bluuebunny/crossref_metadata_2025 huggingface dataset:
Opinions over Targets for Blood Pressure Control after Mechanical Thrombectomy in Patients with Acute Ischemic Stroke: Baseline Survey for the ENCHANTED2/MT Trial in China
Authors: Li Yunke , Zhao Yang , Zhang Xiaoxi , Sun Lingli , Wan Yingfeng , Zhang Yongwei , Yang Pengfei , Song Lili , Liu Jianmin , Anderson Craig S. ,   for the ENCHANTED2/MT investigators
Published: April 2024

Introduction: Although guidelines recommend a target blood pressure 185–180/105–110 mm Hg after mechanical thrombectomy for acute ischemic stroke, there is limited randomized evidence to support this level. We surveyed candidate institutions about the approach to blood pressure management in this patient group in preparation for inviting them to participate in the Enhanced Blood Pressure Control after Endovascular Thrombectomy for the Acute Ischemic Stroke Trial (ENCHANTED2/MT). Methods: Physicians from a professional network of institutions that met mechanical thrombectomy qualification requirements were invited to participate in an online questionnaire covering basic clinical information as well as questions on blood pressure management. Results: We invited 88 sites to participate with 44 (50%) ultimately joining the trial, and a total of 88 physicians finished the survey. The median number of annual mechanical thrombectomy cases performed per site was 89 (IQR: 65–150). Only 38 (43%) institutions strictly adhere to guidelines when managing the blood pressure of mechanical thrombectomy patients. The most popular blood pressure target for reperfusion patients was 140–160 mm Hg (n = 47, 53%) and <120 mm Hg (n = 28, 32%). Fewer hospital stroke beds (40 [21–57] vs. 60 [39–110], p = 0.01) and lower proportion of elevated blood pressure after mechanical thrombectomy (25% [10–50%] vs. 50% [20–70%], p = 0.02) were related to a more aggressive blood pressure target (<120 mm Hg). Urapidil (n = 82, 93%) and calcium channel blockers (n = 87, 99%) were the most widely used antihypertensive drugs, respectively. Conclusion: According to the survey, unstandardized blood pressure management protocols are performed in mechanical thrombectomy patients at institutions across China, which is different from prior survey from another country. More high-quality studies are needed to guide clinical practice.
Clinicopathologic Features and Efficacy of Induction Chemotherapy in Nasopharyngeal Carcinoma: Real-World Experience
Authors: Majidova Nargiz , Sarı Murat , Kahvecioglu Fatma Akdag , Ozcan Erkan , Akdag Mutıianur Ozkorkmaz , Dogan Akif , Yıldırım Sedat , Sonusen Sermin Dinc , Yunusov Emil , Yasar Alper , Celebi Abdussamet , Sever Nadiye , Kocaaslan Erkam , Erel Pınar , Agyol Yesım , Guren Alı Kaan , Arıkan Rukıye , Isık Selver , Balvan Ozlem , Geredelı Caglayan , Uygun Kazım , Hacibekiroglu Ilhan , Kostek Osman , Bayoglu Ibrahım Vedat
Published: April 2024

Introduction: Nasopharyngeal carcinoma (NPC) accounts for 0.01% of all carcinomas, and 70% of patients have locally advanced disease with a poor prognosis. The mainstay therapy is chemoradiotherapy (CRT), and concurrent administration of platinum-based agents and irradiation provides high local control rates. However, induction (neoadjuvant) chemotherapy (ICT) prior to CRT is recommended for large tumors with a high tumor burden at the category 1 level. For ICT, platinum-based doublet or triplet combination regimens are recommended. Selected patients with a high tumor burden at the time of diagnosis who did not receive ICT before CRT were given adjuvant (consolidation) therapy after CRT. This multicenter study aimed to share our experience in treatment of NPC and evaluate the factors associated with survival. Methods: The study included patients diagnosed with NPC who were followed and treated between 2008 and 2022. Hundred and forty-two patients from 6 centers were evaluated. The factors associated with disease-free survival (DFS) and overall survival (OS) were evaluated. Results: The median age of our patients was 51 years (IQR: 16–81 years), and the male:female ratio was 2.5:1. A majority of patients (71%) had stage 3–4 disease. They had locally advanced disease, and 48 patients (34%) received ICT. Twenty patients (14%) received adjuvant therapy. The median follow-up was 41 months (range, 2.7–175.1 months). The median DFS in NPC was 92.6 months (range, 71.9–113.3 months), with a 40th month DFS of 70.9%. The median OS was 113 months (range, 91–135 months), with a 40th month OS of 84.7%. Median DFS was 95.3 months (range, 64.2–126.4 months) in patients who received ICT before CRT, which was longer than in the CRT-only group (p = 0.6). DFS at the 40th month was 75.1% in patients treated with ICT compared to 65.1% in the CRT-only group. Median OS was 117 months (range, 92–142 months) in patients receiving ICT, which was longer than in the CRT-only group (p = 0.4). OS at the 40th month was 86.7% in patients receiving ICT but 83.6% in the CRT-only group. Conclusions: Both the objective response rate and survival were longer in patients who radiologically responded to CRT following ICT. Nonresponse to ICT is a negative predictive indicator. The role of ICT in locally advanced NPC is increasing.
The Relationship between Neurobiological Function and Inflammation in Depressed Children and Adolescents: A Scoping Review
Authors: Schumacher Anett , Muha Jessica , Campisi Susan C. , Bradley-Ridout Glyneva , Lee Andy C.H. , Korczak Daphne J.
Published: April 2024

