Search Results (202)

Emerging research indicates that neuronal activity is maintained by an architectural system of protons in a multi-scale fashion. Proton architecture is formed when organelles (such as mitochondria, endoplasmic reticulum, lysosomes, synaptic vesicles, etc.) are coupled together to produce dynamic energy domains. Techniques have been developed to visualize protons in neurons; recent advances include near-atomic structural imaging of organelle interfaces using cryo-tomography and nanoscale resolution imaging of organelle interfaces and proton tracking using ultra-fast spectroscopy. Results of these studies indicate that protons in neurons do not diffuse randomly throughout the neuron but instead exist in organized geometric configurations. The cristae of mitochondrial cells create oscillating proton micro-domains that are influenced by the curvature of the cristae, hydrogen bonding between molecules, and localized changes in dielectric properties that result in time-patterned proton signals that can be used to determine the metabolic load of the cell and the redox state of its mitochondria. These proton patterns also communicate to the rest of the cell via hydrated aligned proton-conductive pathways at the mitochon-dria-endoplasmic reticulum junctions, through acidic lipid regions, and through nano-tethered contact sites between mitochondria and other organelles, which are typically spaced approximately 10–25 nm apart. Other proton architectures exist in lysosomes, endosomes, and synaptic vesicles. In each of these organelles, the V-ATPase generates steep concentration gradients across their membranes, controlling the rate of cargo removal from the lumen of the organelle, recycling receptors from the surface of the membrane, and loading neurotransmitters into the vesicles. Recent super-resolution pH mapping has indicated that populations of synaptic vesicles contain significant heterogeneity in the amount of protons they contain, thereby influencing the amount of neurotransmitter released per vesicle, the probability of vesicle release, and the degree of post-synaptic receptor protonation. Additionally, proton gradients in each organelle interact with the cytoskeleton: the protonation status of actin and microtubules influences filament stiffness, protein–protein interactions, and organelle movement, resulting in the formation of localized spatial structures that may possess some type of computational significance. At multiple scales, it appears that neurons integrate the proton micro-domains with mechanical tension fields, dielectric nanodomains, and phase-state transitions to form distributed computing elements whose behavior is determined by the integration of energy flow, organelle geometry, and the organization of soft materials. Alterations to the proton landscape in neurons (e.g., due to alterations in cristae structure, drift in luminal pH, disruption in the hydration-structure of the cell, or imbalance in the protonation of cytoskeletal components) could disrupt the intracellular signaling network well before the onset of measurable electrical or biochemical pathologies. This article will summarize evidence indicating that proton–organelle interaction provides a previously unknown source of energetic substrate for neural computation. Using an integrated approach combining nanoscale proton energy, organelle interface geometry, cytoskeletal mechanics, and AI-based multiscale models, this article outlines current principles and unresolved questions related to the subject area as well as possible new approaches to early detection and precise intervention of pathological conditions related to altered intracellular energy flow. Full article

The National Institute of Standards and Technology (NIST) is developing analytical methods to characterize extracellular vesicles (EVs) to support the urgent need for standardized EV reference materials (RMs). This study used orthogonal techniques, cryogenic electron microscopy (Cryo-EM), particle tracking analysis (PTA), asymmetrical flow field-flow fractionation (AF⁴), and microfluidic resistive pulse sensing (MRPS), to evaluate particle size distributions (PSDs) and particle number concentrations (PNCs) of human mesenchymal stem cells (MSCs) and LNCaP prostate cancer cell EVs. Proteomic profiles were assessed by mass spectrometry (MS), and microRNA (miRNA) content of LNCaP EVs was evaluated by small RNA-seq at two independent laboratories. A commercial green fluorescent protein exosome served as a control, except in Cryo-EM, proteomic, and miRNA analyses. Cryo-EM, regarded as the gold standard for morphological resolution, served as PSD reference. PSDs from all methods skewed larger than Cryo-EM, with MRPS closest, AF⁴ most divergent, and PTA intermediate with broader distributions. All techniques reported broad PSDs (30 nm to >350 nm) with PNCs decreasing with increasing particle size, except for AF⁴. Quantitative discrepancies in PNCs reached up to two orders of magnitude across methods and cell sources. MS identified global and EV-specific proteins, including syntenin-1 and tetraspanins CD9, CD63, and CD81. RNA-seq revealed notable inter-laboratory variation. These findings highlight the variability across measurement platforms and emphasize the need for reproducible methods to support NIST’s mission of developing reliable EV reference materials. Full article

