Search Results (21,426)

This study proposes a novel deep learning framework for predicting the shear capacity of slender reinforced concrete (RC) beams without shear reinforcement. The proposed approach employs convolutional neural networks and autoencoders to transform structural data into image representations, reconstruct missing data, and predict shear capacity with high accuracy. Using a dataset of 964 experimental results covering a wide range of beam characteristics, the framework achieves remarkable predictive performance. The image-based methodology enables the model to capture spatial dependencies, while the autoencoder reconstructs incomplete data with a fidelity exceeding 95%. The framework is validated against conventional methods under different data masking levels (10%, 20%, 30%). For 10% masking, the proposed method achieves R² = 0.94, MAE = 0.05, and NSE = 0.93, significantly outperforming ACI 318 and Eurocode 2. Even with 30% masking, the framework maintains robust performance, with R² = 0.85 and NSE = 0.81. These results highlight the scalability and reliability of the model in handling incomplete datasets, as well as its potential to advance structural engineering practice by integrating machine learning techniques with traditional design methodologies. Full article

This paper introduces a novel extension of the classical Lindley distribution, termed the X-Lindley model, obtained by a specific mixture of exponential and Lindley distributions, thereby substantially enriching the distributional flexibility. To enhance its inferential scope, a comprehensive reliability analysis is developed under a generalized progressive hybrid censoring scheme, which unifies and extends several traditional censoring mechanisms and allows practitioners to accommodate stringent experimental and cost constraints commonly encountered in reliability and life-testing studies. Within this unified censoring framework, likelihood-based estimation procedures for the model parameters and key reliability characteristics are derived. Fisher information is obtained, enabling the establishment of asymptotic properties of the frequentist estimators, including consistency and normality. A Bayesian inferential paradigm using Markov chain Monte Carlo techniques is proposed by assigning a conjugate gamma prior to the model parameter under the squared error loss, yielding point estimates, highest posterior density credible intervals, and posterior reliability summaries with enhanced interpretability. Extensive Monte Carlo simulations, conducted under a broad range of censoring configurations and assessed using four precision-based performance criteria, demonstrate the stability and efficiency of the proposed estimators. The results reveal low bias, reduced mean squared error, and shorter interval lengths for the XLindley parameter estimates, while maintaining accurate coverage probabilities. The practical relevance of the proposed methodology is further illustrated through two real-life data applications from engineering and physical sciences, where the XLindley model provides a markedly improved fit and more realistic reliability assessment. By integrating an innovative lifetime model with a highly flexible censoring strategy and a dual frequentist–Bayesian inferential framework, this study offers a substantive contribution to modern survival theory. Full article

Efficient intra-mode decision for depth maps assumes a pivotal role in augmenting the overall performance of 3D-HEVC. Existing research endeavors predominantly rely on fast mode screening strategies grounded in texture characteristics or machine learning techniques. These strategies, to a certain extent, mitigate the complexity of mode search. Nevertheless, these approaches often fall short of fully leveraging the intrinsic spatio-temporal correlations within depth maps. Moreover, strategies relying on deterministic classifiers exhibit insufficient discrimination reliability in regions featuring edge mutations or intricate structures. To tackle these challenges, this paper presents a two-stage fast intra-mode decision algorithm for depth maps, integrating naive Bayes probability estimation and fuzzy support vector machine (FSVM). Initially, it confines the candidate mode space through spatio-temporal prior modeling. Subsequently, FSVM is employed to enhance the decision accuracy in regions with low confidence. This methodology constructs a joint mode decision framework spanning from probability screening to refined classification. By doing so, it significantly reduces the computational burden while preserving rate-distortion performance, thereby attaining an effective equilibrium between encoding complexity and performance. Experimental findings demonstrate that the proposed algorithm reduces the average encoding time by 52.30% with merely a 0.68% increment in BDBR. Additionally, it showcases stable universality across test sequences of diverse resolutions and scenes. Full article

