Search Results (20,498)

Market indices serve as a benchmark for performance comparison, guide asset allocation decisions, and reflect overall market sentiment and economic conditions, thereby influencing investment strategies by representing a segment of the market. Unquestionably, investor sentiment impacts price movement. In this paper, the objectives were to study the effectiveness of the Money Flow Index (MFI) in enhancing the performance of predictive analysis by capturing market psychology, developing an investment strategy, and analyzing the performance of the method mentioned. This study applies machine learning algorithms with technical indicators and optimizes portfolio allocation based on three notable market indices in Southeast Asia (SEA): SET50 in Thailand, STI in Singapore, and VN30 in Vietnam. Firstly, we combined technical indicators with machine learning—Support Vector Classifier (SVC), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—by comparing datasets with and without MFI over the period from 2013 to 2023. The results showed that XGBoost with MFI delivered the best predictive performance across three indices. These findings indicate that MFI significantly enhances prediction accuracy, even during volatile market conditions (COVID-19). Additionally, the predictions were integrated into the Markowitz Mean-Variance (MV) model to construct an optimal portfolio, which was then benchmarked against an equal-weight portfolio (1/N). Ultimately, the findings demonstrate that incorporating the machine learning predictions into the MV framework efficiently generates wealth. Full article

Understanding how quickly trading liquidity recovers after volatility shocks is central to evaluating market resilience and trading costs in financial markets. The purpose of this study is to examine how quickly trading liquidity recovers after volatility-based stress shocks in an emerging equity market and to evaluate whether recovery horizons vary systematically across shock severity, market fear, downside-risk conditions, and sectors. Using a balanced panel of NIFTY-50 firms over 2018–2024, comprising 91,350 firm-day observations, the analysis employs a non-parametric event-time framework, combined with bootstrap inference and episode-level regression diagnostics, to trace the adjustment in market liquidity following episodes of elevated volatility. Liquidity conditions are measured using the Amihud illiquidity indicator, while stress episodes are identified through firm-specific volatility shocks derived from a standardised realised-volatility measure. The framework introduces duration-based recovery metrics—liquidity half-life and time-to-normalisation—to quantify the persistence of post-shock trading frictions relative to firm-specific pre-stress baselines. Across 602 declustered stress episodes, liquidity deteriorates sharply on the stress day and recovers only gradually thereafter. The estimated mean recovery half-life is slightly above five trading days, while nearly one-third of episodes do not fully normalise within twenty trading days, indicating economically meaningful persistence in post-shock illiquidity. Recovery dynamics also vary systematically across stress severity, market-wide fear conditions (India VIX), downside-risk regimes, and sectors, highlighting that market resilience is state-dependent rather than uniform. The findings provide new evidence on the temporal structure of liquidity adjustment in emerging equity markets and introduce operational recovery-horizon metrics that can inform liquidity risk management, trading execution strategies, and market surveillance during periods of elevated volatility. These recovery-horizon measures have direct practical relevance for portfolio managers and institutional traders because they provide an operational basis for planning execution strategies when market liquidity remains impaired after volatility shocks. They are also useful for exchanges and regulators seeking to complement volatility monitoring with post-shock liquidity surveillance, thereby improving the assessment of market functioning during periods of elevated stress. Full article

High-frequency surface acoustic wave (SAW) devices require piezoelectric thin films combining strong electromechanical coupling, high acoustic velocity, and compatibility with scalable fabrication. Lithium niobate (LiNbO₃) is a promising material, but the growth of high-quality thin films remains challenging because of lithium volatility and process-control issues. In this work, chemical beam epitaxy (CBE) was investigated as an alternative route for the deposition of c-axis-oriented LiNbO₃ thin films on C-plane sapphire at a relatively low growth temperature of 400 °C. Structural characterization confirmed high crystalline quality, with clear (006) and (0012) XRD reflections and a rocking-curve full width at half maximum of 0.04°. To evaluate acoustic performance, a SAW delay line and a one-port resonator were fabricated on 350 nm thick films using e-beam lithography. The devices operated in the 1–3 GHz range and exhibited electromechanical coupling factors of about 0.3% for the Rayleigh mode at 1.7 GHz and 3% for the Sezawa mode at 2.75 GHz. Propagation velocities ranged from 5094 to 8250 m/s, and the Rayleigh-mode resonator quality factor reached about 500. These results demonstrate the feasibility of CBE-grown LiNbO₃ films for SAW device applications. Full article

