bluuebunny/crossref_metadata_2025 huggingface dataset:
Deconstruct To Reconstruct
Authors: Montefinese Lorenzo
Published: January 2025

Electronic music can arguably be considered the genre that has most effectively encapsulated and articulated human relationships with noise, machines and technology, embodying many technology-oriented concepts. The 21st century has brought about a post-condition that has had an impact on popular electronic music, with the rise of what has been labelled “deconstructed club”, “post-club”, and “conceptronica”. This paper presents a critique of the aforementioned terms, arguing that electronic dance music has consistently employed deconstructive and decontextualizing strategies in relation to pre-existent genres, elements, and expressive codes. Indeed, it is argued that a logic of assemblage and an aesthetic of metamorphosis are intrinsic to popular electronic music, given its technological and recombinant essence. If electronic dance music is based on avantgardist techniques such as assemblage and aesthetic détournement, it may be conceived, then, as an expression of popular avantgarde or popular modernism. Moreover, conceptual underpinnings may be found in popular electronic music since its inception. This paper, therefore, aims to reframe the analysis and discourses on popular electronic music by considering its deconstructive and conceptual aspects, and proposes that the most interesting facets of its recent developments may, in fact, lie elsewhere.
Automated Nemocentric Listening
Authors: Raponi Martina
Published: January 2025

This contribution is meant to analyze the the techno-cognitive assemblages that streaming platforms are part of, and how streaming platforms create the conditions for a “physiognomy” of listening habits under the premises created by the automatic distributed listening on which their functioning relies. Considering the genealogy of digital music formats (Sterne, 2012), interrogating the retromaniac (Reynolds, 2011) re-mediations taking place within streaming platforms, looking at the shifts and clashes generated by automation (Reynolds, 2011; Burrows and O’Sullivan, 2018), and using noise theory and noise philosophy (Attali, 1985; Brassier, 2011; Malaspina, 2018; Mattin, 2022), this text will attempt to elucidate different non-sonic constraints that inform and shape the way we engage with listening routines, while also analyzing streaming-reliant listening habits through the lens of noise and noise philosophy. If noise is the disruption that becomes prerequisite for a selfless subject (Brassier, 2009), could we argue that noise-reliant algorithmic systems, based on the objectivity of machinic listeners, organize the chaos of desiring subjects by creating the illusion of a unique self, but in reality becoming the ultimate breeding ground for nemocentric (Metzinger, 2004) production of subjectivities? These subjectivities can be considered nemocentric (namely: without a center) because distributed across the cognitive assemblages of platform capitalism, dematerialized and scattered. If noise has been subsumed under the “masking” (Sterne, 2012) premises of platform capitalism, how do nemocentric subjects operate under the prescriptive logics of the very capitalist structures that produced them?
Introduzione
Authors: Tomeo Caterina , Attimonelli Claudia
Published: January 2025

Introduzione vol. 8
Climate Change and National Security Challenges to Pakistan: An Analysis
Authors: Hussain Zahid , Gillani Syed Aleem , Hayat Muhammad Umer
Published: January 2025

Climate change is increasingly recognized as a major national security concern in Pakistan. The country faces many climate-related challenges, including rising temperatures, changing precipitation patterns, extreme weather events, and melting glaciers. These climate changes have a significant impact on various sectors such as agriculture, water resources, healthcare, and infrastructure. This article examines the impact of climate change on Pakistan's national security, the threats and risks associated with environmental degradation, Pakistan's current policy framework on climate change, and how Pakistan should address the climate crisis. Addressing these challenges requires urgent and concerted efforts to integrate climate change adaptation and mitigation strategies into national security policies to protect the well-being and stability of our people.
Knowledge Work and Discontent: A Comparative Analysis of Work Alienation by Age and Experience
Authors: Shahzad Muhammad , Batool Zahira , Anjum Faiza , Mahmood Babak , Chauhdhry Ayesha
Published: January 2025

Organizations are confronted with work alienation among workers, yet there is limited empirical research on how work alienation varies based on age and work experience. This study evaluated the difference in work alienation by age and work experience among agricultural knowledge workers. Using stratified sampling technique, data were collected from 350 respondents. Data were collected through in-person visits and Google forms. Kruskal Wallis test and Bonferroni correction methods were used to evaluated the study hypotheses. The results demonstrated that there are statistically significant differences in work alienation across various age and experience groups of agricultural knowledge workers. The study findings underscore the importance of sociodemographic factors in understanding the processes of work alienation. The study contributes to the existing knowledge of sociodemographic and socioeconomic factors contributing to work alienation and work engagement among various groups of agricultural knowledge workers.
Transgressing Pashtun Boundaries: A Spatial Critique of the Pashtun Woman’s Position in Pashtunwali
Authors: Rehman Fasih Ur , Nawaz Maham , Saeed Sana
Published: January 2025

