Search Results (8,383)

In order to solve the miscalculation problem caused by the distortion and loss of fault information caused by the traditional transmission grid fault diagnosis method due to the severe meteorological environment, a transmission grid fault diagnosis method based on multi-source information fusion is proposed. Firstly, the pulse fault degree, amplitude fault degree and meteorological fault degree are obtained by analyzing the switching, electrical and meteorological information from multiple sources using the binary reasoning spiking neural P systems, Hilbert–Huang transform and meteorological fusion methods, respectively. Then, the fault diagnosis results are obtained by fusing the various fault degrees using the analytic hierarchy process. Finally, simulation experiments are conducted on the standard IEEE39-bus system built by PSCAD simulation software, and the results verify the feasibility and effectiveness of the proposed diagnosis method in this paper. Full article

Renewable energy technologies have historically been offered market support to facilitate their deployment and aid the transition away from fossil fuels. This work shows the costs of subsidising utility-scale renewable electricity generation in the UK, focusing on wind, solar and tidal stream technologies in the Renewables Obligation (RO) and Contracts for Difference (CfD) schemes. The subsidy of each technology is calculated using published data, including an estimate of committed costs over the full project lifetime, which is not always assessed. For the technologies considered, the RO supported 24.8 GW of installed capacity at a lifetime cost of about £103 bn. To date, CfD have been awarded for 45.3 GW of wind, solar and tidal stream, with total lifetime cost of £40 bn, although this is sensitive to future gas generation costs, with a range of £8–71 bn. The CfD scheme offers better value for money to consumers than the previous RO schemes, and this is true for all technologies assessed. By design, the CfD also helps to insulate billpayers from spikes in the wholesale market caused by high fossil fuel prices, decoupling the costs of electricity from gas. Credible scenarios for future deployment out to 2050 are also presented, along with discussion of potential socioeconomic benefits and the mechanisms to achieve these. Full article

IoT devices operate as integrated systems spanning hardware, firmware/software layers, and communication layers. In operational settings, many faults and performance degradations are emergent: they arise from cross-layer interactions, workload changes, and telemetry artifacts, rather than a single physics-of-failure mechanism. These realities make traditional supervised fault classification difficult because labeled fault data are rarely available during deployment, and the fault surface is unknown and a priori. This paper presents a practitioner-oriented, label-free fault detection and diagnosis (FDD) pattern based on Dynamic Time Warping (DTW) for rapid implementation in production IoT telemetry. The method represents a device as a sequence of overlapping episodes and organizes telemetry into interpretable layers (hardware sensors, communication health proxies, and software/firmware-derived KPIs). A reference library of regular episodes is built from an assumed-healthy training window; new episodes are scored using constrained DTW distances against this library, while retaining per-layer and per-channel contributions for attribution. We show that production performance depends strongly on operational parameterization, including episode length, DTW constraints, robust threshold learning, and temporal validation. Within a verified-healthy evaluation window, the tuned configuration achieves an AUROC of 0.97 for the temporally structured faults DTW is suited to (bias, drift, and interaction faults, with spikes detected at an AUROC of 0.93), detecting 100% of injected faults, with a mean delay under 25 min. We further show that constant-value (stuck-at) and missing-data (dropout) faults fall outside DTW’s shape-matching scope (AUROC about 0.66) and are better served by complementary variance- and missingness-based detectors, a consequence of DTW’s shape-matching scope rather than a parameter choice. This work contributes a system-level methodological framework for deploying DTW as an IoT fault-detection-and-diagnosis capability: an episode-and-layer architecture aligned with hardware, communication, and software/firmware ownership; a label-free reference library requiring only assumed-healthy data; per-layer and per-channel attribution for cross-domain triage; and a reproducible operational tuning procedure. Together, these deliver a fast-to-deploy, scalable, and accurate first-line detector for label-scarce IoT systems. Full article

