Search Results (222)

Leakage identification in district heating networks is challenging because leakage-induced transient pressure waves often overlap with pressure disturbances triggered by routine operations such as valve regulation, pump speed variation, and emergency shut-off. In addition, the scarcity of high-quality labeled leakage samples limits the robustness of data-driven models under small-sample conditions. To address these issues, this study proposes a compact and moderately interpretable framework for multi-condition identification from transient pressure-wave signals, integrating signal preprocessing, handcrafted statistical feature extraction, multiclass ReliefF-based feature selection, and class-wise generative adversarial network augmentation in the selected feature space. A dataset containing four representative conditions, namely leakage, valve regulation, pump speed regulation, and emergency valve shut-off, was constructed using an integrated indoor district heating network testbed. After Hampel-based spike suppression and zero-phase Butterworth band-pass filtering within 0.5 to 300 Hz, time- and frequency-domain statistical features were extracted, and a compact subset was selected by multiclass ReliefF. A class-wise generative adversarial network was then used to augment the training set in feature space, while all evaluations were performed strictly on real samples. The results show that feature-space augmentation improves robustness and generalization under operational disturbances and noise. Using random forest as the representative classifier, Accuracy and Macro-F1 increased from 0.960 to 0.985, while leakage recall improved from 0.920 to 0.980. Further comparisons confirmed that the ReliefF-selected subset outperformed representative alternatives such as LASSO and mRMR. Overall, the proposed framework provides an effective solution for distinguishing leakage events from operational disturbances and offers practical support for online monitoring and intelligent operation of district heating networks. Full article

Under high-power disturbances such as HVDC blocking, stability strategies such as generator tripping are employed to ensure the frequency stability of the sending-end power grid. For renewable energy units, rapid emergency power reduction instead of direct tripping can quickly reduce active power and suppress frequency spikes, while maintaining grid connection to provide dynamic reactive power support, avoiding voltage collapse, and smoothly restoring power after a fault, thus improving the transient stability and resilience of a high-proportion renewable energy grid. However, the control performance of rapid emergency power reduction for wind turbines is limited by the converter’s overcurrent capacity and the unit-side load limit. Sudden large-scale active power reduction can easily cause motor speed fluctuations and mechanical stress accumulation, and may trigger current limiting and protection actions when the inverter current is saturated, or the DC bus voltage exceeds the limit, thus strictly limiting the range and duration of the adjustable power. To address the engineering requirements for rapid active power reduction in wind turbines, this paper proposes a control scheme based on low-voltage ride-through (LVRT) energy dissipation circuit reuse, and simultaneously conducts a special study on LVRT reuse conditions. When the unit receives a command to rapidly reduce active power, the scheme uses a percentage current duty cycle control strategy to drive the energy-consuming circuit to quickly dissipate excess energy. Simultaneously, it controls the pitch angle to increase at the maximum adjustment rate, thus completely eliminating excess power. This scheme leverages the existing LVRT hardware of the wind turbine to expand its functionality without requiring additional equipment. Furthermore, research on LVRT reuse conditions provides crucial support for the reliable operation of the scheme, demonstrating both outstanding economic efficiency and engineering practicality. Full article

In side-channel analysis, simple power analysis (SPA) is a widely used technique for recovering secret information by exploiting differences between operations in traces. However, in realistic measurement environments, SPA is often hindered by noise, temporal misalignment, and weak or transient leakage, which obscure secret-dependent features in single or very few power traces. In this paper, we provide a systematic analysis of moving-skewness-based trace preprocessing for enhancing asymmetric leakage characteristics relevant to SPA. The method computes local skewness within a moving window along the trace, transforming the original signal into a skewness trace that emphasizes distributional asymmetry while suppressing noise. Unlike conventional smoothing-based preprocessing techniques, the proposed approach preserves and can even amplify subtle leakage patterns and spike-like transient events that are often attenuated by low-pass filtering or moving-average methods. To further improve applicability under different leakage conditions, we introduce feature-driven window-selection strategies that align preprocessing parameters with various leakage characteristics. Both simulated datasets and real measurement traces collected from multiple cryptographic platforms are used to evaluate the effectiveness of the approach. The experimental results indicate that moving-skewness preprocessing improves leakage visibility and achieves higher SPA success rates compared to commonly used preprocessing methods. Full article

