Search Results (602)

This study comprehensively compares the performance of two non-destructive testing (NDT) techniques—active thermography (AT) and digital shearography (DS)—for identifying various damage types in composite structures. Three distinct composite specimens were inspected: a carbon-fiber-reinforced polymer (CFRP) plate with flat-bottom holes, an aluminum honeycomb core sandwich plate with a circular skin-core disbond, and a CFRP plate with two low-energy impacts damage. The research highlights the significant role of post-processing methods in enhancing damage detectability. For AT, algorithms such as fast Fourier transform (FFT) for temperature phase extraction and principal component thermography (PCT) for identifying significant temperature components were employed, generally making anomalies brighter and easier to locate and size. For DS, a novel band-pass filtering approach applied to phase maps, followed by summing the filtered maps, remarkably improved the visualization and precision of damage-induced anomalies by suppressing background noise. Qualitative image-based comparisons revealed that DS consistently demonstrated superior performance. The sum of DS filtered phase maps provided more detailed and precise information regarding damage location and size compared to both pulsed thermography (PT) and lock-in thermography (LT) temperature phase and amplitude. Notably, DS effectively identified shallow flat-bottom holes and subtle imperfections that AT struggled to clearly resolve, and it provided a more comprehensive representation of the impacts damage location and extent. This enhanced capability of DS is attributed to the novel phase map filtering approach, which significantly improves damage identification compared to the thermogram post-processing methods used for AT. Full article

In this cross-disciplinary investigation, we uncover a suite of previously unexamined factors and their intricate interplay that hold causal relationships with the distribution of Trachurus japonicus in the northern reaches of the South China Sea, thereby extending the existing research paradigms. Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of T. japonicus density. A robust experimental design identified nine key factors significantly influencing this density: mean sea-level pressure (msl-0, msl-4), surface pressure (sp-0, sp-4), Summit ozone concentration (Ozone_sum), F10.7 solar flux index (F10.7_index), nitrate concentration at 20 m depth (N3M20), sonar-detected effective vertical range beneath the surface (Height), and survey month (Month). Crucially, stable causal relationships were identified among Ozone_sum, F10.7_index, Height, and N3M20. Variations in Ozone_sum likely impact surface UV radiation levels, influencing plankton dynamics (a primary food source) and potentially larval/juvenile fish survival. The F10.7_index, reflecting solar activity, may affect geomagnetic fields, potentially influencing the migration and orientation behavior of T. japonicus. N3M20 directly modulates primary productivity by limiting phytoplankton growth, thereby shaping the availability and distribution of prey organisms throughout the food web. Height defines the vertical habitat range acoustically detectable, intrinsically linking directly to the vertical distribution and availability of the fish stock itself. Surface pressures (msl-0/sp-0) and their lagged effects (msl-4/sp-4) significantly influence sea surface temperature profiles, ocean currents, and stratification, all critical determinants of suitable habitats and prey aggregation. The strong influence of Month predominantly reflects seasonal changes in water temperature, reproductive cycles, and associated shifts in nutrient supply and plankton blooms. Rigorous robustness checks (Data Subset and Random Common Cause Refutation) confirmed the reliability and consistency of these causal findings. This elucidation of the distinct biological and physical pathways linking these diverse factors leading to T. japonicus density provides a significantly improved foundation for predicting distribution patterns globally and offers concrete scientific insights for sustainable fishery management strategies. Full article

Rivers play a crucial role in the hydrological cycle and serve as essential freshwater resources for both human populations and ecosystems. Climate change significantly alters precipitation patterns and river discharge variability. However, the impact of precipitation patterns (rainfall and snowfall) and air temperature on river discharge in coastal zones remains inadequately understood. This study focused on Toyama Prefecture, located along the Sea of Japan, as a representative coastal area. We analyzed over 30 years of datasets, including air temperature, precipitation, snowfall, and river discharge, to assess the effects of climate change on river discharge. Trends in hydroclimatic datasets were assessed using the rescaled adjusted partial sums (RAPS) method and the Mann–Kendall (MK) non-parametric test. Furthermore, a correlation analysis and the Structural Equation Model (SEM) were applied to construct a relationship between precipitation, temperature, and river discharge. Our findings indicated a significant increase in air temperature at a rate of 0.2 °C per decade, with notable warming observed in late winter (January and February) and early spring (March). The average river fluxes for the Jinzu, Oyabe, Kurobe, Shou, and Joganji rivers were 182.52 m³/s, 60.37 m³/s, 41.40 m³/s, 38.33 m³/s, and 18.72 m³/s, respectively. The tipping point for snowfall decline occurred in 1992, marked by an obvious decrease in snowfall depth. The SEM showed that, although rainfall dominated the changes in river discharge (loading = 0.94), the transition from solid (snow) to liquid (rain) precipitation may alter the river discharge regime. The percentage of flood occurrence increased from 19% (1940–1992) to 41% (1993–2020). These changes highlight the urgent need to raise awareness about the impacts of climate change on river floods and freshwater resources in global coastal regions. Full article

