Miracle-dz/UniFAR-FTUs huggingface dataset:
The Evolution of X-ray Binaries

These proceedings represent papers presented at the Astrophysics Conference in Maryland, USA. The topic of the Conference was the evolution of x-ray binaries and the papers encompass a wide range of subjects on x-ray astronomy. There were one hundred eighteen papers presented at the Conference and out of these three have been abstracted for the Energy Science and Technology database. (AIP)
Full-genome sequencing and mutation analysis of SARS-CoV-2 isolated from Makassar, South Sulawesi, Indonesia

Introduction A global surge in SARS-CoV-2 cases is occurring due to the emergence of new disease variants, and requires continuous adjustment of public health measures. This study aims to continuously monitor and mitigate the impact of SARS-CoV-2 through genomic surveillance, to determine the emergence of variants and their impact on public health. Methods Data were collected from 50 full-genome sequences of SARS-CoV-2 isolates from Makassar, South Sulawesi, Indonesia. Mutation and phylogenetic analysis was performed of SARS-CoV-2 from Makassar, South Sulawesi, Indonesia. Results Phylogenetic analysis showed that two samples (4%) were of the B.1. 319 lineage, while the others (96%) were of the B.1. 466.2 lineage. Mutation analysis of the spike (S) protein region showed that the most common mutation was D614G (found in 100% of the sequenced isolates), followed by N439K (98%) and P681R (76%). Several mutations were also identified in other genomes with a high frequency, including P323L (nsp12), Q57H (ns3-orf3a), and T205I (nucleoprotein). Conclusion Our findings highlight the importance of continuous genomic surveillance to identify new viral mutations and variants with possible impacts on public health.
Geoparsing: Solved or Biased? An Evaluation of Geographic Biases in Geoparsing

Abstract. Geoparsing, the task of extracting toponyms from texts and associating them with geographic locations, has witnessed remarkable progress over the past years. However, despite its intrinsically geospatial nature, existing evaluations tend to focus on overall performance while paying little attention to its variation across geographic space. In this work, we attempt to answer the question whether geoparsing is solved or biased by conducting a spatially-explicit evaluation, namely an evaluation of the regional variability in geoparsing performance. Particularly, we will analyze the spatial autocorrelation underlying this regional variability. By performing hot and cold spot detection over results of several open-source geoparsers, we observe that none of them performs equally well across geographic space, and some are geographically biased towards some regions but against others. We also carry out a comparative experiment showing that stateof- the-art geoparsers developed with neural networks do not necessarily outperform the off-the-shelf tools across geographic space. To understand the implications behind this observed regional variability, we evaluate geographic biases involved in geoparsing research centered around data contribution and usage, algorithm design, and performance evaluations. Particularly, our spatially-explicit performance evaluation serves as an approach to evaluation bias mitigation in geoparsing.We conclude that previous performance evaluations published in the literature are overly optimistic, thus hiding the fact that geoparsing is far from solved, and geoparsers require debiasing in addition to further considerations when being applied to (geospatial) downstream tasks.
Compressive sensing-based vibration signal reconstruction using sparsity adaptive subspace pursuit