Reliable recognition of people on water from UAV imagery remains a challenging task due to strong glare, wave-induced distortions, partial submersion, and small visual scale of targets. This study proposes a hybrid method for human detection and position recognition in aquatic environments by integrating the YOLO12 object detector with optical-flow-based motion analysis, Kalman tracking, and BlazePose skeletal estimation. A combined training dataset was formed using four complementary sources, enabling the detector to generalize across heterogeneous maritime and flood-like scenes. YOLO12 demonstrated superior performance compared to earlier You Only Look Once (YOLO) generations, achieving the highest accuracy (mAP@0.5 = 0.95) and the lowest error rates on the test set. The hybrid configuration further improved recognition robustness by reducing false positives and partial detections in conditions of intense reflections and dynamic water motion. Real-time experiments on a Raspberry Pi 5 platform confirmed that the full system operates at 21 FPS, supporting onboard deployment for UAV-based search-and-rescue missions. The presented method improves localization reliability, enhances interpretation of human posture and motion, and facilitates prioritization of rescue actions. These findings highlight the practical applicability of YOLO12-based hybrid pipelines for real-time survivor detection in flood response and maritime safety workflows. Full article

Monitoring glacier flow velocity is crucial for understanding ice dynamics, mass balance, and hydrological processes in a changing climate. This study provides a comprehensive systematic review of methodological advances in glacier-velocity retrieval, with a particular focus on debris-covered glaciers that remain underrepresented in current research. We used the PRISMA framework to identify 121 peer-reviewed studies published between 1992 and 2025, which we analyzed to identify key developments, data sources, and performance characteristics. The examined methodologies encompass feature tracking, InSAR, offset tracking, optical flow, deep learning algorithms, and data fusion strategies that integrate optical and SAR datasets. The findings demonstrate a clear trend away from manual and correlation-based approaches towards automated, AI-informed systems, driven by the increasing availability of satellite data and advances in computational power. Accuracy and uncertainty tests indicate persistent problems with debris-covered surfaces due to low surface contrast and heterogeneity. Emerging trends point toward increasing integration of data fusion and glaciological modeling, paving the way for more intelligent, automated, and physically informed monitoring systems. This underscores the necessity for open data, reproducible methodologies, and interdisciplinary collaboration to advance the accuracy and scalability of global glacier-velocity monitoring. Full article

Groundwater is one of the main sources of water supply in tropical developing countries; however, its integrated management is often constrained by limited hydrogeological information and increasing anthropogenic pressures on aquifer systems. This study presents the numerical modeling of groundwater flow in the Neogene–Quaternary aquifer system of the Middle Magdalena Valley (Colombia), focusing on the rural area of Puerto Wilches, which is characterized by strong surface–groundwater interactions, particularly with the Yarirí wetland and the Magdalena River. A three-dimensional model was implemented and calibrated in FEFLOW v.8.1 under steady-state and transient conditions, integrating both primary and secondary data. The dataset included piezometric levels measured with water level meters and automatic loggers, hydrometeorological records, 21 physicochemical and microbiological parameters analyzed in 45 samples collected during three field campaigns under contrasting hydrological conditions, 79 pumping tests, detailed lithological columns from drilled wells, and complementary geological and geophysical models. The results indicate a predominant east–west groundwater flow from the Eastern Cordillera toward the Magdalena River, with seasonal recharge and discharge patterns controlled by the bimodal rainfall regime. Microbiological contamination (total coliforms in 69% of groundwater samples) and nitrate concentrations above 10 mg/L in 21% of wells were detected, mainly due to agricultural fertilizers and domestic wastewater infiltration. Particle tracking revealed predominantly horizontal flow paths, with transit times of up to 800 years in intermediate units of the Real Group and around 60 years in shallow Quaternary deposits, highlighting the differential vulnerability of the system to contamination. These findings provide scientific foundations for strengthening integrated groundwater management in tropical regions under agroindustrial and hydrocarbon pressures and emphasize the need to consolidate monitoring networks, promote sustainable agricultural practices, and establish preventive measures to protect groundwater quality. Full article

