Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Molecular Weight Heparin Treatment Patterns and Outcomes in Cancer Patients with Acute Venous Thromboembolism: A Nationwide Cohort Study in France

Simple Summary Venous thromboembolism (VTE) is a common and often fatal complication in cancer patients. The initial treatment for VTE in cancer patients is typically low molecular weight heparins (LMWH), a type of anticoagulant that prevents VTE from recurring. However, information on outcomes of LMWH treatment in cancer patients with VTE have not typically been collected from large national databases, such as the one in France. The aim of this study was to assess VTE recurrence, bleeding (a common side effect of anticoagulant treatment), and all-cause mortality in cancer patients in France who were prescribed LMWH for VTE in 2013–2018. Our results indicate that cancer patients who experience VTE have unmet medical needs due to high rates of mortality, VTE recurrence, and bleeding after LMWH treatment initiation. Our study may help further research on outcomes in cancer patients with VTE who receive the next generation of therapy, direct oral anticoagulants. Abstract Patients with cancer have an increased risk of developing venous thromboembolism (VTE) and an increased risk of death from VTE. Until recently, the standard of care for treatment of VTE in cancer patients was low molecular weight heparins (LMWH). To determine treatment patterns and outcomes, we performed an observational study using a nationwide health database. Treatment patterns, rates of bleeding, and VTE recurrence at 6 and 12 months were assessed in cancer patients with VTE in France prescribed LMWH in 2013–2018. Of 31,771 patients administered LMWH (mean age 66.3 years), 51.0% were male, 58.7% had pulmonary embolism, and 70.9% had metastatic disease. At 6 months LMWH persistence was 81.6%, VTE recurrence had occurred in 1256 patients (4.0%) at a crude rate per 100 person-months (PM) of 0.90, and bleeding had occurred in 1124 patients (3.5%) at a crude rate per 100 PM of 0.81. At 12 months, VTE recurrence had occurred in 1546 patients (4.9%) at a crude rate per 100 PM of 0.71 and bleeding had occurred in 1438 patients (4.5%) at a crude rate per 100 PM of 0.66. Overall, VTE-related clinical event rates were high among patients administered LMWH, suggesting an unmet medical need.
Increasing the Information Quality of social media on the Political Involvement of Millennials Using Social Media

Political involvement, namely community activity in influencing the methods of preparing and implementing public policies, as well as participating in choosing government leaders. Social media is widely used as a place to engage in political activities. This study aims to examine the quality of social media information, social media reputation, trust, use of social media, and millennial political involvement. The instrument of this research is using a questionnaire created using a Google form that is distributed online. The respondents of this study are millennials. This study contributes to the inconsistency test between social media information quality variables and political involvement. The results of the study prove that there is an influence between the variables use of social media and political involvement. This study also concluded that most of the millennial generation use social media for 3 hours/day to seek information and communicate. Finally, this research can find that the use of social media affects political involvement.
Laser-Based Door Localization for Autonomous Mobile Service Robots

For autonomous mobile service robots, closed doors that are in their way are restricting obstacles. In order to open doors with on-board manipulation skills, a robot needs to be able to localize the door’s key features, such as the hinge and handle, as well as the current opening angle. While there are vision-based approaches for detecting doors and handles in images, we concentrate on analyzing 2D laser range scans. This requires less computational effort, and laser-scan sensors are available on most mobile robot platforms. Therefore, we developed three different machine learning approaches and a heuristic method based on line fitting able to extract the required position data. The algorithms are compared with respect to localization accuracy with help of a dataset containing laser range scans of doors. Our LaserDoors dataset is publicly available for academic use. Pros and cons of the individual methods are discussed; basically, the machine learning methods could outperform the heuristic method, but require special training data when applied in a real application.
Research of item recommendation algorithm based on graph neural network and deep learning

