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Accurate and efficient ship detection in remote sensing imagery is a key enabler of intelligent maritime surveillance operations, supporting real-time decision-making in search and rescue, traffic management, and maritime law enforcement. However, remote ship images pose unique challenges for detection. These include densely distributed targets, complex sea-land backgrounds, large aspect ratios, diverse ship geometries, and high color similarity between ships and their surroundings. To address these issues under the computational constraints of unmanned aerial platforms, we propose GTO-YOLO11n, an enhanced YOLOv11n-based detection model tailored for efficient maritime ship sensing. First, we introduce the GatedFDConvBlock, which employs gated convolutional filtering to strengthen feature extraction for small and elongated ships while suppressing background clutter, thereby reducing missed and false detections in dense scenes. Second, we improve the C2PSA module with a dynamic multi-scale attention design, TSSABlock_DMS, to adaptively model cross-scale feature interactions and enhance robustness to complex maritime environments. Third, we replace the original detection head with OBB_ED, a parameter-sharing head that incorporates depthwise separable convolution (DSConv) and an angle prediction branch to lower model complexity while preserving high-quality localization and classification. To verify the performance of the algorithm, we were conducted on the public datasets HRSC2016, HRSC2016-MS, and ShipRSImageNet. The mAP@50 results were 95.2%, 88.3%, and 76.7%, showing improvements of 3.2%, 2.2%, and 2.6% compared to the original YOLOv11n. Full article

The widespread adoption of electric vehicles (EVs) presents significant opportunities and challenges for power systems, especially in Vehicle-to-Grid (V2G) integration. Accurate modeling of EV charging and mobility behaviour is therefore crucial for enabling reliable and efficient V2G operation. This paper reviews current paradigms of EV behavior modeling, including statistical, data-driven, and decision-oriented approaches, and compares them from a V2G-service-oriented rather than purely algorithm-centric perspective. The analysis focuses on modeling assumptions, data requirements, computational characteristics, and their suitability for different V2G tasks and decision layers. Key challenges are identified, including data availability and heterogeneity, limited cross-scenario generalizability, insufficient integration of physical and behavioral constraints, and computational barriers to large-scale and real-time deployment. To address these limitations, this paper introduces a Task–Data–Deployment perspective framework, which emphasizes aligning modeling paradigms with specific V2G tasks, realistic data conditions, and deployment feasibility. Rather than proposing new algorithms, this perspective provides practical guidance for selecting and applying EV behavior models in real-world V2G systems. These insights clarify current gaps between modeling research and deployment needs, and support the development of scalable, transferable, and operationally viable EV behavior modeling frameworks for future large-scale V2G integration. Full article

In bovine embryo transfer (ET) using in vitro-produced (IVP) embryos, recipient factors and embryo grade are well-established predictors of pregnancy success, but the impact of the laboratory-level developmental competence of IVP embryos remains insufficiently characterized. This retrospective study evaluated factors affecting pregnancy rates following the transfer of IVP beef embryos to dairy recipients. Medical records from 462 ETs were analyzed across three categories: (1) recipient-related factors (parity, body condition, estrus synchronization, corpus luteum characteristics); (2) laboratory factors (cleavage, blastocyst formation, degeneration, embryo grade, developmental stage, cryopreservation); and (3) environmental factors (temperature–humidity index, transport time). Mean comparison and chi-square analyses revealed significant differences in pregnancy rates based on corpus luteum volume, cleavage rates, blastocyst formation rates, degeneration rates, and embryo grade. In binary logistic regression, categorized increases in blastocyst formation rate, degeneration rate, and embryo grade were associated with a 1.45-fold increase, 0.74-fold decrease, and 0.56-fold decrease in pregnancy odds, respectively; no recipient or environmental variables were independent predictors. These findings indicate that developmental competence of IVP embryos is more critical for pregnancy success than recipient or environmental factors, suggesting that optimizing IVP systems to maximize embryo quality is the most effective strategy to improve reproductive efficiency in ET. Full article

