Search Results (1,134)

To enable rapid, non-destructive identification of damage to Yali pears, this study proposes a detection method that integrates short-wave infrared hyperspectral imaging (1000–2500 nm), regularised linear discriminant analysis (R-LDA) and a lightweight convolutional neural network (CNN). The experiments utilised 180 Yali pears (60 each of healthy, with Mechanical scratch and Compression damage specimens) as training samples, whilst a further 300 independent fruits (150 healthy and 150 damaged) were used for fruit-level sorting validation. Following pre-processing using Principal Component Analysis (PCA) to eliminate multicollinearity, the classification performance of three feature extraction strategies—PCA, Independent Component Analysis (ICA) and R-LDA—was compared when combined with the same lightweight CNN. The results indicate that R-LDA’s Fisher discrimination criterion (5.0275) and separation index (2.3543) were both superior to those of PCA and ICA, and its dimension-reduced features exhibited stronger inter-class separability. In pixel-level testing, the R-LDA + lightweight CNN achieved recognition accuracies of 98.60 per cent and 99.32 per cent for Compression damage and background, respectively, and 93.54 per cent and 96.33 per cent for Mechanical scratch and intact tissue, respectively. In a validation study involving the sorting of 300 independent fruits, this method achieved a recall rate of 100.00% for damaged fruits (zero false negatives), with precision and F1 scores of 97.00% and 97.09% respectively, both of which outperformed PCA combined with a lightweight CNN and ICA combined with a lightweight CNN. The above results indicate that the combination of R-LDA discriminant dimensionality reduction and a lightweight CNN can effectively reduce redundancy in hyperspectral data whilst maintaining a high damage detection rate, thereby providing a viable solution for the rapid, non-destructive detection of post-harvest damage in Yali pears. Full article

Masonry structures, frequently encountered both in traditional architecture and in historical buildings, occupy a significant place among structural system types. In these systems, the load-bearing elements typically consist of walls made of materials such as brick and stone. Performance evaluation of masonry structures requires more refined modeling processes due to their brittle behavior under seismic effects and their irregular geometric characteristics. In particular, historical masonry buildings require accurate analysis. The analysis critically depends on the precise identification of the actual geometry, material properties, and structural deficiencies. Within the scope of the study, the effectiveness of combining laser scanning technology with Building Information Modeling (BIM) in the structural analysis and condition assessment of masonry structures was investigated through a case study. Laser scanning point cloud data were processed and transferred into a digital environment. Subsequently, BIM-based software was employed to generate a three-dimensional model using the point cloud data of a historic structure located in Cappadocia, Türkiye. Then, the model was transformed into a finite element model for structural analysis. The finite element model was employed for modal characterization and preliminary structural assessment under self-weight. Throughout the process, axis controls of the walls, misalignments, irregularities, structural discontinuities, and possible structure damage were evaluated digitally to identify potentially vulnerable zones. This study applies the existing Scan-to-BIM-to-Finite Element workflow to complex historical masonry, providing a reliable and practical roadmap for geometric documentation and preliminary structural assessment of the building stock. Full article

The semiconductor industry faces converging pressures from energy-constrained edge electronics and energy-bottlenecked high-performance computing, motivating heterogeneous monolithic three-dimensional (M3D) integration as a system-level response. M3D imposes a strict back-end-of-line (BEOL) thermal budget on upper-tier devices, restricting the channel materials and contact processes available and degrading conventional thin-film transistor performance. The source-gated transistor (SGT), in which drain saturation is set by gate-modulated injection across an engineered source barrier rather than by drain-side channel pinch-off, provides a device-level response: low saturation voltage, high output impedance, large intrinsic gain, and tolerance to channel-length variation, all achieved with moderate-mobility and nonideal-contact channel materials. This review organizes reported SGTs by source-barrier architecture and channel-material platform, develops a spatial characterization framework that complements electrical measurements for unambiguous identification of source-controlled operation, and surveys applications across standalone edge electronics and BEOL-compatible upper tiers in M3D stacks. Integrating non-volatile memory mechanisms into the source barrier further extends SGTs into a compute-in-memory and neuromorphic upper-tier role in which the voltage-invariant saturation current itself functions as a programmable, read-bias-robust state variable. Together, these considerations position SGTs as a flexible architectural primitive for heterogeneous M3D platforms that address the energy demands of both edge and high-performance computing. Full article

