The rapid integration of technology, with increasing speeds, has transformed vehicles into cyber-physical systems by connecting them to each other Vehicle-to-Everything (V2X), significantly expanding the attack surface and leaving them vulnerable to network-based threats. Current cyber intrusion detection systems (CIDS) exhibit performance degradation due to significant class imbalance, limited resilience against adversarial attacks, and insufficient interpretability for security-critical environments. To overcome the identified issues in this study, we propose Hierarchical Classifier-Agnostic Boosted Stacking for Network Intrusion Detection (HCABS-NID), a hierarchical classifier-agnostic boosted stacking architecture for network intrusion detection in connected device ecosystems. The proposed framework adds the Synthetic Minority Over-sampling Technique for Nominal and Continuous features (SMOTENC)-based adaptive class balancing to increase minority attack detection and TreeSHAP to make it multi-level interpretable. As a hierarchical stacking strategy, a two-layer structure includes heterogeneous learners together with meta-learning, calibrated with LightGBM, XGBoost, CatBoost, and TabNet to take advantage of the complementary decision boundaries. Extensive experiments performed on the benchmark dataset from University of New South Wales Network-Based 15 (UNSW-NB15) should enhance generalization performance. HCABS-NID achieved 98.20% accuracy, 97.10% macro F1 score, and 0.989 macro Receiver Operating Characteristic Area Under the Curve (ROC-AUC), in contrast to the latest community-based methods found in the literature. The proposed model achieves 3.40 ms average inference latency, satisfying the real-time processing requirement of the V2X safety systems. Indeed, other analysis architectures show the same 96.8% accuracy at 5% corruption, which underscores their practicality. The results validate that hierarchical ensemble learning, with adaptive imbalance management artificial intelligence (AI) mechanisms, provides a sound, interpretable, and ready-to-use intelligent transportation security package.

Determining strategic locations for the development of integrated agroindustry—encompassing aquaculture areas, industrial zones, and ecotourism sites within coastal regions—presents a complex challenge. Each sector carries distinct interests and characteristics, often leading to spatial conflicts. Moreover, ensuring coastal ecological sustainability must remain a top priority throughout the planning process. A comprehensive approach is required to identify locations that not only minimize environmental impacts but also maximize cross-sectoral economic value. This study aims to identify potential locations for the development of an integrated agroindustry in the aquaculture–ecotourism sector. The Analysis of Operational Area of Nature-Based Tourism Attractions (AOA-NBTA) was employed to assess ecotourism potential. The Analytical Hierarchy Process (AHP) was applied to assign weights to industrial development parameters, while Geographic Information Systems (GIS) were used for spatial analysis of potential locations for integrated agroindustrial development. The AOA-NBTA analysis identified Tanjung Pakis Beach as the most promising ecotourism location, with a cumulative score of 3.175. Spatial overlay analysis in the Bekasi–Karawang coastal region revealed that highly suitable (S1) areas account for 20.27% (1,950.961 ha), suitable (S2) areas 18.10% (1,742.823 ha), and unsuitable areas 61.63% (5,933.175 ha). These findings provide a foundation for spatial decision-making in formulating sustainable development policies, particularly in coastal zones.

Chemical dyes are routinely discharged into ecosystems via textile industry effluents and landfill leachates. Adsorption using engineered adsorbents presents a viable strategy for pollutant removal in water treatment. Activated carbon (AC) and carbon nanoparticles are composite materials that integrate nanomaterials, rendering them less susceptible to these processes. This study involved preparing and characterizing AC using UV-Vis, fourier-transform infrared (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) techniques. We subsequently assessed its capacity to remove methylene blue (MB) under varying conditions of pH, initial dye concentration, adsorbent dosage, and contact time. The dye is often utilized in the textile and chemical industries. The adsorbent achieved a removal efficacy of 99.9% under optimal conditions: a temperature of 25 ℃, a pH range of 7–9, and a contact time of 60–120 minutes. The removal process was characterized by pseudo-second-order kinetics and the Freundlich isotherm model. The results indicated that the adsorbent’s surface was heterogeneous, consisting of many layers. The calculated thermodynamic parameters were $\Delta G^{\circ}$ = -4.24 kJ/mol, $\Delta H^{\circ}$ = -0.0975 kJ/mol, and $\Delta S^{\circ}$ = -0.3125 kJ/kg/K.

The Industrial Era 4.0 has seen industries start shifting towards implementing Decision Support System (DSS) in the manufacturing sector. Technological advancements have made it possible for the development of DSS to be based on Artificial Intelligence (AI) using past data generated by industry, especially in the furniture manufacturing industry. The furniture manufacturing industry is now faced with the challenge of Extreme Programming (XP) model complexity that hinders production and inventory management. The manufacturing industry finds it difficult to comprehend which industries to produce based on the current market trends. This research, therefore, seeks to comprehend how an AI-based DSS system can learn furniture model production trends. Based on such problems, this research can potentially assist in designing an AI-based DSS employing the Autoregressive Integrated Moving Average (ARIMA) model from the XP system development paradigm. This research is segmented into five phases, i.e., problem identification, decision model design, data collection and processing, system development and integration, and implementation. The delivery of this research is a list of best-selling furniture fads from market analysis generated through DSS. These findings are useful in the development of DSS, especially in AI to make predictions of furniture model trends.

The functional value of a watershed is often degraded by anthropogenic activities. Land cover changes, urban expansion, and industrial development can significantly affect river water quality. Consequently, rapid and comprehensive monitoring is required to represent conditions across the entire river system. Advances in Earth observation satellite technology provide efficient tools for monitoring natural resources and environmental quality. This study aims to estimate concentrations of Total Suspended Solids (TSS) and Dissolved Oxygen (DO) in the Krueng Pase River Basin, North Aceh, Indonesia, using satellite imagery. The analysis employed Sentinel-2A data acquired during both dry and rainy seasons from 2020 to 2022, with a spatial resolution of 60 m. Concurrent field measurements collected by the Aceh Environmental Service were used for accuracy assessment. The results reveal seasonal variations in sediment levels within the Krueng Pase Watershed. Validation against in situ observations produced Nash–Sutcliffe Efficiency (NSE) values of 0.949 (very good) for Period I and 0.645 (satisfactory) for Period II. Percent Bias (PBIAS) values were 15.668 (very good) and 21.0307 (very good), respectively. These findings are supported by the estimated DO concentrations, which consistently $>$5 mg/L. Such levels indicate good oxygen conditions, sufficient to sustain productive aquatic biota and showing no evidence of severe pollution. This study demonstrates that satellite imagery-based estimation of TSS and DO concentrations is a reliable approach for land and water management, particularly in evaluating water pollution.