This article examines the role cryptographic methods play in protecting digital assets through blockchain systems, with a particular focus on their adjustment to contemporary challenges and technological trends. An endeavor is undertaken to systematize major cryptographic algorithms, their effective appraisal in data protection, and development prospects under quantum computing threats. The study is relevant because centralized systems increasingly depend on cryptography due to greater regulatory pressures and, above all, a need for security through secrecy. The scientific novelty lies in the detailed comparative analysis of the said methodology (hashing, digital signatures, zero-knowledge proofs) for cases relating to major blockchain platforms (Bitcoin, Ethereum, Zcash), which hence demonstrate varied approaches towards security provision. The study's methodological foundation consists of analyzing 13 sources, merging a qualitative examination of algorithms and ECDSA with zk-SNARKs with a quantitative assessment of their effectiveness. Hash functions and Merkle trees ensure data integrity while reducing the computational costs of verification; asymmetric cryptography and Zero-Knowledge Proofs guarantee authenticity and confidentiality for the function of the transaction. Main findings support that cryptography is the cornerstone technology for blockchain security, but it has to be tailored to meet new challenges. Development in post-quantum algorithms and the infusion of homomorphic encryption will soon become imperative for quantum threats. This paper strongly advocates hybrid solutions that would bring traditional ways merged with novelties, which will provide sustainability over time for digital assets. Thus, this article will be useful for Developers of Blockchain Systems, Cryptographers, Cybersecurity Experts, & Regulators willing to know how protection methods for digital assets evolve.

New advancements in artificial intelligence technology have driven fundamental changes in IT operations by making possible self-governing infrastructure along with intelligent service administration. IT frameworks of a traditional nature present several performance-limiting issues because they depend on humans while only addressing problems after they occur which leads to both poor operational outcomes and elevated operational expenses. AI computing racks bring revolutionary changes to IT systems through their integration of machine learning (ML) algorithms and predictive analytics which enables real-time automation along with self-healing capabilities and intelligent service management decisions. The research evaluates how AI-powered computing racks affect IT infrastructure and demonstrates their ability to improve resource management while boosting security resistance and enhancing operational delivery capabilities. The research tracks real-world deployments through empirical methods while it evaluates how AI computing racks modify workload management systems and decrease system failure frequency and boost prognostic maintenance performance. AI-driven automation delivers substantial cost savings along with increased operational efficiency through selected industry-specific examples that the research analyzes. AI-powered IT operations achieve dual goals of producing automated systems which scale up operations while creating sustainable IT networks. The research delivers direct recommendations to companies that aim to implement AI-driven infrastructure systems through a deployment path which solves infrastructure adoption hurdles including execution expenses together with privacy protection matters and worker skill adaptations. The next-generation enterprise IT framework will establish AI computing racks as its core foundation because they apply intelligent automation to revamp IT operations for enhanced efficiency and security alongside innovation capabilities.

This paper presents methods for improving the shape stability of plush knitwear, which, despite its advantages, tends to deform under load due to the structure of the ground yarn. The structures of two-layer plush knitwear are considered, in which one layer is plush knitwear, and the other layer consists of more shape-stable weaves. Three variants of double-layer plush knitwear with different structures are described. These developments improve shape stability, reduce elongation, enhance thermal protection properties, and expand the range of knitwear applications. The advantages of the new variants include increased durability, improved quality, and an aesthetically pleasing appearance.

program testing is crucial for guaranteeing program dependability, but it has historically included a lot of manual labor, which restricts coverage and raises expenses.  By creating and selecting test cases, anticipating defect-prone locations, and evaluating test results, machine learning (ML)-driven testing approaches automate and improve traditional software testing. This study examines the development of these techniques.  Significant enhancements are provided by ML-driven techniques, such as early fault detection, shorter testing times, and increased test coverage.  The paper offers a thorough synthesis of current developments, contrasting ML-based testing with conventional methods in a number of areas, including efficacy and efficiency in defect identification.  It also highlights important research gaps, talks about real-world implementation issues, and looks at multidisciplinary uses of machine learning technologies, such as deep learning and reinforcement learning. The paper concludes by highlighting machine learning's revolutionary influence on software testing procedures and projecting a time when testing will become more independent, flexible, and incorporated into ongoing software development processes.

This article deals with the intricacies of packaging lead time requirements in order fulfillment in the Business-to-Business (B2B) context, with different requirements being customer-driven and regulation-driven. The article discusses how Oracle E-Business Suite (EBS) as an integrated ERP package handles order promising processes and how it addresses packaging requirements on domestic and international shipments. In the B2B context, customers need materials shipped in particular types of packaging, from generic cartons to specialized packing skids or pallets. As a robust application, Oracle EBS is strongly positioned to address such packaging needs in a diversified manner by sub-applications like Oracle Order Management and Oracle Global Order Promising. The document talks about the setup of packaging specifications in Oracle EBS using common lookups, descriptive flexfields (DFF), and workflows within Oracle Order Management. Order line workflow customization and defaulting rules are also dealt with in the document to further optimize the order promising process, presenting an end-to-end solution to simplify operations. Finally, the paper outlines Oracle EBS benefits in order fulfillment automation, compliance assurance, and customer satisfaction improvement. It highlights the important part that technology plays in coping with the intricacies of contemporary manufacturing and supply chain management and presents Oracle EBS as a central facilitator of operational excellence and long-term business expansion. As a whole, this journal presents a complete image of the dynamics of packaging lead time demands in the B2B model and the important role played by Oracle EBS in mitigating these challenges.

Healthcare IT security threats create multiple threats that endanger patient privacy together with operational processes and medical institution trust levels. Medical organizations that adopt digital adoption methods depend more on electronic health records (EHRs), cloud computing and connected medical devices which creates growing cyberattack risks. The paper examines various difficulties affecting healthcare IT cybersecurity through a focus on expanding data breaches that cause patient trust issues. The research adopts a data-based framework to study security threats which involve ransomware, insider threats, and phishing attacks together with their impact on financial losses and reputational damage. Through this analysis the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR) serve as regulatory emphasis with assessment of worldwide healthcare institution compliance obstacles. This study presents multiple business strategies to combat cybersecurity risks through visionary threat detection tools alongside AI security systems and blockchain protocols alongside complete risk management protocols. This paper analyzes recent verifiable studies from respected sources to locate essential research holes in healthcare cybersecurity defense and provides usable solutions for organizations. Research outcomes demonstrate that healthcare organizations must implement multiple protective measures to protect patient data in order to preserve their institutional trustworthiness. The written work presents usable knowledge for public servants in addition to healthcare leadership teams and information security experts who deal with new digital threats.

Oil and gas drilling operations are among the most complex and high-risk industrial activities, involving hazardous environments that require strict safety protocols and adherence to best practices to prevent accidents, injuries, and environmental harm. This article provides a detailed review of the essential safety protocols and best practices that should be implemented throughout the oil and gas drilling process, from exploration and well construction to production and decommissioning. The study discusses safety management systems, hazard identification, risk assessments, and emergency response plans, with a focus on continuous improvement and safety culture in the industry. The implementation of these safety practices is crucial to ensuring the well-being of workers, protecting the environment, and improving operational efficiency.