Synergy: Cross-Disciplinary Journal of Digital Investigation (2995-4827)
Synergy: Cross-Disciplinary Journal of Digital Investigation (2995-4827) covers a broad array of subjects related to digitalization, science, and security throughout the computerized world. The primary pillar of this publication is digital evidence and multimedia, with the core qualities of provenance, integrity, and authenticity. This publication promotes advances in investigating forensic science, computer science, data science, and artificial intelligence applied to digital evidence involving digital evidence, using scientific practices in digital investigations and reducing the use of technology for criminal purposes. JOURNAL INFORMATION Journal title : Synergy: Cross-Disciplinary Journal of Digital Investigation Initials : Synergy Abbreviation : Cross.Disc.Jour.Dig.Inves. Frequency : Monthly Published DOI : Publisher Association : Antis Publishing House Online ISSN : ISSN 2995-4827 (Online) | Synergy | The ISSN Portal Collaboration Institution : Kokand State University Publisher : Multi Journals Press Citation Analysis : IEEE Reference Style Accreditation : - Abstracting and Indexing : Click here
Integrative Approaches to Aesthetic Education in Higher Pedagogical Institutions: Opportunities and Challenges in the Digital Era
The digital era has transformed higher education by introducing innovative technologies, new pedagogical models, and expanded opportunities for interdisciplinary learning. These developments have significantly influenced aesthetic education, particularly within higher pedagogical institutions responsible for preparing future teachers. This study examines integrative approaches to aesthetic education in higher pedagogical institutions and explores the opportunities and challenges associated with their implementation in the digital era. The research analyzes theoretical foundations of aesthetic education, integrative pedagogy, digital transformation, and teacher professional development. Particular attention is devoted to interdisciplinary integration, digital creativity, cultural literacy, professional culture, and student-centered learning environments. The findings indicate that integrative approaches contribute significantly to the development of aesthetic competence, creativity, critical thinking, and professional identity among future educators. A conceptual model is proposed for integrating aesthetic education into contemporary teacher preparation programs.
Copyright (c) 2026 Synergy: Cross-Disciplinary Journal of Digital Investigation https://creativecommons.org/licenses/by/4.0
An Optimized Machine Learning Framework for Financial Fraud Detection Using SMOTE and Feature Selection
This study presents a machine learning system capable of efficiently detecting financial fraud with the use of unbalanced data treatment strategies. A holistic approach to the problem, combining feature scaling, feature selection, SMOTE (Synthetic Minority Oversampling Technique), and machine learning evaluation was proposed by all in a single experimental pipeline. Method119 randomly over-samples 84 million transactions that occur on average every day with only 2 out of 1000 transactions reflecting fraud241. Model Evaluation- The model accuracy, precision, recall and F1-score and ROC-AUC. In the results of their test, they demonstrate Random Forest won out among all numeric methods with 99.96% accuracy, 97% precision and 91% recall (Contextual F1 = 94 & ROC-AUC =98). The results indicate the effectiveness of combining SMOTE with feature selection to achieve better detection for frauds and balance the classification towards lower outputs, i.e., which are Genuine Transactions. We provide new insights into the reliability of machine learning-based fraud detection system in real financial systems and a general framework for implementing these models.
Copyright (c) 2026 Synergy: Cross-Disciplinary Journal of Digital Investigation (2995-4827)
Synthetic Data, Deepfakes and Digital Consent: The Next Ethical Battlefield in AI
The exponential growth of artificial intelligence has catalyzed unprecedented capabilities in synthetic data generation and deepfake technology, simultaneously introducing complex ethical challenges surrounding digital consent. This paper examines the intersection of these technologies with consent frameworks, analyzing current limitations in governance structures and proposing comprehensive ethical guidelines. Through systematic analysis of 127 documented cases and empirical data visualization, we demonstrate that existing consent mechanisms are inadequate for addressing synthetic media manipulation. Our findings reveal a 340% increase in non-consensual deepfake creation between 2019-2023, with only 23% of jurisdictions implementing specific regulatory frameworks. We propose a multi-layered consent architecture integrating cryptographic verification, blockchain authentication, and standardized ethical protocols for synthetic data deployment.
