Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating Spatial Representation of Learning Content in Virtual Reality Learning Environments

A recent surge in the application of Virtual Reality in education has made VR Learning Environments (VRLEs) prevalent in fields ranging from aviation, medicine, and skill training to teaching factual and conceptual content. In spite of multiple 3D affordances provided by VR, learning content placement in VRLEs has been mostly limited to a static placement in the environment. We conduct two studies to investigate the effect of different spatial representations of learning content in virtual environments on learning outcomes and user experience. In the first study, we studied the effects of placing content at four different places - world-anchored (TV screen placed in the environment), user-anchored (panel anchored to the wrist or head-mounted display of the user) and object-anchored (panel anchored to the object associated with current content) - in the VR environment with forty-two participants in the context of learning how to operate a laser cutting machine through an immersive tutorial. In the follow-up study, twenty-two participants from this study were given the option to choose from these four placements to understand their preferences. The effects of placements were examined on learning outcome measures - knowledge gain, knowledge transfer, cognitive load, user experience, and user preferences. We found that participants preferred user-anchored (controller condition) and object-anchored placement. While knowledge gain, knowledge transfer, and cognitive load were not found to be significantly different between the four conditions, the object-anchored placement scored significantly better than the TV screen and head-mounted display conditions on the user experience scales of attractiveness, stimulation, and novelty.
Tirzepatide: A Promising Drug for Type 2 Diabetes and Beyond

Tirzepatide is a promising drug with dual-acting glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide 1 (GLP-1) receptor activation that has revolutionized the treatment of type 2 diabetes mellitus (T2DM) as an adjunct to diet and exercise. In phase 3 clinical trials (SURPASS 1-5), the dose-dependent efficacy and safety of tirzepatide were assessed by once-weekly subcutaneous injection (5 mg, 10 mg, and 15 mg), as monotherapy or combination therapy, in individuals with T2DM. Tirzepatide has been shown to achieve better glycemic control in terms of glycosylated hemoglobin reduction and improved fasting and postprandial glucose levels as compared to other diabetic medications. Moreover, the studies demonstrate a reduction in body weight (-6.2 to -12.9 kg), and other cardiovascular benefits by altering the lipid profile, reducing blood pressure, and visceral adiposity. Tirzepatide has acceptable side effects and is well tolerated, with a low risk of hypoglycemia. The SURPASS 4 clinical trial has shown positive cardiovascular outcomes in people with T2DM and elevated cardiovascular risk. Additionally, encouraging results from SURMOUNT trials and ongoing SURPASS-CVOT studies will shed more light on cardiovascular safety in the future. In this review, we have summarized the clinical trials and their respective outcomes and highlighted the potential future indications for tirzepatide in the management of obesity, heart failure, and nonalcoholic steatohepatitis.
Comprehensive Analysis of Epstein-Barr Virus LMP2A-Specific CD8+ and CD4+ T Cell Responses Restricted to Each HLA Class I and II Allotype Within an Individual

Latent membrane protein 2A (LMP2A), a latent Ag commonly expressed in Epstein-Barr virus (EBV)-infected host cells, is a target for adoptive T cell therapy in EBV-associated malignancies. To define whether individual human leukocyte antigen (HLA) allotypes are used preferentially in EBV-specific T lymphocyte responses, LMP2A-specific CD8+ and CD4+ T cell responses in 50 healthy donors were analyzed by ELISPOT assay using artificial Ag-presenting cells expressing a single allotype. CD8+ T cell responses were significantly higher than CD4+ T cell responses. CD8+ T cell responses were ranked from highest to lowest in the order HLA-A, HLA-B, and HLA-C loci, and CD4+ T cell responses were ranked in the order HLA-DR, HLA-DP, and HLA-DQ loci. Among the 32 HLA class I and 56 HLA class II allotypes, 6 HLA-A, 7 HLA-B, 5 HLA-C, 10 HLA-DR, 2 HLA-DQ, and 2 HLA-DP allotypes showed T cell responses higher than 50 spot-forming cells (SFCs)/5×105 CD8+ or CD4+ T cells. Twenty-nine donors (58%) showed a high T cell response to at least one allotype of HLA class I or class II, and 4 donors (8%) had a high response to both HLA class I and class II allotypes. Interestingly, we observed an inverse correlation between the proportion of LMP2A-specific T cell responses and the frequency of HLA class I and II allotypes. These data demonstrate the allele dominance of LMP2A-specific T cell responses among HLA allotypes and their intra-individual dominance in response to only a few allotypes in an individual, which may provide useful information for genetic, pathogenic, and immunotherapeutic approaches to EBV-associated diseases.
Towards Adaptive Information Fusion in Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have gained great popularity in tackling various analytic tasks on graph and network data. However, some recent studies raise concerns about whether GCNs can optimally integrate node features and topological structures in a complex graph. In this paper, we first present an experimental investigation. Surprisingly, our experimental results clearly show that the capability of the state-of-the-art GCNs in fusing node features and topological structures is distant from optimal or even satisfactory. The weakness may severely hinder the capability of GCNs in some classification tasks, since GCNs may not be able to adaptively learn some deep correlation information between topological structures and node features. Can we remedy the weakness and design a new type of GCNs that can retain the advantages of the state-of-the-art GCNs and, at the same time, enhance the capability of fusing topological structures and node features substantially? We tackle the challenge and propose an Adaptive Multi-channel Graph Convolutional Network for semi-supervised classification (AM-GCN). The central idea is that we extract the specific and common embeddings from node features, topological structures, and their combinations simultaneously, and use the attention mechanism to learn adaptive importance weights of the embeddings. However, considering that the input topology and feature structure in AM-GCN are still predefined and fixed, once the properties of graph structures are not consistent with tasks, the fusion performance of AM-GCN will be hindered from the beginning. Therefore, we need to adjust the structure and further propose the Label Propagation guided Multi-channel Graph Convolutional Network (LPM-GCN). LPM-GCN introduces edge weights learning on both topology and feature spaces to improve structural homophily, which can better promote the fusion process of graph convolutional networks. Our extensive experiments on benchmark data sets clearly show that our proposed models extract the most correlated information from both node features and topological structures substantially, and improves the classification accuracy with a clear margin.
A Time-Series Data Analysis about the Historical Population of the Philippines using 12-point Moving Average Forecasting

