Search Results (18,733)

Anxiety disorders represent a significant global health challenge, yet a substantial treatment gap persists, motivating the development of scalable digital health solutions. This study investigates the potential of integrating passive physiological data from consumer wearable devices with subjective self-reported surveys to predict state–trait anxiety. Leveraging the multi-modal, longitudinal LifeSnaps dataset, which captured “in the wild” data from 71 participants over four months, this research develops and evaluates a machine learning framework for this purpose. The methodology meticulously details a reproducible data curation pipeline, including participant-specific time zone harmonization, validated survey scoring, and comprehensive feature engineering from Fitbit Sense physiological data. A suite of machine learning models was trained to classify the presence of anxiety, defined by the State–Trait Anxiety Inventory (S-STAI). The CatBoost ensemble model achieved an accuracy of 77.6%, with high sensitivity (92.9%) but more modest specificity (48.9%). The positive predictive value (77.3%) and negative predictive value (78.6%) indicate balanced predictive utility across classes. The model obtained an F1-score of 84.3%, a Matthews correlation coefficient of 0.483, and an AUC of 0.709, suggesting good detection of anxious cases but more limited ability to correctly identify non-anxious cases. Post hoc explainability approaches (local and global) reveal that key predictors of state anxiety include measures of cardio-respiratory fitness (VO₂Max), calorie expenditure, duration of light activity, resting heart rate, thermal regulation and age. While additional sensitivity analysis and conformal prediction methods reveal that the size of the datasets contributes to overfitting, the features and the proposed approach is generally conducive for reasonable anxiety prediction. These findings underscore the use of machine learning and ubiquitous sensing modalities for a more holistic and accurate digital phenotyping of state anxiety. Full article

Tetranychus mexicanus (McGregor, 1950) (Tetranychidae) is considered one of the primary phytosanitary problems in passion fruit crops, resulting in significant production losses. Understanding the impact of this mite species’ activity on the physiology of passion fruit plants can serve as a basis for developing sustainable management strategies. With this in mind, this research sought to analyze, using supervised and unsupervised machine learning models, how T. mexicanus mite infestation influences gas exchange, chlorophyll “a” and chlorophyll “b” levels, fluorescence, and thermal response of passion fruit plants. We tested the hypothesis that juvenile and adult mites alter the physiological and thermal response patterns of plants. Only the variables related to the fluorescent response (Fo, Fm, and Fv) had a significant relationship with mite infestation. In the joint comparison of multiple fluorescent variables, there were differences between the treatments of plants infested and not infested by T. mexicanus. The variables’ initial fluorescence (Fo), maximum fluorescence (Fm), and variable fluorescence (Fv) of chlorophyll a had a direct negative impact on both reproductive activity, as measured by the number of eggs and nymphs produced, and the total number of mites found. The unsupervised model based on multidimensional scaling with the k-means algorithm revealed a clear separation between the groups of infested passion fruit plants (Group 1) and healthy plants (Group 2). The Fo response was described with high accuracy for the reproductive rate (75%) and total infestation of eggs, nymphs, and adults of the mites (99.99%). Kappa values were moderate (Kappa = 0.50) and high (Kappa = 0.99) for reproductive and total rates of T. mexicanus, respectively. Additionally, the thermal response revealed that the infested passion fruit plants had a median temperature of 25.1 °C, compared to a median temperature of 25.7 °C, with notable differences between these medians. Therefore, the T. mexicanus mite altered both the fluorescent and thermal patterns of passion fruit plants. Our findings have implications for the development of early detection tools and the generation of future resistance breeding. Full article

A major challenge in the agricultural industry is finding innovative and sustainable methods that can lead to enhanced crop resistance to abiotic stress factors and increased productivity. Research in recent years has proven the potential of non-thermal plasma in various fields, including agriculture, with relevance in promoting plant growth and development, plant immune response to abiotic stress or pathogen resistance. In the present study, distilled water was activated using dielectric barrier discharge equipment; subsequently, plasma-activated water (PAW) was used to irrigate maize plants subjected to cold stress. Two different maize accessions were studied in this work, SVGB-11742 and SVGB-718, previously identified as highly and moderately resistant to cold stress, respectively. After plant exposure to cold and irrigation with plasma-activated water, morphological, morpho-agronomical and physiological parameters and molecular data were assessed. The two genotypes showed distinct, often opposing, responses to PAW treatment depending on the parameter assessed. Generally, the obtained data at the molecular level showed that treatment with PAW increased the expression of certain genes involved in growth and development of the SVGB-718 variant subjected to cold stress. Irrigation of plants exposed to low temperatures with PAW did not have the predicted effects at the morphological and even the physiological level regarding the concentration of assimilatory pigments and the cold test index. While morphological benefits were limited and genotype-specific, PAW induced significant molecular changes (upregulated stress-responsive genes in SVGB-718), suggesting a priming effect that may not have been captured in the short-term morphological assays. However, the results obtained represent an important background for future studies. Full article

