Search Results (616)

Terpenes are diverse plant metabolites with essential ecological and physiological functions, yet their biosynthetic regulation in lettuce (Lactuca sativa L.) remains poorly understood. By integrating volatile profiling, genome-wide identification, and biochemical characterization of terpene synthase (TPS) genes, we elucidated how methyl jasmonate (MJ) induces terpene formation in lettuce seedlings. Headspace analysis of 10-day-old seedlings revealed that while mock-treated tissues emitted no detectable volatiles, MJ elicitation triggered the de novo production of a terpene blend dominated by (E)-β-ocimene (9.3–14.6%), (E)-β-caryophyllene (37.2–46.9%), and caryophyllene oxide (26.2–41.4%). A genome-wide search identified 54 putative LsTPS genes, often clustered with prenyl transferases or cytochrome P450 genes. Gene expression assays revealed 17 MJ-responsive LsTPS genes; among them, LsTPS21, LsTPS23, LsTPS28, LsTPS51, and LsTPS52 showed strong (>200-fold) induction, with LsTPS52 exceeding a 20,000-fold increase. Functional characterization of six recombinant enzymes demonstrated diverse substrate specificities: LsTPS8 as an α-copaene synthase, LsTPS16 as a linalool synthase, LsTPS24 as an (E)-nerolidol synthase, LsTPS21 and LsTPS23 as (E)-β-ocimene synthases, and LsTPS10 as an (E)-β-caryophyllene synthase. Phylogenetic analyses confirmed conserved domains characteristic of the TPS-a and TPS-b subfamilies. This study presents the first comprehensive framework for MJ-induced terpene biosynthesis in lettuce, offering new insights into Asteraceae terpenoid metabolism. Full article

Complex odor emissions from wastewater treatment plants consist of multiple volatile compounds that exhibit heterogeneous temporal dynamics and low linear correlations, making accurate prediction and interpretation difficult when analyzed on a single-compound basis. This study investigates whether clustering can serve not only as an exploratory tool but as an essential preprocessing step to enhance machine-learning performance in multi-odor prediction systems. A total of 22 designated odorants were continuously monitored, and their pairwise dependencies were evaluated using Pearson correlation and mutual information. Data-driven clustering was performed through K-means, hierarchical linkage, and principal-component–based latent grouping, and the resulting structures were quantitatively compared with functional-group-based chemical classifications using the consistency ratio and Jaccard similarity index. Cluster validity was further examined using the Silhouette Coefficient, Davies–Bouldin Index, and Calinski–Harabasz Index. The predictive contribution of clustering was verified by training XGBoost regression models on both raw and cluster-structured datasets. The clustered dataset yielded higher predictive accuracy, with increased R² and reduced MAE and RMSE across most odorants. SHAP analysis further confirmed that clustering improved model interpretability by stabilizing feature contributions and reducing noise-driven importance shifts. The findings demonstrate that clustering is not a supplementary diagnostic tool, but a prerequisite for building reliable, high-performance machine-learning models in complex odor systems. This integrative framework offers a methodological foundation for multi-odor forecasting, source tracking, and next-generation odor management platforms. Full article

