Food packaging involves the process of enclosing food to keep it safe from outside elements that could contaminate, harm, or decompose it while it is being transported, stored, or sold. Selection of packaging materials used play a critical role in ensuring food safety and extending shelf life. This makes the exploration of sustainable alternatives, such as Natural Fibre Reinforced Green Composites (NFRGCs), essential. In addition to packaging sustainability, NFRGCs have gained prominence in several vital applications in industries, including semi-structural, structural, automotive, aerospace, medical etc. because of their long-term sustainability. This review work focuses on the present and potential applications of green composite films intercalated with turmeric oil. It highlights the significance of the materials now in use as well as their limitations. The necessity of sustainable packaging stems from customer demand, legal pressures, and environmental concerns. Green composites are being investigated as an alternative to synthetic polymer composites due to their good mechanical properties, affordability, biodegradability, improved life cycle, and eco-friendliness. Turmeric oil-intercalated nanocomposites offer unique benefits for food packaging, including improved mechanical and barrier properties, increased antimicrobial and antioxidant properties, and potential environmental benefits. Despite challenges like cost and scalability, they represent a new frontier in packaging innovation.
Solar Photovoltaic System (SPV) is a rapidly expanding renewable energy source integrated into national power grids and off-grid systems worldwide. Therefore, the effective management of power systems and the energy industry needs accurate solar energy forecasting, ranging from short-term predictions of a few seconds to longer-term forecasts up to one week in advance. Hence it is well recognized that the presence of dust particles on the surface of photovoltaic (PV) modules substantially influences their overall performance. Subsequently, the resultant decrease in energy generation significantly impacts the financial earnings. A range of methodologies have been used to address the limitations associated with solar radiation forecasting. The use of machine learning (ML) algorithms provides a range of techniques for predicting solar radiation. Additionally, integrating artificial intelligence (AI) and ML may collectively enhance the optimization of cleaning intervals for photovoltaic (PV) modules. In the same manner, the development of a sun tracking algorithm aims to accurately determine the location of the sun in order to optimize the reception of solar energy. This article aims to critically review the current research on maintenance and tracking strategies for PV panels while presenting a novel application of AI and ML algorithms. Finally, this review presents a comprehensive analysis comparing traditional approaches with AI techniques to demonstrate the significant mpact of AI algorithms in PV systems.
The escalating threat of global warming necessitates a shift towards clean energy production, with renewable energy sources emerging as a promising solution for sustainable electricity generation. However, the inherent intermittency of renewables requires hybrid systems to maintain reliable electricity. This review explores the optimization of dual-mode renewable hybrid power systems, which operate in both grid-connected and autonomous modes, to address power stability challenges. By analyzing state-of-the-art methodologies, including advancements in control algorithms, architecture sizing, and energy management strategies, this work identifies methods for enhancing system efficiency and cost-effectiveness. Key findings demonstrate that optimized dual-mode systems improve power consistency and operational resilience. The review concludes by outlining a roadmap for future research, highlighting innovations in integration techniques, environmental assessments, and regulatory collaboration as crucial steps toward maximizing the impact of dual-mode systems in achieving global energy sustainability goals.
This paper established a robust theoretical framework and applied fuzzy random and probabilistic theories to evaluate the durability and strength of reinforced concrete bridge structures subject to corrosion. The study systematically predicts moment capacity degradation, failure probability, and service life, while accounting for uncertainties in input parameters through Monte Carlo simulation. The MATLAB programming tool was used to calculate and analyze the bridge structures considering the uncertainty of input parameters and Monter Carlo simulation. A targeted application focused on estimating the service life of an aging bridge affected by chloride intrusion yielded noteworthy findings. Notably, the predicted initial corrosion times from both fuzzy and non-fuzzy analyses were found to be comparable, at approximately 29.96 years and 29.94 years, respectively. Additionally, the analysis indicated that the probability of failure for reinforced concrete bridge structures effectively doubles after 25 years. These results underscore the robustness of the proposed model, emphasising that the incorporation of comprehensive empirical data on the input parameters used to calculate durability and resistance, particularly from field experiments, significantly enhances the reliability and accuracy of lifespan and resistance predictions for reinforced concrete structures in practical applications.
This paper introduces a testbed for a Group Acknowledgement Circular Shift (GACS) MAC protocol model designed to balance current consumption in LoRa-based Wireless Sensor Networks. The model leverages a time-slotted approach to allocate transmission times for end devices, using ATmega328p microcontrollers and Ebyte 22 LoRa Modules. The testbed includes a gateway, an application server, and initially four, then fifteen, end devices. Current monitoring circuits were attached to certain end devices to measure current usage during both GACS and Group Acknowledgement (GA) without Circular Shift (CS) models. In a network of four end devices, the GACS model extended battery life uniformly to 463 days with a 3000 mAh battery, while inconsistent battery life was observed in GA without CS. Similarly, the GACS model enabled 434 days of battery life across 15 devices, promoting uniform battery longevity. Results show that the GACS model enhances current consumption fairness, reduces collisions, and improves packet delivery to 99.81%.