Nonlinear water waves (NWWs) can be generated by the vertical bottom disturbance, which represents the conceptual processes of the rise of seabed rupture under seismic loads. To explore the correlation between the disturbance parameters and the wave features, a Reynolds-averaged Navier–Stokes (RANS) model is applied, with the flow turbulence and fluid–structure interaction (FSI) being resolved by the k–ɛ model and the immersed boundary method (IBM), respectively. The free surface is tracked using the volume of fluid (VOF) method. After validating against the theoretical solutions and experimental results, the effects of disturbance duration and bulk on the wave features at the source region (the generation stage) and offshore direction (the propagation stage) are systematically discussed. The fixed maximal vertical displacement is considered, with four moving durations and five disturbance widths being simulated, resulting in four disturbance velocities and five disturbance bulks. The results indicate that the proposed RANS model can accurately create various wave patterns (including the linear, solitary, and tsunami-like waves) generated by bottom disturbances. Special attentions are paid to the tsunami-like wave. The wave evolution exhibits strong dependence on disturbance duration and width, with shorter durations triggering earlier soliton fission and longer widths accelerating phase celerity. These findings highlight the critical role of disturbance parameters in governing soliton formation and energy propagation patterns, which are vital in disaster forecasting. Full article

Accurate typhoon track forecasting is vital for disaster mitigation in East China, a region frequently impacted by landfalling typhoons. Despite advances in numerical weather prediction, uncertainties remain high, especially within 48 h of landfall, due to complex interactions among tropical cyclones, the subtropical high, and mesoscale systems. This study applies Ensemble-based Sensitivity Analysis (ESA) within a high-resolution regional ensemble prediction system (Shanghai Weather And Risk Model System-Ensemble Prediction System, SWARMS-EN) to investigate forecast uncertainties of three representative typhoons—Gaemi, Bebinca, and Kong-rey—that made landfall in East China in 2024. Our results reveal consistent sensitivity patterns across diverse large-scale environments, particularly around the western flank of the subtropical high and in proximity to nearby low-pressure systems. Track uncertainty was closely tied to fluctuations in the steering flow, notably its zonal component. Moreover, binary typhoon interactions emerged as key drivers of forecast divergence. ESA effectively identified sensitive regions where small initial perturbations exert significant downstream influence on typhoon tracks. This study demonstrates the operational value of ESA for diagnosing forecast error sources and guiding targeted observations. By linking forecast uncertainty to physical mechanisms, this research enhances our understanding of typhoon predictability and supports the development of more adaptive and accurate regional forecasting systems. Full article

OpenStack is a popular open-source cloud platform that orchestrates virtualized compute, storage, and networking resources. In such virtualized environments, packet traceability refers to the ability to track the path and transformations of network packets as they traverse virtual switches, routers, and interfaces. This paper presents a comprehensive overview of packet traceability in OpenStack cloud environments. We provide an introduction to the OpenStack architecture with a focus on the Networking component (Neutron) and discuss how packets flow through virtual networking elements. We examine the routing and interface mechanisms that enable communication within and across nodes, and compare single-node versus multi-node OpenStack deployments from a packet tracing perspective. Furthermore, we survey tools and techniques for packet tracing (such as monitoring interfaces, using tcpdump, and Open vSwitch tracing), and highlight the challenges faced (multi-tenancy, overlay networks, etc.) in tracing packets. We offer recommendations for improving traceability, including leveraging built-in OpenStack features and advanced kernel-level tracing technologies. Our goal is to aid cloud administrators and researchers in understanding and improving network packet observability in OpenStack clouds, thereby enhancing troubleshooting and security analysis capabilities. Full article

Special steel manufacturing involves both discrete processing events and continuous physical flows, forming a representative discrete–continuous hybrid production system. However, due to the visually homogeneous surfaces of steel products, the highly dynamic production environment, and frequent disturbances or anomalies, traditional single-source tracking approaches struggle to maintain accurate and consistent material identification. To address these challenges, this paper proposes a multi-source fusion-based material tracking method tailored for discrete–continuous hybrid scenarios. First, a state–event system (SES) is constructed based on process rules, enabling interpretable reasoning of material states through event streams and logical constraints. Second, on the visual perception side, a YOLOv8-SE detection network embedded with the squeeze-and-excitation (SE) channel attention mechanism is designed, while the DeepSORT tracking framework is improved to enhance weak feature extraction and dynamic matching for visually similar targets. Finally, to handle information conflicts and cooperation in multi-source fusion, an improved Dempster–Shafer (D-S) evidence fusion strategy is developed, integrating customized anomaly handling and fault-tolerance mechanisms to boost decision reliability in conflict-prone regions. Experiments conducted on real special steel production lines demonstrate that the proposed method significantly improves detection accuracy, ID consistency, and trajectory integrity under complex operating conditions, while enhancing robustness against modal conflicts and abnormal scenarios. This work provides an interpretable and engineering-feasible solution for end-to-end material tracking in hybrid manufacturing systems, offering theoretical and methodological insights for the practical deployment of multi-source collaborative perception in industrial environments. Full article