As a cushion for the volatility of renewable energy, energy storage systems can achieve peak shaving and valley filling, thereby improving the operational efficiency and economic performance of the power grid. In addition, energy storage systems can absorb renewable energy production, thereby enhancing the safety and reliability of the electrical power system. Nowadays, energy storage systems are facing severe problems such as explosions that are caused by overcharging and discharging. The main reason for the overcharging and discharging of energy storage systems is the inconsistency in the state of the electric core in the charging and discharging process, which not only affects the safety of the electric core, but also influences the overall charging and discharging capacity of the energy storage system. To address this inconsistency of energy storage cores, this paper proposes an energy storage consistency monitoring method under the framework of clustering-classification, which adopts the Belief Peaks Evidential Clustering and Evidential K-Nearest Neighbors classification algorithm. This paper proposes a BPEC-EKNN-based method for battery inconsistency detection and localization. The proposed approach first constructs battery performance evaluation coefficients to characterize inter-cell behavioral differences, and then integrates an enhanced k-nearest neighbor strategy to identify abnormal cells. It also identifies and locates inconsistent battery cells by analyzing the magnitude of the confidence level m (Ω), without relying on predefined thresholds. Also, time-series data as opposed to the evaluation of voltage data at a singular point is engaged to realize the detection and localization of energy storage core consistency anomalies under the consideration of time-series data. The proposed algorithm is capable of identifying inconsistencies among energy storage batteries, with the parameter m (Ω) serving as an indicator of the likelihood of inconsistency. Experimental results on battery pack datasets demonstrate that the proposed method achieves higher detection accuracy and robustness compared with representative statistical threshold-based methods and machine learning approaches, and it can more accurately identify inconsistent battery cells. By applying perturbation analysis to real-time operational data, the algorithm proposed in this paper can detect inconsistencies in battery cells reliably. Full article

Photovoltaic (PV) power generation is inherently intermittent and volatile, complicating power system operation and control. Accurate forecasting is crucial for proactive grid responses and optimal energy resource scheduling. This study proposes a novel hybrid forecasting model that achieves high-precision PV power forecasting by integrating similar-day clustering, generating extreme weather samples, and optimizing the Bidirectional Temporal Convolutional Network (BiTCN) and Bidirectional Gated Recurrent Unit (BiGRU) model via the Animated Oat Optimization (AOO) algorithm. The proposed method outperforms other models in the three evaluation metrics of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). The innovations lie in the integration of similar-day clustering with deep learning and the application of AOO for hyperparameter optimization, which significantly enhances forecasting accuracy and robustness. Full article

The indoor low-voltage power line network is characterized by highly irregular interferences, where background noise coexists with bursty impulsive noise originating from household appliances and switching events. Traditional noise models, which are considered monofractal models, often fail to reproduce the clustering, intermittency, and long-range dependence seen in measurement data. In this paper, a Multifractal Random Walk (MRW) framework tailored for Power Line Communication (PLC) noise modelling is developed. MRW is a continuous time limit process based on discrete-time random walks with stochastic log-normal variance. As such, the formulated MRW framework introduces a stochastic volatility component that modulates Gaussian increments, thus generating heavy-tailed statistics and multifractal scaling laws which are consistent with the measured PLC noise data. Empirical validation is carried out through structure function analysis and covariance of log-amplitudes, both of which reveal scaling characteristics that align well with MRW-simulated predictions. This proposed model captures both the bursty nature and correlation structure of impulsive PLC noise more effectively as compared to the conventional monofractal approaches, thereby providing a mathematically grounded framework for accurate noise generation and the robust system-level performance evaluation of PLC networks. Full article

Under the ongoing electricity market reforms, short-term load forecasting (STLF) is increasingly challenged by pronounced non-stationarity driven by price fluctuations. This study proposes an adaptive STLF framework tailored to price-induced non-stationarity. Firstly, a market state identification method based on load–price joint clustering is developed to structurally model the temporal interactions between price and load. It allows the automatic extraction of typical market patterns and helps uncover how price fluctuations drive load variations. Secondly, a gated mixture forecasting network is proposed to dynamically adapt to the inertia of historical price fluctuations. By integrating parallel branches with an adaptive weighting mechanism, the model dynamically captures historical price features and achieves both rapid response and steady correction under market volatility. Finally, a Transformer-based expert model with multi-scale dependency learning is introduced to capture sequential dependencies and state transitions across different load regimes through self-attention, thereby enhancing model generalization and stability. Case studies using real market data confirm that the proposed approach delivers substantial performance improvements, offering reliable support for system dispatch and market operations. Relative to mainstream baseline models, it reduces MAPE by 1.08–2.62 percentage points. Full article