G-Hypergraphs Induced by Locally Compact Hypergroups
In this paper, we introduce the notion of a g-hypergraph as a natural extension and generalization of both classical graph theory and hypergraph theory. The proposed structure provides a broader mathematical framework for studying relationships among elements in algebraic systems, particularly those arising from hypergroups. By combining the structural properties of graphs with the generalized connectivity of hypergraphs, g-hypergraphs offer a flexible and powerful tool for analyzing complex algebraic and topological interactions. We investigate several important classes of g-hypergraphs induced by hypergroups and examine their fundamental structural characteristics. The paper also examines conditions under which the pair H:=(V,E), consisting of the vertex set and edge set of a g-hypergraph, satisfies desirable topological properties. In particular, we discuss the role of local compactness and provide several applicable sufficient conditions ensuring that the induced g-hypergraph possesses appropriate topological compatibility. These results contribute to the development of a unified framework connecting hypergroup theory, topology, and generalized graph structures. The concepts and results presented in this work may serve as a foundation for further investigations in algebraic graph theory, topological hyperstructures, and related applications in mathematical modeling and discrete structures.
Enterprise Resource Planning Model for Business Process Integration
This study developed an Enterprise Resource Planning (ERP) model for enhancing business process integration within organizations. The increasing complexity of organizational operations and the limitations of fragmented information systems have made seamless integration of business processes a critical requirement for operational efficiency and effective decision-making. Despite widespread adoption of ERP systems, many organizations continue to experience challenges related to poor process alignment, data inconsistency, and limited system utilization. The study adopted a design science research approach to develop a comprehensive ERP model that integrates core business functions through a centralized database and a business process integration engine. The model incorporates modular architecture, workflow automation, and real-time data sharing to ensure coordinated operations across finance, procurement, inventory, human resources, production, and customer management functions. Data for the study were obtained from organizational process analysis and relevant literature, while the model was validated through expert review and scenario-based evaluation. Findings revealed that the proposed ERP model significantly improved business process integration, reduced data redundancy, and enhanced operational efficiency. The model also demonstrated improved system responsiveness, better process visibility, and increased user effectiveness due to automated workflows and centralized data management. Furthermore, the modular and scalable design of the model supports adaptability across different organizational contexts. The study concludes that effective ERP implementation requires a shift from system connectivity to process-oriented integration. The proposed model provides a practical framework for organizations seeking to improve efficiency and achieve seamless coordination of business operations.
Directions for Financing the Activities of the Forestry System of Uzbekistan and the Classification of Indicators for Assessing its Effectiveness
This article analyzes the composition of sources of financing the activities of the forestry system of Uzbekistan, their dynamics of change over 2021–2024, and the main economic indicators assessing the efficiency of the use of financial resources. During the study, state budget allocations, extrabudgetary revenues, income from leasing forest fund lands, international donor funds, environmental funds, and agro-forestry projects were considered as the main directions of the financing system. The article identifies the economic stability and efficiency of the sector through indicators such as the dynamics of financial flows, net profit, profitability, and ROI. The results of the study showed that the diversification of financing in the forestry system, increased international support, and the activation of market mechanisms have strengthened the financial independence of the sector.
Efficiency Indicators of Financial Resources and Financing in the Forestry Sector of Uzbekistan
The article analyzes financial resource flows in Uzbekistan’s forestry sector during 2019–2024 and their impact on efficiency. The database draws on state budget execution reports, statistics from the Ministry of Ecology, and project documentation from FAO, GEF/UNDP, and the World Bank. Monetary indicators were adjusted for inflation using 2019=100 indexation; efficiency was assessed through indicators such as cost per hectare (CPH), cost per seedling (CPS), seedling survival rate, financial leverage (donor + private/public), and the CO₂ per soum metric. The findings indicate that while the state budget has retained its foundational role, the share of grants and concessional loans has increased. At the same time, CPH has declined, and improvements have been observed in seedling survival rates and CO₂ efficiency. The discussion substantiates the need to attract private capital, expand results-based financing mechanisms, and strengthen MRV-based digital monitoring systems. The conclusion offers recommendations on establishing a sustainable financing architecture, implementing territorially differentiated management approaches, and introducing payment mechanisms for ecosystem services.
