Search Results (758)

Computer vision techniques such as three-dimensional digital image correlation (3D-DIC) and three-dimensional point tracking (3D-PT) have demonstrated broad applicability for monitoring the conditions of large-scale engineering systems by reconstructing and tracking dynamic point clouds corresponding to the surface of a structure. Accurate stereophotogrammetry measurements require the stereo cameras to be calibrated to determine their intrinsic and extrinsic parameters by capturing multiple images of a calibration object. This image-based approach becomes cumbersome and time-consuming as the size of the tested object increases. To streamline the calibration and make it scale-insensitive, a multi-sensor system embedding inertial measurement units and a laser sensor is developed to compute the extrinsic parameters of the stereo cameras. In this research, the accuracy of the proposed sensor-based calibration method in performing stereophotogrammetry is validated experimentally and compared with traditional approaches. Tests conducted at various scales reveal that the proposed sensor-based calibration enables reconstructing both static and dynamic point clouds, measuring displacements with an accuracy higher than 95% compared to image-based traditional calibration, while being up to an order of magnitude faster and easier to deploy. The novel approach has broad applications for making static, dynamic, and deformation measurements to transform how large-scale structural health monitoring can be performed. Full article

Monitoring the height of secondary forest regrowth is essential for assessing ecosystem recovery, but current methods rely on field surveys, airborne or UAV LiDAR, and 3D reconstruction from high-resolution UAV imagery, which are often costly or limited by logistical constraints. Here, we address the challenge of scaling up canopy height monitoring by evaluating a recent deep learning model, trained on data from the Amazon and Atlantic Forests, developed to extract canopy height from RGB-NIR Planet NICFI imagery. The research questions are as follows: (i) How are canopy height estimates from the model affected by slope and orientation in natural forests, based on a large and well-balanced experimental design? (ii) How effectively does the model capture the growth trajectories of Pinus and Eucalyptus plantations over an eight-year period following planting? We find that the model closely tracks Pinus growth at the parcel scale, with predictions generally within one standard deviation of UAV-derived heights. For Eucalyptus, while growth is detected, the model consistently underestimates height, by more than 10 m in some cases, until late in the cycle when the canopy becomes less dense. In stable natural forests, the model reveals seasonal artifacts driven by topographic variables (slope × aspect × day of year), for which we propose strategies to reduce their influence. These results highlight the model’s potential as a cost-effective and scalable alternative to field-based and LiDAR methods, enabling broad-scale monitoring of forest regrowth and contributing to innovation in remote sensing for forest dynamics assessment. Full article

To solve the limitations of these models for submicron materials like electrospun nanofiber membranes, a numerical simulation was used to construct a three-dimensional model closer to the actual structure to explore the filtration resistance and efficiency of these membranes. Based on the actual polydisperse electrospun nanofiber filter, the three-dimensional structure (fiber diameter 280 nm–1300 nm, thickness 0.0150 mm–0.0240 mm, and solid volume fraction 11.3–17.7%) was reconstructed by GeoDict software. The filtration resistance was simulated with the FlowDict module (surface velocity 6.89 cm/s, 20 °C), and the filtration efficiency was calculated with the FilterDict module (2.5 μm particles, tracking 20,000). The results are compared with the experimental values, Davids empirical formula, Happel model, and Kuwabara model. The results show that the simulated values of filtration resistance are generally higher than the experimental values (deviation ≤ 20%), among which the simulation and experiment have the highest consistency, followed by the Davids formula (such as the relative error of 41.62% at 9% spinning solution concentration), and the Kuwabara model has the largest error (59.86%). The simulated value of filtration efficiency is higher than the experimental value (deviation ≤ 7%), because the model assumes that the particles adhere directly after contacting the fiber, and the actual sliding off is not considered. This study confirms that numerical simulation can efficiently predict the filtration performance of electrospun nanofiber membranes. Therefore, it provides a basis for optimizing material structure by adjusting spinning parameters and promoting the engineering application of submicron filter materials. Full article

