Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Requirements Classification Based on Graph Attention Network

Requirements classification is a significant task for requirements engineering, which is time-consuming and challenging. The traditional requirements classification models usually rely on manual pre-processing and have poor generalization capability. Moreover, these traditional models ignore the sentence structure and syntactic information in requirements. To address these problems, we propose an automatic requirements classification based BERT and graph attention network (GAT), called DBGAT. We construct dependency parse trees and then utilize the GAT for mining the implicit structure feature and syntactic feature of requirements. In addition, we introduce BERT to improve the generalization ability of the model. Experimental results of the PROMISE datasets demonstrate that our proposed DBGAT significantly outperforms existing state-of-the-art methods. Moreover, we investigate the impact of graph construction methods on non-functional requirements classification. DBGAT achieved the best classification results on both seen (F1-scores of up to 91%) and unseen projects (F1-scores of up to 88%), further demonstrating the strong generalization ability.
Connectomic analysis of unilateral dual-lead thalamic deep brain stimulation for treatment of multiple sclerosis tremor

Abstract Tremor is a common symptom in multiple sclerosis and can present as a severe postural and action tremor, leading to significant disability. Owing to the diffuse and progressive nature of the disease, it has been challenging to characterize the pathophysiology underlying multiple sclerosis tremor. Deep brain stimulation of the ventralis intermedius and the ventralis oralis posterior thalamic nuclei has been used to treat medically refractory multiple sclerosis tremors with variable results. The aim of this study was to characterize multiple sclerosis tremor at the network level by applying modern connectomic techniques to data from a previously completed single-centre, randomized, single-blind prospective trial of 12 subjects who were treated with unilateral dual-lead (ventralis intermedius + ventralis oralis posterior) thalamic deep brain stimulation. Preoperative T1-weighted MRI and postoperative head CTs were used, along with applied programming settings, to estimate the volume of tissue activated for each patient. The volumes of tissue activated were then used to make voxel-wise and structural connectivity correlations with clinically observed tremor suppression. The volume of the tissue-activated analyses identified the optimal region of stimulation at the ventralis oralis posterior ventralis intermedius border intersecting with the dentato-rubro-thalamic tract. A regression model showed strong connectivity to the supplemental motor area was positively associated with tremor suppression (r = 0.66) in this cohort, whereas connectivity to the primary motor cortex was negatively associated with tremor suppression (r = −0.69), a finding opposite to that seen in ventralis intermedius deep brain stimulation for essential tremor. Comparing the structural connectivity to that of an essential tremor cohort revealed a distinct network that lies anterior to the essential tremor network. Overall, the volumes of tissue activated and connectivity observations converge to suggest that optimal suppression of multiple sclerosis tremor will likely be achieved by directing stimulation more anteriorly toward the ventralis oralis posterior and that a wide field of stimulation synergistically modulating the ventralis oralis posterior and ventralis intermedius nuclei may be more effective than traditional ventralis intermedius deep brain stimulation at suppressing the severe tremors commonly seen in complex tremor syndromes such as multiple sclerosis tremor.
When a school is more than just a school: Improving school‐based health in the wake of COVID‐19

Abstract The ongoing COVID‐19 pandemic has posed tremendous challenges for economies and individuals around the world. At the same time, it has also laid bare the blatant and growing inequities that many individuals, particularly children, are confronted with on a daily basis. With communities in lockdowns and schools going virtual in many parts of the United States, the important role that schools and school‐based services play in the lives of many children have gained new attention. Nonetheless, only 3% of American schools have school‐based health centers on campus, and they remain relegated to the fringes of both health care and education. One key limitation has been the lack of appropriately trained health‐care professionals. Over the past 2 years, we have interviewed dozens of individuals about their experiences in school‐based health centers. Based on this study, we explore what it means for a health‐care professional to work in school‐based health care and how it differs from more traditional health‐care settings. Our analysis particularly focuses on training and education, work environments, and their unique demands that come from being embedded within the educational setting. We conclude by addressing the important role that governmental policies could play in augmenting this crucial workforce.
Dyspnea in Post-COVID Syndrome following Mild Acute COVID-19 Infections: Potential Causes and Consequences for a Therapeutic Approach

