Search Results (1,570)

This study examines how emerging sensor-based technologies can augment the personality expression of digital characters across multiple media. While digital animation and games have traditionally relied on movement to convey traits, the integration of motion capture, wearable biosensors, and live coding introduces new opportunities for dynamic, embodied character design. Drawing on the MONOLOVE saga, we developed four prototypes across animation, games, interactive performance, and interactive networked environments. Central to our approach is the Wheel of Personality model, a structured taxonomy that organizes expressive parameters into four categories: Character Structure, Motion–Action, Interaction, and Environment. Each prototype was designed to explore how these categories, mediated through sensor technologies, contribute to the perception of personality traits. An evaluation with 14 participants from diverse backgrounds employed questionnaires and interviews to assess the alignment between intended and perceived character traits. The results show that movement and interaction were consistently identified as the most influential cues, while the impact of environmental factors varied across media. Additional influences included narration and the personality of the audience, underscoring the interpretive nature of perception. We conclude that personality expression emerges from the interplay of multimodal cues and context, offering methodological insights and frameworks for designing expressive and emotionally resonant digital characters in trans-media productions. Full article

Global population growth, intensifying climate change, and escalating food security demands are mounting. In response, modern agriculture must transcend the limitations of traditional experience-based cultivation models to address issues such as low resource utilization, poor environmental adaptability, and significant yield fluctuations. As the core technical support of smart agriculture, agricultural sensors have become the key to transformation. This review systematically introduces the classification and working principles of current mainstream agricultural sensors: according to the monitoring parameters, they can be divided into humidity sensors, light sensors, gas sensors, pressure sensors, nutrient sensors, etc. At the same time, breakthroughs in emerging technologies such as microneedle sensing, nanosensing, and wireless sensor networks are being explored, which are breaking the application limitations of traditional sensors in complex agricultural environments. Combined with specific cases, the practical value of sensor technology is improving in agricultural drought monitoring, soil detection, and agricultural product quality assessment. Looking ahead, if agricultural sensors can overcome existing limitations through breakthroughs in material innovation, multi-sensor unit integration, and artificial intelligence algorithm fusion, this will provide stronger technological support for the further advancement of smart agriculture. Full article

Anaerobic digestion (AD) is increasingly recognized as a key technology for renewable energy generation and sustainable waste management within the circular economy. However, its performance is highly sensitive to feedstock variability and environmental fluctuations, making stable operation and high methane yields difficult to sustain. Conventional monitoring and control systems, based on limited sensors and mechanistic models, often fail to anticipate disturbances or optimize process performance. This review discusses recent progress in electrochemical, optical, spectroscopic, microbial, and hybrid sensors, highlighting their advantages and limitations in artificial intelligence (AI)-assisted monitoring. The role of soft sensors, data preprocessing, feature engineering, and explainable AI is emphasized to enable predictive and adaptive process control. Various machine learning (ML) techniques, including neural networks, support vector machines, ensemble methods, and hybrid gray-box models, are evaluated for yield forecasting, anomaly detection, and operational optimization. Persistent challenges include sensor fouling, calibration drift, and the lack of standardized open datasets. Emerging strategies such as digital twins, data augmentation, and automated optimization frameworks are proposed to address these issues. Future progress will rely on more robust sensors, shared datasets, and interpretable AI tools to achieve predictive, transparent, and efficient biogas production supporting the energy transition. Full article

