bluuebunny/crossref_metadata_2025 huggingface dataset:
THE LEGAL NATURE OF THE JOINT WILL OF THE SPOUSES
Authors: DENGIN A.G.
Published: October 2024

This article examines the peculiarity and legal nature of the joint will of spouses (joint will) in Russian law and modern legal doctrine. The author explores the problem of the legal characteristics of the joint will of spouses, analyzes the existing positions in the legal literature concerning the concept of joint will of spouses. Within the framework of this article, the author defines the problem of the ambiguity of the regulatory legal regulation of the joint will of spouses in domestic legislation and comes to conclusions about the correlation of problems of substantive law concerning the legal characteristics of the joint will of spouses with potential law enforcement problems.
Nursing procedures for assisting victims of violence in emergency units
Authors: Carvalho Jade Fonsêca Ottoni de , Guilhem Dirce Bellezi
Published: October 2024

The objective of this study was to carry out the cross-cultural adaptation of the Questionnaire on Preservation of Traces in Victim Assistance by Nurses to the Brazilian reality. The method used was composed by the steps: (1) semantic equivalence; (2) idiomatic equivalence; (3) empirical equivalence; (4) conceptual equivalence; and (5) pre-test. Based on the experts' analysis and recommendations, adjustments were made regarding semantic and empirical equivalence; during the assessment of idiomatic and conceptual equivalence, no changes were made. The pre-test was performed with 24 nurses and the value of Cronbach's alpha coefficient was α = 0.973. The instrument's feasibility assessment showed that most participants found it easy to understand the questionnaire's instructions and questions and mark the answers, which demonstrates its validity with the sample in the pre-test. The performance of the forensic nurse is essential in emergency services. The adapted instrument is valid for use in the Brazilian context.
Integrated pest management of soybean emerging pests in Brazil and United States: a review
Authors: Santos Juliana Lopes dos , Peluzio Joenes Mucci , Picanço Marcelo Coutinho , Sarmento Renato Almeida
Published: October 2024

Soybean cultivation is crucial for meeting global demand for vegetable oil and protein, with Brazil being the largest global producer, followed by the United States. However, intensifying soja production has led to the emergence of new pests, such as whitefly, thrips, and leaf miners, which cause damage to plants and reduce crop productivity. The integrated pest management approach is essential for sustainable pest control, minimizing pesticide use, and maintaining ecological balance. However, further research is needed on emerging pests and their management strategies.
Machine learning for particle size prediction in iron ore grinding process
Authors: Silva Daniel Henrique Cordeiro , Alves Vladmir Kronemberger , Souza Ernandes Savio
Published: October 2024

The mining industry is finally embracing the new opportunities brought by Industry 4.0, and one opportunity to be explored is the use of machine learning and artificial intelligence tools. To meet the continuous global demand for iron ore and improve the performance of facilities, process optimization is essential and may revolutionize the industry with data-based decision-making and increased efficiency and profitability. In iron ore beneficiation plants, the potential gains from machine learning tools are significant, and tend to increase in the milling process, where the grinding phase incurs the highest energy costs among mineral processing operations. This article presents the application of machine learning techniques for the prediction of the main product quality parameter of a milling plant. CRISP-DM, a well-established approach for data science projects, was applied, and the results demonstrated the effectiveness of the model, achieving satisfactory predictive performance. The deployment reduces reliance on laboratory testing and provides energy savings through proactive process control optimization.
EMS response to infectious disease outbreaks: Prehospital preparedness and intervention
Authors: Alenezi Abdulrahman Ramadhan , Alanazi Mohammed Ayyat , Aldhafeeri Fares Saqir , Alotaibi Bader Naif , Alshamri Ahmed Saer , Alenezi Meshal Falah , Alkhathami Bandar Mohammad Abdullah , Alenazi Nawaf Subhi Dobayan , Alhazmi Fares Khalid Mohammed , Al Khathami ‏Mohammed Mesfer Musaed , Alanazi ‏Anood Fhid , Alomran ‏Razan Walid , Al-Jasser Saleh AbdulLatif , Alharbi Sanad Samah , Gaddourah Ahmad Mohammed Isa , Alrakhimy Hamad Dafalh
Published: October 2024

Background: Emerging infectious diseases (EIDs) often originate from wildlife, with zoonotic transmissions, such as those causing pandemic influenza, Ebola, and COVID-19. Ecological disruptions, including deforestation and land-use changes, have heightened the risk of these diseases by increasing human contact with wildlife. Aim: The aim of this article is to examine emergency medical services (EMS), paramedics, and nursing preparedness and intervention strategies during infectious disease outbreaks. Methods: Methods include analyzing zoonotic spillover factors and reviewing prevention strategies under the One Health framework. Results: The findings reveal that efforts to prevent zoonotic spillover are minimal, with most attention focused on post-spillover measures like vaccine development and healthcare response. Results suggest that a comprehensive approach, including forest conservation, biosecurity in animal husbandry, and regulating wildlife markets, can reduce the risk of future outbreaks. Conclusion: It concludes that true prevention requires addressing the root causes of zoonotic transmission, enhancing community-driven initiatives, and improving biosecurity to mitigate spillover risk.
Prioritizing Intellectual Capital Assets for Strategic Investment Using an Analytic Network Process Approach
Authors: Eva Mafanasy
Published: October 2024

