bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrating Machine Learning Algorithms and Advanced Computing Technology Using an Ensemble Hybrid Classifier
Authors: Shetty Roopashri , M Geetha , G Shyamala , U Dinesh Acharya
Published: July 2024

Ovarian Cancer (OC) is one of the major types of cancers in women worldwide. Despite the standardization of characteristics that can help distinguish benign from malignant ovarian masses, accurate predictive modelling following ultrasound (US) examination and biomarkers for ’progression-free survival’ is lacking in the field of ovarian cancer. Important leading factors in ovarian cancer lethality are the lack of diagnostic procedures and proper screening to detect early-stage ovarian cancer, and the rapid spread of the disease over the surface of the peritoneum. Therefore, developing tools for accurate screening and prognosis, as well as the diagnosis of early stage ovarian cancer, is a current clinical need. In this study, an ensemble classifier was developed as a novel means of ovarian cancer prediction, and its effectiveness was assessed. The ensemble classifier integrates various machine learning algorithms, including support vector machines (SVM), k-nearest neighbors (KNN), decision trees (DT), naïve Bayes (NB), and logistic regression (LR). Because ensembles may integrate the benefits of numerous models, they can mitigate the limitations of each model individually and improve the overall predictive performance, making them popular in the domain of machine learning. To increase predictive performance, an ensemble hybrid approach was created by utilizing a meta-classifier to merge many base classifiers. The performance with respect to various measures of the ensemble classifier was evaluated considering a comprehensive novel dataset of ovarian cancer patients, including tumor markers as well as clinical and ultrasound features. Through extensive cross-validation studies, the hybrid model showed better prediction accuracy of 95% which is approximately 6-17% improved than the baseline classifiers and state-of-the-art ensemble approaches in predicting ovarian cancer. After comparing the performance of the ensemble classifier with other existing classifiers, the ensemble classifier outperformed the individual models and conventional diagnostic techniques in terms of sensitivity (94%) and specificity (95%) through performance evaluation.
Impact of Effective Word Vectors on Deep Learning Based Subjective Classification of Online Reviews
Authors: B Priya Kamath , M Geetha , U Dinesh Acharya , Nandi Ritika , Urolagin Siddhaling
Published: July 2024

Sentiment Analysis tasks are made considerably simpler by extracting subjective statements from online reviews, thereby reducing the overhead of the classifiers. The review dataset encompasses both subjective and objective sentences, where subjective writing expresses the author's opinions, and objective text presents factual information. Assessing the subjectivity of review statements involves categorizing them as objective or subjective. The effectiveness of word vectors plays a crucial role in this process, as they capture the semantics and contextual cues of a subjective language. This study investigates the significance of employing sophisticated word vector representations to enhance the detection of subjective reviews. Several methodologies for generating word vectors have been investigated, encompassing both conventional approaches, such as Word2Vec and Global Vectors for word representation, and recent innovations, such as like Bidirectional Encoder Representations from Transformers (BERT), ALBERT, and Embeddings from Language Models. These neural word embeddings were applied using Keras and Scikit-Learn. The analysis focuses on Cornell subjectivity review data within the restaurant domain, and metrics evaluating performance, such as accuracy, F1-score, recall, and precision, are assessed on a dataset containing subjective reviews. A wide range of conventional vector models and deep learning-based word embeddings are utilized for subjective review classification, frequently in combination with deep learning architectures like Long Short-Term Memory (LSTM). Notably, pre-trained BERT-base word embeddings exhibited exceptional accuracy of 96.4%, surpassing the performance of all other models considered in this study. It has been observed that BERT-base is expensive because of its larger structure.
Experimental Investigation of Modified Level Shift PWM with Capacitor Voltage Balancing on Single Phase Modular Multilevel Converter
Authors: S Balamurugan , M Bhavani , R Sudha , K Padmanaban
Published: July 2024

Modular Multilevel Converters (MMCs) are a prominent voltage source converter topology that is rapidly gaining popularity in medium/high power/voltage applications, including high-voltage DC transmission systems and electric vehicle systems. However, MMC has the critical issue of unbalanced submodule capacitor voltages and circulating current. The MMC distributes DC-link energy evenly among its submodule capacitors, rather than storing it in a large capacitor like conventional voltage source converters. In MMC, the submodule floating capacitors interface the DC input voltage and AC output voltage. Therefore, capacitor voltages must be balanced. The existing capacitor voltage control struggles to handle many gate pulses at medium and high voltages. An effective control scheme is needed to solve the issues mentioned earlier. This paper presents a performance analysis of a 1-Φ MMC using a modified level shift PWM technique based on Universal Control Modulation Scheme (UCMS) and a sorting-based capacitor voltage balancing algorithm. MATLAB/Simulink software implements the proposed control method for a 1-Φ , seven-level MMC. The outcomes of the simulation demonstration reveal that phase disposition PWM offers less harmonic distortion of output phase voltage than the other level shift PWM techniques, such as phase disposition PWM and alternate phase disposition PWM. The simulation results also demonstrate the effective use of the sorting-based capacitor balancing algorithm to regulate the voltages of the submodule capacitors. Finally, the real-time GUI tool validates the simulation results using an experimental prototype of 1-Φ MMC with the dSPACE MicroLabBox 1202.
The Future of Neurodiagnosis: Deep Learning for Earlier Intervention
Authors: Govindarajan Rajkumar , K Thirunadana Sikamani , Kumar Angati Kalyan , N Komal Kumar
Published: July 2024

