The Global Market Valuation Report (GMVR) is the definitive market sizing report in the broadcast and digital media industry.
Providing comprehensive market sizing data for the entire broadcast industry (approximately 150 product categories across nine segments), the GMVR is published through a unique partnership between the IABM, the trade association that represents media technology suppliers worldwide, and Devoncroft Partners, a leading provider of market intelligence products and services to the broadcast and digital media industries.
IABM and Devoncroft have joined forces and established a joint venture – IABM DC LLC – whose sole purpose is to provide high-quality market sizing and forecasting data, and related products and services. The combined experience, skills, and intellectual property of IABM and Devoncroft have been combined to produce the GMVR on an annual basis.
The total market for media technology products and services returned to modest growth in 2016 – projected to be 2.3% up at $51.35bn according to the newly released 2017 Global Market Valuation and Strategy Report (GMVR), published by IABM DC, a joint venture between IABM and Devoncroft Partners.
This follows a dramatic decline of 6.3% in 2015 – which turned out to be even greater than originally projected (-4.3%). Significant currency exchange rate fluctuations continued to play a role in 2016, but the underlying driver the media technology market remains the ripple effect from the fundamental structural change in end-users' business models.
The broadcast and media technology industry has of course been through a number of major transitions over the years – black and white to color, analog to digital, SD to HD – but never before has such a structural shift happened. Today, we are not merely replacing old boxes with new; business models, operations, culture and technology are all fundamentally changing as the power shifts to the viewer with an ever-increasing number of media choices.
End-users are responding by moving to Opex-based, software-driven business and technology models to deliver the agility and flexibility they need to keep up with viewers. This is reflected in the 2017 GMVR – Services were up 3.3% to $29.06bn while Products only grew 0.9% to $22.28bn. This is in line with a six-year trend in the GMVR, reflecting the market's transition from hardware to software.
While the overall size of the market has remained stable in this period, profit margins for software-based products are significantly greater than hardware. Thus, for the same market size, profitability – the driver for investment – looks set to improve significantly as media technology vendors continue to command a premium for their industry-specific solutions, replacing low-margin hardware income lost to IT vendors for their COTS platforms.
There are also significant market opportunities revealed by the GMVR. For example, workflow orchestration grew by 39%, OTT managed services by 17%, and the market for routers – both baseband and IP – also grew.
While IP has yet to translate into a major spending driver for end-users, with all parts of the industry now working together on interoperability initiatives and with new, common standards and codecs coming online, the move to IP-based infrastructures is now beginning to gain momentum.
Some vendors continue to face the challenge of supporting traditional products that are still in use in many facilities while funding development of next-generation, software-based solutions. At the same time, they are having to change their business models to reflect end-users' requirements; revenues that previously would land in a single Capex can now take 5-6 years to monetize. While this is causing a squeeze on cashflow for some in the near-term, in the longer term, shifting to a regular licence-based revenue stream – as seen in other verticals – will provide a more stable and predictable business model.
The GMVR is the only report where broadcast industry vendors come together in a collaborative partnership, under a mutually-agreed framework of strict confidentiality, to share their revenue data, future forecasts, and detailed information on what drives their businesses.
Partner data is augmented by interviews with hundreds of industry insiders (including vendors, service providers, and broadcasters), publicly available information, and exhaustive research into each industry segment, sub-segment, and product category.
The result is the most comprehensive model ever created of the broadcast and digital media industry.
The IABM DC data model used to produce the GMVR encompasses more than 2,500 companies ranging from large service providers and technology vendors to small start-up businesses just breaking into the industry.
One of the key inputs to this data model is the comprehensive data submissions we receive from leading industry vendors who have signed on to be partners in this industry-wide collaborative effort. These partner companies have agreed to share historic and future projected sales data at a granular level according to our segmentation model of the industry. They have also provided extensive input to the report through an interview and feedback process.
In addition to partner data submissions, many data submissions were received from non-partners. Data was also gathered from a wide variety of public and proprietary sources. We also did an exhaustive review of company financials, and interviews with end-users about their past and future planned purchasing strategies.
What data does the GMVR provide?
The report offers detailed market sizing information on products and services sold into professional media contexts.
Because the GMVR is based on actual sales data provided by a large number of broadcast industry vendors and service providers on a collaborative basis, this report provides a unique and unmatched level of detail and data accuracy.
Market sizing in the 2018 edition is available for historical years 2009 through 2017; an estimate for calendar year 2018; and forecast are provided for 2018 through 2020. Sizing for the calendar years 2009 through 2015 are provided on a three-region basis (NALA / EMEA / APAC) for more than 100 product categories and 10 service categories.
High-level forecasting is available for eight product segments (together containing the 150 product categories) and the services segment. More detailed forecasting is available for 29 individual sub-segments.
What qualitative information does the report provide?
A discussion of the primary trends and market drivers of each product category covered by the market sizing is provided with the data on each segment. More general trends impacting the sector (e.g. in the past trends in the behavior of buyers in the broadcast sector was a key topic) are discussed in separate sections and included with the purchase of any segment. Many clients feel that the qualitative information found in the report is one of the best information sources for professionals new to professional media supply and looking to build up knowledge.
How is the GMVR sold?
The GMVR is available via the IABM and/or Devoncroft Partners.
In creating the report, one of our objectives was to allow clients to purchase the entire report (all product segments) or just the specific segments that are of interest to them. Therefore the report was designed from the beginning to be modular. This structure is important because the broadcast industry has a large number of small companies who don't want and/or can't afford to buy a monolithic report about hundreds of product categories that may not be relevant to them.
Within GMVR, there are nine individual product/segment reports, each of which are sold separately.
How can my company participate in future?
Eligible broadcast technology vendors can participate in the report by becoming a partner. This involves the sharing of information with the authors of the report, under NDA, to assist in the production of the detailed market sizing contained in the report. This includes historic sales data, short-term sales projections, and commentary on market dynamics. Confidentiality of partner data is of paramount importance. Partner data submissions will be used only in aggregate, and will not be disclosed in a way that makes it possible to reverse engineer information submitted by any one company.
Work has already begun on the next edition of the report, and we are actively looking for new partners to join this collaborative effort.
How frequently is the GMVR produced?
The GMVR is produced annually.
The report is produced by IABM DC LLC, a joint venture between IABM and Devoncroft Partners.
The report is a collaborative effort. It's a "by the industry, for the industry" project that is coordinated by Devoncroft Partners and the IABM, the trade association that represents broadcast technology suppliers worldwide. All broadcast and digital media technology suppliers are invited to become partners and participate in this project.
© IABM DC LLC 2013 – 2018. All Rights Reserved.


Dramalj is a famous tourist resort on the northern coast of the Adriatic Sea, 35 km south of the river, connected with Crikvenica, which is located along the sea promenade 3 km. Coast in Dramlju whole decorated for swimming and recreation. Here are more gravel, betoniranih and sandy beaches with sports and entertainment. Apartmany Eve in a family house, 4 +1, on the ground floor and the 6 +2 is on the second floor. The house is on a quiet village, tourists can rest in peace on its terrace. The owners of the house willingly offer its guests local wine and brandy, and prepare dishes typical of this house is kraj.Uz grill and a large parking lot. The sea is 10 minutes away on foot.
Accommodation reviews and comments for "Apartmany Eva"


Check out Windyty's gorgeous wind pattern map.
Self-described as "Earth's 36th most influential eco-tweeter," Slate contributor Eric Holthaus tweets and writes gripping stories about weather stuff: how yak dung is contributing to climate change, how California's water-sucking almonds are just misunderstood, and the weather record-breakers that are making it harder to ignore global warming.
Windyty is a mesmerizing, searchable, interactive map of wind patterns around the world. Not only is it beautiful to look at, it's packed with information: You can search for a specific location, zoom in and out to see granular details, toggle along a timeline to see past and future wind patterns, and expand to see detailed weather forecasts. Prepare to settle in—you're going to be playing with this thing for a while.
Mesoscale convective systems! Tornado superoutbreaks! Chaotic cloud patterns!
Weather Channel Senior Meteorologist Stu Ostro is a climate connoisseur.
The Climate Desk combines the resources of nine publications—including the Guardian, Mother Jones, and WIRED—to cover climate change and its connection to the extreme weather that fills our news feeds and inspires Boston's mayor to warn citizens not to jump out of windows into excess snow. Those superstorms, droughts, cyclones, and melting polar ice are all linked—and the polar bears aren't the only ones who are in trouble.
Karl the Fog embodies San Francisco's mysterious microclimates, tracking the city's infamous fog as it shifts around the city. San Franciscans need to follow him to know how many layers of polar fleece to stuff in their messenger bags; shorts-wearing in-landers need to follow him so they can point and laugh at our bone-chilling summer days.


