Search Results (33,673)

Deciphering the coupling mechanisms between post-harvest precursor shaping and roasting thermochemistry is pivotal for precise coffee flavor modulation. This study aimed to investigate the regulation mechanisms of in vitro biomimetic fermentation (BF) on the precursor-roasting reaction network. Integrated multi-omics characterization and sensory evaluation reveal that the BF protocol achieves targeted substrate enrichment, notably amplifying free amino acids—particularly leucine and phenylalanine—by 1.89-fold while accumulating lactate and succinate buffering salt systems. This reconfiguration constructs a matrix with superior thermal buffering capacity (ΔpH 0.17), which optimizes the thermal reaction kinetic window during roasting. Consequently, BF drives a 3.08-fold surge in esterification flux, significantly increasing the abundance of key fruity markers such as ethyl acetate and ethyl isovalerate. It also enhances the diversity of Maillard products, specifically elevating nutty-associated alkylpyrazines (e.g., 2,3,5-trimethylpyrazine). Concurrently, BF improves the thermal stability of bioactive compounds, including 5-caffeoylquinic acid (5-CQA) and trigonelline. Multi-scale molecular dynamics and quantum chemical calculations elucidate that BF-derived organic acid–salt complexes exert a ‘pseudo-catalytic effect,’ lowering activation free energy barriers for critical aroma-generating reactions by approximately 8.5 kcal/mol. This study demonstrates high sensory predictability (predictive model R² = 0.98) and provides a quantitative theoretical framework to advance coffee processing from empirical observation to rational flavor design. Full article

Heritage Building Information Modeling (HBIM) is accelerating the transition from reactive restoration to preventive conservation in architectural heritage management. Nevertheless, research at the heritage-cluster scale remains limited, particularly in terms of multi-source data integration, dynamic value–risk coupling, and lifecycle-oriented decision support. This study proposes an intelligent HBIM-based framework designed to support integrated data processing, automated value–risk assessment, and preventive intervention planning for masonry heritage clusters. The framework is validated through its application to the Suopo Ancient Watchtower Complex in Danba, Sichuan, consisting of 84 polygonal stepped-in stone towers. By integrating 3D laser scanning, unmanned aerial vehicle (UAV) oblique photogrammetry, and historical archival data, a closed-loop workflow is established, spanning data acquisition, parametric semantic modeling, and intervention prioritization. A dedicated parametric component library and hierarchical semantic database tailored to irregular polygonal masonry significantly enhance modeling consistency, semantic coherence, and cross-building reusability. Leveraging the Revit Application Programming Interface (API) and Dynamo, the framework embeds a value–risk model (P = V × R), enabling automated component-level evaluation, real-time visualization of conservation priorities, and one-click generation of intervention lists. Results demonstrate improved modeling accuracy, efficiency, and decision reliability compared with conventional manual workflows. The framework offers a scalable and replicable pathway for sustainable conservation of masonry heritage clusters in high-seismic regions and provides a foundation for future integration with IoT-enabled digital twin systems. Full article

