Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Whole-Hand Interactions with One Fingertip Vibrotactile Feedback on Cooperative VR Game Experience and Performance

Abstract New technologies have recently advanced user experiences in virtual reality (VR), whereas full sensation of diverse modalities has been not achieved yet. If any, haptic feedback has been delivered via bulky gloves. We have developed a novel thimble device that can deliver vibrotactile feedback via one fingertip. With this device, in the present study we investigated the effects of interaction methods and vibrotactile feedback on users' social presence, presence, engagement, workload, and performance in a cooperative VR game. Twenty-six participants wearing a VR headset played a cooperative VR game with the experimenter under four conditions: (1) controllers with no vibrotactile feedback, (2) controllers with vibrotactile feedback, (3) hand tracking with no vibrotactile feedback, and (4) hand tracking with vibrotactile feedback. Results showed that hand tracking improved participants' presence, engagement, and perceived workload compared to the traditional VR controllers. Also, vibrotactile feedback enhanced presence. However, the VR controllers outperformed the hand tracking interactions in completion time. The usability of hand interactions with vibrotactile feedback shows a promising result. We discuss the trade-offs between user experience and performance of the interaction methods and the potential of vibrotactile feedback in the VR environment.
Antibacterial Resistance and Phenotypic Detection of Extended Spectrum Beta-Lactamase (ESBL) Producing Enterobacteriaceae Isolated from Environmental Sources in Nasarawa State, Nigeria

The multidrug resistant member of Enterobacteriaceae is common causes of community related infections. A study on antibacterial resistance and phenotypic detection of Extended Spectrum Beta-Lactamase (ESBL) producing Enterobacteriaceae isolated from environmental sources in Nasarawa state, Nigeria was carried out. A total of 400 samples comprised of air, water, soil, vegetable, and sewage were collected and 89 samples were multidrug resistant. Enterobacteriaceae were isolated and identified using commercial biochemical kit Eneterosystem 18R. The antibacterial susceptibility testing of Enterobacteriaceae isolates was carried out using the clinical and laboratory standards institute (CLSI). The phenotypic confirmation test of ESBL was carried out by double disc synergy test (DDST) method. The occurrence of multidrug resistant isolates shows that Escherichia coli (24.71%) and Klebsiella pneumoniae (19.10%) find to be high while the occurrence of Proteus mirabilis and Citrobacter freundii (6.74%) were low. The Enterobacteriaceae isolates were more resistant to Cefuroxime, Cefexime, Amoxicillin Clavulanate and Imipenem/Cilastatin with percentage resistance ranges from 62.5%–100%. The occurrence of ESBL producers shows that Klebsiella pneumoniae (66.7%) and Shigella flexneri (57.14%) were found to be high while the occurrence of ESBL was low in Proteus mirabilis (20.0%). The Beta Lactam and gentamycin antibiotics were not effective against the MDR isolates and most of the isolates were ESBL producers.
Comparison of Control Algorithms Using a Generalized Model for a Human with an Exoskeleton

This article presents a pictorial representation of a generalized model for a human interacting with an exoskeleton or a force-augmenting device. This model is used for comparing four different control schemes, which are: 1) Kazerooni's algorithm, 2) BLEEX’s algorithm, 3) technique inspired by fictitious gain, and 4) Force control with velocity and position feedback. The hardware and software requirements for the presented control algorithms are also discussed.
Unraveling educational networks: Data-driven exploration through multivariate regression, geographical clustering, and multidimensional scaling

Enhancing rates of school participation holds significant importance for a nation’s educational achievements. This research employs a comprehensive approach that combines various methodologies, including multivariate regression analysis, geographic categorization, and multidimentional visualization, to examine the factors influencing school enrollment in Indonesia. Through the integration of diverse data sources, we investigate the connections among variables such as economic status, school accessibility, educational quality, and societal considerations concerning enrollment rates. This discrete impact of each factor on enrollment variations is analyzed through multivariate regression. Geospatial clustering analysis reveals enrollment trends in different regions, while multidimensional visualization untangles the intricate interplay of influencing factors. This holistic approach facilitates a nuanced comprehension of these dynamics within Indonesia’s varied geographical and society offering guidance in the formulation of more efficient strategies to improve school attendance, tackle enrollment disparities, and advocate for inclusive education based on fundamental determinants.
Comprehensive analysis of an endoplasmic reticulum stress-related gene prediction model and immune infiltration in idiopathic pulmonary fibrosis