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Technical note An Electron Micrograph of Guard Cells Cryofixed by Metal-Contact Type Rapid-Freezing Method
Chieko Saito
2013 Volume 78 Issue 1 Pages 1-2
Released on J-STAGE: August 01, 2013
DOIhttps://doi.org/10.1508/cytologia.78.1
JOURNAL OPEN ACCESS
Cytogenetic Effects of Two Synthetic Pesticides on Mitotic Chromosome on Root Tip Cells of Allium cepa
Mona S. Al-ahmadi
In this study, two agricultural synthetic insecticides were examined for their genotoxic and cytotoxic effects on root tip cells of Allium cepa, namely, Emar non-systemic Insecticide, which is used to eliminate worms on rodents, and Confidor Systemic insecticide that targets sucking insects.Two concentrations (0.2 and 0.1 ml/L) of Emar and one concentration (0.15 ml/L) of Confidor were used for four different time periods (8, 12, 16 and 24 h); tested dosages were assessed for genotoxic and cytotoxic effects to mitotic chromosomes of Allium cepa root tip cells. The results indicate that both insecticides were genotoxic, as they decrease the mitotic index and increase chromosomal aberration. The observed effects were significant after treatment with 0.1 ml/L and 0.2 ml/L of Emare; also, treament with 0.15 ml/L of Confidor was significant at p>0.05 and cytotoxic, especially after treatment for a long time (16, 24 h). The types of chromosome aberration observed were stickiness, distributed, C-metaphase, C-anaphase, lagging chromosome, binucleate cell and bridge.
Chromosomal Characteristic of Nile Tilapia (Oreochromis niloticus) from Mitotic and Meiotic Cell Division by T-Lymphocyte Cell Culture
Weerayuth Supiwong, Alongklod Tanomtong, Praween Supanuam, Kriengkrai ...
2013 Volume 78 Issue 1 Pages 9-14
Chromosomal characteristics of Nile tilapia (Oreochromis niloticus) from mitotic and meiotic cell division were studied. Blood samples were taken from five male and five female fish. Fish chromosome preparation was conducted by T-lymphocyte cell culture and squash technique from testis. The chromosomes were stained by conventional and Ag-NOR staining techniques. The results showed that the diploid chromosome number of O. niloticus was 2n=44 and the fundamental number (NF) was 58 in both male and female. The types of chromosomes were present as 2 large acrocentric, 2 small submetacentric, 10 small acrocentric, and 30 small telocentric chromosomes. There are no strange size chromosomes related to sex. We also observed distinctive secondary constriction/NORs at the region adjacent to the short arms near telomere of a pair of submetacentric chromosomes. The karyotype formula of O. niloticus is as stated:
2n (diploid) 42=La2+S2sm+Sa10+St30.
Karyological Analysis and Morphometrics of Horsfield's Bat, Myotis horsfieldii (Chiroptera, Vespertilionidae)
Praween Supanuam, Alongklod Tanomtong, Sumpars Khunsook, Sarawut Kaews ...
2013 Volume 78 Issue 1 Pages 15-24
DOIhttps://doi.org/10.1508/cytologia.78.15
Karyological analysis and morphometrics of Horsfield's bat (Myotis horsfieldii) from Thailand were studied. Blood samples were taken from five male and five female bats. After standard whole blood lymphocytes were cultured at 37°C for 72 h in presence of colchicine, metaphase spreads were performed on microscopic slides and air-dried. Conventional staining and GTG-banding techniques were applied to stain the chromosome. The results showed that the diploid chromosome number of M. horsfieldii was 2n=44, the fundamental number (NF) was 52 in both male and female. The types of autosomes observed were 6 large metacentric, 4 medium telocentric, 2 small metacentric, 2 small submetacentric, and 28 small telocentric chromosomes. The X chromosome was a large submetacentric chromosome and the Y chromosome was a small acrocentric chromosome. From the GTG-banding technique, the number of bands and location in M. horsfieldii was observed to be 195, and each chromosome pair could be clearly differentiated. Six external morphological characters were measured as well as 13 cranial and dental measurements. The karyotype formula of M. horsfieldii is as follows:
2n (44)=Lm6+M4t+Sm2+S2sm+S28t+XY
Interpopulational Variation of NOR Positions and Karyotypic Analysis of Siamese Catfish (Pseudomystus siamensis) in Thailand
Weerayuth Supiwong, Alongklod Tanomtong, Arunrat Chaveerach, Tawatchai ...
Interpopulational variation of nucleolar organizer region (NOR) position and karyotypic analysis of Siamese catfish (Pseudomystus siamensis) in Thailand were studied. The samples were taken from three locations (8 populations); the mitotic chromosome preparations were conducted directly from kidney cells. Conventional and Ag-NOR staining techniques were applied to stain the chromosomes. The results showed that the diploid chromosomes number was 2n=50 and the fundamental number (NF) was 96 in both sexes. The karyotype of P. siamensis showed two new cytotypes for this species. The chromosome of cytotype A and B showed the presence of metacentric, submetacentric, acrocentric and telocentric autosomes as 22–18–6–4 and 20–20–6–4, respectively, without any apparent sex chromosomal heteromorphism. Data obtained from chromosomal banding technique further suggests an interpopulation NOR polymorphism. Such variation is possibly caused by paracentric inversion. In addition, a pair of the long arms of chromosome pair 2′ in cytotype A and chromosome pair 12′′ in cytotype B showed clearly observable NORs. The karyotype formulas of cytotype A and B for P. siamensis could be deduced as:
Cytotype A: 2n (diploid) 50=L6m+L6sm+L2a+Mm16+M12sm+Ma4+Mt2+St2
Cytotype B: 2n (diploid) 50=L2m+L6sm+Mm14+M12sm+Ma6+Mt2+S4m+S2sm+St2.
Cytogenetical and Molecular Characterization of S. violaceum, S. sisymbrifolium and a Putative Hybrid between ♀ S. violaceum and ♂ S. melongena
Sheikh Shamimul Alam, Lutfun Nahar, Md. Ahashan Habib, Syeda Sharmeen ...
Two wild Solanum species, viz. S. violaceum, S. sisymbrifolium, and a putative hybrid between ♀ S. violaceum and ♂ S. melongena were investigated to elucidate their genomic information. For this reason, the orcein and CMA-stained karyotypes of these three specimens were compared. In addition, RAPD with five primer combinations were conducted to make comparative DNA finger printing. The three specimens were found to possess 2n=24 chromosomes. The karyotypes of the three specimens consisted of mostly metacentric chromosomes with no gradual decrease in chromosomal length suggesting symmetric karyotypes. A pair of CMA-positive satellites was found in both the members of pair II of S. violaceum and the putative hybrid. The CMA-karyotype of S. sisymbrifolium was different from the other two specimens. Solanum violaceum and the putative hybrid showed exactly the same DNA finger printing in two primers (Batch-7736-037 and Batch-7736-039) and almost the same in the other three primer combinations. On the other hand, the RAPD finger printing of S. sisymbrifolium was quite different from the other two specimens. The comparative karyotype and RAPD analysis clearly indicated that the putative hybrid was not actually a hybrid between S. violaceum and S. melongena, but rather showed a close resemblance to the mother plant.
Meiotic Behavior of Induced Reciprocal Translocation Heterozygote during Microsporogenesis of Brassica campestris L. (Brassicaceae)
Girjesh Kumar, Kshama Dwivedi
Although the occurrence of reciprocal translocation has already been discovered in plants, this is the first report on the striking phenomenon of reciprocal translocation (RT) induced through combined treatment in Brassica campestris L. accession number-IC363713. The seeds were subjected to four doses of gamma rays i.e.15, 30, 45, 60 Kr, followed by 0.3% EMS solution, and were sown in triplicates. Occurrence of RT was identified by the reduced reproductive success of plants, which was authenticated by cytological analysis that revealed the high incidence of multivalent association, forming either a ring or a chain. Rings were discerned to occur in alternate orientation, forming eight-shaped structure while simple loops were noticed erratically when the chromosomes were oriented adjacently. On the other hand, the lack of chiasmata in chromosomes was the cause of chains as multivalent association. No such phenomenon has been observed in non-treated sets. Moreover, the spectrum of multivalent association (MA) and univalents in the pollen mother cells (PMCs) from diakinesis to metaphase-I has been found to be increased in parallel with the dose of treatment, although the frequency of bivalents showed a gradual decline in their percentages. The frequency of quadrivalents has prevailed both in ring and chain forms, other than trivalent, hexavalent and other associations. Meanwhile, pollen fertility drastically decreased with the increasing trend of multivalent association. Notwithstanding RT, various other structural chromosome abnormalities, such as unequal separation, laggards and bridges during meiotic course, were recorded.
Spontaneous Inversion Heterozygotes in Aloe barbadensis Mill.
Ramesh Ahirwar, R. C. Verma
A population of cultivated Aloe barbadensis Mill. displayed various types of chromosomal configurations at anaphase/telophase-I and II due to inversion heterozgosity. The population was characterized by the presence of bridges and fragments because of various numbers and positions of cross-overs in the inversion loop. The inversion heterozygote had very low pollen fertility.
Cytological Studies of Some Dicots from the Hills of Mandi District (Himachal Pradesh) in Northwest Indian Himalayas
Vijay Kumar Singhal, Puja Garg, Puneet Kumar
Details of male meiotic course and pollen fertility have been investigated in 45 species of dicotyledonous plants from the chromosomally unexplored area of the Mandi district, between the altitudinal ranges of 800 to 3360 m. All the species have been worked out cytologically for the first time from the study area. The meiotic chromosome count of n=9 for Impatiens micranthemum is the first ever chromosome report for the species. A new intraspecific 12x cytotype (n=42) has been reported in Potentilla gerardiana, supplementing the earlier reported cytotypes (2x, 8x). A new cytotype has been reported in Gentiana argentea (n=9), supplementing the earlier Indian report of n=10. Out of a total 45 species, 31 species (68.89%) showed normal meiotic course and high pollen fertility (91–100%), while 14 species (31.11%) depicted various meiotic irregularities and some pollen sterility (4–40%). The phenomenon of cytomixis involving inter-pollen mother cells (PMCs) migration of chromatin material has been reported in 3 species, Clematis grata (n=8), Gentiana aprica (n=10) and Lamium album (n=9). In these species, cytomixis induces various meiotic abnormalities (enucleated, hypo-, hyperploid, tripolar and multipolar PMCs, chromatin stickiness, laggards and chromatin bridges at anaphases and telophases and micronuclei) and consequently some pollen sterility and variable sized pollen grains. Besides, non-synchronous disjunctions of bivalents at anaphases, abnormal spindles, and interbivalent connections during diakinesis/metaphase-I, univalents and multivalents have also been reported in certain cases. The chromosome counts in the remainder of the species substantiate previous counts by other researchers.
Cytogenetical Studies in Some Diploid Mulberry Varieties (Moraceae)
K. H. Venkatesh, R. Nijagunaiah, Munirajappa
Most Morus species are diploid, having 28 chromosomes, but a few species, specifically, M. tiliafolia Makino, Morus cathyana and M. nigra L., are higher polyploidy. In these species, the chromosome number varies from 2n=28 to 2n=308, with ploidy level from x to 22x. In the present study, three diploid mulberry varieties, namely, V1, S13 and S34, have been analyzed for detailed meiotic studies. Based on chromosome configuration and other meiotic behaviour, x=14 has been considered as the basic number of the genus. Meiosis shows marginal differences. Various meiotic abnormalities, such as the occurrence of one large chromosome at metaphase I, unequal separation at anaphase I, the presence of laggards and precocious movement of chromosomes at anaphase II, and two micronuclei at tetrad, have been observed. The low pollen fertility and seed sets may be due to irregular chromosomal pairing and separation rather than due to genetic or physiological causes.
Fluorescent Banding and RAPD Analysis of Five Cultivars in Vigna unguiculata ssp. sesquipedalis (L.) Verdc.
Sheikh Shamimul Alam, Ruhina Tasmin, Israt Jahan, Md. Ahashan Habib, S ...
Five commercial cultivars of Vigna unguiculata ssp. sesquipedalis (L.) Verdc., viz. Lalbanie, Kagornatki, BARI Borboti-1, White Bean, and Saba, were investigated cytogenetically and at the molecular level for characterization. Although the five cultivars possessed an identical chromosome number (2n=22), they showed variation in respect of other karyotype features. Maximum metacentric chromosomes were found in Saba. The karyotype formulae of Lalbanie, Kagornatki, and White Bean were more or less similar while BARI Borboti-1 was quite different, since in addition to metacentric and sub-metacentric chromosomes, a telocentric chromosome is present in its karyotype. The total length of 2n chromosome complements of Saba was the largest. Different total lengths of diploid complements among these cultivars indicated the deletion of heterochromatic regions. The five cultivars showed distinct CMA-banded karyotypes which were specific for each variety. Heteromorphicity in respect of CMA-band was found in Lalbanie and Saba, suggesting the deletion of heterochromatic banded portion from the respective chromosomes. The highest percentage of GC-rich regions was found in BARI Borboti-1, while the lowest was in Saba. The reason for the variation in the percentage of GC-rich regions is probably the tandem duplication of GC-rich repeats. In spite of a few common RAPD bands, each cultivar has distinct RAPD-fingerprinting pattern. The common band revealed that the five cultivars share similar fragments. Lalbanie, BARI Borboti-1, White Bean, and Saba showed unique bands, useful as specific markers for these varieties. Therefore, with the help of cytogenetical and RAPD analysis, it was possible to characterize each cultivar of Vigna unguiculata ssp. sesquipedalis (L.) Verdc.
Efficiency and Effectiveness of Gamma Rays and Sodium Azide in Sesbania cannabina Poir.
Girjesh Kumar, Nitisha Srivastava
Mutagenic effectiveness and efficiency is an important factor for the selection of a mutagen for a mutation breeding program. Mutagenic effectiveness is a measure of the frequency of mutations induced by a unit mutagen dose, while mutagenic efficiency is a measure of the proportion of mutations in relation to undesirable changes such as lethality, sterility, meiotic aberrations etc. The present study envisages the mutagenic effectiveness and efficiency of individual and combined treatments of chemical and physical mutagens i.e. sodium azide (individual), gamma rays (individual) and sodium azide+gamma ray (combined). For the individual treatment of sodium azide, the seeds of the Sesbania cannabina variety ND-1 were treated with 0.5% solution of sodium azide (SA) for four different time durations, i.e. 3, 5, 7, and 9 h, and for the individual treatment of gamma rays, dry and healthy seeds were treated with 20, 40, 60, and 80 Kr doses of gamma rays. For the combined treatment, the seeds were exposed to four different doses of gamma rays (20, 40, 60, and 80 Kr) and after irradiation seeds were treated with 0.5% solution of sodium azide for 3 h. After treatment, seeds subjected to individual and combined treatment were sown in randomized block design to raise the M1 generation and a study was conducted on germination percentage, survival percentage, pollen fertility percentage, and chromosomal aberrations at different doses of the individual and combined treatments.
Cytogenetic Studies in Two Species of Genus Pimelodella (Teleostei, Siluriformes, Heptapteridae) from Iguatemi River Basin, Brazil
Carlos Alexandre Fernandes, Jenifer Fernanda Damásio, Zaira da ...
Pimelodella is one of the genera belonging to the family Heptapteridae consisting of endemic neotropical fishes; it has shown a wide variability in karyotype. The present study aimed to evaluate cytogenetically two species belonging to the genus Pimelodella from Iguatemi River Basin, MS, Brazil. In the specimens of P.avanhadavae, the diploid number was 2n=52 chromosomes, distributed in 24m+20sm+08st, with multiple NORs systems. The heterochromatin was weakly visualized with C-banding in telomeric and/or centromeric regions of a few chromosomes. For the specimens of P. gracilis, the diploid number was 2n=46 chromosomes, distributed in 20m+18sm+06st+2a to with a simple NORs system, and heterochromatic blocks in the centromeric and telomeric regions, with variable intensity of staining, and conspicuous interstitial blocks. The two species of Pimelodella differ in diploid number and karyotypic formula, indicating that chromosomal rearrangements, such as fissions and/or centric fusions, may have occurred during the diversification of these two species.
A Modified Protocol for Accurate Detection of Cell Fusion-Mediated Premature Chromosome Condensation in Human Peripheral Blood Lymphocytes
Yumiko Suto, Miho Akiyama, Takaya Gotoh, Momoki Hirai
2013 Volume 78 Issue 1 Pages 97-103
When interphase cells are fused with mitotic cells, loosely distributing chromatins in nuclei are induced to form prematurely condensed chromosomes. In this paper we report a modified protocol to unequivocally detect prematurely condensed chromosomes in human peripheral blood lymphocytes that were fused with mitotic Chinese hamster ovary (CHO) cells. To examine cell fusion-mediated premature chromosome condensation (PCC), we conducted morphological analysis by differential interference contrast microscopy and molecular cytogenetic analysis by fluorescence in situ hybridization using pan-centromeric and telomeric peptide nucleic acid (PNA) probes. These modified procedures may serve to improve the usefulness of the technique of PCC in cytogenetic investigations.
Induced Genetic Male Sterility in Nigella sativa L. (Black Cumin)
Suchetana Mukherjee, Animesh K. Datta
Eight male sterile-female fertile mutants possessing a similar phenotype (semi-dwarf with dark green cup shaped pinnae), normal meiotic chromosome behavior and 100.0% sterile pollen grains were scored at M2 (0.1% over mutagenized population; 7845 plants scored) following different mutagen treatments (0.25%, 2 h EMS; 0.5%, 4 h dES; 1.00%, 2 h H2O2; 0.25%, 4 h NaN3; 0.50%, 4 h NH2OH; 100 Gy, gamma irradiations) to dry seeds (moisture content: 19.04%) of Nigella sativa L. (Family: Ranunculaceae; common name: black cumin). The marked phenotype of the M2 mutant plants did not persist in subsequent generations (M3 and M4). Male sterility is reported to be of the non-structural nuclear type. Segregation studies revealed that male sterility was monogenic recessive to male fertility. The male sterile plant type assessed over the generations (M2 to M4) was characterized morphologically, palynologically (pollen attributes studied following SEM analysis, DAPI staining and using stain tests for viability), and cytologically in relation to male fertile normal plants. The results obtained were discussed.
Different Hematopoietic Reconstruction Abilities of Transplanted Cells from Bone Marrow, Spleen, Liver and Peripheral Blood
Guang-Ping Ruan, Jin-Xiang Wang, Xiang Yao, Rong-Qing Pang, Xue-Min Ca ...
Cells from different lineages may exert a different growth capacity following transplantation. To compare the efficacy of different cells on rebuilding hematopoietic function, peripheral leukocytes, spleen cells, liver cells and bone marrow cells derived from GFP transgenic male mice were transfused into irradiated female mice. GFP-positive cells in the peripheral blood of female recipients were detected 20, 40, or 60 days after transplantation. At the 180th day after transplantation, mice were sacrificed and bone marrow cells were collected to detect the Y chromosome-positive rate. Results showed that GFP-positive cells appeared as early as 20 days after transplantation in peripheral blood, and some cells had differentiated to CD4+ or CD8+ T cells. The differentiation rate was different among the four types of donor cells. The Y chromosome-positive rate in bone marrow and the GFP-positive rate in peripheral blood were positively correlated. Although all 4 types of cells were able to rebuild hematopoietic function, spleen and bone marrow cells showed the highest capacity, followed by liver cells. Leukocytes showed the least reconstitution efficacy.

