Miracle-dz/UniFAR-FTUs huggingface dataset:
Screening of Accompanying First Degree Relatives of Patients with Primary Open Angle Glaucoma.

INTRODUCTION Glaucoma is one of the common causes of blindness worldwide, and the leading cause of irreversible blindness . The overall prevalence of glaucoma in Nepal is 1.9%. Though poorly understood, primary open angle glaucoma (POAG) is believed to have a genetic or familial component that may occur through polygenic or multifactorial transmission. First-degree relatives of POAG patients have 4-16% risk of developing POAG. Given the circumstances and the prevalence of POAG in first degree relatives of POAG patients, screening the first degree relative/s accompanying the patient to the hospital can be a cost effective and viable tool for glaucoma screening that will not add any extra cost of travel and accommodation. MATERIALS AND METHODS The authors performed a hospital-based cross-sectional study at a tertiary eye hospital in Nepal. All first-degree family members of POAG patients who accompanied them to the glaucoma clinic underwent a full ophthalmic examination.The optic disc was evaluated and intraocular pressure (IOP) was measured. POAG and glaucoma suspect were defined as per ISGEO classification. RESULTS Sixty one first degree relatives of 54 glaucoma patients were examined. The mean age was 30.67 years (±12.71). Fifty five(90.2%) of 61 of accompanying first degree relatives were off springs, 6 (9.8%) were siblings. Five out of 61 (8.2%) were diagnosed as glaucoma and started on anti-glaucoma medication. Fourteen participants (23%) were glaucoma suspects. Forty two (68.9%) of the participants had no sign of glaucoma. CONCLUSION Ocular examination of the first-degree relatives accompanying POAG patients helped to identify a remarkable number of individuals with glaucoma and thus might be used as an effective and viable tool for screening glaucoma in a hospital setting. Awareness regarding glaucoma is very low even among the first degree relatives of glaucoma patients.
Socio-psychological factors influencing farmers’ willingness to continue participating in collaborative activities of community-based innovation platforms in eastern Uganda

This study employed an extended theory of planned behaviour to assess the socio-psychological factors influencing farmers’ willingness to continue participating (WTCP) in collaborative activities of community-based innovation platforms (CB-IPs). Using a structured questionnaire, data were obtained from a random sample of 231 farmers who had participated in collaborative activities of the CB-IPs. Data were analyzed using partial least squares-structural equation modelling. Findings showed that although farmers’ past-experience did not directly influence their WTCP in collaborative activities of CB-IPs, it was positive and significant when mediated by attitudes and perceived behavioural control (t-value = 6.840; p-value = 0.000). Therefore, it can be concluded that farmers’ WTCP in collaborative activities of CB-IPs is enhanced by three socio-psychological factors: favourable past-experience, positive attitudes towards collaborative activities of CB-IPs, and farmers’ perceived capacity to participate. Hence, conveners of collaborative activities of CB-IPs ought to improve farmers’ attitudes through organizing beneficial activities and building their capacity to participate. This study contributed to the body of knowledge by using a behavioural perspective to examine the socio-psychological factors that influence farmers’ continued participation in collaboration activities of CB-IPs.
Discrete modelling of a rockfall protective system

Abstract. Metallic wire meshes are used worldwide for rockfall protective systems, such as rockfall net barriers and drapery meshes. Within different types of meshes, the doubletwisted hexagonal mesh is commonly used. This paper focuses on the implementation and validation of a computational tool for the simulation of the behaviour of such meshes as a single part and as a component of a rockfall protective system. The discrete element method (DEM) is used to model the rockfall mesh and the impacting blocks. The opensource framework YADE has been extended in this context. Tensile tests of a plane net sheet subjected to a constant strain rate are used to calibrate the numerical model. Finally, the simulation of an impacting block on a horizontally spanned net is investigated where numerical results are compared to experimental results.
Multi-task Fine-tuning for Passage Re-ranking Using BM25 and Pseudo Relevance Feedback

