Miracle-dz/UniFAR-FTUs huggingface dataset:
A Wideband Full-Metal Sidewall-Loaded Magnetoelectric Dipole Array Based on Combined Ridge and Groove Gap Waveguides in the Q-Band

A wideband full-metal sidewall-loaded magnetoelectric (ME) dipole array antenna fed by a combined ridge–groove gap waveguide (GGW) is proposed. Three promising full-metal aperture-coupled <inline-formula> <tex-math notation="LaTeX">$2\times2$ </tex-math></inline-formula> element ME-dipole subarrays are investigated and compared in detail. By introducing sidewalls around the metal pillars and increasing their height to <inline-formula> <tex-math notation="LaTeX">$0.36\lambda _{0}$ </tex-math></inline-formula>, the bandwidth of the subarray is substantially enhanced to 33% for <inline-formula> <tex-math notation="LaTeX">$\text{S}_{11} < -15$ </tex-math></inline-formula> dB. An eigenmode analysis is performed to explain and validate the broadband property of the proposed subarray with taller pins and sidewalls. The combined ridge GW (RGW) and E-plane GGW are employed in the feeding layer for a compact array as well as for lower losses and better isolation between the gap waveguides. Wideband ridge-to-groove (RG) and groove-to-ridge (GR) junctions are designed using stepped transformers for bandwidth enhancement. For verification, an <inline-formula> <tex-math notation="LaTeX">$8\times8$ </tex-math></inline-formula> element sidewall-loaded ME-dipole array is designed and fabricated by computer numerical controlled (CNC) milling technique in the Q-band. The bandwidth for <inline-formula> <tex-math notation="LaTeX">$\text{S}_{11} < -10$ </tex-math></inline-formula> dB is widened to 35.9% from 34.5 to 49.4 GHz. From 34.3 to 34.7 and 35.6 to 48.7 GHz, the antenna efficiency is better than 75%, with a bandwidth of 32.5%. The proposed design is attractive for wideband and higher-frequency wireless applications.
Timely Data Collection for UAV-Based IoT Networks: A Deep Reinforcement Learning Approach

In some real-time Internet of Things (IoT) applications, the timeliness of sensor data is very important for the performance of a system. How to collect the data of sensor nodes (SNs) is a problem to be solved for an unmanned aerial vehicle (UAV) in a specified area, where different nodes have different timeliness priorities. To efficiently collect the data, a guided search deep reinforcement learning (GSDRL) algorithm is presented to help the UAV with different initial positions to independently complete the task of data collection and forwarding. First, the data collection process is modeled as a sequential decision problem for minimizing the average age of information (AoI) or maximizing the number of collected nodes according to specific environment. Then, the data collection strategy is optimized by the GSDRL algorithm. After training the network using the GSDRL algorithm, the UAV has the ability to perform autonomous navigation and decision-making to complete the complexity task more efficiently and rapidly. Simulation experiments show that the GSDRL algorithm has strong adaptability to adverse environments and obtains a good strategy for UAV data collection and forwarding.
A test of the social withdrawal syndrome hypothesis of bulimia nervosa

BACKGROUND The study examined the social withdrawal syndrome (SWS) hypothesis of bulimia nervosa (BN). According to the hypothesis, eating disorders such as BN are associated with a coherent set of social withdrawal cognitions, affect, and behavior. PARTICIPANTS AND PROCEDURE Eight-eight young female adults completed a standardized measure of bulimic symptoms and measures of social withdrawal (affective withdrawal, trust beliefs in close others, and disclosure). Participants were engaged in a laboratory-based peer interaction which yielded the SWS measure of perceived lack of social connectiveness. RESULTS Bulimic symptoms were associated with each measure of social withdrawal. Structural equation modeling analysis confirmed that those measures contributed to a coherent latent factor which was associated with bulimic symptoms. CONCLUSIONS The findings supported the social withdrawal syndrome hypothesis of BN and have implications for the detection and treatment of eating disorders.
Lift and Drag of Kayak Rudders