This study presents an innovative deep learning framework for improved early detection of a debilitating neurodegenerative condition marked by cognitive decline and memory impairment. Timely diagnosis is crucial for effective interventions and improved patient outcomes. Our framework integrates diverse data sources, including structural and functional neuroimaging (MRI and PET) alongside clinical information, to enhance detection precision. Convolutional Neural Networks (CNNs) analyze structural MRI scans, extracting subtle changes in brain structure indicative of early disease progression. Functional insights are gleaned from PET scans, contributing to increased sensitivity. Additionally, longitudinal data is incorporated through Recurrent Neural Networks (RNNs) to capture the disease's temporal evolution. Training on a diverse dataset utilizes transfer learning, optimizing performance even with limited labeled data. Rigorous validation consistently demonstrates the model's effectiveness, achieving a 92% accuracy rate.
A Robust Dual Watermarking using Grey Wolf Optimization, Selective Encryption and Fast Flexible De-Noising Convolution Neural Network
Authors: Shedole Sambhaji Marutirao , V Santhi
Published: July 2024

Digital data interchange in IoT systems has flourished with the advancement of industrial internet technologies. Particularly, more and more digital images created by intelligent and industrial equipment are sent there are security concerns related to the website, server, and cloud. To accomplish this issue, in this article a secure watermarking approach is suggested in this research to effectively improve security, invisibility, and resilience at the same time. The adequate coefficient for information embedding is first determined using an assortment of transform domain techniques Discrete-Wavelet-Transform (DWT), Heisenberg- decomposition (HD), and Tensor-singular-value-decomposition (T-SVD). Using the grey wolf optimization (GWO) approach, we estimated the appropriate embedding factors to provide a reasonable compromise between robustness and invisibility. To enable the suggested approach to offer an additional level of security, a selective encryption technique is used on the watermark image. Moreover, FFDNet—a quick and adaptable de-noising convolutional-neural–network is working to increase the robustness-of-the suggested algorithm. The results demonstrate that the recommended watermarking method generates exceptional imperceptibility, resilience, and security against standard attacks. Additionally, the comparison demonstrates that the suggested algorithm performs better than alternative strategies. The following metrics were reached: 51.6966 dB, 0.9944, 0.9961, and 0.2849 for the peak-signal- to-noise ratio (PSNR), Structural-Similarity-Index (SSIM), number of changing pixels per second (NPCR), and unified-averaged-changed-intensity (UACI) average scores.
EDUCATION ZAKAT COORDINATION ISSUES: STUDENTS’ BIRTH STATE, PARENTS ADDRESS AND POTENTIAL ECONOMIC LOSS
Authors: Ariffin Muhammad Irwan , Affendi Ammar , Ismail Irsyadi , Ruslan Adib Hakim
Published: July 2024

The research examines on the challenges faced by students when applying for zakat with a special focus on the status of their birth state and parent’s residential address. Through a mixed-methods approach, the research conducts a comparative analysis of eligibility criteria and collects testimonies from 21 students through interviews to gain qualitative insights, where the mismatch between birth state and residential address of students and parents causes 47% failure in obtaining zakat application approval. The study estimates the economic loss resulting from operational and structural coordination failures by assessing the number of poor migrants household who may underserved by zakat institutions due to mismatch between birth state and residential address or incomplete residential period requirement, which is estimated at RM 4.2 billion and RM 9.96 billion for Selangor and Malaysia, respectively. A focus group discussion among students of zakat class is conducted to validate the identified problems and gather consensus on zakat application processes and challenges, where majority of them got a wrong understanding about the process. The research emphasizes the importance of a streamlined and coordinated approach to enhance the effectiveness of zakat institutions and mitigate economic losses due to coordination failures.
Risk and protective factors for reduced life-space mobility among older adults with joint disease in Colombia: analysis of secondary data from a nationwide population-based survey
Authors: Gómez-Gonzalez Brenda , Coll-Tello Brenda , Wallis-Mosquera Luis , Valdez-Muñoz Laura Cristina , Altamar-Canales Geraldine
Published: July 2024

