Search Results (940)

To promote sustainable development and guide the responsible use of natural ecosystems, the United Nations introduced the concept of ecosystem accounting. Ecosystem services are key components of ecosystem accounting. Water-related ecosystem services (ES) are of primary importance for Armenia due to relatively dry climate, and dependence on irrigation water for agriculture. This study aims to conduct a pilot-level quantitative scoping assessment and mapping of key water-related regulating ES in accordance with the SEEA-EA guidelines, and to offer recommendations to initiate their accounting in Armenia. We used three Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models—Seasonal Water Yield, Sediment Delivery Ratio, and Urban Flood Risk Mitigation. Input data for these models were sourced from global and national databases, as well as ESRI land cover datasets for 2017 and 2023. Government-reported data on river flow and water consumption were used to assess the ES supply–use balance. The results show that natural ecosystems contribute between 11% and 96% of the modeled ES, with the strongest impact on baseflow supply and erosion prevention. The average current erosion is estimated at 2.3 t/ha/year, and avoided erosion at 46.4 t/ha/year. Ecosystems provide 93% of baseflow, with an average baseflow index of 34%, while on bare ground it is only 3%. Changes in land cover from 2017 to 2023 have resulted in alterations across all assessed ES. Comparison of total water flow and baseflow with water consumption revealed water-deficient provinces. InVEST models show their general operability at the scoping phase of ecosystem accounting planning. Advancing ES accounting in Armenia requires model calibration and validation using local data, along with the integration of InVEST and hydrological and meteorological models to account for the high diversity of natural conditions in Armenia, including terrain, geological structure, soil types, and regional climatic differences. Full article

Simultaneous Localization and Mapping (SLAM) systems require accurate and globally consistent mapping to ensure the long-term stable operation of robots or vehicles. However, for the commercial applications of indoor sweeping robots, the system needs to maintain accuracy while keeping computational and storage requirements low to ensure cost controllability. This paper proposes a dual-point-cloud-registration SLAM based on line features for the mapping of a mobile robot, named DPCR-SLAM. The front-end employs an improved Point-to-Line Iterative Closest Point (PLICP) algorithm for point cloud registration. It first aligns the point cloud and updates the submap. Subsequently, the submap is aligned with the regional map, which is then updated accordingly. The back-end uses the association between regional maps to perform graph optimization and update the global map. The experimental results show that, in the application scenario of indoor sweeping robots, the proposed method reduces the map storage space by 76.3%, the point cloud processing time by 55.8%, the graph optimization time by 77.7%, and the average localization error by 10.9% compared to the Cartographer, which is commonly used in the industry. Full article

Three-dimensional path planning is critical for the successful operation of unmanned aerial vehicles (UAVs), automated guided vehicles (AGVs), and robots in industrial Internet of Things (IIoT) applications. In 3D path planning, the standard Particle Swarm Optimization (PSO) algorithm suffers from premature convergence and a tendency to fall into local optima, leading to significant deviations from the optimal path. This paper proposes an improved PSO (IPSO) algorithm that enhances particle diversity and randomness through the introduction of logistic chaotic mapping, while employing dynamic learning factors and nonlinear inertia weights to improve global search capability. Experimental results demonstrate that IPSO outperforms traditional methods in terms of path length and computational efficiency, showing potential for real-time path planning in complex environments. Full article

The burgeoning economic relationship between Türkiye and Poland, marked by a targeted $10 billion trade volume, has catalyzed significant Turkish engagement in the Polish construction sector. Ranked second globally in international contracting, Turkish firms are increasingly undertaking complex infrastructure projects in Poland, making it a critical European market to analyze. This study develops a comprehensive framework to identify and evaluate the sources of sustainable competitive advantage for Turkish contractors operating in this dynamic environment. The research adopts a qualitative, single-case study methodology, centered on the extensive project portfolio of a leading Turkish firm in Poland. The analytical approach is twofold. First, it employs Porter’s Diamond Framework to deconstruct the existing competitive advantages, revealing a shift from traditional low-cost models to a sophisticated synergy of superior labor management capabilities, strategic local partnerships, and expertise in complex project delivery. These strengths are shown to align directly with Poland’s critical needs, particularly its skilled labor shortage and ambitious infrastructure agenda. Second, a Foresight Analysis is conducted to map plausible future scenarios through 2035, addressing key uncertainties such as geopolitical shifts and the pace of technological adoption. The findings demonstrate that the sustained success of Turkish contractors hinges on their ability to deliver targeted value. The study concludes by proposing a set of “no-regrets” strategies—including accelerated ESG and digital up-skilling, forging deep local partnerships, and developing financial engineering capabilities—designed to secure and enhance their competitive positioning. The results provide an actionable roadmap for industry practitioners and valuable insights for policymakers fostering bilateral economic collaboration. Full article