Adiabatic demagnetization refrigeration (ADR) has attracted considerable attention as an effective approach to reach ultra-low temperatures required for fundamental physics and quantum technologies. Here we directly characterize the cryogenic magnetocaloric performance of the rare-earth-based double-perovskite oxide Gd₂CuTiO₆ (GCTO) through quasi-adiabatic demagnetization measurements. Magnetization measurements show no long-range magnetic transition above 1.8 K and indicate dominant antiferromagnetic (AFM) interactions, consistent with an AFM ordering temperature of $T_{\mathrm{N}}\approx 1.15$ K reported previously. Notably, the isothermal magnetization $M\left(H\right)$ at 1.8 K deviates from an ideal single-ion Brillouin response and is better described by a molecular-field correction for the Gd sublattice, suggesting correlated paramagnetism persisting above $T_{\mathrm{N}}$. In contrast to previous studies that inferred cooling performance from thermodynamic estimates, we directly validate the achievable sub-Kelvin cooling in GCTO through quasi-adiabatic measurements. In the quasi-ADR process starting from $T_0$∼2 K, demagnetization fields of 4, 6, and 9 T yield minimum temperatures of $T_{\min }=761.5$, 452.4, and 289.2 mK, respectively, well below $T_{\mathrm{N}}$. After complete removal of the magnetic field, the sample temperature remains highly stable for at least several tens of minutes, demonstrating a long hold time under quasi-adiabatic conditions. Moreover, the $T\left(H\right)$ curves reveal a characteristic field scale around $H_{\mathrm{c}}$∼1 T, implying a field-induced modification of the low-temperature magnetic-entropy landscape that is relevant to the cooling behavior during demagnetization. These results highlight GCTO as a promising magnetic refrigerant for sub-Kelvin ADR applications and underscore the role of correlated magnetism in optimizing cryogenic magnetocaloric performance. Full article

During the vacuum arc remelting (VAR) process, external convective cooling conditions exert a significant influence on both the heat transfer behavior and solidification microstructure of ingots. In this research, Φ 480 mm IN718 alloy VAR ingots were investigated. A heat transfer model for the VAR mold was established based on the equivalent thermal resistance method to analyze the effects of varying external convective cooling conditions on overall heat transfer performance. Industrial-scale VAR experiments were conducted at different cooling water flow velocities (0.48, 0.73 and 1.30 m/s) to assess how external cooling affects molten pool morphology and microstructure evolution. The results indicate that cooling water flow velocity is the primary factor affecting the heat transfer performance of the VAR mold. Increasing the flow velocity significantly enhances radial heat transfer capability while exerting a relatively limited effect on axial heat transfer. Furthermore, as the cooling water flow velocity increases, the molten pool depth decreases markedly, the pool morphology becomes shallower and more symmetric, and the ingot cooling rate is enhanced. Consequently, dendrite coarsening is effectively suppressed, resulting in a significant reduction in secondary dendrite arm spacing. Specifically, when the flow velocity increases from 0.48 to 1.30 m/s, SDAS decreases by 30.4% at the center, 31.0% at R/2, and 26.5% at the edge, and the SDAS-derived equivalent cooling rate (GR) increases from 6.53–18.25 K/min to 19.41–46.01 K/min across the three representative radial locations. A significant enhancement in the metallurgical quality of the VAR ingot is achieved. Full article

The present study investigates the potential of olive mill wastewater (OMW), supplemented with expired commercial glucose syrup, as a sustainable substrate for the submerged cultivation of Tuber spp. wild mushrooms. OMW contains considerable quantities of phenolic compounds, making it both a challenging pollutant and a promising nutrient source. To assess fungal performance under increasing phenolic stress, culture media were prepared with varying OMW concentrations (0–75% v/v on agar; 0–50% v/v in liquid media), while glucose was adjusted to ~30 g/L using expired glucose syrup. A sequential experimental approach was followed, beginning with Petri dish screenings on substrate/strain selection (measuring the mycelial growth rate; Kr, mm/day), progressing to 25-day shake flask fermentations and subsequently scaling up the most promising strain (Tuber mesentericum) in a controlled stirred-tank bioreactor. Throughout cultivation, substrate consumption (glucose, phenolics), pH evolution and decolorization were evaluated, while the resulting biomass was analyzed for polysaccharides, β-glucans, proteins, lipids, fatty acids, antioxidants, phenolic acids and triterpenoids content. Results showed that increasing OMW concentration enhanced tolerance and metabolic activity in selected Tuber species, with T. mesentericum exhibiting the highest resilience and achieving comparable or higher biomass yields in OMW-based media than in glucose (control). Phenolic removal exceeded 60% in flasks and 50% in the bioreactor, confirming simultaneous bioremediation capacity. Bioreactor cultivation demonstrated efficient substrate utilization and biomass production, while OMW-grown biomass presented high lipid content, enriched with unsaturated fatty acids, high β-glucan levels and increased antioxidant and phenolic profiles. Overall, this study demonstrates that OMW (supplemented with expired glucose syrup) can serve as a cost-effective and environmentally beneficial substrate for Tuber biomass production with dietary and antioxidant properties, offering an alternative source to mushroom carposomes, as well as supporting the circular bioeconomy strategies within olive oil processing industries. Full article