Dyspnea, shortness of breath, and chest pain are frequent symptoms of post-COVID syndrome (PCS). These symptoms are unrelated to organ damage in most patients after mild acute COVID infection. Hyperventilation has been identified as a cause of exercise-induced dyspnea in PCS. Since there is a broad overlap in symptomatology with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), causes for dyspnea and potential consequences can be deduced by a stringent application of assumptions made for ME/CFS in our recent review papers. One of the first stimuli of respiration in exercise is caused by metabolic feedback via skeletal muscle afferents. Hyperventilation in PCS, which occurs early on during exercise, can arise from a combined disturbance of a poor skeletal muscle energetic situation and autonomic dysfunction (overshooting respiratory response), both found in ME/CFS. The exaggerated respiratory response aggravating dyspnea does not only limit the ability to exercise but further impairs the muscular energetic situation: one of the buffering mechanisms to respiratory alkalosis is a proton shift from intracellular to extracellular space via the sodium–proton-exchanger subtype 1 (NHE1), thereby loading cells with sodium. This adds to two other sodium loading mechanisms already operative, namely glycolytic metabolism (intracellular acidosis) and impaired Na+/K+ATPase activity. High intracellular sodium has unfavorable effects on mitochondrial calcium and metabolism via sodium–calcium-exchangers (NCX). Mitochondrial calcium overload by high intracellular sodium reversing the transport mode of NCX to import calcium is a key driver for fatigue and chronification. Prevention of hyperventilation has a therapeutic potential by keeping intracellular sodium below the threshold where calcium overload occurs.
Distributed Orientation Estimate of Single-integrator Multi-agent Systems with Measurement Noises in Time-varying Networks*

This note investigates the distributed orientation estimate problem in the plane for single-integrator multi-agent systems with measurement noises in time-varying networks. A distributed algorithm for all agents’ orientation estimate is designed by combining the stochastic approximation type gain design method and the unbiased estimate strategy. We give the rigorous proof that all agents will asymptotically share a same reference coordinate frame if and only if the magnitude of the measurement noise of the bearing belongs to [0,π/4) when the time-varying communication network together with the corresponding time-varying bearing sensing network are uniformly strongly connected. We finally give a numerical simulation to verify the proposed algorithm and the conclusion.
Self-study on Diligence as a Continuous Professional Development: A Teacher Researcher’s Perspectives

Nepali teachers’ professional qualities are discussed from non-socio-cultural perspectives, which seem insufficient for the continuous professional development of Nepali university teachers. Teachers’ professional attributes that influence their professional and pedagogical practices are rarely researched, particularly in Nepal. The purpose of this self-study was to investigate how I, a university teacher, could improve my pedagogical practices to facilitate a Research Methodology course for Science, Technology, Engineering, Arts and Mathematics (STEAM) graduates. Adapting a socio-cultural perspective, I purposefully asked myself and my 27 graduate students whom I taught in 2021 from August to October. I collected and analyzed information adapting dialogue as a research method. Making dialogues with myself and my students, I unveiled five ways of improving my pedagogical practices. They were: by (1) creating a supportive learning environment, (2) enhancing curiosity, (3) enhancing inquiry skills, (4) enhancing evaluation skills, and (5) developing continuous learning habits. My reflective journal, classroom discussions, activities, and assignments hold evidence of my improved pedagogical practices. Finally, I learnt that my enhanced diligence contributed to my learning and my student’s learning as my professional development.
A-MoMAC: mobility-aware and adaptive vehicular MAC protocol in VANETs

VANET is a special mobile ad hoc network, which is an indispensable part of ITS. One of its main functions is to provide security messages for the vehicle nodes. The high mobility of the nodes leads to frequent changes in the topology of the network, so it is necessary to design efficient and reliable MAC for VANETs. This paper improves on MoMAC by proposing an adaptive MAC protocol based on node density, called A-MoMAC. The protocol is no longer fixed for the time slot division. Before reserving time slots, the node will first adjust the time slot division according to the current traffic conditions in each lane. After the adjustment of frame partition, the node will determine a group of reservable time slots and then attempt to reserve a random time slot. Simulation results show that A-MoMAC is well suited to scenarios where nodes are non-uniform, improving channel utilization and reducing access collision.
Nucleic Acid Nanotechnology for Diagnostics and Therapeutics in Acute Kidney Injury

Acute kidney injury (AKI) has impacted a heavy burden on global healthcare system with a high morbidity and mortality in both hospitalized and critically ill patients. However, there are still some shortcomings in clinical approaches for the disease to date, appealing for an earlier recognition and specific intervention to improve long-term outcomes. In the past decades, owing to the predictable base-pairing rule and highly modifiable characteristics, nucleic acids have already become significant biomaterials for nanostructure and nanodevice fabrication, which is known as nucleic acid nanotechnology. In particular, its excellent programmability and biocompatibility have further promoted its intersection with medical challenges. Lately, there have been an influx of research connecting nucleic acid nanotechnology with the clinical needs for renal diseases, especially AKI. In this review, we begin with the diagnostics of AKI based on nucleic acid nanotechnology with a highlight on aptamer- and probe-functionalized detection. Then, recently developed nanoscale nucleic acid therapeutics towards AKI will be fully elucidated. Furthermore, the strengths and limitations will be summarized, envisioning a wiser and wider application of nucleic acid nanotechnology in the future of AKI.
Gravestones, zombies and dead siblings: graveyards as artefacts for children’s existential questions

