Microplastics (MPs) have emerged as a widespread environmental contaminant, raising growing concerns about their impact on terrestrial ecosystems. This comprehensive review paper highlights the effects of MPs on soil properties, soil organisms, and plants, shedding light on the complex interactions within these critical components of terrestrial environments. In terms of soil properties, plastics, ranging from macroplastics to mesoplastics, microplastics, and nanoplastics, have been found to exert significant influence. They can alter soil physical attributes, including texture, structure, bulk density, water aggregate stability, water holding capacity, and rainwater infiltration. Microplastics can affect soil chemical properties by influencing pH levels, electrical conductivity, nutrient cycling, and enzyme activity, and even can cause heavy metal accumulation in plants. These alterations in soil properties have far-reaching implications for ecosystem health and agricultural productivity. Furthermore, microplastics have substantial repercussions on soil organisms, particularly earthworms, collembolans, and microbial communities comprising bacteria and fungi. These organisms play pivotal roles in nutrient cycling and soil health. Microplastics can disrupt their habitats, affect their behavior, and potentially lead to changes in soil biota composition, with widespread effects throughout the terrestrial food web. Microplastics influence plant growth and development; even the microplastic can be uptaken and translocated within plant tissues. Food safety and ecosystem dynamics are affected by these effects. This review paper emphasizes the urgency of understanding the complex interactions between microplastics and terrestrial ecosystems. It highlights the need for further research to comprehensively assess the extent and implications of microplastic contamination in various soil types, under different environmental conditions, and concerning diverse plastic characteristics. Standardized methodologies for studying these interactions are essential to facilitate comparisons across studies.

Abstract. This paper presents a collaborative effort to develop localized solar photovoltaic (PV) power output (PPV) forecasting models for the Philippines using geospatial data. It underlines the importance of solar energy in the country and discusses the opportunities and challenges associated with PPV forecasting. Project SINAG, a two-year research project, aimed to develop solar PV output forecasting models through a collaborative approach with academic institutions, solar energy industries, and government agencies. Actual PPV data from 43 solar PV installations were analyzed alongside meteorological data from the PAGASA weather bureau, ERA5, AHI-8, and FY- 4A. These datasets were filtered based on a one-year period to ensure quality. The study employed SARIMAX, LSTM, and XGBoost models individually and in hybrid models to develop the forecasting models. Model performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In a case study in Baguio City, the SARIMAX model exhibited strong seasonal dependence, providing more accurate forecasts in dry seasons than in wet seasons. Additionally, the forecasting accuracy of each model (SARIMAX, LSTM, and XGBoost) varied based on the month and location of the installation, emphasizing the need for local and season-based PPV forecasting models. Despite implementation challenges, such as collaboration arrangements, bureaucratic barriers, and budget constraints, the project produced thirteen research publications and provided data for three student theses. This paper also demonstrated diverse engagements and contributions that emphasize the significance of collaborative research in conducting nationwide-scale data-driven projects.

Access to reliable energy is crucial for development, yet many rural areas in southern Bangladesh suffer from electricity shortages, impeding essential services and hindering social and economic progress. This paper proposes integrating renewable energy-based microgrids to provide sustainable and reliable electricity, thereby improving living conditions and boosting economic growth. A detailed survey in Ruma, Bandarban, was conducted for load estimation. Simulation results for on-grid and off-grid microgrids are obtained using HOMER Pro and PVsyst software. The off-grid system includes 21.8 kW of PV, 15 kW of hydro, and 222 kWh of battery storage, while the on-grid system includes a 200 kW PV system and a 15 kW hydro turbine. The levelized cost of energy (LCOE) is 0.15 USD/kWh off-grid and 0.03 USD/kWh on-grid. The on-grid system shows economic sustainability with a 6.8-year break-even point, 13 % IRR, and 8.7 % ROI. Environmental analysis shows significant greenhouse gas reductions, with CO2 emissions decreasing from 227,778 kg/year to 199,016 kg/year. Additionally, a sensitivity analysis is conducted, which underscores the resilience of the proposed hybrid microgrid system to weather variations and cost fluctuations. This paper provides a comprehensive foundation for policymakers to consider renewable microgrids as a solution for rural electrification in southern Bangladesh, utilizing solar and hydropower resources.

