Miracle-dz/UniFAR-FTUs huggingface dataset:
Design, Analysis, and Implementation of Wireless Traveling-Wave Ultrasonic Motors

Wireless motors have been extensively investigated for their flexibility and convenience, but they inevitably suffer from vulnerability to magnetic field interference, insufficient robustness, and the need for numerous power switches. Therefore, a wireless traveling-wave ultrasonic motor (USM) with high robustness is proposed in this article, which artily integrates capacitive power transfer into a USM to derive the brand-new nonmagnetic wireless direct-drive motor. Prominently, differing from the existing wireless motors, the proposed wireless USM avoids the use of fragile microcontrollers and active or passive switches at the motor side, the control process of which can be completely conducted at the primary side, thus facilitating high-degree integration and maintenance-free operation. In addition, the bidirectional motion capability and flexible speed regulation can be readily achieved by changing the sequence and amplitude of two-phase outputs at the primary side, enabling the real sense of wireless direct drive. Finally, theoretical analysis and hardware experimentation are given to verify the feasibility of the proposed wireless USM for nonmagnetic biomedical and aerospace applications.
Medium-term morphodynamics of the Mittelplate area, German North Sea coast

In view of climate change and sea level rise the prediction of the morphological evolution of coastal areas in medium-term and long-term has become a matter of increasing concern in the field of coastal management. Process-based modelling has recently emerged as an effective tool for studying coastal morphodynamics and for predictive estimation of the coastal development. The main focus of the thesis is to study, analyse and assess the medium-term morphodynamics of a complex network of tidal channels, flats and shoals in the Mittelplate area, Dithmarschen Bight, German North Sea coast. In this framework numerical techniques for accelerating morphodynamic simulations are also critically evaluated. Taking full advantage of recent available high-resolution bathymetric surveys over a period of six years, the natural morphodynamics was thoroughly investigated. Essential reasons for the morphological changes are provided on the base of a structural analysis. The medium-term morphological changes were studied by means of numerical modelling. Process-based models for simulating flow, waves and sediment transport were developed, calibrated and validated against collected field data. According to internationally accepted quality criteria the developed models proved to represent well the hydrodynamics and sediment dynamics in the area of concern. By means of coupling of the process models a morphodynamic model was set-up. Benchmark simulation using the full time series of forcing of tide, wind and waves was conducted for a two-year period and the model proved good ability in reproducing the observed morphological changes. The single effects of the driving forces tide, wind and waves on the morphological development were analyzed and assessed. Main mechanisms driving the morphological evolution of the area were identified. An input reduction method, the so-called "representative period method" applied to time series of wind in conjunction with "morphological factor approach", was used to analyse effectiveness of numerically accelerated simulations for the prediction of morphodynamics. The results show that the method was restrictively applicable for reproducing the medium-term morphodynamics because (i) it disregards associated classes of wind speed and wind direction in time series when shortening the long-term wind data to much shorter "representative period" data; (ii) the interaction of tide, wind and waves from long-term is changed or widely lost in the "representative period". Recommendations for the improvement of the applied input reduction method and the model performance are provided.
Does the Implementation of Corporate Social Responsibility Have An Effect toward Profitability?

This study aims to find out how and how the Implementation of Corporate Social Responsibility (CSR) on Profitability (ROA) on Large Trading Sub Sector Companies listed in Indonesia Stock Exchange Period 2014-2016. The background of this research, there are several companies experiencing loss and decrease of profitability (ROA) during year 2014-2016. Research taking samples with non probability sampling, while the technique used is purposive sampling. This research is explanatory research with type of investigation are descriptive and verification. The analysis data used simple linear regression and processed using software Eviews 8 program. The result of research indicate that Corporate Responsibility research on profitability of large trading sub sector companies of production goods listed in Indonesia Stock Exchange in 2014-2016.
Mapping Cumulative Impacts of Human Activities on Marine Ecosystems

given the diversity of human uses and natural resources that converge in coastal waters, the potential independent and cumulative impacts of those uses on marine ecosystems are important to consider during ocean planning. Researchers from SeaPlan and the national Center for Ecological Analysis and Synthesis (nCEAS) designed a study to support the development and implementation of the 2009 Massachusetts ocean Management Plan. This study estimated and visualized the cumulative impacts of human activities on coastal and marine ecosystems in the state and federal waters off of Massachusetts.
Automated Network Performance Characterization for HPC Systems

In this work, we propose the Network Performance Collector (NPC) workflow for automated network performance characterization. The workflow is based on the collection, processing as well as visualization of network performance metrics such as throughput and latency and can be used for analysis with different network performance models. Depending on the chosen model, benchmark tools such as iperf or sockperf as well as microbenchmarks specific to parallel programming models can be automated and orchestrated for data collection with the NPC. The data obtained can then be used by NPC, for example, to validate and characterize the performance of the underlying network or to analyze the system boundaries for a particular application. We provide a prototype implementation of the proposed workflow and demonstrate its effectiveness by automating an extended Roofline model analysis.
Obtaining Spatial Variations in Cabernet Sauvignon (Vitis vinifera L.) Wine Flavonoid Composition and Aromatic Profiles by Studying Long-Term Plant Water Status in Hyper-Arid Seasons

