bluuebunny/crossref_metadata_2025 huggingface dataset:
Implications of Emulating a Dermatologist: A Study of Topical medication usage for dermatoses prescribed by Non-Dermatologists in a rural area
Authors: Devaraj Yogesh , Reddy Mittamedi Nikitha , Kaur Kalra Taranpreet , Yogananda Yadav Priyanka , Nag Neethu , Meenakshi Sundaram Premika
Published: May 2024

Introduction: Over-the-counter (OTC) medications refer to drugs that a patient can purchase without prescription from a registered medical practitioner. Topical OTC medication usage for skin disorders is common in rural India, due to non-availability of Dermatologists. Inappropriate use of such drugs has resulted in various cutaneous adverse effects and antifungal resistance. Aims: To determine the pattern of usage of the different OTC topical medications for various Dermatological indications, their source of prescription and their cutaneous adverse effects. Materials and Methods: Patients presenting to Dermatology OPD with a history of usage of topical OTC drugs for skin disorders were recruited. Details such as type of OTC drug used, the source of its prescription, indications for which it was advised and patients knowledge regarding steroids were documented. Patients were examined for cutaneous adverse effects. Results: Out of 100 patients there were 46 males and 54 females. Dermatophytosis (29%) was the most common indication for which these drugs were used. The common side effects were itching (17.86%) and burning sensation (13.39%). Majority of the drugs were advised by pharmacists (36%) followed by friends and relatives (29%). Betamethasone was the commonest steroid and Betnovate was the commonest brand misused. Majority (97%) of the patients were unaware of topical steroids and their adverse effects. Conclusion: The growing threat of OTC drug abuse is evident from our study. Stringent drug control policies are required to regulate the sale of topical OTC drugs. Creating awareness among patients and General physicians about the adverse effects of OTC topical medications including steroids is truly the need of the hour.
A Brief Review on Essential Medicine
Authors: B. Patel Ashok , V. Abhangi Shivangi , J. Vyas Amitkumar , I. Patel Ajay , J. Chotaliya Urvi
Published: May 2024

The World Health Organization (WHO) initially released its list in 1977, marking the beginning of the Essential Medicine Concept, a significant advancement in medical treatment. One of the most cost-effective aspects of modern healthcare is the appropriate use of vital medications. From expert judgement to evidence-based selection, the selection process has developed. In 1996, the first Indian list was published. Particularly in the public sector and in low- and lower middle-income countries, Essential Medicine Policies (EMLs) have had an impact on the provision of medications and led to a higher availability of essential vs non-essential medicines. Even yet, fair access is not guaranteed by the accessibility of necessary medications, particularly in the public sector.
STEM in Mathematics Teaching and Learning: A Bibliometric Analysis
Authors: Wahyuni Septia
Published: May 2024

STEM education, which integrates science, technology, engineering, and mathematics, has become increasingly important in modern education. This study aims to present a bibliometric analysis of STEM in mathematics teaching and learning topics published in the Scopus database. The data used in this study amounted to 1,787 articles published from 2013 to 2023. This research uses reference management software, namely Publish or Perish for data collection, Microsoft Excel for data analysis, RStudio, and VOSviewer for data visualization. The results showed that STEM in mathematics teaching and learning research increased consistently from 2013 to 2023, with some years experiencing a greater increase than others. A significant increase occurred in 2019–2020. The United States is the most productive country in terms of publications. Yuenyong, C., has the most publications, and Universitas Pendidikan Indonesia is the organization with the most publications. Keywords that are widely used through WordCloud include "students," "STEM education," and "engineering education." Therefore, these findings can serve as a guide for future researchers to conduct relevant research using STEM in mathematics teaching and learning.
Mathematical Modelling for Failure Analysis of Composite Drive Shaft Using Modal Flexibility and Curvature Method
Authors: Rakesh Raushan
Published: May 2024

Early damage identification in composite structures has attracted a lot of attention in post-failure examinations in recent years. In the research, torsional fatigue test of composite drive shafts has been done and numerical methodologies have been employed for the validation for premature failure using modal flexibility and modal curvature methods. Initially, the eigenvalue and eigenvectors of healthy and fractured drive shafts are evaluated using a numerical model and verified by fatigue experimental approach. Flexibility variance and the absolute curvature difference between the drive shafts that are fractured and those that are healthy are evaluated after mass normalization. Variations in absolute modal curvatures and variations in local flexibilities that are utilized to identify the existence, location, and severity of a drive shaft fracture. The position and severity of the fracture are predicted more precisely by the modal curvature approach than by the modal flexibility approach.
Application of Nonlinear Analysis in Evaluating Additive Manufacturing Process for Engineering Design Features: A Study and Recommendations
Authors: Dama Y. B.
Published: May 2024

