Search Results (1,327)

Restricting the use of chemical pesticides in forestry requires the search for alternative solutions. These could be volatile organic compounds produced by three investigated species of bacteria (Bacillus amyloliquefaciens (ex Fukumoto) Priest, B. subtilis (Ehrenberg) Cohn and B. thuringiensis Berliner), which inhibit the growth of the pathogen F. oxysporum Schltdl. emend. Snyder & Hansen in forest nurseries. The highest inhibition of fungal growth (70%) was observed with B. amyloliquefaciens after 24 h of antagonism test, which had a higher content of carbonyl compounds (46.83 ± 8.41%) than B. subtilis (41.50 ± 6.45%) or B. thuringiensis (34.62 ± 4.77%). Only in the volatile emissions of B. amyloliquefaciens were 3-hydroxybutan-2-one, undecan-2-one, dodecan-5-one and tetradecan-5-one found. In contrast, the main components of the volatile emissions of F. oxysporum were chlorinated derivatives of benzaldehyde (e.g., 3,5-dichloro-4-methoxybenzaldehyde) and chlorinated derivatives of benzene (e.g., 1,4-dichloro-2,5-dimethoxybenzene), as well as carbonyl compounds (e.g., benzaldehyde) and alcohols (e.g., benzyl alcohol). Further compounds were found in the interactions between B. amyloliquefaciens and F. oxysporum (e.g., $\alpha$-cubebene, linalool, undecan-2-ol, decan-2-one and 2,6-dichloroanisole). Specific substances were found for B. amyloliquefaciens (limonene, nonan-2-ol, phenethyl alcohol, heptan-2-one and tridecan-2-one) and for F. oxysporum (propan-1-ol, propan-2-ol, heptan-2-one and tridecan-2-one). The amounts of volatile chemical compounds found in B. amyloliquefaciens or in the bacterium–fungus interaction can be used for further research to limit the pathogenic fungus. In the future, one should focus on the compounds that were found exclusively in interactions and whose content was higher than in isolated bacteria. In order to conquer an ecological niche, bacteria increase the production of secondary metabolites, including specific chemical compounds. The results presented are a prerequisite for creating an alternative solution or supplementing the currently used methods of plant protection against F. oxysporum. Understanding and applying the volatile organic compounds produced by bacteria can complement chemical plant protection against the pathogen, especially in greenhouses or tunnels where plants grow in conditions that favour fungal growth. Full article

With the ongoing advancement of industrialization and rapid urbanization, the emission of volatile organic compounds (VOCs) has increased significantly. As key precursors of PM_2.5 and ozone formation, VOCs pose a growing threat to the health of ecosystems. Due to their complex and dynamic transformation processes across air, water, and soil media, the ecological risks associated with VOCs have attracted increasing attention from both the scientific community and policy-makers. This study systematically reviews the core literature on the ecological impacts of VOCs published between 2005 and 2024, based on data from the Web of Science and Google Scholar databases. Utilizing three bibliometric tools (CiteSpace, VOSviewer, and Bibliometrix), we conducted a comprehensive visual analysis, constructing knowledge maps from multiple perspectives, including research trends, international collaboration, keyword evolution, and author–institution co-occurrence networks. The results reveal a rapid growth in the ecological impact of VOCs (EIVOCs), with an average annual increase exceeding 11% since 2013. Key research themes include source apportionment of air pollutants, ecotoxicological effects, biological response mechanisms, and health risk assessment. China, the United States, and Germany have emerged as leading contributors in this field, with China showing a remarkable surge in research activity in recent years. Keyword co-occurrence and burst analyses highlight “air pollution”, “exposure”, “health”, and “source apportionment” as major research hotspots. However, challenges remain in areas such as ecosystem functional responses, the integration of multimedia pollution pathways, and interdisciplinary coordination mechanisms. There is an urgent need to enhance monitoring technology integration, develop robust ecological risk assessment frameworks, and improve predictive modeling capabilities under climate change scenarios. This study provides scientific insights and theoretical support for the development of future environmental protection policies and comprehensive VOCs management strategies. Full article

