Miracle-dz/UniFAR-FTUs huggingface dataset:
Logical Structure-based Pretrained Models for Legal Text Processing

In recent years, we have witnessed breakthroughs in natural language processing coming from pretrained models based on the Transformer architecture. In the field of legal text processing, a special sub-domain of NLP, pretrained models also show promising results. For a legal sentence, although the natural language is used for expression, the real meaning lies in its logical structure. From that observation, we have a hypothesis that the knowledge of recognizing logical structures can support deep learning models to understand the legal text better and achieve a higher performance in the related tasks. To verify our assumption, we design a novel framework to inject the knowledge about recognizing the requisite and effectuation part of a law sentence into Transformer models. Our proposed method is effective and general. By our experiments, we provide informative results about our approach and its performance compared with the baselines.
Traffic forecasting requirements by project type

The method used to prepare a traffic forecast needs to relate to the type of project for which the forecast is prepared. This report describes the various types of projects for which the Texas Department of Transportation uses traffic forecast data. For each project category, the type of traffic forecast data needed to perform the required analyses is identified and the appropriate forecasting procedure and level of forecast accuracy that can be expected is described. The project categories and forecasting requirements discussed include urban transportation planning; feasibility studies; advanced planning; environmental documentation; and geometric, signalized intersection, pavement, and bridge design.
Data mining on road safety: factor assessment on vehicle accidents using classification models

ABSTRACT In this study, we use three data mining classification models to detect factors with the greatest influence on car accidents. Understanding the circumstances in which the drivers and passengers are more likely to be killed or severely injured in an automobile crash is of particular concern in traffic safety. Our experimental objective is exploring the role of different factors on injury risk using a Bayesian network, decision trees and artificial neural networks. To identify relevant patterns and detect the most frequent factors involved in an accident, we conducted an experiment with road accident data, from 2010 to 2012, provided by the Driver and Vehicle Standards Agency (DVSA) of the United Kingdom. Here, we evaluate and discuss our results, which show that the three most frequent factors are light conditions, vehicle manoeuvre and road type. The investigation also found that the age of the vehicle and weather conditions had no significant influence on the degree of injury.
Developing a Personal Leadership Competency Model for Safety Managers: A Systems Thinking Approach

The roles of all levels of management in influencing safety, particularly in a complex work environment, are crucial. Therefore, safety managers need to develop leadership competencies (i.e., effectiveness in terms of person-oriented behaviours) to reinforce their influencing capabilities through their safety responsibilities. However, practising leadership behaviours without considering how and when these behaviours should be executed is not enough. Therefore, this paper develops a personal leadership competency model by adopting the Systems Thinking approach. The model was developed by conducting exploratory factor analysis and confirmatory factor analysis of three behavioural leadership competencies (emotional, social and cognitive) selected to fulfil the holistic view of Systems Thinking. Data were collected via self-administered questionnaire surveys. A total of 180 valid responses were received from construction managers responsible for overseeing site safety. The statistical results revealed three factors belonging to emotional competency—achievement orientation and adaptability, positive outlook, and emotional self-control. Regarding social competency, four factors represented it—teamwork, organisational awareness, coach and mentor, and conflict management. Finally, cognitive competency was found to be formed by two factors—interaction recognition and pattern recognition. All nine identified factors should, in combination, help safety managers to achieve a better understanding of themselves, of others and of their worksite environments.
Improving Social Emotion Prediction with Reader Comments Integration

Social emotion prediction is concerned with the prediction of the reader’s emotion when exposed to a text. In this paper, we propose a comment integration method for social emotion prediction. The basic intuition is that enriching social media posts with related comments can enhance the models’ ability to capture the conversation context, and hence improve the performance of social emotion prediction. We developed three models that use the comment integration method with different approaches: word-based, topic-based, and deep learning-based. Results show that our proposed models outperform popular models in terms of accuracy
Construction of Financial Statement for Islamic Household

This study aims to investigate how to develop sharia accounting to compile Islamic household financial statements. The study was used qualitative research for analytical, empirical data. The respondents for the research are 5 Islamic housewives and accountant educators (accounting lecturers) who were selected by purposive sampling in East Java. The obtained data were analyzed via Miles Huberman's approach with three analytical elements. The results showed that Islamic household financial statements consisted of; (a) statement of financial position, (b) cash flow statement and, (c) statement of changes in funds. The Islamic accounting development model for Islamic household entities uses an integration, interaction, and evolution approach that was developed from empirical facts about the existence of financial statements occurring in Islamic households synchronized with the opinions of educators accountants about Islamic financial statements and then integrated with Islamic teachings.
Visualization Methods and User Interface Design Guidelines for Rapid Decision Making in Complex Multi-Task Time-Critical Environments

relationships among resources should be made visible, interface can be used as an external memory to reduce the operator memory load, operator actions should be kept at skilland rule-based levels of behavior; the system should support knowledge-based behavior for problem solving, interface should be designed for high visual momentum (Woods, 1984) which will allow the users to find as much required information as possible across the displays and combine all available information for obtaining effective solutions to problems.
Non-productive exposure of PBMCs to SARS-CoV-2 induces cell-intrinsic innate immunity responses

