Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceived Discrimination sebagai Mediator Hubungan antara Akulturasi dan Kesejahteraan Psikologis pada Mahasiswa Etnis Tionghoa

Abstract—The purpose of this research wastoexamine the relationship between acculturation and the psychological well-being of ethnic Chinese students with perceived discrimination as a mediator. This study used quantitative methods, with a cross-sectional survey design. The respondents (N=97) were ethnicChinese students studying at public universities, selected using purposive sampling. The data was collected online using the Vancouver Index Acculturation (VIA), Brief Perceived Ethnic Discrimination Questionnaire-Community Version (Brief PEDQ-CV), and Psychological Well-Being Questionnaire (PWB-42). The results of the mediation test showed that perceived discrimination was a full mediator in the relationship between acculturation and the psychological well-being of ethnic Chinese students studying at public universities (z=-1.988; p <0.05). This finding revealed that as a minority, ethnic Chinese students accepted or adapted to the majority culture to reduce discrimination and this mechanism could improve their psychological well-being.The theoretical and practical implications of this research are discussed further. Keywords: acculturation, psychological well-being, perceived discrimination, chinese ethnicity Abstrak—Penelitian bertujuan menguji hubungan antara akulturasi dengan kesejahteraan psikologis mahasiswa etnis Tionghoa dengan perceived discrimination sebagai mediator. Penelitian ini menggunakan metode kuantitatif, dengan design cross sectional survei. Partisipan penelitian (N= 97) merupakan mahasiswa etnis Tionghoa yang berkuliah di perguruan tinggi negeri. Metode sampling yang digunakan yaitu purposive sampling. Pengumpulan data menggunakan tiga skala yaitu Vancouver Index Acculturation, Brief Perceived Ethnic Discrimination Questionnaire-Community Version (Brief PEDQ-CV), danPsychological Well-Being Questionnaire (PWB-42). Hasil uji hipotesis dengan uji mediasi menunjukkan bahwa perceived discrimination menjadi mediator full terhadap hubungan antara akulturasi dengan kesejahteraan psikologis mahasiswa etnis Tionghoa yang berkuliah di perguruan tinggi negeri(z=-1.988; p <0.05). Temuan ini menunjukkan bahwa sebagai minoritas, mahasiswa etnis Tionghoa melakukan penerimaan atau adaptasi terhadap budaya mayoritas untukmenurunkandiskriminasi yang dihadapinya dan mekanisme ini dapat meningkatkan kesejahteraan psikologisnya. Implikasi teoritis dan praktis penelitian ini, didiskusikan lebih lanjut. Kata kunci: akulturasi, kesejahteraan psikologis, diskriminasi yang dirasakan, etnis tionghoa
Phase-Plane Based Model-Free Estimation of Steady-State Metabolic Cost

The steady-state metabolic cost has been used to assess the performance of wearable robotic devices. Recently, real-time assessment of this metabolic cost has been employed in an objective function when optimizing robotic parameters for an individual user, thus personalizing the assistance to minimize human effort. However, the long estimation time needed for the model-based approach to metabolic cost estimation limits the optimization only to light-intensity activities. Here, we hypothesized that model-free, phase-plane estimation (PPE) would reduce the estimation time for the steady-state metabolic cost. First, we developed a phase-plane representation to classify the steady-state (where the change in metabolic rate is zero) and the transient metabolic dynamics. Second, we approximated the transient metabolic dynamics using a data-driven Gaussian mixture model and a real-time respiratory measure. We compared the performance of PPE with that of the model-based method by examining (1) walking performance assisted by a robotic prosthetic foot for individuals with and without Below Knee Amputation (BKA) and (2) squatting and running performance for individuals without BKA. PPE reduced the steady-state estimation time during walking for individuals with and without BKA by 31% and 40%, respectively. It also reduced estimation time by 56% and 24% for squatting and running conditions. These significant reductions in estimation time suggest that the data-driven PPE method can be used to rapidly estimate physical effort when personalizing the assistance from wearable robots. This expands the ability to conduct individual optimization for subjects engaged in physically intensive activities or for individuals with reduced physical strength.
Treatment of Wastewaters Containing Sulfonylurea Herbicides by Electroflotation: Chemical and Ecotoxicological Efficacy