Climate change is increasingly altering rainfall regimes and hydrological processes, posing major challenges to reservoir operation, flood control, and hydropower production. Understanding its impacts on the Ban Chat reservoir in Northwest Vietnam is therefore crucial for ensuring reliable water resource management under future uncertainties. This study aims to assess potential changes in streamflow to the Ban Chat reservoir under different climate change scenarios. The study employed nine Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Future climate projections were bias-corrected using the Quantile Delta Mapping (QDM) method and used as input for the Hydrological Engineering Center–Hydrological Modeling System (HEC-HMS) to simulate future inflows. Streamflow changes were evaluated for near- (2021–2040), mid- (2041–2060), and late-century (2061–2080) periods relative to the baseline (1995–2014). Results show that under SSP1-2.6, mean annual discharge and flood-season flows steadily increase (up to +6.9% by 2061–2080), while storage deficits persist (−27.7% to −13.1%). Under SSP2-4.5, changes remain small, with flood peaks limited to +4.5% mid-century, but severe dry-season deficits continue (−29.5% to −24.4%). In contrast, SSP5-8.5 projects strong late-century increases in mean flows (+7.5%) and flood peaks (+8.2%), though early-century flood flows decline (−2.1%). These findings provide essential scientific evidence for adaptive reservoir operation, hydropower planning, and flood risk management, underscoring the significance of incorporating climate scenarios into sustainable water resource strategies in mountainous regions. Full article

This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. It combines multi-criteria suitability modelling with spatial autocorrelation and Geographically Weighted Regression (GWR). Using fine-scale demographic and retail data, the results show clear regional differences in how drivers operate. Population density is most influential around large metropolitan areas, while the role of points of interest is stronger in smaller regional towns. A separate gap analysis identified forty grid cells with high suitability but no existing retail infrastructure. These locations are spread across both rural and urban contexts, from peri-urban districts in Baden-Württemberg to underserved municipalities in Brandenburg and Bavaria. The pattern is consistent under different model specifications and echoes earlier studies that reported supply deficits in comparable communities. The results are useful in two directions. Retailers can see places with demand that has gone unnoticed, while planners gain evidence that service shortages are not just an urban issue but often show up in smaller towns as well. Taken together, the maps and diagnostics give a grounded picture of where gaps remain, and suggest where investment could bring both commercial returns and community benefits. This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. A multi-criteria suitability surface is constructed from demographic and retail indicators and then subjected to spatial diagnostics to separate visually high values from statistically coherent clusters. “White-spots” are defined as cells in the top decile of suitability with zero (strict) or ≤1 (relaxed) existing shops, yielding actionable opportunity candidates. Global autocorrelation confirms strong clustering of suitability, and Local Indicators of Spatial Association isolate hot- and cold-spots robust to neighbourhood size. To explain regional heterogeneity in drivers, Geographically Weighted Regression maps local coefficients for population, age structure, and shop density, revealing pronounced intra-urban contrasts around Hamburg and more muted variation in Berlin. Sensitivity analyses indicate that suitability patterns and priority cells stay consistent with reasonable reweighting of indicators. The comprehensive pipeline comprising suitability mapping, cluster diagnostics, spatially variable coefficients, and gap analysis provides clear, code-centric data for retailers and planners. The findings point to underserved areas in smaller towns and peri-urban districts where investment could both increase access and business feasibility. Full article

