Search Results (46,247)

The deployment of Wireless Sensor Networks (WSNs) remains challenging and time consuming due to the manual commissioning, configuration, and maintenance of resource-constrained Internet of Things (IoT) devices. Achieving precise network-wide time synchronization in such systems further increases this deployment complexity. This paper presents a novel Real-Time Operating System (RTOS)-integrated time synchronization method that distributes an absolute Coordinated Universal Time (UTC) reference across the network using a single Global Navigation Satellite System (GNSS)-enabled host. The method extends the semantics of the RTOS tick count by directly linking it to a global time reference. Consequently, sensor nodes obtain a notion of UTC time and can execute time-critical tasks at precisely defined moments without requiring a dedicated Real-Time Clock (RTC) or GNSS module on each sensor node. This design reduces both hardware cost and overall system complexity. Experimental results obtained on custom-developed hardware running FreeRTOS demonstrate a task synchronization error below ±30 μs between the GNSS reference and a sensor node operating at a clock frequency of 32 MHz. Such precise network-wide synchronization enables more efficient channel utilization, reduces power consumption, and improves the accuracy of both local and coordinated task execution across multiple devices in WSNs. It therefore serves as a key enabler for self-deployable WSNs. Full article

Acinetobacter baumannii (A. baumannii) represents a major threat because of its multidrug resistance, achieved through its ability to control virulence, and its mechanisms of drug efflux resistance. In this study, we used a combined experimental–computational approach to create and evaluate antimicrobial peptides that targeted the two essential pathogenic proteins, BfmR and AbaQ. The genomic analysis of a clinical isolate showed an extensive resistome and virulence profile, which matched high-risk global lineages. This study conducted molecular docking of an experimental AMP (cathelicidin KR-12 screened from the literature) and a rationally designed synthetic AMP (modified KR-12 analog) with pathogenic proteins, followed by 200 ns molecular dynamics simulations to evaluate both the binding stability and inhibitory potential of the compounds. The disk diffusion assay and microdilution assay were performed against A. baumannii. The study used comparative trajectory analyses, including RMSD, RMSF, radius of gyration, solvent-accessible surface area, principal component analysis, and MM-PBSA free energy calculations, to show that the synthetic AMP created stable electrostatic and hydrogen-bond networks, which caused conformational locking, and reached lower energy states than the experimental peptide. The synthetic AMP showed significant inhibition in validation in vitro. Contrastingly, the experimental AMP had transient interactions and no specificity. The study demonstrates that rationally designed AMPs have therapeutic potential, while the results create a reliable in silico framework to combat multidrug-resistant A. baumannii. Full article

In fire emergency ventilation systems, EC (Electronically Commutated) internal-rotor axial fans are critical devices, but their high-speed operation generates aerodynamic noise often exceeding 90 dB (A). While struts are core structural components regulating flow field stability, their specific geometric impact on trailing-edge vortex shedding and noise generation mechanisms remains unclear. This study investigates three strut configurations: a hexagonal annular type, a hexagonal double-ring type, and a three-pronged type. A coupled numerical model was established using Large Eddy Simulation (LES) and the Ffowcs Williams and Hawkings (FW-H) acoustic analogy. The Q-criterion was employed to analyze vortical structures, with numerical predictions validated against experimental measurements in a semi-anechoic chamber. The results quantitatively demonstrate that optimizing the strut geometry significantly mitigates unsteady flow separation. The three-pronged strut (Model C) effectively dispersed high-velocity airflow, reducing the peak turbulent kinetic energy (TKE) at the inlet by 30% compared to the original design (Model a). Furthermore, Model C achieved a 6.7 dB reduction in the sound pressure level at the blade-passing frequency (BPF), alongside a 14.1% reduction in pressure pulsation amplitude near the blade tip. Structural optimization of struts enables synergistic control over turbulence distribution and pressure fluctuations. By disrupting the phase coherence of shed vortices, the optimized design fundamentally suppresses aerodynamic noise, advancing axial fan design toward precise quantitative aeroacoustic optimization. Full article