In today’s world, Additive Manufacturing (AM) is quickly becoming the dominant manufacturing technology. Massive development has occurred in recent decades, and it is occurring at a much faster rate. It has also progressed from simple prototype to actual end-use items and manufacturing tools. Various manufacturing processes, such as SLS, LENS, FDM, PolyJet, SLA, LTP, DMLS FDM, and binder jet printing, are produced using additive manufacturing techniques. Layer by layer material deposition/addition is a critical component in all these operations, which is why this technology is known as additive manufacturing. Rapid prototyping, 3D printing, digital manufacturing, and other such names are used. Automotive, Aerospace components, medical equipment parts, consumer goods and gadgets, fashion sector, jewelry, and other industries employ additive manufacturing.Currently all designed parts can manufacture using either subtractive or additive manufacturing processes. However, distinct design elements established for production provide additional manufacturing obstacles in metal substation and metal addition manufacturing procedures. As the number of parts made directly utilizing additive manufacturing techniques grows, it is critical to compile a list of the best design for manufacturing principles applicable for various additive manufacturing procedures. This will assist the design community in ensuring that items are developed for additive manufacturing rather than generic strategy and produced.The primary goal of this effort is to evaluate and comprehend the various additive processes and choose the optimal design for manufacturing procedures to be used. As a result, one should be prepared to select a suitable additive manufacturing process for printing based on the design aspects of the component to be produced.
Enhancing Surface Integrity and Quality through Roller Burnishing: A Comprehensive Review of Parameters Optimization, and Applications.
Authors: Avinash A. Somatkar
Published: May 2024

Surface integrity is a critical aspect in ensuring the quality of components or products, particularly in industries such as automotive, aerospace, and machinery where superior surface finish and prolonged functionality of cylindrical parts are essential. Burnishing, a chip-less cold working finishing process, enhances the surface finish of machined workpieces through plastic deformation. This review paper delves into various aspects of the burnishing process and its impact on surface properties, drawing insights from multiple research projects. It explores topics including residual stresses, surface roughness, microhardness, cooling strategies, and modeling techniques. Key findings underscore the influence of burnishing parameters such as speed, feed rate, penetration depth, and number of passes on surface roughness and microhardness. Moreover, the process induces compressive residual strains on the workpiece surface, with their magnitude varying based on operational parameters. The choice of cooling methods, including kerosene, minimum quantity lubrication (MQL), cryogenic cooling, and hybrid cooling, significantly affects surface integrity. Various modeling approaches, such as Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Response Surface Methodology (RSM), aid in predicting and optimizing surface characteristics. Despite the extensive discussion on surface roughness, microhardness, residual stresses, cooling methods, and modeling, out-of-roundness in the burnishing process remains understudied and warrants further investigation. This review contributes valuable insights for enhancing surface qualities in manufacturing applications and deepening our understanding of the burnishing process.
Strong Regular Domination in Litact Graphs
Authors: G. Shankarajyothi
Published: May 2024

Strong regular domination in a litact graph is a novel domination parameter that has been introduced in this paper. A dominating set D⊆V(G) is known as Strong regular dominating set of G, if for each point x∈V(G)-D there is a vertex y∈D with an edge xy∈E(G) and deg⁡(x)≤deg⁡(y) and all vertices of 〈D〉 holds the equal degree. The lowest cardinality of such vertices of D is known as strong regular domination number of G which is represented by γ_str (G). The current study aims by taking strong regular domination on a litact graph m(G) denoted by  γ_str [m(G)]  and to obtain some bounds on γ_str [m(G)]  in terms of various parameters of G such as vertices, edges, maximum degree, diameter and so on and also in terms of various domination parameters of G such as total domination of G, connected domination of G and so on. Furthermore, outcomes resembling those of Nordhaus-Gaddum were also obtained.
Periodic Behaviour of General Systems
Authors: L. Praveen Kumar
Published: May 2024

A continuous function on the product of compact metric spaces to itself and back to the same space is known as a genera system. Where each element's orbit is an infinite sequence and where the first two elements are the same as given and next to it depends on the two elements prior to it form stronger conditions for the orbit. We could define an m-step dynamical system by extending the definition of a compact metric space to its m-times product. Because the system's current state frequently depends directly on the conditions of previous terms, this system appears more realistic. The basic theorems regarding periodic points and their related points, such as fixed point, limit points, recurrent points, will be proved in this paper. We also define the topological transitivity and its properties. In the end we find periodic points with periods one and two for affine maps and periodicity of tent map in dynamical system and generalised dynamical system.
A Distributed Deployment Method for Wireless Sensor Networks Based on Multi Agent Systems
Authors: Chuiju You
Published: May 2024

This paper proposes a distributed deployment method for wireless sensor networks based on multi-agent systems, which addresses the issues of poor self adaptive deployment capability and high deployment costs caused by numerous and diverse types of nodes in the deployment process of wireless sensor networks, leading to blind spots in coverage and redundant nodes. Due to the characteristics of intelligent perception, intelligent communication, and clustering of nodes in wireless sensor network, sensor nodes can be seen as sensing agents, and sensor networks can be seen as distributed multi-agent systems composed of numerous sensing agents. Through the sensing agents' perception of the surrounding environment and communication interaction, a unified deployment model and distributed deployment algorithm with multiple nodes (cluster head nodes) as leaders are established, achieving effective integration of autonomous and leadership mechanisms, distributed management, and centralized management. Taking the problem of sensor network coverage as an example, simulation experiments were conducted, including 12 sensor agents and 1 20 * 20 monitoring areas, to verify how the sensor agents perceive the environment and optimize the migration and deployment process under the coordination of cluster head nodes. The experimental results confirmed the effectiveness of the above models and algorithms. The research results provide a theoretical model and practical approach for the distributed deployment and scheduling of sensor nodes in wireless sensor networks, and can be extended to intelligent Internet of Things systems, such as urban intelligent agent systems, logistics intelligent agent systems, etc., to improve the intelligence of wireless sensor networks deployment and scheduling.
Analysis of corrosion detection error of grounding grid under amplitude characterization
Authors: Jinshan Yu
Published: May 2024

