Search Results (499)

Rapid urbanization and climate change are intensifying heat exposure in cities, making effective adaptation strategies essential. This study presents a streamlined digital twin modeling framework for simulating the impact of nature-based solutions (NBSs) on outdoor thermal comfort, developed within the Intelligent Communities Lifecycle (ICL) software suite. The approach automates the import of urban geometry from OpenStreetMap and integrates geolocated weather data, enabling users to efficiently test scenarios involving NBSs and surface material modifications. Outdoor thermal comfort is quantified using the Universal Thermal Climate Index (UTCI), with results visualized through an interactive cloud-based 3D platform to support participatory urban planning. The methodology is demonstrated in Meunierstraat, Leuven (Belgium), where three planning alternatives are compared across seasonal extremes. Simulations show that targeted NBS interventions, particularly temporary participatory measures, can improve thermal comfort under extreme heat. However, the benefits are seasonally dependent and spatially heterogeneous, emphasizing the value of high-resolution, scenario-based analysis. This integrated workflow enhances both technical evidence and stakeholder engagement. While the tool is capable of linking outdoor comfort improvements with building energy performance and carbon emissions, the present paper focuses solely on the outdoor thermal comfort results, leaving indoor–outdoor coupling analysis as a direction for future work. Full article

In recent years, cycling mobility in urban environments across Spain has grown significantly, driven by sustainability policies and behavioral shifts following the COVID-19 pandemic. However, this growth has been accompanied by an increase in accidents in urban areas, where more than 72.6% of cyclist accidents are concentrated, with large cities being the most affected. This study aims to explore and analyze the factors influencing cycling accidents in Spanish municipalities with populations exceeding 50,000, during the period of 2020–2023. A total of 24 variables were analyzed, encompassing not only innovative cyclist infrastructure network features (line connectivity), but also urban morphology and street infrastructure, weather conditions and mobility (all transportation modes). The methodological approach combines Principal Component Analysis (PCA) with two negative binomial regression models: one addressing all cycling accidents, and another focusing specifically on collisions between cyclists and motor vehicles. PCA shows the complex relations between urban features when comparing cyclist accidents among cities. The main results from the Negative Binomial analysis show that increased bicycle lane length significantly reduces cycling accident risk, while higher intersections with traffic signal density are associated with a greater likelihood of car–bicycle crashes. These findings emphasize the importance of cycling infrastructure provision and intersection design and regulation as key policy levers for improving urban cyclist safety. Future research should seek to corroborate these results through micro-spatial analyses and accident geolocation, assessing their severity and accounting for more detailed data on cycling infrastructure. Finally, the results’ discussion underscores the importance of implementing holistic urban mobility strategies that prioritize cyclist safety. Full article

Conventional Security Operations Center (SOC) solutions struggle to process representative operational alert streams efficiently and adapt to evolving cyber threats, highlighting the need for automated, intelligent threat detection and prioritization. This study presents a custom AI-driven framework that leverages unsupervised learning techniques to support SOC analysts in cyber threat detection, anomaly identification, and alert prioritization. The framework applies several clustering methods: HDBSCAN, DBSCAN, KMeans, and Gaussian Mixture Models for alert segmentation, and integrates anomaly detection using LOF and Isolation Forest, complemented by semi-supervised detection via One-Class SVM. Using textual, categorical, and numerical features from Wazuh alerts across three datasets, the system performs clustering and anomaly detection in the original high-dimensional feature space, with UMAP applied solely for two-dimensional visualization. HDBSCAN consistently produced well-separated clusters with effective noise detection, while, Isolation Forest evaluated via 10-fold cross-validation exhibited stable anomaly flagging and clear score separation across both cyber alert event data and synthetic threat injection experiments. Furthermore, the framework formulates a composite priority ranking that integrates anomaly severity, cluster rarity, and SOC contextual weighting, yielding actionable alert rankings. An interactive, analyst-centric dashboard enables SOC teams to explore top alerts, clusters, associated MITRE techniques, priority rankings, and geolocation data, providing insights while preserving human oversight. Overall, the proposed system transforms complex alert streams into structured insights, enhancing SOC situational awareness, decision support, and operational efficiency. Full article

