Search Results (292)

The phase-sensitive optical time-domain reflectometry (Φ-OTDR) system has shown substantial potential in distributed acoustic sensing applications. Accurate event classification is crucial for effective deployment of Φ-OTDR systems, and various methods have been proposed for event classification in Φ-OTDR systems. However, most existing methods typically rely on sufficient labeled signal data for model training, which poses a major bottleneck in applying these methods due to the expensive and laborious process of labeling extensive data. To address this limitation, we propose CLWTNet, a novel contrastive representation learning method enhanced with wavelet transform convolution for event classification in Φ-OTDR systems. CLWTNet learns robust and discriminative representations directly from unlabeled signal data by transforming time-domain signals into STFT images and employing contrastive learning to maximize inter-class separation while preserving intra-class similarity. Furthermore, CLWTNet incorporates wavelet transform convolution to enhance its capacity to capture intricate features of event signals. The experimental results demonstrate that CLWTNet achieves competitive performance with the supervised representation learning methods and superior performance to unsupervised representation learning methods, even when training with unlabeled signal data. These findings highlight the effectiveness of CLWTNet in extracting discriminative representations without relying on labeled data, thereby enhancing data efficiency and reducing the costs and effort involved in extensive data labeling in practical Φ-OTDR system applications. Full article

To investigate the group behavioral characteristics of Schizothorax wangchiachii during the spawning period, we used acoustic telemetry to track 10 mature individuals (4 females, 12 males) in a semi-controlled stream section (28.1 m × 5.8 m) simulating natural spawning microhabitats from 23 to 26 January 2024. By integrating trajectory similarity analysis and wavelet transform, we examined the aggregation patterns and activity rhythms during natural spawning events. The population formed two relatively stable subgroups, with significantly shorter inter-individual distances during the day (1.69 ± 0.72 m) than at night (2.54 ± 0.85 m, p < 0.01). Aggregation behavior exhibited a dominant ultradian rhythm of 16.5 h, with stable clustering between 09:00 and 16:00 (spawning window: 13:40–14:20) and dispersal from 19:00 to 00:00. Group activity followed a decreasing-then-increasing trend, with higher nighttime activity. Males were more active than females (F = 51.89, p < 0.01); female activity peaked on the spawning day and was influenced by reproductive progression, while male activity was mainly driven by diel rhythms (p < 0.01). A weak positive correlation was found between active time and inter-individual distance in both sexes (r = 0.32, p < 0.05), indicating reduced activity when aggregated. These findings provide insight into the temporal coordination and spatial regulation of reproductive behavior under semi-controlled conditions. However, due to the short monitoring period and experimental setup, caution is warranted when generalizing to the full reproductive season or fully natural habitats. Full article

As populations age and workforces decline, the need for accessible health assessment methods grows. The merging of accessible and affordable sensors such as inertial measurement units (IMUs) and advanced machine learning techniques now enables gait assessment beyond traditional laboratory settings. A total of 52 participants walked at three speeds while carrying a smartphone-sized IMU in natural positions (hand, trouser pocket, or jacket pocket). A previously trained Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM)-based machine learning model predicted gait events, which were then used to calculate stride time, stance time, swing time, and double support time. Stride time predictions were highly accurate (<5% error), while stance and swing times exhibited moderate variability and double support time showed the highest errors (>20%). Despite these variations, moderate-to-strong correlations between the predicted and experimental spatiotemporal gait parameters suggest the feasibility of IMU-based gait tracking in real-world settings. These associations preserved inter-subject patterns that are relevant for detecting gait disorders. Our study demonstrated the feasibility of extracting clinically relevant gait parameters using IMU data mimicking smartphone use, especially parameters with longer durations such as stride time. Robustness across sensor locations and walking speeds supports deep learning on single-IMU data as a viable tool for remote gait monitoring. Full article

Catastrophic floods in monsoon-driven river systems pose significant challenges to flood resilience. In July 2020, China’s Huai River Basin (HRB) encountered an unprecedented basin-wide flood event characterized by anomalous southward displacement of the rain belt. This event established a new historical record with the three typical hydrological stations (Wangjiaba, Runheji, and Lutaizi sections) along the mainstem of the Huai River exceeded their guaranteed water levels within 11 h and synchronously reached peak flood levels within a 9-h window, whereas the inter-station lag times during the 2003 and 2007 floods ranged from 24 to 48 h, causing a critical emergency in the flood defense. By integrating operational hydrological data, meteorological reports, and empirical rainfall-runoff model schemes for the Meiyu periods of 2003, 2007, and 2020, this research systematically dissects the 2020 flood’s spatial composition patterns. Comparative analyses across spatiotemporal rainfall distribution, intensity metrics, and flood peak response dynamics reveal distinct characteristics of southward-shifted torrential rain and flood variability. The findings provide critical technical guidance for defending against extreme weather events and unprecedented hydrological disasters, directly supporting revisions to flood control planning in the Huai River Ecological and Economic Zone. Full article