Dry-farmed vineyards of Vitis vinifera L. cv. País in central–southern Chile represent one of the oldest viticultural systems in the Americas; however, objective compositional evidence supporting valley-scale typicity remains limited. This single-vintage study evaluated whether dry-farmed País wines from the Maule and Itata valleys exhibit compositional and sensory differences under standardized winemaking conditions. Ten monovarietal wines (2018 vintage; n = 5 per valley) were produced by controlled microvinification and analysed for general chemistry, phenolic composition, polysaccharides, chromatic attributes (CIELAB), and volatile compounds (SPME–GC–MS), together with descriptive sensory analysis by a trained panel. Total phenols (~1.2 g GAE L⁻¹), anthocyanins (~130 mg malvidin-3-glucoside equivalents L⁻¹), and tannins were low and comparable between valleys. However, differences were observed in specific compositional domains: Maule wines showed higher flavanols, polysaccharides, and aldehydes, whereas Itata wines exhibited higher ester levels. Sensory evaluation revealed differences in colour intensity, floral aroma, retronasal red-fruit notes, and astringency. Multivariate analysis (PCoA) revealed a structured but partial separation between valleys; however, this pattern was not supported by PERMANOVA, indicating limited statistical evidence for multivariate differentiation. These findings, based on a single vintage, suggest subtle compositional and sensory differences rather than strong valley-level typicity. Full article

Wuhua Yellow Chicken (WYC) is a Guangdong heritage breed known for its characteristic “three yellow” phenotype and distinctive meat flavour. Despite its commercial importance, data on muscle flavour chemistry remain scarce. In this study, 180 one-day-old chicks (90 cocks, 90 hens, 18 replicates of 5 chickens per sex) were raised to 20 weeks under cage conditions, after which slaughter traits, meat physicochemical indices, proximate composition, amino acid and fatty acid profiles, and volatile compounds were measured. Cocks were heavier and had higher eviscerated yields and leg muscle percentages, whereas hens accumulated more abdominal fat (6.47–0.46%, p < 0.01). Shear force was greater in cock breast muscle (2.86–2.13 kg·f, p < 0.01), indicating firmer texture. Cock breast muscle contained more crude protein (26.89%) and less crude fat. Amino acid totals were identical between sexes (21.10 g/100 g), with all six essential amino acids surpassing FAO/WHO reference values; lysine scored highest (168%). Unsaturated fatty acid proportions were 63.33% (cocks) and 66.64% (hens), with PUFA/SFA ratios of 61.95% and 53.60%, respectively. Gas chromatography-mass spectrometry identified 10 volatile compounds in cocks and 14 in hens; aldehydes dominated in both, with hexanal alone accounting for over 50%. Hen muscle contained a richer volatile profile, including additional ketone and ester compounds. These data collectively confirm that WYC is nutritionally dense, organoleptically appealing, and well-suited for further breed promotion. Full article

Recent studies have shown that common household activities produce characteristic patterns in indoor air pollutants, enabling activity inference using environmental measurements alone. However, pollutant-based approaches are usually formulated as flat multi-class classification problems, even though indoor environments are dominated by long baseline periods with no emission-generating activity, leading to false alarms and unstable predictions. This work proposes a gated hierarchical inference framework for recognizing activities from indoor air quality data. A first-stage gate detects whether a time window contains activity-induced pollutant dynamics, while a second-stage classifier conditionally identifies the specific activity only when activity relevance is detected. Multivariate time-series measurements of particulate matter, volatile organic compounds, nitrogen oxides, carbon dioxide, temperature and relative humidity were collected using a portable monitoring system during controlled household cooking and cleaning experiments. Temporal windows were processed using recurrent neural network models in both stages. By separating activity detection from activity identification, the proposed method aligns inference with the physical generation of indoor pollutant signals and improves robustness in baseline-dominated monitoring scenarios while maintaining reliable discrimination among activities. The framework supports unobtrusive activity recognition and enables applications in exposure-aware monitoring and intelligent indoor environmental management. Full article

