Comprehensive Comparative Analysis Research Articles

COMPARATIVE ANALYSIS OF BUSINESS MODELS IN RETAIL: FAST CYCLE VS. LONG-TERM SUSTAINABILITY

This study conducts a comprehensive comparative analysis of fast-cycle and long-term sustainability business models in the retail industry. Through a mixed-methods approach, incorporating both quantitative and qualitative data, this research examines the key characteristics, underlying mechanisms, and impacts of these two contrasting models. The findings reveal stark differences in supply chain structures, design and production processes, marketing strategies, and consumer behavior. While fast-cycle models excel in speed, affordability, and trend responsiveness, they are associated with environmental degradation, ethical concerns, and a culture of disposability. Conversely, sustainable models prioritize ethical sourcing, environmental responsibility, and long-term value creation, but face challenges related to cost, scalability, and the need to balance timelessness with trend relevance. The implications for retailers, policymakers, and consumers are discussed, with a focus on the potential for hybrid models that combine elements of both approaches. This research contributes to the growing body of literature on sustainable business practices and provides valuable insights for retailers seeking to navigate the complex landscape of consumer preferences and sustainability demands.

Cost Analysis of Bladder Catheterization After Pelvic Floor Surgery.

Approximately 15-45% of female patients develop transient postoperative urinary retention (POUR) following pelvic reconstructive surgery. Catheter options for bladder drainage include transurethral indwelling catheter (TIC), intermittent self-catheterization (ISC), and suprapubic tube (SPT). Each strategy has risks and benefits; none have been shown to be clinically superior, and to date, no comprehensive comparative economic analysis has been published. The objective of this study was to evaluate the cost of these different bladder catheterization strategies after transvaginal pelvic surgery. A Canadian universal single-payer (government funded) health system perspective was taken, and a decision tree model was constructed to evaluate the costs associated with each catheterization strategy over a 6-week horizon. Base-cases were set based on recently published clinical data of our institutions, 2 academic tertiary care centers, and based on systematic reviews and meta-analyses. Costs were established in consultation with process stakeholders, in addition to published values. The average cost calculated for management of transient POUR after outpatient pelvic reconstructive surgery was 150.69 CAD (median 154.86; interquartile range [IQR] 131.30-176.33) for TIC, 162.28 CAD (median 164.72; IQR 144.36-189.39) for ISC and 255.67 CAD (median 270.63; IQR 234.32-276.82) for SPT. In costing inpatient surgical data, the average cost calculated was 134.22 CAD (median 123.61; IQR 108.87-151.85) for TIC and 224.61 CAD (median 216.07; IQR 203.86-231.23) for SPT. TIC and ISC were found to be significantly less costly than SPT in managing transient POUR following transvaginal pelvic reconstructive surgery.

Temporal action segmentation for video encryption

Videos contain temporal information, enabling them to capture the dynamic changes of actions and provide richer visual effects. Traditional video encryption methods involve decomposing videos into frames and encrypting them frame by frame, which results in significant resource consumption. This paper proposes a video encryption method based on temporal action segmentation. This methodology involves the identification and extraction of pivotal frames from a video dataset, followed by the encryption of these significant key frames. This approach serves to enhance the efficacy of the video encryption algorithm. The method consists of three modules. The first module uses temporal action segmentation to classify video frames and extract important frames for the second module’s input. The second module encrypts the extracted key frames using a chaos-based encryption algorithm, thereby reducing the time cost of video encryption. The third module outputs the encrypted video. During the encryption process, a large amount of key stream is required. To address this, the paper introduces a new pseudo-random sequence generation method called two-dimensional Gramacy&Lee map (2D-GLM). Comprehensive comparative analysis clearly demonstrates that compared to other systems, 2D-GLM exhibits superior performance and can generate a large number of high-performance pseudo-random sequences. The proposed algorithm is tested on GTEA, and the simulation results demonstrate that it can accomplish video encryption tasks with high security.

Comprehensive characterization of extracellular vesicles produced by environmental (Neff) and clinical (T4) strains of Acanthamoeba castellanii.