Reliable electricity supply in Colombia’s Non-Interconnected Zones requires sustainable and low-cost energy technologies. Vertical-axis hydrokinetic turbines are promising for this purpose; however, their relatively low power coefficient remains a major challenge. This study combines transient 2D and 3D $k$–$\omega$ SST computational fluid dynamics (CFD) simulations with hydraulic channel experiments to investigate a hybrid Darrieus–Savonius turbine. A 27-case Design of Experiments (DoE) based on 2D CFD was first applied to screen the effects of rotor radius ratio (RR), attachment angle (AA), and water velocity. Within the investigated design space, the configuration with $\mathrm{R}\mathrm{R}=0.5$ and $\mathrm{A}\mathrm{A}=0^{\circ }$ produced the most favorable average performance. The selected configuration was subsequently analyzed using 3D CFD and experimentally evaluated at TSR values of 1.0, 1.1, and 1.2. At TSR = 1.0, the 3D model predicted ${\mathrm{C}}_{\mathrm{P}}=0.1525$, closely matching the experimental value of 0.1541 with a relative error of 1.05%. The results demonstrate that 2D CFD is useful for computationally efficient parameter screening and qualitative trend identification, but it overpredicts absolute performance because it neglects blade tip vortices, spanwise flow, and volumetric wake interactions. Three-dimensional CFD is therefore required for reliable performance prediction and analysis of the complex flow structures governing hybrid hydrokinetic turbine behavior. Full article

Reliable personal identification remains a major challenge in forensic medicine, particularly in cases involving decomposition, thermal injury, or absence of DNA and dental records. In this context, anatomically protected and morphologically unique structures such as the sphenoid sinus may serve as valuable biometric markers. The aim of this study was to evaluate the forensic applicability of sphenoid sinus morphology using computed tomography (CT), automated segmentation, and three-dimensional (3D) computational analysis. The proposed framework integrates CT-based image preprocessing, automated 3D segmentation using nnU-Net architecture, and geometric comparison of reconstructed sphenoid sinus models through point cloud analysis and deep learning approaches. Morphological variability, spatial configuration, and structural stability of the sphenoid sinus were analyzed as discriminative biometric features. The GSA-Net–based identification model demonstrated high recognition performance, achieving Top-1 accuracies exceeding 97.8% and Top-3 accuracies up to 100% in controlled datasets. The results support the concept that the sphenoid sinus possesses sufficient individuality and anatomical preservation to enable reliable ante-mortem and post-mortem identification. The study highlights the potential of integrating automated segmentation and AI-driven 3D analysis into forensic workflows and emphasizes the importance of standardized imaging protocols and larger annotated datasets for future clinical and forensic implementation. Full article

Background: Autophagy plays a central role in maintaining cellular homeostasis, regulating growth and development, and responding to multiple stresses. Autophagy-related genes (ATGs) play critical roles in autophagy, yet their functional diversity in soybean (Glycine max) remains underexplored. Methods: Genome-wide identification of GmATG genes was performed using sequence similarity and domain-based searches against the Wm82.gnm4 reference genome, followed by characterization of physicochemical properties, chromosomal distribution, phylogenetic relationships, gene duplication, conserved motifs, gene structure, three-dimensional structural, and promoter cis-acting elements. Tissue-specific expression and multiple stresses response were examined using transcriptome data and profiled by RT-qPCR. Results: A total of 60 GmATG genes belonging to 20 subfamilies were identified in soybean. Gene family expansion was predominantly driven by fragment duplication (33 gene pairs), with the ATG8 family expanding to 12 members, and pan-genomic analysis uncovered prominent copy number variation (6–9 copies) in the ATG18 family. GmATG genes showed distinct expression patterns in response to multiple abiotic and biotic stresses. Specifically, GmATG18f was significantly induced by phosphorus deficiency in the low-phosphorus-tolerant soybean variety Nannong 94-156. GmATG8g, GmATG9d and GmATG13d showed a typical expression trend of initial increase followed by decrease, with expression levels peaking at 6–12 h after salt stress treatment. GmATG8g and GmATG9d were rapidly upregulated at the early drought stress stage, while GmATG13a maintained sustained upregulation. In response to Phomopsis stem rot, GmATG7a/8h/8i/11/13d/18e/18f displayed differential expression in resistant and susceptible soybean materials. Conclusions: This study systematically characterizes the composition, expansion and stress response patterns of the GmATG gene family, revealing functional differentiation among family members. The identified key candidate genes, including abiotic-stress-regulated GmATG8g/9d/13d/18f and biotic-stress-regulated GmATG7a/8h/8i/11/13d/18e/18f, provide valuable genetic resources for the molecular breeding of stress-tolerant soybean. Full article