redpajama_set_name: RedPajamaCommonCrawl


text:
What is Solar PV?
Solar Photovoltaics (PV) is a state of the art, proven application derived from electronic and semiconductor technology. It is used to harness free energy from the sun for the generation of electrical energy, using a process called the Photovoltaic or Photoelectric Effect. Fundamentally, this phenomenon describes how a voltage or current is created when PV materials are exposed to sunlight energy.
Pioneers of this technology were scientists from Bell Laboratories in the United States of America, who in the early 1950's discovered the PV Effect upon observing that an electric charge was generated when sunlight irradiated silicon. In essence, the technology has not changed significantly, but it has improved in versatility, quality, price and efficiency, making it one of more popular renewable energy applications implemented around the world.
Solar cells and how they work
A single PV unit is referred to as a solar cell. In order to form an electric field, solar cells are usually manufactured from semi-conductive materials (predominantly silicon or a variation thereof) and other conductive materials. A particular advantage of PV is that it can be designed to any scale depending on the consumption pattern or requirement of the user. A solar cell is capable of producing 1-2 watts of power each. For a larger output, several cells can be connected to form modules or panels. In turn, these modules or panels can be connected to form arrays, which are the building blocks of a PV system.
Classification of cells
The most common classification of solar cell technologies is according to first, second or third generation, referring to the way they are produced and the materials utilised. The main difference between each generation class is the material used for the semiconductor component and the structure of its crystal.
First generation cells
First generation solar cells include both monocrystalline and polycrystalline cells. These are generally flat plate cells used for rooftop PV applications. Monocrystalline ('one crystal') cells are those produced from silicon wafers cut from a silicon crystal. These solar cell types are usually the most expensive, but also have the highest efficiency rating i.e. between 15 – 24%, due to the purity of the silicon used.
Polycrystalline cells are those produced from molten silicon or thin silicon wafers cut from multiple crystals that were formed together. As a result, this type of cell has a lower production cost but has a lower efficiency rating of 13 – 18%.
Production of crystalline silicon cells
(DGS Scholar School / ©GIZ)
Second generation cells
Cells derived from an assortment of non-silicon materials, for example copper-indium-diselenid, cadmium telluride or amorphous silicon, are referred to as second generation cells. These cells are also known as thin film cells as the thickness of the silicon element is only a few micro-meters in width.
Various types of second generation cells
Copper-Indium-Diselenid (CIS)
Cadmium-Telluride (CdTe)
Amorphous silicon
Second generation products have a lower cost and efficiency, but are suitable for applications that require the solar cells to be more flexible.
Third generation cells
There are also some manufacturers who use a more innovative approach and produce cells from organic materials. Examples include those cells with a polymer or nanocrystal base; concentrated or dye sensitised solar cells. These are referred to as third generation cells and can be considered emerging technologies under research, as there are not commercially proven yet.
How solar panels work
Each solar cell constitutes 2 sheets of silicon i.e. an 'N' type (has an excess of electrons) and a 'P' type (has fewer electrons and more capacity to accept electrons). As a result, an electric field is formed internally due to the oppositely charged layers. Silicon atoms in solar cells are strongly bonded to one another. In this natural state, there is no flow of current; however when sunlight irradiates a cell with sufficient energy, electrons in silicon atoms become energised and move out of their natural state. Through metal contact points on the surface of the cell, the displaced electrons are collected and conveyed to the wires. If a circuit is connected to the panel, then electricity can be generated.
For a comprehensive visual on how solar panels work, watch this short Ted-Ed video by Richard Komp (2016).


Embrace Black — We Are BEST.
The first instalment of Grinders Coffee strategic brand undertaking for the upcoming year was launched at the 2018 Melbourne Cup Carnival as the start of a three-year exclusive coffee partnership between Grinders Coffee Roasters (GrInders) and Victorian Racing Club (VRC).
BEST. Creative Director Jacob Burke and Design Manager Tom Debenham worked to design and project manage the build of the pavilion and activation spaces for the exclusive Birdcage Enclosure and The Park – Flemington's food, fashion and entertainment precinct.
The 'Embrace Black' design draws on the strategic and creative themes for the above the line campaign in development to be launched in early 2019 called 'From the Street's of Melbourne'; the home of Grinders Coffee.
The Birdcage enclosure featured a 6x4 meter pavilion constructed with Spacecubes. The versatile product allowed for a stunning configuration and the perfect framework for the bold interior design - the interior finishes achieving a beautiful, luxurious outcome. Encased in the ceiling a curated display of lighting and balloon installation hovered over the black chevron floor and the walls painted black V-groove both complimented by a black mirrored coffee counter. All of this being put to good use for racegoers with coffee served by Grinders Baristas and former World Latte Artist Champion Caleb Cha.
Meanwhile, in The Park, Grinders served a delicious range of premium coffees from the Grinders 'Short Black' Caravan, a piccolo-sized caravan with matte black styling featured in the relaxed park cocktail themed area.
Local artist Antra Švarcs designed a number of stickers to activate and transform the Grinders Birdcage space throughout the carnival, with guests being given bespoke stickers featuring Švarcs' designs to stick onto the walls to create a unique art installation.
The Grinders race day ambassadors, dressed by Melbourne fashion house NIQUE, dripped in handmade jewellery by Melbourne jewellery designer Megan Moss. Both Megan and Antra are two emerging Melbourne talent who featured in the new 30-sec brand campaign, launched on Cup Day and will be a key part of the 2019 campaign rollout, alongside Melbourne singer-songwriter Janeva, whose track 'You and I' is the track for the campaign.


text:
"""
gnpy.core.science_utils
=======================

Solver definitions to calculate the Raman effect and the nonlinear interference noise