The recommendation algorithm based on graph neural network has advantage than the traditional recommendation algorithm that it has the power to extract the association relationship between users and items, which makes it become a research hotspot. However, most of the item recommendation algorithm models based on graph neural network are too focused on the interaction information between users and items, ignore the user preferences and item attributes contained in the review data of users and items. In order to solve this problem, this thesis proposed a new model. This model used the graph neural network to extract high-order feature from the interaction information between items and users, and deep learning method to get the general feature from review data. Then it got the final feature of items and users by carrying out feature fusion, which used the attention mechanism. The thesis conducted comparative experiments and ablation experiments on four sets of data sets and the evaluation indexes include the normalized discounted cumulative gain and recall rate. Experiments results show that this model is more effective than the existing algorithms, the feature fusion has positive effect on improving the recommendation accuracy.
Pattern and predictors of musculoskeletal pain among bakery workers
in Abeokuta, Nigeria

Introduction. The magnitude of musculoskeletal pain (MSP) and its risk factors among bakery workers are not well known in Nigeria. Therefore, this study investigated the pattern and predictors of musculoskeletal pain (MSP) seen in Nigerian bakery workers. Methods. The analytical cross-sectional survey used a proforma and the standard Nordic questionnaire in assessing demographic characteristics, relevant work-related/ergonomic hazards and MSP information. Participants were selected using a multistage sampling technique with simple random sampling at stage one (25 bakeries) and a consecutive sampling technique at stage two (7 employees from each bakery). Statistics like chi-square and logistic regression were used in analysing the data. Results. one hundred and seventy-seven participants (77.4% males) with a mean age of 29.7 ± 6.7 years, and the average length of working in the bakery was 9.3 ± 5.2 years. Bending for prolonged periods (93.2%) and repetitive hand movements (92.1%) were the most prevalent ergonomic work hazards. in the past year, 72.3% reported MSP with the lower back region most prevalent (80.2%) followed by pain in the neck region (63.8%). Age, gender and educational attainments were significantly associated with the presence of MSP while ergonomic factors such as repetitive bending, overstretching and use of vibration-generating movement were also significantly associated with the presence of MSP among the bakery workers. inadequate staffing, using vibration movement handling and education are the main predictors of the presence of MSP ( 2 = 29.09, p < 0.001; R 2 = 0.219). Conclusions. High prevalence of musculoskeletal pain in the past one year among Nigerian bakery workers predicted by education level, inadequate staffing, and vibration movement handling.
Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset

Depression is a disorder that if not treated can hamper the quality of life. EEG has shown great promise in detecting depressed individuals from depression control individuals. It overcomes the limitations of traditional questionnaire-based methods. In this study, a machine learning-based method for detecting depression among young adults using EEG data recorded by the wireless headset is proposed. For this reason, EEG data has been recorded using an Emotiv Epoc+ headset. A total of 32 young adults participated and the PHQ9 screening tool was used to identify depressed participants. Features such as skewness, kurtosis, variance, Hjorth parameters, Shannon entropy, and Log energy entropy from 1 to 5 sec data filtered at different band frequencies were applied to KNN and SVM classifiers with different kernels. At AB band (8–30 Hz) frequency, 98.43 ± 0.15% accuracy was achieved by extracting Hjorth parameters, Shannon entropy, and Log energy entropy from 5 sec samples with a 5-fold CV using a KNN classifier. And with the same features and classifier overall accuracy = 98.10 ± 0.11, NPV = 0.977, precision = 0.984, sensitivity = 0.984, specificity = 0.976, and F1 score = 0.984 was achieved after splitting the data to 70/30 ratio for training and testing with 5-fold CV. From the findings, it can be concluded that EEG data from an Emotiv headset can be used to detect depression with the proposed method.
Parallel Solver for H(div) Problems Using Hybridization and AMG

