bluuebunny/crossref_metadata_2025 huggingface dataset:
Adoption Dynamics of Digital Payments: An Urban Case Study on E-Money Using the Technology Acceptance Model
Authors: Utomo Rio Guntur , Yasirandi Rahmat , Suwastika Novian Anggis
Published: October 2024

The utilization of e-money in Indonesia has surged, propelled by the expansion of digital payment platforms. Despite their growing prevalence, the dynamics of e-money acceptance within urban environments remain underexplored. This study innovatively extends the Technology Acceptance Model (TAM) by incorporating Perceived Security as a new variable, alongside traditional factors such as Perceived Usefulness, Perceived Ease of Use, Attitude Toward Using, Behavioral Intention to Use, and Facilitating Condition. The research focuses on Padang City, a representative urban landscape, where data was collected from 201 valid respondents through online platforms. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The integration of Perceived Security is a novel aspect of this study, reflecting its crucial role in the contemporary urban context of e-money utilization. Results reveal significant relationships among the studied variables, although the impacts of Facilitating Condition on Perceived Ease of Use and Perceived Usefulness on Behavioral Intention to Use were not supported. The findings underscore that positive attitude toward e-money significantly boost behavioral intentions to use it, primarily influenced by security perceptions and ease of use. These insights have substantial implications for policymakers and businesses focused on enhancing e-money adoption in urban settings. The study highlights the necessity of addressing user perceptions, particularly security, to foster broader acceptance. The limited influence of Facilitating Conditions suggests that improvements in infrastructure must be coupled with efforts to enhance user trust and ease of interaction with e-money platforms. This research contributes to the field by providing a deeper understanding of the factors driving e-money acceptance in urban areas, guiding targeted strategies for digital financial inclusion.
MRI-Based Brain Tumor Classification using ResNet-50 and Optimized Softmax Regression
Authors: Musa Muhammad Nazeer
Published: October 2024

Accurate classification of brain tumors is crucial for effective treatment planning and patient management. This study presents a new hybrid deep learning classification method based on transfer learning by feature extraction to automate the categorization of MRI brain image datasets into four classes: meningioma, glioma, pituitary tumor, and no tumor. The proposed method combines a finely-tuned ResNet-50 model, a state-of-the-art convolutional neural network architecture, with optimized Softmax Regression (SR) for classification. The study explores the use of data augmentation techniques and evaluates the model's performance on both augmented and unaugmented images. The results demonstrate that the proposed method achieves an impressive accuracy of 98.4%, outperforming existing methods for automatic brain tumor detection. Furthermore, a detailed comparative analysis is presented to evaluate the proposed model's accuracy and efficiency against previous state-of-the-art hybrid models for brain tumor classification. The study suggests that the proposed methodology could be employed as a diagnostic tool to aid radiologists in identifying questionable brain regions, potentially improving the accuracy and efficiency of brain tumor diagnosis.
Erratum for “Study on the Prototype of Platform Tools and Practice Related to the ‘Sustainability Scoring Index’ for Disaster Resilience of Local Governments in Japan” (Vol.19, pp. 631-644, 2024)
Authors: Fujisawa Aoba , Ando Koko , Masuda Yukihiro , Fujita Hiroyuki , Araki Michio , Tanaka Kazuaki
Published: October 2024

Due to an authors’ error, the original version of this article contained error in the in-text citations for tables in Section 4.1. The referenced table number should be corrected as follows:

Now reads

The self-assessment by the officials in charge of disaster management in the local governments was based on the Sustainability Scoring Index that was formulated by the authors for this study (Table 3).

Should read

The self-assessment by the officials in charge of disaster management in the local governments was based on the Sustainability Scoring Index that was formulated by the authors for this study (Table 2).

The authors regret this error, and this error has now been corrected in the PDF version of the article.
Internet of things infrared imaging device for assessing lameness in racehorses
Authors: Rizanov Stefan , Yakimov Peter
Published: September 2024

Lameness is a clinical condition that heavily affects racehorses. As a result, their competitive performance is dramatically deteriorated and side effects such as changes in feeding patterns may manifest. Within this work we have presented a developed portable Internet of Things (IoT) infrared imaging device, whose application is aimed towards on-site farm level detection of lameness in equine. Additionally we have presented an in-debt review of the causes of lameness, the underlying biological changes that it induces, its influence over tissue thermal patterns and how exercise affects a racehorse's temperature profile.
Automating flowmeter calibration process: Digital measurements from numerical displays using open-source optical character recognition tools
Authors: Esteves Coelho Gustavo , Pinheiro Alexandre , Silva Ribeiro Álvaro , Simões Catarina , Lages Martins Luís
Published: September 2024

