bluuebunny/crossref_metadata_2025 huggingface dataset:
Educação, arte e literatura: entrelaçando saberes para uma Educação Emancipatória com Jacotot, Libertária com Freire e Revolucionária com Boal
Authors: Santos Antônio Nacílio Sousa dos , Moura Douglas Luiz de Oliveira , Kehler Gabriel dos Santos , Pinheiro Moisaniel Oliveira , Dezem Lucas Teixeira , Mendes Gabriel Barbosa , Cândido Luciana de Fátima Silva , Feitosa Mayara Oliveira , Silva Murilo Borges , Ferreira Samira Borges , Gonçalves Francysco Pablo Feitosa , Santos Madson Douglas Silva dos , Santana Everaldo Costa , Vieira Antonia Pedrosa , Souza Helena Campista de
Published: October 2024

A necessidade da permanência dos estudos literários e artísticos no currículo escolar contemporâneo torna-se uma questão urgente no cenário atual da educação, marcado por uma crescente instrumentalização do conhecimento, onde disciplinas tecnicistas muitas vezes se sobrepõem ao cultivo das artes e das humanidades. Este artigo, portanto, tem como objetivo central demonstrar como a manutenção e o fortalecimento desses estudos são essenciais para a construção de uma educação emancipatória a partir de Jacotot, libertadora através do olhar de Freire e revolucionária por Boal, desafiando a lógica utilitarista que frequentemente norteiam os sistemas educacionais modernos. O artigo utiliza uma metodologia qualitativa (Minayo, 2016) e bibliográfica (Gil, 1999). Assim, diante do contexto de uma educação voltada para o mercado de trabalho, onde a educação ganhou status de “coisificação”, indagamos: quais são as chances de viabilizar uma educação literária e artística que seja verdadeiramente emancipatória e libertadora para os jovens? A pesquisa analisa como as áreas da educação, em especial a arte e a literatura, podem contribuir para essa formação e em que medida são valorizadas ou negligenciadas em favor de disciplinas consideradas mais “úteis” pelo mercado. Dito isso, concluímos a urgência de preservar e valorizar os estudos literários e artísticos no currículo escolar contemporâneo como um imperativo diante das tendências de instrumentalização do conhecimento e da predominância de disciplinas tecnicistas que desconsideram a importância das humanidades. O artigo evidencia que, ao se fundamentar nas ideias de Joseph Jacotot, Paulo Freire e Augusto Boal, é possível conceber uma educação que não apenas forneça conhecimentos aplicáveis, mas que também promova a emancipação, a criatividade e a crítica social.
Benchmarking de tecnologias móveis para promoção da saúde e autocuidado: revisão de escopo
Authors: Oliveira Francisco Ariclene , Leite Álvaro Jorge Madeiro
Published: October 2024

O uso de dispositivos móveis para o desenvolvimento infantil tem crescido, mas sua eficácia varia consideravelmente, sendo essencial avaliar suas funcionalidades para garantir resultados positivos. O objetivo desta pesquisa foi investigar o papel das tecnologias móveis na promoção da saúde e autocuidado infantil. Foi realizada uma revisão de escopo, fundamentada no Benchmarking e no protocolo PRISMA-ScR, com coleta de dados nas lojas Google Play e Apple Store, entre 10 e 20 de outubro de 2023. Foram incluídos aplicativos em português, inglês e espanhol voltados a pais e cuidadores de crianças de 0 a 6 anos. Foram analisados 19 aplicativos, destacando-se que a maioria é em inglês e voltada para um público internacional. Os aplicativos avaliados possuem uma usabilidade aceitável tanto na escala SUS quanto na escala SURE, com escores médios em torno de 79. Conclui-se que a falta de padronização nos aplicativos limita seu impacto, recomendando-se um framework robusto de avaliação para garantir a eficácia dessas ferramentas. Sugere-se ainda que políticas públicas e iniciativas de saúde promovam o uso de aplicativos confiáveis e de alta qualidade para maximizar os benefícios no desenvolvimento infantil.
Avaliação da técnica de amamentação e fatores associados em puérperas e recém-nascidos assistidos em uma maternidade
Authors: Alves Ellen Patricia Fonseca , Pereira Victor Guilherme , Oliveira Sabrina Ferreira de , Murça Lady Thaynara Santos , Santos Anyelle Geovanna Leopoldo , Cruz Brenda Letícia Moura , Guimarães Bruna Carolina Rodrigues , Fonseca Mônica Pereira , Ruas Sélen Jaqueline Souza , Ribeiro Claúdia Danyella Alves Leão
Published: October 2024

