Open Access
zikri alhadi 
, rahmadani yusran , iip permana , hidayatul fajri , roni ekha putera , ory riandini , rizki hidayat , muhammad ilham , axvel gion revo |
Available online: 11-13-2026
Disaster risk reduction (DRR) in multi-hazard regions requires locally grounded governance arrangements that connect policy, institutions, community capacity, local wisdom, risk data, and sustainable development. This study examines how village-level risk governance can support sustainable local development through a contextual policy model for West Sumatra, Indonesia. The province faces earthquakes, tsunamis, floods, landslides, volcanic hazards, and extreme weather, alongside diverse local institutions, including Nagari, the Minangkabau village institution. The study used a qualitative policy research design. Data were collected through semi-structured interviews, focus group discussions (FGDs), and document analysis involving disaster management agencies, local government actors, village authorities, customary and religious leaders, volunteers, and community representatives. The data were analysed using thematic coding supported by NVivo to identify recurring policy issues and synthesise model components. The findings identified weak local regulatory frameworks, limited institutional coordination, inadequate human resource capacity, insufficient risk documentation, fragmented financing, limited integration of science and technology, and weak incorporation of local wisdom into formal disaster policy. The proposed model comprises nine interconnected components: C1 local regulations and institutional arrangements; C2 DRR paradigms, human resource capacity, and disaster literacy; C3 infrastructure, information systems, and preparedness facilities; C4 risk documents and integration into village planning; C5 DRR financing; C6 community participation and multi-actor collaboration; C7 science, technology, and risk data integration; C8 local wisdom and village social capital; and C9 sustainable development and village resilience. Coding-reference counts indicate recurrence rather than causal importance or policy priority. Sustainability provides the model’s overall purpose through livelihood continuity, essential services, environmental protection, and inclusion, while C9 specifies development outcomes for evaluation. The study contributes a synthesis of disaster risk governance, sustainable local development, and local institutional legitimacy. Local wisdom is treated as a potential resource whose contribution depends on inclusive participation and accountability.
This review explores how community-based approaches can offer effective, context-specific strategies that both adapt to and mitigate climate change, while simultaneously advancing the Sustainable Development Goals (SDGs). It interrogates how bottom-up activities have helped strengthen vulnerable populations, especially in climate-affected regions, to be more resilient and achieve a meaningful transition. A structured review of peer-reviewed and grey literature published between 2015 to 2025 was undertaken. The search identified 153 records; after duplicates were removed and titles, abstracts and full texts were screened, 21 publications met the inclusion criteria and were included in the final synthesis. The included sources comprised peer-reviewed journal articles together with 4 grey literature sources from the United Nations SDGs, the Paris Agreement and the Intergovernmental Panel on Climate Change (IPCC). The results reveal that community-based approaches are context-specific, foster social inclusion, and enhance the adaptive capacity. They align with the SDGs 1, 2, 5, 6, 7, 10, 11, 13, 14, 15, 16 and 17. Such initiatives can be scaled up, with policy enabling and partnership, as evidenced in Indonesia's Climate Village Program (Proklim). This review highlights the benefits of using indigenous knowledge in climate practices and the production of co-created knowledge integration, and multi-stakeholder governance. However, gaps in funding, poor financial monitoring, and restrictive legal systems often make it harder for local communities to adapt. This paper integrates new information related to the ability of community-based approaches to bridge the global-local divide in climate governance and offers a new approach to resilience-building based on climate justice in a participatory framework.
This study addresses the strategic engineering management challenge of balancing short-term farm economic profitability with long-term soil carrying capacity restoration in staple crop systems. It aims to evaluate policy trade-offs and inter-agency coordination mechanisms across the Triple Bottom Line (TBL) dimensions—People, Planet, and Profit—to enhance regional rice food security in East Java, Indonesia. A system dynamics (SD) simulation model was engineered using STELLA software, integrating demographic demand, biophysical soil degradation/regeneration loops, farmer financial investment flows, and policy execution dynamics. Model parameters were calibrated using empirical historical data (2021–2025) and stakeholder interviews ($N$ = 45). Structural validity was evaluated through dimensional consistency, extreme-condition tests, and mean relative error (E1), defined as the average percentage deviation between simulated and historical behavior (E1 $\leq$ 4.73%). Four policy intervention scenarios were simulated over a 2026–2030 horizon: Baseline, Economic Intervention, Environmental Intervention, and a Combined Policy Intervention. The Baseline scenario experiences saturation after 2028, with the Sustainable Rice Food Security Index (SFSI) declining to 70.41 by 2030 due to unmitigated soil health degradation. The Economic Intervention scenario enhances immediate farmer profitability (SFSI = 75.43), but performance levels off over time due to ongoing biophysical soil depletion. The Environmental Interventio scenario restricting subsidized chemical fertilizer to 1,034,209 tons/year and transitioning to organic inputs exhibits steady ecological resilience (SFSI = 77.61). The Combined Policy Intervention scenario achieves the highest projected performance (SFSI = 81.12), buffering short-term financial transition shocks while supporting soil carrying capacity restoration. Overall, the simulation results suggest that biophysical soil health restoration constitutes a primary structural leverage point preventing limits-to-growth system degradation. Successful policy execution requires an integrated inter-agency roadmap: the National Food Agency must align price floors, the Ministry of Agriculture must reallocate chemical subsidies toward bio-organic supply chains and extension training, and the Ministry of Environment and Forestry must enforce regional soil organic monitoring.