Large-area metasurfaces were fabricated via a tunable pyramidal interference lithography (PIL) technique, which uses custom-built 2-faced, 3-faced, and 4-faced pyramidal prisms to create metasurfaces with customizable nano- and micro-scale surface feature periodicities. The 2-faced prism produced linear surface relief diffraction gratings, while the 3-faced prism produced metasurfaces with triangular lattices and the 4-faced prism produced metasurfaces with square lattices, all on azobenzene thin films. A double inline prism set-up enabled control over the metasurface feature periodicity, allowing systematic increase in the pattern size. Additional tunability was achieved by placing a prism inline with a lens, allowing precise control over the metasurface feature periodicity. A theoretical model was derived and successfully matched to the experimental results. The resulting metasurfaces were coated with gold and exhibited distinct surface plasmon resonance (SPR) and surface plasmon resonance imaging (SPRi) responses, confirming their functionality. Overall, this work establishes PIL as a cost-effective and highly adaptable metasurface fabrication method for producing customizable periodic metasurfaces for photonic, plasmonic, and sensing applications. Full article

With increasing life expectancy driven by rapid biomedical science advancement, reproductive longevity has become a key concept in women’s health. Preventing reproductive senescence is important not only to extend fertility potential but also to preserve endocrine health, enhance quality of life, and promote healthy aging. The end of ovarian function and fertility is symbolized by menopause, as the most eminent index of reproductive aging. Hormone replacement therapy (HRT) remains the mainstay for managing menopausal symptoms. However, as the use of HRT is often limited, there is a need for safe and effective alternatives. This narrative review summarizes current and emerging approaches targeting different stages of reproductive aging. Both hormonal and non-hormonal therapies for vasomotor and genitourinary symptoms are discussed alongside developing fertility preservation techniques, including oocyte vitrification, ovarian tissue cryopreservation, in vitro follicle maturation, and artificial ovary engineering. Furthermore, evolving and experimental ovarian regenerative strategies, such as stem cell transplantation, intraovarian platelet-rich plasma (PRP) injections, antioxidants, metabolic modulators, telomerase activators, and stem cell-derived extracellular vesicles, offer new prospects for delaying or reversing ovarian aging. Overall, personalized regenerative strategies and innovative solutions may reshape the future of women’s reproductive health and longevity. Full article

The green-making stage is crucial for forming the distinctive aroma and flavor of Tieguanyin tea. Current green-making stage recognition relies on tea makers’ sensory experience, which is labor-intensive and time-consuming. To address these issues, this paper proposes a lightweight automatic recognition model named T-GSR for the accurate and objective identification of Tieguanyin tea green-making stages. First, an extensive set of Tieguanyin tea images at different green-making stages was collected. Subsequently, preprocessing techniques, i.e., multi-color-space fusion and morphological filtering, were applied to enhance the representation of target tea features. Furthermore, three targeted improvements were implemented based on the MobileNet V3 backbone network: (1) an adaptive residual branch was introduced to strengthen feature propagation; (2) the Rectified Linear Unit (ReLU) activation function was replaced with the Gaussian Error Linear Unit (GELU) to improve gradient propagation efficiency; and (3) an Improved Coordinate Attention (ICA) mechanism was adopted to replace the original Squeeze-and-Excitation (SE) module, enabling more accurate capture of complex tea features. Experimental results demonstrate that the T-GSR model outperforms the original MobileNet V3 in both classification performance and model complexity, achieving a recognition accuracy of 93.38%, an F1-score of 93.33%, with only 3.025 M parameters and 0.242 G FLOPs. The proposed model offers an effective solution for the intelligent recognition of Tieguanyin tea green-making stages, facilitating online monitoring and supporting automated tea production. Full article