Magnetic field amplitude detection is an important method for determining the positioning and corrosion degree of the grounding grid, and the conventional analysis only determines the grounding grid status by collecting single-component signals, which makes the diagnosis results of the grounding grid status susceptible to the influence of the collection process. By considering the single-component sensor device in the attitude deflection during the actual substation detection process, the analysis of the difference between the signals measured by the sensor under different attitude deflections is performed, and the degree of influence of the attitude deflection on the magnetic field amplitude characteristics is determined. Further, by analyzing the calculation error of the attitude angle on the corrosion degree of the ground grid, the correspondence between the sensor attitude and the calculation error of the corrosion degree was obtained. Finally, in order to reduce the influence of the sensor attitude on the detection signal, it is proposed that the detection scheme of measuring the three-component sensor signal and the attitude angle at the same time which could improve the accuracy of the detection system.
Exploring Impact of Protein Sequence Local Information to Predict Enzyme-Ligand Binding Residues Using Machine Learning
Authors: Shweta Redkar
Published: May 2024

Enzymes are important for various biochemical reactions in living cells. In drug discovery and drug design, identifying small molecule binding residues in enzymes is a crucial step. Although, identifying ligand-binding residues using computational techniques are improving, accurate prediction remains difficult. Therefore, to address this problem, we used the sequence local information, i.e., sequence neighbors around the target residue, and transformed it into two ways. First, into a Chaos Game Representation (CGR) to form a feature vector and train with an Extreme Gradient Boosting classifier (XGB) and second into a non-numeric feature space and apply the conditional probabilistic based approach. Our results suggest that local protein sequence information along with global information can help to develop precise predictors for small molecule binding residues. We hope our observations could facilitate the researchers to investigate more into enzyme-ligand interaction and drug discovery.
An AQ-SCS Optimization Assisted Optimal Expansion Planning with Fixed and Switchable Capacitors for Distribution System
Authors: Abhilasha Pawar
Published: May 2024

Distribution System has given renewable-based DGs like Solar PV and Wind turbines a lot of attention because of growing worries about global warming and the depletion of fossil fuels. This work proposes a unique hybridized optimization technique for a distribution system expansion plan utilizing an innovative theoretical framework. Two stages are used to tackle the issue with the distribution system expansion plan: master optimization and sub-optimization for every state of the system. The developed Aquila based Sand Cat Swarm (Aq-SCS) optimization method considers the distribution system factors, like DG type, size/capacity, location, PQ power, fixed capacitor, switchable capacitor, and the SPV/wind capacity with load demand uncertainties. The sub-algorithm is employed in conjunction with the DG allocation strategy produced by the master algorithm to deduce the state-dependent operating tactics for each individual DG unit with respect to active and reactive power. The suggested Aq-SCS optimization approach is created by combining the properties of the SCSO with the AOA models. The main objective of achieving the minimal possible cost for the DSEP is verified, and the cycle continues unless the best outcome (least cost) is reached. The analysis of the suggested approach is carried out in 4 cases, such as (i) Without DG and capacitor (ii) With capacitor and no DG (iii) With DG and no capacitor (iv) With both capacitor and DG. The analysis is made in IEEE 33 bus system. The Aq-SCS approach outperforms the conservative approaches SSI-CS, WHO, AQO, and SCSO, according to the analysis conducted in MATLAB/Simulink utilizing an IEEE-33 bus system with five system states.
Novel Path Selector in Wireless Sensor Networks
Authors: B.Harish Goud
Published: May 2024

The Sensor Network is a system with a more number of sensor nodes that can maintain itself. The dispersed nodes sensor converses with one another. the node sensors gather data and transmit it to the base station. The deployment of the WSN spans many different applications, such secure military software, healthcare systems, and monitoring software. Numerous studies have concentrated on boosting communication efficiency and increasing network lifetime. In The conventional style Base station, sensor nodes, and cluster heads (CH) make up the typical WSN architecture. Traditional sensor node communication methods use a lot of energy, cause delays, and have poor network performance. To solve the system's restriction in its current state in this paper, it was suggested to implement Path selector a special node which contains all clusters information in WSN and their sensor energy information of all sensor nodes so that directly we can send data to destination sensor node fast without any delay, energy consumption. There by we can improve packet delivery ratio, throughput decrease delay.
Automatic Diagnosis of Parkinson’s Disease using Handwriting Patterns
Authors: T. Nagamani
Published: May 2024