Objetivou-se avaliar a técnica de amamentação e os fatores associados em puérperas e recém-nascidos assistidos em uma maternidade no norte de Minas Gerais. Estudo transversal conduzido com 160 mulheres e respectivos neonatos em aleitamento materno exclusivo na maternidade de um hospital. Empregou-se a escala LATCH para avaliação da técnica de amamentação. Conduziu-se análise multivariada para definição das variáveis associadas à prática de amamentação. Nutrizes com idade entre 18 e 29 anos (58,8%) e escolaridade entre > 9 a < 12 anos de estudo (58,8%) foram majoritárias. Os neonatos foram, predominantemente, do sexo masculino (53,8%) e avaliados com < 24 horas de vida (69,4%). Menor nível de escolaridade associou-se com dificuldade/não manutenção da pega ao amamentar (OR: 3,66; IC95%: 1,74 - 21,06; p = 0,017). Ainda, registrou-se correlação entre intercorrências no parto, dificuldade/não manutenção da pega (OR: 0,32; IC95%: 0,10 - 0,99; p = 0,048) e necessidade de ajuda parcial/completa no posicionamento para amamentar (OR: 0,16; IC95%: 0,04 - 0,61; p = 0,007). Os resultados atestam a necessidade de uma coordenação do cuidado integrado bem instituída e preparada nos serviços de saúde obstétricos para atender às demandas particulares do público materno e infantil.
Research on Evaluation Method of Natural Gas Hydrate Resources Based on Probability Distribution Model
Authors: Zhao Zhetong , Huang Yiwei , Wang Haoyu
Published: October 2024

Natural gas hydrate, as a highly efficient and clean energy source, has garnered significant attention from both developed and energy-deficient nations. However, to achieve industrial application, several technical challenges must be addressed, including resource exploration, spatial positioning, resource assessment, economic production evaluation, and climate change impact assessment. Currently, our exploration and evaluation system for gas hydrate resources remains immature. This study aims to determine the probability distribution of effective thickness, porosity, and saturation of natural gas hydrate resource parameters in the study area and their variations. Utilizing probability statistics theory, we calculated specific values for all gas hydrate resource parameters from open-source data of 14 exploration well locations, determined their probability distributions, and conducted distribution detection to identify specific distributions. Data analysis was performed using linear graphs and bar charts to summarize patterns. Subsequently, by applying the mathematical formula Q = A×Z×∅×S×E, we categorized natural gas hydrate resource parameters, calculated gas hydrate resources at each effective depth, determined the probability distribution of each obtained data point, and ultimately solved for the total natural gas hydrate resources, yielding a final solution of 31875763695.08.
Design Issues and Solutions for Improving the Comfort of Neo-Ming Style Chair
Authors: Li Zhi , Rina Abd Shukor
Published: October 2024

This paper explores the design issues and solutions for improving the comfort of new Ming-style chairs. It first analyzes the historical origins, traditional aesthetics, and modern users' new demands for comfort in these chairs. Then, it highlights the comfort-related challenges in terms of chair form, materials, structure, and usage scenarios. The study proposes design principles from the perspectives of ergonomics, material innovation, structural optimization, and modern usage scenarios. Specific strategies to enhance comfort are provided, such as optimizing the angle between the chair back and chair surface, improving cushion materials, optimizing the chair's supporting structure, and incorporating intelligent design. This paper also discusses the challenges of integrating technology with traditional design, the application of eco-friendly materials, and the diverse demands of consumer groups. Finally, it summarizes the future trends and challenges in the comfort-focused design of Neo-Ming style chair.
The Relationship between Quality Control and Corporate Brand Strategy in Operations Management
Authors: Chen Chi , Huang Peng
Published: October 2024

In today's competitive business environment, quality control and brand strategy are critical factors for corporate success. This paper aims to explore the close relationship between quality control and corporate brand strategy in operational management. Initially, the paper defines quality control and brand strategy and analyzes their importance in modern corporate operations. Subsequently, it elaborates on the role of quality control in enhancing product and service quality, as well as customer satisfaction, and the central position of brand strategy in shaping corporate image, accumulating brand equity, and competing in the market. Through case analysis, the paper reveals how quality control supports the implementation of brand strategy and how brand strategy influences the decision-making and execution of quality control. Finally, the paper discusses the challenges faced by companies in implementing quality control and brand strategy and proposes corresponding countermeasures. The findings of this study emphasize the importance of the synergistic development of quality control and brand strategy and provide guidance and recommendations for corporate practice.
CNN-BiLSTM-Attention Based Stock Price Prediction and Quantitative Investment Strategy
Authors: Li Chunzhong , Hua Weiqi
Published: October 2024

With the rapid development of technology and the increasing maturity of financial markets, stock price prediction has become a hot topic and an important trend in the financial field. However, the stock market has complexity and volatility, in order to reduce the investment risk and ensure the maximization of benefits, selecting the optimal stock to predict the stock price and formulating the quantitative trading strategy are extremely important issues for financial academics and investors. For the research of stock price prediction and quantitative trading, firstly, the quantitative stock selection model combining XGBoost and multi-factor stock selection model is constructed, and four stocks are screened out, and then the CNN-BiLSTM-Attention model is proposed to predict the stock price trend of the selected stocks, and it is found from the prediction results of the four stocks that the prediction accuracies all reach more than 95%, which is higher than that of the single model prediction. accuracy and passed the validity test of the fitted model. Secondly, based on the quantitative investment portfolio given in the above analysis and the quantitative stock trading strategy of MACD, the prediction results are verified, and the total return of most stocks based on the strategy is higher than 40% at the highest, and the loss is controlled within 10%, which indicates that the trading strategy in this paper is effective and feasible. The study concludes that greater returns can be obtained by calculating the total returns of stock portfolios based on different portfolio approaches.
The Impact of E-commerce Live Streaming on Consumer Purchasing Behavior in the Influencer Economy
Authors: Wang Qi , Binti Musa Rosidah
Published: October 2024