Pashtunwali, an unwritten tribal code of conduct, has been shaping the lives of millions of Pashtuns, outlining the contours of their socio-cultural hierarchies. This study explores Pashtunwali's role in creating spatial hierarchies that place Pashtun women at the peripheries of Pashtun spaces, thus making Pashtunwali a patriarchal tool to exert and maintain spatial dominance of Pashtun patriarchy. Using theoretical underpinnings of spatial theory as propounded by Tim Cresswell, Edward Casey, and David Sibley, this study explores Pashtun women's spatial experiences of in-placeness, out-of-placeness, and transgression within the normative geography constructed through Pashtunwali. The study offers a critique of the Pashtunwali to comprehend how it creates spaces of exclusion for Pashtun women and hampers their leadership within the Pashtun society. The current research concludes that Pashtunwali creates an ambivalent normative geography where Pashtun women experience being out of place and excluded from leadership spaces.
Impact of Technology on Language Learning: A study of Translation Apps and their Usage at Undergraduate Level in University Education
Authors: Ahmad Ali , Zulfiqar Mehwish , Batool Basma
Published: January 2025

Translation apps have a helpful role in language learning, using the technology to present, re-enforce and assess material. These apps are tools to enable language acquisition across different contexts and have become indispensable in university education with the speed of technological advancement. Urlaub and Dessein (2022) indicated that machine and human translation systems are becoming increasingly prevalent in language education. Translation practices cannot omit these tools; we have computer-aided translation technologies and online dictionaries. The impact of translation apps on undergraduate translation students' achievement is investigated; samples were 100 respondents. Students understand the benefits of using translation apps, like improved language skills. Plus, it's much easier to learn than traditional ways, but obstacles prevent the most effective use of these translation apps. Key barriers are the cost of internet access, lack of proper training, and occasional confusion when using the apps. Despite these hurdles, most respondents said they were optimistic and had learned to read from translation apps, describing their utility for language learning. The findings imply that translation apps must be integrated into undergraduate education to facilitate language acquisition and translation skills. The further the potential of these technologies is maximized in academic settings, the greater the addressing of challenges such as those connected to internet costs and training.
Coerciveness and isomorphism of discontinuous Sturm-Liouville problems with transmission conditions
Authors: Kandemir Mustafa , Küçük Murat
Published: January 2025

This study investigates a discontinuous Sturm-Liouville boundary value problem(BVP) on two intervals with functionals and transmission conditions in the direct sum ofSobolev spaces. Moreover, it presents the differential operator generated by the problem underinvestigation. The definition space of this operator is the direct sum of Sobolev spaces, andthe value space of the operator is the space obtained by adding the complex spaces where theboundary conditions are evaluated about the direct sum of Sobolev spaces. This paper establishesthe solvability of the problem and some important spectral properties of the operator, such asisomorphism, Fredholmness, and coerciveness concerning spectral parameters. In addition, theconclusion section discusses how different original problems can be produced.
Genomic exploration of HAK/KUP/KT potassium transporter genes in Citrus sinensis (L.) Osbeck: A comprehensive bioinformatics approach
Authors: Öz Ummahan
Published: January 2025

Citrus sinensis (L.) Osbeck, a member of the Rutaceae family, holds significant economic importance. Potassium (K), an essential macronutrient, is vital in diverse physiological processes, such as photosynthesis, osmoregulation, stress tolerance, and disease resistance. The high-affinity K ion transporters (HAK), K ion uptake permeases (KUP), and K transporters (KT) gene family represents the largest group of K transporters. This study aims to comprehensively analyze HAK/KUP/KT genes in C. sinensis (Cs). Phylogenetic analysis, chromosome distribution, gene structure and conserved protein motif analysis, protein interaction, homology modeling, cis-acting element analysis, functional gene ontology, miRNA analysis, and primer search were performed using CsHAK sequences. Through bioinformatics tools, 25 CsHAK genes were identified and categorized into three distinct groups based on the results of phylogenetic analysis. Furthermore, it has been determined that CsHAK genes play a role in K transport, localizing in organelles and plasma membranes. They are found on the first, second, fifth, seventh, and eighth chromosomes. Furthermore, cis-acting elements associated with stress response and miRNAs have been identified. This study provides a robust foundation for future functional genomics research, offering insights into the genetic landscape of K transporters in C. sinensis. The findings contribute valuable information for crop improvement strategies and enhance our understanding of plant responses to environmental challenges.
Investigation of W, Pb, and Bi elements from neutron shielding point of view
Authors: Gaylan Yasin
Published: January 2025

This study investigates the performances of tungsten, lead, and bismuth elements, which are widely used for shielding gamma-ray, from the neutron shielding point of view using the Monte Carlo N-Particle (MCNP6.2) simulation code. The study analyzed the neutron shielding capacities of Al matrix composites containing 30% W, Pb, and Bi. In addition, the shielding properties of secondary gamma-ray with an energy of 0.478 MeV resulting from neutron-boron interaction were also investigated. The results show that for thermal and intermediate energy neutrons, Al-30%W composite gives very good results by separating from Al-30%Pb and Al-30%Bi composites. However, Al-%30Pb and Al-%30Bi composites are also effective for fast neutrons. Regarding the gamma-ray, it was calculated that the Al-30%Pb composite exhibited the highest shielding performance. These findings provide important data on the contribution of W, Pb, and Bi elements used as gamma-ray shielding to neutron shielding in different neutron energy ranges and reveal that these elements should be used strategically with appropriate combinations depending on the neutron energy in neutron shielding designs.
Influence of various fertilizer regimes on phenological characteristics, polyphenol content, and antioxidant capacity of Amaranthus caudatus L.
Authors: Koç Canan , Ulusu Funda , Ulusu Yakup
Published: January 2025