Abdominal Aortic Aneurysm (AAA) remains a significant public health challenge, with an 82.1% increase in related fatalities from 1990 to 2019. In the United States alone, AAA complications resulted in an estimated 13,640 deaths between 2018 and 2021. In clinical practice, computed tomography angiography (CTA) is the primary imaging modality for monitoring and pre-surgical planning of AAA patients. CTA provides high-resolution vascular imaging, enabling detailed assessments of aneurysm morphology and informing critical clinical decisions. However, manual segmentation of CTA images is labor-intensive and time consuming, underscoring the need for automated segmentation algorithms, particularly when feature extraction from clinical images can inform treatment decisions. We propose a framework to automatically segment the outer wall of the abdominal aorta from CTA images and estimate AAA wall stress. Our approach employs a patch-based dilated modified U-Net model to accurately delineate the outer wall boundary of AAAs and Nonlinear Elastic Membrane Analysis (NEMA) to estimate their wall stress. We further integrate Non-Uniform Rational B-Splines (NURBS) to refine the segmentation. During prediction, our deep learning architecture requires $17\pm 0.02$ milliseconds per frame to generate the final segmented output. The latter is used to provide critical insight into the biomechanical state of stress of an AAA. This modeling strategy merges advanced deep learning architecture, the precision of NURBS, and the advantages of NEMA to deliver a robust and efficient method for computational analysis of AAAs. Full article

The enhanced long-range navigation (eLoran) system serves as an important backup method for the global navigation satellite system (GNSS) system. In long-distance transmission scenarios, the signal propagation delay of the eLoran system is affected by fluctuations in meteorological factors along the path. Regarding these issues, such as the potential timing system errors caused by meteorological factors and the limitation on the accuracy of the timing system, in this paper, an innovative prediction model is proposed to predict the propagation delay data by fusing the propagation delay data of multiple differential reference stations on the path and the path-weighted meteorological data. By collecting and processing actual data, four types of prediction tasks were designed. Comparative analyses of the prediction performance of eight common models were conducted on a unified dataset. The results show that the Pucheng–Zhengzhou path-weighted ten-factor back-propagation neural network (PZWT-BPNN) model performs the best, achieving a balance between prediction accuracy and training efficiency. This model effectively suppresses the timing errors caused by meteorological fluctuations and improves the prediction accuracy of the propagation delay of the system, providing corresponding technical support for key fields such as low-altitude economy and transportation. Full article

Designing structure-preserving numerical schemes for the generalized Poisson-Nernst-Planck (PNP) system is challenging due to its inherent strong nonlinearity and coupling. In this paper, we propose a class of efficient, unconditional energy-stable schemes based on the Stabilized Scalar Auxiliary Variable (S-SAV) framework combined with the finite element method. We construct both first-order (BE-S-SAV) and second-order (BDF2-S-SAV) fully discrete schemes. A distinguishing feature of our approach is the use of a linear decomposition strategy, which decouples the complex nonlinear system into a sequence of linear, constant-coefficient elliptic equations at each time step. This significantly reduces computational complexity by avoiding expensive nonlinear iterations. We provide rigorous theoretical proofs demonstrating that the proposed schemes are unconditionally energy stable and strictly preserve mass conservation. Numerical experiments satisfy the theoretical analysis, confirming optimal convergence rates and demonstrating robust preservation of mass conservation and modified energy stability in the tested regimes. Full article

