Search Results (477)

To address the problems of closed-loop task organization, strong corridor constraints, and path failure after local disturbances in unmanned aerial vehicle (UAV) inspection of gas-insulated switchgear (GIS) rooms, this paper proposes a topology-and-corridor-guided bias-suppressed D* (TCG-BS-D*) method for closed-loop three-dimensional (3D) path planning and local replanning. The proposed method constructs a structured guidance model based on the inspection-corridor topology, generates local 3D path segments according to a predetermined inspection sequence, and forms a nominal closed-loop inspection path through bias suppression and path regularization. Meanwhile, for local maintenance blockage and dynamic disturbance scenarios, an alternative local replanning strategy is applied to the affected path segments. Simulation results show that, under the static closed-loop inspection condition, the proposed method achieves a total path length of 700.22 m, a total inspection time of 269.32 s, an average safety clearance of 8.18 m, 37 large-angle turns, a corridor adherence rate of 80.73%, and a task completion rate of 100%, showing superior performance in inspection efficiency, safety margin, trajectory regularity, and corridor consistency. Under the local blockage condition, the replanned path introduces path-length and time increments of 71.29 m and 25.88 s, respectively, while maintaining the minimum safety clearance at 1.52 m and increasing the corridor adherence rate to 83.91%. Under dynamic disturbance conditions, the minimum dynamic safety clearance is improved from −2.71 m to 17.84 m, effectively eliminating the local dynamic collision risk. The results demonstrate that the proposed method can balance closed-loop path-generation efficiency, corridor-structure consistency, safety margin, and adaptability to local disturbances, providing an effective solution for UAV inspection path planning in GIS rooms. Full article

Accurate solar irradiance forecasting is important for efficient planning of solar energy systems, renewable energy integration, and data-driven energy management in smart cities. This becomes more essential in regions with limited measured data availability and varying climatic conditions, where reliable forecasting can support urban energy planning and smart grid operation. Pakistan faces a scarcity of available solar data and has varying climatic conditions, which makes it ideal for such a study. This study utilizes nine geographically diverse stations to develop a benchmark framework for direct one-step-ahead hourly solar irradiance forecasting. The dataset was subjected to data preprocessing, feature engineering, and multi-model evaluation. A staged approach was adopted for feature selection, starting from a base model comprising three input variables: extraterrestrial radiation, solar zenith angle, and relative humidity. Features were added in an incremental order, which resulted in an optimized four-variable input set through the addition of a lagged clearness index to the base model. The forecasting models evaluated in this study, using these input variables, were ANN, NAR, NARX, LSTM, GRU, SARIMA, and Prophet. Deep learning models outperformed the other considered approaches, with LSTM showing the best overall benchmark performance with an average RMSE of 92.93 W/m², MAE of 66.56 W/m², and R-Squared of 0.872. The performance trends were broadly consistent across the evaluated stations, indicating stable behaviour within the adopted dataset and experimental setup. The study shows that a compact and physically interpretable input feature set, used with recurrent deep learning models, provides an effective solution for hourly solar irradiance forecasting, especially in locations with varying climatic conditions. The proposed benchmark can support smart city applications related to distributed solar generation, energy-aware urban planning, and intelligent operation of renewable-rich power systems. Full article

The rapid development of wind and solar energy necessitates a solution to the problem of the optimal spatial placement of wind farms (WFs) and solar farms (SFs) within electric power systems. The non-stationary generation schedules of WFs and SFs place increased demands on the flexibility of conventional generation, determined by the intensity of net load fluctuations. This paper proposes a methodology for the spatial optimization of WF and SF location, in which the optimization criteria include net load indicators (rate of net load change and net load increment), the base power of the RES system, and the economic criterion of maximum electricity generation. Unlike existing approaches, in which the geographical smoothing effect on power fluctuations is treated as an incidental outcome, the proposed methodology employs it as an explicit optimization criterion for RES placement. The algorithm provides for the preliminary ranking of candidate sites based on the maximum electricity generation criterion, followed by the redistribution of generating capacities among sites with an acceptable capacity factor in accordance with the selected optimization criterion. The methodology was tested on a model comprising six potential wind farm sites and two solar farm sites with a total installed capacity of 600 MW and a maximum power system load of 3000 MW. The obtained results show that the optimal redistribution of installed capacities among sites allows a 31.5% reduction in net load variability intensity to be achieved with an 11.6% reduction in electricity generation relative to the maximum possible. The study is based on idealized daily generation and consumption profiles and is theoretical in nature, proposing a pre-screening tool for RES siting that complements rather than replaces subsequent network-constrained planning studies, including power-flow analysis and grid verification, and establishes a methodological foundation for further development using real multi-year retrospective data. Full article