This paper develops a deep reinforcement learning framework for cryptocurrency portfolio management in which transaction costs are derived from the Riemannian geometry of the underlying volatility model rather than assumed constant. A Proximal Policy Optimisation agent is trained on a reward function grounded in non-equilibrium thermodynamics: we use the free-energy Bellman equation, in which transaction costs are the geodesic slippage on the Fisher information manifold of a maximum-entropy Markov-switching GARCH model, and regime-transition costs are the Wasserstein-2 distance between the calm and turbulent return distributions. A thermodynamic Carnot bound on portfolio efficiency is established and empirically validated. Five hypotheses are tested across Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash over January 2017 to March 2026. The geometric-cost agent achieves statistically superior Sharpe ratios relative to flat-fee baselines on four of five assets; portfolio turnover is reduced by 56 to 83 percent relative to signal-following; the thermodynamic friction point at which the agent prefers no-trade is asset-specific and ordered by turbulent half-life; a joint topological and geometric circuit breaker reduces Maximum Drawdown by 28 to 38 percent; and ablation confirms that every component of the observation vector contributes a statistically significant performance gain. The framework requires liquid cryptocurrency markets with validated parametric volatility models; transferability to other asset classes requires upstream recalibration. Full article

Formaldehyde (HCHO) is a typical volatile organic compound (VOC) that poses significant risks to human health. Long-term exposure, even at low concentrations, has been associated with various malignant diseases, including nasopharyngeal, colon, and brain cancers. Common technologies for HCHO abatement include ventilation, adsorption, photocatalysis, and catalytic oxidation. Among these methods, catalytic oxidation is regarded as the most promising due to its high removal efficiency, low cost, minimal energy consumption, and no toxic by-products. In recent years, supported catalysts with excellent room-temperature activity and high dispersibility have attracted considerable attention. These catalysts can usually be divided into two categories: noble metal catalysts and non-noble metal catalysts. Zirconia (ZrO₂) has become an ideal support owing to its advantages of high specific surface area, abundant and tunable acid–base sites, and strong metal–support interaction (SMSI). Various modification strategies have been developed to improve the catalytic performance of ZrO₂-based systems, such as the construction of phase interfaces and the stabilization of single-atom species. This review summarizes the recent research progress of ZrO₂-based systems for the catalytic oxidation of formaldehyde. It provides a detailed discussion of the physicochemical properties of ZrO₂ supports and the reaction mechanisms involved, and highlights achievements in crystal phase regulation, elemental doping, metal–support interaction, and composite modification. Finally, future challenges and development directions for these catalysts are also outlined. Full article

With the growing demand for strategic metals and the gradual depletion of traditional metal ore deposits, coal and coal-bearing strata are regarded as potential sources of rare metals; consequently, research into the characteristics of associated elements in coal-bearing strata has become one of the primary avenues of searching for new alternative resources. To investigate the sedimentary environmental characteristics and controlling factors of the coal-bearing strata along the southern margin of the Junggar Basin, coal seams 9–15 of the Xishanyao Formation in Sulphur Gully (Early Middle Jurassic) were selected as the subject of this study. This study employed analytical techniques including industrial analysis, total sulphur analysis, X-ray powder diffraction (XRD), X-ray fluorescence spectroscopy (XRF) and inductively coupled plasma mass spectrometry (ICP-MS) to determine the mineralogical and elemental geochemical characteristics of coal samples from Seylangou mining area, specifically from coal seams 9–15 and their overlying and underlying strata. Based on analyses of elemental ratios such as Al₂O₃/TiO₂, Sr/Ba, Rb/Sr, Ni/Co and V/(Ni + V), the source of material during the deposition of this deposit was identified, and the characteristics of the depositional environment, as indicated by palaeosalinity, palaeoclimate and redox conditions, were revealed. The results indicate that the macroscopic coal-rock types of coal seams 9–15 at the Sulphur Gully Coal Mine on the southern margin of the Junggar Basin are predominantly semi-dull to dull, with small amounts of filamentous coal and lustrous coal. The average proportion of the vitrinite group in the coal is 42.75%, the inertinite group is 51.40%, and the liptinite is 2.25%. The average content of inorganic matter in the coal is 3.60%, and the average maximum reflectance of the vitrinite group is 0.651%. The coal represents a transitional stage from low-rank to medium-rank coal, corresponding to a metamorphic stage of Grade I–II. The coal is classified as a bituminous coal with medium total moisture, very low ash, medium-volatile matter, medium-to-high fixed carbon and very low sulphur. The minerals in the coal seam are predominantly kaolinite, calcite and quartz. The major elements in the ceiling of the coal seam are dominated by SiO₂, followed by Al₂O₃; the coal itself is dominated by CaO, SiO₂ and Al₂O₃; and the base plate of the coal seam is dominated by Al₂O₃. The trace elements Cs and Bi are relatively enriched in the coal seam ceiling; Sr is relatively enriched in the coal; whilst Li, Cr and other elements are highly enriched in the coal seam base plate. The source rocks of the coal and the roof consist of deposits of felsic igneous rock (dacite), whilst the source rocks of the floor consist of deposits of intermediate igneous rock (andesite). The depositional environment ranges from marine brackish water at the base to transitional slightly brackish water and then to terrestrial freshwater at the top; the depositional climate was cold and arid, and the depositional environment was oxidising. This study provides valuable insights for further research into the elemental geochemical characteristics, sediment sources and depositional environments of the Xishanyao Formation coal seams in Liuhuangou, Xinjiang. Full article

