Miracle-dz/UniFAR-FTUs huggingface dataset:
Neutralization Activity against SARS-CoV-2 Variants after Booster Vaccination in Populations without COVID-19: A Meta-Analysis

A number of SARS-CoV-2 variants that have evolved to have significant immune escape have emerged worldwide since the COVID-19 outbreak. The efficacy of prime vaccination is waning with the evolution of SARS-CoV-2, and the necessity of booster doses is more and more prominent. Therefore, this study aimed to compare the neutralization activity against the wild type and variants (Beta, Delta, and Omicron) in different prime–boost vaccination regimens. Electronic databases including PubMed, the Cochrane Library, Embase, medRxiv, Wanfang and CNKI were used to retrieve original studies. A total of 16 studies, 9 prime–boost vaccination regimes, and 3134 subjects were included in the meta-analysis and random effect models were used to estimate pooled neutralization titers. The neutralization activity against SARS-CoV-2 showed a significant decline with the evolution of the virus, especially in the populations primed with inactivated vaccines. For homologous immunization, only the populations boosted with mRNA vaccines consistently had a significant rise in neutralization titers (Beta: MD = 0.97; Delta: MD = 1.33; Omicron: MD = 0.74). While the heterologous immunization was more effective, the increment of neutralization titers against wild type, Beta, Delta and Omicron was 1.65 (95% CI: 1.32–1.96), 1.03 (95% CI: 0.53–1.54), 1.46 (95% CI: 1.07–1.85) and 1.15 (95% CI: 0.68–1.61), respectively. With the evolution of SARS-CoV-2, the effectiveness of prime immunization is waning. Although the administration of the booster dose could ameliorate the neutralization titers, homologous immunization regimens were gradually losing their effectiveness. Therefore, a heterologous booster dose is required, especially in populations primed with inactivated vaccines.
Adoption of electronic cards using Wi-Fi platform services by clients of banking sector during COVID-19 pandemic

Wi-Fi technology is one of the most electronic services that recently emerged in the banking sector that provides significant benefits to clients and banks. Thus, the study aims to validate the factors influencing the adoption of Wi-Fi (Near Field Communication) technology by banking sector clients using an extended technology acceptance model. One hundred sixty-two valid surveys were gained from Jordanian clients via an online survey. Data were analyzed using the PLS-SEM technique. Perceived usefulness, perceived ease of use, and perceived bank credibility all have a significant effect on the adoption of Wi-Fi technology services. However, perceived awareness, reference groups, and security concerns have no significant effect on the adoption of Wi-Fi technology services. The research has several implications for theory and practice. It also makes several suggestions for future studies and points out some limitations.
Lane Detection Based on Two-Stage Noise Features Filtering and Clustering

Lane detection is essential for autonomous vehicles, and vision-based lane detection is widely used in the field of intelligent driving cars because of its low cost. Aiming to solve the problem of false detection caused by surrounding vehicles during lane detection, a new multi-lane detection method with strong anti-interference ability is proposed. The main contribution of this work is the utilization of object detection neural network which is applied to remove vehicles from road images as much as possible. Compared with the traditional feature-based detection algorithms, this method can preserve lane line features more accurately and effectively. Firstly, binarization of global optimal threshold method is utilized to extract the basic features of lanes. In order to further remove non-lane line noises and extract lane features as clear as possible, YOLOv4-tiny object detection network is introduced to detect and filter vehicles on the road. Then the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm with denoising function is applied to cluster the different lane features. Finally, an improved RANSAC (Random Sample Consensus) method is used to perform quadratic curve fitting on the clustered features. The experimental results on dataset show that consumed time of a single frame image is only 0.119 second and the precision of the proposed method can reach 94.4%. Compared with other detection algorithms, the proposed algorithm has good real-time performance, stability and robustness in a variety of complex scenarios.
HEALTH CONDITIONS AND TEMPORAL STABILITY OF THE INTERNAL STRUCTURE OF ILLNESS REPRESENTATION IN PATIENTS WITH MYOCARDIAL INFARCTION

The study aimed to examine the effects of health conditions – previous history of cardiovascular disease (high blood pressure, hypertension, angina pectoris), heredity, risk factors for MI (smoking, obesity, hyperlipidemia, physical inactivity), and acute/chronic stress in the previous year, and socio-demographic variables (gender, age, education, relationship, and material status) on the formation and change in the mental representation of MI. In addition, the temporal stability of the internal structure of the illness representation dimensions in MI patients, and the effect the health and socio-demographic conditions may have on it, were also examined. The results point to significant effects of health and socio-demographic factors on the formation and change of myocardial infarction representation. A significant change in the internal structure of the illness representation was already found 1.5-2 months following the event. This change can only be partially explained by the examined health and socio-demographic conditions, given that after partialization of these effects, differences in the construct's internal structure at the two measurement time points remain significant. The results are discussed according to the theoretical background and previous research findings. Future research should explore the effects of some other variables that could further explain the temporal changes in the MI representation, but also the initial representation formation.
Denoising Medical Images Using Filters