This master’s thesis studies the kayak rudder flow phenomena and uses analytic, numerical and experimental methods to describe the force coefficients of kayak rudders. Also an analysis of the sport of flatwater kayaking is conducted – amongst others with regard to the motion of the kayak hull and with regard to the propulsion. The former is based on measurement data of a K1 kayak. Further the focus is put on the rudder, and the gained knowledge finds e.g. utilization in modeling of the dynamic angle of attack. Simulations based on CAD-data of a kayak rudder type are conducted (mostly RANS models) and discussed. Assumptions in this framework are that the boundary layer of the kayak hull is not accounted for and that possible effects of a transitional flow are not incorporated. Naturally, also the process and the solutions used to face the challenges of the grid development are described. Furthermore, a comparative analysis of different simulations (meshes, turbulence models and two sizes of the rudder) is undertaken. The main outcome are force coefficients and lift-to-drag ratios. They are based on angles of attack: 0 ≤ α ≤ 7.5 and found for three different Reynolds numbers. These force coefficients are compared to analytical methods and discussed in the context of general flow phenomena and its modeling. The mainly discussed and derived analytical methods are slender body and lifting line theory (SBT & LLT). If the known error of LLT at small aspect ratios is empirically taken into account, the simulation results are in excellent agreement with the LLT. However, all of the studied sizes of the kayak rudder exceed the slenderness approximation of SBT. The simulation results are combined with the study of the kayak-hull motion, and the approximate dynamic power loss caused by the rudder is deduced. For the smallest rudder that is approximately 1.2 W. In addition, an experiment is designed and build up to measure lift and drag of kayak rudders. First runs are conducted. They deliver a reasonable preliminary result for the drag force at zero angle of attack (compared with the simulations), but could unfortunately not be pursued further. This master’s thesis was not part of any ongoing project, and one can conclude that a further study in the field of kayak rudders most certainly could benefit from the achievements of this work.
Longitudinal Study for the Detection and Quantification of Campylobacter spp. in Dairy Cows during Milking and in the Dairy Farm Environment

Campylobacteriosis outbreaks have repeatedly been associated with the consumption of raw milk. This study aimed to explore the variation in the prevalence and concentration of Campylobacter spp. in cows’ milk and feces, the farm environment and on the teat skin over an entire year at a small German dairy farm. Bi-weekly samples were collected from the environment (boot socks), teats, raw milk, milk filters, milking clusters and feces collected from the recta of dairy cows. Samples were analyzed for Campylobacter spp. , E. coli, the total aerobic plate count and for Pseudomonas spp. The prevalence of Campylobacter spp. was found to be the highest in feces (77.1%), completely absent in milking equipment and low in raw milk (0.4%). The mean concentration of Campylobacter spp. was 2.43 log10 colony-forming units (CFU)/g in feces and 1.26 log10 CFU/teat swab. Only a single milk filter at the end of the milk pipeline and one individual cow’s raw milk sample were positive on the same day, with a concentration of 2.74 log10 CFU/filter and 2.37 log10 CFU/mL for the raw milk. On the same day, nine teat swab samples tested positive for Campylobacter spp. This study highlights the persistence of Campylobacter spp. for at least one year in the intestine of individual cows and within the general farm environment and demonstrates that fecal cross-contamination of the teats can occur even when the contamination of raw milk is a rare event.
Evaluation of In-Cloth versus On-Skin Sensors for Measuring Trunk and Upper Arm Postures and Movements

Smart workwear systems with embedded inertial measurement unit sensors are developed for convenient ergonomic risk assessment of occupational activities. However, its measurement accuracy can be affected by potential cloth artifacts, which have not been previously assessed. Therefore, it is crucial to evaluate the accuracy of sensors placed in the workwear systems for research and practice purposes. This study aimed to compare in-cloth and on-skin sensors for assessing upper arms and trunk postures and movements, with the on-skin sensors as the reference. Five simulated work tasks were performed by twelve subjects (seven women and five men). Results showed that the mean (±SD) absolute cloth–skin sensor differences of the median dominant arm elevation angle ranged between 1.2° (±1.4) and 4.1° (±3.5). For the median trunk flexion angle, the mean absolute cloth–skin sensor differences ranged between 2.7° (±1.7) and 3.7° (±3.9). Larger errors were observed for the 90th and 95th percentiles of inclination angles and inclination velocities. The performance depended on the tasks and was affected by individual factors, such as the fit of the clothes. Potential error compensation algorithms need to be investigated in future work. In conclusion, in-cloth sensors showed acceptable accuracy for measuring upper arm and trunk postures and movements on a group level. Considering the balance of accuracy, comfort, and usability, such a system can potentially be a practical tool for ergonomic assessment for researchers and practitioners.
Hydrogen and Silicon Effects on Hexagonal Close Packed Fe Alloys at High Pressures: Implications for the Composition of Earth's Inner Core