Intellectual Capital Assets (ICAs) refer to the intangible resources that are critical to the creation of organizational value out of human, customer, innovation and process capital. High impact ICAs should be identified and prioritized for investment in by organizations as they strive to increase their value. This research aims at assessing and ranking ICAs by using Analytic Network Process (ANP) model for capturing the interactions between the assets. The method used to gather the pairwise data aimed at supervising CEOs, scientific supervisors, shareholders, employees and customers in order to determine the relative value each ICA contributes to value creation. A mathematical model is employed to translate qualitative assessments into quantitative results with regard to Consistency Index (CI) and Consistency Ratio (CR) to check the reliability of the judgments. Cost benefit analysis shows the ICAs which yield the greatest return on investment. Our findings suggest that knowledge sharing and innovation are the most dominant ICA that positively and significantly affects value creation. These assets involve moderate investment but have huge returns, while other assets such as employee training and customer relations also have greater potential but involve high investment. The results offer practical guidance to organizations in terms of how to effectively prioritize ICAs to support their achievement of strategic objectives in creating innovative value.
Risk Perception Disparities in E-Business: Analyzing SME Roles, Experience and Legal Compliance Challenges
Authors: Yue Chiang Wang
Published: October 2024

This study examines how e-business experience and organizational roles impact on the perception of dependency risks including external service provider risks, legal and compliance risks, and security risks among SMEs. Thus, the first research objective is to examine how and to what extent general and specific e-business experience influence the perceived risks and their priorities. In the present research, a mixed research approach and Kruskal-Wallis test were adopted to compare the risk perception of the respondents. We dissect the findings further by applying qualitative analysis on the impressions shared by the respondents. The findings show that e-business experience has direct effects on risk perception and the firms with less e-business experience perceive dependency risks as more severe, especially in terms of website developer, hardware, and software dependencies. On the other hand, larger and more experienced SMEs consider legal and compliance risks as more important especially where the cross-border transactions and data privacy issues are involved. Moreover, IT staff showed better appreciation of the reputational and employee risk factors more than the non-IT staff showing that technical knowledge is key to risk perception. Based on these results, this study posits that as SMEs gain e-business experience, their risk perceptions change and so do their understanding of external vulnerabilities and regulations.
Hybridization of Machine Learning Models for Alzheimers Disease Classification
Authors: A Nirmala Devi , M Latha
Published: October 2024

Alzheimer's disease (AD), is a gradual cognitive decline and memory impairment. It is a major health concern worldwide. Despite intensive research efforts, accurate and early diagnosis remains difficult to achieve, largely due to the complexity of AD pathology and the absence of definitive biomarkers. Existing diagnostic approaches often rely on costly and invasive procedures, leading to delays in diagnosis and treatment initiation, and limiting the effectiveness of therapeutic interventions. To overcome these issues, this work suggests a novel approach for AD classification using EEG signals. EEG signals offer a non-invasive and cost-effective means of assessing brain activity, making them an attractive candidate for biomarker discovery and disease classification. The proposed work integrates preprocessing, feature extraction, and classification methodologies to accurately differentiate between AD, normal/healthy states, and Frontotemporal Dementia (FTD). The proposed solution begins with Sequential Savitzky-Golay filtering (SEQ-SG) to enhance the quality of EEG signals by reducing noise and enhancing relevant features. Subsequently, an Improved Principal Component Analysis (IPCA) approach is employed for feature extraction, incorporating feature scaling using StandardScaler to ensure uniform contribution from all features. Finally, classification is achieved using a hybrid approach named HMLCAD (Hybridization of Machine Learning for Classification of Alzheimer's Disease), which combines Random Forest and Gradient Boosting through a voting classifier ensemble. This methodology offers a promising framework for accurate and early detection of AD, enabling timely intervention and improved patient outcomes.
ECBoA-OFS: An Ensemble Classification Model for Botnet Attacks based on Optimal Feature Selection using CPR in IoT
Authors: G Chandana Swathi , G Kishor Kumar , A P Siva Kumar
Published: October 2024

The rapid growth of the Internet of Things (IoT) has indeed introduced new security challenges, and the proliferation of compromised IoT devices has become a significant concern. Botnet attacks, where multiple corrupted devices are managed by a particular object, have become a widespread threat in IoT environments. These are used for a variety of malicious activities, including distributed DDoS attacks, data breaches, and malware distribution. However, detecting IoT botnets poses several challenges due to the resource constraints inherent in many IoT devices. The limitations in computation, storage, and communication capabilities make it challenging to deploy complex ML and deep learning models directly on these devices. This paper proposes an ensemble classification model ECBoA-OFS (Ensemble Classification for Botnet Attack Prediction using Optimal Feature Selection). It focuses on enhancing the accuracy of botnet attack prediction through the integration of ensemble methods and optimal feature selection. It describes a method for optimal feature selection in the context of analyzing the behavior of BoA and malicious traffic flow features in a network using Central Pivot Ranges (CPR). Feature selection is an important step in machine learning and data analysis because it supports to identification of the most important features for a given problem, thereby improving model performance and interpretation. The extracted features are used for model training and ensemble classification for prediction. To evaluate ECBoA-OFS, the N-BaIoT-2021 dataset consisting of regular IoT network traffic and BoA traffic records of corrupted IoT devices is utilized, considering detection precision, sensitivity, specificity, accuracy, and F1-score. Although all ensemble classifier models achieved better detection accuracy through optimal feature selection, the proposed ECBA-OFS shows better results compared to other ensemble classifier results.
An Improved and Secured 3-Factor Authentication Scheme for WSN in IoT Based Applications
Authors: Sengodan Ramesh
Published: October 2024