The solvers take as input instances of the spectral information, the fiber and the simulation parameters
"""

from numpy import interp, pi, zeros, shape, where, cos, reshape, array, append, ones, argsort, nan, exp, arange, sqrt, \
    empty, vstack, trapz, arcsinh, clip, abs, sum
from operator import attrgetter
from logging import getLogger
import scipy.constants as ph
from scipy.integrate import solve_bvp
from scipy.integrate import cumtrapz
from scipy.interpolate import interp1d
from scipy.optimize import OptimizeResult
from math import isclose

from gnpy.core.utils import db2lin, lin2db
from gnpy.core.exceptions import EquipmentConfigError
from gnpy.core.parameters import SimParams

logger = getLogger(__name__)
sim_params = SimParams.get()

def propagate_raman_fiber(fiber, *carriers):
    raman_params = sim_params.raman_params
    nli_params = sim_params.nli_params
    # apply input attenuation to carriers
    attenuation_in = db2lin(fiber.params.con_in + fiber.params.att_in)
    chan = []
    for carrier in carriers:
        pwr = carrier.power
        pwr = pwr._replace(signal=pwr.signal / attenuation_in,
                           nli=pwr.nli / attenuation_in,
                           ase=pwr.ase / attenuation_in)
        carrier = carrier._replace(power=pwr)
        chan.append(carrier)
    carriers = tuple(f for f in chan)

    # evaluate fiber attenuation involving also SRS if required by sim_params
    raman_solver = fiber.raman_solver
    raman_solver.carriers = carriers
    raman_solver.raman_pumps = fiber.raman_pumps
    stimulated_raman_scattering = raman_solver.stimulated_raman_scattering

    fiber_attenuation = (stimulated_raman_scattering.rho[:, -1])**-2
    if not raman_params.flag:
        fiber_attenuation = tuple(fiber.params.lin_attenuation for _ in carriers)

    # evaluate Raman ASE noise if required by sim_params and if raman pumps are present
    if raman_params.flag and fiber.raman_pumps:
        raman_ase = raman_solver.spontaneous_raman_scattering.power[:, -1]
    else:
        raman_ase = tuple(0 for _ in carriers)

    # evaluate nli and propagate in fiber
    attenuation_out = db2lin(fiber.params.con_out)
    nli_solver = fiber.nli_solver
    nli_solver.stimulated_raman_scattering = stimulated_raman_scattering

    nli_frequencies = []
    computed_nli = []
    for carrier in (c for c in carriers if c.channel_number in sim_params.nli_params.computed_channels):
        resolution_param = frequency_resolution(carrier, carriers, sim_params, fiber)
        f_cut_resolution, f_pump_resolution, _, _ = resolution_param
        nli_params.f_cut_resolution = f_cut_resolution
        nli_params.f_pump_resolution = f_pump_resolution
        nli_frequencies.append(carrier.frequency)
        computed_nli.append(nli_solver.compute_nli(carrier, *carriers))

    new_carriers = []
    for carrier, attenuation, rmn_ase in zip(carriers, fiber_attenuation, raman_ase):
        carrier_nli = interp(carrier.frequency, nli_frequencies, computed_nli)
        pwr = carrier.power
        pwr = pwr._replace(signal=pwr.signal / attenuation / attenuation_out,
                           nli=(pwr.nli + carrier_nli) / attenuation / attenuation_out,
                           ase=((pwr.ase / attenuation) + rmn_ase) / attenuation_out)
        new_carriers.append(carrier._replace(power=pwr))
    return new_carriers

def frequency_resolution(carrier, carriers, sim_params, fiber):
    def _get_freq_res_k_phi(delta_count, grid_size, alpha0, delta_z, beta2, k_tol, phi_tol):
        res_phi = _get_freq_res_phase_rotation(delta_count, grid_size, delta_z, beta2, phi_tol)
        res_k = _get_freq_res_dispersion_attenuation(delta_count, grid_size, alpha0, beta2, k_tol)
        res_dict = {'res_phi': res_phi, 'res_k': res_k}
        method = min(res_dict, key=res_dict.get)
        return res_dict[method], method, res_dict

    def _get_freq_res_dispersion_attenuation(delta_count, grid_size, alpha0, beta2, k_tol):
        return k_tol * abs(alpha0) / abs(beta2) / (1 + delta_count) / (4 * pi ** 2 * grid_size)

    def _get_freq_res_phase_rotation(delta_count, grid_size, delta_z, beta2, phi_tol):
        return phi_tol / abs(beta2) / (1 + delta_count) / delta_z / (4 * pi ** 2 * grid_size)

    grid_size = sim_params.nli_params.wdm_grid_size
    delta_z = sim_params.raman_params.space_resolution
    alpha0 = fiber.alpha0()
    beta2 = fiber.params.beta2
    k_tol = sim_params.nli_params.dispersion_tolerance
    phi_tol = sim_params.nli_params.phase_shift_tolerance
    f_pump_resolution, method_f_pump, res_dict_pump = \
        _get_freq_res_k_phi(0, grid_size, alpha0, delta_z, beta2, k_tol, phi_tol)
    f_cut_resolution = {}
    method_f_cut = {}
    res_dict_cut = {}
    for cut_carrier in carriers:
        delta_number = cut_carrier.channel_number - carrier.channel_number
        delta_count = abs(delta_number)
        f_res, method, res_dict = \
            _get_freq_res_k_phi(delta_count, grid_size, alpha0, delta_z, beta2, k_tol, phi_tol)
        f_cut_resolution[f'delta_{delta_number}'] = f_res
        method_f_cut[delta_number] = method
        res_dict_cut[delta_number] = res_dict
    return [f_cut_resolution, f_pump_resolution, (method_f_cut, method_f_pump), (res_dict_cut, res_dict_pump)]

def raised_cosine_comb(f, *carriers):
    """ Returns an array storing the PSD of a WDM comb of raised cosine shaped
    channels at the input frequencies defined in array f
    :param f: numpy array of frequencies in Hz
    :param carriers: namedtuple describing the WDM comb
    :return: PSD of the WDM comb evaluated over f
    """
    psd = zeros(shape(f))
    for carrier in carriers:
        f_nch = carrier.frequency
        g_ch = carrier.power.signal / carrier.baud_rate
        ts = 1 / carrier.baud_rate
        pass_band = (1 - carrier.roll_off) / (2 / carrier.baud_rate)
        stop_band = (1 + carrier.roll_off) / (2 / carrier.baud_rate)
        ff = abs(f - f_nch)
        tf = ff - pass_band
        if carrier.roll_off == 0:
            psd = where(tf <= 0, g_ch, 0.) + psd
        else:
            psd = g_ch * (where(tf <= 0, 1., 0.) + 1 / 2 * (1 + cos(pi * ts / carrier.roll_off * tf)) *
                          where(tf > 0, 1., 0.) * where(abs(ff) <= stop_band, 1., 0.)) + psd
    return psd

class SpontaneousRamanScattering:
    def __init__(self, frequency, z, power):
        self.frequency = frequency
        self.z = z
        self.power = power

class StimulatedRamanScattering:
    def __init__(self, frequency, z, rho, power):
        self.frequency = frequency
        self.z = z
        self.rho = rho
        self.power = power

class RamanSolver:
    def __init__(self, fiber=None):
        """ Initialize the Raman solver object.
        :param fiber: instance of elements.py/Fiber.
        :param carriers: tuple of carrier objects
        :param raman_pumps: tuple containing pumps characteristics
        """
        self._fiber = fiber
        self._carriers = None
        self._raman_pumps = None
        self._stimulated_raman_scattering = None
        self._spontaneous_raman_scattering = None

    @property
    def fiber(self):
        return self._fiber

    @property
    def carriers(self):
        return self._carriers

    @carriers.setter
    def carriers(self, carriers):
        self._carriers = carriers
        self._spontaneous_raman_scattering = None
        self._stimulated_raman_scattering = None

    @property
    def raman_pumps(self):
        return self._raman_pumps

    @raman_pumps.setter
    def raman_pumps(self, raman_pumps):
        self._raman_pumps = raman_pumps
        self._stimulated_raman_scattering = None

    @property
    def stimulated_raman_scattering(self):
        if self._stimulated_raman_scattering is None:
            self.calculate_stimulated_raman_scattering(self.carriers, self.raman_pumps)
        return self._stimulated_raman_scattering

    @property
    def spontaneous_raman_scattering(self):
        if self._spontaneous_raman_scattering is None:
            self.calculate_spontaneous_raman_scattering(self.carriers, self.raman_pumps)
        return self._spontaneous_raman_scattering

    def calculate_spontaneous_raman_scattering(self, carriers, raman_pumps):
        raman_efficiency = self.fiber.params.raman_efficiency
        temperature = self.fiber.operational['temperature']

        logger.debug('Start computing fiber Spontaneous Raman Scattering')
        power_spectrum, freq_array, prop_direct, bn_array = self._compute_power_spectrum(carriers, raman_pumps)

        alphap_fiber = self.fiber.alpha(freq_array)

        freq_diff = abs(freq_array - reshape(freq_array, (len(freq_array), 1)))
        interp_cr = interp1d(raman_efficiency['frequency_offset'], raman_efficiency['cr'])
        cr = interp_cr(freq_diff)

        # z propagation axis
        z_array = self.stimulated_raman_scattering.z
        ase_bc = zeros(freq_array.shape)

        # calculate ase power
        int_spontaneous_raman = self._int_spontaneous_raman(z_array, self._stimulated_raman_scattering.power,
                                                            alphap_fiber, freq_array, cr, freq_diff, ase_bc,
                                                            bn_array, temperature)

        spontaneous_raman_scattering = SpontaneousRamanScattering(freq_array, z_array, int_spontaneous_raman.x)
        logger.debug("Spontaneous Raman Scattering evaluated successfully")
        self._spontaneous_raman_scattering = spontaneous_raman_scattering

    @staticmethod
    def _compute_power_spectrum(carriers, raman_pumps=None):
        """
        Rearrangement of spectral and Raman pump information to make them compatible with Raman solver
        :param carriers: a tuple of namedtuples describing the transmitted channels
        :param raman_pumps: a namedtuple describing the Raman pumps
        :return:
        """

        # Signal power spectrum
        pow_array = array([])
        f_array = array([])
        noise_bandwidth_array = array([])
        for carrier in sorted(carriers, key=attrgetter('frequency')):
            f_array = append(f_array, carrier.frequency)
            pow_array = append(pow_array, carrier.power.signal)
            ref_bw = carrier.baud_rate
            noise_bandwidth_array = append(noise_bandwidth_array, ref_bw)

        propagation_direction = ones(len(f_array))

        # Raman pump power spectrum
        if raman_pumps:
            for pump in raman_pumps:
                pow_array = append(pow_array, pump.power)
                f_array = append(f_array, pump.frequency)
                direction = +1 if pump.propagation_direction == 'coprop' else -1
                propagation_direction = append(propagation_direction, direction)
                noise_bandwidth_array = append(noise_bandwidth_array, ref_bw)