Volatile organic compounds (VOCs) are important for teas’ quality and act as a critical evaluative criterion in teas. The distinctive aromatic profile of tea not only facilitates tea classification but also has potential applications in aroma-driven product innovation. In this review, we summarized the tea aroma from tea classification, VOCs extraction methodologies, and VOCs detection techniques. Moreover, the potential utilization of tea aroma in the future, such as applications in essential oil refinement, food flavor enhancement, and functional fragrance for personal health care, was proposed. Our review will provide a solid foundation for further investigations in tea aroma and offer significant insights into the development and application of tea fragrance. Full article

Seaweed is a sustainable ingredient that has been suggested to improve the nutritional aspects as well as the sensory properties of different food products. The objective of this study was to evaluate the flavor properties of extracts from brown seaweed (Ascophyllum nodosum) and sugar kelp (Saccharina latissimi) obtained at different temperatures. These varieties commonly grow in the Atlantic Ocean. The seaweed samples were extracted using water at three different temperatures (50 °C, 70 °C, and 90 °C). The volatile fraction of the extracts was extracted with headspace solid-phase microextraction and analyzed by gas chromatography–mass spectrometry. The headspace chemical composition varies significantly among seaweed extracts and at different extraction temperatures. Major classes of identified compounds were aldehydes, ketones, alcohols, hydrocarbons, and halogenated compounds. Extracts were also evaluated using temporal check-all-that-apply (with 84 untrained participants). The different temperatures had minimal impact on the flavour properties of the brown seaweed samples, but the extraction temperature did influence the properties of the sugar kelp samples. Increasing the extraction temperature seemed to lead to an increase in bitterness, savouriness, and earthy flavor, but future studies are needed to confirm this finding. This study continues the exploration of the flavor properties of seaweeds and identifies the dynamic flavor profile of brown seaweed and sugar kelp under different extraction conditions. Full article

Electricity markets in Europe have undergone major changes in the last decade, mainly due to the increasing share of variable renewable energy sources (RES), changing demand patterns, and geopolitical factors—particularly the war in Ukraine, tensions over energy imports, and disruptions in natural gas supplies. These changes have led to increased electricity price volatility, reducing the reliability of traditional forecasting tools. This research analyses the potential of static and adaptive linear regression as electricity price forecasting tools in the context of three countries with different energy mixes: Germany, France and the Czech Republic. The static regression approach was compared with an adaptive approach based on incremental model updates at monthly intervals. Testing was carried out in three different scenarios combining stable and turbulent market periods. The quantitative results showed that the adaptive model achieved a lower MAE and RMSE, especially when trained on data from high-volatility periods. However, models trained under turbulent conditions performed poorly in stable environments due to a shift in market dynamics. The results supported several of the hypotheses formulated and demonstrated the need for localised, flexible and continuously updated forecasting. Limitations of the adaptive approach and suggestions for future research, including changing the length of training windows and the use of seasonal models, are also discussed. The research confirms that modern markets require adaptive analytical approaches that account for changing RES dynamics and country specificities. Full article

Glancing Angle Deposition (GLAD) has emerged as a versatile and powerful nanofabrication technique for developing next-generation gas sensors by enabling precise control over nanostructure geometry, porosity, and material composition. Through dynamic substrate tilting and rotation, GLAD facilitates the fabrication of highly porous, anisotropic nanostructures, such as aligned, tilted, zigzag, helical, and multilayered nanorods, with tunable surface area and diffusion pathways optimized for gas detection. This review provides a comprehensive synthesis of recent advances in GLAD-based gas sensor design, focusing on how structural engineering and material integration converge to enhance sensor performance. Key materials strategies include the construction of heterojunctions and core–shell architectures, controlled doping, and nanoparticle decoration using noble metals or metal oxides to amplify charge transfer, catalytic activity, and redox responsiveness. GLAD-fabricated nanostructures have been effectively deployed across multiple gas sensing modalities, including resistive, capacitive, piezoelectric, and optical platforms, where their high aspect ratios, tailored porosity, and defect-rich surfaces facilitate enhanced gas adsorption kinetics and efficient signal transduction. These devices exhibit high sensitivity and selectivity toward a range of analytes, including NO₂, CO, H₂S, and volatile organic compounds (VOCs), with detection limits often reaching the parts-per-billion level. Emerging innovations, such as photo-assisted sensing and integration with artificial intelligence for data analysis and pattern recognition, further extend the capabilities of GLAD-based systems for multifunctional, real-time, and adaptive sensing. Finally, current challenges and future research directions are discussed, emphasizing the promise of GLAD as a scalable platform for next-generation gas sensing technologies. Full article