Wireless Sensor Networks (WSN) has top-notch capacity for numerous domains of utility due to the potential to sense and apprehend unattended environments. IoT is playing an increasingly more essential position in clever healthcare, smart transportation and elegant grid using WSN. Therefore, challenges like privacy and data security associated with IoT wireless sensor networks because of sensor node being captured easily, consume less computing energy and low storage. Designing a secure authentication scheme will enable secure communication in WSN. Here, we proposed a improved three-factor authentication scheme for WSN utilizing biometric, password and smart card withstand against security attacks. Subsequently, by comparing the security, functional and performance parameters of our proposed scheme to the other schemes, our scheme provides better and efficient one. Therefore, our scheme is fairly workout in real time IoT applications involved in WSN scenario.
Developing an Adaptive Learning Recommendation Algorithm and System for MOOCs
Authors: Zhang Ying
Published: October 2024

Massive Open Online Courses (MOOC) based learning platform had totally changed the educational environment by providing easy and accessible learning opportunities for global learners. But even such environment display high dropout and low learner engagement which remain a significant challenge to be addressed. To handle the challenge of this study, propose an Adaptive Learning Recommendation System (ALRS) that is designed to personalize learning paths based on individual preferences and performance metrics. The study employed Open University Learning Analytics Dataset (OULAD) and build recommendation model that combine k-means Clustering, Content-based Filtering, Collaborative Filtering, and Random Forest (RF) classifiers to make course recommendations. The proposed model have shown better recommendation when compared to other models with Precision of 0.92, Recall of 0.89, F1 Score of 0.90, and AUC of 0.95. Also the proposed model had shown the lowest Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) at 0.042 and 0.205, respectively.
Public Participation Monitoring: Social Media Data Mining and Analysis of User Engagement Patterns
Authors: Zhang Jing , Tang Lijun
Published: October 2024

Social Media Platforms (SMP) like Twitter and Facebook have become public influencing medium in today’s digital age which facilitate community interactions. These necessitates the monitoring of public participation in these SMP. This work performs such an study by examining the user engagement patterns in social media in Indonesia during the national elections (#Pemilu2024), Ramadan celebrations (#Ramadan2024), and climate change discussions (#ClimateChange). The data for the study was collected during a period of six months from (January to June 2024) using event-specific hashtags and keywords. To identify the user engagement patterns datamining and Natural Language Processing (NLP) tools were utilized. The findings show that the Twitter platform has higher user engagement in morning with news updates and visual updates at evening. The Facebook show user engagement in afternoon with videos and evening engagement with shared articles. The Sentiment Analysis (SA) and network analysis was performed over the dataset and the findings have shown that higher positive sentiments towards elections and Ramadan but argumentative towards climate change discussions. The Twitter show rapid communication effectiveness compared to Facebook. Further the youth prefer faster update and older expect detailed content sharing.
Analysis of the Evolution of Spatio Temporal Pattern of Rural Industrial Integration and its Influencing Factors
Authors: Ge Linling , Kongruang Chuleerat
Published: October 2024

Regional development has a significant impact on Rural Industrial Integration (RII), which substantially boosts economic growth in rural regions and decreases the economic disparity between rural regions and urban areas. Addressing the Spatio-Temporal Patterns (STP) of RII and the factors that impact these developments is essential for today's economies to attempt balanced regional development successfully. The objective of the present study is to investigate the STP of RII during time considering Zhejiang Province, China, as a case study. The present research examines the primary social, economic, and environmental variables that result in RII applying spatial economic frameworks like Adaptive Geographically Weighted Regression (AGWR) and Multiscale Geographically Weighted Regression (MGWR). The research study evaluated how they relate and impact these factors to integration across multiple spatial scales. With AGWR and MGWR values achieving 0.0083 and 0.0085, respectively, the study indicated that the most significant variable determining RII is the development of urban infrastructure. Significant grouping impacts have been shown by the spatial autocorrelation (Moran's I) for this metric, which attained values that were as high as 0.4205. Significant variables comprised the cost of investment and the urban-rural per capita disposable income (PCDI) proportion, with PCDI ratio ratios of 0.0053 (AGWR) and 0.0056 (MGWR), respectively.
Enhancing the Vehicular Public Key Infrastructure to Develop Privacy-Preserving Authentication Scheme for Vanet by Using PCM and MSS Scheme
Authors: N Jyothi , Terdal Sujatha
Published: October 2024

