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 through magnetohydrodynamic (MHD) surfaces has attracted considerable research interest due to its relevance for polymer processing, heat treatment, metallurgical production and biomedical transport systems. Despite extensive literature on MHD Casson nanofluids, the concurrent effects of two-way stretching, porous media, thermal radiation, viscous dissipation, Brownian motion, thermophoresis, double diffusion and non-Fourier heat conduction have received relatively little attention in the same analytical framework. To bridge this gap, this study develops an analytical model for the unsteady flow of MHD Casson nanofluids across a two-way stretch surface by incorporating the Cattaneo-Christov formulation of the heat flux. This model accounts for finite-speed thermal propagation and thermal relaxation effects that are not considered in the conventional Fourier heat conduction model. The resulting non-linear partial differential equations are reduced to a system of coupled similarity equations and analysed by means of the homotopy analysis method (HAM). The velocity, temperature, and nanoparticle concentration profiles are analyzed in relation to the effects of the respective control parameters. We see that stronger magnetic forces and greater permeability of the porous medium reduce the speed of the liquid, while thermal radiation and viscous dissipation increase the temperature. The nanoparticle concentration and the thermal boundary layer properties are affected by Brownian motion and thermal phenomena, and the increase in the thermal diffusion parameter in the Cattaneo-Christov model decreases the temperature profile compared to the classical Fourier formulation, indicating the influence of finite-speed heat propagation and thermal relaxation effects. The analytical results presented here provide a comprehensive view of the combined momentum, thermal and mass transfer phenomena in cryogenic nanofluids and may be useful in designing and improving useful for improving 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.
This study examines the association between board structure and characteristics and the quality of sustainability reporting, using an integrated reporting-based proxy. Thirty-seven companies listed on the Johannesburg Stock Exchange (JSE) in South Africa were selected. Panel data were collected on board size, women on the board, ethnic diversity, the number of financial experts on the board, the average age of board members and their independence. Three control variables were included, namely profitability, firm age and firm size. Sustainability reporting quality (SRQ) was operationalised using the Ernst and Young (EY) Excellence in Integrated Reporting Awards, a categorical rating with four levels: Progress to be made, Average, Good, and Excellent. A multinomial logit model with firm level clustered standard errors was applied to assess the relationship between board structure and SRQ. Under the sample conditions, the results indicate a statistically significant positive association between ethnic diversity and the highest SRQ category. Board size and average board age showed negative and positive associations respectively, but neither was statistically significant after clustering. Board independence, financial expertise and gender diversity were not statistically associated with SRQ. The findings offer insights for policymakers on board composition, with ethnic diversity emerging as the characteristic most strongly associated with high-quality sustainability disclosure under the sample conditions examined.
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.
Multi-tier supply chains are exposed to operational disruptions that can spread through interconnected supplier–customer relationships. Existing risk assessments often rank individual firms or links without considering whether these relationships form continuous routes of concentrated vulnerability. This study investigates critical-path identification as a decision-analytics problem in which relationship-level performance and network structure are considered jointly. A two-phase framework was developed by integrating the Criteria Importance Through Intercriteria Correlation (CRITIC) method with evolutionary path optimization. In the first phase, CRITIC was used to derive objective weights for 14 operational performance criteria and to calculate a criticality index for each supplier–customer link. In the second phase, two optimization models were formulated to identify the path with the highest cumulative criticality and the path with the highest average link criticality subject to a minimum path length. The framework was applied to an automotive supply chain consisting of 19 enterprises and 35 directed links. The cumulative model identified a five-link path with a total criticality of 3.274 and an average criticality of 0.655, whereas the average-criticality model identified a four-link path with a total criticality of 2.738 and an average of 0.684. Both models selected the same initial relationship but produced different subsequent routes. The results indicate that link-level rankings alone cannot identify the most critical continuous route because path selection also depends on connectivity, topological position, and the optimization objective. The framework provides a reproducible basis for prioritizing supplier relationships, directing monitoring resources, and selecting risk-mitigation measures across multi-tier supply networks.
Information asymmetry remains a fundamental constraint on the allocation of external finance, as the organisational capabilities and internal quality of firms cannot be fully observed by external financiers. Although managerial quality, human capital, innovation, digitalisation, relational networks and financial transparency have each been linked to financing outcomes, their interrelated nature has received considerably less attention. To address this gap, a unified theoretical framework is developed in which these observable organisational attributes are conceptualised as indicators of a latent construct, termed Organisational Quality (OQ). Drawing on information asymmetry theory and signalling theory, the framework is tested using longitudinal data from 2,017 Vietnamese manufacturing small and medium-sized enterprises (SMEs), comprising 6,051 firm-year observations from the SMEs Survey. OQ is operationalised as a reflective latent construct within a structural equation modelling (SEM) framework, through which its associations with access to external finance, productive investment and firm performance are examined. Strong empirical support is obtained for the proposed framework. Higher OQ is found to be positively associated with access to external finance, and this association is significantly stronger under conditions of greater information asymmetry. Access to external finance is, in turn, positively associated with productive investment, while productive investment is positively associated with firm performance. The mediation results further indicate that OQ is associated with firm performance through both direct and indirect pathways, with a sequential pathway operating through improved access to external finance and subsequent productive investment. These relationships remain stable across alternative measurement approaches, model specifications and measures of firm performance. The findings provide three principal contributions. First, organisational characteristics that have traditionally been examined separately are integrated into a common latent organisational dimension. Second, OQ is operationalised and empirically validated as a reflective latent construct within an integrated SEM framework. Third, evidence is provided that OQ is associated with improved access to external finance, greater productive investment and enhanced firm performance, while its financing-related association becomes more pronounced as information asymmetry increases.
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