Search Results (91)

Persistent scatterer (PS) pixel selection is crucial in the PS-InSAR technique, ensuring the quality and quantity of PS pixels for accurate deformation measurements. However, traditional methods like the amplitude dispersion index (ADI)-based method struggle to balance the quality and quantity of PS pixels. To adequately select high-quality PS pixels, and thus improve the deformation measurement performance of PS-InSAR, the multi-temporal feature extraction network (MFN) is constructed in this paper. The MFN combines the 3D U-Net and the convolutional long short-term memory (CLSTM) to achieve time-series analysis. Compared with traditional methods, the proposed MFN can fully extract the spatiotemporal characteristics of complex SAR images to improve PS pixel selection performance. The MFN was trained with datasets constructed by reliable PS pixels estimated by the ADI-based method with a low threshold using ∼350 time-series Sentinel-1A SAR images, which contain man-made objects, farmland, parkland, wood, desert, and waterbody areas. To test the validity of the MFN, a deformation measurement experiment was designed for Tongzhou District, Beijing, China with 38 SAR images obtained by Sentinel-1A. Moreover, the similar time-series interferometric pixel (STIP) index was introduced to evaluate the phase stability of selected PS pixels. The experimental results indicate a significant improvement in both the quality and quantity of selected PS pixels, as well as a higher deformation measurement accuracy, compared to the traditional ADI-based method. Full article

The management of groundwater depth (GWD) in alluvial soils under irrigation in arid climates is critical for soil and water conservation, given its influence on salt dynamics and water availability for crops. GWD is influenced by the interaction of irrigation water supply and drainage system design and operation. Controlling GWD is a significant issue in the Hetao Irrigation District due to continuous irrigation, arid climate, and high risks of soil salinization, which concerns farmers and water management authorities. To address this issue, a study was conducted based on open-air laboratory experimentation to rigorously assess the effects of GWD on soil salt dynamics and capillary rise contribution to maize cultivation under level basin irrigation. Data collected served as the basis for parameterizing and calibrating the HYDRUS-1D model, facilitating simulation of soil water and salt dynamics to enhance understanding of GWD effects ranging from 1.25 m to 2.25 m. It was concluded that during calibration and validation, the model demonstrated strong performance; SWC simulations achieved R² > 0.69, RMSE < 0.03 cm³ cm⁻³, and NSE approaching 1; and EC simulations yielded R² ≥ 0.74 with RMSE < 0.22 S cm⁻¹. Additionally, the simulated bottom boundary moisture flux closely matched the measured values. The most favorable GWD range should be between 1.75 m and 2.0 m, minimizing the negative impacts of irrigation-induced soil salinity while maximizing water use efficiency and crop productivity. A higher GWD causes crop water stress, while a lower value results in a greater risk of soil salinity. This study anticipates future field application in Hetao to assess drainage system effectiveness and variability in salinity and productivity effects. Full article

In the earthquakes that occurred in the Pazarcık (Mw = 7.7) and Elbistan (Mw = 7.6) districts of Kahramanmaraş Province on 6 February 2023, many buildings collapsed in the Hayrullah neighborhood of the Onikişubat district. In this study, we investigated whether there was a soil amplification effect on the damage occurring in the Hayrullah neighborhood of the Onikişubat district of Kahramanmaraş Province. Firstly, borehole, SPT, MASW (multi-channel surface wave analysis), microtremor, electrical resistivity tomography (ERT), and vertical electrical sounding (VES) tests were carried out in the field to determine the engineering properties and behavior of soil. Laboratory tests were also conducted using samples obtained from bore holes and field tests. Then, an idealized soil profile was created using the laboratory and field test results, and site dynamic soil behavior analyses were performed on the extracted profile. According to The Turkish Building Code (TBC 2018), the earthquake level DD-2 design spectra of the project site were determined and the average design spectrum was created. Considering the seismicity of the project site and TBC (2018) criteria (according to site-specific faulting, distance, and average shear wave velocity), 11 earthquake ground motion sets were selected and harmonized with DD-2 spectra in short, medium, and long periods. Using scaled motions, the soil profile was excited with 22 different earthquake scenarios and the results were obtained for the equivalent and non-linear models. The analysis showed that the soft soil conditions in the area amplified ground shaking by up to 2.8 times, especially for longer periods (1.0–2.5 s). This level of amplification was consistent with the damage observed in mid- to high-rise buildings, highlighting the important role of local site effects in the structural losses seen during the Kahramanmaraş earthquakes. Full article