Electroflotation (EF) is an electroanalytical technique applied for separating substances suspended in phase and is reported to be efficient for effluents and wastewater treatments. To date, there are no studies employing EF for the treatment of wastewater containing toxic organic compounds. In the present study, EF was tested as an electro-oxidative process to decontaminate wastewater containing the sulfonylurea herbicide chlorimuron-ethyl. To evaluate the value of EF, both the chemical and ecotoxicological efficacies were assessed in non-treated and treated wastewaters; the former monitored the herbicide and degradation products in the reaction medium through analytical chromatographic methods, whereas the latter comprised testing the 72 h growth of the green microalga Raphidocelis subcapitata and the 48 h lethality of the cladoceran Daphnia magna. Analysis by HPLC-UV allowed the monitoring of the electrochemical reaction, and a degradation mechanism based on gas chromatography–mass spectrometry was proposed. Despite the compromised herbicide structure, non-treated and treated wastewaters were similarly toxic for the microalgae Raphidocelis subcapitata and the invertebrate Daphnia magna. Even though EF did not remove the wastewater toxicity, the results indicate that toxic organic compounds are potentially oxidized by EF while signaling the need to combine chemical and ecotoxicological approaches to gauge the environmental sustainability of EF.
Downside and upside risk spillovers from commercial banks into China’s financial system: a new copula quantile regression-based CoVaR model

In this paper, we investigate the downside and upside risk spill-overs from three kinds of commercial banks (state-owned commercial banks (SOCBs), joint-stock commercial banks (JSCBs) and city commercial banks (CCBs)) to China ’ s financial system by proposing a new copula quantile regression-based CoVaR model. We find that (i) the dynamic risk spillovers show heterogeneity over time, specifically that its downward trend is significant after the stock market disaster in 2015; (ii) JSCBs display the largest risk spillovers, indicating that JSCBs are the main contributors to systemic risk in China ’ s financial system; and (iii) the risk spillovers are not symmetrical, as the upside risk spillovers are smaller than the downside risk spillovers. Our results have crucial implications for financial regulators and investors who want to measure and prevent systemic financial risk and optimise their investment strategies.
Perceptions of Fatigue and Safety Climate Pertaining to Residency Duty-Hour Restrictions

Introduction: The Accreditation Council for Graduate Medical Education (ACGME), which sets the standards for residency training, instituted work-hour restrictions in 2003. Our purpose was to assess residents’ perceptions of fatigue and local safety climate specific to these duty-hour restrictions. Methods: All residents (N=433) at one university were emailed a link to a survey in 2019. The survey included demographic details, on-call descriptors, an 18-point climate survey (CS), and the 33-point Chalder Fatigue Questionnaire (CFQ). The CS was adapted from a commonly used safety climate scale and intended to measure the respondent’s perceptions of their program’s attitudes and practices around resident duty-hour compliance. A Pearson correlational analysis was used to determine if there were associations between the variables. Results: Mean CS score was 12.89 (95% confidence interval, CI 12.32-13.46, N=164, 48.5%). Respondents were most likely to disagree with “Residents are told when they are at risk of working beyond ACGME duty-hour restrictions,” where 57 (34.7%) disagreed or strongly disagreed. Mean CFQ score was 16.02 (95% CI 14.87-17.17, N=113, 26.1%). As the CS score improved, CFQ scores decreased indicating an inverse relationship between duty-hour climate and fatigue (r=-0.328, p<0.05). Having a protected post-call day off, and having either the Program Director, Chief Resident, or Senior Resident decide that a resident takes a post-call day off were all associated with higher CS scores. Conclusion: We found that the CS had good internal consistency and evidence of construct validity. An inverse relationship between CS score and fatigue suggests that the level of fatigue is higher among residents in programs where residents perceived that ACGME duty-hour compliance was less important.
Maximization of High-Efficiency Operating Range of Spoke-Type PM E-Bike Motor by Optimization Through New Motor Constant