This study investigates the impact of e-commerce live streaming on consumer purchasing behavior, focusing on the current market size, the promotion efforts of mainstream media and businesses, and conversion rates. The results show that the market for live streaming is expanding rapidly, with businesses and media viewing it as a key traffic source and marketing tool. The conversion rate of live stream content is also higher than traditional e-commerce marketing methods. However, the influence of live streaming is not entirely positive, highlighting the need for increased regulation and more rigorous market standards. E-commerce platforms and influencers must also deepen their understanding of consumer needs to build trust and optimize their content to suit purchasing behavior.
Assessing the Impact of Leadership Styles on Organizational Effectiveness in Chinese Vocational Schools
Authors: Wu Wenfeng , MERIALSES ROSALIE D.
Published: October 2024

This research examines the impact of leadership styles on the organizational efficacy of vocational institutions in China. The main aim is to evaluate the effects of several leadership dimensions—communication, decision-making, motivation, empowerment, conflict management, relationship building, and ethical behavior—on these schools' overall operation and performance. Utilizing a descriptive-comparative and regression analytic methodology, data was gathered from instructors and students at five vocational schools in Guangdong Province via structured questionnaires. The research examined the opinions of leadership effectiveness among instructors and students, finding notable disparities between the two groups, with teachers consistently providing more favorable leadership evaluations. The regression study further substantiated that leadership styles substantially influence critical aspects of organizational success, including strategy planning, resource management, employee engagement, performance management, and student orientation.
Innovation and Practice of Diversified Business Model of New Energy Power Generation Enterprises
Authors: Chen Guanbao
Published: October 2024

With the growing global demand for clean energy, new energy power generation enterprises are facing new opportunities and challenges. This paper explores the diversified business model of new energy power generation enterprises. Firstly, the theoretical basis of the business model of new energy power generation enterprises is analyzed, including the characteristics and income sources of single and multiple industrial chain models. The advantages and applications of different business models are analyzed through the study of cases at home and abroad, such as the European aggregated generation side resource model, the United States focused on controllable load demand response model, and the domestic cases of Shanghai and Shenzhen virtual power plant projects and distributed photovoltaic power generation. Then it puts forward the diversified business model innovation strategy of new energy power generation enterprises, covering the innovation of comprehensive energy services, the development of power side energy storage business, and the expansion of key businesses and profit models. Finally, the research conclusions are summarized and the future research directions are prospected. In short, the exploration of diversified business models of new energy power generation enterprises is of great significance to promote the development of clean energy and improve the competitiveness of enterprises.
Analysis of Landing Buffer Characteristics of Unmanned Airdrop Rescue Vehicle
Authors: Zhang Xin
Published: October 2024

Taking an unmanned airdrop rescue vehicle as the research object, in order to reduce the impact overload of the airdrop landing, the exhaust type airbag buffer mode suitable for the airdrop vehicle is determined. Firstly, the scheme of the airdrop system is designed. According to the basic assumption of the airdrop process, the thermodynamic analysis model of the buffer bag is derived and the parameters are designed. Secondly, the finite element model of airdrop system with airbag buffer is established based on the airbag finite element algorithm (CV method). Finally, the buffering effect is evaluated by analyzing the change of tire force. The calculation results show that the system overload is reduced from 35g to 22g by using airbag buffer, and the buffer effect is obvious.
Construction of an Adolescent Spinal Health Intelligent Early Warning and Intervention System under the Proactive Health Model
Authors: Miao Feng
Published: October 2024

This study explores the construction of an intelligent early warning and intervention system for adolescent spinal health and its application under a proactive health model. The research demonstrates that through real-time monitoring and personalized interventions, the Spinal Health Index of adolescents in the intervention group significantly improved, increasing from 65 to 85 points, while the control group's index slightly decreased from 60 to 58 points. Personalized intervention strategies, such as the combination of exercise and nutritional interventions, were found to be the most effective, indicating that daily activity levels significantly impact spinal health. The system plays a crucial role in the management of adolescent spinal health by facilitating real-time monitoring, personalized interventions, and modifications in health behaviors. Despite limitations including constraints in sample size and geographical scope, a relatively short intervention period, and insufficient data diversity, future research can enhance universality and model generalizability by expanding sample sizes, prolonging the intervention period, and increasing data diversity. Looking forward, the integration of multimodal data, optimization of the user interface, and establishment of long-term tracking mechanisms will further enhance system performance and promote the improvement of spinal health management in adolescents.
Research on the Innovation Path of Enterprise Management Mode in the Era of Big Data
Authors: Wang Jing
Published: October 2024

