Pulau Weh, with Sabang as its capital, is located at the westernmost tip of Indonesia. Beyond its significance for domestic and international tourism, the island hosts several national strategic projects. Currently, the landscape of Pulau Weh is dominated by non-urban areas (agriculture, forests, and open land), including critical conservation and protected forest zones. These areas provide essential Cultural Ecosystem Services (CES) that benefit the local island community. This study aims to analyze land-use changes and local community perceptions of CES values within the forest conservation areas of Pulau Weh, Aceh Province. The research aligns with the Sustainable Development Goals (SDGs), specifically Goals 1 (No Poverty), 11 (Sustainable Cities and Communities), and 13 (Climate Action). A mixed-methods approach was employed, integrating ArcGIS analysis to evaluate spatial functional changes and statistical analysis using Principal Component Analysis (PCA) via SPSS to measure public perception. The results indicate that the 2023 land-use composition comprises built-up areas (497.69 ha; 4.49%), woodlands (5,203 ha; 46.89%), agricultural land (3,111 ha; 28.04%), grasslands (2,071 ha; 18.67%), water bodies (157 ha; 1.41%), and bare land (55.76 ha; 0.50%). Based on community perceptions, the forest conservation areas in Sabang contribute significantly to CES values, including recreation, cultural heritage, aesthetics, education, social relations, health, spirituality, and ecological/tourism benefits. Notably, disaster mitigation was the only value perceived as non-significant by the community.

The study of non-Newtonian nanofluid flow over magnetohydrodynamic (MHD) surfaces has attracted considerable research interest due to its relevance to polymer processing, heat treatment, metallurgical production, and biomedical transport systems. Despite extensive studies on MHD Casson nanofluids, the combined effects of bidirectional stretching, porous-medium resistance, thermal radiation, viscous dissipation, Brownian motion, thermophoresis, double diffusion, and non-Fourier heat conduction have received relatively limited attention within a unified analytical framework. To address this gap, this study develops an analytical model for the unsteady flow of MHD Casson nanofluids over a bidirectional stretching surface by incorporating the Cattaneo–Christov heat-flux formulation. The proposed model accounts for finite-speed thermal propagation and thermal relaxation effects that are not considered in the conventional Fourier heat conduction model. The resulting nonlinear partial differential equations are transformed into a coupled system of similarity equations and analysed using the Homotopy Analysis Method (HAM). The effects of the governing parameters on the velocity, temperature, and nanoparticle concentration profiles are systematically analysed. The results show that stronger magnetic forces and increased permeability of the porous medium reduce the fluid velocity, whereas thermal radiation and viscous dissipation enhance the temperature distribution. Brownian motion and thermophoresis influence nanoparticle concentration and thermal boundarylayer characteristics, while increasing the thermal relaxation parameter in the Cattaneo–Christov model reduces the temperature profile compared with the classical Fourier formulation, highlighting the effects of finite-speed heat propagation and thermal relaxation. The analytical results provide comprehensive insights into the coupled momentum, heat, and mass transfer characteristics of cryogenic nanofluids and may contribute to the design and optimisation of thermal management systems and polymer-processing applications.

Greywater waste is a type of wastewater that comes from bathroom water, washing that enters the drainage system, and is supplemented with rainwater whose downstream flow enters the river. This research aims to enhance the effectiveness of greywater treatment by using a physical model comprising a treatment process, namely a cattious wetland filter. This study applied the vetigenetic wetland method with and without media. The observed research variables were wastewater discharge variations, wetland residence time at intervals of 2 days, 4 days and 7 days, and the number of vetinous root stems, as well as water quality parameters (Y) consisting of pH, odor, color, turbidity, total dissolved solids (TDS), nitrate (NO$_3$$^-$), nitrite (NO$_2$$^-$), fecal coliform, and iron (Fe). The data analysis was based on descriptive evaluation of water quality parameters under different operational conditions, including variations in hydraulic retention time, flow rate, and plant density. The study is expected to show that using a physical model of a media-filtration root wetland can improve wastewater quality, with the output meeting clean-water quality standards that will be used as input to the pond. The qualified water quality consists of pH (7.9), NO$_3$$^-$ 2.319 mg/L, NO$_2$$^-$ 0.0922 mg/L, Fe 0.0286 mg/L, and the unqualified water quality consists of: odor 0.77, color 10.6 true colour units (TCU), turbidity 5.44 nephelometric turbidity units (NTU), TDS 175 mg/L, fecal coliform 85 colony‑forming unit (CFU)/100ml. The output comprises five parameters that do not meet treated effluent quality standards and will be used as inputs to the pond; therefore, further research on phytoremediation is warranted. A constructed wetland using vetiver grass effectively cleans fishpond greywater and reduces pollutant levels. This means it could be used as an initial or final step in water treatment when reuse is limited. However, in this study, the treated water didn’t always meet all health standards. This suggests extra treatment steps are needed, along with more real-world testing to ensure safety.

