Search Results (3,419)

Azimuth multi-channel (AMC) Synthetic Aperture Ladar (SAL) is a promising technique for overcoming the inherent trade-off between azimuth resolution and swath width in single-channel SAL, by replacing temporal sampling with spatial sampling. However, due to the micron-scale wavelength, AMC SAL is extremely sensitive to non-cooperative target motion: even millimeter-level radial velocities can induce significant inter-channel phase deviations, leading to severe azimuth ambiguities (false targets). To address this critical issue, a motion-compensated reconstruction framework for AMC SAL is proposed for micro-motion targets. The relationship between target radial motion and inter-channel phase deviations is theoretically derived, and a parametric strategy based on a Minimum Azimuth Ambiguity-to-Signal Ratio (MAASR) criterion is proposed to estimate the radial velocity. Simulation results demonstrate that the uncompensated processing suffers from strong ambiguities (AASR = −2.90 dB) and a notable azimuth position shift (−42 samples), whereas the proposed method suppresses false targets to the noise floor (<−40 dB) and corrects the position error. These simulation results indicate that the proposed method enables AMC SAL imaging for the non-cooperative moving target with millimeter-level radial velocity. Full article

The security of biometric data is a critical challenge in modern information security due to their uniqueness and non-revocability. Compromise of biometric characteristics leads to irreversible consequences; therefore, storing or transmitting them in plaintext is unacceptable. This paper addresses the confidentiality and integrity of fingerprint data using cryptographic protection methods. Considering the specific nature of biometrics, fingerprint features are used only to generate a cryptographic secret rather than being stored directly. To protect the derived secret, a modified threshold secret-sharing scheme based on non-positional polynomial notation and the Chinese Remainder Theorem is proposed. The method generates a cryptographic secret from fingerprint minutiae described by spatial coordinates and ridge orientation. Concatenating minutiae coordinates and converting them into binary form produces a unique value deterministically linked to a specific user. Compared to the classical Shamir scheme, the modified scheme reduces the computational complexity of secret reconstruction from O($n {log}^{2}n$) to O(k log k), decreases data storage requirements by 30–40% through compact polynomial remainders, and increases successful secret reconstruction by 12–15% in the presence of noise in biometric samples. The results show that the proposed algorithm can be effectively applied in biometric authentication systems to protect personal data in distributed environments. Security analysis confirms resistance to major attack classes and demonstrates practical applicability in real-world systems. Full article

Coherent diffraction imaging (CDI) is a fast-growing imaging technique. Among all CDI methods, coherent modulation imaging (CMI) has strong potential for dynamic imaging because of its ability to form an image from a single diffraction pattern. However, current CMI methods mostly reconstruct the exit wave distribution behind the object plane, which is seriously affected by the illumination artifact. Recently, some improved CMI methods have been developed to resolve the problem. However, many of these methods still need two diffraction patterns—one empty-sample diffraction pattern and another snapshot measurement. Recent advances in randomized probe imaging have shown that a single diffraction pattern suffices for quantitative reconstruction when the probe is pre-calibrated. Herein, we propose a modified CMI algorithm to reconstruct pure object function with single diffraction pattern, thereby simplifying the experimental process. Moreover, the proposed method can also work in the situation where the modulation effect is weak. Both numerical simulations and optical experiments have been conducted to verify the proposed method. Full article