DC bias will cause the magnetization working point of the transformer core to shift and cause local saturation, and generate abnormal vibration through the magnetostrictive effect, which threatens the safe operation of the transformer. Aiming at the problem that the time–frequency characteristics of transformer vibration signals under DC bias are complex and the adjacent bias levels are difficult to distinguish, this paper proposes a 2DWT-CNN-Transformer diagnostic method that combines two-dimensional discrete wavelet transform, a convolutional neural network, and Transformer Encoder. Firstly, the multi-physical-field finite element model of three-phase three-column transformer is established, and the L0–L5 six-class DC bias dataset is constructed. Secondly, the one-dimensional vibration signal is reconstructed into a two-dimensional matrix, and the multi-subband time–frequency features of LL, LH, HL, and HH are extracted by two-dimensional discrete wavelet transform. The local texture features are extracted by the CNN, and the multi-head self-attention mechanism of Transformer Encoder is introduced to establish the global dependence and enhance the discrimination ability of adjacent bias levels. Compared with the traditional time–frequency-feature deep learning model, the proposed method achieves higher accuracy, especially in the high-noise environment of 15 dB, where it can still maintain accuracy of 96.23%. The visualization results further show that the model can form a more compact intra-class aggregation and a clearer inter-class boundary. This also provides an effective solution for the identification and evaluation of transformer DC bias states based on vibration signals in complex environments in the future. Full article

Urban residents spend an estimated 80–90% of their time indoors, yet urban indoor air quality (IAQ) science remains fragmented across pollutant types, settings, mitigation strategies, and governance contexts, contributing to an estimated 6.7 million deaths annually from indoor air pollution worldwide. This review asks the following question: what are the critical, multi-dimensional research and governance gaps in urban IAQ, and how can they be systematically derived and organized into a reference framework for future research and policy? A PRISMA 2020-aligned hybrid systematic evidence synthesis, combining bibliometric science mapping and structured thematic synthesis, was conducted across 105 records, primarily published between 2011 and 2026, with three pre-2011 foundational records retained, spanning 15 national contexts. A seven-stage hybrid deductive–inductive derivation procedure was applied to the coded corpus to produce the Multi-Dimensional Gap Identification Framework (MGIF), organizing research gaps across five dimensions: knowledge, methodological, technological, policy and implementation, and equity and urban context. Recurrent gaps include the absence of multi-pollutant mixture assessment in monitoring frameworks, the lack of standardized measurement protocols limiting cross-study comparability, a systematic gap between laboratory-validated and field-measured intervention performance, the near-total absence of enforceable indoor air quality standards across most jurisdictions, and the severe underrepresentation of Global South populations in both primary evidence and regulatory design. Building on the MGIF output, the Urban Indoor Air Quality Nexus (UIAQN) is proposed as a four-level conceptual organizing architecture linking pollutant dynamics, building systems, personal exposure, and governance mechanisms. Both frameworks are grounded in the coded corpus, have not been subjected to external validation, and are designed as structured reference architectures for future research investment, standard harmonization, and equity-centered policy design rather than as empirically validated predictive models. Full article

Distinguishingstructured nonlinear temporal behaviour from stochastic variability remains a fundamental challenge in the analysis of noisy and nonstationary time series, particularly when conventional nonlinear methods are sensitive to noise and finite observational records. This study presents a simulation-based modified Singular Spectrum Analysis (SSA) framework for investigating finite-time nonlinear temporal structures through signal extraction, component-wise simulation, and eigenvalue distribution analysis. The proposed framework decomposes time series into interpretable components and systematically compares empirical behaviour with white-noise processes and canonical nonlinear benchmark systems. Using a noisy chaotic benchmark with a known three-dimensional state-space structure, a direct comparison with standard SSA criteria was conducted to examine component identification under the same benchmark setting, with the proposed framework identifying three components compared with two indicated by the conventional criteria. The methodology is demonstrated using COVID-19 case time series from the United Kingdom (UK) and the Kingdom of Saudi Arabia (KSA), together with monthly sunspot numbers as an independent application, as representative real-world time series. The modified SSA identified distinct temporal structures within these datasets, with eigenvalue distributions differing from those expected under pure white-noise processes while exhibiting similarities to canonical nonlinear benchmark systems. Reconstructed phase spaces displayed bounded attractor-like geometries consistent with structured nonlinear temporal behaviour over finite observational intervals. The proposed framework provides a complementary nonparametric statistical methodology for investigating structured nonlinear temporal behaviour in noisy observational time series through signal extraction and simulation-based benchmarking. Full article