Background/Objectives: Perceptual awareness and decision formation unfold gradually as sensory evidence increases. Near the threshold of awareness, small differences in neural processing efficiency can be amplified into marked behavioral variability. Open-skill athletes are trained to make rapid decisions under dynamic and uncertain conditions, yet it remains unclear whether their perceptual advantage reflects enhanced early sensory sensitivity or more efficient late-stage evidence accumulation. This study aimed to identify the processing stage at which open-skill sports expertise exerts its influence. Methods: Twenty-five open-skill athletes and twenty-three non-athlete controls completed a visual orientation discrimination task with eight graded levels of stimulus visibility, ranging from subliminal to clearly visible. Behavioral performance was analyzed together with hierarchical drift–diffusion modeling to estimate latent decision parameters. Event-related potentials (ERPs) were recorded using a 64-channel EEG system during an active decision task and a passive viewing task, focusing on early (N2, P2) and late (P3) components. ERP–behavior correlations were examined across visibility levels. Results: No group differences were observed at the lowest visibility levels. Group differences emerged selectively at intermediate to high visibility levels, where athletes showed higher accuracy and a tendency toward faster responses. Drift–diffusion modeling revealed that this advantage was driven by higher drift rates in athletes, with no group differences in non-decision time, boundary separation, or starting point. Early ERP components (N2, P2) were strongly modulated by stimulus visibility but showed no consistent group differences. In contrast, the P3 component exhibited earlier and more pronounced differentiation across visibility levels in athletes. In the passive viewing task, group differences were substantially reduced. ERP–behavior analyses showed stronger and earlier P3–behavior coupling in athletes. Conclusions: Open-skill sports expertise selectively optimizes late-stage evidence accumulation and its translation into behavior, rather than enhancing unconscious or early sensory processing. Full article

The rapid aging of bridges has increased interest in real-time, data-driven monitoring for predictive maintenance and safety management; however, practical deployment on in-service bridges remains limited. This paper presents lessons learned from long-term field deployment of real-time bridge joint monitoring systems on three in-service highway bridges and demonstrates how these insights can support the transition toward Industry 5.0. A unified framework is introduced to integrate key enabling technologies, including Internet of Things (IoT), digital twins, and artificial intelligence (AI), into a practical, human-centric monitoring architecture. Best practices for achieving durable, site-compliant, and cost-effective system design are summarized, with emphasis on sensor selection, wireless communication strategies, modular system development, and maintaining seamless operation. The development of a Docker-based analytics and visualization platform illustrates how interactive dashboards enhance human–machine collaboration and support informed decision-making. The role of advanced analytical tools, including digital twins, AI, and statistical modeling, in providing reliable structural assessments is highlighted, along with guidance on balancing cloud and edge computing for energy-efficient performance under constraints such as limited power, weather exposure, and site accessibility. Overall, the findings support the development of scalable, resilient, and human-centric real-time monitoring systems that advance data-driven decision-making and directly contribute to the realization of Industry 5.0 objectives in bridge health management. Full article

Flexible Job Shop Scheduling Problems with setup and transportation times (FJSP-TS) involve assigning operations to machines and sequencing them under additional time constraints, making the problem highly complex and common in modern manufacturing systems. Discrete Particle Swarm Optimization (DPSO) is one of the mainstream meta-heuristic methods for solving such scheduling problems, and this paper proposes a hybrid optimization approach based on DPSO to enhance solution quality. To reduce the complexity of meta-heuristic search and improve solution accuracy, a decoupled framework is introduced: DPSO is employed to optimize the operation sequence globally, while a Multi-Agent System (MAS) handles machine sequence. Furthermore, to enhance the state representation and decision-making capability of Machine Agents, a Heterogeneous Graph Neural Network (HGNN) integrated with Multi-head Attention is utilized to efficiently extract comprehensive features from the scheduling environment. Experimental results on 30 benchmark instances demonstrate that the proposed method achieves notable performance improvements in key scheduling metrics. Our method reduces the average makespan by 5.7%, total setup time by 8.9%, and total transportation time by 4.8% compared to representative optimization approaches. Full article

Cyclodextrins (CDs) are cyclic oligosaccharides composed of α(1 → 4) linked glucose units, which are widely used as solubilizers and stabilizers in the food, pharmaceutical and cosmetic industries. Among the CDs, γ-CD has attracted much attention due to its larger hydrophobic cavity and higher solubility. However, the industrial production of γ-CD is limited by lack of suitable enzymes and production process shortcomings. In this study, various strategies of improving heterologous enzyme production and optimization of the starch conversion process were applied to increase the production of γ-CD. A γ-cyclodextrin glucanotransferase with good product specificity from Bacillus sp. FJAT-44876 (BFγ-CGTase) and a liquefying β-CGTase from Bacillus sp. 1011 (Bsβ-CGTase) were successfully secreted by Pichia pastoris. After codon optimization and using the one-factor-at-a-time (OFAT) principle to improve the fermentation, the yield of recombinant BFγ-CGTase was increased 13.3 times to 463 U/L. Next a process was established involving Bsβ-CGTase-assisted starch liquefaction and simultaneous pullulanase debranching and BFγ-CGTase production of γ-CD. The yield of γ-CD increased by 17.67% via optimizing the amounts of BFγ-CGTase and BtPul used for the reaction. Overall, combination of the various improvements provided a new process for efficient preparation of γ-CD. Full article