Background/Objectives: The Democratic Republic of the Congo (DRC) has a history of low coverage (<50%) with all first-year-of-life vaccines for children aged 12–23 months, resulting in frequent outbreaks of vaccine-preventable diseases. In response, the DRC’s Expanded Program on Immunization (EPI) is applying innovations to improve vaccination coverage, including using geospatial data to inform vaccination planning (geo-referenced microplans). This report describes a proof of concept to geo-locate, by locality of residence, never-vaccinated children (NVC) or incompletely vaccinated children (IVC); use those data to prepare geo-referenced microplans for rounds of Periodic Intensification of Routine Immunization (PIRIs); and implement the PIRIs. Methods: In 2022, in Kindu and Kibombo Health Zones (HZs), Maniema Province, DRC, children aged 0–23 months were enumerated with inquiries about their vaccination status and reasons for non-vaccination by locality of residence. The enumeration was coupled with the collection of the localities’ geographic coordinates, facilitating the spatial illustration of estimated proportions of NVC by locality. Coordinates for HZ and health area (HA) landmarks and borders were also collected. We created maps that informed geo-referenced PIRI microplans, placing an emphasis on deploying vaccination teams to localities with high proportions of NVC, especially those in remote and riverine locations. To account for inclusion of children aged up to 59 months in the PIRIs, enumeration data were extrapolated to estimate the numbers of NVC and IVC in this wider age range. Volunteers mobilized communities for the PIRIs, HA staff vaccinated age-eligible children, and vaccination teams were geographically tracked. Results: In Kindu, 29,837 children aged 0–23 months were enumerated in 430 localities; among them, 38% were NVC and 6% IVC. In Kibombo, 9582 children aged 0–23 months were enumerated in 168 localities; among them, 50% were NVC and 16% IVC. In both HZs, reasons for never vaccination were primarily associated with knowledge- or belief-related factors, while reasons for incomplete vaccination were associated with access-related factors. Between HAs and localities, there was heterogeneity in the proportions of NVC and IVC and in the reasons for non-vaccination. The numbers of NVC and IVC aged 0–59 months were estimated at 28,220 and 4613 in Kindu and 12,038 and 3785 in Kibombo. Approximately 2000 health staff and community volunteers were engaged for implementation of each of the three PIRIs. The number of children vaccinated during the three PIRIs ranged from 15,500 to 26,500 and from 10,500 to 15,500 in Kindu and Kibombo, respectively. Data suggest that vaccinated children originated from >90% of localities identified during the cartography. Tracking data showed that vaccination teams visited localities with high proportions of NVC, including those that were remote and riverine. Conclusions: Geo-referenced microplanning with engagement of health staff and communities succeeded in vaccinating at least 40,000 children who were not routinely benefiting from health services in two HZs in the DRC; similar innovative strategies could be considered elsewhere. Applying new technologies to existing microplanning strategies can enhance their success. Full article

Modern combine harvesters can collect real-time geolocated yield data, but it is subject to errors. Various protocols have been proposed to clean this data, each with varying levels of complexity. This data is valuable for precision agriculture to implement site-specific management and to train models to predict yield using remote sensing data. Machine learning and deep learning techniques have shown their potential for precision agriculture, and their performance shows no significant differences between models trained with data cleaned using a computationally demanding protocol or a simpler one, such as parametric filtering. However, parametric filtering approaches primarily rely on statistics that are highly sensitive to data distribution and do not effectively filter inliers. The objective of this study is to develop a data-cleansing method that leverages robust statistical measures, specifically the median and interquartile range, to effectively identify and filter outliers and inliers while retaining valid observations in datasets collected from combine harvesters, thereby minimizing the influence of non-normal data distributions. Different levels of data cleaning were applied to a total of 7399 ha of wheat and barley crops, and the quality of each cleaning level was compared. The selected protocol improved the spatial structure of the data, deleting up to 42% and 33% of the data at the polygon level, for wheat and barley, respectively. It increased the mean and median, and decreased the standard deviation and coefficient of variation of the data. Between 78.7% and 82.9% of the fields showed a normal distribution after applying the selected method, and machine learning performance improved compared with the raw data. Compared with previous data cleaning studies, the present work proposes an automatic, low-computational, parametric filtering method that uses robust statistics for non-normal distributions. In addition, its scalability has been demonstrated by applying the method to a large dataset, improving data quality and the performance of yield-prediction ML models in all cases. Full article