Parkinson's Disease (PD) is a neuro-degenerative syndrome characterized by motor and non-motor signs, and early detection is crucial for effective intervention. This paper presents a novel approach for PD detection using computer vision and machine learning techniques applied to Spiral-Wave handwriting analysis. The dataset comprises frontal handwritten images obtained through the Spiral-Wave test, capturing subtle motor control differences. Our methodology involves resizing images to a standardized 200x200 pixels, converting them to grayscale, and applying thresholding for improved feature abstraction. Histogram of Oriented Gradients (HOG) is employed to capture shape and texture information. The development of a strong approach for deriving significant features from Spiral-Wave handwriting patterns and the usage of machine learning classifiers for precise PD analysis are the two main goals of this work. The emphasis is on using Random Forest and K-Nearest Neighbours (KNN) classifiers for Spiral and Wave pictures, respectively, in conjunction with the Histogram of Oriented Gradients (HOG) approach for feature extraction. For Spiral images, a Random Forest Classifier is utilized, achieving an accuracy of 86.67%. The classifier's interpretability is enhanced through an analysis of feature importance, revealing critical HOG features for distinguishing between healthy and PD-afflicted patterns. The Wave images are classified using a K-Nearest Neighbours (KNN) model, attaining an accuracy of 76.67%. Performance metrics, including precision, recall, and F1-score, offer a nuanced assessment of the KNN model's capabilities.
Artwork Traceability and Anti-Counterfeiting Model Based on Block Chain Technology
Authors: Rui Yin
Published: May 2024

The Chinese art market fostered the advancement of culture and spirituality. Nevertheless, false and counterfeit artwork is not uncommon. The art technology and security introduce the innovative method to determine Artwork Traceability and Anti-Counterfeiting. The aim of the work is to prevent the creation and circulation of fake or unauthorized copies of artworks. Because of the cryptographic security and immutability of block chains, it would be challenging to falsify artwork records or make accurate duplicates. This would foster confidence in the art market and shield collectors and artists from monetary losses and harm to their reputations. The data on mobile Application Data Center (ADC) platform and the cleansing the data using sub-aperture keystone transform matched filtering (SAKTMF) and the preprocessed data is fed into the Hierarchically Gated Recurrent Neural Network (HGRNN) classify the art work and Anti-Counterfeiting. The Bald Eagle Search Optimization (BES) Algorithm is used to optimize the weight parameter of HGRNN. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such as Dual-Branch Multi-Scale Feature Fusion network (DMF-Net), Region Convolutional Neural Network (R-CNN) and practical Byzantine fault tolerance (PBFT) algorithm. The gained results of the proposed HGRNN-BES method attains higher accuracy 97%, 96%, and 99%, higher precision 98%, 99%, and 97%.
Design and Performance Evaluation of Network Intrusion Detection System Based on Deep Learning
Authors: Lin Yang
Published: May 2024

Today’s internets are made up of nearly half million various networks. In any network connection, detecting attacks by their kinds is challenging task as various attacks may have several connections, their number vary from few to hundreds of network connections. In this paper, Design and Performance Evaluation of Network Intrusion Detection System Based on Deep Learning (NID-SPGAN-STO) is proposed.  Initially, input data are collected by NSL-KDD dataset. Afterward, data are fed to preprocessing. In preprocessing, Distributed Set-Membership Fusion Filtering is used to remove redundant and biased records from input data. The pre-processed output is given to feature selection for selecting optimal features utilizing Piranha foraging Optimization Algorithm. Finally the selected features are transferred into the Semantic-Preserved Generative Adversarial Network (SPGAN) for detecting Network Intrusion, like DoS, Probe, R2L, U2R and Normal. Generally SPGAN doesn’t reveal some adoption of optimization techniques for computing optimal parameters for promising precise network intrusion detection. Hence Siberian Tiger Optimisation (STO) is used to enhance weight parameters of SPGAN. The proposed NID-SPGAN-STO method is implemented using Python. To detect network intrusion detection, performance metrics likes precision, sensitivity, FI-score, specificity, accuracy, RoC, computational time are considered. The NID-SPGAN-STO method attains 30.58%, 28.73% and 25.62%, higher precision, 20.48%, 24.73%, 29.32% higher specificity and 30.98%, 26.66% and 21.32% higher F-score, 26.78%, 34.47%, and 22.86% higher recall  analysed, with existing techniques likes improved binary gray wolf optimizer with SVM for intrusion detection system in WSNs (NID-SVM-IDS), network intrusion detection system utilizing deep learning (NID-DNN), design with improvement of efficient network intrusion detection system utilizing ML methods (NID-IDS-ANN) respectively.
English Speaking Assessment Algorithm Based on Deep Learning
Authors: Xiaoli  Hu
Published: May 2024

