Search Results (427)

Low-rank recovery has emerged as a powerful methodology for the restoration of degraded images. Conventional low-rank recovery techniques, however, predominantly rely on nuclear norm or weighted nuclear norm minimization to separate sparse noise. A significant limitation of this approach is its dependence on full singular value decomposition, which imposes a substantial computational burden, thereby hindering its practical applicability. This paper presents a novel image denoising model integrating the weighted nuclear norm and deep image prior. The weighted nuclear norm is introduced to accurately characterize the global structural properties of images, ensuring the consistency of the overall image structure after denoising. Meanwhile, the deep image prior is employed to effectively capture local details, which helps avoid the blurring of textures and edges often caused by excessive noise removal. The complementary advantages of the two components enable the proposed model to achieve superior performance compared with existing denoising methods. To efficiently compute the proposed model, we design the bilinear factorization method and the alternating direction method of multipliers. Experiments show that the proposed method outperforms mainstream approaches in both restoration accuracy and computational efficiency, exhibiting rapid convergence and robust algorithm stability, thereby demonstrating excellent comprehensive performance. Full article

This paper presents an ARMv8/NEON-oriented implementation of NCC-Sign targeting the NTT-friendly trinomial parameter sets (NCC-Sign-1/3/5), whose dominant cost arises from mixed-radix NTT computations with $n=2^a·3^b$. We design lane-local SIMD kernels—including a four-lane Montgomery multiply–reduce, a centered modular reduction pass, a fused stage-0 butterfly, and streamlined radix-2/radix-3 pipelines—and extend them with three further optimizations: (i) radix-2 multi-stage butterfly merging to halve intermediate load/store traffic, (ii) a stride-3 vectorization technique exploiting NEON structure load/store instructions (vld3q/vst3q) to fully vectorize small-len radix-3 stages that would otherwise fall back to scalar execution, and (iii) NEON-parallel pointwise Montgomery multiplication. Using cycle-accurate PMU measurements under identical toolchains for baseline and optimized builds on Apple M1 Pro, we observe geometric-mean speedups of 1.40× for key generation, 2.24× for signing, and 2.01× for verification across NCC-Sign-1/3/5, with per-kernel gains of up to 5–6× for NTT/INTT and 7.5× for pointwise multiplication. To contextualize these results, we provide a direct comparison with the NEON-optimized ML-DSA (Dilithium) implementation of Becker et al. on the same platform, a cross-platform evaluation on Arm Cortex-A72 (Raspberry Pi 4), a Montgomery-versus-Barrett microbenchmark supporting our design choice, and an empirical constant-time assessment via dudect confirming that no timing leakage is detected in any NEON kernel under 30 million measurements. Full article

This paper presents a new perspective on the energy decay of a nonlocal truncated Timoshenko–Ehrenfest system. By using the non local elasticity theory, this model is a generalization of the standard truncated Timoshenko system. The well-posedness of the proposed model is established via the Faedo-Galerkin method. Energy stability and decay properties are then derived using suitable multiplier techniques. Finally, a numerical scheme is constructed, and the exponential decay of the discrete energy is given special attention. Numerical simulations are provided to illustrate and validate the theoretical results. Full article

To address the issue of missing position in flight trajectory data collected by Automatic Dependent Surveillance-Broadcast (ADS-B) systems, a flight trajectory tensor completion model based on truncated Schatten p-norm minimization is proposed. First, the low-rank characteristics of the trajectory set are validated using Singular Value Decomposition (SVD); based on this, the data is transformed into a three-dimensional tensor structure. Next, a regularization strategy combining the Schatten p-norm with a singular value truncation mechanism is introduced to construct the trajectory tensor completion model, which suppresses noise and interference from minor components while preserving the main variation patterns of the trajectories. Finally, the model is optimized and solved using the Alternating Direction Method of Multipliers (ADMM) to obtain the completed trajectories. Taking historical ADS-B trajectory data from Orly Airport to Toulouse Airport as an example, the completion results of the proposed model under different missing patterns, missing rates, and flight phases are analyzed from both qualitative and quantitative perspectives. Experimental results show that compared with other representative models, the proposed model achieves the best completion performance under different missing patterns and missing rates; the completion performance during the cruise phase is better than during the ascent and descent phases. The proposed model can serve as a preprocessing technique for flight trajectory data in air traffic, providing more complete and reliable data support for various downstream applications. Full article