As AI-driven educational systems increasingly rely on deep neural networks, their vulnerability to adversarial perturbations raises concerns about assessment integrity, fairness, and reliability. Adversarial example purification is attractive for such deployments because it removes input perturbations without modifying the already deployed models. However, most existing purification methods are inherently goal-free: denoising-based approaches apply blind heuristic operators, while reconstruction-based methods rely on stochastic sampling guided by natural image priors. These methods typically suppress perturbations at the cost of weakening semantic details or inducing structural distortions. To address this limitation, we propose a novel goal-directed purification framework, termed adversarial energy field optimization for adversarial example purification (AEFOP). AEFOP formulates purification as a constrained optimization problem by defining a learnable adversarial energy which quantifies how far an input deviates from the benign region. This allows adversarial examples to be explicitly pushed from high-energy regions toward low-energy benign regions along an interpretable descent trajectory. Specifically, we build an adversarial energy network and optimize the energy field via a two-stage strategy: adversarial energy field shaping, which enforces distance-like energy behavior and correct gradient directions, and task-driven energy field calibration, which unrolls the descent process to calibrate the field with classification-consistency and semantic-preservation objectives. Extensive experiments across multiple attack scenarios demonstrate that AEFOP achieves superior purification accuracy and high visual quality while requiring only a few gradient steps during inference, offering a practical and efficient robustness layer for vision-based AI services in education. Full article

Large-scale green tides dominated by Ulva species have recurred annually in the Southern Yellow Sea for nearly two decades, yet early detection remains challenging due to the patchy distribution of incipient floating macroalgae. This study investigated the spatiotemporal dynamics of Ulva micropropagules during the 2020 outbreak using a systematic cultivation assay. Seawater samples were collected from 23 stations across the Subei Shoal and adjacent waters in April, May, and July, and incubated under controlled laboratory conditions to enumerate Ulva germling densities. Results revealed that Ulva micropropagule abundance peaked in April, with high-density foci concentrated in the Subei Shoal region—particularly in aquaculture areas of Neopyropia J. Brodie & L.-E. Yang, 2020—confirming this zone as one of the important sources. Abundance declined progressively through May and July as macroalgae drifted northward under wind and current forcing. This method effectively identified putative source regions and reconstructed initial dispersal patterns prior to satellite-detectable macroalgal aggregation. These findings demonstrate that Ulva micropropagule monitoring provides a cost-effective, sensitive tool for early warning and Ulva source tracking, offering finer-scale propagule distribution data to inform precision management strategies for mitigating green tide impacts on coastal marine ecosystems. Future research should expand investigations into Ulva micropropagule dynamics to elucidate their mechanistic processes and ecological significance in green tide initiation and development. Full article

Background: Musculoskeletal disorders (MSDs) are a major contributor to global disability. Exercise-based rehabilitation is widely recommended as first-line management; however, in clinical practice, it is frequently combined with adjunct therapeutic modalities, and the incremental effectiveness of these approaches remains unclear. The present review addressed the research question: Do adjunct modalities provide additional benefits beyond exercise-based rehabilitation alone in individuals with musculoskeletal disorders? Methods: This systematic review was conducted according to PRISMA 2020 guidelines and prospectively registered in the PROSPERO database (registration number CRD420261309183). Electronic searches were performed in PubMed/MEDLINE, Scopus, Web of Science, and the Cochrane Central Register of Controlled Trials to identify controlled clinical trials evaluating exercise-based rehabilitation delivered alone or combined with adjunct modalities. Outcomes included pain, functional disability, physical performance, strength, structural or imaging-based measures, biomechanical variables, injury risk, and work-related outcomes. Due to methodological heterogeneity across studies, a structured narrative and tabular synthesis were performed. Results: Twenty-one controlled clinical trials were included, encompassing tendinopathies (n = 7), knee osteoarthritis (n = 5), post-ACL reconstruction (n = 2), chronic spinal pain (n = 3), sarcopenia (n = 2), low bone mass (n = 2), and occupational musculoskeletal conditions (n = 1), with sample sizes ranging from 22 to 823 participants. Pain outcomes were reported in 18 studies (86%) and functional outcomes in 16 studies (76%). Exercise-based rehabilitation consistently produced clinically meaningful improvements across studies, whereas adjunct modalities demonstrated short-term advantages in a limited number of trials but rarely showed sustained long-term superiority. Conclusions: Evidence from controlled clinical trials indicates that exercise-based rehabilitation is an effective primary intervention for improving pain, functional capacity, and physical performance across diverse musculoskeletal conditions. Adjunct modalities may provide condition-specific or short-term benefits but do not consistently enhance long-term outcomes beyond structured exercise programs. Full article

