Search Results (235)

Acetaminophen, more commonly known as paracetamol (APAP), is one of the most widely used analgesics and antipyretic drugs worldwide. Its presence in the environment poses a risk to the organisms it comes into contact with, which is why it has been classified as an emerging contaminant. Given its adverse effects and continuous discharge into water bodies, it is necessary to study efficient, environmentally sustainable processes for its complete removal. Denitrification is a biological process that has been studied for the biodegradation of recalcitrant compounds and certain pharmaceuticals such as 17β-estradiol and ampicillin, transforming them into harmless products such as N₂ and HCO₃⁻. In the present study, the biodegradation of 6 mg L⁻¹ of APAP-C was evaluated through a denitrifying process. Batch experiments were conducted, achieving acetaminophen (APAP) removal efficiencies (E_APAP-C) of 83.3 ± 0.86% and nitrate removal efficiencies (E_N-NO3⁻) of 100%. The substrates were predominantly converted into HCO₃⁻ and N₂, with yields greater than 0.9, while intermediates such as NO₂⁻ were observed only transiently during the reaction. At the end of the experimental period, no secondary metabolites were detected, indicating that intermediates did not accumulate to quantifiable levels. Full article

Lettuce (Lactuca sativa L.) is an important leafy vegetable crop, yet the efficiency and reliability of genome editing platforms in lettuce remain limited, particularly for precision base editing applications. In this study, we established an optimized PEG-mediated protoplast transformation system for lettuce through systematic evaluation of key parameters, including protoplast density, incubation time, plasmid size, and transformation method. Under optimized conditions, a maximum transient transformation efficiency of up to 81% was achieved. Using this optimized protoplast platform, we comparatively evaluated the performance of three single-base editing systems—adenosine base editor (ABE), glycosylase-based guanine base editor (gGBE), and rice alkylpurine DNA glycosylase-mediated A-to-K base editor (rAKBE)—targeting the LsALS gene, encoding acetolactate synthetase as a herbicide target with great value in weed control. Among the tested editors, ABE exhibited the highest A-to-G editing efficiency, reaching 9.3%. In contrast, gGBE and rAKBE showed lower editing efficiencies. Together, this study established a robust and reproducible protoplast-based platform for transient genome editing in lettuce and provides a practical framework for the rapid evaluation of base editing tools and target sites, firstly for gGBE and rAKBE evaluation in lettuce. The optimized system facilitates functional genomics studies and supports the development of precision breeding strategies in lettuce. Full article

Gardenia jasminoides Ellis is a commercially important medicinal and ornamental plant; however, its functional genomics remain poorly understood because of the lack of efficient cell-based research tools. To address this limitation, we established an optimized method for isolating viable protoplasts from petal and mesophyll tissues of G. jasminoides and developed a polyethylene glycol (PEG)-mediated transient expression system. For petal protoplast isolation, the optimal enzyme combination consisted of 3.0% cellulase R-10 and 1.0% macerozyme R-10 supplemented with 0.5 M D-mannitol, yielding 5.26 × 10⁶ protoplasts per gram fresh weight (FW) with 80.63% viability. For mesophyll protoplast isolation, 1.5% cellulase R-10 and 0.5% macerozyme R-10 supplemented with 0.5 M D-mannitol produced 8.75 × 10⁶ protoplasts g⁻¹ FW with 84.55% viability. PEG-mediated transient transformation was optimized at 20% PEG4000 for petal protoplasts and 40% PEG4000 for mesophyll protoplasts, resulting in efficient GFP expression. This system was successfully applied to subcellular localization analyses of floral regulatory proteins (GjAP3, GjPI, and GjSEP) and defense-related proteins (GjNPR1 and GjTGA2), as well as to the validation of protein–protein interactions between GjSEP and GjPI and between GjNPR1 and GjTGA2 using bimolecular fluorescence complementation and yeast two-hybrid assays. Collectively, these results establish a reliable and species-specific protoplast-based platform for rapid functional characterization of genes in G. jasminoides, providing an effective tool for future studies on gene regulation, metabolic engineering, and molecular breeding in this horticultural plant species. Full article