This paper presents a methodology for obtaining digital machine-readable measurements from numerical displays images. The proposed method provides means to digitalize an automate a previously manual and labour-intensive laboratory procedure for flowmeters calibration. The proposed method allows to obtain machine-readable readings from remote numerical displays with available-off-the-shelf hardware and open-source software. By using smartphones for remote image capture and streaming and the Tesseract open-source OCR engine, is possible to leverage the infrastructure’s digital transition, improve procedures efficiency and effectiveness while promoting sustainable actions with cost reductions.
Thrust ball bearing fault identification from visualized data using optimized deep network
Authors: Evanchalin S. Mary , Milton T. , Tamil Selvi F. M. , Chaudhari Anagha , Mogra Ashish
Published: September 2024

Bearings are the most common components employed in machine parts. The bearings' movement facilitates the smooth motion. They also help with friction reduction. The faults in bearings are often caused by tribological parameters. Various methods have been developed to identify faults in bearings, but they often fail to predict these accurately. This work has concentrated on designing an effective fault-recognizing model. Therefore, a framework, the Zebra-based Radial Basis Prediction Mechanism (ZbRBPM), was proposed in this work. Initially, the bearing datasets are collected, and mineral oil (MO) lubrication is added to minimize wear and friction. The primary goal of the work is to detect and classify the fault in the thrust ball bearing. The bearing vibration data included a normal vibration signal, a ball fault, and inner and outer race faults. Hence, Zebra fitness is enhanced for the optimization of tribological parameters and fault identification. The proposed model is executed in the MATLAB system. Finally, performance criteria like accuracy, precision, recall, F-score, error rate, computation time, speed, specific wear rate, friction torque, and energy consumption are validated. The performance of the proposed ZbRBPM gains better accuracy rate as 99.5 %, 99 % of precision and f-score and 99.4 % of recall. Also it significantly reduced the prediction error rate into 0.5 % with lower computation time and very low wear and friction.
Staffing of anaesthesiology and resuscitation services in obstetric hospitals the Russian Federation
Authors: Guseva E. V. , Filippov O. S. , Ketskalo M. V. , Ganieva A. R. , Kulikova E. V.
Published: September 2024

Introduction. One of the strategies for reducing maternal mortality is to improve the provision of medical care to pregnant women, women in labor and maternity hospitals in need of intensive care. Sufficient provision of anesthesiology and intensive care units of maternity institutions with highly qualified personnel is the most important factor determining the availability and quality of medical care for women.

   The purpose of the study: to analyse the staffing of the anaesthesiology and resuscitation services of obstetric hospitals in the Russian Federation.

   Materials and methods. Based on the data of 83 subjects of the Russian Federation, the indicators of staffing (per 10,000 women of fertile age) with anaesthesiologists-resuscitators (for 2007–2022) and nurses anaesthesiologists (for 2021–2022), staffing levels and compatibility ratios of anaesthesiologist-resuscitators and nurses anaesthesiologists in obstetric hospitals of group II and III (level) (for 2022) in the Russian Federation, federal districts and subjects of the Russian Federation were analysed. Based on data from 29 subjects of the Russian Federation (for 2021), the average age of anaesthesiologists-resuscitators and nurses anaesthesiologists working in obstetric hospitals was analysed.

   Results. In 2007–2022, the number of anaesthesiologists-resuscitators in obstetric hospitals of the Russian Federation increased by 27.2 %, and the availability of anaesthesiologists-resuscitators (per 10,000 women of fertile age) increased by 43.9 %. The number of nurse anaesthesiologists in obstetric hospitals in 2021–2022 increased by 0.8 %, the availability of nurse anaesthetists (per 10,000 women of fertile age) decreased by 0.8 %. In 2022, the staffing level of anaesthesiologists-resuscitators in obstetric hospitals of Group II was 85.6 %, Group III – 86.0 %, and nurses anaesthesiologists – 87.8 % and 88.4%, respectively. The compatibility rate of anaesthesiologists-resuscitators in obstetric hospitals of group II was 1.42, group III – 1.53, and nurse anaesthesiologists – 1.23 and 1.34 respectively (2022). The average age of anaesthesiologists-resuscitators working in obstetric hospitals was 43.3 ± 4.3 years, nurse anaesthesiologists – 42.1 ± 3.9 years (2021).

   Conclusion. In order to eliminate staff shortages, it is necessary to implement measures to attract medical staff in the anaesthesiology and resuscitation services of obstetric institutions, which will decrease the burden on medical staff and prevent maternal deaths.
Assessment of “Knowledge, attitudes and practice” of the population of the Republic of Tajikistan regarding COVID-19
Authors: Muhsinzoda G. M. , Ruziev M. M. , Mirzoaliev Y. Y. , Jafarov N. J. , Tursunov R. A.
Published: September 2024

Introduction. Despite the high level of vaccination, some groups of the population still lack reliable information about vaccines and resources with which to familiarize themselves with it. Social and behavioral changes are a key element of an effective response to COVID-19, such as increasing the level of knowledge of the population through the dissemination of information and educational materials on vaccination of the population against coronavirus infection.