This article proposes a security-based authentication as well as efficient certificate management approach for VANET to detect fraudulent nodes with better precision, less latency and overhead. The primary purpose of the developed system is to establish effectual and heftiness of VANET security that lead to the stability of overall network. VANETs are composed of vehicles and Road Side Units (RSUs) assisting with network management and the vehicles connect with one another and RSUs to furnish roadside information and safety solutions. Security is an essential factor in VANETs because the confidentiality of humans (passengers) is paramount; hence, Vehicular Public Key Infrastructure (VPKI) is utilised to offer authentication and safety services in VANETs. The developed structure provides an encrypted VANET transmission infrastructure by utilising the concepts of Merkle Signature Scheme (MSS) and Pseudo-code Certificate Management (PCM) to minimise overhead for communication and latency while ensuring entity authenticity. Messages are authenticated by sender, encoded with a vehicular public key distributed by a PCM-MSS and decrypted by the destination, resulting in every transmission including a certification from a reliable authority. During that verification, the transmitter and receiver of message’s authentication and validation is accomplished. Simulation findings show that the proposed approach improves the reliability of identifying hostile nodes and PDR while reducing authentication delays and overhead.
Machine Learning for Genomic Expression Classification-Based Phenotype Prediction in Topological Data Analysis
Authors: M Narender , Mohsin Karrar S. , T Ragunthar , Papasani Anusha , Ayasrah Firas Tayseer , R Anjaneyulu Naik
Published: October 2024

Genomic data has become more prevalent due to sequencing and Machine Learning (ML) innovations, which have increased the biological genomics study. The multidimensional nature of this data provides challenges to phenotype prediction, which is required for individualized health care and the research investigation of genetic problems; nevertheless, it holds tremendous potential for understanding the association between genes and physical features. The authors of this paper introduce a new technique for symptom prediction from data from genomes, which combines Topological Data Analysis (TDA), Graph Convolutional Networks (GCN), and Support Vector Machines (SVM). The proposed method aims to address these challenges. By using TDA for multifaceted feature extraction, GCN to analyze gene interaction networks, and SVM for reliable classification in high-dimensional spaces, the above technique overcomes the drawbacks of conventional approaches. This TDA-GCN-SVM model has been demonstrated to be implemented in a method that is superior to conventional methods on distinct tumor datasets in terms of accuracy and additional measures. A novel method for genomic study and a more significant comprehension of genomic data analysis are both caused by this innovation, which is an enormous achievement in precision healthcare.
Exploring Feature Relationships in Brain Stroke Data Using Polynomial Feature Transformation and Linear Regression Modeling
Authors: Parvathi Sitanaboina S L , B Aruna Devi , Kulkarni Gururaj L , Murugan Sangeetha , Vijayammal Bindu Kolappa Pillai , Neha
Published: October 2024

A Cerebral vascular accident, commonly known as a stroke, is a pathological condition that impacts the brain due to the rupture of capillaries. It occurs when there is a disturbance in the typical blood circulation and essential physiological processes of the brain. Stroke prediction plays a crucial role in early diagnosis and intervention, potentially improving patient outcomes. This paper proposes a machine learning model that leverages polynomial feature transformation and linear regression modeling for stroke prediction. The model addresses the challenge of capturing non-linear relationships between features and the target variable while maintaining interpretability. The proposed approach involves preprocessing data by separating categorical and numerical features, applying one-hot encoding to categorical features, and generating polynomial features up to the second degree for numerical features. This tailored preprocessing is facilitated by a Column Transformer. For model development, a machine learning pipeline is constructed, splitting the data into training and testing sets. Despite utilizing polynomial features, linear regression is employed as the final model, allowing for the capture of both linear and non-linear relationships while maintaining interpretability. This work contributes to stroke prediction by offering a balanced approach that considers model complexity and interpretability, showcasing the potential of linear regression with polynomial features for accurate predictions and insights into feature-target relationships. The proposed model exhibited superior performance compared to other existing models, achieving a remarkable testing accuracy of 99.2%.
Optimized CNN-BiLSTM with Attention: A High Performance Model for Predicting Heart Disease Using Cleveland and Framingham Datasets
Authors: K Kayalvizhi , S Kanchana , S Silvia Priscila , C Padmavathy , Ramana Murthy Banda SNV , Thangavel Veeramani
Published: October 2024

Worldwide, some 17.9 million survives are lost each year due to heart disease (HD), which is acknowledged by the World Health Organisation (WHO) as top cause of mortality. In order to simplify further action, HD prediction—a difficult problem—can give a computerised estimate of the HD level. Improving patient outcomes and allowing for timely medical interventions are both made possible by early detection and accurate calculation of HD. As a result, HD prediction has garnered a great deal of interest from healthcare facilities around the globe. There has been encouraging progress in the detection of cardiac illness thanks to recent developments in machine learning (ML). Transparency and explainability, in addition to generalisability and robustness, are crucial for ML models to be used in therapeutic settings. The efficient prediction and diagnosis of numerous diseases was greatly aided by systems based on Deep Learning (DL). By combining Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BiLSTMs), besides Attention Mechanisms (CNN-AM), this paper aims to build a strong HD prediction scheme. Minimal preparation is necessary for this procedure. To extract spatial features, CNN is used. To extract temporal characteristics, Bi-LSTM is used. Lastly, to filter out the outcomes of the more to ighted channel output classification, two channel to ights are allotted through the attention mechanism. The proposed model's parameters are fine-tuned using a new optimisation approach known as Newton-Raphson-based Optimiser (NRO), which ultimately leads to better classification accuracy. With accuracy of 95.3% on the Cleveland dataset and 98.1% on the Framingham dataset, respectively, the optimised CNN-BiLSTM-AM model demonstrated the best performance in the experimental findings.
Mengenali Potensi Diri Melalui Tes Minat dan Karir Berbasis Aplikasi BK Almas untuk Pemetaan Karir Remaja di Desa Sepakat
Authors: Anuar Arman Bin , Marhani Marhani , Fahmi Ainul , Nasriandi Nasriandi , Pribadi Imam
Published: October 2024

