Search Results (51)

Lumbar shear forces are increasingly recognized as critical contributors to lower-back injury risk, yet most ergonomic assessment tools—most notably the Revised NIOSH Lifting Equation (RNLE)—do not directly estimate shear loading. This study develops and evaluates a family of linear mixed-effects regression models that statistically predict L4/L5 lumbar shear force exposure using traditional NIOSH lifting parameters combined with posture descriptors extracted from digital human models. A harmonized dataset of 106 peak-shear lifting postures was compiled from five controlled laboratory studies, with lumbar shear forces obtained from validated biomechanical simulations implemented in the Siemens JACK (Siemens software, Plano, TX, USA) platform. Twelve model formulations were examined, varying in fixed-effect structure and hierarchical random effects, to quantify how load magnitude, hand location, sex, and joint posture relate to simulated task-level anterior–posterior shear exposure at the lumbar spine. Across all models, load magnitude and horizontal reach emerged as the strongest and most stable predictors of shear exposure, reflecting their direct mechanical influence on anterior spinal loading. Hip and knee flexion provided substantial additional explanatory power, highlighting the role of whole-body posture strategy in modulating shear demand. Upper-limb posture and coupling quality exhibited minimal or inconsistent effects once load geometry and lower-body posture were accounted for. Random-effects analyses demonstrated that meaningful variability arises from individual movement strategies and task conditions, underscoring the necessity of mixed-effects modeling for representing hierarchical structure in lifting data. Parsimonious models incorporating subject-level random intercepts produced the most stable and interpretable coefficients while maintaining strong goodness-of-fit. Overall, the findings extend the NIOSH framework by identifying posture-dependent determinants of lumbar shear exposure and by demonstrating that simulated shear loading can be reliably predicted using ergonomically accessible task descriptors. The proposed models are intended as statistical predictors of task-level shear exposure that complement—rather than replace—comprehensive biomechanical simulations. This work provides a quantitative foundation for integrating shear-aware metrics into ergonomic risk assessment practices, supporting posture-informed screening of manual material-handling tasks in field and sensor-based applications. Full article

Radiographic imaging remains a cornerstone of diagnostic practice. However, accurate interpretation faces challenges from subtle visual signatures, anatomical variability, and inter-observer inconsistency. Conventional deep learning approaches, such as convolutional neural networks and vision transformers, deliver strong predictive performance but often lack anatomical grounding and interpretability, limiting their trustworthiness in imaging applications. To address these challenges, we present SpineNeuroSym, a neuro-geometric imaging framework that unifies geometry-aware learning and symbolic reasoning for explainable medical image analysis. The framework integrates weakly supervised keypoint and region-of-interest discovery, a dual-stream graph–transformer backbone, and a Differentiable Radiographic Geometry Module (dRGM) that computes clinically relevant indices (e.g., slip ratio, disc asymmetry, sacroiliac spacing, and curvature measures). A Neuro-Symbolic Constraint Layer (NSCL) enforces monotonic logic in image-derived predictions, while a Counterfactual Geometry Diffusion (CGD) module generates rare imaging phenotypes and provides diagnostic auditing through counterfactual validation. Evaluated on a comprehensive dataset of 1613 spinal radiographs from Sunpasitthiprasong Hospital encompassing six diagnostic categories—spondylolisthesis (n = 496), infection (n = 322), spondyloarthropathy (n = 275), normal cervical (n = 192), normal thoracic (n = 70), and normal lumbar spine (n = 258)—SpineNeuroSym achieved 89.4% classification accuracy, a macro-F1 of 0.872, and an AUROC of 0.941, outperforming eight state-of-the-art imaging baselines. These results highlight how integrating neuro-geometric modeling, symbolic constraints, and counterfactual validation advances explainable, trustworthy, and reproducible medical imaging AI, establishing a pathway toward transparent image analysis systems. Full article