The rational placement of pollutant monitoring sensors has long been a prominent research focus in ocean environment science. Our method integrates an incremental Proper Orthogonal Decomposition with a mobility-constrained sensor selection strategy, enabling efficient monitoring and dynamic adaptation to spatio-temporal field changes. At each time step, the position of the sensors is updated based on the incoming measurements to minimize the reconstruction error while adhering to movement constraints. This online approach considers the need for mobility distance, making it suitable for long-term deployments in resource-limited scenarios. The proposed framework is validated in three scenarios: a linear advection–diffusion system with multiple moving pollution sources, the distribution of particulate matter with an aerodynamic diameter smaller than 2.5 μm (PM2.5) across the United States, and scalar transport in flows past side-by-side cylinder arrays in the ocean. The results demonstrate that the method achieves high reconstruction accuracy with significantly fewer sensors. This study conducts a comparative analysis of three typical mobility constraints and their respective effects on reconstruction accuracy. In addition, the proposed localized sensor mobility strategy effectively tracks evolving plume structures and maintains a low approximation error, providing a generalizable solution for sparse monitoring of the marine environment. Full article

With the characteristic of “decentralized distribution and local power supply”, small hydropower (SHP) in China has become a core means of solving the problem of insufficient power supply in rural and remote mountainous areas, effectively promoting the improvement of local livelihoods. However, for a long time, SHP has had many problems, such as irrational development, old equipment, and poor economic efficiency, resulting in some rivers with connectivity loss and reduced biodiversity, etc. The Chishui River Watershed is an ecologically valuable river in the upper reaches of the Yangtze River. As an important habitat for rare fish in the upper reaches of the Yangtze River and the only large-scale tributary that maintains a natural flow pattern, the SHP plants’ dismantling and ecological restoration practices in the Chishui River Watershed can set a model for regional sustainable development. This paper adopts the methods of literature review, field research, and case study analysis, combined with the comparison of ecological conditions before and after the dismantling, to systematically analyze the effectiveness and challenges of SHP rectification in the Chishui River Watershed. The study found that after dismantling 88.2% of SHP plants in ecologically sensitive areas, the number of fish species upstream and downstream of the original dam site increased by about 6.67% and 70%, respectively; the natural hydrological connectivity has been restored to the downstream of the Tongzi River, the Gulin River and other rivers, but there are short-term problems such as sediment underflow, increased economic pressure, and the gap of alternative energy sources; the retained power stations have achieved the success and challenges of power generation and ecological management ecological flow control and comprehensive utilization, achieving a balance between power generation and ecological protection. Based on the above findings, the author proposes dynamic monitoring and interdisciplinary tracking research to fill the gap of systematic data support and long-term effect research in the SHP exit mechanism, and the results can provide a reference for the green transition of SHP. Full article

This study develops an incompressible two-phase flow solver based on the open-source OpenFOAM platform, employing the volume-of-fluid (VOF) method to track the gas–liquid interface and utilizing the MULES algorithm to suppress numerical diffusion. This study provides a comprehensive investigation of the spreading dynamics of droplet pairs near walls, along with the presentation of a corresponding mathematical model. The numerical model is validated through a two-dimensional axisymmetric computational domain, demonstrating grid independence and confirming its reliability by comparing simulation results with experimental data in predicting drConfirmedoplet collision, spreading, and deformation dynamics. The study particularly investigates the influence of surface wettability on droplet impact dynamics, revealing that increased contact angle enhances droplet retraction height, leading to complete rebound on superhydrophobic surfaces. Finally, a mathematical model is presented to describe the relationship between spreading length, contact angle, and Weber number, and the study proves its accuracy. Analysis under logarithmic coordinates reveals that the contact angle exerts a significant influence on spreading length, while a constant contact angle condition yields a slight monotonic increase in spreading length with the Weber number. These findings provide an effective numerical and mathematical tool for analyzing the spreading dynamics of droplet pairs. Full article