Accurate estimation of upper-limb joint angles is essential in biomechanics, rehabilitation, and wearable robotics. While inertial measurement units (IMUs) offer portability and flexibility, systems requiring multiple inertial sensors can be intrusive and complex to deploy. In contrast, optical motion capture (MoCap) systems provide precise tracking but are constrained to controlled laboratory environments. This study presents a deep learning-based approach for estimating shoulder and elbow joint angles using only three IMU sensors positioned on the chest and both wrists, validated against reference angles obtained from a MoCap system. The input data includes Euler angles, accelerometer, and gyroscope data, synchronized and segmented into sliding windows. Two recurrent neural network architectures, Convolutional Neural Network with Long-short Term Memory (CNN-LSTM) and Bidirectional LSTM (BLSTM), were trained and evaluated using identical conditions. The CNN component enabled the LSTM to extract spatial features that enhance sequential pattern learning, improving angle reconstruction. Both models achieved accurate estimation performance: CNN-LSTM yielded lower Mean Absolute Error (MAE) in smooth trajectories, while BLSTM provided smoother predictions but underestimated some peak movements, especially in the primary axes of rotation. These findings support the development of scalable, deep learning-based wearable systems and contribute to future applications in clinical assessment, sports performance analysis, and human motion research. Full article

Optical sensor technologies are reshaping obesity detection by enabling non-invasive, dynamic analysis of biomechanical and morphological biomarkers. This review synthesizes recent advances in three key areas: optical gait analysis, vision-based pose estimation, and depth-sensing voxel modeling. Gait analysis leverages optical sensor arrays and video systems to identify obesity-specific deviations, such as reduced stride length and asymmetric movement patterns. Pose estimation algorithms—including markerless frameworks like OpenPose and MediaPipe—track kinematic patterns indicative of postural imbalance and altered locomotor control. Human voxel modeling reconstructs 3D body composition metrics, such as waist–hip ratio, through infrared-depth sensing, offering precise, contactless anthropometry. Despite their potential, challenges persist in sensor robustness under uncontrolled environments, algorithmic biases in diverse populations, and scalability for widespread deployment in existing health workflows. Emerging solutions such as federated learning and edge computing aim to address these limitations by enabling multimodal data harmonization and portable, real-time analytics. Future priorities involve standardizing validation protocols to ensure reproducibility, optimizing cost-efficacy for scalable deployment, and integrating optical systems with wearable technologies for holistic health monitoring. By shifting obesity diagnostics from static metrics to dynamic, multidimensional profiling, optical sensing paves the way for scalable public health interventions and personalized care strategies. Full article

This paper addresses the critical gap in the existing literature regarding the combined buoyancy–Marangoni convection of power-law fluids in three-dimensional porous media with complex evaporation surfaces. Previous studies have rarely investigated the convective heat transfer mechanisms in such systems, and there is a lack of effective methods to accurately track fractal evaporation surfaces, which are ubiquitous in natural and engineering porous media (e.g., geological formations, industrial heat exchangers). This research is significant because understanding heat transfer in these complex porous media is essential for optimizing energy systems, enhancing thermal management in industrial processes, and improving the efficiency of phase-change-based technologies. For this scientific issue, a general model is designed. There is a significant temperature difference on the left and right sides of the model, which drives the internal fluid movement through the temperature difference. The upper end of the model is designed as a complex evaporation surface, and there is flowing steam above it, thus forming a coupled flow field. The VOF fractal reconstruction method is adopted to approximate the shape of the complex evaporation surface, which is a major highlight of this study. Different from previous research, this method can more accurately reflect the flow and phase change on the upper surface of the porous medium. Through numerical simulation, the influence of the evaporation coefficient on the flow and heat transfer rate can be determined. Key findings from numerical simulations reveal the following: (1) Heat transfer rates decrease with increasing fractal dimension (surface complexity) and evaporation coefficient; (2) As the thermal Rayleigh number increases, the influence of the Marangoni number on heat transfer diminishes; (3) The coupling of buoyancy and Marangoni effects in porous media with complex evaporation surfaces significantly alters flow and heat transfer patterns compared to smooth-surfaced porous media. This study provides a robust numerical framework for analyzing non-Newtonian fluid convection in complex porous media, offering insights into optimizing thermal systems involving phase changes and irregular surfaces. The findings contribute to advancing heat transfer theory and have practical implications for industries such as energy storage, chemical engineering, and environmental remediation. Full article

