Search Results (2,333)

Every year about fifty volcanoes erupt on average, posing a serious threat for populations living in the neighboring areas. To mitigate the volcanic risk, many satellite monitoring systems have been developed. Information from the medium infrared (MIR) and thermal infrared (TIR) bands of sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) is commonly exploited for this purpose. However, the potential of daytime shortwave infrared (SWIR) observations from the Sea and Land Surface Temperature Radiometer (SLSTR) aboard Sentinel-3 satellites in supporting the near-real-time monitoring of thermal volcanic activity has not been fully evaluated so far. In this work, we assess this potential by exploring the contribution of a normalized hotspot index (NHI) in the monitoring of the recent Home Reef (Tonga Islands) eruption. By analyzing the time series of the maximum NHI_SWIR value, computed over the Home Reef area, we inferred information about the waxing/waning phases of lava effusion during four distinct subaerial eruptions. The results indicate that the first eruption phase (September–October 2022) was more intense than the second one (September–November 2023) and comparable with the fourth eruptive phase (June–August 2024) in terms of intensity level; the third eruption phase (January 2024) was more difficult to investigate because of cloudy conditions. Moreover, by adapting the NHI algorithm to daytime SLSTR SWIR data, we found that the detected thermal anomalies complemented those in night-time conditions identified and quantified by the operational Level 2 SLSTR fire radiative power (FRP) product. This study demonstrates that NHI-based algorithms may contribute to investigating active volcanoes located even in remote areas through SWIR data at 500 m spatial resolution, encouraging the development of an automated processing chain for the near-real-time monitoring of thermal volcanic activity by means of night-time/daytime Sentinel-3 SLSTR data. Full article

The ongoing debate over whether polycentric urban structures reduce commuting times has yielded conflicting conclusions, highlighting the need for empirical findings in diverse urban contexts and analyses that consider a range of influencing factors. This study analyzed the effects of employment center characteristics on commuting times, using the Seoul Metropolitan Area (SMA) as a case study. A cutoff method identified employment centers within the SMA. Differences in commuting behavior, including average commuting time and mode share, were observed among workers at different employment centers. A multilevel regression model estimated the effect of employment center characteristics, such as industry composition and nearby housing prices, on workers’ commuting time. Key findings include a positive relationship between public transportation (PT) density and commuting time, suggesting that well-designed PT systems may encourage longer commutes. Manufacturing and finance, insurance, and real estate (FIRE) industries negatively impacted commuting times, with manufacturing being associated with the geographic location of centers and FIRE industries being associated with high-income workers, which likely contributed to shorter commutes. On the other hand, the positive relationship between housing prices and commuting times highlights the need for affordable housing near employment centers to reduce commuting times. These findings underscore the complex interactions between each employment center’s characteristics and workers’ socioeconomic factors in shaping commuting behavior. Full article

This study introduces an integrated lower limb robotic orthosis with near-field electrospinning (NFES) piezoelectric sensors and a fuzzy logic-based gait phase detection system to enhance mobility assistance and rehabilitation. The exoskeleton incorporates embedded pressure sensors within the insoles to capture ground reaction forces (GRFs) in real-time. A fuzzy logic inference system processes these signals, classifying gait phases such as stance, initial contact, mid-stance, and pre-swing. The NFES technique enables the fabrication of highly oriented nanofibers, improving sensor sensitivity and reliability. The system employs a master–slave control framework. A Texas Instruments (TI) TMS320F28069 microcontroller (Texas Instruments, Dallas, TX, USA) processes gait data and transmits actuation commands to motors and harmonic drives at the hip and knee joints. The control strategy follows a three-loop methodology, ensuring stable operation. Experimental validation assesses the system’s accuracy under various conditions, including no-load and loaded scenarios. Results demonstrate that the exoskeleton accurately detects gait phases, achieving a maximum tracking error of 4.23% in an 8-s gait cycle under no-load conditions and 4.34% when tested with a 68 kg user. Faster motion cycles introduce a maximum error of 6.79% for a 3-s gait cycle, confirming the system’s adaptability to dynamic walking conditions. These findings highlight the effectiveness of the developed exoskeleton in interpreting human motion intentions, positioning it as a promising solution for wearable rehabilitation and mobility assistance. Full article

