bluuebunny/crossref_metadata_2025_split huggingface dataset:
The optimal cut-points of alanine aminotransferase for screening metabolic syndrome in Iranian adults
Authors: Asgari Samaneh , Azizi Fereidoun , Hadaegh Farzad 
Published: June 2023

Abstract
Background
Studies reported that the activity of the alanine aminotransferase (ALT) enzyme becomes the main biomarker for screening liver cell damage such as non-alcoholic fatty liver disease (NAFLD). Since individuals with metabolic syndrome (Mets) are at high risk for NAFLD, we aimed to determine the gender-specific ALT thresholds for screening Mets in the Tehranian population.
Methods
We did a cross-sectional study (2018–2022) of 4968 adults aged 20–70 years (2732 female). Multivariable logistic regression analysis was done to assess the association between ALT level and the prevalence of Mets and each component. Moreover, the gender-specific ALT cut-off point was determined using the maximum Youden's index. The area under the receiver operating characteristics curves (AUC) was calculated to derived thresholds and compared to the previously introduced cut-off points from the American College of Gastroenterology (ACG; ALT > 19 U/L for females, > 29 U/L for males). We also examined the diagnostic performance of the derived cut-off points in the 11,147 individuals (7,154, women) from Atherosclerosis Risk in Communities (ARIC) study.
Results
The odds ratio (OR) of the logistic regression shows that each 5-U/L increase in ALT level was associated with prevalent Mets [19% for females and 8% for males] and its components (ranging from 7–19% in females and 3–10% in males; all p-value < 0.05). The suggested cut-off point for ALT among men was 21 U/L with a sensitivity of 72.1% and specificity of 47.1%; considering the threshold of 18 U/L the corresponding values were 57.9% and 66.5%, respectively. Compared to the ACG guideline, the AUC of our suggested threshold increased in males (60% vs. 56%, respectively) while for females it remained the same as in the pretest (≈ 62%). Using ARIC data, our suggested threshold showed almost the same AUC values compared to the ACG threshold among females (58% vs. 57%, respectively), while among males, the highest AUC was observed for our suggested cut-off points (56%), followed by ACG thresholds (53%).
Conclusion
The cut-off point for screening Mets among Iranian women was almost the same with the lower suggested threshold of American guidelines but remarkably lower for defined abnormal ALT levels in males.
Assessing Adherence to Infection Control Standards in Emergency Departments of Hospitals During the COVID-19 Pandemic
Authors: Alraimi Ammar , Al-Fadhli Abdulmajeed 
Published: June 2023

Abstract
This study evaluated the implementation of infection prevention standards in the emergency departments of selected hospitals. Data was collected from physicians and nurses using a questionnaire assessing multiple dimensions of infection control measures. Responses from 322 participants were statistically analyzed for validity and stability using Pearson’s coefficient and Cronbach’s alpha. Findings revealed that, while infection control standards were largely implemented across all dimensions, gaps were identified. Specifically, a lack of sterilization indicators, failure to isolate respiratory patients, and inadequate patient and family education were noted. Infection control practices require strengthening, especially isolation procedures. Study outcomes provide healthcare professionals and policymakers with evidence to further improve infection prevention controls and reduce healthcare-associated infection risks, especially during the spread of epidemics such as the COVID-19 epidemic. Enhancing measures in line with the study's recommendations can help create safer care environments in hospitals.
Learning with Albinism: Potential of Digital Technologies in Enhancing Learning Environment for Visually Impaired Pupils
Authors: Mwangakala Hilda , Manyengo Patrick Renatus , Mlaki Enedy 
Published: June 2023

