Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of rational tornado design wind speed for residential woodframe structures

ii LIST OF ABBREVIATIONS AND SYMBOLS . iv ACKNOWLEDGEMENTS . vi LIST OF FIGURES . ix LIST OF TABLES . xiii LIST OF APPENDICES . xiv CHAPTER 1: INTRODUCTION . 1 1.1 Background . 1 1.2 Motivation . 4 1.3 Literature Review . 6 1.3. 1 Tornado Forces on Buildings . 6 1.3. 2 Performance Based Design . 7 1.4 Research Purpose . 14 1.4. 1 Scope and Objective . 14 1.4. 2 Study Limitations . 15 CHAPTER 2: FRAGILITY MODELING . 16 2.1 Development of Baseline Structures . 16 2.2 Fragility Analysis . 22 2.3 Limit States . 24
Harnessing Collective Intelligence on Social Networks

Crowdsourcing is an approach to replace the work traditionally done by a single person with the collective action of a group of people via the Internet. It has established itself in the mainstream of research methodology in recent years using a variety of approaches to engage humans in solving problems that computers, as yet, cannot solve. Several common approaches to crowdsourcing have been successful, including peer production (in which the participants are inherently interested in contributing), microworking (in which participants are paid small amounts of money per task) and games or gamification (in which the participants are entertained as they complete the tasks). An alternative approach to crowdsourcing using social networks is proposed here. Social networks offer access to large user communities through integrated software applications and, as they mature, are utilised in different ways, with decentralised and unevenly-distributed organisation of content. This research investigates whether collective intelligence systems are facilitated better on social networks and how the contributed human effort can be optimised. These questions are investigated using two case studies of problem solving: anaphoric coreference in text documents and classifying images in the marine biology domain. Social networks themselves can be considered inherent, self-organised problem solving systems, an approach defined here as ‘groupsourcing’, sharing common features with other crowdsourcing approaches; however, the benefits are tempered with the many challenges this approach presents. In comparison to other methods of crowdsourcing, harnessing collective intelligence on social networks offers a high-accuracy, data-driven and low-cost approach.
Time-lapse seismic modeling and production data assimilation for enhanced oil recovery and CO2 sequestration

Time-Lapse Seismic Modeling and Production Data Assimilation for Enhanced Oil Recovery and CO2 Sequestration. (December 2008) Ajitabh Kumar, B.Tech., Indian School of Mines, Dhanbad; M.S., The University of Texas at Austin Chair of Advisory Committee: Dr. Akhil Datta-Gupta Production from a hydrocarbon reservoir is typically supported by water or carbon dioxide (CO2) injection. CO2 injection into hydrocarbon reservoirs is also a promising solution for reducing environmental hazards from the release of green house gases into the earth’s atmosphere. Numerical simulators are used for designing and predicting the complex behavior of systems under such scenarios. Two key steps in such studies are forward modeling for performance prediction based on simulation studies using reservoir models and inverse modeling for updating reservoir models using the data collected from field. The viability of time-lapse seismic monitoring using an integrated modeling of fluid flow, including chemical reactions, and seismic response is examined. A comprehensive simulation of the gas injection process accounting for the phase behavior of CO2-reservoir fluids, the associated precipitation/dissolution reactions, and the accompanying changes in porosity and permeability is performed. The simulation
Relationship between Social support as Perceived by Adolescent and their Psychological well-being

social support play an important role in influencing students' psychological well-being . Purpose of the study was to assess the relationship between perceived social support and psychological well-being among adolescents. Design: was descriptive co-relational research design. Multi-stage random sample of 361 students was randomly selected This study was conducted at one mixed secondary and other mixed preparatory school at Daragil, Al-Shohada, Menoufyia Governorate and one mixed secondary and other mixed preparatory school at Daragil, Al-Shohada, Menoufyia Governorate . Two tools were used for data gathering. Structured interview questionnaire, Multidimensional Perceived Social Support Scale and Psychological wellbeing scale. Results: revealed that, more than half of the studied sample had moderate social support and the majority of the studied sample (80.9%) had mild psychological well-being. There was a highly statistically significant positive correlation between social support and psychological well-being of students. Conclusion, this study concluded that positive correlation existed between social support and psychological well-being. Therefore, it is recommended that:(1)plan and implement an educational programme for educating parents and the other caregiver about different pictures of social support. (2) Plan and implement an educational programme for providing all sources of caregivers with knowledge about the effects of social support on development of psychological well-being of their children and skills appropriate to the needs and development of their adolescent.
Ethiopian Healthcare Workers' Experiences During the COVID-19 Pandemic