   The purpose of the study: to assess the level of knowledge about COVID-19, behavioral practices, as well as attitudes towards vaccination and other ongoing anti-epidemic measures among the population of the Republic of Tajikistan.

   Materials and methods. The study was conducted by the NGO “Subhi Tandurusti” from February to May 2024. To assess the level of respondents in the Knowledge, Attitudes and Practices survey regarding issues related to COVID-19, a questionnaire survey was conducted using a specially designed questionnaire. The study involved 2,500 respondents from 15 different districts and cities of the republic.

   Results. About 93 % of respondents (n = 2322) correctly answered this question regarding modes of transmission of the virus, indicating a high level of awareness of modes of transmission of COVID-19. Relatively high rates of correct answers were recorded to the question “Is it possible to get sick with COVID-19 several times?” 69.5 % of respondents (n = 1737) answered this question correctly, indicating an understanding that it is possible to get sick with COVID-19 several times. Attitude of respondents regarding the safety and effectiveness of vaccination: 78.3 % of respondents (n = 1957) expressed confidence in its safety, 11.3 % (n = 282) did not know the answer, and 10.4 % (n = 261) did not consider the vaccine safe. 2,430 people (97.20 %) have already received the full course of vaccination against COVID-19, 42 people (1.68 %) have only received the first dose of the vaccine and are planning to receive the second. The most popular source of information about COVID-19 was television programs – 44.0 % of respondents (n = 1101), followed by information materials (25.2 %, n = 631) and the Internet, including websites and social networks (22.8 %, n = 571). At the same time, 75.6 % (n = 1889) of respondents rated information and educational materials about COVID-19 as very useful and reliable. To the question “Have you been tested?”: 639 people (25.6 %) “never” got tested, 588 (23.5 %) “often” and 461 (18.4 %) “always” got tested.

   Conclusion. The results of the study showed that the interviewees had sufficient knowledge about COVID-19, including the transmission of the virus through various routes and clinical symptoms of the disease, as well as the importance of vaccination. Public health initiatives help better protect public health during emergencies such as infectious disease pandemics.
The Evolution of the Land Drilling Rig
Authors: Wright Blake
Published: October 2024

_

The earliest land rigs used materials like bamboo and iron and were powered by animals such as horses or oxen. These rigs used percussive drilling to create the bore, repeatedly raising and dropping heavy iron bits—effective but slow. By the time Edwin Drake was drilling the Titusville well in 1859, steam engines had been introduced to help power now metal pipe down into the Earth’s crust. By the 1950s, steam-powered rigs gave way to diesel engines. The 1960s would see the advent of self-elevating rigs—the transition from derricks to mast. During the 1970s, electrical power using on-site generators was introduced to drive systems on the rig, and the advent of new downhole tools would allow drillers to directionally drill while maintaining rotation. In the mid-1980s, the first steerable drilling system was introduced.

Soon, the evolution would move from the drill floor and downhole into the doghouse (driller’s cabin). Gone were the mechanical hand brakes and foot pedal systems, replaced by touchscreens and joysticks. The land rig has come a long way.

Hoisting capability and rig-up requirements were key drivers in the design of older units rather than an ability to store materials and move. Older rig-floor configurations were modified to accommodate pipe-handling equipment instead of designed to integrate pipe-handling equipment. These units were not driven by location sizes nor multiwell pad capabilities.

Most of this would change with the coming of the new millennia, and by 2010, it would seem like new innovations would be integrated into land rig design every year—from larger variable skid systems allowing for pad and batch drilling to app-based automation driving efficiency and safety profile.

Topdrives? Onshore?

Most of what drove innovation in land rigs can be traced back to development in the offshore sector. Much of what would become standard technology on land had its roots offshore, starting with one of the most important technology transfers of the 1990s—topdrives.
Technology Focus: Artificial Lift (October 2024)
Authors: Romer Michael
Published: October 2024

Summer blockbuster season has passed, but it’s always nice to grab a bucket of popcorn and escape reality with a movie. Some movies even teach lessons—as in, sometimes it takes hard work and incremental improvements to advance. Chugging raw eggs, sprinting steps, and boxing meat slabs in epic training montages can be effective (Rocky), but so can optimization with computational flow dynamics. Paper IPTC 24243 shows that redesign of a jet pump sliding-sleeve coupling can considerably reduce pressure losses and increase throughput.

The scientific method can even be exciting. Who doesn’t enjoy hypothesizing, experimenting, measuring, cleaning records, analyzing, and scaling up—especially if it pays off with a huge computer-generated explosion in the desert (Oppenheimer)? Paper SPE 218124 demonstrates the benefits of digitizing and analyzing operational data from electrical submersible pumps (ESPs) to improve run life and profitability. Paper OTC 34898 describes a comprehensive testing program to qualify load-bearing ESP deployment cable for corrosive conditions.

