Search Results (1,225)

Schmallenberg virus (SBV) is a negative-sense RNA virus transmitted by insect vectors, causing arthrogryposis-hydranencephaly syndrome in newborn ruminants. Since its discovery in Germany and the Netherlands in 2011, SBV has rapidly spread across multiple European countries, resulting in significant economic losses in the livestock industry. With the increasing global animal trade and the expanded range of insect transmission, the risk of SBV introduction into non-endemic regions is also rising. As the gold standard for serological testing, the virus neutralization test (VNT) is crucial for tracking the spread of SBV and evaluating the efficacy of vaccines. However, in non-endemic regions, the lack of local viral strains and the biosafety risks associated with introducing foreign strains pose challenges to the implementation of VNT. In this study, we employed reverse genetics techniques using vesicular stomatitis virus (VSV) to substitute the VSV G protein with the envelope glycoproteins of SBV, thereby successfully generating and rescuing the recombinant virus rVSVΔG-eGFP-SBVGPC. The recombinant virus was then thoroughly characterized in terms of SBV Gc protein expression, viral morphology, and growth kinetics. Importantly, rVSVΔG-eGFP-SBVGPC exhibited SBV-specific cell tropism and was capable of reacting with SBV-positive serum, enabling the measurement of neutralizing antibody titers. The results suggest that this recombinant virus can serve as a feasible alternative for SBV neutralization tests, with promising potential for application in serological screening and vaccine evaluation. Full article

Power flow analysis of low-voltage network (LVN) is one of the most crucial methods for achieving refined management of such networks. To accurately calculate the three-phase (TP) probabilistic power flow (PPF) distribution in LVN, this paper first draws on the injection-type Newton method; by leveraging TP power measurements relative to the neutral point obtained from smart meters, the injected power is expressed in terms of injected current equations, thereby establishing TP power flow models for various components within the low-voltage distribution transformer area grid. Subsequently, addressing the stochastic fluctuation models of load power and photovoltaic output, this paper employs conventional numerical methods and an improved Latin hypercube sampling technique. Utilizing linearized power flow equations and based on the improved semi-invariant method (SIM) and Gram–Charlier (GC) series fitting, a calculation method for three-phase PPF in low-voltage distribution transformer area grids using the improved semi-invariant is proposed. Finally, simulations of the proposed three-phase PPF method are conducted using the IEEE-13 node distribution system. The simulation results demonstrate that the proposed method can effectively perform three-phase PPF calculations for the distribution transformer area grid and accurately obtain probabilistic distribution information of the TP power flow within the grid. Full article

This paper establishes a rigorous theoretical framework for analyzing the existence and uniqueness of solutions to Cohen–Grossberg bidirectional associative memory neural networks (CGBAMNNs) incorporating four distinct types of time-varying delays: leakage, neutral, distributed, and transmission delays. This study makes three key contributions to the field: First, it overcomes the fundamental challenge posed by the system’s inherent inability to be expressed in vector–matrix form, which previously limited the application of standard analytical techniques. Second, the work develops a novel and generalizable methodology that not only proves sufficient conditions for solution existence and uniqueness but also, for the first time in the literature, provides an explicit representation of the unique solution. Third, the proposed framework demonstrates remarkable extensibility, requiring only minor modifications to be applicable to a wide range of delayed system models. Theoretical findings are conclusively validated through numerical simulations, confirming both the robustness of the proposed approach and its practical relevance for complex neural network analysis. Full article

In response to the urgent global call for “dual carbon” targets, the sustainable transformation of public museums has become a focal issue in both academic research and engineering practice. This study proposes and empirically validates an integrated management framework that unites digital twin modeling, artificial intelligence, and green energy systems for next-generation green smart museums. A unified, closed-loop platform for data-driven, adaptive management is implemented and statistically validated across distinct deployment scenarios. Empirical evaluation is conducted through the comparative analysis of three representative museum cases in China, each characterized by a distinct integration pathway: (A) advanced digital twin and AI management with moderate green energy adoption; (B) large-scale renewable energy integration with basic AI and digitalization; and (C) the comprehensive integration of all three dimensions. Multi-dimensional data on energy consumption, carbon emissions, equipment reliability, and visitor satisfaction are collected and analyzed using quantitative statistical techniques and performance indicator benchmarking. The results reveal that the holistic “triple synergy” approach in Case C delivers the most balanced and significant gains, achieving up to 36.7% reductions in energy use and 41.5% in carbon emissions, alongside the highest improvements in operational reliability and visitor satisfaction. In contrast, single-focus strategies show domain-specific advantages but also trade-offs—for example, Case B achieved high energy and carbon savings but relatively limited visitor satisfaction gains. These findings highlight that only coordinated, multi-technology integration can optimize performance across both environmental and experiential dimensions. The proposed framework provides both a theoretical foundation and practical roadmap for advancing the digital and green transformation of public cultural buildings, supporting broader carbon neutrality and sustainable development objectives. Full article