Microbial communities play a pivotal role in material cycling, energy flow, and pollutant degradation within river ecosystems. Thus, gaining a clear understanding of how wastewater discharge affects microbial community structure and function is essential for the protection and management of the surface water environment. In this study, a total of 9 samples were collected from the Sha River in March 2024. Subsequently, 16S rRNA sequencing technology combined with investigation of physicochemical properties of water was used to investigate the compositional diversity, spatial distribution, and explore the environmental effects of wastewater discharged on microorganisms. The sequencing results of species at the phylum level revealed that the dominant microbial phyla in the Sha River were primarily Proteobacteria (55.4%), Actinobacteriota (24.0%), Bacteroidota (14.3%), and Verrucomicrobiota (2.6%). The most dominant phylum, Proteobacteria, exhibited varying abundances across different sampling sites in the Sha River basin, with the highest abundances observed at Sites S2, S4, S5, and S6. This is mainly due to the fact that the upstream areas of Sites S2, S4, S5, and S6 are characterized by high concentrations of COD and NH₃-N, which are caused by wastewater discharge. Quantitative analysis was also conducted using the Source Tracker model; the results showed that S2 (36.7%) and S4 (31.3%) in the upper reaches of the Sha River are the primary contributors to the microbial community in the downstream catchment area (S6). The study found that the impact of wastewater discharge on the microbial community in the downstream water body exhibits a “longitudinal persistence of microbial signatures” even though the physicochemical pollution indicators of the water body have decreased. These findings of this study represent the application in microbial source tracking in the upstream and downstream sections of rivers, providing strong support for formulating more effective environmental protection strategies in the Sha River basin. Full article

Rapid urban growth in Middle Eastern cities has intensified congestion-related challenges, yet traffic data-based decision making remains limited. This study leverages crowd-sourced travel time data from the Google Maps API to evaluate temporal and spatial patterns of congestion across multiple strategic routes in Jeddah, Saudi Arabia, a coastal metropolis with a complex road network characterized by narrow, high-traffic corridors and limited public transit. A real-time Congestion Index quantifies traffic flow, incorporating free-flow speed benchmarking, dynamic profiling, and temporal classification to pinpoint congestion hotspots. The analysis identifies consistent peak congestion windows and route-specific delays that are critical for travel behavior modeling. In addition to congestion monitoring, the framework contributes to urban sustainability by supporting reductions in traffic-related emissions, enhancing mobility equity, and improving economic efficiency through data-driven transport management. To our knowledge, this is the first study to systematically use the validated, real-time Google Maps API to quantify route-specific congestion in a Middle Eastern urban context. The approach provides a scalable and replicable framework for evaluating urban mobility in other data-sparse cities, especially in contexts where traditional traffic sensors or GPS tracking are unavailable. The findings support evidence-based transport policy and demonstrate the utility of publicly accessible traffic data for smart city integration, real-time traffic monitoring, and assisting transport authorities in enhancing urban mobility. Full article

Precise and robust localization for micro Unmanned Aerial Vehicles (UAVs) in GNSS-denied environments is hindered by the lack of diverse datasets and the limited real-world performance of existing visual matching methods. To address these gaps, we introduce two contributions: (1) the SatLoc dataset, a new benchmark featuring synchronized, multi-source data from varied real-world scenarios tailored for UAV-to-satellite image matching, and (2) SatLoc-Fusion, a hierarchical localization framework. Our proposed pipeline integrates three complementary layers: absolute geo-localization via satellite imagery using DinoV2, high-frequency relative motion tracking from visual odometry with XFeat, and velocity estimation using optical flow. An adaptive fusion strategy dynamically weights the output of each layer based on real-time confidence metrics, ensuring an accurate and self-consistent state estimate. Deployed on a 6 TFLOPS edge computer, our system achieves real-time operation at over 2 Hz, with an absolute localization error below 15 m and effective trajectory coverage exceeding $90\%$, demonstrating state-of-the-art performance. The SatLoc dataset and fusion pipeline provide a robust and comprehensive baseline for advancing UAV navigation in challenging environments. Full article