This study investigates strategies for urban-scale renewable energy integration through a photovoltaic-centric approach, with a case study of a district in Barcelona. The methodology integrates spatial and morphological data using a geographic information system (GIS)-based and clustering framework to address challenges of CO₂ emissions, air pollution, and energy inefficiency. Rooftop availability and photovoltaic (PV) design constraints are analysed under current urban regulations. The spatial analysis incorporates building geometry and solar exposure, while an evolutionary optimisation algorithm in Grasshopper refines shading analysis, energy yield, and financial performance. Clustering methods (K-means and 3D proximity) group PV panels by solar irradiance uniformity and spatial coherence to enhance system efficiency. Eight PV deployment scenarios are evaluated, incorporating submodule integrated converter technology under a solar power purchase agreement model. Results show distinct trade-offs among PV scenarios. The standard fixed tilted (31.5° tilt, south-facing) scenario offers a top environmental and performance ratio (PR) = 66.81% but limited financial returns. In contrast, large- and huge-sized modules offer peak financial returns, aligning with private-sector priorities but with moderate energy efficiency. Medium- and large-size scenarios provide balanced outcomes, while a small module and its optimised rotated version scenarios maximise energy output yet suffer from high capital costs. A hybrid strategy combining standard fixed tilted with medium and large modules balances environmental and economic goals. The district’s morphology supports “solar neighbourhoods” and demonstrates how multi-scenario evaluation can guide resilient PV planning in Mediterranean cities. Full article

Steam networks are widely used for industrial heat supply. High-temperature heat pumps (HTHPs) are an increasingly attractive low-emission solution to traditional steam generation, which could also improve the operational efficiency and energy demand flexibility of industrial processes. This work characterises 4-bar steam supply via HTHPs and aims to assess how variations in power input that result from flexible HTHP operation may affect steam flow and temperature, both with and without a downstream steam accumulator (SA). First, steady-state modelling is used for system design. Then, dynamic component models are developed and used to simulate the system response to HTHP power input variations. The performance of different SA integration layouts and sizes is evaluated. Results demonstrate that steam supply fluctuations closely follow changes in HTHP operation. A downstream SA is shown to mitigate these variations to an extent that depends on its capacity. Practical SA sizing recommendations are derived, which allow for the containment of steam supply fluctuations within acceptability. By providing a basis for evaluating the financial viability of flexible HTHP operation for steam provision, the results support clean technology’s development and uptake in industrial steam and district heating networks. Full article

The efficient management of urban parking systems has emerged as a pivotal issue in today’s smart cities, where increasing vehicle populations strain limited parking infrastructure and challenge sustainable urban mobility. Aligned with the United Nations 2030 Agenda for Sustainable Development and the strategic goals of smart city planning, this study presents a sustainability-driven, multiagent simulation-based framework to model, analyze, and optimize smart parking dynamics in congested urban settings. The system architecture integrates ground-level IoT sensors installed in parking spaces, enabling real-time occupancy detection and communication with a centralized system using low-power wide-area communication protocols (LPWAN). This study introduces an intelligent parking guidance mechanism that dynamically directs drivers to the nearest available slots based on location, historical traffic flow, and predicted availability. To manage real-time data flow, the framework incorporates message queuing telemetry transport (MQTT) protocols and edge processing units for low-latency updates. A predictive algorithm, combining spatial data, usage patterns, and time-series forecasting, supports decision-making for future slot allocation and dynamic pricing policies. Field simulations, calibrated with sensor data in a representative high-density urban district, assess system performance under peak and off-peak conditions. A comparative evaluation against traditional first-come-first-served and static parking systems highlights significant gains: average parking search time is reduced by 42%, vehicular congestion near parking zones declines by 35%, and emissions from circling vehicles drop by 27%. The system also improves user satisfaction by enabling mobile app-based reservation and payment options. These findings contribute to broader sustainability goals by supporting efficient land use, reducing environmental impacts, and enhancing urban livability—key dimensions emphasized in sustainable smart city strategies. The proposed framework offers a scalable, interdisciplinary solution for urban planners and policymakers striving to design inclusive, resilient, and environmentally responsible urban mobility systems. Full article