Objective: To determine the association between life space and self-reported joint disease in the 2015 SABE Colombia survey. Methods: Cross-sectional observational study of secondary data collected during the SABE Colombia 2015 survey. Life space was considered the dependent variable, and self-reported joint disease as an independent variable. An ordinal probit model was used to determine the different influences of each variable including biological, sociodemografic, health condition and geriatric syndromes on the life space of each respondent, categorized as follows: 1=Confined to bedroom, 2=Confined to home, 3=Neighborhood, 4=Around town, 5=Out of town. Results: 6158 respondents reported joint disease, corresponding to an overall prevalence of 25.99% (95%CI 24.53–28.81%), stratified by life space as follows: confined to room, 3.73%; housebound, 10.31%; neighborhood, 13.64%; town, 49.24%; and out of town, 23.08%. Among female respondents, 78.26% were confined to their homes. The mean (SD) age of this population was 71.8 (8.3) years; respondents confined to their sleeping quarters were on average older. Men were less likely to have reduced life-space mobility, with the probability decreasing by 1.00% compared to women. Having the frailty phenotype increased the probability of having one’s life space confined to the bedroom by 0.69% compared to nonfrail respondents. Conclusion: Our findings suggested that being female, older, having a lower score on the Lawton scale, and being frail are factors that were associated with reduced life spaces. On the other hand, being male, having a higher socioeconomic status, higher educational attainment, good self-perception of health, and belonging to social groups were associated with a greater likelihood of having unlimited life-space Mobility.
Tax morale and taxpayers’ compliance among SMEs in Nigeria
Authors: Alabi Adeyemi Wasiu , Atanda Fatai Abiodun , Akintoye Ishola Rufus , Kajola Sunday Gboyega
Published: July 2024

This research delved into the dynamics of tax compliance among Small and Medium Enterprises (SMEs) in Nigeria, focusing on the influence of tax morale. The study examined key dimensions of tax morale, including religious beliefs; trust in governance, income levels, financial literacy, and educational attainment. Adopting a descriptive survey design, the investigation encompassed the entirety of the 386,655 registered SMEs across selected Nigerian states. Utilizing Taro Yamane's statistical formula and a multistage sampling method, a representative sample of 400 SMEs was meticulously selected. The ensuing regression analysis revealed compelling insights. Religious convictions, trust in governance, and higher income levels emerged as substantial and positive determinants of taxpayer compliance, encompassing both voluntary and enforced aspects. Financial literacy exhibited a positive influence on compliance, though statistically insignificant, hinting at a potential impact that lacks statistical robustness. Educational attainment, while positively affecting compliance, demonstrated significance primarily in enforced compliance, with no statistically significant impact on voluntary compliance. In conclusion, the study underscores the pivotal role of tax morale in shaping compliance behaviour among SMEs in Nigeria. The findings advocate for governmental initiatives aimed at augmenting transparency and accountability in tax administration to cultivate a positive tax morale, consequently fostering enhanced compliance within the SME sector.
Strengthening the security of end-to-end communication in photonic networks
Authors: Ali Sellami , Djaouida Benlahcene
Published: July 2024

The burgeoning Internet and Internet-of-Things (IoT) sectors necessitate robust cryptographic methods to ensure data security, integrity, and authentication over unsecured networks. Traditional public key cryptography, reliant on computationally hard problems, faces threats from quantum computing advancements. Quantum Key Distribution (QKD) presents a solution through the generation of unconditionally secure cryptographic keys using quantum mechanics. This paper explores the enhancement of QKD protocols to establish secure end-to-end communication in photonic networks. The proposed method involves a QKD system that generates two types of weak quantum signals: one with randomly varied intensity, polarization, or phase, and another with random frequency fluctuations. These signals are used to establish a shared key between a transmitter (Alice) and multiple receivers (Bobs) by measuring the quantum states. This dual signal approach enhances protection against Photon Number Splitting attacks and improves key length. Key Management Agents (KMAs) securely handle QKD-generated keys for data encryption before transmission, ensuring only intended recipients can decrypt the messages. The system leverages optical fiber or free-space optical links to transmit weak quantum signals and synchronization signals, facilitating key distribution even under existing network constraints. The proposed architecture allows for the secure exchange of cryptographic keys between Alice and multiple Bobs, ensuring private and authenticated communication over public channels. The approach mitigates potential eavesdropping by enabling the detection of any interception attempts through Quantum Bit Error Rate (QBER) estimation. This study underscores the promise of QKD as a foundational element of future communication systems, providing uncrackable quantum keys and paving the way for secure photonic networks. Incremental advancements in quantum devices, networking, and infrastructure are essential to fully realize QKD's potential for robust cryptographic security.
The influence of the piezoelectric effect on stress distribution in semiconductor layers
Authors: Abdallah Zine , Madjid Berrabah Hamza , Bachir Bouderba
Published: July 2024

This study investigates thin structures by analyzing stress distribution in two different scenarios. In the first scenario, the absence of the E3 and d31 parameters is considered, while the second scenario examines their influence. The parameters vary according to a mixing rule based on thickness, with h1, h2, and h3 representing the distinct layers of the structure. The coordinate origin is positioned on the lower surface of the structure. The piezoelectric effect in semiconductor layers refers to the phenomenon where mechanical stress within the material generates an electric charge due to the inherent piezoelectric properties of the material. This effect significantly influences the stress distribution in semiconductor layers, altering their mechanical and electrical behavior. Understanding this impact is crucial for optimizing the design and performance of semiconductor devices, as it affects the material's ability to handle stress and distribute it evenly, thereby enhancing the reliability and efficiency of electronic components. This comparative analysis enhances the understanding of the piezoelectric effect on stress distribution in semiconductor layers. By comparing these two scenarios, significant differences in stress distribution are observed when piezoelectric effects are considered. Understanding how piezoelectric effects influence stress distribution aids engineers in designing better semiconductor layers, enhancing both performance and reliability. This research underscores the critical importance of considering piezoelectric effects in the design and analysis of thin structures. By incorporating these effects, engineers can develop semiconductor devices that are not only more efficient but also significantly more robust. The study's findings highlight the necessity of integrating piezoelectric considerations into engineering practices to optimize material properties and structural integrity, ultimately leading to advancements in semiconductor technology and contributing to the development of high-performance, reliable electronic devices.
A curricularização da extensão nos cursos de tecnologia em gastronomia
Authors: Coura Helena Luiza Oliveira , Coura Marcel Oliveira
Published: July 2024