Periodic artifacts such as ringing (Gibbs), herringbone (spike/corduroy), and zipper patterns degrade the quality of brain MRI. We present a reproducible framework that (i) synthetically generates periodic artifacts with controllable severity directly in k-space, (ii) normalizes pattern orientation through a Radon-guided alignment step, and (iii) corrects them in the wavelet domain using a 2D DWT (AA/AD/DA/DD) with a band-weighted loss. The evaluation was conducted using DLBS T1-weighted 3T MRI volumes with synthetically generated periodic artifacts. It combined global image-quality metrics (SSIM, PSNR) with per-band metrics to quantify how correction concentrates on high-frequency components, and included ablation studies, mixed-artifact stress tests, and structural preservation analyses. Compared with several baseline architectures, the proposed approach shows improvements in structural fidelity and a reduction in periodic patterns (SSIM: $0.985\pm 0.022;$ PSNR: $43.337\pm 5.364$; reduction in concentrated error in high-frequency bands), while preserving unaffected structures. These findings indicate that, within a controlled synthetic benchmark, aligning the pattern orientation prior to learning and optimizing correction in the wavelet domain enables suppression of synthetically generated periodic artifacts while limiting over-smoothing. Full article

Fully maintenance-free smart collars for range cattle, sheep and deer must survive years of uncontrolled grazing under highly variable shade and motion conditions. This paper presents an ultra-low-power buck converter governed by a fast integral terminal sliding mode controller (FITSMC) with a fixed-time observer. A new reaching law retains the initial sliding manifold and a negative-power term maintains the constant switching gain to preserve robustness near the surface while attenuating chattering without widening the bandwidth. The fixed-time observer estimates the irradiance and load changes and provides a feed-forward correction, tightening the output regulation regardless of initial conditions. Load step tests with moderate resistance swings showed the proposed method recovers noticeably faster and exhibits slightly lower overshoot than a recent method based on a two-phase power reaching law, while visible inductor current spikes are also suppressed. Simulations under daily grazing profiles confirmed tight output regulation adequate for microwatt data logging and periodic long-range (LoRa) bursts. The sleep mode quiescent current remained in the 9 microamps range, eliminating the need for manual recharge across multi-season field deployments. By integrating robust power electronics with collar-grade solar harvesting, the circuit offers a truly maintenance-free energy path for untethered livestock wearables and supports sustainable precision agriculture. Full article

Background: MSCs possess strong immunoregulatory properties and play a central role in maintaining immune homeostasis by limiting inflammatory responses. Their function is highly plastic and influenced by environmental cues, including viral signals. How SARS-CoV-2-derived antigens affect MSC immunoregulation remains incompletely understood. This study aimed to investigate the impact of SARS-CoV-2 peptides on MSC-mediated immune modulation of T-cells. Methods: MSCs were stimulated directly with SARS-CoV-2 spike protein S peptides or cocultured with SARS-CoV-2 peptide-activated T-cells. TLR4 surface expression and receptor downstream signaling were assessed to evaluate pathway activation. MSC immunoregulatory function was analyzed by measuring suppression of TNF-α and IFN-γ expression and induction of CD4⁺FOXP3⁺ regulatory T-cells. TLR4 inhibition and lipopolysaccharide (LPS) stimulation were used to examine pathway specificity and interaction. Results: SARS-CoV-2 peptides activated TLR4-associated signaling in MSCs, increasing TLR4 expression and NF-κB phosphorylation. Peptide-treated MSCs showed impaired suppression of pro-inflammatory cytokines and reduced induction of regulatory T-cells. TLR4 inhibition prevented these effects. LPS induced similar effects, while combining LPS and peptide stimulation partially restored physiological T-cell cytokine suppression. Conclusions: SARS-CoV-2 peptides modulate MSC immunoregulatory function on T-cells via TLR4-dependent mechanisms. Full article