Against the backdrop of accelerating global energy transition, developing high-performance energy-storage systems is crucial for achieving carbon neutrality. Traditional electrode materials are limited by a single densification storage mechanism and low conductivity, struggling to meet demands for high energy/power density and a long cycle life. Carbon/high-entropy alloy nanocomposites provide an innovative solution through multi-component synergistic effects and cross-scale structural design: the “cocktail effect” of high-entropy alloys confers excellent redox activity and structural stability, while the three-dimensional conductive network of the carbon skeleton enhances charge transfer efficiency. Together, they achieve synergistic enhancement via interfacial electron coupling, stress buffering, and dual storage mechanisms. This review systematically analyzes the charge storage/attenuation mechanisms and performance advantages of this composite material in diverse energy-storage devices (lithium-ion batteries, lithium-sulfur batteries, etc.), evaluates the characteristics and limitations of preparation techniques such as mechanical alloying and chemical vapor deposition, identifies five major challenges (including complex and costly synthesis, ambiguous interfacial interaction mechanisms, lagging theoretical research, performance-cost trade-offs, and slow industrialization processes), and prospectively proposes eight research directions (including multi-scale structural regulation and sustainable preparation technologies, etc.). Through interdisciplinary perspectives, this review aims to provide a theoretical foundation for deepening the understanding of carbon/high-entropy alloy composite energy-storage mechanisms and guiding industrial applications, thereby advancing breakthroughs in electrochemical energy-storage technology under the energy transition. Full article

With increasing energy demand and advancing carbon neutrality goals, arid regions—key areas for centralized photovoltaic (PV) station development in China—urgently require efficient and accurate remote sensing techniques to support spatial distribution monitoring and ecological impact assessment. Although numerous studies have focused on PV station extraction, challenges remain in arid regions with complex surface features to develop extraction frameworks that balance efficiency and accuracy at a regional scale. This study focuses on the Inner Mongolia Yellow River Basin and develops a PV extraction framework on the Google Earth Engine platform by integrating spectral bands, spectral indices, and topographic features, systematically comparing the classification performance of support vector machine, classification and regression tree, and random forest (RF) classifiers. The results show that the RF classifier achieved a high Kappa coefficient (0.94) and F1 score (0.96 for PV areas) in PV extraction. Feature importance analysis revealed that the Normalized Difference Tillage Index, near-infrared band, and Land Surface Water Index made significant contributions to PV classification, accounting for 10.517%, 6.816%, and 6.625%, respectively. PV stations are mainly concentrated in the northern and southwestern parts of the study area, characterized by flat terrain and low vegetation cover, exhibiting a spatial pattern of “overall dispersion with local clustering”. Landscape pattern indices further reveal significant differences in patch size, patch density, and aggregation level of PV stations across different regions. This study employs Sentinel-2 imagery for regional-scale PV station extraction, providing scientific support for energy planning, land use optimization, and ecological management in the study area, with potential for application in other global arid regions. Full article

