The incorporation of natural fillers like eggshells in polymers has gain attention due to their potential capability to enhance some properties while providing possible cost savings. In this paper, quail eggshells were used as bio-based filler in silicone rubber and their effects on the mechanical properties were investigated. For the composite manufacturing, samples containing 4.8 wt.% (5 phr), 9.1 wt.% (10 phr) and 13 wt.% (15 phr) of crushed quail eggshells were manually prepared. The mechanical characterization tests considered are compression, tensile and hardness. Based from the results, the sample with 4.8 wt.% filler achieved the highest compressive strength of 2.79 MPa and hardness of 53.3 which correspond to improvements of about 11 % and 6 % as compared to the plain rubber, respectively. These enhancements can be associated with the good dispersion of the filler at lower loading. However, higher filler contents resulted to a decrease in mechanical properties which could be linked to the possible agglomeration of crushed eggshells and weak filler to matrix interaction caused by lack of surface treatment. Nevertheless, the improvements attained by adding quail eggshells at lower percentage in rubber can still make it an alternative filler to consider.

Extreme weather, temperature fluctuations, and long-term changes in demand patterns are just a few of the previously unheard-of stresses that climate change brings to power systems. These occurrences jeopardize grid dependability, put traditional security measures to the test, and reveal weaknesses in operational procedures and infrastructure. This study examines the various ways that climate change affects the protection and dependability of power systems, highlighting the necessity of adaptive engineering techniques. Dynamic line rating (DLR), climate-integrated load forecasting, and adaptive protection schemes backed by machine learning and wide-area monitoring are important tactics. The review highlights important research gaps in probabilistic coordination, climate downscaling, and sensor trust while synthesizing recent developments. This work advances the development of climate-resilient power systems by coordinating technical innovation with resilience objectives.

The development of reliable wireless networks is crucial for advancing sustainability. Not only does it facilitate remote work and telecommunication which are critical remote services such as telemedicine and distance education, they are also essential in supporting sustainable practices like the application of IoT in monitoring environmental conditions and energy usage. To ensure that these networks work optimally, it is essential that the datasets used in their development are not only accurate but are also distinct. This study contributes to this end by analyzing the datasets generated by the COST 2100, a model that is used extensively in wireless communications. Using ANOVA, the researchers determined if the dataset are indeed distinct as signals bounce about multiple clustering which use Multiple Input, Multiple Output (MIMO) Technology similar to modern wireless systems like 5G. Results show that the different variables or dimensions are distinct from each other. Thus, the datasets generated by COST2100 are suitable to be utilized in further preprocessing methods of wireless multipath clustering, ultimately contributing to building a more sustainable wireless communication system.

A sustainable electronics engineering program effectively prepares graduates to tackle the changing technological, environmental, and societal challenges. In this context, this study examines the relationship between Outcome-Based Education (OBE), quality assurance mechanisms, and student performance in the Electronics Engineering Licensure Examination with the goal of enhancing the development of the program and making it more sustainable. To do this, the paper analyzed a five-year dataset to examine key factors such as accreditation by the Philippine Technological Council (PTC), international rankings (QS and THE), and recognition as Centers of Excellence (COE) or Centers of Development (COD) by the Commission on Higher Education (CHED). Regression modeling of the data gathered revealed that the linear interaction model most effectively predicts student performance, with an R-squared value of 0.85, highlighting the emphasis on OBE and quality assurance to improve academic results. The study concluded that emphasizing interactions among program attributes can guide curriculum revisions to enhance student success and ultimately, its sustainability. It suggested that future studies integrate machine learning (ML) techniques to improve the predictive capabilities of model to enhance quality assurance measures. This may be done by utilizing ML methodologies from related fields such as human detection systems and ECG analysis and apply it to educational research. Such an implementation can enhance data-driven decision-making processes, thereby improving the quality of education and student performance in the Electronics Engineering Licensure Examination and ultimately, making the program more sustainable.

The integration of Internet of Things (IoT) technology in the agricultural sector using advanced sensor systems has garnered significant interest in the recent year, especially in promoting food security. This study shows an application of this to Spondias purpurea (Philippine Pias Prunes), an important fruit in the Philippine Archipelago. The developed system places significant importance on the connection of Internet of Things (IoT) devices and the Google Cloud Platform. This integration enables real-time monitoring, data storage, and analysis, therefore providing valuable insights into enhancing the drying process and mitigating spoilage by maintaining moisture levels within the recommended range of 12-14%. The technology provides farmers with the opportunity to extend the shelf life of the prunes, reduce food wastage and increased profitability. While the system focused on Spondias purpurea, the system is highly adaptable and scalable to other fruits and crops. The research employed a DHT11 sensor that is linked to a Raspberry Pi Microcontroller, together with a Google Cloud-Based Platform for the purpose of data storage and processing. Results of the experiments indicate that the temperature measurements remain consistent at varying conditions. Moreover, the humidity levels remain to be high while the prune’s moisture content continue to be steady. To enhance the system's functionality, future endeavours should focus on integrating the system with other agricultural processes. Additionally, it is recommended to broaden the scope of the cost-benefit analysis by considering aspects such as the initial investment, maintenance costs, energy consumption, and potential rewards in terms of product quality, loss reduction, and increased output.