One of the world's most serious challenges is the exponential expansion of the population. The Philippines is included in this category of "less developed countries. " Moreover, the said country's population expansion is unstoppable. With at least three newborns born per minute, the country has one of the greatest population growth rates. Consequently, the researcher's objective is to use a machine-learning algorithm to forecast the possible Philippine population growth for the upcoming year. Employing Knowledge Discovery in Database (KDD) as the step-by-step process in determining solutions and answering each of the research questions of the study.With this, the researchers were able to forecast data using time series data analysis about the historical populations of the Philippines utilizing a 12-point moving average. Based on the accuracy performance of the model, the algorithm can be used as a reliable source and was considered a good fit with a 90.27% accuracy.
A Unified Acceleration Solution Based on Deformable Network for Image Pixel Processing

Recently, deep neural networks, including convolutional neural networks, deformable convolutional networks, and generative adversarial networks, have shown promising potential for image pixel processing tasks, such as image super-resolution and denoising. These various networks bring different memory accesses and complex computation patterns, challenging their hardware deployments seriously. Therefore, this brief proposes a field-unified method to transform various convolutions into a specific deformable convolution with an adaptive receptive field. Based on the transformation, a tile engine, together with a novel position-decoupling computing flow, is developed to unify the multiple memory access patterns, which maintains the consistency of input/output layout formats. Then, a high-level hardware architecture is presented to flexibly support multiple types of convolutional layers. The proposed design is implemented on a Zynq UltraScale+ FPGA board. Experimental results show that our design obtains around $1.4\times $ computation efficiency improvement, surpassing existing works in terms of hardware flexibility and computation efficiency significantly.
A New Multiscale Multiattention Convolutional Neural Network for Fine-Grained Surface Defect Detection

Surface defect detection (SDD) is essential in industry to ensure the quality of products. Fine-grained SDD has been developed to cope with the high intraclass and low interclass differences in defect samples. However, the sizes and appearances of the defects are various, making it still challenging to identify the defects accurately. To overcome this drawback, this article proposes a new multiscale multiattention convolutional neural network (MSMA-SDD) for fine-grained SDD. First, a new multiscale CNN network is developed, which uses features from different layers to match the defects with different sizes. Second, a new multiattention module is proposed, which generates compact attention map to assist MSMA-SDD focusing on the tiny defects. Third, the multiscale and multiattention-based MSMA-SDD is adopted in the fine-grained SDD task. The proposed MSMA-SDD is conducted on three datasets. The experimental results show that MSMA-SDD is superior to the current most advanced method, and the accuracy are 100%, 99.59%, and 99.57%, respectively, which has verified its potential in the SDD field.
Research on Topology of Dual Arm Hybrid Direct Current Circuit Breaker and Its Control Scheme

