The catastrophic 2021 eruption of Mount Semeru severely degraded local ecosystems, necessitating rapid pioneer vegetation establishment to restore landscape stability. While conventional satellite imagery often obscures critical micro-scale ecological interactions, ultra-high-resolution unmanned aerial vehicles (UAVs) provide an unprecedented tool for assessing post-disaster resilience. This study aimed to quantify the multitemporal recovery rate and model the micro-topographic constraints dictating the spatial heterogeneity of pioneer colonization. A 127.08-ha primary lahar corridor within Volcanic Hazard Zone III was mapped using UAVs (2.7 cm/pixel) over a three-year period (2022, 2023, 2025). Vegetation cover was extracted using the Excess Green (ExG) index and automated Otsu thresholding. The methodology was rigorously validated using 300 multitemporal ground-truth points, yielding an overall accuracy of 93.00%, an F1-score of 0.933, and a Cohen’s Kappa coefficient of 0.860, confirming exceptional classification reliability. Post-classification spatial analysis revealed a net positive vegetation expansion rate of +1.88 ha/year, indicating a successful transition from initial colonization to ecological stabilization. However, recovery was profoundly heterogeneous, governed entirely by geomorphological constraints rather than uniform macro-climatic factors. Population-based zonal statistics identified significant ecological preferences for higher peripheral elevations (mean 3,081.0 m), gentler slopes acting as natural seed traps (45.5°), and Southwest-facing aspects that provide essential micro-climatic buffering against equatorial desiccation. These findings offer a strategic paradigm shift for environmental management, recommending that artificial restoration and erosion control interventions in active stratovolcanoes be precisely targeted at these highly resilient micro-topographical hotspots to accelerate the formation of a natural biological shield.

Complex engineering systems, including multi-degree-of-freedom structural assemblies, microelectromechanical systems resonators, electromechanical transducers, and fluid–structure interaction systems, are frequently represented by reduced-order nonlinear models in which a limited number of interacting modes govern the dominant dynamic response. The validity of such reductions depends fundamentally on whether the underlying Hamiltonian admits a second independent first integral that enables exact modal decoupling or whether the modal interaction remains intrinsically non-integrable. This question was investigated for a symmetric two-mode quartic Hamiltonian whose mathematical structure, although originally derived from the Friedmann–Robertson–Walker (FRW) cosmological model, is shared by a broad class of coupled nonlinear oscillators encountered in engineering applications and therefore provides a representative analytical surrogate for their dynamical behavior. The Kovalevskaya exponent method was applied to the similarity-invariant system, and the complete spectrum of exponents associated with each family of particular solutions was derived analytically. It was demonstrated that the Kovalevskaya exponents were generically irrational, indicating the absence of an additional analytic first integral and thereby establishing the generic non-integrability of the Hamiltonian in the Liouville sense. Three exceptional parameter regimes, within which all Kovalevskaya exponents remained rational, were identified explicitly and characterized analytically. Furthermore, Lyapunov’s theorem on holomorphic first integrals was employed to construct families of periodic solutions in the vicinity of every equilibrium configuration. The local dynamics were consequently classified into stable operating regimes and buckling-type unstable regimes, and the number of periodic vibration families was shown to be governed systematically by the model parameters $k, m^2, \Lambda$, and $\lambda$. This reveals the parameter regimes in which modal interactions remain unavoidable and reduced-order decoupling becomes mathematically inadmissible. These results provide a rigorous analytical criterion for assessing the applicability of reduced-order models and identifying parameter regions in which full nonlinear multi-physics simulations may be advisable to accurately predict stability, nonlinear energy transfer, and global system dynamics.

Small-scale wind energy systems continue to attract attention as distributed renewable energy solutions; however, improving electrical output under varying operating conditions remains a challenge due to nonlinear interactions between environmental and operational parameters. This study investigates the optimization and predictive modelling of the current output of a tubular wind energy conversion system (WECS) through a statistically guided experimental framework. A response surface methodology based on central composite design (RSM–CCD) was employed to evaluate the combined influence of operating time and wind speed on system performance. Experimental observations were analysed using a quadratic polynomial model and analysis of variance (ANOVA) to establish the response relationship and identify operating conditions associated with improved electrical output. The results showed that the developed model achieved strong agreement between predicted and experimental responses with a coefficient of determination ($R^2$) of 0.962. Wind speed was identified as the dominant factor affecting current generation. The optimization analysis showed that the highest current output of 491.897 A was obtained at an operating time of 30 min and a wind speed of 6.88 m/s, corresponding to a desirability value of 0.998. The results indicate that the proposed modelling framework provides an effective approach for describing system response and identifying favourable operating conditions for compact wind energy systems. This study offers a practical methodology for experimental optimization and supports further development of data-driven performance enhancement strategies in renewable energy applications.