Passage re-ranking is a machine learning task that estimates relevance scores between a given query and candidate passages. Keyword features based on the lexical similarities between queries and passages have been traditionally used for the passage re-ranking models. However, such approaches have a limitation; it is difficult to find semantic and contextual features beyond word-matching information. Recently, several studies based on neural pre-trained language models such as BERT overcome the limitations of traditional keyword-based models and they show significant performance improvements. Such ranking models have the advantage of finding the contextual features of queries and documents better than traditional keyword-based methods. However, these deep learning-based models require large amounts of data for training. Such training data is usually manually labeled with high cost, and how to utilize the data efficiently is an important issue. This paper proposes a fine-tuning method for efficient training of the neural re-ranking model. The proposed model utilizes data augmentation by simultaneously learning the ranking and MLM tasks during the fine-tuning stages. For the MLM task, different parts of a passage are masked at each training epoch. Even if only one pair of query and passage is given, the model is exposed to diverse cases with passages dynamically masked from the one. Also, the probability distribution of term importance is trained on the model.We calculate term importance weight by two novel methods using BM25 and pseudo relevance feedback. Terms are sampled and masked according to the importance weight. The ranking model learns representation based on the term weight distribution by executing the MLM task. A novel method with pseudo relevance feedback is applied for calculating term importance. It enables the neural ranking models to form representation according to feedbacks from an initial search stage. The proposed model is trained with data from the MS MARCO leaderboard for the re-ranking task. Our model achieves the state-of-the-art MRR@10 score in the leaderboard except for the ensemble-based method. In addition, our model demonstrates significant performance in three different evaluation metrics: MRR@10, Mean Rank, and Hit@(5,10,20,50).
Exploring Overlay Topology Cost-Termination Tradeoff in Blockchain Vicinity-Based Consensus

Private blockchain platforms tend to apply deterministic consensus mechanisms as a more efficient alternative to the proof-based consensus. Deterministic mechanisms tolerate two types of failures, Byzantine, and Crash-Fault. Byzantine-Fault tolerant consensus assumes restrictive assumptions of time and number of failures to guarantee the validity, while the termination depends on node message broadcasting. Crash-Fault tolerant consensus mechanisms induce lower overhead and faster termination than Byzantine-Fault tolerant mechanisms at the cost of not tolerating malicious behaviors. This paper explores different overlay topologies to assess a lightweight consensus mechanism based on vicinity voting with reliable message broadcasting. The paper proposes different ways to compose the consensus quorum according to the vicinity models. Vicinity models applied in the overlay network allow for relaxing the trade-off between agreement and termination. Analytical and experimental results for different physical topologies show that certain vicinity models guarantee higher number of nodes reached at the time of reaching the consensus threshold (higher than 90% of all nodes) with a lower cost at the exchange of lower fault tolerance. Other models induce a lower agreement while increase fault tolerance (higher than 50%).
Willingness to pay for attributes of Templestay and implications on marginal utility of different meditation forms

Templestays are a form of cultural tourism in which participants visit a Buddhist temple to experience their culture. Using a choice experiment, the marginal willingness to pay (MWTP) is estimated for various attributes of Templestay programs of location, accommodations, and type of meditation. Respondents are willing to pay a premium for private accommodations and to experience the natural mountain settings over an urban setting. The MWTP varies by type of meditation and by how often respondents have participated in a Templestay. For the most physically demanding form of meditation, 108 prostrations, there is weak evidence marginal utility decreases with increasing number of visits. Brewing tea and talking to a monk do not lead to a change in marginal utility. The most spiritually demanding meditation, Seon meditation, shows increasing marginal utility. Seon meditators could benefit from additional stays, as the benefits of this form of meditation can only be achieved through practice and learning from experienced meditators. It is expected that these results will provide information for the diversification of Templestay experience programs.
“Bad Vibrations”: Sensing Toxicity From In-Game Audio Features

Toxicity in online gaming is a problem that causes harm to players, developers, and gaming communities. Toxic behaviors persist in online multiplayer games for a number of reasons, and continue to go unchecked due in large part to a lack of reliable methods to accurately detect toxicity online, in real-time, and at scale. In this article, we present a modeling approach that uses features derived from in-game verbal communication and game metadata to predict if Overwatch games are toxic. With logistic regression models, we achieve accuracy scores of 86.3% for binary (high vs. low toxicity) predictions. We discuss which features were most salient, potential application of our predictive model, and implications for toxicity detection in games. Our approach is a low-cost, low-effort, and noninvasive contribution to holistic efforts in combating toxicity in games.
Fragile Bonds of Recognition: Exploring the Social Underpinnings of Sentiments of Exclusion in Post-1989 East Germany

Abstract Recently, as a corollary of intensified efforts to understand the rise of right-wing populism, the topic of social recognition has gained renewed attention in sociological research. It seems that a sense of misrecognition and exclusion is shaped as much by cultural as by economic factors. Just how these elements are interlinked, however, remains a black box. In this article, I offer an empirical contribution to this problem: I demonstrate that social recognition is nourished in everyday interpersonal relations and that people negotiate ideas of economic deservingness in their social surroundings—so much so, in fact, that they make social ties dependent on them. The article studies the case of the post-1989 societal shifts in formerly communist-ruled East Germany, a context marked by a pervasive sense of social exclusion today. In interviews with 41 individuals who lived through this rupturing process, I identify a crucial dynamic of social misrecognition in how respondents evaluate other peoples’ strategies of coping with the economic fallout of this time and how they draw—often deeply personal—boundaries between themselves and others on these grounds.
Design of Nonorthogonal Multiple Access Systems: An Information Bottleneck Perspective

