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Stormwater runoff is a significant contributor of phosphorus (P) loading to waterbodies around the world. Green stormwater infrastructure (GSI) that uses filtration media, such as bioretention, can effectively retain suspended solids and associated particulate P, but is commonly less effective for soluble P retention. The addition of aluminum-based drinking water treatment residuals (DWTRs) may increase P-sorbing capacity of GSI media, though guidance is needed for material selection and to reduce risk of potential contamination. This study examined the P removal capacities of DWTRs (n=11) from drinking water treatment plants in the New England region (northeastern USA). DWTRs were compared for P-sorption potential using batch isotherm and column experiments and characterized for several material properties as well as arsenic leaching and per and polyfluoroalkyl substances (PFAS) content. Results indicate that P retention capacity of DWTRs is generally high (>1,000 mg P kg-1) but varies by approximately one order of magnitude. Lower DWTR bulk density and greater oxalate-extractable Al + Fe were correlated with greater P retention in column experiments. Our findings also indicate that the potential for significant arsenic leaching is low. PFAS were detected in 36% of DWTRs, often at low levels near the method detection limit, with three DWTRs having higher levels of certain PFAS. The addition of DWTRs to GSI is promising for enhanced soluble P removal on a decadal scale (10-90 years), but additional research on As, PFAS, and other contaminants should be pursued prior to use, especially in areas with known or suspected source water contamination. Achieving effective long-term P removal requires selecting DWTRs with favorable material properties (e.g., drier, lower bulk density, greater oxalate-extractable Al + Fe), and mixture with sand at up to 10% DWTR by volume and potentially higher if proven to not impede hydraulic conductivity. Field monitoring of DWTR-enhanced infrastructure at multiple time points postinstallation (e.g., years 1, 5, 10, 20, and 30) is needed to confirm P removal longevity over expected infrastructure lifespans.

<title>Abstract</title> Despite recent advances in bladder cancer treatment, therapeutic resistance remains a critical challenge, necessitating the identification of novel molecular targets. This study used an integrative in silico approach to evaluate the prognostic value and molecular landscape of the immune regulatory molecule CD276 (B7-H3). Analyses of TCGA and GTEx datasets showed that CD276 is significantly upregulated in tumor tissues compared with normal controls, with higher expression in Stage 2 and beyond relative to normal and Stage 1. Kaplan Meier and univariate Cox regression analyses indicated that high CD276 expression is associated with poorer overall survival. Mechanistically, CD276 expression was associated with PI3K–Akt pathway enrichment and a myeloid/macrophage enriched immune contexture, including M2 like macrophage signatures. Finally, pancancer pharmacogenomic profiling across large cancer cell line panels suggested a broad resistance-associated pattern in CD276 high models, with reduced sensitivity (higher IC50) to PI3K/mTOR inhibitors and several cytotoxic agents used in urothelial cancer regimens (e.g., gemcitabine and methotrexate). Collectively, these findings support CD276 as a candidate biomarker linked to aggressive biology and motivate experimental and clinical validation of CD276 directed strategies in advanced bladder cancer.

Groundwater resource that can be utilized by humans to meet their needs. A study on the potential distribution of groundwater was conducted at Merauke IIb Prison, Merauke Regency, South Papua Province using the 1D geoelectrical measurement method analysis of the Werner-Schlumberger configuration. The data acquisition was performed on two measurement paths. The length of the first path (GL-LPS1) was 300 m, with a spacing of 2.5 - 25 meters. The second path (GL-LPS2) is 180 m, with a spacing of 2.5 - 20 meters. The results showed that these two test wells contained brackish-salty water while one test well was fresh water. Based on the data analysis, the dominant resistivity value was &lt;315 Ωm in both paths which was interpreted as clay, sand and gravel. Moreover, the distribution of aquifers on the first path was at a depth of 83.5-170 meters, while in path 2 the distribution of aquifers was at a depth of 90-110 meters. Thus, the analysis results indicated heterogeneity in the groundwater quality. Despite similar local lithologies, this indicates different water sources at each location. Therefore, water supply management must be considered in a prison environment.

This experimental and modeling study explores the integration of renewable energy-based pretreatment methods (solar thermal and ultrasonic) with anaerobic digestion (AD) for sustainable sludge management and enhanced biogas production. Building on prior experimental work that utilized microwave pretreatment, the study employs machine learning (ML) to model and optimize AD performance under renewable energy pretreatment. Experimental validation was conducted using lab-scale continuously stirred tank reactors (CSTRs) with a comprehensive dataset of experimental runs. Key findings demonstrate that solar thermal and ultrasonic methods achieve 20.5% ± 1.8% and 18.7% ± 2.1% higher methane production (295 ± 22 and 285 ± 20 mL CH₄/g VS, respectively) and 30.9% ± 2.1% greater chemical oxygen demand (COD) solubilization compared to microwave pretreatment (245 ± 18 mL CH₄/g VS), while reducing energy consumption by 40.1% ± 3.2% and 35.6% ± 2.8%, respectively. ML models (Random Forest and Gradient Boosting) demonstrated high accuracy (R² = 0.952 ± 0.018 and 0.948 ± 0.022, respectively) in predicting biogas yield and identifying optimal pretreatment parameters. Comprehensive life cycle assessment including upstream emissions shows 49% and 37% carbon footprint reduction for solar thermal and ultrasonic systems, respectively, compared to microwave pretreatment. This work provides both experimental validation and theoretical framework for future large-scale implementation and highlights the potential of ML-driven optimization to advance sustainable sludge-to-energy conversion, offering significant implications for reducing operational costs.