With the continuous development of the market economy and the advent of the big data era, traditional management models cannot meet the requirements for enterprise development, which poses certain obstacles to business growth. Therefore, enterprises must continuously leverage the commercial value embedded in the big data era to innovate their management models. In practical terms, internal managers and relevant personnel can innovate based on traditional management models, fully utilizing the advantages of big data to optimize and innovate enterprise management models. By utilizing information technology for extensive data analysis and integration, businesses can be guided in the right direction, provided with reasonable operational plans, and ensured safe decision-making, thereby achieving healthy and sustainable development. This approach enables enterprise management to keep pace with the progress of the times and enhances the role of management in business operations.
Research on the Digital Transformation Process from Dependent to Self-Empowered Upgrading
Authors: Yan Jinxia
Published: October 2024

In the face of many challenges in the digital era, how to accelerate the digital transformation of enterprises has become a topic of widespread concern in academia and industry. In the face of many challenges in the digital era, how to accelerate the digital transformation of enterprises has become a topic of widespread concern in academia and industry. Considering that the stage of this solution is to first rely on relevant enterprises to upgrade and build digital systems, and then cultivate their own digital capabilities, so as to complete the iteration of digital system capabilities and achieve continuous self-empowerment upgrades, it is named as the research on the process of upgrading from dependent to self-empowered to digital transformation.
Research on Bank Data Governance Organization Architecture Planning
Authors: Huang Jian
Published: October 2024

Banking service capacity building is a critically essential area of study and research because banks, as vital components of the financial system, are key drivers of economic development and industrial progress. In light of the continuous improvement of financial innovation and scientific and technological knowledge, bank data information regarding the current state of the commercial banking industry displays a geometric multiple growth pattern. The complex state of bank data is also brought about by these data, which serve as a reference for bank architecture and planning. Because of this, efficient data management is required to fully reap the benefits of data. In short, the bank's data governance work will further enhance the bank's service capabilities and achieve its comprehensive value improvement.
COMPARISON OF DRUG-ELUTING STENTS VERSUS BARE-METAL STENTS FOR THE TREATMENT OF CORONARY ARTERY DISEASE
Authors: AHMAD I , ULLAH R , KUMAR S , TIPU AA , EJAZ M , AZIZ A , HASAN F , SHARFI FM , REHMAN S
Published: October 2024

Coronary artery disease is a condition that affects millions of people worldwide and can lead to serious complications such as heart attack or stroke. Objectives: The study compares drug-eluting stents to bare-metal stents for treating coronary artery disease. Methods: The present study aimed to compare the efficacy and safety of drug-eluting stents (1) versus bare metal stents (BMS) for the treatment of coronary artery disease (CAD). The study was conducted at Cardiology Department Hayat Abad Medical Complex Peshawar Pakistan, from 01 January 2022 till 30 June 2022. The study included a total of 384 patients with CAD who underwent percutaneous coronary intervention (PCI) with either DES or BMS. Results: The study included a total of 384 patients with CAD who underwent PCI with either DES or BMS. The mean age of the study participants was 58.2 ± 9.4 years, and 75.8% were male. The baseline characteristics, including demographic data, clinical presentation, cardiovascular risk factors, and angiographic findings, were comparable between the two groups. Conclusion: In conclusion, our study contrasting medication-eluting stents versus exposed metal stents for the treatment of coronary supply route sickness found that drug-eluting stents were related to a lower chance of unfriendly heart occasions, including objective vessel revascularization, myocardial dead tissue, and cardiovascular demise, contrasted with uncovered metal stents.
UNVEILING CLINICAL AND ANGIOGRAPHIC PREDICTORS OF RESTENOSIS FOLLOWING PERCUTANEOUS CORONARY INTERVENTION (PCI) IN LEFT ANTERIOR DESCENDING (LAD) ARTERY LESIONS: INSIGHTS FROM A RETROSPECTIVE COHORT STUDY
Authors: DURRANI T , HUSSAIN I , ASMA S , DURRANI H , KHAN P , HAIDER A
Published: October 2024

