Search Results (364)

Earth Observation (EO) and Remote Sensing (RS) data are widely used in various fields, including weather, environment, and natural disaster modeling and prediction. EO and RS done through geostationary satellite constellations in fields such as these are limited to a smaller region, while sun synchronous satellite constellations have discontinuous spatial and temporal coverage. This limits the ability of EO and RS data for near-real-time weather, environment, and natural disaster applications. To address these limitations, we introduce Now Observation Assemble Horizon (NOAH), a multi-modal, sensor fusion dataset that combines Ground-Based Sensors (GBS) of weather stations with topography, vegetation (land cover, biomass, and crown cover), and fuel types data from RS data sources. NOAH is collated using publicly available data from Environment and Climate Change Canada (ECCC), Spatialized CAnadian National Forest Inventory (SCANFI) and United States Geological Survey (USGS), which are well-maintained, documented, and reliable. Applications of the NOAH dataset include, but are not limited to, expanding RS data tiles, filling in missing data, and super-resolution of existing data sources. Additionally, Generative Artificial Intelligence (GenAI) or Generative Modeling (GM) can be applied for near-real-time model-generated or synthetic estimate data for disaster modeling in remote locations. This can complement the use of existing observations by field instruments, rather than replacing them. UNet backbone with Feature-wise Linear Modulation (FiLM) injection of GBS data was used to demonstrate the initial proof-of-concept modeling in this research. This research also lists ideal characteristics for GM or GenAI datasets for RS. The code and a subset of the NOAH dataset (NOAH mini) are made open-sourced. Full article

The intangible accessibility of real estate markets via platforms like Airbnb profoundly influences the urban development industry, propelled by the dynamics of short- to medium-term rentals for tourists. The suggested study aims to examine the association between the prices of listed properties and the influence of proximity to tourist attractions on location-driven pricing. The city of Rome acts as a case study from which to derive pertinent conclusions and proof on the phenomena intended for exploration. The methodological approach relies on a comprehensive classification of locations recognized as tourist attractions, drawn from public resources, travel guides, search engines, and online trends. The identified attractionswere subsequently classified to analyze how spatial proximity influences price formation. Data on short-term rental listings were obtained from the Inside Airbnb platform. The results enable the characterization of Rome as a polycentric urban system, composed of multiple tourism hubs whose spatial interactions are closely associated with prevailing hotel pricing patterns. This study emphasizes the influence of tourist demand on land values, a phenomenon intricately connected to urban gentrification and the capitalization of the real estate market. These findings enhance comprehension of tourism’s impact on the geographical and economic structure of cities. Full article

Precision agriculture increasingly recognizes the transformative potential of unmanned aerial systems (UASs) for crop monitoring and field assessment, yet research consistently highlights significant usability barriers as the main constraints to widespread adoption. Complex mission planning processes, including detailed flight plan creation and way point management, pose substantial technical challenges that mainly affect non-expert operators. Farmers and their teams generally prefer user-friendly, straightforward tools, as evidenced by the rapid adoption of GPS guidance systems, which underscores the need for simpler mission planning in UAS operations. To enhance accessibility and safety in UAS control, especially for non-expert operators in agriculture and related fields, we propose a Secure UAS Control Framework (SAUCF): a comprehensive system for natural-language-driven UAS mission management with integrated dual-factor biometric authentication. The framework converts spoken user instructions into executable flight plans by leveraging a language-model-powered mission planner that interprets transcribed voice commands and generates context-aware operational directives, including takeoff, location monitoring, return-to-home, and landing operations. Mission orchestration is performed through a large language model (LLM) agent, coupled with a human-in-the-loop supervision mechanism that enables operators to review, adjust, or confirm mission plans before deployment. Additionally, SAUCF offers a manual override feature, allowing users to assume direct control or interrupt missions at any stage, ensuring safety and adaptability in dynamic environments. Proof-of-concept demonstrations on a UAS plat-form with on-board computing validated reliable speech-to-text transcription, biometric verification via voice matching and face authentication, and effective Sim2Real transfer of natural-language-driven mission plans from simulation environments to physical UAS operations. Initial evaluations showed that SAUCF reduced mission planning time, minimized command errors, and simplified complex multi-objective workflows compared to traditional waypoint-based tools, though comprehensive field validation remains necessary to confirm these preliminary findings. The integration of natural-language-based interaction, real-time identity verification, human-in-the-loop LLM orchestration, and manual override capabilities allows SAUCF to significantly lower the technical barrier to UAS operation while ensuring mission security, operational reliability, and operator agency in real-world conditions. These findings lay the groundwork for systematic field trials and suggest that prioritizing ease of operation in mission planning can drive broader deployment of UAS technologies. Full article