A sharing framework based on Zero-Knowledge Proof (ZKP) and Proxy Re-encryption (PRE) technologies offers a promising solution for sharing Student Electronic Academic Records (SEARs). As core credentials in the education sector, student records are characterized by strong identity binding, the need for long-term retention, frequent cross-institutional verification, and sensitive information. Compared with electronic health records and government archives, they face more complex security, privacy protection, and storage scalability challenges during sharing. These records not only contain sensitive data such as personal identity and academic performance but also serve as crucial evidence in key scenarios such as further education, employment, and professional title evaluation. Leakage or tampering could have irreversible impacts on a student’s career development. Furthermore, traditional blockchain technology faces storage capacity limitations when storing massive academic records, and existing general electronic record sharing solutions struggle to meet the high-frequency verification demands of educational authorities, universities, and employers for academic data. This study proposes a dedicated sharing framework for students’ electronic academic records, leveraging PRE technology and the distributed ledger characteristics of blockchain to ensure transparency and immutability during sharing. By integrating the InterPlanetary File System (IPFS) with Ethereum Smart Contract (SC), it addresses blockchain storage bottlenecks, enabling secure storage and efficient sharing of academic records. Relying on optimized ZKP technology, it supports verifying the authenticity and integrity of records without revealing sensitive content. Furthermore, the introduction of gate circuit merging, constant folding techniques, Field-Programmable Gate Array (FPGA) hardware acceleration, and the efficient Bulletproofs algorithm alleviates the high computational complexity of ZKP, significantly reducing proof generation time. The experimental results demonstrate that the framework, while ensuring strong privacy protection, can meet the cross-scenario sharing needs of student records and significantly improve sharing efficiency and security. Therefore, this method exhibits superior security and performance in privacy-preserving scenarios. This framework can be applied to scenarios such as cross-institutional academic certification, employer background checks, and long-term management of academic records by educational authorities, providing secure and efficient technical support for the sharing of electronic academic credentials in the digital education ecosystem. Full article

Determining an appropriate sequence of interrelated activities is one of the keys to developing a complex product. One of the approaches used to sequence activities consists of solving the feedback length minimization problem (FLMP). Several metaheuristic algorithms for this problem have been reported in the literature. However, they suffer from high computational costs when dealing with large-scale problem instances. To address this research gap, we propose a fast hybrid heuristic for the FLMP, which integrates the simulated annealing (SA) technique with the variable neighborhood search (VNS) method. The local search component of VNS relies on a fast insertion neighborhood exploration procedure performing only $O\left(1\right)$ operations per move. Using rigorous statistical tests, we show that the SA-VNS hybrid is superior to both SA and VNS applied individually. We experimentally compare SA-VNS against the insertion-based simulated annealing (ISA) heuristic, which is the state-of-the-art algorithm for the FLMP. The results demonstrate the clear superiority of SA-VNS over ISA. The SA-VNS hybrid technique produces equally good or better results across all tested problem instances. In particular, SA-VNS is able to find better solutions than ISA on all instances of size 150 or more. Moreover, SA-VNS requires two orders of magnitude less CPU time than the ISA algorithm. Thus, SA-VNS achieves excellent performance regarding solution quality and running time. Full article

This study aimed to develop a methodology to evaluate, through RGB image processing, the wheat cultivar TRIO Calibre under three irrigation levels (100, 50, and 25%), with or without the application of Bacillus aryabhattai, in Brazilian Cerrado soil. The experimental scheme was a $3\times 2$ factorial design with five replicates. Images were collected, numbered, and organized into files, which were transformed to grayscale. During processing, the grayscale level co-occurrence matrix (GLCM) technique was applied and implemented in four main directions (0°, 45°, 90°, and 135°), and 13 statistical descriptors were extracted. At physiological maturity, the plants were harvested, and the following yield components were evaluated: plant height (PH), number of spikes per plant (NS), number of grains per spikes (NGS), average grain weight (AGW), and total prodution of grains (TPG). Irrigation influenced all the variables, with higher TPG and NS at 100% and 50% water and higher AGW at 25% water. The results indicated that the “contrast” descriptor in the 90° and 135° GLCM directions was the most efficient in differentiating treatments, which presented better performance in the 90° direction and was significantly correlated with the NS ($r=-0.48$, $p<0.05$) and TPG ($r=-0.46$, $p<0.05$). The analyses demonstrated that the methodology has the potential to be adapted for the analysis of under controlled conditions, contributing to more sustainable agricultural practices. Full article