Background: Indocyanine green (ICG) fluorescence has seen extensive application across medical and surgical fields, praised for its real-time navigation capabilities and low toxicity. Initially employed to assess liver function, ICG fluorescence is now integral to liver surgery, aiding in tumor detection, liver segmentation, and the visualization of bile leaks. This study reviews current protocols and ICG fluorescence applications in liver surgery, with a focus on optimizing timing and dosage based on clinical indications. Methods: Following PRISMA guidelines, we systematically reviewed the literature up to 27 January 2024, using PubMed and Medline to identify studies on ICG fluorescence used in liver surgery. A systematic review was performed to evaluate dosage and timing protocols for ICG administration. Results: Of 1093 initial articles, 140 studies, covering a total of 3739 patients, were included. The studies primarily addressed tumor detection (40%), liver segmentation (34.6%), and both (21.4%). The most common ICG fluorescence dose for tumor detection was 0.5 mg/kg, with administration occurring from days to weeks pre-surgery. Various near-infrared (NIR) camera systems were utilized, with the PINPOINT system most frequently cited. Tumor detection rates averaged 87.4%, with a 10.5% false-positive rate. Additional applications include the detection of bile leaks, lymph nodes, and vascular and biliary structures. Conclusions: ICG fluorescence imaging has emerged as a valuable tool in liver surgery, enhancing real-time navigation and improving clinical outcomes. Standardizing protocols could further enhance ICG fluorescence efficacy and reliability, benefitting patient care in hepatic surgeries. Full article

Accurate fruit detection is of great importance for yield assessment, timely harvesting, and orchard management strategy optimization in precision agriculture. Traditional optical imaging methods are limited by lighting and meteorological conditions, making it difficult to obtain stable, high-quality data. Therefore, this study utilizes near-field millimeter-wave MIMO-SAR (Multiple Input Multiple Output Synthetic Aperture Radar) technology, which is capable of all-day and all-weather imaging, to perform high-precision detection of apple targets in orchards. This paper first constructs a near-field millimeter-wave MIMO-SAR imaging system and performs multi-angle imaging on real fruit tree samples, obtaining about 150 sets of SAR-optical paired data, covering approximately 2000 accurately annotated apple targets. Addressing challenges such as weak scattering, low texture contrast, and complex backgrounds in SAR images, we propose an innovative detection framework integrating Dynamic Spatial Pyramid Pooling (DSPP), Recursive Feature Fusion Network (RFN), and Context-Aware Feature Enhancement (CAFE) modules. DSPP employs a learnable adaptive mechanism to dynamically adjust multi-scale feature representations, enhancing sensitivity to apple targets of varying sizes and distributions; RFN uses a multi-round iterative feature fusion strategy to gradually refine semantic consistency and stability, improving the robustness of feature representation under weak texture and high noise scenarios; and the CAFE module, based on attention mechanisms, explicitly models global and local associations, fully utilizing the scene context in texture-poor SAR conditions to enhance the discriminability of apple targets. Experimental results show that the proposed method achieves significant improvements in average precision (AP), recall rate, and F1 score on the constructed near-field millimeter-wave SAR apple dataset compared to various classic and mainstream detectors. Ablation studies confirm the synergistic effect of DSPP, RFN, and CAFE. Qualitative analysis demonstrates that the detection framework proposed in this paper can still stably locate apple targets even under conditions of leaf occlusion, complex backgrounds, and weak scattering. This research provides a beneficial reference and technical basis for using SAR data in fruit detection and yield estimation in precision agriculture. Full article