Industry 4.0 advanced technologies are increasingly used to monitor workers and reduce accident risks to ensure workplace safety. In this paper, we present an on-premise, rule-based safety management system that exploits the fusion of data from an Ultra-Wideband (UWB) Real-Time Locating System (RTLS) and AI-based video analytics to enforce context-aware safety policies. Data fusion from heterogeneous sources is exploited to broaden the set of safety rules that can be enforced and to improve resiliency. Unlike prior work that addresses PPE detection or indoor localization in isolation, the proposed system integrates an UWB-based RTLS with AI-based PPE detection through a rule-based aggregation engine, enabling context-aware safety policies that neither technology can enforce alone. In order to demonstrate the feasibility of the proposed approach and showcase its potential, a proof-of-concept implementation is developed. The implementation is exploited to validate the system, showing sufficient capabilities to process video streams on edge devices and track workers’ positions with sufficient accuracy using a commercial solution. The efficacy of the system is assessed through a set of seven safety rules implemented in a controlled laboratory scenario, showing that the proposed approach enhances situational awareness and robustness, compared with a single-source approach. An extended validation is further employed to confirm practical reliability under more challenging operational conditions, including varying camera perspectives, diverse worker clothing, and real-world outdoor conditions. Full article

Sweet potato (Ipomoea batatas) is a globally important crop and one of a growing number of plants recognized as naturally transgenic, harboring Agrobacterium-derived T-DNA genes whose functions remain largely uncharacterized. In this proof-of-concept study, we applied CRISPR/Cas9 technology to generate targeted knockouts of the Ib-rolB/C and Ib-rolD-like genes located within the sweet potato cellular T-DNA2 (IbT-DNA2) region. Mutations were introduced into sweet potato callus cultures using an optimized genome editing protocol, with most edits consisting of single-nucleotide insertions. Knockout of Ib-rolB/C did not affect callus growth but significantly reduced levels of chlorogenic acid derivatives. Validation in planta using transient expression in I. batatas leaves confirmed the suppressive effect of Ib-rolB/C disruption on polyphenol content. In contrast, Ib-rolD-like knockout lines showed reduced biomass accumulation and downregulation of cell cycle–related genes, but did not display significant changes in metabolite content in either callus cultures or leaf tissues. These findings suggest that Ib-rolB/C and Ib-rolD-like may differentially contribute to growth and secondary metabolism in sweet potato. Full article

Selecting accurate climatic data is crucial for reliable simulations of Renewable Energy Systems (RESs) and the assessment of building energy performance, particularly under ongoing global climate change. Typical Meteorological Year (TMY) datasets are widely used to represent long-term average weather conditions. However, they may not fully capture regional climatic variability, recent temperature or solar radiation trends, potentially leading to substantial discrepancies in simulation outcomes. Despite the widespread use of TMY and reanalysis datasets, limited studies have systematically compared multiple contemporary meteorological databases in the context of RES simulations across Europe. This study evaluates and compares five meteorological databases—Meteonorm, TMY, TMYx, ERA5, and SARAH3—for twenty European capitals located between 38° and 56° N. A transient model developed in TRNSYS was employed to assess the performance of photovoltaic and solar collector systems with different datasets. The results reveal significant differences between datasets, with deviations reaching up to 200–300 kWh/m² in annual total horizontal radiation and 40–50% in simulated useful energy gains. PV efficiency remained relatively stable across Europe (17.7–18.7%) with very low standard deviation (<0.12%), while SC efficiency showed higher variability (25.8–28.7%). The findings demonstrate that the choice of climatic database can substantially influence energy yield predictions, technical optimization, thereby introducing significant uncertainty into the economic bankability assessment of renewable energy projects, especially in Central and Northern Europe, where climatic variability is more pronounced. The study emphasizes the need for careful database selection and periodic validation of TMY datasets in the context of evolving climatic conditions to ensure accurate, risk-aware, and future-proof energy system simulations. Full article

