Search Results (4,276)

As global agriculture shifts to intelligence and precision, crop attribute detection has become foundational for intelligent systems (harvesters, UAVs, sorters). It enables real-time monitoring of key indicators (maturity, moisture, disease) to optimize operations—reducing crop losses by 10–15% via precise cutting height adjustment—and boosts resource-use efficiency. This review targets harvesting-stage and in-field monitoring for grains, fruits, and vegetables, highlighting practical technologies: near-infrared/Raman spectroscopy (non-destructive internal attribute detection), 3D vision/LiDAR (high-precision plant height/density/fruit location measurement), and deep learning (YOLO for counting, U-Net for disease segmentation). It addresses universal field challenges (lighting variation, target occlusion, real-time demands) and actionable fixes (illumination compensation, sensor fusion, lightweight AI) to enhance stability across scenarios. Future trends prioritize real-world deployment: multi-sensor fusion (e.g., RGB + thermal imaging) for comprehensive perception, edge computing (inference delay < 100 ms) to solve rural network latency, and low-cost solutions (mobile/embedded device compatibility) to lower smallholder barriers—directly supporting scalable precision agriculture and global sustainable food production. Full article

By increasing the number of vehicles, an intelligent parking system can help drivers in finding parking slots by providing real-time information. To address this issue, this study developed an Arduino-based automated parking system integrating sensors to assist drivers in quickly discovering available parking slots with real-time space detection and dynamic access control. This system consists of ultrasonic sensors, NodeMCU, an LCD screen, a servo motor, and an Arduino Uno. Each ultrasonic sensor is assigned a specific number corresponding to its slot number, which helps to identify the locations. These sensors were connected to the NodeMCU to collect, process, and transfer data to the Arduino board. If the ultrasonic sensor cannot detect the vehicle in the parking space, the LCD screen will show the number of specific slots. The Arduino will use the servo motor to open the entrance gate if a vehicle is detected by another ultrasonic sensor next to it. Otherwise, the system prevents any vehicle from entering the parking area when all of the available spaces are occupied. The system prototype is constructed and empirically evaluated to verify its performance and efficiency. The results indicate that the system successfully monitors parking spot occupancy and validates its capacity for real-time information updates. Full article

This paper presents a Vibration-based Track Anomaly Detection (VTAD) system designed for real-time monitoring of urban tram infrastructure. The novelty of VTAD is that it converts existing public transport vehicles into distributed mobile sensor platforms, eliminating the need for specialized diagnostic trains. The system integrates low-cost micro-electro-mechanical system (MEMS) accelerometers, Global Positioning System (GPS) modules, and Espressif 32-bit microcontrollers (ESP32) with wireless data transmission via Message Queuing Telemetry Transport (MQTT), enabling scalable and continuous condition monitoring. A stringent ±6σ statistical threshold was applied to vertical vibration signals, minimizing false alarms while preserving sensitivity to critical faults. Field tests conducted on multiple tram routes in Zagreb, Croatia, confirmed that the VTAD system can reliably detect and locate anomalies with meter-level accuracy, validated by repeated measurements. These results show that VTAD provides a cost-effective, scalable, and operationally validated predictive maintenance solution that supports integration into intelligent transportation systems and smart city infrastructure. Full article

Crowdsourced delivery plays a key role in fresh food retailing, where tight time limits and perishability require fast, reliable fulfillment. However, real-time order–courier assignment is challenging because orders arrive in bursts, couriers’ locations and availability change, capacities are limited, and many decisions must be made simultaneously. We propose Attn-QMIX, a novel attention-augmented multi-agent reinforcement learning framework that models each order as an agent and learns coordinated matching strategies through centralized training with decentralized execution. The framework develops a new capacity-aware multi-head attention mechanism that captures complex order–courier interactions and dynamically prevents courier overload and integrates it with a QMIX-based mixing network equipped with hypernetworks to enable effective credit assignment and global coordination. Extensive experiments on a real-world road network show that Attn-QMIX outperforms five representative methods. Compared with a novel cooperative ant colony optimization method, it reduces total cost by up to 2.30% while being up to 3403 times faster in computation. Full article

