Search Results (212)

The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasting due to their ability to model complex relationships among variables. However, their application raises two main critical issues: (i) the risk of overfitting, especially with small datasets or with noisy data; (ii) the interpretive issues associated with the “black box” nature of many models. Within this framework, this paper proposes a methodological approach that addresses both these issues, comparing the predictive performance of three ML algorithms—k-Nearest Neighbors (kNN), Random Forest (RF), and the Artificial Neural Network (ANN)—applied to the housing market in the city of Salerno, Italy. For each model, overfitting is preliminarily assessed to ensure predictive robustness. Subsequently, the results are interpreted using explainability techniques, such as SHapley Additive exPlanations (SHAPs) and Permutation Feature Importance (PFI). This analysis reveals that the Random Forest offers the best balance between predictive accuracy and transparency, with features such as area and proximity to the train station identified as the main drivers of property prices. kNN and the ANN are viable alternatives that are particularly robust in terms of generalization. The results demonstrate how the defined methodological framework successfully balances predictive effectiveness and interpretability, supporting the informed and transparent use of ML in real estate valuation. Full article

The distribution of forest aboveground biomass density (AGBD) is a key indicator of carbon stock and ecosystem health in the Eastern Himalayas, which represents a global biodiversity hotspot that sustains diverse forest types across an elevation gradient from lowland rainforests to alpine meadows and contributes to the livelihoods of more than 200 distinct indigenous communities. This study aimed to identify the key factors influencing forest AGBD across this region by analyzing the underlying biophysical and anthropogenic drivers through machine learning (random forest). We processed AGBD data from the Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR and applied filtering to retain 30,257 high-quality footprints across ten ecoregions. We then analyzed the relationship between AGBD and 17 climatic, topographic, soil, and anthropogenic variables using random forest regression models. The results revealed significant spatial variability in AGBD (149.6 ± 79.5 Mg ha⁻¹) across the region. State-wise, Sikkim recorded the highest mean AGBD (218 Mg ha⁻¹) and Manipur the lowest (102.8 Mg ha⁻¹). Within individual ecoregions, the Himalayan subtropical pine forests exhibited the highest mean AGBD (245.5 Mg ha⁻¹). Topographic factors, particularly elevation and latitude, were strong determinants of biomass distribution, with AGBD increasing up to elevations of 2000 m before declining. Protected areas (PAs) consistently showed higher AGBD than unprotected forests for all ecoregions, while proximity to urban and agricultural areas resulted in lower AGBD, pointing towards negative anthropogenic impacts. Our full model explained 41% of AGBD variance across the Eastern Himalayas, with better performance in individual ecoregions like the Northeast India-Myanmar pine forests (R² = 0.59). While limited by the absence of regionally explicit stand-level forest structure data (age, stand density, species composition), our results provide valuable evidence for conservation policy development, including expansion of PAs, compensating avoided deforestation and modifications in shifting cultivation. Future research should integrate field measurements with remote sensing and use high-resolution LiDAR with locally derived allometric models to enhance biomass estimation and GEDI data validation. Full article

As global climate change intensifies and the transition to clean energy accelerates, lithium-ion batteries—critical components of electric vehicles—are becoming increasingly vital in international trade networks. This study investigates the structural evolution and determinants of the electric vehicle lithium-ion battery trade network among European Union (EU) member states from 2012 to 2023, employing social network analysis and the multiple regression quadratic assignment procedure method. The findings demonstrate the transformation of the network from a centralized and loosely connected structure, with Germany as the dominant hub, to a more interconnected and decentralized system in which Poland and Hungary emerge as the leading players. Key network metrics, such as the density, clustering coefficients, and average path lengths, reveal increased regional trade connectivity and enhanced supply chain efficiency. The analysis identifies geographic and economic proximity, logistics performance, labor cost differentials, energy resource availability, and venture capital investment as significant drivers of trade flows, highlighting the interaction among spatial, economic, and infrastructural factors in shaping the network. Based on these findings, this study underscores the need for targeted policy measures to support Central and Eastern European countries, including investment in logistics infrastructure, technological innovation, and regional cooperation initiatives, to strengthen their integration into the supply chain and bolster their export capacity. Furthermore, fostering balanced inter-regional collaborations is essential in building a resilient trade network. Continued investment in transportation infrastructure and innovation is recommended to sustain the EU’s competitive advantage in the global electric vehicle lithium-ion battery supply chain. Full article