High voltage Direct Current (HVDC) is more suitable for long distance and large capacity power transmission, which can effectively improve Grid-connected absorption capacity of distributed generation. DC circuit breaker, as the core equipment of HVDC, which can quickly cut off fault current and prevent faults current from expanding. DC circuit breakers are mainly divided into such three types as mechanical, solid, and hybrid, of which the hybrid DC circuit breaker has the advantages of strong carrying capacity, small loss, easy extinguishing of arc and fast cutting off the fault current and has gradually become the main development direction of DC circuit breaker. In this paper, by studying a typical hybrid DC circuit breaker, which has such disadvantages as large capacity of arrester, none zero-voltage opening of solid state switch set and long time of fault current removal, a hybrid double bridge hybrid DC circuit breaker is proposed. The two kinds of circuit breakers are compared by simulation, which proves that the dual-arm hybrid DC circuit breaker proposed in this paper is more economical, the fault current is cut off faster, and the solid-state switch group can realize Zero voltage switching (ZVS).
Radiological Incident System using Augmented Reality (RISAR)

This paper presents an Augmented Reality (AR) solution called RISAR that allows for the real-time visualisation of Radiological hazards based on sensor data captured from detectors as well as Unmanned Aerial and Ground Vehicles. RISAR improves safety for first responders during radiological events by enhancing their situation awareness. This lowers the risk of harm, and with it any health impacts and costs.
RESULTS OF SURGICAL TREATMENT OF CARPAL TUNNEL SYNDROME USING TWO VARIATIONS OF OPEN TECHNIQUE

ABSTRACT Transverse carpal ligament (TCL) opening is the treatment of choice for carpal tunnel syndrome. However, complications such as loss of grip strength and anterior displacement of the median nerve are described as common complications associated with this technique. Thus, techniques that reconstruct or extend TCL are described to reduce the incidence of these complications. Objective: To evaluate the effectiveness of TCL enlargement through Z-plasty and the reduction of complications by comparing it with the complete opening of the ligament. Materials and Methods: A prospective and randomized intervention study was conducted in 56 patients. They were divided into 2 groups: 1) complete opening of TCL 2) TCL enlargement via Z-plasty. We evaluated grip strength, sensitivity, and functional evaluation using the QuickDASH questionnaire. Results: There was no statistically significant difference in the improvement of scores with QuickDASH between the two techniques. The sensitivity test was better in patients subjected to TCL enlargement, whereas grip strength increased in the group subjected to complete TCL opening. Conclusion: According to the results of this study, the complete opening of the TCL showed no reduction in grip strength, although it showed inferior recovery to postoperative sensitivity. Both techniques were equivalent in functional improvement. Thus, Z-plasty showed no identifiable benefits for TCL enlargement. Level of Evidence III, Randomized Clinical Trial.
Determination of an Asymmetrical Nine Phase Induction Machine Stator and Rotor Inductances Using Winding Function Approach

It is an established fact that the simplest means of parameter extraction of an induction machine (e.g resistances and inductances) can be determined from no load (also known as synchronous) test and locked rotor (short circuit ) test. To find the resistance of an electric machine for single or multiple phase induction machine pose no problem, because it is a straight forward approach. However, in multiphase induction machine, with phase number greater than three, such approaches are not utterly sufficient to determine the inductances of the machine. An efficient and easily applicable, analytical approach for calculating the inductance of an asymmetrical multiple phase induction machine based on machine geometric information is presented. An algorithm translated to a Matlab script is developed to numerically evaluate the inductances of any multiphase induction machine using turn function and winding function approach is thoroughly established. Important conclusions regarding the processes to obtain the inductance are presented.
Evaluating interest rate term-structure using extensions of the Diebold and Li three factors model

This paper evaluates methods that employ Kalman Filter to estimate Diebold and Li (2006) extensions in a state-space representation, applying the Nelson and Siegel (1987) function as measure equation and different specifications for the transition equation that determines level, slope and curvature dynamics. The models that were analyzed have the following structures in transition equation: (1) AR(1) specification, employing a diagonal covariance matrix for the residuals; (2) VAR(1) specification, employing a covariance matrix calculated with Cholesky decomposition; (3) VAR(1) extension, inserting variables related to the Covered Interest Rate Parity (CIRP); (4) VAR(1) extension, including stochastic volatility components. The major findings of this paper were: (1) evaluating the latent variables dynamics, the curvature was the factor that fitted better to the stochastic volatility component; (2) in a broad sense, even though the simplest VAR(1) model was the one that provided the best out-of-sample performance in the most part of maturities and forecasting horizons, the extension inserting variables related to the CIRP was able to overcome the former specification in some of these simulations.
Examining Nutrition Knowledge, Skills, and Eating Behaviours in People with Severe Mental Illness: A Cross-Sectional Comparison among Psychiatric Inpatients, Outpatients, and Healthy Adults