Sometimes an overly precise plan draws us to the edge of our seats. The protagonist has recruited a crew of experts, rehearsed obsessively, ensured that every loose end has been tied, and yet some unknown still (temporarily) derails the vault raid (Ocean’s Eleven). Paper SPE 218028 attempts to bypass chance and minimize production interference by orchestrating a network of intermittently gas lifted wells.

Is physics limiting what you can do? Then change the rules. You could move existence into a computer simulation (The Matrix) or perhaps start over with a novel material. Paper IPTC 23949 introduces fiber-reinforced thermoplastic sucker rods to accomplish more with less.

Who doesn’t enjoy a quality sequel? There must be an infinite number of things that can be jumped with a car (The Fast and the Furious). Paper SPE 215010 analyzes failures of gas lift valves retrieved from subsea wells and could be considered a follow-up to paper OTC 30943, an artificial lift paper featured in 2021.

Closure also can satisfy, even if it’s just the final act for a high school chemistry teacher with a side gig (Breaking Bad). With that in mind, it’s been an honor and a pleasure to pen the Artificial Lift Technology Focus page these past few years. I look forward to seeing who the directors cast next for this character.

Recommended additional reading at OnePetro: www.onepetro.org.

SPE 218124 Digitalizing the Management of Electric Submersible Pump Failures Through Failure Prevention and Post-Mortem Analysis Tools by Michael Ellsworth, Suncor Energy, et al.

SPE 218028 Optimization of Gas-Injection Network Using Genetic Algorithm: A Solution for Intermittent Gas Lift Wells by Ankit Garg, Oil and Natural Gas Corporation, et al.

IPTC 24243 Increasing Oil Production With Jet Pumps by Reducing Pressure Losses in the Jet

Pump/Sliding Sleeve Coupling by J. Soria, Sertecpet, et al.
Technology Focus: Data Analytics (October 2024)
Authors: Yang Junjie
Published: October 2024

In recent years, advancements in artificial intelligence, particularly the revolutionary applications of deep-learning-based computer vision and large language models, have significantly affected the oil and gas industry. These technological developments have underscored the growing importance and diversity of data analytics across various aspects of the industry’s daily operations.

Maintaining this momentum, data analytics practitioners in the industry have reexamined existing workflows and realized the substantial benefits that artificial intelligence brings, including increased efficiency and expedited turnaround times.

Organizations have allocated resources to develop a big-data-driven, multidisciplinary platform that leverages artificial intelligence and machine learning to enhance rapid and robust decision-making in asset management and risk mitigation. Integrated data-management platforms use both proprietary data and public domain data to streamline and automate processes that otherwise could be tedious and time-consuming.

Successful case studies have been shared in the literature on topics such as candidate screening and production forecasting. A prominent area of focus garnering extensive attention is the integration of artificial intelligence and domain knowledge, exemplified by physics-informed machine learning.

Several innovative workflows using proxy or surrogate models in conjunction with machine learning and deep learning have been published. These workflows demonstrate promising test cases that extract valuable insights from physics-based modeling results and provide production forecasts through a statistical approach. Undoubtedly, we can anticipate a surge in research and publications on this topic in the coming years, which will contribute significantly to reshaping our industry.

As advancements in generative artificial intelligence and big data continue to accelerate, swiftly adapting and pivoting toward digital transformation will be essential in the emerging digital era.

Recommended additional reading at OnePetro: www.onepetro.org.

SPE 217665 Well Construction in the Era of Big Data: It’s Not Data Analytics, It’s Engineering With Data by G.S. Payette, ExxonMobil, et al.

IPTC 24126 Deep-Learning-Based Long Short-Term Memory for Production Data Analysis of Hydraulically Fractured Wells by Fengyuan Zhang, China University of Petroleum-Beijing, et al.

URTeC 3857064 Application of Data Analytics to Chemometric Analysis of Conventionally Produced Oil Samples From the Delaware Basin by Timothy Carpp, Texas A&M University, et al.
Forecasting extremes of football players' performance in matches.
Authors: Nowak Michal , Bok Bartosz , Wilczek Artur , Kamola Mariusz , Oleksy Łukasz
Published: October 2024