The use of digital tools in agroecological crop production can help mitigate current farming challenges such as labour shortage and climate change. The aim of this study was to map digital tools used in crop production, assess their impacts across economic, environmental, and social dimensions, and determine their potential as enablers of agroecology. A systematic search and screening process, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses methodology, identified 453 relevant studies. The results showed that most digital tools are applied for crop monitoring (83.4%), with unmanned aerial vehicles (37.7%) and camera sensors (75.2% combined) being the most frequently used technologies. Farm Management Information Systems (57.6%) and Decision Support Systems (25.2%) dominated the tool categories, while platforms for market access, social networking, and collaborative learning were rare. Most tools addressed the first tier of agroecology, which refers to input reduction, highlighting a strong focus on efficiency improvements rather than systemic redesign. Although digital tools demonstrated positive contributions to social, environmental, and economic dimensions, studies concentrated mainly on economic benefits. Future research should investigate the potential role of digital technologies in advancing higher tiers of agroecology, emphasising participatory design, agroecosystem services, and broader coverage of the agricultural value chain. Full article

The biogeochemical cycling of nitrogen (N) in natural waterbodies, ranging from freshwaters to estuaries and seawater, is fundamental to the health of aquatic ecosystems. Anthropogenic pressures (agricultural runoff, atmospheric deposition, and wastewater discharge) have profound effects on these cycles, leading to widespread problems, such as eutrophication, harmful algal blooms, and contamination of drinking water sources. Monitoring of different N-species—ammonium (NH₄⁺), nitrite (NO₂⁻), nitrate (NO₃⁻) ions, dissolved organic nitrogen (DON), and total nitrogen (TN)—is of crucial importance to protect and mitigate environmental harm. Traditional analytical methodologies, while providing accurate laboratory data, are hampered by logistical complexity, high cost, and the inability to capture transient environmental events in near-real time. In response to this demand, miniaturised microfluidic technologies offer the opportunity for rapid, on-site measurements with significantly reduced reagent/sample consumption and the development of portable sensors. Here, we review and critically evaluate the principles, state-of-the-art applications, inherent advantages, and ongoing challenges associated with the use of microfluidic colorimetry for N-species in a variety of environmental waterbodies. We explore adaptations of classical colorimetric chemistry to microfluidic-based formats, examine strategies to mitigate complex matrix interferences, and consider future trajectories with autonomous platforms and smart sensor networks for simultaneous multiplexed N-species determination. Full article

The Domain Name System (DNS) is a fundamental component of the Internet, yet its distributed and caching nature makes it susceptible to various attacks, especially cache poisoning. Although the use of random port numbers and transaction IDs has reduced the probability of cache poisoning, recent developments such as DNS Forwarder fragmentation and side-channel attacks have increased the possibility of cache poisoning. To counteract these emerging cache poisoning techniques, this paper proposes the DNS Cache Sensor (DNS-Sensor) system, which operates as a distributed sensor network for DNS security. Like environmental sensors monitoring physical parameters, DNS-Sensor continuously scans DNS cache records, comparing them with authoritative data to detect anomalies with sensor-grade precision. It involves checking whether the DNS cache is consistent with authoritative query results by continuous observation to determine whether cache poisoning has occurred. In the event of cache poisoning, the system switches to a disaster recovery resolution system. To expedite comparison and DNS query speeds and isolate the impact of cache poisoning on the disaster recovery resolution system, this paper uses a local top-level domain authoritative mirror query system. Experimental results demonstrate the accuracy of the DNS-Sensor system in detecting cache poisoning, while the local authoritative mirror query system significantly improves the efficiency of DNS-Sensor. Compared to traditional DNS, the integrated DNS query and DNS-Sensor method and local top-level domain authoritative mirror query system is faster, thus improving DNS performance and security. Full article