In this study, the effects of different doses of organic (OF), vermicompost (VC), and chemical (CF) fertilizers on phenological characteristics, polyphenolic compounds, and antioxidant capacity of Amaranthus caudatus L. were evaluated. The study was carried out using a randomized block design with four replications. The findings revealed that the fastest germination occurred in the CF-6 L/da group, with a mean time of 7 days, while the slowest germination was observed in the OF-1200 mL/da group, with a mean time of 16 days. Regarding flowering time, the earliest flowering occurred on the 68th day in the CF-3 L/da group, while the latest flowering occurred on the 79th day in the OF-1200 mL/da group. The longest vegetative period was observed in the OF-1200 mL/da group with 215 days due to the effect of fertilizer applications and the fact that the study was carried out in the summer season. The closest vegetative period to the control group was 187-190 days in the CF-6 L/da group. The treatment groups' total phenolic content (TPC) and total flavonoid content (TFC) exhibited a range of 0.29-2.46 mg GAE/g and 0.50-1.26 mg QE/g, respectively. The highest TPC and TFC values among the treatment groups were determined in the OF-300 mL/ha and VC-2 L/ha groups, respectively. The IC50 values of 2,2-Diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity of methanol extracts exhibited a range of 3.34 to 6.80 mg/mL, with the OF-300 mL/da (50 mg/mL) group demonstrating the highest radical scavenging activity, exhibiting an 89.95% inhibition rate.
On σ-primeness and σ-semiprimeness in rings with involution
Authors: Yeşil Didem , Karalarlıoğlu Camcı Didem , Albayrak Barış
Published: January 2025

This study introduces the structure of $\left\vert
\mathcal{S}_{R}^{\sigma}\right\vert $-$\sigma$-semiprime ring, $\left\vert \mathcal{S}_{R}^{\sigma}\right\vert $-$\sigma$-prime ring, and source of $\sigma$-primeness , which have not been previously explored, and presents new results. We define a subset $$
P_{R_{\sigma}} = \bigcap_{a\in R} S_{R_{\sigma}}^{a}
$$ of ring $R$ as $S_{R}^{\sigma}=\left\{ \left. a\in R\right\vert aRa=aR{\sigma}(a)=(0)\right\} $, where $\sigma$ is an involution is referred to as the source of $\sigma$-primeness of $R$. Additionally, we have established some relationships between the prime radical $\beta(R)$ and $\mathcal{S}_{R}^{\sigma}$.
Study on the Impact of Digital Inclusive Finance on Residents' Cultural Consumption: Evidence From China
Authors: Chi Qishui , Chen Jiehui
Published: January 2025

This article establishes a fixed effects spatial Durbin model to explore the impact of digital inclusive finance development on residents' cultural consumption. We found that the development of digital inclusive finance has a positive promoting effect on the cultural consumption of residents, indicating that the development of digital inclusive finance can significantly enhance the cultural consumption of local residents; The negative coefficient of spatial spillover effect indicates the need to strengthen cooperation and exchange between regions to promote communication and collaboration in digital inclusive finance. Based on the research findings, this article proposes policy recommendations such as emphasizing spatial spillover effects, breaking regional spatial and industrial boundaries to share the dividends of the digital economy.
Knocking the Future: Unraveling the Role of Guests’ Experience Using Chatbot on their Acceptance and Intention to Visit Saudi Arabian Hotels
Authors: M. Hasanein Ahmed , Ayad Tamer Hamdy
Published: January 2025

This study investigates the direct impact of hotel guests’ perceptions towards using Chatbot on their acceptance and usage intention to visit the hotel, as well as the indirect role of actual use of Chatbot in the correlation between guests’ acceptance and usage of Chatbot and their intention to visit Saudi Arabian hotels. Self-administered surveys were provided to a randomly selected sample of hotel guests as part of the research, which used a quantitative technique. Structural equation modeling (SEM) analysis of 429 valid responses showed substantial positive influence by “PE”, “EE”, “SI”, and “FC” on “UEX” and “IVH”. The study also proved that “UEX” mediates the relation between all independent variables and “IVH”. These insights provide executives and decision-makers in the hotel industry with thorough guidance on utilizing this innovative technology. This will empower them to tackle novel technological obstacles and optimize its utilization to optimize its advantageous impacts on hotel guests.
AI-Enabled Unmanned Agents for Real-Time 3D Scene Analysis in Military Operations
Authors: Macario Giuseppe
Published: January 2025