As a candidate technology of B5G cellular Internet of Things(IoT), grant-free non-orthogonal multiple access (NOMA) has arisen worldwide concerns. However, the non orthogonality leads to the mutual interference between users which reduces the reliability of NOMA systems. Recent research in the design of NOMA transmission signal and multiuser detection are carried out separately which is difficult to achieve the global optimum. To solve this problem, we study the joint optimization of transmission and detection for NOMA system from an information bottleneck (IB) perspective. Specifically, we resort to deep learning to parameterize the transmitter and receiver of NOMA systems. Furthermore, the loss function for training is derived and analyzed based on information bottleneck and variational inference. Simulation results show that, the proposed method has better bit error rate (BER) performence with the same overloading factor compared to existing new techniques.
SimCSE for Encrypted Traffic Detection and Zero-Day Attack Detection

Traffic detection has attracted much attention in recent years, playing an essential role in intrusion detection systems (IDS). This paper proposes a new approach for traffic detection at the packet level, inspired by natural language processing (NLP), using simple contrastive learning of sentence embeddings (SimCSE) as an embedding model. The new approach can learn the features of traffic from raw packet data. Experiments were conducted on two well-known datasets to evaluate our approach. For detecting malicious activity, our model achieved an accuracy of 99.99% on the USTC-TFC2016 dataset, whereas for detecting virtual private network (VPN) activity, our model achieved an accuracy of 99.98% on the ISCXVPN2016 dataset. Furthermore, the resulting model was found to be robust based on zero-day attack detection, which shows the model’s ability to detect attacks that have not been seen before. Experiments show that our approach can effectively detect network traffic and outperforms many other state-of-the-art methods.
Analysis of an ideal dual-impedance network using reference disposition modulation

A single-impedance network consists of two inductors and capacitors connected in an X-shape to provide single-stage energy conversion. This structure eliminates the need for two-stage energy conversion that is currently employed in traditional converters. A dual-impedance network includes two individual sets of inductors and capacitors connected in parallel. This network possesses the flexibility of using two different sources as inputs to meet the load demand. In order to provide voltage boosting, the switching must be performed selectively to shoot-through (ST) the power sources. A modified modulation method has been implemented to introduce the ST for switching. The modified modulation method shares the operating principle with the in-phase disposition modulation method for multilevel inverters. However, the reference signals are dispositioned via the modified modulation method rather than employing carrier disposition via the traditional modulation methods. Therefore, the circuit analysis of a dual-impedance network is evaluated whose switching is achieved by a modified modulation method for an ideal condition. The simulations are carried out using the MATLAB/PSIM environment and the results are presented.
Morbidity Patterns in Primary Care in Hong Kong: Protocol for a Practice-Based Morbidity Survey

Background Up-to-date and accurate information about the health problems encountered by primary care doctors is essential to understanding the morbidity pattern of the community to better inform health care policy and practice. Morbidity surveys of doctors allow documentation of actual consultations, reflecting the patient’s reason for seeking care as well as the doctor’s diagnostic interpretation of the illness and management approach. Such surveys are particularly critical in the absence of a centralized primary care electronic medical record database. Objective With the changing sociodemographic profile of the population and implementation of health care initiatives in the past 10 years, the aim of this study is to determine the morbidity and management patterns in Hong Kong primary care during a pandemic and compare the results with the last survey conducted in 2007-2008. Methods This will be a prospective, practice-based survey of Hong Kong primary care doctors. Participants will be recruited by convenience and targeted sampling from both public and private sectors. Participating doctors will record the health problems and corresponding management activities for consecutive patient encounters during one designated week in each season of the year. Coding of health problems will follow the International Classification of Primary Care, Second Edition. Descriptive statistics will be used to calculate the prevalence of health problems and diseases as well as the rates of management activities (referral, investigation, prescription, preventive care). Nonlinear mixed effects models will assess the differences between the private and public sectors as well as factors associated with morbidity and management patterns in primary care. Results The data collection will last from March 1, 2021, to August 31, 2022. As of April 2022, 176 doctor-weeks of data have been collected. Conclusions The results will provide information about the health of the community and inform the planning and allocation of health care resources. Trial Registration ClinicalTrials.gov NCT04736992; https://clinicaltrials.gov/ct2/show/NCT04736992 International Registered Report Identifier (IRRID) DERR1-10.2196/37334
The Effect of Time Management on Student’s Interest in Learning Mathematics