Energy storage systems (ESS) play a central role in renewable energy integration, grid reliability, and the transition toward low-carbon energy systems. In Malaysia, however, the indicators used to evaluate ESS remain fragmented, limiting comparison across technologies and weakening the evidence available for investment, policy, and sustainable supply chain decisions. This study investigates how ESS performance has been evaluated in the Malaysian energy transition and develops a structured framework for linking engineering performance with sustainable supply chain management (SSCM). A systematic review of 40 eligible studies was conducted using bibliometric mapping and thematic analysis. The reported indicators were identified, coded, and classified into technical, economic, operational, and policy/environmental dimensions. The results showed that capacity and sizing were the most frequently reported indicators, followed by renewable energy integration and system reliability or availability. Battery-based systems dominated the reviewed literature, particularly in photovoltaic (PV)-coupled applications, whereas long-duration storage, grid-scale services, lifecycle assessment, and end-of-life considerations received limited attention. Although levelized cost of energy (LCOE) and net present cost (NPC) were commonly reported, none of the retained studies explicitly evaluated the levelized cost of storage (LCOS). The findings indicate that current assessment practices remain concentrated on project-level technical and financial performance and provide insufficient support for evaluating material sourcing, lifecycle impacts, regulatory conditions, and supply chain resilience. The proposed framework connects ESS performance evaluation with technology selection, investment appraisal, supplier assessment, environmental management, and policy planning. It provides a systematic basis for developing national performance benchmarks and supports more consistent ESS decision-making in Malaysia and other Association of Southeast Asian Nations (ASEAN) energy systems.

Mobile photovoltaic (PV) power systems provide a flexible electricity supply for remote locations, emergency operations, and other off-grid applications. Their practical operation requires continuous assessment of power conversion performance under changing environmental conditions. This study investigates the efficiency, power output, and operational reliability of a mobile PV system through an Internet of Things (IoT)-enabled monitoring platform and a multilayer perceptron (MLP) model. Solar irradiance, panel temperature, voltage, current, power, and battery state of charge (SOC) were recorded under outdoor operating conditions, yielding approximately 1,600 observations. An MLP with two hidden layers was trained using the Levenberg–Marquardt algorithm, and the data were divided into training and testing subsets at a ratio of 80:20. Operational reliability was evaluated by comparing measured and predicted power outputs against statistically defined control limits. The PV panel achieved an average operating efficiency of approximately 15%, whereas the efficiency of the solar charge controller (SCC) reached 60%. For the normalized dataset, the MLP produced mean squared error (MSE) values of 0.002402 and 0.001951, root mean squared error (RMSE) values of 0.049012 and 0.044173, mean absolute error (MAE) values of 0.033774 and 0.027760, and $R^2$ values of 0.964491 and 0.970248 for PV and controller power, respectively. The predicted outputs remained within the established control limits throughout the observation period. These findings indicate that the proposed framework can support real-time power-performance assessment and the early identification of abnormal operating conditions in mobile PV systems.

Agentic data pipelines, in which large language models select and invoke tools through the Model Context Protocol, consume tool outputs, and iteratively execute multi-step analytical or operational workflows, are increasingly being deployed in production environments. However, the observability infrastructure required to diagnose failures in such systems remains underdeveloped. Conventional distributed tracing effectively captures service-to-service execution but often represents large language model invocations as opaque spans and fails to preserve causal relationships across large language model-tool boundaries. Consequently, incident diagnosis can require an agent's execution trajectory to be reconstructed manually from chronologically ordered spans. To address this limitation, causal span linking across large language model and tool invocations was defined as a first-class observability primitive for agentic data pipelines. Hops-to-root-cause was introduced as the directed acyclic graph distance between a symptom span—the earliest span tagged error=true—and the identified causal span, with deterministic tie-breaking applied. The proposed approach was evaluated on a synthetic corpus comprising 20 incidents generated using a fully disclosed construction protocol. Compared with a flat-span baseline, causally linked tracing reduced the mean hops-to-root-cause from 6.4 to 1.5, corresponding to a reduction by a factor of 4.3. The greatest improvements were observed for incidents involving multi-hop tool chains. Causal span linking was further complemented by deterministic replay and by a human-artificial intelligence collaborative diagnostic workflow. Together, causal span linking, deterministic replay, and human-artificial intelligence collaborative diagnosis were established as complementary observability primitives for improving the reproducibility, interpretability, and efficiency of root-cause analysis in agentic data pipelines.

Immersive technologies are increasingly used by cultural institutions to create context-sensitive visitor experiences, yet conventional media branding pipelines rely largely on predefined visual assets and provide limited support for real-time adaptation. This study investigates how generative artificial intelligence (AI) can be integrated into an adaptive systems architecture while preserving institutional visual identity. A mixed-method design was employed, comprising an analysis of immersive branding pipelines, case studies of five cultural institutions, the development of two prototype application scenarios, and an evaluation by nine experts. The proposed architecture connected contextual data acquisition, generative processing, constraint validation, immersive rendering, and user feedback within a closed-loop workflow. A Validator module was introduced to examine generated outputs against predefined color and geometric constraints and to initiate regeneration or fallback procedures when violations were detected. The case analysis produced a mean adaptivity score of 4.2 out of 10 for the existing implementations. Expert evaluation of the proposed architecture yielded mean scores of 4.78 for personalization, 4.56 for visual identity flexibility, and 3.89 for brand consistency. Generation latency ranged from 1.2 to 1.8 s in the augmented reality (AR) scenario and from 2.5 to 4.0 s in the virtual reality (VR) scenario. The findings indicate that generative AI can be incorporated into a feedback-controlled branding pipeline without removing deterministic control over core visual elements. The proposed architecture provides a systems engineering basis for coordinating content generation, identity validation, and immersive delivery, while the observed latency and limited evaluation sample identify priorities for edge deployment and larger-scale experimental validation.