We conducted a comprehensive comparative analysis of extracellular vesicles (EVs) from two Acanthamoeba castellanii strains, Neff (environmental) and T4 (clinical). Morphological analysis via transmission electron microscopy revealed slightly larger Neff EVs (average = 194.5 nm) compared to more polydisperse T4 EVs (average = 168.4 nm). Nanoparticle tracking analysis (NTA) and dynamic light scattering validated these differences. Proteomic analysis of the EVs identified 1,352 proteins, with 1,107 common, 161 exclusive in Neff, and 84 exclusively in T4 EVs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) mapping revealed distinct molecular functions and biological processes and notably, the T4 EVs enrichment in serine proteases, aligned with its pathogenicity. Lipidomic analysis revealed a prevalence of unsaturated lipid species in Neff EVs, particularly triacylglycerols, phosphatidylethanolamines (PEs), and phosphatidylserine, while T4 EVs were enriched in diacylglycerols and diacylglyceryl trimethylhomoserine, phosphatidylcholine and less unsaturated PEs, suggesting differences in lipid metabolism and membrane permeability. Metabolomic analysis indicated Neff EVs enrichment in glycerolipid metabolism, glycolysis, and nucleotide synthesis, while T4 EVs, methionine metabolism. Furthermore, RNA-seq of EVs revealed differential transcript between the strains, with Neff EVs enriched in transcripts related to gluconeogenesis and translation, suggesting gene regulation and metabolic shift, while in the T4 EVs transcripts were associated with signal transduction and protein kinase activity, indicating rapid responses to environmental changes. In this novel study, data integration highlighted the differences in enzyme profiles, metabolic processes, and potential origins of EVs in the two strains shedding light on the diversity and complexity of A. castellanii EVs and having implications for understanding host-pathogen interactions and developing targeted interventions for Acanthamoeba-related diseases.IMPORTANCEA comprehensive and fully comparative analysis of extracellular vesicles (EVs) from two Acanthamoeba castellanii strains of distinct virulence, a Neff (environmental) and T4 (clinical), revealed striking differences in their morphology and protein, lipid, metabolites, and transcripts levels. Data integration highlighted the differences in enzyme profiles, metabolic processes, and potential distinct origin of EVs from both strains, shedding light on the diversity and complexity of A. castellanii EVs, with direct implications for understanding host-pathogen interactions, disease mechanisms, and developing new therapies for the clinical intervention of Acanthamoeba-related diseases.

Comparative proteomic analysis of the ovarian fluid and eggs of Siberian sturgeon

BackgroundSturgeon species are living fossils that exhibit unique reproductive characteristics, and elucidation of the molecular processes governing the formation and quality of sturgeon eggs is crucial. However, comprehensive data on the protein composition of sturgeon ovarian fluid (OF) and eggs and their functional significance are lacking. To address this knowledge gap, the aim of the present study was to conduct a comprehensive comparative proteomic analysis of Siberian sturgeon OF and eggs using liquid chromatography–mass spectrometry (LC–MS/MS).ResultsA total of 617 proteins were identified in OF, and 565 proteins were identified in eggs. A total of 772 proteins showed differential abundance. Among the differentially abundant proteins, 365 were more abundant in OFs, while 407 were more abundant in eggs. We identified 339 proteins unique to OFs and 287 proteins specific to eggs, and further investigated the top 10 most abundant proteins in each. The functional annotation of the OF proteins highlighted their predominant association with immune system processes, including the complement and coagulation cascade, neutrophil and leukocyte-mediated immunity, cholesterol metabolism, and regulation of the actin cytoskeleton. Analysis of egg proteins revealed enrichment in metabolic pathways, such as oxidative phosphorylation and fatty acid metabolism, and protein ubiquitination and translation. OF-specific proteins included extracellular matrix and secretory vesicles, and eggs were enriched in proteins localized to mitochondria and ribosome components.ConclusionsThis study presents the first comprehensive characterization of the protein composition of sturgeon OF and eggs and elucidates their distinct functional roles. These findings advance our understanding of sturgeon reproduction, OF-egg signaling and the origin of OF proteins. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium with the dataset identifier PXD044168 to ensure accessibility for further research.