Filters supporting dynamic reconfiguration that use the spectral parameter approximation (SPA) technique, together with other methodologies, and the interpolated spectral parameter approximation (ISPA) technique offer dynamic adjustment of the cutoff frequency (f_c) with a narrow transition bandwidth and a very wide f_c range. However, they suffer from a high multiplier requirement, leading to increased hardware resource usage. With fewer multipliers, we suggest the Multiply and Accumulate (MAC)-based SPA (MAC-SPA) and MAC-based interpolated SPA (MAC-ISPA) filter in this article. This article describes a unified MAC structure utilizing Time-Division Multiplexing (TDM) that uses the resource-sharing concept to implement an MAC-SPA and MAC-ISPA filter. The developed dynamically reconfigurable filter is implemented and realized using a 0.18 µm CMOS process. Additional testing was done on the Xilinx xc6vlx760-1ff1760 FPGA device. Relative to the filter that incorporates SPA along with the modified coefficient decimation method (MCDM), the obtained results reveal that the proposed MAC-SPA and MAC-ISPA channel filters, synthesized on FPGA, achieve a reduction in occupied slice count by approximately 7% and 4.76%, respectively. Although their operating speeds are slightly lower by about 9.4% for the MAC-SPA filter and 13.89% for the MAC-ISPA filter, this tradeoff is offset by significant savings in hardware resources, making both designs more area-efficient with only a modest reduction in speed. Full article

At temperatures higher than 5 °C in the cooling chambers of refrigeration systems, bacteria multiply rapidly on fresh fishes, thereby leading to an increased risk of foodborne diseases. Maintaining the storage temperature within the recommended bounds of 0 °C and 5 °C is needed to maintain food safety and quality. This study presents model predictive control of a data-driven medium-temperature cold storage system using subspace system identification techniques. The identified linear model presents a holistic view of the whole system, with each subsystem cohesively linked together. The data-driven model was developed from synthetic data derived from a high-fidelity simulation benchmark model of a supermarket refrigeration system from Aalborg University, Denmark. The benchmark model consists of a medium-temperature closed display case, the suction manifold, and the compressor rack. The data of the expansion valve, suction pressure, compressor capacity, heat transfer rate, and ambient temperature were taken as inputs, while the data of the air and goods temperatures were taken as outputs based on expert knowledge. A linear model predictive controller was designed to control the temperature outputs of the identified linear model, and the outputs were compared with the proportional–integral dead band control used in the benchmark. Simulation results for 24 h showed that the model predictive controller was able to achieve an air temperature and a goods temperature within the recommended temperature range of 0 °C and 5 °C that guarantees safe storage of fresh fishes. These results imply that a reduced-order model of a commercial refrigeration system that is robust, reliable, and stable can be developed and controlled to achieve the goal of food safety, thereby guaranteeing food security and reducing costs. Full article

Column generation is a fundamental technique for solving large-scale combinatorial optimization problems such as unit commitment and vehicle routing, yet its performance is often limited by dual oscillation. This study explores the intrinsic cause of this phenomenon from the perspective of shadow price theory and demonstrates that dual oscillation arises from the lack of marginal interpretability of Lagrange multipliers when multiple dual solutions coexist. To address this issue, an improved column generation framework is proposed in which traditional multipliers are replaced with minimum-norm multipliers that possess clear economic meaning and act as directional shadow prices. A generalized pricing subproblem is formulated, and partial minimum-norm multipliers are obtained through convex quadratic optimization to guide column generation. Numerical experiments on a simplified single-period unit commitment case and large-scale cutting stock problems showed that the proposed approach eliminated invalid column generation and achieved speedy convergence to the optimal solution within only two iterations for the unit commitment case, and the classical column generation exhibited slow convergence with dual oscillation in large-scale scenarios while the improved algorithm achieved fast and stable convergence. The results indicate that the stabilization method enhances the consistency of dual variables and provides a more robust foundation for the theoretical and practical development of column generation algorithms. Full article

