Search Results (1,131)

Improving the accuracy of stable state identification and collapse early warning for unstable rock masses is an urgent challenge in slope engineering. In this study, a simplified dynamic model of falling-type unstable rock masses was established, and the dynamic response characteristics of unstable rock masses under different constraint conditions were investigated by combining modal analysis. Finally, physical model tests were carried out to explore the evolution of relevant time-domain and frequency-domain dynamic characteristic parameters during the entire process of falling-type unstable rock masses on slopes, ranging from a stable state, through the propagation of dominant structural planes, to final collapse. The results show that (1) the dominant frequency of the rock mass is independent of the magnitude and direction of excitation forces; (2) the coefficient of variation and waveform factor undergo significant changes during the critical failure stage; and (3) the acceleration amplitude ratio and natural frequency can synergistically and sensitively trace the progression of fracture development within the rock mass. An identification method for the stability stages of typical falling-type unstable rock masses was proposed, which integrates four time–frequency dynamic indicators. The stability state of unstable rock masses was divided into three phases: stable, fundamentally stable, and critical instability. This work provides a valuable reference for instability monitoring of falling-type unstable rock masses. Full article

Falls are a prominent issue in society and the second leading cause of unintentional death globally. Traditional gait analysis is a process that can aid in identifying factors that increase a person’s risk of falling through determining their gait parameters in a controlled environment. Advances in wearable sensor technology and analytical methods such as deep learning can enable remote gait analysis, increasing the quality of the collected data, standardizing the process between centers, and automating aspects of the analysis. Real-world gait analysis requires two problems to be solved: high-accuracy Human Activity Recognition (HAR) and high-accuracy terrain classification. High accuracy HAR has been achieved through the application of powerful novel classification techniques to various HAR datasets; however, terrain classification cannot be approached in this way due to a lack of suitable datasets. In this study, we present the Context-Aware Human Activity Recognition (CAHAR) dataset: the first activity- and terrain-labeled dataset that targets a full range of indoor and outdoor terrains, along with the common gait activities associated with them. Data were captured using Inertial Measurement Units (IMUs), Force-Sensing Resistor (FSR) insoles, color sensors, and LiDARs from 20 healthy participants. With this dataset, researchers can develop new classification models that are capable of both HAR and terrain identification to progress the capabilities of wearable sensors towards remote gait analysis. Full article

Forensic anthropology, a specialized branch of biological anthropology, plays a crucial role in the identification of human remains, particularly when conventional methods such as fingerprinting are not applicable. In Ecuador, its relevance has increased in response to challenges such as intentional deaths, forced disappearances, violence, mass fatalities, and migration-related deaths. Despite its growing importance, the field faces significant limitations, including restricted access to advanced technologies, limited training opportunities for local forensic anthropologists, and insufficient resources for research and the application of advanced methodologies for victim identification. This article examines the development and current state of forensic anthropology in Ecuador, emphasizing the urgent need for population-specific standards, the establishment of a national osteological collection, and stronger institutional support. It also highlights the contributions of bioarchaeological research and its potential to enhance forensic practices. By analyzing the challenges of identifying skeletonized human remains and other instances of human rights violations, the study underscores the necessity of advancing forensic anthropology in the country. The article further discusses how interdisciplinary efforts have contributed to forensic knowledge in Ecuador and concludes by emphasizing the importance of ethical guidelines, technological integration, and improved infrastructure to strengthen forensic anthropology as both a scientific discipline and a humanitarian tool. Full article

Prediction accuracy for complex flexible support systems is often limited by insufficiently characterized thermo-mechanical couplings and nonlinearities. To address this, we propose a multilevel hybrid parallel–serial model that integrates the thermo-viscous effects of a Squeeze Film Damper (SFD) via a coupled Reynolds–Walther equation, the structural flexibility of a squirrel-cage support using Finite Element analysis, and the load-dependent stiffness of a four-point contact ball bearing based on Hertzian theory. The resulting state-dependent system is solved using a force-controlled iterative numerical algorithm. For validation, a dedicated bidirectional excitation test rig was constructed to decouple and characterize the support’s dynamics via frequency-domain impedance identification. Experimental results indicate that equivalent damping is temperature-sensitive, decreasing by approximately 50% as the lubricant temperature rises from 30 °C to 100 °C. In contrast, the system exhibits pronounced stiffness hardening under increasing loads. Theoretical analysis attributes this nonlinearity primarily to the bearing’s Hertzian contact mechanics, which accounts for a stiffness increase of nearly 240%. This coupled model offers a distinct advancement over traditional linear approaches, providing a validated framework for the design and vibration control of aero-engine flexible supports. Full article