RT: a Retrieving and Chain-of-Thought framework for few-shot medical named entity recognition.

This article aims to enhance the performance of larger language models (LLMs) on the few-shot biomedical named entity recognition (NER) task by developing a simple and effective method called Retrieving and Chain-of-Thought (RT) framework and to evaluate the improvement after applying RT framework. Given the remarkable advancements in retrieval-based language model and Chain-of-Thought across various natural language processing tasks, we propose a pioneering RT framework designed to amalgamate both approaches. The RT approach encompasses dedicated modules for information retrieval and Chain-of-Thought processes. In the retrieval module, RT discerns pertinent examples from demonstrations during instructional tuning for each input sentence. Subsequently, the Chain-of-Thought module employs a systematic reasoning process to identify entities. We conducted a comprehensive comparative analysis of our RT framework against 16 other models for few-shot NER tasks on BC5CDR and NCBI corpora. Additionally, we explored the impacts of negative samples, output formats, and missing data on performance. Our proposed RT framework outperforms other LMs for few-shot NER tasks with micro-F1 scores of 93.50 and 91.76 on BC5CDR and NCBI corpora, respectively. We found that using both positive and negative samples, Chain-of-Thought (vs Tree-of-Thought) performed better. Additionally, utilization of a partially annotated dataset has a marginal effect of the model performance. This is the first investigation to combine a retrieval-based LLM and Chain-of-Thought methodology to enhance the performance in biomedical few-shot NER. The retrieval-based LLM aids in retrieving the most relevant examples of the input sentence, offering crucial knowledge to predict the entity in the sentence. We also conducted a meticulous examination of our methodology, incorporating an ablation study. The RT framework with LLM has demonstrated state-of-the-art performance on few-shot NER tasks.

Microservices Security Vulnerability Remediation approach using Veracode and Checkmarx

Abstract — As organizations increasingly adopt microservices architectures for building scalable and resilient applications, ensuring the security of these distributed systems becomes paramount. In this empirical study, we conduct a comprehensive comparative analysis to assess the efficacy of three leading security scanning tools, namely Veracode, Snyk, and Checkmarx, in identifying and remedying security vulnerabilities within microservices applications deployed on the AWS and Azure cloud platforms. The study aims to provide nuanced insights into the performance, usability, and integration capabilities of these tools, offering valuable guidance to organizations striving to fortify their microservices-based infrastructures. By meticulously evaluating scanning capabilities, vulnerability detection accuracy, remediation guidance comprehensiveness, CI/CD pipeline integration proficiency, and overall ease of use, our research sheds light on the relative strengths and weaknesses of each tool in the context of modern cloud-native application security. Through meticulously designed experiments utilizing realistic microservices application scenarios encompassing diverse vulnerability types, including injection attacks, authentication bypasses, and insecure configurations, we present a thorough examination of the tools' capabilities and limitations. The findings from our study contribute to the evolving discourse on microservices security, emphasizing the critical importance of selecting appropriate security scanning solutions tailored to the unique requirements and constraints of cloud-based microservices architectures. By leveraging the insights gleaned from our comparative analysis, organizations can make well-informed decisions regarding tool selection and deployment strategies, thereby bolstering the resilience of their microservices ecosystems against an ever-expanding threat landscape.

Theoretical insights into IT governance and compliance in banking: Perspectives from African and U.S. regulatory environments

This review paper provides a comprehensive comparative analysis of IT governance and compliance within the banking sectors of Africa and the U.S., highlighting the intricacies of navigating diverse regulatory environments. Through examining governance strategies, compliance mechanisms, and best practices, the paper underscores the adaptation of frameworks like COBIT, ISO/IEC 27001, and ITIL to meet regional regulatory demands and operational challenges. Key insights reveal significant differences in regulatory landscapes, technological maturity, and cybersecurity and risk management approaches. The analysis identifies best practices and lessons that can be leveraged globally, suggesting areas for future research including emerging technologies and the impact of global regulations. This work aims to enhance understanding of IT governance and compliance, offering valuable perspectives for banks, regulators, and policymakers. Keywords:  IT Governance, Compliance, Banking Sector, Regulatory Environment, Cybersecurity, Emerging Technologies.