This paper presents a subtractive-dither-assisted background calibration technique for a 2 GS/s 12 bit pipelined analog-to-digital converter (ADC). A large 7 bit pseudo-random dither is injected in both the flash and the multiplying digital-to-analog converter (MDAC) to decorrelate the differential nonlinearity (DNL) errors caused by the inherent quantization error nonlinearity, capacitor mismatching, and inter-stage amplifier nonlinearity from the input signal. Designed in a 28 nm CMOS process with a 1 V supply, post-layout simulations demonstrate a 10.2 dB improvement in spurious-free dynamic range (SFDR), from 73.8 dB to 84.4 dB, with dithering enabled under a close-to-Nyquist input frequency of 985 MHz. Although the injected dither cannot be completely removed in the digital domain, the proposed ADC exhibits only a 0.5 dB degradation in signal-to-noise-and-distortion ratio (SNDR) for full-scale input, achieving an SNDR of 62.3 dB and an effective number of bits (ENOB) of 10.1 bits. Dithering also improves static performance, with DNL and INL optimized to +0.54/−0.53 LSBs and +0.85/−0.88 LSBs, respectively. Moreover, the proposed dither-based calibration technique introduces an additional power consumption of less than 2 mW. Full article

Arrhythmia is a life-threatening cardiovascular disease if not detected early. While deep learning models have demonstrated strong performance in ECG-based arrhythmia classification, deploying these models on resource-constrained wearable devices remains challenging. In this paper, we present a quantization-compatible neural architecture search (NAS) framework that discovers ultra-compact minority-aware convolutional neural networks (CNN). We formulate NAS as a multi-objective optimization problem, jointly maximizing balanced accuracy and minority-classes recall while minimizing model size and computational complexity. Furthermore, we constrain our search space to INT8-compatible operations. We evaluated our framework on the MIT-BIH Arrhythmia Database. We discovered NanoCNN models for binary and multi-class classification tasks. The models trained without augmentation achieved 98.7% and 98.21% overall accuracies outperforming the state-of-the-art. The discovered models required 38.3 K and 51.5 K multiply-accumulate operations (MAC) per inference, enabling their deployment on ARM Cortex-M4 microcontrollers. With augmentation and other minority-aware interventions, our model attained 91.6% balanced accuracy. Our results validated the effectiveness of the adopted search and training techniques for arrhythmia screening and diagnosis. Full article

Infectious diseases are contributing to a major public health challenge worldwide, affecting individuals across all age groups and regions. An infectious disease is a pathological condition caused by harmful microorganisms. These are bacteria, viruses, fungi, or parasites that enter the body, multiply, and disturb normal physiological functions, leading to clinical manifestations. At present, the detection of infectious disease is mainly based on vital signs and a limited set of biomarkers. This limited approach fails to fully capture the complications of infection-related physiological changes. To address these limitations, vital signs and a broad range of hematological and biochemical biomarkers are integrated with machine learning and explainable artificial intelligence (XAI). The data set used in this study was collected from the Kaggle data source. The dataset consists of vital sign values, such as body temperature, systolic and diastolic blood pressure, respiratory rate, heart rate, and oxygen saturation, along with blood-based biomarkers including albumin, base excess, bicarbonate, bilirubin, blast cells, calcium, creatinine, gamma-glutamyl transferase (GGT), glucose, hematocrit, hemoglobin, lactate, leukocytes, neutrophils, C-reactive protein (CRP), platelets, potassium, sodium, alanine aminotransferase (TGP/ALT), activated partial thromboplastin time (TTPA), and urea. These parameters provide a complete view of the patient’s physiological and biochemical state during infection. Feature selection was performed using a hybrid approach combining correlation filtering, mutual information, tree-based feature importance, and XAI validation (SHAP, permutation sensitivity) to ensure both predictive accuracy and interpretability. The integration of these techniques supports accurate classification and AI-assisted decision-making. The findings of this study highlight the importance of integrating both vital sign monitoring and laboratory assessments for effective infectious disease management. Full article