Abstract
Pupils with albinism face significant challenges in accessing quality education due to visual impairment, lack of appropriate learning resources, assistive technologies, and supportive teaching strategies. Traditional classroom settings often fail to cater to their specific needs, leading to educational disparities and hindered academic progress. The study aimed to investigate the learning environment of pupils with Albinism (PA) in inclusive classrooms and the teaching strategies employed by teachers to support their education. The study utilized an exploratory qualitative research design to explore the learning challenges experienced by pupils with albinism at Buhangija Primary School in the Shinyanga region of Tanzania. A total of twenty (20) participants participated in the study, comprising seven (7) special needs teachers, eleven (11) STD VI pupils with albinism, one (1) Headteacher, and one (1) Academic mistress. Data was collected through face-to-face interviews and Focus Group Discussions (FGDs). The study's findings revealed significant challenges faced by pupils with albinism in the learning process. These challenges included the absence of assistive devices and large print books, difficulties reading from the chalkboard, inadequate teacher support, and stigmatization. Additionally, the overcrowded classrooms posed additional barriers for pupils with visual impairments, hindering their learning experience. However, the study identified several Information and Communication Technologies (ICTs) equipment, such as Digital Tablets, Optical Character Recognition (OCR), Screen reader, Braille translation software, Braille writing equipment, Closed-Circuit Television (CCTV), Braille embosser, Scanners, as practical assistive devices that could improve the learning environment for pupils with albinism. These devices have the potential to enhance their learning capabilities and facilitate their academic performance, bringing them on par with their non-albino peers..
Random variable matrix on graph neural networks to extract global spatial features
Authors: Kan Yuanliang , Hu Yao 
Published: June 2023

Abstract
Graph neural networks (GNN) is a method to extract spatial information between adjacent nodes. But it cannot extract spatial information between nodes that are far apart. In this paper, to address this issue, we assume that the spatial features between any two nodes are random variables that follow certain distributions, and the variances of the random variables increase as the expectations increase. And we set out the conditions that the random variable should satisfy. This approach has three benefits: 1) Being able to fit the spatial relationships of long-distance nodes. 2) Improving the robustness of the model. 3) Improvement on the most basic GNN model means that this method can be applied to any other GNN models. The experiments on real-world datasets demonstrate the superiority of the model.
An Optimal Feature Selection and Mrblstm-based Autonomous Vehicle Detection System in Challenging Weather Conditions
Authors: Vellaidurai Arthi , Rathinam Murugeswari 
Published: June 2023

Abstract
Recently, self-driving cars with automation procedures such as queuing aid in traffic jams, parking assistance, lane-keep guidance, and crash evasion have been initiated. These automated driving systems and sophisticated video traffic surveillance methods laboriously rely on cameras and sensor fusion technologies. The poor performance of AVs and driver assistance systems in adverse weather conditions (ADWC), which might include snow, fog, rain, and hail, is one of the most serious problems in the development of these technologies. In order to detect items like vehicles and pedestrians in difficult situations like bad weather, it is vital to design the methods to do so. This paper proposes a deep learning (DL) model, namely a Modified ReLU-based Bidirectional Long Short Term Memory (MRBLSTM) for autonomous vehicle detection (VD) in ADWC. It is mainly composed of six stages. Initially, the proposed system collects the data from the publicly available dataset for VD. After that, image restoration is done as a preprocessing step on the collected data using the Retinex algorithm. Next, the Modified Canny Edge Detection Operator (MCO) algorithm preserves the edges from the restored images with the help of a bilateral filter and Otsu’s approach. Then, using feature extraction models for HoG, HaaR-like features, LBP, and SIFT, the features are retrieved from the edge-detected images. The optimal features are selected from the extracted features to lower the dimensions of the features using the Cauchy mutation-included Coyotes Optimization Algorithm (CMCOA). Finally, the vehicles are detected using the MRBLSTM. The proposed strategy has experimented on the DAWN dataset, and testing outcomes have demonstrated the proposed technique's efficacy, which surpasses state-of-the-art VD systems under ADWC.
An Engaging User Experience Framework for Mobile Augmented Reality
Authors: Ramtohul Arvind , Khedo Kavi Kumar 
Published: June 2023