This work focuses on maximizing the high-efficiency operating range of a spoke-type permanent magnet (PM) motor by minimizing losses with a proposed motor constant to extend the cruise range for E-bikes. Multiple factors are usually involved in reducing copper and iron losses, and the efficiency may not be easily manipulated in the design processes for high-performance motors. The motor efficiency may directly be obtained through simulations upon completion of designs, resulting in a long product development period. Therefore, this paper proposes a simple optimization method covering copper and iron losses to expand motor high-efficiency ranges. A 300 W spoke-type PM motor for E-bikes is used as the target. The torque density is enhanced and the power losses are minimized based on the proposed new motor constant, which is developed to comprehensively evaluate motor performance and efficiency. The Genetic Algorithm is used in conjunction with the new motor constant to maximize the high-efficiency operating range. The analysis results reveal that the optimized spoke-type PM motors can achieve a higher torque density and broader high-efficiency range than that without being optimized. A prototype is fabricated, and experiments are conducted to validate the analysis.
Semi-supervised dual-graph regularization non-negative matrix factorization with local coordinate and orthogonal constraints for image clustering

Abstract. Due to significant performance in the representation of data points, non-negative matrix factorization (NMF) has been widely applied in machine-learning fields, such as dimension reduction, image representation, feature extraction, data mining, and so on. However, classical NMF suffers from a common issue, low efficiency in representing the internal geometric structure of data and sparsity limitation. To circumvent this problem, we innovatively propose a semi-supervised NMF algorithm called semi-supervised dual-graph regularization non-negative matrix factorization (LOSDNMF), into which dual-graph and bi-orthogonal constraints are embedded to reduce the inconsistency between the original matrix and the basic vectors while maintaining the manifold structures of the data and feature spaces. This strategy can fully explore the potential geometry information of the data, which is extremely beneficial to enhance the learning ability of the model. In addition, the local coordinate constraints are introduced to ensure good sparsity of the coefficient matrix and simplify the calculation. Furthermore, an iterative updating scheme for the optimization problem of LOSDNMF and its convergence proofs are also provided in detail. The effectiveness of the proposed method is verified on eight benchmark datasets. Experimental results show that our method can effectively improve clustering performance.
Two-Stream Spatial Graphormer Networks for Skeleton-Based Action Recognition

In skeleton-based human action recognition, Transformer, which models the correlations between joint pairs in global topology, has achieved remarkable results. However, compared to many researches on changing graph topology learning in graph convolution network (GCN), Transformer self-attention ignores the topology of the skeleton graph when capturing the dependencies between joints. To address these problems, we propose a novel two-stream spatial Graphormer network (2s-SGR), which uses self-attention incorporating structural encodings to model joint and bone information, and which consists of two networks, the joint stream spatial Graphormer network (Js-SGR) and the bone stream spatial Graphormer network (Bs-SGR). First, in the Js-SGR, while Transformer models joint correlations in the global topology of the space, the topology of the joints and the edge information of the bones are introduced into the self-attention through custom structural encodings. At the same time, joint motion information is modeled in spatial-temporal blocks. The added information on structure and motion can effectively capture the dependencies of nodes between frames and enhance feature representation. Second, for the second-order information of the skeleton, the Bs-SGR adapts to the structure of the bone by adjusting the custom structural encodings. Finally, the global spatial-temporal features of joints and bones in the skeleton are fused and input into the classification network to obtain action recognition results. Extensive experiments on three large-scale datasets, NTU-RGB+D 60, NTU-RGB+D 120, and Kinetics, demonstrate that the performance of the 2s-SGR proposed in this paper is at the state-of-the-art level and is effectively validated by ablation experiments.
Transitive inverse-consistent image registration and evaluation