Enabling near-real-time safety glove detection through edge computing and transfer learning: comparative analysis of edge and cloud computing-based methods

PurposeComputer vision and deep learning (DL) methods have been investigated for personal protective equipment (PPE) monitoring and detection for construction workers’ safety. However, it is still challenging to implement automated safety monitoring methods in near real time or in a time-efficient manner in real construction practices. Therefore, this study developed a novel solution to enhance the time efficiency to achieve near-real-time safety glove detection and meanwhile preserve data privacy.Design/methodology/approachThe developed method comprises two primary components: (1) transfer learning methods to detect safety gloves and (2) edge computing to improve time efficiency and data privacy. To compare the developed edge computing-based method with the currently widely used cloud computing-based methods, a comprehensive comparative analysis was conducted from both the implementation and theory perspectives, providing insights into the developed approach’s performance.FindingsThree DL models achieved mean average precision (mAP) scores ranging from 74.92% to 84.31% for safety glove detection. The other two methods by combining object detection and classification achieved mAP as 89.91% for hand detection and 100% for glove classification. From both implementation and theory perspectives, the edge computing-based method detected gloves faster than the cloud computing-based method. The edge computing-based method achieved a detection latency of 36%–68% shorter than the cloud computing-based method in the implementation perspective. The findings highlight edge computing’s potential for near-real-time detection with improved data privacy.Originality/valueThis study implemented and evaluated DL-based safety monitoring methods on different computing infrastructures to investigate their time efficiency. This study contributes to existing knowledge by demonstrating how edge computing can be used with DL models (without sacrificing their performance) to improve PPE-glove monitoring in a time-efficient manner as well as maintain data privacy.

On cost-efficient parallel iterative solvers for 3D frequency-domain seismic multisource viscoelastic anisotropic wave modeling

Solving large sparse linear systems in 3D frequency-domain seismic wave modeling, especially in viscoelastic anisotropic media, poses significant challenges due to the increasing number of discrete moduli and nonzero elements in the linear system matrix. The computational load surpasses that of acoustic or viscoacoustic media, making it even more challenging when dealing with multisource problems. Popular scientific tools for solving a linear system, such as multifrontal massively parallel sparse direct solver (MUMPS), structured matrix package (STRUMPACK), and portable extensible toolkit for scientific computation (PETSc), can be used, but their applicability to our specific problem has not been comprehensively evaluated. Our study aims to tackle the challenges in solving large, sparse, complex-valued symmetric linear systems with multiple right-hand side vectors for 3D frequency-domain seismic wave modeling. We have adopted the preconditioned conjugate gradient iterative algorithms as the foundation for our research, introducing two highly cost-effective parallel iterative solvers: the parallel symmetric successive overrelaxation conjugate gradient (P-SSORCG) and the parallel incomplete Cholesky conjugate gradient (P-ICCG). These novel solvers are subjected to a comprehensive comparative analysis against well-established scientific tools, such as MUMPS, STRUMPACK, and PETSc, in the context of 3D frequency-domain seismic wave modeling. We show their promising performances in a practical 3D SEG/EAGE overthrust model and demonstrate that the grouped P-SSORCG offers an efficient alternative to parallel direct solvers, particularly in situations wherein computational resources are limited.

DFT aided comparative screening of mechanical, optoelectronic, and transport properties of double perovskites Cs2ScAuX6 (X = Cl, Br, and I) for green energy applications

A comprehensive comparative analysis has been performed to investigate the Cs2ScAuX6 (X = Cl, Br, and I) employing density functional theory. The WIEN2k code has been applied to optimize the structural arrangement of perovskites and calculate various aspects such as mechanical, optoelectronic, and transport characteristics. The results demonstrated that these perovskite compounds had both structural and chemical stability. The analyzed elastic characteristics of the Cs2ScAuX6 have shown that these compounds are mechanically stable, have anisotropy toughness, and provide durability against plastic deformation. The electronic characteristics are determined precisely and accurately using the Trans-Blaha-modified Becke-Johnson (TB-mBJ) potential. The compounds Cs2ScAuCl6, Cs2ScAuBr6, and Cs2ScAuI6 are semiconductors and have band gaps of 1.6, 1.5, and 1.05 eV, respectively, demonstrating an indirect nature. An investigation examines the optical characteristics within the energy range of 0–6 eV. The chosen materials reveal transparency in the lower energy spectrum of incoming photons and exhibit significant absorption and transmission of light. Therefore, it can be inferred that Cs2ScAuX6 materials possess optical properties that make them suitable for utilization in photovoltaic devices. Further, these materials elaborate substantial electrical conductivity, power factors, and figures of merit (ZT), making them efficient thermoelectric resources. At last, these results would be advantageous for experimental researchers and have also unveiled the substantial capability of these substances to be utilized in solar energy harvesting and thermoelectric devices.