Wireless sensors produce large amounts of data in long-term online monitoring following the Shannon–Nyquist theorem, leading to a heavy burden on wireless communications and data storage. To address this problem, compressive sensing which allows wireless sensors to sample at a much lower rate than the Nyquist frequency has been considered. However, the lower rate sacrifices the integrity of the signal. Therefore, reconstruction from low-dimension measurement samples is necessary. Generally, the reconstruction needs the information of signal sparsity in advance, whereas it is usually unknown in practical applications. To address this issue, a sparsity adaptive subspace pursuit compressive sensing algorithm is deployed in this article. In order to balance the computational speed and estimation accuracy, a half-fold sparsity estimation method is proposed. To verify the effectiveness of this algorithm, several simulation tests were performed. First, the feasibility of subspace pursuit algorithm is verified using random sparse signals with five different sparsities. Second, the synthesized vibration signals for four different compression rates are reconstructed. The corresponding reconstruction correlation coefficient and root mean square error are demonstrated. The high correlation and low error result mean that the proposed algorithm can be applied in the vibration signal process. Third, implementation of the proposed approach for a practical vibration signal from an offshore structure is carried out. To reduce the effect of signal noise, the wavelet de-noising technique is used. Considering the randomness of the sampling, many reconstruction tests were carried out. Finally, to validate the reliability of the reconstructed signal, the structure modal parameters are calculated by the Eigensystem realization algorithm, and the result is only slightly different between original and reconstructed signal, which means that the proposed method can successfully save the modal information of vibration signals.
A new high-torque retarder based on combined effects of magnetorheological fluid and eddy current

In this article, a new high-torque retarder combining the effects of magnetorheological fluid and eddy current is researched. The new retarder provides a part of the braking torque generated by the shear stress of the magnetorheological fluid and an additional braking torque generated by the effect of the eddy current on the rotors. This operating concept is realized by a common magnetic excitation circuit generated by a new structure with several separated coils. The configurations and design details of the new retarder, including the structure, material selection, and magnetic circuit, are discussed. The mathematical models of braking torque caused by the magnetorheological fluid and eddy current are also derived. Then, a finite element analysis is performed to verify the magnetic field design of the new retarder. Finally, a prototype is fabricated, and the relevant parameters are tested. The experimental result shows that the new retarder provides not only a stable braking torque at low speed but also a great increment of braking torque varied with rotation speed, which effectively improves the total braking torque compared with conventional magnetorheological retarders.
Periodontal and Peri-Implant Health Status in Traditional vs. Heat-Not-Burn Tobacco and Electronic Cigarettes Smokers: A Systematic Review

The aim of the present systematic review was to evaluate and possibly differentiate the effects of traditional cigarettes, heat-not-burn tobacco, and electronic cigarettes on periodontal and peri-implant health status. Electronic cigarettes and heat-not-burn tobacco have become very popular in recent years and have been proposed to consumers as a safer alternative to conventional tobacco smoke, although their effect on periodontal and peri-implant health remains unclear. The study protocol was developed according to PRISMA guidelines, and the focus question was formulated according to the PICO strategy. A literature search was conducted across PubMed/MEDLINE and the COCHRANE library from 2003 to April 2022. From the 1935 titles initially identified, 18 articles were finally included in the study and extracted data were qualitatively synthesized. It may be carefully concluded that e-cigarettes may cause attenuated clinical inflammatory signs of periodontitis and, hypothetically, of peri-implantitis when compared to conventional tobacco smoke. Both alternative smoking products, containing nicotine, may likewise exert negative effects on periodontal and peri-implant health, as demonstrated by in vitro studies. Further investigations are needed to assess the impact of electronic cigarettes and heat-not-burn tobacco products on periodontal and peri-implant health status.
Evaluating the Effectiveness of Community-Based Dementia Caregiver Intervention on Caregiving Burden, Depression, and Attitude Toward Dementia: A Quasi‐experimental Study

Objective Accumulating evidence of the effects of dementia caregiving on individuals, society, and health has generated intervention studies to reduce the stress among family caregivers of people with dementia. This study aims to evaluate the effectiveness of a family support program, community-based dementia caregiver intervention (CDCI), among family caregivers of people with dementia compared with a control group (no intervention). Patients and Methods This study is a quasi-experimental, non-randomized controlled trial conducted in six dementia relief centers of a community healthcare center in Korea. Family caregivers of 83 patients with dementia were recruited; of these 78 were included in the final study, with 40 in the intervention group and 38 in the control group. Analysis of covariance (ANCOVA) was used to compare the mean difference in the scores of the total short-form Zarit Burden Interview (SZBI), personal strain, role strain, depression, and attitude between the groups. Results Compared with controls, in the intervention group, the adjusted mean score of personal strain (F = 4.353, t = 0.041) and attitude toward dementia (F = 10.284, t = 0.002) differed significantly after the intervention, with a small to moderate effect. There was no significant difference in the total SZBI, role strain, or depression mean score. Conclusion The findings suggest that CDCI may be an effective intervention strategy to reduce personal strain and enhance the attitudes of family caregivers of people with dementia.
Tetrahedral Profiled Carbon Rovings for Concrete Reinforcements