Background: Despite significant improvements in blood safety, the risk of transfusion-transmitted infections persists, particularly from emerging and re-emerging viruses. For red blood cell (RBC) products, this risk is exacerbated by the fact that there is no routine testing for many of these pathogens, and effective, commercially available pathogen inactivation technologies specifically for RBCs are still lacking. This gap in the safety framework means that viruses capable of establishing an asymptomatic viremia—a characteristic of many arboviruses like Zika, dengue, and West Nile virus—present a tangible threat to the blood supply, highlighting the need for broad-spectrum countermeasures. Study Design and Methods: This study aims to investigate the antiviral activity of green tea extract (GTE) and its key catechins, epigallocatechin gallate (EGCG) and epicatechin gallate (ECG), against ZIKV in both cellular models and red blood cell (RBC) products. In vitro antiviral activity was assessed using A549 cells treated with GTE (150 μg/mL) or purified EGCG/ECG (20 μM). Mechanistic studies focused on viral attachment inhibition. Additionally, ZIKV-spiked RBC products were co-incubated with GTE (300 μg/mL) for 1 h to evaluate virucidal effects. Erythrocyte integrity was confirmed via hemolysis assays. Results: Co-treatment with GTE or catechins suppressed ZIKV replication by ≥3.64 logs (p < 0.001) in A549 cells. GTE and catechins primarily inhibited viral attachment. In RBCs, GTE reduced viral infectivity by 99.99% (4-log reduction) without compromising erythrocyte membrane integrity or cellular viability. Furthermore, RBCs with added GTE demonstrated a lower hemolysis rate during storage for up to 60 days. Conclusions: GTE exhibits potent virucidal activity against ZIKV in blood matrices, highlighting its potential as a pathogen reduction agent to enhance transfusion safety. Further development of GTE-based additive solutions or technologies is warranted. Full article

This study investigates the multi-scale tribo–thermo–viscoelastic performance of a sustainable bio-based FormuLITE epoxy reinforced with single and hybrid carbon nanofillers (0.1 wt.% total loading) under dry sliding up to 50 N. Pin-on-disk tests at 10, 30, and 50 N showed a consistent reduction in contact pressure and wear volume in the order: neat epoxy > 0.1 CNT > 0.1 GNP > 0.1 ND > 0.1 CNT/GNP > 0.1 CNT/ND > 0.1 GNP/ND. At 50 N and 1500 m sliding distance, neat epoxy exhibited a wear volume of 13.43 mm³ and contact pressure of 13.4 N/cm², while the GNP/ND hybrid reduced wear to 4.86 mm³ and contact pressure to 6.2 N/cm², corresponding to reductions of 64% and 54%, respectively. The accelerating wear coefficient decreased from 2.9 × 10⁻⁶ to 8.5 × 10⁻⁷, confirming slower damage accumulation in hybrid systems. Time-dependent contact pressure analysis revealed reduced asymptotic intensity and suppressed mid-cycle pressure spikes, indicating enhanced tribolayer stability. Effective surface hardness increased from 0.18 GPa (neat epoxy) to 0.30 GPa (GNP/ND), while normalized wear decreased from 1.00 to 0.36. Enhanced damping behavior and improved thermal conductivity in hybrid systems promoted stress redistribution and minimized flash-temperature localization. An interfacial energy-partition framework calibrated to experimental wear data quantitatively linked effective driving pressure, tribofilm stabilization, and surface hardness to material removal. The results demonstrate that wear mitigation in sustainable bio-epoxy systems is governed by coupled mechanical, viscoelastic, and thermal energy redistribution, with GNP/ND hybrids providing the most stable tribological interface under severe sliding. The findings contribute to the development of durable and sustainable bio-epoxy composite systems for engineering applications, supporting broader goals of responsible material utilization and sustainable industrial innovation aligned with the United Nations Sustainable Development Goals (SDG 9 and SDG 12). Full article

The clinical severity of COVID-19 is influenced by cellular and humoral immune responses, as well as the dynamics of viral replication. In line with this, the current study examined systemic and mucosal immunity responses alongside viral load in unvaccinated SARS-CoV-2-infected individuals during the period of Omicron predominance. Between 2022 and 2023, when Omicron prevalence was at its peak, 48 SARS-CoV-2-positive cases with varied severity were recruited using positive PCR testing, and 48 negative controls were recruited using negative PCR testing at Moi Teaching and Referral Hospital, Kenya. Severe patients showed higher viral loads and systemic anti-spike IgG levels compared to moderate and asymptomatic individuals. Asymptomatic individuals had higher mucosal anti-spike IgG and receptor-binding domain (RBD) levels compared to severe patients. Systemic IFN-α mRNA transcripts were higher in asymptomatic individuals compared to patients with severe COVID-19 and healthy individuals. Severe patients had significantly lower expression of IFN-γ mRNA transcript levels in both blood and mucosa, as well as significantly lower systemic IFI-16 mRNA transcript levels. These findings reflect associations observed in a cross-sectional design and should not be interpreted as causal mechanisms. Suppressed interferon responses, both mucosal and systemic, were associated with severe disease. In conclusion, high systemic IgG and viral loads and low interferon responses were closely linked to severe COVID-19 outcomes. Full article