Solid-state fermentation (SSF) is a long-established biotechnological approach gaining renewed interest for its ability to enhance nutrient availability and improve the functional properties of agro-industrial by-products. This strategy is particularly relevant for early post-weaning piglets, which are highly susceptible to weaning stress due to an immature digestive system and a gut microbiota not yet adapted to solid feed. In this study, the fermentation parameters of flaxseed cake were optimized using a Plackett–Burman experimental design. Protease activity was selected as the response variable due to its relevance for improving protein degradation and potential digestibility in fermented feed ingredients. Accordingly, based on the statistical analysis, the conditions selected for the in vivo trial were 1% molasses, 0.5% yeast extract, 0.05% CaCl₂, 0.5% NaCl, 7.5% inoculum (4.12 × 10⁹ CFU/mL), 60% moisture, and 72 h fermentation. Fermentation time was identified as the main factor positively influencing protease production, while higher CaCl₂ concentrations and inoculum levels negatively affected enzyme activity. Optimization increased protease activity, microbial viability and free amino acid content. In addition, SSF reorganizes the carbohydrate profile by reducing structural fiber fractions, with neutral detergent fiber and acid detergent fiber decreasing by 27% and 29%, respectively, while simultaneously increasing soluble carbohydrates by 14.67%. Phytic acid content being also reduced by 23.81%. A pilot nutritional trial on post-weaned piglets (35 days old) showed that including 8% fermented flaxseed cakes (FFSC group) improved body weight, average daily gain, feed conversion ratio, and diarrhea score, without affecting average daily feed intake, compared with 8% unfermented flaxseed cakes (FSC group). These performance improvements were accompanied by changes in fermentation metabolites and gut microbial composition. Lower isovalerate concentrations suggested reduced proteolysis, while higher propionate levels may contribute to increased blood glucose availability in the FFSC group. These changes coincided with a shift in microbial composition, characterized by a reduced abundance of methanogenic archaea and increased abundances of taxa such as Lactobacillus, Enterococcus, and members of the Lachnospiraceae and Eubacteriaceae families. Full article

Free space detection in assisted driving applications is essential to provide information to vehicles about traversable surfaces and potential obstacles to be avoided. The current trend in free space detection favors the use of deep learning techniques. However, Deep Neural Networks require extensive training that considers as many scenarios as possible, which makes it difficult to create a model that can be generalized to all types of surfaces. Additionally, their lack of explainability contrasts with the growing interest in geometrically grounded and safety-oriented design principles for autonomous vehicle systems. To address these limitations, we propose a geometric approach that incorporates coplanarity conditions and normal vector estimation, removing the dependence on datasets for different types of surfaces. Additionally, the stereoscopic images are clustered in superpixels. The use of images clustered in superpixels allows us to obtain shorter processing times, in addition to taking advantage of the spatial and color information provided by the superpixels to increase the robustness of the three-dimensional reconstruction of the scene. Experimental results show that the proposed superpixel-based approach achieves competitive performance compared to unsegmented dense stereo methods, while significantly reducing algorithmic complexity. These results demonstrate the viability of integrating superpixel clustering into stereo-based free space estimation frameworks. Full article

Aiming to address the complex coupling of transient impulses and steady-state components in vibration signals of scroll compressors in air conditioning systems, this study proposes a physically driven heterogeneous dual-path adaptive weighting network (PDW-Net). The approach constructs a physics-inspired weighting module based on kurtosis and energy criteria, enabling adaptive reconstruction of transient impulses and steady-state vibration components. Feature extraction and decision-level fusion are achieved through a heterogeneous dual-branch network comprising a Fast Fourier Transform (FFT)-based one-dimensional convolutional neural network (1D-CNN) and a Short-Time Fourier Transform (STFT)-based two-dimensional convolutional neural network (2D-CNN). In experimental validation covering four typical fault conditions—condenser failure, refrigerant deficiency, refrigerant overcharge, and main shaft wear—the PDW-Net achieved an average diagnostic accuracy of 97.87% (standard deviation: 2.60%), with 100% accuracy in identifying refrigerant deficiency and normal operating states, demonstrating significant superiority over existing mainstream methods. Ablation studies reveal that the adaptive weighting mechanism contributes most substantially to performance, as its removal results in a 34.24 percentage point drop in accuracy. Replacing the heterogeneous dual-branch structure with a homogeneous counterpart reduces accuracy by 16.18 percentage points, robustly validating the efficacy of the physics-guided and heterogeneous fusion design. Full article