Abstract
This research proposes an Engaging User Experience (ENUX) framework which caters for the influential User Experience (UX) and User Engagement (UE) dimensions for a MAR system. The ENUX framework extends on the UX and UE theoretical groundings, the multimodal and multisensory techniques and performance metrics to effectively enhance the UX and UE in MAR systems. Designers and developers in the MAR field have focused on providing a usable system rather than concentrating on a user-centric system that may lead to an enhanced UX and UE. The recommendations from the ENUX framework adopt a user-centered perspective to ensure a high-quality MAR experience in various application areas, and therefore can demonstrate the following: point of engagement, period of engagement, maximum engagement and re-engagement. An initial MAR prototype for a cultural heritage site has been developed using the instrumental, cognitive and sensory dimensions from the ENUX framework. A field study of sixty-six users has evaluated the MAR prototype to carry out an impact analysis of the contribution of the ENUX framework. The findings have confirmed that the partial development of the ENUX framework have significantly improved the UX and UE in the MAR prototype and is a promising start to implement the overall ENUX framework. The framework lays a solid foundation for the assessment of the UX and UE factors in MAR systems, which has the potential to assist the MAR system developers to identify and improve the most UX and UE influential factors in their systems.
Antimicrobial evaluation of metal microneedles made by local electrodeposition-based additive manufacturing on metal-coated substrates
Authors: Sachan Gregory , Schürch Patrik , Koelmans Wabe W. , Hepp Edgar , Vanderwal Lyndsi , Stafslien Shane , Narayan Roger J. 
Published: June 2023

Abstract
Electrochemical-based additive manufacturing of metals has many potential uses for the manufacturing of medical devices with small-scale features. In this study, we examined the in vitro antimicrobial properties of metal microneedles made by local electrodeposition-based additive manufacturing called CERES (Exaddon AG, Switzerland) on metal substrates. Three-by-three arrays of copper microneedles were created on copper-coated silicon substrates. To understand the effect of a galvanic couple between gold microneedles and a copper substrate on the antimicrobial activity of the microneedle device, three-by-three arrays of copper microneedles were created on gold-coated silicon substrates. Scanning electron microscopy was used to understand the microstructure of the microneedles; the microneedles were shown to possess hollow bores and sharp tips. X-ray photoelectron spectroscopy indicated the presence of copper, carbon, oxygen, silicon, and nitrogen as well as the absence of toxic impurities for the copper microneedles on copper-coated silicon substrates. X-ray photoelectron spectroscopy indicated the presence of copper, carbon, oxygen, copper, gold, and silicon as well as the absence of toxic impurities for the copper microneedles on gold-coated silicon substrates. In vitro cell colonization studies involving the Gram-positive bacterium Staphylococcus epidermidis, the Gram-negative bacterium Escherichia coli, and the opportunistic fungal pathogen Candida albicans at two hour and twenty four hour colonization at 37oC showed generally stronger activity for copper microneedles on copper-coated silicon substrates than for copper microneedles on gold-coated silicon substrates and uncoated silicon substrates. The copper microneedles on gold-coated silicon substrates showed stronger antimicrobial activity than uncoated silicon substrates except for twenty four hour colonization with Escherichia coli. The results of this study show potential strategies for creating antimicrobial microneedles for medical applications via local electrodeposition-based additive manufacturing.
Sentimentally attached to hometown: CEO hometown identity and corporate environmental violations
Authors: 周 志芳 , 杨 卓轩 , 刘 金浩 , 他 彭伟 , 朱 彭 
Published: June 2023

AbstractCEO hometown identity has received high attention from both academic and practical circles. However, scholars must learn more about the impact of CEO hometown identity on corporate environmental behavior. In this study, Shanghai and Shenzhen A-share listed companies in heavily polluting industries in China from 2010 to 2019 are taken as a sample to empirically test the impact of CEO hometown identity on corporate environmental violations and the regulatory role of financing constraints. The research results show that CEO hometown identity reduces corporate environmental violations, but financing constraints weaken this impact. It is also found that the conclusion remains valid when the enterprise is in a region with external solid environmental supervision or an industry with fierce market competition. Even in the face of higher financing constraints, CEO hometown identity can effectively reduce the possibility of corporate environmental violations. This study enriches the theory of place attachment, contributes to the theoretical research and management practice of CEO hometown identity and corporate environmental behavior, and provides essential references for improving corporate environmental performance and promoting the construction of ecological civilization.
Multi-Class Pesticide Residues and Their Associated Dietary Health Risks from Tomato and Aubergine/Eggplant; the Highly Consumed Vegetables in Pakistan
Authors: BIBI AASMA , ANJUM FARIDA , HUSSAIN SYED ROOHUL , AHAD ABDUL , DEEN AZEEM UD , SULEMAN MUHAMMAD , JAN BILAL 
Published: June 2023