Image registration is widely used for finding correspondences and comparing morphology in populations of biological forms. Due to the shape complexity, discretized approximation of continuous space, and so on, it is hard to find perfect registration and the point-wise ground truth correspondence rarely exists. In order to improve registration performance, registration errors and desired properties were studied to constrain the transformation searching space. New registration methods were developed to generate correspondences with desired properties. Evaluation framework and experiments were established for methods validation and comparison. Transitive inverse-consistent non-reference (TINR) registration methods were developed to jointly estimate correspondences between groups of three images while minimizing inverse consistency and transitivity errors. Registering three images simultaneously provides a means for minimizing the transitivity error which is not possible when registering only two images. The clustered TINR (CTINR) extended this method to register groups of more than three images and was implemented by first clustering the group to sub-groups and applying the TINR method inside each sub-group. Transitive inverse-consistent implicit reference (TIIR) registration methods were also developed to jointly register images to an implicit reference. By construction, the set of transformations are transitive and inverse consistent. The TIIR registration method was proved mathematically to provide smaller registration error compared to pair-wise registration.
The Association Between Long-Term Spicy-Food Consumption and the Incidence of Chronic Postsurgical Pain After Cesarean Delivery: An Observational Study

Background Our previous study found that a long-term diet incorporating spicy foods can reduce the human basal pain threshold. Capsaicin is the pungent ingredient in chili peppers. Transient receptor potential vanilloid type1 is the capsaicin receptor expressed in the oral cavity and is the primary sensory neuron of the “pain” pathway. Few studies have examined the association between long-term spicy diet and chronic postsurgical pain (CPSP). Women who underwent elective cesarean section (eCS) have consistent characteristics of CPSP. This study aimed to investigate the relationship between a long-term spicy diet and the incidence of CPSP after eCS. Methods Participants were divided into a low frequency group (LF, numerical rating scale (NRS)<5) for spicy food consumption and a high frequency group (HF, NRS≥5) by receiver operator characteristic analysis. The primary outcome was the incidence of CPSP three months after eCS. Propensity score matching (PSM) analysis was performed between the two frequency groups. Stepwise logistic regression analysis was then performed. Results Of the 1029 enrolled patients, data from 982 were analyzed 3 months after eCS. After PSM, the incidence of CPSP in the HF group (30.1% [108/359]) was higher than that in the LF group (19.8% [71/359]; P = 0.001). Compared with the LF group, the risk of CPSP in the HF group increased 1.61 times by 3 months (95% CI 1.18–2.20, P = 0.003). PSM results found that 1 year, the incidence of CPSP in the HF group (15.2% [56/369]) was higher than that in the LF group (8.1% [30/369], P = 0.003). Conclusion With an NRS≥5 as a boundary, women who consumed spicy food ≥ 2 days/week were more likely to have CPSP than those who consumed spicy food < 2 days/week.
Audiovisual Emotional Congruency Modulates the Stimulus-Driven Cross-Modal Spread of Attention

It has been reported that attending to stimuli in visual modality can spread to task-irrelevant but synchronously presented stimuli in auditory modality, a phenomenon termed the cross-modal spread of attention, which could be either stimulus-driven or representation-driven depending on whether the visual constituent of an audiovisual object is further selected based on the object representation. The stimulus-driven spread of attention occurs whenever a task-irrelevant sound synchronizes with an attended visual stimulus, regardless of the cross-modal semantic congruency. The present study recorded event-related potentials (ERPs) to investigate whether the stimulus-driven cross-modal spread of attention could be modulated by audio-visual emotional congruency in a visual oddball task where emotion (positive/negative) was task-irrelevant. The results first demonstrated a prominent stimulus-driven spread of attention regardless of audio-visual emotional congruency by showing that for all audiovisual pairs, the extracted ERPs to the auditory constituents of audiovisual stimuli within the time window of 200–300 ms were significantly larger than ERPs to the same auditory stimuli delivered alone. However, the amplitude of this stimulus-driven auditory Nd component during 200–300 ms was significantly larger for emotionally incongruent than congruent audiovisual stimuli when their visual constituents’ emotional valences were negative. Moreover, the Nd was sustained during 300–400 ms only for the incongruent audiovisual stimuli with emotionally negative visual constituents. These findings suggest that although the occurrence of the stimulus-driven cross-modal spread of attention is independent of audio-visual emotional congruency, its magnitude is nevertheless modulated even when emotion is task-irrelevant.
Snowmelt Leads to Seasonal Nitrous Acid Formation Across Northwestern China