Abstract. Solar photovoltaic (PV) technology has been gaining popularity in the Philippines as an alternative source of sustainable energy. In such technology, module tilt angle and weather conditions are among the system parameters that have substantial impacts on PV system performance. Previous studies have considered either tilt angles or weather conditions, but not the combined impact of these two parameters on solar PV power output. The objective of this study is to examine the effects of weather variables and tilt angle on the output of solar photovoltaic (PV) systems in Muntinlupa City, the Philippines. Three 120W monocrystalline solar PV panels were used and set up to three different tilt angles (i.e., 5°, 10°, and 15°). The fifth generation of the ECMWF's global climate and weather reanalysis (ERA5) dataset was used to gather hourly weather information such as surface solar radiation, wind speed, wind direction, relative humidity, ambient temperature, and total precipitation. Three principal components (PC), which together account for 95% of the variability, were identified using principal components analysis (PCA), which was used to address multicollinearity among the weather parameters. To assess the effects of tilt angle, time, and PCs on solar PV production, analysis of covariance (ANCOVA) was carried out. Results show that all weather variables, except for wind speed and total precipitation, have a significant impact on solar PV production with configuration producing the best results. Moreover, a significant difference in mean solar PV production was observed among the three tilt angles. From 6:00 AM to 2:00 PM, solar PV output gradually increases and declines thereafter. Outputs this study can help in optimizing the design and configuration of solar PV systems in the Philippines by considering weather variables and module tilt angle. Lastly, this study provides useful information for system designers, installers, and policymakers in improving energy generation and utilization, encouraging the use of renewable energy sources, and advancing sustainable energy objectives.

Abstract. The sustainable energy transition in the Philippines requires accurate forecasting of solar PV output to optimize energy efficiency and grid management. While existing studies have emphasized the positive correlation between solar irradiance and PV production, this study aims to explore whether forecasting improves with the inclusion of weather data. This research conducts a comparative analysis between relying solely on solar irradiance against integrating various weather parameters to enhance solar PV output forecasting. The study focuses on three distinct locations (Pangasinan, Negros Occidental, and Davao Del Norte) and employs two models per each site: Model 1 (M1), which relies only on solar irradiance as predictors, and Model 2 (M2), which incorporates solar irradiance and weather parameters. Using Fifth Generation ECMWF Reanalysis (ERA5) Data, Principal Component Analysis (PCA) is conducted on the significant weather parameters. Extreme Gradient Boosting (XGBoost) with 5-fold nested cross-validation is applied for solar PV output forecasting. Models are assessed using Mean Absolute Percentage Error (MAPE) and skill scores. Results showthat while solar irradiance alone suffices for predicting solar PV output in Negros Occidental, incorporating weather parameters improves forecasting accuracy in Davao Del Norte and Pangasinan. This paper recommends caution in generalizing the findings to different regions with varying weather patterns, as the forecasting performance of the models is influenced by data quality, specific location, and prevailing weather conditions.

In the past few decades, the transmission of data over an unsecure channel has resulted in an increased rate of hacking. The requirement to make multimedia data more secure is increasing day by day. Numerous algorithms have been developed to improve efficiency and robustness in the encryption process. In this article, a novel and secure image encryption algorithm is presented. It is based on a modified chaotic logistic map (CLM) that provides the advantage of taking less computational time to encrypt an input image. The encryption algorithm is based on Shannon’s idea of using a substitution–permutation and one-time pad network to achieve ideal secrecy. The CLM is used for substitution and permutation to improve randomness and increase dependency on the encryption key. Various statistical tests are conducted, such as keyspace analysis, complexity analysis, sensitivity analysis, strict avalanche criteria (SAC), histogram analysis, entropy analysis, mean of absolute deviation (MAD) analysis, correlation analysis, contrast analysis and homogeneity, to give a comparative analysis of the proposed algorithm and verify its security. As a result of various statistical tests, it is evident that the proposed algorithm is more efficient and robust as compared to previous ones.