        # Final sorting
        ind = argsort(f_array)
        f_array = f_array[ind]
        pow_array = pow_array[ind]
        propagation_direction = propagation_direction[ind]

        return pow_array, f_array, propagation_direction, noise_bandwidth_array

    def _int_spontaneous_raman(self, z_array, raman_matrix, alphap_fiber, freq_array,
                               cr_raman_matrix, freq_diff, ase_bc, bn_array, temperature):
        spontaneous_raman_scattering = OptimizeResult()

        dx = sim_params.raman_params.space_resolution
        h = ph.value('Planck constant')
        kb = ph.value('Boltzmann constant')

        power_ase = nan * ones(raman_matrix.shape)
        int_pump = cumtrapz(raman_matrix, z_array, dx=dx, axis=1, initial=0)

        for f_ind, f_ase in enumerate(freq_array):
            cr_raman = cr_raman_matrix[f_ind, :]
            vibrational_loss = f_ase / freq_array[:f_ind]
            eta = 1 / (exp((h * freq_diff[f_ind, f_ind + 1:]) / (kb * temperature)) - 1)

            int_fiber_loss = -alphap_fiber[f_ind] * z_array
            int_raman_loss = sum((cr_raman[:f_ind] * vibrational_loss * int_pump[:f_ind, :].transpose()).transpose(),
                                    axis=0)
            int_raman_gain = sum((cr_raman[f_ind + 1:] * int_pump[f_ind + 1:, :].transpose()).transpose(), axis=0)

            int_gain_loss = int_fiber_loss + int_raman_gain + int_raman_loss

            new_ase = sum((cr_raman[f_ind + 1:] * (1 + eta) * raman_matrix[f_ind + 1:, :].transpose()).transpose()
                             * h * f_ase * bn_array[f_ind], axis=0)

            bc_evolution = ase_bc[f_ind] * exp(int_gain_loss)
            ase_evolution = exp(int_gain_loss) * cumtrapz(new_ase * exp(-int_gain_loss), z_array, dx=dx, initial=0)

            power_ase[f_ind, :] = bc_evolution + ase_evolution

        spontaneous_raman_scattering.x = 2 * power_ase
        return spontaneous_raman_scattering

    def calculate_stimulated_raman_scattering(self, carriers, raman_pumps):
        """ Returns stimulated Raman scattering solution including
        fiber gain/loss profile.
        :return: None
        """
        # fiber parameters
        fiber_length = self.fiber.params.length
        raman_efficiency = self.fiber.params.raman_efficiency

        if not sim_params.raman_params.flag:
            raman_efficiency['cr'] = zeros(len(raman_efficiency['cr']))
        # raman solver parameters
        z_resolution = sim_params.raman_params.space_resolution
        tolerance = sim_params.raman_params.tolerance

        logger.debug('Start computing fiber Stimulated Raman Scattering')

        power_spectrum, freq_array, prop_direct, _ = self._compute_power_spectrum(carriers, raman_pumps)

        alphap_fiber = self.fiber.alpha(freq_array)

        freq_diff = abs(freq_array - reshape(freq_array, (len(freq_array), 1)))
        interp_cr = interp1d(raman_efficiency['frequency_offset'], raman_efficiency['cr'])
        cr = interp_cr(freq_diff)

        # z propagation axis
        z = append(arange(0, fiber_length, z_resolution), fiber_length)

        def ode_function(z, p):
            return self._ode_stimulated_raman(z, p, alphap_fiber, freq_array, cr, prop_direct)

        def boundary_residual(ya, yb):
            return self._residuals_stimulated_raman(ya, yb, power_spectrum, prop_direct)

        initial_guess_conditions = self._initial_guess_stimulated_raman(z, power_spectrum, alphap_fiber, prop_direct)

        # ODE SOLVER
        bvp_solution = solve_bvp(ode_function, boundary_residual, z, initial_guess_conditions, tol=tolerance)

        rho = (bvp_solution.y.transpose() / power_spectrum).transpose()
        rho = sqrt(rho)    # From power attenuation to field attenuation
        stimulated_raman_scattering = StimulatedRamanScattering(freq_array, bvp_solution.x, rho, bvp_solution.y)

        self._stimulated_raman_scattering = stimulated_raman_scattering

    def _residuals_stimulated_raman(self, ya, yb, power_spectrum, prop_direct):

        computed_boundary_value = zeros(ya.size)

        for index, direction in enumerate(prop_direct):
            if direction == +1:
                computed_boundary_value[index] = ya[index]
            else:
                computed_boundary_value[index] = yb[index]

        return power_spectrum - computed_boundary_value

    def _initial_guess_stimulated_raman(self, z, power_spectrum, alphap_fiber, prop_direct):
        """ Computes the initial guess knowing the boundary conditions
        :param z: patial axis [m]. numpy array
        :param power_spectrum: power in each frequency slice [W].
        Frequency axis is defined by freq_array. numpy array
        :param alphap_fiber: frequency dependent fiber attenuation of signal power [1/m].
        Frequency defined by freq_array. numpy array
        :param prop_direct: indicates the propagation direction of each power slice in power_spectrum:
        +1 for forward propagation and -1 for backward propagation. Frequency defined by freq_array. numpy array
        :return: power_guess: guess on the initial conditions [W].
        The first ndarray index identifies the frequency slice,
        the second ndarray index identifies the step in z. ndarray
        """

        power_guess = empty((power_spectrum.size, z.size))
        for f_index, power_slice in enumerate(power_spectrum):
            if prop_direct[f_index] == +1:
                power_guess[f_index, :] = exp(-alphap_fiber[f_index] * z) * power_slice
            else:
                power_guess[f_index, :] = exp(-alphap_fiber[f_index] * z[::-1]) * power_slice

        return power_guess

    def _ode_stimulated_raman(self, z, power_spectrum, alphap_fiber, freq_array, cr_raman_matrix, prop_direct):
        """ Aim of ode_raman is to implement the set of ordinary differential equations (ODEs)
        describing the Raman effect.
        :param z: spatial axis (unused).
        :param power_spectrum: power in each frequency slice [W].
        Frequency axis is defined by freq_array. numpy array. Size n
        :param alphap_fiber: frequency dependent fiber attenuation of signal power [1/m].
        Frequency defined by freq_array. numpy array. Size n
        :param freq_array: reference frequency axis [Hz]. numpy array. Size n
        :param cr_raman: Cr(f) Raman gain efficiency variation in frequency [1/W/m].
        Frequency defined by freq_array. numpy ndarray. Size nxn
        :param prop_direct: indicates the propagation direction of each power slice in power_spectrum:
        +1 for forward propagation and -1 for backward propagation.
        Frequency defined by freq_array. numpy array. Size n
        :return: dP/dz: the power variation in dz [W/m]. numpy array. Size n
        """

        dpdz = nan * ones(power_spectrum.shape)
        for f_ind, power in enumerate(power_spectrum):
            cr_raman = cr_raman_matrix[f_ind, :]
            vibrational_loss = freq_array[f_ind] / freq_array[:f_ind]

            for z_ind, power_sample in enumerate(power):
                raman_gain = sum(cr_raman[f_ind + 1:] * power_spectrum[f_ind + 1:, z_ind])
                raman_loss = sum(vibrational_loss * cr_raman[:f_ind] * power_spectrum[:f_ind, z_ind])

                dpdz_element = prop_direct[f_ind] * (-alphap_fiber[f_ind] + raman_gain - raman_loss) * power_sample
                dpdz[f_ind][z_ind] = dpdz_element

        return vstack(dpdz)

class NliSolver:
    """ This class implements the NLI models.
        Model and method can be specified in `sim_params.nli_params.method`.
        List of implemented methods:
        'gn_model_analytic': eq. 120 from arXiv:1209.0394
        'ggn_spectrally_separated_xpm_spm': XPM plus SPM
    """

    def __init__(self, fiber=None):
        """ Initialize the Nli solver object.
        :param fiber: instance of elements.py/Fiber.
        """
        self._fiber = fiber
        self._stimulated_raman_scattering = None

    @property
    def fiber(self):
        return self._fiber

    @property
    def stimulated_raman_scattering(self):
        return self._stimulated_raman_scattering

    @stimulated_raman_scattering.setter
    def stimulated_raman_scattering(self, stimulated_raman_scattering):
        self._stimulated_raman_scattering = stimulated_raman_scattering

    def compute_nli(self, carrier, *carriers):
        """ Compute NLI power generated by the WDM comb `*carriers` on the channel under test `carrier`
        at the end of the fiber span.
        """
        if 'gn_model_analytic' == sim_params.nli_params.method:
            carrier_nli = self._gn_analytic(carrier, *carriers)
        elif 'ggn_spectrally_separated' in sim_params.nli_params.method:
            eta_matrix = self._compute_eta_matrix(carrier, *carriers)
            carrier_nli = self._carrier_nli_from_eta_matrix(eta_matrix, carrier, *carriers)
        else:
            raise ValueError(f'Method {sim_params.nli_params.method} not implemented.')

        return carrier_nli

    @staticmethod
    def _carrier_nli_from_eta_matrix(eta_matrix, carrier, *carriers):
        carrier_nli = 0
        for pump_carrier_1 in carriers:
            for pump_carrier_2 in carriers:
                carrier_nli += eta_matrix[pump_carrier_1.channel_number - 1, pump_carrier_2.channel_number - 1] * \
                    pump_carrier_1.power.signal * pump_carrier_2.power.signal
        carrier_nli *= carrier.power.signal

        return carrier_nli

    def _compute_eta_matrix(self, cut_carrier, *carriers):
        cut_index = cut_carrier.channel_number - 1
        # Matrix initialization
        matrix_size = max(carriers, key=lambda x: getattr(x, 'channel_number')).channel_number
        eta_matrix = zeros(shape=(matrix_size, matrix_size))

        # SPM
        logger.debug(f'Start computing SPM on channel #{cut_carrier.channel_number}')
        # SPM GGN
        if 'ggn' in sim_params.nli_params.method:
            partial_nli = self._generalized_spectrally_separated_spm(cut_carrier)
        # SPM GN
        elif 'gn' in sim_params.nli_params.method:
            partial_nli = self._gn_analytic(cut_carrier, *[cut_carrier])
        eta_matrix[cut_index, cut_index] = partial_nli / (cut_carrier.power.signal**3)

        # XPM
        for pump_carrier in carriers:
            pump_index = pump_carrier.channel_number - 1
            if not (cut_index == pump_index):
                logger.debug(f'Start computing XPM on channel #{cut_carrier.channel_number} '
                             f'from channel #{pump_carrier.channel_number}')
                # XPM GGN
                if 'ggn' in sim_params.nli_params.method:
                    partial_nli = self._generalized_spectrally_separated_xpm(cut_carrier, pump_carrier)
                # XPM GGN
                elif 'gn' in sim_params.nli_params.method:
                    partial_nli = self._gn_analytic(cut_carrier, *[pump_carrier])
                eta_matrix[pump_index, pump_index] = \
                    partial_nli / (cut_carrier.power.signal * pump_carrier.power.signal**2)
        return eta_matrix

    # Methods for computing GN-model
    def _gn_analytic(self, carrier, *carriers):
        """ Computes the nonlinear interference power on a single carrier.
        The method uses eq. 120 from arXiv:1209.0394.
        :param carrier: the signal under analysis
        :param carriers: the full WDM comb
        :return: carrier_nli: the amount of nonlinear interference in W on the carrier under analysis
        """
        beta2 = self.fiber.params.beta2
        gamma = self.fiber.params.gamma
        effective_length = self.fiber.params.effective_length
        asymptotic_length = self.fiber.params.asymptotic_length

        g_nli = 0
        for interfering_carrier in carriers:
            g_interfering = interfering_carrier.power.signal / interfering_carrier.baud_rate
            g_signal = carrier.power.signal / carrier.baud_rate
            g_nli += g_interfering**2 * g_signal \
                * _psi(carrier, interfering_carrier, beta2=beta2, asymptotic_length=asymptotic_length)
        g_nli *= (16.0 / 27.0) * (gamma * effective_length) ** 2 /\
                 (2 * pi * abs(beta2) * asymptotic_length)
        carrier_nli = carrier.baud_rate * g_nli
        return carrier_nli