This paper presents an innovative system for dynamic 3D scene perception tailored for battlefield environments, leveraging unmanned intelligent agents equipped with binocular vision and inertial measurement units (IMUs). The system processes binocular video streams and IMU data to deploy advanced deep learning techniques, including instance segmentation and dense optical flow prediction, facilitated by a specially curated target dataset. The integration of a ResNet101+FPN backbone for model training results in a combat unit type recognition accuracy of 91.8%, a mean Intersection over Union (mIoU) of 0.808, and a mean Average Precision (mAP) of 0.606. A dynamic scene localization and perception module utilizes these deep learning outputs to refine pose estimations and enhance localization accuracy by overcoming environmental complexities and motion-induced errors, typically associated with SLAM methodologies. Application tests conducted within a simulated battlefield metaverse environment demonstrate a 44.2% improvement in self-localization accuracy over traditional ORB-SLAM2 Stereo methods. The system efficiently tracks and annotates dynamic and static battlefield elements, continuously updating global maps with precise data on agent poses and target movements. This work not only addresses the dynamic complexity and potential information loss in battlefield scenarios but also lays a foundational framework for future enhancements in network capabilities and environment reconstruction methodologies. Future developments will focus on precise identification of combat unit models, multi-agent collaboration, and the application of 3D scene perception to advance real-time decision-making and tactical planning in joint combat scenarios. This approach holds significant potential for enriching the battlefield metaverse, fostering deep human-machine interaction, and guiding practical military applications.
Evaluation and Comparison of Machine Learning Algorithms for Effective Image Classification with Fault-Tolerance
Authors: Dyubele Sithembiso , Pretty Cele Noxolo , Mbangata Lubabalo , Monyeki Phirime
Published: January 2025

Image classification is critical in computer vision, with numerous applications ranging from e-commerce to medical imaging. This study provides a comprehensive evaluation of traditional machine learning algorithms for image classification, implementing and analysing novel fault tolerance mechanisms amongst these algorithms. The authors compared the performance of K-Nearest Neighbors (KNN), Decision Trees, Random Forest, and XGBoost on both Fashion MNIST and CIFAR-10 datasets. The comparison was extended to include Support Vector Machine (SVM), Logistic Regression, and Naive Bayes classifiers in order to expand the evaluation of these models on the indicated datasets. Key findings demonstrated the superiority of ensemble methods, particularly XGBoost, which achieved 89.31% of accuracy on Fashion MNIST and 54.93% on CIFAR-10, consistently outperforming other models across various configurations. Random Forest exhibited robust performance as the secondbest model, reaching 87.42% and 51.64% of accuracy on the respective datasets. The significant performance gap between datasets demonstrated the challenges that traditional machine learning models face with complex image data. Implementing the fault tolerance framework in this study has also shown a remarkable effectiveness, achieved a 94.6% recovery rate while maintaining model accuracy within 0.1% of standard implementations. This was achieved with minimal computational overhead (2.3% of training time and 1.8% of memory usage), making it highly practical for production deployments. The system significantly reduced operational failures, decreasing crashes from 5.2 to 0.3 per day and increasing average uptime from 4.3 to 12.0 hours. The study also reveals important insights regarding model scalability and resource requirements, with memory usage varying significantly across models (325MB to 8,923MB). These findings provide valuable guidance for practitioners in selecting and implementing machine learning models for image classification tasks, particularly in scenarios where both performance and system reliability are critical. This research contributes to the field by demonstrating the feasibility of implementing robust fault tolerance in machine learning systems without compromising accuracy while also providing comprehensive performance comparisons across different model architectures and dataset complexities. The developed framework serves as a foundation for building more reliable machine-learning systems for real-world applications.
Optimizing Healthcare Ecosystem Performance - A Computational Study of Integrated Patient Assistance in Primary Care
Authors: Nucci Francesco , Papadia Gabriele
Published: January 2025

Home health care professionals provide medical services to patients in their homes. With rising demand, it’s crucial to manage operational costs effectively while ensuring satisfaction for patients. This study presents a bi-objective optimization model aimed at resolving routing and scheduling challenges in home health care, with a focus on both system efficiency and patient accessibility. A Mixed-Integer Linear Programming Model (MILP) is developed. To tackle computational time challenges, we propose a Non-dominated Sorting Genetic Algorithm II to solve the multi-objective optimization problems. The evaluation of Pareto fronts demonstrates the method’s efficiency. We apply the method in a real-world case study to provide managerial implications.
A Random Forest and Edge Vector Ensemble Model for Segmenting Aerial Satellite Forest Images
Authors: Mapoka Trust Tshepo , Kwenda Clopas , Bakasa Wilson
Published: January 2025