Location information is the most crucial information used in context-aware applications, e-commerce and IoT-based consumer applications. Traditional methods doesn’t focus on network coverage, accuracy, hardware cost, and noise in dense environment. To defeat these issues, this paper presents a novel localization algorithm for UWB nodes adopting self-calibration and ToA measurement for context-aware applications. The Link quality induction values are used instead of RSSI for distance estimation by costing technique. A calibration factor (CF) is further introduce to automatically update the location information in mobility. As the signal strength can be distorted heavily due to shadowing and multi-path fading, the localization is estimated in noisy condition and extended Kalman filtering (EKF) is applied to refine the node coordinates. Simulation results shows that the positioning error is decreased with an overall accuracy of 0.23m and standard-deviation of 0.76m.

3D printed shelters offer a promising approach to accelerating shelter provision and enhancing resilience in disaster-affected regions. This study investigates the potential of 3D printing technology in revolutionizing shelter construction, particularly for disaster relief and humanitarian aid. By examining the technical feasibility, social implications, and environmental impacts of 3D printed shelters, this research aims to identify key challenges and opportunities for their widespread implementation. The study explores the integration of essential utilities into 3D printed shelters, their adaptability to various environmental conditions, and the importance of community engagement in the construction process. Additionally, the economic viability and environmental sustainability of this technology are assessed. Through a comprehensive analysis of existing research and case studies, this paper provides insights into the potential of 3D printing to address the critical need for rapid, affordable, and resilient shelter solutions in disaster-affected areas.

The Philippines is advancing its renewable energy goals to achieve a 35% share by 2030. This study evaluates solar photovoltaic (PV), and wind power output forecasting methods currently employed in the Philippines, aiming to assess their accuracy against electricity market standards and identify potential improvements. The study systematically reviews articles emphasizing forecasting methods, including physical, statistical, machine learning, and hybrid models. The methodologies encompass a range of forecasting horizons and utilize a diverse set of input variables that influence forecasting accuracy. A key finding from the literature is the variability in the accuracy of these forecasting models, with many not meeting the stringent Mean Absolute Percentage Error (MAPE) threshold of 18% set by the Philippines' Wholesale Electricity Spot Market (WESM). This emphasizes the need for enhanced forecasting models to mitigate economic losses and improve grid stability significantly. Furthermore, this study suggests integrating more sophisticated, data-driven forecasting models to improve accuracy. Such advancements are critical for managing the intermittent nature of solar and wind energy and making informed decisions on energy policy and investment in the Philippines. The study also identifies gaps in current forecasting practices and recommends avenues for future research, particularly in developing models that align better with the operational standards and real-time demands of the energy market.

Wave energy derived from oceanic kinetic forces is a highly promising renewable energy source. As global efforts to incorporate renewable energy into the grid increase, accurate wave energy forecasting becomes essential for optimizing energy harvesting and grid integration. This paper examines the latest developments in machine learning (ML) approaches, focusing on deep learning (DL), ensemble methods, and hybrid models used for forecasting ocean wave energy. It highlights the strengths and weaknesses of various approaches in capturing the complex nonlinear dynamics of ocean waves, including predicting energy flux, significant wave height (SWH), and wave period. Additionally, the paper explores how hybrid models, combining physical models with ML, have emerged as powerful tools for improving forecast accuracy over traditional methods. This review concludes with insights into future directions, emphasizing the potential of advanced techniques like transformers, generative adversarial networks (GANs), and real-time data assimilation for enhancing prediction reliability and computational efficiency.