The migrating myoelectric complex (MMC) is the electrical basis of fasting small intestinal motility. Although oxytocin (OT) regulates gastrointestinal functions through oxytocin receptors (OTRs), its effect on jejunal MMC activity during fasting remains unclear. This study investigated the effects of OT on jejunal MMC activity in fasted rats and evaluated the involvement of OTRs, glucagon-like peptide-1 receptors (GLP-1Rs), and nitric oxide (NO) pathways. Bipolar electrodes were implanted at three jejunal sites in adult male Sprague Dawley rats for MMC recordings. After recovery and 18 h fasting, OT was administered intraperitoneally (4–32 µg/kg) following one hour of basal recording. To assess mechanisms, rats were pretreated with the OTR antagonist atosiban (2 mg/kg), the GLP-1R antagonist exendin (9–39) (200 µg/kg), or the nitric oxide synthase inhibitor NG-nitro-L-arginine (L-NNA; 5 mg/kg) before OT (16 µg/kg). Oxytocin dose-dependently reduced spike frequency and MMC cycle number (p < 0.05–0.001 vs. vehicle). Atosiban completely reversed these effects (p < 0.001 vs. OT), while exendin (9–39) partially attenuated them (p < 0.01–0.001 vs. OT). L-NNA showed no significant effect. These findings indicate that OT inhibits jejunal MMC activity via OTR-dependent mechanisms with partial involvement of GLP-1R signaling but not NO pathways. Full article

Spiking neural networks (SNNs) are well suited for modeling temporally evolving information due to their event-driven and dynamic neuronal mechanisms. Nevertheless, the majority of existing SNN topologies are constructed through algorithmic procedures rather than guided by constraints from biological brain connectivity, which weakens their biological plausibility. In our earlier work, we developed a spiking neural network (SNN) by incorporating topological information from functional brain networks extracted from functional magnetic resonance imaging (fMRI) data of healthy individuals, and named the resulting model fMRISNN. Nevertheless, the fMRI data used in previous work were resting-state fMRI. Compared with resting-state fMRI, task-state fMRI can capture brain-region coordination patterns induced by specific task stimuli, and the resulting functional brain network is therefore more closely related to the corresponding task. Motivated by this advantage, this study replaces the resting-state topology used in previous fMRISNN studies with a task-state, number/digit-related fMRI topology and validates the resulting Task-fMRISNN on handwritten digit recognition. The experimental results demonstrate that the proposed Task-fMRISNN outperforms the Rest-fMRISNN in terms of recognition accuracy, lesion robustness, and noise robustness. In addition, the Task-fMRISNN achieves significantly better performance than several baseline models constructed using algorithmically generated topologies. While deep convolutional neural networks (CNNs) may deliver superior absolute recognition performance, the proposed fMRISNN provides a more compact model structure and shows potential resource-efficiency advantages due to its sparse and event-driven computational characteristics. Full article

Identifying soil nutrient limiting factors and fertilization effects in the irrigated silty soil region of Ningxia is key to improving wheat (Triticum aestivum L.) quality and yield. A field experiment was conducted with five treatments: conventional fertilization (TF), recommended fertilization (RF), nitrogen deficiency (RF-N), phosphorus deficiency (RF-P), and potassium deficiency (RF-K). The results showed that under RF, soil nutrients remained at relatively high levels, with no significant differences compared with TF. In contrast, RF-N significantly reduced soil mineral nitrogen, total nitrogen, and organic matter compared with TF, and inhibited plant growth, photosynthesis, and plant accumulation of nitrogen, phosphorus, and potassium. Wheat yields under RF and RF-K showed no significant differences from those under TF, whereas RF-N and RF-P significantly reduced yields by 42.68% and 22.69%, respectively, relative to RF, mainly due to decreases in spike length and grain number per spike. The increase in yield was associated with synergistic increases in grain number per spike, spike number per hectare, and spike length. Yield components were significantly positively correlated with soil organic matter, total phosphorus, and mineral nitrogen, with soil total phosphorus identified as the environmental factor most strongly associated with wheat yield. Grain protein content was significantly positively correlated with soil mineral nitrogen, while starch content was significantly negatively correlated, indicating that mineral nitrogen is a key factor regulating grain quality. In summary, nitrogen fertilizer is the primary limiting factor in this region. Applying nitrogen, phosphorus, and potassium together synergistically enhances wheat yield by increasing soil total phosphorus levels and improves grain quality by regulating soil mineral nitrogen. Thus, this combined fertilization strategy provides a foundation for precise nutrient management and the simultaneous improvement of both yield and quality. Full article