Pengabdian kepada masyarakat ini bertujuan untuk mengidentifikasi potensi diri remaja di Desa Sepakat melalui tes minat dan karir berbasis aplikasi BK Almas guna pemetaan karir yang efektif. Aplikasi BK Almas sebagai instrumen bimbingan karir berbasis teknologi untuk membantu remaja mengenali minat dan bakat mereka serta memberikan panduan karir yang lebih akurat. Metode yang digunakan meliputi seminar bimbingan dan konseling, serta penerapan tes minat dan karir melalui aplikasi BK Almas pada remaja berusia 15-18 tahun di Desa Sepakat. Hasil tes menunjukkan minat terbesar pada bidang sosial dan konvensional, diikuti oleh multi-kategori, dengan minat yang rendah pada bidang kewirausahaan, investigasi, artistik, dan realistis. Identifikasi masalah karir mengungkapkan bahwa remaja di desa ini sering kali kekurangan informasi dan bimbingan karir yang memadai, serta menghadapi tekanan eksternal dalam pengambilan keputusan karir. Implementasi aplikasi BK Almas memberikan solusi inovatif untuk pemetaan karir, membantu remaja memahami potensi diri dan merencanakan masa depan karir dengan lebih baik. Pengabdian ini diharapkan dapat menjadi model bagi program bimbingan karir di daerah pedesaan lainnya, menciptakan generasi muda yang lebih siap dan percaya diri menghadapi tantangan karir di masa depan.
PkM Pemberdayaan Kelompok Rumah Madu Cendana Melalui Pemanfaatan Digital Marketing
Authors: Patmaniar Patmaniar , Tenriawaru Eka Pratiwi , Ulvah Ulvah
Published: October 2024

Rumah Madu Cendana merupakan salah satu kelompok budidaya lebah yang ada di Desa Cendana Putih Kecamatan Mappedeceng Kabupaten Luwu Utara Provinsi Sulawesi Selatan. Rumah Madu Cendana membudidayakan lebah tidak bersengat yang disebut dengan trigon biroi sebanyak 20 stup. Budidaya lebah trigon biroi dilakukan di samping rumah salah satu anggota kelompok Rumah Madu Cendana. Hasil budidaya Rumah Madu Cendana berupa madu trigon biroi. Proses produksi madu trigon biroi dilakukan dengan cara tradisional dan sederhana, yaitu dengan memeras kantong madu. Program pengabdian kepada masyarakat merupakan implementasi hibah Direktorat Riset, Teknologi, dan Pengabdian kepada Masyarakat (DRTPM) tahun 2024 yang bertujuan untuk memberdayakan kelompok Rumah Madu Cendana untuk meningkatkan kualitas dan kuantitas produksi madu trigona biroi. Metode yang diterapakan dalam program Pengabdian Kepada Masyarakat ini bersifat partisipatori yaitu tim pengusul bersama mitra Rumah Madu Cendana terlibat secara proaktif dalam setiap kegiatan. Pelaksanaan program dilakukan dengan edukasi pengemasan produk dan pemasaran berbasis digital marketing. Hasil kegiatan menunjukkan bahwa kelompok Rumah Madu Cendana memberikan respon yang sangat positif. Selain itu, kelompok Rumah Madu Cendana juga mendapatkan pengetahuan tentang pengemasan yang menarik dan efisien serta mendapatkan pengetahuan tentang pentingnya pemasaran berbasis digital marketing.
Penerapan Metode Pembelajaran Kooperatif Tipe Group Investigation untuk Meningkatkan Hasil Belajar Peserta Didik
Authors: Ririn Ririn , Zuhdi Rasyid
Published: October 2024