The yield of common bean (Phaseolus vulgaris L.) is limited by abiotic stresses such as drought and high temperatures, which frequently occur simultaneously under field conditions. This study examined 100 bean genotypes under three environmental conditions, namely, the rainy season (optimal conditions), full irrigation in the dry season (high-temperature stress), and terminal drought in the dry season (combined stress), via a 10 × 10 triple-lattice design. Agronomic parameters evaluated included days to flowering (DF), days to physiological maturity (DM), plant height (PH), aerial biomass (BIO), grain yield (YLD), and 100-seed weight (100SW). The natural temperature exceeded 35 °C during the reproductive stage of the dry season. Combined stress revealed differential adaptive mechanisms in the tested germplasms, indicating that the response to multiple stresses is more complex than the sum of individual stress responses. The average yield under optimal conditions was 1344 kg/ha, decreasing to 889 kg/ha (66.1%) under irrigation with high temperatures and to 317 kg/ha (23.6%) under terminal drought with high temperatures. Under terminal drought with high temperatures, the number of days to maturity decreased by 5%, and the seed weight decreased by 20%. The G69-33-PT and G-19158 genotypes presented high yields under high-temperature stress, with yields above 1800 kg/ha, suggesting specific physiological mechanisms for tolerance to elevated temperatures. Under combined stress, genotypes G69-Sel25, Pinto Mestizo, and Dalia presented yields above 680 kg/ha, indicating adaptations in terms of water use efficiency and tolerance to high temperature. The identification of genotypes with differential stress tolerance provides valuable genetic resources for breeding programs. The diverse origins of superior germplasms (bred lines, landraces, and commercial cultivars) highlight the importance of exploring various germplasms in the search for sources of abiotic stress tolerance for breeding projects aimed at developing cultivars adapted to climate change scenarios where drought and high temperatures occur simultaneously. Full article

Dental caries can cause premature loss of deciduous teeth, affecting children’s growth and development. Endodontic treatment using polymer posts is an effective solution. This study explores biodegradable root canal posts made from Polylactic Acid (PLA), Polycaprolactone (PCL), and amorphous calcium phosphate (ACP), aiming to enhance mechanical properties, minimize polymer degradation acidity, and prevent inflammation. A root canal post with a spherical head and serrated structure was designed and produced via micromolding and optimized using the Taguchi experimental method. The melt temperature, injection speed, and holding speed were analyzed for their influence on shrinkage, revealing an optimal rate of 2.575%, representing the sum of axial and radial shrinkage. The melt temperature had the highest impact (55.932%), followed by holding speed (33.575%), with there being minimal effect from injection speed. The composite exhibited a flexural strength of 21.936 MPa, a modulus of 2.083 GPa, and a hydrophilic contact angle of 73.73 degrees. Cell survival tests confirmed biocompatibility, with a survival rate exceeding 70% and no toxicity. These findings highlight the potential of PLA/PCL/ACP composites, combined with injection molding, for developing biodegradable root canal posts in primary teeth. Full article

