The increasing worldwide energy requirements, combined with sustainable urban growth, drive the need for inventive building technologies. Building integrated photovoltaic and thermal systems (BIPV/T) generate both electricity and thermal energy while enabling nearly zero-energy buildings (NZEBs) to achieve their energy goals. Our analysis examines the technological, economic, and environmental aspects of BIPV/T systems and their application within building elements such as roofs, facades, and glazing areas. The review also examines supportive policy frameworks for BIPV/T implementation while pinpointing adoption barriers like steep initial investments and incomplete regional policies. The present review is based on a systematic literature review from January 2010 to March 2025 from the Scopus, Web of Science, and IEEE Xplore databases. A search string consisting of the combination of the keywords building integrated photovoltaic and thermal systems (BIPV/T), nearly zero-energy buildings (NZEBs), solar technologies, Aesthetic and Architectural Integration, Energy Efficiency, and Building codes. A total of 75 articles were selected after screening and eligibility assessment. This study seeks to provide guidance for researchers, architects, and policymakers to progress BIPV/T integration towards sustainable urban development.

The study investigates the concentration of chemical elements in biological tissues (placenta and blood) of women from the Akmola region, Kazakhstan to assess the impact of environmental pollution on maternal and newborn health. The research conducted from 2018 to 2021 involved 67 placental and umbilical cord blood samples collected from women in four Akmola districts. The study utilized instrumental neutron activation analysis and electronic microscopy to determine the concentration of 28 chemical elements. Statistical methods were applied to analyze the distribution, including the mean values, standard deviations, and frequency distribution curves. Significant variability in chemical element concentrations was observed across samples, with notable differences in rare earth elements and heavy metals. Elements such as sodium (Na), calcium (Ca), and chromium (Cr) displayed high variation. The study identified a strong environmental influence on the accumulation of toxic elements in the placenta and blood. The accumulation of chemical elements in biological tissues was heterogeneous, influenced by natural and anthropogenic factors. Blood was found to be more sensitive to environmental contamination compared to the placenta, indicating the need for enhanced environmental health monitoring in the region.

This study evaluates the operational cost efficiency and environmental implications of transitioning from diesel to electric buses, using the Trans Jogja public transport system in Indonesia as a case study. Employing a total cost of ownership (TCO) framework and emissions analysis, the study compares the financial performance and greenhouse gas (GHG) emissions between diesel and battery electric buses. Results show that electric buses incur approximately 50% higher operating costs, primarily due to elevated capital expenditures and depreciation. Moreover, under Indonesia's coal-dominated electricity grid, electric buses generate higher indirect CO₂ emissions than their diesel ones, highlighting a critical energy-emission paradox. However, electric buses eliminate tailpipe pollutants such as NO_x and PM2.5, offering considerable public health benefits. A systemic scenario analysis reveals that full fleet electrification without concurrent reform in the energy sector could raise annual emissions by over 2,200 tons. The study identifies key barriers—including high upfront costs, limited charging infrastructure, and regulatory misalignment—and proposes phased policy interventions. Recommendations include targeted subsidies, contract revisions, integration with renewable energy, and technical capacity-building. The findings offer valuable insights for Indonesian cities seeking to scale sustainable urban mobility through electric transportation.

The freight forwarding industry serves as a crucial bridge between importers, exporters, and shipping and transportation companies. By facilitating the smooth movement of goods across borders, freight forwarders play a vital role in global trade. However, this industry also significantly impacts environmental stability due to the emission of harmful gases, carbon footprints, waste generation, and improper disposal practices, such as dumping waste into the soil. These activities contribute to environmental degradation and pose serious threats to natural ecosystems. Therefore, it is essential for the freight forwarding industry to adopt green initiatives and sustainable practices to minimize its environmental impact and promote long-term ecological balance. This study attempts exploratory research on green logistics practices and the challenges of their implementation in the case of freight forwarding industry in Hyderabad, India. Using primary research with 150 employees in freight forwarding companies, the paper explores the levels of awareness and adoption, as well as challenges to green logistics management. The study tested the following five hypotheses: educational gaps, economic barriers, customer demand, industry structure, and heterogeneity. Using convenience random sampling and quantitative data analysis, the results show that employees have considerable gaps in education and awareness, as only 28.0% of employees are also very familiar with green logistics concepts. The major barriers inhibiting the widescale adoption included high upfront costs (74.7%), education and awareness challenges (65.3%), customer expectations for competitive pricing (62.7%), and extended installation time (60.0%) All five hypotheses were confirmed with chi-square statistics from 19.76 to 45.72 (p<0.05). We highlight that the diversity of company sizes within the freight forwarding industry results in a spectrum of behavior when it comes to adopting green practices. Micro-level enterprises are facing much more significant challenges (58.0%) than higher-level firms at these conditions, coupled with highly uneven resource distribution (60.7%) Despite these barriers, the majority of respondents acknowledge the significance of green logistics concerning his/her company for operational efficiency (93.3%) and competitive advantage (86.7%). The results highlight a vital relationship where comprehensive education programs, targeted financial support and collaborative efforts from stakeholders can help highlight the more sustainable environmental approach to this activity.