Chrysanthemum (Chrysanthemum × morifolium Ramat.) is a globally popular ornamental plant, but most cultivars lack efficient petal-based transient transformation systems, limiting floral trait molecular mechanism exploration. Protoplasts are versatile tools for gene localization, interaction, and functional characterization. Here, we established a petal protoplast isolation and transient transformation system for C. morifolium ‘Wandai Fengguang’ via L₉(3⁴) orthogonal design: optimal isolation (0.6 M mannitol, 8 h enzymatic digestion time, 0.4% macerozyme R-10, 4% cellulase R-10) and transformation (40% PEG4000, 12 μg plasmid, 10 min transfection, a protoplast density of 1 × 10⁶ protoplasts mL⁻¹). Under these conditions, protoplast yield was 5.14 × 10⁶ protoplasts g⁻¹·FW, viability 87.41%, and transformation efficiency 51.50%, with good applicability for six additional germplasms. We further analyzed CmVIT1 protein localization. Compared with the previous system, this system significantly improved protoplast yield and transformation efficiency, facilitating the transient transformation of genes related to floral traits in chrysanthemum and providing a methodological framework for other horticultural crops. Full article

The increasing complexity and variability of operating conditions, along with the occurrence of low-probability cascading failures, imposes more stringent requirements on data-driven intelligent methods for power system stability analysis. This paper proposes an intelligent enhancement approach for transient voltage stability assessment in modern power systems, considering improved conditional generative adversarial network (CGAN)-based sample balancing. Firstly, an improved CGAN incorporating an enhanced feature-distance metric is developed to accurately capture the distribution characteristics of real samples, effectively alleviating training issues such as gradient vanishing and mode collapse during adversarial learning. Secondly, an intelligent sample enhancement method for transient voltage stability is established based on the improved CGAN, which effectively complements the initial dataset and ensures the predictive performance of intelligent models under extreme operating conditions. Finally, a transient voltage stability assessment framework integrating a convolutional neural network and a transformer is proposed to enable efficient extraction of low-dimensional features and achieve accurate evaluation of transient voltage stability states. Full article

Accurate fault diagnosis of rotating machinery in complex industrial environments demands an optimal trade-off between feature representation capability and computational efficiency. Existing single-modality models relying solely on 1D time-series signals or heavy 2D time-frequency images often fail to simultaneously capture high-frequency transient impacts and long-range degradation trends. CLiST (Complementary Lightweight Spatiotemporal Network), a novel lightweight multimodal framework driven by time-frequency fusion, was proposed to overcome this limitation. The architecture of CLiST employs a synergistic dual-stream design: a LightTS module efficiently extracts global operational trends from 1D vibration signals with linear complexity, while a structurally pruned LiteSwin integrated with Triplet Attention captures local high-frequency textures from 2D continuous wavelet transform (CWT) images. This mechanism establishes explicit cross-dimensional dependencies, effectively eliminating feature blind spots without excessive computational overhead. The experimental results show that CLiST not only achieves perfect accuracy on the fundamental CWRU benchmark but also exhibits exceptional spatial generalization when independently evaluated on non-dominant sensor axes of the XJTUGearbox dataset. Furthermore, validation on the real-world dataset (Guangzhou port) proves that the framework has excellent robustness to the attenuation of the signal transmission path and reduces the performance fluctuation between remote measurement points. Ultimately, CLiST delivers highly reliable AI-driven image and signal-processing solutions for vibration monitoring in industrial equipment. Full article

The vibration signals of Electro-Hydrostatic Actuators (EHAs) exhibit strong non-linearity and non-stationarity, particularly under complex coupling mechanisms, making the extraction of intrinsic fault features computationally challenging. Conventional deep learning approaches often lack mathematical interpretability and struggle to decouple superimposed fault signatures from incomplete datasets. To address these issues, this paper proposes the Enhanced Continuous Wavelet Transform Capsule Network (ECWTCN), an intelligent decoupled diagnosis framework designed for multiscale signal analysis. The architecture integrates a wavelet-kernel convolution layer to extract physically interpretable time–frequency features across multiple scales, effectively capturing transient impulses associated with incipient faults. Furthermore, a novel maximized aggregation routing algorithm is introduced to optimize the dynamic routing process, enhancing global feature aggregation. A distinct advantage of the ECWTCN is its capability to generalize distinct fault patterns, enabling the identification of unseen compound faults by training exclusively on normal and single-fault samples. Comparative experiments show that the proposed method delivers strong multi-label classification performance under operating condition A, achieving a Subset Accuracy of 93.7% and a Label Ranking Average Precision of 0.998. Complexity analysis further confirms the method’s efficiency in terms of FLOPs and parameter size. This work presents a robust, lightweight, and mathematically interpretable solution for the analysis of complex signals in high-reliability equipment. Full article