This study addresses critical knowledge gaps in adobe construction by systematically investigating soil mineralogy–additive effectiveness relationships and developing dual-additive optimization strategies for flood sediment valorization. Four Thai soil types—Nakhon Pathom (NPT), Sisaket (SSK), Uttaradit (UTT), and September 2024 Chiang Rai flood sediment (CRI)—were characterized using XRD and EDS analyses. Twelve adobe formulations incorporating rice husk (3.45%) and graduated bentonite concentrations (5–15%) were evaluated for mechanical and thermal properties. UTT soil with balanced mineralogy (42.1% SiO₂, 40.4% Al₂O₃) achieved optimal mechanical performance (3.12 ± 0.11 MPa compressive strength), while CRI demonstrated superior thermal insulation (0.200 ± 0.009 W/m·K). Rice husk systematically enhanced compressive strength across all soils (13.6–82.5% improvement) while reducing thermal conductivity to 0.211–0.278 W/m·K. Dual-additive optimization of CRI enabled application-specific customization: rice husk alone maximized strength (1.34 ± 0.09 MPa), while bentonite combinations optimized thermal performance (0.199 ± 0.015 W/m·K). Microstructural analysis revealed distinct reinforcement mechanisms and matrix densification effects. This research establishes predictive frameworks for material selection based on soil composition, demonstrates viable flood waste valorization pathways, and supports Thailand’s Bio-Circular-Green economic framework through sustainable construction material development. Full article

The pursuit of sustainable and environmentally benign energy storage solutions has propelled significant interest in organic batteries, which utilize redox-active organic compounds as electrode materials. A pivotal component in determining their electrochemical performance, safety, and long-term stability is the electrolyte. Polymer-based electrolytes (PBEs) have emerged as promising candidates owing to their intrinsic advantages, such as enhanced thermal stability, mechanical integrity, and the mitigation of leakage and flammability risks associated with conventional liquid electrolytes. Unlike previous reviews that broadly cover solid electrolytes, this review specifically focuses on the unique developments of polymer-based electrolytes tailored for organic batteries over the past few years. This review presents a comprehensive overview of the recent progress in PBEs specifically designed for organic battery systems. It systematically examines various categories, including solid polymer electrolytes (SPEs), valued for their structural simplicity and stability; gel polymer electrolytes (GPEs), noted for their high ionic conductivity and processability; and polymer-inorganic composite electrolytes, which synergistically integrate the mechanical flexibility of polymers with the ionic conductivity of inorganic fillers. Additionally, the review delves into the latest advancements in ionogels and poly(ionic liquid) electrolytes, highlighting their potential to overcome existing limitations and enable next-generation battery performance. The article concludes with a critical discussion on prevailing challenges and prospective research directions, emphasizing the importance of advanced material design, interfacial engineering, and sustainable synthesis approaches to facilitate the practical realization of high-performance organic batteries. Full article