ABSTRACT This article builds on a qualitative study of interactions and negotiations with respect to existential questions in an ECEC department with children from 3 to 6 years of age, as part of a larger empirical study on the same topic. In the Norwegian Ministry of Education and Research’s 2017 curriculum for Early Childhood Education and Care (ECEC), there is a stated requirement that the staff shall explore and wonder at existential questions together with the children. This article highlights how a walk through a local graveyard with its material artefacts functioning as cultural tools may open a new context for interaction and negotiation regarding death as an existential issue. Vygotsky’s sociocultural theory of mediated action and Bakhtin’s theory of multivoicedness form the framework for the interpretation together with Wartofsky’s three-level theory of artefacts. The method employed to establish the data was participated observation with a handheld video camera.
Reliable Transiently-Powered Communication

Frequent power failures can introduce significant packet losses during communication among energy harvesting batteryless wireless sensors. Nodes should be aware of the energy level of their neighbors to guarantee the success of communication and avoid wasting energy. This paper presents TRAP (TRAnsiently-powered Protocol) that allows nodes to communicate only if the energy availability on both sides of the communication channel is sufficient before packet transmission. TRAP relies on a novel modulator circuit, which operates without microcontroller intervention and transmits the energy status almost for free over the radiofrequency backscatter channel. Our experimental results showed that TRAP avoids failed transmissions introduced by the power failures and ensures reliable intermittent communication among batteryless sensors.
Novel Insights into the Therapeutic Potential of Lung-Targeted Gene Transfer in the Most Common Respiratory Diseases

Over the past decades, a better understanding of the genetic and molecular alterations underlying several respiratory diseases has encouraged the development of new therapeutic strategies. Gene therapy offers new therapeutic alternatives for inherited and acquired diseases by delivering exogenous genetic materials into cells or tissues to restore physiological protein expression and/or activity. In this review, we review (1) different types of viral and non-viral vectors as well as gene-editing techniques; and (2) the application of gene therapy for the treatment of respiratory diseases and disorders, including pulmonary arterial hypertension, idiopathic pulmonary fibrosis, cystic fibrosis, asthma, alpha-1 antitrypsin deficiency, chronic obstructive pulmonary disease, non-small-cell lung cancer, and COVID-19. Further, we also provide specific examples of lung-targeted therapies and discuss the major limitations of gene therapy.
Highly Efficient Fault Diagnosis of Rotating Machinery Under Time-Varying Speeds Using LSISMM and Small Infrared Thermal Images

The existing fault diagnosis methods of rotating machinery constructed with both shallow learning and deep learning models are mostly based on vibration analysis under steady rotating speed. However, the rotating speed frequently changes to meet practical engineering needs. The shallow learning models largely depend on domain experience of feature extraction, and training a deep learning model requires large samples and a long time. In addition, vibration monitoring has the shortcomings of contact measurement, small coverage, and noise interference. To address these problems, this article proposes a new fault diagnosis method with the least square interactive support matrix machine (LSISMM) and infrared thermal images. In this method, a novel matrix-form classifier called LSISMM is constructed under the concept of nonparallel interactive hyperplanes to fully leverage the structure information of infrared thermal images. To improve the computation efficiency, a new least square loss constraint is designed for LSISMM. Besides, we derive an effective solution framework based on the alternating direction method of the multiplier (ADMM) framework. The constructed LSISMM is directly used to analyze the collected thermal images of rotating machinery under time-varying speeds. Experiment results demonstrate that the proposed method is superior to state-of-the-art methods in terms of diagnosis accuracy and efficiency, especially under small thermal image samples.
Trajectory Adaptation and Safety Control via Control Barrier Functions for Legged Robots

Optimizing footholds as well as their exact transition trajectories during swing phase is computationally expensive hence can not satisfy the real-time requirements when traversing challenging terrain. This paper presents a two- level safety control strategy, which balances the time cost for planning and safety for tracking. The top level obtains a preliminary trajectory through time efficient optimization but no safety is completely guaranteed. The bottom level formulates a Quadratic Programming problem incorporating Control Barrier Functions(CBFs) constraints to adapt the trajectory thus guaranteeing safety. Safety control is further improved by terrain map estimation with Fourier series and height compensation with empirical functions. The former smooths the sharp edges to satisfy CBFs’ condition and the latter delivers a longer horizon to mitigate boundary riding dilemma. In the experiment section, our two-level safety control method is validated on Qingzhui hexapod robot in stairs climbing scenario.
ASSOCIATION BETWEEN ENJOYMENT FACTOR AND PHYSICAL ACTIVITY LEVEL AMONG TERTIARY EDUCATION STUDENTS