Background: The growing demand for innovative biomaterials with antimicrobial properties has driven research into natural and synthetic compounds, such as propolis and nanosilver, known for their antimicrobial efficacy. Methods: This study uses Google Trends data to analyze global search interest in five key terms—propolis, antimicrobial, antibacterial, nanosilver, and biomaterials—over a ten-year period (starting November 2014). The objective is to evaluate temporal variations, quantify correlations between the terms, and explore how external events, such as the COVID-19 pandemic, have influenced public and clinical interest in these topics. Search data were extracted, normalized, and analyzed using multivariate time series methods, including vector autoregression (VAR) modeling, Impulse Response Function (IRF) analysis, and forecast error variance decomposition (FEVD). Stability, causality, and inter-period relationships were assessed using statistical analysis, with results visualized through time series plots and impulse response coefficients. Results: Key findings reveal significant interdependencies between search terms, with surges in one often resulting in immediate or short-term increases in others. Notable trends include a marked increase in COVID-19 interest for nanosilver, propolis, and antibacterial, followed by a return to baseline levels, while antimicrobial maintained a sustained upward trajectory. Biomaterials experienced initial declines but later stabilized at elevated levels. Conclusions: These findings underscore the oscillating nature of public interest in antimicrobial and biomaterial innovations, highlighting opportunities for targeted research and commercialization. By adapting future material development to emerging trends and clinical needs, dentistry can use these insights to develop infection control strategies, improve restorative materials, and deal with persistent challenges such as antimicrobial resistance, peri-implantitis, and tooth caries treatment. Full article

This paper employs a Time-Varying Parameter Vector Autoregression Directional–Spillover (TVP-VAR-DY) model to investigate the dynamic spillover effects among China’s energy, real estate, and stock markets from 2013 to 2023, with a focus on the impact of extreme events. The findings show that the total conditional spillover index (TCI) typically remains below 40% in the absence of extreme events, but significantly increases during such events, reaching 51.09% during the 2015 stock market crisis and nearing 60% during the COVID-19 pandemic in 2020. Specifically, the oil and gas market exhibited a net spillover index of 4.61%, emerging as a major source of risk transmission. In contrast, the real estate market, which had a net spillover index of −9.38%, became a net risk absorber. The net spillover index indicates that the risk transmission role of different markets towards other markets is dynamically changing over time and is closely related to significant global or domestic economic events. These results indicate that extreme events not only directly impact specific markets but also rapidly propagate risks through complex inter-market linkages, exacerbating systemic risks. Therefore, it is recommended to enhance market monitoring, improve transparency, and optimize risk management strategies to cope with uncertainties in the global economy and financial markets. Full article

In today’s urban environment, disasters are not isolated events but part of continuous, complex processes that threaten both sustainable urban development and effective emergency management. Traditional emergency management practices are hindered by departmental silos and fragmented information exchanges, which often lead to conflicting interests, unclear responsibilities, ineffective tools, and imprecise task divisions. In response, our study repositions emergency management within the broader context of sustainable urban development by emphasizing resource optimization, strengthened inter-agency coordination, and strategic decision support to achieve UN Sustainable Development Goal 11. Based on observations from 31 departments in Dongtai City, we identified key contradictions within the current activity system. Guided by activity theory, we designed the Cross-Organizational Emergency Intelligence System (COEIS), which synchronizes real-time data across agencies via a novel information exchange mechanism. Implementation in a real-world setting and evaluation using grounded theory demonstrated that the COEIS enhances collaborative efficiency and decision support capabilities, thereby improving inter-organizational resilience. This study makes both theoretical and practical contributions by integrating the DSRM, activity theory, and grounded theory, offering a replicable pathway for transforming fragmented crisis management infrastructures into sustainable and resilient networks aligned with urban development strategies. Full article