In night-time construction scenarios of under-construction nuclear power plants, some yellow lights and open flames exhibit highly similar visual characteristics, resulting in frequent false alarms of fire sources. Such false alarm information tends to drown out real fire alarm signals, which not only severely disrupts construction operations but also endangers fire safety. To address this problem, this paper proposes an intelligent fire risk identification method based on an enhanced YOLOv8n (named YOLO-Fire). Specifically, shallow convolutional layers embedded with a coordinate attention mechanism are integrated into the Backbone of YOLOv8n; the Neck is optimised to improve the efficiency of multi-scale feature fusion; and the Head is enhanced to strengthen the localization and classification branches. Additionally, a composite loss function combining classification loss, regression loss, and similarity loss is designed, coupled with night-scene-specific data augmentation techniques and a two-stage progressive training strategy. Experimental results show that YOLO-Fire reduces the false alarm rate by 14.3%, increases the mean average precision (mAP@0.5) for open flames by 11.3% to 75.2%, and maintains an inference speed of over 85 frames per second (FPS). This study achieves an optimal balance between false alarm control, small object detection accuracy, and real-time processing efficiency, effectively resolving the misclassification issue between open flames and lights in night-time construction scenarios, and providing precise and efficient intelligent technical support for fire risk prevention and control during the construction phase of nuclear power plants. Full article

The advent of smart farms, enabled by information and communication technologies (ICT) and the Internet of Things (IoT), has improved productivity and sustainable agriculture. However, the large-scale implementation of smart farms is currently hampered by physical constraints. These constraints have led to the concept of open-field smart farming as a viable alternative. In this paradigm, data from unmanned aerial vehicles (UAVs) play a central role in effective and sustainable agricultural management. The quantitative analysis of such data requires highly reliable technological solutions. The objective of this study is to conduct a comparative analysis of image binarization algorithms for UAV-based soybean canopy extraction across growth stages and to contribute to the development of an image labeling methodology. UAVs were used to capture images of soybean fields at different growth stages, and a comparative analysis was performed using binarization image algorithms. The performance of each algorithm was evaluated using Normalized Cross Correlation (NCC) and Mean Absolute Error (MAE). The results indicate that the Excess Green (ExG) and Excess Green minus Excess Red (ExGR) vegetation indices provide accurate and stable soybean canopy extraction across growth stages when combined with Adaptive and Otsu binarization algorithms. These indices are particularly suitable for extracting soybean canopy from UAV-based data, thereby expanding the scope of precision analysis in the agricultural sector and providing data for advancing precision agriculture technology. This study contributes to the standardization and efficient use of UAV-based agricultural data processing. However, since manual weeding was performed prior to image acquisition to ensure that only soybean plants were present, reflecting standard agricultural practices in South Korea, additional validation would be required for application in fields where weeds are naturally present. Full article