The rapid proliferation of Internet of Things (IoT) devices and applications has led to an increasing demand for energy-efficient and secure communication in wireless sensor networks. In this article, we firstly propose an intelligent approach to maximize the energy efficiency of the UAV in a secure sensor network with wireless power transfer (WPT). All sensors harvest energy via downlink signal and use it to transmit uplink information to the UAV. To ensure secure data transmission, the UAV needs to optimize the transmission parameters to decode received information under malicious interference from an attacker. Code Division Multiple Access (CDMA) is adopted to improve uplink communication robustness. To maximize the UAV’s energy efficiency in data collection tasks, we formulate a constrained optimization problem that jointly optimizes charging power, charging duration, and data transmission duration. Applying Deep Deterministic Policy Gradient (DDPG) algorithm, we train an action policy to dynamically determine near-optimal transmission parameters in real time. Numerical results validate the superiority of proposed intelligent approach over exhaustive search and gradient ascent techniques. This work provides some important guidelines for the design of green secure wireless-powered sensor networks. Full article

After attending both the “Decade to Educate in the Sustainable Development and the Agenda 30 of the UNESCO” and the “ACS GCI Pharmaceutical Roundtable”, which focused on sustainable chemistry, in this article, a green chemistry contribution to the Kamal Qureshi protocol is offered; thus, DIM^® and several of its analogs (3,3′-diindolylmethanes) were suitably produced under the green chemistry protocol. In the first stage, the substrate indol-3-yl carbinol was evaluated using mechanochemistry (the best mode) in comparison to other activating methods (near-infrared and microwave electromagnetic irradiation and ultrasound), wishing to highlight the employment of both TAFF^®, an excellent and well-characterized natural catalyst (bentonitic clay), and acetone, a green solvent, in addition to the analysis of the procedures in real-time. In the second stage, the mechanochemical methodology was extended to produce a set of fifteen DIMs, in the last stage, the use of a green metric exhibited the greenness of the approach, with it being important to highlight that, to our knowledge, after a search in the literature, this is the first time that the process has been evaluated to demonstrate its greenness. Full article

Immersive environments have brought a great transformation in human–computer interaction by enabling realistic and interactive experiences within simulated or augmented spaces. In these immersive environments, virtual assets such as custom avatars, digital artwork, and virtual real estate play an important role, often holding a substantial value in both virtual and real worlds. However, this value also makes them attractive to fraudulent activities. As a result, ensuring the authenticity and integrity of virtual assets is of concern. This study proposes a cryptographic solution that leverages digital signatures and hash algorithms to secure virtual assets in immersive environments. The system employs RSA-2048 for signing and SHA-256 hashing for binding the digital signature to the asset’s data to prevent tampering and forgery. Our experimental evaluation demonstrates that the signing process operates with remarkable efficiency; over ten trials, the signing time averaged 17.3 ms, with a narrow range of 16–19 ms and a standard deviation of 1.1 ms. Verification times were near-instantaneous (0–1 ms), ensuring real-time responsiveness. Moreover, the signing process incurred a minimal memory footprint of approximately 4 KB, highlighting the system’s suitability for resource-constrained VR applications. Simulations of tampering and forgery attacks further validated the system’s capability to detect unauthorized modifications, with a 100% detection rate observed across multiple trials. While the system currently employs RSA, which may be vulnerable to quantum computing in the future, its modular design ensures crypto-agility, allowing for the integration of quantum-resistant algorithms as needed. This work not only addresses immediate security challenges in immersive environments but also lays the groundwork for broader applications, including regulatory compliance for financial virtual assets. Full article