Based on training history, accurate predictive modelling of an athlete's performance in competition can be a cornerstone for prior optimal planning of exercise mix and intensity. While universal in many sports, such a goal is challenging in football due to the complexity of factors leading to the final score and the complexity of the preceding training process. We developed and tested a range of models, the best of which were to forecast selected play performance indices with an accuracy of 10-20%. Such score applies to models run on raw player location data and aggregating performance indices developed with expert knowledge in the football training domain. Results show that individual player models perform better than collective ones and that more recent training data are better predictors. While we consider the accuracy of the models still of limited reliability, their transparency and present quality make them useful in the daily planning of training activities that impact player performance in the coming match. Additionally, observations of training parameters generated in short-term intervals are more effective and correlated with extreme match results than long-term dates. Specific training parameters may be key in predicting exceptional football player performance, but they may also vary from person to person.
Microbial colonization effects on drag in suspended sediment transport
Authors: Xu Fan , Zhang Naiyu , Li Haochen , Thompson Charlotte , Townend Ian , He Qing
Published: October 2024

The dynamic interplay between microbial communities and sediment transport shapes continental landscapes and influences particulate matter fluxes across the Earth’s surface. Microbial colonization, with its ubiquitous presence, transforms individual sediment grains into aggregates with intricate and varied morphologies, making sediment transport prediction difficult. Consequently, current models often simplify this morphological complexity, assuming that aggregates experience fluid drag equal to that of smooth spheres or idealised shapes. To test this hypothesis, we applied a novel microscopic methodology, enabling a high-resolution investigation of drag across diverse aggregates. In contrast to the prevailing paradigm that recognizes the aggregate size or gross shape as the primary controls, we discovered a critical yet previously overlooked mechanism: microbial colonization significantly alters the fine-scale aggregate morphology, increasing drag by factors of 1–4 compared with smooth surfaces. We propose a morphology-corrected drag law that accounts for this complexity, reconciling the differences in drag across diverse aggregates. Our findings shift the focus from size or gross shape variability to fine-scale morphologies, enabling greater accuracy in transport predictions, promoting new measurement strategies, and marking a transformative step in conceptualizing microbially colonized aggregates in fluvial, coastal and oceanic systems.
Educational Opportunities and Barriers to Learning During Post-Take Ward Rounds. A Systematic Review.
Authors: Babiker Alwathig , Bello Jibril
Published: October 2024

Background: Bedside teaching is essential to medical education and is considered an effective method for teaching technical and nontechnical skills. The post take ward round (PTWR) is often the first review of patients’ presentations, diagnoses, and management plans and serves as a teaching forum. Although PTWRs are vital in inpatient care, their educational values remained under-researched. This review aims to identify educational opportunities and barriers to learning during PTWRs.
Methods: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search was conducted in bibliographic databases, PubMed, Medline, the Cochrane Library, Scopus Web of Science, the Cumulative Index to the Nursing and Allied Health Literature (CINAHL), Embase, PsycINFO, and Google Scholar, and 14 articles were identified and appraised by the Medical Education Research Study Quality Instrument (MERSQI). A modified Kirkpatrick's framework was used to guide data extraction. Themes about learning were identified, integrated, and presented in evidence tables.
Results: The highest and lowest MERSQI scores for individual studies were 13.5 and 6.5, respectively. The MERSQI mean score for the included studies was 10.08 (SD 1.82). Thirteen studies (92.8%) reported Kirkpatrick’s level 1(Reactions), 12 (85.7%) reported level 2 (knowledge, skill, and attitude), 7 (50%) reported level 3 (Behaviour), and only 2 (14.2%) reported level 4 (outcomes). Clerking and presentation, using pre- and post-round times for discussions, questioning, demonstration, and active participation, were perceived as educational opportunities. However, time pressures, workload, a lack of feedback, and inadequate preparation and engagement are barriers to learning.
Conclusion: PTWRs are opportunities to learn nontechnical skills. They improve knowledge and skills related to diagnostic tests and care escalation. Learning during PTWRs requires preparation, engagement, attention to active participation, a feedback culture, role modelling, and team structure.
Double Trouble-Hyperhomocysteinemia and Lipoprotein(a) Induced Myocardial Infarction in Very Young - A Case Report
Authors: Saha Rupendra , DUGGAL BHANU , CHAWLA RAGHURAJ
Published: October 2024

Background
Acute coronary syndrome is a category that includes unstable angina, non-ST-elevation myocardial infarction (NSTEMI), and ST-elevation myocardial infarction (STEMI). Currently, the most significant cause of death globally is coronary artery disease. It is generally not suspected in a young person, especially without a significant family history. Hyperhomocysteinemia and high lipoprotein(a) are important risk factors and can be used in screening tools.
Case presentation
A 23-year-old male presented with an evolved anterior wall myocardial infarction, which was confirmed by ECG findings and Echocardiography findings. Subsequent coronary angiography was suggestive of a Single vessel with branch vessel disease, which was successfully revascularised with bifurcation stenting. OCT study of the lesion was suggestive of fibro atheromatous lesion with thrombosis. On workup, high lipoprotein(a) and hyperhomocysteinemia were found to be the risk factors for his disease.
Conclusion
Thus, Lipoprotein(a) and familial hyperhomocysteinemia may not always be associated with a family history of young ASCVD and are important risk factors for screening Intracoronary imaging should be used liberally to understand pathophysiology of the disease and also optimising interventions in such cases
Differential impacts of jogging and rope skipping in college students in China: a randomized controlled trial
Authors: Chen Yuxuan , Wu Junjie , Xu Zhuo , Chen Rucheng , Sun Qinghua
Published: October 2024