Images, since their invention, have proved extremely vital in a lot of processes that happen in our daytoday life. The medicine industry is one of the most crucial lifelines, which makes use of images quite extensively. Information amassed from these can provide medical professionals an insight into problem at hand, and if gathered timely, can pave way for faster and more accurate diagnosis while providing appropriate treatments. Modifying the images also helps in detecting diseases beforehand. Images are very commonly affected by different types of noise due to external factors such as interference. A common way of noise removal i.e. denoising an image is using filters. This leads to better quality of images for more accurate information. Hence, enhancement, noise-removal and filtration of noisy images is a significant aspect of the process. The paper provides an understanding of various image denoising methods and reviews the results for the same. Keywords— Medical Image Processing; Image Processing; Image Filtering; Denoising
Structure–Function Insights into the Fungal Endo-Chitinase Chit33 Depict its Mechanism on Chitinous Material

Chitin is the most widespread amino renewable carbohydrate polymer in nature and the second most abundant polysaccharide. Therefore, chitin and chitinolytic enzymes are becoming more importance for biotechnological applications in food, health and agricultural fields, the design of effective enzymes being a paramount issue. We report the crystal structure of the plant-type endo-chitinase Chit33 from Trichoderma harzianum and its D165A/E167A-Chit33-(NAG)4 complex, which showed an extended catalytic cleft with six binding subsites lined with many polar interactions. The major trait of Chit33 is the location of the non-conserved Asp117 and Arg274 acting as a clamp, fixing the distorted conformation of the sugar at subsite –1 and the bent shape of the substrate, which occupies the full catalytic groove. Relevant residues were selected for mutagenesis experiments, the variants being biochemically characterized through their hydrolytic activity against colloidal chitin and other polymeric substrates with different molecular weights and deacetylation percentages. The mutant S118Y stands out, showing a superior performance in all the substrates tested, as well as detectable transglycosylation capacity, with this variant providing a promising platform for generation of novel Chit33 variants with adjusted performance by further design of rational mutants’. The putative role of Tyr in binding was extrapolated from molecular dynamics simulation.
Resource Allocation in UAV-Assisted Networks: A Clustering-Aided Reinforcement Learning Approach

As an aerial base station, unmanned aerial vehicle (UAV) has been considered as a promising technology to assist future wireless communications due to its flexible, swift and low cost features, where resource allocation is the basis for ensuring energy-efficient UAV-assisted networks. This paper formulates a joint optimization problem of user association, UAV trajectory design and power control to maximize the channel capacity among all ground users at a limited power level in a downlink transmission. To tackle the mixed-integer non-linear programming problem, this paper proposes a clustering-aided reinforcement learning approach consisting of three consecutive stages. Firstly, modified expectation-maximization unsupervised learning algorithm is investigated to cluster the ground users, which reduces the dimensions and hence, the association complexity is reduced as well. Then, Kuhn-Munkres algorithm is incorporated for user association, which associates a UAV with the ground users via matching to the cluster, and assigns the UAVs to the centroid of the matching cluster for pre-placement, with the aim of speeding up the convergence of the following deep reinforcement learning algorithm. Finally, a multi-agent twin delayed deep deterministic (MATD3) policy gradient is proposed to solve the non-convex sub-problem, which determines the transmit power and designs the fine-tuned trajectory of UAVs. By incorporating low-bias value estimation technique, the reward of the proposed MATD3 algorithm is improved. Simulation results have demonstrated that our proposed approach achieves higher reward as well as converging faster than existing reinforcement algorithms. Besides, the clustering-aided reinforcement learning has lower computational complexity than the benchmark schemes.
The Effects of Active Video Game Exercise Based on Self-Determination Theory on Physical Fitness and Cognitive Function in Older Adults