Introduction: Neurobiological dysfunction is associated with depression in children and adolescents. While research in adult depression suggests that inflammation may underlie the association between depression and brain alterations, it is unclear if altered levels of inflammatory markers provoke neurobiological dysfunction in early-onset depression. The aim of this scoping review was to provide an overview of existing literature investigating the potential interaction between neurobiological function and inflammation in depressed children and adolescents. Methods: Systematic searches were conducted in six databases. Primary research studies that included measures of both neurobiological functioning and inflammation among children (≤18 years) with a diagnosis of depression were included. Results: Four studies (240 participants; mean age 16.0 ± 0.6 years, 62% female) meeting inclusion criteria were identified. Studies primarily examined the inflammatory markers interleukin 6, tumor necrosis factor alpha, C-reactive protein, and interleukin 1 beta. Exploratory whole brain imaging and analysis as well as region of interest approaches focused on the anterior cingulate cortex, basal ganglia, and white matter tracts were conducted. Most studies found correlations between neurobiological function and inflammatory markers; however, depressive symptoms were not observed to moderate these effects. Conclusions: A small number of highly heterogeneous studies indicate that depression may not modulate the association between altered inflammation and neurobiological dysfunction in children and adolescents. Replication in larger samples using consistent methodological approaches (focus on specific inflammatory markers, examine certain brain areas) is needed to advance the knowledge of potential neuro-immune interactions early in the course of depression.
Corpus Callosum Length and Cerebellar Vermian Height in Fetal Growth Restriction
Authors: Putra Manesha , Hamidi Odessa P. , Driver Camille , Peek Emma E. , Bolt Matthew A. , Gumina Diane , Reeves Shane A. , Hobbins John C.
Published: April 2024

Introduction: Growth-restricted fetuses may have changes in their neuroanatomical structures that can be detected in prenatal imaging. We aim to compare corpus callosal length (CCL) and cerebellar vermian height (CVH) measurements between fetal growth restriction (FGR) and control fetuses and to correlate them with cerebral Doppler velocimetry in growth-restricted fetuses. Methods: This was a prospective cohort of FGR after 20 weeks of gestation with ultrasound measurements of CCL and CVH. Control cohort was assembled from fetuses without FGR who had growth ultrasound after 20 weeks of gestation. We compared differences of CCL or CVH between FGR and controls. We also tested for the correlations of CCL and CVH with middle cerebral artery (MCA) pulsatility index (PI) and vertebral artery (VA) PI in the FGR group. CCL and CVH measurements were adjusted by head circumference (HC). Results: CCL and CVH were obtained in 68 and 55 fetuses, respectively. CCL/HC was smaller in FGR fetuses when compared to control fetuses (difference = 0.03, 95% CI: [0.02, 0.04], p < 0.001). CVH/HC was larger in FGR fetuses compared to NG fetuses (difference = 0.1, 95% CI: [−0.01, 0.02], p = < 0.001). VA PI multiples of the median were inversely correlated with CVH/HC (rho = −0.53, p = 0.007), while CCL/HC was not correlated with VA PI. Neither CCL/HC nor CVH/HC was correlated with MCA PI. Conclusions: CCL/HC and CVH/HC measurements show differences in growth-restricted fetuses compared to a control cohort. We also found an inverse relationship between VA PI and CVH/HC. The potential use of neurosonography assessment in FGR assessment requires continued explorations.
LIKE FATHER, LIKE DAUGHTER . . . OR LIKE MOTHER, LIKE DAUGHTER? Parental Models of Women Students at France’s Fourth Largest Engineering College (UTBM) in 1985–2016
Authors:   University of Technology of Belfort-Montbéliard, Heyberger Laurent
Published: April 2024