Background: Economic logistics in fire and rescue services is a critical determinant of operational readiness, fiscal sustainability, and resilience to large-scale emergencies. Despite its strategic importance, logistics remains under-researched in Central and Eastern European contexts, where legacy governance structures and EU-funded modernization coexist with systemic inefficiencies. This study focuses on the Slovak Fire and Rescue Service (HaZZ) as a case to explore how economic logistics systems can be restructured for greater performance and value. Objective: The objective of this paper was to evaluate the structure, performance, and reform potential of the logistics system supporting HaZZ, with a focus on procurement efficiency, lifecycle costing, digital integration, and alignment with EU civil protection standards. Methods: A mixed-methods design was applied, comprising the following: (1) Institutional analysis of governance, budgeting, and legal mandates based on semi-structured expert interviews with HaZZ and the Ministry of Interior officers (n = 12); (2) comparative benchmarking with Germany, Austria, the Czech Republic, and the Netherlands; (3) financial analysis of national logistics expenditures (2019–2023) using Total Cost of Ownership (TCO) principles, completed with the visualization of cost trends and procurement price variance through original heat maps and time-series graphs. Results: The key findings are as follows: (1) HaZZ operates a formally centralized but practically fragmented logistics model across 51 district units, lacking national coordination mechanisms and digital infrastructure; (2) Maintenance costs have risen by 42% between 2019 and 2023 despite increasing capital investment due to insufficient lifecycle planning and asset heterogeneity; (3) Price variance for identical equipment categories across regions exceeds 30%, highlighting the inefficiencies in decentralized procurement; (4) Slovakia lacks a national Logistics Information System (LIS), unlike peer countries which have deployed integrated digital platforms (e.g., CELIS in the Czech Republic); (5) Benchmarking reveals high-impact practices in centralized procurement, lifecycle-based contracting, regional logistics hubs, and performance accountability—particularly in Austria and the Netherlands. Impacts: Four high-impact, feasible reforms were proposed: (1) Establishment of a centralized procurement framework; (2) national LIS deployment to unify inventory and asset tracking; (3) adoption of lifecycle-based and performance-based contracting models; (4) development of regional logistics hubs using underutilized infrastructure. This study is among the first to provide an integrated economic and institutional analysis of the Fire and Rescue Service logistics in a post-socialist EU member state. It offers a structured, transferable reform roadmap grounded in comparative evidence and adapted to Slovakia’s hybrid governance model. The research bridges gaps between modernization policy, procurement law, and digital public administration in the context of emergency services. Full article

Tree crown detection of high-resolution UAV forest remote sensing images using computer technology has been widely performed in the last ten years. In forest resource inventory management based on remote sensing data, crown detection is the most important and essential part. Deep learning technology has achieved good results in tree crown segmentation and species classification, but relying on high-performance computing platforms, edge calculation, and real-time processing cannot be realized. In this thesis, the UAV images of coniferous Pinus tabuliformis and broad-leaved Salix matsudana collected by Jingyue Ecological Forest Farm in Changping District, Beijing, are used as datasets, and a lightweight neural network U-Net-Light based on U-Net and VGG16 is designed and trained. At the same time, the IP core and SoC architecture of the neural network accelerator are designed and implemented on the Xilinx ZYNQ 7100 SoC platform. The results show that U-Net-light only uses 1.56 MB parameters to classify and segment the crown images of double tree species, and the accuracy rate reaches 85%. The designed SoC architecture and accelerator IP core achieved 31 times the speedup of the ZYNQ hard core, and 1.3 times the speedup compared with the high-end CPU (Intel CoreTM i9-10900K). The hardware resource overhead is less than 20% of the total deployment platform, and the total on-chip power consumption is 2.127 W. Shorter prediction time and higher energy consumption ratio prove the effectiveness and rationality of architecture design and IP development. This work departs from conventional canopy segmentation methods that rely heavily on ground-based high-performance computing. Instead, it proposes a lightweight neural network model deployed on FPGA for real-time inference on unmanned aerial vehicles (UAVs), thereby significantly lowering both latency and system resource consumption. The proposed approach demonstrates a certain degree of innovation and provides meaningful references for the automation and intelligent development of forest resource monitoring and precision agriculture. Full article