The attribute of image segmentation significantly impacts the validity of the resulting classification, making it an essential step in the image classification process. Present segmentation methods cannot produce a feature set that yields a segmented image of good quality. This work creates a method that yields a set of believable attributes and produces segmented images of excellent quality. The authors aim to create a novel machine-learning model to enhance the image segmentation quality of aerial satellite images using metrics such as Intersection Over Union (IoU), Receiver Operating characteristic (ROC) curves, and accuracy. The Random Forest (RF) algorithm-based machine learning model is intended to separate forested and non-forest regions from aerial satellite images. Finding edges and separating layered objects are two computer vision problems that can be solved using RF, a supervised machine learning model. Our method objectively assesses the quality of image segmentation by examining the places at which all image objects overlap with the real image regions of a scene item. After generating a collection of features, the RF performed the segmentation process using the Gabor filters and edge detection techniques, such as the Canny and Sobel filters. Segmented images were compared against real masks. The suggested model’s superior segmentation capability, with 90% accuracy, is evaluated against several baseline algorithms, including Linear Discriminant Analysis (LDA), Linear Support Vector Machine (LSVM), and Gaussian Naive Bayes (GNB). For SVM, GNB, and LDA, the corresponding accuracy rates are 81%, 89%, and 85%.
A Script Language Creation for Event Visualization and Data Storage in an Intelligent Video Surveillance System
Authors: Subbotin Alexey , Delhibabu Radhakrishnan , Zhukova Nataly
Published: January 2025

The development of a macro-based script language for storing data in a database base and visualizing events in an intelligent video surveillance system is discussed. An overview of the built-in script languages used in systems is presented. A comparative analysis of general-purpose script languages is given. A problem with the speed of data access has been identified. Our script programming language based on macros and templates for visualizing events and storing data is proposed as a solution to the problem. The proposed approach made it possible to reduce data access time by 19.34% relative to the fastest PHP script language and by 6.32 times relative to the slowest Ruby language.
Application of Deep Learning Algorithms in Segmentation of Mandibular Nerve Canal in Orthopantomogram (Panoramic) Radiographs: A State-Of-Art Systematic Review
Authors: Dashti Mahmood , Khosraviani Farshad , Amirzade-Iranaq Mohammad Hosein , Tajbakhsh Neda , Rezaei Taleshi Seyede Fateme , Sofia Ani  , Tara Azimi
Published: January 2025

Objectives: To assess the current landscape and efficacy of artificial intelligence (AI) and deep learning (DL) algorithms in detecting and segmenting mandibular canals in orthopantomogram (panoramic) radiographs. Methods: Research on the detection and segmentation of the mandibular canal for developing AI models was conducted by searching five major electronic databases. The PICO question was, “Are 2D radiographic images suitable for utilizing deep learning algorithms to identify the infra-alveolar nerve?” The included studies adapted customized assessment criteria based on QUADAS-2 for quality assessments. Results: 255 records were identified during the initial electronic search. After a thorough evaluation, six studies specifically addressing the detection and segmentation of mandibular canals were selected for inclusion. Various outcome metrics were reported. The dice coefficient varies between 0.78 and 0.97 between models. Also, sensitivity (recall) varies from 0.83 to 0.99, indicating high performance in various DL models. Conclusion: The AI models discussed in the included studies vary in performance. Additionally, the outcome metrics reported were not consistent, making it difficult to compare all the deep learning (DL) models comprehensively. The impressive performance of these DL models should be evaluated using external datasets to compare their effectiveness and train them to achieve better results.
On Possible Advantages of Conscious Systems and a Turing Test for Consciousness
Authors: Otte Ralf
Published: January 2025

Earlier articles have suggested that physical systems with inherent quantum processes could be capable of producing hypercomplex system states. These system states could have special energy states, which are referred to as hypercomplex energy states because they cannot be measured, although they physically exist and could even store information. Hypercomplex system states could be aggregated into consciousness in certain systems, such as the human brain. But technical systems could also potentially generate and use hypercomplex system states, which would create a new level of AI systems. Systems capable of consciousness must and will differ significantly in their performance from systems that cannot utilize such states. This performance difference could be used to distinguish systems with technical consciousness (so-called ”machine consciousness”) from systems without ”consciousness”. Based on this, the article presents a so-called Turing test for consciousness for technical systems, in particular AI systems. The hypotheses in this and previous articles are predictions that arise from mathematical considerations, but they still have to be subjected to experimental evaluation. This article presents the design for initial experiments.
Cancelable Biometric Template Generation using Eigenfeature Regularization
Authors: Singh Onkar , Jaiswal Ajay , Kumar Nitin , Kumar Naveen
Published: January 2025

Cancelable biometrics addresses biometric data’s privacy and security concerns. We present two new cancelable biometrics template generation methods: RP-RegSt and RP-RegSb. The suggested approaches use random permutations and regularized eigenfeature extraction to generate cancelable biometrics templates, which can be reissued if compromised. We also show that applying random permutation to generate cancelable biometric templates enhances recognition accuracy. The suggested approaches are tested on six publicly accessible databases: three iris databases (UBIRIS.v1, CASIA-V1, and IITD Iris), two face databases (Georgia Tech and AT&T), and one ear database (IITD Ear). The superiority of the proposed methods is demonstrated by comparing them to three state-of-the-art random permutation-based cancelable biometric template generation techniques. The suggested approaches’ performance on challenging databases with substantial biometric image variation, such as Georgia Tech and UBIRIS, shows their robustness and efficacy. The privacy concern is addressed as the templates are irreversible (non-invertible) and immune to imposter attacks, while brute force analysis shows the templates are secure. The templates satisfy the diversity (unlinkability) and revocability properties.
Deep Learning Model to Evaluate Alzheimer's disease Through Multi-View Clustering
Authors: Nimbare Sneha , Paygude Priyanka , Dhumane Amol , Rathi Snehal , Bidve Vijaykumar
Published: January 2025