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection induces heterogeneous immune responses that influence both acute disease severity and long-term immune remodeling. A key question in the context of infection and vaccination is whether SARS-CoV-2 exerts direct oncogenic effects or instead acts as a transient immunological stressor capable of reinforcing tumor-permissive pathways. Current evidence does not support classical viral oncogenesis. Rather, severe infection is characterized by early interferon (IFN) imbalance followed by NF-κB-dominant inflammatory amplification, promoting sustained IL-6/JAK–STAT3 and MAPK signaling, chronic cytokine production, metabolic reprogramming, and impaired antitumor immune surveillance. At the molecular level, viral structural proteins modulate host signaling networks. The spike (S1) protein engages TLR2/TLR4–MyD88 pathways, activating NF-κB and MAPK cascades, while the membrane (M) protein reinforces NF-κB–STAT3 circuits linked to epithelial–mesenchymal transition and inflammatory gene expression. These mechanisms intensify pre-existing oncogenic signaling without initiating malignant transformation. Tissue-specific responses are further shaped by IFN competence, renin–angiotensin system balance, and metabolic context. In parallel, immune evasion programs shared by chronic viral infection and cancer, including checkpoint upregulation, impaired antigen presentation, and suppressive myeloid expansion, may be transiently reinforced following severe infection. In contrast, SARS-CoV-2 vaccination induces spatially restricted, self-limited innate activation without sustained inflammatory signaling or persistent antigen exposure. By preventing severe disease and chronic immune dysregulation, vaccination interrupts pathways hypothesized to intersect with cancer biology, with no evidence of increased cancer incidence. Ongoing longitudinal studies are required to clarify the long-term oncologic implications of post-infectious immune remodeling. Full article

This study proposes a wide-input high-step-up DC–DC converter with high efficiency and high voltage conversion ratio. Two coupled inductors were adopted to achieve a parallel-charging and series-discharging energy transfer mechanism, and a voltage multiplier circuit was integrated to increase the voltage gain. The proposed topology uses a single pulse width modulation signal to drive two main switches synchronously, resulting in a low switch count and simple control circuit, concurrently achieving a wide input voltage range of 24 V to 48 V. The proposed converter comprises an active switched inductor combined with a voltage multiplier circuit, achieving a high voltage gain without relying on high duty cycle operation or high-turns-ratio design. The leakage energy of the coupled inductors was recycled through a passive-clamp circuit, effectively suppressing the voltage spikes of the switching devices and reducing their voltage stress. Finally, a 1 kW converter was implemented to verify the feasibility of the proposed topology through steady-state analysis, circuit simulation, and hardware experiments. The maximum efficiencies achieved were 94.7% and 96.2% at input voltages of 24 V and 48 V, respectively. Full article

A robust analytical method based on Captiva EMR-Lipid solid-phase extraction and HPLC-MS/MS was developed and validated for the simultaneous determination of 19 aromatic amine antioxidants (AAs) and two p-phenylenediamine-derived quinones (PPD-Qs) in human plasma. The optimized protocol effectively removed phospholipid interferences from complex blood matrix, significantly mitigating ion suppression and improving the recovery of hydrophobic AAs compared to conventional liquid–liquid extraction. Method validation demonstrated good accuracy (spike recoveries: 73.0–96.8%), precision (RSD < 11%), and sensitivity with method detection limits ranging from 0.81 to 21 pg/mL. The method was successfully applied to plasma samples from 20 adults, in which 11 AAs were detected at total concentrations of 240–710 pg/mL. Diphenylamine derivatives, particularly bis(4-tert-butylphenyl)amine (DBDPA) and diphenylamine (DPA), were identified as the predominant compounds, contributing over 69% of the total AA burden. No PPDs or PPD-Qs were detected, which may be attributed to their biotransformation and urinary excretion, as well as the limited sample size. This study provides a comprehensive biomonitoring tool for assessing combined human exposure to multiple AAs and establishes a foundation for further investigation into their health implications. Full article