Understanding why households choose particular urban neighborhoods requires bridging traditional rational-choice explanations with emerging evidence on cognitive, social, and informational influences. This study investigates how territorial intelligence (TI)—defined as the availability and use of spatial data, planning information, and participatory knowledge platforms—interacts with behavioral factors to shape residential relocation decisions. Employing an explanatory sequential mixed-methods design, we surveyed 356 recent movers in Casablanca, Morocco, and conducted 20 follow-up semi-structured interviews. Quantitative analysis shows that each additional consulted data source increased the odds of selecting a central, transit-rich location by 45 %, while prior awareness of development plans raised those odds by 60 %, controlling for income, tenure, affordability, dwelling attributes, and socio-demographics. Data use also predicted higher post-move satisfaction, particularly when individual housing preferences aligned with chosen locations. Qualitative findings reveal that residents view territorial data as a tool for “future-proofing” but also experience information overload, leading some to revert to heuristics or social advice. The interplay of rational cost–benefit logic, bounded cognitive processing, and TI-mediated knowledge underscores the need for planning strategies that combine economic fundamentals with behaviorally informed data provision. By integrating micro-level decision evidence with the territorial intelligence framework, the study offers practical guidance for urban planners aiming to nudge residential choices toward more sustainable, policy-consistent outcomes. Full article

The Internet of Things (IoT) offers promising solutions for smart agriculture, particularly in the monitoring of livestock. This paper proposes a contactless, low-cost system for individual cow identification and monitoring in a dairy barn using a single Pan–Tilt–Zoom (PTZ) camera and a YOLOv8 deep learning model. The PTZ camera periodically scans the barn, capturing images that are processed to detect and recognize a specific target cow among the herd without any wearable sensors. The system embeds barn area metadata in each image, allowing it to estimate the cow’s location and compute the frequency of its presence in predefined zones. We fine-tuned a YOLOv8 object detection model to distinguish the target cow, achieving high precision in identification. Experimental results in a real barn environment demonstrate that the system can identify an individual cow with 85.96% Precision and 68.06% Recall, and the derived spatial occupancy patterns closely match ground truth observations. Compared to conventional methods requiring multiple fixed cameras or RFID-based wearables, the proposed approach significantly reduces equipment costs and animal handling stress. It should be noted that the present work serves as a proof-of-concept for targeted cow tracking that identifies and follows a specific individual within a herd rather than a fully generalized multi-cow identification system. Full article

Antarctica, largely free from geopolitical borders, serves as a critical site for scientific research, environmental monitoring and climate studies. The continent’s ice cap holds over 60% of the Earth’s freshwater and provides a stable climatological record spanning 800,000 years. In this study, the relationship between changes in atmospheric CO₂ levels over the past century and the microstructural characteristics of Antarctic ice was investigated. While it is well-documented that CO₂ fluctuations have driven the periodic expansion and retreat of ice sheets, no research to this day has explored how variations in CO₂ concentrations influence the physical integrity of ice at the microscopic scale. To address this, grain size, anisotropy, irregularity, and solidity of surface and near-surface ice samples collected over the past 70 years were analyzed. These microstructural features were compared against historical atmospheric greenhouse gas data from multiple Antarctic research stations, including records from the Scripps Institution of Oceanography, the Japanese Antarctic Research Expedition, and the NOAA Global Monitoring Laboratory. Results reveal a correlation between rising CO₂ levels and changes in ice microstructure, particularly an increase in the grain size as well as the reduction in the grain aspect ratio and in the morphological solidity. The study remains limited by significant sources of variability, including differences in sampling depths, geographical locations, seasonal effects, and inconsistencies in analytical tools and methodologies reported across the literature. Despite these limitations, this proof-of-concept study elicits the need for continued meta-analyses of existing climate datasets. Such efforts could provide deeper insights into the role of greenhouse gas concentrations in defining the microstructural stability of Antarctic ice, which is critical for predicting ice sheet integrity and its contribution to sea level rise. Full article