Background Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease. This study aimed to investigate the involvement of endoplasmic reticulum stress (ERS) in IPF and explore its correlation with immune infiltration. Methods ERS-related differentially expressed genes (ERSRDEGs) were identified by intersecting differentially expressed genes (DEGs) from three Gene Expression Omnibus datasets with ERS-related gene sets. Gene Set Variation Analysis and Gene Ontology were used to explore the potential biological mechanisms underlying ERS. A nomogram was developed using the risk signature derived from the ERSRDEGs to perform risk assessment. The diagnostic value of the risk signature was evaluated using receiver operating characteristics, calibration, and decision curve analyses. The ERS score of patients with IPF was measured using a single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. Subsequently, a prognostic model based on the ERS scores was established. The proportion of immune cell infiltration was assessed using the ssGSEA and CIBERSORT algorithms. Finally, the expression of ERSRDEGs was validated in vivo and in vitro via RT-qPCR. Results This study developed an 8-ERSRDEGs signature. Based on the expression of these genes, we constructed a diagnostic nomogram model in which agouti-related neuropeptide had a significantly greater impact on the model. The area under the curve values for the predictive value of the ERSRDEGs signature were 0.975 and 1.000 for GSE70866 and GSE110147, respectively. We developed a prognostic model based on the ERS scores of patients with IPF. Furthermore, we classified patients with IPF into two subtypes based on their signatures. The RT-qPCR validation results supported the reliability of most of our conclusions. Conclusion We developed and verified a risk model using eight ERSRDEGs. These eight genes can potentially affect the progression of IPF by regulating ERS and immune responses.
Post-Incident Netizen Engagement in Sports:

Abstract - The tragic football stampede in Kanjuruhan in October 2022 resulted in significant public concern and engagement, prompting a study to analyze online public sentiment and engagement in the aftermath of the event, focusing on the role of social media in shaping public perceptions and reputation of such incidents. The research employed a mixed-methods approach, including content and sentiment analysis of online news articles and social media posts. The findings contributed to the understanding of the impact of social media on public sentiment and engagement, as well as the need for further research on the effects of social media on public discourse, particularly in the context of tragic events. The study found that social media played a crucial role in shaping public perceptions, selling discussions, and elevating the popularity of such incidents. The analysis provided valuable insights into the implications of social media engagement and the need for further research to understand its broader impact and the influence of cultural and social factors on public responses to tragic events.
Average Length of Stay, Average Preventable Readmission Rates, and Average Total Cost of Care: Is there a Relationship?

Average Length of Stay (ALOS), Average Preventable Readmission Rates (APRRs), and Average Total Cost of Care (ATCc) were examined to see if any relationship exists among the variables. Secondary data from the Texas Hospital data collection database was used for the study. Out of 379 acute care hospitals in Texas, 65 hospitals were selected for analysis using a G*Power analysis. Demographic analysis, Spearman’s Rank correlation, and Regression Analyses were conducted to explore the relationship. The results of the Spearman’s Rho correlation showed a significant negative relationship between ALOS and ATCc [ALOS – ATCc (rs = - .271, p = .016)], and for APRRs and ATCc, there was no statistically significant relationship [APRRs – ATCc (rs = .065, p = .564)]. The multiple regression analysis showed there was no statistically significant relationship between the variables [ALOS – ARRs – ATCc (F (2,62) = 1.584, p = . 211)]. The results showed that ALOS and APRRs do not necessarily predict ATCc. Consequently, based on the study results ATCc is not solely determined by ALOS and APRRs. The absence of a significant relationship between ALOS, APRRs, and ATCc does not necessarily indicate inefficiency in practice or lack of effectiveness. The results showed that there are other mediating factors impacting care that were not assessed in the study that need to be examined carefully which may have implications for practice, research, and methodology. Future research should be considered to determine these moderating factors to better understand the complex relationship between ALOS, APRRs, and ATCc. An understanding of these complex dynamics may inform managerial strategies and help with critical clinical and fiscal decisions in a healthcare setting. Keywords: Average Length of Stay, Average Preventable Readmission Rates, Average Total Cost of care
Axial Flux Permanent Magnet Motor Topologies Magnetic Performance Comparison

Axial flux permanent magnet motors have attracted increasing attention due to their compact topology and high torque density. Many topological variations have arisen over time; however, limited research has directly compared the differences in magnetic performance of these topologies. This paper carries out a comprehensive investigation, employing both analytical and 3D finite element analyses, to compare the magnetic performance of three topologies: yokeless and segmented armature (YASA), axial flux internal rotor (AFIR), and offset AFIR. The findings reveal that each topology offers specific advantages for different applications. The YASA topology excels in minimizing core losses; the AFIR configuration achieves the highest torque density; and the offset AFIR topology shows the highest efficiency. The offset AFIR topology appears to offer advantages for a wide array of applications due to its higher power factor and lower permanent magnet loss, leading to reduced costs for converter design and cooling system design.
Resituating cultural landscapes : pan-European strategies for sustainable management.

