Search Results (22,345)

Objective: Continuous pressure-flow cerebral bio-signals are critical for monitoring cerebrovascular dynamics but are often disrupted by missing data segments caused by artifacts from a variety of sources. These missing segments refer to segments of the signal that do not contain any valid physiological data. Such interruptions fragment the signals, resulting in discontinuities that compromise their overall integrity. Therefore, reconstructing missing values and preserving signal continuity are essential for ensuring the stable computation of signal trajectories and the accuracy of derived cerebrovascular indices. Methods: To address this issue, this systematic scoping review aimed to identify and synthesize existing interpolation and imputation strategies for handling missing segments in continuous pressure-flow cerebral bio-signals. Following the Cochrane and Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a comprehensive search of five electronic databases was conducted from their inception to 24 September 2024, and updated on 16 June 2025, using a detailed search string. Results: The initial searches yielded 19,403 results, and 8 studies were filtered and included in the review. All included studies employed interpolation techniques, such as linear and spline interpolation algorithms, to correct distorted signal segments. However, none of the included studies directly utilized interpolation or imputation strategies to reconstruct or completely fill missing data segments. Conclusions: This reveals a critical knowledge gap, as no study has systematically addressed the utilization of interpolation or imputation strategies for missing segments in pressure-flow cerebral bio-signals. Therefore, this systematic review emphasizes the need for specialized methodologies and standardized frameworks to enable reliable recovery of missing data segments in pressure-flow cerebral bio-signals, which is critical for advancing real-time neurocritical care monitoring and experimental neuroscience/psychological research. Significance: This systematic review lays the groundwork for future research into physiologically informed interpolation and imputation strategies for pressure-flow cerebral bio-signals in clinical and research applications. Full article

Ultrafast physical random bit generators (PRBGs) are essential components for modern applications in secure communication, quantum cryptography, encrypted optical fiber sensing and artificial intelligence. While optical chaos-based PRBGs offer high-speed capabilities, conventional systems often rely on discrete components that suffer from system complexity and environmental instability. This paper proposes and experimentally demonstrates a robust, integrated solution using a two-section mutual injection DFB laser. The device was fabricated using the reconstruction equivalent chirp (REC) technique, which provides precise control over grating phase variation while utilizing low-cost, high-volume fabrication methods. The laser sections, each measuring 450 μm in length, were designed with a free-running wavelength difference of 0.3 nm to ensure a flat optical spectrum and enhanced chaotic dynamics. By optimizing the bias currents, we achieved a chaos RF bandwidth of 20.1 GHz. Notably, the resulting chaotic signal lacks time-delayed signatures, which simplifies the randomness extraction process. To generate random bits, the chaotic waveform was sampled by an 8-bit analog-to-digital converter at 100 GSa/s. Following post-processing through delay-subtracting and the extraction of the four least significant bits (4-LSBs), we realized a total physical random bit rate of 400 Gb/s. The randomness of the generated sequence was successfully verified using the NIST SP 800-22 statistical test suite. This approach offers a compact, energy-efficient, and high-performance integrated chaotic source suitable for secure communication and high-performance computation. Full article

Nowadays, there is a deep interest in developing more compact user facilities, and plasma technology is one of the most promising techniques, not only for acceleration modules, but also for what is ancillary to the delivery of radiation to users, such as free electron lasers. In this regard, significant efforts have been made to miniaturize diagnostic stations, detection devices, and transfer lines, e.g., based on active plasma lenses. However, conventional undulators are still too cumbersome and expensive to meet the requirements of compactness and sustainability. For the aforementioned reasons, advanced undulator concepts have aroused great interest in pushing the frontier beyond conventional, magnet-based undulators. In this regard, a promising, very compact alternative is the use of the betatron motion of electrons in an ion channel to emulate an undulator device. This paper reports a feasibility study aiming to develop plasma-based undulator devices at SPARC_LAB as the test facility of the EuPRAXIA@SPARC_LAB project. In particular, this work provides a systematic assessment of free-electron-laser amplification in a plasma ion-channel undulator under experimentally realistic beam parameters, delivering quantitative predictions for gain and radiation performance in this configuration. Full article