Accurate identification of oil–water two-phase flow patterns is essential for ensuring the safety and operational efficiency of oil and gas extraction systems. While traditional methods using empirical models and sensor technologies have provided basic insights, they often struggle to capture the nonlinear features of complex operational conditions. To address the challenge of data scarcity commonly found in experimental settings, this study employs a data augmentation strategy that combines the Synthetic Minority Over-sampling Technique (SMOTE) with Gaussian noise injection, effectively expanding the feature space from 60 original experimental nodes. Next, a physics-constrained attention mechanism model was developed that incorporates a physical constraint matrix to effectively mask irrelevant feature interactions. Experimental results show that while the standard attention model (83.88%) and the baseline BP neural network (84.25%) have limitations in generalizing to complex regimes, the proposed physics-constrained model achieves a peak test accuracy of 96.62%. Importantly, the model demonstrates exceptional robustness in identifying complex transition regions—specifically Dispersed Oil-in-Water (DO/W) flows—where it improved recall rates by about 24.6% compared to baselines. Additionally, visualization of attention scores confirms that the distribution of attention weights aligns closely with fluid-dynamic mechanisms—favoring inclination for stratified flows and flow rate for turbulence-dominated dispersions—thus validating the model’s interpretability. This research offers a novel, interpretable approach for modeling dynamic feature interactions in multiphase flows and provides valuable insights for intelligent oilfield development. Full article

Background: Currently, there is a considerable number of studies addressing vagus nerve stimulation (VNS) for the treatment of different stroke-related outcomes. We aimed to promote a broad view of the outcomes studied and what are the opportune outcomes to be studied involving this therapeutic strategy for the treatment of post-stroke complications. Methods: This is a scoping review that followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Two investigators conducted independent searches on PubMed/MEDLINE, Scopus, and Embase till July 2025. Randomized clinical trials and preclinical studies using invasive or non-invasive vagus nerve stimulation conducted with a population diagnosed with stroke were included. Results: Forty-one experimental studies and sixteen clinical trials were included. The outcomes found were neuroprotection; motor, functional, and cognitive rehabilitation; dysphagia; comparison of different stimulation intensities; safety, efficacy, and feasibility of the non-invasive approach; comparison between transcutaneous auricular vagus nerve stimulation (taVNS) and transcutaneous cervical vagus nerve stimulation (tcVNS); and comparison between two models of ischemia (permanent and transient). Preclinical studies mostly investigated molecular elements involved in neuroprotection, neuroinflammation, and cellular apoptosis, while clinical studies evaluating the effectiveness of this technique used for rehabilitation and its comparison or combination with other techniques remain scarce. Conclusions: Most studies investigating the effects of VNS on different post-stroke outcomes are experimental studies. Clinical studies are still scarce and with limited analysis of outcomes. Full article

Many studies on solar heating systems have examined individual techniques to enhance the performance of solar water collectors, such as flow obstructions, increased turbulence, nanofluids, and investment in thermal storage. The benefits of integrating these sustainability strategies into a single, sustainable system have yet to be fully established. This work displays a hybrid water-heating system that contains a solar water collector (SWC) and an electric water heater (EWH), a photovoltaic panel (PV), and nano-additives to increase the outlet water temperature and improve thermal efficiency. Numerical and experimental analyses were used to estimate the influence of water flow rate (2.5, 3.5, and 4.5 L/min) and different Al₂O₃ concentrations (0.1%, 0.2%, and 0.3%) on system performance using U-shaped pipe in SWC model. The results highlight that lower flow rates consistently yield higher ΔT values because water spends a longer time in the collector, allowing it to absorb more heat. Also, when using water only, the collector efficiency increases pro-aggressively with flow rate. A significant performance enhancement is observed upon incorporating Al₂O₃ nanoparticles into the fluid, with a 0.1% Al₂O₃ volume concentration improving efficiency by ~7.4% over water. At 0.3%, the highest improvement is recorded, yielding a ~9.3% gain in efficiency compared to the base case. Full article