Major traumas result from the application of multiple force components that, in adulthood, can lead to high mortality and morbidity. In forensic practice, pathological consequences arising from the rapid flexion–extension of an adult victim’s soma are observed, with typical intracranial and ophthalmological findings. The totality of these findings allows for a contribution to the definition of the Shaken Adult Syndrome (SAS). A comprehensive review, employing the PRISMA methodology, was conducted on international works pertaining to SAS. This resulted in the identification of six scientific papers, which were analyzed separately. It emerged that, for the diagnosis of SAS, the same diagnostic triad as Shaken Baby Syndrome is valid, comprising subdural hemorrhages, retinal hemorrhages, and encephalopathy. This syndrome appears to encompass a broad spectrum of pathological conditions, ranging from whiplash to diffuse axonal injury (DAI). At the conclusion of this work, we proposed a diagnostic flowchart that allows for suspected predictive diagnosis of SAS, both in live patients presenting to emergency medical services and in post-mortem cadavers. For this purpose, the collection of anamnesis and circumstantial data, the detection of external injuries, and the execution of cranial CT scans will be essential. Ultimately, microscopic examinations of the brain with specific immunomarkers and of ocular structures will enable the identification of pathognomonic findings for SAS. Full article

Demagnetization faults in direct-drive permanent magnet synchronous motors (DDPMSM) can cause significant performance degradation and unplanned downtime. Traditional fault location methods, which rely on manual feature extraction, exhibit limited accuracy and efficiency in complex and variable operating conditions. To address these limitations, this study presents a demagnetization fault location method based on a search coil employing a data-driven one-dimensional convolutional neural network (1D-CNN). Firstly, the arrangement of search coils was determined, and a partitioned mathematical model was established, using the residual back electromotive force (back-EMF) of the search coil over a single electrical cycle as the fundamental unit. Secondly, the residual back-EMF in the search coil is analyzed under various demagnetization parameters and operating conditions to assess the robustness of the proposed method. Furthermore, a 1D-CNN-based fault location model was developed using residual back-EMF signals as the input and targeting the identification of demagnetized permanent magnet types. Simulation and experimental results demonstrate that the proposed method can effectively detect and locate demagnetization faults across different operating conditions. When the demagnetization degree is not less than 10%, the fault location accuracy reaches 99.58%, and the minimum detectable demagnetization degree is 8%. The approach demonstrates excellent robustness and generalization, offering an intelligent solution for demagnetization fault location in PMSMs. Full article

This study presents an in silico proof of concept exploring whether deep learning models can perform conditional mitochondrial DNA (mtDNA) sequence prediction across species boundaries. A CNN–BiLSTM model was trained under a leave-one-species-out (LOSO) scheme on complete mitochondrial genomes from 21 vertebrate species, primarily archosaurs. Model behavior was evaluated through multiple complementary tests. Under context-conditioned settings, the model performed next-nucleotide prediction using overlapping 200 bp windows to assemble contiguous 2000 bp fragments for held-out species; the resulting high token-level accuracy (>99%) under teacher forcing is reported as a diagnostic of conditional modeling capacity. To assess leakage-free performance, a two-flank masked-span imputation task was conducted as the primary evaluation, requiring free-running reconstruction of 500 bp interior spans using only distal flanking context; in this setting, the model consistently outperformed nearest-neighbor and demonstrated competitive performance relative to flank-copy baselines. Additional robustness analyses examined sensitivity to window placement, genomic region (coding versus D-loop), and random initialization. Biological plausibility was further assessed by comparing predicted fragments to reconstructed ancestral sequences and against composition-matched null models, where observed identities significantly exceeded null expectations. Using the National Center for Biotechnology Information (NCBI) BLAST web interface, BLASTn species identification was performed solely as a biological plausibility check, recovering the correct species as the top hit in all cases. Although limited by dataset size and the absence of ancient DNA damage modeling, these results demonstrate the feasibility of conditional mtDNA sequence prediction as an initial step toward more advanced generative and evolutionary modeling frameworks. Full article