Integrating stakeholders as the active creators and beneficiaries of cultural landscapes is an under-developed element of sustainable heritage research. Through work focusing on some of the most significant monuments in European history (Late Iron Age oppida: c.200BC-AD60), this paper will explore how communities (including farmers, small-medium enterprises (SMEs), wildlife organisations and residents) understand and experience cultural landscapes. Focusing on perceptions data from Bagendon and Salmonsbury, UK, these oppida sites represent the pilot phase of the EU funded REFIT project, and work on 2 further sites, Bibracte (France) and Ulaca (Spain), will follow. The perceptions studies will examine how existing understanding and use of these cultural landscapes can aid the integration of non-archaeological stakeholders into wider archaeological research and support the development of future cultural landscape management strategies, at a pan-European level, that offer greater mutual benefit to all those with a vested interest in cultural landscapes.
Molecular Mechanics of Liquid and Gas Slip Flow

By taking into account the inhomogeneity introduced by the presence of a solid boundary, slip-flow theory extends the range of applicability of the venerable Navier–Stokes description to smaller scales and into the regime where confinement starts to be important. Due to the inherently atomistic nature of solid–fluid interactions at their interface, slip flow can be described, at least in principle, predictively at this level. This review aims to summarize our current understanding of slip flow at the atomistic level in dilute gases and dense liquids. The discussion extends over the similarities and differences between slip in gases and liquids, characterization and measurement of slip by molecular simulation methods, models for predicting slip, and open questions requiring further investigation.
An Assessment of Factors Affecting Electronic Waste Management Within University Students’ Environment

This study aimed at assessing factors affecting the awareness of electronic waste management at among University students. The study was conducted at a University located in Dar Es Salaam-Tanzania; the case for study is not disclosed for security reasons. In particular, the study focuses on factors such as user awareness, budgets and policy and their link to e-waste management. The study was motivated by the fact that students in higher learning Institutions are faced by the challenge of e-waste accumulation, with a steady increase. The study adopted the mixed research approach. In total, the study used 377 respondents in addressing its objective. In addition, the study used probability sampling technique to obtain the sample for study. Further to this, the study cleaned data, and used descriptive statistics (frequencies and percent) in responding to its objectives. The findings revealed that the University student awareness of electronic waste management is low. Also, findings revealed that the University does not have budget allocated for electronic waste management, and there is the lack of policy at the University to provide students on awareness of electronic waste management. Students should be empowered with electronic wastes management knowledge.
Implementation of Xception and EfficientNetB3 for COVID-19 Detection on Chest X-Ray Image via Transfer Learning

COVID-19 is a highly contagious infectious disease caused by the SARS-CoV-2 virus that can cause respiratory issues. The utilization of X-ray imaging has the potential to serve as an alternative means of detecting COVID-19 by offering insights into the condition of the lungs. Rapid and automated analysis of medical images and patterns can be achieved through deep learning techniques. In this study, we propose methods for the automatic classification of COVID-19 from Chest X-Ray images using CNN with transfer learning techniques, namely Xception and EfficientNetB3 architectures, as well as an ensemble of both architectures working in parallel. Additionally, we use Grad-CAM to visualize the regions of the X-ray image that are most important for the classification decision. The classification of COVID-19 is carried out for four types of classes: COVID-19, normal, bacterial pneumonia, and viral pneumonia. The proposed classifier models achieve an overall accuracy of 94.44% for the Xception classifier, 95.28% for the EfficientNetB3 classifier, and 94.44% for the parallel classifier. The accuracy value is higher than the other comparison classifiers accuracy values. Overall, the proposed classifiers can be recommended as tools to assist radiologists and clinical practitioners in diagnosing and following up with COVID-19 cases.
UCG: A Universal Cross-Domain Generator for Transferable Adversarial Examples