Urban tree detection is fundamental to effective forestry management, biodiversity preservation, and environmental monitoring—key components of sustainable smart city development. This study introduces a deep learning framework for urban tree crown segmentation that exclusively leverages high-resolution satellite imagery from GeoEye-1, WorldView-2, and WorldView-3, thereby eliminating the need for additional data sources such as LiDAR or UAV imagery. The proposed framework employs a Residual U-Net architecture augmented with Attention Gates (AGs) to address major challenges, including class imbalance, overlapping crowns, and spectral interference from complex urban structures, using a custom composite loss function. The main contribution of this work is to integrate data from three distinct satellite sensors with varying spatial and spectral characteristics into a single processing pipeline, demonstrating that such well-established architectures can yield reliable, high-accuracy results across heterogeneous resolutions and imaging conditions. A further advancement of this study is the development of a hybrid ground-truth generation strategy that integrates NDVI-based watershed segmentation, manual annotation, and the Segment Anything Model (SAM), thereby reducing annotation effort while enhancing mask fidelity. In addition, by training on 4-band RGBN imagery from multiple satellite sensors, the model exhibits generalization capabilities across diverse urban environments. Despite being trained on a relatively small dataset comprising only 1200 image patches, the framework achieves state-of-the-art performance (F1-score: 0.9121; IoU: 0.8384; precision: 0.9321; recall: 0.8930). These results stem from the integration of the Residual U-Net with Attention Gates, which enhance feature representation and suppress noise from urban backgrounds, as well as from hybrid ground-truth generation and the combined BCE–Dice loss function, which effectively mitigates class imbalance. Collectively, these design choices enable robust model generalization and clear performance superiority over baseline networks such as DeepLab v3 and U-Net with VGG19. Fully automated and computationally efficient, the proposed approach delivers cost-effective, accurate segmentation using satellite data alone, rendering it particularly suitable for scalable, operational smart city applications and environmental monitoring initiatives. Full article

Bathing water quality (BWQ) monitoring and prediction are essential to safeguard public health by informing bathers about the risk of exposure to faecal indicator bacteria (FIBs). Traditional monitoring approaches, such as manual sampling and laboratory analysis, while effective, are often constrained by delayed reporting, limited spatial and temporal coverage, and high operational costs. The integration of artificial intelligence (AI), particularly machine learning (ML), with automated data sources such as environmental sensors and satellite imagery has offered novel predictive and real-time monitoring opportunities in BWQ assessment. This systematic literature review synthesises current research on the application of AI in BWQ assessment, focusing on predictive modelling techniques and remote sensing approaches. Following the PRISMA methodology, 63 relevant studies are reviewed. The review identifies dominant modelling techniques such as Artificial Neural Networks (ANN), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Hybrid and Ensemble Boosting algorithms. The integration of AI with remote sensing platforms such as Google Earth Engine (GEE) has improved the spatial and temporal solution of BWQ monitoring systems. The performance of modelling approaches varied depending on data availability, model flexibility, and integration with alternative data sources like remote sensing. Notable research gaps include short-term faecal pollution prediction and incomplete datasets on key environmental variables, data scarcity, and model interpretability of complex AI models. Emerging trends point towards the potential of near-real-time modelling, Internet of Things (IoT) integration, standardised data protocols, global data sharing, the development of explainable AI models, and integrating remote sensing and cloud-based systems. Future research should prioritise these areas while promoting the integration of AI-driven BWQ systems into public health monitoring and environmental management through multidisciplinary collaboration. Full article

On-machine measurement (OMM) enables real-time dimensional feedback in production, yet accuracy is often degraded by thermal drift, sensor noise, and environmental disturbances. This motivates intelligent error-prediction methods to ensure reliable, high-precision machining. This study proposes a hybrid deep learning model integrating a Convolutional Neural Network (CNN), a Bidirectional Long Short-Term Memory (Bi-LSTM) network, and a Whale Optimization Algorithm (WOA) for precise OMM error prediction. Initially, raw measurement data underwent preprocessing to remove noise and outliers. Subsequently, we use a CNN to extract features and a Bi-LSTM to model time-dependent patterns. Finally, WOA optimizes model hyperparameters globally, further boosting predictive accuracy. Comparative experiments show that the proposed model reduces RMSE, MAE, and MAPE by approximately 53.58%, 54.96%, and 57.65%, respectively, while improving the R² score by about 11.17% over baseline methods. Results confirm the method’s superior nonlinear prediction capabilities, significantly enhancing machining accuracy and production efficiency, and demonstrating promising industrial application potential. Full article