Augmented analytics shifts much of the analytical work in business intelligence (BI) to artificial intelligence (AI), leaving non-technical users with one remaining safeguard: verifying AI-generated insights before acting on them. This paper examines whether trust in augmented analytics is associated with less verification and how verification relates to perceived decision quality. Survey data from 350 non-technical BI users in Vietnam were analysed with partial least squares structural equation modelling (PLS-SEM), using a newly developed ten-item verification behaviour scale (VER). The results suggest that trust is negatively associated with verification, and verification is positively associated with perceived decision quality. The indirect association of trust through verification is negative, whereas its direct association remains positive, consistent with competitive mediation. Perceived explainability is associated with higher trust, but its direct association with verification is not significant, and the hypothesized moderating role of AI literacy receives no statistical support. Because the design is cross-sectional and self-reported, the paths represent associations rather than causal effects. Automation complacency and objective decision quality were not measured, and the verification scale was supported by initial validation evidence from one sample. The findings motivate linking insights to their source data, second checks for high-value decisions and direct monitoring of verification; the effectiveness of these proposals remains untested. The study develops a survey measure of verification behaviour with initial validation evidence and shows that the positive trust–decision quality association coexists with a negative indirect association through verification.
Complex engineering systems often involve interacting transport fields in which diffusion, field-driven fluxes, production or decay, and nonlinear feedback jointly control stability, transport, and localization. Although originating in mathematical biology, the Keller–Segel system is a canonical coupled-field transport model with this structure and provides a stringent benchmark for computational methodologies aimed at complex and multiphysics transport problems. A multidimensional hybrid numerical–analytical formulation based on the Generalized Integral Transform Technique (GITT) is developed for a parabolic–parabolic Keller–Segel system with homogeneous Neumann boundary conditions. Spatial averages are treated analytically, zero-mean fields are expanded in normalized Neumann eigenfunctions, and exact cosine-product selection rules generate a sparse nonlinear modal system. The transformed equations are advanced using CVODE, a variable-step, variable-order ordinary differential equation (ODE) solver from the SUNDIALS (Suite of Nonlinear and Differential/Algebraic Equation Solvers) package, with variable-order backward differentiation formulas (BDF), together with a matrix-free scaled preconditioned generalized minimal residual (SPGMR) solver in two dimensions. Systematic one- and two-dimensional convergence studies are performed for chemotactic sensitivities $\chi$ = 1–5 and are complemented by independent conservative finite-volume/method-of-lines solutions in physical space. The finite-volume discretizations exhibit second-order self-convergence and closely reproduce the converged GITT fields. The parametric study quantifies the transition from weak relaxation to strong aggregation and the growth of genuinely two-dimensional mixed modes. For $\chi$ = 5, successive truncations up to 7,920 spatial modes (15,840 coupled ODEs) resolve a strongly localized regime. Physical and spectral localization scales contract consistently, while normalized profiles collapse onto an approximately invariant shape. Independent late-time extrapolations yield characteristic times clustered near $t$ $\approx$ 2.184. The results demonstrate a high-resolution hybrid framework for nonlinear coupled-field partial differential equation (PDE) systems with cross-field transport, evolving localization, and demanding multiscale resolution, while providing numerical evidence compatible with possible finite-time singularization without constituting a mathematical proof of blow-up.
The rapid diffusion of artificial intelligence (AI) is adding a fast-growing workload to the data centres that host it, with consequences for electricity systems, household energy costs, and the environment. This article examines the sustainability implications of AI-driven data-centre expansion in Europe along three connected dimensions—environmental sustainability, energy and social equity, and the sustainability of the digital transition—and asks who gains and who pays. Doctrinal analysis of European Union (EU) law and policy was combined with a comparative, descriptive assessment of published data. The study synthesised evidence from international agencies, EU institutions, national statistical offices, regulators, and peer-reviewed research, and traced household electricity costs and data-centre development from 2023 onward in three purposively selected cases: Ireland, the United Kingdom, and Spain. Data-centre electricity demand, of which AI workloads are an increasingly important component, is growing far faster than overall demand and is highly concentrated geographically. In exposed regions this concentration is associated with grid-capacity constraints and may contribute to upward pressure on network and system costs. Household prices in the period were, however, shaped predominantly by gas prices, taxation, subsidies, renewable output, and network charges, and the study does not estimate causal effects. The expansion also generates environmental externalities, notably carbon emissions and a growing stream of electronic waste. Because energy costs weigh most heavily on low-income households, these pressures carry a distributional risk: a potential “double burden” in which households already in energy poverty benefit least from AI while bearing part of its infrastructure costs. The article maps the EU response by legal status—measures in force, formally proposed, and under discussion—and argues that assessments of AI’s social value should internalise its energy, climate, resource, and distributional costs. It closes with actor-specific recommendations on clean-supply additionality, cost-reflective network-cost allocation, safeguards for vulnerable consumers, hardware circularity, and sustainability metrics.