This work presents the fabrication and characterization of a bow-tie antenna integrated uni-traveling carrier photodiode (UTC-PD) on a silicon carbide (SiC) substrate for efficient terahertz (THz) wave generation. The proposed device exploits the superior thermal conductivity and mechanical robustness of SiC to overcome the self-heating limitations associated with conventional indium phosphide (InP)-based photodiodes. An epitaxial layer transfer technique was utilized to bond InP/InGaAs UTC-PD structures onto SiC. The study systematically examines the influence of critical geometric parameters, specifically the mesa diameter and length between the antenna arms, on the emitted THz intensity in the 300 GHz frequency band. Experimental results show that the THz radiation efficiency is primarily governed by the mesa diameter, reflecting the trade-off between light absorption, device capacitance, and bandwidth, while the length between the antenna arms exhibits only a weak influence within the investigated parameter range. The fabricated device demonstrates strong linearity between photocurrent and THz output power up to 7.5 mA, after which saturation occurs due to space-charge effects. This work provides crucial insights for optimizing SiC-based bow-tie antenna integrated UTC-PD devices to realize robust, high-power THz sources vital for future high-data-rate wireless communication systems such as beyond 5G and 6G networks. Full article

In this paper, we present a novel method for background estimation and updating in video sequences, utilizing an innovative approach that combines an intelligent truncated mean, the stationary wavelet transform (SWT), and advanced thresholding techniques. This method aims to significantly enhance the accuracy of moving object detection by mitigating the impact of outliers and adapting background estimation to dynamic scene conditions. The proposed approach begins with a robust initial background estimation, followed by moving object detection through frame subtraction and gamma correction. Segmentation is then performed using SWT, coupled with adaptive thresholding methods, including hard and soft thresholding. These techniques work in tandem to effectively reduce noise while preserving critical details. Finally, the background is selectively updated to integrate new information from static regions while excluding moving objects, ensuring a precise and robust detection system. Experimental evaluation on the CDnet 2014 and SBI 2015 datasets demonstrates that the proposed method improves the F1 score by 12.5 percentage points (from 0.7511 to 0.8765), reduces false positives by up to 65%, and achieves higher PSNR values compared to GMM_Zivk, SuBSENSE, and SC_SOBS. These results confirm the robustness of the hybrid approach based on truncated mean and SWT in dynamic and challenging environments. Full article

In modern Industry 4.0 environments, real-time scheduling presents a complex challenge requiring both formal correctness guarantees and optimal performance. Background: Traditional approaches fail to provide an optimal integration between formal correctness guaranteeing and optimization, and such failure either produces suboptimal results or a correct result lacking guarantee, and studies have indicated that poor scheduling decisions could cause productivity losses of up to 20–30% and increased operational costs of up to USD 2.5 million each year in medium-scale manufacturing facilities. Methods: This work proposes a new hybrid approach by integrating Extended Time Petri Nets (ETPNs) and Finite-State Automata (FSAs) with formal modeling, abstracting ETPNs by extending conventional Time Petri Nets to deterministic time and priority systems, accompanied by Genetic Algorithms (GAs) to optimize the solution to tackle a multi-objective optimization problem. Our solution tackles indeterministic problems by incorporating suitable priority resolution methods and GA to pursue optimal solutions to very complex scheduling problems and starting accurately from standard real-time scheduling-policy models such as DM, RM, and EDF-EDF. Results: Experimental evaluation has clearly verified performance gains up to 48% above conventional techniques, covering completely synthetic and practical case studies, including 31–48% improvement on synthetic benchmarks, 24% increase on resource allocation, and total elimination of constraint violations. Conclusions: The new proposed hybrid technique is, to a considerable extent, a dramatic advancement within real-time scheduling techniques and Industry 4.0, successfully and effectively integrating optimal correctness guaranteeing and favorable GA-aided optimization techniques, which particularly guarantee optimal correctness to safe-related applications and provide considerable improvements to support efficient and optimal performance, extremely helpful within Industry 4.0. Full article