Background: Gold (Au) complexes have emerged as promising anticancer candidates due to their distinct coordination chemistry and ability to modulate thiol-dependent and redox-regulated cellular pathways, particularly thioredoxin reductase (TrxR). In recent years, structurally diverse Au(I) and Au(III) complexes have been reported with potent in vitro anticancer activity; however, cross-study comparability and design principles remain unclear. Aim: This systematic review critically evaluates anticancer Au(I/III) complexes reported since 2016, with the specific aim of identifying how oxidation state, coordination geometry, and ligand class influence in vitro potency, selectivity, and translational potential. Methods: A PRISMA-guided literature search was performed in Scopus, Web of Science, PubMed, and ScienceDirect for studies published between January 2016 and March 2025. Two independent reviewers screened titles/abstracts and full texts according to predefined inclusion criteria. Only original studies reporting anticancer activity of structurally characterized Au(I/III) complexes in human cancer models were included. After the removal of duplicates, 1100 records were screened at the title and abstract level. Of these, 240 articles were assessed in full text for eligibility. Ultimately, 128 studies reporting anticancer activity of structurally characterized Au(I/III) complexes met the inclusion criteria and were included in the qualitative synthesis. Biological potency data were harmonized to μM units where applicable, and results were synthesized qualitatively due to heterogeneity in experimental design. Results: A total of 128 studies met the inclusion criteria. Au(I) complexes—particularly phosphine- and N-heterocyclic carbene (NHC)-based compounds—consistently showed sub-micromolar cytotoxicity in TrxR-dependent cancer cell lines, whereas Au(III) complexes displayed greater structural diversity but variable stability and redox behavior. In vivo efficacy was reported for a limited subset of compounds and was frequently constrained by solubility, systemic toxicity, or metabolic instability. Conclusions: The available evidence indicates that anticancer activity of gold complexes is strongly dependent on oxidation state, ligand environment, and redox stability. While Au(I) scaffolds show more reproducible in vitro potency, successful translation to in vivo models remains limited. This review defines structure–activity and structure–liability relationships that can guide the rational design of next-generation gold-based anticancer agents. Full article

Background: Pregnancy induces substantial cardiovascular remodeling, yet whether maternal cardiac adaptation differs across ethnic groups remains unclear. Body size, ventricular geometry, and thoracoabdominal configuration may modulate key functional indices such as left ventricular ejection fraction (LVEF) and global longitudinal strain (LV-GLS). This systematic review compared echocardiographic characteristics between Asian and Western healthy pregnant women in late gestation and explored physiological mechanisms underlying observed differences. Methods: A comprehensive search of PubMed, Scopus, and EMBASE identified studies reporting transthoracic echocardiography in healthy singleton third-trimester pregnancies across Asian and Western populations. Extracted variables included anthropometry, ventricular dimensions and volumes, LVEF, and LV-GLS. Pooled estimates were calculated using inverse-variance weighting, with heterogeneity quantified using the I² statistic. Study quality was assessed with the NIH Case–Control Quality Assessment Tool. Comparative forest plots visualized population differences. Results: Twenty studies involving 1431 participants (578 Asian and 853 Western women) met inclusion criteria. Asian women consistently exhibited smaller ventricular chambers, higher LVEF, and more favorable LV-GLS. Importantly, these differences persisted after indexing LV-GLS to BSA, indicating that body-size normalization attenuates—but does not eliminate—population differences in myocardial deformation. Western women demonstrated slightly attenuated GLS despite preserved LVEF, plausibly attributable to larger cardiac size, higher wall stress, greater diaphragmatic elevation, and increased extrinsic thoracic compression. Between-study heterogeneity was substantial (I² > 95%) due to variation in imaging platforms, strain software, and population characteristics. Methodological quality was fair, with frequent lack of sample-size justification and incomplete confounder adjustment. Conclusions: Healthy Asian pregnant women display a hyperdynamic systolic phenotype, whereas Western women show a physiologically appropriate, load-related attenuation of LV-GLS with preserved LVEF. These findings highlight the need for ethnicity-associated and anatomy-aware echocardiographic reference values and support incorporating thoracic geometric indices, such as the modified Haller Index, into strain interpretation during pregnancy. Full article