Accurate wind power prediction during ramp events remains challenging due to wind speed volatility. This study proposes a hybrid forecasting framework combining improved variational mode decomposition (VMD), a novel ramp factor (RF), and the Informer model. First, a dynamic adaptive VMD method is employed to filter noise and identify abrupt wind speed changes. Subsequently, a similar period matching algorithm, enhanced by the RF and wind speed similarity coefficients, captures historical convergence features. Finally, the Informer network fuses these features with NWP data. Experimental results demonstrate that the proposed method significantly outperforms existing models in accuracy during ramp events, enhancing grid stability. Full article

The growing interest in clean-label and naturally preserved foods has pushed the scientific community to research essential oils (EOs) as sustainable, multifunctional alternatives to chemical preservatives. These plant volatile compounds exhibit strong antimicrobial and antioxidant activities, making them promising ingredients for natural preservation. Fermented meat products, though highly nutritional, are particularly at risk of microbial spoilage and contamination by foodborne pathogens due to their complex microbiota and processing conditions. This review examines the role of EOs as natural antimicrobials in fermented meat systems, summarizing their mechanisms of action, efficiency against key pathogens, and impact on safety, shelf life, and sensory attributes. Additionally, it discusses technological challenges related to volatility, stability, and sensory alterations, and outlines mitigation strategies such as encapsulation, nanoemulsions, and controlled-release delivery systems. By critically presenting current progress and identifying research gaps such as standardization and matrix interactions, this review contributes to the development of effective, natural, and clean-label preservation strategies. These insights support innovation and sustainability in the meat processing industry by bridging the gap between antimicrobial efficacy and sensory acceptability. Full article

Invasive pulmonary mold infections (IPMIs) are critical complications in immunocompromised patients, contributing significantly to morbidity and mortality. Diagnosing pathogens like Aspergillus species (spp.) and the Mucorales remains challenging due to non-specific clinical presentations and the limitations of traditional culture methods. This review provides an up-to-date synopsis of IPMI diagnostic tools, focusing on their diagnostic performance, turnaround time (TAT), and cost-effectiveness. We conducted a narrative review of the current literature regarding clinical evaluation, radiographic findings, invasive diagnostics, and non-invasive assays, including next-generation sequencing (NGS) and volatile organic compounds (VOCs). Chest computerized tomography (CT) remains a vital first step, though classic signs like the “halo” or “reverse halo” are neither sensitive nor specific. Traditional diagnostics are limited by low sensitivity and delayed results. While plasma microbial cell-free DNA (mcfDNA) NGS offers rapid TAT (24–48 h) and high specificity, its suboptimal sensitivity for Aspergillus spp. (<50%) and high cost remain significant barriers. Investigational VOC “breath tests” show promising sensitivity (77–96%) but lack standardization. Future research must prioritize the standardization of non-invasive microbiologic testing modalities, particularly those with rapid TAT such as bedside “breath tests” and high-throughput mcfDNA NGS. Development of clinical algorithms that balance cost-effectiveness with timely pathogen diagnosis based on the patient’s degree of immunosuppression is essential to improve survival in high-risk populations. Full article