Hexagonal close‐packed (hcp) structured Fe‐Ni alloy is believed to be the dominant phase in the Earth's inner core. This phase is expected to contain 4%–5% light elements, such as Si and H. While the effects of individual light element candidates on the equation of state (EoS) of the hcp Fe metal have been studied, their combined effects remain largely unexplored. In this study, we report the equations of state for two hcp‐structured Fe‐Si‐H alloys, namely Fe0.83Si0. 17H0.07 and Fe0.83Si0. 17H0.46, using synchrotron X‐ray diffraction measurements up to 125 GPa at 300 K. These alloys were synthesized by cold compression of Fe‐9wt%Si in either pure H2 or Ar‐H2 mixture medium in diamond‐anvil cells. The volume increase caused by a H atom in hcp Fe‐Si‐H alloys is approximately eight times greater than that by a Si atom. We used the improved data set to develop a composition‐dependent EoS that covers a wide range of compositions. Our calculated density and bulk sound velocity of hcp Fe‐Si‐H alloys suggest a large trade‐off between Si and H contents in fitting the seismic properties of the inner core. Combining our new EoS with geophysical and geochemical constraints, we propose 1.6–3 wt% Si and 0.15–0.6 wt% H in the Earth's inner core.
Amorphization of Ethenzamide and Ethenzamide Cocrystals—A Case Study of Single and Binary Systems Forming Low-Melting Eutectic Phases Loaded on/in Silica Gel

The applicability of different solvent-free approaches leading to the amorphization of active pharmaceutical ingredients (APIs) was tested. Ethenzamide (ET), an analgesic and anti-inflammatory drug, and two ethenzamide cocrystals with glutaric acid (GLU) and ethyl malonic acid (EMA) as coformers were used as pharmaceutical models. Calcinated and thermally untreated silica gel was applied as an amorphous reagent. Three methods were used to prepare the samples: manual physical mixing, melting, and grinding in a ball mill. The ET:GLU and ET:EMA cocrystals forming low-melting eutectic phases were selected as the best candidates for testing amorphization by thermal treatment. The progress and degree of amorphousness were determined using instrumental techniques: solid-state NMR spectroscopy, powder X-ray diffraction, and differential scanning calorimetry. In each case, the API amorphization was complete and the process was irreversible. A comparative analysis of the dissolution profiles showed that the dissolution kinetics for each sample are significantly different. The nature and mechanism of this distinction are discussed.
A Real-Time Predictive Model for Identifying Course Dropout in Online Higher Education

Course dropout is a concern in online higher education, mainly in first-year courses when different factors negatively influence the learners' engagement leading to an unsuccessful outcome or even dropping out from the university. The early identification of such potential at-risk learners is the key to intervening and trying to help them before they decide to drop out. This article focuses on this challenging problem by providing a predictive dropout model with distinctive characteristics from previous approaches. First, the identification is in real time by providing a daily dropout prediction. Second, a temporal window of variable size is defined to evaluate the likelihood of being a dropout learner at the activity level. Such contributions will serve as a basis for designing and applying intervention mechanisms to reverse the course dropout at-risk situation. The predictive model and the temporal window have been evaluated on data from an authentic online learning setting in two first-year undergraduate courses. We show the accuracy of correctly identifying at-risk learners within activities and the model performance to detect actual course dropout learners.
Vocabularies of Motive for Corporate Social Responsibility: The Emergence of the Business Case in Germany, 1970–2014