“Yashil Makon” (Green Nation initiative)
MRV (Monitoring, Reporting and Verification)
Management Information System (MIS) with Creative Knowledge, Overcoming Challenges in Financial Risk Management and Decision-Making
Management Information Systems (MIS) as essential tools which help businesses make strategic choices and protect their financial interests throughout the entire United States. The high costs of implementation together with restricted technical skills and data security worries and organizational opposition to change create barriers which prevent MIS systems from working properly. A cross-sectional survey was conducted among 325 professionals from service, finance, manufacturing, and IT sectors in small, medium, and large US organizations. The survey collected information on MIS implementation challenges, their perceived severity, strategies adopted to mitigate these challenges, and organizational performance indicators. The study used descriptive statistics to analyze data using SPSS included challenge profiles and respondent details and multiple regression to identify MIS factors that affected strategic decision-making performance. The study found that organizations faced three primary MIS implementation challenges which included expensive implementation costs at 22.8% and insufficient trained personnel at 21.2% and worries about data protection at 19.4%. 56.3% of respondents rated these challenges as high or very high. Organizations used three main strategies to address their needs through employee training at 25.2% and cybersecurity investment at 21.8% and cloud-based MIS solution implementation at 19.7%. The study found three important factors which predict strategic decision-making success through regression analysis of MIS system quality (β = 0.184, p = 0.048) and organizational support (β = 0.176, p = 0.044) and data security system performance (β = 0.162, p = 0.049). Organizations in the US develop their strategic decision-making abilities through effective MIS systems which combine strong system infrastructure with protected data management and dedicated organizational backing to establish MIS as their fundamental strategic resource.
CYBERSECURITY RISK PREDICTION USING HYBRID AI MODELS IN FINANCIAL AND HEALTHCARE SOFTWARE SYSTEMS
Financial and healthcare software systems are among the most targeted sectors for cyberattacks due to the high value of sensitive data, strict regulatory requirements, and the increasing adoption of cloud-native and interconnected digital infrastructures. Traditional security tools struggle to anticipate risks that evolve rapidly, such as zero-day exploits, ransomware, and insider threats, especially in environments where large volumes of structured and unstructured data must be secured in real time. To address these challenges, this study explores the use of hybrid artificial intelligence (AI) models—combining machine learning classifiers with generative and deep learning approaches—for predicting cybersecurity risks in critical financial and healthcare applications. Hybrid AI systems integrate the strengths of discriminative models for anomaly detection with the adaptive capabilities of generative models for simulating novel attack scenarios, thereby improving accuracy, reducing false positives, and enhancing resilience against previously unseen threats. Empirical evaluations conducted on publicly available security datasets, including UNSW-NB15 and healthcare intrusion logs, demonstrate that hybrid AI models can achieve over 95% detection accuracy while maintaining lower computational overhead compared to standalone deep learning architectures. Moreover, the proposed approach aligns with compliance frameworks such as HIPAA and PCI-DSS by incorporating explainability and traceability into the prediction pipeline. These findings highlight the transformative potential of hybrid AI for risk prediction in high-stakes domains, offering a pathway toward more reliable, proactive, and regulation-compliant cybersecurity defenses in financial and healthcare systems.
Copyright (c) 2025 Synergy: Cross-Disciplinary Journal of Digital Investigation (2995-4827)
SMART CONTRACTS FOR AUTOMATED COMPLIANCE
Compliance with industry regulations and internal governance policies has become a critical challenge for enterprise software systems, especially as organizations face increasing scrutiny in domains such as finance, healthcare, and critical infrastructure. Traditional compliance management often relies on manual audits, fragmented monitoring tools, and post-event remediation, which can lead to high operational costs, delays, and persistent risks of non-compliance. Recent studies show that regulatory non-compliance costs enterprises an average of $14.8 million annually in fines, remediation, and reputational damage (Ponemon Institute, 2022), underscoring the urgent need for proactive and automated enforcement mechanisms. This paper explores the application of smart contracts—self-executing code deployed on blockchain networks—as a framework for automated compliance and regulatory enforcement in enterprise software ecosystems. Smart contracts can encode regulatory requirements, security policies, and service-level agreements directly into programmable logic, ensuring that compliance is continuously monitored and enforced without human intervention. We present a prototype implementation using Hyperledger Fabric in which financial transaction software was integrated with smart contracts for compliance with Know Your Customer (KYC) and General Data Protection Regulation (GDPR) requirements. Experimental results from this implementation demonstrate that smart contract–driven compliance reduced policy violations by 72%, improved audit readiness by providing 100% tamper-proof logs, and added only a 5–8% overhead in transaction processing time compared to baseline systems. Beyond regulatory enforcement, the integration of smart contracts into enterprise software offers additional benefits: real-time auditing, automated remediation of policy breaches, and interoperability with existing DevSecOps pipelines. However, challenges remain in scaling solutions across heterogeneous enterprise systems, addressing privacy concerns in transparent ledgers, and achieving regulatory acceptance of blockchain-based evidence. In conclusion, smart contracts represent a transformative enabler of trust, efficiency, and accountability in enterprise compliance management. By embedding regulations directly into software execution, organizations can shift from reactive compliance practices to continuous, automated, and verifiable enforcement. Future research should focus on lightweight smart contract protocols, hybrid on-chain/off-chain architectures, and cross-industry standardization to enable broader adoption in enterprise environments.