Restenosis, the re-narrowing of coronary arteries post-stenting, continues to be a significant clinical issue, particularly in Left Anterior Descending (LAD) artery lesions. Despite advances in stent technology, including drug-eluting stents (DES), restenosis remains a significant contributor to adverse cardiovascular events such as myocardial infarction and repeat revascularisation. Understanding the clinical and angiographic factors that predict restenosis is crucial for improving patient outcomes. Objective: This study aims to identify clinical and angiographic predictors of restenosis in patients undergoing Percutaneous Coronary Intervention (PCI) for LAD artery lesions. Methods: A retrospective cohort study was conducted at MTI-Hayatabad Medical Complex from January 1, 2023, to December 31, 2023. A total of 162 patients who underwent PCI for de-novo LAD lesions were included. Data regarding patient demographics, comorbidities, lesion characteristics, and stent types were extracted from hospital records. Angiographic follow-up was performed at 12 months post-PCI to assess restenosis, defined as ≥50% luminal narrowing. Multivariate logistic regression analysis was used to identify independent predictors of restenosis.
Results: Among the 162 patients, 15.4% (n=25) developed restenosis within 12 months. Diabetes mellitus (OR: 2.74; 95% CI: 1.21–6.24; p=0.014), lesion length (OR: 1.08 per mm; 95% CI: 1.01–1.15; p=0.025), and stent type (DES vs. BMS; OR: 0.42; 95% CI: 0.18–0.98; p=0.046) were identified as significant predictors. Smoking and hypertension were not found to be statistically significant. Conclusion: Diabetes mellitus, lesion length, and the use of drug-eluting stents are independent predictors of restenosis in LAD artery lesions. These findings underscore the need for tailored stenting strategies and enhanced post-PCI management in high-risk patients to reduce restenosis rates and improve long-term outcomes.
In vitro propagation of Dendrocalamus hamiltonii Nees & Arn. ex Munro through nodal explants from mature culms
Authors: Bisht Prabha , Pant Manu , Kant Abhinav , Mir Rayees
Published: October 2024

An efficient procedure for in vitro propagation through axillary bud proliferation has been developed for Dendrocalamus hamiltonii Nees & Arn. ex Munro. Best results for bud induction were obtained in liquid MS medium supplemented with 15µM BAP. MS supplemented with 10µM BAP was found to be optimal for shoot multiplication giving an average shoot multiplication rate of 9.13 folds. Multiplied shoots were further cultured onto MS medium supplemented with auxin for in vitro rooting. Optimal rooting (66.13%) was obtained on MS medium supplemented with 25µM IBA in four weeks. The in vitro raised plantlets with well developed shoots and roots were subsequently hardened in green house conditions. A survival rate of over 90% was achieved on transferring the plants to field conditions where they exhibited normal growth.
Standardization of genomic DNA extractions from lac-host trees, Schleichera oleosa and Butea monosperma, for PCR-based genetic diversity studies
Authors: Saha Dipnarayan
Published: October 2024

Isolation of good quality genomic DNA from the polysaccharide and phenol-rich leaf tissues were attempted from Kusum (Schleichera oleosa) and Palas (Butea monosperma) trees, to be used in PCR-based genetic diversity studies. Out of three different methods tried, a CTAB-based method adapted from Doyle and Doyle (1990) was found suitable for isolation of good quality genomic DNA from the young leaves of Kusum and Palas trees. The genomic DNA isolated from young leaves of Kusum and Palas through above method were found to have satisfactory purity index (>1.7) at 260/280 nm absorption and high yield as well. The genomic DNA isolated through CTAB method from the young leaves of Kusum and Palas were found suitable for RAPD-PCR reactions.
Analysis of Objective Functions for Ribonucleic Acid Multiple Sequence Alignment Fusion Based on Harmony Search Algorithm
Authors:   Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Malaysia, Mubarak Mubarak ,   School of Computer Sciences, Universiti Sains Malaysia, Penang, 11800, Malaysia,   School of Computer Sciences, Universiti Sains Malaysia, Penang, 11800, Malaysia,   School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester LE1 9BH, UK,   Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Malaysia,   Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Malaysia, Abdullah Rosni , Omar Mohd. Adib Hj. , Ahmed Abdulghani Ali , Omar Nurul Aswa , Mostafa Salama A.
Published: October 2024

Four kinds of smaller molecules known as ribonucleotide bases-adenine (A), cytosine (C), guanine (G), and uracil (U) combine to form the linear molecule known as ribonucleic acid (RNA). Aligning multiple sequences is a fundamental task in bioinformatics. This paper studies the correlation of different objective functions applying to RNA multiple sequence alignment (MSA) fusion generated by the Harmony search-based method. Experiments are performed on the BRAliBase dataset containing different numbers of test groups. The correlation of the alignment score and the quality obtained is compared against coffee, sum-of-pairs (SP), weight sum-of-pairs (WSP), NorMD, and MstatX. The results indicate that COFFEE and SP objective functions achieved a correlation coefficient (R²) of 0.96 and 0.92, respectively, when compared to the reference alignments, demonstrating their effectiveness in producing high-quality alignments. In addition, the sum-of-pairs takes less time than the COFFEE objective function for the same number of iterations on the same RNA benchmark.
The Detection of Glaucoma in Fundus Images Based on Convolutional Neural Network
Authors:   Department of Computer, College of Science for Women, University of Babylon, Babylon, Iraq, Ali Ali
Published: October 2024