Studying the differential effects of jogging and rope skipping provides useful insights for teens, adolescents, and even adults to make choices and maintain healthy physical activities, which may also have positive significance for the promotion of public physical fitness and health. A total of 92 college students were enrolled in the study and randomly divided into jogging group, rope skipping group and control group. Tests and questionnaires were conducted before and right after the intervention, and a questionnaire survey was conducted again half a year after the intervention. The results showed that the standing long jump performance and the cardiopulmonary function in the jogging group were significantly improved after the intervention. The scores of 800 or 1,000 meters and the total scores of physical test in the rope skipping group and the control group were decreased significantly. The improvement of standing long jump performance in the jogging group was significantly higher than that in the control group. Jogging have more positive significance for physical fitness and cardiopulmonary function maintenance and improvement. If you want to improve your physical fitness in a shorter period of time, then jog.
Early Detection of Mental Health Issues through Machine Learning: A Comparative Analysis of Predictive Models
Authors: kortas Habiba , Jemili Farah , Korbaa Ouajdi
Published: October 2024

Mental health is a pivotal aspect of human well-being, yet it remains under-recognized and stigmatized in many societies. This study explores the integration of artificial intelligence and machine learning techniques to enhance the early detection and management of mental health disorders. By analyzing comprehensive datasets encompassing sociodemographic, medical, and environmental factors, we developed and evaluated several predictive models. Our approach included logistic regression, K-nearest neighbors, decision trees, random forests, bagging, boosting, stacking, and neural networks. The models were trained and tested to predict mental health issues, with performance metrics including accuracy, precision, recall, and F1-score. The results indicate that ensemble methods and neural networks outperform traditional algorithms in predicting mental health conditions, offering a promising direction for early intervention and proactive mental health management. This research highlights the potential of machine learning to transform mental health care by providing accurate, data-driven insights for early detection and intervention.
Computational Insights into Transition Metal-Based BaCoX3 (X = Cl, Br, I) Halide Perovskites for Spintronics, Photovoltaics, and Renewable Energy Devices
Authors: Rahman Arafat , Kabir Alamgir , Mahmud Tareq
Published: October 2024

Ab-initio simulations using density functional theory (DFT) were employed to investigate the structural, mechanical, electronic, magnetic, optical, and thermoelectric properties of halide perovskites BaCoX3 (X = Cl, Br, I). Structural optimization and mechanical stability assessments confirm the reliability of these perovskites in a hexagonal P63mc symmetry. The stability of the ferromagnetic phase was validated through total crystal energy minimization via Murnaghan’s equation of state. Electronic band structures and density of states, derived from the generalized gradient approximation (GGA), reveal a semiconducting ferromagnetic nature in the spin up channel, spotlighting their potential in semiconductor spintronic applications. Phonon dispersion analysis of BaCoCl3 demonstrated positive phonon frequencies across the entire Brillouin zone, confirming its dynamical stability. The elastic constants confirm the mechanical stability and ductile nature of the perovskites. Optical and dielectric properties of these perovskites show significant UV absorption and photoconductivity, making them highly suitable for optoelectronic and solar cell applications. Finally , transport properties, such as the Seebeck coefficient, electrical conductivity, thermal conductivity, power factor, and figure of merit (ZT) unveil their exceptional thermoelectric performance. Combining half-metallic ferromagnetic traits with superior ther-moelectric and optoelectronic performance positions BaCoX3 compounds as exceptional candidates for applications in spintronics, optoelectronics, and thermoelectrics. This comprehensive investigation demonstrates their ability to excel across a diverse array of advanced technological applications.
A Comparative Study of Rigid Three-Dimensional Image Registration Methods for Powder Bed Fusion with Laser Beam of Metals
Authors: Chand Keerthana , Fritsch Tobias , Oster Simon , Ulbricht Alexander , Poka Konstantin , Bruno Giovanni
Published: October 2024