Copyright (c) 2023 Synergy: Cross-Disciplinary Journal of Digital Investigation (2995-4827)
FAULT DIAGNOSIS IN WIRELESS SENSOR NETWORKS USING IMPROVED FIREFLY ALGORITHM AND SUPPORT VECTOR MACHINE
Wireless Sensor Network (WSN) consists of a large number of sensor nodes deployed in target areas for specific applications. Reliable transmission of data from the cluster head to the base station for processing is one of the most important challenges to ensure the proper performance of applications in wireless sensor networks. The distinction between good and bad data should be properly realized. Classification of applications in fault detection in wireless sensor network is one of the significant solutions in this field. A new model for detecting the accuracy of collected data based on improved support vector machine (SVM) classification for clustering data into cluster heads in a hierarchical wireless sensor network is proposed in this research. Optimum parameter setting in support vector machine to achieve accurate classification is done through improved firefly algorithm. Environmental information such as temperature, humidity, etc. is transmitted from the sensor nodes to the cluster heads so that the error data is categorized and transmitted to the base station. The simulation results of the proposed method show that the proposed approach provides an effective way to send correct data for WSN applications. This system achieves more than 99% accuracy throughout the data learning process. Data test results proved that the performance of the proposed method is more accurate compared to the base paper method (with accuracy equal to 0.9864).
OPTIMIZING SPECTRUM SENSING WITH TWIN-FOLD POWER DETECTION IN COGNITIVE RADIO NETWORKS
By allowing dynamic and intelligent spectrum access, Cognitive Radio Networks (CRNs) are revolutionizing wireless communication and solving the problem of underutilization of spectrum. A structured methodology for spectrum sensing is presented in this study, with an emphasis on the energy detection technique, which is a popular method because of its ease of use and low processing needs. Beginning with the detection of an unknown signal, the suggested approach consists of seven steps that determine if a primary user (PU) is present or absent based on energy levels that are observed. The determined threshold value (λ) serves as a benchmark for comparing the energy of the detected signal. Accurately determining spectrum occupancy is made easier by this comparison. The system reduces false detection and increases decision reliability by averaging the values over several sensing instances. In order to provide secondary users with effective spectrum access, the spectrum must be definitively classified as either occupied or unoccupied in the last stage. By ensuring reliable and energy-efficient identification in CRNs, this methodology helps to create wireless communication systems that are more adaptable and dependable.
COMPONENTS OF EGG PRODUCTION OF EGG-LAYING CHICKENS AND THEIR ASSOCIATION WITH EGG PRODUCTION
Egg production in laying hens is a complex biological process influenced by multiple factors, including genetics, hormonal regulation, nutrition, and management conditions. However, the interaction between these components and their direct impact on total productivity remains insufficiently studied. This study aims to identify the key physiological, biological, and management-related components affecting the egg production performance of Borkovskaya Barvistaya hens and to analyze the correlation between these parameters and overall egg yield. The experiment was conducted over a 52-week period on hens kept in standard two-tier cage systems. Variables such as age at first egg, age at 50% production, egg weight at 30 weeks, peak production intensity, intensity index, and average weekly production growth were measured. Data were processed using correlation analysis. The highest correlation with total egg yield was observed in the intensity index (r = 0.97). Early sexual maturity and faster ascent to peak production also positively correlated with higher productivity. The relatively low peak production value (84.8%) suggests potential for genetic enhancement. The study highlights the strong interdependence between physiological indicators and egg productivity. The integration of egg-laying curve components into selection strategies is recommended to enhance breeding efficiency and improve long-term egg production outcomes.
Egg-laying hens, Borkovskaya Barvistaya breed, intensity index, sexual maturity criterion, average weekly growth.