In Vitro Glucose Measurement from NIR and MIR Spectroscopy: Comprehensive Benchmark of Machine Learning and Filtering Chemometrics

The quantitative analysis of glucose using spectroscopy is a topic of great significance and interest in science and industry. One conundrum in this area is deploying appropriate preprocessing and regression tools. To contribute to addressing this challenge, in this study, we conducted a comprehensive and novel comparative analysis of various machine learning and preprocessing filtering techniques applied to near-infrared, mid-infrared, and a combination of near-infrared and mid-infrared spectroscopy for glucose assay. Our objective was to evaluate the effectiveness of these techniques in accurately predicting glucose levels and to determine which approach was most optimal. Our investigation involved the acquisition of spectral data from samples of glucose solutions using the three aforementioned spectroscopy techniques. The data was subjected to several preprocessing filtering methods, including convolutional moving average, Savitzky-Golay, multiplicative scatter correction, and normalisation. We then applied representative machine learning algorithms from three categories: linear modelling, traditional nonlinear modelling, and artificial neural networks. The evaluation results revealed that linear models exhibited higher predictive accuracy than nonlinear models, whereas artificial neural network models demonstrated comparable performance. Additionally, the comparative analysis of various filtering methods demonstrated that the convolutional moving average and Savitzky-Golay filters yielded the most precise outcomes overall. In conclusion, our study provides valuable insights into the efficacy of different machine learning techniques for glucose measurement and highlights the importance of applying appropriate filtering methods in enhancing predictive accuracy. These findings have important implications for the development of new and improved glucose quantification technologies.

DDoS Attack Detection and Classification using Machine Learning Algorithms

Abstract: In the dynamic realm of networking, Software Defined Networking (SDN) emerges as a transformative force, offering centralized control and programmable capabilities that empower network administrators to efficiently manage and secure network infrastructures. However, amidst the ever-present threat of distributed denial-of-service (DDoS) attacks, the need for robust detection mechanisms is imperative. This research proposes a machine learning-based approach to enhance DDoS attack detection and classification within SDN environments. Leveraging the SelectKBest algorithm for feature selection and employing various classifiers such as Decision tree, KMeans++, XGBoost, etc.. with a focus on Random Forest as the most effective, the project aims to bolster detection accuracy and efficiency. Through comprehensive experimentation and comparative analysis, the efficacy of the proposed methodology in identifying DDoS threats is demonstrated, contributing to the ongoing efforts in fortifying cybersecurity defenses against sophisticated adversarial tactics.

International EV policies: A comparative review of strategies in the United States and Nigeria for promoting electric vehicles