Using wheat flour milling (WFM) co-products in pig diets may reduce feed cost. Still, energy digestibility is lower for WFM co-products than for feed grains. Inadequate information exists about their fermentation characteristics and the relationship between digestible energy (DE) value and chemical characteristics or in vitro energy digestibility. The objectives were to (1) determine the chemical characteristics, in vitro and in vivo DE values, and energy digestibility of WFM co-products in growing pigs; (2) determine their in vitro microbial fermentation characteristics, and (3) establish relationships between in vivo DE value of WFM co-products and their chemical composition, fermentation characteristics, or in vitro digestibility values. Across Canada, 94 WFM co-products were sampled and characterized for their chemical composition and in vitro dry matter (DM) and energy digestibility using pepsin, pancreatin, and a multi-enzyme complex containing arabinase, β-glucanase, hemicellulase, xylanase, and cellulase. The in vivo energy, DM digestibility and DE value of 9 WFM co-products (2 shorts, 5 millrun, 1 middling, and 1 bran) were determined using a corn-based diet and 40 growing pigs in two periods to obtain 8 observations per diet. After in vitro digestion, the 9 WFM co-product samples were subjected to microbial fermentation using fresh fecal inoculum in a cumulative gas-production technique. The WFM co-products had a high content of crude fiber (up to 7.9% in shorts, 9.9% in millrun, 7.1% in middlings, and 12.0% in bran) and crude protein (CP; up to 27.8% in shorts, 20.0% in millrun, 22.1% in middlings, 15.9% in bran). The DE values ranged from 2.84 to 3.74 Mcal/kg DM among WFM co-products. Among chemical characteristics, neutral detergent fiber was the best predictor (R² = 0.81) for in vivo DE value, followed by crude fiber (R² = 0.78), and acid detergent fiber (R² = 0.72). The in vitro DE values predicted (R² = 0.80) in vivo DE values of 9 WFM co-products. Based on principal component analysis, total gas and short-chain fatty acid production varied among WFM co-products and was associated with the CP content of WFM co-products. In conclusion, WFM co-products contain high crude protein and have a high DE value for growing pigs but vary substantially in nutritional value. Full article

This investigation explored the chemical constituents and biological activities of the steam-distilled oil of L. usitatissimum (SDOLU), employing sophisticated techniques including LC-HRMS, GC-MS, and GC-FID. The analysis identified a diverse array of 17 phenolic compounds, with linoleoyl chloride (64.05%) and linoleic acid (10.39%) as the major fatty acid components. The SDOLU demonstrated remarkable antioxidant capacity, effectively neutralizing free radicals in both DPPH^• (IC₅₀: 19.80 μg/mL) and ABTS^•+ (IC₅₀: 57.75 μg/mL) scavenging assays, alongside robust electron-donating activity in reducing ability tests. Moreover, the SDOLU showed significant inhibition of key enzymes implicated in metabolic and neurodegenerative disorders, including α-amylase (IC₅₀: 531.44 μg/mL), acetylcholinesterase (IC₅₀: 13.23 μg/mL), and carbonic anhydrase II (IC₅₀: 281.02 μg/mL). Collectively, these results highlight the SDOLU as a valuable natural source of multifunctional bioactivities with potential applications in combating oxidative stress and enzyme-related global diseases. Further studies are warranted to validate its therapeutic efficacy and expand its industrial utilization. Full article

This study examines the performance of a GaN HEMT with a 150 nm gate length, fabricated on silicon carbide, across various operational modes, including direct current (DC), radio frequency (RF), and small-signal parameters. The evaluation of DC, RF, and small-signal performance under diverse bias conditions remains a relatively unexplored area of study for this specific technology. The DC characteristics revealed relatively little I_ds at zero gate and drain voltages, and the current grew as V_gs increased. Essential measurements include I_dss at 109 mA and I_dssm at 26 mA, while the peak g_m was 62 mS. Because transconductance is sensitive to variations in V_gs and V_ds, it shows “Vth _roll-off,” where Vth decreases as V_ds increases. The transfer characteristics corroborated this trend, illustrating the impact of drain-induced barrier lowering (DIBL) on threshold voltage (Vth) values, which spanned from −5.06 V to −5.71 V across varying drain-source voltages (V_ds). The equivalent-circuit technique revealed substantial non-linear behaviors in capacitances such as C_gs and C_gd concerning V_gs and V_ds, while also identifying extrinsic factors including parasitic capacitances and resistances. Series resistances (R_gs and R_gd) decreased as V_gs increased, thereby enhancing device conductivity. As V_gs approached neutrality, particularly at elevated V_ds levels, the intrinsic transconductance (g_mo) and time constants (τ_gm, τ_gs, and τ_gd) exhibited enhanced performance. f_t and f_max, which are essential for high-frequency applications, rose with decreasing V_gs and increasing V_ds. When V_gs approached −3 V, the S₂₁ and Y₂₁ readings demonstrated improved signal transmission, with peak S₂₁ values of approximately 11.2 dB. The stability factor (K), which increased with V_ds, highlighted the device’s operational limits. The robust correlation between simulation and experimental data validated the equivalent-circuit model, which is essential for enhancing design and creating RF circuits. Further examination of bias conditions would enhance understanding of the device’s performance. Full article