Cell-intrinsic responses mounted in vivo in PBMCs during mild and severe COVID-19 differ quantitatively and qualitatively. Whether they are triggered by signals emitted by productively infected cells of the respiratory tract or are, at least partially, resulting from physical interaction with virus particles, remains unclear. Here, we analyzed susceptibility and expression profiles of PBMCs from healthy donors upon ex vivo exposure to SARS-CoV and SARS-CoV-2. In line with the absence of detectable ACE2 receptor expression, human PBMCs were refractory to productive infection. Bulk and single cell RNA-sequencing revealed JAK/STAT-dependent induction of interferon-stimulated genes, but not pro-inflammatory cytokines. This SARS-CoV-2-specific response was most pronounced in monocytes. SARS-CoV-2-RNA-positive monocytes displayed a lower ISG signature as compared to bystander cells of the identical culture. This suggests a preferential invasion of cells with a low ISG base-line profile or delivery of a SARS-CoV-2-specific sensing antagonist upon efficient particle internalization. Together, non-productive physical interaction of PBMCs with SARS-CoV-2-but not SARS-CoV particles stimulates JAK/STAT-dependent, monocyte-accentuated innate immune responses that resemble those detected in vivo in patients with mild COVID-19.
Capacitor aluminum shell end surface defect detection based on machine vision

When an aluminum electrolysis capacitor is produced,some defects may occur such as end surface pits,unqualified explosion- proof line etc,and a set of detection system based on machine vision is introduced in this paper. On the platform of Visual C + +,a series of methods is presented including image pre- processing,image binary-conversion,position and angularity correction,difference image method etc. Experiments prove that this system is safe and reliable.
Opinion: On the Way towards the New Paradigm of Atherosclerosis

Abstract Atherosclerosis is a multicausal disease characterized by the formation of cholesterol-containing plaque in the pronounced intima nearest to the heart’s elastic-type arteries that have high levels of blood circulation. Plaques are formed due to arterial pressure-induced damage to the endothelium in areas of turbulent blood flow. It is found in the majority of the Western population, including young people. This denies the monogenic mechanism of atherogenesis. In 1988, Orekhov et al. and Kawai et al. discovered that the presence of atherogenic (modified, including oxidized ones) LDLs is necessary for atherogenesis. On the basis of our discovery, suggesting that the overloading of enterocytes with lipids could lead to the formation of modified LDLs, we proposed a new hypothesis explaining the main factors of atherogenesis. Indeed, when endothelial cells are damaged and then pass through the G2 phase of their cell cycle they secrete proteins into their basement membrane. This leads to thickening of the basement membrane and increases its affinity to LDL especially for modified ones. When the enterocyte transcytosis pathway is overloaded with fat, very large chylomicrons are formed, which have few sialic acids, circulate in the blood for a long time, undergo oxidation, and can induce the production of autoantibodies. It is the sialic acids that shield the short forks of the polysaccharide chains to which autoantibodies are produced. Here, these data are evaluated from the point of view of our new model.
Bilinear Multi-Head Attention Graph Neural Network for Traffic Prediction

: Trafﬁc forecasting is an important component of Intelligent Transportation System (ITS) and it has the sig-niﬁcance for reducing trafﬁc accidents and improving public safety. Due to the complex spatial-temporal dependencies and the uncertainty of road network, the research on this problem is quite challenging. Some of the latest studies utilize graph convolutional networks (GCNs) to model spatial-temporal relationships. However, these methods are only based on the linear weighted summation of the neighborhood to form the representation of the target node, which cannot capture the signal between pairwise node interactions. In many scenes, adding pairwise node interaction features is an essential way to better represent the target node. Therefore, in this article, we propose an end-to-end novel framework named Bilinear Multi-Head Attention Graph Neural Network (BMHA-GNN) for trafﬁc prediction. We propose a new aggregation operator which utilizes the weighted sum of pairwise interactions of the neighbour nodes and improves the representation ability of GCN based models. We adopt the encoder-decoder framework, the encoder module outputs the representation of trafﬁc data, and the decoder module outputs the prediction results. The multi-head attention mechanism is introduced to aggregate information of different neighbour nodes automatically and stabilize the training process. Extensive experiments are conducted on two real-world datasets (METR-LA, PEMS-BAY) showing that the proposed model BMHA-GNN achieves the state-of-the-art results.
Fruit-Derived Anthocyanins: Effects on Cycling-Induced Responses and Cycling Performance