BLOCKCHAIN PLATFORMS REVISITED: A TECHNICAL AND CRITICAL OVERVIEW OF DISTRIBUTED SYSTEMS
This study offers a comprehensive technical and conceptual review of blockchain platforms and the foundational technologies that underpin them. Beginning with cryptographic primitives such as hash functions and elliptic curve cryptography, the paper explores the structural and security components that facilitate decentralized trust in distributed systems. Core concepts including proof-of-work algorithms like Hashcash, state machines, Merkle trees, and the Merkle Patricia Tree are examined for their roles in data verification, consensus, and transaction integrity. The work also delves into the evolution of blockchain paradigms through early proposals like B-money, and analyzes critical challenges such as the Byzantine Generals Problem and the double-spending dilemma. Advanced topics such as the CAP theorem, SPECTRE's consensus model, and Pedersen commitments are included to illustrate emerging approaches in blockDAGs and privacy-preserving transactions. Emphasis is placed on the balance between liveness, safety, and censorship resistance in modern blockchain design. By synthesizing both theoretical foundations and practical implementations, the paper presents an informed and critical perspective on the evolution, limitations, and future direction of blockchain technologies.
ON Q-ANALOGUES REGARDING AL-ZUGHAIR TRANSFORM
Solving difficult differential equations depends critically upon integral transforms such like Laplace,, and Fourier. through adding q-calculus, the newly proposed q-Al-Zughair transform increases the range regarding the Al-Zughair transform, and becomes more flexible within the favor of the solving equations within q-deformed systems, including quantum mechanics. This work applies it towards ordinary, and partial differential equations, and derives the q-Al-Zughair transform, and investigates its essential features, including linearity, and inversion. Comparative study shows how better this novel method happens towards being than conventional transforms. The results imply, that broadening the applications regarding integral transforms within many domains may benefit coming from the q-Al-Zughair transform.
MODERN DATA STORAGE STRATEGIES: BALANCING PERFORMANCE, SCALABILITY, AND COST
In today’s data-driven world, selecting the optimal data storage strategy is critical for achieving a balance between performance, scalability, and cost-efficiency. This article explores modern data storage paradigms, encompassing traditional relational databases, NoSQL solutions, cloud storage options, and emerging technologies such as distributed and object storage systems. We examine key factors influencing storage decisions, including workload characteristics, access patterns, data volume growth, and budget constraints. Through a detailed analysis, we provide best practices and architectural guidelines to help organizations design flexible storage infrastructures that meet evolving business needs while optimizing resource utilization. By understanding the trade-offs and leveraging innovative approaches, enterprises can ensure high availability, rapid data access, and sustainable scalability without compromising on cost control.
AN EMPIRICAL STUDY OF THE INFLUENCE OF PERSONAL LIFE-MEANING ORIENTATIONS ON PARENTAL ATTITUDES
This empirical study explores the impact of life-meaning orientations on parental attitudes, focusing on the interplay between individual existential values and parenting practices. Life-meaning orientations serve as cognitive-emotional frameworks through which individuals interpret their existence, guiding decisions and interpersonal behavior. The study involves 300 parents from diverse socio-demographic backgrounds and utilizes mixed methods, including validated questionnaires and focus group interviews. Statistical and thematic analysis reveals significant correlations between clearly defined life-meaning orientations (such as existential or altruistic) and adaptive, emotionally supportive parenting styles. Conversely, unclear or crisis-oriented life goals often coincide with authoritarian or indifferent parenting strategies. The research highlights how personal value systems shape family dynamics and affect child development. These findings underline the importance of supporting parents in the development of their own life orientations to promote positive family environments and holistic child wellbeing.
CLASSIFICATION OF DATE FRUITS USING DEEP LEARNING MODEL
Fruits are the most widely grown of all farm products worldwide and have the characteristic of possessing many varieties that differ from each other on the basis of their external characteristics such as color, length, diameter, and shape. These external attributes are equally critical in the definition of fruit types. However, identifying varieties of fruits using outside appearance only could require professional knowledge, and hence may consume lots of time and effort. The focus in the current study is to recognize various types of date fruits using artificial intelligence techniques. The varieties targeted are: Ajwa, Galaxy, Medjool, Meneifi, Nabtat Ali, Rutab, Shaishe, Sokari, and Sugaey. A total dataset of 1,658 images of the mentioned varieties was used in this research, which was obtained by means of a computer vision system (CVS). In contrast to conventional hand-crafted feature extraction-based work on color and shape, in this research study, a Convolutional Neural Network (CNN) model was employed for the direct classification of the images. The model indicated a very high accuracy of 98.8%, which validates the effectiveness and appropriateness of deep neural networks in classifying varieties of date fruit accurately.