A thorough understanding of the resistivity response characteristics of ethanol-contaminated soil is of great significance for the development of non-destructive geophysical detection techniques and for supporting contaminated site investigation and assessment. This experimental study aims to systematically investigate the resistivity behavior of ethanol-contaminated sandy soils, with a focus on the coupled mechanisms of multiple factors, including water content, ethanol concentration, particle size distribution, and contamination time. It is hypothesized that water content serves as the dominant factor controlling resistivity, whereas ethanol concentration and contamination time regulate resistivity by altering the physicochemical properties of the pore fluid. Under laboratory conditions, silt, fine sand, and medium sand were selected as the test materials. Resistivity was systematically measured using a Miller Soil Box with increasing water content, Wenner array configuration across varying water contents (3–24%), ethanol concentrations (40–98%), and contamination durations (0–144 h). The experimental results indicate the following: (1) Regardless of the presence of ethanol contamination, the resistivity of sandy soil decreases with increasing water content following a power-law relationship. The decrease is most pronounced at low water contents (3–9%), and gradually stabilizes at higher water contents. The results show that, at a constant water content, resistivity systematically and consistently follows the order: silt > medium sand > fine sand. (2) The influence of ethanol concentration on resistivity is constrained by water content levels, and the overall increase in resistivity is primarily attributed to ion dilution and the obstruction of conductive pathways. (3) Over time, resistivity exhibits a two-stage increasing trend, associated with ethanol volatilization and water loss. Resistivity changes in fine sand samples contaminated with ethanol at concentrations ranging from 75% to 95% follow a two-stage pattern. The initial phase of growth is characterized by a gradual increase over a period of 0–48 h, followed by a more rapid increase during the subsequent phase, which extends from 48 to 144 h. The results show that higher initial ethanol concentrations enhance the sensitivity of resistivity to temporal changes. Comprehensive analysis indicates that the resistivity variation mechanism under multi-factor coupling conditions can be summarized as follows: the water content is the dominant factor in the regulation of the conductive pathways; the particle size distribution determines pore structure and the characteristics of the particle interface; ethanol concentration and contamination time dynamically alter pore fluid properties, collectively regulating the resistivity response. Although the experiments were conducted under controlled laboratory conditions and the results have certain limitations, they provide a preliminary reference for interpreting resistivity responses in relatively homogeneous sandy contaminated sites and offer theoretical support for the application of resistivity methods in contamination identification and dynamic monitoring. Full article

Polymer-based product use is rapidly increasing worldwide, resulting in critical social, environmental, ecological, economic, and health effects. Worldwide efforts have increasingly focused on solutions to the equilibrium consumption, production, and disposal of plastics to tackle these issues. The frontiers of biodegradable and bio-based polymers are continually advancing in pursuit of sustainability. Therefore, designing ecological bioplastics made of both biodegradable and bio-based polymers reveals chances to overcome plastic pollution and resource depletion. Polymeric materials are mainly used to manufacture different products at the beginning of their lifespans and which become waste after usage. Numerous sustainability strategies and polymer recycling methods are described and mostly classified into chemical, mechanical, and thermal recycling processes. This manuscript presents a New Polymers Frontier in Recycling and Sustainability Using an Ensemble of Deep Learning with a Heuristic Search Algorithm (NPFRS-EDLHSA). This work is devoted to computational polymer typology, which is based on machine learning algorithms applied to data on physicochemical properties. Although polymer classification can facilitate downstream materials research, the present study does not directly simulate recycling, environmental impacts, or sustainability. The main contributions made by this work include (i) an exploratory analysis of ensemble deep learning models to classify polymers by type on a small and unbalanced dataset; (ii) an evaluation of the effect of feature selection with a heuristic optimization methodology; and (iii) a comparison of the effects on classification performance under limited data conditions. This research sets out to provide a methodological explanation, not arguments for industrial-scale applicability. For the polymer-type classification process, the proposed NPFRS-EDLHSA model designs an ensemble of deep learning techniques, namely a bidirectional recurrent neural network (BiRNN) model, a bidirectional gated recurrent unit (BiGRU) method, and a graph autoencoder (GAE) technique. Finally, the grasshopper optimization algorithm (GOA) adjusts the hyperparameter values of the ensemble models optimally and results in an improved classification performance. A wide-ranging set of experiments was conducted to validate the performance of the NPFRS-EDLHSA method. The experimental results indicated that the NPFRS-EDLHSA technique achieved a better performance than an existing model. Full article