The business case constitutes an important instrumental motive for corporate social responsibility (CSR), but its relationship with other moral and relational motives remains controversial. In this article, we examine the articulation of motives for CSR among different stakeholders in Germany historically. On the basis of reports of German business associations, state agencies, unions, and nongovernmental organizations from 1970 to 2014, we show how the business case came to be a dominant motive for CSR by acting as a coalition magnet: the vocabulary was used strategically by key policy entrepreneurs, while being ambiguous for flexible interpretations by different stakeholders, and thereby growing in attractiveness. As a resulting discourse coalition emerged among business, state, and civil society actors, the moral and relational motives for CSR became increasingly marginalized. The article offers a new approach to studying motives and contributes to understanding the complementary or competing nature of different motives for CSR.
Interpretable Emotion Classification Using Multidomain Feature of EEG Signals

Research on affective computing by physiological signals has become a new and important research branch of artificial intelligence. However, most of the research focuses only on the improvement of emotion classification models while neglecting the correlation between subjective emotional labels and implicit electroencephalogram (EEG) feature information. In this study, we proposed an interpretable emotion classification framework based on food images as visual stimuli and a proposed EEG dataset acquired by a portable single-electrode EEG device. Then, we construct a multidomain feature extraction method based on sliding time windows, which can effectively extract single-channel EEG features. Finally, we chose the extreme gradient boosting (XGBoost) model as our classifier in the proposed classification framework. The optimal accuracy of our single-channel EEG classification model was 94.76%, which reached the classification performance of multichannel EEG classification. Based on XGBoost, we interpret the global and the local emotion feature selection method by calculating Shapley additive explanation (SHAP) values. Through the analysis of interpretable methods, we can not only obtain the contribution degree of features from three types of emotional labels (positive, negative, and neutral) but also know whether features have a positive or negative effect on classification results. The source codes of this work are publicly available at: https://github.com/VCMHE/MDF-EEG.
Topological and algebraic aspects of quantization : symmetries and statistics

We discuss the inequivalent quantizations of a physical system with configuration space Q. A brief review is given of rigorous results concerning the number and type of quantizations available to such a system. The example of n identical particles moving on an arbitrary manifold is considered in some detail On discute les quantifications inequivalentes d'un systeme physique a espace de configuration Q. On presente une breve revue des resultats rigoureux concernant le nombre et le type de quantifications possibles pour un tel systeme. L'exemple de n particules identiques en mouvement sur une variete arbitraire est considere en detail
Streptococcus salivarius as an Important Factor in Dental Biofilm Homeostasis: Influence on Streptococcus mutans and Aggregatibacter actinomycetemcomitans in Mixed Biofilm

A disturbed balance within the dental biofilm can result in the dominance of cariogenic and periodontopathogenic species and disease development. Due to the failure of pharmacological treatment of biofilm infection, a preventive approach to promoting healthy oral microbiota is necessary. This study analyzed the influence of Streptococcus salivarius K12 on the development of a multispecies biofilm composed of Streptococcus mutans, S. oralis and Aggregatibacter actinomycetemcomitans. Four different materials were used: hydroxyapatite, dentin and two dense polytetrafluoroethylene (d-PTFE) membranes. Total bacteria, individual species and their proportions in the mixed biofilm were quantified. A qualitative analysis of the mixed biofilm was performed using scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM). The results showed that in the presence of S. salivarius K 12 in the initial stage of biofilm development, the proportion of S. mutans was reduced, which resulted in the inhibition of microcolony development and the complex three-dimensional structure of the biofilm. In the mature biofilm, a significantly lower proportion of the periodontopathogenic species A. actinomycetemcomitans was found in the salivarius biofilm. Our results show that S. salivarius K 12 can inhibit the growth of pathogens in the dental biofilm and help maintain the physiological balance in the oral microbiome.
Scenario-Based Assessment of Moral Sensitivity: Case of Iranian Public Librarians

