Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Beliefs, Behaviors, and Opinions About Blood Donation in Telangana, India—A Cross Sectional Community-Based Study

Background and Objectives: Blood is an essential body fluid primarily required for regulating the body's systems and maintaining homeostasis. In developed and developing countries, concern about the demand and supply for blood is increasing. The current study aims to assess the beliefs, behaviors, and opinions of the public toward blood donation. Methods: This was a cross-sectional study in which a self-created questionnaire with 17-items was used for data collection. The self-administered questionnaire was disseminated between November 2019 and January 2020 through social media (WhatsApp© and Facebook©). Data was analyzed using SPSS program version 26. Results: A total of 356 questionnaires were completed with a response rate of 89%. The majority of participants were male 253 (71.1%), 336 (94.4%) considered blood donation important, 350 (98.3%) believed that blood donation saves lives, and 254 (71.3%) agreed to receive blood from voluntary donors. One-hundred sixty-seven (49.4%) were willing to donate blood voluntarily. The barriers to blood donation were fear of needles 86 (24.2%), fear of contracting a chronic disease 84 (23.6%), and lack of time 40 (11.2%). One day off (91.9%) and receiving a token 73.6% were common motivational factors for blood donation. Overall, 57% of the participants had favorable attitudes toward blood donation and 41.9% were knowledgeable. Favorable attitudes were significantly associated with being married (P = 0.018) and having university level of education (P = 0.005). Younger participants (18–29 years) had a statistically significant better knowledge than older participants (≥30 years). Conclusion: The respondents displayed positive beliefs, opinions, and motivation toward blood donation. Additionally, most of them considered blood donation an important act and a national duty of every individual and are willing to donate in the future.
Learnability of Linear Thresholds from Label Proportions

We study the problem of properly learning linear threshold functions (LTFs) in the learning from label proportions (LLP) framework. In this, the learning is on a collection of bags of feature-vectors with only the proportion of labels available for each bag. First, we provide an algorithm that, given a collection of such bags each of size at most two whose label proportions are consistent with (i.e., the bags are satisﬁed by) an unknown LTF, efﬁciently produces an LTF that satisﬁes at least (2 / 5) -fraction of the bags. If all the bags are non-monochromatic (i.e., bags of size two with differently labeled feature-vectors) the algorithm satisﬁes at least (1 / 2) -fraction of them. For the special case of OR over the d -dimensional boolean vectors, we give an algorithm which computes an LTF achieving an additional Ω(1 /d ) in accuracy for the two cases. Our main result provides evidence that these algorithmic bounds cannot be signiﬁ-cantly improved, even for learning monotone ORs using LTFs. We prove that it is NP -hard, given a collection of non-monochromatic bags which are all satisﬁed by some monotone OR, to compute any function of constantly many LTFs that satisﬁes (1 / 2 + ε ) -fraction of the bags for any constant ε > 0 . This bound is tight for the non-monochromatic bags case. The above is in contrast to the usual supervised learning setup (i.e., unit-sized bags) in which LTFs are efﬁciently learnable to arbitrary accuracy using linear programming, and even a trivial algorithm (any LTF or its complement) achieves an accuracy of 1 / 2 . These techniques however, fail in the LLP setting. Indeed, we show that the LLP learning of LTFs (even for the special case of monotone ORs) using LTFs dramatically increases in complexity as soon as bags of size two are allowed. Our work gives the ﬁrst inapproximability for LLP learning LTFs
Effect of Site and Population Characteristics on Freeway Bottleneck Capacity

In an attempt to explain variations in prequeue flow (PQF) and queue discharge flow (QDF), one- and two-stage regression models that link high-volume PQF and QDF at freeway bottlenecks to site geometric, vehicle population, and driver population characteristics were developed and evaluated. One-stage models linked site characteristics directly to PQF and QDF; two-stage models used the average time gap in the critical lane and the ratio of critical lane flow to flow per lane as intervening variables (where time gap is the time separation between the rear of the lead vehicle and the front of the following one and critical lane is the lane with the highest flow). Explanatory variables included number of lanes, vertical alignment, proportion of heavy vehicles, and population characteristics derived from census data. Models were identified by stepwise regression analysis and evaluated by comparing predicted PQF and QDF values with the results of Highway Capacity Manual (HCM) capacity calculations and average values of PQF and QDF measured at the sites used to develop the models and at a second group of sites used for model verification. In most cases, the initial models developed by the study performed similarly–better than the HCM (which tends to overestimate PQF and QDF)–but in no case did calculated PQF and QDF values correlate with measured flows for the verification sites. After one site with apparently anomalous data was eliminated, the only significant explanatory variable was the number of lanes. Although the number of lanes explains some variation in PQF and QDF among bottlenecks, a great deal more remains unexplained.
Socio-economic characteristics of cassava producers and processors in Enugu State, Nigeria

