Search Results (493)

Natural disasters like wildfires pose significant threats to communities, which necessitates timely and effective disaster response strategies. While Aspect-based Sentiment Analysis (ABSA) has been widely used to extract sentiment-related information at the sub-sentence level, the corresponding field of Aspect-based Emotion Analysis (ABEA) remains underexplored due to dataset limitations and the increased complexity of emotion classification. In this study, we used EmoGRACE, a fine-tuned BERT-based model for ABEA, which we applied to georeferenced tweets of the 2020 California wildfires. The results for this case study reveal distinct spatio-temporal emotion patterns for wildfire-related aspect terms, with fear and sadness increasing near wildfire perimeters. This study demonstrates the feasibility of tracking emotion dynamics across disaster-affected regions and highlights the potential of ABEA in real-time disaster monitoring. The results suggest that ABEA can provide a nuanced understanding of public sentiment during crises for policymakers. Full article

Rangelands form the ecological and economic backbone of Namibia, yet the woody plant dynamics that sustain these landscapes remain sporadically quantified across the semi-arid interior. We investigated the characteristics (stand structure, regeneration, richness, diversity, composition, ecological importance, and indicator species) of woody communities along a pronounced south-to-north rainfall gradient (85–346 mm yr⁻¹) at five representative sites: Warmbad, Gibeon, Otjimbingwe, Ovitoto, and Sesfontein. Field sampling combined point-centered quarter surveys (10 points site⁻¹) and belt transects (15 plots site⁻¹). The basal area increased almost ten-fold along the gradient (0.4–3.4 m² ha⁻¹). Principal Coordinates Analysis (PCoA) arranged plots in near-perfect rainfall order, and Permutational Multivariate Analysis of Variance (PERMANOVA) confirmed significant site differences (F_3,56 = 9.1, p < 0.001). Nanophanerophytes dominated hyper-arid zones, while microphanerophytes appeared progressively with increasing rainfall. Mean annual precipitation explained 45% of the variance in mean height and 34% of Shannon diversity but only 5% of stem density. Indicator value analysis highlighted Montinia caryophyllacea for Warmbad (IndVal = 100), Rhigozum trichotomum (75.8) for Gibeon, Senegalia senegal (72.6) for Otjimbingwe, and Senegalia mellifera (97.3) for Ovitoto. Rainfall significantly influences woody structure and diversity; however, other factors also modulate density and regeneration dynamics. This quantitative baseline can serve as a practical toolkit for designing site-specific management strategies across Namibia’s aridity gradient. Full article

Reservoir-induced landslides threaten infrastructures and downstream communities, making continuous deformation monitoring vital. Time-series InSAR, notably the SBAS algorithm, provides high-precision surface-displacement mapping but suffers from voids due to layover/shadow effects and temporal decorrelation. Existing deep-learning approaches often operate on fixed-size patches or ignore irregular spatio-temporal dependencies, limiting their ability to recover missing pixels. With this objective, a hybrid spatio-temporal Graph Attention (ST-GAT) framework was developed and trained on SBAS-InSAR values using 24 influential features. A unified spatio-temporal graph is constructed, where each node represents a pixel at a specific acquisition time. The nodes are connected via inverse distance spatial edges to their K-nearest neighbors, and they have bidirectional temporal edges to themselves in adjacent acquisitions. The two spatial GAT layers capture terrain-driven influences, while the two temporal GAT layers model annual deformation trends. A compact MLP with per-map bias converts the fused node embeddings into normalized LOS estimates. The SBAS-InSAR results reveal LOS deformation, with 48% of missing pixels and 20% located near the Dasu dam. ST D-GAT reconstructed fully continuous spatio-temporal displacement fields, filling voids at critical sites. The model was validated and achieved an overall R² (0.907), ρ (0.947), per-map R² ≥ 0.807 with RMSE ≤ 9.99, and a ROC-AUC of 0.91. It also outperformed the six compared baseline models (IDW, KNN, RF, XGBoost, MLP, simple-NN) in both RMSE and R². By combining observed LOS values with 24 covariates in the proposed model, it delivers physically consistent gap-filling and enables continuous, high-resolution landslide monitoring in radar-challenged mountainous terrain. Full article

