Miracle-dz/UniFAR-FTUs huggingface dataset:
LSTM-Autoencoder Network for the Detection of Seismic Electric Signals

Seismic electric signals (SESs) are essential short-term precursors of earthquakes. Accurate and efficient detection of SESs is significant to short-term predictions of earthquakes. However, SESs are usually disturbed by various noises and are thus difficult to recognize. Although conventional techniques have made substantial efforts in improving the SES detection accuracy, the success rates of SES detection at certain stations are still less satisfactory due to the complexity and diversity of noises. In this study, we apply deep learning to extract SESs and develop a novel deep learning network based on geoelectric field characteristics by combining the long short-term memory (LSTM) blocks with an autoencoder structure and a time-step attention module. The detection results of both synthetic and real data demonstrate that our proposed network yields superior performance in detecting embedded SESs in the presence of severe noise interference compared with traditional methods and several well-known networks. Moreover, our novel network shows the excellent ability of massive data processing, generalization, and migration, which can process one day’s worth of data in only milliseconds, adapt to SESs whose durations and amplitudes are different from those of the training set and be easily transferred to newly acquired data. The proposed novel method can provide more efficient and accurate detection results, which will broaden the data availability of hazard mitigation based on SESs.
Additive manufacturing of Ku/K-band waveguide filters: a comparative analysis among selective-laser melting and stereo-lithography

This study describes an experimental research activity on the manufacturing of passive waveguide components by means of two additive technologies, namely the selective-laser melting and the stereo-lithography processes. The former is a power-bed fusion process, for which aluminium, titanium, and steel powders have been considered in this study. The latter is based on the photopolymerisation of a resin vat. This technology has been applied in conjunction with an acrylonitrile–butadiene–styrene-like polymer and a subsequent electrolytic copper plating for the metallisation of the internal channels. Wideband filters operating in Ku/K band have been developed as benchmarks, for which detailed comparisons between simulations and measurements are reported.
The intention to migrate from more developed to less developed areas: evidence from Hong Kong

ABSTRACT This paper examines migration from less developed areas to more developed areas. Based on recent data collected in Hong Kong in 2020, we explore the intention of Hong Kong Chinese residents aged between 18 and 50 to move to mainland cities in the Greater Bay Area (GBA). Despite rapid economic growth in the area, many local Hong Kong Chinese residents still consider mainland cities in GBA as less developed. In this study, we provide an alternative framework for exploring the ways that economic and non-economic factors can influence the intention to migrate from less developed areas to more developed areas. The results of our analyses demonstrate that non-economic adjustment cost, such as the level of familiarity with the destination, social networks, and ability to speak Putonghua, plays an important role in shaping the intention of working-age adults in Hong Kong to move to mainland cities in GBA.
The microRNA Expression Profiling in Heart Failure: A Systematic Review and Meta-Analysis

Background Heart failure (HF) is a main consequence of cardiovascular diseases worldwide. Abnormal expression levels of microRNAs (miRNAs) in HF are observed in current studies. Novel biomarkers miRNAs may play an important role in the development of HF. Nevertheless, the inconsistency of miRNA expression limits the clinical application. We thus perform this systematic review of the miRNAs expression profiling to identify potential HF biomarkers. Methods The electronic databases of Embase, Medline, and Cochrane Library were systematically searched to identify the miRNA expression profiles between HF subjects and non-HF controls before May 26th, 2021. The pooled results were shown as log10 odds ratios (logORs) with 95% confidence intervals (CI) using random-effect models. Subgroup analyses were conducted according to species, region, and sample source. The quality assessment of included studies was independently conducted based on Diagnostic Accuracy Study 2 (QUADAS-2). The sensitivity analysis was conducted based on sample size. Results A total of 55 miRNA expression articles reporting 276 miRNAs of HF were included. 47 consistently up-regulated and 10 down-regulated miRNAs were identified in the overall analysis, with the most up-regulated miR-21 (logOR 8.02; 95% CI: 6.76–9.27, P < 0.001) and the most down-regulated miR-30c (logOR 6.62; 95% CI: 3.04–10.20, P < 0.001). The subgroup analysis of sample source identified 35 up-regulated and 10 down-regulated miRNAs in blood sample, the most up-regulated and down-regulated miRNAs were miR-210-3p and miR-30c, respectively. In the region sub-groups, let-7i-5p and miR-129 were most up-regulated and down-regulated in Asian countries, while in non-Asian countries, let-7e-5p and miR-30c were the most dysregulated. It’s worth noting that miR-622 was consistently up-regulated in both Asian and non-Asian countries. Sensitivity analysis showed that 46 out of 58 (79.31%) miRNAs were dysregulated. Conclusion A total of 57 consistently dysregulated miRNAs related to HF were confirmed in this study. Seven dysregulated miRNAs (miR-21, miR-30c, miR-210-3p, let-7i-5p, miR-129, let-7e-5p, and miR-622) may be considered as potential non-invasive biomarkers for HF. However, further validation in larger-scale studies are needed to verify our conclusions.
Enhancing automatic control learning through Arduino-based projects