ABSTRACT This study aims to evaluate the moral sensitivity of Iranian public librarians using a scenario-based approach. Based on an adopted moral sensitivity testing model nine scenarios were developed to assess bribery, discrimination, hypocrisy, contempt, blackmail, collusion, cover-up, whistleblowing, and disregarding merits. The scenarios were sent to a sample of 588 librarians via e-mail. The results indicated that the librarians had a moderate level of moral sensitivity and their moral development was at the “conventional level II. ” This suggests that their focus has been on social aspects and upstream regulations in making moral judgments while considering legality and morality as equivalent.
LcWRKY17, a WRKY Transcription Factor from Litsea cubeba, Effectively Promotes Monoterpene Synthesis

The WRKY gene family is one of the most significant transcription factor (TF) families in higher plants and participates in many secondary metabolic processes in plants. Litsea cubeba (Lour.) Person is an important woody oil plant that is high in terpenoids. However, no studies have been conducted to investigate the WRKY TFs that regulate the synthesis of terpene in L. cubeba. This paper provides a comprehensive genomic analysis of the LcWRKYs. In the L. cubeba genome, 64 LcWRKY genes were discovered. According to a comparative phylogenetic study with Arabidopsis thaliana, these L. cubeba WRKYs were divided into three groups. Some LcWRKY genes may have arisen from gene duplication, but the majority of LcWRKY evolution has been driven by segmental duplication events. Based on transcriptome data, a consistent expression pattern of LcWRKY17 and terpene synthase LcTPS42 was found at different stages of L. cubeba fruit development. Furthermore, the function of LcWRKY17 was verified by subcellular localization and transient overexpression, and overexpression of LcWRKY17 promotes monoterpene synthesis. Meanwhile, dual-Luciferase and yeast one-hybrid (Y1H) experiments showed that the LcWRKY17 transcription factor binds to W-box motifs of LcTPS42 and enhances its transcription. In conclusion, this research provided a fundamental framework for future functional analysis of the WRKY gene families, as well as breeding improvement and the regulation of secondary metabolism in L. cubeba.
Graph coloring and clustering algorithms for science and engineering applications

In this dissertation, efficient algorithms are proposed for two important combinatorial problems in science and engineering applications: parallel graph coloring and subspace clustering. For parallel distance-1 and distance-2 coloring problems, a framework that unifies several existing techniques for creating or facilitating concurrency are presented for distributed-memory computers. Extensions for similar graph and hypergraph coloring problems are also described. Through extensive set of experiments, the algorithms are shown to be efficient and scalable. In addition, new algorithms are proposed for particular instances of subspace clustering problem. A generalized cluster pattern model is introduced to discover complex relationships in large datasets and a search algorithm that utilizes established techniques from related combinatorial problems is designed to solve this problem. Also, biclustering, a special case of subspace clustering, is considered and a new biclustering algorithm is proposed to identify co-regulated genes in large gene expression datasets. The algorithm is the first one to utilize a well known statistical correlation measure in biclustering context. In order to extract useful correlation information from multiple gene expression datasets, a novel method is introduced as well. Both subspace clustering and biclustering algorithms are shown to perform well at identifying targeted relationships in large datasets. The biclustering algorithm is also applied on real gene expression datasets and returned promising clustering results.
A Fast-Response Ultraviolet Phototransistor with a PVK QDs/ZnO Nanowire Heterostructure and Its Application in Pharmaceutical Solute Detection

The sensitivity and photoelectric noise of UV photodetectors are challenges that need to be overcome in pharmaceutical solute detection applications. This paper presents a new device concept for a CsPbBr3 QDs/ZnO nanowire heterojunction structure for phototransistors. The lattice match of the CsPbBr3 QDs and ZnO nanowire reduces the generation of trap centers and avoids carrier absorption by the composite center, which greatly improves the carrier mobility and high detectivity (8.13 × 1014 Jones). It is worth noting that by using high-efficiency PVK quantum dots as the intrinsic sensing core, the device has a high responsivity (6381 A/W) and responsivity frequency (300 Hz). Thus, a UV detection system for pharmaceutical solute detection is demonstrated, and the type of solute in the chemical solution is estimated by the waveform and the size of the output 2f signals.