Enjoyment plays an important key role in the involvement of physical activity. The purpose of the present was to determine the relationship between enjoyment factors and physical activity level among students in UiTM Alor Gajah. 437 respondents (N=437) were voluntarily involved. Physical Activity Enjoyment Scales (PACES) and International Physical Activity Questionnaire (IPAQ-SF) used to quantify enjoyment and physical activity level. Descriptive analysis (Mean ± Standard Deviation) used to describe the demographic value. However, for inferential statistics, Chi-Square Test for Independence was used. There was a significant association [𝑥2(58, n=437) = 103.52, p = . 001, Cramer’s V = 0.344] between enjoyment factor and physical activity level among students in UiTM Alor Gajah. People who have higher enjoyment in physical activity were likely to have a higher physical activity level compared to people who have lower enjoyment. The present study provides valuable and reliable knowledge for adolescents and young people as guidance for preparation programmed to encourage physical activity is necessary to help young people change unhealthful lifestyle patterns and increase physical activity to improve their health.
Toolbox for studying neurovascular coupling in vivo, with a focus on vascular activity and calcium dynamics in astrocytes

Abstract. Significance: Insights into the cellular activity of each member of the neurovascular unit (NVU) is critical for understanding their contributions to neurovascular coupling (NVC)—one of the key control mechanisms in cerebral blood flow regulation. Advances in imaging and genetic tools have enhanced our ability to observe, manipulate and understand the cellular activity of NVU components, namely neurons, astrocytes, microglia, endothelial cells, vascular smooth muscle cells, and pericytes. However, there are still many unresolved questions. Since astrocytes are considered electrically unexcitable, Ca2 + signaling is the main parameter used to monitor their activity. It is therefore imperative to study astrocytic Ca2 + dynamics simultaneously with vascular activity using tools appropriate for the question of interest. Aim: To highlight currently available genetic and imaging tools for studying the NVU—and thus NVC—with a focus on astrocyte Ca2 + dynamics and vascular activity, and discuss the utility, technical advantages, and limitations of these tools for elucidating NVC mechanisms. Approach: We draw attention to some outstanding questions regarding the mechanistic basis of NVC and emphasize the role of astrocytic Ca2 + elevations in functional hyperemia. We further discuss commonly used genetic, and optical imaging tools, as well as some newly developed imaging modalities for studying NVC at the cellular level, highlighting their advantages and limitations. Results: We provide an overview of the current state of NVC research, focusing on the role of astrocytic Ca2 + elevations in functional hyperemia; summarize recent advances in genetically engineered Ca2 + indicators, fluorescence microscopy techniques for studying NVC; and discuss the unmet challenges for future imaging development. Conclusions: Advances in imaging techniques together with improvements in genetic tools have significantly contributed to our understanding of NVC. Many pieces of the puzzle have been revealed, but many more remain to be discovered. Ultimately, optimizing NVC research will require a concerted effort to improve imaging techniques, available genetic tools, and analytical software.
Collision Avoidance Route Planning for Autonomous Medical Devices Using Multiple Depth Cameras

Radiography is one of the most widely used imaging techniques in the world. Since its inception, it has continued to evolve, leading to the development of intelligent and automated radiography systems that are able to perceive parts of their environment and respond accordingly. However, such systems do not provide a complete view of the examination space and are therefore unable to detect multiple objects and fully ensure the safety of patients, staff and equipment during the execution of the movement. In this paper, we present a system architecture based on ROS (Robot Operating System) to solve these challenges and integrate an autonomous X-ray device. The architecture retrieves point clouds from range sensors placed at specific locations in the examination room. By integrating different subsystems, the architecture merges the data from the different sensors to map the space. It also implements downsampling and clustering methods to identify objects and later distinguish obstacles. A subsystem generates bounding boxes based on the detected obstacles and feeds them to a motion planning framework (MoveIt! ) to enable collision avoidance during motion execution. At the same time, a subsystem implements a deep neural network model (PointNet) to classify the detected obstacles. Finally, the developed system architecture provided promising results after being deployed in a Gazebo simulated examination space and on a use case test platform.
Psychological Resistance Recognition Based on Acoustic Features Extraction

Psychological resistance can express the change of the client's psychological state, which is considered to be an important factor affecting psychological counseling. This paper proposed a method of psychological resistance recognition based on acoustic features extraction. The method includes three main steps, i.e., statistical acoustic feature extraction, feature selection, and classification. Our method uses statistical acoustic features to express psychological resistance, and analyzes the application value of high correlation features obtained from feature selection in the recognition of different psychological resistance. This method can be used to determine the high correlation features of psychological resistance adaptively, and avoid the deficiency of subjective judgment. A speech database of psychological resistance is built for research needs, in which three psychological consultants and four subjects are involved in psychological counseling with 1200 sentences in speech. The experimental results verified the effectiveness of our method.