This study aims to determine the effect of time management on student interest in learning at Mts s Addinussyarifiah in class VII using time management indicators. Time management is the process of consciously planning and controlling the time spent on certain activities, especially to increase effectiveness, efficiency, and productivity. From the results of the study, the conclusions obtained are: 1. Time management for class VIII Mts s Addinussyarifiah 2021 belongs to the sufficient category from the indicators that have been implemented, 2. From the value of the mathematics learning outcomes of class VIII Mts s Addinussyarifiah 2021 students are classified as good category according to the distribution of the questionnaire, 3. There is an influence between time management and interest in learning mathematics in class VIII Mts s Addinussyarifiah which is quite significant based on descriptive analysis of the indicators that have been implemented.
Empathy and Burnout in Veterinarians and Veterinary Nurses: Identifying Burnout Protectors

ABSTRACT Burnout in animal health care providers (AHCPs), namely, veterinarians and veterinary nurses, is highly prevalent. Although empathy can be a potential risk factor for burnout in these professionals, research has not empirically addressed the association between empathy and AHCP burnout. This study’s main aims were: (a) to analyze the association between empathy and burnout for AHCPs, distinguishing affective and cognitive empathy toward humans and empathy toward animals; (b) to analyze the possible protective effects of justice perceptions, professional identification, and meaningful work, to counteract the negative impact of empathy on burnout; and (c) to extend analyses to control for variables that might act as burnout protectors or risk factors, such as gender, years of professional experience, workload, income, and the perception that professionals suffer when performing euthanasia procedures. Convenience samples of 229 veterinarians and 96 veterinary nurses were collected in Portugal. The participants were invited to complete an anonymous online survey with self-reported measures. The measures used assessed burnout (exhaustion and disengagement), empathy for humans (cognitive and affective) and empathy for animals, justice perceptions, professional identification, and meaningful work. Multiple regression analysis showed that affective empathy was a significant risk factor for exhaustion in veterinarians and veterinary nurses. Empathy for animals was a significant risk factor for veterinarian exhaustion. Neither of the measures of empathy was a significant predictor of disengagement for veterinarians or veterinary nurses. Professional identification and justice perceptions, namely belief in a just world, were found to be significant burnout protectors. It is recommended that justice perceptions and professional identification should receive special attention in interventions to prevent and/or reduce burnout among AHCPs. The alternative possibility of preventing burnout through a decrease in empathy is not advisable, however, because empathy is a vital AHCP skill.
Evaluation of the CSE-UCLA model on the performance of school counselor in Indonesia

The problem and the aim of the study. Evaluation is an attempt to review whether the program follows the objectives and achieves the expected goals. The evaluation of the UCLA CSE model was chosen because this model is very suitable for evaluating ongoing Education programs. This study aims to find out how the performance of counselors in schools using the UCLA SCE evaluation model. Research methods. This research is evaluation research with five stages: the assessment system, program planning, implementation program, Program Improvement, and Certification Program. The research subjects were 180 people divided into 150 students and 30 counselors from state high school 1 Palangkaraya, state high school 1 Kuala Kapuas, state high school 1 Sampit, state high school 1 Katingan, State Senior High School 1 Pangkalan Lada. The research instruments used in this study were interviews, questionnaires, observation, and documentation. This research instrument was used to obtain data related to the performance of counselors in Indonesian schools. Data analysis was carried out in-depth by analyzing the percentage of the questionnaire results that the research subject had filled in. Results. The results showed that the guidance and counseling services had been running in a good category. The analysis results show that the evaluation of the UCLA CSE model carried out shows that every aspect gets a score with good criteria. The assessment aspect gets a score (72.40), the planning program (69.20), program implementation (70.00), improvement program (70.16), and program certification 73.20 if. The average evaluation result is (70.99) good category. The research findings show that the results of the very good category have not been found, so school counselors need to improve the quality of service to achieve the very good category. Excellent service will help school counselors gain the trust of students and parents. Conclusion. Evaluation of the program using the UCLA CSE model conducted in several schools in Indonesia shows that the school's guidance and counseling service program has been running in a good category. Support from the system needs to be strengthened to recognize guidance and counseling in schools. School counselors need to socialize guidance and counseling services so that students become more confident and confident in the existence of guidance and counseling. The government also needs to provide a strategic position of guidance and counseling in the world of education so that the existence of school counselors is increasingly recognized and has an important role in the world of education.
A simple introduction to the economics of storage: shifting demand and supply over time and space

The literature on electrical energy storage (EES) is technical and complex which this paper aims to simplify. It quantifies the current scale, costs and value of different types of EES and compares them to peaking generators and interconnectors. Worldwide, dams have 2,700 times the storage capacity of pumped storage, which accounts for 99% of conventional EES, batteries making up the rest. Indirect use of hydro power, and in future, electric vehicles, adds to their value and if accessible at reasonable cost, would be cheaper than conventional EES. EES, peakers and DC interconnectors can offer flexibility services which considerably enhance their value, but hopes of a battery revolution enabling a smarter electricity system should not be exaggerated.