Glaucoma is a common disease affecting the human retina, primarily caused by elevated intraocular pressure. Early intervention is crucial to prevent damage to the affected organs, which could lead to their dysfunction. This paper focuses on enhance diagnosis accuracy of the system to determine if a patient is at risk of developing glaucoma. In this paper a novel convolutional neural network (CNN) designed, specifically for the detection of glaucoma in fundus images. This architecture optimizes for the unique characteristics of fundus imagery, enhancing detection accuracy, and also compiled a large and diverse dataset of fundus images, crucial for training and validating our CNN model. The dataset includes a significant number of images with detailed annotations, ensuring robust model training. In addition, implemented sophisticated image preprocessing methods to enhance the quality of the fundus images. These techniques, including noise reduction and contrast enhancement, significantly improve the input data quality for the CNN. The system operates in three stages. First, it preprocesses the image by cropping, enhancing, and resizing it to a consistent 256×256 pixels. Next, it employs an advanced feature extraction to analyses key features of the optic disc and optic cup in retinal images. Finally, the Soft-Max function classifies the images, identifying those with glaucoma and distinguishing them from normal eye samples. The model's performance was thoroughly evaluated using various metrics like accuracy, Sensitivity, specificity, and the area under the curve are metrics used to evaluate the performance of a diagnostic test. Sensitivity measures the test's ability to correctly identify positive cases, specificity assesses its accuracy in identifying negative cases, and the area under the curve indicates the overall effectiveness of the test across different thresholds. The results achieved by the proposed system were thoroughly analyzed, revealing a high accuracy rate in glaucoma classification, reaching 99%.
Computer Aided Brain Tumor Diagnosis using Coati Optimization Algorithm with Explainable Artificial Intelligence Approach
Authors:   Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia, Wajdi Wajdi
Published: October 2024

Brain tumors (BT) are a difficult and dangerous medical condition, and the accurate and early analysis of these tumors is crucial for suitable treatment. Explainability in clinical image diagnosis role a vital play in the correct analysis and treatment of tumors that supports medical staff's optimum understanding of the image analysis performances rely upon deep methods. Artificial intelligence (AI), in certain deep neural networks (DNNs) has attained remarkable outcomes for clinical image analysis in many applications. However, the need for explainability of deep neural approaches has been assumed that major restriction before executing these approaches in medical practice. Explainable AI, or XAI, is a vital module in this context as it supports medical staff and patients in understanding the AI's decision-making model, enhancing trust and transparency. It leads to optimum patient care and performance but making sure that medical staff can make learned decisions depends on AI-driven insights. Therefore, this study develops a novel Computer-Aided Brain Tumor Diagnosis using Coati Optimization Algorithm with an Explainable Artificial Intelligence (CABTD-COAXAI) approach. The purpose of the CABTD-COAXAI technique is to exploit XAI and hyperparameter-tuned deep learning (DL) approaches for automated BT analysis. To accomplish this, the CABTD-COAXAI technique follows a Gaussian filtering (GF) based noise removal process. Besides, the CABTD-COAXAI technique utilizes the EfficientNetB7 methods for the feature extraction process. Additionally, the hyperparameter tuning of the EfficientNetB7 method is performed by the use of COA. Furthermore, the classification of the BT process can be performed by the usage of a convolutional autoencoder (CAE). Finally, the CABTD-COAXAI system combines the XAI method named LIME to effectively understand and explainability of the black-box model for automated BT diagnosis. The simulation result of the CABTD-COAXAI technique has been tested on a benchmark BT database. The extensive outcomes inferred that the CABTD-COAXAI method reaches superior performance over other models in terms of different measures
A Novel Behavioral Monitoring based Trust Model for enhancing Edge Security using Adaptive Neuro-fuzzy Inference System
Authors:   Research Scholar, Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamilnadu, India, D. D. ,   Assistant Professor, Department of Information Technology, Annamalai University, Chidambaram, Tamilnadu, India, Kumar K. Santhosh
Published: October 2024

The Internet of Things (IoT) is in a recent state of instability due to the flooding of virtual data. It is believed that IoT and cloud computing have met their maximum thresholds and loading them with data after this point will only deteriorate their performance. Hence, edge computing has been introduced to mitigate the processing burden of IoT. To meet the security demands of edge computing, we intend to combine the method of blockchain along with edge computing for a better solution. Accordingly, this paper proposes the introduction of a novel blockchain model that is based on artificial neural networks and trust estimation called the behavioral monitoring trust estimation model. Performance metrics such as accuracy, precision, recall, and F-measure are calculated under normal conditions and under the injection of attacks like false data injection, booting attack, and node capturing. The proposed behavioral monitoring trust classification model is compared with existing classifiers like Naive Bayes, K-nearest neighbor, Auto Encoder, Random Forest, and Support Vector Machine, and is found to have improved performance. Additional evaluation parameters like execution time, encryption time, storage cost, computational overhead, energy efficiency, and packet drop possibility are also calculated for the proposed model and compared with existing blockchain techniques of Bitcoin, Ethereum, Hyperledger, Direct and indirect trust model, and mutual trust chain based blockchain model. The proposed model achieved an accuracy of 95%, a precision score of 90%, a recall score of 94%, and an F-measure of 94% indicating superior performance.
Enhancing Cloud Computing Efficiency with Crocodile Optimization Algorithm: A Novel Approach to Distributed Workload and VM Management
Authors:   Computer Engineering, University of Technology, Baghdad, Iraq, Ibrahim Ibrahim ,   University of Debrecen, Department of Mathematical and Computational Science, Debrecen, Hungary,   University of Debrecen, Department of Mathematical and Computational Science, Debrecen, Hungary, Barry Warshine , Badjajian Narek
Published: October 2024