Background: Aging and physical inactivity are associated with declines in physical fitness and cognitive function. Active video games have proven to be beneficial for the physical health of older adults, but the exact effect of active video games on physical fitness and cognitive function was still unclear. Based on self-determination theory (SDT), which is a widely used theory of healthy behavior change, this study aimed to explore the effects of an active video game intervention on fitness and cognitive function in older adults. Methods: A total of 38 participants (mean age = 65.68 ± 3.78 years, 24 female) were randomly assigned to either an intervention group (active video game training) or a control group (no additional intervention). The participants in the intervention group trained for a total of 36 sessions (3 times per week for 50–55 min each) for 12 weeks. The control group continued with their normal daily living. The pre- and posttest measurements included: IPAQ-C score and physical fitness (BMI, body fat percent, blood pressure, reaction time, sit and reach, vital capacity, grip strength, static balance, blood biochemical tests for liver function, kidney function, blood lipids, glucose and insulin levels) and cognitive functions (processing speed, spatial ability, working memory, language ability, associative memory). Result: The intervention group showed a significantly smaller decrease in total average physical activity relative to the control group. BMI, vital capacity, systolic blood pressure, diastolic blood pressure, and spatial cognition significantly improved after training in the intervention group (BMI: F = 9.814, p = 0.004, d = −0.93, vital capacity: F = 4.708, p = 0.038, d = 0.67, systolic blood pressure: F = 5.28, p = 0.028, d = −0.68, diastolic blood pressure: F = 6.418, p = 0.016, d = −0.86, spatial cognition: F = 8.261, p = 0.007, d = 0.72). Three measures of static balance (closed eyes) also showed improvements after training (total length of swing: F = 3.728, d = −0.62, total velocity of swing: F = 3.740, d = −0.62, total area of swing: F = 2.920, d = −0.70). No significant training effects were evident in the results from the blood biochemical tests. Conclusion: This study indicates a positive influence of active video game training on physical fitness and cognitive function. The use of SDT-based active video game exercise as a feasible, safe, and effective training method for improving community older adults’ healthy, promoting group cohesion, and increasing motivation to exercise.
An Instructional Design for Data Warehousing: Using Design Science Research and Project-based Learning

The business intelligence industry is supported by data warehouses. A data warehouse is a database that supports fast queries for strategic decision making. Students should be able to leave university and use their skills to the benefit of an organisation. The aim of design science is to create new knowledge through development of an artifact. The paper reports on a design science research project to create an instructional design for a module in data warehousing. The design of the instructional design is informed by project-based learning. The aim of the module is to equip students for the demands of the business intelligence industry.
Automated Scheduling Algorithm Selection and Chunk Parameter Calculation in OpenMP

Increasing node and cores-per-node counts in supercomputers render scheduling and load balancing critical for exploiting parallelism. OpenMP applications can achieve high performance via careful selection of scheduling <monospace>kind</monospace> and <monospace>chunk</monospace> parameters on a per-loop, per-application, and per-system basis from a portfolio of advanced scheduling algorithms (Korndörfer <italic>et al.</italic>, 2022). This selection approach is time-consuming, challenging, and may need to change during execution. We propose <bold>Auto4OMP</bold>, a novel approach for automated load balancing of OpenMP applications. With Auto4OMP, we introduce three scheduling <italic>algorithm selection methods</italic> and an <italic>expert-defined chunk parameter</italic> for OpenMP's <monospace>schedule</monospace> clause's <monospace>kind</monospace> and <monospace>chunk</monospace>, respectively. Auto4OMP extends the OpenMP <monospace>schedule(auto)</monospace> and <italic>chunk</italic> parameter implementation in LLVM's OpenMP runtime library to automatically select a scheduling algorithm and calculate a chunk parameter during execution. Loop characteristics are inferred in Auto4OMP from the loop execution over the application's time-steps. The experiments performed in this work show that Auto4OMP improves applications performance by up to <inline-formula><tex-math notation="LaTeX">$11\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>11</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="ciorba-ieq1-3189270.gif"/></alternatives></inline-formula> compared to LLVM's <monospace>schedule(auto)</monospace> implementation and outperforms manual selection. Auto4OMP improves MPI+OpenMP applications performance by <italic>explicitly</italic> minimizing thread- and <italic>implicitly</italic> reducing process-load imbalance.
Principals’ digital instructional leadership during the pandemic: Impact on teachers’ intrinsic motivation and students’ learning

The COVID-19 pandemic caused education systems to embrace remote schooling and online learning. In the context of this dramatic change, the principal's role has also changed. Instead of interacting face to face, school leaders had to become distant leaders operating digitally. The field has no knowledge of digital instructional leadership. The study used new and adapted measures to explore principals’ digital instructional leadership, its mechanisms of operation, and its outcomes. In particular, the research examined how digital instructional leadership affects perceived student learning in online settings through teachers’ intrinsic motivation for digital instruction (i.e. the mediator). The study used data from 380 teachers in Bahrain. Results indicated support for mediation. This is an empirical exploratory study, and therefore it is limited in scope. Nevertheless, its concepts, measures, and findings offer valuable contributions to research and practice. The limitations, findings, and implications of the study are discussed. The significance of the study derives from the growing incorporation of hybrid schooling in education and digital instructional leadership practices in mainstream principalship.
Effects of Cuisine-Based Chinese Heart-Healthy Diet in Lowering Blood Pressure Among Adults in China: Multicenter, Single-Blind, Randomized, Parallel Controlled Feeding Trial