In intelligent transportation systems and urban traffic management, accurate vehicle area intrusion detection based on surveillance imagery plays a critical role in ensuring road safety and operational efficiency. However, under real-world road surveillance conditions characterized by complex backgrounds, varying illumination, occlusion, and scale variations, mainstream detection algorithms often suffer from high false detection and missed detection rates, limiting their reliability and practical deployment. To address these challenges, this paper proposes YOLOv10-Intrusion, a high-precision vehicle area intrusion detection framework based on an improved version of YOLOv10s. The proposed algorithm incorporates Omni-Dimensional Dynamic Convolution (ODConv) and a custom-designed RCS_M module to enhance feature extraction and fine-grained recognition capability. In addition, a Bidirectional Feature Pyramid Network (BiFPN) is employed to optimize multi-scale feature fusion at the neck level. These improvements collectively reduce false detections and missed detections while improving model recall and mean Average Precision (mAP). Furthermore, the Wise-IoU (WIoU) loss function replaces the original Complete IoU (CIoU) loss to accelerate convergence and stabilize bounding box regression under complex surveillance conditions. A dedicated vehicle area intrusion dataset is constructed from real-world road surveillance footage, covering five vehicle categories across diverse road environments and lighting conditions. Experimental results demonstrate that, compared with the baseline YOLOv10s, YOLOv10-Intrusion achieves improvements of 1.5, 3.3, 3.6, and 2.8 percentage points in Precision, Recall, mAP@0.5, and mAP@0.5:0.95, respectively, and outperforms other mainstream detection algorithms in vehicle area intrusion detection tasks. Full article

Fault diagnosis of rolling bearings in wind turbines is significantly challenged by strong noise, non-stationary signals, and multi-source interference. To address these issues, a Multi-Scale Attention Residual Graph Convolutional Network (MSAR-GCN) is proposed. First, a fully connected graph is constructed in the frequency domain using a temporal segmentation strategy, which preserves full spectral resolution and captures cross-frequency coupling features via node embeddings. Second, a multi-scale residual module with a cross-layer pyramid structure is designed to extract features at varying granularities, integrated with a dynamic multi-head attention mechanism to adaptively emphasize damage-sensitive frequency bands. Additionally, a hierarchical feature distillation mechanism is employed to compress high-dimensional features, ensuring model lightweighting while retaining critical fault information. Experimental validations on CWRU and JNU datasets demonstrate that MSAR-GCN achieves 97.02% and 92.5% accuracy under −10 dB Gaussian noise, respectively, outperforming existing methods by over 4%. Specifically, the model exhibits exceptional robustness, maintaining 93.09% accuracy under severe non-Gaussian impulsive noise. With verified feature separability and high computational efficiency, the proposed method offers a promising solution for high-precision, real-time industrial fault diagnosis. Full article

Background: Ketogenic dietary therapies (KDTs), characterized by substantial carbohydrate restriction and increased dietary fat intake, were originally developed for the treatment of drug-resistant epilepsy but have recently attracted broader scientific interest. In the context of population aging and the increasing prevalence of cognitive impairment and dementia, their potential relevance for brain health has received growing attention. Experimental and emerging clinical evidence suggests that ketogenic metabolism may influence biological processes involved in brain aging, including cerebrovascular regulation, neuroinflammatory signaling, and cerebral energy metabolism. Objective: This narrative review aims to synthesize current evidence on the relationship between ketogenic dietary therapies and brain health, with particular emphasis on cerebrovascular mechanisms, neuroinflammatory pathways, and neuroprotective processes relevant to aging. The review also briefly introduces the Semmelweis Study as an example of a translational research framework for evaluating nutrition-related interventions in real-world preventive settings. Methods: A narrative literature review was conducted using structured searches of major scientific databases to identify experimental and human studies investigating ketogenic dietary interventions, cerebrovascular mechanisms, and neuroprotective outcomes. Publications related to the Semmelweis Study were included solely to illustrate implementation-oriented research approaches and not as evidence supporting dietary efficacy. Results: Available evidence indicates that ketogenic dietary interventions may modulate several biological pathways relevant to brain health, including cerebral energy metabolism, mitochondrial function, oxidative stress regulation, and inflammatory signaling. However, the current evidence base is dominated by preclinical studies and short-term human investigations, and direct evidence linking ketogenic dietary therapies to long-term cerebrovascular or cognitive outcomes remains limited. Conclusions: Ketogenic dietary therapies represent metabolically distinct dietary strategies with potential relevance for cerebrovascular and neuroprotective mechanisms. Nevertheless, human evidence remains heterogeneous and insufficient to support broad clinical recommendations. Future research should prioritize well-designed long-term human studies with clearly defined metabolic, cerebrovascular, and cognitive endpoints. Translational research frameworks may facilitate the evaluation of feasibility, safety, and implementation of ketogenic interventions in aging populations. Full article