Energy resources are abundant in high-altitude regions, and the construction of power transmission and transformation projects has important value. However, harsh natural environments can increase project costs. To address the issue of insufficient accuracy caused by the impact of extreme weather factors on cost predictions for power transmission and transformation projects in high-altitude regions, this paper first constructs a four-dimensional influencing factor system covering climate and environment, engineering scale, material consumption, and technological economy. On this basis, a hybrid deep-learning model combining an improved whale optimization algorithm (IWOA) and a convolutional neural network (CNN) is then proposed. The model improves the training accuracy of CNNs and avoids falling into local optima through the use of an SGDM optimizer, the L2 regularization method, and the Bayesian optimization method. Nonlinear convergence factors and adaptive weights are introduced to enhance the WOA’s ability to optimize the CNN’s learning rate. The case analysis results show that, compared with the comparison model, the proposed IWOA-CNN model exhibits a better convergence performance and fitting effect in the training set and a better prediction effect on the test set. Its mean absolute percentage error is as low as 1.51%, which is 10.1% lower than the optimal comparison model. The root mean square error is reduced to 5.07, and the sum of squared errors is reduced by 72.4%, demonstrating high prediction accuracy. The comparative analysis of scenarios further confirms the crucial role of climate environment; that is, the prediction accuracy of models containing a climate dimension is improved by 51.6% compared to models without such a climate dimension, indicating that the nonlinear impact of low temperatures, frozen soil, and other characteristics of high-altitude regions on costs cannot be ignored. The research results of this paper enrich the method system and application scenarios for the cost prediction for power transmission and transformation projects and provide theoretical reference for engineering predictions in other complex geographical environments. Full article

To quantitatively describe the frequency domain spectroscopy (FDS) characteristics of transformer oil-paper insulation under varying temperature, moisture, and aging conditions, a modified Cole–Cole model is introduced. This model decomposes the dielectric spectrum into polarization, DC conduction, and hopping conduction components, with parameters reflecting insulation characteristics. Methods for determining initial parameter values and optimizing the objective function are proposed. Using a three-electrode setup, FDS measurements were conducted on oil-paper insulation samples at different temperatures, and extracted parameters were analyzed for their variation patterns. Within the frequency range of 1.98 × 10⁻⁴ Hz to 1 × 10³ Hz, the model achieves a goodness-of-fit (R²) exceeding 0.97 for both real and imaginary permittivity components, with the sum of squared errors reduced from 259 to 57.35 at 70 °C, outperforming the fundamental Cole–Cole and Ekanayake’s models. Temperature significantly affects the relaxation and DC conductivity components; both adhere to the Arrhenius equation, enabling precise condition assessment of transformer insulation. Full article

The quality of water in the Red River is a complex interplay between human-induced changes and inherent natural variables. This research utilized the snapshot sampling approach, garnering water quality data from 45 sampling sites in the Red River and crafting 24 environmental indicators related to land use and inherent natural determinants at the catchment scale. Through Spearman rank correlation and redundancy analyses, relationships among land use, natural variables, and water quality were elucidated. Our variance partitioning revealed differentiated impacts of land use and natural factors on water quality. Pivotal findings indicated superior water quality in the Red River, driven mainly by land use dynamics, which showed a distinct geomorphic gradient. Specific land use attributes, like cropland patch density, grassland’s largest patch index, and urban metrics, were pivotal in explaining variations in parameters such as total nitrogen, ammonia, and temperature. Notably, the configuration of land use had a more profound influence on water quality than merely its components. In terms of natural influences, while topography played a dominant role in shaping water quality, other factors like soil and weather had marginal impacts. Elevation was notably linked with metrics like total phosphorus and suspended solids, whereas precipitation and slope significantly determined electrical conductivity and chlorophyll-a models. In sum, incorporating both land use configurations and natural determinants offers a more comprehensive understanding of water quality disparities in the Red River’s ecosystem. For holistic water quality management, the focus should not only be on the major contributors like croplands and urban areas but also on underemphasized areas like grasslands. Tweaking cropland distribution, recognizing the intertwined nature of land use and natural elements, and tailoring land management based on topographical variations are essential strategies moving forward. Full article

This study exploited the skin temperature signal derived from a wrist-worn wearable device to define potential digital biomarkers for glycemia levels. Characterization of the skin temperature signal measured through the Empatica E4 device was obtained in 16 subjects (data taken from a dataset freely available on PhysioNet) by deriving standard metrics and a set of novel metrics describing both the current and the retrospective behavior of the signal. For each subject and for each metric, values that correspond to when glycemia was inside the tight range (70–140 mg/dL) were compared through the Wilcoxon rank-sum test against those above or below the range. For hypoglycemia characterization (below range), retrospective behavior of skin temperature described by the metric CV_{T SD} (standard deviation of the series of coefficient of variation) proved to be the most effective both in daytime and nighttime (100% and 50% of the analyzed subjects, respectively). On the other side, for hyperglycemia characterization (above range), differences were observed between daytime and nighttime, with current behavior of skin temperature, described by M2_T (deviation from the reference value of 32 °C), being the most informative during daytime, whereas retrospective behavior, described by SD_{T hhmm} (standard deviation of the series of means), showed the highest effectiveness during nighttime. Proposed variability features outperformed standard metrics, and in future studies, their integration with other digital biomarkers of glycemia could improve the performance of applications devoted to non-invasive detection of glycemic events. Full article