In the Powder Bed Fusion with Laser Beam of Metals (PBF-LB/M), precise rigid three-dimensional (3D) image registration between X-ray Computed Tomography (XCT) scans and Computer-Aided Design (CAD) models is an important step for the quantification of distortions in produced parts. In this study, we conduct a comparative analysis of various rigid 3D registration methods useful for quality assurance of PBF-LB/M parts including feature-based, intensity-based, and point cloud-based approaches. To quantify the accuracy of rigid registration, we introduce a gold standard for 3D image registration and accuracy measurement in Additive Manufacturing (AM), with a proof of concept demonstrated in PBF-LB/M. The performance of each registration method is evaluated based on measures of alignment accuracy and computational efficiency. Our results indicate significant differences in the efficacy of these methods, with point cloud based Coherent Point Drift (CPD) showing superior performance in both alignment and computational efficiency. The rigidly registered 3D volumes are used to estimate the deformation field of the printed parts relative to the nominal CAD design using Digital Volume Correlation (DVC). The quality of the estimated deformation field is assessed using the Dice score metric. This study provides insights into methods for enhancing the precision and reliability of AM process.
Gradually Thermal Diffusing of Silver on Amorphous GeSe Thin Film; Structural and Optical Properties
Authors: Rashad M. , EL-Mahdy Ahmed F. M. , Moustafa Samar , Fares Hesham
Published: October 2024

Binary glasses of GeSe are prepared by melt quench technique. Two layers of thin film preparation have been done by the conventional thermal evaporation technique on glass substrate. GeSe with 340 ± 5 nm thickness is prepared as first layer, then thin silver layer are evaporated on top of the GeSe film. The GeSe with Ag on top of the film were annealed at different time of 30, 60, 90, and 180 and 210 min at 573 K of temperature. Subsequently, we have analyzed the films using scanning electron microscopy (SEM) and X-ray diffraction (XRD) to confirm the successful diffusion of Ag on GeSe films. XRD measurements show that as prepared Ag/GeSe have amorphous natures. Optical transmission and reflection spectra of the studied thin films are measured in the wavelength range of 200 − 2500 nm at room temperature. The optical properties of the new films were studied before and after annealing at different annealing times due to gradually thermal diffusing of Silver on GaAs. The absorption coefficient (α) as an optical constant is determined as a function of annealing times. Moreover, the values of the third-order nonlinear optical susceptibility increased with an increase of annealing temperatures due to gradually thermal diffusing of Silver. The results indicate that Ag/GeSe has great potential for various applications including optical sensors and optoelectronics.
Do You Actually Need an LLM? Rethinking Language Models for Customer Reviews Analysis
Authors: Xiao Yang , Li Yunke , Chen Shaoyujie , Rad Ryan , Barker Hayden
Published: October 2024

Large language models (LLMs) have demonstrated impressive capabilities in natural language processing, but their high computational costs raise questions about their practical utility compared to small language models (SLMs). This study presents a comprehensive comparison of SLMs and LLMs on two critical customer review analysis tasks: sentiment polarity classification and correlation analysis with product categories. We evaluate state-of-the-art SLMs (DistilBERT, ELECTRA) and LLMs (Flan-T5, Flan-UL2) using benchmark datasets, assessing accuracy, F1 scores, computational runtime, memory usage, and FLOPs. Our findings reveal that LLMs excel in sentiment polarity classification but at significantly higher computational costs, while SLMs demonstrate superior performance and efficiency in the domain-specific correlation analysis task. To optimize the trade-off between accuracy and efficiency, we propose a novel hybrid system integrating SLMs and LLMs through a tiered processing strategy. This research provides valuable insights into the strategic utilization of language models for customer review analysis, enabling businesses to make informed decisions that balance computational resources and accuracy requirements. Our results have important implications for the practical application of AI in business analytics and customer insight generation.
Persistent Right Aortic Arch, Megaesophagus and Tracheal Stenosis in a White-tailed Deer Newborn (Odocoileus virginianus ustus) at Antisana National Park, Ecuador
Authors: A. F.J. Cabrera , Delgado-Ruilova E.M. , Sáenz C. , B. A. Ordoñez
Published: October 2024

Persistent Right Aortic Arch (PRAA) with retention of the left ductus arteriosus constitutes the most prevalent clinically significant form of vascular ring anomalies in both dogs and cats, accounting for approximately 95% of aortic arch developmental anomalies. This anomaly causes esophageal compression in weaned animals, leading to restricted passage of solid food, postprandial regurgitation, and potential development of acquired megaesophagus. Notably, neonatal, or lactating animals usually do not exhibit these consequences. While PRAA is well-documented in dogs and cats, its occurrence in wild or domesticated ungulates is a rare and intriguing phenomenon, shedding light on congenital cardiovascular anomalies in such species. In this case, a female neonate White-tailed Deer, weighing 2 kg, was delivered to the TUERI Wildlife Hospital at San Francisco de Quito University, Ecuador, following its discovery by tourists in the Antisana National Park. Despite receiving substitute feeding and fluid therapy, the specimen faced challenges in milk transit to the abomasum, resulting in postprandial regurgitation. Imagenologic examinations aimed at elucidating the cause of regurgitation were hindered by forelimbs’ muscle contractions. Nonetheless, these procedures facilitated a presumptive diagnosis of secondary Megaesophagus attributed to a Persistent Right Fourth Aortic Arch (PRAA)., The specimen was euthanized, and the carcass underwent necropsy, revealing a PRAA with distinctive anatomical features, including esophageal dilation, tracheal stenosis, and a constricting vascular ring surrounding both esophagus and trachea. This unique case underscores the importance of considering congenital anomalies in wildlife health assessments and contributes to our understanding of cardiovascular anomalies in wildlife species.
Matrix of Cognitive Filters as a Tool for Lingua-Createme Analysis in English Online Discourse
Authors: Taymur Mariya
Published: October 2024