ABSTRACT Systems dynamics and automatic control is classified as one of the toughest courses in the college of engineering at Qassim University according to the statistical analysis of the final grades. This motivated us to think about a new approach to teach this course by extending the design term project to include experimental part beside the theoretical analysis. This approach has become feasible due to the publicity of the low-cost microcontrollers such as the Arduino in the recent years. In this paper, we present our experience in conducting Arduino-based projects and how we used them to support the teaching of all the automatic control subjects in the course such as the mathematical modelling, the stability analysis, the controller design and the implementation of the PID controller. It was found that the new approach improves the attention and the overall performance of the students in the course which was reflected in the students’ marks in the quizzes and exams. Moreover, it motivated the students to be creative by utilising the control theory to design new systems that can be useful for their local community.
Lockdown Duration and Training Intensity Affect Sleep Behavior in an International Sample of 1,454 Elite Athletes

Objective: To investigate the effect of 1) lockdown duration and 2) training intensity on sleep quality and insomnia symptoms in elite athletes. Methods: 1,454 elite athletes (24.1 ± 6.7 years; 42% female; 41% individual sports) from 40 countries answered a retrospective, cross-sectional, web-based questionnaire relating to their behavioral habits pre- and during- COVID-19 lockdown, including: 1) Pittsburgh sleep quality index (PSQI); 2) Insomnia severity index (ISI); bespoke questions about 3) napping; and 4) training behaviors. The association between dependent (PSQI and ISI) and independent variables (sleep, napping and training behaviors) was determined with multiple regression and is reported as semi-partial correlation coefficient squared (in percentage). Results: 15% of the sample spent < 1 month, 27% spent 1–2 months and 58% spent > 2 months in lockdown. 29% self-reported maintaining the same training intensity during-lockdown whilst 71% reduced training intensity. PSQI (4.1 ± 2.4 to 5.8 ± 3.1; mean difference (MD): 1.7; 95% confidence interval of the difference (95% CI): 1.6–1.9) and ISI (5.1 ± 4.7 to 7.7 ± 6.4; MD: 2.6; 95% CI: 2.3–2.9) scores were higher during-compared to pre-lockdown, associated (all p < 0.001) with longer sleep onset latency (PSQI: 28%; ISI: 23%), later bedtime (PSQI: 13%; ISI: 14%) and later preferred time of day to train (PSQI: 9%; ISI: 5%) during-lockdown. Those who reduced training intensity during-lockdown showed higher PSQI (p < 0.001; MD: 1.25; 95% CI: 0.87–1.63) and ISI (p < 0.001; MD: 2.5; 95% CI: 1.72–3.27) scores compared to those who maintained training intensity. Although PSQI score was not affected by the lockdown duration, ISI score was higher in athletes who spent > 2 months confined compared to those who spent < 1 month (p < 0.001; MD: 1.28; 95% CI: 0.26–2.3). Conclusion: Reducing training intensity during the COVID-19-induced lockdown was associated with lower sleep quality and higher insomnia severity in elite athletes. Lockdown duration had further disrupting effects on elite athletes’ sleep behavior. These findings could be of relevance in future lockdown or lockdown-like situations (e.g., prolonged illness, injury, and quarantine after international travel).
A Novel Machine Learning-Based Scheme for Spectrum Sharing in Virtualized 5G Networks