Cloud computing has introduced itself as a mighty mechanism for delivering customers through the service model with on-demand, scalable, and instant access to computer resources. It will conduct effective load balancing and resource management, high importance so that the cloud system works with optimized performance and resource utilization. This gives a new strategy in load balancing and virtual machine (VM) control in cloud computing applied in the field using the Crocodile Optimization Algorithm (COA) for better performance. Inspired by crocodile hunting behaviors, the COA-based strategy is adopted to balance loads and manage VMs. This approach seeks to balance the number of the workload given to VMs with respect to the processing power of VMs and also the distribution of workload. It best uses resources in such a way that tasks are dynamically distributed to VMs in such a way that response time is at its minimum, and thus overall efficiency is enhanced in cloud computing. On the other hand, COA-based load balancing incorporates VM management techniques like migration and scaling to be adjustable in relation to the changing conditions of the workload. This allows dynamically adjusting the allocation of resources with respect to current demands, in such a way that assures optimal utilization of computational resources with high performance. The proposed approach was evaluated using simulations through CloudSim, one of the most adopted tools for simulating cloud computing. The COA effectively works are divided between the VM, which in turn will lead to better response time for the user request and improve cloud resource utilization. That is to mean, subsequent research would be some type of unique attempt in the area of load balancing and VM management in cloud computing, based on the Crocodile Optimization Algorithm. This approach improves efficient cloud computing through the balancing of load distribution, maximization of resource utilization, and lowering of response time.
Securing Drug Traceability: Block chain-Enhanced Privacy Protection and Anti-Counterfeit Measures in Pharmaceutical Supply Chains
Authors:   Industrial Engineering Department, Penn State University, University park, State college, USA, Thamer Thamer ,   E-Commerce Department, Saudi Electronic University , Kingdom of Saudi Arabia, Alhussain Thamer
Published: October 2024

The pharmaceutical industry encounters numerous challenges in the management of medications and ensuring their authenticity, as well as safeguarding sensitive information within the supply chain. Maintaining the integrity of drug manufacturing processes, transaction records, and patient data from unauthorized access or tampering is crucial. Any breach in security could undermine trust throughout the entire supply chain. To mitigate these concerns, a multi-layered approach is employed. Initially, data encryption using QR codes with Attribute-Based Encryption provides a foundation for securing information. This is followed by an innovative strategy that combines Red Panda Optimization (RPO) Algorithm and Group Teaching Optimization algorithms (GTOA) to optimize encryption key selection. Finally, Multi-Party Computation (MPC) protocols along with Shamir's Secret Sharing enhances overall security measures. These procedures ensure that only authorized individuals have access to critical information essential for identifying counterfeit products and maintain confidentiality through Secure MPC verification without compromising sensitive details.
Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Cloud Environments
Authors:   Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia, Mohd Mohd ,   Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia,   Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia,   Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia,   Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia,   College of Computer Sciences and Information Technology, Majmaah University, Majmaah, Saudi Arabia, .. Mohd Faizal Ab , Azmi Zafril Rizal Bin M , Firdaus Ahmad , Nuhu Abdul Hafeez , Hussain Syed Shuja
Published: October 2024

Distributed Denial of Service (DDoS) attacks pose a significant threat to cloud computing environments, necessitating advanced detection methods. This review examines the application of Machine Learning (ML) and Deep Learning (DL) techniques for DDoS detection in cloud settings, focusing on research from 2019 to 2024. It evaluates the effectiveness of various ML and DL approaches, including traditional algorithms, ensemble methods, and advanced neural network architectures, while critically analyzing commonly used datasets for their relevance and limitations in cloud-specific scenarios. Despite improvements in detection accuracy and efficiency, challenges such as outdated datasets, scalability issues, and the need for real-time adaptive learning persist. Future research should focus on developing cloud-specific datasets, advanced feature engineering, explainable AI, and cross-layer detection approaches, with potential exploration of emerging technologies like quantum machine learning.
Enhancing Urban Connectivity: Dynamic Implementation and Integration of Multi-IRS Systems in Smart Cities
Authors:   Computer Engineering, University of Technology, Sinaa Street 62, Baghdad Iraq, Israa Israa ,   Computer Engineering, University of Technology, Sinaa Street 62, Baghdad Iraq,   Mustansiriyah University / College of Engineering / Electrical Engineering Department, Baghdad Iraq,   Faculty of Information Technology and Robotics, Vitebsk State Technological University, Belarus,   Faculty of Information Technology and Robotics, Vitebsk State Technological University, Belarus, Mahmod Alza A. , Ahmed Alaa H , Drominko Sergey , Kovachiskaya Erina
Published: October 2024