Abstract
An analytical method was optimized and validated for the routine analysis of eighteen commonly used and widely reported pesticides in Tomato and Aubergine/Eggplant. Acetate-buffered QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) extraction with PSA (primary secondary amines) cleanup through dispersive SPE (solid phase extraction) was employed. The analytical method was validated at three spiking levels (0.05, 0.01, 0.005mg/Kg) using GC-µECD (gas chromatograph-micro electron capture detector). GC-MSD (gas chromatograph-mass spectrometric detector) was used for both confirmation and quantification using SIM (selective ion monitoring) mode. The LOD and LOQ (limit of detection and quantification) for the studied compounds ranged from 0.0025 to 0.01mg/Kg. The recoveries ranged from 72 – 129% and 70 - 122% with an associated precision (%RSD) of -30 – 23% and -27 – 30%, in Tomato and Aubergine/Eggplant, respectively. The method was applied to fresh samples of Tomato (n = 33) and Aubergine/Eggplant (n = 27) collected from local markets of Khyber Pakhtunkhwa, Pakistan. Over 76% of the Tomato and 56% of the Aubergine/Eggplant samples were found positive. The chronic and acute health risk assessments have shown that both of these vegetables are unlikely to pose any unacceptable health threat to their consumers.
Introducing the Double Validation Metric for Radar Sensor Models
Authors: Elster Lukas , Rosenberger Philipp , Holder Martin , Mori Ken , Staab Jan , Peters Steven 
Published: June 2023

Abstract

In automated vehicles, environment perception is performed by various sensor types, such as cameras, radars, lidars, and ultrasonics. Simulation models of these, sensors as required in virtual validation methods, are available in various degrees of detail. However, proving the validity of such models is a subject of research. New metrics and methods for credibility assessment of simulation are needed to standardize the validation process in the future. The so-called Double Validation Metric (DVM) has shown advantages and allows an intuitive interpretability of the validation results. The DVM has so far only been applied to lidar sensor models. In this paper, an extension to the DVM is introduced, which is called the DVM Map. A static measurement scenario is conducted in reality and transferred into simulation. The novel method is demonstrated on the obtained real and simulated radar sensor data. In this simple scenario special focus is put on the position accuracy of GNSS reference sensors. Therefore, their impact on the result of sensor model validation is discussed. The paper shows that the method provides a more detailed and accurate validation of a radar simulation, revealing previously undetected simulation errors. Errors due to the environment model, signal propagation, and signal processing are separated and satellite imagery is used for intuitive visualization of the results. This method is a complementary tool to existing validation techniques to improve the interpretability and judging the trustworthiness of radar simulations.
The Psychological Benefits of Forest Bathing in Individuals with Fibromyalgia and Chronic Fatigue Syndrome/ Myalgic Encephalomyelitis: A Pilot Study
Authors: Serrat Mayte , Royuela-Colomer Estíbaliz , Alonso-Marsol Sandra , Ferrés Sònia , Auer William , Muro Anna , Nieto Ruben , Feliu-Soler Albert 
Published: June 2023

Abstract
Background/Objectives.
The main objective of the present study is to assess the short-term effects of Forest Bathing (FB) conducted in a Mediterranean forest on individuals with fibromyalgia (FM) and/or Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME) on perceived pain, fatigue, state anxiety, positive and negative affect, mood states, and state mindfulness. Methods.
A total of 44 participants with FM and/or CSF/ME agreed to participate in this study. The FB session consisted of a 3km silent walk, lasting three hours long and guided by a specialized psychologist and a mountain guide to guarantee the safety of the activity. Paired-sample t-tests were used to analyze the pre-post changes in perceived pain, fatigue, state anxiety, positive and negative affect, mood states and mindfulness.
Results.
All reported variables but self-reported pain showed statistically significant pre-post variations after the FB session. Particularly, large-to-very-large improvements in positive and negative affect, state anxiety, tension, depression, anger, and vigor were found. Small-to-moderate effect sizes for fatigue, friendliness and state mindfulness were also reported. Conclusions.
This study provides preliminary evidence of the short-term benefits of FB in individuals with FM and/or CFS/ME, especially on state anxiety and negative affect.
Are refugees better together? a study on the influence of ethnic clusters and dispersal policy on refugee integration
Authors: Adee Md Iltemas Amin , Lee Grace Hooi Yean , Rahman Habib 
Published: June 2023