Network virtualization allows the coexistence of multiple network slices over a shared physical infrastructure, each delivering a service with own requirements in terms of quality of service, coverage, and time span. Spectrum is an expensive commodity, so it must be managed among these slices in the most efficient way. However, current spectrum sharing techniques are too rigid to address all combinations of service requirements and properly exploit the new air interface flexibility and network deployment options introduced by the Fifth Generation (5G) mobile networks. In this paper, we extend the state of the art first by proposing a novel spectrum sharing scheme that supports an unlimited number of radio networks. Second, we propose a 5G compliant novel service-based network management architecture to integrate Machine Learning (ML) algorithms for Radio Resource Management (RRM), including spectrum sharing. Finally, we propose a new three-stage ML framework that exploits forecasting, clustering and reinforcement learning algorithms to implement the proposed spectrum sharing scheme. Our proposed solution can allow an arbitrary number of 5G network slices to share spectrum more effectively and more dynamically either with each other or with other radio networks.
Using K-Means Clustering in Python with Periodic Boundary Conditions

Periodic boundary conditions are natural in many scientific problems, and often lead to particular symmetries. Working with datasets that express periodicity properties requires special approaches when analyzing these phenomena. Periodic boundary conditions often help to solve or describe the problem in a much simpler way. The angular rotational symmetry is an example of periodic boundary conditions. This symmetry implies angular momentum conservation. On the other hand, clustering is one of the first and most basic methods used in data analysis. It is often a starting point when new data are acquired and understood. K-means clustering is one of the most commonly used clustering methods. It can be applied to many different situations with reasonably good results. Unfortunately, the original k-means approach does not cope well with the periodic properties of the data. For example, the original k-means algorithm treats a zero angle as very far from an angle that is 359 degrees. Periodic boundary conditions often change the classical distance measure and introduce an error in k-means clustering. In the paper, we discuss the problem of periodicity in the dataset and present a periodic k-means algorithm that modifies the original approach. Considering that many data scientists prefer on-the-shelf solutions, such as libraries available in Python, we present how easily they can incorporate periodicity into existing k-means implementation in the PyClustering library. It allows anyone to integrate periodic conditions without significant additional costs. The paper evaluates the described method using three different datasets: the artificial dataset, wind direction measurement, and the New York taxi service dataset. The proposed periodic k-means provides better results when the dataset manifests some periodic properties.
Impacts of Corporate Social Responsibility on Employees’ Mental Fatigue: Employees’ Ethical Perspective

With the rise of cost of living and coronavirus disease 2019 (COVID-19) global pandemic therewithal, finding reliable measures to reduce employees’ mental fatigue has become a great challenge. In this context, scholars have mainly focused on solutions for relieving employees’ mental fatigue from the perspective of human resource management but barely from employees’ ethical perspectives and that of internal and external corporate social responsibility (CSR) and employees’ ethics. This study uses hierarchical regression analysis and attempts to formulate and analyze the relationship between CSR, perceptions of corporate hypocrisy, and employees’ mental fatigue along with the mediating role of ethical egoism and altruistic choice. It also conceptualizes models and develops various hypotheses and theoretical logic. A total of 250 questionnaires were distributed, and 176 valid responses were subsequently gathered. The results show that employees’ mental fatigue significantly reduces when either internal or external CSR has a positive impact on employees’ altruistic choice and significantly increases either internal or external CSR has a negative effect on ethical egoism. Similarly, reducing perceptions of corporate hypocrisy can enhance the positive impact of external CSR on altruistic choice, which consequently reduces employees’ mental fatigue.
Sphingolipid Long-Chain Base Phosphate Degradation Can Be a Rate-Limiting Step in Long-Chain Base Homeostasis