To enhance the speed regulation accuracy and robustness of permanent magnet synchronous motor (PMSM) drives under complex operating conditions, this paper proposes a dual-loop active disturbance rejection control strategy optimized by an opposition-based learning hybrid optimization algorithm (DLADRC-OBLHOA). First, the vector control system and ADRC model of the PMSM are established. Then, a nonlinear function, ifal, is introduced to improve the performance of the speed-loop ADRC. Meanwhile, an active disturbance rejection controller is also introduced into the current loop to suppress current disturbances. To address the challenge of tuning multiple ADRC parameters, an opposition-based learning hybrid optimization algorithm (OBLHOA) is developed. This algorithm integrates chaotic mapping for population initialization and employs opposition-based learning to enhance global search capability. The proposed OBLHOA is utilized to optimize the speed-loop ADRC parameters, thereby achieving high-precision speed control of the PMSM system. Its optimization performance is validated on 12 benchmark functions from the IEEE CEC2022 test suite, demonstrating superior convergence speed and solution accuracy compared to conventional heuristic algorithms. The proposed strategy achieves superior speed regulation accuracy and reliability under complex operating conditions when deployed on high-performance processors, but its effectiveness may diminish on resource-limited hardware. Moreover, simulation results show that the DLADRC-OBLHOA control strategy outperforms PI control, traditional ADRC, and ADRC-$ifal$ in terms of tracking accuracy and disturbance rejection capability. Full article

Robots play a crucial role in experimental smart cities and are ubiquitous in daily life, especially in complex environments where multiple robots are often needed to solve problems collaboratively. Researchers have found that the swarm intelligence optimization algorithm has a better performance in planning robot paths, but the traditional swarm intelligence algorithm cannot be targeted to solve the robot path planning problem in difficult problem. Therefore, this paper aims to introduce a fuzzy controller, mutation factor, exponential noise, and other strategies on the basis of particle swarm optimization to solve this problem. By judging the moving speed of different particles at different periods of the algorithm, the individual learning factor and social learning factor of the particles are obtained by fuzzy controller, and using the leader particle and random particle, designing a new dynamic balance of mutation factor, with the iterative process of the adaptation value of continuous non-updating counter and continuous updating counter to control the proportion of the elite individuals and random individuals. Finally, using exponential noise to update the matrix of the population every 50 iterations is a way to balance the local search ability and global exploration ability of the algorithm. In order to test the proposed algorithm, the main method of this paper is simulated on simple scenarios, complex scenarios, and random maps consisting of different numbers of static obstacles and dynamic obstacles, and the algorithm proposed in this paper is compared with eight other algorithms. The average path deviation error of the planned paths is smaller; the average distance of untraveled target is shorter; the number of steps of the robot movements is smaller, and the path is shorter, which is superior to the other eight algorithms. This superiority in solving multi-robot cooperative path planning has good practicality in many fields such as logistics and distribution, industrial automation operation, and so on. Full article

Smart grids (SGs) are becoming increasingly complex with the integration of communication, protection, and automation technologies. However, this digital transformation has introduced new vulnerabilities, especially false data injection attacks (FDIAs) and Denial of Service (DoS) attacks. FDIAs can subtly corrupt measurement data, misleading operators without triggering traditional bad data detection (BDD) methods in state estimation (SE), while DoS attacks disrupt the availability of sensor data, affecting grid observability. This paper presents a deep learning-based framework for detecting and localizing FDIAs, including under DoS conditions. A hybrid CNN, Transformer, and BiLSTM model captures spatial, global, and temporal correlations to forecast measurements and detect anomalies using a threshold-based approach. For further detection and localization, a Multi-layer Perceptron (MLP) model maps forecast errors to the compromised sensor locations, effectively complementing or replacing BDD methods. Unlike conventional SE, the approach is fully data-driven and does not require knowledge of grid topology. Experimental evaluation on IEEE 14–bus and 118–bus systems demonstrates strong performance for the FDIA condition, including precision of 0.9985, recall of 0.9980, and row-wise accuracy (RACC) of 0.9670 under simultaneous FDIA and DoS conditions. Furthermore, the proposed method outperforms existing machine learning models, showcasing its potential for real-time cybersecurity and situational awareness in modern SGs. Full article