Intercrystal scatter reduces system sensitivity and spatial resolution, a phenomenon that has been extensively studied in positron emission tomography (PET) systems. However, the issue is even more significant in high-energy systems. The purpose of this study is to propose a practical crosstalk rejection technique and demonstrate its applicability in high-energy photon-counting systems. The effect of inter-crystal scattering interactions between ⁶⁰Co γ photons and lutetium yttrium oxyorthosilicate (LYSO) scintillator crystals is investigated through Monte Carlo simulations conducted using the Geant4 toolkit. To suppress the crosstalk phenomenon, a field-programmable gate array (FPGA)-based algorithm is proposed to suppress inter-crystal scattering events, characterized by a time window of 5 nanoseconds and detector window sizes of one or two. The 250 mm Fe steel penetration model is used to evaluate the proposed algorithm, showing improved radiation image quality, particularly with a detector window size of two, which performs better under low-count-rate conditions. Laboratory testing indicates that the proposed algorithm can enhance steel penetration (SP) by 60–70 mm of Fe when compared to the existing current integration system under the same settings. The suggested method has been proven effective in producing higher-quality images and demonstrates good adaptability by adapting the detector window width according to different system count rates. Full article

In this paper, the fixed-time control for high-order nonlinear multi-agent systems under unknown stochastic time delay is investigated via an event-triggered approach. First of all, RBF neural networks are utilized to approximate the system’s uncertain nonlinearities. After that, an event-triggered scheme, which is designed with a relative threshold for more flexible control, is proposed to alleviate the communication burden. In consideration of the unknown stochastic time delay in the inter-communication among high-order nonlinear multi-agent systems, the Lyapunov–Krasovskii functional (LKF) is used to construct the system’s Lyapunov function, specifically targeting the adverse effects caused by time delay. Further, the fixed-time stability theory is employed to ensure that the convergence time remains independent of the initial values. Finally, the proposed control strategy is validated through numerical simulations. Full article

As precipitation analysis reveals critical statistical characteristics, temporal patterns, and spatial distributions of rainfall and snowfall events, it plays an important role in planning urban drainage systems, flood forecasting, hydrological modeling, and climate studies. It helps engineers design climate-resilient infrastructure capable of withstanding extreme weather events, which is becoming increasingly important as precipitation patterns change over time. With precipitation analysis, multiple valuable information can be determined, such as storm intensity, duration, and frequency. To enhance understanding of precipitation data and analysis results, researchers often use graphical representation methods to show the data in visual formats. Although existing precipitation analysis and basic visual representations are helpful, it is critical to have a comprehensive analysis and visualization system to detect significant patterns and anomalies in high-resolution temporal precipitation data more effectively. This study presents a visual analytics system enabling interactive analysis of hourly precipitation data across all U.S. states. Multiple coordinated visualizations are designed to support both single and multiple-station analysis. These visualizations allow users to examine temporal patterns, spatial distributions, and statistical characteristics of precipitation events directly within visualizations. Case studies demonstrate the usefulness of the designed system by evaluating various historical storm events. Full article

Event data loggers are frequently used to record the date and time of tip events in tipping-bucket rain gauges. The HOBO^® pendant event data logger is one such commercially available device commonly used for this purpose. It can record the contact closure of a TBGR reed switch at a maximum timing resolution of 1 s, tied to the timing of the logger clock, which is set each time the logger is launched. These event loggers are ideal for the routine recording of rainfall. This paper addresses the issue of whether they can also be relied upon when estimating short-term intensities, for which they were not designed. New experiments show that for a series of switch closures at fixed intervals other than exact multiples of 1 s, the HOBO^® logger fails to record evenly spaced tip events. Thus, for example, with pulses at fixed 2.75 s intervals, the logger records some events as occurring at 2 s intervals and others at 3 s intervals. This quantization error means that there can be large errors in the logged time between bucket tip events. In natural rainfall, tip events can occur at any time, and inter-tip times, from which intensity can be estimated, will generally not be an integral number of seconds. Consequently, particularly in intense rain, the logger behaviour just described can lead to erroneous estimates of the rainfall rate estimated from the duration of individual inter-tip times. Possible solutions are discussed. Full article