This study assessed the effectiveness and sustainability of using barge versus feeder vessels to transport containerized cargo to Bangkok Port, Thailand. A survey of 387 stakeholders in marine logistics was conducted from October to December 2024. Multiple regression analysis (MRA) showed that cost-effectiveness, environmental impact, and operational flexibility primarily influenced transport mode choice, explaining 56.2% of the variance. Cost-effectiveness emerged as the key factor, while environmental impact was the strongest predictor of perceived sustainability. While operators favored feeders due to cost and time efficiency, barges scored higher due to environmental friendliness and operational flexibility. Notably, 68% of respondents preferred barges for short routes under 100 km due to their role in reducing road congestion and pollution. Furthermore, 73% expected greater barge use over the next five years, driven by technology and environmental policies. Improved waterway infrastructure would lead 82% to use barges more frequently, and 76% believed better intermodal integration would enhance logistics efficiency. This study is limited to the context of Thailand’s domestic maritime logistics and stakeholder perceptions, which may not be fully generalizable to other ASEAN or global port systems. Future research should explore multi-country comparative studies and assess longitudinal trends as green port policies evolve across Southeast Asia.

In this work, we introduce a hybrid method that combines Long Short-Term Memory (LSTM) neural networks with Taylor Series Expansion (TSE) to solve high-dimensional Fredholm Integral Equations of the second kind (SFIEs). Specifically, we focus on systems with up to 10000 dimensions, which are common in fields like fluid dynamics, electromagnetics, and quantum mechanics. Traditional methods for solving these equations, such as discretization, collocation, and iterative solvers, face significant challenges in high-dimensional spaces due to their computational cost and slow convergence. LSTM networks approximate the solution functions, and Taylor Series Expansion refines the approximation, ensuring higher accuracy and computational efficiency. Numerical experiments demonstrate that the hybrid method significantly outperforms traditional approaches in both accuracy and stability. This method provides a promising approach to solving complex high-dimensional integral equations efficiently in scientific and engineering applications.

Wireless propagation is a crucial technology in modern advancements, requiring highly accurate prediction. Path loss propagation is influenced by various parameters that must be accounted for to predict the signal route over the entire distance and refine breakpoint models with precise interference calculations. The breakpoint distance is defined as the point separating two distinct trends of path loss, each following a different path loss exponent. This paper reviews the Fresnel, Perera, and True breakpoints in a dual-slope model reference at 2 GHz, using a fixed exponent of n₁ = 2 before the breakpoint and n₂ = 4 after. It then proposes a distance-adaptive exponent model that considers a steady path by incorporating a flexible exponent based on environmental factors, mitigating the abrupt change in path loss exponent at breakpoints observed in the dual-slope model, which leads to discontinuities. The comparison results under similar conditions demonstrate that both models perform similarly over short distances of up to 100 meters, while the dual-slope model is more suitable for distances of up to 1 km. However, due to its stability and consistency, the distance-adaptive exponent model is more appropriate for longer distances. Validation using RMSE, followed by comparative analysis, confirms that our model offers higher stability in interference scenarios. These findings will assist researchers and wireless designers in predicting and selecting the most accurate and effective propagation model.