Background: A major technical challenge in single-cell transcriptomics is the absence of an integrative analytic pipeline that can simultaneously leverage gene regulatory network (GRN) architecture, AI-assisted gene panel discovery, and functional relevance analyses to generate coherent biological insights. Existing approaches often treat these components independently, focusing on clusters, marker genes, or predictive features without integrating them into a mechanistically grounded framework. Consequently, comprehensive screening that links regulatory association, gene signature screening, and functional interpretation within single-cell datasets remains limited, underscoring the need for an integrated strategy. Methods: We developed an integrative bioinformatics pipeline based on Gene regulatory network–AI–Functional Analysis (GAFA), combining latent-space integration, unsupervised clustering, diffusion pseudotime analysis, lineage-resolved generalized additive modeling, GRN inference, and machine learning-based gene panel discovery. This framework enables systematic mapping of cell-state structure, reconstruction of differentiation and effector trajectories, and identification of transcriptional and regulatory features strongly associated with clinical outcomes. As a case study, we applied the pipeline to NK cell transcriptomes from six CML patients (two early relapse, two late relapse, two durable treatment-free remission—TFR; 15 samples) collected at TKI discontinuation and 6–12 months after therapy cessation. Results: We reanalyzed publicly available scRNA-seq data from a previously published CML cohort to evaluate NK-cell transcriptional programs associated with treatment-free remission and relapse. We resolved six transcriptionally distinct NK cell states spanning CD56^bright-like cytokine-responsive, early activated, terminally mature, cytotoxic, lymphoid trafficking, and HLA-DR⁺ immunoregulatory populations, each exhibiting outcome-specific compositional differences. Pseudotime analysis revealed two major NK cell lineages—a maturation trajectory and a cytotoxic effector trajectory. TFR samples displayed balanced occupancy of both lineages, whereas early relapse samples showed marked depletion of the maturation branch and preferential accumulation in cytotoxic end states. AI-guided feature selection and random forest modeling identified an 18-gene panel that distinguished NK cells from TFR and relapse samples in an exploratory manner. Among them, CST7, FCER1G, GNLY, GZMA, and HLA-C were conventional NK-associated genes, whereas ACTB, CYBA, IFITM2, IFITM3, LYZ, MALAT1, MT2A, MYOM2, NFKBIA, PIM1, S100A8, S100B, and TSC22D3 were novel. The GRN inference further uncovered outcome-specific regulatory modules, with RUNX3, EOMES, ELK4, and REL regulons enriched in TFR, whereas FOSL2 and MAF regulons were enriched in relapse, and their downstream targets linked to IFN-γ signaling, metabolic reprogramming, and immunoregulatory feedback circuits. Conclusions: This AI-enabled single-cell analysis demonstrates how NK cell state composition, differentiation trajectories, and regulatory network rewiring collectively shape TFR versus relapse following TKI discontinuation in CML. The integrative pipeline provides a modular framework that could be extended to additional datasets for data-driven biomarker discovery and mechanistic stratification, and highlights candidate transcriptional regulators and NK cell programs that may be leveraged to improve remission durability, pending validation in larger patient cohorts. Full article

Scanning electrochemical microscopy (SECM) provides spatially resolved electrochemical information but is constrained by long acquisition times arising from dense spatial sampling requirements. This work investigates whether physics-informed signal representations can improve neural reconstruction of SECM approach curve signals from sparse measurements. We propose an exponential function-based Neural Tangent Kernel (NTK) framework in which SECM signals are encoded using deterministic exponential feature mappings aligned with diffusion-controlled electrochemical dynamics. A layer-wise NTK checkpointing mechanism is employed to filter covariantly insignificant components during training, reducing redundancy while preserving dominant signal modes. The method is evaluated on synthetically generated SECM signals designed to replicate characteristic approach curve behavior. Quantitative performance is assessed using root mean square error (RMSE), mean absolute error (MAE), relative error (%), and the coefficient of determination (R²). Compared to a random Gaussian (Fourier feature) baseline (RMSE = 0.0952, MAE = 0.0547, Rel.Err = 17.68%), the proposed exponential mappings achieve consistently lower reconstruction error, with the best configuration yielding RMSE = 0.0858, MAE = 0.0375, and relative error = 11.10% under identical training conditions. Results demonstrate that incorporating physically motivated exponential feature representations into NTK-aware learning improves reconstruction fidelity and stability for low-dimensional electrochemical signals, highlighting the potential of physics-informed kernel methods for accelerated SECM data acquisition. Full article