DEEP LEARNING TECHNIQUES FOR TIME SERIES DATA MINING ANOMALY DETECTION
As communications innovations develop and ventures become automated, a wide scope of applications and frameworks have arisen to provide and produce enormous measures of data. Numerous techniques have been proposed to separate key indicators from a lot of data to address the state of the whole framework. Inconsistencies utilizing such indicators are identified quickly to forestall likely accidents and limit economic misfortunes. Multivariate anomaly detection of time series data is especially difficult because it requires concurrent consideration of time, dependencies, and relationships between factors. Profound learning-based works have made tremendous advancements around here. Representations of huge scope successions in records may be prepared and advanced in an unaided way and identify oddities from the data. Be that as it may, the vast majority of them are unmistakable to an individual use case and in this manner require space information for legitimate arrangement. This paper provides a logical foundation for anomaly detection in time series data and surveys state-of-the-workmanship certifiable applications. We likewise investigate techniques appropriate for profound time series anomaly detection models utilizing a few standard datasets. At long last, we present a plan for choosing and preparing a fitting model, a profound learning-based time series anomaly detection procedure.
THERMAL ACCUMULATORS AND THE DIFFERENCE IN HEAT STORAGE
In solar thermal and cooling supply schemes, heat storage-based accumulators (such as water, gravel, crushed stones, and others) are widely used. In some cases, heat accumulation is achieved by heating a water layer surrounding stones. At the same time, the working materials used in the above-mentioned heat accumulators, in desert and semi-desert areas, for supplying low-power consumers with hot water, have certain disadvantages, mainly of an economic nature. These include the limited availability of drinking water and the high cost of electricity and thermal energy used to accelerate the process (such as pumps, ventilators, large containers, etc.).
USING LINEAR PROGRAMMING MATHEMATICS IN PRODUCTION PLANNING APPLIED SEARCH
The aim of this research is to use operation research approaches in agricultural production planning to determine the optimal max of agricultural crops that maximize the productivity of one acre. Therefor 400 acres of laned called Moradia boycott 6 were selected after the building a linear programming model in light of agricultural constricts and Use the Simplex and graphite methods. It was found that allocution of the Land for the cultivation of the wheat Crop by 70% and Barley by 30% Lead to maximize the program.
Copyright (c) 2024 Synergy: Cross-Disciplinary Journal of Digital Investigation (2995-4827)
THE EFFECT OF REHABILITATION EXERCISES IN RESTORING THE EFFICIENCY OF THE ADDUCTOR MUSCLE WITH PARTIAL TEAR IN AL-SUFIA CLUB FOOTBALL PLAYERS
The aim of the research is to prepare rehabilitation exercises in order to restore the efficiency of the adductor muscle with partial tears in Al-Sufia Club football players, and to identify the effect of these rehabilitation exercises in restoring the efficiency of the adductor muscle with partial tears. The researchers used the experimental method because it suits the nature of the problem. The research sample consisted of Al-Sufia Club football players who were intentionally selected, numbering four players. A single-group design with pre- and post-tests was employed. The rehabilitation exercises were applied for six weeks, with two units per week during the first and second weeks. The data were processed statistically, and the researchers concluded that the rehabilitation exercises have a positive effect in restoring the efficiency of the adductor muscle injured by partial tears. It was recommended to apply the research findings to samples of players in other sports, such as volleyball and handball, who have the same injury.
THE EFFECT OF HIGH-INTENSITY PHYSICAL EFFORT ON CERTAIN VARIABLES OF THE ELECTRICAL ACTIVITY OF THE MUSCLES ACTING ON THE KNEE JOINT, BLOOD ACIDITY LEVEL (PH), AND THEIR RELATIONSHIP TO THE ACCURACY OF PERFORMING THE DIAGONAL SPIKING SKILL IN VOLLEYBALL
The world has witnessed significant scientific and technical progress in the application of modern scientific and technological foundations in the sports field. This progress has contributed to raising the scientific level in general and the sports level in particular. This is evident in many sports, including volleyball, which has seen substantial development in the way it performs and implements its offensive and defensive skills. This development is due to the positive application of many sciences, such as physiology, anatomy, training, and other aspects of physical education (Al-Dulaimi and Dhanoun, 1991).