With the challenge of increasing global carbon emissions and climate change, the importance of solar energy as a clean energy source is becoming more pronounced. Accurate solar radiation prediction is crucial for planning and operating solar energy systems. However, the accurate measurement of solar radiation faces challenges due to the high cost of instruments, strict maintenance, and technical complexity. Therefore, this paper proposes a deep learning approach that integrates the Sparrow Search Algorithm (SSA), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks for solar radiation forecasting. The study utilizes solar radiation data from Songjiang District, Shanghai, China, from 2019 to 2020 for empirical analysis. Initially, a correlation analysis was conducted to identify the main factors affecting the intensity of solar radiation, including temperature, clear-sky GHI, solar zenith angle, and relative humidity. Subsequently, the forecasting effectiveness of the model was compared on datasets of 10 min, 30 min, and 60 min, revealing that the model performed best on the 60 min dataset, with a determination coefficient (R²) of 0.96221, root mean square error (RMSE) of 65.9691, and mean absolute error (MAE) of 37.9306. Moreover, comparative experimental results show that the SSA-CNN-LSTM model outperforms traditional LSTM, BiLSTM, and CNN-LSTM models in forecasting accuracy, confirming the effectiveness of SSA in parameter optimization. Thus, the SSA-CNN-LSTM model provides a new and efficient tool for solar radiation forecasting, which is of significant importance for the design and management of solar energy systems. Full article

Detailed hydraulic modeling of a water distribution network (WDN) in an urban area is implemented therein, based on data from geoinformatic tools (GIS), to investigate and analyze the network’s hydraulic response to different scenarios of operation. A detailed mapping of the water meters of the consumers in the urban district is therefore conducted in the frame of a District Metered Area (DMA) zoning. Different consumptions according to water meters and patterns of daily water demand, resulting from both theoretical and measured data from a limited number of smart meters, are used in the hydraulic simulations. The analysis conducted assists common engineering practice to identify critical locations for constructing new hydraulic infrastructure, resulting in the restructuring and reorganization of the DMA, assisting to face existing and common problems of WDNs within the general framework of DMA design and efficient water management. A case study on the WDN of Efkarpia, located in the city of Thessaloniki, Greece, satisfying the principal design criteria of DMAs, is presented in this work, under both normal and emergency conditions. Hydraulic analysis is performed based on different scenarios, mainly consisting of different consumptions according to water meters and different demand patterns, all resulting in high pressures in the southern part of the DMA. Hydraulic simulations are then performed considering two basic operating scenarios, namely the operation of the old DMA of Efkarpia and a new DMA, which is reduced in size. The two scenarios are compared in terms of estimated pressures in the studied area, as well as in terms of energy consumption in the upstream pumping station. The comparisons reveal that the new DMA outperforms the old one, with a large increase in the pressure at nodes where low pressures were assessed in the old DMA, a reduction in daily pressure variation up to 45%, and quite significant energy savings assessed around 21.6%. Full article