Background: The COVID-19 pandemic has caused widespread health and socioeconomic disruptions around the world. Understanding the impact that this crisis has had on health workers and the delivery of routine health care services within countries provides evidence on pandemic preparedness and response. Here, we conduct an investigation into these factors for the Ethiopian context. Methods and findings: We conducted an online cross-sectional survey with Ethiopian health care professionals between August 27 and October 10, 2020 via existing research networks. The variables of interest were confidence in COVID-19 related knowledge, training and experience, the adoption of precautionary health practices, risk perceptions, and respondent concerns. The majority of surveyed health care professionals in Ethiopia reported seeing fewer patients than usual during the COVID-19 crisis, gaps in pandemic training, inadequate access to personal protective equipment (PPE) and barriers to accessing COVID-19 testing. Most health care professionals were also deeply concerned and worried about their own COVID-19 risks and the likelihood that they would transmit the disease to others. Conclusions: Our study findings point to a possible reduction in routine health care services during the COVID-19 pandemic and gaps in pandemic preparedness in Ethiopia. The ministry of health and other stakeholders should work towards improving access to PPE and testing, and identify approaches to ensure that essential healthcare provision (such as immunizations) is not disrupted during crises akin to the COVID-19 outbreak.
Integrating endometrial proteomic and single cell transcriptomic pipelines reveals distinct menstrual cycle and endometriosis-associated molecular profiles

Endometriosis is a debilitating gynecological disorder affecting approximately 10% of the female population. Despite its prevalence, robust methods to classify and treat endometriosis remain elusive. Changes throughout the menstrual cycle in tissue size, architecture, cellular composition, and individual cell phenotypes make it extraordinarily challenging to identify markers or cell types associated with uterine pathologies since disease-state alterations in gene and protein expression are convoluted with cycle phase variations. Here, we developed an integrated workflow to generate both proteomic and single-cell RNA-sequencing (scRNA-seq) data sets using tissues and cells isolated from the uteri of control and endometriotic donors. Using a linear mixed effect model (LMM), we identified proteins associated with cycle stage and disease, revealing a set of genes that drive separation across these two biological variables. Further, we analyzed our scRNA-seq data to identify cell types expressing cycle and disease-associated genes identified in our proteomic data. A module scoring approach was used to identify cell types driving the enrichment of certain biological pathways, revealing several pathways of interest across different cell subpopulations. Finally, we identified ligand-receptor pairs including Axl/Tyro3 - Gas6, that may modulate interactions between endometrial macrophages and/or endometrial stromal/epithelial cells. Analysis of these signaling pathways in an independent cohort of endometrial biopsies revealed a significant decrease in Tyro3 expression in patients with endometriosis compared to controls, both transcriptionally and through histological staining. This measured decrease in Tryo3 in patients with disease could serve as a novel diagnostic biomarker or treatment avenue for patients with endometriosis. Taken together, this integrated approach provides a framework for integrating LMMs, proteomic and RNA-seq data to deconvolve the complexities of complex uterine diseases and identify novel genes and pathways underlying endometriosis.
Genome-wide insights into adaptive hybridisation across the Schistosoma haematobium group in West and Central Africa

Schistosomiasis remains a public health concern across sub-Saharan Africa; current control programmes rely on accurate mapping and high mass drug administration (MDA) coverage to attempt disease elimination. Inter-species hybridisation can occur between certain species, changing epidemiological dynamics within endemic regions, which has the potential to confound control interventions. The impact of hybridisation on disease dynamics is well illustrated in areas of Cameroon where urogenital schistosomiasis, primarily due to Schistosoma haematobium and hybrid infections, now predominate over intestinal schistosomiasis caused by Schistosoma guineensis. Genetic markers have shown the ability to identify hybrids, however the underlying genomic architecture of divergence and introgression between these species has yet to be established. In this study, restriction site associated DNA sequencing (RADseq) was used on archived adult worms initially identified as; Schistosoma bovis (n = 4), S. haematobium (n = 9), S. guineensis (n = 3) and S. guineensis x S. haematobium hybrids (n = 4) from Mali, Senegal, Niger, São Tomé and Cameroon. Genome-wide evidence supports the existence of S. guineensis and S. haematobium hybrid populations across Cameroon. The hybridisation of S. guineensis x S. haematobium has not been demonstrated on the island of São Tomé, where all samples showed no introgression with S. haematobium. Additionally, all S. haematobium isolates from Nigeria, Mali and Cameroon indicated signatures of genomic introgression from S. bovis. Adaptive loci across the S. haematobium group showed that voltage-gated calcium ion channels (Cav) could play a key role in the ability to increase the survivability of species, particularly in host systems. Where admixture has occurred between S. guineensis and S. haematobium, the excess introgressive influx of tegumental (outer helminth body) and antigenic genes from S. haematobium has increased the adaptive response in hybrids, leading to increased hybrid population fitness and viability.
Genomic data support the taxonomic validity of Middle American livebearers Poeciliopsis gracilis and Poeciliopsis pleurospilus (Cyprinodontiformes: Poeciliidae)