Due to high temperatures and repeated load, asphalt pavements commonly experience rutting distress, a challenge that can be considerably reduced by modifying the binder components. This research focused on evaluating the performance of asphalt binders with single-use masks (SUMs) when subjected to high temperatures. For this purpose, dynamic shear rheometer (DSR)-based frequency sweep, temperature sweep, and multiple stress creep recovery (MSCR) experiments were performed on various asphalt binders, including both unmodified and SUM-modified (SUMM) samples. To explore the effects of temperature, SUM content, and loading frequency on the rutting performance of the SUMM samples, a statistical modeling-based response surface methodology (RSM) was utilized, enabling the creation of predictive mathematical models. To investigate the internal morphology of the binders, fluorescence microscopy (FM) was applied. Data from rheological analyses revealed that the addition of SUM markedly boosted the high-temperature resistance of the asphalt binder. Findings from the MSCR analysis indicated that the SUMM samples achieved lower nonrecoverable compliance (J_nr) and greater percent recovery (R) values than the reference asphalt, suggesting that SUMs significantly enhance the binder’s resistance to rutting. Data analysis demonstrated that the chosen independent variables had a considerable effect on the asphalt’s complex modulus (G*) and rutting performance (G*/sin (δ)), prompting the formulation of predictive models for rutting susceptibility. Moreover, the FM examination demonstrated that the SUM was homogeneously integrated across the asphalt matrix. Full article

Objectives: The ability to efficiently regulate body temperature is crucial during endurance activities such as trail running, especially during competitive events in hot conditions. Over the past decade, passive hyperthermia exposure has grown significantly in popularity as a means of improving acclimatization and performance in hot environments. The present study aims to compare the physiological changes that occur in a group of professional athletes due to passive sauna exposure (80–90 °C) and their own response to maximal aerobic performance. Methods: Twelve professional trail runners (eight men and four women) were tested in three conditions: (i) baseline; (ii) before; and (iii) after (a) passive dry sauna exposure and (b) a maximal endurance test. In both cases, physiological parameters such as heart rate, tympanic temperature, arterial and muscle oxygen saturation, and blood concentrations of glucose, total cholesterol, high-density lipoprotein (HDL) and hemoglobin were measured. Results: Sauna exposure produced similar trends in cardiovascular and metabolic responses to those occurring during exercise, but at a much lower physiological level. Glucose and HDL levels were both significantly elevated (or tended to be so) after sauna and exercise (p < 0.03 and p < 0.01, respectively). Athletes who mobilized the sum of substrates (glucose and HDL) performed the exercise test faster (r = −0.76; p < 0.004). The response of arterial oxygen saturation (decreased) was similar during sauna and exercise, but opposite at the muscular level (increased during sauna and decreased during exercise). Additionally, inter-individual variability in responses was noted for most of the other parameters, suggesting the existence of ‘responders’ and ‘non-responders’ to thermal stimuli. Conclusions: The physiological responses of trained endurance athletes are moderately impacted by passive sauna use. However, individual changes could be correlated with endurance performance and optimizing individualization. Heat stimuli promote different physiological responses in terms of cardiac function, oxygen kinetics and substrate mobilization, albeit to a lesser extent than exercise. Greater substrate mobilization during maximal endurance exercise was found to be correlated with better performance. Further studies are needed to explore the concepts of metabolic flexibility, as described here, and how heat exposure may improve systemic health and performance. Full article