Previous evidence has shown that the consumption of fruit-derived anthocyanins may have exercise benefits. This review aimed to summarize the effects of fruit-derived anthocyanins on cycling-induced responses and cycling performance. Medline, Science Direct, Cochrane Library, and SPORTDiscus online databases were searched. Nineteen articles met the inclusion criteria. The fruit-derived anthocyanins used in these studies were from cherry (n = 6), blackcurrant (n = 8), pomegranate (n = 2), açai (n = 1), and juçara fruit (n = 2), and were offered in juice, pulp, powder, freeze-dried powder, and extract form. The supplementation time ranged from acute consumption to 20 days, and the amount of anthocyanins administered in the studies ranged from 18 to 552 mg/day. The studies addressed effects on oxidative stress (n = 5), inflammation (n = 4), muscle damage (n = 3), fatigue (n = 2), nitric oxide biomarkers (n = 2), vascular function (n = 2), muscle oxygenation (n = 2), performance (n = 14), substrate oxidation (n = 6), and cardiometabolic markers (n = 3). The potential ergogenic effect of anthocyanin supplementation on cycling-induced responses seems to be related to lower oxidative stress, inflammation, muscle damage, and fatigue, and increased production of nitric oxide, with subsequent improvements in vascular function and muscle oxygenation leading to improved performance. In addition, the observed increase in fat oxidation can direct nutritional strategies to change the use of substrate and improve performance.
Forming Character With Morals Prophet Muhammad Saw

Character education can be characterized by a whole-souled, wholesome, moral, and disposition education at rendering good decisions, maintaining what is good, and wholeheartedly displaying goodness in daily life. The prophet (peace and blessings be upon him) was the messenger of god and the educator. He got disciplined directly from his Lord so as to have the most perfect character on the face of this earth, so as to become a great man with the testimony of god enshrined in his word: “and how virtuous you truly are” the great testimony of god of the prophet (peace) is evidence that his moral character is great and glorious since the creation of it. He was renowned among his kind as honest and trustworthy. As a majestic being, he had the glorious duty of the supreme being, the edifying and perfecting of men, as the prophecy says: “that I was sent forth to perfect men. ” He succeeded in carrying out a great commission by turning the evil community into a society of faith and civility, while printing out noble characters
Documenting insanity: Paperwork and patient narratives in psychiatric history

Paperwork plays a key role in a how institutions accommodate, refuse, or manage disabled people. This article develops modes for reading paperwork that build on each other, beginning with (a) recognizing the institutional pressures at work in shaping bureaucratic practices, then (b) considering how a person's relationship to disability influences how they might encounter these practices, and ultimately (c) noticing how the encounter between disabled/mad people and an institution might create something new, what the author calls archival excess. These methods for reading are in conversation with disability studies, medical humanities, and document studies, and ultimately work toward a goal adapted from the principles of Disability Justice: recognizing the wholeness of disabled subjects in institutional archives.
Development of an Air-Source Heat Pump Integrated with a Water Heating / Dehumidification Module

A residential-sized dual air-source integrated heat pump (AS-IHP) concept is under development in partnership between ORNL and a manufacturer. The concept design consists of a two-stage air-source heat pump (ASHP) coupled on the air distribution side with a separate novel water heating/dehumidification (WH/DH) module. The motivation for this unusual equipment combination is the forecast trend for home sensible loads to be reduced more than latent loads. Integration of water heating with a space dehumidification cycle addresses humidity control while performing double-duty. This approach can be applied to retrofit/upgrade applications as well as new construction. A WH/DH module capable of ~1.47 L/h water removal and ~2 kW water heating capacity was assembled by the manufacturer. A heat pump system model was used to guide the controls design; lab testing was conducted and used to calibrate the models. Performance maps were generated and used in a TRNSYS sub-hourly simulation to predict annual performance in a well-insulated house. Annual HVAC/WH energy savings of ~35% are predicted in cold and hot-humid U.S. climates compared to a minimum efficiency baseline.
False Alert Detection Based on Deep Learning and Machine Learning

Among the large number of network attack alerts generated every day, actual security incidents are usually overwhelmed by a large number of redundant alerts. Therefore, how to remove these redundant alerts in real time and improve the quality of alerts is an urgent problem to be solved in large-scale network security protection. This paper uses the method of combining machine learning and deep learning to improve the effect of false alarm detection and then more accurately identify real alarms, that is, in the process of training the model, the features of a hidden layer output of the DNN model are used as input to train the machine learning model. In order to verify the proposed method, we use the marked alert data to do classification experiments, and finally use the accuracy recall rate, precision, and F1 value to evaluate the model. Good results have been obtained.
Kansei Engineering Concept in Instructional Design : A Novel Perspective in Guiding the Design of Instructional Materials