Municipally provided water is low-cost, considered safe in most communities, and usually fluoridated to improve oral health. Yet, many Chicago region families report relying on other water sources. We investigated if safety and quality concerns were associated with these decisions; we also investigated whether there were spatial trends related to lead risk associated with water choice preferences. We used self-reported water consumption behavior data from the Coordinated Oral Health Promotion (CO-OP) Study, a longitudinal cohort of young children and their families. Respondents’ residences (N = 331) were geolocated at the census tract level. We evaluated associations between parent demographics, estimated lead risk and water preferences. Among those who “Never” gave their children tap water, we investigated demographic characteristics associated with viewing tap water as “Not safe”. Sixty-five percent (n = 216) of caregivers report that their child “Never” drinks tap water. Ordinal logistic regression indicates that parents aged <30 years are more likely to respond “Never” relative to “Sometimes” or “Always” (OR = 1.89; CI = 1.04, 3.40). Among those in the “Never” category, we grouped reasons into safety concerns (n = 114), observed quality concerns (n = 48), and preference (n = 40). We found that the decision not to give children municipal water is not aligned with the estimated lead risk. Understanding water consumption choice mechanisms is important for communities seeking safe and quality drinking water. Full article

Unexploded ordnance (UXO) poses a significant hazard in controlled outdoor testing/training areas. This paper assesses the effectiveness of aerial drone-mounted magnetometry for detecting buried UXO located outside the designated landing areas of the National Training Center (CENAD) of San Gregorio (Zaragoza, Spain), considered the largest manoeuvre area in Europe. To this end, a high-resolution aeromagnetic survey was conducted using a GEM GSMP-35U proton magnetometer mounted on a hexacopter drone. Data were collected at flight heights of 7 m and 2 m above ground level along a grid with 1 m line spacing. For its validation, eleven UXOs were deliberately buried at known coordinates to evaluate the system’s sensitivity and spatial resolution under operational conditions. The results demonstrate the capability of aerial drone-based magnetometry to detect small magnetic anomalies (with amplitudes between 2 and 18 nT) associated with buried UXO in complex environments characterised by high ferromagnetic noise, achieving signal-to-noise ratios greater than 5 (SNR > 5) at 2 m height and a geolocation accuracy of approximately 0.5 m. These findings support the use of unmanned aerial magnetometry as a viable tool for identifying hazardous remnants in military training ranges and field scenarios, enabling coverage of 0.53 ha in less than one hour of effective flight time. Full article

The increasing complexity of fleet operations often forces drivers and administrators to alternate between fragmented tools for geolocation, messaging, and spreadsheet-based reporting, which slows response times and increases cognitive load. This study evaluates a comprehensive architectural framework designed to automate fleet management in personnel transport companies. The research proposes a unified methodology integrating Internet-of-Things (IoT) telemetry, cloud analytics, and Conversational AI to mitigate information fragmentation. Through a Lean UX iterative process, the proposed system was modeled and validated, with 30 participants (10 administrators and 20 drivers) who performed representative operational tasks in a simulated environment. Usability was assessed through the System Usability Scale (SUS), obtaining a score of 71.5 out of 100, classified as “Good Usability”. The results demonstrate that combining conversational interfaces with centralized operational data reduces friction, accelerates decision-making, and improves the overall user experience in fleet management contexts. Full article