Weak grid infrastructure and the absence of flexible storage are among the principal barriers to reliable, low-carbon energy access in sub-Saharan transmission systems. This paper proposes a hierarchical multi-objective framework for the optimal siting and sizing of battery energy storage systems (BESSs), applied to the 130-bus Mali transmission network within the EMERGE project. The upper level employs NSGA-II to simultaneously maximize daily price arbitrage revenue and minimize active power losses; the lower level solves a network-constrained DC optimal power flow with thermal branch limits enforced as hard linear inequalities via the Power Transfer Distribution Factor (PTDF) matrix. Over 500 generations, the framework identifies Bus 91 (SIRAKORO II, 150 kV) as the dominant storage location, achieving a maximum daily revenue of approximately €10,033 at a marginal loss increment of $6.7\times {10}^{-3}$ MWh. The resulting Pareto front gives Mali system planners a quantitative tool for trading off private investment returns against grid-level environmental impact, demonstrating that rigorous network-constrained BESS planning is technically tractable and economically viable in the resource-constrained context of sub-Saharan energy transitions. Full article

Individual tree modeling (ITM) is an effective system for thinned stands, especially in teak (Tectona grandis Linn F.) plantations, allowing the estimation of individual-tree-specific variables. Heartwood diameter and volume have high added value and can be estimated in living trees. Therefore, we developed an ITM system for clonal teak stands capable of projecting technical intervention ages and quantifying heartwood production throughout the rotation in the Eastern Brazilian Amazon. The system included equations for total tree height, site index, and taper of both stem and heartwood, with volumes obtained by integrating the respective taper equations. Future diameters and heights were projected using models based on the algebraic difference approach (ADA) and the generalized algebraic difference approach (GADA). Ages of technical intervention were defined by the maximum mean annual increment in volume with bark. The Lundqvist-Korf-ADA base model was the most accurate in estimating future trees’ diameters and heights. The inclusion of the number of trees as a covariate to represent thinning had a significant and positive impact on variable projections. Optimal technical rotations ranged from 17.1 to 21.3 years, considering volume with bark. An increase in the proportion of heartwood was observed, reaching 78% of the diameter and 53% of the volume at rotation ages. The modeling system developed in the present study enables the estimation of technical rotation ages and the quantification of heartwood production throughout the rotation, which provides reliable information for silvicultural planning and decision-making in the management of clonal teak stands. Full article

This paper presents a full-scale case study on real-time corrosion monitoring in an underground reinforced-concrete potable water tank built in 1968. The study aims to demonstrate how continuous electrochemical monitoring can support durability assessment and predictive maintenance in ageing water-retaining infrastructure, where direct inspection is often limited and exposure conditions are spatially variable. Fourteen monitoring points were installed in beams, columns and domes subjected to different exposure conditions. Corrosion potential, concrete resistivity, corrosion current density and temperature were recorded every 3 h and used to assess the corrosion state of the reinforcement. The monitored durability indicators were reinforcement section loss, estimated from corrosion current density using Faraday’s law, and corrosion-induced crack-width evolution, used as a serviceability-related indicator for maintenance planning. The results show that beams remained predominantly passive, with corrosion current densities below 0.1 µA/cm² and incremental sectional losses below approximately 2 µm during the monitoring period. Columns showed the highest vulnerability, particularly at lower elevations subjected to prolonged immersion, with estimated incremental section losses reaching approximately 4–6 µm and a clear correlation between submerged time and corrosion progression. Domes exhibited intermediate behaviour, with occasional activation events associated with environmental fluctuations. A multivariable model combining resistivity and temperature was used to interpret corrosion kinetics, while Faraday-based section-loss estimates were coupled with empirical crack-width models to forecast serviceability indicators up to 2045. These forecasts are presented as scenario-based maintenance-support indicators rather than deterministic predictions of future damage, since corrosion propagation and crack development may evolve nonlinearly under changing exposure conditions. The proposed approach demonstrates how continuous corrosion monitoring can be linked to durability limit-state assessment, enabling risk-informed and performance-based maintenance of critical water infrastructure. Full article