Reliable energy access in rural schools is necessary for educational and community development. Solar hybrid systems, particularly Direct Current (DC) Coupled designs, offer a sustainable solution. However, they face significant scalability, maintenance complexity, and charge controller dependency challenges. This paper addresses these issues and presents an adaptive model to optimize DC Coupled solar hybrid systems. Applying Basic Qualitative Inquiry, semi-structured interviews were conducted during this study with five experts, including site officers, maintenance contractors, and a senior electrical engineer. The results reveal that rigid system design, high upgrading costs, and charge controller inefficiencies limit scalability and long-term performance. System reliability will suffer from a lack of maintenance, especially battery management and overheating prevention. The Adaptive DC Coupled System Solution Model (ADCSM) was proposed to overcome these challenges, including adaptive system restructuring, AC-DC hybrid integration, advanced charge controller optimization technologies, and intelligent monitoring technologies. This paradigm improves the system's flexibility, efficiency, and sustainability and provides a cost-effective and scalable strategy for policymakers, engineers, and researchers to manage rural energy. Keywords: Solar Hybrid Systems, DC Coupled, Scalability Challenges, Charge Controller Optimization, Renewable Energy in Rural Schools.

ABSTRACTDevelopment in urban areas is indicated by the existence of infrastructure and facilities that support the lives of its people. This encourages urban areas to develop into centers of community activities and influences the use of space in the area (Bai et al., 2021). Based on Temanggung Regency Regional Regulation Number 1 of 2024 concerning the Temanggung Regency Spatial Planning Plan (RTRW) for 2024-2044, the Parakan urban area is designated as one of the urban areas in Temanggung Regency which is a strategic node for the development of the Magelang-Temanggung corridor area. This is one of the factors that influences development and encourages population growth in the area. This population growth increases the need for housing as one of the basic human needs. The analysis method used in this study is quantitative analysis. The analysis carried out includes calculating population projections, calculating the backlog of housing needs, analyzing land availability, and spatial analysis in determining the location for housing needed in the planning year. The final results of the study show that the population will increase in 2043 with a total land area needed for settlements reaching 35,13 ha, while land availability until 2043 is 243,97 ha. This study contributes to spatial planning by integrating housing demand projections and land availability mapping, this approach is still rarely applied in spatial planning studies in an area.Keywords: housing needs, land availability, urban area ABSTRAKPerkembangan pada kawasan perkotaan diindikasikan dengan keberadaan prasarana dan sarana yang menunjang kehidupan masyarakatnya. Hal ini mendorong kawasan perkotaan berkembang menjadi pusat kegiatan masyarakat dan mempengaruhi pemanfaatan ruang kawasannya (Bai et al., 2021). Berdasarkan pada Peraturan Daerah Kabupaten Temanggung Nomor 1 Tahun 2024 tentang Rencana Tata Ruang Wilayah (RTRW) Kabupaten Temanggung Tahun 2024-2044, kawasan perkotaan parakan ditetapkan sebagai salah satu wilayah perkotaan di Kabupaten Temanggung yang menjadi simpul strategis pengembangan Wilayah koridor Magelang- Temanggung. Hal ini menjadi salah satu faktor yang mempengaruhi terjadinya perkembangan dan mendorong pertumbuhan penduduk di kawasan. Pertumbuhan penduduk tersebut meningkatkan kebutuhan hunian sebagai salah satu kebutuhan dasar manusia. Metode analisis yang digunakan dalam penelitian ini adalah analisis kuantitatif. Analisis yang dilakukan meliputi perhitungan proyeksi penduduk, perhitungan backlog kebutuhan rumah, analisis ketersediaan lahan, dan analisis spasial dalam menentukan lokasi untuk hunian yang dibutuhkan pada tahun perencanaan. Hasil akhir penelitian menunjukkan bahwa populasi meningkat pada tahun 2043 dengan total luas lahan yang dibutuhkan untuk permukiman mencapai 35,13 ha, sementara ketersediaan lahan hingga tahun 2043 sebesar 243,97 ha. Penelitian ini berkontribusi pada perencanaan tata ruang dengan mengintegrasikan proyeksi kebutuhan rumah dan pemetaan ketersediaan lahan, pendekatan ini masih jarang diterapkan dalam studi perencanaan tata ruang di suatu kawasan.Kata Kunci: kebutuhan rumah, ketersediaan lahan, kawasan perkotaan