This study investigates risk contagion and dependence structures between U.S. and Chinese technology-related stock markets, focusing on the electronics and semiconductor sectors. We employ DCC-GARCH models to capture time-varying correlations and copula models to analyze nonlinear and tail dependencies. To highlight extreme risk dynamics, we extend the analysis to Value-at-Risk (VaR) series derived from a GARCH(1,1)-Skewed-t model. Empirical results reveal three major findings. First, volatility clustering and negative skewness are evident across markets, with extreme downside risks concentrated during the 2015 Chinese stock market crash and the 2020 COVID-19 pandemic. Second, copula results show stronger upper-tail dependence in cross-border broad markets and more symmetric dependence within domestic Chinese markets, while U.S. sectoral linkages exhibit the highest vulnerability during downturns. Third, dynamic copula analysis indicates that downside contagion is episodic and crisis-driven, whereas rebound co-movements are structurally persistent. These findings contribute to understanding systemic vulnerability in global technology markets. They provide insights for investors, regulators, and policymakers on monitoring cross-market contagion and managing systemic risk under stress scenarios. Full article

Against the backdrop of electricity market liberalization and deregulation, accurate electricity price forecasting is critical for optimizing power dispatch and promoting the low-carbon transition of energy structures. However, electricity prices exhibit inherent complexities such as seasonality, high volatility, and non-stationarity, which undermine the efficacy of traditional forecasting methodologies. To address these challenges, this study proposes a four-stage Decomposition-Reconstruction-Optimization-Integration (DROI) framework, coupled with an econometric breakpoint test, to evaluate forecasting performance across distinct time segments of Spanish electricity price data. The framework employs CEEMDAN for signal decomposition, decomposing complex price sequences into intrinsic mode functions to retain essential features while mitigating noise, followed by frequency-based data reconstruction; integrates the Improved Sparrow Search Algorithm (ISSA) to optimize initial model parameters, minimizing errors induced by subjective factors; and leverages Convolutional Neural Networks (CNN) for frequency-domain feature extraction, enhanced by an attention mechanism to weight channels and prioritize critical attributes, paired with Long Short-Term Memory (LSTMs) for temporal sequence forecasting. Experimental results validate the method’s robustness in both interval forecasting (IPCP = 100% and IPNAW is the smallest, Experiment 1.3) and point forecasting tasks (MAPE = 1.3758%, Experiment 1.1), outperforming naive approaches in processing stationary sequence clusters and demonstrating substantial economic utility to inform sustainable power system management. Full article

Modern power grids face increasing stress from volatile, high-dynamics loads, such as Electric Vehicle (EV) charging clusters and intermittent renewable energy sources. Accurate transformer thermal monitoring via the International Electrotechnical Commission (IEC) 60076-7 and the Institute of Electrical and Electronics Engineers (IEEE) C57.91 standards is crucial, yet their methodologies differ significantly. This study develops a comprehensive MATLAB simulation framework to quantify these differences. The analysis compares physical thermal models across multi-stage cooling—Oil Natural Air Natural (ONAN), Oil Natural Air Forced (ONAF), and Oil Forced Air Forced (OFAF)—and insulation aging models. It is demonstrated that divergence in transformer life estimation stems primarily from the physical thermal models. A ‘reversal of conservatism’ is identified, where ‘conservative’ is defined as predicting higher hot-spot temperatures and enforcing a larger safety margin. Results prove that while the IEC model is thermally more conservative during cooling failures (static mode), the IEEE model is consistently more conservative during normal active cooling. Additionally, 2D “heat maps” are presented to define safe operational zones, and the catastrophic impact of cooling system failures is quantified. These findings provide a quantitative outline for managing transformer state under increasingly demanding loading schemes. Full article