Historicamente, a extensão universitária esteve associada a uma função social das universidades. A sua finalidade formativa emerge de um contexto recente, especialmente depois da realização do l Encontro de Pró-Reitores de extensão das Universidades Públicas Brasileiras, em 1987, onde se formulou um novo conceito de extensão. Com a publicação da Constituição Federal (1988), no art. 207, ficou estabelecida a equidade entre ensino, pesquisa e extensão, por meio do Princípio da Indissociabilidade. A partir de então, sob a égide do FORPROEX, tem início a um tensionamento, junto as instancias superiores, para tornar a extensão universitária uma política pública do ensino superior. Em face do exposto, foram promulgadas diversas normativas com o intento de regulamentar a extensão nos currículos, o que culminou na publicação da Resolução CNE/CES nº 7/2018, que regulamentou o Plano Nacional de Educação (2014), instituiu a curricularização da extensão, definindo que 10% (dez por cento) da carga horária total dos cursos superiores, sejam reservados para a extensão. Os Cursos Superiores de Tecnologia têm especificidades que os diferenciam das outras categorias: são cursos de curta duração, cujos currículos são mais especializados e voltados ao mercado de trabalho. À vista disso, a partir da revisão bibliográfica e documental, este artigo analisou o cumprimento da Resolução CNE/CES nº 7/2018 em quatro Projetos Pedagógicos de Cursos de Tecnologia em Gastronomia, ofertados em Institutos Federais da Educação Profissional, da Região Nordeste. Dado o exposto, esse estudo se justifica, principalmente em razão da crescente procura pelos cursos Tecnológicos do ensino superior. Os resultados apontam que, não obstante todas as determinações legais, apenas um dos cursos tem a carga horária da extensão definida na matriz curricular, demonstrando que essa atividade ainda é tratada como uma ação complementar em três, dos quatro cursos pesquisados.
Os teoremas de incompletude de Gödel e a conjectura de Goldbach
Authors: Sales Francisco Odécio , Freires Kevin Cristian Paulino , Sousa Maria Aparecida de Moura Amorim , Marreiros Emerson Charles do Nascimento , Ferreira João Raimundo Silva , Nascimento Marcos André Maia do , Silva Micael Campos da , Lima Francisco Felipe Ramos Rodrigues
Published: July 2024

Christian Goldbach foi um matemático russo do século XVIII que, embora visse a Matemática mais como um hobby, fez contribuições significativas, especialmente na Teoria dos Números. Entre suas correspondências com o famoso matemático Leonhard Euler, surgiu a conjectura que hoje leva seu nome: a Conjectura de Goldbach. Esta conjectura afirma que todo número par maior que 2 pode ser expresso como a soma de dois números primos (iguais ou diferentes). Embora seu enunciado seja extremamente simples, a demonstração dessa conjectura requer conhecimentos matemáticos muito avançados, tanto que, até hoje, permanece sem uma prova definitiva. Ao longo dos séculos, muitos matemáticos dedicaram suas carreiras ao estudo e à tentativa de provar a Conjectura de Goldbach. Esses esforços não só avançaram o entendimento dessa conjectura específica, mas também contribuíram significativamente para o desenvolvimento da Teoria dos Números como um todo. A pesquisa sobre essa conjectura atravessou diferentes épocas, refletindo tanto o contexto matemático quanto o histórico de cada período. O objetivo desta pesquisa é elencar os avanços mais importantes alcançados por matemáticos ao longo do tempo no estudo da Conjectura de Goldbach. Iremos explorar as contribuições de diferentes eras e entender melhor o contexto histórico em que essa conjectura surgiu e evoluiu. Desde as correspondências iniciais de Goldbach e Euler no século XVIII até os avanços computacionais e teóricos dos séculos XX e XXI, esta pesquisa buscará destacar as principais descobertas e métodos desenvolvidos. Além disso, tentaremos compreender o impacto dessas descobertas no campo mais amplo da Matemática. Ao final, destacaremos os resultados mais significativos que, em um primeiro momento, podem servir como base para pesquisas futuras. A esperança é que, ao mapear o progresso feito até agora, possamos identificar novas abordagens e perspectivas que ajudem a resolver de uma vez por todas esse enigma matemático fascinante.
The Impact of Food Insecurity on Maternal and Neonatal Outcomes
Authors: Tian Wendy , Schopper Claire , Ryan Emma , Mossesso Kelly , Guise David , Daggy Joanne , Haas David , Scott Nicole
Published: July 2024

Purpose: The purpose of this study was to examine the relationship between food security status and pregnancy outcomes.