Background/Objectives: Obesity remains a major global health burden, with metabolic–bariatric surgery being the most efficient long-term treatment strategy. However, both variability in perioperative care and postoperative complications persist. To address these challenges, the EUropean PErioperative MEdical Networking (EUPEMEN) protocol for bariatric surgery was developed to standardize care and enhance perioperative outcomes across European healthcare settings. Methods: The protocol was formulated through close collaboration among experts from multiple disciplines, involving surgeons, anesthetists, nurses, and nutritionists. Its development included a literature review, expert consensus, and the creation of structured perioperative guidelines covering the preoperative, intraoperative, and postoperative phases. Focus areas include patient education, nutritional optimization, early mobilization, opioid-sparing analgesia, and minimally invasive surgical techniques, supported by educational materials and manuals. Technical activities included the development of detailed multimodal rehabilitation manuals translated into five languages, the creation of an open-access online learning platform, training of future educators through a “train the trainer” approach, organization of multiplier promotional events, international collaboration meetings to refine the protocol, and revision and standardization of existing perioperative care guidelines to ensure evidence-based, unified practices across Europe. Results: Implementation of the EUPEMEN protocol aims to reduce postoperative complications, enhance recovery, and decrease hospitalization time. Standardized rehabilitation pathways and access to free educational platforms promote consistent care delivery across diverse healthcare environments. Key strategies include early oral intake, limited use of invasive devices, and comprehensive patient preparation. Conclusions: The EUPEMEN protocol introduces an evidence-based, multidisciplinary framework for optimizing perioperative management in bariatric surgery. While variability in resources and adherence may present potential obstacles, its application holds significant promise for improving perioperative outcomes. Future studies are necessary to assess its long-term impact and adaptability in different healthcare settings. Full article

Understanding the spatiotemporal dynamics of medicinal plant distributions and their quality responses under climate change is essential for formulating forward-looking conservation and utilization strategies. In response to the increasing depletion of wild resources of Alpinia officinarum Hance, one of the ‘Ten Major Guangdong Medicinal Materials’, this study developed an integrated modeling platform incorporating nine algorithms. These included generalized linear models, machine learning techniques, and a MaxEnt model optimized using ENMeval (Regularization Multiplier (RM) = 3, Feature Class (FC) = LQH). The platform was applied to simulate habitat suitability evolution under current climatic conditions (1970–2000) and for two future periods (2050s: 2041–2060; 2090s: 2081–2100) across four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). Furthermore, Co-kriging interpolation was coupled to conduct a comprehensive quality zoning based on the dual “ecological-chemical” dimension. Analysis of key environmental factors revealed that the distribution of A. officinarum is primarily constrained by hydrothermal conditions, with a suitable annual temperature ranges from 19.96 to 29.05 °C and a dry-season precipitation requirement between 56.64 and 185.65 mm. Model projections indicate that future warming does not promote habitat expansion; instead, it drives a latitudinal shift in the suitability centroid toward lower latitudes. The cumulative effects of different emission pathways vary markedly: the high-emission scenario (SSP585) triggers severe habitat contraction by the 2090s, while habitat loss under the SSP370 scenario remains relatively manageable. By overlaying the spatially heterogeneous distribution of galangin, this study delineated southeastern Yunnan, southeastern Guangxi, southwestern Guangdong, and northern Hainan as core “integrated quality regions”. These findings not only reveal the sensitivity and vulnerability of A. officinarum Hance to climate change but also provide spatially explicit guidance for in situ germplasm conservation and the selection of high-quality cultivation bases. Full article