In recent years, a novel decomposition of fluid motion has been proposed, which mathematically defines a type of fluid rigid rotation distinct from vorticity, termed the Liutex quantity. Since its introduction, Liutex has been successfully applied to describe fluid vortices and has emerged as an internationally recognized third-generation vortex identification method. This new motion decomposition undoubtedly leads to a revised description of rotational and deformational motions, thereby necessitating a new description of dynamics. Therefore, based on the Stokes assumption and the novel Liutex decomposition, this paper constructs a new constitutive equation and derives a new set of fluid dynamic equations. The research findings reveal two key insights: first, the new shear stress in the fluid is no longer symmetric; second, in addition to traditional forces such as body force, pressure, and viscous force, an additional force induced by Liutex-based rigid rotation is identified. Furthermore, the new dynamic framework encompasses traditional fluid dynamics, with the latter being a special case when Liutex equals the traditional vorticity. It is anticipated that the proposed equations will find significant applications in the study of fluid vortices and turbulence and will undoubtedly stimulate research interest in the field of fluid mechanics. Full article

Cables in cable-supported bridges are critical structural components with exceptional tensile capacity, and their assessment is essential for the safety of both the cables themselves and the entire bridge. Microwave radar, a non-contact and efficient measurement technique, has emerged as a promising tool for bridge cable evaluation. This study demonstrates the deployment of microwave radar on bridge decks to efficiently measure the displacements of multiple cables, enabling coverage of all cables while effectively eliminating low-frequency components caused by deck deformation and radar motion using the LOWESS method. The measured cable displacements can be directly used to characterize vibrations, particularly for detecting vortex-induced vibrations (VIVs), without the need for numerical integration of accelerations. Furthermore, microwave radar is applied to free-decay testing for cable damping evaluation, providing an improved signal-to-noise ratio and eliminating the need for sensors installed via elevated platforms, thereby enhancing the reliability of damping assessments. The effectiveness of these approaches is validated through field testing on two cable-stayed bridges. Full article

To address the limitations of existing cable damage identification methods in terms of environmental robustness and measurement dependency, this study proposes a novel damage identification approach based on the second-order difference characteristics of main beam deflection. Through theoretical derivation, the intrinsic relationship between cable damage and local deflection field disturbances in the main beam was revealed, leading to the innovative definition of a second-order difference of deflection (DISOD) index for damage localization. By analyzing the second-order deflection differences at the anchorage points of a three-cable group (a central cable and its two adjacent cables), the damage status of the central cable can be directly determined. The research comprehensively employed finite element numerical simulations and scaled model experiments to systematically validate the method’s effectiveness in identifying single-cable and double-cable (both adjacent and non-adjacent) damage scenarios under various noise conditions. This method enables damage localization without direct cable force measurement, demonstrates anti-noise interference capability, achieves rapid and accurate identification, and provides a technically promising solution for the health monitoring of long-span cable-stayed bridges. Full article

Innovations are the driving force of change and are essential even in traditional activities such as regional food production. This is especially important considering that locally produced food can be a healthier, more organic, and sustainable alternative to mass-produced food. In this context, the research aims to identify the characteristics of innovative producers and their implications for creating effective policies in the rural regional food sector in Poland. A survey research study using an electronic questionnaire was conducted among a group of 400 regional food producers in Poland in July 2024. Differences between the groups analyzed of regional food producers were examined using a series of non-parametric tests. The results indicate that innovative regional food producers differ significantly from non-innovative producers in many aspects. In terms of raw materials, finance, knowledge, and skills, the differences concern the greater reliance on external resources, as well as a weaker connection with family knowledge and skills, compared to non-innovative producers. The contribution of the research includes the identification of conditions that facilitate the innovativeness of regional food producers, as well as the features that enable or hinder this process. The dissemination of innovations among regional food producers in Poland requires financial and non-financial support. Support for innovation is a crucial component of an effective rural development policy in Poland. Full article

Background: The Identification of Functional Ankle Instability (IdFAI) questionnaire is widely used to screen for functional ankle instability (FAI), but its link to objective landing kinetics in multidirectional sports like netball is not well-understood. This study aimed to (i) compare landing kinetics between idFAI stratified netball players, and (ii) examine associations between IdFAI scores with dynamic postural stability (DPS) indices and peak vertical ground reaction forces (PvGRF) during vertical drop landings. Methods: A cross-sectional exploratory study using a repeated-measures landing protocol was conducted on female university netball players (n = 24), stratified into FAI (n = 12) and non-FAI (n = 12) groups using the IdFAI (≥11 indicating possible FAI). Participants completed 18 unilateral drop jump landings in forward (FW), diagonal (DI), and lateral (LA) directions. Ground reaction forces (GRFs) were recorded to obtain DPS and PvGRF metrics (1000 Hz). Mann–Whitney U tests compared FAI groups, and Spearman correlations assessed associations (p < 0.05). Results: Players with FAI showed greater anteroposterior instability during LA landings (U = 33.5, p = 0.020, ES = 0.65). IdFAI scores correlated moderately with lateral anteroposterior deficits (rs = 0.473, p = 0.020, CI = 0.062–0.746). Conclusions: These findings suggest that players with greater FAI display increased anteroposterior instability during LA landings, with higher IdFAI scores moderately associated with these deficits. Despite the small exploratory, hypothesis-generating sample, the results emphasize the practical relevance of direction-targeted landing-stability training to improve DPS in vertical landings. This may provide insight into ankle-injury risk among FAI netball players, given that LA landings represent a documented ankle sprain mechanism. Full article