    # Methods for computing the GGN-model
    def _generalized_spectrally_separated_spm(self, carrier):
        gamma = self.fiber.params.gamma
        f_cut_resolution = sim_params.nli_params.f_cut_resolution['delta_0']
        f_eval = carrier.frequency
        g_cut = (carrier.power.signal / carrier.baud_rate)

        spm_nli = carrier.baud_rate * (16.0 / 27.0) * gamma ** 2 * g_cut ** 3 * \
            self._generalized_psi(carrier, carrier, f_eval, f_cut_resolution, f_cut_resolution)
        return spm_nli

    def _generalized_spectrally_separated_xpm(self, cut_carrier, pump_carrier):
        gamma = self.fiber.params.gamma
        delta_index = pump_carrier.channel_number - cut_carrier.channel_number
        f_cut_resolution = sim_params.nli_params.f_cut_resolution[f'delta_{delta_index}']
        f_pump_resolution = sim_params.nli_params.f_pump_resolution
        f_eval = cut_carrier.frequency
        g_pump = (pump_carrier.power.signal / pump_carrier.baud_rate)
        g_cut = (cut_carrier.power.signal / cut_carrier.baud_rate)
        frequency_offset_threshold = self._frequency_offset_threshold(pump_carrier.baud_rate)
        if abs(cut_carrier.frequency - pump_carrier.frequency) <= frequency_offset_threshold:
            xpm_nli = cut_carrier.baud_rate * (16.0 / 27.0) * gamma ** 2 * g_pump**2 * g_cut * \
                2 * self._generalized_psi(cut_carrier, pump_carrier, f_eval, f_cut_resolution, f_pump_resolution)
        else:
            xpm_nli = cut_carrier.baud_rate * (16.0 / 27.0) * gamma ** 2 * g_pump**2 * g_cut * \
                2 * self._fast_generalized_psi(cut_carrier, pump_carrier, f_eval, f_cut_resolution)
        return xpm_nli

    def _fast_generalized_psi(self, cut_carrier, pump_carrier, f_eval, f_cut_resolution):
        """ It computes the generalized psi function similarly to the one used in the GN model
        :return: generalized_psi
        """
        # Fiber parameters
        alpha0 = self.fiber.alpha0(f_eval)
        beta2 = self.fiber.params.beta2
        beta3 = self.fiber.params.beta3
        f_ref_beta = self.fiber.params.ref_frequency
        z = self.stimulated_raman_scattering.z
        frequency_rho = self.stimulated_raman_scattering.frequency
        rho_norm = self.stimulated_raman_scattering.rho * exp(abs(alpha0) * z / 2)
        if len(frequency_rho) == 1:
            def rho_function(f): return rho_norm[0, :]
        else:
            rho_function = interp1d(frequency_rho, rho_norm, axis=0, fill_value='extrapolate')
        rho_norm_pump = rho_function(pump_carrier.frequency)

        f1_array = array([pump_carrier.frequency - (pump_carrier.baud_rate * (1 + pump_carrier.roll_off) / 2),
                         pump_carrier.frequency + (pump_carrier.baud_rate * (1 + pump_carrier.roll_off) / 2)])
        f2_array = arange(cut_carrier.frequency,
                          cut_carrier.frequency + (cut_carrier.baud_rate * (1 + cut_carrier.roll_off) / 2),
                          f_cut_resolution)  # Only positive f2 is used since integrand_f2 is symmetric

        integrand_f1 = zeros(len(f1_array))
        for f1_index, f1 in enumerate(f1_array):
            delta_beta = 4 * pi**2 * (f1 - f_eval) * (f2_array - f_eval) * \
                (beta2 + pi * beta3 * (f1 + f2_array - 2 * f_ref_beta))
            integrand_f2 = self._generalized_rho_nli(delta_beta, rho_norm_pump, z, alpha0)
            integrand_f1[f1_index] = 2 * trapz(integrand_f2, f2_array)  # 2x since integrand_f2 is symmetric in f2
        generalized_psi = 0.5 * sum(integrand_f1) * pump_carrier.baud_rate
        return generalized_psi

    def _generalized_psi(self, cut_carrier, pump_carrier, f_eval, f_cut_resolution, f_pump_resolution):
        """ It computes the generalized psi function similarly to the one used in the GN model
        :return: generalized_psi
        """
        # Fiber parameters
        alpha0 = self.fiber.alpha0(f_eval)
        beta2 = self.fiber.params.beta2
        beta3 = self.fiber.params.beta3
        f_ref_beta = self.fiber.params.ref_frequency
        z = self.stimulated_raman_scattering.z
        frequency_rho = self.stimulated_raman_scattering.frequency
        rho_norm = self.stimulated_raman_scattering.rho * exp(abs(alpha0) * z / 2)
        if len(frequency_rho) == 1:
            def rho_function(f): return rho_norm[0, :]
        else:
            rho_function = interp1d(frequency_rho, rho_norm, axis=0, fill_value='extrapolate')
        rho_norm_pump = rho_function(pump_carrier.frequency)

        f1_array = arange(pump_carrier.frequency - (pump_carrier.baud_rate * (1 + pump_carrier.roll_off) / 2),
                          pump_carrier.frequency + (pump_carrier.baud_rate * (1 + pump_carrier.roll_off) / 2),
                          f_pump_resolution)
        f2_array = arange(cut_carrier.frequency - (cut_carrier.baud_rate * (1 + cut_carrier.roll_off) / 2),
                          cut_carrier.frequency + (cut_carrier.baud_rate * (1 + cut_carrier.roll_off) / 2),
                          f_cut_resolution)
        psd1 = raised_cosine_comb(f1_array, pump_carrier) * (pump_carrier.baud_rate / pump_carrier.power.signal)

        integrand_f1 = zeros(len(f1_array))
        for f1_index, (f1, psd1_sample) in enumerate(zip(f1_array, psd1)):
            f3_array = f1 + f2_array - f_eval
            psd2 = raised_cosine_comb(f2_array, cut_carrier) * (cut_carrier.baud_rate / cut_carrier.power.signal)
            psd3 = raised_cosine_comb(f3_array, pump_carrier) * (pump_carrier.baud_rate / pump_carrier.power.signal)
            ggg = psd1_sample * psd2 * psd3

            delta_beta = 4 * pi**2 * (f1 - f_eval) * (f2_array - f_eval) * \
                (beta2 + pi * beta3 * (f1 + f2_array - 2 * f_ref_beta))

            integrand_f2 = ggg * self._generalized_rho_nli(delta_beta, rho_norm_pump, z, alpha0)
            integrand_f1[f1_index] = trapz(integrand_f2, f2_array)
        generalized_psi = trapz(integrand_f1, f1_array)
        return generalized_psi

    @staticmethod
    def _generalized_rho_nli(delta_beta, rho_norm_pump, z, alpha0):
        w = 1j * delta_beta - alpha0
        generalized_rho_nli = (rho_norm_pump[-1]**2 * exp(w * z[-1]) - rho_norm_pump[0]**2 * exp(w * z[0])) / w
        for z_ind in range(0, len(z) - 1):
            derivative_rho = (rho_norm_pump[z_ind + 1]**2 - rho_norm_pump[z_ind]**2) / (z[z_ind + 1] - z[z_ind])
            generalized_rho_nli -= derivative_rho * (exp(w * z[z_ind + 1]) - exp(w * z[z_ind])) / (w**2)
        generalized_rho_nli = abs(generalized_rho_nli)**2
        return generalized_rho_nli

    def _frequency_offset_threshold(self, symbol_rate):
        k_ref = 5
        beta2_ref = 21.3e-27
        delta_f_ref = 50e9
        rs_ref = 32e9
        beta2 = abs(self.fiber.params.beta2)
        freq_offset_th = ((k_ref * delta_f_ref) * rs_ref * beta2_ref) / (beta2 * symbol_rate)
        return freq_offset_th

def _psi(carrier, interfering_carrier, beta2, asymptotic_length):
    """Calculates eq. 123 from `arXiv:1209.0394 <https://arxiv.org/abs/1209.0394>`__"""
    if carrier.channel_number == interfering_carrier.channel_number:  # SCI, SPM
        psi = arcsinh(0.5 * pi**2 * asymptotic_length * abs(beta2) * carrier.baud_rate**2)
    else:  # XCI, XPM
        delta_f = carrier.frequency - interfering_carrier.frequency
        psi = arcsinh(pi**2 * asymptotic_length * abs(beta2) *
                      carrier.baud_rate * (delta_f + 0.5 * interfering_carrier.baud_rate))
        psi -= arcsinh(pi**2 * asymptotic_length * abs(beta2) *
                       carrier.baud_rate * (delta_f - 0.5 * interfering_carrier.baud_rate))
    return psi

def estimate_nf_model(type_variety, gain_min, gain_max, nf_min, nf_max):
    if nf_min < -10:
        raise EquipmentConfigError(f'Invalid nf_min value {nf_min!r} for amplifier {type_variety}')
    if nf_max < -10:
        raise EquipmentConfigError(f'Invalid nf_max value {nf_max!r} for amplifier {type_variety}')

    # NF estimation model based on nf_min and nf_max
    # delta_p:  max power dB difference between first and second stage coils
    # dB g1a:   first stage gain - internal VOA attenuation
    # nf1, nf2: first and second stage coils
    #           calculated by solving nf_{min,max} = nf1 + nf2 / g1a{min,max}
    delta_p = 5
    g1a_min = gain_min - (gain_max - gain_min) - delta_p
    g1a_max = gain_max - delta_p
    nf2 = lin2db((db2lin(nf_min) - db2lin(nf_max)) /
                 (1 / db2lin(g1a_max) - 1 / db2lin(g1a_min)))
    nf1 = lin2db(db2lin(nf_min) - db2lin(nf2) / db2lin(g1a_max))

    if nf1 < 4:
        raise EquipmentConfigError(f'First coil value too low {nf1} for amplifier {type_variety}')

    # Check 1 dB < delta_p < 6 dB to ensure nf_min and nf_max values make sense.
    # There shouldn't be high nf differences between the two coils:
    #    nf2 should be nf1 + 0.3 < nf2 < nf1 + 2
    # If not, recompute and check delta_p
    if not nf1 + 0.3 < nf2 < nf1 + 2:
        nf2 = clip(nf2, nf1 + 0.3, nf1 + 2)
        g1a_max = lin2db(db2lin(nf2) / (db2lin(nf_min) - db2lin(nf1)))
        delta_p = gain_max - g1a_max
        g1a_min = gain_min - (gain_max - gain_min) - delta_p
        if not 1 < delta_p < 11:
            raise EquipmentConfigError(f'Computed \N{greek capital letter delta}P invalid \
                \n 1st coil vs 2nd coil calculated DeltaP {delta_p:.2f} for \
                \n amplifier {type_variety} is not valid: revise inputs \
                \n calculated 1st coil NF = {nf1:.2f}, 2nd coil NF = {nf2:.2f}')
    # Check calculated values for nf1 and nf2
    calc_nf_min = lin2db(db2lin(nf1) + db2lin(nf2) / db2lin(g1a_max))
    if not isclose(nf_min, calc_nf_min, abs_tol=0.01):
        raise EquipmentConfigError(f'nf_min does not match calc_nf_min, {nf_min} vs {calc_nf_min} for amp {type_variety}')
    calc_nf_max = lin2db(db2lin(nf1) + db2lin(nf2) / db2lin(g1a_min))
    if not isclose(nf_max, calc_nf_max, abs_tol=0.01):
        raise EquipmentConfigError(f'nf_max does not match calc_nf_max, {nf_max} vs {calc_nf_max} for amp {type_variety}')

    return nf1, nf2, delta_p

redpajama_set_name: RedPajamaGithub


The University of Texas System Board of Regents named Secretary of the U.S. Air Force Heather Wilson – a former university president and U.S. congresswoman from New Mexico – as the sole finalist for the president of UT El Paso.
The University of Texas System Board of Regents will hold a special called meeting via telephone conference call at 9:30 a.m., Friday, March 8, in Austin.
The Cancer Prevention & Research Institute of Texas (CPRIT) has announced its latest round of grants, and once again UT institutions will receive the majority of the funding.
Visit the News Archive to search for news by date.


text:
La sonate  203 (380) en mi mineur est une œuvre pour clavier du compositeur italien Domenico Scarlatti.