Background: More than one-fifth of the world’s population consumes Chinese cuisines regularly, but no evidence-based healthy diets fitting the Chinese food culture are available for implementation. Methods: A multicenter, patient- and outcome assessor–blind, randomized feeding trial was conducted among 265 participants with 130 to 159 mm Hg baseline systolic blood pressure (SBP) for 4 major Chinese cuisines (Shangdong, Huaiyang, Cantonese, Szechuan). After a 7-day run-in period on a control diet matching the usual local diets, participants were randomized to continue with the control diet or the cuisine-based Chinese heart-healthy diet for another 28 days. The primary outcome was SBP, and secondary outcomes included diastolic blood pressure and food preference score. Linear regression models were used to estimate the intervention effects and adjustments for the center. The incremental cost per 1 mm Hg reduction in SBP was also calculated. Results: A total of 265 participants were randomized (135 on the Chinese heart-healthy diet and 130 on the control diet), with 52% women, mean age of 56.5±9.8 years, and mean SBP and diastolic blood pressure of 139.4±8.3 and 88.1±8.0 mm Hg, respectively, at baseline. The change in SBP and diastolic blood pressure from baseline to the end of the study in the control group was –5.0 (95% CI, –6.5 to –3.5) mm Hg and –2.8 (95% CI, –3.7 to –1.9) mm Hg, respectively. The net difference of change between the 2 groups in SBP and diastolic blood pressure were –10.0 (95% CI, –12.1 to –7.9) mm Hg and –3.8 (95% CI, –5.0 to –2.5) mm Hg, respectively. The effect size did not differ among cuisines (P for interaction=0.173). The mean food preference score was 9.5 (with 10 the best preferred) at baseline, and the net change during intervention was 0.1 (95% CI, –0.1 to 0.2; P=0.558). The incremental cost-effectiveness ratio per 1 mm Hg SBP reduction was CNY 0.4 (USD 0.06) per day. No difference in the number of adverse events was found between the 2 groups (P=0.259), and none of the adverse events was associated with the intervention. Conclusions: The Chinese heart-healthy diet is effective, palatable, and cost-effective in reducing blood pressure in Chinese adults with high blood pressure, with a clinically significant effect applicable across major Chinese cuisine cultures. Registration: URL: https://www.clinicaltrials. gov; Unique identifier: NCT03882645.
ALTERNATIVE METHOD FOR ON SITE EVALUATION OF THERMAL TRANSMITTANCE

Thermal transmittance or U-value is an indicator of the building envelope thermal properties and a key parameter for evaluation of heat losses through the building elements due to heat transmission. It can be determined by calculation based on thermal characteristics of the building element layers. However, this value does not take into account the effects of irregularities and degradation of certain elements of the envelope caused by aging, which may lead to errors in calculation of the heat losses. An effective and simple method for determination of thermal transmittance is in situ measurement, which is governed by the ISO 9869-1:2014 that defines heat flow meter method. This relatively expensive method leaves marks and damages surface of the building element. Furthermore, the final result is not always reliable, in particular when the building element is light or when the weather conditions are not suitable. In order to avoid the above mentioned problems and to estimate the real thermal transmittance value an alternative experimental method, here referred as the natural convection and radiation method, is proposed in this paper. For determination of thermal transmittance, this method requires only temperatures of inside and outside air, as well as the inner wall surface temperature. A detailed statistical analysis, performed by the software package SPSS ver. 20, shows several more advantages of this method comparing to the standard heat flow meter one, besides economic and non-destructive benefits.
Subarray‐based time‐delay low sidelobes methods for space‐time coding array

A space-time coding array (STCA) system transmits an identical waveform with a small time offset across the array elements. It shares the characteristics of high angular resolution and instant wide angular coverage, leading to the low probability of interception as seen in the traditional multiple-input and multiple-output radar. Angle-time two-dimensional matched filter is required in the STCA. However, the sidelobe level (SLL) of the multi-dimensional ambiguity function in the range and angle domains is not as low as that desired in practical applications and the range resolution decreases as the number of elements increases. Here, two kinds of subarray-based time-delay methods are proposed to improve the resolution and reduce the SLL of the ambiguity function for STCA. Both the regular and irregular subarrays are considered, which increase the degrees of freedom and thus enhance the range resolution. Moreover, the SLL of the ambiguity function is reduced. Simulation results are provided to demonstrate the effectiveness of the proposed methods.