The continuous-flow technologies in organic synthesis for the production of active pharmaceutical ingredients (APIs) are nowadays more and more applied. In-silico process design is a powerful tool able to support organic synthesis in the field of scale-up and process development. Process design feasibility and reliability depend on the availability of a well-defined chemical reaction kinetic scheme, information which is usually derived from experimental datasets collected on purpose. The latter approach is time-consuming and demanding in terms of resources. Different possibilities are here proposed to valorize widely available experimental data from explorative works with different approaches, depending on the nature, richness, and structure of the datasets. The kinetic parameters (i.e., reaction order, kinetic constant, and activation energy) of some interesting organic reactions have been approximately estimated by applying different computational methodologies, thanks to built-in experimental databases. The numerical algebra approach dealing with linear and non-linear regression analysis for the kinetic parameters has been initially considered and related to the database information for oseltamivir synthesis. The Bayesian statistic was applied to the ibuprofen case through the application of the Markov Chain Monte Carlo (MCMC) method for reaction order estimation. At last, a Machine Learning (ML) approach has been applied to the Rolipram and Pregabalin case study. The in-house developed T-ReX experimental kinetic constant database was exploited, with application of the k-Nearest neighbor algorithm for classification and regular expression pattern recognition. Advantages and limitations of the three approaches are discussed. Full article

Spontaneous intracerebral hemorrhage (ICH) is associated with substantial mortality and morbidity. Current management paradigms rely heavily on the rapid interpretation of neuroimaging and clinical data, yet are frequently constrained by limitations in processing speed, diagnostic accuracy, and prognostic precision. Artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), offers transformative potential to circumvent these challenges across the entire continuum of ICH care. This comprehensive review synthesizes the rapidly evolving landscape of AI applications in ICH management. Through a systematic evaluation of recent literature, we examine studies focused on the development, validation, or critical appraisal of AI-driven technologies for ICH care. Our analysis encompasses automated neuroimaging, computer-assisted surgical navigation, brain–computer interfaces (BCIs), prognostic modeling, and fundamental research into disease mechanisms. AI has demonstrated performance comparable to that of clinical experts in automating hematoma segmentation, predicting complications such as hematoma expansion, and refining surgical planning via augmented reality. Furthermore, BCIs present innovative therapeutic avenues for motor rehabilitation. However, the translation of these technological advances into routine clinical practice is impeded by substantial challenges, including data heterogeneity, model opacity (“black-box” issues), workflow integration barriers, regulatory ambiguities, and ethical concerns surrounding accountability and algorithmic bias. The integration of AI into ICH care signifies a paradigm shift from standardized treatment protocols toward dynamic, precision medicine. Realizing this vision necessitates interdisciplinary collaboration to engineer robust, generalizable, and interpretable AI systems. Key priorities include the establishment of large-scale multimodal data repositories, the advancement of explainable AI (XAI) frameworks, the execution of rigorous prospective clinical trials to validate efficacy, and the implementation of adaptive regulatory and ethical guidelines. By systematically addressing these barriers, AI can evolve from a mere analytical tool into an indispensable clinical partner, ultimately optimizing patient outcomes. Full article

Landslides occurring on reservoir banks in steep, high-gradient canyon areas pose a significant risk of surge disasters when they slide into the water. This can endanger the lives and property of downstream residents and damage coastal infrastructure. Therefore, researching the formation mechanisms, disaster evolution, and risk assessment of the landslide-surge disaster chain in such areas is essential. This paper takes the Laowuchang landslide in the Shuibuya Reservoir area of the Qingjiang River, China, as its research object. Using GeoStudio 2018 software, it evaluates the landslide’s stability under varying reservoir water levels and rainfall conditions. For potential unstable scenarios identified, a full-chain numerical simulation of the landslide–tsunami disaster was conducted based on the Tsunami Squares method, with a focus on analyzing the wave characteristics during generation, propagation, and run-up processes. Furthermore, the paper assesses the risk of landslide–tsunami disasters in the Laowuchang landslide area. The research findings indicate that: (1) Under the long-term continuous river incision, limestone of the Triassic Daye Formation slides along weak interlayers, inducing large-scale collapses. Subsequently, part of the landslide mass is transported by water, while most accumulates in the near-shore area of the Qingjiang River, ultimately shaping the present morphology of the landslide. (2) The Laowuchang landslide is stable under static water levels of 375 m and 400 m, with corresponding safety factors of 1.137 and 1.167, respectively. Under combined static water level and heavy rainfall conditions, the slope stability decreases significantly, with safety factors of 1.034 and 1.064, respectively. Under reservoir drawdown conditions, the slope tends to be unstable, with a safety factor of 1.047. (3) Numerical simulation results indicate that if the Laowuchang landslide fails into water by the speed of 12 m/s and with a volume of 2 million m³, the maximum initial wave height can reach 15.9 m. The tsunami’s affected range spans 10 km upstream and downstream from the landslide mass, with four houses and one substation within a 2 km up and downstream falling into high-risk areas. If abnormal increases in landslide displacement occur, relocation and risk avoidance measures should be implemented. The findings of this study provide a scientific basis for the prevention and response to landslide–tsunami disasters in similar high and steep canyon terrains. Full article