Reconfigurable intelligent surfaces (RISs), recognized as one of the most promising key technologies for sixth-generation (6G) mobile communications, are characterized by their minimal energy expenditure, cost-effectiveness, and straightforward implementation. In this study, we develop a novel communication channel model that integrates RIS-enabled base stations with unmanned ground vehicles. To enhance the system’s adaptability, we implement a fluid antenna system (FAS) at the unmanned ground vehicle (UGV) terminal. This innovative model demonstrates exceptional versatility across various wireless communication scenarios through the strategic adjustment of active ports. The inherent dynamic reconfigurability of the FAS provides superior flexibility and adaptability in air-to-ground communication environments. In the paper, we derive and study key performance characteristics like the autocorrelation function (ACF), validating the model’s effectiveness. The results demonstrate that the RIS-FAS collaborative scheme significantly enhances channel reliability while effectively addressing critical challenges in 6G networks, including signal blockage and spatial constraints in mobile terminals. Full article

We propose and experimentally demonstrate an Integrated Multiband-Mode Multiplexing Photonic Lantern (IM3PL) that enables the selective excitation of high-order modes and stable modal preservation across multiple wavelength bands. As a proof-of-concept configuration, the IM3PL integrates a custom-designed input fiber array composed of three 980 nm single-mode fibers (SMFs) and two few-mode fibers (FMFs) operating at 1310 nm and 1550 nm, respectively. Simulations verify that 980 nm input signals can selectively excite $L{P}_{01}$, $L{P}_{11a}$, and $L{P}_{11b}$ modes at the FMF output, while the modal integrity of high-order linear polarized modes is preserved at 1310 nm and 1550 nm. The fabricated IM3PL device is experimentally validated via near-field pattern measurements, confirming the selective excitation at 980 nm and low-loss, mode-preserving transmission at the signal bands. This work offers a scalable and reconfigurable solution for multiband high-order-mode multiplexing, with promising applications in mode-division multiplexed fiber communication systems and multiband high-mode fiber lasers. Full article

The pervasive scale of networks brought about by smart city applications has created infeasible energy footprints and necessitates the inclusion of sensing sustained operations with minimal human intervention. Consequently, integrated sensing and communication (ISAC) is emerging as a key technology for 6G systems. ISAC systems realize dual functions using shared spectrum, which complicates interference management. This motivates the development of advanced signal processing and multiplexing techniques. In this context, extremely large antenna arrays (ELAAs) have emerged as a promising solution. ELAAs offer substantial gains in spatial resolution, enabling precise beamforming and higher multiplexing gains by operating in the near-field (NF) region. Despite these advantages, the use of ELAAs increases energy consumption and exacerbates carbon emissions. To address this, NF multiple-input multiple-output (NF-MIMO) systems must incorporate sustainable architectures and scalable solutions. This paper provides a comprehensive review of the various methodologies utilized in the design of energy-efficient NF-MIMO-based ISAC systems. It introduces the foundational principles of the latest research while identifying the strengths and limitations of green NF-MIMO-based ISAC systems. Furthermore, this work provides an in-depth analysis of the open challenges associated with these systems. Finally, it offers a detailed overview of emerging opportunities for sustainable designs, encompassing backscatter communication, dynamic spectrum access, fluid antenna systems, reconfigurable intelligent surfaces, and energy harvesting technologies. Full article

