Standalone applications are widely deployed in mission-critical environments due to their high execution speed, reliability, and ability to operate without continuous network connectivity. In contrast, web-based applications offer scalability, centralized data management, and ubiquitous accessibility, but often suffer from latency, offline limitations, and dependency on network availability. Relying exclusively on either paradigm is insufficient for modern enterprise systems that demand both responsiveness and scalability. This paper presents an edge-cloud hybrid software architecture that integrates standalone and web applications to achieve offline resilience, centralized coordination, and scalable collaboration. In the proposed framework, an edge-based local application performs time-sensitive operations and maintains persistent local storage, while a cloud-backed server manages global synchronization, multi-user access, and security enforcement. A conflict-aware synchronization mechanism based on RESTful services and distributed consistency principles ensures reliable data convergence across heterogeneous environments. The framework is implemented and experimentally evaluated using a hospital management system case study. Experimental results demonstrate low synchronization latency, stable resource utilization, and high fault tolerance under increasing workloads. The findings confirm that the proposed hybrid architecture effectively bridges offline and online computing, making it suitable for mission-critical domains such as healthcare, finance, and transportation.
Ensuring transparency, integrity, and security across the vaccine supply chain is essential to prevent counterfeiting, unauthorized data manipulation, and logistical inefficiencies that can adversely affect public health systems. Traditional vaccine supply chain management systems primarily rely on centralized databases, which are susceptible to tampering, single points of failure, and limited real-time visibility for participating stakeholders. These limitations reduce trust among manufacturers, distributors, healthcare providers, and regulatory authorities and increase the risk of counterfeit or compromised vaccines entering the distribution network. This research presents a blockchain-based vaccine supply chain management model developed using Hyperledger Fabric, a permissioned blockchain framework specifically designed for enterprise and healthcare-oriented applications. The proposed system integrates chaincode-driven automation to enforce predefined business rules, SHA3-256 cryptographic hashing to ensure data immutability, and PBFT-based consensus mechanisms to validate transactions reliably in a distributed environment. Role-based authentication is implemented to ensure that only authorized participants can access and update the system, enabling secure and controlled data sharing among all stakeholders involved in the vaccine lifecycle. In addition, a middleware layer developed using the Go programming language and the Gorilla Mux framework facilitates seamless and secure communication between client applications and the blockchain network through RESTful APIs. The proposed architecture enables immutable record-keeping, efficient end-to-end traceability of vaccine batches, and stringent verification of transactions across all stages of the supply chain, including manufacturing, distribution, and administration. Experimental testing conducted through API calls and Postman validates the system’s ability to automate operational workflows while effectively preventing unauthorized data modifications. The results demonstrate improved operational reliability, enhanced data integrity, reduced risk of counterfeit vaccines, and increased transparency and trust among stakeholders. This study highlights how permissioned blockchain networks such as Hyperledger Fabric can modernize vaccine logistics and contribute to safer, more transparent, and reliable public health supply chain systems.
Agility in software development has evolved from a niche methodology to a mainstream paradigm that enables organizations to respond rapidly to changing market demands and customer expectations. This paper explores the integration of agility into software development through modern agile methodologies such as Scrum, Extreme Programming (XP), and Kanban. It highlights the core benefits of agility, including improved adaptability, faster delivery cycles, and enhanced customer satisfaction, while also addressing persistent challenges such as documentation gaps, progress measurement, and organizational resistance to change. Beyond current practices, the paper examines the future trajectory of agile development, emphasizing the growing influence of emerging technologies such as artificial intelligence (AI) and DevOps. These technologies are reshaping agile workflows by enabling greater automation, predictive analytics, and continuous delivery. Drawing on recent scholarly and industry research, the paper outlines best practices for successful agile transformation, including the alignment of agile practices with strategic business goals, the cultivation of a supportive organizational culture, and the use of modern tools to enhance transparency and performance analytics. By synthesizing empirical findings and forward-looking insights, this study provides a comprehensive roadmap for agile adoption and continuous improvement, offering valuable guidance for practitioners, managers, and researchers aiming to build resilient, customer-centric software systems in increasingly complex and technology-driven environments.