To achieve synergistic, efficient degradation of volatile, harmful gases in asphalt and to scientifically quantify inhibitor dosage, this study proposes a dosage optimization method that integrates nonlinear regression with a multi-response satisfaction function. Focusing on a proprietary composite volatile gas suppressant, we systematically measured the concentration trends of ammonia, nitrogen oxides, sulfur dioxide, and hydrogen sulfide emitted from three asphalt systems: base asphalt, SBS modified asphalt (Styrene-Butadiene-Styrene modified asphalt), and rubber modified asphalt under different suppressant dosages (0%, 0.02%, 0.04%, 0.06%, 0.08%, and 0.10%). First, high-precision prediction models (R² > 0.95) were established using nonlinear regression to relate different inhibitor dosages to corresponding gas concentrations. Based on a satisfaction function, the multi-objective degradation effects were normalized into a comprehensive satisfaction index, and the optimal dosage was then determined. The results indicate: (1) the constructed models can accurately predict the concentrations of volatile harmful gases at various dosages; (2) the predicted optimal blending ratios vary by asphalt type, specifically 0.082% for base asphalt, 0.079% for SBS modified asphalt, and 0.080% for rubber modified asphalt; and (3) at the optimal blending ratios, all four gases achieve high and balanced degradation levels, resulting in the best overall degradation performance. At the same time, road performance tests confirmed that this blending ratio has no significant negative impact on the high-temperature and low-temperature stability or water stability of the asphalt mixture. Compared with traditional single-factor empirical methods, this approach represents a methodological upgrade from qualitative description to quantitative prediction, and from single-objective comparison to multi-objective synergistic optimization, providing data and theoretical support for the precise, efficient, and engineering-applicable use of asphalt volatile gas inhibitors. Full article

Climate warming in Mediterranean vineyards accelerates grape ripening and increases the incidence of sunburn and berry shriveling, leading to imbalances in grape composition and wine quality. This study evaluated the combined effects of a non-positioned training system (asymmetric sprawl) and foliar application of kaolin particle film on vine microclimate, agronomic performance and wine aroma profile in a Syrah cv. vineyard under warm conditions. Vine canopy temperature was monitored by UAV thermography at veraison and harvest, while grape damage, yield components and vegetative balance were assessed at harvest. Wines obtained from each treatment were analysed for chemical composition, volatile compounds and sensory attributes. Kaolin application significantly reduced canopy temperature, particularly under water-limited conditions at veraison (up to 1.9 °C), and the combination with sprawl training decreased the proportion of sunburnt and shrivelled clusters. These microclimatic modifications were associated with higher ethanol content, improved colour intensity and increased total polyphenol index in wines. The combined strategy also enhanced the concentration of key aroma compounds, especially terpenes and fruity esters, resulting in higher values of citrus, floral and fruity aromatic series. Sensory evaluation confirmed a better overall appreciation of wines produced from vines managed with both practices. Overall, the integration of canopy architecture modification and reflective particle film represents an effective strategy to mitigate heat stress effects in warm viticultural regions, improving grape physiological performance and contributing to the preservation of wine aromatic quality under climate change scenarios. Full article

Thymol has been granted “Generally Recognized as Safe” status by the US Food and Drug Administration. However, its application as a natural preservative is constrained by limitations such as poor water solubility and high volatility. In this study, a dual-protein complex was prepared using mung bean protein and whey protein isolate to stabilize thymol-loaded oil-in-water (O/W) Pickering emulsions. The results demonstrated that the dual-protein system was driven by hydrogen bonding, electrostatic attraction, and hydrophobic interactions. Compared to single-protein systems, the dual-protein Pickering emulsions possessed smaller droplet sizes, lower polydispersity indices, and higher surface charges and surface hydrophobicity. Additionally, the dual protein enhanced emulsifying activity, thermal stability, and 30-day storage stability. Notably, the complex formed a continuous three-dimensional porous network structure at the mung bean protein (MBP) to whey protein isolate (WPI) ratio of 50%:50%. Benefiting from this structure and high surface hydrophobicity, the 50%:50% formulation achieved the highest thymol encapsulation efficiency. In terms of functional properties, this optimized emulsion demonstrated notable antibacterial activity and antioxidant activity; it demonstrated antibacterial activity against Shewanella putrefaciens and Staphylococcus aureus. Furthermore, the IC₅₀ value for the 50%:50% formulation was 192.25 ± 1.93 μg/mL (DPPH) and 161.74 ± 0.71 μg/mL (ABTS). In summary, the 50%:50% formulation enhanced the emulsifying activity, encapsulation efficiency, and bioactivity of the emulsion. This system provides an effective strategy for the stabilization and encapsulation of hydrophobic active compounds in emulsions. Full article