Early diagnosis of Alzheimer's disease (AD) plays a crucial role in the development and effectiveness of interventions, and neuroimaging stands out as an up-and-coming field for the initial identification of Alzheimer's disease. Earlier models utilized various methods to analyze images of Alzheimer's disease, such as deep learning models or unsupervised matrix factorization processes. Neither of these techniques alone can produce satisfactory results while clustering multi-view photos of Alzheimer's disease. This motivates our research to create a deep learning model for obtaining the most important Alzheimer's disease factors from MRI and classifying brain images into different stages. To achieve optimal results in multi-view clustering, the proposed model integrates a deep learning technique (Channel Boost-Convolution Neural Network) with an inverse matrix factorization method, forming an ensemble approach. The experiment analyzes several images to evaluate the implemented technique for the performance of RMSE, which are about 2.32 better than the various compared models. The results show that combining the deep learning model with Inverse matrix factorization for Alzheimer's disease multi-view image clustering works well, the Transformers can further improve multi-view clustering in deep learning.
A Diabetes Diagnosis Model using Optimized Long Short-Term Memory Based on Improved Particle Swarm Optimization
Authors: J Revathy , S.K Jayanthi
Published: January 2025

Diabetes, a chronic disease, arises when the body is either unable to utilize the insulin generated by the pancreas or is unable to create enough of it. It could be the deadliest if left undiagnosed and untreated. If diabetes is identified early enough, a person can receive the right care and live a healthy life. An automated system is required to identify diabetes from clinical and physical data when the traditional method is laborious. The paper proposed a new diabetes classifying model based on optimized long short-term memory (LSTM). The proposed method uses a new variant of particle swarm optimization (PSO) based on partial opposition-based learning (POBL) and a local search algorithm (LSA) approach called PLPSO for optimizing hyperparameters of LSTM (PLPSO-LSTM). PSO uses the POBL during the initialization phase to increase population diversity and the LSA during the updating position to increase exploitation. The proposed model has been tested using four diabetes datasets for analyzing its performance. These results show that optimized PLPSO-LSTM performs better than other state-of-the-art algorithms.
Effectiveness of Fenugreek Seeds against Polycystic Ovarian Syndrome
Authors: Zafar Umrah , Amjad Umar Ali , Ghaffar Bareera , Fatima Arooj , Habiba Umm-e , Malik Rubia , Mustafa Shahmeer , Sattar Naveed , Abbas Tasawar , Ashraf Raheel
Published: January 2025

Polycystic Ovarian Syndrome (PCOS) is one of the most common hormonal issues which affects most of the women among reproductive years which can lead to infertility, miscarriages, irregular menstrual cycles, and excessive facial hairs. Infertility, obesity, lipid metabolism problems, and insulin resistance all are the major causes of PCOS. Its diagnosis is done with the help of ultrasound and blood hormonal tests. Asia, Europe, and the Mediterranean region are home to fenugreek. Fenugreek may regulate estrogen and testosterone levels, which is effective against PCOS. In women with polycystic ovary syndrome, fenugreek improved body weight, number of ovarian cysts, ovary size, irregular hair growth, and monthly regularity. This evaluates the impact of fenugreek on insulin resistance in PCOS- affected women. Sonographic results and menstrual cycle are improved in PCOS women receiving adjuvant therapy to the fenugreek seed extract. The effectiveness of a patented, standardized Trigonella foenum- graecum extract (Furocyst®) as a phytotherapeutic for the efficient management of PCOS is reported. Thus, fenugreek seeds act as functional foods that can give various health related benefits beyond basic nutrition. The aim of this study is to highlight the therapeutic effects of fenugreek seeds in the management of PCOS.
The Use of Preservatives in Food: A Shift Toward Natural Alternatives
Authors: Imran Sajid Muhammad
Published: January 2025