Introduction: An incomplete high anterior column fracture of the acetabulum is commonly considered to require completion of the fracture. However, reduction may become more difficult after completing the incomplete fracture due to plastic deformation. This study describes a surgical technique of compression osteosynthesis without completing the incomplete fracture and evaluates the clinical and radiographic outcomes. Materials and Methods: In this retrospective study, 25 patients with incomplete high anterior column fractures met the inclusion criteria. The fracture was reduced and stabilized by compression osteosynthesis through the anterior iliac approach without completing the incomplete fracture in the iliac wing. Patient demographics, the mechanism of injury, associated injuries, time to surgical reconstruction, operation time, and postoperative complications were analyzed. The quality of reduction and outcome were evaluated according to Matta’s criteria. Results: The mean operation time was 110 ± 23 min (range, 75–160). All fractures achieved bone union at a mean of 10.2 ± 1.4 weeks (range, 8–14). The quality of fracture reduction was graded as anatomical in 22 hips, imperfect in one and poor in two. Clinical results were excellent in 19 patients and good in six, and radiographic results were excellent in 22 patients and good in three. No statistically significant differences were observed between patients with and without quadrilateral plate fractures. Lateral femoral cutaneous nerve injury occurred in 13 patients (52%), mostly without significant symptoms. One patient experienced vascular injury. Conclusions: Incomplete high anterior column fractures can be effectively reduced and stabilized by compression osteosynthesis through the anterior iliac approach without completing the incomplete fracture in the iliac wing. This case series demonstrated favorable clinical and radiographic outcomes using this surgical technique. However, because this study was a retrospective case series with a small sample size and no comparative control group, further studies are required to confirm these findings. Full article

Accurate shape control is paramount for ensuring the quality of hot-rolled strip products, which is significantly challenged by the high dimensionality, inherent nonlinearity, and strong coupling of process parameters. While machine learning (ML) methods have demonstrated superior predictive performance in product quality modeling, the inherent “black-box” nature and lack of transparency severely undermine system reliability and hinder practical deployment. Existing explainable artificial intelligence (XAI) approaches predominantly rely on statistical correlations while overlooking the underlying causal mechanisms among coupled variables, which severely limits the validity of explanations. To address these limitations, a causal XAI diagnosis and optimization framework for hot-rolled strip shape is proposed. Initially, a hybrid causal structure learning module is established, which integrates domain knowledge with the NOTEARS-MLP algorithm to accurately reconstruct the causal topology and decode the complex coupling mechanisms among process parameters. Subsequently, a high-performance quality prediction module utilizing AutoML techniques is constructed to establish a robust predictive baseline. Furthermore, a causal XAI and quality optimization module is introduced, which incorporates causal constraints into standard Shapley additive explanation (SHAP) analysis for transparent diagnosis, and employs piecewise linear analysis (PLR) to generate sample-specific optimization strategies. Comprehensive experimental validation demonstrates that the prediction module significantly outperforms state-of-the-art ML approaches across multiple performance metrics. Additionally, comparative analysis reveals that the optimization strategy based on causal feature attribution exhibits 14.7% defect rate reduction over the associational baseline, which is effective, efficient and establishes a new benchmark for causal explainability in industrial process optimization applications. Full article