DEVELOPMENT OF AN ALGORITHM AND SOFTWARE TOOL THAT TAKES INTO ACCOUNT THE CORROSION OF MATERIALS IN THE WATER DISCHARGE SYSTEM OF A HYDRAULIC STRUCTURE
The longevity and effectiveness of infrastructure are significantly impacted by corrosion in hydraulic structures' water discharge systems. To ensure structural integrity, save maintenance costs, and increase the lifespan of hydraulic structures, a sophisticated algorithm and software tool for evaluating and forecasting material corrosion in such settings must be developed. The goal of this study is to create a computer model that incorporates several material and environmental parameters that affect corrosion rates. To correctly model corrosion processes, the algorithm will consider variables such water pH, temperature, flow velocity, dissolved oxygen levels, and material composition. The suggested software application would offer real-time monitoring and forecasting capabilities by using machine learning, predictive analytics, and empirical data. This will help engineers with decision-making processes pertaining to maintenance planning and material selection. Engineers will be able to input precise parameters and obtain corrosion risk assessments and mitigation plans thanks to the software's user-friendly interface. To improve its forecast accuracy, the system will also use real-world data and case studies. The creation of this technology would greatly enhance safety, optimise hydraulic structure management, and reduce financial losses from unanticipated corrosion-related breakdowns. By providing a reliable, approachable method for evaluating corrosion in water discharge systems, this research seeks to close the gap between theoretical corrosion models and real-world engineering applications. Better material selection, the scheduling of preventative maintenance, and the general sustainability of hydraulic structures will all be made possible by the use of this algorithm and software.
Revolutionizing Supply Chain Optimization with AI-Driven Predictive Analytics
In an increasingly complex and dynamic global economy, supply chain optimization has become a critical factor for business success. Traditional supply chain management approaches often struggle to adapt to rapidly changing market conditions, demand fluctuations, and unforeseen disruptions. Artificial Intelligence (AI)-driven predictive analytics is transforming this landscape by providing real-time insights, demand forecasting, risk mitigation, and end-to-end operational efficiency. This paper explores how AI-powered predictive analytics revolutionizes supply chain management by leveraging machine learning, big data analytics, and advanced algorithms to enhance decision-making. By analyzing vast datasets and identifying patterns, AI enables businesses to anticipate demand shifts, optimize inventory levels, streamline logistics, and minimize disruptions. Companies such as Amazon, Tesla, and Walmart are already leveraging AI to enhance efficiency, reduce costs, and improve customer satisfaction. Key case studies highlight successful implementations across industries, demonstrating how predictive analytics enhances supplier collaboration, warehouse automation, and logistics management. Furthermore, the paper discusses the challenges of AI adoption, including data quality issues, implementation costs, and ethical considerations, and proposes solutions for overcoming these barriers. As AI continues to evolve, its integration into supply chain management will drive greater resilience, agility, and sustainability, empowering businesses to maintain a competitive edge. The study concludes that AI-driven predictive analytics is not just an enhancement but a necessity for future-proofing supply chains in an era of increasing uncertainty and rapid technological advancement.
BEYOND BITCOIN: THE EVOLUTION OF BLOCKCHAIN FOR SECURE DECENTRALIZED APPLICATIONS
Blockchain technology has evolved far beyond its initial application in cryptocurrencies like Bitcoin, emerging as a transformative force in secure, decentralized applications (DApps) across industries. This article explores the expanding role of blockchain in reshaping data security, transparency, and trust in fields such as finance, healthcare, supply chain management, and governance. By leveraging smart contracts, decentralized consensus mechanisms, and cryptographic security, blockchain provides tamper-resistant, trustless ecosystems that enhance efficiency and reduce reliance on centralized authorities. We examine the key technological advancements driving blockchain’s evolution, including scalability solutions, interoperability frameworks, and energy-efficient consensus algorithms that address traditional limitations. Additionally, real-world case studies highlight how enterprises and governments are adopting blockchain to enhance data integrity, prevent fraud, and streamline operations. As blockchain technology continues to mature, its integration with artificial intelligence (AI), the Internet of Things (IoT), and next-generation cryptographic protocols is shaping a future of secure, decentralized digital infrastructures. This paper concludes by discussing the challenges that remain—such as regulatory concerns, scalability bottlenecks, and adoption barriers—and how continued innovation is driving blockchain’s potential to redefine the digital economy and establish a new era of secure, decentralized applications.