The 2030 Agenda for Sustainable Development faces considerable implementation challenges in border regions, where natural and socio‑economic processes transcend administrative boundaries. This study assesses progress towards selected Sustainable Development Goals (SDGs) in two Russian border regions: the Republic of Buryatia and Zabaikalsky Krai, which share borders with China and Mongolia, and identifies directions for bilateral cooperation based on the transboundary challenges. Based on official data from the Federal State Statistics Service of Russia for 2010–2024, the authors analysed 39 indicators covering SDGs 6, 8, 11, 13, 15 and 17. Growth rates were calculated and compared with national averages. The results reveal contrasting development models: Zabaikalsky Krai shows strong economic growth (gross regional product per capita index 114.3% in 2023) but critical deficits in access to quality drinking water (58.1% vs. national 89.2%), safe sanitation (49.2%), buses for persons with reduced mobility (3.2%), and protected forest areas (0.94%). The Republic of Buryatia performs better in social and environmental spheres (urban water access 86.0%, sanitation 80.5%, equipped buses 61.9%, reforestation ratio 247%) but lags economically—gross regional product (GRP) per capita index at 101.1%—and faces unique constraints from strict Baikal environmental norms (only 2.5% of wastewater meets those standards). Both regions suffer extreme wildfires and dispose of almost all municipal solid waste in landfills without recycling. The conclusions indicate that joint bilateral actions should include harmonised environmental standards and monitoring methodologies, creation of cross‑border demonstration zones, and coordinated wildfire and water management. These findings can inform regional strategies, bilateral environmental projects, and statistical harmonisation within BRICS and the Shanghai Cooperation Organisation.

Perishable agricultural supply chains face significant uncertainties in production yield, market demand, and availability of resources. This paper presented a multi-period, multi-echelon, and multi-product mathematical model for a perishable agricultural supply chain network comprising farms, cold storage facilities, and wholesale markets. The model determined optimal allocation of cultivation area, harvest timing, duration of storage, and product flow distribution to minimize total supply chain costs, including production, transportation, storage, and import costs. A key innovation is the integration of interval programming to handle uncertainty in production costs, transportation rates, and water availability. Three metaheuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA) were employed to solve the nondeterministic polynomial (NP)-hard problem. A comprehensive case study of three major perishable crops, i.e., potato, onion, and tomato, across 31 Iranian provinces and 24 candidate cold storage facilities attested to the effectiveness of the model. Results demonstrated that PSO outperformed GA and SA in solution quality by achieving a total cost of 64.14 trillion Rials, with 93.7% of costs attributed to production, 5.2% to transportation, and 1.1% to storage. The model reduced final product prices to one-third of market averages. Sensitivity analyses revealed that production cost was the most sensitive parameter, with a 100% increase triggering economically viable imports. Water availability reduction below 10% of baseline rendered domestic production infeasible. The model achieved 19.9% average water savings (1.062 billion cubic meters annually) through optimized cultivation patterns. The proposed framework provided agricultural policymakers with a quantitative tool for balancing domestic production, investment in storage capacity, and import decisions under uncertainty.

This study assesses the degree to which adaptive governance principles are incorporated into four regional development planning documents of North Sumatra Province, aiming to manage the increasing energy crises and climate threats. Four aspects of adaptive governance are included in the analysis using a document-based evaluation framework: (1) reactivity to uncertainty; (2) scenario-based projection; (3) flexibility of development indicators; and (4) institutional coordinating capacity. The 2025–2045 Regional Long-Term Development Plan (Rencana Pembangunan Jangka Panjang Daerah, RPJPD), the 2025–2029 Technocratic Draft of the Regional Medium-Term Development Plan (Rencana Pembangunan Jangka Menengah Daerah, RPJMD), the 2024–2026 Regional Development Plan (Rencana Pembangunan Daerah, RPD), and the Development Planning Agency’s 2024–2026 Strategic Plan are among the documents examined. Document content was categorized using a qualitative thematic coding process in relation to the assessment matrix. The findings show that planning is still technocratic and linear, lacking institutional tools to facilitate adaptive decision-making, risk-responsive indicators, and alternative scenarios. This disparity indicates that the planning tools required to anticipate crisis-driven disruptions in the energy and environmental sectors are not aligned with the sustainability agenda. In order to improve provincial readiness and hasten the achievement of Sustainable Development Goals (SDGs) 7, 13, and 16, the study suggests integrating risk-based planning, dynamic performance indicators, and cross-sector institutional platforms.