Spatially selective nerve stimulation is an active area of research, with the capability to reduce side effects and increase the clinical efficacy of nerve stimulation technologies. Several research groups have demonstrated proof-of-concept devices capable of performing spatially selective stimulation with multi-contact cuff electrodes in vivo; however, optimizing the technique is difficult due to the large possibility space granted by a multi-electrode cuff. Our work attempts to elucidate the most valuable stimulation montages (current ratios between stimulating electrodes) provided by a multi-contact cuff. We characterized the performance of five different montage types when stimulating fibers in different “electrode configurations”, with configurations including up to three rings of electrode contacts, 13 different counts of electrodes per ring, and five electrode arc lengths per electrode count (for 195 unique configurations). Selected montages included several methods from prior art, as well as our own. Among montage types, the most spatially selective stimulation was one we refer to as “X-Adjacent” stimulation, in which three adjacent electrodes are active per ring. Optimized X-adjacent montages achieved an average fiber specificity of 71.9% for single-ring electrode configurations when stimulating fibers located at a depth of two-thirds of the nerve radius, and an average fiber specificity of 77.2% for two-ring configurations. These values were the highest among montages tested, and in combination with our other metrics, led these montages to perform best in the majority of cost functions investigated. This success leads us to recommend X-Adjacent montages to researchers exploring spatially selective stimulation. Full article

The methylation of CpG sites with 5mC mark is a dynamic epigenetic modification. However, the relationship between the methylation and the surrounding genomic sequence context remains poorly explored. Investigation of the allele methylation provides an opportunity to decipher the interplay between differences in the primary DNA sequence and epigenetic variation. Here, we performed high-coverage long-read whole-genome direct DNA sequencing of one individual using Oxford Nanopore technology. We also used Illumina whole-genome sequencing of the parental genomes in order to identify allele-specific methylation sites with a trio-binning approach. We have compared the results of the haplotype-specific methylation detection and revealed that trio binning outperformed other approaches that do not take into account parental information. Also, we analysed the cis-regulatory effects of the genomic variations for influence on CpG methylation. To this end, we have used available Deep Learning models trained on the primary DNA sequence to score the cis-regulatory potential of the genomic loci. We evaluated the functional role of the allele-specific epigenetic changes with respect to gene expression using long-read Nanopore RNA sequencing. Our analysis revealed that the frequency of SNVs near allele-specific methylation positions is approximately four times higher compared to the biallelic methylation positions. In addition, we identified that allele-specific methylation sites are more conserved and enriched at the chromatin states corresponding to bivalent promoters and enhancers. Together, these findings suggest that significant impact on methylation can be encoded in the DNA sequence context. In order to elucidate the effect of the SNVs around sites of allele-specific methylation, we applied the Deep Learning model for detection of the cis-regulatory modules and estimated the impact that a genomic variant brings with respect to changes to the regulatory activity of a DNA loci. We revealed higher cis-regulatory impact variants near differentially methylated sites that we further coupled with transcriptomic long-read sequencing results. Our investigation also highlights technical aspects of allele methylation analysis and the impact of sequencing coverage on the accuracy of genomic phasing. In particular, increasing coverage above 30X does not lead to a significant improvement in allele-specific methylation discovery, and only the addition of trio binning information significantly improves phasing. We investigated genomic variation in a single human individual and coupled computational discovery of cis-regulatory modules with allele-specific methylation (ASM) profiling. In this proof-of-concept analysis, we observed that SNPs located near methylated CpG sites on the same haplotype were enriched for sequence features suggestive of high-impact regulatory potential. This finding—derived from one deeply sequenced genome—illustrates how phased genetic and epigenetic data analyses can jointly put forward a hypotheses about the involvement of regulatory protein machinery in shaping allele-specific epigenetic states. Our investigation provides a methodological framework and candidate loci for future studies of genomic imprinting and cis-mediated epigenetic regulation in humans. Full article

Most urban areas have Traffic Management Centers that rely partially on communication with 9-1-1 centers for incident detection. This level of awareness is often lacking for rural interstates spanning several 9-1-1 centers. This paper presents a novel approach to extending TMC visibility by automatically monitoring regional 9-1-1 dispatch channels using off-the-shelf hardware and open-source speech-to-text libraries. Our study presents a proof-of-concept study servicing 71 miles of rural I-65 in Indiana, successfully monitoring four county dispatch centers from a single location, and efficiently transcribing live audio within 60 s of broadcast. This work’s primary contribution is demonstrating the feasibility and practical value of automated incident detection systems for rural interstates. This technology is implementation-ready for extending the visibility of Traffic Management Centers in rural interstate segments. Further work is underway for developing scalable procedures for integrating multiple remote sites, extracting more diverse keyword sets, investigating optimal speech-to-text models, and assessing the technical aspects of the experimental procedures of this manuscript. Full article