Présentation
La sonate  203, en mi mineur, notée Vivo non molto, forme le second membre d'une paire, avec la sonate K. 198 de même tonalité ; toutes les deux se trouvent dans le  du manuscrit de Parme. Les sonates  à 205 ne figurent pas dans les manuscrits de Venise et la  y est présentée seule, dans le , 27. La sonate commence avec une série de notes ornées et se poursuit avec de curieuses syncopes asymétriques. L'œuvre est peut-être liée à une autre : la Toccata I en ré mineur (1744) du compositeur et organiste Vicente Rodríguez (1685–1760).

Manuscrits
Le manuscrit principal est le numéro 21 de  (Ms. A. G. 31409), copié en 1752 ; les autres sont Münster  (Sant Hs 3966) et Vienne  (VII 28011 E). Une copie figure à Lisbonne, ms. FCR/194.1 ().

Interprètes
La sonate  203 est défendue au piano, notamment par Jenő Jandó (1999, Naxos), Linda Nicholson (2004, Capriccio), Carlo Grante (2009, Music & Arts, ) et Alberto Urroz (2017, IBS) ; au clavecin par Luciano Sgrizzi (1964, Accord), Huguette Dreyfus (1978, Denon), Scott Ross (1985, Erato), Luc Beauséjour (1995 Analekta), Andreas Staier (1991, DHM), Richard Lester (2003, Nimbus, ) et Pieter-Jan Belder (Brilliant Classics) et Mario Martinoli (2015, Etcetera). Janne Rättyä la joue à l'accordéon (2014, Ondine).

Notes et références

Sources

Articles connexes
 Vivo : 125, 153, 168, 178, 195, 201, 205, 218, 222, 224, 242, 262, 264, 271, 273, 433.

Liens externes

203
Œuvre en mi mineur

redpajama_set_name: RedPajamaWikipedia


text:
About Poplar Bluff
Starting With the Early Settlers
The character of the early settlers of this county is evidenced in the choice they made for the county seat. They could well have chosen the Cane Creek area, site of the first settlement. It would no doubt have been a convenience to them and a financial benefit, but they chose to have their county seat in the center of the county on a navigable river to make it more easily accessible to all who would come here to live. The river in the early days of the 19th century.was used like a major highway is today.
They chose an uninhabited bluff on Black River on which to place the town that would contain their governing offices, and they named it for the beautiful poplar trees that grew profusely on that bluff. They called it Poplar Bluff and they set out to build a town, their county seat. By the end of 1850 a town had been started and some ten families lived in Poplar Bluff. In 1855 the first court house was built and the town grew, and finally, on Feb. 9, 1870, Poplar Bluff was incorporated.
The City's Historical Preservation Commission has more information on the history of Poplar Bluff and the area. The website features numerous history articles and photos, an online coloring book, puzzles, a trivia quiz and a place for Poplar Bluff area residents and former residents to share their recollections.


That carousel provides a distinctive and appropriately nostalgic backdrop for the band's video for the song "Preoccupied," which debuted online last month. Directed by Milwaukee filmmaker Damien Blue, a Madison native and friend of the band, the video is a surrealist, color-drenched embodiment of the emotions that inspired the song.
That disconnect is underscored by the fact that the video has no explicit narrative. Instead, it's shot as a series of images at various locations around Madison — an indoor pool, an outdoor concert, the spinning carousel. There's a textural approach to the storytelling, and the joyful imagery provides a powerful contrast to the song's unapologetic jadedness.
The members of Slow Pulp have been playing together for about five years, releasing a six-song EP in 2017. The group has developed a sound that brings to mind the psychedelic-influenced indie rock of bands like Tame Impala. But within that framework, the EP showcases impressive versatility in songwriting and influences, slipping easily from driving, distortion-heavy post-punk to lighter, more melodic indie pop.
The band is currently working on revisiting and refining older demo recordings, and the next goal is to release a single. Most of the band members are visual artists in addition to being musicians, and they hope to continue exploring video and other visual mediums to enhance and contextualize their creative work in new ways. And they hope other bands will follow suit.


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text:
elwang@fullerton.edu
Office: GH-427
Dept: 657-278-3163
Elise Wang
My research focuses on the language of justice in medieval literature, and I am particularly interested in how "old" ideas of guilt and morality can expand our understanding of our own, contemporary problems. My current book project concerns felony in medieval literature and argues that some aspects of felony were originally and essentially literary, which means that I treat poets as serious legal thinkers and read legal documents with an eye to their language and form. I am also working on a project on secrecy and conspiracy narratives, following up on a long-standing interest in conspiracy theories (both medieval and modern). Before coming to CSUF in 2020, I spent two years teaching at Duke University, and before that received my doctorate from Princeton University. During that time, I also spent five years teaching writing and literature in New Jersey correctional facilities.
2018, Ph.D., Princeton University
2011, M.St., Oxford University
2007, A.B., Harvard University
Literatures of late medieval England; Piers Plowman; legal history; punishment theory; premodern race and critical race theory; conspiracy narratives; early global literatures.
MW 11:30am -1:00pm
Last Published 1/25/23


text:
Les conclusions factuelles du juge de première instance à l'égard de l'abus de procédure méritent déférence en appel
La Cour d'appel nous enseigne souvent que l'appel n'est pas un forum pour refaire le procès de première instance. Cela est particulièrement vrai en matière d'abus de procédure comme le rappelle l'Honorable juge Nicholas Kasirer dans la décision récente rendue dans l'affaire Pileggi c. Paliotti (2018 QCCA 2172). En effet, les conclusions factuelles d'abus du juge de première instance méritent déférence.
Dans cette affaire, le juge Kasirer est saisi de la demande de permission d'en appeler d'un jugement de première instance qui a rejeté un recours en diffamation. Ce jugement a également conclu à l'abus de procédure.
Le juge Kasirer note d'emblée que le Requérant tente de refaire le procès de première instance. Rappelant que ce n'est pas le rôle de la Cour d'appel, le juge Kasirer souligne que les conclusions d'abus méritent déférence:
[13] As to the finding of abuse, here too the petitioner contests the judge's evaluation of the facts leading to his conclusion that the petitioner's action was "an inappropriately aggressive use of court procedure". The judge noted that, after weighing the evidence, that the action had no foundation in the absence of any proof of damage and with questionable evidence of fault. Deference is owed to the judge in respect of this factual aspect of a finding of abuse of proceedings: Centre commercial Innovation inc. v. Institut de dermopigmentation, 2011 QCCA 1954 (CanLII), para. [2]. By his various arguments, the petitioner seeks to retry this matter which, needless to say, is not the function of the Court. The sum of his arguments is a far cry from constituting a question meriting the attention of the Court.
[14] It is of no moment that, prior to the judge's full review of the facts, motions to dismiss the action brought by the defendants had been dismissed. In response to these proceedings, the petitioner was given the benefit of the doubt. This does not change the fact that when all the evidence was in, the judge was entitled to conclude that the action amounted to an abuse of proceedings.
[15] The judge plainly understood the petitioner's conduct before the courts as amounting to a civil wrong that caused harm, in particular as an exercise designed to put the defendants and the Spiritual Center under personal and financial strain. While the judge does not use these specific terms found in article 51 C.C.P., one understands he is of the view that the petitioner's conduct before the court was "excessive" and "unreasonable", and amounts to an "attempt to defeat the ends of justice" by using the defamation suit to achieve another purpose – in short, a civil fault that gives rise to a claim for compensation.
Référence : [2019] ABD 4
Libellés : 51 C.p.c., abus de procédure, Appel, Permission d'en appeler


2306596 Youth_Brad_Keselowski_Navy_Race_Day_T-Shirt http://images.fanatics.com/lf http://dmimages.ff.p10/chains/2306596.txt false We are unable to customize this item with the text you have entered. Please try a different entry again. Your current entry cannot be processed due to guidelines for past and present player names. Please create a new entry.
Let your young fan show off some Brad Keselowski spirit with this Race Day tee. A bold driver number with additional high-quality graphics adorn the chest. With this tee, your youngster will have the perfect race day look!


LONDON – Breaking from the rest of the recording industry, EMI Group said Monday it will begin selling songs online that are free of copy-protection technology through Apple Inc.'s iTunes Store. The deal, however, doesn't include music from the label's biggest act, The Beatles.
ITunes customers will soon be able to buy songs by the Rolling Stones, Norah Jones, Coldplay and other top-selling artists for $1.29, or 30 cents more than the copy-protected version.
The premium tunes also will be offered in a higher quality than the 99-cent tracks.
EMI Chief Executive Eric Nicoli said The Beatles music catalog is excluded from the deal but the company was "working on it." He declined to set a time frame for negotiations over the catalog.
The announcement followed calls by Apple Chief Executive Steve Jobs earlier this year for the world's four major record companies, including EMI Group PLC, to start selling songs online without copy-protection software.
The technology, known as digital rights management, or DRM, is designed to combat piracy by preventing unauthorized copying or sharing, but it also can be a consumer headache. Some music players, for instance, support one type of DRM software but not others.
The DRM used by Apple does not work with competing services or devices, meaning that consumers can download songs from iTunes to work only on their computers or iPod music players. The lock between the download services and players has drawn criticism from European industry regulators, who argue that it limits buyer choice.
Jobs said he planned to offer around half of all music in the iTunes store under the premium package by the end of the year, but declined to say whether the company was in discussions with other leading record companies.