This paper details the design and implementation of a fuzzy logic control system for an advanced driver-assistance system (ADAS) that adjusts brake force based on proximity sensing, vehicle speed, and road conditions. By employing a cost-effective ultrasonic sensor (HC-SR04) and an STM32 microcontroller, the system facilitates real-time adjustments to braking force, enhancing both vehicle safety and driver comfort. The fuzzy logic controller processes three inputs to deliver a smooth and adaptive brake response, thus addressing the shortcomings of traditional binary systems that can lead to abrupt and unsafe braking actions. The effectiveness of the system is validated through several test cases, demonstrating improved responsiveness and safety across various driving scenarios. This paper presents a cost-effective model for a straightforward braking system using fuzzy logic, laying the groundwork for the development of more advanced systems in emerging technologies. Full article

This study investigates the dynamic response mechanisms of discharge capacity in the Han River Estuary to hydrological process changes at the Yangtze–Han River confluence. By constructing a one-dimensional hydrodynamic model for the 265 km Xinglong–Hankou reach, we quantitatively decouple the synergistic effects of riverbed scouring (mean annual incision rate: 0.12 m) and Three Gorges Dam (TGD) operation through four orthogonal scenarios. Key findings reveal: (1) Riverbed incision dominates discharge variation (annual mean contribution >84%), enhancing flood conveyance efficiency with a peak flow increase of 21.3 m³/s during July–September; (2) TGD regulation exhibits spatiotemporal intermittency, contributing 25–36% during impoundment periods (September–October) by reducing Yangtze backwater effects; (3) Nonlinear interactions between drivers reconfigure flow paths—antagonism occurs at low confluence ratios (R < 0.15, e.g., Cd increases to 45 under TGD but decreases to 8 under incision), while synergy at high ratios (R > 0.25) reduces Hanchuan Station flow by 13.84 m³/s; (4) The 180–265 km confluence-proximal zone is identified as a sensitive area, where coupled drivers amplify water surface gradients to −1.41 × 10⁻³ m/km (2.3× upstream) and velocity increments to 0.0027 m/s. The proposed “Natural/Anthropogenic Dual-Stressor Framework” elucidates estuary discharge mechanisms under intensive human interference, providing critical insights for flood control and trans-basin water resource management in tide-free estuaries globally. Full article

MET exon 14 skipping mutations have emerged as significant driver alterations in non-small-cell lung cancer (NSCLC), contributing to tumor progression. This study examines the immune microenvironment in NSCLC patients with these mutations and its prognostic implications. We performed multiplex immunofluorescence (mIF) staining on formalin-fixed paraffin-embedded (FFPE) tissue samples from nine NSCLC patients, including four recurrent/metastatic and five non-recurrent/non-metastatic patients. Two panels assessed immune cell markers (CD8, CD4, CD20, CD68, and FoxP3) and immune checkpoints (PD-L1, LAG3, and TIM3). Immune cell infiltration and checkpoint expression were analyzed using HALO^TM software (version 3.6.4134.464). Nearest neighbor analysis was conducted to assess the proximity of immune cells to tumor cells. Univariate Cox regression analysis assessed factors associated with disease-free survival (DFS). CD8+TIM3+ and CD8+LAG3+ cells were predominantly located in the tumor parenchyma of recurrent/metastatic patients but localized to the stroma in non-recurrent/non-metastatic patients. Non-recurrent/non-metastatic patients exhibited a higher density of tertiary lymphoid structures and closer proximity of CD20+ B cells, CD8+TIM3+, and CD8+LAG3+ cells to tumor cells compared to recurrent/metastatic patients, though the differences were not statistically significant. Cox regression analysis suggested a potential association between higher densities of CD8+TIM3+ cells and improved DFS (HR = 0.89), though these findings did not reach statistical significance. Our findings suggest that differences in immune microenvironmental factors, particularly those related to immune checkpoint expression (TIM3 and LAG3), may influence clinical outcomes in NSCLC patients with MET exon 14 skipping mutations. Further studies are needed to validate these observations and explore potential therapeutic implications. Full article