The efficient management of urban parking systems has emerged as a pivotal issue in today’s smart cities, where increasing vehicle populations strain limited parking infrastructure and challenge sustainable urban mobility. Aligned with the United Nations 2030 Agenda for Sustainable Development and the strategic goals of smart city planning, this study presents a sustainability-driven, multiagent simulation-based framework to model, analyze, and optimize smart parking dynamics in congested urban settings. The system architecture integrates ground-level IoT sensors installed in parking spaces, enabling real-time occupancy detection and communication with a centralized system using low-power wide-area communication protocols (LPWAN). This study introduces an intelligent parking guidance mechanism that dynamically directs drivers to the nearest available slots based on location, historical traffic flow, and predicted availability. To manage real-time data flow, the framework incorporates message queuing telemetry transport (MQTT) protocols and edge processing units for low-latency updates. A predictive algorithm, combining spatial data, usage patterns, and time-series forecasting, supports decision-making for future slot allocation and dynamic pricing policies. Field simulations, calibrated with sensor data in a representative high-density urban district, assess system performance under peak and off-peak conditions. A comparative evaluation against traditional first-come-first-served and static parking systems highlights significant gains: average parking search time is reduced by 42%, vehicular congestion near parking zones declines by 35%, and emissions from circling vehicles drop by 27%. The system also improves user satisfaction by enabling mobile app-based reservation and payment options. These findings contribute to broader sustainability goals by supporting efficient land use, reducing environmental impacts, and enhancing urban livability—key dimensions emphasized in sustainable smart city strategies. The proposed framework offers a scalable, interdisciplinary solution for urban planners and policymakers striving to design inclusive, resilient, and environmentally responsible urban mobility systems. Full article

With the rapid development of sixth-generation (6G) wireless networks and large-scale multiple-input multiple-output (MIMO) technology, the number of antennas deployed at base stations (BSs) has increased significantly, resulting in a high probability that users are in the near-field region. Note that it is difficult for the traditional far-field plane-wave model to meet the demand for high-precision beamforming in the near-field region. In this paper, we jointly optimize the power and the number of antennas to achieve the maximum energy efficiency for the users located in the near-field region. Particularly, this paper considers the resolution constraint in the formulated optimization problem, which is designed to guarantee that interference between users can be neglected. A low-complexity optimization algorithm is proposed to realize the joint optimization of power and antenna number. Specifically, the near-field resolution constraint is first simplified to a polynomial inequality using the Fresnel approximation. Then the fractional objective of maximizing energy efficiency is transformed into a convex optimization subproblem via the Dinkelbach algorithm, and the power allocation is solved for a fixed number of antennas. Finally, the number of antennas is integrally optimized with monotonicity analysis. The simulation results show that the proposed method can significantly improve the system energy efficiency and reduce the antenna overhead under different resolution thresholds, user angles, and distance configurations, which provides a practical reference for the design of green and low-carbon near-field communication systems. Full article

The risk of developing symptomatic knee osteoarthritis (KOA) during a lifetime, i.e., pain, aching, or stiffness in a joint associated with radiographic KOA, was estimated in 2008 to be around 40% in men and 47% in women. The clinical and scientific communities lack an efficient diagnostic method to effectively monitor, evaluate, and predict the evolution of KOA before and during the therapeutic protocol. In this review, we summarize the main methods that are used or seem promising for the diagnosis of osteoarthritis, with a specific focus on non- or low-invasive methods. As standard diagnostic tools, arthroscopy, magnetic resonance imaging (MRI), and X-ray radiography provide spatial and direct visualization of the joint. However, discrepancies between findings and patient feelings often occur, indicating a lack of correlation between current imaging methods and clinical symptoms. Alternative strategies are in development, including the analysis of biochemical markers or acoustic emission recordings. These methods have undergone deep development and propose, with non- or minimally invasive procedures, to obtain data on tissue condition. However, they present some drawbacks, such as possible interference or the lack of direct visualization of the tissue. Other original methods show strong potential in the field of KOA monitoring, such as electrical bioimpedance or near-infrared spectrometry. These methods could permit us to obtain cheap, portable, and non-invasive data on joint tissue health, while they still need strong implementation to be validated. Also, the use of Artificial Intelligence (AI) in the diagnosis seems essential to effectively develop and validate predictive models for KOA evolution, provided that a large and robust database is available. This would offer a powerful tool for researchers and clinicians to improve therapeutic strategies while permitting an anticipated adaptation of the clinical protocols, moving toward reliable and personalized medicine. Full article