This study evaluated the productivity and egg quality of Japanese quails reared under different LED colors and rearing systems. A total of 720 female quails were assigned to a 3 × 2 factorial arrangement with three LED colors (red, green, and white) and two rearing systems (cage and floor) until 20 weeks of age. Production performance was evaluated across specific age intervals, while physical egg quality traits were analyzed using a Repeated-measures General Linear Model to assess temporal changes. No significant overall interactions between LED color and rearing system were observed (p > 0.05). However, significant interactions between treatment and time (p < 0.05) revealed that red LED light progressively enhanced productivity, while the floor system significantly improved feed efficiency and income during the early laying phase (weeks 6–12). Specifically, red LED light significantly improved hen-day production, egg mass, feed efficiency, and income-to-cost ratio compared to other colors (p < 0.05). Physical egg quality traits remained consistent across treatments (p > 0.05) but were significantly influenced by time (p < 0.05). In conclusion, red LED light optimizes long-term profitability, whereas the floor system offers distinct advantages during the onset of lay. Full article

Multispectral (MS) satellite imagery provides rich spectral information for surface and atmospheric interpretation, yet its spatial resolution is often limited by sensor design. In this study, we propose a Transformer-based MS super-resolution framework that uses high-resolution panchromatic (PAN) imagery to supply complementary spatial detail cues for MS reconstruction and explicitly separates spatial enhancement from spectral preservation. In the spatial branch, PAN features are aligned to the MS grid via Pixel-Unshuffle and encoded with shifted-window self-attention to capture long-range spatial dependencies efficiently. In the spectral branch, spectral self-attention treats bands as tokens to learn inter-band correlations and maintain spectral consistency. The two representations are fused through channel concatenation and a 1 × 1 convolutional module, followed by a reconstruction head that upsamples the fused features to generate high-resolution MS outputs. For training, low-resolution MS inputs are synthesized from KOMPSAT-3A MS imagery using a degradation pipeline that combines modulation transfer function-based blur, downsampling, and additive Gaussian noise; the operation order is randomly permuted to emulate diverse acquisition conditions. In addition, Bayesian optimization is employed to explore network configurations through jointly considering the normalized mean absolute error and inference time. Experiments demonstrate that the proposed approach attains 46.23 dB PSNR, 0.9735 SSIM, and 3.12 ERGAS with approximately 167.4 K parameters, achieving a high restoration quality and computational efficiency across diverse degradation settings. Full article

Digital–intelligent integration (DII) has emerged as a pivotal driver for high-quality urban development, offering a pathway to overcome pressing resource and environmental constraints. By harnessing data as a core production factor and integrating advanced intelligent technologies, DII can substantially elevate urban green economic efficiency (GEE). This study constructs a quasi-natural experiment using the staggered rollout of national big data comprehensive pilot zones (initiated in 2012) and smart-city pilot programs (from 2016 onward). Employing a rigorous staggered difference-in-differences (DID) estimator on panel data from 279 Chinese prefecture-level cities over 2010–2021, we find that DII causally increases GEE by 5.03 percentage points (p < 0.01). This benchmark result remains robust across a comprehensive set of checks, including parallel-trend validation, placebo tests, double/debiased machine learning, two-stage least squares with historical IT-sector instruments, and controls for overlapping policies (e.g., ETS, low-carbon pilots, green finance zones). Mechanism analysis, conducted via a sequential 2SLS control-function approach with lagged mediators and Sobel–Goodman mediation tests, reveals three theoretically grounded channels: (i) enhanced urban ecological resilience (mediates 62%, z = 4.68), (ii) accelerated green technological innovation (55%, z = 4.12, measured by IPC/Y02 patent share), and (iii) heightened entrepreneurial vitality (58%, z = 4.39, new firms per 10,000 residents). Heterogeneity tests show pronounced effects in growing and mature resource-based cities (+1.21% and +11.21%), high-fintech cities (+11.35%), and high-river-density areas (+10.29%) but insignificant impacts in declining resource-exhausted cities (joint F p = 0.08). This study makes four key contributions: (1) it innovatively constructs a continuous DII policy variable by exploiting the synergistic timing of dual pilots, thereby overcoming the limitation of analyzing policies in isolation; (2) it opens the “theoretical black box” by integrating institutional theory and information economics into a unified conceptual framework that explicitly links DII to GEE through reduced transaction costs and alleviated information asymmetry; (3) it enriches the mediation identification strategy in staggered settings using 2SLS control functions and sequential G-estimation, addressing endogeneity in intermediary variables more rigorously than traditional three-step approaches; and (4) it delivers nuanced evidence on the contextual conditions (when and where) under which DII yields the strongest green dividends, providing actionable guidance for China’s “dual-carbon” goals and the global green transition. Full article