In this study, we present a pilot investigation using a single Purple Heart plant (Tradescantia pallida) to explore whether bioelectrical signals for dual-purpose classification tasks: environmental state detection and human emotion recognition. Using an AD8232 ECG sensor at 400 Hz sampling rate, we recorded 3 s bioelectrical signal segments with 1 s overlap, converting them to mel-spectrograms for ResNet18 CNN (Convolutional Neural Network) classification. For lamp on/off detection, we achieved 85.4% accuracy with balanced precision (0.85–0.86) and recall (0.84–0.86) metrics across 2767 spectrogram samples. For human emotion classification, our system achieved optimal performance at 73% accuracy with 1 s lag, distinguishing between happy and sad emotional states across 1619 samples. These results should be viewed as preliminary and exploratory, demonstrating feasibility rather than definitive evidence of plant-based emotion sensing. Replication across plants, days, and experimental sites will be essential to establish robustness. The current study is limited by a single-plant setup, modest sample size, and reliance on human face-tracking labels, which together preclude strong claims about generalizability. Full article

Cultural heritage conservation faces escalating challenges from environmental threats and resource constraints, necessitating innovative preservation strategies that balance predictive accuracy with interpretability. This study presents a hybrid neuro-fuzzy framework addressing critical gaps in heritage conservation practice through sequential integration of feedforward neural networks (FF-NNs) and Mamdani-type fuzzy inference systems (MFISs). The system processes multi-sensor data (temperature, vibration, pressure) through a two-stage architecture: an FF-NN for pattern recognition and an MFIS for interpretable decision-making. Evaluation on 1000 synthetic heritage building monitoring samples (70% training, 30% testing) demonstrates mean accuracy of 94.3% (±0.62%), precision of 92.3% (±0.78%), and recall of 90.3% (±0.70%) across five independent runs. Feature importance analysis reveals temperature as the dominant fault detection driver (60.6% variance contribution), followed by pressure (36.7%), while vibration contributes negatively (−2.8%). The hybrid architecture overcomes the accuracy–interpretability trade-off inherent in standalone approaches: while the FF-NN achieves superior fault detection, the MFIS provides transparent maintenance recommendations essential for conservation professional validation. However, comparative analysis reveals that rigid fuzzy rule structures constrain detection capabilities for borderline cases, reducing recall from 96% (standalone FF-NN) to 47% (hybrid system) in fault-dominant scenarios. This limitation highlights the need for adaptive fuzzy integration mechanisms in safety-critical heritage applications. Full article

Street vitality is an important indicator of urban attractiveness and sustainable development, and it has become a central topic in contemporary urban planning and research. Using the PRISMA methodology, this review systematically examines four major technologies including machine learning (ML), space syntax, GPS, and sensors, together with six categories of data that are commonly used in street vitality studies. The analysis traces the methodological development of these approaches and identifies application trends across both macro and micro spatial scales. ML has become the leading technology in this field, showing strong performance in dynamic modeling, pattern recognition, and the integration of multiple data sources. GPS provides high temporal accuracy for tracking mobility and identifying spatiotemporal dynamics. UAVs and sensor networks make it possible to observe environmental and behavioral responses in real time. When combined, these technologies support four main research themes: the built environment and vitality, pedestrian mobility and urban dynamics, spatial and visual characterization, and social interaction. Other complementary data sources, including social media, online maps, surveys, and government statistics, expand analytical coverage and improve contextual interpretation across different spatial and cultural settings. The review emphasizes the need to connect advanced technologies and diverse data sources with broader concerns of governance, ethics, and civic participation, while maintaining a focus on methodological and data-based synthesis. By clarifying the technological pathways and data foundations of street vitality research, this study provides a structured reference for researchers, urban designers, and policymakers who aim to develop evidence-based and socially responsive frameworks for urban space evaluation and planning. Full article