Advancements in biosensing technologies have introduced opportunities for non-invasive, real-time monitoring of salivary biomarkers, enabling progress in fields ranging from personalized medicine to public health. Identifying and prioritizing the most critical analytes to measure in saliva is essential for estimating physiological status and forecasting performance in applied contexts. This study examined the value of 12 salivary analytes, including hormones, metabolites, and enzymes, for predicting cognitive and physical performance outcomes in military personnel (N = 115) engaged in stressful laboratory and field tasks. We calculated a series of features to quantify time-series analyte data and applied multiple regression techniques, including Elastic Net, Partial Least Squares, and Random Forest regression, to evaluate their predictive utility for five outcomes of interest: the ability to move, shoot, communicate, navigate, and sustain performance under stress. Predictive performance was poor across all models, with R-squared values near zero and limited evidence that salivary analytes provided stable or meaningful performance predictions. While certain features (e.g., post-peak slopes and variance metrics) appeared more frequently than others, no individual analyte emerged as a reliable predictor. These results suggest that salivary biomarkers alone are unlikely to provide robust insights into cognitive and physical performance outcomes. Future research may benefit from combining salivary and other biosensor data with contextual variables to improve predictive accuracy in real-world settings. Full article

Abundant large organic aggregates, which form mucous webs up to a few decimeters in length, have been observed in Baía de Todos os Santos (BTS), northeastern Brazil. This communication presents preliminary results from field (February 2015) and laboratory (June 2015) experiments that aimed to determine preliminary values for respiration and near-maximum photosynthesis and the impact of macroaggregates on respiration rates. The experiments included the determination of respiration in controls, with the mechanical removal and addition of macroaggregates. The field experiment during a flood tide presented the lowest respiration rate (−7.0 ± 0.7 µM L⁻¹ d⁻¹), average net primary production (8.9 ± 4.5 µM L⁻¹ d⁻¹), and gross primary production (16.0 ± 10 µM L⁻¹ d⁻¹), with a ratio of gross primary production to respiration of 2.3. The control experiments during an ebb tide showed a mean respiration rate of 8.7 ± 2.3 µM L⁻¹ d⁻¹, whereas, after macroaggregate removal, this was 9.5 ± 4.5 µM L⁻¹ d⁻¹. In the laboratory experiments, the control sample respiration rate of 18.4 ± 1.4 µM L⁻¹ d⁻¹ was slightly increased to 20.6 ± 0.1 µM L⁻¹ d⁻¹ after aggregate removal. The addition of aggregates to the control sample increased the respiration rate by approximately 3-fold, to 56.5 ± 4.8 µM L⁻¹ d⁻¹. These results indicate that macroaggregates could have an important role in pelagic and benthic respiration, as well as in the whole bay’s metabolism. Full article

The production of hydroponic fresh produce presents unique food safety and intervention challenges. A systematic approach was used to map and characterize the evidence on hydroponic food safety. Quantitative data describing the effectiveness of intervention studies were extracted, synthesized, and assessed for quality. A search of electronic databases yielded 131 relevant papers related to hydroponic food safety. Thirty-two studies focusing on food safety interventions reported 53 different interventions using chemical (n = 39), physical (n = 10), multiple-hurdle (n = 2), and biological (n = 2) approaches. Human pathogen indicators and surrogates were most often studied (n = 19), while pathogenic strains like Salmonella spp. (n = 9), Shiga toxin-producing Escherichia coli (STEC) (n = 5), Listeria monocytogenes (n = 2), and viruses (Hepatitis A virus (HAV), n = 1; norovirus (NoV), n = 1) were studied less frequently. Of fourteen articles (43.8%) investigating pre-harvest interventions, most (42.9%) did not specify the hydroponic system type. Gaps remain in the available evidence regarding the efficacy of interventions for controlling human pathogens in near-commercial hydroponic systems. The quality assessment revealed a significant lack of detailed reporting on methods and outcomes, making it difficult to translate the findings into practical recommendations for the industry; therefore, this review provides recommendations for the scientific community to improve future research design and reporting in this field. Full article