In this paper, a scalable parallel solver is proposed for H(div) problems discretized by arbitrary order finite elements on general unstructured meshes. The solver is based on hybridization and algebraic multigrid (AMG). Unlike some previously studied H(div) solvers, the hybridization solver does not require discrete curl and gradient operators as additional input from the user. Instead, only some element information is needed in the construction of the solver. The hybridization results in a H1-equivalent symmetric positive definite system, which is then rescaled and solved by AMG solvers designed for H1 problems. Weak and strong scaling of the method are examined through several numerical tests. Our numerical results show that the proposed solver provides a promising alternative to ADS, a state-of-the-art solver [12], for H(div) problems. In fact, it outperforms ADS for higher order elements.
Optimal Deployment in Moving Target Defense against Coordinated Cyber–Physical Attacks via Game Theory

This work proposes a method for the intelligent deployment of distributed flexible AC transmission system (D-FACTS) devices. In recent years, in the field of moving target defense (MTD) strategies to detect coordinated cyber–physical attacks (CCPAs), establishing how to deploy D-FACTS devices has become an important research point. Although some research results have been proposed, the obtained deployment solutions are unintelligent due to not carefully considering smart attackers’ behaviors. A method for achieving the intelligent deployment of D-FACTS devices is proposed in this paper. First, the basic concept of corrupting CCPAs is summarized; second, based on considering practical constraints and the basic concept, a protected transmission line set is confirmed; and third, a zero-sum game model is formulated, and a robust Nash equilibrium solution is computed. Due to the game’s characteristics, this solution reflects the smart attackers’ sense of action. Relying on the solution, those lines that are most likely to be tripped form a new protected transmission line set. Finally, a comprehensive algorithm using a metric proposed in previous studies is proposed for finding an intelligent solution for the deployment of D-FACTS devices. We validated our results through extensive simulations using IEEE 14-bus, 30-bus, and 118-bus power systems provided by MATPOWER and the real-world load profiles from New York State. Our work, in tracking the targets that attackers are most likely to attack, opens up new ideas for the intelligent deployment of D-FACTS devices.
A new finite element formulation for internal acoustic problems with dissipative walls

This work concerns the variational formulation and the numerical computation of internal acoustic problems with absorbing walls. The originality of the proposed approach, compared to other existing methods, is the introduction of the normal fluid displacement field on the damped walls. This additional variable allows to transpose formulations in frequency domain to time domain when the fluid is described by a scalar field (pressure or fluid displacement potential). With this new scalar unknown, various absorbing wall models can be introduced in the variational formulation. Moreover, the associated finite element matrix system in symmetric form can be solved in frequency and time domain. Copyright © 2006 John Wiley & Sons, Ltd.
A Suggested Answer To Wallstrom's Criticism (I): Zitterbewegung Stochastic Mechanics

Wallstrom's criticism of existing formulations of stochastic mechanics is that they fail to derive the empirical predictions of orthodox quantum mechanics because they require an ad hoc quantization condition on the postulated velocity potential, \emph{S}, in order to derive Schr\"odinger wave functions. We propose an answer to this criticism by modifying the Nelson-Yasue formulation of non-relativistic stochastic mechanics for a spinless particle with the following hypothesis: a spinless Nelson-Yasue particle of rest mass $m$ continuously undergoes a driven steady-state oscillation of `zitterbewegung' (\emph{zbw}) frequency, $\omega_{c}=\left(1/\hbar\right)mc^{2}$, in its instantaneous mean forward (and backward) translational rest frame. With this hypothesis we show that, in the lab frame, \emph{S} arises from imposing the constraint of conservative diffusions on the time-symmetrized steady-state phase of the \emph{zbw} particle, satisfies the required quantization condition, and evolves in time by the Hamilton-Jacobi-Madelung equations (when generalized to describe a statistical ensemble of \emph{zbw} particles). From the mathematical equivalence of Schr\"odinger's equation with the Hamilton-Jacobi-Madelung equations plus the quantization condition, Schr\"odinger wave functions for a spinless particle (in and excluding external fields) are thereby recovered. We also apply this `zitterbewegung stochastic mechanics' (ZSM) to the case of a central potential and show that it predicts angular momentum quantization. This paper sets the foundation for Part II, which will (primarily) work out the many-particle version of ZSM.
Deep Reinforcement Learning for Resource Demand Prediction and Virtual Function Network Migration in Digital Twin Network