Methods: This was a single-center cross-sectional cohort study of postpartum women in Indianapolis, IN. A validated 10 question Household Food Security Survey module was used to assess food security for patients who spoke English, Spanish, or Haitian-Creole. Correlating with the USDA definitions of food security, total scores were “high,” “marginal,” “low,” and “very low.” Scores were analyzed as inadequate (marginal, low, or very low) vs. adequate (high) food security. If a score fell within any category but “adequate” food security, patients were provided with information on community resources. Prenatal and postpartum data were abstracted via chart review, collecting: standard demographics, language, parity, diagnosis of diabetes, diagnosis of hypertensive disorder(s), pre-pregnancy weight, weight at time of delivery, BMI at time of delivery, gestational age at delivery, Edinburgh Postnatal Depression Scale (EPDS) score, and delivery mode. Neonatal variables included: weight and sex. The primary outcome was occurrence of gestational diabetes (GDM). These outcomes were compared between patients who had adequate vs. inadequate food security. Logistic regression was used to examine the association between food insecurity and each of the dichotomous outcomes. Linear regression was used to model the relationship between sex-specific birthweight z-scores and food insecurity. All adjusted models included language and maternal age. The Indiana University institutional review board approved the study (IRB# 14116).

Results: Two hundred six surveys were completed from June 2022 through December 2022. Ninety-three participants (45.4%, 95% CI 38.4-52.4%) were categorized as inadequate food security. The incidences of marginal, low, and very low food security were similar with 31, 30, and 28 participants, respectively. Race/ethnicity and language were different between adequate and inadequate groups (p=0.006 and <0.001 respectively). The incidence of GDM was 7.9% and was not different for those with inadequate food security (OR 0.96 95% CI 0.33-2.68). Food insecurity was protective against delivery via C-section (aOR 0.50 95% CI 0.25-0.98). Food insecurity increases odds of EPDS > 9 (aOR 4.32 95% CI 1.41-16.2).

Conclusions: Inadequate food security status is highly prevalent in our patient population and was associated with racial/ethnic minority status and non-English speaking populations. Inadequate food security was found to be associated with a decreased odds of cesarean delivery and higher odds of postpartum depression.
Analysis of Solution-Focused Brief Therapy Theories and Counseling Techniques in Islamic View
Authors: Sholihah Maidatus , Mierrina Mierrina , Thohir Mohmmad
Published: July 2024

This article aims to explain and understand the Solution Focused Brief Therapy (SFBT) technique and its development. This article uses the literature review method by discussing themes through relevant books and journals. The analysis technique used is content analysis. The results of this article show that the process of SFBT techniques and approaches need to carry Islamic values so that it does not only treat physically but also forms a kaffah Muslim by Islamic teachings. The reliability of a counselor is also needed so that this rapid counseling technique can run optimally. Islam-based SFBT not only helps counselors deal with life's problems but also helps them achieve worldly and afterlife happiness and peace of mind, by living life by the rules and instructions of Allah SWT. This method encourages counselors to focus on solutions and their potential so that they can overcome problems effectively and build a better future. The combination of the SFBT approach and Islamic values provides a powerful and holistic framework for counseling, helping individuals develop mentally, emotionally, and spiritually.
THYROID FUNCTION AND COGNITIVE DEVELOPMENT: A CROSS-SECTIONAL STUDY AT THE DEPARTMENT OF PEDIATRICS, LRH PESHAWAR
Authors: KHAN I , AYESHA . , MUHAMMAD A
Published: July 2024

Thyroid hormones play an essential role in brain development and cognitive function. Hypothyroidism can adversely affect children's mental development, leading to long-term developmental delays. Objective: This research aims to assess the correlation between thyroid hormones and child mental health, specifically examining the effects of changes in thyroid hormones on the cognitive outcomes of children receiving treatment at Lady Reading Hospital (LRH) Peshawar. Methods: A cross-sectional study was conducted in the Department of Pediatrics at LRH Peshawar from March 05, 2021, to September 05, 2021. One hundred children aged 2-8 years were randomly selected for the study. The mean age was 4.5 years, with a standard deviation of 1.3 years. Blood tests for TSH, T3, and T4 were performed to evaluate thyroid function. Cognitive development was assessed using standardised cognitive development tests appropriate for the children's ages. Descriptive statistics were used to analyse the data, and inferential statistics were employed to compare cognitive test scores between children with normal thyroid function and those with hypothyroidism, with a significance level set at p < 0.05. Results: The study found that thyroid function significantly impacts cognitive development. Children with normal thyroid hormone levels had better cognitive abilities compared to those with hypothyroidism. The mean cognitive test score for children with normal thyroid function was 85%, whereas children with hypothyroidism had a mean score of 65%. The p-value for this correlation was less than 0.05, indicating a statistically significant difference. Conclusion: Both hypothyroidism and hyperthyroidism significantly affect brain development in children. Early diagnosis and treatment of thyroid dysfunction are crucial to prevent developmental impacts. Incorporating thyroid function screening into routine health check-ups can enhance children's cognitive development.
FAKTOR – FAKTOR YANG MEMPENGARUHI PERTUMBUHAN EKONOMI MELALUI BELANJA MODAL
Authors: Muhariyanto  , Rudianti Walid , Ghonimah Zumroatun Ainiyah
Published: July 2024