Rice wine, as a traditional fermented beverage, has its quality and flavor influenced by a combination of multiple factors. This review provides an overview of the key aspects of rice wine production, including raw material selection and processing, the regulation of quality by brewing techniques, the mechanisms of microbial community interaction during fermentation, and the types and formation mechanisms of major compounds in rice wine (including flavor compounds and non-volatile components). The study highlights that different raw materials and processing methods significantly impact the fundamental flavor profile of rice wine, while fermentation conditions and dynamic changes in microbial communities determine its flavor complexity and stability. Additionally, this review examines various factors affecting the quality and flavor of rice wine, such as fermentation environment, microbial metabolism, and control of harmful substances, and summarizes modern research and technological advancements, emphasizing the potential of digital and intelligent technologies in enhancing the quality and safety of rice wine. Finally, future research directions are proposed to promote modernization and quality improvement of the rice wine industry. Full article

Urban road travel time prediction is a critical component of intelligent transportation systems. However, accurate prediction remains challenging due to the high volatility and randomness of urban road traffic. This study compares the predictive performance of deep learning methods with traditional shallow learning methods for urban road travel time prediction, and explores the potential for improving prediction effectiveness through multi-run training strategies for hyperparameter optimization and ensemble learning. The study employs urban road travel time data from Shenzhen, China, designing 10 prediction scenarios with different sliding time window lengths and prediction scales. To address the randomness issue in deep learning models, we adopt a strategy of conducting five independent training runs for each hyperparameter configuration and using statistical measures to obtain stable performance evaluations. For model comparison, we implement multiple shallow learning models (linear regression, ridge regression, LASSO regression, random forest) and deep learning models (LSTM-DNN, DNN), and construct ensemble learning models based on linear regression and LSTM-DNN as base learners. The experimental results demonstrate that: (1) deep learning models generally outperform shallow learning models in terms of Mean Absolute Percentage Error (MAPE), particularly the LSTM-DNN model which achieves the best MAPE values across all prediction scenarios with 30 min sliding time windows; (2) in terms of Root Mean Square Error (RMSE), shallow learning models such as random forest perform better in most scenarios; (3) ensemble learning models show certain advantages in some prediction scenarios, but the improvement effects are limited and scenario-dependent. The research results provide empirical reference for method selection in urban road travel time prediction, while also revealing the limitations of single data sources and the differences in model performance under different evaluation metrics, offering directions for future research. Full article

The decarbonization of electricity generation by 2030 and the realization of a net-zero economy by 2050 are central to the United States’ climate strategy. However, large-scale renewable integration introduces operational challenges, including extreme ramping, unsafe dispatch, and price volatility. This review investigates how demand–response (DR) aggregators and distributed loads can support these climate goals while addressing critical operational challenges. We hypothesize that current DR aggregator frameworks fall short in the areas of distributed load operational flexibility, scalability with the number of distributed loads (prosumers), prosumer privacy preservation, DR aggregator and prosumer competition, and uncertainty management, limiting their potential to enable large-scale prosumer participation. Using a systematic review methodology, we evaluate existing DR aggregator and prosumer frameworks through the proposed FCUPS criteria—flexibility, competition, uncertainty quantification, privacy, and scalability. The main results highlight significant gaps in current frameworks: limited support for decentralized operations; inadequate privacy protections for prosumers; and insufficient capabilities for managing competition, uncertainty, and flexibility at scale. We conclude by identifying open research directions, including the need for game-theoretic and machine learning approaches that ensure privacy, scalability, and robust market participation. Addressing these gaps is essential to shape future research agendas and to enable DR aggregators to contribute meaningfully to US climate targets. Full article