This study investigates rural settlement patterns and land suitability in Rwanda’s Western Highlands, a mountainous region highly vulnerable to geohazards like landslides and flooding. Its primary aim is to inform sustainable, climate-resilient development planning in this fragile landscape. We employed high-resolution satellite imagery, a Digital Elevation Model (DEM), and comprehensive geospatial datasets to analyze settlement distribution, using Thiessen polygons for influence zones and Kernel Density Estimation (KDE) for spatial clustering. The Analytic Hierarchy Process (AHP) was integrated with the GeoDetector model to objectively weight criteria and analyze settlement pattern drivers, using population density as a proxy for human pressure. The analysis revealed significant spatial heterogeneity in settlement distribution, with both clustered and dispersed forms exhibiting distinct exposure levels to environmental hazards. Natural factors, particularly slope gradient and proximity to rivers, emerged as dominant determinants. Furthermore, significant synergistic interactions were observed between environmental attributes and infrastructure accessibility (roads and urban centers), collectively shaping settlement resilience. This integrative geospatial approach enhances understanding of complex rural settlement dynamics in ecologically sensitive mountainous regions. The empirically grounded insights offer a robust decision-support framework for climate adaptation and disaster risk reduction, contributing to more resilient rural planning strategies in Rwanda and similar Central African highland regions. Full article

Background/Objectives: In the last decade, there has been a notable increase in the prevalence of cognitive decline among the elderly population. This phenomenon is further compounded by the concurrent rise in life expectancy, indicating a growing concern for the health and well-being of individuals in this demographic. Dementia has become a disease with a strong social impact, not exclusively limited to its health dimension. It is generally accepted that lifestyle factors and psychological attitudes toward life challenges may serve as protective mechanisms against pathological cognitive decline. The objective of this contribution is to evaluate the impact of lifestyle factors (e.g., physical activity, employment history, nutrition, technology use, etc.), stressors (e.g., illness, rare events, abandonments, home moving, etc.), and sociability (e.g., marriage, active friend network, children proximity, work relationships, etc.) at the onset of pathological cognitive frailty. Methods: In this study, a semi-structured interview was administered to 32 individuals over the age of 65 during their initial neuropsychological evaluation for suspected dementia. Results: Linear regressions with Mini Mental State Examination scores indicated that lifestyle and sociability factors offer a degree of protection against cognitive decline, while stressors were found to be unrelated to this phenomenon. Conclusions: The utilization of contemporary technologies, the possession of a driver’s license, and the maintenance of an active social network have been demonstrated to possess a high degree of predictive value with respect to cognitive reserve in the context of aging. Full article

Urban expansion threatens sustainable development in densely populated countries like Bangladesh. This study aims to quantitatively identify and evaluate the key drivers influencing the spatial distribution of urban surfaces (SDUS) in Chattogram City, providing insights into urban growth patterns over 30 years. Using Landsat 5 and 9 imageries, the Normalized Difference Built-up Index (NDBI) was computed for 1993 and 2023 to map urban surface changes. A total of 16 geospatial variables representing potential drivers were analyzed. Four statistical and machine learning methods, including GeoDetector, Distributed Random Forest (DRF), global Geographically Weighted Random Forest (GWRF), and local GWRF, were employed to quantify individual and interactive influences on SDUS. The Geodetector analysis identified the central business district (CBD) as the most influential driver of urban surface distribution, with a q statistic of 0.22, followed by river proximity (q = 0.14) and administrative boundaries (q = 0.13). Across all models, CBD consistently ranked as a dominant factor. In the Distributed Random Forest (DRF) model, CBD showed the highest importance score (0.57), followed by coastlines (0.35) and rivers (0.35). The DRF model achieved the highest performance (R² = 0.612), outperforming the global GWRF (R² = 0.59) and local GWRF (R² = 0.529). Although variables like the proximity of administrative location and forests have low individual impacts, they show a stronger coupled influence. This industrial port-based economy expanded, facing challenges of uncontrolled urbanization, poor governance, and environmental issues. Promoting mixed land use planning, decentralizing urban governance, and improving coordination among implementing agencies may better resolve these issues. This work may help planners and policymakers in planning future cities and developing policies to promote sustainable urban growth. Full article