Semantic segmentation of high-resolution remote sensing images remains a challenging task due to the intricate spatial structures, scale variability, and semantic ambiguity among ground objects. Moreover, the reliable delineation of fine-grained boundaries continues to impose difficulties on existing CNN- and transformer-based models, particularly in heterogeneous urban and rural environments. In this study, we propose GPRNet, a novel geometry-aware segmentation framework that leverages geometric priors and cross-stage semantic alignment for more precise land-cover classification. Central to our approach is the Geometric Prior-Refined Block (GPRB), which learns directional derivative filters, initialized with Sobel-like operators, to generate edge-aware strength and orientation maps that explicitly encode structural cues. These maps are used to guide structure-aware attention modulation, enabling refined spatial localization. Additionally, we introduce the Mutual Calibrated Fusion Module (MCFM) to mitigate the semantic gap between encoder and decoder features by incorporating cross-stage geometric alignment and semantic enhancement mechanisms. Extensive experiments conducted on the ISPRS Potsdam and LoveDA datasets validate the effectiveness of the proposed method, with GPRNet achieving improvements of up to 1.7% mIoU on Potsdam and 1.3% mIoU on LoveDA over strong recent baselines. Furthermore, the model maintains competitive inference efficiency, suggesting a favorable balance between accuracy and computational cost. These results demonstrate the promising potential of geometric-prior integration and mutual calibration in advancing semantic segmentation in complex environments. Full article

We study stochastic variants of the Kairat-II and Kairat-X equations in (3 + 1) dimensions, two canonical models in soliton theory. Random fluctuations are incorporated through a Wiener process, yielding a multiplicative stochastic embedding of the wave fields. By combining the enhanced direct algebraic technique with the new projective Riccati equation approach, we obtain closed-form stochastic soliton solutions and analyze how noise modulates their amplitude and localization. The solutions are illustrated with consistent 3D surface plots (mean field vs. sample paths) and 2D time traces to highlight wave geometry and variability. In addition, we employ the energy balance approach to separate kinetic and potential contributions and to verify an energy balance relation for the derived solutions, thereby clarifying their physical plausibility and stability under noise. The results provide exact, easily verifiable benchmarks for stochastic nonlinear wave models and a practical template for incorporating randomness into nonlinear dispersive systems. Full article

The performance of large-scale photovoltaic (PV) power plants is strongly influenced by array layout parameters including module tilt angle, azimuth angle, and row spacing. These geometric variables jointly determine solar irradiance geometry, shading losses, and land-use efficiency, affecting annual energy yield and levelized cost of electricity. To achieve multi-objective comprehensive optimization of array layout parameters for a PV power generation system, a collaborative optimization strategy for PV array layout based on the lemur optimization (LO) algorithm is proposed in this paper. The method couples the Perez anisotropic irradiance model with a dynamic shading irradiance geometric model to simulate the effective insolation, incorporating land availability, shading thresholds, and maintenance access requirements. In addition, the LO algorithm is employed to solve resulting nonlinear and constrained problems, enabling an efficient global search across large parameter spaces. The case studies in Lianyungang, Dalian, and Fuzhou City show that the proposed scheme based on the LO algorithm improves annual energy yield compared with the existing optimization schemes, providing new theoretical methods and engineering application paths for the optimal layout of PV arrays. Full article

The pattern of the front contact metallization critically influences solar cell efficiency. This study introduces a novel explicit geometry optimization approach for designing the front contact metallization patterns. In the proposed approach, the front contact patterns are represented by wide Bezier curves with variable widths, where each curve’s geometry is defined by both control points and control circles. The control point coordinates and the control circle radii are taken as design variables. To ensure physical feasibility during the design process, one of the end control points of each curve is fixed at the current extraction point. Unlike geometry optimization techniques employing fixed-width Bezier curves, our approach provides enhanced design flexibility through continuous width modulation along the front contact paths. Simulation experimental validation across the simple solar cell geometries demonstrates the proposed method’s superior performance relative to both the solid isotropic material with penalization (SIMP) approach and geometry optimization method using a fixed-width Bezier. Furthermore, the optimized front contact metallization structures outperform the conventional H-pattern designs. Specifically, for a solar cell with a size of 3.5 cm, compared to a solar cell with conventional H-pattern front contact electrodes, the conversion efficiency, open-circuit voltage, short-circuit current, and fill factor of the solar cell with curve-shaped front contact metallization are relatively increased by 0.415%, 0.0011 V, and 5.091 A·m⁻², and 0.904%, respectively, while the material coverage ratio is reduced by 1.974%. The methodology’s versatility is further evidenced by its successful adaptation to free-form solar cell configurations. Full article