English as a Foreign Language (EFL) students perform when speaking in public. An increasingly globalized world, effective public speaking is critical, but EFL students struggle to perform it, despite importance of qualities such as eye contact, speech pauses, there is presently no objective examination of such elements. A summative assessment has historically been the predominant form of evaluation in college English speaking assessments. Exam-centric teaching has considerable negative effect on foreign language training. In this research work, English Speaking Assessment Algorithm Based on Deep Learning (ESA-NEGCN-NBOA) is proposed. Initially, input video data are gathered from the multiple video dataset (MVD).The input video data is then pre-processed using Deep Attentional Guided Image Filtering (DAGIF) to remove presence of signal-dependent noise and improve lack of pixels from the regions and enhanced the video data. The data that has been pre-processed is utilized to Feature extraction using New General Double Integral Transform (NGDIT), which extract the significant attributes such as mel-frequency cepstral coefficients, energy, speech rate and pitch. Then NEGCN is proposed to improve students spoken English performance by assessmenting the English speakers. In general, NEGCN doesn’t express some adaption of optimization approaches for determining optimal parameters to promise exact improvement of assessment. Therefore, NBOA is proposed to enhance weight parameter of NEGCN for English speaking assessments, which precisely assess the English speaking. Performance measures such as accuracy, assessment error, evaluation time, pretest and posttest are examined when the proposed ESA-NEGCN-NBOA method is put into practice. The proposed ESA-NEGCN-NBOA method attains21.36%, 23.42% and 19.29% higher accuracy, 23.36%, 18.42% and 28.27% lower evaluation error, 20.36%, 27.42%, 28.17% lesser evaluation time analysed with existing techniques, likes innovative strategy towards oral English assessment utilizing machine learning, data mining,  blockchain methods(IST-OEA-ML), machine learning assessment system for spoken English depend on linear predictive coding (AS-SE-LPC-ML), multimodal transfer learning for oral presentation assessment (MM-TL-OPA) respectively.
Music Teaching Mode of Colleges and Universities Based On Hierarchically Gated Recurrent Neural Network (HGRNN) and Lyrebird Optimization Algorithm (LOA)
Authors: Kegang Lu
Published: May 2024

Colleges and universities play a crucial role in nurturing talent and providing highly skilled individuals for various sectors of society. Through modifications over time, the model of music education at colleges and universities has advanced. However, there are still numerous issues that demand careful consideration. This manuscript proposes a hierarchically gated recurrent neural network (HGRNN) optimized with the lyrebird Optimization Algorithm (LOA) for predicting music teaching mode of colleges and universities (MTM-HGRNN-LOA). Initially, the data is collected via real time basis. Afterward, the data is fed to an unscented trainable kalman filter (UTKF) based pre-processing process. In the pre-processing segment, it enhances training rate and eliminates the of batch size dependency. The pre-processing output is given to modified spline-kernelled chirp let transform (MSKCT). The input signal undergoes feature extraction to derive the primary features, which are subsequently combined to yield more comprehensive features in an efficient manner. After that, the extracted features are given to a hierarchically gated recurrent neural network and lyrebird optimization algorithm for effectively classifying the music teaching mode for best, good, normal, satisfactory and poor. The weight parameters of hierarchically gated recurrent neural network are optimized using the lyrebird optimization algorithm. The proposed method is implemented in python and evaluated their performance with existing methods. The performance metrics, like precision, F1-score, accuracy, specificity, sensitivity, and ROC is analysed to the proposed method's performance. The proposed MTM-HGRNN-LOA methods of accuracy are provide 97% best, 98% good, 95% normal, 98% satisfactory and 97% poor music teaching mode. The existing methods MTM-CNN, MTM-BPNN and MTM-GNN, the specificity becomes 90%, 70%, 79% best, 77%, 75%, 65% good, 66%, 85%, 84% normal, 59%, 58%, 70% satisfactory, 61%, 79%, 81% poor music teaching mode. The results show that the proposed MTM-HGRNN-LOA method outperforms other existing techniques, such as online vocal music teaching quality using Back Propagation neural network and convolutional neural network based College-Level Music Teaching Quality Evaluation.
Product Design Interaction and Experience Based on Virtual Reality Technology
Authors: Qianqian Hu
Published: May 2024

Artificial intelligence involves imitating human thought, consciousness, and other aspects. This is similar to having machine with human brain that can think, produce independently. However, unlike human brain, it has a speed, memory advantage. A virtual reality interactive glove is built using a nine-axis inertial sensor, an artificial intelligence deep learning algorithm. In this research work, Product Design Interaction and Experience Based on Virtual Reality Technology (PDIE-VRT-SCGAN) were proposed. Initially, input gestures data are gathered from the Virtual Reality Experiences (VRE) dataset. The input gestures data is then pre-processed using Multi-Window Savitzky-Golay Filter (MWSGF) to reduce noises, increase overall quality of the gestures data. In order to improving overall user engagement in product design interactions on virtual reality (VR) technology, the pre-processed gestures data are then fed into an adversarial network called a Semi-Cycled Generative Adversarial Network (SCGAN). In general, SCGAN does not express some adaption of optimization strategies for determining optimal parameters to promise exact to improving overall user engagement in product design interactions using VR technology. Therefore, FOX-inspired Optimization (FIO) is proposed to enhance weight parameter of SCGAN method, which precisely improving the user experience in product design interaction. The efficacy of PDIE-VRT-SCGAN method is assessed using a number of performance criteria, including tracking accuracy, frame rate, latency, rendering time, error rate, and user error. The proposed PDIE-VRT-SCGAN method attains 22.36%, 25.42% and 18.17% higher tracking accuracy, 21.26%, 15.42% and 19.27% higher latency, 28.36%, 25.32% and 28.27% higher frame rate compared with existing methods, such as design and implementation of virtual reality interactive product software depend on artificial intelligence deep learning algorithm(DVRI-PS-AI-DL), virtual evaluation system for product designing utilizing virtual reality (VES-PD-VR), and analysis of unsatisfying user experiences with unmet psychological needs for virtual reality exergames utilizing deep learning approach (AUUE-UP-VRE-DLA) respectively.
Software Requirements Engineering and User Experience Design Modeling of Big Data Analysis using Convolution-Bidirectional Temporal Convolutional Network
Authors: Xu Yang
Published: May 2024

