The healthcare industry increasingly faces critical challenges related to data privacy, integrity, and system interoperability. This research project presents a blockchain-based healthcare management system built using Hyperledger Fabric, a permissioned blockchain framework designed to ensure secure handling of sensitive medical records. The system architecture includes a secure backend developed with Node.js and Express, and an intuitive frontend using ReactJS. The smart contract (chaincode), written in Go, enables key operations such as patient registration, diagnosis tracking, and logical data deletion. Docker and Docker Compose were utilized for consistent and scalable deployment, while system security was reinforced through JSON Web Token (JWT) authentication and role-based access control. Initial testing under minimal load conditions showed promising results in preserving data immutability, providing transparent audit trails, and enhancing overall system usability. Although comprehensive stress testing was beyond the current scope, the system design demonstrates strong potential for integration into real-world healthcare infrastructures. This project highlights the viability of blockchain in improving the trust, transparency, and security of medical data in remote healthcare environments. Future enhancements may include performance benchmarking under high-load scenarios and improved interoperability with existing healthcare systems.

This paper investigates the impact of artificial intelligence (AI) on consumer purchasing power and environmental behavior in the context of the digital economy. It examines how economic, technological, and socio-cultural factors interact with consumer stress levels, thereby influencing decisions regarding the purchase of environmentally oriented products. An optimization model of the consumer basket is proposed, which accounts for AI-driven personalized recommendations, budget constraints, individual preferences, and the psychological state of consumers. The model demonstrates that AI enhances the efficiency of product selection, mitigates the negative effects of stress, and promotes conscious consumption. Practical implementation of the model enables forecasting consumer behavior, forming optimal “green” baskets, and ensuring both economic and ecological efficiency in the digital economy. The findings highlight the role of AI as an integrator of economic, behavioral, and technological factors in shaping sustainable consumption strategies.

The current paper will provide a detailed overview of sustainable technologies of laser welding will encompass technological advancements that will ensure a higher degree of energy expenditure, diminished emissions, and embracement of application of environmentally friendly materials. Laser welding is introduced as a more efficient and the most accurate alternative to dusty welding processes as it can decrease energy usage, material waste, or harmful emissions. It identifies the ways in which innovative technologies, including green lasers, systems based on inverters, and automated process control, influence the environmental goals and retain the high quality of products produced by products operating in several sectors, including automotive, aerospace, and construction. The review considers lifecycle assessments (LCA) to calculate the environmental impact of laser welding and compares its energy usage with the old methods all of which include MIG and TIG welding methods. Emission mitigation best practices such as real-time fume-removal and the use of biodegradable consumable materials are presented to show how it is possible to advance workplace safety and environmental compliance. In addition, the use of inorganic materials such as recycled minimum and biodegradable fluxes are demonstrated to be of bond importance to environmentally focused production. This paper will illustrate how mainstream welding in the automation field, application of AI, and real-time monitoring can enable laser welding be the pioneer in the green field of manufacturing. Regulatory framework and policy recommendations are also used to incorporate these sustainable technologies. The report has also concluded by giving a call to the industry players to embrace the sustainable laser welding as a sustainable strategic and environmental friendly decision as the manufacturing future proof.

One of the most innovative variants of the solution to the field of prosthetic sockets development is hybrid composite laminates that provides a higher level of mechanical efficiency, comfort during usage, and the sustainability of materials. This literature survey addresses how varying types of fibers, including glass, carbon, aramid and natural fibers (like flax) can be incorporated into polymer matrices in a strategic approach to satisfy the multifaceted requirements of a prosthetic. The different types of fibers offer individual mechanical advantages of tensile strength, impact resistance, fatigue resistance and lightweight structure, and this aspect makes the hybrid laminates an alternative to solid materials. The paper explains the role of the choice of the matrix, thermoplastics, thermoset and bio-based resins and the effect they have on the performance of the composite, biocompatibility, and cost. Structural integrity and flexibility of design is further improved by manufacturing techniques such as alternative advanced layering sequences, vacuum-assisted curing, additive manufacturing processes, and so on. Mechanical testing, ISO compliance tests are aspects of quality control that guarantee uniformity in the levels of production. The hybrid laminates can be used in the place of the traditional prosthetic materials, where tensile strength, fatigue, affects absorption are the major aspects, after which they can be designed to fit the individual anatomy by way of being custom-designed. The review indicates both the improvement in mechanical performance, as well as end-user responses, as a larger number of amputees stated that it provided better comfort, reduced weight, and enhanced effects of the functionality. The current research prospects underline the combination of smart materials, environmentally friendly composits, as well as AI-based design optimization. With the changing status of manufacturing technology, the hybrid composite laminates will become available and offer high quality and affordable prosthetic solutions to the world population. This critical review is the basic source of reference to those researchers, clinicians, and engineers working to create the second generation of prosthetic design.1 Hybrid Composite Laminate Introduction to Prosthetics.