This article analyses the feminisation of the student population of the University of Technology of Belfort-Montbéliard (UTBM) from its foundation to 2016. It positions the case of UTBM in the French landscape of engineering schools, but also in the academic discussions of the causes of the over-selection of girls (paternal or maternal transmission, other explanatory model). Thanks to the large number of files used (N = 14,243), a comparison of French, Chinese, and Moroccan students of UTBM makes it possible to identify national models of choice and transmission between parents and their children. For French students, paternal model is more influential, nevertheless the maternal model seems to determine with greater influence the choice of girls, in spite of the small number of mothers who are engineers.
Modern and comprehensive soil studies in grape agrocenoses in Azerbaijan
Authors: Ikhtiar Anara Nasirova
Published: April 2024

The article presents some results of a comparative analysis of the agrochemical, microbiological, and environmental properties of grape plants and soil types common in regions located on the slopes of the Lesser Caucasus. The objectives of this study were to assess the current state and quality of soils used for vineyards in some villages of the Ganja-Kazakh economic region, taking into account the long-term use of fertilizers and chemicals to protect plantations from various diseases for the resulting wine materials. Growing and exporting grapes is of great importance for the development of the economy of the Republic of Azerbaijan. Since it is a break-even plant, expanding the area under grapes to attract wetlands to agriculture has been an important issue for soil scientists in recent years. During the period of rapid development of viticulture in the republic, ensuring rapid harvesting and longevity of vineyards is one of the important scientific and practical tasks.
Determinants of community participation in ecotourism programs in Bali, Indonesia
Authors: Utama I Gusti Bagus Rai , Junaedi I Wayan Ruspendi , Krismawintari Ni Putu Dyah
Published: April 2024

In the case of Indonesia, most of the tourist attractions offered and advertised are protected forests. This research seeks to find a balance between these two interests, so the purpose of this research is actually to determine the determinants of local community involvement in ecotourism development in Bali. This research uses quantitative methods described in the form of descriptive, and factor analysis according to the results of the survey with informants in the ecotourism of West Bali National Park, Buyan Tamblingan, Bali Mangrove, Batur Kintamani, and Lembongan. Eight factors determine community participation in ecotourism programs in Bali, namely: i) the role of leaders and business opportunities, ii) the factor of mindset and industry cooperation, iii) the factor of income, socialization, and commitment, iv) the factor of awareness of nature conservation, v) the factor of optimism to get money and knowledge, vi) the factor of facilities and independence, vii) the factor of training, interest, and participation, and viii) the factor of skills and funding.
A review of the antimicrobial benefits of naturally extracted nanomaterials
Authors: S.I Asiya , Sarangapani Anandhi
Published: April 2024

Nanotechnology finds immense potential due to the unique characteristics of nanomaterials. Though noble metal nanoparticles, particularly gold and silver nanoparticles possess advanced properties their conventional production methods pose environmental hazards. This paper explores the eco-friendly approaches using natural sources for synthesizing gold and silver nanoparticles. Rose and pomegranate extracts were used to synthesize gold nanoparticles. Evaluation using ultraviolet-visible (UV-Vis) spectroscopy confirmed the nanoparticle formation, and Fourier transform infrared spectroscopy (FT-IR) analysis recognized the presence of plant-derived compounds for stabilizing these particles. In-depth observations of their size and form were provided using electron microscopy, and these findings were aligned with the inferences made from the UV-Vis data. Silver nanoparticles were produced using Ocimum gratissimum leaf extract (OGE), exhibiting dose-dependent antimicrobial effects against bacterial strains. A comparative analysis demonstrated the distinct antibacterial characteristics of silver and gold nanoparticles against several bacterial strains. These nanoparticles demonstrated enhanced inhibitory effects when employed in combination with antibiotics, suggesting the possibility of dealing with antibiotic resistance. The study presents opportunities for producing nanomaterials with minimal impact on the environment and for addressing antibiotic resistance. Further research can enhance the process and find more useful applications as this green synthesis approach can bring about significant improvements in many areas.
Implementation approach in legal research
Authors: Hamzani Achmad Irwan , Widyastuti Tiyas Vika , Khasanah Nur , Mohd Rusli Mohd Hazmi
Published: April 2024

The use of a research approach in legal research will determine the results. There are three categories of approaches in legal research. The normative approach is the most widely used. This research aims to examine how the approach should be implemented in legal research. This research uses a conceptual approach which is still within the scope of the normative approach. Research data was collected by searching articles published in 23 law journals. The results of this research show that the approach to legal research is the use of perspective in discussing legal issues. There are three legal research approaches, namely normative, empirical, and philosophical approaches with all their variants. The normative approach reviews legal problems from a positive law perspective. The empirical approach examines legal problems as a cultural reality. A philosophical approach examines legal problems from an ideal perspective. The approach to legal research should be applied according to the type of research, research data, and level of research. The normative approach is the most widely used. This is because law is mostly understood as a set of rules. Sequentially, of the 256 articles studied, 70% of legal research used a “normative approach”, 19% empirical, and 11% philosophical.
Development of a soil electrical conductivity measurement system in paddy fields
Authors: Ho The-Anh , Bui Van-Huu , Nguyen Van-Khanh , Nguyen Dinh-Tu , Nguyen Chi-Ngon
Published: April 2024