This paper proposes a novel electric wheelchair that uses the surface electromyographic signal (sEMG) signals generated by the occlusal muscles to control the wheelchair during occlusion, instead of the traditional electric wheelchair that requires users to use their hands or feet for control. In this work, the myoelectric signal controls the electric wheelchair so that users with limited mobility and paraplegia can operate the electric wheelchair using the myoelectric signal generated during clenching. This is achieved through the seamless transmission of user data and GPS paths to the cloud and is facilitated by the state-of-the-art Wi-Fi 6E communication technology. By leveraging cloud connectivity, the system can instantly relay critical information, such as the user’s location and movement patterns, ensuring a prompt emergency response. Furthermore, several standard methods exist to set up the myoelectric signal electrodes and analyze the signals. This novel electric wheelchair can change the daily activities of many users who have difficulty walking. This work is presented as a proof-of-concept feasibility study rather than a comprehensive clinical validation. Full article

The acid phosphatase (AP) test is widely utilised in forensic biology laboratories to examine for the presence of semen on crime evidence. If semen is present, the AP-positive areas are marked on the exhibit to indicate the precise location of the semen stain. However, documenting AP-positive areas with a crayon is time-consuming and laborious. In this proof-of-concept study, we evaluated the use of Saral Wax-Free Transfer Taper (TP) as an alternative tool for tracing the boundaries of AP-positive areas. We demonstrated that the TP pigment did not inhibit PCR amplification, as indicated by consistent internal PCR control (IPC) C_T values during real-time DNA quantification. While a reduction in DNA yield was observed under stress-test conditions, where TP pigment was intentionally included in the samples, complete STR profiles were still recovered with no allele dropout. Importantly, the documenting time for AP mapping was reduced by approximately five-fold with TP compared to crayon, underscoring its potential to enhance efficiency in forensic laboratory workflows. Full article

Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low clinical value in diagnosis. Interpretation of the acoustic characteristics of BSs, i.e., using a phonoenterogram (PEG), may aid in diagnosing various GI conditions non-invasively. Use of artificial intelligence (AI) and improvements in computational analysis can enhance the use of PEGs in different GI diseases and lead to a non-invasive, cost-effective diagnostic modality that has not been explored before. The purpose of this work was to develop an automated AI model, You Only Listen Once (YOLO), to detect prominent bowel sounds that can enable real-time analysis for future GI disease detection and diagnosis. A total of 110 2-minute PEGs sampled at 44.1 kHz were recorded using the Eko DUO^® stethoscope from eight healthy volunteers at two locations, namely, left upper quadrant (LUQ) and right lower quadrant (RLQ) after IRB approval. The datasets were annotated by trained physicians, categorizing BSs as prominent or obscure using version 1.7 of Label Studio Software^®. Each BS recording was split up into 375 ms segments with 200 ms overlap for real-time BS detection. Each segment was binned based on whether it contained a prominent BS, resulting in a dataset of 36,149 non-prominent segments and 6435 prominent segments. Our dataset was divided into training, validation, and test sets (60/20/20% split). A 1D-CNN augmented transformer was trained to classify these segments via the input of Mel-frequency cepstral coefficients. The developed AI model achieved area under the receiver operating curve (ROC) of 0.92, accuracy of 86.6%, precision of 86.85%, and recall of 86.08%. This shows that the 1D-CNN augmented transformer with Mel-frequency cepstral coefficients achieved creditable performance metrics, signifying the YOLO model’s capability to classify prominent bowel sounds that can be further analyzed for various GI diseases. This proof-of-concept study in healthy volunteers demonstrates that automated BS detection can pave the way for developing more intuitive and efficient AI-PEG devices that can be trained and utilized to diagnose various GI conditions. To ensure the robustness and generalizability of these findings, further investigations encompassing a broader cohort, inclusive of both healthy and disease states are needed. Full article