Broccoli (Brassica oleracea var. italica) is an important crop valued for its nutritional and health-promoting properties, yet its biotechnological improvement is limited by low effectivity and genotype-dependent transformation protocols. The absence of reliable transient expression systems further constrains functional genomics and genome-editing applications. Here, we optimized regeneration and transformation protocols for different broccoli genotypes. Endoreduplication patterns in young tissues were analyzed by flow cytometry to identify suitable explants, and combinations of plant growth regulators were tested to develop an efficient organogenic medium. Stable transformation was achieved via Agrobacterium tumefaciens using nptII and eGFP markers. Cotyledons and hypocotyls up to day 7 showed similar endoreduplication patterns, with abundant 2n cells, but hypocotyls exhibited higher regeneration capacity. The optimized medium supported efficient organogenesis while maintaining diploidy. Transformation efficiency reached 10.4% in ‘S1’ and 2.8% in ‘Naxos’, highlighting genotype dependence. In parallel, a transient expression system was established using cotyledon-derived protoplasts and electroporation-mediated DNA delivery. GFP expression was confirmed through fluorescence microscopy, confocal imaging, and Western blotting. These protocols provide a robust toolkit for broccoli genetic manipulation, facilitating molecular biology studies in the native plant, functional genomics and genome-editing strategies, including CRISPR-based approaches. Full article

Response latency and sensor noise are universal challenges in closed-loop control systems. In the context of semi-active suspensions, these issues also exist and manifest as critical bottlenecks. Due to the highly transient nature of road shocks, the inherent physical actuation delays of the hardware, combined with the phase lag introduced by traditional signal filtering, often cause the control response to significantly lag behind the physical excitation. To address this issue from a predictive perspective, this study proposes a Physics-Informed Gated Convolutional Neural Network (PI-GCNN) designed to predict future multi-modal energy evolution, thereby enabling feedforward control. Unlike traditional feedback mechanisms, the proposed framework employs the Continuous Wavelet Transform (CWT) to convert short-horizon inertial data into time–frequency scalograms, effectively isolating transient shock features from background vibrations. A novel physics-guided gating mechanism is embedded within the network architecture to regulate feature activation. This mechanism is trained using an asymmetric sparse physics loss, which combines L1 regularization with adaptive spectral consistency constraints to enforce noise suppression on flat roads while ensuring sensitivity to impacts. Extensive validation was conducted using high-fidelity heavy truck simulations and the public PVS 9 real-world dataset. The results confirm that the PI-GCNN achieves a predictive phase lead of approximately 100–200 ms over real-time baselines, creating a valuable actuation window for suspension dampers. Furthermore, the model demonstrates exceptional computational efficiency, with a parameter count of 0.10 M and a single-frame inference latency of 0.25 ms, making it highly suitable for deployment on resource-constrained automotive edge computing platforms. Full article