This study aims to develop a data collection system for measuring soil electrical conductivity (EC) in Paddy Fields using the Wenner measurement principle, with one electrode configuration based on the Veris 3100 measuring machine, and a second electrode configuration with fixed electrodes. The result is a system that measures soil EC using the fixed electrode configuration for more stable results compared to the coulter electrode configuration on the Veris 3100. This system comprises three main components: a 7-inch industrial touchscreen monitor used for real-time monitoring and storing collected data on external memory, a controller that provides reverse direct current (DC) power supply to the electrodes and measures voltage and current parameters on the electrodes, two mechanical configurations including six electrodes in contact with the soil. The system is developed to operate best after soil preparation and before seed sowing. The collected data is processed and compared to actual measurements using an artificial neural network (ANN), resulting in an R2=0.7449, equivalent to linear regression. The system has been successfully installed and operated on a handheld tractor and tested in Paddy Fields on the outskirts of Can Tho City, Vietnam.
Electronic health records with decision support systems for sharper diagnoses: bibliometric analysis
Authors: Abdelaziz Toka , Rosa Elyse Maria
Published: April 2024

Clinical decision support systems (CDSS) and electronic health records (EHRs) are computer systems designed to assist decision-makers in making optimal and streamlined judgments on disease diagnosis, treatment, patient care, and health institution management. This study conducted descriptive and bibliometric evaluations of CDSS integrated with EHRs studies published in journals included in the Scopus database from 2007 to 2024. During the initial phase, the publications were distributed based on their publication year, nation, institution, journal, and citation numbers, as part of a descriptive analysis. During the second stage, the articles were subjected to bibliometric analysis, which involved doing common keyword analyses. The research yielded 409 papers about CDSS and EHRs. The United States has been identified as the country with the highest number of studies on this issue. The journal with the highest number of citations observed was Studies in Health Technology and Informatics. Furthermore, the text showcases visual representations of co-citations and cooperation between authors from different institutions and countries. This study aims to present a systematic framework for examining CDSS with EHRs to improve diagnosis and offer a comprehensive viewpoint to researchers and specialists in the field.
A novel PQ improvement in multi-parallel feeder distribution system using multi-convertible UPQC device
Authors: Seshu Moturu , Sundaram P. Kalyana , Ramesh Maddukuri Venkata
Published: April 2024

In the current situation, the use of non-linear power electronic devices in automated industries, arc-furnaces, and adjustable speed drives. The nature of large-sized non-linear loads causes harmonic pollution, voltage interruptions, and voltage sag/swell issues, which are the main issues in modern multi-parallel feeder distribution network. Several mitigation techniques among the above, the multi-convertible universal power-quality conditioner has reliable performance, robust operation and also mitigation of both current-voltage allied power quality (PQ) issues accordingly. The multi-convertible universal power-quality conditioner (Mcon-UPQC) device mitigates power-quality issues by extracting deviated supply voltage and distorted load current by using feasible control algorithm. But, the computational delay, complex mathematical transformation and response delay are the major problem identified in conventional control algorithms. In this work, a novel generalized vector reference control algorithm has been proposed for extraction of fundamental reference voltage-current signals. The performance and effectiveness of proposed control algorithm of Mcon-UPQC device is validated by using MATLAB-Simulink computing tool, simulated outcomes are illustrated with valid comparisons.
Fractional order sliding mode control for power quality improvement in the distribution system
Authors: Sreejyothi Khammampati R. , Kumar P. Venkatesh , Jayakumar J.
Published: April 2024

This paper presents fractional order sliding mode control (FOSMC) based distribution system compensator (DSTATCOM) for power quality improvement in the distribution system. The three-phase two-level inverter-based voltage source converter (VSI) with DC-link capacitor is used as DSTACOM. In this paper, the FOSMC-based DSTATCOM improves supply current harmonics, load balancing, and reactive power and reduces THD. The sinusoidal pulse width modulation (SPWM) is generating gating pulses for VSI. The performance of the presented system is verified in MATLAB/Simulink software. The simulations are verified source voltage, current and load current as well as compensating current. The FOSMC has maintained a constant supply current when connecting non-linear load. The hardware results are also presented in the manuscript. The hardware results are supply current, voltage, compensating current, and load current.
A novel hybrid-fuzzy logic based UVC technique for solar-PV/grid integrated water-pumping system
Authors: Sharma Pidatala Prabhakara , Rao Lingineni Shanmukha , Kumar Moparthi Ranjith , Vidurasri Malineni
Published: April 2024