Preservatives are defined as anything that elongates the shelf life of food. It helps to prevent the spoilage of food by microorganisms and enhance its flavor, taste, and color. It is estimated that a total of 2,00,000 tons of food preservatives are used annually [1]. Salt, honey, and sugar are the natural preservatives used to preserve food for centuries. With the increase in food demand, many chemicals have been prepared to preserve food more effectively and efficiently.
Artificial preservatives have increased greatly during the last decade because of modernization, the food industry, and media advertisement [2]. The artificial food preservatives can provide us with prompt effects but in the long run, it could act as a poison if constantly ingested. When converted into nitrous acid inside, these artificial preservatives can be toxic: nitrates and nitrites. These preservatives can cause cancer in various organs. Benzoates which are used as antimicrobial agents cause asthma, allergies, and rashes.
Sorbates, another class of antimicrobial agents, have a lower rate of conversion into harmful substances within the body. There are some reports claiming it causes skin conditions such as urticaria and dermatitis [3]. As a result of this, there is an increased public awareness of the harmful effects of artificial preservatives on health, leading the public to demand safer alternatives.
This demand led many researchers to find alternative natural preservatives that can give the same antimicrobial and antioxidant efficacy. The widely studied natural preservatives alternative to artificial preservatives are polyphenols. Polyphenols are the secondary metabolites produced by plants in response to infection, stress conditions, and UV exposure.
Polyphenols have shown promising effects and no side effects on human health have been observed. It has been noticed that polyphenols have low stability, and their antioxidant activity is not efficient if they are directly used in food products. Various environmental factors such as heat, pH, electrolyte concentration, and oxidants affect their stability. Encapsulation has emerged as an effective solution to improve polyphenols' stability and controlled release in food products. It not only improves the stability and antioxidant character, but it is also seen that sensory characteristics (aroma, flavor, and food acceptability), shelf life, and antioxidant activity of polyphenols have shown better scores after encapsulation.
In conclusion, while artificial preservatives have been widely used for their effectiveness, growing concerns over their health implications have led to increased interest in natural alternatives like polyphenols. Encapsulation technology shows promise in addressing the challenges of polyphenol instability, offering a safer and more sustainable option for food preservation.
Factors Affecting Digital Financial Service Adoption in Bangladesh: Evidence from SEM-ANN Approaches
Authors: Sadik Nafis , Rahman Md. Mominur
Published: January 2025

The rapid growth of Digital Financial Services (DFS) has revolutionized the financial landscape in developing countries, including Bangladesh. Despite its growing importance, understanding the factors influencing DFS adoption remains limited, particularly when leveraging advanced analytical frameworks. This study investigates the key drivers affecting the adoption of DFS in Bangladesh by employing Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) approaches. Using survey data collected from 340 DFS users, the SEM analysis validates the proposed relationships, identifying financial literacy, trust, access to capital, digital payment usage, and digital financial inclusion as significant factors driving DFS adoption. ANN modeling further highlights the relative importance of these predictors, revealing financial literacy as the most influential factor, closely followed by digital financial inclusion, trust in digital financial services, access to capital and digital payment usage. This dual-method analysis provides nuanced insights for policymakers, financial institutions, and technology providers, aiming to enhance DFS adoption and promote financial inclusion in Bangladesh.
The Effectiveness of Remittances from Migrant Workers in Overcoming Poverty in Developing Asian Countries
Authors: Ghannili Farawi , Lucky P Muhammad
Published: January 2025

This study explores the effect of remittances on poverty in developing Asian countries. This study aims to examine the effect of remittances on poverty by developing the Swamy and Arora model. There are relative influences in understanding the effect of remittances in alleviating poverty in developing Asian countries, namely geographical location and destination country income. Identify and understand the key variables that significantly affect poverty. This paper is the first to address the risk of poverty by utilizing poor migrants to increase household consumption and reduce poverty in the country of origin. We use Generalized Least Square (GLS) and Generalized Method of Moment (GMM) models to explore and understand the conditions of the poor who are sent money by migrants. The findings reveal a significant difference between poverty as measured through consumption expenditure and the income of the remitted poor. These insights are reassuring in that remittances can increase household consumption and do little to increase the income of the poor. remittances of 5% of GDP can help in alleviating poverty conditions in the home country, and directly benefit the country's economy, and alleviate domestic policies for poverty alleviation.
A Comprehensive Multi-Dimensional Risk Monitoring Model for Illegal Financial Activities
Authors: Zhou Zhong-qiang , Li Ling , Huang Ping
Published: January 2025

The rapid expansion of internet finance and fintech has introduced increasingly sophisticated illegal financial activities that challenge traditional regulatory frameworks. Existing regulatory approaches often lack the depth and adaptability to effectively identify and manage these evolving risks. This study proposes a comprehensive, multi-dimensional risk monitoring model tailored to detect illegal financial activities, based on four core dimensions: investor behavior, intermediary platforms, investment products, and regional factors. The model integrates six primary and 26 secondary indicators, enabling quantitative assessment of risk levels and early warning detection. By applying this model to empirical data, our findings highlight specific risk drivers and propose policy recommendations aimed at strengthening regulatory measures. This framework provides regulatory bodies with a practical tool for timely intervention, thereby enhancing financial stability and protecting investor interests.
Impact of Covid-19 on Firm Performance: A Systematic Literature Review Using PRISMA Framework
Authors: Sattar Mahnoor , Jalal Rafayat
Published: January 2025