The spatial variability in vineyard soil might negatively affect wine composition, leading to inhomogeneous flavonoid composition and aromatic profiles. In this study, we investigated the spatial variability in wine chemical composition in a Cabernet Sauvignon (Vitis vinifera L.) vineyard in 2019 and 2020. Because of the tight relationships with soil profiles, mid-day stem water potential integrals (Ψstem Int) were used to delineate the vineyard into two zones, including Zone 1 with relatively higher water stress and Zone 2 with relatively lower water stress. Wine from Zone 2 generally had more anthocyanins in 2019. In 2020, Zone 1 had more anthocyanins and flavonols. Zone 2 had more proanthocyanidin extension and terminal subunits as well as total proanthocyanidins in 2020. According to the Principal Component Analyses (PCA) for berry and wine chemical composition, the two zones were significantly different in the studied wine aromatic compounds. In conclusion, this study provides evidence of the possibility of managing the spatial variability of both wine flavonoid composition and aromatic profiles through connecting vineyard soil variability to grapevine season-long water status.
Current Sensor Incipient Fault Diagnosis in PMSM Drive Systems Using Novel Interval Sliding Mode Observer

This article presents a novel method for timely and accurate detection of incipient faults in current sensors of permanent magnet synchronous motor (PMSM) drive systems by utilizing an interval sliding mode observer (SMO). Initially, a hybrid logic dynamic model is developed to incorporate current sensor incipient faults, system parameter perturbation, and unknown disturbances. The current sensor incipient faults are then reconstructed using state augmentation and nonsingular coordinate transformation techniques. Subsequently, an adaptive interval SMO with a fast convergence rate and the ability to suppress high-frequency chattering is designed to estimate the reconstructed state accurately. The residual between the actual value and the observed value of the reconstructed state is employed as the detection variable, and adaptive upper and lower thresholds are designed for incipient fault diagnosis of the current sensor. The proposed method is capable of diagnosing both incipient faults and significant faults in current sensors. Experimental results validate the accuracy and robustness of the diagnostic method.
Primary energy consumption and economic growth: the case of Greece

This paper examines the relationship between primary energy consumption and economic growth in Greece for the period 1965-2019 by using annual data. The main objective of this study is to investigate whether there is a causal relationship between economic growth and energy consumption. In terms of econometric specificity, ordinary least squares regression (OLS) is used to determine the model in the first step, while the vector self-regulating model (VAR) and the Wald test are used to detect causality. The study differs from the literature in terms of examining the individual energy sources. Total primary energy consumption in Greece is examined in relation to economic growth and individual energy sources separately. According to the results, primary energy consumption in Greece has a big impact on economic growth. The energy derived from non-renewable energy sources has the highest consumption rates. Causality tests show that there is a causal relationship between wind energy consumption and the GDP per capita while causality is observed from the GDP per capita to oil, coal, solar and hydropower consumption.
Misinformation on COVID-19 Vaccine and Its Associated Factors among Residents in Gondar, Ethiopia, 2022

Introduction. In the year 2019, a new strain of severe acute respiratory syndrome emerged in Wuhan, Hubei province, China, causing a new pneumonia outbreak. Misinformation about the coronavirus 2019 vaccine, combined with inequitable vaccine delivery around the world, is the bottleneck of the most promising instruments for ending pandemic. Vaccine confidence and vaccination rate can be affected by misinformation. The majority of COVID-19 vaccine misinformation has been on vaccine development, safety, and effectiveness as well as COVID-19 denial. Hence, this study is aimed at assessing the magnitude of misinformation on the COVID-19 vaccine and associated factor among residents in Gondar, Ethiopia, Method. A community-based cross-sectional study was conducted from May 8 to June 10, 2022, among 844 residents in Gondar town. A multistage cluster sampling method was employed to select study participants. An interview-administered questionnaire was used to collect the required data. The data were entered using EpiData 4.6 and transferred into SPSS version 20 for analysis. Descriptive statistics and bivariable and multivariable logistic regression analyses were done. An adjusted odds ratio (AOR) with 95% CI was used to determine explanatory factors associated with misinformation on the COVID-19 vaccine. Result. The proportion of misinformation about the COVID-19 vaccine was found to be 48%. Being male (AOR=1.48, 95% CI: 1.03, 2.13), vaccination status (AOR: 7.37, CI: 4.59, 11.83), history of COVID-19 (AOR: 0.21, CI: 0.5, 0.78), health self-efficacy (AOR: 0.6, CI: 0.42, 0.86), perceived severity (AOR: 1.47, CI: 1.02, 2.12), knowledge towards COVID-19 vaccine (AOR: 1.85, CI: 1.28,2.67), and attitude towards COVID-19 (AOR: 1.9, CI: 1.32, 2.76) vaccine were significant factors associated with misinformation on COVID-19 vaccine. Conclusion and Recommendation. The overall proportion of misinformation on the COVID-19 vaccine was found at 48%. Increasing community awareness of the COVID-19 vaccine and clarifying misunderstandings about it through media campaigns may help to lessen the tendency for misinformation in the community.
An Investigation about Gene Modules Associated with hDPSC Differentiation for Adolescents