This paper presents the development and characteristics of geopolymer foams modified with paraffin-based phase change materials (PCMs) encapsulated in diatomite. The aim was to increase both the thermal insulation and heat storage capacity of the foams while maintaining sufficient mechanical strength for construction applications. Eleven variants of composites with different PCM fractions (5–10% by mass) and grain sizes (<1.6 mm to >2.5 mm) were synthesized and tested. The inclusion of PCM encapsulated in diatomite modified the porous structure: the total porosity increased from 6.6% in the reference sample to 19.6% for the 1.6–1.8 mm_10% wt. variant, with pore diameters ranging from ~4 to 280 µm. Thermal conductivity (λ) ranged between 0.090–0.129 W/m·K, with the lowest values observed for composites 2.0–2.5 mm_5–10% wt. (≈0.090–0.091 W/m·K), which also showed high thermal resistance (R ≈ 0.287–0.289 m²·K/W). The specific heat (Cp) increased from 1.28 kJ/kg·K (reference value) to a maximum value of 1.87 kJ/kg·K for the 2.0–2.5 mm_10% mass variant, confirming the effective energy storage capacity of PCM-modified foams. Mechanical tests showed compressive strength values in the range of 0.7–3.1 MPa. The best structural performance was obtained for the 1.6–1.8 mm_10% wt. variant (3.1 MPa), albeit with a higher λ (≈0.129 W/m·K), illustrating the classic trade-off between porosity-based insulation and mechanical strength. SEM microstructural analysis and mercury porosimetry confirmed the presence of mesopores, which determine both thermal and mechanical properties. The results show that medium-sized PCM fractions (1.6–2.0 mm) with moderate content (≈10% by weight) offer the most favorable compromise between insulation and strength, while thicker fractions (2.0–2.5 mm) maximize thermal energy storage capacity. These findings confirm the possibility of incorporating natural PCMs into geopolymer foams to create multifunctional materials for sustainable and energy-efficient building applications. A unique contribution to this work is the use of diatomite as a natural PCM carrier, ensuring stability, compatibility, and environmental friendliness compared to conventional encapsulation methods. Full article

Thermal imaging is increasingly applied in remote sensing to identify material degradation, monitor structural integrity, and support energy diagnostics. However, its adoption is limited by the low spatial resolution of thermal sensors compared to RGB cameras. This study proposes a modular pipeline to generate thermally enriched 3D point clouds by fusing RGB and thermal imagery acquired simultaneously with a dual-sensor unmanned aerial vehicle system. The methodology includes geometric calibration of both cameras, image undistortion, cross-spectral feature matching, and projection of radiometric data onto the photogrammetric model through a computed homography. Thermal values are extracted using a custom parser and assigned to 3D points based on visibility masks and interpolation strategies. Calibration achieved 81.8% chessboard detection, yielding subpixel reprojection errors. Among twelve evaluated algorithms, LightGlue retained 99% of its matches and delivered a reprojection accuracy of 18.2% at 1 px, 65.1% at 3 px and 79% at 5 px. A case study on photovoltaic panels demonstrates the method’s capability to map thermal patterns with low temperature deviation from ground-truth data. Developed entirely in Python, the workflow integrates into Agisoft Metashape or other software. The proposed approach enables cost-effective, high-resolution thermal mapping with applications in civil engineering, cultural heritage conservation, and environmental monitoring applications. Full article

Thermoset fibre-reinforced composites are widely used in high-end industries, but a growing demand for more sustainable and recyclable alternatives conveyed the research efforts towards thermoplastics. To expand their usage, new approaches to their manufacture and mechanical performance must be tackled and tailored to each engineering challenge. The present study designed, manufactured and tested advanced multi-layer laminated composites of thermoplastic polypropylene prepreg reinforced with continuous woven fibreglass with interlayer toughening through thermoplastic polyurethane elastomer (TPU) layers manufactured by fused filament fabrication. The manufacturing process was iteratively optimized, resulting in successful adhesion between layers. Three composite configurations were produced: baseline glass fibre polypropylene (GFPP) prepreg and two multi-layer composites, with solid and honeycomb structured TPU layers. Thermal and mechanical analyses were conducted with both the polyurethane elastomer and the manufactured laminates. Tensile testing was conducted on additively manufactured polyurethane elastomer specimens, while laminated composites were tested in three-point bending. The results demonstrated the potential of the developed laminates. TPU multi-layer laminates exhibit higher thermal stability compared to the baseline GFPP prepreg-based composites. The addition of elastomeric layers decreases the flexural modulus but increases the ability to sustain plastic deformation. Multi-layer laminate composites presenting honeycomb TPU layers exhibit improved geometric and mechanical consistency, lower delamination and fibre breakage, and a high elastic recoverability after testing. Full article