Cassava production in Nigeria is by far the largest output in the world; a third more than the production in Brazil and almost double the production in Indonesia and Thailand. Results of socio economic characteristics established that the mean age and mean household size of the study farmers were 50 years and 8 persons respectively. More than half (57.9%) of the cassava producers and processors were males, with about 83.5% of them having some form of formal education and 78.5% of them married. The mean farm size and mean farming/processing experience of the cassava producers and processors were 1.6 hectares and 22 years respectively. Mean annual farm income and mean annual non-farm income of the respondents were N171, 963.82 and N77649.25, with 62.6% of them not having access to farm credit. As much as 72.4% of the cassava producers and processors used both improved cassava production and processing technologies. About 51 percent of the cassava producers/processors employed use of both family and hired labour on their cassava production and processing enterprises. Also, 53.2% of the study farmers did not belong to any cooperative associations. In conclusion, the cassava farmers/processors engaged primarily in farming, although majority of them operated small farm sizes whose output are small. Consequently, these farmers engage in other off-farm income generating activities whose returns are smaller compared to returns generated from farming.
Linear Convergence of Gradient Methods for Estimating Structured Transition Matrices in High-dimensional Vector Autoregressive Models

In this paper, we present non-asymptotic optimization guarantees of gradient descent methods for estimating structured transition matrices in high-dimensional vector autoregressive (VAR) models. We adopt the projected gradient descent (PGD) for single-structured transition matrices and the alternating projected gradient descent (AltPGD) for superposition-structured ones. Our analysis demonstrates that both gradient algorithms converge linearly to the statistical error even though the strong convexity of the objective function is absent under the high-dimensional settings. Moreover our result is sharp (up to a constant factor) in the sense of matching the phase transition theory of the corresponding model with independent samples. To the best of our knowledge, this analysis constitutes first nonasymptotic optimization guarantees of the linear rate for regularized estimation in high-dimensional VAR models. Numerical results are provided to support our theoretical analysis.
IN THE PROCESS OF SOFTWARE DEVELOPMENT: AVAILABLE RESOURCES AND APPLICABLE SCENARIOS

For many decades, the cost, time and quality are the main concern of software engineering. The main objective of any software organization is to produce high quality software product within a shorter time and minimum cost. Software reuse is one of the main strategies concerns about using available resources to enhance the productivity of software development and the quality of software products. It aims at using existing software products and components in the development of new software systems. However, various types of software components available in different sources are used in the reuse strategy. This makes the reuse strategy confusable and its efficiency and effectiveness debatable. Selecting unsuitable component or scenario makes the reuse inefficient and ineffective. This study discusses the types of software components, their sources, characteristics and applicable scenarios for developing and reusing these components. A dataset from the literature is used to calculate and compare the cost of reuse processes. The results show that software reuse is an efficient strategy comparing with the normal development. Although, considering the reusability of software components required extra cost to the normal development, it could efficiently save the cost of the development of new software system. Moreover, using existing software components in the development of new reusable component is the most efficient strategy, which required even less than the cost of developing normal component.
Social Responsibility Costs and Performance of Listed Oil and Gas companies in Nigeria: An Investigative Analysis

The study was carried out to investigate the influence of social responsibility costs on performance of listed Oil and Gas companies in Nigeria. This was motivated by the claim of stakeholders’ theory which emphasises that social responsibility enhances economic benefits of companies. In this study, effort was made to examine the influence amongstthree social responsibility activitiesproxied by health associated cost, infrastructural expansioncost and educational initiative costs and performance. The dependent variable, performance, was proxied by profit margin.The study adoptedex-post facto design. Sample size was made up of five listed Oil and Gas companies for a period of eleven years from 2009 to 2019. Data was obtained through secondary sources, mainly from the annual reports of the sampled companies. Descriptive statistics, linear and multiple regression analysis were the analytical techniques utilised for the study. Findings revealed that a significant positive relationship exists between health associated cost (HAC) and profit margin(PM) while infrastructural expansion cost (IEC)and educational initiative cost (EIC) do not show any significant influence. However, the joint influence of the variables (HAC, IEC and EICshow statistically significant relationship with performance. In conclusion, aggregate social responsibility costs engender the performance of listed Oil and Gas companies in Nigeria. It was therefore recommended among others that Oil and Gas companies should take cognizance of social interest and invest more in health, infrastructural and educational activities in order to enhance their overall profitability.
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation

Large pretrained language models (LMs) like BERT have improved performance in many disparate natural language processing (NLP) tasks. However, fine tuning such models requires a large number of training examples for each target task. Simultaneously, many realistic NLP problems are"few shot", without a sufficiently large training set. In this work, we propose a novel conditional neural process-based approach for few-shot text classification that learns to transfer from other diverse tasks with rich annotation. Our key idea is to represent each task using gradient information from a base model and to train an adaptation network that modulates a text classifier conditioned on the task representation. While previous task-aware few-shot learners represent tasks by input encoding, our novel task representation is more powerful, as the gradient captures input-output relationships of a task. Experimental results show that our approach outperforms traditional fine-tuning, sequential transfer learning, and state-of-the-art meta learning approaches on a collection of diverse few-shot tasks. We further conducted analysis and ablations to justify our design choices.
Supply Network Formtion and Fragility

We model the production of complex goods in a large supply network. Each firm sources several essential inputs through relationships with other firms. Individual supply relationships are at risk of idiosyncratic failure, which threatens to disrupt production. To protect against this, firms multisource inputs and strategically invest to make relationships stronger, trading off the cost of investment against the benefits of increased robustness. We find that equilibrium aggregate production is robust to idiosyncratic disruptions. Nevertheless, there is a regime in which arbitrarily small systemic shocks cause arbitrarily steep drops in output, so that the the supply network is fragile. The endogenous configuration of supply networks provides a new channel for the powerful amplification of shocks. Date Printed. February 5, 2021. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number #757229) (Elliott); the Joint Center for History and Economics; the Pershing Square Fund for Research on the Foundations of Human Behavior; and the National Science Foundation under grant SES-1629446 (Golub). We thank seminar participants at the 2016 Cambridge Workshop on Networks in Trade and Macroeconomics, Tinbergen Insititute, Monash Symposium on Social and Economic Networks, Cambridge-INET theory workshop (2018), Binoma Workshop on Economics of Networks (2018), Paris School of Economics, Einaudi Institute for Economics and Finance, Seventh European Meeting on Networks, SAET 2019 and the Harvard Workshop on Networks in Macroeconomics. We thank Joey Feffer, Riako GranzierNakajima, Yixi Jiang, and Brit Sharoni for excellent research assistance. For helpful conversations we are grateful to Daron Acemoglu, Nageeb Ali, Pol Antrás, David Baqaee, Vasco Carvalho, Olivier Compte, Krishna Dasaratha, Selman Erol, Marcel Fafchamps, Emmanuel Farhi, Alex Frankel, Sanjeev Goyal, Philippe Jehiel, Matthew O. Jackson, Chad Jones, Annie Liang, Eric Maskin, Marc Melitz, Marzena Rostek, Emma Rothschild, Jesse Shapiro, Ludwig Straub, Alireza Tahbaz-Salehi, Larry Samuelson, Andrzej Skrzypacz, Juuso Välimäki and Rakesh Vohra. Electronic copy available at: https://ssrn.com/abstract=3525459
CAN WE THINK AGAIN REGARDING THE IMPORTANCE OF BINOCULAR VISION THERAPY IN TREATING IXT CASES......A CASE STUDY

Background: Intermittent exotropia is a condition in which part of the times when one eye has drifted outwards, and other times when the eyes are straight. Some patients or family members notice the exotropia more when the patient is tired or daydreaming. Prevalence of basic exotropia accounts for approximately 50% of all intermittent exotropia. Practitioners view treating these patients, especially adults with childhood-onset exotropia, as challenging. Contradictory opinions still exist regarding prescribing any treatment for adult strabismus, including surgical realignment and optometric Binocular vision therapy (BVT). Case reports: An adult patients with moderate-size intermittent exotropia of the basic type who presented with asthenopia, headaches, and diplopia. 10 to 20 in-office BVT sessions were endorsed to reduce the magnitude and frequency of the deviation as well as improve their binocularity and decrease their symptoms. After completing OBT, patients became orthotropic for all distances, had normal fusional vergence ability, and had normal near points of convergence. Advised to continue HBT and Long term outcome was evaluated 1 year post therapy. Conclusion: Optometric Binocular Vision Therapy plays important role and highly effective in patients with intermittent exotropia of the basic type. Most importantly it eliminated the patients' symptoms of asthenopia and diplopia without the need for surgery. Index Terms Vision Therapy, Intermittent Exotropia, Prisms.
Information Technology and Financial Performance of Money Deposit Banks in Nigeria