The continual depletion of fossil fuels and increased green-house emissions are persuading the consumers to install micro-renewable energy sources-based water pumping system. Among numerous energy sources, the solar-PV plays a significant role in water pumping application due to its virtuous, environment friendly, noise-free and abundant nature, so on. Along with solar-PV, the grid integrated system enables the continuous operation of water pumping system during varying temperature and irradiance conditions, and also delivers available solar-PV energy to grid during non-functional of pumping system. The above operations are carried by using bidirectional inverter which is controlled by using unit-vector control (UVC) technique. It consists of proportional-integral controller, which is not suited for regulation of DC-link voltage at desired level because of improper selection of gain values. In this work, an intelligent hybrid-fuzzy logic based UVC technique evidences the intelligent knowledge base for better regulation of DC-link voltage and power-flow of bidirectional inverter. The performance and operation of proposed hybrid-fuzzy logic control UVC technique for solar-PV/Grid integrated water-pumping system is evaluated under various operating cases by using MATLAB/Simulink tool; simulated results are conferred with superlative comparisons.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-based perturb and observe method
Authors: Aziz Jafar Muhammad Ihsan , Zakaria Muhammad Iqbal , Dahlan Nofri Yenita , Kamarudin Muhammad Nizam , El Fezazi Nabil
Published: April 2024

Photovoltaic systems have emerged as a promising energy resource that caters to the future needs of society, owing to their renewable, inexhaustible, and cost-free nature. The power output of these systems relies on solar cell radiation and temperature. In order to mitigate the dependence on atmospheric conditions and enhance power tracking, a conventional approach has been improved by integrating various methods. To optimize the generation of electricity from solar systems, the maximum power point tracking (MPPT) technique is employed. To overcome limitations such as steady-state voltage oscillations and improve transient response, two traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb and observe (P&O), have been modified. This research paper aims to simulate and validate the step size of the proposed modified P&O and FLC techniques within the MPPT algorithm using MATLAB/Simulink for efficient power tracking in photovoltaic systems.
Smart grid deployment: from a bibliometric analysis to a survey
Authors: Wetinhoun Stéphane , Houngue Pélagie , Roland M. Ahouandjinou Sèmèvo Arnaud , Degila Jules
Published: April 2024

Smart grids are one of the last decades' innovations in electrical energy. They bring relevant advantages compared to the traditional grid and significant interest from the research community. Assessing the field's evolution is essential to propose guidelines for facing new and future smart grid challenges. In addition, knowing the main technologies involved in the deployment of smart grids (SGs) is important to highlight possible shortcomings that can be mitigated by developing new tools. This paper contributes to the research trends mentioned above by focusing on two objectives. First, a bibliometric analysis is presented to give an overview of the current research level about smart grid deployment. Second, a survey of the main technological approaches used for smart grid implementation and their contributions are highlighted. To that effect, we searched the Web of Science (WoS), and the Scopus databases. We obtained 5,663 documents from WoS and 7,215 from Scopus on smart grid implementation or deployment. With the extraction limitation in the Scopus database, 5,872 of the 7,215 documents were extracted using a multi-step process. These two datasets have been analyzed using a bibliometric tool called bibliometrix. The main outputs are presented with some recommendations for future research.
A fast charge algorithm for Li-ion battery for electric vehicles
Authors: Bouzaid Sohaib , El Mehdi Laadissi , El Ballouti Abdessamad
Published: April 2024

The renewable solar energy industry and electric vehicle industry are today seeking for fast battery pack recharging methods to achieve higher performances, and fast energy recovery for energy storage systems (ESS) and for electric vehicles. The charge rate of batteries impacts directly the temperature which in turn impacts the capacity fade, thus it should be kept low to prevent the cells from warming up. This not only limits the charging rate but also puts us on a trade-off, a long lifetime or a fast recharge. In this study, we tried to achieve fast charging using a new charging method that combine two charging methods, without much deterring the capacity of the battery, in order to be able to maintain a long battery lifetime. Charging time of around 82 min was achieved for a 1.8 Ah battery. We compared our findings with the literature with known charging profiles.
Electric vehicle and photovoltaic advanced roles in enhancing the financial performance of a manufacturing and commercial setup
Authors: Nassereddine Mohamad , Nassreddine Ghalia , ElHassan Tamima
Published: April 2024

Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network.
A review on features and methods of potential fishing zone
Authors: Ya’acob Norsuzila , Nik Dzulkefli Nik Nur Shaadah , Abdul Aziz Mohd Azri , Yusof Azita Laily , Umar Roslan
Published: April 2024

This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Data mining for predictive analysis in gynecology: a focus on cervical health
Authors: Andrade-Arenas Laberiano , Rubio-Paucar Inoc , Yactayo-Arias Cesar
Published: April 2024