Textile reinforcements have established themselves as a convincing alternative to conventional steel reinforcements in the building industry. Due to their high load-bearing capacities in addition to a smaller concrete cross section required, the bond between textile and concrete is extremely important. In contrast to ribbed steel bars ensuring a stable mechanical interlock with concrete (form fit), the bond force of carbon rovings has so far been transmitted primarily via the coating of the textile, i.e. by an adhesive bond with the concrete matrix (material fit). However, this material fit must be activated over relatively large yarn areas, which does not allow material-efficient utilization of the mechanical load capacity of the textile reinforcement. Solutions involving profiled rovings promise significant improvements in the bonding behavior by creating an additional mechanical interlock with the concrete matrix. In order to achieve a form-fit effect between roving and concrete, a roving geometry inspired by ribbed steel bars has to be created. For this purpose, an innovative profiling process was developed and implemented.
Nonlinear model predictive control of a class of continuum robots using kinematic and dynamic models

Controlling continuum robots with precision is particularly a challenging task due to the complexity of their mathematical models and inaccuracies in modeling approaches. Therefore, most advanced control schemes have shown poor performances, especially in trajectory tracking accuracy. This paper presents a proposed Nonlinear Model Predictive Control (NMPC) scheme to solve the trajectory tracking of a class of continuum robots, namely Cable-Driven Continuum Robot (CDCR). However, since NMPC schemes are often limited by the computational burden associated with the optimization algorithms to be solved at each sampling time, the Particle Swarm Optimization (PSO) algorithm is used to solve the arising optimization problem NMPC, thanks to its simplicity and fast convergence. The proposed NMPC-PSO scheme is applied to the developed kinematic and dynamic models of the considered CDCR. Based on the kinematic and dynamic model, the two proposed controllers have been validated against numerical simulations of two-dimensional CDCR with two bending sections for set-point stabilization and point-to-point trajectory tracking. For both controllers, the performance of tracking accuracy and computation time is analyzed and compared. Moreover, the obtained simulation results are compared to the available literature works. In view of the results obtained on the considered CDCR, the proposed NMPC-PSO scheme can track in real-time the desired trajectory with high accuracy and much less execution time than other advanced control schemes, which makes it an alternative for real-time applications.
Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art

ABSTRACT In recent years, since researchers began to study on Unmanned Aerial Vehicles (UAVs), UAVs have been integrated into today's everyday life, including civilian area and military area. Many researchers have tried to make use of UAVs as an ideal platform for inspection, delivery, surveillance, and so on. In particular, machine learning has been applied to UAVs for autonomous flight that enables UAVs do designated task more efficiently. In this paper, we review the history and the classification of machine learning, and discuss the state-of-the-art machine learning that has been applied to UAVs for autonomous flight. We provide control strategies including parameter tuning, adaptive control for uncertain environment, and real-time path planning, and object recognition that have been described in the literature. GRAPHICAL ABSTRACT
Knowledge building in software developer communities