In water-scarce regions such as Xinjiang, China, agricultural development is constrained not only by limited water resources but also by a strong reliance on water-saving irrigation technologies. Drip irrigation is a key measure for improving irrigation efficiency and promoting the sustainable development of water-saving agriculture. However, defects arising during the manufacture of labyrinth Drip emitters—the core components of drip irrigation systems—can undermine system reliability, leading to channel blockage and non-uniform irrigation. To tackle this issue, a defect detection approach is developed by integrating Digital Twin technology with an enhanced YOLOv8 model for online inspection of labyrinth Drip emitters on drip irrigation tape production lines. In parallel, a self-built dataset covering six defect categories is established. Supported by the DT framework, the standard YOLOv8 network is refined to strengthen its capability in identifying complex micro-defects. Specifically, DySnakeConv is introduced to better represent the curved and slender characteristics of labyrinth channels; DySample is incorporated to improve the reconstruction and representation of fine-grained details; an Efficient Multi-Scale Attention module is adopted to capture richer contextual information while suppressing background noise; and Inner-SIoU is applied to optimize the bounding-box regression process. Experimental results show that the model achieves 89.6% precision, 90.9% recall, and 93.9% mAP50. Compared with the baseline YOLOv8, precision, recall, and mAP50 are improved by 7.3, 3.9, and 3.3 percentage points, respectively. Under the same training conditions, the proposed model outperforms YOLOv10 and YOLOv11 in accuracy-related metrics. Specifically, compared with YOLOv11, precision, recall, and mAP50 are improved by 4.8, 5.0, and 2.6 percentage points, respectively; compared with YOLOv10, they are improved by 10.0, 7.7, and 7.3 percentage points, respectively. Meanwhile, the model maintains a lightweight size of 3.7 M parameters and a real-time inference speed of 150.2 FPS, demonstrating a favorable accuracy–efficiency trade-off. By extending manufacturing-level quality control to agricultural applications, the approach helps ensure uniform irrigation and improve water-use efficiency, providing practical technical support for precision agriculture in arid regions. Full article

The main objective of the present study is to reveal the palette of pigments and the other specific constituent materials as well as the techniques used by the Roman artists to create the mural paintings discovered in the ancient city of Tomis, the modern-day Constanţa, Romania’s largest seaport and a major tourist hub on the Black Sea. This paper is an archeometric study based on the physical, chemical and biological analyses of the archeological Roman mural painting fragments from the ancient city of Tomis dating from the 5th to 6th century A.D. and to our knowledge is among the very few research studies carried out so far on the ancient Roman wall painting discovered in Romania. The methods of scientific investigation employed directly on the archeological fragments, on samples taken from the fragments and on the cross-sections prepared from the samples were: optical microscopy (OM), digital microscopy, X-ray fluorescence spectrometry (XRF) and attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR). Examination and analysis of the archeological mural fragments revealed that the painted fragments consist of ground support and successive layers of color displaying specific characteristics of the artistic technique, such as imitation of marble cladding or meticulous smoothing of the surface to achieve a glossy and compact finish. It was also found that fragments exhibit subtle variations in different colors, identified in general terms, showing seven color tones: cinnabar red, red-violet, red ochre, yellow ochre, white, gray-blue, gray-black and black. The physical–chemical and biological analyses carried out provide the diagnosis and theoretical basis for choosing an appropriate conservation methodology and the correct restoration treatment of the discovered mural painting, with a view to its museum display through exhibition and virtual reconstruction and scientific use by the setting up of a useful database for researchers or specialists in museums on Roman archeology and art. Full article