During his second presidency, Barack Obama traveled through several Alaskan cities and became the first president to visit the Arctic Circle. The White House hoped to bring into focus the effects of climate change and improve ties with Native Americans. Obama stayed at Kotzebue, a small town of 3,000, that is battling coastal erosion.
Andrea Martin returns as Berthe in revival of "Pippin"
Pippin is a musical by Stephen Schwartz and Roger O. Hirson. Director of the original Broadway production, Bob Fosse contributed to the libretto. During the Broadway revival, the musical was performed at the Music Box Theatre. Andrea Martin, a Tony award winner, signed up for a limited engagement to play the role of Berthe again.
The Sims 4 is the fourth major title in the life simulation video game series The Sims. The Sims 4 is the first PC game to top all-format charts in two years. The game has received mixed reviews since its release, with the majority of criticism directed towards its lack of content.
Celaena travels to Wendlyn, where she must train as a Fae with the immortal warrior Rowan Whitethorn. Tensions high between them, the pair have to work together to stop evil forces from rising, as well as learning to accept herself as the Queen of Terrasen. Meanwhile, in Adarlan, Chaol teams with General Aedion Ashryver to rebel against the king.
Microsoft announced its acquisition of Nokia's devices and services businesses, which closed with the formation of a Finnish subsidiary, Microsoft Mobile. On smartphones, the Nokia name was phased out in favour of Microsoft branding on future Lumia products.
The eastern span replacement of the San Francisco–Oakland Bay Bridge was a construction project to replace a seismically unsound portion of the Bay Bridge with a new self-anchored suspension bridge and a pair of viaducts. The bridge is in the U.S. state of California and crosses the San Francisco Bay between Yerba Buena Island and Oakland.
John received the first Brits Icon Award for his "lasting impact" on UK Culture. Rod Stewart presented him the award on stage at the London Palladium before the two performed a duet of "Sad Songs". John's 31st album, The Diving Board, produced by T-Bone Burnett was released in September 2013 and reached No. 3 in the UK and No. 4 in the US.
In 1967 he conducted the first human to human heart transplant ever. Unfortunately, Bernard's patient, Louis Washkansky, died of pneumonia. It was a consequence of a therapy by immunosuppressants. Barnard also invented therapy for intestinal atresia (malformation of the intestine found in small children).
Swissair Flight 111 took off from John F. Kennedy International Airport in NYC and was on its way to Cointrin International Airport in Geneva. The plane crashed into the Atlantic Ocean at the entrance to St. Margarets Bay, Nova Scotia. All of the passengers and crew members onboard of McDonnell Douglas MD-11 were killed.
"You Are Not Alone" is a song by American recording artist Michael Jackson from his 9th studio album HIStory: Past, Present and Future, Book I. Commercially, the song was a significant success. It holds a Guinness World Record as the first song in the 37-year history of the Billboard Hot 100 to debut at number 1; it was later certified platinum by the RIAA.
"Jeremy" is a song by the American rock band Pearl Jam, with lyrics written by vocalist Eddie Vedder and music written by bassist Jeff Ament. "Jeremy" was released in 1992 as the third single from Pearl Jam's debut album Ten. In 1993, the "Jeremy" video was awarded 4 MTV Video Music Awards, including Best Video of the Year, which made them discuss openly sense of this award.
Marcus Ericsson is a Swedish racing driver currently racing for Sauber in Formula One, where he is contracted to the end of the 2018 season. Ericsson completed the 2013 GP2 season with DAMS and debuted in Formula One in 2014 with Caterham F1.
Zedd is a Russian-German record producer, DJ, multi-instrumentalist, and songwriter. He primarily produces and performs electro house music, but has diversified his genre and musical style, drawing influences from progressive house, dubstep, and classical music.
Human Rights Now! was a worldwide tour of 20 benefit concerts on behalf of Amnesty International that took place over 6 weeks in 1988. The shows featured Bruce Springsteen and the E Street Band, Sting, Peter Gabriel, Tracy Chapman, and Youssou N'Dour, plus guest artists from each of the countries where concerts were held.
It was a format of optical media disc. It combined the technologies of standard compact disc and LaserDisc. CD Video discs had a distinctive gold colour, to differentiate them from regular silver-coloured audio CDs. This is a characteristic that would later be replicated in HVD, a more advanced disc format.
First signs of the shipwreck were pieces of debris, found the previous day. The pictures of the wreckage taken by remotely controlled deep-sea vehicle Argo followed soon. RMS Titanic lies at a depth of about 3.8 km; about 600 km south-southeast off the coast of Newfoundland. The ship is broken in two parts.
The Milperra Massacre was a firearm battle between rival motorcycle gang members. The shootout had its roots in an intense rivalry that developed after a group of Comancheros broke away and formed the first Bandidos Motorcycle Club chapter in Australia. Seven people were killed and twenty-eight injured when the two groups clashed at Milperra.
George Harrison was an English guitarist, singer-songwriter, and producer who achieved international fame as the lead guitarist of the Beatles. Harrison married Dark Horse Records' secretary Olivia Trinidad Arias in 1978. They had met at the A&M Records offices in Los Angeles, and together had one son, Dhani Harrison.
Famous English writer, J. R. R. Tolkien, best known for his high fantasy works The Hobbit, The Lord of the Rings and The Silmarillion, died at the age of 82, in a coastal resort town of Bournemouth, on the south coast of England. He was buried together in a grave with his previously deceased wife at Wolvercote Cemetery in Oxford.
"You Wear It Well" is a song written by Rod Stewart and Martin Quittenton, performed by Stewart. It utilizes an arrangement markedly similar to that of "Maggie May," one of Stewart's hits from the previous year. Stewart recorded "You Wear It Well" for the 1972 album Never a Dull Moment. The song became an international hit, reaching number 1 on the UK Singles Chart.
In 1968 the ATM was pioneered in the U.S. by Donald Wetzel, who was a department head at a company called Docutel. Docutel was a subsidiary of Recognition Equipment Inc of Dallas, Texas, which was producing optical scanning equipment and had instructed Docutel to explore automated baggage handling and automated gasoline pumps.
Salma Hayek Pinault is a Mexican and American film actress, producer, and former model. She began her career in Mexico starring in the film El Callejón de los Milagros for which she was nominated for an Ariel Award. In 1991 Hayek moved to Hollywood and came to prominence with roles in films such as Desperado, Dogma, and Wild Wild West.
The band recorded a six-song demo in September 1965 at World Pacific Studios, Los Angeles, California. This has circulated widely since then as a bootleg recording. The band took their name from the title of Aldous Huxley's book The Doors of Perception, itself derived from a line in William Blake's The Marriage of Heaven and Hell.
Keanu Charles Reeves is a Canadian actor, director, producer, and musician. He gained fame for his starring role performances in several blockbuster films, including Bill and Ted franchise, The Devil's Advocate, and the Matrix franchise. Reeves has earned critical acclaim for his acting.
Walter Cronkite became an anchor of the program titled Walter Cronkite with the News. On September 2, the program, retitled CBS Evening News, became the first half-hour weeknight news broadcast of network television and was moved to 6:30 p.m. Eastern time.
The Third Man is a British film directed by Carol Reed, written by Graham Greene, and starring Joseph Cotten, Valli, Orson Welles, and Trevor Howard. It centers on Holly Martins, an American who is given a job in Vienna by his friend Harry Lime, but when Holly arrives in Vienna he gets the news that Lime is dead.
She was one of the seven astronauts killed during the Challenger disaster. Their death caused suspension of the Space shuttle flights. McAuliffe was trained as a teacher. She joined NASA as a first member of the Teacher in Space Project. Before the disastrous flight, she prepared lessons to be broadcasted from the orbit.
William Everett Preston was an American musician whose work included R&B, rock, soul, funk, and gospel. Preston was a top session keyboardist in the 1960s, during which he backed artists such as Little Richard, Sam Cooke, Ray Charles, Rev. James Cleveland and the Beatles.
Hồ Chí Minh read the Proclamation during a public meeting in front of thousands of people, at what is now Ba Đình Square, Hà Nội, announcing the birth of the Democratic Republic of Vietnam and the country's independence from France.
The surrender of Japan brought the hostilities of WWII to a close. It was formally signed more than 2 weeks after the Emperor's recorded speech to the nation announced it. The surrender ceremony took place aboard of the USS Missouri in Tokyo Bay. Following the ceremony, investigations into Japanese war crimes began quickly.
After being arrested with her family by the Gestapo, Franks were held and questioned, then transferred to the Huis van Bewaring, an overcrowded prison. After two days, they were moved to the Westerbork transit camp where they were put to hard labor and stayed there until their deportation to the Auschwitz concentration camp.
An operetta-style musical by Rudolf Friml and Herbert Stothart, "Rose-Marie" premiered at the Imperial Theatre on Broadway. It was the longest running musical of the 20s until it was surpassed by The Student Prince. It tells the story of a French Canadian girl that loves miner Jim Kenyon who falls under suspicion for murder.
A Trip to the Moon is a French adventure film directed by Georges Méliès. The film follows a group of astronomers who travel to the Moon in a cannon-propelled capsule, explore the Moon's surface, escape from an underground group of Selenites, and return to Earth with a captive Selenite.
In September 1789, Congress created a permanent institution for the management of government finances. Alexander Hamilton took the oath of office as the first Secretary of the Treasury on September 11, 1789. Hamilton had served as George Washington's aide-de-camp during the Revolution and was of great importance in the ratification of the Constitution.
The fire gutted the medieval City of London inside the old Roman city wall. It consumed 13,200 houses, 87 parish churches, St Paul's Cathedral, and most of the buildings of the City authorities. It is estimated to have destroyed the homes of 70,000 of the City's 80,000 inhabitants.
The Third Crusade, led by Richard I of England and Philip II of France captured the important cities of Acre and Jaffa, however, the Christians failed to take Jerusalem. It ended with a treaty between Richard and Saladin that allowed unarmed Christian pilgrims and merchants to visit Muslim-controlled Jerusalem.
The Battle of Actium decided the outcome of the Final War of the Roman Republic. It was a naval battle between Octavian and the forces of Mark Antony and Cleopatra on the Ionian Sea. Octavian emerged victorious from the conflict and the Senate named him the Augustus, which is viewed as the beginning of the Roman Empire.


Will This Concepts x Nike Air Monarch Collaboration Actually Release?
Few things in sneaker culture are more intriguing than the current adoration of the Air Monarch. Everyone's favorite "dad shoe" that has seen more 10am laps around the mall than a security guard, is new and hot again. The culture that surrounds the Monarch is largely to credit, popularizing chunky retro designs from the late '80s and early '90s.
Cambridge-based Concepts Creative Director Deon Point recently alluded to an Air Monarch collaboration on his Instagram and also gave a slight preview of the shoe. Then, last night, Jordan Brand's Frank Cooker vindicated the collaborative style in its entirety.
Predominately dressed in an all-black upper, the midsole appears in white atop a sporty gum outer. A Concepts hang tag is seen near the heel while co-branding marks the shoe's tongue. And while this exciting Concepts x Nike Air Monarch shoe would be the perfect bow to tie up 2017, its future is extremely uncertain.
Point was recently a guest on Sole Collector's Full Size Run and alluded to the shoe's completion in extremely limited numbers. Yet he went on to express that the shoe may or may not release — in limbo so to speak. Would you cop given a release?


The price of the BTC has risen by approximately 35 percent since the beginning of the week and currently stands at USD 8,218. After experiencing a bear market almost throughout the last three months, the prices of almost all cryptocoins are now pointing up again – led by the BTC, all larger cryptocoins increased.
This is probably also due to the fact that the story of the approval for the Bitcoin ETF is once again bubbling in the rumour mill. Rather than this, however, the increased trade volume on the Asian market is likely to be responsible for the rise in BTC – the Japanese and Korean markets in particular stand out here.
Geopolitically, the increasing trade war between the Trump-led US government and China is also playing a role in the price increase of bitcoin. The trade war had led China to devalue its national currency, which in turn led wealthy Chinese to hedge their assets through bitcoin purchases.
I'm especially excited about Bitcon's price rising again lately since I've started to invest a bit in spring 2018 and I'd be happy if my loss would finally get turned into profit.


Community-wide "pop-up" style art activities will help promote health and community engagement in unexpected ways and locations. Open to all 352 residents, the goal is to create healthy communities through everyday acts of creativity – founded on the idea that anyone, using even the simplest of materials, can create something amazing.
352Creates is two days of community art-making and creative activities throughout the 352 area code (around Gainesville, Florida).