Arsenic contamination poses a severe health risk in regions with high water vulnerability and limited treatment infrastructure. This study evaluates a photovoltaic-powered water treatment system for arsenic removal in La Yarada Los Palos District, Tacna, Peru, where arsenic concentrations reached up to 0.0417 mg/L, significantly surpassing the World Health Organization (WHO) limit of 10 µg/L (0.01 mg/L) for drinking water. The system integrates a natural sedimentation pretreatment stage in a geomembrane-lined reservoir, followed by oxidation with sodium hypochlorite, coagulation, and adsorption. Arsenic removal efficiencies ranged from 99.72% to 99.85%, reducing residual concentrations below WHO guidelines. Pretreatment significantly improved performance, reducing turbidity by up to 66.67% and TSS by up to 70.37%, optimizing subsequent treatment stages. Operationally, pretreatment decreased cleaning frequency from six to four cleanings per month, while backwashing energy consumption dropped by 33% (from 45.72 kWh to 30.48 kWh). The photovoltaic system leveraged the region’s high solar radiation, achieving an average daily generation of 20.31 kWh and an energy surplus of 33.08%. The system’s performance was evaluated within the context of existing arsenic removal technologies, demonstrating that the integration of natural sedimentation and renewable energy constitutes a viable operational alternative. Given the regulatory framework in Peru, where arsenic limits align with WHO standards, conventional water treatment systems are normatively and technically unfeasible under national legislation. Furthermore, La Yarada Los Palos District faces challenges due to its limited infrastructure for conventional electrification via power grid, as identified in national reports on rural electrification and gaps in access to basic services. Beyond its performance in the study area, the system’s modular design allows adaptation to diverse water sources with varying arsenic concentrations, turbidity levels, and other physicochemical characteristics. In remote regions with limited access to the power grid, such as the study site, photovoltaic energy provides a self-sustaining and replicable alternative, particularly in arid and semi-arid areas with high solar radiation. These conditions are not exclusive to Latin America but are also prevalent in remote regions of Africa, the Middle East, Asia, and Oceania, where groundwater arsenic contamination is a significant issue and renewable energy availability can enhance water treatment sustainability. These findings underscore the potential of using sustainable energy solutions to address water contamination challenges in remote areas. The modular and scalable design of this system enables its replication in regions with adverse hydrogeological conditions, integrating renewable energy and pretreatment strategies to enhance water treatment performance. The framework presented in this study offers a replicable and efficient approach for implementing eco-friendly water treatment systems in regions with similar environmental and resource constraints. Full article

Background and Purpose: Previous studies have shown that socioeconomic status influences cognitive health in adults. Therefore, it is important for the development of healthy aging policies to further investigate the effect of specific socioeconomic factors on cognitive function in older people and the possible mechanism. In this study, three specific socioeconomic factors (i.e., income, occupation, and education) were used as independent variables, and social support and social participation were used as the parallel or serial mediating variables to investigate the effect on cognitive function in community-dwelling older adults and the specific pathway of influence. Methods: A cross-sectional study was conducted in the Pudong New District of Shanghai, China. A total of 970 community-dwelling older adults aged over 60 years old who had lived in their current location for more than 5 years were enrolled. Socioeconomic factors in older adults, including income, education level, and occupation before retirement, were investigated, and their cognitive function and social support and social participation levels were measured using the MoCA, MSPSS, and the quantity of participation in social activities, respectively. Covariates, including lifestyle, health status, sleep quality, and nutritional status, were assessed using a self-designed questionnaire, the PSQI, and the MNA-SF scale. Omnibus mediation effect analysis was adopted to examine the mediation effect, and the mediation analysis was performed using the SPSS PROCESS program. Results: Community-dwelling older adults with higher income, more complex occupation, or higher education level had a better cognitive function, with β_{medium income} = 1.949 and β_{high income} = 3.799 compared to low-income level (all p < 0.001), β_{medium occupational complexity} = 1.262 and β_{high occupational complexity} = 1.574 compared to low occupational complexity level (all p < 0.01), and β_{medium education} = 1.814 and β_{high education} = 1.511 compared to low education level (all p < 0.001). Social participation significantly mediated the above relationship (all p < 0.001); the relative indirect effect of medium and high income through social participation was respectively β_{medium income} = 0.356 and β_{high income} = 0.777 compared to low income, accounting for 18.36% and 20.45% of the total effect; the relative indirect effect (β) of medium and high occupational complexity compared to low level of occupational complexity was 0.358 and 0.561, accounting for 28.36% and 35.64% of the total effect; while the relative indirect effect (β) of medium and high education compared to low education level was 0.311 and 0.562, with 17.14% and 39.19% of the total effect. Social support significantly mediated the relationship of income and education with cognitive function (all p < 0.001), with the indirect effect (β) of medium and high levels of income or education compared to their low levels being 0.132 and 0.160, or 0.096 and 0.156, respectively, accounting for 4.21% and 6.77%, or 5.29% and 10.32%, of their total effects. Serial mediation analysis showed that income and education significantly affected social participation through social support and subsequently cognitive function (all p < 0.01), with the relative serial indirect effects (β) of medium and high levels of income or education compared to their low levels being 0.065 and 0.078, or 0.043 and 0.070, respectively, accounting for 3.3% and 2.0%, or and 2.4–4.6% of their total effects. Conclusions: This study demonstrates that social support and social participation independently and cumulatively mediate the relationship between socioeconomic conditions and cognitive function in community-dwelling older adults. Therefore, improving the social support systems and encouraging older adults to actively participate in social activities may be beneficial in preventing or improving cognitive decline in community-dwelling older adults. The findings also provide new insights for the future improvement of cognitive function in community-dwelling older adults in the future. Full article