Rapid and often chaotic urbanisation in post-Soviet cities such as Almaty challenges the quality, availability, and accessibility of public spaces for residents, given the cities’ historical development. Meanwhile, global research is focused on the concepts of Third Places, coworking spaces in the Western context, and urban experience in cities with transitional economies, but the heritage of centrally planned urban development lacks spatial explicit analysis. The purpose of the current study is to analyse the evolution, current situation, and distribution of collaborative spaces (public spaces that combine work and connectedness) in Almaty. The methodology includes four phases of qualitative analysis: (1) a historical–typological analysis of architectural functions since the beginning of the 20th century until the 2025, (2) spatial mapping analysis of the existing typologies such as libraries, museums, coworking spaces, research and development (R&D) institutions and universities, and community centres, (3) longitudinal statistical analysis, and (4) historical graphic analysis. Analysis is conducted through the lens of advanced levels of human needs that concern self-education and self-development. This approach helped to propose a new definition of collaborative space. The results also show examples of sustainable urban structure with collaborative spaces in Almaty’s old centre (“Zolotoi Kvadrat”—Golden Square) and a critical deficit of new multifunctional spaces for work and socialisation in recently developed districts. The study reveals that Almaty’s evolution occurred through incremental infill development over the old grid, without the integrated development of the public realm and existing structural connections. As a result, the research explores the connection between collaborative spaces and their indirect influence on the general well-being in Almaty. Full article

Urban Unmanned Aerial Vehicle (UAV) logistics operations are frequently constrained by the intersection of limited battery endurance and dynamic ground traffic. When mobile depots are delayed by congestion, onboard drone fleets experience extended idling periods, leading to constrained sortie generation and reduced asset utilization. To address this bottleneck, this paper introduces a traffic-predictive multi-UAV dispatch framework for deterministic day-ahead planning under modeled urban operating conditions. By coupling a count-derived macroscopic speed surrogate learned using XGBoost with a Particle Swarm Optimization (PSO)–Mixed-Integer Linear Programming (MILP) optimization architecture, the framework synchronizes mobile depot trajectories with forecasted low-congestion windows and pre-allocates endurance-feasible parallel aerial sorties. Controlled computational experiments across 30 synthetic routing instances demonstrate the potential value of this approach within the stated modeling assumptions. Compared to baseline clustered deployments, the traffic-aware framework raises mean fleet utilization from 0.43 to 0.63—a 46.2% relative improvement driven by temporal compression of the mission window rather than an absolute increase in flight hours. Furthermore, the proposed framework reduces total mission completion time by 69.87% relative to the conventional truck-only baseline, while achieving a 29.58% incremental gain over static speed drone deployments. These findings suggest that incorporating predictive ground traffic information into day-ahead UAV scheduling can improve modeled fleet efficiency; however, field validation with measured route-level speeds, real delivery demand, and operational constraints remains necessary before deployment-level claims can be made. Full article

Node-level cargo-volume forecasting in logistics sorting networks requires modeling temporal dynamics together with directed inter-node dependencies and planned topology perturbations. This study addresses 1-h-ahead forecasting under a partial-information boundary, where historical node volumes, the pre-change network structure, and planned route-topology changes are available before prediction, whereas continuous post-change dynamic edge weights and realized post-change graph states are unavailable. We propose a perturbation-aware framework that represents the sorting system as a directed network and integrates temporal features, pre-change structural descriptors, topology-change encodings, perturbation-response proxies, and similarity-assisted support for data-scarce nodes within a unified forecasting protocol. A shared random forest backbone is used only to assess the incremental value of these representations. Experiments on 57 sorting centers show that temporal dynamics dominate under stable-network conditions. Under topology perturbation, topology-change signals reduce test weighted absolute percentage error (WAPE) from 18.10% to 17.11%, and perturbation-response proxies further reduce it to 16.91%. For data-scarce nodes, similarity support reduces test WAPE from 29.43% to 26.68%, with consistent gains under 10%, 20%, and 30% sample-retention settings. These results suggest that the framework provides an interpretable and information-admissible representation strategy for node-level forecasting in directed networked systems. Full article