The endophytic bacterium Bacillus subtilis 10-4 is a potent bioinoculant, previously shown to enhance growth and resilience to abiotic/biotic stresses across various crops. However, the genetic basis underlying these beneficial traits remains unexplored. In this study, a whole-genome analysis of B. subtilis 10-4 was performed to gain the molecular determinants of its plant-beneficial effects. The Illumina MiSeq-based assembly revealed a genome of 4,278,582 bp (43.5% GC content) distributed across 19 contigs, encoding 4314 predicted protein-coding sequences, 42 tRNAs, and 6 rRNAs. This genomic architecture is comparable to other sequenced B. subtilis strains. The genomic annotation identified 331 metabolic subsystems with a total number of 1668 functions, predominantly associated with amino acid (281) (16.9%) and carbohydrate (247) (14.9%) metabolism. In silico genomic analysis uncovered a diverse repertoire of genes significant for plant growth and stress resilience. These included genes for colonization (i.e., exopolysaccharide production, biofilm formation, adhesion, motility, and chemotaxis), nutrient acquisition (i.e., nitrogen, phosphorus, iron, potassium, and sulfur metabolisms), and synthesis of bioactive compounds (auxins, salicylic acid, siderophores, gamma-aminobutyric acid, vitamins, and volatiles) and antimicrobials. The latter was supported by identified biosynthetic gene clusters (BGCs) for known antimicrobials (100% similarity) bacilysin, bacillaene, subtilosin A, and bacillibactin, as well as clusters for surfactin (82%), fengycin (80%), and plipastatin (46%), alongside a unique terpene cluster with no known similarity. Additionally, genes conferring abiotic stress tolerance via glutathione metabolism, osmoprotectants (e.g., proline, glycine betaine), detoxification, and general stress response were identified. The genomic evidence was consistent with observed plant growth improvements in laboratory assays (radish, oat) and a field trial (wheat) upon 10-4 inoculation. Thus, the findings elucidate the genomic background of B. subtilis 10-4’s beneficial effects, solidifying its potential for utilization as a bioinoculant in sustainable crop production under changing climate accompanied by multiple environmental stresses. Full article

The volatile profiles of beef steaks (Longissimus lumborum) were analysed both raw and grilled to five different internal temperatures, 55 °C, 60 °C, 71 °C, 77 °C, and 85 °C, representing very-rare, rare, medium rare, well-done, and very well-done, respectively. Volatile organic compounds (VOCs) were extracted using direct immersion high-capacity sorptive extraction (DI-HiSorb) and analysed by gas chromatography–mass spectrometry (GC–MS). A total of ninety-one VOCs were detected with forty-two significantly impacted by the degree of doneness, thirty of which had Variable in Projection score > 1. Principal Component Analysis produced three distinct clusters, (i) raw, (ii) very-rare, rare, and medium-rare, (iii) and well-done and very well-done. Direct immersion high-capacity sorptive extraction (DI-HiSorb) provided a comprehensive volatile profile of grilled beef steak across different degrees of doneness and revealed that the abundance of methyl esters. The main findings were that in relation to the degree of doneness methyl esters were significantly reduced, with both aldehydes and pyrazines increasing due to thermal lipid oxidation, Strecker degradation, and Maillard reaction, highlighting the significance of internal temperature on the volatile profile of steak during grilling. Full article

Transformations in HEIs (Higher Education Institutions) in recent years have positioned education alongside research, development, and innovation, creating the necessary framework for achieving a positive impact on society and economies. A Principal Factor Analysis was employed using 19 variables from eight Eastern European countries over a three-year period (2022–2024). The six main factors are noted with F1 (innovation and collaboration in R&D), F2 (performance and investment in academic research), F3 (advanced technological production and talent influx), F4 (evolution over time/systemic progress), F5 (cluster development), and F6 (investment in education). These explain over 83% of the total variance, ensuring a robust representation of the original data. The results of the analysis show, in some countries, strengths in specific areas (e.g., EE in innovation, CZ in academic research, and SK in high-tech manufacturing). Meanwhile, a general trend of decreasing scores at the systemic progress level can be observed in most nations, suggesting a slowdown in the overall development momentum. At the same time, significant volatility was observed in cluster development (F5) and investment in education (F6) across the sample. These findings provide a condensed, multidimensional framework for comparative analysis and policy formulation, highlighting specific strengths and vulnerabilities in the regional innovation landscape. Full article