This paper examines a previously unknown anonymous Hebrew letter inserted into a postwar edition of Shem HaGedolim, found in the library of the Jewish University in Budapest. The letter, composed in Győr in 1947, consists almost entirely of passages copied from Tiferet Chayim, a hagiographic genealogy of the Sanz Hasidic dynasty. Although derivative in content, the letter’s form and placement suggest it was not meant for transmission but instead served as a private act of mourning and historiographical preservation. By situating the letter within the broader context of post-Holocaust Jewish and Hasidic memory practices, including yizkor books, rabbinic memoirs, and grassroots commemorative writing, this study proposes that the document constitutes a “micro-yizkor”: a bibliographic ritual that aimed to re-inscribe lost tzaddikim into sacred memory. Drawing on theories of trauma, religious coping, and bereavement psychology, particularly the Two-Track Model of Bereavement, the paper examines the letter as both a therapeutic and historiographical gesture. The author’s meticulous copying, selective omissions, and personalized touches (such as modified honorifics and emotive phrases) reflect an attempt to maintain spiritual continuity in the wake of communal devastation. Engaging scholarship by Michal Shaul, Lior Becker, Gershon Greenberg, and others, the analysis demonstrates how citation, far from being a passive act, functions here as an instrument of resistance, memory, and redemptive reconstruction. The existence of such a document can also be examined through the lens of Maurice Rickards’ insights, particularly his characterization of the “compulsive note” as a salient form of ephemera, materials often inserted between the pages of books, which pose unique challenges for interpreting the time capsule their authors sought to construct. Ultimately, the paper argues that this modest and anonymous document offers a rare window into postwar Ultra-orthodox religious subjectivity. It challenges prevailing assumptions about Hasidic silence after the Holocaust and demonstarates how even derivative texts can serve as potent sites of historical testimony, spiritual resilience, and bibliographic mourning. The letter thus sheds light on a neglected form of Hasidic historiography, one authored not by professional historians, but by the broken-hearted, writing in the margins of sacred books. Full article

Steel lazy wave risers (SLWRs) are critical in offshore hydrocarbon transport for linking subsea wells to floating production facilities in deep-water environments. The incorporation of buoyancy modules reduces curvature-induced stress concentrations in the touchdown zone (TDZ); however, extended operational exposure under cyclic environmental and operational loads results in repeated seabed contact. This repeated interaction modifies the seabed soil over time, gradually forming a trench and altering the riser configuration, which significantly impacts stress patterns and contributes to fatigue degradation. Accurately reconstructing the riser’s evolving profile in the TDZ is essential for reliable fatigue life estimation and structural integrity evaluation. This study proposes a simulation-based framework for the autonomous tracking of SLWRs using a fin-actuated autonomous underwater vehicle (AUV) equipped with a monocular camera and multibeam echosounder. By fusing visual and acoustic data, the system continuously estimates the AUV’s relative position concerning the riser. A dedicated image processing pipeline, comprising bilateral filtering, edge detection, Hough transform, and K-means clustering, facilitates the extraction of the riser’s centerline and measures its displacement from nearby objects and seabed variations. The framework was developed and validated in the underwater unmanned vehicle (UUV) Simulator, a high-fidelity underwater robotics and pipeline inspection environment. Simulated scenarios included the riser’s dynamic lateral and vertical oscillations, in which the system demonstrated robust performance in capturing complex three-dimensional trajectories. The resulting riser profiles can be integrated into numerical models incorporating riser–soil interaction and non-linear hysteretic behavior, ultimately enhancing fatigue prediction accuracy and informing long-term infrastructure maintenance strategies. Full article