Kemampuan dalam meningkatkan hasil belajar bagi siswa dengan melalui proses hingga mencapai hasil yang memuaskan dapat membangkitkan rasa semangat dan percaya diri siswa akan pemahaman materi untuk meningkatkan hasil belajarnya. Dengan adanya cara baru dalam meningkatkan hasil belajar siswa salah satunya menggunakan metode kooperatif tipe Group Investigation dapat membuat siswa lebih semangat, kreatif dan ada nilai sosialnya yaitu dalam pembelajaran kooperatif ini juga siswa mampu untuk bekerja sama dalam kelompok kecil, adanya ketergantungan positif (saling membutuhkan), saling membantu, dan saling memberikan motivasi agar mencapai tujuan dan hasil yang diinginkan.. Peneliti melakukan penelitian ini bertujuan untuk mengetahui ada tidaknya Peningkatan Pembelajaran Kooperatif Tipe Group Investigation Untuk Hasil Belajar Siswa. Data hasil penelitian ini dikumpulkan dengan tes prestasi belajar. Sedangkan analisisnya menggunakan analisis deskriktif. Setelah semua data dilakukan analisis, diperoleh peningkatan hasil belajar dari awalnya mencapai 65,80 dengan ketuntasan belajar 53,27 di siklus I Hasil siklus ke II rata- rata meningkat menjadi 75,63 dengan Ketuntasan belajar 73,61 dan di siklus ke III meningkat lagi menjadi 85,47 ketuntasan belajar 88,30 Ternyata hasil di siklus III sudah melampui kreteria yang diusulkan sesuai indikator keberhasilan penelitian. Hal ini dibuktikan dari pengumpulan data tentang hasil belajar siswa termasuk dalam kategori baik dengan melalui 3 siklus. Atas dasar perolehan data tersebut peneliti berkesimpulan bahwa dengan Penerapan Metode Kooperatif Tipe Group Investigation Mampu Meningkatkan Hasil Belajar Siswa.
Assessing the effect of international migration on the livelihood of the Bini people of Edo State, Nigeria
Authors: Braimah Ehiagwina Sunday , Gberevbie Daniel Eseme , Chidozie Felix , Osimen Goddy Uwa
Published: October 2024

This study investigates the impact of international migration on the Bini people of Edo State, Nigeria who are renowned for their rich cultural heritage, arts and historical migration patterns. The primary objective is assessing how international migration affects their economic and social life. This   study posits that migration is primarily motivated by the search for better employment opportunities with individuals making well-considered decisions to enhance their economic prospects using neoclassical theory. Furthermore, the new economic migration theory is applied to examine the broader impacts of migration on the Bini community’s lifestyle in Edo South. Data for this research were gathered through focused group interviews and structured questionnaires. A linear regression analysis was conducted to assess the role of remittances in the community’s economic dynamics. The analysis indicates that remittances are crucial in shaping the Bini people’s economic activities. It also suggests that these remittances should be strategically invested in business ventures to maximise the benefits. This study concludes that international migration plays a significant role in improving the livelihood of the Bini people enhancing their economic stability and growth. The findings highlight the need to channel remittances effectively into productive investments to benefit the community.  The Bini people can better leverage the economic advantages provided by international migration leading to sustained community development and economic resilience by strategically investing in business ventures.
Change detection in remote sensing image using a modified logarithmic mean-based thresholding
Authors: Abdelwhab Ouahab , Rabhi Seddik
Published: October 2024

In this paper, we propose a novel approach to change detection in remote sensing imagery by modifying the logarithmic mean-based thresholding technique (MLMBTICD). This method introduces a preprocessing step using a mean filter to enhance the accuracy of detecting changes between multi-temporal satellite images. The mean filter reduces noise and smoothens the images before calculating the logarithmic difference, which improves the quality of the change detection process. The proposed approach was tested on two benchmark datasets: the Onera dataset, which contains satellite images of urban regions, and the Ottawa dataset, consisting of RADARSAT-2 images. The effectiveness of the MLMBTICD method was evaluated using Overall Accuracy (OA) and Kappa metrics. The results demonstrate that our method achieves better performance compared to the original logarithmic thresholding method, yielding improved change detection accuracy. The preprocessing step significantly enhances the quality of the detected changes, making the proposed method a robust and efficient solution for various remote sensing applications, including land use monitoring, urban development, and environmental change analysis.
Clinical features and predictors of early neurological deterioration in patients with Trousseau syndrome-related cerebral infarction
Authors: Gao Weiwei , She Jingjing , Cai Lijuan , Li Huaiyi , Yang Qingwei , Chen Xingyu , Zhu Renjing
Published: October 2024

Objective: This study aimed to explore the clinical features of Trousseau syndrome (TS)-related cerebral infarction and determine predictors of early neurological deterioration (END).
Methods: A retrospective study was conducted on patients with TS-related cerebral infarction admitted to the Zhongshan Hospital Affiliated with Xiamen University, from December 2015 to December 2023. Patients were categorized into groups with and without END. The laboratory findings and imaging data were compared, and receiver operating characteristic (ROC) curves were constructed to determine the best predictors of END.
Results: The study included 30 patients with TS-related cerebral infarction, comprising 17 males and 13 females with an average age of 70.93 ± 8.7 years. Five patients experienced END. Lung cancer was the most common primary malignancy (n=13, 43.33%), with most patients (n=22, 73.33%) having stage IV disease with extensive metastasis. The primary neurological symptom was hemiplegia (n=17, 56.67%). The mean NIHSS score at admission was 9.33 ± 5.83. Imaging revealed varying stages of cerebral infarction in 10 patients (38.46%). Within three months, 25 patients (83.33%) died, 8 of whom (32%) succumbed to tumor-related complications. The incidence of END in TS-related cerebral infarction patients was 16.67% (5/30). Patients in the END group had significantly greater D-dimer levels and lower fibrinogen (Fib) levels upon admission (P < 0.001, P = 0.042). Comparisons of ROC curves revealed that D-dimer (AUC 0.916, optimal cutoff 17.86, sensitivity 1.000, specificity 0.840) and Fib (AUC 0.788, optimal cutoff 2.54, sensitivity 1.000, specificity 0.740) significantly predicted END (P = 0.004, P = 0.045). The combined use of these markers improved the AUC to 0.952, with a sensitivity of 1.00 and specificity of 0.920, surpassing those of the individual markers (P = 0.002).
Conclusion: TS-related cerebral infarction most commonly occurs in patients with advanced-stage lung cancer with widespread metastasis and is associated with poor long-term prognosis. Elevated D-dimer and decreased fibrinogen levels are significant predictors of END in patients with TS-related cerebral infarction.
Emergency department CT scan utilization and findings in geriatric patients presenting with non-traumatic headache
Authors: Özen Olcay Handan , Emektar Emine , Kutbay Ferhat , Gürdoğan Deniz , Yılmaz Aydın Yasemin , Çevik Yunsur
Published: October 2024