To address the problems of state estimation bias, dynamic threat response lag, and insufficient safety margin in formation coordination caused by the mismatch between the three-dimensional continuous motion model and the discrete sampling characteristics of sensors in UAV multi-aircraft collaborative inspection missions under complex dynamic environments, this paper studies a trajectory planning method that integrates model predictive control and multi-constraint optimization. By constructing a three-dimensional continuous motion model of the UAV and discretizing it using the Euler integral method, the mapping deviation between the continuous motion characteristics and the discrete working mechanism of the airborne system is solved. Based on the model predictive control method, a patrol trajectory tracking planning model is designed, and state increment and integral augmentation strategies are introduced to transform global reference trajectory tracking into a constrained quadratic programming problem in the rolling time domain, achieving high-precision closed-loop tracking. Furthermore, a dynamic environment model coupling static terrain height field and sudden spherical threat is constructed to systematically characterize the static obstacles and random dynamic threats faced by the UAV in complex scenarios such as mountains and hills. On this basis, multiple constraints such as flight altitude, pitch angle, horizontal turning angle, terrain safety margin, and multi-aircraft collision avoidance are integrated to establish a comprehensive objective function that includes range cost, attitude penalty, and safety cost. Through a collaborative mechanism of global optimization and local online correction, a reference trajectory that meets the requirements of formation safety and flight efficiency is generated and used as the input command for the tracking planning model, forming a closed-loop architecture of global optimization generation, local closed-loop tracking, and dynamic real-time correction for trajectory planning. Experimental results show that the success rate of dynamic obstacle avoidance in complex dynamic environments is always higher than 99.9%, and the mean square error of trajectory tracking is stable in the range of 0.02–0.04 km, which verifies its significant advantages in dynamic adaptability, tracking accuracy and formation safety. Full article

Vision-and-Language Navigation in continuous environments (VLN-CE) requires embodied agents to ground natural language instructions into reliable long-horizon motion decisions under partial observability. Despite their strong semantic understanding and reasoning abilities, Multimodal Large Language Model (LVLM) struggle when directly applied to VLN, as they lack explicit spatial grounding, embodied memory, and awareness of geometric and reachability constraints, leading to perceptual misalignment and cascading decision errors in complex scenes. To address these limitations, we propose STAMP, a Spatial-Temporal Anchored Motion Planning framework for zero-shot VLN-CE, which systematically bridges the gap between pretrained world knowledge and embodied navigation. STAMP adopts a hierarchical design that decouples high-level semantic reasoning from low-level motion execution, enabling a frozen LVLM to operate over a structured, navigation-oriented abstraction. Its core novelty lies in a multimodal spatial-temporal anchoring mechanism that explicitly encodes instruction-relevant landmarks, action semantics, depth-aware geometry, and historical navigation context, together with an explicit Chain-of-Navigation reasoning process that constrains decision-making to navigation-critical cues. Furthermore, STAMP incrementally constructs an online, backtracking-enabled topological map, supporting robust planning under uncertainty. Extensive experiments demonstrate the effectiveness of the proposed STAMP framework, achieving performance comparable to state-of-the-art zero-shot methods on VLN-CE benchmarks and in real-world settings. Full article

Rapid urbanization and climate change are exerting unprecedented pressure on regional water resources, particularly in emerging megacities. This study examines the Xiong’an New Area (XNA) in the water-stressed North China Plain, where high-intensity urbanization coincides with rigorous ecological restoration mandates. To overcome the limitations of single-model assessments, a coupled SD–PLUS–InVEST framework was developed, integrating System Dynamics for socio-economic and policy drivers, Patch-Generating Land-Use Simulation for fine-scale urban expansion, and InVEST for hydrological process assessment. Projecting spatiotemporal water dynamics to 2035 under three Shared Socio-Economic Pathways (SSPs), results reveal that urbanization-driven water demand growth consistently outpaces climate-induced supply gains. While precipitation increases are projected to raise water yield by 8.91–19.58% by 2035, demand surges by up to ~26% under the extensive expansion scenario (SSP5–8.5), driven predominantly by impervious surface proliferation. External water transfers are projected to sustain 40–45% of total supply by 2035, yet this dependency introduces systemic vulnerabilities. Quantitative assessment further indicates severe spatiotemporal mismatches, with Seasonal Water Shortage Rates of 26.1–27.3% and a Spatial Mismatch Index rising from 0.44 to 0.98. These findings indicate that climate-driven precipitation increments alone cannot offset water deficits induced by unregulated urban sprawl, and that integrating strategic land-use planning, resilient infrastructure, and adaptive governance is essential for water security in rapidly developing regions. Full article