Seismic inversion is a key technique for quantitative characterization of subsurface elastic parameters and detailed reservoir description. However, due to the limited bandwidth of seismic signals and the strong heterogeneity of complex reservoirs, conventional inversion methods struggle to simultaneously achieve high vertical resolution and lateral continuity. To address these challenges, an intelligent elastic parameter inversion method based on kernel density estimation within a Bayesian framework is proposed. First, kernel density estimation is introduced to augment the training samples, thereby alleviating data scarcity. Second, a hybrid architecture integrating convolutional modules, Mamba, and cross-attention mechanisms is constructed to achieve collaborative modeling of local spatial features and long-range temporal dependencies. The cross-attention mechanism is further employed to adaptively weight and fuse multi-source features, thus enhancing the representation capability of the model. Subsequently, by designing a joint loss function, the strengths of deterministic inversion and data-driven approaches are effectively integrated, ensuring physical consistency while enhancing data adaptability, thereby improving the stability and accuracy of the inversion results. Furthermore, the neural network outputs are used as the initial model for Bayesian inversion to construct a probabilistic inversion framework for elastic parameter inversion. Finally, experimental results demonstrate that the proposed method improves the R² values of inversion results by more than 8.0% and 5.0% compared with conventional methods in thin interbedded models and real data experiments, respectively. Full article

Background: Diabetic vascular complications are a major cause of poor prognosis in patients with diabetes mellitus (DM). Mitophagy activation is a potential therapeutic target for type 2 diabetes mellitus (T2DM), but the role of low-intensity pulsed ultrasound (LIPUS) in this context remains unclear. Methods: The biological effects of LIPUS on endothelial cells under high glucose conditions were systematically evaluated using high glucose-treated human umbilical vein endothelial cells (HUVECs) and aortic tissues from diabetic rats as models, in combination with bioinformatics analysis and standard molecular and cellular biology techniques. Histological staining was further used to assess the protective role of LIPUS in the aortas of diabetic rats. Results: Bioinformatics analysis predicted that high glucose induces mitochondrial dysfunction, suppresses autophagy in HUVECs, impairs endothelial cell function, and activates fibroblasts. In vitro results were in agreement with these predictions. LIPUS treatment significantly counteracted these effects, restoring migration (p < 0.001) and angiogenesis (p < 0.05), increasing proliferation (p < 0.001), and decreasing apoptosis (p < 0.05). Mechanistically, LIPUS enhanced mitophagy, and its therapeutic effects were markedly diminished upon addition of the autophagy inhibitor 3-Methyladenine (3-MA). In vivo, LIPUS attenuated aortic endothelial damage and reduced collagen deposition in diabetic rats (p < 0.01). Conclusions: LIPUS may ameliorate hyperglycemia-induced endothelial cell dysfunction by activating mitophagy, and it also attenuates pathological damage in the abdominal aorta of diabetic rats, thereby providing experimental evidence for its application in the treatment of diabetic macrovascular complications. Full article