This paper provides a comprehensive comparative analysis of the strategies employed by the United States and Nigeria in promoting electric vehicles (EVs) within their respective national contexts. As the world shifts towards sustainable energy solutions, EVs have emerged as a pivotal component in reducing greenhouse gas emissions and mitigating climate change. Drawing upon a range of scholarly literature, governmental reports, and policy documents, this study examines the policy frameworks, incentives, and challenges faced by both countries in fostering the adoption of EVs. In the United States, a diverse array of federal, state, and local initiatives have been implemented to incentivize EV adoption, including tax credits, rebates, infrastructure investments, and regulatory mandates. Moreover, collaborations between the government, industry stakeholders, and research institutions have facilitated technological advancements and market growth in the EV sector. However, challenges such as range anxiety, high upfront costs, and inadequate charging infrastructure continue to impede widespread adoption. Conversely, Nigeria, while grappling with its own set of socio-economic and infrastructural challenges, has exhibited a nascent interest in promoting EVs as part of its broader sustainable development agenda. Despite lacking a comprehensive national policy framework, various initiatives such as tax exemptions, import duty waivers, and pilot projects have been introduced to stimulate EV uptake. Moreover, Nigeria's abundant renewable energy resources offer a promising opportunity for leveraging EVs to enhance energy security and reduce dependence on fossil fuels. Nevertheless, significant barriers such as limited infrastructure, affordability constraints, and institutional capacity gaps pose formidable obstacles to scaling up EV adoption in the country. By juxtaposing the experiences of the United States and Nigeria, this study contributes valuable insights into the diverse approaches and lessons learned in crafting effective international EV policies amidst varying socio-economic contexts. Through a nuanced understanding of the policy landscape, policymakers, industry stakeholders, and researchers can identify strategies for overcoming barriers and accelerating the transition towards a sustainable transportation paradigm.

Comprehensive Comparative Analysis of the JAZ Gene Family in Common Wheat (Triticum aestivum) and Its D-Subgenome Donor Aegilops tauschii.

JASMONATE-ZIM DOMAIN (JAZ) repressor proteins work as co-receptors in the jasmonic acid (JA) signalling pathway and are essential for plant development and environmental adaptation. Despite wheat being one of the main staple food crops, until recently, comprehensive analysis of its JAZ gene family has been limited due to the lack of complete and high-quality reference genomes. Here, using the latest reference genome, we identified 17 JAZ genes in the wheat D-genome donor Aegilops tauschii. Then, 54 TaJAZs were identified in common wheat. A systematic examination of the gene structures, conserved protein domains, and phylogenetic relationships of this gene family was performed. Five new JAZ genes were identified as being derived from tandem duplication after wheat divergence from other species. We integrated RNA-seq data and yield QTL information and found that tandemly duplicated TaJAZ genes were prone to association with spike-related traits. Moreover, 12 TaJAZ genes were located within breeding selection sweeps, including 9 tandemly duplicated ones. Haplotype variation analysis of selected JAZ genes showed significant association of TaJAZ7A and TaJAZ13A with thousand-grain weight. Our work provides a clearer picture of wheat JAZ gene evolution and puts forward the possibility of using these genes for wheat yield improvement.

COMPARATIVE ANALYSIS OF “KITE RUNNER” AND “A THOUSAND SPLENDID SUNS” BY KHALED HOSSEINI: THEMES, CHARACTERS, AND SOCIOPOLITICAL CONTEXT

This comparative analysis explores the novels "Kite Runner" and "A Thousand Splendid Suns" by Khaled Hosseini, examining their thematic elements, character development, and sociopolitical context. Through a comprehensive comparative analysis, this article aims to shed light on the similarities and differences between the two novels.

Beyond the fractures: A comprehensive Comparative analysis of Affordable and Accessible laboratory parameters and their coefficients for prediction and Swift confirmation of pulmonary embolism in high-risk orthopedic patients

BackgroundPulmonary embolism (PE) poses a significant challenge in diagnosis and treatment, particularly in high-risk patient populations such as those hospitalized for orthopedic reasons. This study explores the predictive and diagnostic potential of laboratory parameters in identifying PE among orthopedic patients. ObjectivesThe purpose of this study was to determine whether selected (inexpensive and readily available) laboratory parameters and their coefficients can be used to diagnose pulmonary embolism and whether they are applicable in predicting its occurrence. Material and methodsSelected laboratory parameters were determined twice in 276 hospitalized orthopedic patients with suspected PE: PLT, MPV, NEU, LYM, D-dimer, troponin I, age-adjusted D-dimer and their coefficients. Depending on the angio-CT results, patients were divided into groups. Selected popular laboratory coefficients were calculated and statistically analyzed. Optimal cutoff points were determined for the above laboratory tests and ROC curves were plotted. ResultsD-dimer/troponin I [p = 0.008], D-dimer [p = 0.001], age-adjusted D-dimer [p = 0.007], NLR/D-dimer [p = 0.005] and PLR [p = 0.021] are statistically significant predictors of PE. D-dimer/troponin I [p < 0.001], troponin I [p = 0.005] and age-adjusted D-dimer [p = 0.001] correlated with the diagnosis of PE after the onset of clinical symptoms. ConclusionsIn the context of orthopedic patients, cost-effective laboratory parameters, particularly the D-dimer/troponin I ratio and age-adjusted D-dimer, exhibit considerable potential in predicting and diagnosing PE. These findings suggest that combining readily available laboratory tests with clinical observation can offer a viable and cost-effective diagnostic alternative, especially in resource-constrained settings. Further studies with larger and diverse patient populations are recommended to validate these results.