Dynamic Vision Sensor (DVS) is an event-based imaging technology inspired by biological photoreceptors, which holds great promise for edge computing. The event streams produced by DVS are often contaminated by Background Activity (BA) noise and hot-pixel noise, which degrade downstream processing. Existing filters typically use fixed parameters, resulting in poor adaptability to changing illumination. In this paper, we propose a lightweight Adaptive Event-based Filtering Spiking Neural Network (AEFSNN) to address these limitations. Inspired by homeostatic plasticity, AEFSNN dynamically adjusts neuronal thresholds by monitoring the input-to-output spike ratio, allowing the network to autonomously converge to an optimal operating point across different lighting conditions. Furthermore, we introduce a novel neuronal wake-up mechanism that inhibits processing neurons until triggered by valid input, which effectively suppresses redundant events generated by neighboring activity. Experiments show that AEFSNN is more robust under varying illumination. Compared with current filters, our method increases the Signal-to-Noise Ratio (SNR) of the output data by 1.42–2.33 dB. Additionally, the filtered data improves classification accuracy on downstream tasks, validating its practical value for neuromorphic vision systems. Full article

Recent advances in artificial intelligence have made power efficiency a primary objective in system design. In this context, stochastic computing (SC), which processes probabilistic bitstreams using simple logic, and spiking neural networks (SNNs), a neuromorphic paradigm, have gained prominence as alternative approaches. This study proposes a Stochastic Computing Neural Network (SC-NN) framework that minimizes the intrinsic errors of stochastic computing and leverages the isomorphism between one-count operations on bitstreams and spike-rate computations in spiking neural networks, yielding improvements in accuracy and hardware efficiency. In contrast to earlier studies that utilized independent random number sequences of 10 bits or higher, our study employed a practically implementable 8-bit linear feedback shift Register (LFSR)-based pseudo-random bitstream. Using 4 taps and 255 seeds improves the realism of the hardware. Despite the inherent accuracy ceiling of pseudo-random sequences, the proposed method achieves higher accuracy. Applied to an 8-bit SC-based neural network accelerator, the proposed design improves accuracy by 35% over a conventional FSM baseline, while reducing power and area by 43.8% and 17.2%, respectively, and decreasing delay by 5.5%. These improvements translate to a 2.3× enhancement in the Figure of Merit (FoM), which was further verified through physical layout and FPGA results. Overall, this work introduces a new paradigm that enables simultaneous gains in accuracy and efficiency for low-power AI by suppressing the error sources and embedding the structural similarity between SNNs and SC into the design. Full article

Digital transformation in water conservancy and urban water utilities demands perception systems that are accurate, fast, and energy-efficient and maintainable over long service lifecycles at the edge. We present HydroSNN, a neuromorphic computer-vision framework that couples an event-driven sensing pipeline with a spiking-transformer backbone to support monitoring of canals, reservoirs, treatment plants, and buried pipeline networks. By reducing always-on compute and unnecessary data movement, HydroSNN targets sustainability goals in smart water infrastructure: lower operational energy use, fewer site visits, and improved resilience under harsh illumination and weather. HydroSNN introduces three novel components: (i) spiking temporal tokenization (STT), which converts asynchronous events and optional frames into latency-aware spike tokens while preserving motion cues relevant to hydraulics; (ii) physics-guided spiking attention (PGSA), which injects lightweight mass-conservation/continuity constraints into attention weights via a differentiable regularizer to suppress physically implausible interactions; and (iii) cross-modal self-supervision (CM-SSL), which aligns RGB frames, event streams, and low-cost acoustic/vibration traces using masked prediction to reduce annotation requirements. We evaluate HydroSNN on public water-surface and event-vision benchmarks (MaSTr1325, SeaDronesSee, DSEC, MVSEC, DAVIS, and DDD20) and report accuracy, latency, and an operation-based energy proxy. HydroSNN improves mIoU/F1 over strong CNN/ViT baselines while reducing end-to-end latency and the estimated energy proxy in event-driven settings. These efficiency gains are practically relevant for off-grid or power-constrained deployments and support sustainable development by enabling continuous, low-power monitoring and timely anomaly response. These results demonstrate that event-driven spiking vision, augmented with simple physics guidance, offers a practical and efficient solution for resilient perception in smart water infrastructure. Full article