Background/Objectives: Within their host trees, mountain pine beetles (MPBs, Dendroctonus ponderosae) interact with many fungal species, each releasing a unique profile of volatile organic compounds (VOCs). The FVOCs released by the two primary symbionts of MPBs, Grosmannia clavigera and Ophiostoma montium, have been found to enhance MPB attraction in the field and laboratory studies. Opportunistic, saprophytic fungal species, such as Aspergillus sp. and Trichoderma atroviride, are also common in MPB galleries and can negatively impact MPB fitness. However, little is known about the FVOCs produced by these fungal species and how they may impact MPB feeding and attraction. Methods: To address this knowledge gap, we characterized the FVOC profile of T. atroviride, and performed bioassays to test the effects of its FVOCs on MPB attraction and feeding activity. Results: Our chemical analysis revealed several FVOCs from T. atroviride known to inhibit the growth of competing fungal species and impact subcortical-beetle attraction. Conclusions: From those FVOCs, we recommended four compounds—2-pentanone, 2-heptanone, 2-pentanol, and phenylethyl alcohol—for use in future field tests as anti-attraction lures for MPBs. In bioassays, we also observed strong MPB repellency from FVOCs released by T. atroviride, as well as the mild effects of FVOCs on MPB feeding activity. Our findings highlight the potential for these FVOCs to be utilized in the development of more effective MPB anti-attractant lures, which are crucial for the monitoring and management of low-density MPB populations. Full article

Water scarcity is a growing global challenge, intensified by climate change, seawater intrusion, and pollution. While conventional desalination methods are energy-intensive, solar-driven interfacial evaporators offer a promising low-energy solution by leveraging solar energy for water evaporation, with the resulting steam condensed into purified water. Despite advancements, challenges persist, particularly in addressing volatile contaminants and biofouling, which can compromise long-term performance. The integration of photocatalysts into solar-driven interfacial evaporators has been proposed as a solution, enabling pollutant degradation and microbial inactivation while enhancing water transport and self-cleaning properties. This review critically assesses testing methodologies for solar-driven interfacial evaporators incorporating both photothermal and photocatalytic functions. While previous studies have examined materials and system design, the added complexity of photocatalysis necessitates new testing approaches. First, solar still setups are analyzed, particularly concentrating on the selection of materials and geometry for the transparent cover and water-collecting surfaces. Then, performance evaluation tests are discussed, with focus on the types of tested pollutants and analytical techniques. Finally, key challenges are presented, providing insights for future advancements in sustainable water purification. Full article

Background: Allergic rhinitis (AR) is a prevalent allergic disorder characterized by a complex pathogenesis. Drawing on traditional Chinese medicine theory and contemporary pharmacological principles, this study developed an inhalation-based herbal formulation, ZHW, to explore a novel non-invasive therapeutic approach. Objective: To investigate the therapeutic effects of ZHW on AR and elucidate its underlying mechanisms and potential targets through an integrated analysis of network pharmacology and proteomics. Materials and Methods: The volatile components of ZHW were analyzed by gas chromatography–mass spectrometry (GC-MS). The mouse model of AR was induced by OVA sensitization. The therapeutic efficacy of ZHW was assessed based on nasal symptom scores, histopathological examination, and inflammatory cytokine levels. Furthermore, the underlying mechanisms and potential targets of ZHW were investigated through integrated network pharmacology and proteomics analyses. Results: GC-MS analysis identified 39 bioactive compounds in ZHW. Inhalation treatment with ZHW demonstrated significant anti-allergic effects in OVA-sensitized mice, as evidenced by (1) reduced sneezing frequency and nasal rubbing behaviors; (2) decreased serum levels of IL-4, histamine, and OVA-specific IgE; (3) attenuated IL-4 concentrations in both nasal lavage fluid and lung tissue; (4) diminished nasal mucosal thickening; and (5) suppression of inflammatory cell infiltration. Integrated network pharmacology and proteomics analyses indicated that ZHW’s therapeutic effects were mediated through the modulation of multiple pathways, including the PI3K-Akt signaling pathway, the B cell receptor signaling pathway, oxidative phosphorylation, and the FcεRI signaling pathway. Key molecular targets involved Rac1, MAPK1, and SYK. Molecular docking simulations revealed strong binding affinities between ZHW’s primary bioactive constituents (linalool, levomenthol, linoleic acid, Linoelaidic acid, and n-Valeric acid cis-3-hexenyl ester) and these target proteins. Conclusions: The herbal formulation ZHW demonstrates significant efficacy in alleviating allergic rhinitis symptoms through multi-target modulation of key signaling pathways, including PI3K-Akt- and FcεRI-mediated inflammatory responses. These findings substantiate ZHW’s therapeutic potential as a novel, non-invasive treatment for AR and provide a strong basis for the development of new AR therapies. Future clinical development will require systematic safety evaluation to ensure optimal therapeutic outcomes. Full article