The trajectory tracking the control of autonomous underwater vehicle (AUV) systems faces considerable challenges due to strong inter-axis coupling and complex time-varying external disturbances. This paper proposes a novel fixed-time control scheme incorporating a switching threshold-based event-driven strategy to address critical issues in multi-AUV formation control, including full-state constraints, unmeasurable states, model uncertainties, limited communication resources, and unknown time-varying disturbances. A rapid and stable dimensional augmented state observer (RSDASO) was first developed to achieve fixed-time convergence in estimating aggregated disturbances and unmeasurable states. Subsequently, a logarithmic barrier Lyapunov function was constructed to derive a fixed-time control law that guarantees bounded system errors within a predefined interval while strictly confining all states to specified constraints. The introduction of a switching threshold event-triggering mechanism (ETM) significantly reduced communication resource consumption. The simulation results demonstrate the effectiveness of the proposed method in improving control accuracy while substantially lowering communication overhead. Full article

Xiphosurids (Merostomata, Xiphosura) are a group of chelicerates with a rich and complex evolutionary history that is constantly being updated through new discoveries. In this study, we re-estimated the divergence time of the extant horseshoe crab species with new fossil calibration points and addressed the inter- and intraspecific relationships of the American horseshoe crab through a phylogeographic perspective. In order to achieve our objectives, three datasets were compiled from fragments of different lengths of the COI gene that include sequences from 154 individuals, representing the Mexican populations. In addition to these, the datasets also included previously published sequences corresponding to individuals from different US populations and Asian horseshoe crab species. Firstly, we estimated the divergence times of extant horseshoe crab species by Bayesian methods using multiple fossil calibration points. Subsequently, we investigated the phylogeographic relationships and demographic history of Limulus polyphemus in the Americas utilizing various datasets. The time of divergence of the two Asian species clades was estimated to be approximately 127 million years ago (Ma). Phylogeographic relationships between the Asian and American species are linked through a minimum of 86 mutational steps. In America, phylogeographic relationships reflect differentiation between US and Mexican populations of L. polyphemus. We detect signs of demographic expansion for the Mexican population during the last 75,000 years, as well as an absence of phylogeographic structuring. The evolutionary history of horseshoe crabs is older than previously believed; however, the current distribution and demographic changes have probably been influenced by environmental events of the recent past, such as the glacial–interglacial periods that occurred during the Pleistocene. Full article

The microservices architectural style has gained widespread adoption in recent years thanks to its ability to deliver high scalability and maintainability. However, the development process for microservices-based applications can be complex and challenging. Indeed, it often requires developers to manage a large number of distributed components with the burden of handling low-level, recurring needs, such as inter-service communication, brokering, event management, and data replication. In this article, we present Microhooks: a novel framework designed to streamline the development of microservices by allowing developers to focus on their business logic while declaratively expressing the so-called low-level needs. Based on the inversion of control and the materialized view patterns, among others, our framework automatically generates and injects the corresponding artifacts, leveraging 100% build time code introspection and instrumentation, as well as context building, for optimized runtime performance. We provide the first implementation for the Java world, supporting the most popular containers and brokers, and adhering to the standard Java/Jakarta Persistence API. From the user perspective, Microhooks exposes an intuitive, container-agnostic, broker-neutral, and ORM framework-independent API. Microhooks evaluation against state-of-the-art practices has demonstrated its effectiveness in drastically reducing code size and complexity, without incurring any considerable cost on performance. Based on such promising results, we believe that Microhooks has the potential to become an essential component of the microservices development ecosystem. Full article

The West Kunlun Mountains (WKL) gather lots of large-scale glaciers, which play an important role in the climate and freshwater resource for central Asia. Despite extensive studies on glaciers in this region, a comprehensive understanding of inter-annual variations in glacier area, flow velocity, and terminus remains lacking. This study used a deep learning model to derive time-series glacier boundaries and the sub-pixel cross-correlation method to calculate inter-annual surface flow velocity in this region from 71 Sentinel-2 images acquired between 2016 and 2024. We analyzed the spatial-temporal variations of glacier area, velocity, and terminus. The results indicate that, as follows: (1) The glacier area in the WKL remained relatively stable, with three glaciers expanding by more than 0.5 km² and five glaciers shrinking by over 0.5 km² from 2016 to 2024. (2) Five glaciers exhibited surging behavior during the study period. (3) Six glaciers, with velocities exceeding 50 m/y, have the potential to surge. (4) There were eight obvious advancing glaciers and nine obvious retreating glaciers during the study period. Our study demonstrates the potential of Sentinel-2 for comprehensively monitoring inter-annual changes in mountain glacier area, velocity, and terminus, as well as identifying glacier surging events in regions beyond the study area. Full article