Ensuring the long-term sustainable utilisation of supplemented cropland has become a critical challenge for implementing China’s requisition–compensation balance of farmland (RCBF) policy, particularly in the fragmented hilly and mountainous regions of Southwest China. Existing studies generally evaluate land suitability and utilisation performance separately, making it difficult to identify mismatches between theoretical suitability and actual utilisation and thereby limiting targeted regulation. To address this limitation, this study proposes a suitability–utilisation deviation identification (SUDI) framework, which integrates four sequential analytical components: three-dimensional suitability assessment, suitability–utilisation deviation identification, driving mechanism analysis, and sustainable regulation. Taking Beibei District of Chongqing as a case study, supplemented cropland parcels were identified using the 2020–2024 land change survey data. A three-dimensional suitability evaluation system incorporating production, ecological, and utilisation attributes was established to quantify theoretical land suitability. Actual utilisation performance was characterised using the land economic utilisation coefficient, and suitability–utilisation deviation was identified through residual analysis between theoretical suitability and utilisation intensity. A Bayesian-optimised Extreme Gradient Boosting-SHAP (XGBoost-SHAP) model was subsequently employed to reveal the nonlinear effects and interaction mechanisms of the driving factors. The results indicate the following: (1) supplemented cropland in Beibei District is predominantly characterised by medium-to-high suitability, with high-suitability patches exhibiting a mosaic spatial pattern of local aggregation and overall dispersion; (2) suitability–utilisation deviation is dominated by under-utilised plots, whereas well-matched and over-intensified plots account for substantially smaller proportions, indicating that insufficient realisation of land suitability is the prevailing utilisation pattern; and (3) the land economic utilisation coefficient is the dominant factor driving suitability–utilisation deviation, while high-standard farmland construction and plot area exhibit significant mitigating effects. Moreover, significant interaction effects between utilisation intensity and location-related variables reveal that unfavourable spatial conditions amplify deviation risk under intensive land use. The proposed SUDI framework extends conventional suitability assessment by explicitly linking suitability evaluation with utilisation performance, driving mechanism analysis, and differentiated regulation. It provides a transferable analytical framework for diagnosing suitability–utilisation mismatch and supports dynamic management and sustainable utilisation of supplemented cropland in fragmented hilly and mountainous regions. Full article

Background: The frontal aslant tract (FAT) is a white matter pathway associated with language and executive functions. The aim of the study was to identify and analyze this tract using DTI tractography. Methods: The study included 26 healthy adults. Probabilistic multi-fiber tractography and deterministic single-fiber tractography were applied. The seed region (ROI) was defined in the inferior frontal gyrus, and the target region in the superior frontal cortex. FA, MD, and tract volume were analyzed. Results: The FAT was reconstructed in all participants using probabilistic analysis. The left FAT showed higher FA (p = 0.012), greater volume (p = 0.008), and lower MD (p = 0.021) than the right. Probabilistic tractography demonstrated greater tract volume compared to deterministic tractography. Conclusions: The FAT can be identified using DTI tractography. Probabilistic tractography produced more extensive FAT reconstructions than deterministic tractography; however, anatomical accuracy was not directly assessed. Full article

The ICARUS experiment is part of the Short-Baseline Neutrino program at Fermilab and aims to search for the possible existence of sterile neutrinos in the O (1 eV) mass range, addressing anomalies observed by the LSND and MiniBooNE experiments. The ICARUS-T600 detector is a Liquid Argon Time Projection Chamber that provides high-resolution three-dimensional imaging and precise calorimetric measurements of ionizing particles. This technology enables detailed studies of neutrino interactions over a wide energy range, from a few $\mathrm{keV}$ to several hundred $\mathrm{GeV}$. Event reconstruction relies on a software framework that applies pattern recognition algorithms to transform raw detector signals into fully reconstructed interaction topologies, including vertices, particle tracks, and electromagnetic showers. In some cases, however, a single particle track may be incorrectly split into multiple segments, leading to underestimated energy reconstruction and potential failures in particle identification. Since these effects can result in the loss of otherwise valid events, we designed a stitching algorithm that identifies the broken tracks and reconnects (“stitches”) the segments. This approach improves track reconstruction quality, energy estimation and overall event reconstruction efficiency. Full article