This study investigates the convergence of blockchain, artificial intelligence (AI), near-field communication (NFC), and mobile technologies in electronic payment (e-payment) systems, proposing an innovative integrative framework to deconstruct the systemic innovations and transformative impacts driven by such technological synergy. Unlike prior research, which often focuses on single-technology adoption, this study uniquely adopts a cross-technology convergence perspective. To our knowledge, this is the first study to empirically map the multi-technology convergence landscape in e-payment using scientometric techniques. By employing bibliometric and thematic network analysis methods, the research maps the intellectual evolution and key research themes of technology convergence in e-payment systems. Findings reveal that while the integration of these technologies holds significant promise, improving transparency, scalability, and responsiveness, it also presents challenges, including interoperability barriers, privacy concerns, and regulatory complexity. Furthermore, this study highlights the potential for convergent technologies to unintentionally deepen the digital divide if not inclusively designed. The novelty of this study is threefold: (1) theoretical contribution—this study expands existing frameworks of technology adoption and digital governance by introducing an integrated perspective on cross-technology adoption and regulatory responsiveness; (2) practical relevance—it offers actionable, stakeholder-specific recommendations for policymakers, financial institutions, developers, and end-users; (3) methodological innovation—it leverages scientometric and topic modeling techniques to capture the macro-level trajectory of technology convergence, complementing traditional qualitative insights. In conclusion, this study advances the theoretical foundations of digital finance and provides forward-looking policy and managerial implications, paving the way for a more secure, inclusive, and innovation-driven digital payment ecosystem. Full article

Bilateral teleoperation systems have been widely used in many fields of robotics, such as industrial manipulation, medical treatment, space exploration, and deep-sea operation. Delays in communication, known as an inevitable issues in practical implementation, especially for long-distance operations and challenging communication situations, can destroy system passivity and potentially lead to system failure. In this work, we address the time-delayed three-channel teleoperation design problem to guarantee system passivity and achieve high transparency simultaneously. To realize this, the three-channel teleoperation structure is first reformulated to form a two-channel-like architecture. Then, the wave variable technique is used to handle the communication delay and guarantee system passivity. Two novel wave variable compensators are proposed to achieve delay-minimized system transparency, and energy reservoirs are employed to monitor and regulate the energy introduced via these compensators to preserve overall system passivity. Numerical studies confirm that the proposed method significantly improves both kinematic and force tracking performance, achieving near-perfect correspondence with only a single-trip delay. Quantitative analyses using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Dynamic Time Warping (DTW) metrics show substantial error reductions compared to conventional wave variable and direct transmission-based three-channel teleoperation approaches. Moreover, statistical validation via the Mann–Whitney U test further confirms the significance of these improvements in system performance. The proposed design guarantees passivity with any passive human operator and environment without requiring restrictive assumptions, offering a robust and generalizable solution for teleoperation tasks with communication time delay. Full article

Remote sensing plays an important role in understanding the degradation and restoration processes of alpine grasslands. However, the extreme climatic conditions of the region pose difficulties in collecting field spectral data on which remote sensing is based. Thus, in-depth knowledge of the spectral characteristics of alpine grasslands and an accurate assessment of their restoration status are still lacking. In this study, we collected the canopy hyperspectral data of plant communities in the growing season from severely degraded grasslands and actively restored grasslands of different ages in 13 counties of the “Three-River Headwaters Region” and determined the absorption characteristics in the red-light region as well as the trends of red-light parameters. We generated a model for estimating the crude protein content of plant communities in different grasslands based on the screened spectral characteristic covariates. Our results revealed that (1) the raw reflectance parameters of the near-infrared band spectra can distinguish alpine Kobresia meadow from extremely degraded and actively restored grasslands; (2) the wavelength value red-edge position (REP), corresponding to the highest point of the first derivative (FD) spectral reflectance (680–750 nm), can identify the extremely degraded grassland invaded by Artemisia frigida; and (3) the red valley reflectance (Rrw) parameter of the continuum removal (CR) spectral curve (550–750 nm) can discriminate among actively restored grasslands of different ages. In comparison with the Kobresia meadow, the predictive model for the actively restored grassland was more accurate, reaching an accuracy of over 60%. In conclusion, the predictive modeling of forage crude protein content for actively restored grasslands is beneficial for grassland management and sustainable development policies. Full article