An issue that is often heard among the public in APBD management is allocating capital expenditure as an effort to increase economic growth. This research aims to examine the influence of Regional Original Income, General Allocation Funds, Special Allocation Funds on economic growth through Capital Expenditures in district/city governments in Central Java province in 2015-2019. The research data used in this research comes from secondary data. The method in this research uses a quantitative method with a saturated sample, meaning that the entire population is sampled. The population used is all districts/cities in Central Java province. The data analysis technique used is path analysis using SPSS as a statistical test tool. The research results prove that Regional Original Income directly has a significant effect on Economic Growth, while General Allocation Funds and Special Allocation Funds directly have an insignificant effect on economic growth. Capital expenditures have no direct effect on economic growth. Regional Original Income, General Allocation Funds and Special Allocation Funds have a significant effect on Capital Expenditures. Regional Original Income, General Allocation Funds and Special Allocation Funds have no effect on Economic Growth through the intervening variable Capital Expenditure. The implication of this research is that there are differences in the results of previous research and adding the capital expenditure variable as an intervening variable.
PENGARUH KEKUATAN MONITORING, MODAL, NPL DAN ASET TERHADAP MARGIN LABA PADA BPR KARESIDENAN PEKALONGAN
Authors: Supriyatun Supriyatun , Kusumawardhani Eny , Karyawati Danik , Sumawan Sumawan
Published: July 2024

This research aims to identify the influence of monitoring strength, capital, non-performing loans (NPL), and assets on net interest margin (NIM) in people's credit banks (BPR) in the Pekalongan area. The data analysis method used is multiple linear regression using secondary data obtained from the financial reports of BPRs and related institutions. The data used in this research uses secondary data from Conventional Rural Banks (BPR), which operates in the former Pekalongan Residency, Central Java for the 2018-2020 period, from Rural Bank Publication Financial Reports (LKP) which consist of balance sheets, Profit and Loss reports, administrative accounts and other information which is an integral part of the Published Financial Report, obtained from the Bank Indonesia directory www.bi.go.id. The research results show that monitoring strength, capital, and assets have a significant influence on NIM, while NPL does not have a significant influence. The implication of this research is the importance of managing monitoring power, capital, and assets effectively to increase NIM and the financial health of BPRs in the Pekalongan Resident area. It is hoped that this research can contribute to the understanding of the factors that influence BPR financial performance and become a consideration for related parties in making decisions regarding business strategy and banking policy.
PENGARUH NIM, DEP, LOANS, SIZE TERHADAP CAR PADA BANK UMUM DI BURSA EFEK INDONESIA
Authors: Karyawati Danik , Marsana Marsana , Sunarya Sunarya , Sumawan Sumawan
Published: July 2024

This research aims to analyze the influence of Net Interest Margin (NIM), Deposit Equity Ratio (DEP), Loans to Assets Ratio (LOANS), and bank size (SIZE) on the Capital Adequacy Ratio (CAR) in commercial banks listed on the Stock Exchange Indonesia (IDX). The data used is for 4 years, namely 2014, 2015, 2016, and 2017 with a total of 28 banking companies, using the pooled data method to obtain 112 data. The panel regression analysis method is used to test the relationship between these variables. The research results show that NIM has a significant positive influence on CAR, indicating that banks with higher net interest margins tend to have higher capital adequacy ratios. In addition, DEP and LOANS have an insignificant negative effect on CAR, indicating that banks with a higher deposit to equity ratio tend to have a lower CAR. The higher the LOA, the lower the CAR level. However, SIZE does not have a significant influence on CAR in this context. These findings provide important insights for bank management and regulators in understanding the factors that influence bank capital adequacy levels and designing appropriate policies to ensure financial system stability
Gorilla Troops Optimizer with Deep Learning-based Multi-Criteria Decision Making for Traffic Analysis in V2X Networks
Authors: Djamal Djamal ,   Department Of Mathematics, University Of Blida 1, Blida, Algeria,   Sudan University of Science and Technology, Faculty of Science, Khartoum, Sudan, Al-basheer Othman Al
Published: July 2024