Sphingolipid long-chain bases (LCBs) are building blocks for membrane-localized sphingolipids, and are involved in signal transduction pathways in plants. Elevated LCB levels are associated with the induction of programmed cell death and pathogen-derived toxin-induced cell death. Therefore, levels of free LCBs can determine survival of plant cells. To elucidate the contribution of metabolic pathways regulating high LCB levels, we applied the deuterium-labeled LCB D-erythro-sphinganine-d7 (D7-d18:0), the first LCB in sphingolipid biosynthesis, to Arabidopsis leaves and quantified labeled LCBs, LCB phosphates (LCB-Ps), and 14 abundant ceramide (Cer) species over time. We show that LCB D7-d18:0 is rapidly converted into the LCBs d18:0P, t18:0, and t18:0P. Deuterium-labeled ceramides were less abundant, but increased over time, with the highest levels detected for Cer(d18:0/16:0), Cer(d18:0/24:0), Cer(t18:0/16:0), and Cer(t18:0/22:0). A more than 50-fold increase of LCB-P levels after leaf incubation in LCB D7-d18:0 indicated that degradation of LCBs via LCB-Ps is important, and we hypothesized that LCB-P degradation could be a rate-limiting step to reduce high levels of LCBs. To functionally test this hypothesis, we constructed a transgenic line with dihydrosphingosine-1-phosphate lyase 1 (DPL1) under control of an inducible promotor. Higher expression of DPL1 significantly reduced elevated LCB-P and LCB levels induced by Fumonisin B1, and rendered plants more resistant against this fungal toxin. Taken together, we provide quantitative data on the contribution of major enzymatic pathways to reduce high LCB levels, which can trigger cell death. Specifically, we provide functional evidence that DPL1 can be a rate-limiting step in regulating high LCB levels.
Cell Fate following Irradiation of MDA-MB-231 and MCF-7 Breast Cancer Cells Pre-Exposed to the Tetrahydroisoquinoline Sulfamate Microtubule Disruptor STX3451

A tetrahydroisoquinoline (THIQ) core is able to mimic the A and B rings of 2-methoxyestradiol (2ME2), an endogenous estrogen metabolite that demonstrates promising anticancer properties primarily by disrupting microtubule dynamic instability parameters, but has very poor pharmaceutical properties that can be improved by sulfamoylation. The non-steroidal THIQ-based microtubule disruptor 2-(3-bromo-4,5-dimethoxybenzyl)-7-methoxy-6-sulfamoyloxy-1,2,3,4-tetrahydroisoquinoline (STX3451), with enhanced pharmacokinetic and pharmacodynamic profiles, was explored for the first time in radiation biology. We investigated whether 24 h pre-treatment with STX3451 could pre-sensitize MCF-7 and MDA-MB-231 breast cancer cells to radiation. This regimen showed a clear increase in cytotoxicity compared to the individual modalities, results that were contiguous in spectrophotometric analysis, flow cytometric quantification of apoptosis induction, clonogenic studies and microscopy techniques. Drug pre-treatment increased radiation-induced DNA damage, with statistically more double-strand (ds) DNA breaks demonstrated. The latter could be due to the induction of a radiation-sensitive metaphase block or the increased levels of reactive oxygen species, both evident after compound exposure. STX3451 pre-exposure may also delay DNA repair mechanisms, as the DNA damage response element ataxia telangiectasia mutated (ATM) was depressed. These in vitro findings may translate into in vivo models, with the ultimate aim of reducing both radiation and drug doses for maximal clinical effect with minimal adverse effects.
Modeling of Take-Over Performance in Highly Automated Vehicle Guidance

Withdrawing the driving task from automated vehicle guidance at system limits is a key safety and controllability aspect of future highly automated vehicles. Based on a series of driving simulator studies, major factors that influence driver’s performance in take-over scenarios are identified and a validated, quantitative modeling approach for temporal and qualitative aspects of the take-over is proposed.
Genome-Resolved Characterization of Structure and Potential Functions of the Zebrafish Stool Microbiome