Although PINNs have demonstrated strong predictive capabilities in forward problems, their performance in inverse problems remains inadequate, largely due to unquantifiable noise encountered during the multi-parameter identification of prestressed concrete beams. Experimental measurements are often noisy, sparse, or asymmetric, while numerical or analytical models, although physically consistent, are typically approximate and lack regularization from well-defined multi-fidelity data. To address this limitation, this paper proposed a multi-fidelity data and prior-enhanced physics-informed neural network (MF-rPINN), which integrates multi-fidelity data with physics prior relational constraints to guide parameter identification using only sparse experimental observations. The MF-rPINN architecture is designed to enforce consistency between each training iteration and a prescribed set of experimental measurements, while embedding the second-order displacement function into the loss function. Experimental results demonstrate that the proposed MF-rPINN achieves accurate parameter identification even under noisy and incomplete observations, owing to the combined regularization effects of governing physical laws and the second-order displacement prior. The minimum relative errors of the elastic modulus are −6.49% and −9.32% under different and identical loading conditions, respectively, while the minimum relative errors of the prestress force are 0.65% and 4.51%. Compared with classical analytical approaches, MF-rPINN exhibits superior robustness and is capable of predicting continuous displacement fields of prestressed concrete beams while simultaneously identifying prestress force and elastic modulus. These advantages highlight the potential of MF-rPINN as a reliable surrogate modeling tool for practical engineering applications. Full article

Effective identification of strain-hardening parameters is essential for predictive plasticity models used in automotive applications. However, the performance of Bayesian optimization depends strongly on kernel hyperparameters in the Gaussian-process surrogate, which are often kept fixed. In this work, we propose a likelihood-based online hyperparameter strategy within Bayesian optimization to identify strain-hardening parameters in plasticity. Specifically, we used the rational polynomial strain-hardening scheme for the plasticity model to fit the force vs. displacement response of automotive structural steel in tension. An in-house Bayesian optimization framework was first developed, and an online hyperparameter tuning algorithm was further incorporated to advance the optimization scheme. The optimization histories obtained from the fixed and online-tuning hyperparameters were compared. For the same number of iterations, the online hyperparameter adaptation reduced the final residual by approximately 20.4%, 24.0%, and 3.8% for Specimens 1–3, respectively. These results demonstrate that the proposed strategy can significantly improve the efficiency and quality of strain-hardening parameter identification. The results show that the online tuning scheme improved the optimization efficiency. This proposed strategy may be readily extensible to other materials and identification problems where enhancing optimization efficiency is needed. Full article

Under the dual driving forces of negative population growth and the cross-regional agglomeration of factors, the trend of urban shrinkage in China continues to intensify. This study examines 461 small towns in 26 mountainous counties of Zhejiang Province, constructing a multi-dimensional shrinkage identification model based on “population–economy–land use.” The spatiotemporal patterns of shrinkage were visualized using ArcGIS 10.8, while the driving factors were analyzed using the MGWR method. ① From 2010 to 2020, the shrinkage phenomenon in small towns across the 26 mountainous counties rapidly spread, with medium- and severe-shrinking towns increasing markedly, showing an irreversible trend. ② The spatial evolution pattern shows a phased characteristic, transitioning from “disordered scattered points” to “striped aggregation.” A “V”-shaped shrinkage belt formed along the “Kaihua–Jingning–Yongjia” axis, demonstrating strong spatial aggregation. ③ The shrinkage of small towns is driven by multiple factors. Rugged mountainous terrain constrains development, while urbanization and industrial restructuring, coupled with outmigration of young and middle-aged workers, accelerate aging and limit local specialty industries. Transportation, social services, and policy frameworks further influence shrinkage patterns. In response to the continuous shrinkage trend of small towns in mountainous areas, future efforts should adopt coordinated strategies such as smart shrinkage, industrial restructuring, and institutional innovation to achieve structural and systemic reshaping. Full article