PRIVACY-PRESERVING AI MODELS FOR CLOUD AND EDGE COMPUTING SECURITY
The rapid adoption of cloud and edge computing has revolutionized modern digital infrastructure, enabling seamless data processing and real-time analytics. However, this shift has also introduced significant security and privacy challenges, particularly concerning the exposure of sensitive data to potential cyber threats. Traditional security frameworks often fail to provide adequate protection in distributed environments, necessitating the development of privacy-preserving AI models to safeguard critical information while maintaining computational efficiency. This paper explores the role of privacy-preserving AI models in enhancing cloud and edge computing security, focusing on techniques such as federated learning, homomorphic encryption, secure multi-party computation (SMPC), and differential privacy. By leveraging these approaches, AI-driven security frameworks can detect cyber threats, enforce access control, and mitigate privacy risks without compromising data confidentiality. Additionally, we examine the integration of zero-trust security models with AI-driven privacy mechanisms to strengthen cloud-edge ecosystems against emerging cyber threats. Through a comparative analysis of traditional security methods versus AI-enhanced privacy models, we highlight the advantages of distributed learning architectures, encrypted inference techniques, and privacy-aware anomaly detection. Furthermore, real-world case studies from industry leaders (e.g., AWS, Microsoft Azure, Google Cloud) demonstrate how privacy-preserving AI is being implemented to protect sensitive workloads in cloud-edge environments. Despite its transformative potential, implementing privacy-preserving AI presents challenges, including computational overhead, algorithmic bias, and adversarial vulnerabilities. We discuss ongoing research efforts and emerging trends such as decentralized AI, quantum-safe encryption, and edge-native privacy protocols to address these issues. This paper concludes by providing recommendations for researchers, cloud service providers, and policymakers to drive the adoption of secure and privacy-aware AI models in cloud and edge computing. By advancing AI-driven privacy solutions, the industry can achieve a balance between security, performance, and compliance, ensuring a trusted and resilient digital ecosystem for the future.
ORGANIZATION OF ADDITIONAL SERVICES IN HOTELS: SCIENTIFIC ANALYSIS AND PRACTICAL ASPECTS
This article examines the issues of organizing additional services in hotels as a key element in increasing competitiveness and customer satisfaction. Modern trends in the hospitality industry that influence the development of additional services, as well as their role in forming guest loyalty, are analyzed.
ARTIFICIAL INTELLIGENCE IN AUDIT PLANNING AND EXECUTION: ENHANCING DECISION-MAKING ACCURACY IN MULTINATIONAL COMPANIES IN NIGERIA
This study examines the impact of Artificial Intelligence (AI) integration in audit planning and execution on decision-making accuracy in multinational companies operating in Nigeria. Specifically, it investigates how AI-driven technologies, such as data mining, machine learning, and image recognition, influence audit quality. Using a survey research design, the study collects primary data from 120 accounting firms in Nigeria that have adopted AI in their auditing processes. Descriptive statistics, correlation analysis, and Ordinary Least Squares (OLS) regression were employed to analyze the data. The results suggest that while data mining has a significant positive effect on audit quality, machine learning and image recognition have limited independent effects. The interaction between data mining and machine learning, however, shows a stronger impact on audit quality, implying that combining AI technologies enhances audit efficiency. The study highlights the need for strategic AI integration, regulatory support, and continuous capacity building in the Nigerian auditing sector to fully exploit AI’s potential. The findings contribute to the growing body of knowledge on AI in auditing and provide insights into the challenges and opportunities of implementing AI in audit processes in developing economies.
ADVANCED ALGORITHM FOR EXPEDITING IMAGE PROCESSING
Processing images more effectively is possible when a novel technique based on fractal image compression is applied. Range blocks and domain blocks are the overlapping and non-overlapping portions of an image that are separated to achieve a fast encoding time Typically, domain blocks are bigger than range blocks. This study proposed a novel rapid full-search algorithm technique to drastically cut down on encoding time, one that expands the search until a suitable match is located or the search is finished. Using the nearest points in the range blocks as a starting point, this method searches the range block for the best matching domain. Despite being straightforward to use, the recommended rapid full-search strategy outperforms the traditional search strategy. The proposed method's search reduction, peak signal-to-noise ratio, compression ratio, and encoding time are all assessed. Test results show that the proposed method may reduce the overall search time by 89% and encode a 512x512 gray scale Lena image in 0.31 seconds.