A transient numerical framework incorporating dynamic mesh techniques was developed to simulate the launch process. On this basis, a thermal–fluid–structural multi-physics coupling paradigm was proposed to interpret the evolution of the flow field and the associated load response throughout the entire firing sequence. The results show that flow development follows a multi-stage dynamic pattern, comprising gas-impact filling, gap-jet formation, and subsequent free-jet expansion. A pronounced spatially heterogeneous phase lag was observed in the pressure–Mach number response. This phenomenon arises from a mismatch among the characteristic time scales of pressure-wave propagation, flow inertia, and shock–boundary-layer interaction. Quantitative analysis further indicates that the spatial superposition of high-temperature zones, high-Mach regions, and elevated-pressure areas activates a thermal–fluid–structural positive-feedback loop that drives the local peak temperature to approximately 2.5 × 10³ K. The temperature response lags behind the pressure maximum by approximately 30–50 ms, reflecting the governing time scale of thermal inertia. In addition, vortical structures near the tube base account for nearly 15% of the total thrust. These findings provide a theoretical foundation for predicting transient peak loads in concentric cylindrical systems and for optimizing instantaneous thermal protection strategies. Full article

The Panoma Field in the Hugoton Embayment, Kansas, has produced significant gas resources from thousands of wells perforating the Permian Chase and Council Grove Groups. Variability in gas production from these formations is controlled by facies-influenced petrophysical properties. The use of geological facies data in numerical modeling is often limited to delineating regions of interest without intrinsic use in estimating petrophysical properties. Machine learning provides opportunities to integrate facies data into the numerical model-building process. In this study, we employ facies data in optimizing a numerical model permeability matrix scaling parameter using Monte Carlo Simulation of Markov Switching Dynamic Regression and machine learning. Realizations of the scaling parameter are included in a machine learning facies prediction workflow to identify the parameter that maximizes facies prediction accuracy, with test accuracy as high as 83%. A 3D numerical model was constructed to represent the interlayered carbonate, shale, and non-marine sandstones facies typical of the Council Grove intervals. Multiple field development and completion scenarios were evaluated to maximize cumulative gas recovery and assess the role of facies distribution on reservoir performance. History matching results of historical gas production demonstrate strong coupling between facies distribution and the optimized permeability, emphasizing the importance of facies data integration in reservoir property modeling and gas production estimation in Permian reservoirs. This implies that probabilistically constrained permeability scaling using the Monte Carlo and machine learning workflow produces more realistic modeling compared to traditional approaches. Full article