This study investigated the impact of preparing lesson plans and conducting microteaching activities—aligned with the learning outcomes of the mathematics curriculum—on the development of sustainability beliefs among teacher candidates. The rationale behind this research stems from the growing global emphasis on sustainability and the urgent need to embed sustainability literacy into teacher education programs, particularly in disciplines such as mathematics, which are often perceived as abstract and value-neutral. There is a recognized gap in equipping pre-service teachers with the pedagogical skills and conceptual awareness needed to integrate sustainability meaningfully into mathematics instruction. Employing a mixed-methods design, the Sustainability Belief Scale was administered to 45 teacher candidates (22 in the experimental group and 23 in the control group) as both a pre-test and post-test. During the intervention, participants in the experimental group collaboratively designed lesson plans and delivered them through microteaching sessions. Throughout the process, they maintained individual reflective journals. The lesson plans and microteaching performances were evaluated using instructor-developed rubrics. Data were analyzed using both quantitative statistical techniques and qualitative content analysis. The findings indicate that integrating sustainability themes into mathematics education significantly enhances teacher candidates’ sustainability beliefs and informs their pedagogical orientations. This study underscores the importance of structured, practice-based learning experiences—such as sustainability-focused microteaching—as a means to develop the competencies needed for education for sustainable development in mathematics classrooms. Full article

The interaction of manganese(II) with deprotonated 3,5–dibromo–salicylaldehyde (3,5–diBr–saloH) in the absence or the presence of the N,N′-donors 2,2′–bipyridylamine (bipyam), 2,2′–bipyridine (bipy), 1,10–phenanthroline (phen), and 2,9–dimethyl–1,10–phenanthroline (neoc) as co-ligands yielded five neutral mononuclear complexes, namely Mn(3,5-diBr-salo)₂(CH₃OH)₂] (complex 1), [Mn(3,5-diBr-salo)₂(bipyam)] (complex 2), [Mn(3,5-diBr-salo)₂(bipy)] (complex 3), [Mn(3,5-diBr-salo)₂(phen)] (complex 4), and [Mn(3,5-diBr-salo)₂(neoc)] (complex 5), respectively. The resultant complexes were characterized with physicochemical and spectroscopic techniques, and single-crystal X-ray crystallography was applied to determine the crystal structure of complex 2. The evaluation of the potential biological profile of the complexes focused on the interaction with linear calf-thymus (CT) DNA, and bovine (BSA) and human (HSA) serum albumin. According to the data derived, the complexes interact intercalatively and strongly with CT DNA and associate tightly and reversibly with both albumins studied. Full article

This paper proposes the expressions for the motor airgap torque harmonics induced by a cascaded H-bridge inverter operating with failed cells. These variable frequency drive systems (VFDs), are widely used in oil and gas applications, where a torsional vibration evaluation is a critical challenge for field engineers. This paper proposes mathematical expressions that are crucial for an accurate torsional analysis during the design stage of VFDs, as required by international standards such as API 617, API 672, etc. By accurately reconstructing the electromagnetic torque from the stator voltages and currents in the (αβ0) reference frame, the obtained expressions enable the precise prediction of the exact locations of torque harmonics induced by the inverter under various real-world operating conditions, without the need for installed torque sensors. The neutral-shifted and peak-reduction fault-tolerant control techniques are commonly adopted under faulty operation of these VFDs. However, their effects on the pulsating torques harmonics in machine air-gap remain uncovered. This paper fulfils this gap by conducting a detailed evaluation of spectral characteristics of these fault-tolerant methods. The theoretical analyses are supported by MATLAB/Simulink 2024 based offline simulation and Typhoon based virtual real-time simulation results performed on a (4.16 kV and 7 MW) vector-controlled induction motor fed by a 7-level cascaded H-bridge inverter. According to the theoretical analyses- and simulation results, the Neutral-shifted and Peak-reduction approaches rebalance the motor input line-to-line voltages in the event of an inverter’s failed cells but, in contrast to the normal mode the carrier, all the triplen harmonics are no longer suppressed in the differential voltage and current spectra due to inequal magnitudes in the phase voltages. These additional current harmonics induce extra airgap torque components that can excite the lowly damped eigenmodes of the mechanical shaft found in the oil and gas applications and shut down the power conversion system due torsional vibrations. Full article