Background/Objectives: Gestational diabetes mellitus (GDM) is one of the most common metabolic complications of pregnancy and is associated with adverse maternal and neonatal outcomes. Current screening strategies are generally performed during the end of the second trimester, limiting opportunities for early intervention. Increasing evidence suggests that placental and fetal ultrasound markers detectable in early pregnancy may reflect the pathophysiological changes preceding the clinical diagnosis of GDM. This narrative review aims to summarize the current evidence regarding first- and second-trimester placental and fetal ultrasound markers associated with the subsequent development of GDM. Methods: A narrative review of the literature was conducted focusing on studies evaluating ultrasound markers of placental morphology, placental vascularization and function, uterine artery Doppler indices, placental volume, placental thickness, and fetal biometric and functional parameters in relation to later GDM diagnosis. Twenty-two studies were identified and analyzed to assess their clinical utility and predictive value. Results: Increasing evidence suggests that placental and fetal ultrasound markers detectable in early pregnancy may reflect pathophysiological changes preceding the clinical diagnosis of GDM. Three-dimensional power Doppler, particularly the vascularization–flow index, has demonstrated reduced placental vascularization in pregnancies subsequently complicated by GDM. Placental elastography and artificial intelligence-based radiomic analysis also show promising predictive potential. Uterine artery Doppler has demonstrated limited and inconsistent value. Fetal findings suggest that conventional biometry has limited predictive utility, whereas fetal growth velocity and markers of adiposity, including anterior abdominal wall thickness and abdominal overgrowth, more consistently identify early adaptations associated with later GDM. However, substantial methodological heterogeneity limits interpretation. Conclusions: Early placental and fetal ultrasound markers represent promising non-invasive tools for the early identification of pregnancies at risk for GDM. Further large-scale prospective studies are required to validate their predictive performance and establish standardized assessment protocols. Integrating maternal clinical characteristics and biochemical parameters may enhance future screening strategies for GDM. Full article

Three-dimensional topological magnetic defects, such as Bloch points, are of significant interest for high-frequency spintronics due to their unique particle-like properties and effective inertial mass. We investigate the nucleation, stabilization, and driven dynamics of an isolated Bloch point in a ferromagnetic multilayer with alternating in-plane and perpendicular magnetic anisotropy. Using micromagnetic simulations, we show that a perpendicular magnetic field stabilizes a head-to-head Bloch point state, while a transient in-plane field pulse drives the defect into gyrotropic and nutation motion. To model the dynamics, we develop a collective-coordinate Lagrangian description of the Bloch point core. We further demonstrate that the time-dependent core displacement generates a transverse charge current via spin pumping and inherent spin-to-charge conversion within the multilayer system. The resulting current spectrum contains low-frequency and high-frequency components, including an intrinsic nutation mode in the gigahertz range. Our findings expand the capabilities for electrical control and identification of complex spin configurations, contributing to the development of active three-dimensional spintronic devices. Full article

The reliable identification of microseismic events is an important prerequisite for microseismic monitoring. However, the complex underground mining environment and limited monitoring resources make it challenging for existing identification algorithms to balance accuracy and efficiency. Therefore, this paper proposes a lightweight microseismic signal classification model, MS-Mamba, based on time–frequency image analysis. This model efficiently integrates global time–frequency dependencies and local detail features through Lightweight Receptive Field Feature Interaction (LRFFI) and uses the embedded DB-Mamba to enhance the modeling of temporal and frequency-domain features. The inverted residual module is also introduced to complement local high-dimensional feature representations, thereby boosting classification performance without substantial computational overhead. To accommodate different computational budgets, three model variants of different scales are designed. Experimental results show that MS-Mamba achieves a favorable balance between accuracy and efficiency across different scales. Among them, MS-Mamba-B achieves the highest accuracy of 98.66%; MS-Mamba-T still reaches 97.94% with only 0.13 GFLOPs of computational cost. These results indicate that MS-Mamba achieves a favorable trade-off between classification accuracy and computational efficiency. Full article