Dental pulp stem cells (DPSCs) have the property of self-renewal and multidirectional differentiation so that they have the potential for future regenerative therapy of various diseases. The latest breakthrough in the biology of stem cells and the development of regenerative biology provides an effective strategy for regenerative therapy. However, in the medium promoting differentiation during long-term passage, DPSCs would lose their differentiation capability. Some efforts have been made to find genes influencing human DPSC (hDPSC) differentiation based on hDPSCs isolated from adults. However, hDPSC differentiation is a very complex process, which involves multiple genes and multielement interactions. The purpose of this study is to detect sets of correlated genes (i.e., gene modules) that are associated to hDPSC differentiation at the crown-completed stage of the third molars, by using weighted gene coexpression network analysis (WGCNA). Based on the gene expression dataset GSE10444 from Gene Expression Omnibus (GEO), we identified two significant gene modules: yellow module (742 genes) and salmon module (9 genes). The WEB-based Gene SeT AnaLysis Toolkit showed that the 742 genes in the yellow module were enriched in 59 KEGG pathways (including Wnt signaling pathway), while the 9 genes in the salmon module were enriched in one KEGG pathway (neurotrophin signaling pathway). There were 660 (7) genes upregulated at P10 and 82 (2) genes downregulated at P10 in the yellow (salmon) module. Our results provide new insights into the differentiation capability of hDPSCs.
Ray-Tracing and Physical-Optics Model for Planar Mikaelian Lens Antennas

This article proposes a ray-tracing and physical-optics (RT-PO) model that allows for an accurate and time-efficient evaluation of planar Mikaelian lens antennas implemented by parallel plate waveguides (PPWs). With an intrinsic flat shape and axis-symmetry of refractive-index distribution characteristics, the planar Mikaelian lens antennas are easy to fabricate and integrate into standard planar feeds. A numerical computation of the ray paths based on Snell’s law describes the phase of the electric field in the lens aperture, while the ray-tube power conservation theory is employed to evaluate the amplitude. The field equivalence principle is then used to calculate the far-field of the lens antenna. The information on far-field directivity, gain, and dielectric efficiency is further obtained, considering a small loss in the dielectric materials. Our approach is validated by comparing the results of a particular Mikaelian lens antenna with those computed using a commercial full-wave simulator, demonstrating high accuracy and a significant reduction in computation resources and times.
Assessment of inter-individual variability in hamstring muscle recovery after a sport-specific sprint training in women and men

Background: Hamstring muscles are most affected by multiple sprint-based sports as a result of muscle strain during sprinting, leading to reduced performance and increased risk of injury. Therefore, the purpose of the study was to assess inter-individual variability in hamstrings recovery after a sport-specific repeated-sprint training (RST), through sprint-specific markers of muscle recovery and associated muscle damage biomarkers in women and men. Methods: Healthy females (n = 14) and males (n = 15) underwent 10 repeated 40-m sprints with a 3-min rest pause between each repetition. Force-generating capacity (FGC) by the 90° hip :20° knee test and range of motion Jurdan test, together with serum biomarkers [sarcomeric mitochondrial creatine kinase (sMtCK), oxidative stress, irisin] were tested at baseline and 24-, 48- and 72-h post-exercise through a repeated measures design. Participants were classified according to FGC loss into high responders (HR) and low responders (LR). Results: 21 individuals (10 females, 11 males) were classified as HR (FGC loss >20% and recovery >48 h), while 8 individuals (4 females, 4 males) were classified as LR. HR individuals showed unrecovered maximal voluntary isometric contraction (MVIC) torque until 72 h post-training (p = 0.003, np 2 = 0.170), whereas only HR males showed decreased range of motion (p = 0.026, np 2 = 0.116). HR individuals also showed increased sMtCK (p = 0.016, np 2 = 0.128), oxidative stress (p = 0.038, np 2 = 0.106) and irisin (p = 0.019, np 2 = 0.123). Conclusion: There is inter-individual variability in the muscular response to a sport-specific RST, identifiable by MVIC torque assessment. The findings support that the 90° hip :20° knee test is a powerful indirect test to screen hamstrings recovery in both women and men, in a cost-effective way. However, the Jurdan test might not be able to monitor hamstrings recovery in sportswomen after RST. Decreases in muscle capacity are linked to damage to muscle sarcolemma and mitochondria until 72 h post-exercise. Overall, 72 h will not be adequate time to restore hamstrings structure and function after a sport-specific RST in both female and male responders.
EXPERIMENTAL STUDY OF RELATIONSHIPS BETWEEN METAMORPHIC GRADE,PORE CHARACTERISTICS AND PERMEABILITY OF COAL