The Internet of Things (IoT) enables intelligent services varying with the complex and realtime environment to achieve network benefits, where network function virtualization (NFV) can dynamically provide virtualized network functions (VNFs) for IoT devices. In the NFV-enabled IoT architecture, a service function chain (SFC) consists of an ordered set of VNFs. However, the energy consumption of the VNF migration and SFC reconfiguration is one major issue owing to the dynamic characteristic of the IoT network. In this article, we propose a new paradigm digital twin (DT) to create the virtual twin of physical objects in the IoT network, then, we formalize the problem as a mathematical model, which aims to minimize the energy consumption. To this end, we prove this problem is NP-hard and propose an algorithm bidirectional gated recurrent unit (Bi-GRU) based on federated learning to predict the resource requirement. Further more, according to the prediction result, which utilizing the deep reinforcement learning (DRL) algorithm for decision making of the VNF migration. Simulation results show that our proposed method can effectively reduce the number of VNFs to be migrated and economize the energy consumption of the DT IoT network.
Differences in Telomere Length between Adolescent Females with Anorexia Nervosa Restricting Type and Anorexia Nervosa Binge-Purge Type

Physiological and psychological distress may accelerate cellular aging, manifested by shortening of telomere length (TL). The present study focused on TL shortening in anorexia nervosa (AN), an illness combining physiological and psychological distress. For that purpose, we measured TL in 44 female adolescents with AN at admission to inpatient treatment, in a subset of 18 patients also at discharge, and in 22 controls. No differences in TL were found between patients with AN and controls. At admission, patients with AN-binge/purge type (AN-B/P; n = 18) showed shorter TL compared with patients with AN-restricting type (AN-R; n = 26). No change in TL was found from admission to discharge, despite an improvement in body mass index standard deviation score (BMI-SDS) following inpatient treatment. Older age was the only parameter assessed to be correlated with greater TL shortening. Several methodological changes have to be undertaken to better understand the putative association of shorter TL with B/P behaviors, including increasing the sample size and the assessment of the relevant pathological eating disorder (ED) and non-ED psychological correlates in the two AN subtypes.
Perception of Students and Teachers Regarding Online Teaching of English Language: A Case of Secondary School Hyderabad - Pakistan

This study explored the perceptions of students and teachers regarding online English teaching and learning. The research study was carried out using quantitative descriptive method, in which qualitative data was collected through semi-structured interviews and analyzed through thematic analysis. Meanwhile the quantitative data was collected using close-ended questionnaires, separate for both teachers and students. The data collected was validated through a pilot study. For the reliability of the data, closed-ended responses were tested using the Cronbach Alpha test. The value for this study’s close-ended tool is 0.7. The findings of the study suggests that teaching of English language virtually is more beneficial for teachers and students as it offers many Perceived Usefulness (PU) advantages namely; accessibility, time saving, awareness of new technology and its applications. It was also found to be challenging to some extent as Perceived Ease of Usefulness (PEOU). The challenges may include, major power breakdown, lack of resources, poor network connections, lack of professional and academic trainings about the use of modern technology and its applications by the teachers and the students. The pandemic era was the time of complete closure of schools; hence, teaching virtually was considered a blessing in disguise.
Intense Pulsed Neutron Source FRANZ in the 1 - 500 keV Range