Poeciliopsis (Cyprinodontiformes: Poeciliidae) is a genus comprised of 25 species of freshwater fishes. Several well-known taxonomic uncertainties exist within the genus, especially in relation to the taxonomic status of Poeciliopsis pleurospilus and P. gracilis. However, to date, no studies have been conducted to specifically address the taxonomic status of these two species. The goal of this study was to examine the taxonomic validity of P. pleurospilus and P. gracilis using genomic data (ddRADseq) in phylogenetic, population genetic, and species delimitation frameworks. Multiple analyses support the recognition of both taxa as distinct species and also permits us to revise their respective distributions. A species delimitation analysis indicates that P. pleurospilus and P. gracilis are distinct species, each of which consists of two distinct lineages that are geographically structured. Phylogenetic and population genetic analyses provide clear evidence that individuals of P. gracilis are distributed north and west of the Isthmus of Tehuantepec in both Pacific and Atlantic river systems in Mexico, whereas individuals of P. pleurospilus are distributed in both Atlantic and Pacific river systems south and east of the Isthmus of Tehuantepec, from southern Mexico to Honduras.
Screening coffee genotypes for brown eye spot resistance in Brazil

Several researchers have attempted to develop coffee plants that are resistant to brown eye spot (BES); however, no coffee cultivars are resistant to the disease. In the present study, a blend of strains from Cercospora coffeicola was inoculated into 19 Brazilian commercial cultivars and 41 accessions from the Germplasm Collection of Minas Gerais to evaluate the genetic resistance ability within the population and select superior genotypes for the breeding program. After predicting the genotypic values of the estudied material, the evaluations number necessary for selecting genotypes with accuracy and efficiency was determined based on the data of severity to BES. The action of defense mechanisms plant was also investigated by assessing the levels of total soluble phenolic compounds and soluble lignin in contrasting genotypes for disease susceptibility. Based on the results, the accession MG 1207 Sumatra, had an intrinsic genetic capacity to maintain low levels of severity to BES. The genotype MG 1207 Sumatra can substantially contribute to the development of new cultivars, which may lead to the reduced use of pesticides. According to the accuracy and efficiency results obtained, four evaluations BES severity are sufficient to achieve accuracy, providing expressive genetic gains. Finally, the levels of lignin and phenolic compounds were not found to be associated with the resistance of coffee genotypes to BES.
User concerns regarding information sharing on social networking sites: The user’s perspective in the context of national culture

Information sharing via social networking systems (SNS) is a common practice among academics, as well as others, that brings substantial benefits. At the same time, privacy concerns are widespread among SNS users, which may tend to inhibit their maximising the benefit from using the systems. This paper investigates the proposition that SNS user attitudes and behaviour are affected by privacy concerns, and that the effects are subject to significant cultural factors. A broad assessment of the literature provides the context for the study. Working in the context of Saudi Arabia, we apply a mixed-methods approach beginning with in-depth interviews, exposing in detail a range of views and concerns about privacy and SNS use, also allowing us to identify three key factors that bear on SNS usage and users’ concerns. Analysis of these factors in the light of the “theory of reasoned action” derives a structural model predicting several hypotheses relating the factors and users’ attitudes and behaviour. We assess the model through development of a questionnaire, administered to a large pool of academic participants, that allows us to examine how the model responds in general, and via multigroup partial least squares analyses, differentially to gender and to culturally distinct (Arab vs. non-Arab) constituents of the participant group. Results show good support for the hypotheses and clear gender and culture effects. Picking up issues from the interviews, discussion focuses on users’ views about SNS providers’ privacy policies and their inadequacy regarding culturally specific ethical concerns. We argue that these views may reflect different regulatory environments in combination with other cultural factors.
Spatial patterns of West Nile virus distribution in the Volgograd region of Russia, a territory with long-existing foci