Kinetic façades represent a climate-responsive design solution that improves building adaptability by responding to seasonal needs such as daylighting and shading. They offer an attractive retrofit strategy that improves both the esthetics and environmental performance of buildings. This study investigated the integration of an origami-inspired kinetic façade into a student dormitory building located in Dhahran, Saudi Arabia. Using numerical simulations, 35 façade configurations were analyzed under varying conditions of façade orientations, closure ratios (from 5% to 95%), and cavity depths (from 20 cm to 100 cm). The findings highlight the critical impact of kinetic façade design characteristics on daylight availability and solar exposure and the required trade-off between these two variables. In this context, this study observed that at higher façade closure ratios, increasing cavity depth could effectively mitigate daylight reduction by promoting reflected daylight penetration inside the cavity. As for heat gains and cooling load reduction, mid-range façade closure, 50 cm in this study, achieved balanced performance across the three examined orientations. However, the southern façade showed slightly higher efficiency compared to the eastern and western façades, which achieved lower cooling reductions and showed a similar UDI compromise. Thus, a dynamic façade operation is recommended, where higher closure ratios could be applied during peak solar hours on the east in the morning and the west in the afternoon to maximize cooling savings, while moderate closure ratios can be maintained on the south to preserve daylight. Future work should incorporate real-time climatic data and smart control technologies to further optimize kinetic façade performance. Full article

We present a photoacoustic spectroscopy (PAS)-based method using a laser Doppler vibrometer (LDV) for real-time detection of laser-induced damage (LID) in optical components. By measuring audible frequency surface vibrations, the method enables remote, non-contact, and sensitive detection. Experiments with various dielectric optics (slide glass and single-layer coatings) and pulse durations (7 ns and 360 ps) of an Nd:YAG laser (wavelength of 1064 nm) showed detection accuracy comparable to microscopy. Vibration spectra correlated with natural modes calculated by finite element modeling, and vibrations according to the detecting location were observed. The method remained effective under typical mounting conditions, demonstrating its practical applicability. This PAS-LDV approach offers a promising tool for in situ monitoring of LID in high-power laser systems. Full article

Photovoltaic power generation prediction is crucial for the effective integration of renewable energy into the grid, real-time grid balancing, and the optimization of energy storage systems. However, PV power generation is highly dependent on environmental factors such as weather conditions. Photovoltaic power generation prediction is crucial for the effective integration of renewable energy into the grid, real-time grid balancing, and the optimization of energy storage systems. However, PV power generation is highly dependent on environmental factors such as weather conditions. Effectively integrating environmental information remains a major challenge for photovoltaic power forecasting. This study proposes a hybrid deep learning model that incorporates an adaptive neural network to capture the latent relationships between PV power generation and environmental variables, thereby enhancing forecasting accuracy. The adaptive graph neural network employs a data-driven directed graph structure, where TCN and variable interaction layers are alternately stacked to better model the spatiotemporal coupling among variables for long-term PV output forecasting. The proposed model was evaluated on three sites located in different regions, with a fixed input length of 96 and output horizons ranging from 96 to 768 steps. Compared with state-of-the-art baselines, the model achieved average improvements of 2.19% and 1.57% in MSE and MAE at a 384-step horizon, and 2.81% and 2.47% at a 768-step horizon, respectively, demonstrating superior performance in long-term PV output forecasting tasks. Full article

Since it is difficult to obtain the positions of dynamic loads on structures, this paper suggests a new method to identify the locations of dynamic loads step-by-step based on the correlation coefficients of dynamic responses. First, a recognition model for dynamic load position based on a finite-element scheme is established, with the finite-element domain divided into several regions. Second, virtual loads are applied at the central points of these regions, and acceleration responses are calculated at the sensor measurement points. Third, the maximum correlation coefficient between the calculational and measured accelerations is obtained, and the dynamic load is located in the region with the virtual load corresponding to the maximum correlation coefficient. Finally, this region is continuously subdivided with the refined mesh until the dynamic load is pinpointed in a sufficiently small area. Different virtual load construction methods are proposed according to different types of loads. The frequency response function, unresolvable for the actual problem due to the unknown location of the real dynamic load, can be transformed into a solvable form, involving only known points. This transformation simplifies the analytical process, making it more efficient and applicable to analysis of the dynamic behavior of the system. The identification of the dynamic load position in the entire structure is then transformed into a sub-region approach, focusing on the area where the dynamic load acts. Simulations for case studies are conducted to demonstrate that the proposed method can effectively identify positions of single and multiple dynamic loads. The correctness of the theory and simulation model is verified with experiments. Compared to recent methods that use machine learning and neural networks to identify positions of dynamic loads, the approach proposed in this paper avoids the heavy computational cost and time required for data training. Full article