The seamless integration of real-time operational technology (OT) with big data architectures remains a critical bottleneck in developing robust robotic Digital Twins. Furthermore, evaluating stochastic motion planners strictly within pristine simulations obscures vital real-world challenges such as sensor noise, communication latency, and mechanical stress. This study presents a high-throughput, real-time Hardware-in-the-Loop (HIL) pipeline integrating ROS 2, Apache Kafka, and Functional Mock-up Units (FMUs). Using a UR10e manipulator in a constrained industrial environment, we conducted extensive physical benchmarking of 11 Open Motion Planning Library (OMPL) algorithms across 10 repetitions, generating a comprehensive dataset of 785,192 samples. The proposed IT/OT architecture achieved deterministic millisecond-level synchronization, bounding end-to-end communication latency between 0.09 and 15.51 ms. Physical executions revealed a macroscopic “topological divergence” between simulation and reality, with spatial deviations peaking at 457.65 mm due to real-world point-cloud noise. While algorithms like EST and KPIECE demonstrated optimal geometric efficiency (e.g., a mean path length of 14.57 m) and hardware-friendly dynamics, traditional planners like RRT generated severe inertial spikes of up to 100 N, demonstrating substantial unsuitability for continuous industrial deployment. The primary contribution is a methodologically novel, rigorously validated big data pipeline and the release of an open-source, 50 Hz multimodal dataset (spatial, temporal, and dynamic forces), bridging the sim-to-real gap and providing a foundational benchmark for future data-driven robotic applications. Full article

Environmental stressors such as poor water quality, overstocking, and temperature spikes force shrimp to divert energy from growth and immunity to maintain homeostasis, increasing their susceptibility to opportunistic pathogens. Despite this risk, information on how these conditions affect viral and bacterial abundance, diversity, and community structure in shrimp farms remains scarce. To address this gap, this study offers a broad metagenomic analysis of the viral and bacterial communities in a shrimp farm, uncovering their diversity and ecological significance. In total, 13,572 viral operational taxonomic units (vOTUs) were recovered. Most viruses belonged to the realm Duplodnaviria, with Caudoviricetes dominating the libraries. Additionally, some contigs were linked to the Iridoviridae, a family that can affect fish and shrimp. Taken together, these findings highlight the critical role of virus–host interactions in marine environments and underscore the utility of metagenomic analysis for monitoring and safeguarding aquaculture health. Full article

Background: SARS-CoV-2 fusion inhibitory peptides represent promising antiviral candidates. Recently, a 19-mer peptide (PN19)—designed in our laboratory to mimic the internal fusion peptide of the SARS-CoV-2 spike S2 subunit—demonstrated potent antiviral activity and stable conformational features. Objectives: To investigate how this antiviral activity depends on membrane interactions, we designed synthetic PN19 lipopeptide derivatives and evaluated their efficacy against SARS-CoV-2 replication. Methods: Lipopeptides were synthesized by conjugating cholesterol to either the N- or C-terminus of the PN19 peptide, utilizing a Gly/Ser pentapeptide (GSGSG) and/or various polyethylene glycol (PEG) spacers. Antiviral activity against SARS-CoV-2 variants was evaluated by plaque reduction assays, and cytotoxicity was assessed in Vero E6 cells. Results: The lipopeptides exhibited potent inhibitory activity at sub-micromolar concentrations. Compared to the unmodified PN19 peptide, antiviral efficacy was significantly enhanced by cholesterol conjugation at either terminus. Evaluation of six PN19 lipopeptides bearing the GSGSG sequence and different PEG spacers revealed that C-terminal cholesterol conjugation yielded higher antiviral activity than N-terminal derivatives. Furthermore, thirteen shorter PN19 lipopeptide derivatives (8–13-mers) confirmed this robust efficacy, which was most pronounced with C-terminal cholesterol conjugation and further enhanced by the spacers. Noteworthy, all tested PN19 lipopeptides displayed broad activity against multiple SARS-CoV-2 variants in the absence of cytotoxicity. Conclusions: Collectively, peptides conjugated with cholesterol at the C-terminus emerged as highly potent inhibitors of SARS-CoV-2, likely driven by enhanced peptide–membrane interactions. These findings warrant further investigation to fully elucidate the role of lipidation in the inhibitory mechanism, supporting the development of novel antiviral lipopeptides for SARS-CoV-2 therapy. Full article