Microalgae-based carbon sequestration is promising for implementing carbon neutrality and reducing greenhouse gas emissions. However, as the technology remains in its early developmental stages, it presents a range of risks that may deter potential investors. To address these risks, this study proposes a group-based decision-making framework for the risk evaluation of microalgae carbon sequestration projects. Fifteen risk indicators are identified and categorized into four groups, including economic, technical, market, and environmental. To handle uncertainty and vagueness in the assessment, the framework uses trapezoidal fuzzy numbers and hesitant fuzzy linguistic sets to evaluate benchmark values. An expert credibility model is developed to assign weights to expert opinions by combining the subjective RANCOM method and the objective centroid method, both adapted for a fuzzy linguistic environment. A generalized aggregation operator is then used to combine expert evaluations. This operator integrates weighted and ordered averaging techniques and converts probabilistic linguistic terms into trapezoidal fuzzy numbers. The final risk level is determined using a fuzzy comprehensive evaluation method. The results indicate a medium-high level of risk, with a similarity score of 0.960. This suggests that while microalgae carbon sequestration holds great promise, effective planning and risk management are essential. For project managers and investors, this proposed framework helps quantify risk. It provides practical guidance for improving decision-making and strengthening project management. Full article

Harnessing the synergistic potential of graphene oxide-doped titanium dioxide (GO-TiO₂), this study pioneers an advanced photocatalytic approach by incorporating graphene oxide-doped titanium dioxide (GO-TiO₂) as a catalyst to enhance the photocatalytic degradation of levofloxacin (LVX), with optimisation of parameters using response surface methodology (RSM) and artificial neural networks (ANNs). By adjusting key operational parameters such as catalyst dosage, LVX concentration, pH, and percentage dopant in TiO₂, the study aimed to maximise degradation efficiency. The RSM statistical model highlighted optimal conditions, i.e., neutral pH, 0.1 g/g dopant, 1.1 g/L catalyst, and 25 ppm LVX concentration, achieving a degradation efficiency close to 80% (R² = 0.88). An ANN model was also developed, offering a three-layer neural network that accurately predicts LVX degradation under varied conditions, with R² reaching 0.97. Current modelling techniques frequently fail to strike a balance between practical insights for optimising photocatalytic degradation and predictive accuracy. By combining the parametric insights of RSM with the nonlinear predictive power of ANN, this study closes that gap and develops a sustainable, data-driven framework for effectively breaking down pharmaceutical pollutants and developing environmentally friendly wastewater treatment methods. Full article

The increasing frequency of structural failures on construction sites emphasizes the critical role of rigorous supervision in ensuring the quality of both construction processes and materials. Current regulatory frameworks mandate the production of detailed supervision reports to provide comprehensive evaluations of construction quality, material compliance, and site records. This study proposes a novel approach to harnessing unstructured reports for automated quality assessment. Employing text mining techniques, a sentiment lexicon specifically tailored for quality performance evaluation was developed. A corpus-based manual classification was conducted on 291 relevant words and 432 sentences extracted from the supervision reports, assigning sentiment labels of negative, neutral, and positive. This sentiment lexicon was then utilized as fundamental information for the Quality Performance Evaluation Model (QPEM). To validate the efficacy of the QPEM, it was applied to supervision reports from 30 construction sites adhering to legal standards. Furthermore, a Pearson correlation analysis was performed with the actual outcomes based on the legal requirements, including quality test failure rate, material inspection failure rate, and inspection management performance. By leveraging the wealth of unstructured data continuously generated throughout a project’s lifecycle, this model can enhance the timeliness of inspection and management processes, ultimately contributing to improved construction performance. Full article