Tensegrity structures, by definition composed of compressed members suspended in a network of tensile cables, are characterised by a high strength-to-weight ratio and the ability to undergo reversible deformations. Their application as cores of sandwich panels represents an innovative approach to lightweight design, enabling the regulation of mechanical properties while reducing material consumption. This study presents a finite element modelling procedure that combines analytical determination of prestress using singular value decomposition with implementation in the ABAQUS™ 2019 software. Geometry generation and prestress definitions were automated with Python 3 scripts, while algebraic analysis of individual modules was performed in Wolfram Mathematica. Two models were investigated: M1, composed of four identical modules, and M2, composed of four modules arranged in two mirrored pairs. Model M1 exhibited a linear elastic response with a constant global stiffness of 13.9 kN/mm, stable regardless of the prestress level. Model M2 showed nonlinear hardening behaviour with variable stiffness ranging from 0.135 to 1.1 kN/mm and required prestress to ensure static stability. Eigenvalue analysis confirmed the full stability of M1 and the increase in stability of M2 upon the introduction of prestress. The proposed method enables precise control of prestress distribution, which is crucial for the stability and stiffness of tensegrity structures. The M2 configuration, due to its sensitivity to prestress and variable stiffness, is particularly promising as an adaptive sandwich panel core in morphing structures, adaptive building systems, and deployable constructions. Full article

This paper focuses on the vibration analysis of a prototype helicopter rotor test stand, with particular attention to the dynamic response of its electric propulsion system. The stand is driven by an induction motor and equipped with composite rotor blades of various geometries, including blades with shape memory alloy (SMA)-based torsion actuators for angle of attack (AoA) adjustment. These variable geometries significantly influence the system’s dynamic behavior, where resonance phenomena may pose risks to structural integrity. The objective was to investigate how selected operational parameters specifically motor speed and AoA affect the vibration response of the propulsion system. Structural vibrations were measured using a tri-axial piezoelectric accelerometer system integrated with calibrated signal conditioning and high-resolution data acquisition modules. This setup enabled precise, time-synchronized recording of dynamic responses along all three axes. Fast Fourier Transform (FFT) and Power Spectral Density (PSD) methods were applied to identify dominant frequency components, including those associated with rotor harmonics and SMA activation. The highest vibration amplitudes were observed at an AoA of 16°, but all results remained within the vibration limits defined by MIL-STD-810H for rotorcraft drive systems. The study confirms the importance of sensor-based diagnostics in evaluating electromechanical propulsion systems operating under dynamic loading conditions. Full article

Accurate placement and geometry of simulation plots are essential for spatially explicit modeling of forest ecosystems. This study introduces a Monte Carlo-based approach for optimizing the spatial alignment of simulation plots with their source polygons, improving their ability to represent stand-level heterogeneity. The method is implemented in GenSimPlot, an open-source Python plugin for QGIS (version 3.30) that automates the generation, placement, and refinement of simulation plots using simple geometric shapes. Monte Carlo optimization iteratively adjusts translation, rotation, and scaling parameters to maximize spatial congruence, thereby enhancing the fidelity of forest growth simulations. A built-in hyperparameter tuning module based on random search enables users to explore optimal parameter settings systematically. In addition, GenSimPlot supports the extraction of qualitative and quantitative environmental variables and terrain from raster datasets, facilitating integration with forest growth models and broader ecological simulations. The proposed approach improves plot representativeness and enables robust scenario analysis across heterogeneous landscapes. Full article