This paper presents an effective approach to reducing noise and vibration levels in ultra-high-voltage (UHV) shunt reactors based on structural optimization and damping techniques. The main sources of vibration are associated with magnetostriction of electrical steel and electromagnetic forces in the magnetic system, which induce structural excitation of the reactor tank. A combined numerical and experimental methodology is employed, including finite element modeling (FEM) of the reactor tank and field measurements of vibration displacement and acoustic noise. In contrast to previous studies focused primarily on material properties, this work emphasizes the role of structural modifications in controlling vibration transmission. The proposed solutions include the use of nitrile butadiene rubber (NBR) damping elements, optimization of the magnetic system geometry, and reinforcement of the tank structure using vertical and horizontal stiffeners. The FEM analysis in the frequency range of 50–150 Hz shows that the maximum displacement amplitude reaches 16.2 μm at the tank bottom and 10.5 μm at the tank walls. Experimental results confirm a reduction in vibration levels to 13 μm and a sound power level of 88 dBA, which meets regulatory requirements. The proposed approach improves the vibroacoustic performance and operational reliability of UHV reactors and can be effectively applied in the design of modern high-voltage power equipment. Full article

High-resolution satellite imagery is pivotal for accurate analysis in remote sensing applications, including land-use monitoring, urban planning, and environmental assessment. However, obtaining such data is often costly and limited. Consequently, super-resolution techniques, such as deep learning models and fine-tuning strategies like LoRA, offer a promising alternative to the critical research challenge, especially given the diversity and large scale of satellite datasets. While deep learning-based super-resolution models have been very promising recently, their effectiveness, efficiency, and scalability across heterogeneous satellite scenes are not well studied. This work studies the performance of representative deep learning Super-Resolution frameworks, including the Enhanced Super-Resolution Generative Adversarial Network. (ESRGAN), Swin Transformer for Image Restoration (SwinIR), and latent diffusion models (LDM), under unified experimental conditions using the WorldStrat dataset. The main goal is to establish whether adaptation strategies for parameter efficiency can boost reconstruction quality while reducing computational and training costs. Toward this goal, we investigate hybrid sequential pipelines, ensemble averaging, and Low-Rank Adaptation (LoRA)–based fine-tuning. The experiments indicate that these pipelines, which use multi-model methods, achieve only marginal performance gains while incurring substantial increases in computational complexity. LoRA-Based Fine-Tuning, by contrast, has demonstrated superiority in enhancing reconstruction accuracy and quality across all model families, despite using only a small percentage of trainable parameters. LoRA-based models demonstrate superiority over multi-model methods in both efficiency and performance. The presented results confirm that LoRA is an effective and accessible technique for high-fidelity satellite-based super-resolution image synthesis. The manuscript identifies LoRA as one of the enabling technologies advancing the state of the art in Deep Learning-based Super Resolution for large-scale satellite-based image synthesis. Full article

Background/Objectives: This study aimed to clarify the cardioprotective effects of combined empagliflozin and sacubitril/valsartan therapy in an experimental rat model of heart failure (HF). The main research question was whether dual treatment provides greater functional and molecular benefit than either monotherapy, with particular emphasis on oxidative stress, inflammation, apoptosis, and JAK2/STAT3 signaling. Methods: HF was induced in rats by 7-day isoproterenol administration and confirmed four weeks later by echocardiographic evidence of reduced ejection fraction (<55%). The animals were then assigned to healthy control, untreated HF, empagliflozin, sacubitril/valsartan, and combined empagliflozin/sacubitril/valsartan groups. Following four weeks of treatment, ex vivo cardiac function was evaluated using the Langendorff technique. Serum cardiospecific markers and natriuretic peptides were measured by ELISA. Oxidative stress parameters were determined in coronary venous effluent, while myocardial gene expression of selected (anti)oxidative, (anti)inflammatory, (anti)apoptotic, and signaling markers was assessed by RT-PCR. Myocardial collagen content was evaluated using Picrosirius red staining. Results: HF rats exhibited impaired ex vivo myocardial function, elevated cardiac injury markers, increased oxidative stress, upregulation of pro-inflammatory and pro-apoptotic genes, activation of JAK2/STAT3 signaling, and increased myocardial collagen content. Both monotherapies produced partial benefit. In contrast, combined treatment achieved the most pronounced improvement in contractile performance, attenuated oxidative stress more consistently, reduced expression of TNF-α, IL-1β, IL-6, IL-17, Bax, CASP-3, and CASP-9, favorably modulated JAK2, STAT3, mTOR, and PPARγ expression, and decreased myocardial collagen content. Conclusions: Dual empagliflozin and sacubitril/valsartan therapy exerted broader cardioprotective effects than either monotherapy, likely through coordinated antioxidant, anti-inflammatory, anti-apoptotic, and signaling-related mechanisms. Full article