Cold-mercury gilding uses mercury as an adhesive to bond gold foil onto the surface of copper and silver artifacts. This technique and mercury gilding (fire gilding) both belong to the Au-Hg system and are closely related in technology. Clarifying the technical differences between them is of great significance for revealing the developmental sequence of ancient gilding technologies. On the basis of reconstructing traditional fire gilding, simulated cold-mercury-gilded samples were successfully prepared using experimental archeological methods, and multi-scale characterization was performed using SEM-EDS, XRD, and XPS. The results show that the surface of cold-mercury-gilded samples displays a micromorphology of folded and overlapped gold foil accompanied by locally dense particle aggregation. The cross-section of the gold layer exhibits a multilayer stacked structure, in which mercury is enriched at the gold layer/substrate interface and forms an AuHgCu/Ag diffusion layer. Room-temperature-stable Au-Hg and Ag-Hg phases such as Au₂Hg and AgHg are present in the gold layer, reflecting complex phase transformation behavior of the Au-Hg/Ag-Hg system at room temperature. During cold-mercury gilding, liquid mercury first adheres to the gold foil, and then interdiffusion and phase reactions occur between mercury, gold, and copper/silver atoms at room temperature. Intermetallic compounds and diffusion layers formed at the interface achieve firm bonding between the gold layer and the substrate. Both cold-mercury gilding and mercury gilding achieve metallurgical bonding through atomic interdiffusion. However, affected by differences in the initial state of mercury and operating temperature, the phase transformation and atomic diffusion behaviors of the system differ significantly, which are ultimately reflected in the cross-sectional structure of the gold layer, the composition of the interfacial diffusion layer, and the types of phases. Therefore, mercury-gilded artifacts show superior gold layer durability and bonding strength with the substrate compared with cold-mercury-gilded artifacts. Both techniques pioneered the application of mercury in metallic gilding and represent important innovations in ancient surface decoration technology. Full article

Accurate long-horizon air-quality forecasting becomes difficult when historical observations are missing or irregularly sampled because reconstruction errors can propagate into downstream predictions. In this work, we propose the TILSTM method, a task-aligned hybrid architecture that integrates a Transformer-based imputation module with an LSTM forecaster designed to jointly enforce a causal horizon boundary that restricts imputation strictly to the historical look-back window, an observed-preserving merge that leaves measured values unchanged, and a time-aware decay gate applied selectively to imputed positions. The model is trained end-to-end using a combined forecasting loss and a self-supervised imputation loss computed on artificially masked observed entries. We evaluate TILSTM on hourly PM10 forecasting from 21 monitoring stations in Slovenia across three forecasting horizons and three missingness regimes. Among the compared methods, TILSTM shows the clearest and most consistent gains at the 24 h horizon, while at medium horizons, the relative ranking becomes more dependent on the missingness regime. In pooled error summaries, TILSTM achieves the lowest MAE and RMSE at the 168 h horizon under the real and near_origin missingness regimes, while the overall results indicate that no single method is uniformly best across all long-horizon settings. Full article

Matrix Product States (MPSs) have become an indispensable symmetry-based representation for simulating quantum systems on near-term hardware by constraining entanglement entropy through a fixed bond dimension $\mathsf{\chi }$. This study identifies a critical “rank explosion” phenomenon that destabilizes this low-rank manifold during conditional quantum diffusion processes. We empirically demonstrate that the introduction of conditional guidance—essential for semantic control—injects global correlations that drive the effective Schmidt rank to increase by $4\times$ (from $\mathsf{\chi }=4$ to $16$), saturating the simulation limits and necessitating quantum circuits with approximately $1.8\times {10}^3$ Controlled-NOT (CNOT) gates. Such circuit depths fundamentally exceed the operational coherence budgets of Noisy Intermediate-Scale Quantum (NISQ) devices. To mitigate this structural instability, we propose Rank-Aware Conditional Synthesis (RACS), a sampling framework that maintains the latent trajectory within a prescribed MPS manifold through step-wise manifold projection and time-shift error correction. Experimental results on real-world semantic data reveal that RACS reduces reconstruction error, or Mean Squared Error (MSE) by 30.8% and enhances latent trajectory smoothness by 36.8% compared to conventional post hoc truncation. At a fixed hardware-efficient rank of $\mathsf{\chi }=4$, RACS achieves a +4.8% fidelity gain and exhibits superior robustness against depolarizing noise. By resolving the tension between conditional expressivity and entanglement constraints, RACS provides a principled, hardware-aware methodology for high-fidelity quantum generative modeling. Full article