Software development has become a cognitive and collaborative knowledge-based endeavor where developers and organizations, faced with a variety of challenges and an increased demand for extensive knowledge support, push the boundaries of existing tools and work practices. Researchers and industry professionals have spent years studying collaborative work and communication media, however, the landscape of social media is rapidly changing. Thus, instead of trying to model the use of specific technologies and communication media, I seek to model the knowledge-building process itself. Doing so will not only allow us to understand specific tool and communication media use, but whole ecosystems of technologies and their impact on software development and knowledge work, revealing aspects not only unique to specific tools, but also aspects about the combination of technologies. In this dissertation, I describe the empirical studies I conducted aimed to understand social and communication media use in software development and knowledge curation within developer communities. An important part of the thesis is an additional qualitative meta-synthesis of these studies. My meta-analysis has led to a model of software development as a knowledge building process, and a theoretical framework: I describe this newly formed framework and how it is grounded in empirical work, and demonstrate how my primary studies led to its creation. My conceptualization of knowledge building withing software development and the proposed framework provide the research community with the means to pursue a deeper understanding of software development and contemporary knowledge work. I believe that this framework can serve as a basis for a theory of knowledge building in software development, shedding light on knowledge flow, knowledge productivity, and knowledge management.
Extracardiac Vagal Stimulation-Assisted Cardioneuroablation: Dynamically Evaluating the Impact of Sequential Ganglionated Plexus Ablation on Vagal Control of SAN and AVN in Patients with Sinoatrial Node Dysfunction

Cardioneuroablation (CNA) is proposed as a promising therapy for patients with sinoatrial node dysfunction (SND) that is mediated by excessive vagal tone. However, a series of urgent questions about CNA remain unanswered. From December 2020 to March 2022, six patients with symptomatic SND who underwent CNA were summarized in this report. Sequential CNA targeting Ao-SVC GP, PMLGP, RAGP, and LSGP was performed in patients, guided by fractionated intracardiac electrograms and dynamically evaluated by extracardiac vagal stimulation (ECVS). The results showed that Ao-SVC GP ablation led to a significant increase in heart rate (HR) and the elimination of sinus arrest evoked by ECVS, while the vagal responses of atrial ventricular block were eliminated by the ablation of PMLGP and LSGP. Post-procedure HR increased up to 64–86% of the maximum HR of an atropine test at baseline. The median HR from Holter monitoring increased from 52.8 ± 2.1 bpm at baseline to 73.0 ± 10.4 bpm after the procedure (p = 0.012) and to 71.3 ± 10.1 bpm at the six-month follow-up (p = 0.011). Bradycardia-related symptoms disappeared in all patients at the six-month follow-up. This case series reveals the feasibility of using the ECVS-assisted sequential CNA technique and indicates the critical role of ECVS in dynamically evaluating the impact of sequential CNA on the vagal control of SAN and AVN.
Homophily Effect in Trauma-Informed Classroom Training for School Personnel

A national shortage of youth mental health professionals necessitates training others (e.g., school staff) to help youth with behavioral and mental health issues. Professional training in trauma-informed classroom (TIC) practices could increase school staff’s awareness of adverse childhood experiences (ACEs). The purpose was to determine the effect of homophily on participants’ perceptions or knowledge of TIC training. Mental Health America of Greater Houston (MHAGH) offered TIC training from 2019 to 2020 to Texas educators (N ≈ 29,900) from nine school districts that experienced significant natural and human-made traumatic events. Proportional stratified random samples were selected based on trainer type (experts vs. peer trainers). Perception was measured with close-ended items on five-point scales. Knowledge was measured with content-specific questions. Independent t-tests and two-way ANOVA revealed no significant interaction effects (i.e., trainer and test type) and no differences existed in perception or knowledge by trainer type. TIC training can be equally effective when delivered by homophilous peers (i.e., school staff) and heterophilous experts (i.e., mental health experts). COVID-19 worsened the effects of ACEs and youth mental health issues. High-quality training will increase school staff’s use of TIC practices. MHAGH’s train-the-trainer model helps educators supporting youth affected by ACEs and other life stressors.
Metabolomic Profiles of Multidrug-Resistant Salmonella Typhimurium from Humans, Bovine, and Porcine Hosts