This study develops and validates a data-driven model based on an artificial neural network (ANN) to predict the effective shear strength parameters (effective angle of internal friction and effective cohesion) of granular soils from basic index properties. A database of 1,105 secondary records, obtained from standardized geotechnical tests performed at Peruvian universities, was used for training and internal validation. External verification was carried out with 39 independent samples tested by consolidated-drained direct shear in accordance with ASTM International D3080/D3080M and Peruvian Technical Standard (Norma Técnica Peruana, NTP) 339.171. The network, with three hidden layers, dropout, and batch normalization, uses particle size distribution, Atterberg limits, dry density, and moisture content as input variables. In the external test, the model achieved a coefficient of determination ($R^2$) of 0.86 for the effective angle of internal friction (mean absolute error [MAE] = 1.21°; root mean square error [RMSE] = 1.75°) and 0.77 for the effective cohesion (MAE = 1.09 kPa; RMSE = 2.14 kPa). Unlike previous studies based on limited or heterogeneous datasets, this work uses a broad database of Peruvian soils, which facilitates regional calibration. Gradient and sensitivity analyses indicated that particle size distribution is the most influential factor on shear strength, whereas moisture content has a comparatively smaller influence. The results suggest that the proposed network may serve as a preliminary estimation tool for the shear strength of Peruvian granular soils when basic index properties are available. However, given the small external test set and the regional scope of the data, its extension to other soil types or regions requires further validation.
The strength assessment of thick-walled composite cylinders under internal pressure requires consideration of the nonuniform stress distribution and the interaction of stress components arising from material orthotropy and axial restraint. Conventional assessments based primarily on circumferential stress may not adequately represent the multiaxial stress state governing failure initiation. This study investigates the elastic stress response and initial failure of an internally pressurized thick-walled cylinder made of Hexcel AS4/3501-6 carbon fiber/epoxy composite, with particular attention to the effects of wall thickness and three-dimensional stress interaction. An analytical formulation was developed using orthotropic elasticity under plane-strain conditions, and the radial, circumferential, and axial normal stresses were determined across the cylinder wall. The three-dimensional Tsai–Wu criterion was then applied to evaluate the combined influence of these stresses on failure initiation and to determine the allowable internal pressure. The calculations showed a nonuniform stress distribution, with the circumferential stress reaching its maximum at the inner surface and decreasing toward the outer wall. For the representative configuration, the maximum circumferential stress was approximately 188 MPa, while the axial stress remained nonzero because of the imposed axial constraint. Variations in wall thickness produced substantial changes in the stress distribution and the predicted failure response. The analysis also established a procedure for identifying the pressure at which the Tsai–Wu failure threshold is first reached. These findings clarify the role of orthotropic elasticity and multiaxial stress interaction in the strength assessment of pressurized composite cylinders. The analytical framework provides a basis for evaluating initial failure margins and examining the influence of wall thickness on structural pressure capacity.
The integration of artificial intelligence (AI) into engineering practice is bringing new dimensions to the role of a leader, including professional judgment, collaboration, and responsibility for decisions made with AI’s support. However, desirable personal leadership orientations do not necessarily correlate with desirable work conditions. This study examines this perceived capability–enablement divide among engineers in Iraq. Survey data were collected from 144 engineers working in Iraq using 39 Likert-scale questions. The primary within-respondent comparison used bootstrap estimation, the Wilcoxon signed-rank test, and a cumulative ordinal fixed-effects model covering 5,616 item responses. Adjusted associations were examined using ordinary least squares regression with heteroskedasticity-consistent type 3 (HC3) standard errors and complete-profile permutation tests. Individual Leadership Orientation (ILO) was rated more favorably ($M$ = 3.789, standard deviation [SD] = 0.435) than Organizational Enablement (OE) ($M$ = 3.479, SD = 0.848). The mean paired difference was 0.309 points (95% bootstrap confidence interval [CI] [0.187, 0.443], $p$ $<$ .001; Cohen’s d$_z$ = 0.394). Using the ordinal model, and controlling for respondent and item differences, the same directionality was found; that is, endorsement of Organizational Enablement was associated with lower odds (odds ratio [OR] = 0.450, 95% CI [0.380, 0.534], $p$ $<$ .001). AI-use frequency was associated with ILO, but its association did not differ significantly from that with OE ($p$ = .242). Education was associated much more strongly with individual orientation than enablement. The results suggest that there is a perceived gap in capability and enablement among the engineers participating, rather than any broad opposition to AI. The results indicate that leadership orientation and perceived organizational support are separate factors to consider when designing responsible AI implementation for engineering organizations.
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