We present a brief overview of the field of charge density waves (CDW) in condensed systems with focus set to the underlying mechanisms behind the CDW ground state. Our intention in this short review is not to count all related facts from the vast volume of literature about this decades-old and still developing field, but rather to pinpoint the most important, mostly theoretical ones, presenting the development of the field. Starting from the “early days”, mainly based on weakly coupled, chain-like quasi-1D systems and Peierls instability, in which the Fermi surface nesting has been the predominant and practically paradigmatic mechanism of the CDW ground state stabilisation, we track the change in paradigms while entering the field of layered quasi-2D systems, with Fermi surface far away from the nesting regime, in which rather strong, essentially momentum-dependent interactions and particular reconstructions of the Fermi surface become essential. Examples of real quasi-1D materials, such as organic and inorganic conductors like Bechgaard salts or transition metal trichalcogenides and bronzes, in which experiment and theory have been extremely successful in providing detailed understanding, are contrasted to layered quasi-2D materials, such as ${\mathrm{high-T}}_c$ superconducting cuprates, intercalated graphite compounds or transition metal dichalcogenides, for which the theory explaining an onset of the CDWs constitutes a frontier of this fast-evolving field, strongly boosted by development of modern ab initio calculation methods. Full article

Under the upsurge of research on the integration of Simultaneous Localization and Mapping (SLAM) and neural implicit representation, existing methods exhibit obvious limitations in terms of environmental semantic parsing and scene understanding capabilities. In response to this, this paper proposes a SLAM system that integrates a full attention mechanism and a multi-scale information extractor. This system constructs a more accurate 3D environmental model by fusing semantic, shape, and geometric orientation features. Meanwhile, to deeply excavate the semantic information in images, a pre-trained frozen 2D segmentation algorithm is employed to extract semantic features, providing a powerful support for 3D environmental reconstruction. Furthermore, a multi-layer perceptron and interpolation techniques are utilized to extract multi-scale features, distinguishing information at different scales. This enables the effective decoding of semantic, RGB, and Truncated Signed Distance Field (TSDF) values from the fused features, achieving high-quality information rendering. Experimental results demonstrate that this method significantly outperforms the baseline-based methods in terms of mapping and tracking accuracy on the Replica and ScanNet datasets. It also shows superior performance in semantic segmentation and real-time semantic mapping tasks, offering a new direction for the development of SLAM technology. Full article

This study presents an automatic PID control strategy for Tapping-Mode Atomic Force Microscopy (TM-AFM) that addresses the impacts of energy dissipation on tip–sample interactions. Our methodology integrates energy analysis to quantify the critical relationship between energy loss and phase lag dynamics in tapping mode. Additionally, systematic decomposition of interaction force is performed to enable the reconstruction of system transfer functions. The study in this work examines the fluctuations of PID gains during critical oscillations. A SIMULINK-based virtual TM-AFM is developed to simulate practical measurement conditions, based on which a lookup table for PID gains across various phase lags is generated. The efficacy of the proposed algorithm is experimentally validated through the experiments of a calibration nanogrid and two kinds of coated silicon samples, demonstrating the improved tracking accuracy and the improvement of surface height of 5.4% compared to regular control scheme. Full article

Introduction: Total temporomandibular joint replacement (TMJR) is a well-established surgical solution for patients with severe TMJ disorders. It aims to relieve chronic pain, restore jaw mobility, and significantly enhance quality of life. This systematic review evaluates QoL outcomes following TMJR, analyzes complication profiles, compares custom versus stock prostheses, explores pediatric applications, and highlights technological innovations shaping the future of TMJ reconstruction. Methods: A systematic search of PubMed, Embase, and the Cochrane Library was conducted throughout April 2025 in accordance with PRISMA 2020 guidelines. Sixty-four studies were included, comprising 2387 patients. Results: Primary outcomes assessed were QoL improvement, pain reduction, and functional gains such as maximum interincisal opening (MIO). Secondary outcomes included complication rates and technological integration. TMJR consistently led to significant pain reduction (75–87%), average MIO increases of 26–36 mm, and measurable QoL improvements across physical, social, and psychological domains. Custom prostheses were particularly beneficial in anatomically complex or revision cases, while stock devices generally performed well for standard anatomical conditions. Pediatric TMJR demonstrated functional and airway benefits with no clear evidence of growth inhibition over short- to medium-term follow-up. Complications such as heterotopic ossification (~20%, reduced to <5% with fat grafting), infection (3–4.9%), and chronic postoperative pain (~20–30%) were reported but were largely preventable or manageable. Recent advancements, including CAD/CAM planning, 3D-printed prostheses, augmented-reality-assisted surgery, and biofilm-resistant materials, are enhancing personalization, precision, and implant longevity. Conclusions: TMJR is a safe and transformative treatment that consistently improves QoL in patients with end-stage TMJ disease. Future directions include long-term registry tracking, growth-accommodating prosthesis design, and biologically integrated smart implants. Full article