Southern Russia remains affected by West Nile virus (WNV). In the current study, we identified the spatial determinants of WNV distribution in an area with endemic virus transmission, with special reference to the urban settings, by mapping probable points of human infection acquisition and points of virus detection in mosquitoes, ticks, birds, and mammals during 1999–2016. The suitability of thermal conditions for extrinsic virus replication was assessed based on the approach of degree-day summation and their changes were estimated by linear trend analysis. A generalized linear model was used to analyze the year-to-year variation of human cases versus thermal conditions. Environmental suitability was determined by ecological niche modelling using MaxEnt software. Human population density was used as an offset to correct for possible bias. Spatial analysis of virus detection in the environment showed significant contributions from surface temperature, altitude, and distance from water bodies. When indicators of location and mobility of the human population were included, the relative impact of factors changed, with roads becoming most important. When the points of probable human case infection were added, the percentage of leading factors changed only slightly. The urban environment significantly increased the epidemic potential of the territory and created quite favorable conditions for virus circulation. The private building sector with low-storey houses and garden plots located in the suburbs provided a connection between urban and rural transmission cycles.
A Family of Horizon-penetrating Coordinate Systems for the Schwarzschild Black Hole Geometry with Cauchy Temporal Functions

We introduce a new family of horizon-penetrating coordinate systems for the Schwarzschild black hole geometry that feature time coordinates, which are speciﬁc Cauchy temporal functions, i.e., the level sets of these time coordinates are smooth, asymptotically ﬂat, spacelike Cauchy hypersurfaces. Coordinate systems of this kind are well suited for the study of the temporal evolution of matter and radiation ﬁelds in the joined exterior and interior regions of the Schwarzschild black hole geometry, whereas the associated foliations can be employed as initial data sets for the globally hyperbolic development under the Einstein ﬂow. For their construction, we formulate an explicit method that utilizes the geometry of – and structures inherent in – the Penrose diagram of the Schwarzschild black hole geometry, thus relying on the corresponding metrical product structure. As an example, we consider an integrated algebraic sigmoid function as the basis for the determination of such a coordinate system. Finally, we generalize our results to the Reissner– Nordstr¨om black hole geometry up to the Cauchy horizon. The geometric construction procedure presented here can be adapted to yield similar coordinate systems for various other spacetimes with the same metrical product structure.
Bootstrap Confidence Regions for Learned Feature Embeddings

Abstract Algorithmic feature learners provide high-dimensional vector representations for non-matrix structured data, like image or text collections. Low-dimensional projections derived from these representations, called embeddings, are often used to explore variation in these data. However, it is not clear how to assess the embedding uncertainty. We adapt methods developed for bootstrapping principal components analysis to the setting where features are algorithmically derived from nonmatrix data. We empirically compare the derived confidence areas in simulations, varying factors influencing feature learning and the bootstrap, like feature learning algorithm complexity and bootstrap sample size. We illustrate the proposed approaches on a spatial proteomics dataset, where we observe that embedding precision is not uniform across all tissue types. Code, data, and pretrained models are available as an R compendium in the supplementary materials. Supplementary files for this article are available online.
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification

Federated learning (FL) has rapidly risen in popularity due to its promise of privacy and efficiency. Previous works have exposed privacy vulnerabilities in the FL pipeline by recovering user data from gradient updates. However, existing attacks fail to address realistic settings because they either 1) require toy settings with very small batch sizes, or 2) require unrealistic and conspicuous architecture modifications. We introduce a new strategy that dramatically elevates existing attacks to operate on batches of arbitrarily large size, and without architectural modifications. Our model-agnostic strategy only requires modifications to the model parameters sent to the user, which is a realistic threat model in many scenarios. We demonstrate the strategy in challenging large-scale settings, obtaining high-fidelity data extraction in both cross-device and cross-silo federated learning.
On solutions of the distributional Bellman equation

In distributional reinforcement learning (RL), not only expected returns but the complete return distributions of a policy are taken into account. The return distribution for a fixed policy is given as the solution of an associated distributional Bellman equation. In this note, we consider general distributional Bellman equations and study the existence and uniqueness of their solutions, as well as tail properties of return distributions. We give necessary and sufficient conditions for the existence and uniqueness of return distributions and identify cases of regular variation. We link distributional Bellman equations to multivariate affine distributional equations. We show that any solution of a distributional Bellman equation can be obtained as the vector of marginal laws of a solution to a multivariate affine distributional equation. This makes the general theory of such equations applicable to the distributional reinforcement learning setting.
Fortuitous Forgetting in Connectionist Networks

Forgetting is often seen as an unwanted characteristic in both human and machine learning. However, we propose that forgetting can in fact be favorable to learning. We introduce"forget-and-relearn"as a powerful paradigm for shaping the learning trajectories of artificial neural networks. In this process, the forgetting step selectively removes undesirable information from the model, and the relearning step reinforces features that are consistently useful under different conditions. The forget-and-relearn framework unifies many existing iterative training algorithms in the image classification and language emergence literature, and allows us to understand the success of these algorithms in terms of the disproportionate forgetting of undesirable information. We leverage this understanding to improve upon existing algorithms by designing more targeted forgetting operations. Insights from our analysis provide a coherent view on the dynamics of iterative training in neural networks and offer a clear path towards performance improvements.