This study aims to inform evidence-based policy for climate change adaptation in the Philippines by estimating the climate sensitivity of household electricity demand among select cities in Metro Manila during the COVID-19 pandemic. Localized climate data from 2001 to 2021 were matched to households based on their city of residence to construct measures of climate variability. Heat Index (HI), which combines temperature and relative humidity, provides a measure of perceived thermal discomfort. Cooling Degree Days (CDD), defined as the number of days when daily HI exceeds a long-run threshold of 34.82 °C (the 75th percentile of historical HI), serve as another indicator of exposure to extreme heat conditions. Correlated random effects (Mundlak) regressions reveal strong climate sensitivity of electricity use. Household electricity consumption increases when high temperature coincides with high humidity. A 1 °C rise in HI leads to a 19 kWh increase in monthly electricity use, while an additional CDD raises monthly consumption by about 6 kWh. The estimated positive relationship between temperature, humidity, and electricity consumption is consistent with increased demand for cooling services during periods of higher thermal stress. Overall, the results suggest that households adjust their electricity consumption in response to perceived heat conditions through a range of cooling-related behaviors. However, electricity demand among lower-income households is significantly less responsive to climate fluctuations, which may indicate limited adaptive capacity amidst greater thermal discomfort. Conversely, higher-income households exhibit greater responsiveness, highlighting their potential for targeted adoption of energy-efficient appliances and rooftop solar systems. The results underscore the importance of climate-sensitive energy policies that simultaneously advance climate mitigation goals and address equity concerns.

In this research, both Synthetic Aperture Radar (SAR) and optical satellite data are used independently to evaluate the urban expansion, thermal change (Land Surface Temperature (LST)/Urban Heat Island (UHI)), and particulate air quality (PM$_{10}$) ten-year forecasts for Dehradun, Uttarakhand, India for the decade 2014–2024. The Sentinel-1 C-band SAR data (backscatter amplitude $\sigma^{\circ}$ and interferometric coherence $\gamma$) provide a cloud-penetrating and structurally-based evaluation of urban extent. The data used include Landsat-8 OLI/TIRS, which are used for supervised classification of land use and land cover (LULC) by maximum likelihood. The maximum likelihood classification (MLC) and LST retrieval using Mono-Window Algorithm (MWA). The both sensors are processed through independent analytical pipelines and offering corroborating lines of evidence for urban change. The results show important environmental changes: There was an increase in urban area of +28.3 km$^2$ (+3.9%). agricultural land increased by +55.9 km$^2$ (+8.15%), while forest cover declined sharply by -83.1 km$^2$ (-12.05%)—figures with area adjusted uncertainty estimates. The mean LST increased by $\sim$+3.0 °C over the study transect, with mean annual air temperature rising from 21.22 °C to 24.21 °C (+14.1%), which is in line with the rise in temperature at the station, confirming intensification of the Surface Urban Heat Island (SUHI). The concentration of PM$_{10}$ at all the three Central Pollution Control Board (CPCB) monitoring stations it was consistently found that levels of SO$_2$ exceeded the national annual standard for India (60 $\mu$g/m$^3$) by 2.5–2.7 times throughout the study period. These are analyzed in a framework of Environmental Impact Assessment (EIA), which links to Identified land cover change to environmental impact pathways, management implications and policies Himalayan Mountain cities suggestions for urban planning, green space protection and air quality control.

Flexible inflatable structures play an important role in emergency sealing and protection systems where rapid deployment and adaptive contact with complex boundaries are required. However, the operational performance of inflatable sealing systems is strongly influenced by the coupled interaction among internal pressure evolution, nonlinear membrane deformation, and interface contact behaviour under confined conditions. This study investigates the pressure-deformation-contact coupling behaviour of a Z-fold inflatable barrier during the sealing process of an underground tunnel. A nonlinear finite element model was developed using ABAQUS, in which the Yeoh hyperelastic constitutive model was adopted to describe the large deformation behaviour of the membrane material. The inflation process was simulated using the fluid cavity method, and the interaction between the inflatable membrane and tunnel boundary was defined through contact analysis. The evolution of structural morphology, stress distribution, volume variation, and contact development was systematically analysed to reveal the coupled mechanical response of the sealing system. The results showed that the deployment process consisted of three successive stages, including gravitational descent, inflation-driven expansion, and stable sealing. The complete deployment process was achieved within approximately 30 s. The maximum von Mises stress during inflation was 12.15 MPa, while the stabilized stress decreased to approximately 10.13 MPa, remaining considerably below the material tensile strength of 200 MPa. The airbag volume increased from 0.16 m³ to 5.79 m³, and the final contact area with the tunnel wall reached 4.55 m², corresponding to a sealing coverage rate of 81.3%. The results indicate that the sealing performance of inflatable tunnel barriers is governed by the coupled evolution of pressure loading, nonlinear structural deformation, and boundary contact interaction. This study provides new insights into the multiphysics response mechanism of flexible inflatable systems and offers a numerical basis for the design and optimization of rapid-response sealing devices in underground engineering applications.