Using high-resolution remote sensing imagery to obtain the wind turbine height is a fast and effective method for monitoring the status of wind turbines after natural disasters such as earthquakes, landslides, and typhoons. A height estimation method tailored for wind turbines is proposed using high-resolution satellite images. First, deep learning techniques are employed to identify wind turbines and extract their shadow information from GaoFen-2 (GF-2) satellite imagery. Specifically, YOLOv5-CBAM and MSASDNet are used for target recognition and shadow extraction, achieving an identification accuracy of 96% and a shadow extraction accuracy of 82.53%. Next, the line-by-line scanning method is applied to remove blade shadow from the whole wind turbine shadow. By calculating the number of pixels occupied by the shadow length of the wind turbine after removing the blade shadow and multiplying by the image resolution, the wind turbine shadow length is obtained. Finally, a spatial geometry model involving the satellite angles, solar angles, and wind turbine shadow length is constructed to retrieve the wind turbine height. An experiment was conducted using GF-2 satellite remote sensing data from a wind farm in Huailai County of China. The actual heights of wind turbines in the estimation area were measured by the field experiment, and the average absolute error was verified to be 2.2 m, demonstrating the effectiveness of the proposed method. The experimental results show that this method can detect the post-disaster status of wind turbines. Full article

The Probe Of Extreme Multi-Messenger Astrophysics (POEMMA) is a proposed dual-satellite mission to observe Ultra-High-Energy Cosmic Rays (UHECRs), increase the statistics at the highest energies, and observe Very-High-Energy Neutrinos (VHENs) following multi-messenger alerts of astrophysical transient events, such as gamma-ray bursts and gravitational wave events, throughout the universe. POEMMA–Balloon with radio (PBR) is a small-scale version of the POEMMA design, adapted to be flown as a payload on one of NASA’s suborbital Super Pressure Balloons (SPBs) circling over the Southern Ocean for more than 20 days after a launch from Wanaka, New Zealand. The main science objectives of PBR are: (1) to observe UHECRs via the fluorescence technique from suborbital space; (2) to observe horizontal high-altitude air showers (HAHAs) with energies above the cosmic ray knee (E > 3PeV) using optical and radio detection for the first time; and (3) to follow astrophysical event alerts in the search of VHENs. The PBR instrument consists of a 1.1 m aperture Schmidt telescope similar to the POEMMA design, with two cameras on its focal surface: a Fluorescence Camera (FC) and a Cherenkov Camera (CC). In addition, PBR has a Radio Instrument (RI) optimized for detecting EASs (covering the 60–660 Mhz range). The FC observes UHECR-induced EASs in the ultraviolet (UV) spectrum using an array of 9216-pixel Multi-Anode Photo-Multiplier Tubes (MAPMTs) imaged every 1 $\mathsf{\mu }$s. The CC uses a 2048-pixel Silicon Photo-Multiplier (SiPM) imager to observe cosmic-ray-induced HAHAs and search for neutrino-induced upward-going EASs. The CC covers a spectral range of 320–900 nm, with an integration time of 10 ns. This contribution provides an overview of PBR instruments and their current status. Full article

Background/Objectives: Varicocele repair can improve semen parameters and pregnancy rates in appropriately selected men; however, persistence or recurrence remains a common cause of treatment failure with ongoing infertility or scrotal pain. Because mechanisms and definitions vary across studies, counseling and salvage selection can be challenging. This review synthesizes contemporary evidence on why varicocele recur and provides an anatomy-informed approach to evaluation and retreatment. Methods: A narrative evidence synthesis was performed using PubMed/MEDLINE, prioritizing clinical practice guidelines, systematic reviews, meta-analyses, and contemporary adult and adolescent clinical series addressing mechanisms of failure, diagnostic workup, and outcomes of salvage microsurgery and endovascular therapy. Results: Recurrence rates vary by technique and follow-up, with the lowest rates reported in contemporary microsurgical subinguinal series. The dominant drivers of failure are incomplete venous control and complex reflux pathways, including duplicated internal spermatic veins and missed collaterals such as cremasteric, external spermatic, gubernacular, and deferential veins. Clinical examination remains central; Doppler ultrasonography is most useful when pain persists or semen parameters and testicular growth do not improve. Venography can define culprit channels in complex or multiply treated cases and enables targeted embolization. Retreatment achieves high anatomic success with consistent improvements in semen parameters and meaningful pregnancy rates in available series, with modality-specific complication profiles. Conclusions: Recurrent varicocele should be managed with structured reassessment that links venous anatomy and the index procedure to the salvage option. Microsurgical redo is generally favored after non-microscopic repairs, whereas endovascular occlusion is often preferred after prior surgery or when venographic mapping is needed. Full article