Despite growing importance, agricultural landscapes face threats, like fragmentation, shrinkage, and degradation, due to climate change. Although remote sensing and GIS are widely used in monitoring croplands, integrating machine learning, remote sensing, GIS, and landscape metrics for the holistic management of this landscape remains underexplored. Thus, this study monitored crop patterns using random forest (94% accuracy), the role of geographical factors (such as elevation, aspect, slope, maximum and minimum temperature, rainfall, cation exchange capacity, NPK, soil pH, soil organic carbon, soil type, soil water content, proximity to drainage, proximity to market, proximity to road, population density, and profit per hectare production), diversity, combinations, and fragmentation using landscape metrics and a fragmentation index. Findings revealed that slope, rainfall, temperature, and profit per hectare production emerged as significant drivers in shaping crop patterns. However, anthropogenic drivers became deciding factors during spatial overlaps between crop suitability zones. Rice belts were the least fragmented and highly productive with a risk of monoculture. Croplands with a combination of soybean, black grams, and maize were highly fragmented, despite having high diversity with comparatively less production per field. These diverse fields were providing higher profits and low risks of crop failure due to the crop combinations. Equally, intercropping balanced the nutrient uptakes, making the practice sustainable. Thus, it can be suggested that productivity and diversity should be prioritized equally to achieve sustainable land use. The development of the PCA-weighted fragmentation index offers an efficient tool to measure fragmentation across similar agricultural regions, and the integrated approach provides a scalable framework for holistic management, sustainable land use planning, and precision agriculture. Full article

Intercity cooperation networks are critical for addressing regional imbalances and advancing sustainable regional development, yet existing studies typically focus on specific functional domains, rather than the overall intercity cooperation network. To bridge this gap, this study examines the intercity cooperation network in the Pearl River–Xijiang Economic Belt (21 cities, 2014–2023), analyzing its spatio-temporal evolution and influence mechanisms through Social Network Analysis (SNA) and partial least squares structural equation modeling (PLS-SEM). The results reveal the following: (1) the network has undergone three policy-driven development stages: initial–accelerated–steady; (2) a spatial pattern of “east—dominant, west—weak” has emerged, shaped by the radiating influence of core cities; and (3) institutional proximity and cooperation investment are key drivers of network formation, while geographical and organizational proximity exhibit negative impacts. These findings underscore the need for related regional development strategies to foster a more vital and open cooperation network. Overall, this study deepens the understanding of intercity cooperation by revealing its macro-level patterns and influence mechanisms, and provides practical implications for policymakers committed to promoting sustainable regional development. Full article

The photochemical production of tropospheric ozone (O₃) is very closely linked to seasonal cycles and peaks in solar radiation occurring during warm seasons. In the Mediterranean Basin, which is a hotspot for climate and air mass transport mechanisms, boreal warm seasons cause a notable increase in tropospheric O₃, which unlike stratospheric O₃ is not beneficial for the environment. At the Lamezia Terme (code: LMT) World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) station located in Calabria, Southern Italy, peaks of tropospheric O₃ were observed during boreal summer and spring seasons, and were consequently linked to specific wind patterns compatible with increased photochemical activity in the Tyrrhenian Sea. The finding resulted in the introduction of a correction factor for O₃ in the O₃/NO_x (ozone to nitrogen oxides) ratio “Proximity” methodology for the assessment of air mass aging. However, some of the mechanisms driving O₃ patterns and their correlation with other parameters at the LMT site remain unknown, despite the environmental and health hazards posed by tropospheric O₃ in the area. In general, the issue of ozone photochemical pollution in the region of Calabria, Italy, is understudied. In this study, the behavior of O₃ at the site is assessed with remarkable detail using nine years (2015–2023) of data and correlations with surface temperature and solar radiation. The evaluations demonstrate non-negligible correlations between environmental factors, such as temperature and solar radiation, and O₃ concentrations, driven by peculiar patterns in local wind circulation. The northeastern sector of LMT, partly neglected in previous works, yielded higher statistical correlations with O₃ than expected. The findings of this study also indicate, for central Calabria, the possibility of heterogeneities in O₃ exposure due to local geomorphology and wind patterns. A case study of very high O₃ concentrations reported during the 2015 summer season is also reported by analyzing the tendencies observed during the period with additional methodologies and highlighting drivers of photochemical pollution on larger scales, also demonstrating that near-surface concentrations result from specific combinations of multiple factors. Full article