Photovoltaic (PV) performance in desert environments is significantly hindered by soiling and partial shading. To bridge the gap in empirical data for extreme Saharan conditions, this study presents a novel techno-economic assessment of uniform horizontal shading (UHS) specifically conducted in Mauritania. Controlled outdoor experiments were performed using a 250 W crystalline silicon PV module and a PVPM 2540C I–V curve tracer, applying progressive shading levels from 2.5% to 20%. The novelty of this work lies in the integration of high-resolution experimental I–V/P–V characterization with a localized techno-economic model for five pre-commercial PV plants. It was observed that PV modules are exceptionally sensitive to shading; specifically, a mere 10% shaded area leads to a catastrophic 90% drop in power and current, while the voltage remains remarkably stable. Thermographic analysis further validates the thermal gradients and bypass diode functionality. By quantifying the financial impacts, this research highlights that cumulative economic losses across the five real-world sites reached 87.95%, exceeding 55,000 MRU. These findings provide a strategic framework for optimizing PV systems in arid terrains and offer a robust tool for enhancing the design and operation of large-scale solar applications in desert environments. Full article

Temporal monitoring of bioprocesses is inherently complex because process variables do not evolve independently over time, and their interpretation changes as the reaction progresses. In many existing abnormality detection methods, sensor signals are analyzed at isolated time points or temporal characteristics are only weakly reflected through model structures. As a result, such approaches struggle to explain or detect abnormal behavior that emerges differently across reaction states. This study proposes TPP-TimeNet, a time-aware artificial intelligence framework developed to improve abnormality detection in bioprocess monitoring. Unlike conventional methods, the proposed framework explicitly incorporates reaction time as contextual information. Multivariate process signals are reorganized into sliding windows that reflect reaction-state transitions rather than uniform time segmentation. Temporal behavior inside each window is captured using a sequential encoding model, and reaction-state information is subsequently integrated to form state-dependent representations. Through this design, the model can distinguish between temporal patterns that are similar in shape but occur at different points in the reaction timeline. This capability leads to improved sensitivity to abnormal events that may otherwise remain undetected. Abnormality is evaluated at the window level using a probabilistic scoring scheme with a fixed threshold, enabling consistent and reproducible decision-making. The performance of TPP-TimeNet was evaluated using publicly available process control datasets from Kaggle. The datasets were reinterpreted in a bioprocess context by mapping variables such as temperature, pH, and pressure. Experimental results show that the proposed method outperforms traditional machine learning models as well as deep learning approaches that focus only on temporal features, achieving higher accuracy, sensitivity, and F1-score. These findings suggest that incorporating explicit reaction-state awareness is essential for effective abnormality detection in bioprocess monitoring systems. Full article

Gas-filled discharge capillaries are widely used in the field of plasma-based particle accelerators, due to their compactness, cost-effectiveness and versatility for different applications. Technological improvement of such plasma sources is necessary to enable high energy gain acceleration at the meter scale, as required for next-generation particle colliders and light sources. Beam quality preservation within such an acceleration length involves accurate tuning of the plasma properties. In particular, precise tailoring of the plasma density distribution is required to control the emittance growth of particle bunches during the acceleration process. In this context, this paper presents a scalable and versatile approach for the design of meter-scale discharge capillaries, aimed at achieving fine tuning of the plasma density distribution, with the possibility of locally controlling the density profile by acting on the source geometry. Forty-centimeter-long capillaries are designed using numerical fluid dynamics simulations and tested in a dedicated plasma module. Different arrangements of the gas inlets are tested, with their number and diameter varied, to assess the effect of the capillary geometry on the plasma properties. Plasma density measurements show that a higher number of inlets with variable diameter along the plasma formation channel provides an enhancement in the homogeneity of the electron plasma density distribution. Longitudinal density plateaus are observed along most of the plasma channel length, with a center-to-end density uniformity of up to 80%. The experimental results highlight the proposed approach’s capability to modulate the longitudinal plasma density distribution by acting on the capillary geometry, thus providing uniform density profiles over the meter scale, as required for plasma-based acceleration experiments. Full article