This study investigated the impact of thawing methods on the roasting quality and flavor of reduced-salt marinated large yellow croaker to optimize processing protocols for frozen products. Three thawing methods, low-temperature thawing (LTT), room-temperature thawing (RTT), and flowing-water thawing (FWT), were systematically evaluated. Freshly marinated (FM) and non-thawed (WT) samples served as controls. Key parameters, including thawing efficiency, physicochemical properties, texture, color, sensory attributes, and volatile organic compounds (VOCs), were analyzed. The results showed that FWT achieved the fastest thawing (14.67 min), significantly outperforming RTT (32.57 min) and LTT (591 min) (p < 0.05). Moisture content and springiness remained stable across treatments (p > 0.05). For color parameters, lightness (L*), yellowness (b*), and browning index (BI) showed no significant variations (p > 0.05), while the total color difference (ΔE) was significantly affected by thawing methods (p < 0.05). FWT exhibited the lowest salt retention (3.49 g/100 g), a 18.8% reduction compared to WT (4.30 g/100 g). Texture analysis revealed that FWT samples maintained optimal hardness and chewiness, with sensory scores second only to WT. Volatile profiling identified distinct “thermal–oxygen–temporal” effects, referring to the respective influences of heating conditions, oxidative environments, and processing time on flavor compound formation. RTT and WT treatments significantly increased the relative 1-propanethiol and 5-methyl-2-furanmethanol (>10% increase) contents, respectively, and markedly reduced the 2-butanol levels (<0.3%) due to volatilization losses. GC-IMS and electronic nose analysis established a robust correlation network among three major VOC clusters (aldehydes/alcohols, esters/acid/sulfides, and ketones), with sensory scores showing strong positive correlations with the alkane- and aromatic-sensitive sensors (W5C/W1C) of the electronic nose (r > 0.90) and negative correlations with other sensors (r < −0.70). These findings demonstrate that FWT offers the best balance of efficiency, salt reduction, and sensory quality, making it a superior method for reduced-salt marinated large yellow croaker industrial applications. Full article

This study investigates the volatility dynamics of the Bahrain All Share Index (BAX) between 2010 and 2025, a period marked by COVID-19 and regional geopolitical shocks. Using ARMA (1,1) to model returns and four GARCH-family models (ARCH, GARCH, EGARCH, GJR-GARCH) to capture volatility, we provide new evidence from a bank-based frontier market that has received limited empirical attention. The results reveal that returns are stationary and exhibit volatility clustering. Among the competing models, EGARCH (1,1) provides the best fit—exhibiting the lowest AIC and SIC values and the highest log-likelihood—revealing a significant leverage effect whereby negative shocks generate stronger volatility than positive shocks. This asymmetric volatility pattern contradicts earlier findings for Bahrain but aligns with theoretical expectations for bank-based financial systems. The findings carry implications for investors in terms of portfolio risk management, derivative pricing, and asset allocation. They also have important implications for regulators and policymakers, suggesting that counter-cyclical buffers and interest rate adjustments could be applied to stabilize the market in anticipation of negative shocks. These insights enrich the scarce literature on volatility in small frontier markets and contribute to a more nuanced understanding of the volatility dynamics in the MENA region. Full article

With the rapid increase in the renewable energy penetration rate in distribution networks, the volatility and uncertainty on the power supply side have become prominent; thus, it is urgent to fully utilize the regulation potential of the flexible load on the user side to maintain the dynamic balance of power. A multi-level evaluation method for flexible load regulation potential based on ensemble clustering is proposed in the paper. First, a data-driven approach based on ensemble clustering is adopted to quantify the user-level regulation potential of flexible load. Second, the bus-level regulation potential of the flexible load is obtained by aggregation calculation. Finally, a quantitative evaluation of the system-level regulation potential of flexible load in the distribution network is realized by constructing three optimization models with different objectives. Case studies show that the proposed method can effectively evaluate the regulation potential of flexible load in the distribution network from multiple levels, i.e., user level, bus level, and system level. Full article