The Frankfurt Neutron source at the Stern-Gerlach-Zentrum FRANZ will provide 1 ns short n-pulses emerging from a 7 Li target hit by an intense 2.0 ± 0.2 MeV p-beam. The pulse rate will be 250 kHz typically. The integrated n-flux will be up to 10 7 per cm 2 in a distance of 0.8 m from the target. Together with a Gamma calorimeter it will be possible for instance to measure differential n-capture cross sections at samples containing a low number of target atoms only (rare isotopes) as needed for nuclear astrophysics. Additionally to this operation mode there will be an option for higher duty factors with unspecified micro pulse structure. In this case detector components etc. could be tested with respect to their durability against non-ionizing radiation at nfluxes around 10 10 per cm 2 s. The proton driver consists of a volume proton source on a 150 kV high voltage platform, a 100 ns chopper, RFQ and IH-DTL, followed by a Mobley-type buncher system. It is aimed to focus up to 5C 10 10 protons onto the Li-target within 1 ns. Solid as well as fluid Li-targetsolutions are under development at FZ Karlsruhe, Germany. The accelerator is under construction and will be located at Frankfurt, about 35 km away from GSI Darmstadt, which is deeply involved in this project. 1. Motivation
Automatic Concrete Dam Deformation Prediction Model Based on TPE-STL-LSTM

Concrete dam deformation prediction is important for assessing the safety of dams. A TPE-STL-LSTM deformation prediction model for concrete dams is established by introducing the TPE algorithm based on the decomposition–prediction model. Taking the Wanjiazhai gravity dam project as an example, a prediction model for the top deformation of 14 dam sections was established and the parameters were determined. The model was used for deformation prediction and compared with the measured deformation and similar methods to predict deformation for verification. The results show that the model has good prediction effect and matches well with the measured data; the accuracy is better than the Autoregressive Integrated Moving Average model and the Support Vector Machine model; and the model achieves the automatic determination of all parameters. The model can be used for dam engineering safety assessment, effectively improving the analysis accuracy and analysis efficiency.
Exploring the flexural and impact properties of pure flax/epoxy and Kevlar/flax/epoxy composites through experimental and numerical analysis

Hybrid composites offer a viable alternative to conventional composites in terms of reduced cost and environmental impact. This study investigated the effect of hybridizing flax with Kevlar on the flexural and impact properties of Kevlar/flax/epoxy composites using three-point bending, drop-weight, and Charpy impact tests. The tested specimens were made of unidirectional flax fibers (F) and woven Kevlar fibers (K) in four configurations: unidirectional flax/epoxy [016F] (UFE), angle-ply flax/epoxy [±454F]S (AFE), woven Kevlar/unidirectional flax/epoxy [0–902K/06F]S (UKFE), and woven Kevlar/angle-ply flax/epoxy [0–902K/±453F]S (AKFE). The three-point bending test results showed that the ultimate strength and the flexural modulus of the UKFE increased by 15% compared to pure UFE and by more than threefold for AKFE compared to AFE laminates. Additionally, the results of the drop-weight test revealed a significant increase in the impact force by 30% for both unidirectional and angle-ply laminate configurations. Similarly, Charpy test results indicated a 60% improvement in the impact energies of the flax laminates. The predictions of the proposed finite element model agreed very well with the experimental results. These findings demonstrate the effectiveness of hybridizing two layers of Kevlar onto flax/epoxy composites in enhancing the composite's impact and flexural properties.
COVID-19 Therapeutic Potential of Natural Products

Despite the fact that coronavirus disease 2019 (COVID-19) treatment and management are now considerably regulated, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still one of the leading causes of death in 2022. The availability of COVID-19 vaccines, FDA-approved antivirals, and monoclonal antibodies in low-income countries still poses an issue to be addressed. Natural products, particularly traditional Chinese medicines (TCMs) and medicinal plant extracts (or their active component), have challenged the dominance of drug repurposing and synthetic compound libraries in COVID-19 therapeutics. Their abundant resources and excellent antiviral performance make natural products a relatively cheap and readily available alternative for COVID-19 therapeutics. Here, we deliberately review the anti-SARS-CoV-2 mechanisms of the natural products, their potency (pharmacological profiles), and application strategies for COVID-19 intervention. In light of their advantages, this review is intended to acknowledge the potential of natural products as COVID-19 therapeutic candidates.