Compared to the general population, people with severe mental illness (SMI) have an increased risk of weight gain and metabolic syndrome, but also of malnutrition, in part due to unhealthy lifestyle behaviours. The aim of this cross-sectional study was to identify barriers to healthy eating, including nutrition knowledge and skills in people with SMI. For this purpose, we compared the means of anthropometric data such as body mass index, waist-to-hip ratio, and interview data on nutrition knowledge and skills, health-related variables, eating behaviours, personality, motivation, and attitudes in 65 inpatients and 67 outpatients of the Psychiatric Hospital of the University of Zurich and 64 healthy adults using ANOVA and chi-squared tests. The results showed that patients with SMI had worse nutritional status and lifestyle compared to the healthy controls, including disordered (e.g., night eating) and unhealthy (e.g., high intake of sugary foods) eating habits. However, levels of nutrition knowledge, cooking and food skills, and motivation to eat healthily were not significantly lower in the psychiatric patients than in the healthy adults and were not associated with weight change. Based on our findings, nutritional support for people with SMI is urgently needed and should include not only educational but also behavioural and long-term approaches.
Image Classification Using Deep Learning Models for Use in Plant Seed Identification

Deep learning models have been applied to various different tasks of image classification for large sets of diverse images. This study aims to determine the significance of certain factors in the development of an optimal deep learning model purposed for plant seed identification. This study will involve a series of experiments conducted in a 2k factorial design, which would represent certain network design factors for the training of a state-of-the-art model based on convolutional neural networks. For this study, the chosen factors for consideration were the base model used for transfer learning, the optimizer function used for training, and the total epoch count used for training. The main objective of the model is the accurate classification of an image dataset composed of plant seed images belonging to multiple distinct plant types.
Identification of a Depressive State Among Users of the Vkontakte Social Network

The article examines the analysis of the depressive state of social network users. It is noted that the VKontakte social network will be used as a social platform for collecting information in the study. It is noted that a combination of vocabulary-based and machine learning methods is used to achieve the highest accuracy. Two methods based on vocabulary are considered: the dictionary-based method and the corpus method. The stages of analysis are considered: collecting data obtained using the VK_API script creation module, preprocessing data through the natural language processing pipeline (deleting raw data that does not carry a semantic role), creating a model and evaluating it. It is noted that the implementation of this task uses a high-level Python programming language with dynamic strict typing and automatic memory management, the syntax of which contains a natural language processing module (NLTK). The paper presents 4 machine learning classifiers: support vector machine (SVM), k—nearest neighbor method (KNN), random forest, logistic regression, LSTM. It is revealed that machine learning algorithms such as decision tree, support vector machine, logistic regression and LSTM demonstrate good accuracy in detecting the depressive mood of a social network user. The LSTM network showed the greatest accuracy during this experiment. In conclusion, the main conclusions on the work done are formulated.
Evaluation of Transgenerational Effects of Sublethal Imidacloprid and Diversity of Symbiotic Bacteria on Acyrthosiphon gossypii

Simple Summary Acyrthosiphon gossypii is a sap-sucking pest that causes direct cotton damage by sucking sap and indirect damage by honeydew contamination and induced fungal growth. Chemical pesticides are widely used to combat the damage caused by A. gossypii. However, the escalating use of pesticides has had a notable impact on the resurgence and resistance of pests. In addition, microbes that live in insects as symbionts have recently been found to protect their hosts against toxins. In this study, we determined the toxicity and sublethal effects of the neonicotinoid insecticide imidacloprid on A. gossypii and further determined the effects of imidacloprid on symbiotic bacteria. The sublethal concentrations of imidacloprid had a negative effect on the reproduction and longevity of the G0 generation of A. gossypii, increased the population growth parameters of the G1 and G2 generations, but not those of the G3 generation, and altered the abundance of symbiotic bacteria. Our results revealed the transgenerational and sublethal effects of imidacloprid on A. gossypii, which may have implications for the endosymbiont-insecticide of the major pest. Abstract Symbiotic bacteria and hormesis in aphids are the driving forces for pesticide resistance. However, the mechanism remains unclear. In this study, the effects of imidacloprid on the population growth parameters and symbiotic bacterial communities of three successive generations of Acyrthosiphon gossypii were investigated. The bioassay results showed that imidacloprid had high toxicity to A. gossypii with an LC50 of 1.46 mg·L−1. The fecundity and longevity of the G0 generation of A. gossypii decreased when exposed to the LC15 of imidacloprid. The net reproductive rate (R0), intrinsic rate of increase (rm), finite rate of increase (λ), and total reproductive rate (GRR) of G1 and G2 offspring were significantly increased, but those of the control and G3 offspring were not. In addition, sequencing data showed that the symbiotic bacteria of A. gossypii mainly belonged to Proteobacteria, with a relative abundance of 98.68%. The dominant genera of the symbiotic bacterial community were Buchnera and Arsenophonus. After treatment with the LC15 of imidacloprid, the diversity and species number of bacterial communities of A. gossypii decreased for G1–G3 and the abundance of Candidatus-Hamiltonella decreased, but Buchnera increased. These results provide insight into the resistance mechanism of insecticides and the stress adaptation between symbiotic bacteria and aphids.