The theoretical foundations of neural network integration of system software modules lie in the field of combining formal methods for automation, optimization and management of development, integration and maintenance of complex software systems and systems engineering. The article proposes a formalism of operator schemes for constructing programs with deterministically connected modules for any class of algorithms. The structures of description and execution of programs are considered. In programs, there are data (information) and control transfer links between operators. When training neural networks, program structures indicate only the links that control their execution. Distributed links between inputs and outputs of sets of operators are associated with modules. Program operator modules are numbered. This numbering is preserved in program execution. Programs of modules can be sequential, parallel and sequential-parallel. The structure of programs with deterministically connected modules is considered. In the proposed formalism of operator schemes, the solvability of the problem of constructing programs with deterministically connected modules is proved. These fundamentals provide a theoretical basis for developing systems in which neural networks act as tools for complex automation and optimization of design, integration, and management of system software modules. In the future, the development of this concept will contribute to the creation of self-regulating, adaptive, and scalable system architectures.
The proposed approach to creating AGI using brain-like neural networks combines principles that teach systems to effectively generalize, remember, plan, and reason across a variety of tasks, drawing on ideas from neuromorphic architectures, dynamic hierarchical information processing, and hybrid neural-symbolic methods. Brain-like architectures dynamically process event-driven information over time, provide online learning and low power consumption, and implement research projects on hardware-based implementation of online plasticity. Within the framework of brain-like neural networks, this involves hybrid approaches to world reconstruction and action planning. The goal is to enable the network to reason, manipulate symbols, and use rules, similar to natural intelligence. This is achieved using differentiable logic, modal induction, neural memory processors, neural program interpreters, and neural-modular networks for task composition. This improves long-term memory, planning, and reasoning accuracy with a limited training set. It also enables skill transfer from one task to another, simulating alternative actions without multiple interactions with the real world. Brain-like neural networks have a modular, scalable architecture with routing and an ensemble of specialized modules. Neural modules for vision, planning, dialogue, and motor control can be dynamically assembled without complete retraining. Memory, planning, and perception are separated into separate modules with mechanisms for collaboration and joint goal learning. AGI based on brain-like neural networks enables knowledge transfer across domains. Neuromorphic modules motivate themselves to explore and investigate the environment, which supports long-term adaptability, similar to biological mechanisms. This is essential for AGI to function in the real world.
This is the study that presents an AI-Python-based chatbot that helps students to learn programming by demonstrating solutions to such problems as debugging errors, solving syntax problems or converting abstract theoretical concepts to practical implementations. Traditional coding tools like Integrated Development Environments (IDEs) and static analyzers do not give robotic help while AI-driven code assistants such as GitHub Copilot focus on getting things done. To close this gap, our chatbot combines static code analysis, dynamic execution tracing, and large language models (LLMs) to provide the students with relevant and practical advice, hence promoting the learning process. The chatbot’s hybrid architecture employs CodeLlama for code embedding, GPT-4 for natural language interactions, and Docker-based sandboxing for secure execution. Evaluated through a mixed-methods approach involving 1,500 student submissions, the system demonstrated an 85% error resolution success rate, outperforming standalone tools like pylint (62%) and GPT-4 (73%). Quantitative results revealed a 59.3% reduction in debugging time among users, with pre- and post-test assessments showing a 34% improvement in coding proficiency, particularly in recursion and exception handling. Qualitative feedback from 120 students highlighted the chatbot’s clarity, accessibility, and confidence-building impact, though critiques included occasional latency and restrictive code sanitization. Emphasizing the ethical aspects of the project, the bias principle led to the discrimination of gendered reasons for 83% and the GDPR-iPad-like procedures to anonymity were followed. The chatbot's productivity points to its ability to make coding education available to everyone and to give 24/7 aid to students in some not well-funded schools. Future work will expand multilingual support through localized datasets and culturally adapted examples, integrate gamification to enhance engagement, and develop collaborative learning features. By balancing technical innovation with pedagogical empathy, this research provides a blueprint for AI tools that prioritize educational equity and long-term skill retention over mere code completion. The chatbot exemplifies how AI can augment human instruction, fostering deeper conceptual understanding in programming education.