This is in preparation to stand out in urban connectivity to be used faster for Multi-Intelligent Reflecting Surfaces (Multi-IRS) in the latest thirst response. It will determine in advance the application of IRS technology for electromagnetic wave control, so that it is fine-tuned at full power to boost signal transmission and coverage across the urban areas in high-density population. It outlines flexible strategies on how to integrate the Multi-IRS system with both past and urban future establishments in a view of making connected connectivity. In reality, multi-IRS integrated with foundational smart city technologies such as IoT, 5G networks, AI, and others are nothing but a leap toward accomplishing unparalleled data flow and connectivity, both very essential for the modern urban ecosystem. Detailed case studies have demonstrated how multi-IRS systems can enable the breaking of traditional barriers in connectivity: more essentially, it can offer higher bandwidth, lower latency, and increased communication effectiveness. This development marks one of the serious steps under the concept of smart cities, where the data will be spreading and flowing without barriers between the multifarious urban systems and services. Lastly, the paper concludes with a future-looking view of urban connectivity underscored through continuous innovation and research of multi-IRS applications within the smart city landscape. The study points out the fact that dynamic IRS implementation creates an indispensable role in the pathway for upcoming development in smart city connectivity solutions, thus making a case for sustained collaborative efforts in research, policy formulating, and technological innovation for realizing the full potential of IRS technology in taming the connectivity challenges of contemporary urban settings. Performance comparison between a sequential beam search and a proposed model across varying Rician Factors, showing the proposed model's superior channel gain progression from -57 dB at 5 dB to -48 dB at 30 dB, outperforming the sequential method in environments with strong direct signals.
Study on Naming Common Infectious Diseases in Treatise on Cold Pathogenic and Miscellaneous Diseases
Authors: Yu Wenxin
Published: October 2024

The naming factors of the representative infectious diseases in Treatise on Cold Pathogenic and Miscellaneous Diseases are various, mainly according to the cause, time and symptoms of the disease. From the perspective of cognitive linguistics, these names are mostly named by metaphor and metonymy. The types of metaphors are mainly abiotic metaphors, plant metaphors and animal metaphors, while metonymy highlights the key features. This reflects the wisdom of the ancients to transform abstract pathological phenomena into concrete perceptions. This naming principle not only highlights the essence of TCM culture, but also promotes the inheritance and development of TCM theories.
The Contemporary Transformation of Mythical Women: Helen and Penelope in Margaret Atwoods Reconstruction
Authors: Zhou Yining
Published: October 2024

The feminist movement has driven the reinterpretation of classical texts such as Homers epics, with Margaret Atwoods works exemplifying this trend. Atwood has created poetry and novels rooted in the tradition of feminist rewriting, utilizing mythological female figures like Helen and Penelope to explore their inner worlds and unveil the long-concealed mechanisms of patriarchal power. Employing techniques such as multiple perspectives and nonlinear narratives, she disrupts the constraints of a singular authoritative discourse. By merging the ancient with the modern, Atwood highlights the shared experiences of women while portraying the awakening of female consciousness. She transforms the stereotypical images of the whore and the virtuous wife into complex, autonomous individuals, challenging traditional gender role definitions. Atwoods rewritings are not merely retellings of the original stories but also explorations of the deeper structures of gender power. She weaves a rich cultural network of women, opening new frontiers for literature of their own.
Research Progress on Photothermal Therapy of Malignant Tumor Cells
Authors: Li Jingxuan , Xu Shuo
Published: October 2024

Malignant tumors, also known as cancer, are among the diseases with the highest incidence and mortality rates in the world. Photothermal therapy (PTT) involves injecting materials with high photothermal conversion efficiency (PTA) into the human body and using the principle of photothermal conversion to kill malignant tumor cells at the lesion site with high temperatures. This paper reviews various methods of photothermal therapy for malignant tumors, summarizes the basic therapeutic logic of photothermal therapy, and forecasts the future development prospects of this treatment method.
New intelligent flower arrangement system
Authors: Huang Luxing
Published: October 2024

Currently, the art of flower arrangement plays a significant role in both personal life and collective work. However, for non-professionals, creating flower arrangements with high aesthetic value can be challenging. With the rapid advancement of artificial intelligence technology, its application in the field of flower arrangement offers new possibilities for addressing this issue. To achieve a balance between aesthetic appeal and efficiency in intelligent flower arranging, this paper proposes an innovative intelligent flower arrangement assistance system. This system encompasses core functions such as intelligent recognition of floral materials, matching suitable options from a database, realizing flower arrangement designs, and constructing the database. Through a series of experiments, the system has demonstrated a good balance between aesthetic appeal and efficiency in flower arrangement design, with results indicating the effectiveness and feasibility of the proposed method.