Information on tree plantation structures, such as tree type, density, and tree height, is essential for developing smart agriculture and plantation management strategies to support production estimation and investment, including biomass for carbon sequestration estimation. In this study, multisource remote sensing data from radar (Sentinel-1 C band), optical (Pléiades), and drones (multispectral drone) were used to support effective and cost-efficient sustainable tree plantation management in Siak Regency, Riau Province, Indonesia. Tree plantation maps were created using the difference backscatter VH and VV from Sentinel-1. Tree counting was then performed using Pléiades red, green, and blue visible bands and multispectral drone bands using a maximum a posteriori pixel-based classifier integrated with a filter function and statistical estimation. The validation of the tree map using manual measurements yielded accuracies ranging from approximately 79% to 97%. Tree heights were calculated from the difference between the Digital Surface Model (DSM) derived from drone data and the Digital Terrain Model (DTM) obtained from DEM Nasional (DEMNAS) data. Further improvements in the current map accuracy can be achieved using a combination of remote sensing and field measurements of tree structure inventories. • Tree plantations map, tree structure and tree height inventory produced using integrated SAR, optical imaging and UAV • Tree counting developed using The Maximum A Posteriori (MAP) classifier of high-resolution satellite image and drone • Tree height developed using drone DSM and DTM data

In this study, it is presented how to obtain climate data from NASA MERRA climate data by NASA MERRA climate data, recording and reducing the climate distribution zone in Malaysia into 60 stations, in analyzing and calculating the cost of climate distribution in 3 regions of India by northern Bangladesh, central Bangladesh and southern Bangladesh: (1). The average temperature from 1985-2020, (2). Average wind speed from 1985-2021, (3). The average amount of sunshine from 1985-2020, (4). Average rainfall from 1985-2020 and (5). Average moisture of the weather from 1985-2020 to incorporate into a graduate radio, to bring such information to calculate, to calculate parameter value, concrete face construction design by comparing it to the AASHTO 1993 (or 1998) design standard and the road technical standards in Vietnam to be used to design concrete roads to be appropriate and in accordance with the reality of the weather conditions in the Lao PDR.

This study investigates the factors affecting crack development in red clay roadbeds and slopes through a combined computational and experimental approach. A finite-element model of a prefabricated cracked red clay fracture was established using a cohesive zone model, integrating fracture mechanics theory with numerical analysis. The model was based on an improved three-point bending test system to obtain raw data. The effects of prefabricated crack depth, width, shape, and support span on crack propagation in red clay were systematically investigated. The results indicate that the reliability of the model is confirmed by the three-point bending test. The degree of curvature of the crack depth on the Mode I fracture extension was more significant compared with Mixed mode I–II fractures. Variations in prefabricated cracks fracture had minimal impact on peak load and the degree of curvature of the crack extension in Mode I. The maximum crack width and length occurred at a prefabricated crack width of 2 mm, with the lowest curvature observed. Conversely, a crack width of 6.5 mm led to the highest curvature in Mixed mode I–II fractures. The alteration of the prefabricated crack shapes revealed that, for Mode I fracture, triangular prefabricated cracks resulted in the maximum crack width and length, and the lowest degree of crack curvature. The shape of the prefabricated crack had a minor effect on peak load, with triangular tips yielding the longest crack extension length and highest degree of curvature in Mixed mode I–II fractures. Support span had a limited effect on the crack extension path of Mode I fracture; increasing the span led to a 60.4% reduction in maximum crack width and a 28.9% reduction in maximum crack length. In Mixed mode I–II fractures, the support span inversely correlated with maximum crack width and length.

Longitudinal cracking poses a serious threat to the longevity and functionality of continuously reinforced concrete pavement (CRCP). Using structural, traffic, and climatic data taken from the Long-Term Pavement Performance (LTPP) database, this study presents a machine learning system based on a gradient boosting machine (GBM) optimized using particle swarm optimization (PSO) to forecast longitudinal cracking. The proposed PSO-GBM model achieved the lowest mean RMSE (2.661) and highest R2 (0.984) across fivefold cross-validation, outperforming baseline GBM, linear regression, random forest, artificial neural networks (ANN), and support vector regression (SVR). Compared to traditional and untuned models, the PSO-GBM offers improved generalization and a stronger ability to capture nonlinear interactions among variables. Feature importance and sensitivity analyses identified L3 thickness, age, and AADTT as key predictors. Despite the model’s exceptional predictive accuracy, computational demands and data availability may limit its practical application. However, the results offer useful information for transportation organizations looking to improve maintenance planning techniques and incorporate intelligent predictive tools into pavement management systems.