This study examined high-performing teacher teams in Title I elementary schools that demonstrated significant student growth in foundational reading skills. Using an explanatory sequential mixed-methods design, we identified K-2-grade-level teams within a district with the highest growth on Acadience Reading. The study explored (1) student growth and proficiency outcomes in these high-growth teams and (2) how teachers described their instructional practices and outcomes. Focus group interviews were conducted, and thematic analysis revealed key factors, including collaboration, data responsiveness, professional development, and content/pedagogical knowledge. Recommendations and directions for future research are discussed. Full article

The purpose of this study was to evaluate the potential of using fuzzy C-means clustering, AMMI and GGE biplot analysis methods to predict the yield of chickpea (Cicer arietinum L.) genotypes grown in various environments. The trials were conducted in the Central, Silvan and Hazro districts of Diyarbakir province and Kiziltepe district of Mardin province in the Southeastern Anatolia Region of Türkiye. During the 2016 growing season, 19 chickpea genotypes were tested across four distinct environments. Multiple location experiments were used to assess the genotypes’ performance and stability. The study employed a two-factor experimental design in randomized blocks with four replications in each environment. As a result, the genotype FLIP98-206C showed the highest performance for yield (1727.3 kg ha⁻¹) at the Diyarbakır location among all locations. On the other hand, the Diyar-95 variety showed the lowest yield (723.5 kg ha⁻¹) at the Hazro location among all locations. The Diyarbakir location was determined as an ideal test environment for genotype selection in fuzzy C-means clustering, AMMI and GGE biplot analysis. The Silvan region was determined as the weakest environment for this purpose. It is considered that the determination of genotypes with high yield and stability in this research, in which different analysis methods were used in combination, will contribute to agricultural production. Full article

In the rice-wheat system, using straw for soil incorporation provides better soil health and improves agricultural production. The experiment was performed in Babaiqiao town, Jiangsu Province, China’s Luhe District, Nanjing City, in June 2024 using a Shichao TG-500 tractor equipped with a Qingxuan 1GKN-180 rotary cultivator. The impacts of the three tillage practices, deep rotary tiller with straw (DRTS), shallow rotary tiller with straw (SRTS), and no-tillage with straw return (NTSR), on the level of soil disturbance were observed in the single-factor and two-factor interaction experiments. Based on the profilometry analysis, it was observed that DRTS had the highest value of soil disturbance while SRTS had a moderate disturbance value and NTSR minimized disturbance. The effects of working depths, forward speed, and rotation speed on the straw return rate have been evaluated by further investigations. The results showed that enhancing straw return rates was significantly impacted by changing the tilling depths and the rotation speeds, especially when using deeper tillage and moderate to high rotary speeds. The investigation found that the forward speed, blade rotation speed, and tillage depth explained the overall rates of straw return, soil breaking, and soil flatness. In the research, the response surface design employed was the Box–Behnken Design (BBD). The optimal operating parameters were 14.23 cm of plowing depth, 297.6 rpm for the rotary blades, and 3.23 km/h for forward speed. Achieved were the following parameters: 94.766% soil breakage rate, 84.97% straw return rates, and 16.36 mm soil flatness. The findings demonstrate the potential to implement strategies through operational parameters to significantly enhance agricultural practices. Full article