To explore how varying viewpoints influence the accuracy of distributed fusion in asynchronous, nonlinear visual-field systems, this study investigates fusion strategies for multi-target tracking. The primary focus is on how different sensor perspectives affect the fusion of nonlinear moving-target data and the spatial segmentation of such targets. We propose a differential-view nonlinear multi-target tracking approach that integrates the Gaussian mixture, jump Markov nonlinear system, and the cardinalized probability hypothesis density (GM-JMNS-CPHD). The method begins by partitioning the observation space based on the boundaries of distinct viewpoints. Next, it applies a combined technique—the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and SOS (stochastic outlier selection)—to identify outliers near these boundaries. To achieve accurate detection, the posterior intensity is split into several sub-intensities, followed by reconstructing the multi-Bernoulli cardinality distribution to model the target population in each subregion. The algorithm’s computational complexity remains on par with the standard GM-JMNS-CPHD filter. Simulation results confirm the proposed method’s robustness and accuracy, demonstrating a lower error rate compared to other benchmark algorithms. Full article

Understanding morphological descriptions of plants documented by ancient peoples over 1000 years ago and identifying the species they described are critical for reconstructing the natural geographic distribution of plant taxa, tracking taxonomic variations, and inferring historical climate dynamics. Analyzing shifts in plant communities and climatic conditions during this period is essential to unravel the interplay among floristic composition, climate fluctuations, and anthropogenic impacts. However, research in this field remains limited, with greater emphasis placed on plant taxa from hundreds of millions of years ago. Investigations into flora and climate during the last two millennia are sparse, and pre-millennial climatic conditions remain poorly characterized. In this study, a historical text written 1475 years ago was analyzed to compile plant names and morphological features, followed by taxonomic identification. The research identified three gymnosperm species (one in Pinaceae, two in Cupressaceae), 1 Tamaricaceae species (dicotyledon), and 19 dicotyledon species. However, three plant groups could only be identified at the genus level. Using textual analysis and woody plant coexistence methods, the climate of 1475 years ago in western Henan Province, located in the middle-lower Yellow River basin in East Asia, was reconstructed. Results indicate that the mean temperature of the coldest month (MTCM) was approximately 1.3 °C higher than modern values. In comparison, the mean temperature of the warmest month (MTWM) and mean annual temperature (MAT) were lower than present-day levels. This suggests slightly cooler overall conditions with milder seasonal extremes in ancient Luoyang—a finding supported by contemporaneous studies. Furthermore, annual precipitation (AP), precipitation of the warmest quarter (PWQ), and precipitation of the coldest quarter (PCQ) in the Luoyang region 1475 years ago exceeded modern measurements, despite the area’s monsoonal climate. This suggests significantly higher atmospheric moisture content in ancient air masses compared to today. This study provides floristic and climatic baseline data for advancing our understanding of global climate variability at millennial scales. Full article

Harmful algal blooms (HABs) are one of the major environmental concerns, as they have various negative effects on public and environmental health, recreational services, and economics. HAB modeling is challenging due to inconsistent and insufficient data, as well as the nonlinear nature of algae formation data. However, it is crucial for attaining sustainable development goals related to clean water and sanitation. From this point of view, we employed the sparse identification nonlinear dynamics (SINDy) technique to model microcystin, an algal toxin, utilizing dissolved oxygen as a water quality metric and evaporation as a meteorological parameter. SINDy is a novel approach that combines a sparse regression and machine learning method to reconstruct the analytical representation of a dynamical system. The model results indicate that MAPE values of approximately 2% were achieved in three out of four lakes, while the MAPE value of the remaining lake is 11%. Moreover, a model-driven and web-based interactive tool was created to develop environmental education, raise public awareness on HAB events, and produce more effective solutions to HAB problems through what-if scenarios. This interactive and user-friendly web platform allows tracking the status of HABs in lakes and observing the impact of specific parameters on harmful algae formation. Full article