In vehicle routing problems, long-distance transportation poses a significant challenge to the optimization of transportation costs while adhering to regulations. This study investigates a special type of logistics problem that focuses on liquid transportation systems involving full truckload delivery and the rest–break–drive periods of truck drivers over long distances according to the regulations of the United States. Based on an exact solution algorithm, this work combines a long-distance full truckload fluid transportation problem with the concept of truck driver schedules for the first time. The goal is to optimize transportation expenses while managing challenges related to the rest–break–drive periods of truck drivers, time windows, trailer varieties, customer segments, food and non-food products, a diverse fleet, starting locations, and the diverse tasks of vehicles. In order to reach optimality, a construction heuristic and the column generation method were employed, supplemented by several acceleration strategies. Performance analysis, carried out with artificial input sets mirroring real-life scenarios, indicates that low optimality gaps can be obtained in an appropriate amount of time for large-scale long-haul liquid transportation. Full article

A modular, multi-functional (encompassing data acquisition, management, preprocessing, and transmission) sensing (MMFS) system based upon the Internet of Things (IoT) paradigm is discussed in this paper with the goal of continuous real-time, multi-sensor and multi-location monitoring of aircraft (including drones) structural performances during flight. According to industrial and system requirements, a microcontroller and four sensors (strain, acceleration, vibration, and temperature) were selected and integrated into the system. To enable the determination of potential in-flight failures and estimates of the remaining useful service life of the aircraft, resistance strain gauge networks, piezoelectric sensors for capturing structural vibrations and impact, accelerometers, and thermistors have been integrated into the MMFS system. Real flight tests with Evektor’s Cobra VUT100i and SportStar RTC aircraft have been undertaken to demonstrate the features of recorded data and provide requirements for the MMFS functional design. Real flight test data were analysed, indicating that a sampling rate of 1000 Hz is necessary to balance representation of relevant features within the data and potential loss of quality in fatigue life estimation. The design and evaluation of the performance of a prototype (evaluated via representative stress/strain experiments using an Instron Hydraulic 250 kN machine within laboratories) are detailed in this paper. Full article

GRAS family plays a critical role in plant growth and stress responses. In this study, we identified 47 GRAS (DlGRAS) genes in the longan genome and conducted a comprehensive bioinformatics analysis of these genes. RNA-seq analysis revealed that the expression of these DlGRAS genes differed during early SE and across various longan tissues. The quantitative real-time PCR (qRT-PCR) results indicated that the DlGRAS genes exhibited differential expression during the early SE of longan, with most of them showing high expression at the globular embryo (GE) stage. Under GA₃ treatment, the transcript levels of DlGRAS12/15 decreased significantly. In contrast, exogenous ABA promoted the expression of DlGRAS6/10/23, indicating that DlGRAS genes are responsive to hormones. Compared with CaMV35S-driven GUS expression, the promoters of DlGRAS10/22 increased GUS expression, GA₃ and ABA treatments enhanced promoter activity. DlGRAS10/22 were located in the nucleus. Overexpression of DlGRAS10/22 in longan SE significantly promoted the transcription levels of SE-related genes, including DlGID1, DlGA20ox2, DlLEC1, DlFUS3, DlABI3 and DlLEC2. Therefore, DlGRAS may be involved in the early morphogenesis of longan SE through the hormone signaling pathway. Full article

Within the framework of the Healthy China strategy, daylighting in primary and secondary schools is crucial for students’ health and learning efficiency. Most schools in China still face insufficient and uneven daylighting, along with limited outdoor solar exposure, underscoring the need for systematic optimization. Guided by the “Daylighting School” concept, this study proposes a campus design model that integrates indoor daylighting with outdoor activity opportunities and explores a generative optimization approach. The research reviews daylighting and thermal performance metrics, summarizes European and American “Daylighting School” experiences, and develops three classroom prototypes—Standard Side-Lit, High Side-Lit, and Skylight-Lit—together with corresponding campus layout models. A two-stage optimization experiment was conducted on a high school site in Guangzhou. Stage 1 optimized block location and functional layout using solar radiation illuminance and activity accessibility distance. Stage 2 refined classroom configurations based on four key performance indicators: sDA, sGA, UOD, and APMV-mean. Results show that optimized layouts improved activity path efficiency and daylight availability. High Side-Lit and Skylight-Lit classrooms outperformed traditional Side-Lit in illuminance, uniformity, and glare control. To improve efficiency, an ANN-based prediction model was introduced to replace conventional simulation engines, enabling rapid large-scale assessment of complex classroom clusters and providing architects with real-time decision support for daylight-oriented educational building design. Full article