A novel thermal conductivity prediction model was developed to address the complex influence of pore structure in porous materials. This model incorporates pore size (d) and a pore distribution parameter (t) to calculate the material’s thermal conductivity. To validate the model’s accuracy, geopolymer foamed concrete (GFC) samples with varying pore structures were fabricated. These utilized ground granulated blast furnace slag (GGBS) as the precursor, a mixed solution of sodium hydroxide (NaOH) and sodium silicate as the alkaline activator, and sodium stearate (NaSt), hydroxypropyl methylcellulose (HPMC), and sodium carboxymethyl cellulose (CMC-Na) as foam stabilizers. Conventional pore size characterization techniques exhibit limitations; consequently, this research implements a high-fidelity machine vision-driven image analysis methodology. Pore size measurement is achieved through a combined technical approach involving equivalent diameter modeling and morphological optimization. The feasibility of the proposed theory is validated by our experimental data and data from previous literature, with the error between experimental and theoretical values maintained within 5%. The value of t increases with increasing porosity and increasing disorder in pore distribution. Based on the experimental data obtained in this study and the research data from previous scholars’ studies, the t value for porous materials can be categorized according to porosity: when porosity is approximately 30%, t ≈ 0.9; when porosity is 55~65%, t ranges from 1.2 to 1.3; and when porosity is approximately 80%, t ranges from 1.9 to 2.2. Full article

The High Temperature Gas-cooled Reactor (HTGR) is characterized by a high output temperature and inherent safety due to its fuel design. However, the double heterogeneity of the reactor component structure poses a challenge in thermal analyses, where fuel temperature is a key safety parameter. In this paper, a methodology for coupled thermal and neutron calculations with power discretization is developed to accurately reflect the spatial phenomena occurring in the moderator. The method is based on the point generation of power in the thermal model, and these points are determined based on the location of the fuel in the neutron model. The multi-physics interface capabilities of the Serpent code were used to investigate several configurations of the thermal model mesh and its alignment with the fuel. The impact of the radial discretization of power density was further analyzed in detail. The study revealed that the highest accuracy was achieved when the thermal model mesh was aligned with the TRi-structural ISO-tropic (TRISO) fuel particle size, and the TRISO particle arrangement was centered relative to the mesh cells. Moreover, it was found that due to the power–temperature feedback phenomena, the power is shifted outwards within a range of 1% of the relative power density. Full article

Deep coalbed methane (CBM) resources possess significant potential. However, their development is challenged by geological characteristics such as high in situ stress and low permeability. Furthermore, existing production strategies often prove inadequate. In order to achieve long-term stable production of deep coalbed methane reservoirs and increase their final recoverable reserves, it is urgent to construct a scientific and reasonable drainage system. This study focuses on the deep CBM reservoir in the Daning-Jixian Block of the Ordos Basin. First, a thermal–hydraulic–mechanical (THM) multi-physics coupling mathematical model was constructed and validated against historical well production data. Then, the model was used to forecast production. Finally, key control measures for enhancing well productivity were identified through production strategy adjustment. The results indicate that controlling the bottom-hole flowing pressure drop rate at 1.5 times the current pressure drop rate accelerates the early-stage pressure drop, enabling gas wells to reach the peak gas production earlier. The optimized pressure drop rates for each stage are as follows: 0.15 MPa/d during the dewatering stage, 0.057 MPa/d during the gas production rise stage, 0.035 MPa/d during the stable production stage, and 0.01 MPa/d during the production decline stage. This strategy increases peak daily gas production by 15.90% and cumulative production by 3.68%. It also avoids excessive pressure drop, which can cause premature production decline during the stable phase. Consequently, the approach maximizes production over the entire life cycle of the well. Mechanistically, the 1.5× flowing pressure drop offers multiple advantages. Firstly, it significantly shortens the dewatering and production ramp-up periods. This acceleration promotes efficient gas desorption, increasing the desorbed gas volume by 1.9%, and enhances diffusion, yielding a 39.2% higher peak diffusion rate, all while preserving reservoir properties. Additionally, this strategy synergistically optimizes the water saturation and temperature fields, which mitigates the water-blocking effect. Furthermore, by enhancing coal matrix shrinkage, it rebounds permeability to 88.9%, thus avoiding stress-induced damage from aggressive extraction. Full article