The study of user perception and interaction with applications is referred to as user experience, or UX. The intricacy and versatility of software products, from requirements engineering to product functionality are well recognized. UX evaluations are often depends on prototypes, but it's important to consider the semantics embedded in software requirements to ensure project success. In this manuscript, Software Requirements Engineering and User Experience Design Modeling of Big Data Analysis using Convolution-Bidirectional Temporal Convolutional Network (SRE-UEDM-BDA-CBTCN) is proposed. The input data are collected from Requirements dataset. The collected data are given to the Convolution-Bidirectional Temporal Convolutional Network (CBTCN) to Design Modeling of Big Data Analysis user experience based on the dataset. In general, CBTCN does not express any adaption of optimization techniques for determining the ideal parameters to accurate Design user experience. Hence, African Vultures Optimization Algorithm (AVOA) is proposed in this work to improve the weight parameter of CBTCN. The proposed model is implemented and the efficiency is evaluated utilizing some performance metrics like accuracy, precision, specificity, sensitivity and F1-Score. The proposed SRE-UEDM-BDA-CBTCN method provides 28.46%, 21.34 and 33.81% higher accuracy, 22.88%, 26.52% and 34.63% higher Precision and 28.46%, 21.34 and 33.81% higher specificity compared with the existing techniques like Holistic big data integrated artificial intelligent modeling to improve privacy and safety in data management of smart cities (AIM-BDI-SDM), Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach (MHA-UED-MLA) and Towards Measuring User Experience based on Software Requirements (TM-UEB-SR).
From Swipe to Learn: Unleashing the Potential of Tik Tok as a Nano Learning Tool
Authors: Mariappan Logenthini
Published: May 2024

Nano-learning is a highly targeted approach that breaks down complex subjects into manageable chunks, with the goal of presenting brief and straightforward ideas in an interesting way. This approach is similar to social media content consumption found on platforms like TikTok. Introducing TikTok as a nano-learning tool for teaching English grammar can be a viable option, considering the platform's popularity and effectiveness in delivering short and simple notes. This study employed a mixed-mode strategy. A survey was done with 500 TikTok users between the ages of 18 and 25. Measures of learning efficacy and engagement, as well as questions about the frequency and kinds of educational content consumed on TikTok, were also part of the study. Fifty TikTok users between the ages of 18 and 25 were interviewed using a semi-structured format as part of a qualitative approach. Key themes were found through thematic analysis of the audio-recorded and typed-in transcripts of the interviews. The findings suggest that TikTok has the potential to be an effective nano-learning tool, with a majority of users consuming educational content on a regular basis and reporting high levels of engagement with the content.
Rainfall Prediction Using Deep Learning Algorithms
Authors: Huthaifa Abuhammad
Published: May 2024

Water scarcity presents a significant challenge for many countries. Given that rainfall serves as the primary source of drinking water, irrigation, and various other purposes, the accuracy of rainfall prediction holds utmost importance for decision-makers. Climate change has exacerbated the unpredictability of rainfall patterns, posing challenges to sustainable water resource management. Artificial Intelligence (AI) techniques, especially deep neural networks, have demonstrated effectiveness in predicting rainfall patterns and enhancing water resource management. This study involved reprocessing data obtained from the Jordanian Ministry of Water and Irrigation Jordan Valley Authority to create a dataset. The dataset was then evaluated using Long Short-Term Memory (LSTM), a type of Recurrent Neural Network (RNN), and Support Vector Machine (SVM) models for rainfall prediction. Evaluation metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared were employed, revealing the reliability of the forecasting models.
Infecção humana pelo vírus monkeypox: uma revisão bibliográfica
Authors: Milagres Camilo Isaac , D ́Acri Antônio Macedo , Lopes Vanessa Knauf
Published: May 2024