Kansei Engineering is a proven methodology for translating human feelings into a product design. It is capable of enhancing users’ satisfaction on a product by analyzing the relationships between their feelings and design parameters. Though it has been widely implemented on consumer and industrial products, its use on instructional materials remains a potential domain to be explored and examined. Furthermore, in recent years, instructional designers have paid increasing attention to affective issues in improving learners’ learning experience. Thus, this paper offers a fresh perspective by integrating Kansei Engineering methodology in the instructional design process. In particular, it describes how Kansei Engineering methods can be used to elicit important design elements of instructional materials that would in turn induce positive emotions on the learners and optimize effective learning.
Whale Optimization-based Prediction for Medical Diagnostic

This study aims to improve disease detection accuracy by incorporating a discrete version of the Whale Optimization Algorithm (WOA) into a supervised classification framework (KNN). We devise the discrete WOA by redefining the related components to operate on discrete spaces. More precisely, we redefine the notion of distance (between individuals in WOA), and propose a random exploration function to include more diversity. The latter includes the random move defined in the WOA algorithm, as well as two other random techniques based on the crossover and mutation operators. To assess the performance of our proposed method, we conducted experiments on two benchmark medical datasets. The results demonstrate the efficacy of the hybrid
Genetic Characterization of Multidrug-Resistant E. coli Isolates from Bloodstream Infections in Lithuania

Extraintestinal pathogenic Escherichia coli (ExPEC) isolates are a main cause of bloodstream infections. The aim of this study was to characterize 256 β-lactam–resistant, bacteremia-causing E. coli isolates collected from 12 healthcare institutions in Lithuania in 2014 and 2018. All isolates were identified as E. coli via MALDI-TOF MS and VITEK ®2. In addition, the isolates were analyzed for the presence of 29 resistance genes and 13 virulence genes, divided into phylogenetic groups (A, B1, B2, C, D, E, and F), and characterized using rep-PCR genotyping methods (BOX-PCR and (GTG)5-PCR). Analyzing the results of this study showed tetA-strB-sul2-TEM-NDM-strA-fosA-AIM-sul3-aadA-CTX-M-9 to be the most common resistance gene combination (67.2% of all isolates). Additionally, the most common virulence genes established were fimH (98.4% of all isolates), fyuA (91.8%), and traT (81.3%) and the most common gene combination was fuyA-fimH-iroN (58.6% of all isolates). Next, the isolates were separated into four phylogenetic groups: A, B1, B2, and F, where group A isolates were detected at a significantly higher frequency (79.3% of all isolates). Finally, a total of 235 genotyping profiles were established using rep-PCR methods, and all profiles were separated into fourteen genotypic clusters, with each cluster containing profiles with a variety of virulence and resistance genes not restricted to any specific cluster. The results of this study elucidate E. coli antimicrobial resistance patterns by highlighting the variability and diversity of resistance and virulence genes and providing phylogenetic classification, genetic profiling, and clustering data. These results may improve clinical control of multidrug-resistant infections in healthcare institutions and contribute to the prevention of potential outbreaks.
Laptop use and its antecedents among educators : a review of the literature.

The rapid growth of Information and Communication Technology (ICT) has enabled the creation of mobile technology that can be used to enhance the instructional process. One of the recent mobile devices introduced in classroom instruction is the laptop. Indeed, schools around the globe are using laptops for educational purposes with increasing frequency. Based on a review of the literature, this paper explores how educators use the laptop. It then explores the antecedents of laptop use among the educators.
Magic Mirror: I and My Avatar - A Versatile Augmented Reality Installation Controlled by Hand Gestures

An aircraft conflict occurs when two or more aircraft cross at a certain distance at the same time. Specific air traffic controllers are assigned to solve such conflicts. A controller needs to consider various types of information in order to solve a conflict. The most common and preliminary information is the coordinate position of the involved aircraft. Additionally, a controller has to take into account more information such as flight planning, weather, restricted territory, etc. The most important challenges a controller has to face are: to think about the issues involved and make a decision in a very short time. Due to the increased number of aircraft, it is crucial to reduce the workload of the controllers and help them make quick decisions. A conflict can be solved in many ways, therefore, we consider this problem as a multi-label classification problem. In doing so, we are proposing a multi-label classification model which provides multiple heading advisories for a given conflict. This model we named CRMLnet is based on a novel application of a multi-layer neural network and helps the controllers in their decisions. When compared to other machine learning models, our CRMLnet has achieved the best results with an accuracy of 98.72% and ROC of 0.999. The simulated data set that we have developed and used in our experiments will be delivered to the research community.