The inherent randomness, high volatility, and non-stationarity of photovoltaic (PV) power generation pose substantial threats to the stability of modern power grids. Developing high-precision forecasting models is essential for grid operation, yet conventional architectures often encounter a performance bottleneck: they struggle to simultaneously achieve high computational efficiency for long-range dependency modeling and robust perception for local, abrupt fluctuations. To address these limitations, this paper proposes HDCF-Mamba, a novel forecasting framework that resolves the feature distribution gap between long-range trends and short-term volatility. The core innovation lies in the Heterogeneous Dual-branch Cross-Fusion (HDCF) mechanism, which enables the synergetic integration of a Mamba-based global branch and a Multi-Kernel Filter Unit-based multi-scale local branch. Specifically, we integrate the Mamba Selective State Space Mechanism into the global branch to efficiently capture long-term dependencies with $O\left(L\right)$ linear complexity, fundamentally overcoming the quadratic computational bottleneck of Transformers. Meanwhile, the Multi-Scale Feature Extraction Module (MSFEM) acts as a local compensator to capture high-frequency power fluctuations caused by transient weather changes. Unlike simple hybrid models that rely on linear addition, our HDCF design utilizes a temporal concatenation mechanism to ensure non-linear alignment of these heterogeneous features. Extensive experiments on four real-world PV operational datasets (including publicly available benchmark datasets and actual photovoltaic power station monitoring data: ECD-PV, LSP-PV, APS-PV, and PSB-PV) demonstrate that HDCF-Mamba consistently outperforms state-of-the-art models, achieving a reduction in Mean Absolute Error (MAE) of up to 11.4% compared to iTransformer and 8% compared to SCINet, while maintaining superior computational efficiency. Full article

With increasingly stringent international limits on diesel particulate matter emissions, Continuous-Regeneration Particulate Filters (CRPFs) have been widely applied in heavy-duty vehicle (HDV) exhaust systems. However, their impacts on the complete gaseous pollutant profile remain insufficiently characterized. This study investigated the effects of three CRPF configurations on gaseous emissions from a China III diesel engine under the World Harmonized Transient Cycle (WHTC). Regulated pollutants (CO, total hydrocarbons (THC), NOx, and CO₂) and unregulated pollutants (benzene series compounds and aldehydes) were measured before and after CRPF installation. The results demonstrated that CRPFs achieved high reduction efficiencies for CO (98.5–99.9%) and THC (77.4–99.9%) through catalytic oxidation, while showing negligible effects on NOx (0.2–3.0% reduction) and slight increases in CO₂ (0.07–0.55%). For unregulated pollutants, aldehydes were effectively reduced (formaldehyde: 84.1–100.0%; acetaldehyde: 47.4–100.0%), whereas benzene series compounds exhibited variable responses, with some species showing increased emissions. These findings reveal complex pollutant transformation mechanisms within CRPF systems and provide references for optimizing aftertreatment configurations to meet China VI and subsequent emission standards, thereby contributing to the mitigation of air pollution, the protection of public health, and the promotion of sustainable societal development. Full article

The rapid expansion of distributed cloud platforms introduces critical security challenges, specifically non-deterministic race conditions like Time-of-Check to Time-of-Use (TOCTOU) vulnerabilities. Traditional passive detection methods often fail to identify these transient “Heisenbugs” due to the asynchronous nature of multi-threaded control planes. To address this, we propose a novel DAG-Guided Active Fuzzing framework. Our approach constructs a Directed Acyclic Graph (DAG) to map causal dependencies of API operations and implements deterministic proactive scheduling. By injecting microsecond-level delays into identified race windows, the system enforces adversarial interleavings to expose hidden order and atomicity violations. Validated on 32 verified vulnerabilities across six distributed systems (including Hadoop and OpenStack), our method achieves an overall Recall (Detection Rate) of 68.8% across the entire dataset and a peak Precision of 92% in reproducibility tests, significantly outperforming random fuzzing baselines ($p<0.01$). Furthermore, the framework maintains a low runtime overhead of 11.5%. These findings demonstrate a favorable trade-off between detection depth and system efficiency, establishing the approach as a robust toolchain for transforming theoretical concurrency risks into reproducible security findings in large-scale cloud infrastructure. Full article