Autonomous navigation for unmanned vehicles in complex, unstructured environments remains challenging, especially in GPS-denied or obstacle-dense scenarios, limiting their practical deployment in logistics, inspection, and emergency response applications. To overcome these limitations, this paper presents a tightly integrated air-ground collaborative system comprising three key components: (1) an aerial perception module employing a YOLOv8-based vision system onboard the UAV to generate real-time global obstacle maps; (2) a low-latency communication module utilizing FAST DDS middleware for reliable air-ground data transmission; and (3) a ground navigation module implementing an A* algorithm for optimal path planning coupled with closed-loop control for precise trajectory execution. The complete system was physically implemented using cost-effective hardware and experimentally validated in cluttered environments. Results demonstrated successful UGV autonomous navigation and obstacle avoidance relying exclusively on UAV-provided environmental data. The proposed framework offers a practical, economical solution for enabling robust UGV operations in challenging real-world conditions, with significant potential for diverse industrial applications. Full article

The increasing complexity and digitization of modern supply chains have created an urgent demand for transparent, traceable, and trustworthy systems of data management. Blockchain, with its core features of immutability, decentralization, and smart contracts, has emerged as a promising solution for strengthening data-driven supply chain operations. This paper presents a narrative review synthesizing insights from academic research, industry reports, and regulatory documents to examine blockchain’s role in enhancing transparency, traceability, and trust. References were identified through targeted searches of major databases and gray literature sources, with emphasis on diverse sectors and global perspectives, rather than exhaustive coverage. The review maps how blockchain’s technical capabilities—such as data integrity preservation, access control, automated validation, and provenance tracking—support these outcomes, and assesses the empirical indicators used to evaluate them. A sectoral applicability analysis distinguishes contexts in which blockchain adoption offers clear advantages from those where benefits are limited. The review also identifies critical research gaps, including inconsistent definitions of core concepts, insufficient interoperability standards, overreliance on subjective performance measures, and lack of longitudinal cost–benefit evidence. Finally, it proposes directions for future research, including the development of sector-specific adoption frameworks, integration with complementary technologies, and cross-border regulatory harmonization. Full article

Heatwaves are increasing in frequency and intensity owing to climate change, posing severe health risks to urban populations, particularly vulnerable groups. This study investigates public perceptions, adaptive behavior, and policy awareness regarding extreme heat in Gwangju Metropolitan City, South Korea, a heat-prone urban area. Using a mixed-methods approach, we analyzed primary survey data from 814 residents and secondary data from the 2020 Gwangju Citizen Heatwave Awareness Survey. Statistical analyses, including chi-squared and t-tests, examined differences across socioeconomic age groups. Results indicate that while general awareness of heatwave risks is high, low-income residents exhibit lower perceived severity, limited access to mechanical cooling, and greater reliance on passive avoidance behaviors. Awareness and use of municipal cooling shelters were low, with satisfaction hindered by concerns over accessibility, cleanliness, and operational hours. Television and emergency text alerts were the main information channels; however, trust and perceived usefulness were limited. Policy recommendations include spatially targeted shelter placement informed by vulnerability mapping, improved operational standards, diversified risk communication, and enhanced community engagement. This study underscores the importance of equity-driven adaptation strategies and provides practical insights for global municipalities facing similar climate-related heat risks. Full article

Accurate and real-time environmental perception is essential for the safe and efficient execution of deep-sea mining operations. Semantic segmentation of forward-looking sonar (FLS) images plays a pivotal role in enabling environmental awareness for deep-sea mining vehicles (DSMVs), but remains challenging due to strong acoustic noise, blurred object boundaries, and long-range semantic dependencies. To address these issues, this study proposes DSM-Seg, a novel hybrid segmentation architecture combining Convolutional Neural Networks (CNNs) and Receptance Weighted Key-Value (RWKV) modeling. The architecture integrates a Physical Prior-Based Semantic Guidance Module (PSGM), which utilizes sonar-specific physical priors to produce high-confidence semantic guidance maps, thereby enhancing the delineation of target boundaries. In addition, a RWKV-Based Global Fusion with Semantic Constraints (RGFSC) module is introduced to suppress cross-regional interference in long-range dependency modeling and achieve the effective fusion of local and global semantic information. Extensive experiments on both a self-collected seabed terrain dataset and a public marine debris dataset demonstrate that DSM-Seg significantly improves segmentation accuracy under complex conditions while satisfying real-time performance requirements. These results highlight the potential of the proposed method to support intelligent environmental perception in DSMV applications. Full article