Zebrafish have been used as a model organism for more than 50 years and are considered an excellent model for studying host-microbiome interactions. However, this largely depends on our understanding of the zebrafish gut microbiome itself. Despite advances in sequencing and data analysis methods, the zebrafish gut microbiome remains highly understudied. This study performed the de novo metagenome assembly and recovery of the metagenome-assembled genomes (MAGs) through genome binning (and refinement) of the contigs assembled from the zebrafish stool. The results indicate that majority of the MAGs had excellent quality i.e. high completeness (≥90%) and low contamination levels (≤5%). MAGs mainly belong to the taxa that are known to be members of the core zebrafish stool microbiome, including the phylum Proteobacteria, Fusobacteriota, and Actinobacteriota. However, most of the MAGs remained unclassified at the species level and reflected previously unexplored microbial taxa and their potential novelty. These MAGs also contained genes with predicted functions associated with diverse metabolic pathways that included carbohydrate, amino acid, and lipid metabolism pathways. Lastly, we performed a comparative analysis of Paucibacter MAGs and reference genomes that highlighted the presence of novel Paucibacter species and enriched metabolic potential in the recovered MAGs.
Biochemical and cellular characteristics of the 30! 50 exonuclease TREX2

TREX2 is an autonomous nonprocessive 30 ! 50 exonuclease, suggesting that it maintains genome integrity. To investigate TREX2’s biochemical and cellular properties, we show that endogenous TREX2 is expressed widely in mouse tissues and human cell lines. Unexpectedly, endogenous human TREX2 is predominantly expressed as a 30-kDa protein (not 26 kDa, as previously believed), which is likely encoded by longer isoforms (TREX2 and/or TREX2) that possess similar capacity for selfassociation, DNA binding and catalytic activity. Site-directed mutagenesis analysis shows that the three functional activities of TREX2 are distinct, yet integrated. Mutation of amino acids putatively important for homodimerization significantly impairs both DNA binding and exonuclease activity, while mutation of amino acids (except R163) in the DNA binding and exonuclease domains affects their corresponding activities. Interestingly, however, DNA-binding domain mutations do not impact catalytic activity, while exonuclease domain mutations diminish DNA binding. To understand TREX2 cellular properties, we find endogenous TREX2 is down regulated during G2/M and nuclear TREX2 displays a punctate staining pattern. Furthermore, TREX2 knockdown reduces cell proliferation. Taken together, our results suggest that TREX2 plays an important function during DNA metabolism and cellular proliferation.
Contextual Attention Network for Emotional Video Captioning

This paper investigates an emerging and challenging task—emotional video captioning. Formally, given a video, the task aims to not only describe the factual content of the video, but also discover the emotional clues in the video. We propose a novel Contextual Attention Network (CANet), which recognizes and describes the fact and emotion in the video by semantic-rich context learning. To be specific, at each time step, we first extract visual and textual features from both input video and previously generated words. Then, we apply the attention mechanism to these features to capture informative contexts for captioning. We train the CANet model with the joint optimization of cross-entropy loss <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_{CE}$</tex-math></inline-formula> and contrastive loss <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_{CL}$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_{CE}$</tex-math></inline-formula> constrains the semantics of the generated sentence to be close to human annotation and <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_{CL}$</tex-math></inline-formula> encourages discriminative representation learning from positive and negative pairs of video and caption. Experiments on two emotional video captioning datasets (i.e., EmVidCap and EmVidCap-S) demonstrate the superiority of CANet compared to the state-of-the-art approaches.
Characterization and phylogenetic analysis of the complete chloroplast genome of Cyanthillium cinereum (L.) H. Rob

Abstract Cyanthillium cinereum is a member of the tribe Vernonieae from the family Compositae. The tribe was traditionally placed in the subfamily Cichorioideae, but is recently proposed to be placed in its own subfamily Vernonioideae. The complete chloroplast genome (cp genome) of the genus Cyanthillium is sequenced for the first time. The cp genome of C. cinereum is 152,750 bp in length. It contained a large single copy (LSC) region (83,871 bp), and small single copy (SSC) region (18,487 bp), and two inverted repeats (IRs, 25,196 bp). Phylogenetic analysis of 20 species was conducted. C. cinereum and Gymnanthemum amygdalinum which are members of tribe Vernonieae nested outside of the monophyletic clade formed by members of subfamily Cichorioideae. The findings would be useful to understand the phylogeny of the genus Cyanthillium and the subfamily Vernonioideae.