Low-dose computed tomography (LDCT) can effectively reduce ionizing radiation; however, the associated image noise and artifacts can severely compromise the accuracy of clinical diagnosis. To address the challenge of balancing noise suppression and detail preservation in LDCT images, this study proposes a deep learning (DL)-based image denoising method termed Progressive Fusion Distillation Network (PFDN). Building upon the Information Multi-distillation Network (IMDN), the proposed method incorporates a pixel attention (PA) mechanism and a progressive fusion strategy, and further designs a Pixel Parallel Extraction Block (PPEB) together with a Progressive Fusion Distillation Block (PFDB) to fully exploit multi-scale and multi-channel features, thereby optimizing the image denoising network through efficient feature separation and re-fusion. In addition, by explicitly leveraging the noise characteristics specific to LDCT images, the method establishes an end-to-end training framework suitable for medical imaging. Experimental results demonstrate that PFDN not only effectively reduces image noise and artifacts, but also enhances overall image quality while preserving diagnostically relevant image structures under the adopted evaluation setting. Full article

Wind turbine blade surface defect detection remains challenging due to large variations in defect scales, blurred edge textures, and severe interference from complex backgrounds, which often lead to insufficient detection accuracy and high false and missed detection rates. To address these issues, this paper proposes an improved RTDETR-based detection framework, termed CEA-DETR, for wind turbine blade surface defect inspection. First, a Cross-Scale Multi-Edge feature Extraction (CSME) backbone is designed by integrating multi-scale pooling and edge-enhancement units with a dual-domain feature selection mechanism, enabling effective extraction of fine-grained texture and edge features across different scales. Second, an Efficient Multi-Scale Feature Fusion Network (EMSFFN) is constructed to facilitate deep cross-level feature interaction through adaptive weighted fusion and multi-scale convolutional structures, thereby enhancing the representation of multi-scale defects. Furthermore, an adaptive sparse self-attention mechanism is introduced to reconstruct the AIFI module, strengthening global dependency modeling and guiding the network to focus on critical defect regions under complex background conditions. Experimental results demonstrate that CEA-DETR achieves mAP50 and mAP50:95 of 89.4% and 68.9%, respectively, representing improvements of 3.1% and 6.5% over the RT-DETR-r18 baseline. Meanwhile, the proposed model reduces computational cost (GFLOPs) by 20.1% and parameter count by 8.1%. These advantages make CEA-DETR more suitable for deployment on resource-constrained unmanned aerial vehicles (UAVs), enabling efficient and real-time autonomous inspection of wind turbine blades. Full article

Bone marrow cell classification and nucleus segmentation in microscopic images are fundamental tasks for computer-aided diagnosis of lymphocytic leukemia. However, bone marrow cells from different subtypes exhibit high morphological similarity, and structural information is often constrained under optical microscopic imaging, posing challenges for stable and effective feature representation. To address this issue, we propose MTLL (Multitask Model on Lymphocytic Leukemia), a novel multitask approach that performs cell classification and nucleus segmentation within a unified network to exploit their complementary information. The model constructs a hybrid backbone for shared feature representation based on a CNN-Transformer architecture, in which Fuse-MBConv modules are tightly integrated with multilayer multi-scale transformers to enable deep fusion of local texture and global semantic information. For the segmentation branch, we design an AM (Atrous Multilayer Perceptron) decoder that combines atrous spatial pyramid pooling with multilayer perceptrons to fuse multi-scale information and accurately delineate nucleus boundaries. The classification branch incorporates prior knowledge of cell nuclei structures to capture subtle variations in cellular morphology and texture, thereby enhancing the model’s ability to distinguish between leukemia subtypes. Experimental results demonstrate that the MTLL model significantly outperforms existing advanced single-task and multi-task models in both lymphocytic leukemia classification and cell nucleus segmentation. These results validate the effectiveness of the multi-task feature-sharing strategy for lymphocytic leukemia diagnosis using bone marrow microscopic images. Full article