Generating transferable adversarial examples is a challenging issue in adversarial attacks. Existing works on transferable adversarial examples generation mainly focus on models with similar architectures and trained on the same data domain. However, in practice, information such as the model architecture type and training data domain is unlikely to be revealed in deployed models. In this work, we introduce the Universal Cross-domain Generator (UCG), a pioneering framework for transferable adversarial examples that is the first to simultaneously address both cross-domain and cross-architecture challenges in adversarial attacks. The design of UCG is mainly inspired by two key observations. First, there exists some commonality in attention regions even when the structures of models are different. Second, there exists prevalent instability of intermediate-feature maps across cross-domain models. We accordingly design an attention transfer mechanism and a roughness abatement mechanism to enhance the cross-architecture and cross-domain transferability of the generated adversarial examples. Moreover, we propose an integrated transformation processing technique to improve the transferability of the generated adversarial examples under different transformations. Experimental results demonstrate that, compared with state-of-the-art solutions, UCG improves the average transferable attack success rate by 14.6%, 7.8%, and 7.9% in the cross-architecture task (convolutional neural networks (CNNs) to vision transformers (ViTs)), coarse-grained cross-domain tasks, and fine-grained cross-domain tasks, respectively.
DISCO AND RENDERINGS OF PRESENT PERCEPTIONS IN HIGHLAND PAPUA

This article draws on ethnographic and historical materials to interpret several related photographs and ritual images created by Fuyuge-speakers of highland Papua. The images are analysed as techniques of vision. That is, the images are shown to bring forth present perceptions in a timely manner, as evaluated by Fuyuge men and women. It is shown how the representations are informed by distinctive Fuyuge retentions of the past in the present, and extensions of the present in the future. The article suggests that the tendency to 'read' photographic images for their semiotic connotations misses the local per? ceptions at work in their creation and use. Rather, photographic and ritual images are analogous to the relations of persons that produce them, with their mutually connected
Calculation of the System Unavailability Measures of Component Importance Using the D2T2 Methodology of Fault Tree Analysis

A recent development in Fault Tree Analysis (FTA), known as Dynamic and Dependent Tree Theory (D2T2), accounts for dependencies between the basic events, making FTA more powerful. The method uses an integrated combination of Binary Decision Diagrams (BDDs), Stochastic Petri Nets (SPN) and Markov models. Current algorithms enable the prediction of the system failure probability and failure frequency. This paper proposes methods which extend the current capability of the D2T2 framework to calculate component importance measures. Birnbaum’s measure of importance, the Criticality measure of importance, the Risk Achievement Worth (RAW) measure of importance and the Risk Reduction Worth (RRW) measure of importance are considered. This adds a vital ability to the framework, enabling the influence that components have on system failure to be determined and the most effective means of improving system performance to be identified. The algorithms for calculating each measure of importance are described and demonstrated using a pressure vessel cooling system.
The Effect of Phrase Vector Embedding in Explainable Hierarchical Attention-Based Tamil Code-Mixed Hate Speech and Intent Detection

The substantial growth in social media users has led to a significant increase in code-mixed content on social media platforms. Millions of users on these platforms upload pictures and videos and post comments regarding their recent or exciting activities. Responding to this uploaded content, a few users occasionally use offensive language to insult others or specific groups. Social media platforms encounter challenges identifying and removing hate speech and objectionable content in various languages. Hate speech, in its general sense, refers to harmful posts directed at individuals or groups based on factors such as their sexuality, religion, community affiliation, disability, and others. Typically, offensive language is directly or indirectly utilized in hate speech posts to insult someone, causing psychological distress to users. In light of this, we propose developing a system to automatically block, remove, or report posts written in code-mixed Tamil containing hate speech. We have gathered code-mixed Tamil comments from Twitter and the Helo App, categorizing them as hate speech and classifying their intent. We have identified three categories of hate speech intent, namely Targeted Individual (TI), Targeted Group (TG), and Others (O). The Targeted Individual (TI) class encompasses posts aimed at a specific individual target. At the same time, the Targeted Group (TG) category primarily focuses on identifying people based on their religion, community, gender, and other characteristics. The Others (O) category encompasses untargeted offensive posts and other posts containing offensive language. In this context, we propose using a phrase-based, Explainable Hierarchical Attention model for hate speech detection. The results demonstrate that the proposed method is more effective in identifying and explaining hate speech and offensive language in social media posts.
Update on trends in sex ratio at Birth in India

With additional data available at more regular intervals on sex ratio at birth, it is easier to track changes without depending on the decennial censuses. However, the diversity of sources also means differences in trends across sources and regions. A brief analysis of the latest available data is presented to identify the most recent trends and concerns in states that need attention, like north-western and eastern states of India.