The vibronic spectra of the first excited singlet state (S₁) and the cation spectra of the ground state of the cation (D₀) of jet-cooled cyclopropylbenzene (CPB) were investigated using resonance-enhanced multiphoton ionization (REMPI) and photoelectron velocity-map imaging techniques, respectively. The vibronic spectra indicated the existence of only the bisected conformer, a finding corroborated by quantum chemical calculations. The S₀ → S₁ electronic transition originated at 36,858.5 cm⁻¹, with an adiabatic ionization energy of 66,846 ± 15 cm⁻¹. Vibrational levels in both states were assigned with the assistance of theoretical geometry optimization and frequency calculations. These experimental spectra and theoretical calculations provided valuable insights into the structural and vibrational characteristics of CPB in its excited and cationic states. Full article

The precise and timely diagnosis of grapevine diseases is paramount for ensuring food security and mitigating economic losses within the viticulture sector. While existing deep learning models offer high accuracy, their computational intensity and hardware requirements often hinder their use in portable or low-power field systems. This study addresses this gap by proposing GrapeLeafNet, a lightweight convolutional neural network optimized for efficient feature extraction. GrapeLeafNet introduces a strategic hybrid approach that combines the low parameter efficiency of models like SqueezeNet with the rapid feature propagation advantages offered by shallow architectures such as AlexNet. By eliminating the sequential processing latency caused by SqueezeNet’s 18-layer deep structure and the excessive 61-million-parameter memory burden of AlexNet, this model establishes a critical balance between low latency and high accuracy through its optimized 7-layer architecture. Characterized by an original integration of standard convolutional layers, batch normalization, and max pooling, GrapeLeafNet achieves high computational efficiency with only 1.6 million parameters and a 6.26 MB memory footprint. This structural optimization enhances deep feature hierarchies, enabling the model to focus on distinctive pathological signs within complex leaf patterns and maximize classification sensitivity by filtering out irrelevant features. The evaluation was conducted using the Niphad Grape Leaf Disease (NGLD) dataset, incorporating data augmentation to mitigate inherent class imbalances. Additionally, data augmentation techniques were employed to mitigate inherent class imbalances within the dataset. Experimental results demonstrate that GrapeLeafNet achieved 97.06% accuracy and a 94.77% F1-score on the original dataset, outperforming recent benchmarks by 2.46%. Following augmentation, performance reached 98.29% accuracy and a 98.16% F1-score, representing a 6.16% higher F1-score than contemporary models. GrapeLeafNet exhibits high robustness against asymmetric class distributions and establishes a significant performance margin over existing architectures. Its lightweight nature, combined with superior accuracy and F1-score metrics, makes it an ideal candidate for integration into mobile devices and real-time agricultural monitoring systems. Full article