This study presents the design and implementation of a solar power generation system (SPGS) to harness solar energy as an alternative power source for greenhouse operations. The system is developed to support vertical hydroponic crop cultivation while operating independently through an off-grid configuration. The specific objectives of this research are to optimize the energy efficiency of the SPGS, ensure the reliability of power supply for hydroponic operations, and evaluate the system's effectiveness in supporting sustainable agricultural practices. The SPGS utilizes solar panels to convert solar radiation into direct current (DC), which is stored in batteries or converted to alternating current (AC) to power various loads. The results showed that the SPGS operated effectively and was capable of supplying consistent energy to the greenhouse. The highest recorded solar irradiance was 1072.82 W/m², resulting in a voltage of 42.8 V and current of 6.9 A. The maximum power output reached 295.32 W, with the solar panel system achieving an efficiency of 18.72%. The combination of a solar energy system specifically created and fine-tuned for greenhouse use, along with a vertical hydroponic system. This research offers a customized energy approach that guarantees effective energy collection, storage, and delivery, perfectly aligned with the fluctuating energy needs of a greenhouse setting.

Hyperparameter search was found not making good use of compute resources as surrogate-based optimizers consume extensive memory and demand long set-up time. Meanwhile, projects running with fixed budgets require lean tuning tools. The current study presents Bounding Box Tuner (BBT) and conducts tests of its capability to attain maximum validation accuracy while reducing tuning time and memory use. The project team compared BBT with Random Search, Gaussian Processes for Bayesian Optimization, Tree-Structured Parzen Estimator (TPE), Evolutionary Search and Local Search to decide on the optimum option. Modified National Institute of Standards and Technology (MNIST) classification with a multilayer perceptron (0.11 M weights) and Tiny Vision Transformer (TinyViT) (9.5 M weights) were adopted. Each optimizer was assigned to run 50 trials. During the trial, early pruning stopped a run if validation loss rose for four epochs. All tests applied one NVIDIA GTX 1650 Ti GPU; the key metrics for measurement included best validation accuracy, total search time, and time per trial. As regards the perceptron task, BBT reached 97.88% validation accuracy in 1994 s whereas TPE obtained 97.98% in 2976 s. Concerning TinyViT, BBT achieved 94.92% in 2364 s, and GP-Bayesian gained 94.66% in 2191 s. It was discovered that BBT kept accuracy within 0.1 percentage points of the best competitor and reduced tuning time by one-third. The algorithm renders the surrogate model unnecessary, enforces constraints by design and exposes solely three user parameters. Supported by the evidence of these benefits, BBT was considered to be a practical option for rapid and resource-aware hyperparameter optimization in deep-learning pipelines.

PT Supreme Energy is a company engaged in developing geothermal energy to produce electricity. In the operation of Geothermal Power Plants (GPP), water vapor is extracted from the bowels of the earth, then the steam is condensed into water. When the condensate produced by GPP is not reinjected, the water has the potential to produce pollutants. One method of processing pollutants is the phytoremediation technique, which uses aquatic plants with the construction of constructed wetlands. This research aims to test the effectiveness, adaptability, and removal ability of aquatic plants to reduce condensate water pollutants. This research used a Randomized Block Design (RBD) with 1 (level) of treatment. The treatment consisted of 10 types of aquatic plant seedlings. The research results showed that 9 types of plants had a survival rate above 100%, namely H. coronarium J. Koenig), T. angustifolia, I. formosana, T. dealbata, A. calamus, J. effusus, P. umbrela, C. papyrus, D. bicolor, while N. alba had a survival rate of 76%. Removal values for the parameters Fe, Cu, Co, Bo, pH, BOD, COD, Ammonia, Nitrite, Nitrate and TSS show varying results for each cell/plant. Specifically for Co metal, the removal value is 0 in each cell. The highest removal was found in Cell 2 (treatment of H. coronarium and T. angustifolia plants) with Fe metal removal values (41.07%), pH (3.97%), ammonia (16.25%), nitrate (33.11%) and TSS values (33.78%). Removal of metals, Cu (16.67%) and Bo (19.11%), COD (56.65%), and nitrite (0.05%) were found in Cell 5 (treatment of P. umbrella and C. papyrus plants). So, H. coronarium, T. angustifolia, P. umbrella and C. papyrus can be used as phytoremediation plants to reduce pollutants, especially pollutants in condensate.