The eastern Baltic region is rich in hemiboreal forests, which are both commercially important and provide habitats for rare and/or endangered forest-dwelling species, which are sensitive to accelerating climatic changes. Under the intensifying climatic disturbances that are stressing forests worldwide, sanitary logging is a widely used harvesting technique for the mitigation of commercial losses. The effects of salvage logging on the biodiversity of forests remain ambiguous due to the larger scale and higher intensity of timber harvesting, which can alter the recovery of stands and succession of their vegetation. Furthermore, EU legislation is increasingly emphasizing conservation/restoration and mandating its implementation. The recovery of ecosystems, and hence the biodiversity of disturbed managed forests, can take several decades to centuries, depending on the site conditions. Long-term (~60 years, four remeasurements) changes in the composition and structure of vegetation, as an indicator of overall health and nutrient cycling, were studied in conventionally managed hemiboreal forests. Potential forest transformation (paludification) risks associated with large-scale logging were assessed in mixed coniferous stands in the Baltics, Latvia. Following logging, the stands were conventionally managed, including artificial regeneration. According to ground cover vegetation, 50 years was the period for the disturbance effects to start subsiding, as a dynamic equilibrium was reached and the canopies of regenerating trees were closing. A gradual decrease in moisture levels in the middle parts of salvage-logged areas, and later at their edges, indicated that the stands have escaped paludification, likely as the climate has been warming. Distance from the edge of the salvage-logged areas had a secondary effect on ground cover vegetation recovery after storms, alleviating concerns about the explicit negative impact of the scale of harvesting. Thus, in managed seminatural forest landscapes with a historically small to moderate scale of anthropogenic disturbance, salvage logging at a scale locally deemed as large had a transient effect in the Baltics. This indicates successful regeneration of the forest ecosystem over a timeframe shorter than the conventional rotation period, suggesting overall conservation efficiency of conventionally managed forests. Accordingly, salvage logging can be sustainable in terms of biodiversity and forest continuity in the long run under traditional management, as environmental changes accelerate. Full article

Short-Term Residential Load Forecasting (STRLF) is a core task in smart grid dispatching and energy management, and its accuracy directly affects the economy and stability of power systems. Current mainstream methods still have limitations in addressing issues such as complex temporal patterns, strong stochasticity of load data, and insufficient model interpretability. To this end, this paper proposes an explainable and efficient forecasting framework named KAN+Transformer, which integrates Kolmogorov–Arnold Networks (KAN) with Transformers. The framework achieves performance breakthroughs through three innovative designs: constructing a Reversible Mixture of KAN Experts (RMoK) layer, which optimizes expert weight allocation using a load-balancing loss to enhance feature extraction capability while preserving model interpretability; designing an attention-guided cascading mechanism to dynamically fuse the local temporal patterns extracted by KAN with the global dependencies captured by the Transformer; and introducing a multi-objective loss function to explicitly model the periodicity and trend characteristics of load data. Experiments on four power benchmark datasets show that KAN+Transformer significantly outperforms advanced models such as Autoformer and Informer; ablation studies confirm that the KAN module and the specialized loss function bring accuracy improvements of 7.2% and 4.8%, respectively; visualization analysis further verifies the model’s decision-making interpretability through weight-feature correlation, providing a new paradigm for high-precision and explainable load forecasting in smart grids. Collectively, the results demonstrate our model’s superior capability in representing complex residential load dynamics and capturing both transient and stable consumption behaviors. By enabling more accurate, interpretable, and computationally efficient short-term load forecasting, the proposed KAN+Transformer framework provides effective support for demand-side management, renewable energy integration, and intelligent grid operation. As such, it contributes to improving energy utilization efficiency and enhancing the sustainability and resilience of modern power systems. Full article

KANECS is a 3D multigroup neutronics code based on the Simplified Spherical Harmonics (SP_N) approximation and the Continuous Galerkin Finite Element Method (CGFEM). In this work, the code is extended to solve the time-dependent neutron kinetics by implementing a fully implicit backward Euler scheme for the neutron transport equation and an implicit exponential integration for delayed neutron precursors. These schemes ensure unconditional stability and minimize temporal discretization errors, making the method suitable for fast transients. The new formulation transforms each time step into a transient fixed-source problem, which is solved efficiently using the GMRES solver with ILU preconditioning. The kinetics module is validated against established benchmark problems, including TWIGL, the C5G2 MOX benchmark, and both 2D and 3D mini-core rod-ejection transients. KANECS shows close agreement with the reference solutions from well-known neutron transport codes, with consistent accuracy in normalized power evolution, spatial power distributions, and steady-state eigenvalues. The results confirm that KANECS provides a reliable and accurate framework for solving neutron kinetics problems. Full article