Current-source DC links and their associated power converters require continuous conduction mode (CCM), necessitating specialized switching device configurations. These topologies have gained significant attention due to the increasing adoption of current-mode power distribution systems. The operation of a current-source DC-DC converter relies on temporary magnetic energy storage, typically regulated using established switch-mode power conversion techniques. For a stable current step up or step down the use of the tapped inductor concept can provide an ultimate stable solution for current adjustment and the proposed concept is now developed on a step-down current source DC-DC power converter for the first time to reveal in the power electronics field. The use of tapping concept is similar to a coupled inductor and this allows flexible current modification. In this article, this concept is extended to a family of Tapped inductor current-based DC-DC together with soft-switching to reduce the loss of the switching devices. The key advantage is that it can offer a wide range of current conversions with high efficiency. The theoretical and experimental analysis of the proposed converter family is presented. An experimental prototype of the converter was built and tested, operating with a switching frequency of 100 kHz and accommodating input currents ranging from 1 A to 10 A. The converter achieved current conversion ratios of 0.8, 0.67 and 0.57 times the input current, with an output power range of 1 W to 314 W. The maximum efficiency of 88% was achieved at an output power of 314 W. The high efficiency and wide current conversion range of this current-based converter make it suitable for a variety of applications such as current driving LED systems, photovoltaic (PV) system current source control, and constant current fast charging systems for electric vehicles (EVs). Full article

Opportunities abound in microfluidic technologies to impact how we understand extremely complex systems with many constituents which change with time and space. In these technologies, separation science plays a central role towards understanding everything from biology and healthcare to environmental monitoring to the search for life in the Solar system. Separations can amplify the capabilities of detection modalities by isolating targets and/or increasing their concentration while removing background constituents which can interfere with their sensing. In essence, separations increase the amount of information that can be gathered from a sample. The ideal features of next-generation separations capability are present in gradient insulator-based dielectrophoresis (g-iDEP), enabled by the length scale and precision of microfluidics. It acts through electric field interactions with particles, which enables unbiased (label-free) separations since all relevant particles, from atoms to cells, have an accessible response to electricity—either through linear (electrophoresis) or higher-order gradient (dielectrophoresis and related) effects. The technique isolates and concentrates, enabling improved detection function and multidimensional separations. Its foundational theoretical capabilities give it separations power on the order of 1:10⁸, beyond the resolving power of the best mass spectrometers and ultra-high resolution spectroscopies. Experimental evidence is amassing that shows it to be a powerful tool that can resolve tiny differences in cells (antibiotic resistance versus susceptible in unlabeled paired isolates across many species) and differentiate single-point mutations in proteins. Its capabilities are still emerging, and this work aims to quantify the current practice and connect those approaches to the ultimate capabilities of the technique towards quantifying the dynamic range and resolving power of the strategy as a whole. The technique uses two methods of quantifying the electrophysical properties of the target, voltage sweep and spatial methods. The voltage sweep method is lower-resolution and serves as a search mode, while the spatial method is higher-resolution and quantifies the properties over a smaller defined range determined via the sweep method. These quantification methods are examined by collating existing experimental data, performing relevant Monte Carlo simulations, and finite element model calculations. These are summarized to understand the mechanisms currently limiting the technique, facilitate quantitative comparisons with traditional separation science capabilities in terms of resolution and dynamic range, and compare them to the theoretical limits of the strategy. Full article

Fault section location in distribution networks is a key technology for enhancing power supply reliability and fault recovery efficiency. However, the existing fault section location techniques all have certain limitations, such as being affected by fault types and working conditions. Therefore, to achieve fault section location in different fault types for resonant-grounded distribution networks, such as grounding faults and arc faults, this paper proposes a comprehensive method based on DC attenuation components of grounding wire currents and transient polarity of zero-sequence currents. Firstly, by analyzing the cable distribution model, it is found that the DC attenuation components of grounding wire currents in healthy and fault lines are different. The transient polarities of zero-sequence currents of healthy and fault lines are opposite. Based on this, a fault section location method using DC attenuation components is proposed. Meanwhile, to improve the accuracy of the location method, a fault section location method based on transient polarity is also developed. Then, a high-reliability fault section location method is formed by complementing each other through two methods. Finally, 10 kV distribution network fault simulation models are established using PSCAD/EMTDC and RTDS. The experimental results show that the proposed method can accurately locate the fault section under different fault types, fault angles, fault resistances and noise conditions. Full article