28 snowpack samples were collected across northern Xinjiang, northwestern China in January 2018. 16 of these liquid snowmelt samples, 15 with pH < 6.8 and one with the highest nitrate anion (NO3−) concentration, were selected to investigate the photochemical gaseous nitrous acid (HONO) production rate (P(HONO)(g)), which was observed to range from 4.0 to 180.9 ppt min−1 at room temperature. Surprisingly, the ratio of HONO production rate and NO3− concentration (P(HONO)(g)/[NO3ˉ]) was significantly higher than those obtained from the photolysis of aqueous NO3− solutions with similar pH, due to the complex organic compounds present in the snowmelt samples, enhancing HONO formation or the coexistence of other photochemical HONO sources. We also found that P(HONO)(g) was highly correlated with the pH of snowmelt samples, which were significantly lower in rural/remote areas than those from urban/industrial sites, suggesting a significant influence of anthropogenic activities on pH and P(HONO)(g) of snowmelt.
Solar System Dynamics: Expansion of the Disturbing Function

The Solar System is a complex and fascinating dynamical system. This is the first textbook to describe comprehensively the dynamical features of the Solar System and to provide students with all the mathematical tools and physical models they need to understand how it works. It is a benchmark publication in the field of planetary dynamics and destined to become a classic. Clearly written and well illustrated, Solar System Dynamics shows how a basic knowledge of the two- and three-body problems and perturbation theory can be combined to understand features as diverse as the tidal heating of Jupiter's moon Io, the origin of the Kirkwood gaps in the asteroid belt, and the radial structure of Saturn's rings. Problems at the end of each chapter and a free Internet Mathematica® software package are provided. Solar System Dynamics provides an authoritative textbook for courses on planetary dynamics and celestial mechanics. It also equips students with the mathematical tools to tackle broader courses on dynamics, dynamical systems, applications of chaos theory and non-linear dynamics.
Moth-Flame Optimization for Early Prediction of Heart Diseases

Heart disease is among the leading causes of mortality globally. Predicting cardiovascular disease is a major difficulty in clinical data analysis. AI has been demonstrated to be powerful in deciding and anticipating an enormous measure of information created by the health domain. We provide a unique method for finding essential traits employing machine learning approaches in this paper, which enhances the effectiveness of identifying heart diseases. Decision tree (DT), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN) are the classification techniques used to create the proposed system. Ensemble stacking integrates the four classification models to create a single best-fit predictive model using logistic regression. Many explorations have been directed at the identification of cardiac infection; however, the exactness of the outcomes is poor. Accordingly, to further enhance the efficiency, Moth-Flame Optimization (MFO) algorithm is proposed. The feature selection strategies are used to improve the classification accuracy while shortening the execution time of the classification system. Medical data are used to assess the probability of heart disease based on BP, age, gender, chest ache, cholesterol, blood sugar, and other variables. Results revealed that the proposed system excelled other existing models, obtaining 99% accuracy in the Cleveland dataset.
There are More Than 2**(n/17) n-Letter Ternary Square-Free Words

We prove that the `connective constant' for ternary square-free words is at least $2^{1/17} = 1.0416 . $, improving on Brinkhuis and Brandenburg's lower bounds of $2^{1/24}=1.0293 . $ and $2^{1/22}=1.032 . $ respectively. This is the first improvement since 1983.