This study is essential for medium- and long-term land-use management, as land-use patterns directly influence local economic and social development. Geographic Information System (GIS) techniques are fundamental tools for analyzing a wide range of geomorphological processes, including relief fragmentation density, relief energy, soil texture, slope gradient, and slope orientation. The present research focuses on the Pesceana river basin in the Southern Carpathians, Romania. It addresses three main objectives: (1) to analyze land-use dynamics derived from CORINE Land Cover (CLC) data between 1990 and 2018, along with the long-term distribution of the Normalized Difference Vegetation Index (NDVI) for the period 2000–2025; (2) to evaluate the basin’s natural potential byintegrating topographic data (contour lines and profiles) with relief fragmentation density, relief energy, vegetation cover, soil texture, slope gradient, aspect, the Stream Power Index (SPI), and the Topographic Wetness Index (TWI); and (3) to assess the spatial distribution of habitat types, characteristic plant associations, and soil properties obtained through field investigations. For the first two research objectives, ArcGIS v. 10.7.2 served as the main tool for geospatial processing. For the third, field data were essential for geolocating soil samples and defining vegetation types across the entire 247 km² area. The spatiotemporal analysis from 1990 to 2018 reveals a landscape in which deciduous forests clearly dominate; they expanded from an initial area of 80 km² in 1990 to over 90 km² in 2012–2018. This increase, together with agricultural expansion, is reflected in the NDVI values after 2000, which show a sharp increase in vegetation density. Interestingly, other categories—such as water bodies, natural grasslands, and industrial areas—barely changed, each consistently representing less than 1 km² throughout the study period. These findings emphasize the importance of land-use/land-cover (LULC) data within the applied GIS model, which enhances the spatial characterization of geomorphological processes—such as vegetation distribution, soil texture, slope morphology, and relief fragmentation density. This integration allows a realistic assessment of the physical–geographic, landscape, and pedological conditions of the river basin. Full article

This study provides a comprehensive, long-term evaluation of inter-sensor radiometric calibration biases for the NOAA OMPS Nadir and CrIS instruments using four complementary validation methodologies implemented within the Inter-Sensor Radiometric Bias Assessment (iSensor-RCBA) portal, a component of the STAR Integrated Calibration/Validation System. Overall, SDR data quality from the three OMPS Nadir instruments and three CrIS instruments aboard SNPP, NOAA-20, and NOAA-21 remains stable. The iSensor-RCBA portal has also proven to be a powerful diagnostic resource, enabling the detection of both new and previously unrecognized calibration issues and anomalies. Using the 32-day averaged difference method, we were the first to discover and identify the root cause of an inconsistency near 280 nm in inter-sensor radiometric biases between the SNPP and NOAA-20 OMPS NP instruments. The same method also revealed an unusual radiometric feature in NOAA-21 CrIS SDRs over the southern high latitudes during spring and summer. In addition, we derived decade-long degradation rates at 11 Metop-B GOME-2 wavelengths using an independent dataset—Simultaneous Nadir Overpass observations between SNPP OMPS and Metop-B GOME-2. Furthermore, iSensor-RCBA monitoring confirmed two geolocation anomalies in SNPP CrIS through a new approach involving SNO-based inter-sensor biases between GOES-16 ABI and SNPP CrIS. These cases demonstrate that iSensor-RCBA is not only a monitoring visualization tool but also a diagnostic tool that delivers unique, complementary insight into instrument performance, enabling early identification of radiometric and geolocation issues across JPSS and other satellite missions. Importantly, the analysis methods used in this study are broadly applicable to current and future missions, including JPSS-03, JPSS-04, and non-NOAA satellite systems. Full article

The transition toward Agriculture 5.0 requires intelligent and autonomous monitoring systems capable of providing early, accurate, and scalable crop health assessment. This study presents the design and field evaluation of an artificial intelligence (AI)–based unmanned aerial vehicle (UAV) system for the detection of Botrytis cinerea in vineyards using multispectral imagery and deep learning. The proposed system integrates calibrated multispectral data with vegetation indices and a YOLOv8 object detection model to enable automated, geolocated disease detection. Experimental results obtained under real vineyard conditions show that training the model using the Chlorophyll Absorption Ratio Index (CARI) significantly improves detection performance compared to RGB imagery, achieving a precision of 92.6%, a recall of 89.6%, an F1-score of 91.1%, and a mean Average Precision (mAP@50) of 93.9%. In contrast, the RGB-based configuration yielded an F1-score of 68.1% and an mAP@50 of 68.5%. The system achieved an average inference time below 50 ms per image, supporting near real-time UAV operation. These results demonstrate that physiologically informed spectral feature selection substantially enhances early Botrytis cinerea detection and confirm the suitability of the proposed UAV–AI framework for precision viticulture within the Agriculture 5.0 paradigm. Full article