The translocation of sucrose into spike grains during the grain-filling stage directly affects rice yield and quality. The sugar transporters OsSWEET11 and OsSWEET15 are key sucrose transporters essential for rice (Oryza sativa L.) grain filling. To elucidate their effects on grain traits, we analyzed sequence polymorphisms of these two genes in 139 landrace rice varieties from Yunnan, China, and conducted association and haplotype analyses. Our results indicated that grain filling degree was closely associated with grain shape, where wider grains negatively impacted grain plumpness. The association analysis revealed eight significant SNPs: six located in the coding region of OsSWEET15 that influenced grain length, thickness, density, and 1000-grain weight (TGW), while two SNPs in OsSWEET11 affected TGW and the thickness of milled rice grains. Haplotype analysis further validated these trait associations: OsSWEET15 Hap2 and Hap3 conferred longer grains (with Hap2 additionally increasing TGW and Hap3 enhancing grain density/plumpness), whereas Hap1 produced narrower and thicker grains. Consistently, OsSWEET11 Hap2 was also linked to higher TGW. The superior haplotypes identified here deepen our understanding of the genetic basis of rice grain filling and serve as potential molecular markers for marker-assisted rice breeding. Full article

Price spikes are rare but economically significant events observed across electricity, financial, commodity, and cryptocurrency markets. Their abrupt magnitude, heavy-tailed distributions, and severe class imbalance make them difficult to forecast using conventional time-series methods. This systematic literature review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, synthesises recent deep learning approaches to forward-looking price-spike prediction and classification. Searches of Scopus, Web of Science, and IEEE Xplore identified studies published between 2020 and 2026. Following screening and full-text eligibility assessment of approximately 300 studies, only 20 met the inclusion criteria and were included in the final synthesis, comprising 19 peer-reviewed papers and one doctoral thesis. The review develops a structured taxonomy spanning spike definitions, task formulations, model architectures, input design, and evaluation practices. A central finding is that predictive performance is driven more by problem formulation, label construction, and evaluation design than by model architecture. While architectures have diversified to include recurrent networks, transformers, graph neural networks, and hybrid frameworks, improvements are often attributable to differences in how the prediction problem is defined rather than the models themselves. Key limitations stem from inconsistent spike definitions and insufficient treatment of class imbalance, leading to a misalignment between modelling objectives and evaluation practices, further exacerbated by the absence of standardised benchmarks. These issues hinder comparability and can lead to overstated model performance by masking poor detection of rare but economically critical spike events. The review therefore identifies clear directions for future research, including standardised spike labelling, adoption of rare-event-appropriate evaluation frameworks, and problem formulations that explicitly target extreme-event prediction. Full article

Background/Objectives: A current debate addresses where syntactic Merge primarily resides: the left-hemisphere posterior inferior frontal gyrus (IFG) or the temporo-parietal cortex. For proponents of the former, the temporo-parietal cortex supports more effortful processing; for the latter, the IFG supports integration and conflict resolution. We examine aperiodic activity in processing wh-filler-gap dependencies in French for evidence from network dynamics addressing engagement in syntax across L1 and L2. Methods: We extracted aperiodic activity 1/f components (considering offsets as a reflection of neuronal spiking and exponents as a reflection of excitatory–inhibitory balance) out of power spectrum density at 0.5–40 Hz across occipital and bilateral frontal and temporo-parietal regions of interest (ROIs) in reading. Results: Greater exponents arose in temporo-parietal than frontal ROIs in L1 and L2, with strong spiking and regulation suggested by greater offsets and exponents in the occipital ROI in L2—unlike L1—and with potential modulation by L1–L2 representation overlaps. These patterns suggest distributed cell assemblies for L1 and L2 processing. Increased regulation in temporo-parietal ROIs across L1 and L2 cell assemblies might suggest a structural function across temporo-parietal cortices in syntactic processing. Conclusions: Aperiodic activity reflecting connectivity in L1 and L2 processing supports distinct L1 and L2 cell assemblies, with L2 patterns suggesting potential overlap between L1 and L2 circuit modules. Greater exponents in bilateral temporo-parietal ROIs across L1 and L2 indicate increased regulation, supporting the engagement of lateralized temporo-parietal cortices in computations. These effects are discussed by considering advances in syntactic theory and the biology of language readiness. Full article