Multi-criteria decision-making (MCDM) is employed for analyzing traffic in a Vehicle-to-Everything (V2X) network. V2X suggests communication among vehicles and other entities, containing pedestrians, infrastructure, and other vehicles. Traffic analysis and management in V2X networks need effectual decision-making approaches, which assume several conditions. MCDM contains estimating and choosing alternatives depending on several conditions or objectives. In the context of traffic analysis in V2X networks, MCDM algorithms are employed for decision-making concerning traffic flow optimizer, resource allocation, route planning, and congestion management. Deep learning (DL) approaches are trained to analyze massive counts of data gathered from several sources from the V2X network. These sources contain traffic sensors, GPS data, vehicle-to-infrastructure (V2I) communication, and historical traffic designs. By processing this data, DL approaches extract useful insights and create informed decisions depending on various conditions. Therefore, this article proposes a gorilla troops optimizer with deep learning-based MCDM for traffic analysis (GTODL-MCDMTA) technique in the V2X network. The purpose of the GTODL-MCDMTA algorithm is to identify the traffic flow prediction for improving route planning and resource allocation with the consideration of various factors into account. In the presented GTODL-MCDMTA technique, the input data is pre-processed to remove noise and normalize it for analysis. Next, the GTO algorithm is used for the feature selection process. Besides, the deep extreme learning machine (DELM) model is used for the forecast of traffic movement. Finally, the seeker optimization algorithm (SOA) has been utilized for the parameter tuning of the DELM technique. A brief set of simulation outcomes can be applied to emphasize the promising outcomes of the GTODL-MCDMTA technique. The experimental outcome demonstrates the efficiency and efficiency of the GTODL-MCDMTA approach in handling the complexity and dynamic nature of V2X network traffic analysis.
Enhanced Wetland Classification using Deep Learning based Fusion Approach on Multi-Source Remote Sensing Images
Authors: Bahromjon Bahromjon ,   Tashkent State University of economics, Tashkent, Uzbekistan,   Tashkent State University of economics, Tashkent, Uzbekistan, Ibrahim Maha
Published: July 2024

Accurate remote sensing (RS) monitoring of wetland ground objects is an enormous importance for ecological preservation. Wetland classification on multi-source remote sensing images (MS-RSI) includes leveraging data from different sensors for accurately describing and categorizing wetland regions. This method normally incorporates data from infrared, radar, and optical sensors to take a wide-ranging view of wetland features. Advanced image processing methodologies, comprising machine learning (ML) approaches are often implemented for analyzing these multi-source images as well as recognizing spectral and spatial patterns indicative of wetland characteristics. The interaction of various RS data increases the accuracy and robustness of wetland classification models, allowing a more complex analysis of wetland ecosystems and aiding environmental observation, conservation, and control measures. To accomplish effective training for wetland mapping through the RS, it is essential for a significant training data that comprises a numerous array of class variants. In this article, we propose an Enhanced Wetland Classification using a Deep Learning based Fusion Approach (EWC-DLFA) on MS-RSI. The proposed EWC-DLFA technique examines the MS-RSI for wetland classification using the DL model which can be used for other land cover classification types. To accomplish this, the EWC-DLFA technique utilizes the data from multiple sources such as Sentinel-1 (SAR), Landsat-8, Sentinel2 (multi-spectral), and digital elevation model (DEM). In the presented EWC-DLFA technique, a deep convolutional neural network-based EfficientNetB-5 model can be applied for the extraction of features from the multi-source images. For increasing the performance of the EfficientNet-B5 model, the marine predators algorithm (MPA) based hyper parameter tuning process can be applied. Finally, an ensemble of three ML classifiers such as extreme learning machine (ELM), multilayer perceptron (MLP), and gradient boosting machine (GBM) are used to classify the wetland into different types such as fen, bog, marsh, swamps, and upland. The performance of the EWC-DLFA technique can be validated using a large set of simulations. The resultant values pointed out that the EWC-DLFA technique reaches better performance over other models on wetland classification.
Religius Obligation dan Social Influence Terhadap Keputusan Muslim Generasi Z Untuk Menabung di Bank Syariah (Studi Pada Muslim Generasi Z di Kabupaten Jepara)
Authors: Yuliani Naili Rahma , Ansori Miswan
Published: July 2024

This study aims to address whether religious obligation and social influence affect the decision to save in Islamic banks among Muslim Generation Z in Jepara Regency. The research employs a quantitative approach with primary data collected through online questionnaires. A purposive sampling method was used to select 96 respondents. The analytical technique applied is multiple linear regression. The findings indicate that both religious obligation and social influence have a significant impact on the decision to save in Sharia banks. Interestingly, social influence has a greater impact compared to religious obligation in shaping this decision. These findings highlight the importance of community and social norms in shaping Generation Z's financial behavior when choosing financial institutions for saving and provide valuable insights for Sharia banks to develop more effective marketing strategies
Model Evaluasi Program Pendidikan Terhadap Peningkatan Kualitas Pembelajaran
Authors: Iqbal Muhammad , Savitri Intan , Hanifah Fathia , Alfiana Kholizah Nur , Sari Dinda Aulia , Munawwarah Tarisa
Published: July 2024

The quality of education in an educational institution is currently one of the important elements in the sustainability of the educational institution itself, both in terms of the quality of infrastructure, the quality of teaching and education staff or the quality of learning, all of this is one unit that must be maintained and updated in order to make education quality. An educational evaluation model, where matters related to evaluation data are collected is the main step in conducting educational evaluations, especially the quality of learning. Through research using this qualitative method, the author intends to provide readers with views regarding educational evaluation models, especially evaluations that lead to improving the quality of learning with appropriate steps. Evaluation models may appear to vary from one another, but the aims and objectives are the same, namely carrying out data or information collection activities relating to the object being evaluated.
Assessment of the Effect of Cost Leadership Strategies on Organizational Performance of Deposit Taking Saccos in Meru County
Authors: Gikunda Ronny Gitobu , Sawe Christine Jeptoo
Published: July 2024