Objectives: Venous malformations (VMs) that infiltrate the muscular layer, involve or are closely adjacent to critical nerves or vessels, or are located deep within or very close to major organs in the thoracic or abdominal cavities are challenging to access during sclerotherapy, which we defined as inaccessible VMs. This study proposed an integrated real-time stereotactic MRI-guided sclerotherapy with bleomycin-polidocanol foam (RSMS-BPF) for the treatment of inaccessible VMs, focusing on its clinical feasibility, efficacy, and safety. Methods: A retrospective study was conducted involving patients treated with RSMS-BPF between 2019 and 2021. During the sclerotherapy, the intraoperative magnetic resonance imaging (MRI) was combined with an optical navigation system to guide precise needle placement and track BPF, a foam sclerosant optimized for MRI visibility. Radiological response was assessed by lesion volume, while clinical improvement was evaluated through patients’ description of their symptoms. Rigorous follow-up and documentation of complications were conducted. Results: A total of 42 patients (mean age 23.6 ± 1.6 years; 18 males) were treated in 64 sclerotherapy sessions. The treatment achieved an overall response rate of 89.5%. Imaging analysis revealed an average lesion volume reduction of 59.6%. 57.9% of patients achieved good or excellent radiological responses. After a median follow-up of 12.25 months, 60.53% of patients reported complete or significant relief. Lesion depth did not affect treatment efficacy (p = 0.43). Minor complications included skin hyperpigmentation (5.3%, 2/38) and blisters (2.6%, 1/38), with no major complications observed. Conclusions: RSMS-BPF demonstrated satisfactory efficacy and safety in VMs treatment, particularly for inaccessible VM lesions. It enables authentic real-time dynamic tracking during sclerotherapy, achieving unparalleled precision targeting while minimizing procedural risks. These findings strongly support routine integration of RSMS-BPF as first-line therapy for complex vascular malformations with critical anatomical constraints. Full article

Forecasting real-world time series in domains with strong event sensitivity and regional variability poses unique challenges, as predictive models must account for sudden disruptions, heterogeneous contextual factors, and structural differences across locations. In tackling these challenges, we draw on the concept of symmetry that refers to the balance and invariance patterns across temporal, multimodal, and structural dimensions, which help reveal consistent relationships and recurring patterns within complex systems. This study is based on two multimodal datasets covering 12 tourist regions and more than 3 years of records, ensuring robustness and practical relevance of the results. In many applications, such as monitoring economic indicators, assessing operational performance, or predicting demand patterns, short-term fluctuations are often triggered by discrete events, policy changes, or external incidents, which conventional statistical and deep learning approaches struggle to model effectively. To address these limitations, we propose an event-aware multimodal time-series forecasting framework with graph-based regional transfer built upon an enhanced PatchTST backbone. The framework unifies multimodal feature extraction, event-sensitive temporal reasoning, and graph-based structural adaptation. Unlike Informer, Autoformer, FEDformer, or PatchTST, our model explicitly addresses naive multimodal fusion, event-agnostic modeling, and weak cross-regional transfer by introducing an event-aware Multimodal Encoder, a Temporal Event Reasoner, and a Multiscale Graph Module. Experiments on diverse multi-region multimodal datasets demonstrate that our method achieves substantial improvements over eight state-of-the-art baselines in forecasting accuracy, event response modeling, and transfer efficiency. Specifically, our model achieves a 15.06% improvement in the event recovery index, a 15.1% reduction in MAE, and a 19.7% decrease in event response error compared to PatchTST, highlighting its empirical impact on tourism event economics forecasting. Full article

Background/Objectives: Monitoring community health and tracking SARS-CoV-2 evolution were critical priorities throughout the COVID-19 pandemic. However, widespread shortages of personal protective equipment, the necessity for social distancing, and the redeployment of healthcare personnel to clinical duties presented significant barriers to traditional sample collection. Methods: In this study, we evaluated the feasibility of using self-collected saliva specimens for the qualitative detection of SARS-CoV-2 infection. Following confirmation of reliable viral detection in saliva, we established a large-scale surveillance program in Arizona, USA, to enable clinical diagnosis and genomic sequencing from self-collected samples. Between April 2020 and December 2023, we tested approximately 1.4 million saliva samples using RT-PCR, identifying 94,330 SARS-CoV-2 infections. Whole genome sequencing was performed on 69,595 samples, yielding 54,040 high-quality consensus genomes. Results: This surveillance approach enabled real-time monitoring of general infection trends that matched regional case counts. We monitored multiple wave-like introductions of viral lineages over the course of the pandemic. We identified three periods of S gene target failure on a commercial assay and assessed its ability to make fast, genotyping assignment during the pandemic (PPV = 0.98, 95% CI = 0.97–0.99; NPV = 0.94, 95% CI = 0.94–0.96). The co-location of clinical testing and sequencing capabilities within the same facility resulted in low turnaround time from the sample collection to the generation of sequencing data (median = 12 days, IQR: 9.0–19.75). Conclusions: Our findings support the use of self-collected saliva as a scalable, cost-effective, and practical strategy for infectious disease surveillance in future pandemics. Full article