Currently, data mining based on the application of detection of important patterns that allow making decisions according to cervical cancer is a problem that affects women from the age of 24 years and older. For this purpose, the Rapid Miner Studio tool was used for data analysis according to age. To perform this analysis, the knowledge discovery in databases (KDD) methodology was used according to the stages that this methodology follows, such as data selection, data preparation, data mining and evaluation and interpretation. On the other hand, the comparison of methodologies such as the standard intersectoral process for data mining (Crips-dm), KDD and sample, explore, modify, model, evaluate (Semma) is shown, which is separated by dimensions and in each dimension both methodologies are compared. In that sense, a graph was created comparing algorithmic models such as naive Bayes, decision tree, and rule induction. It is concluded that the most outstanding result was -1.424 located in cluster 4 in the attribute result date.
Analysis of research on the implementation of Blockchain technologies in regional electoral processes
Authors: Ainur Jumagaliyeva , Elmira Abdykerimova , Asset Turkmenbayev , Gulzhan Muratova , Amangul Talgat , Shekerbek Ainur
Published: April 2024

Implementation of Blockchain technologies in online voting system is becoming increasingly popular in modern society and has significantly efficiency in governance. This article explores how Blockchain technologies can boost government operations, making them more transparent and effective. It focuses on an in-depth analysis of current research and methods on Blockchain-based electronic voting systems. The aim of this study is investigated and analysis the potential contributions of Blockchain technology to e-voting by drawing insights from global best practices. According to literature review and case studies of Blockchain implementation in government are conducted to identify existing systems and methods of e-voting, identifying their strengths and weaknesses by analyzing European countries and preparing the ground for future alternatives. Additionally, it examined the role of public education in fostering trust and understanding of Blockchain technology and analyzed the legislative landscape in neighboring jurisdictions to solidify Blockchain’s role in decision-making processes. The results of the study provide a comprehensive perspective, and the findings emphasize the relevance of the study, its contribution to understanding the problems and prospects of introducing Blockchain into electoral processes at the regional level.
Expert system for diagnosing learning disorders in children
Authors: Andrade-Arenas Laberiano , Yactayo-Arias Cesar
Published: April 2024

Given the urgent need for early detection of learning disorders such as dysgraphia, dyslexia, and dyscalculia in children, this study aimed to evaluate an expert system developed in Python to facilitate early diagnosis of these disorders. The background highlights the importance of providing parents, educators, and health professionals with an effective tool for early detection of these disorders. In 21 simulated cases, the system showed impressive performance with an accuracy rate of 95%, a precision of 100%, a sensitivity of 93%, and a specificity of 100%. Furthermore, the acceptability evaluation, conducted with 15 parents selected by convenience sampling, showed a high level of satisfaction, with an overall mean of 4.78 and a standard deviation of 0.45, indicating consistency in responses. In conclusion, this study confirms the effectiveness of the expert system in the early diagnosis of learning disabilities, providing parents, educators, and health professionals with a valuable tool. Despite these encouraging results, the need for additional research is recognized to address limitations and improve the external validity of the system to ensure its widespread utility and adaptability in real clinical settings.
Analysis of the emotional coloring of text using machine and deep learning methods
Authors: Abdykerimova Lazzat , Abdikerimova Gulzira , Konyrkhanova Assem , Nurova Gulsara , Bazarova Madina , Bersugir Mukhamedi , Kaldarova Mira , Yerzhanova Akbota
Published: April 2024

The presented scientific article is a comprehensive study of machine learning and deep learning methods in the context of emotion recognition in text data. The main goal of the study is to conduct a comprehensive analysis and comparison of various machine learning and deep learning methods to classify emotions in text. During the work, special attention was paid to the analysis of traditional machine learning algorithms, such as multinomial naive Bayes (MNB), multilayer perceptron (MLP), and support vector machine (SVM), as well as the use of deep learning methods based on long short-term memory (LSTM). The experimental part of the study involves the analysis of different data sets covering a variety of text styles and contexts. The experimental results are analyzed in detail, identifying the advantages and limitations of each method. The article provides practical recommendations for choosing the optimal method depending on the specific tasks and context of the application. The data obtained is important for the development of intelligent systems that can effectively adapt to the emotional aspects of interaction with users. Overall, this work makes a significant contribution to the field of emotion recognition in text and provides a basis for further research in this area.
An advanced approach for accurate pneumonia detection using combined deep convolutional neural networks
Authors: El Zein Ola M. , Ghannam Naglaa E.
Published: April 2024