O ano de 2022 ficou marcado por um aumento mundial de casos de infecções pelo vírus monkeypox, sendo considerado a maior disseminação desse vírus para fora do continente africano. A total compreensão acerca do aumento expressivo do número de casos de mon-keypox ainda é desafiadora, pois engloba diversas manifestações clínicas e particularida-des que dificultam o diagnóstico e o tratamento, aumentando o potencial de transmissão do vírus entre seres humanos. Assim como políticas públicas estão sendo adotadas a fim de diminuir a transmissão do vírus, um alarme também ocorre entre profissionais da saúde que buscam compreender o contexto do aumento do número de casos em todo mundo, sobre-tudo em homens jovens. Este trabalho busca elucidar lacunas de conhecimento que ainda existem sobre o tema, como vias de transmissão da doença, contexto epidemiológico, par-ticularidades do manejo clínico das lesões dermatológicas e advento de novas drogas para tratamento. Foram utilizadas as bases de dados National Library of Medicine (PubMed), Literatura Latino-americana e do Caribe em Ciências da Saúde (LILACS) e Scientific Eletronic Library Online (SciELO), buscando-se artigos publicados de 1980 a 2022, nos idiomas português, inglês e espanhol, respeitando-se os critérios de inclusão e exclusão adotados. Pelo modelo de estratificação PRISMA, foram considerados elegíveis 40 arti-gos que contribuíram para o resultado do estudo, no qual a via sexual foi apontada como potencial para transmissão da doença, com prevalência de casos em indivíduos jovens do sexo masculino. Também foi possível identificar características morfológicas do vírus que o permitem sobreviver por mais tempo em superfícies inertes, assim como constatou-se a contribuição da interrupção da vacinação contra varíola para o crescimento expressivo do número de casos. Dessa forma, este estudo mostrou-se eficiente para estabelecer as relações causais que culminaram no aumento da incidência da infecção humana pelo monkeypox mundialmente, assim como contribuiu para a elucidação dos protocolos de diagnóstico e de tratamento a fim de auxiliar as condutas médicas envolvendo a doença.
Sífilis no sistema prisional do Rio Grande do Norte: uma série histórica
Authors: Dantas Amanda Almeida de Medeiros , Silva Gislainhy Aline Pires da , Alves Silvana Faustino , Luz Eduardo Edino da
Published: May 2024

Introdução: A sífilis é uma infecção bacteriana sistêmica, causada pelo Treponema pallidum, de transmissão predominantemente sexual e que apresenta alta prevalência em populações de risco, como as pessoas privadas de liberdade, em razão da exposição cons-tante às situações que aumentam a vulnerabilidade às infecções sexualmente transmissíveis. Objetivo: Descrever o perfil epidemiológico dos casos de sífilis adquirida identificados no sistema prisional do Rio Grande do Norte (RN). Métodos: Esta é uma análise descritiva dos casos de sífilis adquirida notificados pelas instituições do sistema prisional, no RN, entre os anos de 2011 e 2022. Os dados foram obtidos por meio do Sistema de Informação de Agravos de Notificação (SINAN), considerando as seguintes variáveis: ano de diagnós-tico, sexo, faixa etária, escolaridade, raça/cor e unidade prisional. Resultados: No período analisado, foram identificados 566 casos de sífilis adquirida notificados, no SINAN, pro-venientes dos presídios, dos Centros de Detenção Provisória (CDP), das cadeias públicas e dos complexos penais do RN. As instituições presentes nos municípios de Nísia Floresta e Ceará-Mirim apresentaram os maiores percentuais de casos notificados — 38,3 e 24,2%, respectivamente. Predominaram o sexo masculino (95,6%), a faixa etária de 20 a 29 anos (47,3%), a raça/cor parda (47,2%) e a escolaridade entre a 5ª e a 8ª série incompleta (17,7%). Observou-se um elevado percentual de casos com a informação ignorada de raça/cor (35%) e de escolaridade (62,7%). Conclusão: Os resultados obtidos no estudo reforçam a neces-sidade de que gestores públicos e profissionais de saúde desenvolvam estratégias efetivas para a prevenção da sífilis em unidades prisionais. Ressalta-se a importância da utilização de métodos educativos, de rastreio da infecção e de tratamento precoce dos casos identifi-cados e dos seus contatos. Nessa perspectiva, a ampliação das ações intersetoriais em saúde, considerando as especificidades contextuais, poderá contribuir para o controle da doença e a melhoria da qualidade de vida da população prisional.
Vigilância da gestante infectada pelo HIV por meio da estratégia de relacionamento de sistemas de informação em saúde
Authors: Dantas Amanda Almeida de Medeiros , Silva Gislainhy Aline Pires da , Alves Silvana Faustino , Nogueira Maria Vanessa
Published: May 2024

Introdução: A vigilância da infecção pelo HIV na gestante/parturiente/puérpera visa acompanhar continuamente o comportamento da infecção entre gestantes e crianças para que as medidas de controle e prevenção da transmissão vertical do HIV sejam planejadas, implementadas, monitoradas e avaliadas. Objetivo: Analisar os registros das gestantes infectadas pelo HIV e de crianças expostas ao HIV mediante o cruzamento de bancos de dados, para recuperação de informações subnotificadas. Métodos: Este é um estudo descritivo e retrospectivo de relacionamento de base de dados, com a coleta de informações no Sistema de Informação de Agravos de Notificação (SINAN), no Sistema de Controle Logístico de Medicamentos (SICLOM) e nos registros de entrega da fórmula infantil para crianças expostas ao HIV (RFI), no Rio Grande do Norte (RN), entre os anos de 2012 e 2022. Resultados: Foram notificados 1.126 casos de gestantes infectadas pelo HIV, no SINAN, entre 2012 e 2022, no RN. O cruzamento desse banco de dados com os demais descritos anteriormente mostrou que foram registrados 1.168 casos de gestantes com HIV, no mesmo período, no estado. O estudo revelou que houve uma subnotificação de 42 (3,7%) gestantes com HIV, no SINAN, identificada nos anos de 2012 e 2014 a 2020. Já nos anos de 2013, 2021 e 2022, observou-se o sub-registro de crianças expostas ao HIV. O maior percentual de subnotificação de gestante HIV foi identificado na 3ª região de saúde (27,6%), e o percentual mais elevado de sub-registro de crianças expostas ao HIV foi observado na 5ª região de saúde. Conclusão: Os resultados apresentados apontam para falhas na notificação das gestantes infectadas pelo HIV e das crianças expostas ao HIV, reforçando a importância do cruzamento de bancos de dados como uma estratégia para a redução do sub-registro das informações, possibilitando, assim, melhor aproximação da situação epidemiológica real.
Alta prevalência da hepatite B em população imigrante e refugiada de Goiás
Authors: Martins Thaynara Lorrane Silva , Silva Grazielle Rosa da Costa e , Silva Carla de Almeida , Gomes Davi Oliveira , Silva Bruno Vinícius Diniz e , Carneiro Megmar Aparecida dos Santos , Pacheco Leonora Rezende , Araujo Natalia Motta de , Teles Sheila de Araujo , Caetano Karlla Antonieta Amorim
Published: May 2024