A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks

Efficient spectrum sharing is essential for maximizing data communication performance in Vehicular Networks (VNs). In this article, we propose a novel hybrid framework that leverages Multi-Agent Reinforcement Learning (MARL), thereby combining both centralized and decentralized learning approaches. This framework addresses scenarios where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. We introduce the QMIX technique with the Deep Q Networks (DQNs) algorithm to facilitate collaborative learning and efficient spectrum management. The DQN technique uses a neural network to approximate the Q value function in high-dimensional state spaces, thus mapping input states to (action, Q value) tables that facilitate self-learning across diverse scenarios. Similarly, the QMIX is a value-based technique for multi-agent environments. In the proposed model, each V2V agent having its own DQN observes the environment, receives observation, and obtains a common reward. The QMIX network receives Q values from all agents considering individual benefits and collective objectives. This mechanism leads to collective learning while V2V agents dynamically adapt to real-time conditions, thus improving VNs performance. Our research finding highlights the potential of hybrid MARL models for dynamic spectrum sharing in VNs and paves the way for advanced cooperative learning strategies in vehicular communication environments. Furthermore, we conducted an in-depth exploration of the simulation environment and performance evaluation criteria, thus concluding in a comprehensive comparative analysis with cutting-edge solutions in the field. Simulation results show that the proposed framework efficiently performs against the benchmark architecture in terms of V2V transmission probability and V2I peak data transfer.

Exploring Machine Learning Techniques for Accurate Prediction of Methane Hydrate Formation Temperature in Brine: A Comparative Study

AbstractAccurate estimation of formation conditions plays a pivotal role in effectively managing various processes related to hydrates, including flow assurance, deep-water drilling, and hydrate-based technology development. The formation temperature of methane hydrates in the presence of brine greatly affects the efficacy and accuracy of these processes. This work presents a comprehensive and novel comparative analysis of nine distinct machine learning models for accurate prediction of formation temperatures of methane hydrate. This study investigated the application of major machine learning (ML) algorithms including multiple linear regression (MLR), long short-term memory (LSTM), radial basis function (RBF), support vector machine (SVM), artificial neural network (ANN), gradient boosting regression (GBR), gradient process regression (GPR), random forest (RF), and K-nearest neighbor (KNN). The model accuracy was validated against a large dataset comprising of over 1000 data points with diverse range of salt concentrations. In this regard, model accuracies were compared using several metrics including R2, ARD, and AARD. The experimental results exhibited KNN algorithm to be fast-converging, accurate, and consistent over the entire range of data points with an R2 score of 0.975 and AARD of 0.385%. The results enable efficient and accurate temperature estimation with ML algorithms for multiple hydrate-related processes.

Regional aspects of autism morbidity in the Kabardino-Balkar Republic

The article presents the results of a comprehensive scientific comparative analysis of the results of the study of statistical and epidemiological indicators of autism incidence in the population of the Kabardino-Balkar Republic for 2019–2021, which is of practical importance in describing the diverse manifestations of autism incidence dynamics on the territory of a separate region of the Russian Federation. The results of the study of the probable relationship of age, sex, place of residence and social everyday features with the prevalence of autism in the population of the Kabardino-Balkar Republic showed that autism is more prevalent among the child population of the Kabardino-Balkar Republic. The leading position among the identified cases of autism is in male children under 14 years of age living in urban settings. Any peculiarities in the change of living conditions were not revealed. This suggests a connection between autism and demographic indicators, with no connection with social features of residence in the region.