In addressing the limited absolute positioning accuracy of industrial robots, which stems from the discrepancy between the nominal kinematic model and the physical entity, this paper proposes a novel paradigm for online compensation based on data-driven error prediction. The present study utilized a KUKA KR4 R600 robot as the experimental platform to construct a high-fidelity digital twin system capable of real-time synchronization. Within this framework, a new machine learning model, termed the Global Configuration-Error Forest (GCE-Forest), was developed and validated. The fundamental principle of GCE-Forest, based on the Random Forest algorithm, is its offline learning of the complex, highly non-linear mapping from the robot’s six-dimensional joint space configuration to its three-dimensional end-effector Cartesian error space. This facilitates online, feedforward, and predictive compensation for the nominal trajectory during robot operation. Through rigorous comparative experiments, the superiority of the proposed GCE-Forest was established. The final outcomes of dynamic trajectory tracking validation demonstrate that the system, by accurately predicting a mean nominal error of 0.1977 mm, successfully reduced the average spatial positioning error of the end-effector to 0.0845 mm, achieving an accuracy improvement of 57.25%. This research provides comprehensive validation of the method’s robust performance, offering a low-cost, non-invasive, and highly effective solution for significantly enhancing robotic accuracy. Full article

Background/Objectives: Acute ischemic stroke is a leading cause of mortality and long-term disability globally, with limited reliable early predictors of functional outcomes and survival. This study aimed to assess the prognostic value of two novel predictors: the hypoperfusion intensity ratio calculated from mean transit time and time-to-drain maps (HIR-MTT–TTD), derived from computed tomography perfusion (CTP) imaging parameters, and the Inflammation–Coagulation Index (ICI), which integrates systemic inflammatory (C-reactive protein and white blood cell count) and hemostatic (D-dimer) markers. Methods: This prospective, single-center observational study included 60 patients with acute ischemic stroke treated with intravenous thrombolysis and underwent pre-treatment CTP imaging. HIR-MTT–TTD evaluated collateral status and perfusion deficit severity, while ICI integrated C-reactive protein (CRP), white blood cell (WBC) count, and D-dimer levels. Functional outcomes were assessed using the National Institutes of Health Stroke Scale (NIHSS), Barthel Index, and modified Rankin Scale (mRS) at 24 h, 3 months, and 1 year. Results: Of 60 patients, 53.3% achieved functional independence (mRS 0–2) at 1 year. Unadjusted Cox models showed HIR-MTT–TTD (HR = 6.25, 95% CI: 1.48–26.30, p = 0.013) and ICI (HR = 1.08, 95% CI: 1.00–1.17, p = 0.052) were associated with higher 12-month mortality, worse mRS, and lower Barthel scores. After adjustment for age, BMI, smoking status, and sex, these associations became non-significant (HIR-MTT–TTD: HR = 2.83, 95% CI: 0.37–21.37, p = 0.314; ICI: HR = 1.07, 95% CI: 0.96–1.19, p = 0.211). Receiver operating characteristic (ROC) analysis indicated moderate predictive value, with ICI (AUC = 0.756, 95% CI: 0.600–0.867) outperforming HIR-MTT–TTD (AUC = 0.67, 95% CI: 0.48–0.83) for mortality prediction. Conclusions: The study introduces promising prognostic tools for functional outcomes. Elevated HIR-MTT–TTD and ICI values were independently associated with greater initial stroke severity, poorer functional recovery, and increased 1-year mortality. These findings underscore the prognostic significance of hypoperfusion intensity and systemic thrombo-inflammation in acute ischemic stroke. Combining the use of the presented indices may enhance early risk stratification and guide individualized treatment strategies. Full article