A recent study conducted in Meru County, Kenya, aimed to investigate the impact of cost leadership strategies on the performance of deposit-taking Savings and Credit Cooperative Societies (SACCOs). The study, which was based on insights from resource-based theory, utilized a descriptive survey design targeting 96 top management personnel across 12 active deposit-taking SACCOs in the region. Primary data for the study was collected through questionnaires distributed to CEOs, accountants, credit managers, marketing managers, and executive board members of the selected SACCOs. Secondary data, on the other hand, was sourced from the yearly financial statements and other reports of these SACCOs. Both primary and secondary data were analyzed using correlation and regression analysis, alongside descriptive and inferential statistics. The findings of the study highlighted the significant impact of cost leadership strategies on the performance of SACCOs in Meru County. These strategies collectively explained a substantial portion (76.30%) of the variations in SACCOs’ performance. Specifically, an increased adoption of the cost leadership strategy resulted in notable performance improvements. In light of these findings, the study recommended initiatives aimed at enhancing capacity building within SACCOs and fostering greater stakeholder engagement to facilitate their growth and overall performance.
PRINCIPAIS ACHADOS DA NEUROPATIA ASSOCIADOS À COVID-19: UMA REVISÃO NARRATIVA DE LITERATURA
Authors: Nogueira Guilherme Nobre , Gonçalves Rafaela Fernandes , Isolan Gustavo Rassier
Published: July 2024

A COVID-19, uma doença de transmissão respiratória que afetou mais de 700 milhões de pessoas entre 2019 e 2023, não se limita apenas às complicações pulmonares. A infecção pelo SARS-CoV-2 também afeta o sistema nervoso periférico (SNP), resultando em diversas complicações neurológicas. Este artigo tem como objetivo revisar os achados das neuropatias em pacientes com COVID-19. Foi realizada uma revisão narrativa de nove artigos publicados entre 2021 e 2023, selecionados através de buscas no PubMed e na Biblioteca Virtual em Saúde (BVS) com os descritores “Neuropathy signs” e “Covid-19”. As manifestações clínicas observadas incluem parestesia, alodinia, paresia, astenia, mialgia, disgeusia e anosmia, sendo mais comuns em pacientes graves. Pacientes com condições neurológicas prévias têm maior risco de desenvolver novas manifestações neurológicas. Cerca de 50% dos pacientes com alterações sensoriais iniciais evoluíram para alterações neuromusculares após sete meses. A recuperação de força muscular foi insuficiente em muitos casos, mesmo com fisioterapia. O padrão de neuropatia foi predominantemente polineuropatia distal e simétrica, com poucos casos de mononeuropatia, e houve uma forte associação entre a Síndrome de Guillain-Barré e a COVID-19. Conclui-se que a COVID-19 está fortemente associada a complicações neurológicas, especialmente em pacientes graves e com condições neurológicas prévias. É crucial um monitoramento rigoroso e intervenções terapêuticas precoces para melhorar os prognósticos neurológicos desses pacientes.
ASSESSMENT PRACTICES, SKILLS, AND UTILIZATION OF ASSESSMENT TOOLS BY THE SECONDARY SCHOOL TEACHERS IN SCHOOLS DIVISION OF ZAMBALES
Authors: A. Bellido EdD Willyn , M. Ebal, EdD Irene
Published: July 2024

The study’s main purpose was to the examine assessment skills, practices, and utilization of assessment tools of 534 Secondary School Teachers in the Division of Zambales for the school year 2021-2022. Descriptive research design was used to assess the perception of the teachers in utilizing classroom assessment skills, practices and tools to monitors students learning and their performance inside the classroom. Descriptive (frequency, percentage and mean) and inferential statistics (Pearson r and ANOVA) were utilized for the analysis and interpretation of data. Major results showed that teacher-respondents utilized in the study have doctorate units, teaching science subject, with few seminars/trainings attended related to classroom assessment, they have more than enough teaching experiences. The teacher-respondents perceived they were very skillful in using classroom assessment. The teacher-respondents perceived they had always done classroom assessment practices and often utilized traditional and authentic assessment. There was no significant difference on the perceived level of agreement of teacher-respondents with the use of classroom assessment skills. There was no significant difference on the perceived level of agreement of teacher-respondents with the use of classroom assessment skills. There was a significant difference on the use of classroom assessment tools among secondary school teachers. There was a significant relationship on the use of classroom assessment skills and the classroom assessment practices. There was a significant relationship between the utilization of assessment tools and the classroom assessment skills. In light of the findings, it is recommended that the teacher may use the findings of the pretest to alter and update instructions before a unit of study, determine which pupils need additional practice, and adapt and revise instructions as needed. Teachers may utilize classroom assessment skills and practices to check on students' comprehension of ideas and a variety of assessment tools, including traditional and authentic assessment to obtain evidence on students' learning. School administrators should work with other stakeholders and ensure that teachers are sent for in-service training or workshops in assessment on a regular basis to make certain that they maintain current classroom assessment skills. Department of Education and curriculum planners may provide teachers with more comprehensive assessment tools to address the needs of students who are at risk of falling behind and equip teachers with traditional and authentic assessment tools to measure a broader variety of competencies. Further research can be conducted on other areas of assessment which are not included in this study.