This study examines wood vinegar extracted from Eucalyptus camaldulensis through the process of pyrolysis at various temperatures as an eco-friendly wood preservative, particularly against subterranean termite attacks on Triplochyton scleroxylon and Terminalia superba. Wood vinegar of Eucalyptus camaldulensis wood at the three temperatures (300°C, 400°C, and 500°C) were prepared into three concentration levels (50, 75 and 100%) and then tested for its preservative qualities. To assess its efficacy, treated wood samples were also incubated in a timber graveyard exposed to natural termite populations for 12 weeks. Data were analysed using descriptive statistics and ANOVA at α_0.05.The treated wood samples demonstrated enhanced resistance to termite damage; notably, those treated with wood vinegar from pyrolysis at 500°C showed no signs of termite activity or damage with weight loss of 0.4% and 1.8% for Triplochyton scleroxylon and Terminalia superba respectively, whereas samples treated with a 50% diluted solution from 300°C and 400°C exhibited only light termite attack with weight loss in the range of 0.8-5.7% , in stark contrast to the untreated controls which sustained significant damage with weight loss of 92%. This study not only underscores the benefits of using wood vinegar as an eco-friendly alternative but also encourages further exploration into its application across the wood preservation industry to promote environmental sustainability especially in Sub-Saharan Africa.

Cardiovascular diseases (CVDs) are still the primary cause of death globally, and heart disease is the most critical contributor. Precise diagnosis early on prevents poor outcomes, but available diagnosis techniques are typically costly, invasive, and dependent on expert interpretation. This study explores whether artificial intelligence (AI), in the guise of deep learning and bio-inspired optimization, can provide a robust, non-invasive predictive diagnosis of heart disease. Two baseline algorithms, Multi-Layer Perceptron (MLP) and Artificial Neural Network (ANN), were trained and tested on the Cleveland heart disease database. While baseline performance was found to have outstanding predictive capability, this was constrained by sensitivity to hyperparameters. To alleviate this, tuning was performed using the new bio-inspired metaheuristic, the Aquila Optimizer (AO), which draws inspiration from the hunting strategy of Aquila eagles. Experimental results show that AO significantly enhances model performance: the AO-tuned MLP achieved accuracy of 98.05% and ideal precision of 100%, while the AO-tuned ANN produced optimal performance with 98.54% accuracy, 100% precision, 97.09% recall, and a Matthews Correlation Coefficient (MCC) of 0.9712. Convergence of the fitness function confirmed AO's capability to achieve stable and near-optimum solutions in few iterations. Results emphasize the benefit of ANNs for structured clinical data and validate that AO is a phenomenal tool for optimizing neural networks in medicine.

Advancements in digital technologies have transformed the way services are delivered, making e-service quality a critical determinant of customer satisfaction, trust, and loyalty in online environments. Traditional service quality models such as SERVQUAL are insufficient in capturing the unique features of electronic services, which are largely characterized by automation, self-service interfaces, and limited human interaction. To address this limitation, Parasuraman et al. (2005) introduced the E-S-QUAL model, defining e-service quality as the degree to which online platforms enable effective and efficient service transactions. This study conceptually examines the core dimensions of e-service quality and their role in shaping consumer perceptions in online service settings. The E-S-QUAL framework comprises four key dimensions: efficiency, system availability, fulfilment, and privacy. Efficiency relates to the ease of use, speed, and navigational convenience of websites, which significantly enhances perceived service quality and customer satisfaction. System availability emphasizes the reliability and technical stability of online platforms, as uninterrupted service delivery is essential for reducing perceived risk and building consumer trust. Fulfilment refers to the accuracy of service promises, including correct order processing and timely delivery, which strengthens customers’ confidence in service providers. Privacy and security address concerns related to data protection and transaction safety, which remain critical factors influencing customers’ willingness to engage in online transactions. Prior empirical evidence confirms that these dimensions collectively contribute to e-satisfaction and long-term customer loyalty. The study underscores the importance of continuous technological enhancement and customer-oriented system design in sustaining superior e-service quality.