The D-region ionosphere (60–100 km altitude) is critical for radio communication and space weather research but cannot be easily measured because it is too low for satellites and too high for balloons. The most effective technique is to remotely sense by measuring Very-Low-Frequency (VLF, 3–30 kHz) waves emitted from man-made transmitters, a technique that was traditionally utilized to estimate the average ionospheric condition between the transmitter and receiver. Recently, various methods have been proposed to remotely sense the D-region ionosphere in large areas using network observation of VLF transmitter signals. The key component of these methods is the VLF propagation model, and the Long-Wavelength Propagation Capability (LWPC) model is employed in most cases due to its relatively fast computation speed. However, it is still too long and thus insufficient for real-time remote sensing. To overcome this limitation, we have proposed a neural network model to replace the LWPC model and to shorten the computation time of VLF propagation. This model is specifically obtained using the transfer learning method by retraining the last three layers of the well-established VGG16, GoogLeNet, and ResNet architectures. We have tested different methods to organize the input data for these neural network models and verified their performance using the validation dataset and real measurements. Among the three models, GoogLeNet outperforms the other two, and the root mean squared error (RMSE), with respect to LWPC results, is as low as 0.334. Moreover, the proposed neural network model can dramatically reduce the computation time. The computation time to calculate the signal distribution near the transmitter is 1184 s if one uses the LWPC model but 0.87 s if the present neural network model is used. The performance of this model is also excellent for ionospheric conditions that are not included in the validation dataset. Therefore, this model is robust and can be used to remotely sense, in real time, the D-region ionosphere in large areas, as well as various scientific and engineering needs. Full article

Clustering algorithms are widely used in statistical data analysis as a form of unsupervised machine learning, playing a crucial role in big data mining research for Maritime Intelligent Transportation Systems. While numerous studies have explored methods for optimizing ship trajectory clustering, such as narrowing dynamic time windows to prevent errors in time warp calculations or employing the Mahalanobis distance, these methods enhance DBSCAN (Density-Based Spatial Clustering of Applications with Noise) by leveraging trajectory similarity features for clustering. In recent years, machine learning research has rapidly accumulated, and multiple studies have shown that HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) outperforms DBSCAN in achieving accurate and efficient clustering results due to its hierarchical density-based clustering processing technique, particularly in big data mining. This study focuses on the area near Taichung Port in central Taiwan, a crucial maritime shipping route where ship trajectories naturally exhibit a complex and intertwined distribution. Using ship coordinates and heading, the experiment normalized and transformed them into three-dimensional spatial features, employing the HDBSCAN algorithm to obtain optimal clustering results. These results provided a more nuanced analysis compared to human visual observation. This study also utilized O notation and execution time to represent the performance of various methods, with the literature review indicating that HDBSCAN has the same time complexity as DBSCAN but outperforms K-means and other methods. This research involved approximately 293,000 real historical data points and further employed the Silhouette Coefficient and Davies–Bouldin Index to objectively analyze the clustering results. The experiment generated eight clusters with a noise ratio of 12.7%, and the evaluation results consistently demonstrate that HDBSCAN outperforms other methods for big data analysis of ship trajectory clustering. Full article

This work introduces a model for lead-acid battery health monitoring in automobiles, focusing on detecting degradation before complete failure. With the proliferation of electronic modules and increasing power demands in vehicles, along with enhanced sensor data availability, this study aims to investigate battery lifespan. Dead batteries often lead to customer dissatisfaction and additional expenses due to inadequate diagnosis. This study seeks to enhance predictive diagnostics and provide drivers with timely warnings about battery health. The proposed method employs the Detrended Cross-Correlation Analysis Coefficient for end-of-life detection by analyzing the cross-correlation of voltage signals from batteries in different states of health. The results demonstrate that batteries with a good state of health exhibit a coefficient consistently within the statistically significant cross-correlation zone across all time scales, indicating a strong correlation with reference batteries over extended time scales. In contrast, batteries with a deteriorated state of health compute a coefficient below 0.3, often falling within the non-significant cross-correlation zone, confirming a clear decline in correlation. The method effectively distinguishes batteries nearing the end of their useful life, offering a low-computational-cost alternative for real-time battery monitoring in automotive applications. Full article