The adaptive cycle engine (ACE) can modulate thermal cycle characteristics by adjusting variable geometry components, enabling rational distribution of bypass flow rates. As a key component of the ACE, the rear variable area bypass injector (RVABI) significantly influences the engine bypass ratio and consequently alters engine performance. RVABIs are typically categorized into three configurations based on their design: Translation Type, Rotary Type, and Hole Type. Previous studies have not fully elucidated the overall operating characteristics, internal flow mechanisms, and applicable scenarios of these different RVABI configurations. To address this problem, this paper first introduces and validates a three-dimensional (3D) simulation methodology for RVABIs. Subsequently, criteria for reasonably evaluating the operating characteristics of different RVABI configurations are defined. Following this, the differences in operating characteristics and internal flow mechanisms among the three RVABI configurations are systematically compared. Finally, the application scenarios for each configuration are identified. This work provides valuable insights to guide the configuration selection and parameter design of RVABIs in practical engineering applications. Full article

Electric vehicle (EV) battery thermal management systems have gradually improved owing to the increasing power demand of EVs. This study aims to optimize the channel geometry of sandwich double-sided cold plates for EV battery cooling under 100% state of charge and 2C-rate charging conditions. For precise and accurate optimization, the conventional one-dimensional analysis model of the sandwich double-sided cold plate was converted into a three-dimensional computational fluid dynamics (CFD) model. Non-dimensional parameters were selected as the main variables of the channel geometry, and nine additional channel shapes were derived based on them. Battery modules with the derived channel shapes were subjected to CFD analysis in the Reynolds number range of 500 to 20,000. The goodness factor was calculated from these correlations, and optimization was performed using the Taguchi method. The results revealed that the wetted area of the channel had a greater impact on battery cooling than the number of channels. This study proposed more generalized design guidelines by employing non-dimensionalized parameters across a wide range of Reynolds numbers. The rectangular channel-based correlations developed in this study showed improved prediction accuracy compared to conventional annular pipe-based correlations and are expected to be applicable to various battery thermal management system designs in the future. Full article

Hydrogen-bond-like M···H–C interactions in square-planar d⁸ metal complexes have recently gained attention as structure-directing elements and design motifs in asymmetric catalysis. In this study, we explore these weak interactions not as static features, but as key modulators of molecular motion. We synthesized four cis-[N,N′-pentamethylenebis(iminomethylazolato)]M(II) (M = Pt, Pd), including iminomethyl-2-imidazole, iminomethyl-5-imidazole, and iminomethylpyrrolato Pt(II) complexes and an iminomethylpyrrolato Pd(II) analog. All complexes display reversible flipping of the alkylene bridge across the coordination plane, with the M···H–C interaction alternately engaging from above or below. This dynamic motion was characterized by variable-temperature ¹H NMR spectroscopy, revealing activation parameters for the flipping process. X-ray crystallography confirmed geometries consistent with hydrogen-bond-like interactions, while NBO analysis based on DFT calculations provided insight into their electronic nature. Interestingly, although Pt and Pd display comparable M···H–C distances, solvent effects dominate the flipping kinetics over metal identity. These findings highlight the role of hydrogen-bond-like M···H–C interactions not only in structural stabilization, but also in regulating conformational dynamics. Full article

The Hard X-ray Modulation Telescope (HXMT), China’s first X-ray astronomy satellite, has significantly contributed to the study of fast variability in black hole X-ray binaries through its broad energy coverage (1–250 keV), high timing resolution, and sensitivity to hard X-rays. This review presents a comprehensive overview of timing analysis techniques applied to black hole X-ray binaries using Insight-HXMT data. We introduce the application and comparative strengths of several time-frequency analysis methods, including traditional Fourier analysis, wavelet transform, bicoherence analysis, and Hilbert-Huang transform. These methods offer complementary insights into the non-stationary and nonlinear variability patterns observed in black hole X-ray binaries, particularly during spectral state transitions and quasi-periodic oscillations. We discuss how each technique has been employed in recent Insight-HXMT studies to characterize timing features such as low-frequency QPOs, phase lags, and power spectrum evolution across different energy bands. Moreover, we present novel phenomena revealed by Insight-HXMT observations, including the detection of high-energy QPOs, spectral parameter modulation with QPO phase, and a new classification scheme for QPO types. The integration of multiple analysis methods enables a more nuanced understanding of the accretion dynamics and the geometry of the inner accretion flow, shedding light on fundamental physical processes in relativistic environments. Full article