Business diversity in proximity-based environments is emerging as an important requirement in urban planning, especially with the rise of concepts such as the 15-min city, which aim to enhance urban vitality. While many studies have focused on assessing vitality through the conditions defined by Jane Jacobs, few have specifically measured commercial diversity and analyzed its relationship with place popularity, attendance, and tourism activity. Using geo-localized data on businesses and Google Maps reviews in Paris, a diversity index was constructed based on Shannon entropy derived from business categories—Culture and leisure, Food and beverage, Retail stores, Local services—and explored its correlations through statistical analysis. The study reveals a higher level of commercial diversity in central areas compared to the outskirts, as indicated by spatial clustering analysis, along with a positive association between diversity and attendance. However, no significant relationship was observed between commercial diversity and the popularity of the selected establishments. These findings may inform policymakers and urban planners in designing more locally diversified cities and, more broadly, in promoting sustainable urban vitality. Full article

Gender-based violence is a widespread and persistent social scourge. The most effective strategy to reduce its impact is prevention, which has led to the adoption of a hand gesture conventionally recognized as a request for help. In addition, in cases of confirmed risk, a Judge may order the potential aggressor to wear an electronic bracelet to prevent them from approaching the victim. However, these measures have proven largely insufficient, as incidents of gender-based violence continue to recur. To address this limitation, the author developed an application, named “no pAIn app”, based on artificial intelligence (AI), designed to create a virtual shield for potential victims. The app, which can run on both smartphones and smartwatches, automatically sends help requests with geolocation data when AI detects a real danger situation. The process is fully autonomous and does not require any user intervention, ensuring fast, discreet, and reliable assistance even when the victim cannot act directly. Scenario-based tests in realistic domestic environments showed that configured danger keywords were reliably detected in the vast majority of test cases, with end-to-end alert delivery typically completed within two seconds. Preliminary battery profiling indicated approximately 5% consumption over 24 h of continuous operation confirming the feasibility of long-term daily use. Full article

Satellite on-board registration is becoming increasingly prevalent since it shortens the data processing chain, enabling users to acquire actionable information more efficiently. However, current on-board processing hardware exhibits severely constrained storage and computational resources, making traditional ground-based methods infeasible in terms of storage and time efficiency. Meanwhile, real-time orbit parameters are normally less accurate, causing a large initial geolocation offset. In this paper, we propose a novel registration framework based on a well-designed lightweight universal database to address the challenges of limited storage as well as poor initial accuracy. Firstly, for the global matching step, a lightweight universal database is designed by storing a feature vector of control points instead of a traditional basemap (such as Digital Orthophoto Map and Digital Surface Model) for on-board processing. We replace the keypoint detection stage with a sparse sampling strategy, which significantly improves time efficiency. In addition, the sparsely sampled control points avoid the problem of keypoint repeatability, allowing the proposed method to perform robust global matching with few control points and little storage usage. Then, for the local matching step, we introduce relative total variation to extract the most obvious and significant structures from the basemap, so that unimportant feature or noise can be omitted from the database. Combined with Run-Length Encoding, the masked binary edge feature yields high precision with considerably reduced storage. Quantitative experiments demonstrate that the proposed reference database occupies less than $5\%$ of raw image storage, while maintaining efficiency and accuracy comparable to SOTA methods. Full article

The substantial domain discrepancy inherent in multi-source and multi-view imagery presents formidable challenges to achieving precise drone-based multi-view geo-localization. Existing methodologies primarily focus on designing sophisticated backbone architectures to extract view-invariant representations within abstract feature spaces, yet they often overlook the rich and discriminative frequency-domain cues embedded in multi-view data. Inspired by the principles of $\pi$-Noise theory, this paper proposes a frequency-domain Positive-Incentive Information Screening (PIIS) mechanism that adaptively identifies and preserves task-relevant frequency components based on entropy-guided information metrics. This principled approach selectively enhances discriminative spectral signatures while suppressing redundant or noisy components, thereby improving multi-view feature alignment under substantial appearance and geometric variations. The proposed PIIS strategy demonstrates strong architectural generality, as it can be seamlessly integrated into various backbone networks including convolutional-based and Transformer-based architectures while maintaining consistent performance improvements across different models. Extensive evaluations on the University-1652 and SUES-200 datasets have validated the great potential of the proposed method. Specifically, the PIIS-N model achieves a Recall@1 of 94.56% and a mean Average Precision (mAP) of 95.44% on the University-1652 dataset, exhibiting competitive accuracy among contemporary approaches. These findings underscore the considerable promise of frequency-domain analysis in advancing multi-view geo-localization. Full article