A capacitive screen-printed electrode immunosensor operating in non-faradaic mode by dispensing redox probes was developed for the Coronavirus 2 Spike (S) protein. This new strategy enabled direct detection of the S protein by measuring changes in the electrochemical capacitance resulting from antigen–antibody interactions on the electrode surface, altering interfacial dielectric properties. To enhance analytical sensitivity and provide an electrode surface with attractive capacitive and conductive properties, an in-house graphite ink-based screen-printed electrode was developed and subsequently modified with a polypyrrole (PPy) layer in bulk-synthesized in the presence of Cetyltrimethylammonium bromide (CTAB). CTAB acted as a dispersing and structure-directing agent, promoting homogeneous distribution and guiding the PPy polymerization, resulting in a composite with improved charge density storage and high conductivity. Analytical signals of the S proteins in spiked serum were detected by measuring the Specific Capacitances taken from cyclic voltammograms. This capacitive immunosensor achieved a linear range from 1 to 100 µg/mL (R² = 0.989, p < 0.05), with a limit of detection of 0.45 µg/mL of S protein, which falls within the clinical range for COVID-19 diagnostics. Probe-free detection without ferri/ferrocyanide steps minimizes errors by probe adsorptions and is easy to use as a point-of-care, unlike conventional immunosensors. Full article

Antigen detection provides rapid and convenient diagnosis of respiratory infections. This study develops an innovative dual-fluorescence aptasensing method based on polydopamine-functionalized MXene (PDA-MXene) for the simultaneous detection of spike protein and hemagglutinin protein. The method employs green- and red-emitting quantum dot (QD) probes as fluorescence reporters, and the PDA-MXene as an effective adsorption and separation substrate. Coupled with a centrifugation-assisted separation strategy, this design method reduces background interference and enhances detection reliability. The method demonstrates good analytical performance, with detection limits of 0.82 ng/mL for spike protein and 2.11 ng/mL for hemagglutinin protein in single-channel mode. The dual-channel mode enables reliable and simultaneous quantification of both target proteins with minimal spectral cross-talk. Furthermore, this method exhibits high specificity against interferents including ions, proteins, and toxins. Artificial saliva, chosen as real sample, is spiked with target proteins to investigate the practical applicability of the method, showing recovery rates for both target proteins between 100 and 114 sensing strategy is simple to operate and allows the detection of new targets by simply replacing the azide-modified aptamer lyophilized powder. It therefore holds promising application for the simultaneous detection of multiple proteins in point-of-care testing and health monitoring fields. Full article

Sowing density affects the tillering and the number of spikes, which are important wheat yield components. Meanwhile, biostimulants stimulate plant growth and development, which usually improves the yield and grain quality. In our experiment, we investigated the impact of different grain sowing densities (200, 250, 300, 350, 400 and 450 grains m⁻²) and the timing of application of Methylobacterium symbioticum Pascual et al. 2021 bacteria (control, tillering, stem elongation) on winter wheat (“RGT Kilimanjaro” variety) grain yield size and quality. The three-year experiment (2022/2023–2024/2025) was conducted in a split-plot design. The content of macroelements in the soil (Haplic Cambisol) was high, and the contents of micronutrients were medium or low. It was shown that varied weather conditions modified plant responses in individual years. In general, along with the increase in canopy density, the physiological parameters of plants (Fv/Fm, Fv/Fo, PI, RC/ABS), gas exchange parameters (Gs, E, Ci, PN) and SPAD index values. The highest grain yield was obtained in 2023, and the yield in 2025 was significantly lower by 0.39 t ha⁻¹. On average, in the conducted experiment, the best results were obtained with a sowing density of 350 grains m⁻² and 400 grains m⁻². The yields obtained at these densities were 8.21 t ha⁻¹ and 8.34 t ha⁻¹, respectively. However, the highest protein content (14.6%) was identified at a sowing density of 300 grains m⁻². The application of M. symbioticum bacteria, especially in the stem elongation stage, had a positive effect on the yield as well as on the grain protein and gluten content. In contrast, antioxidant capacity was generally higher in the control treatment, while total phenols and flavonoids were most favorably affected by biostimulant application at the tillering stage. PCA and Pearson correlation analysis revealed an inverse relationship between physiological performance and antioxidant-related traits, indicating that climatic variability played an important role in modulating bioactive compound accumulation. Overall, moderate sowing densities combined with M. symbioticum application at stem elongation improved wheat productivity and grain quality, while antioxidant-related traits were mainly influenced by environmental conditions. Full article