The study investigated effect electronic banking on financial performance of money deposit banks in Nigeria. The objective was to find out the contributions of web banking, Remita, automatic teller machine, National Electronic Fund Transfer, point of sale and Ebillspay on financial performance of Money Deposit banks in Nigeria. The data for the study was secondary gotten from Central bank of Nigeria (CBN) statistical Bulletin, National Bureau of Statistics bulletin and financial reports for seven years from 2011-2017. Research design for the study is ex-post facto and Least square multiple regression was adopted with help of e-view 9 to test the hypothesis in the study and outcome of the study unveiled that ATM, POS and WEB BANKING to have a significant but negative relationship with financial performance, while NEFT, REMITA, and EBILLSPAY were shown to have positive but insignificant association with financial performance of the banks in Nigeria. We conclude that there is a significant and positive relationship between electronic payments channels and financial performance of the banks in Nigeria. Those banks in Nigeria should educate their customers on the use of Ebillspay and National Electronic Fund Transfer as it is the most poorly used among the others.
Study of Bacteriospermia Isolated from Male Infertility in Al-Najaf City, Iraq

Sanaa H. J. Al-Khafaji Email: shadahsn4@gmail.com Fertility and Sterility Cente, Sadr City Education, Baghdad, Iraq ABSTRACT Background: Bacteriospermia is the presence of bacteria in seminal fluid samples. Bacteriospermia can affect sperm function, leading to an abnormal fertility process. It is a major health condition that has a detrimental impact on male fertility and is usually caused by acute or persistent bacterial infections. It has been discovered that about 15% of infertile men and women are infertile. Bacterial infections can damage the prostate, epididymis, testis, and urethra, among other parts of the male genitourinary system. Escherichia coli, Staphylococci, Streptococci, and Enterococcus faecalis are the most commonly isolated pathogenic bacteria. On the other hand, the male urinary system is not fully sterile, as some bacteria, such as Staphylococcus epidermidis, have been found in otherwise healthy reproductive men. The effect of various bacteria on sperm quality is unknown at this time. The ever-increasing number of infertile couples has piqued scientific curiosity in the effects of bacteriospermia on male reproductive potential during the last few decades. To confirm the link between bacteriospermia and seminal characteristics, including motility and vitality, researchers looked into a variety of pathophysiologic pathways. Both direct bacterial interaction and immunological competent cell participation impact spermatogenesis, degrade semen function, and impede the urogenital canal. Objectives: To explore the association between bacterial species found in guys’ lower genital tracts and the quality of their sperm and male infertility. Methods: The study included the amassment and examination of (175) samples of semen samples from infertile people for the period from 14/2/2021 to 17/5/2021, whose ages ranged between (19–40) years, isolation and identification of some bacteria by the automated VITEK-2 compact system utilizing Gram-negative identification cards (GN-ID) of bacteriosperm, the antibiotic sensitivity test was studied on bacteria isolates by the disk diffusion technique on Muller Hinton Agar (MHA). Results: The highest bacterial infection rate among infected patients was Staphylococcus haemolyticus, which amounted to 26 isolates, followed by E. coli with 23 isolates, and 20 followed by Staphylococcus epidermidsi and Staphylococcus saprophyticus two isolates and
CONCRETE CONE FAILURE OF HEADED STUD UNDER DIFFERENT FIRE EXPOSURE

The paper presents the results of numerical investigations on the concrete cone failure of single headed stud exposed to different fire. The fire exposures investigated include the standard fire and hydrocarbon fire as per EN1991-1-2. A sequentially coupled 3D thermal-stress analysis is performed using Ansys® Mechanical for the investigation. The unique feature of the Finite Element model is that it uses quadratic (higher order) elements. A good comparison between the predicted degradation in concrete cone capacity in the present study and other numerical results available in literature (for standard fire exposure) demonstrates the usability of the model. The concrete cone capacities under hydrocarbon fire exposure were reached 30 minutes earlier as compared to standard fire exposure.
Mitigating Forgetting in Online Continual Learning with Neuron Calibration

Inspired by human intelligence, the research on online continual learning aims to push the limits of the machine learning models to constantly learn from sequentially encountered tasks, with the data from each task being observed in an online fashion. Though recent studies have achieved remarkable progress in improving the online continual learning performance empowered by the deep neural networks-based models, many of today’s approaches still suffer a lot from catastrophic forgetting, a persistent challenge for continual learning. In this paper, we present a novel method which attempts to mitigate catastrophic forgetting in online continual learning from a new perspective, i.e., neuron calibration. In particular, we model the neurons in the deep neural networks-based models as calibrated units under a general formulation. Then we formalize a learning framework to effectively train the calibrated model, where neuron calibration could give ubiquitous beneﬁt to balance the stability and plasticity of online continual learning algorithms through inﬂuencing both their forward inference path and backward optimization path. Our proposed formulation for neuron calibration is lightweight and applicable to general feed-forward neural networks-based models. We perform extensive experiments to evaluate our method on four benchmark continual learning datasets. The results show that neuron calibration plays a vital role in improving online continual learning performance and our method could substantially improve the state-of-the-art performance on all the evaluated datasets.