This study proposes a hybrid approach to analyze and forecast non-stationary financial time series by combining statistical models with deep neural networks. A model is introduced that integrates three key components: the Generalized Autoregressive Score (GAS) model, which captures volatility dynamics; an attention mechanism (ATT), which identifies the most relevant features within the sequence; and a Long Short-Term Memory (LSTM) neural network, which receives the outputs of the previous modules to generate price forecasts. This architecture is referred to as GAS-ATT-LSTM. Both unidirectional and bidirectional variants were evaluated using real financial data from the Nasdaq Composite Index, Invesco QQQ Trust, ProShares UltraPro QQQ, Bitcoin, and gold and silver futures. The proposed model’s performance was compared against five benchmark architectures: LSTM Bidirectional, GARCH-LSTM Bidirectional, ATT-LSTM, GAS-LSTM, and GAS-LSTM Bidirectional, under sliding windows of 3, 5, and 7 days. The results show that GAS-ATT-LSTM, particularly in its bidirectional form, consistently outperforms the benchmark models across most assets and forecasting horizons. It stands out for its adaptability to varying volatility levels and temporal structures, achieving significant improvements in both accuracy and stability. These findings confirm the effectiveness of the proposed hybrid model as a robust tool for forecasting complex financial time series. Full article

The phenomenon of rising forward price volatility, both historical and implied, as maturity approaches is referred to as the Samuelson effect or maturity effect. Disregarding this effect leads to significant mispricing of early-exercise options, extendible options, or other path-dependent options. The primary objective of the research is to identify a practical way to incorporate the Samuelson effect into the evaluation of commodity derivatives. We choose to model the instantaneous variance employing the exponential decay parameterizations of the Samuelson effect. We develop efficient calibration techniques utilizing historical futures data and conduct an analysis of statistical errors to provide a benchmark for model performance. The study employs 15 years of data for WTI, Brent, and NG, producing excellent results, with the fitting error consistently inside the statistical error, except for the 2020 crisis period. We assess the stability of the fitted parameters via cross-validation techniques and examine the model’s out-of-sample efficacy. The approach is generalized to encompass seasonal commodities, such as natural gas and electricity. We illustrate the application of the calibrated model of instantaneous variance for the evaluation of commodity derivatives, including swaptions, as well as in the evaluation of power purchase agreements (PPAs). We demonstrate a compelling application of the Samuelson effect to a widely utilized auto-callable equity derivative known as the snowball. Full article

The black fig fly, Silba adipata (Diptera: Lonchaeidae), is an invasive pest recently introduced to Mexico, where it has rapidly spread across fig-producing regions. Despite its economic importance, effective monitoring strategies remain poorly studied. The present study evaluated the response of S. adipata adults to visual (color) and olfactory (attractant) cues under laboratory and field conditions in fig orchards. No significant color preferences were observed in laboratory choice tests using nine colors or in field trials using traps of four different colors. In the laboratory, traps containing 2% ammonium sulfate solution, torula yeast + borax, or Captor + borax, captured similar numbers of flies, whereas CeraTrap^® was less attractive. Traps containing 2% ammonium sulfate were more effective than 2% ammonium acetate, though attraction was comparable when ammonium acetate was diluted to 0.2% or 0.02%. In the field, torula yeast + borax and 2% ammonium sulfate mixed with fig latex outperformed the 2% ammonium sulfate solution alone, although seasonal variation influenced trap performance. A high proportion of field-captured females were sexually immature. Torula yeast + borax attracted high numbers of non-target insects and other lonchaeid species, which reduced its specificity. In contrast, traps containing fig latex mixtures showed higher selectivity, although some S. adipata adults could not be sexed due to specimen degradation. These findings highlight the value of torula yeast pellets and 2% ammonium sulfate plus fig latex for monitoring this pest, but merit validation in field studies performed over the entire crop cycle across both wet and dry seasons. Future studies should evaluate other proteins, ammonium salt combinations and fig latex volatiles in order to develop effective and selective monitoring or mass trapping tools targeted at this invasive pest. Full article