Pavement surface defects such as potholes pose significant safety risks and accelerate infrastructure deterioration. Accurate and automated detection of such defects requires both advanced sensing technologies and robust deep learning models. In this study, we propose PoFormer, a Transformer–CNN hybrid framework designed for precise segmentation of pavement potholes from heterogeneous image datasets. The architecture leverages the global feature extraction ability of Transformers and the fine-grained localization capability of CNNs, achieving superior segmentation accuracy compared to state-of-the-art models. To construct a representative dataset, we combined open source images with high-resolution field data acquired using a multi-sensor pavement inspection vehicle equipped with a line-scan camera and infrared/laser-assisted lighting. This sensing system provides millimeter-level resolution and continuous 3D surface imaging under diverse environmental conditions, ensuring robust training inputs for deep learning. Experimental results demonstrate that PoFormer achieves a mean IoU of 77.23% and a mean pixel accuracy of 84.48%, outperforming existing CNN-based models. By integrating multi-sensor data acquisition with advanced hybrid neural networks, this work highlights the potential of 3D imaging and sensing technologies for intelligent pavement condition monitoring and automated infrastructure maintenance. Full article

The low-altitude economy (LAE), encompassing urban air mobility, drone logistics and sub 3000 m aerial surveillance, demands secure, intelligent infrastructures to manage increasingly complex, multi-stakeholder operations. This survey evaluates the integration of Internet of Things (IoT) networks, artificial intelligence (AI) decision-making and blockchain trust mechanisms as foundational enablers for next-generation LAE ecosystems. IoT sensor arrays deployed at ground stations, unmanned aerial vehicles (UAVs) and vertiports form a real-time data fabric that records variables from air traffic density to environmental parameters. These continuous data streams empower AI models ranging from predictive analytics and computer vision (CV) to multi-agent reinforcement learning (MARL) and large language model (LLM) reasoning to optimize flight paths, identify anomalies and coordinate swarm behaviors autonomously. In parallel, blockchain architectures furnish immutable audit trails for regulatory compliance, support secure device authentication via decentralized identifiers (DIDs) and automate contractual exchanges for services such as airspace leasing or payload delivery. By examining current research and practical deployments, this review demonstrates how the synergistic application of IoT, AI and blockchain can bolster operational efficiency, resilience and trustworthiness across the LAE landscape. Full article

The agricultural sector remains one of the most hazardous working environments, with viticulture posing particularly high risks due to repetitive manual tasks, pesticide exposure, and machinery operation. This study explores the potential of vision-based Artificial Intelligence (AI) systems to enhance occupational health and safety by evaluating their coherence with human expert assessments. A dataset of 203 annotated images, collected from 50 vineyards in Northern Italy, was analyzed across three domains: manual work activities, workplace environments, and agricultural machinery. Each image was independently assessed by safety professionals and an AI pipeline integrating convolutional neural networks, regulatory contextualization, and risk matrix evaluation. Agreement between AI and experts was quantified using weighted Cohen’s Kappa, achieving values of 0.94–0.96, with overall classification error rates below 14%. Errors were primarily false negatives in machinery images, reflecting visual complexity and operational variability. Statistical analyses, including McNemar and Wilcoxon signed-rank tests, revealed no significant differences between AI and expert classifications. These findings suggest that AI can provide reliable, standardized risk detection while highlighting limitations such as reduced sensitivity in complex scenarios and the need for explainable models. Overall, integrating AI with complementary sensors and regulatory frameworks offers a credible path toward proactive, transparent, and preventive safety management in viticulture and potentially other high-risk agricultural sectors. Furthermore, vision-based AI systems inherently act as optical sensors capable of capturing and interpreting occupational risk conditions. Their integration with complementary sensor technologies—such as inertial, environmental, and proximity sensors—can enhance the precision and contextual awareness of automated safety assessments in viticulture. Full article