Superconducting transition-edge sensors (TESs) exhibit excellent single-photon detection performance. The quantum efficiency (QE), which quantifies the probability that an incident photon is absorbed and converted into a measurable signal, is strongly governed by the optical properties of the constituent thin films. Specifically, for typical TES device architectures where optical transmission is negligible, maximizing the QE requires the minimization of surface reflectance to ensure high photon absorptance. In this work, we systematically study how anodic oxidation modifies the optical response of superconducting titanium (Ti) thin films that are relevant for TES devices. Anodization is carried out under well-controlled constant-current conditions in an aqueous electrolyte containing ammonium pentaborate and ethylene glycol. Experimentally, we show that anodic oxidation substantially reduces the ultraviolet (UV) reflectance and induces a monotonic redshift of the reflectance minimum as the anodic oxidation cutoff voltage (V_ocv) increases. Finite-difference time-domain (FDTD) simulations based on spectroscopic ellipsometry data reproduce the measured spectra with good fidelity for most samples, validating the extracted optical constants. By comparing samples prepared at different current densities and oxidation times, we identified V_ocv as the primary parameter controlling the reflectance response, because it determines the thickness and effective optical properties of the anodic TiO_x layer. Under optimized conditions, reflectance values below 1% in the 320.9–340.2 nm wavelength range and below 2% in the 316.3–346.3 nm range are achieved, indicating a significant enhancement in potential absorptance. These results demonstrate that anodic oxidation provides a simple, post-fabrication, and voltage-tunable route for engineering the UV optical response of Ti-based TES structures and for enhancing their potential QE by suppressing reflection losses. Full article

Pomegranate peel, accounting for 35–50% of the fruit weight, is an underutilized agri-food by-product. This study applied, for the first time, fermentation with Saccharomyces cerevisiae as a simple and sustainable strategy to simultaneously obtain tannin-rich extracts and polysaccharide fractions with potential prebiotic activity. Peels from two cultivars, Wonderful and G1, differing in peel thickness, were subjected to three fermentation protocols (air- and not air-exposed) and monitored at 25 °C over 48 and 72 h. HPLC-DAD analysis showed that yeast-inoculated fermentation increased total tannin concentration in dry extracts (up to 70%) without inducing chemical modifications to tannin profiles. As determined by Dynamic Light Scattering, fermentation promoted significant depolymerization of native polysaccharides, while DOSY-¹H-NMR analyses revealed the presence of reduced molecular weight fractions down to 26 kDa. In vitro growth assays confirmed that fermented polysaccharides were more efficiently utilized as a carbon source by Bifidobacterium breve and Lactiplantibacillus plantarum compared to non-fermented controls, likely thanks to polysaccharide depolymerization induced by fermentation. The study demonstrated that air-exposed S. cerevisiae fermentation was an effective process alternative to chemical or enzymatic hydrolysis for modifying pomegranate peel pectin directly within a complex matrix, while simultaneously enhancing tannin recovery. This approach represents a possible sustainable strategy for pomegranate peel valorization into functional ingredients. Full article