This article addresses the non-trivial problem of classifying thoracic diseases in chest X-ray (CXR) images. A single CXR image may exhibit multiple diseases, making this a multi-label classification problem. Additionally, the inherent class imbalance makes the task even more challenging as some diseases occur more frequently than others. Our methodology is based on transfer learning aiming to fine-tune a pretrained DenseNet121 model using CXR images from the NIH Chest X-ray14 dataset. Training from scratch would require a large-scale dataset containing millions of images, which is not available in the public domain for this multi-label classification task. To address class imbalance problem, we propose a rank-based loss derived from the Zero-bounded Log-sum-exp and Pairwise Rank-based (ZLPR) loss, which we refer to as focal ZLPR (FZLPR). In designing FZLPR, we draw inspiration from the focal loss where the objective is to emphasize hard-to-classify examples (instances of rare diseases) during training compared to well-classified ones. We achieve this by incorporating a “temperature” parameter to scale the label scores predicted by the model during training in the original ZLPR loss function. Experimental results on the NIH Chest X-ray14 dataset demonstrate that FZLPR loss outperforms other loss functions including binary cross entropy (BCE) and focal loss. Moreover, by using test-time augmentations, our model trained using FZLPR loss achieves an average AUC of 80.96% which is competitive with existing approaches. Full article

Waste wheat straw (WS) is a common agricultural waste with a low acquisition cost and a high annual yield, making it a promising feedstock for a biorefinery. In this work, efficient co-production of reducing sugars and xylo-oligosaccharides (XOSs) from WS was realized through FeCl₃-assisted p-toluene sulfonic acid (PTSA) pretreatment. The effects of reaction conditions (PTSA content, FeCl₃ loading, pretreatment duration, and temperature) on lignin and xylan elimination and enzymolysis were analyzed. The results manifested that the enzymolysis of WS substantially elevated from 22.0% to 79.3% through the treatment with FeCl₃-PTSA/water (120 °C, 60 min). The xylan removal and delignification were 79.7% and 66.6%, respectively. XOSs (4.0 g/L) were acquired in the pretreatment liquor. The linear fitting about LogR₀ with enzymolysis, delignification, xylan elimination and XOSs content was investigated to explain the reasons for the elevated enzymolysis and to clarify the comprehensive understanding of WS enzymolysis through the FeCl₃-PTSA/water treatment. In addition, the recycling test of FeCl₃-PTSA/water manifested a good recycling ability for WS treatment, which would reduce the pretreatment cost and enhance the economic benefit. To sum up, FeCl₃-assisted PTSA treatment of biomass for co-production of reducing sugars and XOSs is an alternative method of waste biomass valorization. Full article

With the acceleration of construction industrialization and carbon reduction goals, prefabricated steel structures are widely used for their efficiency and strength. However, steel’s poor fire resistance limits its use. At high temperatures, steel weakens, leading to collapse risks. Common fire protection methods like rock wool, fire-resistant boards, and coatings focus on single materials, leaving composite systems for modular steel columns understudied. This study systematically examines the fire resistance of modular steel columns with composite protective layers through tests and simulations. It finds that rock wool shrinks under heat, reducing its effectiveness by approximately 66.7%, and suggests construction improvements to mitigate this issue. A simplified fire resistance formula is proposed, showing that the total fire resistance of multi-layer systems approximates the sum of each layer’s resistance. These insights offer practical design guidance and fill a key research gap in composite fire protection for modular steel structures. Full article

Background: In response to the growing demand for the real-time, in-field characterization of odorous anthropogenic emissions, this study develops and uses a MEMS-based micropacked thermal desorption Gas Chromatography system coupled with a PhotoIonization Detector (GC/PID) for Hot Mix Asphalt (HMA) plant emissions. Methods: The innovative portable device, Pyxis GC, enables the high-sensitivity profiling of Volatile Organic Compounds (VOCs), particularly aldehydes and ketones, with sub-ppb detection limits using ambient air as the carrier gas. A comprehensive experimental design optimized the preconcentration parameters, resulting in an efficient, green analytical method evaluated via the Green Analytical Procedure Index (GAPI). Sorbent comparison showed quinoxaline-bridged cavitands outperform the conventional materials. Results and conclusions: The method was successfully deployed on site for source-specific sampling at an HMA plant, generating robust emission fingerprints. To assess environmental impact, a Generalized Additive Model (GAM) was developed, incorporating the process temperature and Sum of Odour Activity Values (SOAV) to predict odour concentrations. The model revealed a significant non-linear influence of temperature on emissions and validated its predictive capability despite the limited sample size. This integrated analytical–statistical approach demonstrates the utility of MEMS technology for real-time air quality assessment and odour dispersion modelling, offering a powerful tool for environmental monitoring and regulatory compliance. Full article