Pneumonia, a lung infection caused by viral or bacterial agents, poses a significant health risk by affecting one or both lungs in humans. Accurate diagnosis, particularly in pediatric cases, is crucial for timely intervention. chest X-rays (CXRs) are a common and non-invasive diagnostic tool to detect pneumonia-related abnormalities. Nonetheless, the minimal radiation exposure suitable for pediatric diagnosis poses a challenge in accurately detecting pneumonia in children. This work proposes a concatenation model that combines two pre-trained convolutional neural networks (CNNs) depending on the transfer learning (TL) technique and optimizes the training parameters to build a highly accurate model for detecting pediatric pneumonia from CXR images. The concatenated extracted features from the two pre-trained CNNs are passed through a convolutional layer to select more valuable semantic features to reduce the extracted features, which helps reduce the model parameters and execution time. Experimental results demonstrate that the feature concatenation technique, along with optimization of training parameters, surpasses the performance of individual CNNs and several state-of-the-art methods. The proposed method achieves a classification accuracy of 98.5%, precision of 99.5%, sensitivity of 98.4%, and F1 score of 99.1%. The primary objective of the proposed approach is to aid radiologists in achieving accurate pneumonia diagnosis in real-time.
Ambient intelligent framework for modelling critical medical events based on context awareness
Authors: Subramaniyam Manjunath , Parthasar Srirangapatna Sampathkumararan
Published: April 2024

With the rapid pace of communication technology, the modern communication system still encounters challenges in meeting the dynamic requirements of users. Facilitating emergency services for patients without a caretaker side by is quite challenging. This work contributes a solution towards state-of-the-art research problems by introducing a novel architecture using collaboration, coordination and user activity detection using contextual information. A prototype is built and experiment is carried out to emphasize the importance of real-time activity-based context awareness in ambient intelligence (AmI) applications. The primary contributions of this work are introduction of novel architecture and usage of both static and dynamic activity-based contextual parameters. The secondary contribution of this model is to integrate ambient intelligence with context awareness to offer higher accuracy in determining the critical condition of a patient. Initially, analytical models are built using the context-based attributes that consider both clinical and non-clinical entities based on the minimal and essential vital information of patient. This paper further discusses the experimental model, which is highly cost-efficient both from an operational and usage viewpoint. Different assessment environments have been used for assessing the performance of the model.
Implementation of suitable information technology governance frameworks for Moroccan higher education institutions
Authors: Abdelilah Chahid , Ahriz Souad , El Guemmat Kamal , Mansouri Khalifa
Published: April 2024

This article aims to present formal governance practices of information technology adapted to the general context of Moroccan universities. The study consists of two main phases: the conceptualization phase and the operationalization phase. During the conceptualization phase, the authors reviewed relevant literature on best practices and their associated frameworks in higher education institutions (HEIs). The results revealed that universities had varying levels of maturity in terms of good practices and often used multiple information system frameworks, which can cause organizational and technical problems. In order to find a solution to this situation, the authors conducted in-depth interviews with chief information officers (CIOs) and university officials from four Moroccan universities during the operationalization phase. These interviews enabled them to propose an effective baseline of best practices and an algorithmic approach to assist managers in choosing between two combinations of frameworks that cover all the mechanisms of the baseline. This solution would enable optimal, agile, and easy-to-implement information technology governance in Moroccan universities while avoiding the multiplicity of frameworks.
Enhancing online learning: sentiment analysis and collaborative filtering from Twitter social network for personalized recommendations
Authors: El maazouzi Qamar , Retbi Asmaa , Bennani Samir
Published: April 2024

Online learning presents a major challenge for learners, namely the diversification of courses and information overload. In response to this issue, recommender systems are widely used. Nowadays, social networks have become a global platform where individuals share a multitude of information. For instance, Twitter is a social network where users exchange messages and interact with various communities. These interactions on social networks have created a new dimension in the field of online learning. In this article, we propose a novel approach that combines sentiment analysis of learners’ reviews on social networks with collaborative filtering methods to provide more personalized and relevant course recommendations. To achieve this, we explored different models to analyze the sentiments of tweets related to online courses. Additionally, we used collaborative filtering based on k-nearest neighbors (KNN). Our results demonstrate that integrating sentiment analysis provides more relevant recommendations. This has also been shown based on the calculation of root mean square error (RMSE) compared to a traditional approach. In this study, we demonstrated that by leveraging this information from social networks like Twitter, online learning platforms can enhance the effectiveness of their course recommendations, tailoring them to each individual learner’s needs.
Automated DeepLabV3+ based model for left ventricle segmentation on short-axis late gadolinium enhancement-magnetic cardiac resonance imaging images
Authors: Awang Damit Dayang Suhaida , Sulaiman Siti Noraini , Osman Muhammad Khusairi , A. Karim Noor Khairiah , Setumin Samsul
Published: April 2024