Background & Objective: In geriatric patients, headache is a common reason for emergency department (ED) visits. Because secondary headaches are more common in geriatric patients than in younger patients, the former should be carefully excluded at presentation. In this study, we aimed to investigate the role of brain computed tomography (CT) in geriatric patients presenting to the ED with non-traumatic headache, the indications for imaging, the CT findings, and factors associated with detection of pathology.
Methods: Patients aged 65 years and older who presented to the ED with headache between January 1, 2023 and December 31, 2023, who underwent a brain CT scan, and who had complete data were included in the study. Patients with an intracranial mass, acute-subacute ischemic findings, intraparenchymal and subarachnoid hemorrhage, and cerebral edema on brain CT scan were considered as having significant pathology.
Results: The study included 384 patients. CT scan was normal in 61.2% of the patients and 10.4% had significant pathology. It was observed that 24.5% of the patients underwent CT scan for prolonged headache and 19.3% for severe headache. Logistic regression analysis showed that severe headache, loss of consciousness, anticoagulant use, neurological deficit, and elevated blood pressure at presentation were predictors of the detection of significant pathology on CT scan.
Conclusion: In conclusion, 10.4% of geriatric patients presenting to the emergency department with headache have significant CT findings, and the impact of these findings on the emergency management of patients cannot be ignored. Incorporating these findings into clinical decision-making guidelines could improve the effectiveness of emergency management protocols by facilitating rapid and specific diagnostic interventions for geriatric patients presenting with headache.
A comparison of infectious and autoimmune meningoencephalitis: Clinical presentation, biochemical markers and MRI findings
Authors: Sukumaran Vinoshini Nair , Abdul Rashid Khairunnisa , Abdullah Suhaila , Rahmat Kartini , Abd Kadir Khairul Azmi , Norlisah Ramli
Published: October 2024

Objective: This study investigates distinguishing patterns in lesion distribution, relevant clinical presentation and biochemical markers in MRI to differentiate infective encephalitis (IE) and autoimmune encephalitis (AE).
Methods: Retrospective study of adult patients with a confirmed diagnosis of IE and AE admitted to the Neurology unit from January 2012 to December 2020 with MRI Brain. Selected cases with confirmed IE (according to the 2013 Infective Encephalitis Consortium diagnostic criteria) or antibody-positive AE (detection of Neuronal auto-antibody from blood or CSF), coupled by clinical presentation. MRI brain lesion distribution, lobar involvement, enhancement, haemorrhage, vasculopathy and atrophy were analysed.
Results: Forty-seven patients (21 IE and 26 AE, respectively) were selected. IE group are older (48.0 ± 16.81) compared to AE (28.4 ± 14.10). Fever and vomiting were significant in IE (p<0.001), whereas psychosis, seizure, movement disorder, and tumour (ovarian teratoma) were significant in the AE cohort. Cerebrospinal fluid (CSF) analysis showed elevated leucocytes with polymorphism and high protein levels in IE (2.081 g/L ± 2.93 vs AE (0.352g/L ± 0.18). MR imaging detected abnormal findings in 61.9% of cases with infectious encephalitis (IE), while 76.9% of cases with autoimmune encephalitis (AE) exhibited normal MRI results. Asymmetrical lesions and inferior frontal lobe distribution (57.1%) were significantly prevalent in IE (p<0.05). Enhancement patterns and haemorrhage were rarely observed in AE patients.
Conclusion: IE presented at an older age, with the majority having MRI findings such as asymmetrical lesions, leptomeningeal enhancement, and involvement of medial temporal, hippocampus and inferior frontal cortex. IE clinically presents with fever, vomiting, and elevated CSF leucocytes and protein levels. AE presents at a younger age with seizure, psychosis, movement disorder, tumour (ovarian teratoma). Fewer AE have MRI findings, and if present, tend to be symmetrical in distribution. Lobar involvement in the inferior frontal was the MRI feature that significantly favoured the diagnosis of IE compared to AE. IE and AE have distinct clinical, biochemical, and MRI abnormalities that can be discriminated between the 2 entities.
Cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy (CARASIL): A rare cause of ischemic stroke in young
Authors: B Kale Gautam , Ranganath Prajnya , M K Murthy Jagarlapudi
Published: October 2024

Cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy (CARASIL) is the second known genetic form of cerebral small vessel disease after cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). It is a very rare single gene disorder affecting cerebral small blood vessels. Diagnosis of CARASIL should strongly be suspected in a young non-hypertensive patient with lacunar stroke in the basal ganglia and brainstem and alopecia limited to scalp.
Characteristics of stroke inpatients in a single- center in Kunming: A multi-ethnic settlement with a northern sub-tropical low-latitude plateau mountain monsoon climate
Authors: Zhu Hongyan , Chen Jiajie , Fu Hao , Meng Qiang
Published: October 2024

Objective: This study aimed to evaluate the characteristics of stroke inpatients in Kunming in southwest China.
Methods: Basic demographic information and risk factors of stroke inpatients admitted to a first-rate tertiary referral hospital in Kunming from January 1, 2015, to September 15, 2022, were retrospectively analysed. In total, 7,108 stroke inpatients with ischemic stroke (IS), primary intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH) were included.
Results: IS was the most common type of stroke (75.97%), followed by ICH (13.45%) and SAH (10.58%). There were significant differences among the three groups in sex, age, ethnic, residence, occupation, and medical expenses (P<0.05). There was also a statistically significant difference in the proportion of inpatients between the four seasons (P<0.05). Hypertension, dyslipidemia, and diabetes were common risk factors for stroke. There were significant differences in the incidence of smoking, alcohol use, hypertension, dyslipidemia, diabetes, atrial fibrillation, and pulmonary infection among the three stroke subtypes (P<0.05).
Conclusion: Demographic information and risk factors were different among the three stroke subtypes. The number of hospitalized patients from all three groups varied slightly in different seasons in Kunming. Different types of cerebrovascular disease patients in the region have different characteristics from demographic information to risk factors.
Associations between the Framingham Risk Score and cognitive functions in people with multiple sclerosis
Authors: ERSOY Alevtina , Baloglu Senanur , Tanoglu Ceyda , Hayme Serhat
Published: October 2024

Background & Objective: Cognitive impairment is common in people with multiple sclerosis (MS). The Framingham Risk Score (FRS) is an algorithm used to estimate the 10-year risk of developing cardiovascular disease (CVDr). This study aimed to evaluate the possible associations between FRS and cognitive impairment in people with MS.
Methods: Demographic characteristics, laboratory parameters, clinical findings, vascular risk factors, and Brief International Cognitive Assessment for MS (BICAMS) test results, FRS and CVDr scores were recorded for 110 MS patients.
Results: FRS and CVDr scores exhibited a significant negative correlation with the number of correct responses in the Symbol Digit Modalities Test (p < 0.001 for all) and California Verbal Learning Test–Second UK Edition (CVLT-II) test (p < 0.001 for all), and significant positive correlation with the number of incorrect responses in the CVLT-II (p < 0.001 for all). The total score of the Brief Visuospatial Memory Test Revised had a negative correlation with FRS and CVDr (p < 0.001 for both). BICAMS scores showed no significant correlation with smoking status, homocysteine, and uric acid levels.
Conclusion: We suppose that vascular risk factors and FRS and CVDr scores may be associated with the deterioration of working memory, information processing speed, verbal learning, and memory in people with MS. Based on the potential impact of vascular risk factors on cognitive functions, our findings suggest that lifestyle changes, appropriate treatment, and using a multidisciplinary approach toward vascular risk factors during MS management may exhibit a positive effect on cognition in people with MS.
The effectiveness and safety of bevacizumab in improving the efficacy of antiseizure medication in treating refractory epilepsy induced by stereotactic radiosurgery in meningiomas
Authors: Hou Wenwen , Wang Zheng , Wang Xinyu , Yu Xiaofen
Published: October 2024

Background: Refractory epilepsy (RE) increases rapidly after stereotactic radiosurgery (SRS), but the reports on RE treatment are rare, with various methods and efficacy. In recent years, bevacizumab (BEV) has been widely used as it is effective in eliminating intracranial oedema and reducing radioactive damage. The aim of this study was to evaluate the application of additional BEV to RE after SRS in the real world.
Methods: Seizure freedom and seizure response were defined as 100% and > 50% reduction in seizure frequency at baseline and 6-, 9- and 12-month follow-ups. The sustained seizure-free (SSF), sustained seizure response (SSR) was used to assess the effectiveness of BEV. The number of anti-seizure medications (ASM), seizure severity (NH3), and epilepsy quality of life rating scale (QOLIE-31) scores were compared before and 12 months after treatment.
Results: Forty-one patients were included from January 2020 to December 2022. During the 1-year follow-up, 5 patients (12.2%) achieved SSF lasting 12 months, and 4.9% and 7.3% enjoyed SSF more than 6 months and 9 months, respectively. Twelve patients (29.3%) achieved SSR lasting for 12 months, and 19.5% and 24.4% of the study cohort achieved SSR more than 6 months and 9 months, respectively. Patients’ ASM, NH3, and QOLIE-31 scores significantly improved 12 months after treatment, and the adverse reactions were controllable.
Conclusion: This study is the first to explore and report the additional use of BEV in the treatment of RE after SRS in meningiomas. BEV was effective and safe in the treatment of SRS-induced RE.