Post-war reconstruction in Aleppo requires more than replacing damaged buildings; it demands an understanding of the city’s historically layered urban fabrics, their differing socio-spatial logics, and their unequal capacities for recovery. Following severe conflict-related destruction during the Syrian civil war, particularly between 2012 and 2016, and the additional impact of the February 2023 earthquake, Aleppo’s recovery is further complicated by the heritage significance of its Ancient City, inscribed on the UNESCO World Heritage List in 1986 and included on the List of World Heritage in Danger since 2013. This study examines how urban morphology can guide reconstruction through a comparative analysis of four neighborhoods representing major phases of Aleppo’s development: Jdaideh, Azizieh, Mohafaza, and Jabal Badro. Using a qualitative historical–morphological approach, the research analyzes figure–ground relations, street-network structure, degrees of transition between public, semi-public, semi-private, and private spaces, and landmark–node systems to identify the spatial characteristics, temporal persistence, and planning meaning of each district. The findings show that Aleppo is not a homogeneous urban system but a city composed of distinct fabrics with different strengths, vulnerabilities, and reconstruction needs. The comparison further demonstrates that density alone is not an adequate indicator of urban quality or resilience. The study concludes that reconstruction should be based on fabric-specific strategies, including preservation-sensitive rehabilitation, reinforcement of public nodes, balanced connectivity, governance-aware phasing, and incremental upgrading. Urban morphology is therefore proposed as a practical, but not exhaustive, framework for context-sensitive recovery in conflict-affected and historically layered cities. Full article

Achieving deep decarbonization of the residential building sector is essential for meeting Canada’s climate commitments and Net Zero targets. However, large-scale residential retrofit planning is often constrained by the time, cost, and expertise required for detailed building energy modelling. This study evaluates the applicability of a typology-based retrofit planning tool developed by Homes to Zero (HTZ) as a simplified alternative to conventional simulation-based analysis. Two representative Canadian residential archetypes—a detached bungalow and a two-storey semi-detached home located in Toronto—were analyzed using both the HTZ platform and detailed hourly energy simulations conducted in eQuest (DOE-2.2 engine). Baseline energy consumption and greenhouse gas (GHG) emissions were first compared across the two modelling approaches. Results show strong agreement for the bungalow case, with differences of less than 1% for electricity and natural gas consumption and approximately 4% for total emissions. For the two-storey dwelling, baseline electricity estimates were identical while natural gas consumption differed by approximately 17%, highlighting the sensitivity of physics-based simulations to envelope and operational assumptions. Retrofit scenarios were then compared using single-measure GHG reductions derived from HTZ and incremental simulation results from eQuest. While both tools identified electrification through air-source heat pumps as the dominant emission-reduction strategy, differences were observed in the magnitude of savings for envelope upgrades and secondary measures. The HTZ platform also provides approximate retrofit cost estimates, enabling order-of-magnitude budgeting, whereas eQuest requires separate costing analysis. This study is framed as a screening-level benchmark rather than a full validation exercise, highlighting the trade-off between scalability and modelling fidelity in residential retrofit planning. The results suggest that typology-based tools can provide credible screening-level guidance for residential retrofit planning and large-scale policy analysis, while detailed simulation remains valuable for evaluating integrated retrofit packages and design-level decisions. Full article

Cities worldwide face persistent inequalities in access to urban green spaces, a condition associated with reduced physical activity and poorer mental wellbeing. In high-density metropolises, land scarcity further intensifies these disparities. Although recent studies have highlighted the potential of small-scale green spaces, limited attention has been paid to their system-level and spatially differentiated roles within urban green infrastructure. Consequently, the equality implications of micro-scale interventions such as pocket parks across urban–rural gradients remain insufficiently understood. This study addresses this gap by examining the accessibility impacts of 475 pocket parks in conjunction with 433 large parks in Shanghai, using a multidimensional, citywide analytical framework. The Gaussian two-step floating catchment area (G2SFCA) method was applied within the 15-min community life circle framework to assess service coverage, population served, and per capita accessible green space, as well as their urban–rural differentiation patterns. Results indicate that the inclusion of pocket parks modestly increases overall service coverage (+3.41%) but substantially improves population access (+7.83%), converting 143.79 km² of previously unserved areas into areas with basic green space provision. Spatial effects vary along the urban–rural gradient: pocket parks generate high marginal population-service benefits and improve spatial equality in urban cores, strengthen green space service networks in peri-urban areas, and produce incremental accessibility gains in outer suburbs. Taken together, these findings provide a novel system-level understanding of how pocket parks function within urban green infrastructure networks, offering policy-relevant evidence to guide equality-oriented planning in high-density cities. Full article