Accurate segmentation of myocardial scar tissue on late gadolinium enhancement-magnetic cardiac resonance imaging (LGE-CMR) is exceptionally vital for clinical applications, enabling precise diagnosis and effective treatment of various cardiac diseases, such as myocardial infarction and cardiomyopathies. However, the ventricle (LV) variations in the size and shape, artifacts, and image resolution of LGE-CMR has made automatic segmentation of myocardial scar tissue more challenging. While many existing approaches delineate the LV myocardium region using multi-modal segmentation, these models may be computationally complex and suffer from misalignment. Therefore, this study proposed an automatic dual-stage DeepLabV3+ based approach tailored for myocardial scar segmentation on short-axis LGE-MRI exclusively. To segment myocardial scar tissue, the second stage employs the segmented LV chamber from the previous stage. The encoder part of the framework utilizes a MobileNetV2 and ResNet50 backbone for the first and second segmentation, respectively, aiming for optimal resolution of feature maps. Both stages tailor an improved Atrous Spatial Pyramid Pooling module in the DeepLabV3+ model with fine-tuned dilated atrous rates to effectively extract the LV chamber and myocardial scar from the complex LGE-MRI background. Based on the results, the proposed dual-stage network recorded an outstanding segmentation performance, with mean Dice score of 96.02% for LV chamber segmentation and 68.01% for scar tissue extraction.
A comparative analysis of convolutional neural networks for breast cancer prediction
Authors: Al Tawil Arar , Shaban Amneh , Almazaydeh Laiali
Published: April 2024

Breast cancer continues to be a substantial worldwide health concern, affecting millions of individuals each year; this emphasizes the critical nature of early detection in order to enhance patient prognoses. The present study aims to assess the classification performance of three convolutional neural network (CNN) architectures-visual geometry group 19 (VGG19), AlexNet, and residual network 50 (ResNet50)-with respect to breast cancer detection in medical images. Thorough assessments, encompassing metrics such as accuracy, precision, recall, and F-score, were undertaken to evaluate the diagnostic performance of the models. ResNet50 consistently outperforms other models, as evidenced by its highest accuracy and F-score. The research highlights the significant importance of carefully choosing suitable architectures for medical image analysis, with a specific focus on the detection of breast cancer. In addition, it demonstrates the capacity of deep learning models, such as ResNet50, to improve the diagnosis of breast cancer with exceptional precision and sensitivity, which is critical for reducing the occurrence of false positives and negatives in clinical environments. In addition, computational efficiency is taken into account; AlexNet is recognized as the most efficient model, which is advantageous in environments with limited resources. This study advances medical image processing by demonstrating the potential of CNNs in the detection of breast cancer. The results of this study establish a fundamental basis for sub- sequent inquiries and suggest approaches to improve timely detection and treatment, which will ultimately be advantageous for both patients and healthcare professionals.
Optimizing loss functions for improved energy demand prediction in smart power grids
Authors: Nussipova Fariza , Rysbekov Shynggys , Abdiakhmetova Zukhra , Kartbayev Amandyk
Published: April 2024

In this paper, our aim is to improve the accuracy and effectiveness of energy demand forecasting, particularly within modern electricity transmission systems and smart grid technology. To achieve this, we developed a hybrid approach that combines machine learning, representation learning, and other deep learning techniques. This approach is based on extracting essential features, including time-based attributes, identifiable trends, and optimal lags. The outcome of our investigation is the observation that triplet losses demonstrate remarkable accuracy, particularly when employed with a larger margin size and for longer prediction lengths. This finding signifies a substantial improvement in the precision and reliability of energy demand forecasting within modern electricity transmission systems. Our research not only improves predictive modeling in the power grid but also demonstrates the practical use of advanced analytics in addressing renewable energy integration challenges, refining energy demand forecasting for efficient management, system operation, and market analysis.
Learner’s attention detection in connected smart classroom using internet of things and convolutional neural networks
Authors: Riad Mustapha , Qbadou Mohammed , Aoula Es-Saâdia
Published: April 2024

Detecting learner attention is an essential part of learning assessment. Consequently, it becomes an essential requirement for adaptive intelligent teaching systems, to identify specific needs and anticipate orientations. In this article, we propose a new model of a connected smart classroom, based on the internet of things, artificial intelligence and machine learning to detect in real time learners' attention and marking their presence during the execution of a teacher-assisted pedagogical activity, as well as to adapt the most suitable learning objects to these learners. The proposed model is based on head position, gaze direction, yawning and eye-state analysis as facial landmarks detected by cameras connected via the Bluetooth low energy network and transmitted to a developed convolutional neural network. In addition, a series of experiments have been conducted to evaluate the performance and efficiency of the model developed. The findings demonstrate that the model developed can be used to precisely capture the status of learners in the classroom in terms of attention and identification. In this way, these interesting findings can be used to adapt teaching activities to the individual needs of learners, and to identify areas where they have difficulties and needs extra help.