Introdução: A cada dia cresce o número de imigrantes estrangeiros e refugiados em condição de vulnerabilidade individual, social e programática, favorecendo a manutenção e a aquisição de infecções transmissíveis, como a hepatite B. No Brasil, não foram identificados dados a respeito das condições de vida e saúde associadas à infecção pelo vírus da hepatite B (HBV) em população de outras nacionalidades. Objetivo: Investigar a epidemiologia do HBV em populações imigrantes estrangeiras e refugiadas residentes no estado de Goiás. Métodos: Estudo observacional, de corte transversal e analítico, realizado na região do centro goiano. A coleta de dados ocorreu de julho de 2019 a janeiro de 2020, e foram recrutados 365 imigrantes estrangeiros e refugiados com idade igual ou acima de 2 anos. Os participantes foram entrevistados e testados para a detecção dos marcadores sorológicos do HBV: HBsAg, Anti-HBc e anti-HBs. Resultados: A maioria era do sexo masculino (57%), tinha entre 18 e 50 anos (79,2%) e com mais de 11 anos de estudo (55,8%). Quanto ao país de nascimento, 57,8% eram do Haiti e 35,6% da Venezuela, e majoritariamente estavam no Brasil há menos de 2 anos (71,2%). A prevalência de infecção e exposição ao HBV foi de 6,6% (intervalo de confiança de 95% [IC95%] 4,5–9,6) e 27,9% (IC95% 23,6–32,8), respectivamente, e mais de um terço (38,1%) não apresentou marcador sorológico para o HBV. Uso inconsistente do preservativo e relação sexual sob efeito de álcool ou outras drogas são alguns dos comportamentos de risco identificados. Conclusão: A taxa de HBsAg encontrada no estudo foi cerca de 18 vezes maior do que a encontrada na população em geral do Brasil. Além disso, este estudo evidenciou um cenário de vulnerabilidade, ressaltando a necessidade de políticas públicas de saúde que conheçam e sejam capazes de criar estratégias efetivas para prevenção e controle do HBV em imigrantes estrangeiros e refugiados.
O lado “positivo” da vida: a capacidade de resiliência em pacientes que vivem com HIV/AIDS no estado do Paraná, Brasil
Authors: Camargo Angela Maria de
Published: May 2024

A descoberta do diagnóstico do HIV/AIDS configura na vida de um indivíduo um impacto que envolve vários aspectos de seu desenvolvimento, assim como a modificação do seu contexto existencial. Trata-se de pesquisa de caráter exploratório descritivo, utilizando-se de abordagem qualitativa, a ser desenvolvida por meio de métodos de pesquisa de campo e documental, com coleta de dados primários e secundários, por meio de relato pessoal. Foi utilizada, para a análise dos dados, a visão de Vanistendael, por meio do modelo de casita da resiliência. Fizeram parte da pesquisa 13 pacientes portadores de HIV/AIDS. Os objetivos foram conhecer o percurso de resiliência em pacientes que vivem com AIDS no estado do Paraná, apresentar as diferentes realidades vivenciadas pelos pacientes após o diagnóstico reagente para HIV/AIDS, como os pacientes com AIDS percebem a doença, seu tratamento e prognóstico, mudanças diárias ocorridas em sua vida a partir da convivência com a doença, existência de fatores de proteção promotores da resiliência e reconhecer fatores de risco psicossociais que interferem no processo de resiliência. Durante as análises dos dados, surgiram fatores considerados de risco e de proteção. Esses fatores podem ser biológicos/pessoais, familiares ou ambientais. Observou-se que os participantes P10 e P13 são indivíduos plenamente resilientes, e os demais encontram-se no percurso da resiliência. Fica clara a necessidade de mais estudos envolvendo a análise da capacidade de resiliência dos indivíduos que vivem com AIDS e a necessidade de os profissionais não limitarem somente a protocolos. A recuperação e a inserção desses pacientes como indivíduos sociais devem fazer parte desse processo. A capacidade de resiliência pode facilitar a reorganização pessoal em todos os aspectos. Atua em três eixos fundamentais: o ensino, a pesquisa e a extensão, podendo ser uma ferramenta de apoio e desenvolvimento de novas pesquisas que visam ao desenvolvimento da resiliência dos pacientes em acompanhamento nos diversos serviços de saúde.