Internal and external pore characteristics of raw coal sample of medium and high degrees of metamorphism which were obtained from Liupanshui mining area were studied by using TESCAN VEGA II scanning electron microscope(SEM)-energy-dispersive X-ray(EDX) and ASAP2020 specific surface microanalyzer.Triaxial seepage experiments of constant different gas pressures were carried out by independently developed traixial seepage device.The results show that:(1) There is positive correlation between metamorphic grade of coal and interior and external pore characteristics.The higher the metamorphic grade is,the higher the fractal dimension is and the greater the raw coal adsorption capacity and pore volume are. (2) There is also positive correlation between internal and external pore characteristics of raw coal and permeability.The more developed the internal and external pore,the greater the permeability is. (3) Under the conditions of constant mean effective stress and temperature,with the increasing gas pressure,at first the permeability sharply decreases and then tends to decrease gradually;and the relationship between permeability and gas pressure obeys exponential distribution.The research results have some theoretical significance to gas disaster prevention and development and utilization of coalbed methane of Liupanshui mining area.
Research on face recognition algorithm based on FaceNet and coordinate attention

The development of information technology has made the field of deep learning face recognition develop rapidly. The traditional face detection and recognition algorithm can perform well under constrained conditions, but under unconstrained conditions, its effect will be greatly discounted when low quality images and partial occlusion of faces are encountered. Based on MTCNN and FaceNet, this paper adopts two strategies to solve the above two problems respectively. On the one hand, by introducing the face image quality assessment function to solve the problem of low quality pictures, before face detection, a quality assessment of the face image is done, and only the image whose quality score reaches the threshold can be input into the model. On the other hand, the Coordinate attention mechanism is introduced to deal with the problem of partial occlusion of the face, which improves the recognition ability of the model by adaptively enhancing the weight of the unocclusion area of the face. Experimental results show that compared with existing algorithms, the accuracy of the proposed algorithm is significantly improved.
A cluster-based co-evolutionary optimization method for bilevel multi-objective optimization

The research motivation of multi-objective bilevel optimization mainly stems from the need to solve practical problems and improve decision-making efficiency. On the one hand, bilevel optimization helps to solve the complexity and uncertainty in real life, thereby improving decision-making efficiency and robustness. On the other hand, by promoting the development and application of AI technology, bilevel optimization also provides support for sustainable development. Although the application of bilevel optimization has proven to be beneficial in addressing various real-life problems. However, recent studies indicate that achieving both high speed and high-quality optimization through existing algorithms remains challenging. This difficulty arises due to the NP-hard nature of the bilevel optimization problem. The nested structure method, commonly used to tackle this problem, involves each upper level solution independently performing the lower level optimization task. This approach significantly increases the number of evaluations for the lower level. To address this issue, our proposed method leverages the similarity in lower level optimization to group upper level solutions, enabling co-evolution of lower level solutions within the same group. Consequently, this approach substantially reduces the number of evaluations required for lower level solutions. Additionally, our method pairs parents and offspring, the optimized lower level solutions of the parents are utilized to optimize the lower level solutions of the offspring. This approach accelerates the optimization process for the lower level. To validate the effectiveness of our algorithm, we have applied it to a suite of test problems, demonstrating satisfactory performance.
Analysing the influence of Universities’ content strategy on the level of engagement on social media

Social media have become a key tool in the institutional communication of universities to disseminate content and establish interaction and dialogue with their publics. Content strategy in social networks is a relevant aspect to inform their audiences about their daily activities and position universities in the digital sphere. This article studies the influence of the different types of content posted by universities on their social networks on the level of engagement of their publics. We conducted a content analysis of more than 90,000 posts by 70 universities in three regions (Europe, the United States and Latin America) on their institutional profiles on three social networks (Twitter, Facebook and LinkedIn). The results show that the level of engagement achieved by the universities’ posts is very low. Universities clearly prioritize institutional content over functional content, and organizational topics are the most published on social networks. Institutional content achieves a higher level of engagement than functional content, and posts on organizational topics have the best level of engagement. Our study might refute the hypothesis that “functional content will generate a higher level of engagement than institutional content on social networks. ” Thus, it can be concluded that the combination of content on social networks suggests that universities mainly use social networks to develop an institutional positioning strategy on social media.