Abstract
This paper investigated whether ethnic clusters play a role in refugee integration across different races. There are two hypotheses for this study, first, refugees in districts with higher ethnic clusters have better refugee integration, and second, therefore, the dispersal policy is expected to harm refugee integration. I analyzed individual-level data from the Socio-Economic Panel (SOEP), a wide-ranging longitudinal study of private households in Germany, and district-level demographic and economic data from the German statistics department. In this paper, both perspectives of random or non-random refugee allocation are considered while investing the influence of ethnic clusters on refugee integration. A quasi-experimental approach, difference in differences (DID) analysis, is used as the benchmark model to study the causal effect of the refugee influx. In addition, as a robustness check, propensity score matching in the difference-in-differences setup is used to overcome the non-random factor of refugee allocation issued by certain scholars. The results showed that Arabs are better at economically and socially integrating into areas with higher refugee concentration, whereas Asians are worse off. However, ethnic clusters showed a negative relationship but had no causal impact on integration for African and European refugees.
Optical mode division multiplexing inspired photonic neural network accelerator
Authors: Yin Ruoyu , Xiao Huifu , Jiang Yongheng , Han Xu , Zhang Pu , Zhou Xudong , Yuan Mingrui , Ren Guanghui , Mitchell Arnan , Tian Yonghui 
Published: June 2023

Abstract

On-chip photonic neural network (PNN) is emerging as an attractive solution for artificial neural networks due to its high computing density, low energy consumption, and compact size. Matrix-vector multiplication (MVM) plays a key role in on-chip PNN, which can achieve high-speed multiply-accumulate operation. Most of the current schemes implement MVM by adopting wavelength division multiplexing technology to accumulate the power of different wavelengths together, resulting in using a mass of laser sources. Additionally, real-number-field MVM is inevitable for realizing precise PNNs, while limited by the nature of light, effective solutions to perform negative value computing are still inadequate. Here, we propose and demonstrate a PNN accelerator based on wavelength and mode hybrid multiplexing technology to reduce the use of multi-wavelength lasers, which can satisfactorily play real-number-field computing (including positive and negative domain) based on a newly presented transformation mapping approach, avoiding the demanding experimental setup and the sacrificing weight modulation depth. As a proof-of-concept, a fabricated accelerator for image convolution and letter pattern detection has been demonstrated successfully, achieving a computing density of 1.37 TOPS/mm2 under the 22.38 Gbaud modulation rate.
A Comparison of Two Recent Approaches, Exploiting Pipelined FFT and Memory-Based FHT Architectures, for Resource-Efficient Parallel Computation of Real-Data DFT
Authors: Jones Keith John 
Published: June 2023

Abstract
This paper provides a comparison and assessment of both the performance and the capabilities of two recently developed approaches to the problem of computing the realdata DFT. The approaches exploit pipelined FFT and memorybased FHT architectures and aim to produce resourceefficient parallel solutions as required for use in resource and power constrained environments. The FFTbased solutions involve multiPE pipelined designs, geared to streaming (or serial) operation, that exploit the conjugate symmetric nature of the realdata DFT spectrum. The FHTbased solutions, which are suitably optimized versions of the regularized FHT, are geared to batch (or block-based) operation and involve a memorybased single-PE design that exploits partitioned memory in order to achieve eightfold parallelism within the PE. After outlining the performance objectives of each approach the study highlights the key properties and relative advantages/disadvantages of each, showing how the arithmetic complexity may be traded off against the memory requirement in order to optimize the use of the available silicon resources on the target computing device and to meet the appropriate timing objectives or constraints. A number of additional design issues not addressed with recent real-data FFT research – in particular, those relating to design simplicity, regularity and scalability – are also discussed which enable a more comprehensive assessment of a solution’s capabilities.