The article introduces the matrix of cognitive filters as a research method that makes it possible to model the process of inferring the meaning of lingua-createmes in multimodal online discourse. Multimodal linguistic systems are based on a complex interaction of multimodal components. These components combine with basic cognitive mechanisms in decoding various complex phenomena in digital Internet discourse. The research revealed a classification of key cognitive mechanisms that generate the meaning of creative multimodal elements: banner blindness, evaluative categorization, conceptualization, (de)compression of information, profiling, conceptual metaphor, conceptual metaphthonymy, and conceptual integration. The research material involved 18,000 monomodal verbal and multimodal lingua-createmes selected by continuous sampling from English-language Internet resources, as well as visual and verbal corpora. The method proved effective in modelling the general cognitive resonance of lingua-createmes and predicting cognitive resonance / dissonance (positive or negative) in communication. The practical lingua-cognitive experiment involved 335 English-speaking respondents from eleven countries. They were asked to use the matrix of cognitive filters to perform a step-by-step decoding of several creative multimodal elements. The method proved highly effective for detecting cognitive resonance or mental tension, i.e., positive or negative cognitive dissonance. The method demonstrated good prospects for obtaining additional data on reactions to lingua-creative discursive elements.
The Variety of Educational Communicative Situations of Virtual Urbanism as an Effective Tool for the Formation of Students’ Communicative Competence in Learning Foreign Languages in Russia, Kazakhstan and China
Authors: Baymahan Aknur , Bel'gibaeva Gul'barshyn , Karabulatova Irina , Hu Jiadong , Zou Jinna
Published: October 2024

The formation of foreign languages communicative competence in modern electronic and digital society is directly conditioned by the achievements of computer psycholinguistics, neurolinguistics, starting from digital neuropedagogy using the natural landscape to construct a virtual environment with a change in the speech and behavioral standard in urbanism and the priority of digital reality. The purpose of the study is to define and substantiate the methodology of digital educational resources, which gradually forms communicative competencies by immersing students in the virtual environment of urban society.The hypothesis is based on students' understanding of the process of learning foreign languages with an emphasis on the specifics of neurocognitive connections in the formation of communicative competencies using didactic computer-mediated digital technologies of various electronic educational resources by comparing real and virtual urban space. The research material is a variety of educational communicative situations of virtual urbanism. The object of this research is the process of learning a foreign language, including analog situations of the real city environment and simulation modeling of a virtual urban space. The study clarifies the concepts of communicative competence, virtual urbanism, digital technologies, indicating the parameters of the main classifications.
Emoticon in Development of Virtual Graphic Emotionology
Authors: Ivanova-Yakushko Mariya , Anumyan Karpis , Karabulatov Mihail
Published: October 2024

Emoticons are a vivid example of graphic emotionology in virtual communication. The interdisciplinary character of this research made it possible to combine linguistics with exact and natural sciences, including the young science of emotional artificial intelligence. Transmitting psycho-emotional experiences is important for effective virtual communication. Emotional pictography provides a more accurate understanding of the emotive content in a message. It relies on associative links between the image of a specific psycho-emotional state and its perception by a carrier of a specific linguistic culture. Basic emoticons are of universal nature, and internet users with different cultural and linguistic backgrounds tend to interpret them similarly. However, members of different ethno-linguistic communities sometimes decode the same emotions differently, which may lead to misunderstanding, as well as to serve as a marker on the friend – foe axis. Emoticons represent a clearly verified range of emotive states, which can be appealed to in virtual communication. The authors developed a classification of emoticons and proposed graphic emotionology as a new branch of linguistic semiotics. The metaphorical nature of the emoticon as a coded emotion with additional evaluative shades is revealed as a certain unified image or a complex of signs due to the accepted emotive norms of speech behavior. In addition, emoticons provide efficient compression during virtual communication. Intercultural virtual communication enriches the range of emoticons both linguistically and culturally, thus developing graphic emotionology internationally. Emoticons provide insight into the development of graphic emotionology.
Withdrawn: Camel FGF21 Protein Enhances FGF21 Signaling and its Targeted Compounds Screening
Authors: Gao Yuan , Yong Fang , Ji Wangye , Zhang Lili , Hou Jibao , Ma Ruilin , Zhao Shuqin , Ge Huizhen , Wu Xiaoyu
Published: October 2024

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