Antecedent soil moisture is a critical driver of hydrological and erosive processes, directly affecting runoff generation and soil loss. An accurate assessment of soil water content (SWC) variability is therefore essential for sustainable land and water management, particularly in arid and semiarid regions. This study explores the use of two emerging nuclear techniques, cosmic ray neutron sensors (CRNS) and proximal gamma ray spectroscopy (PGRS), to monitor SWC at the field scale in a semiarid agricultural field in NE Spain. Changes in soil moisture induced by a 16 mm rainfall event were monitored to evaluate the sensitivity and response of both techniques under dry and wet conditions. A stationary CRNS, located in the centre of the study field, recorded neutron counts at hourly intervals over a two-week period. Complementary PGRS surveys were conducted before and after the rainfall event, including (i) stationary measurements at the four corners of a 20 × 20 m plot, and (ii) mobile stop-and-go measurements along ten transects across the plot, with a spatial resolution of one metre. The results captured clear temporal dynamics in SWC, inferred from neutron count variations, as well as significant differences in ⁴⁰K (cps) measurements, between dry and wet conditions. These differences were observed when comparing the data from both stationary and mobile surveys conducted before and after the event. The integration of CRNS and PGRS offers complementary insights into scale, temporal dynamics and spatial variability, validating and highlighting the potential of these sensors for soil moisture monitoring. Both techniques demonstrated high sensitivity to variations in soil water content, and their complementary capabilities offer a robust, multi-scale approach with clear applications for precision agriculture and soil conservation. Full article

Vibration can have a significant impact on long-term health, driver comfort, and vehicle performance. With a focus on steering wheel vibrations, this study examines both general and local vibrations that affect the driver. Under real-world conditions, a series of controlled test drives were conducted, with high-precision accelerometers mounted on the driver’s seat and steering wheel recording vibration data. The measurements were conducted in accordance with ISO 5349 and ISO 2631-1, which guaranteed a consistent assessment of vibration exposure. The results suggest that the daily vibration exposure for general vibrations at the driver’s seat is significantly lower than the legal limit, as evidenced by the presence of significant frequencies in the vertical (Z) axis. Nevertheless, steering wheel vibrations may cause pain due to their proximity to the resonance frequencies of the human hand–arm system, which have frequency maxima at approximately 35 Hz and harmonic 70 Hz. Additionally, the vibration intensity was elevated at vehicle velocities between 70 and 80 km/h, suggesting the potential presence of a resonance effect within the suspension or powertrain. The results emphasize the significance of advanced vibration reduction strategies in enhancing driver comfort and safety, including the implementation of a well-designed steering system and enhanced seat absorption. This research offers valuable insights for automotive engineers and ergonomics specialists who are interested in minimizing long-term health risks and vibration-induced fatigue. The aim of this study is to indicate the areas of the drive system fault that have a direct impact on the vibrations of the body structure. The article presents an analysis of the recorded vibration results based on which of the areas of change in the comfort of using the vehicle were selected. Full article

Land subsidence poses a significant risk in built-up environments, particularly in geologically complex and tectonically active regions. In this study, we integrated Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques—Persistent Scatterer Interferometry (PS-InSAR) and Small Baseline Subset (SBAS)—with Global Navigation Satellite System (GNSS) observations to assess ground deformation in the Metamorphosis (MET0) area of Attica, Greece. A Kalman filtering approach was applied to fuse displacement measurements from GNSS, PS-InSAR, and SBAS, reducing noise and improving temporal consistency. Additionally, the PS and SBAS vertical displacement data were fused using Kalman filtering to enhance spatial coverage and refine displacement estimates. The results reveal significant subsidence trends ranging between −10 mm and −24 mm in localized zones, particularly near hydrographic networks and active fault systems. Fault proximity, fluvial processes, and unconsolidated sediments were identified as key drivers of displacement. Random Forest regression analysis, coupled with Partial Dependence analysis, demonstrated that distance to faults, proximity to streams, and the presence of stream drops and debris zones were the most influential factors affecting displacement patterns. This study highlights the effectiveness of integrating multi-sensor remote sensing techniques with data-driven machine learning analysis (Kalman filtering) to improve land subsidence assessment. The findings highlight the necessity of continuous geospatial monitoring for infrastructure resilience and geohazard risk mitigation in the Attica region. Full article