Simple Summary The global threat that is imposed by the resistance the pathogens develop to antimicrobial drugs is escalating. Tools to detect the resistance (with evidence on molecular and cellular outcomes) would reveal intricate mechanisms through which novel drugs could be developed. Approaches such as metabolomics, which involve metabolite detection, provide scientific evidence of metabolite expression of antimicrobial-resistant pathogens. The current study involved metabolomics of antimicrobial-resistant Salmonella Typhimurium collected from various hosts (human, porcine, bovine) and were exposed to antimicrobial drugs—ampicillin, chloramphenicol, streptomycin, sulfisoxazole, and tetracycline—as one set of the experiment. The same isolates were also cultured with no drug exposure as a comparison. There are certain pathways of metabolite expression that are impacted by drug exposure when compared to no drug exposure, meaning that the expressed metabolites could be potential targets for drug companies for the treatment of antimicrobial-resistant pathogens. Abstract Antimicrobial resistance (AMR) is a global public health threat, yet tools for detecting resistance patterns are limited and require advanced molecular methods. Metabolomic approaches produce metabolite profiles and help provide scientific evidence of differences in metabolite expressions between Salmonella Typhimurium from various hosts. This research aimed to evaluate the metabolomic profiles of S. Typhimurium associated with AMR and it compares profiles across various hosts. Three samples, each from bovine, porcine, and humans (total n = 9), were selectively chosen from an existing library to compare these nine isolates cultured under no drug exposure to the same isolates cultured in the presence of the antimicrobial drug panel ACSSuT (ampicillin, chloramphenicol, streptomycin, sulfisoxazole, tetracycline). This was followed by metabolomic profiling using UPLC and GC–mass spectrometry. The results indicated that the metabolite regulation was affected by antibiotic exposure, irrespective of the host species. When exposed to antibiotics, 59.69% and 40.31% of metabolites had increased and decreased expressions, respectively. The most significantly regulated metabolic pathway was aminoacyl-tRNA biosynthesis, which demonstrated increased expressions of serine, aspartate, alanine, and citric acid. Metabolites that showed decreased expressions included glutamate and pyruvate. This pathway and associated metabolites have known AMR associations and could be targeted for new drug discoveries and diagnostic methods.
Differentially Expressed miRNAs in Age-Related Neurodegenerative Diseases: A Meta-Analysis

To date, no neurodegenerative diseases (NDDs) have cures, and the underlying mechanism of their pathogenesis is undetermined. As miRNAs extensively regulate all biological processes and are crucial regulators of healthy brain function, miRNAs differentially expressed in NDDs may provide insight into the factors that contribute to the emergence of protein inclusions and the propagation of deleterious cellular environments. A meta-analysis of miRNAs dysregulated in Alzheimer’s disease, Parkinson’s disease, multiple system atrophy, progressive supranuclear palsy, corticobasal degeneration, dementia with Lewy bodies and frontotemporal lobar degeneration (TDP43 variant) was performed to determine if diseases within a proteinopathy have distinct or shared mechanisms of action leading to neuronal death, and if proteinopathies can be classified on the basis of their miRNA profiles. Our results identified both miRNAs distinct to the anatomy, disease type and pathology, and miRNAs consistently dysregulated within single proteinopathies and across neurodegeneration in general. Our results also highlight the necessity to minimize the variability between studies. These findings showcase the need for more transcriptomic research on infrequently occurring NDDs, and the need for the standardization of research techniques and platforms utilized across labs and diseases.
Biomimetic materials for fouling resistance

In this paper, we first introduce different types of natural species that can prevent hard and soft fouling or have self cleaning ability. Then, we state several theories that may be helpful to fabricate different kinds of fouling resisted surfaces. After that we introduce different types of fouling classified by a framework that based on the modulus, including hard fouling, marine fouling, and other types of fouling, and for each type, we state how to fabricate surfaces that can prevent it or lower the adhesion. Overall, we summarize different ways that can prevent fouling stick on the surfaces and it can also be a guide to direct manufacturing different fouling-resistant surfaces.