This study reports the development and optimization of a Melissa officinalis oil-based nanoemulgel for transdermal delivery using a Design-of-Experiments (DoE) approach. A Central Composite Design (CCD) was applied to optimize Tween 80 concentration and homogenization time, resulting in a nanoemulsion with a droplet size of 127.31 nm, PDI of 17.7%, and zeta potential of −25.0 mV, indicating good colloidal stability. FTIR analysis confirmed the presence of functional groups such as O–H, C=O, and C–O–C, supporting the oil’s phytochemical richness and therapeutic potential. DSC analysis revealed enhanced thermal stability and successful encapsulation, while SEM imaging showed a uniform and spherical microstructure. The drug release followed Higuchi kinetics (R² = 0.900), indicating diffusion-driven release, with the Korsmeyer–Peppas model (n = 0.88) suggesting anomalous transport. Antibacterial studies showed inhibition of Staphylococcus aureus (MIC = 250 µg/mL) and Escherichia coli (MIC = 500 µg/mL). In vivo anti-inflammatory testing demonstrated significant edema reduction (p < 0.05) using a carrageenan-induced rat paw model. These results support the potential of Melissa nanoemulgel as a stable and effective topical therapeutic for inflammatory and microbial skin disorders. Full article

The reintegration of waste into the production chain represents a sustainable method of reducing environmental impact while promoting economic growth. This also aligns with social and environmental demands. In this study, composites were produced from commercial and recycled gypsum, polyvinyl acetate (PVA) emulsions, and chemically treated short green coconut fibers, and characterized by physical and mechanical analyses. The addition of PVA improved paste workability, extended setting time, and reduced porosity, while fiber pretreatment enhanced adhesion and tensile performance. XRD, FTIR, and TGA-DTA confirmed modifications in crystallinity, bonding, and thermal stability due to the combined action of PVA and fibers. Compared with the recycled gypsum reference (RG), the optimized composite (R50C50P5F10) exhibited a 69.1% reduction in sorptivity (from 5440 × 10⁻⁴ to 1680 × 10⁻⁴ kg/m²·s^0.5), a 27.9% increase in flexural tensile strength (from 2.65 to 3.39 MPa), and a 15.1% increase in compressive strength (from 6.18 to 7.12 MPa). Surface hardness values remained statistically equivalent to RG but complied with normative requirements, maintaining all formulations within the moderate hardness category (55–80 Shore C). The results demonstrate the technical feasibility of incorporating recycled gypsum and agro-industrial fibers into gypsum composites, providing a sustainable route for developing more durable construction materials. Full article

Municipal solid waste (MSW) disposal is one of the areas of sustainability development of modern countries including the Republic of Kazakhstan. Annually, more than 4 million tons of MSW are generated, and this amount continues to grow. Additionally, approximately 120 million tons of waste have already accumulated in landfills across the country. It is essential to select an MSW disposal technology that is environmentally friendly, minimizes the generation of more hazardous waste, and maximizes energy efficiency. Ideally, the technology should not only reduce energy consumption but also generate energy and valuable by-products that have market demand. The aim of this study is to conduct experimental research to evaluate the efficiency and environmental impact of incinerating both unsorted and sorted municipal solid waste. As a result of the experiment, the volumes of flue gases and the concentrations of harmful substances produced by the combustion of both unsorted and sorted waste were determined. Additionally, an analysis of the slag and ash generated from the combustion of sorted MSW was conducted. The obtained results enable the development of a waste-free technological scheme for a plant designed for the complete utilization of municipal solid waste. Full article

In deep-sea oil and gas development scenarios, deep-sea dual-channel connectors often face the risk of seal failure due to internal and external temperature difference loads. To address this issue, this paper systematically establishes equivalent heat transfer models for the key parts of the connector based on the third-type boundary condition. On this basis, the quantitative correlation between the equivalent thermal conductivity, composite heat transfer coefficient and temperature of each part is explored. Using the finite element numerical simulation method, the transient temperature field of the connector under three working conditions (heating, cooling and temperature shock) is simulated and analyzed, revealing the temperature distribution characteristics and temperature change trends of the maximum temperature difference of each key component of the connector; combined with thermal–structural coupling simulation, the temperature field is converted into static load, to determine the behavior of the contact stress on the sealing surface under different temperature–pressure coupling working conditions; in addition, by placing the test prototype in a high-low temperature cycle chamber, the seal performance tests under pressurized and non-pressurized working conditions are carried out to verify the reliable sealing performance of the connector under variable temperature conditions. The results of this paper provide comprehensive theoretical support and an experimental basis for the thermodynamic optimization design of deep-sea connectors and the improvement of the reliability of the sealing system. Full article