Slips, trips, and falls (STFs) are a major occupational hazard that contributes significantly to workplace injuries and the associated financial costs. The application of traditional fall detection techniques in the real world is limited because they are usually based on simulated falls. By using kinematic data from real near-fall incidents that occurred in physically demanding work environments, this study overcomes this limitation and improves the ecological validity of fall detection algorithms. This study systematically tests several machine-learning architectures for near-fall detection using the Prev-Fall dataset, which consists of high-resolution inertial measurement unit (IMU) data from 110 workers. Convolutional neural networks (CNNs), residual networks (ResNets), convolutional long short-term memory networks (convLSTMs), and InceptionTime models were trained and evaluated over a range of temporal window lengths using a neural architecture search. High-validation F1 scores were achieved by the best-performing models, particularly CNNs and InceptionTime, indicating their effectiveness in near-fall classification. The need for more contextual variables to increase robustness was highlighted by recurrent false positives found in subsequent tests on previously unobserved occupational data, especially during biomechanically demanding activities such as bending and squatting. Nevertheless, our findings suggest the applicability of machine-learning-based STF prevention systems for workplace safety monitoring and, more generally, applications in fall mitigation. To further improve the accuracy and generalizability of the system, future research should investigate multimodal data integration and improved classification techniques. Full article

According to the World Health Organization, the global population is aging, with cognitive and memory functions declining from the age of 40–50. Individuals aged 65 and older are particularly prone to dementia. Therefore, we developed an interactive system for visual cognitive training to prevent dementia and delay the onset of memory loss. The system comprises three “three-dimensional objects” with printed 2D barcodes and near-field communication (NFC) tags and operating software processing text, images, and multimedia content. Electroencephalography (EEG) data from a brainwave sensor were used to interpret brain signals. The system operates through interactive games combined with real-time feedback from EEG data to reduce the likelihood of dementia. The system provides feedback based on textual, visual, and multimedia information and offers a new form of entertainment. Thirty participants were invited to participate in a pre-test questionnaire survey. Different tasks were assigned to randomly selected participants with three-dimensional objects. Sensing technologies such as quick-response (QR) codes and near-field communication (NFC) were used to display information on smartphones. Visual content included text-image narratives and media playback. EEG was used for visual recognition and perception responses. The system was evaluated using the system usability scale (SUS). Finally, the data obtained from participants using the system were analyzed. The system improved hand-eye coordination and brain memory using interactive games. After receiving visual information, brain function was stimulated through brain stimulation and focused reading, which prevents dementia. This system could be introduced into the healthcare industry to accumulate long-term cognitive function data for the brain and personal health data to prevent the occurrence of dementia. Full article

The increasing prevalence of pollen allergies and their health impact, coupled with the limitations of the current pollen measurement system, require the development of new monitoring strategies and better dissemination of the information to the population. The measurements of a Beenose real-time pollen sensor located in Pouilly, near Metz (France), and a Hirst reference station in the centre of Metz, are considered for the study of the most allergenic species from 20 March to 25 June 2023, mainly Betulaceae and grass. These measurements, which are concordant, are correlated to symptom data obtained from the LivePollen app, which allows users to voluntarily report their allergic symptoms. Strong correlations are found between the symptom reports and the pollen concentrations shifted by one day, depending on the pollen species and the period of interest. The limitations of the data collection methods, the quality of user reports, and the influence of air quality are discussed. Such studies should be extended to other locations and time periods. Considering these promising first results, it seems that future real-time pollen monitoring can help allergy sufferers and healthcare professionals to better diagnose, anticipate, and reduce allergic crises by correlating their symptoms with pollen peaks. Full article

The acquisition of depth information sensorial data is critically important in medical applications, such as the monitoring of the elderly or the extraction of human biometrics. In such applications, compressing the stream of depth video data plays an important role due to bandwidth constraints on transmission channels. This paper introduces a novel lightweight compression system that encodes the semantics of the input depth video and can operate in both lossless and L-infinite near-lossless compression modes. A quantization technique that targets the L-infinite norm for sparse distributions and a new L-infinite compression method that sets bounds on the quantization error is proposed. The proposed codec enables the control of the coding error on every pixel in the input video data, which is crucial in medical applications. Experimental results show an average improvement of 45% and 17% in lossless mode compared to standalone JPEG-LS and CALIC codecs, respectively. Furthermore, in near-lossless mode, the proposed codec achieves superior rate-distortion performance and reduced maximum error per frame compared to HEVC. Additionally, the proposed lightweight codec is designed to perform efficiently in real time when deployed on an embedded depth-camera platform. Full article