Amidst the massive expansion of Covid-19 literature, this paper aims to articulate existing findings, pinpoint practical implications, and provide promising directions for future exploration using a systematic literature review. Following the PRISMA framework, this research analyzed relevant 60 articles published in ABDC ranked and SCOPUS indexed journals during 2020-2024. Moreover, ABS rankings were used to provide an additional layer of quality assessment of sample articles. Using Excel, VOSviewer and Biblioshiny, the authors analyzed the corresponding studies, and results indicate that most research investigating this issue published in B ranked (as per ABDC), Q1 ranked (as per SCOPUS) and ABS-2 and 1 ranked (as per ABS) journals primarily by Elsevier and Emerald publishers. Besides, 43% of papers are Asia based (China contributing most), and the most used databases were CSMAR, BvD, Compustat, Datastream, and Prowess. Quantitative research methods such as Pooled OLS, Logistic regression, Fixed effect model, 2SLS, 3SLS, DID etc. dominated the literature, whereas qualitative methods such as surveys and conceptual studies were rarely used. Around half of the sampled papers used only accounting-based measures (ROA, ROE, NPM, EBITDA, EPS) as dependent variables. Despite the overwhelmingly negative impact of Covid-19 on firm performance, 16.67% of the sampled papers reported mixed results in various regions and countries due to several firm-specific, macro-economic, and industry-specific factors. These articles provide important implications for managers, board of directors, investors, and regulators. Practitioners should adopt a sector- and region-specific approach to combat pandemics.Future researchers may focus on conducting both short-term and long-term studies incorporating factors like government stimulus package, business sensitivity to multiple phases of Covid-19 and the country’s preparedness using longitudinal studies, surveys, meta-analysis, in-depth interviews, observations, and case studies to fully comprehend the effects of Covid-19.
An Overview of the Impact of Green Finance on Green Total Factor Productivity
Authors: Zhan Meng-yao , Zhang Mu
Published: January 2025

This paper discusses the impact of green finance on the green total factor productivity of enterprises, and aims to summarize and analyze the research dynamics, trends and key issues of green finance on green total factor productivity. Since China put forward the goal of “double carbon”, green finance has become an important tool for optimizing resource allocation, enhancing technological innovation and measuring high-quality development in new industries, and its impact has been widely reflected in the social, scientific, technological and economic fields. The purpose of this paper is to comprehensively analyze the research results on the impact of various aspects of green finance on green total factor productivity, including green finance as a whole, green bonds, carbon finance and green credit, and provide theoretical references and practical guidance for future policymaking. Finally, this review discusses the outlook of the relationship between green finance and green total factor productivity in the future.
The Reality of Psychological and Social Adjustment among Visually Impaired Students in Higher Education Institutions in the Sultanate of Oman
Authors: El-Sherbiny Mohamed , Al-Hajj Amjad , Ibrahim Ahmed , Al reqadi Muadh , Al-Abri Ruwa
Published: January 2025

The study aimed to identify the reality of psychological adjustment of visually impaired students in institutions of higher education in the Sultanate of Oman, identify the variables that impact the psychological adjustment of visually impaired students, to identifying the reality of social adjustment of visually impaired students, and determining the variables that influence the social adjustment of visually impaired students in institutions of higher education in the Sultanate of Oman. This study is considered a descriptive-analytical study, using the comprehensive social survey method for all visually impaired students in higher education institutions in the Sultanate of Oman (64) visually impaired students. A questionnaire was used for visually impaired undergraduate students from various colleges and universities in the Sultanate of Oman. Regarding the results of the study, they indicated that the relative strength of the psychological adjustment dimension was 74.4%, which is a high value, and this is considered an indicator of a good level of psychological adjustment among the study population. The results also indicated that the services provided to visually impaired students helped them feel happy in their university studies, as an important determinant of their psychological adjustment, and that visually impaired students often do not suffer from negative feelings towards themselves. The relative strength of the social compatibility dimension was 70.8%, which is a value ranging between medium and high.
Requisitos del mercado laboral y su relación con el perfil de egreso en la Escuela de Electrónica y Telecomunicaciones y la Escuela de Mecánica de Metales de la UNE
Authors: Soria Cuellar Fidel Tadeo , Moreno Casachagua Raúl Humberto , Quintana Ortiz Miguel , Sotelo Raymondi Amador
Published: January 2025

The research work entitled Labor market requirements and graduation profile in the Schools of Electronics and Telecommunications, and Metalmechanics at UNE, had the following objective: To determine the relationship between the demands of the teaching labor market and the graduation profile of the study programs in the Professional Schools of Electronics and Telecommunications, and Metalmechanics; corresponding to the 2020 study plan. Taking into consideration the programs of Electronics and Informatics, Telecommunications and Informatics and Metallurgy - Jewelry. Whose population consisted of 28 students of the tenth cycle, to whom the standardized test administered by the Ministry of Education (MINEDU) was applied in the first phase of the competition for the appointment of teachers, this test was used to measure the teaching work requirement, also a questionnaire was administered to measure the perception of the graduation profile that students of the last cycle of the professional career have. Once the information was obtained and analyzed, we observed that only 25% of the 100% of those evaluated were qualified. And in relation to the graduate profile variable, 92% have the perception that the graduate profile is optimal. Finally, Pearson's correlation showed that there is no correlation between the demands of the teaching labor market and the graduate profile of the tenth cycle students.