The aim of this review is to analyze current routes for the administration and absorption of vitamin B12 in older adults, with a special focus on the sublingual route using orodispersible films, and evaluate the advances, materials, and challenges associated with this method of administration. Thus, the review aims to provide an updated overview of safe and effective alternatives for preventing and treating vitamin B12 deficiency in this age group. Vitamin B12 deficiency predominantly affects older adults. After the age of 70, absorption decreases, and deficiency occurs most frequently due to age-related gastric atrophy, decreased gastric acid production, reduced intrinsic factor secretion, and inadequate dietary vitamin B12 intake. This narrative review examines traditional and current treatments for vitamin B12 administration in older adults, with a focus on sublingual administration (SL) via orodispersible films (ODFs) to enhance absorption, adherence, and accessibility. SL vitamin B12 bioavailability, advantages versus disadvantages, ODF formulations (polymers such as pregelatinized starch, HPMC, and chitosan), and pharmaceutical process challenges (solvent casting and hot-melt extrusion) were explored in the reviewed in vitro and in vivo studies. According to the collected evidence, the sublingual route appears to offer rapid absorption directly into the bloodstream, with efficacy comparable to/superior to intramuscular (IM)/oral (OP) routes of administration, representing a patient-centered innovation for older adults that overcomes painful treatments and gastrointestinal/swallowing barriers. Future longitudinal clinical trials should validate long-term efficacy, standardize materials, and scale up to viable industrial production, addressing issues related to chemical stability and polypharmacy. Full article

Introduction: Psychosocial functioning and body image are key dimensions of mental well-being and performance. Among professional dancers, competitive environments, aesthetic demands, and physical–emotional overload contribute to increased anxiety, stress, and mood disturbances, potentially impairing performance and heightening injury risk. Objective: To investigate longitudinal variations in psychosocial and emotional indicators among professional dancers throughout a season of rehearsals and performances. Methods: Thirteen dancers (9 women and 4 men) from a professional company were assessed across eight time points using the Brunel Mood Scale (BRUMS), State–Trait Anxiety Inventory (STAI-State), Recovery–Stress Questionnaire for Athletes (REST-Q 76 Sport), and Body Shape Questionnaire (BSQ). Data was analyzed using repeated-measures ANOVA with Bonferroni post hoc tests (p < 0.05). Results: Negative mood dimensions progressively increased (p < 0.01; η²p = 0.46, large), while vigor decreased (p = 0.03; η²p = 0.29, medium), indicating an inversion of the typical “iceberg” profile. Overall stress levels increased (p = 0.02; g = 0.53, power = 0.81) and perceived recovery declined (p = 0.04; g = 0.41, power = 0.78). State anxiety rose consistently (p < 0.01; η²p = 0.42), and body dissatisfaction, assessed via the BSQ, increased from “no concern” to “high concern” classifications (p = 0.03; g = 0.59, power = 0.84). Conclusions: Overall, the findings indicating a longitudinal pattern of increased psychometric strain indicators, inferred exclusively from psychometric trends, and conceptually consistent with a possible imbalance between perceived demands and perceived recovery, rather than reflecting objectively measured workload or recovery processes. Full article

Accurate forecasting of lottery sales is crucial for strategic planning in volatile consumer markets driven by trend shifts, multi-scale seasonality, and calendar effects. This study proposes a Diagnostic Decomposition–Correction Hybrid (DDC-Hybrid) framework integrating Prophet and SARIMA through a residual diagnostics and correction pipeline. Specifically, Prophet is employed to model long-term trend changes and interpretable holiday impacts, while SARIMA is subsequently used to correct the residual series, capturing short-range temporal dependence that remains statistically significant after decomposition. From an information-theoretic perspective, the framework can be viewed as a two-stage uncertainty reduction process, where decomposition extracts low-frequency informative components and residual correction harvests remaining predictive information. Using monthly lottery sales in China (2008–2025), we conduct a comprehensive evaluation of SARIMA, Prophet, and the proposed hybrid approach. The DDC-Hybrid demonstrates improved predictive accuracy, yielding the lowest error rates. Beyond predictive accuracy, we further examine varying holiday effects through statistical testing. We also find that lottery sales contain a pronounced quadrennial (48-month) seasonal cycle associated with mega-sport events, which improves long-horizon stability. The results suggest that the proposed diagnostic hybrid modeling approach enhances forecasting accuracy and provides practical insights for lottery sales management. Full article