Performance Loss Research Articles

Assessing Project Resilience Through Reference Class Forecasting and Radial Basis Function Neural Network

A project needs to be able to anticipate potential threats, respond effectively to adverse events, and adapt to environmental changes. This overall capability is known as project resilience. In order to make efficient project decisions when the project is subjected to disruption, such as adjusting the project budget, reformulating the work plan, and rationalizing the allocation of resources, it is necessary to quantitatively understand the level of project resilience. Therefore, this paper develops a novel approach for forecasting project performance, illustrating the changes in performance levels during the disruption and recovery phases of a project and thus quantitatively assessing project resilience. While there are several methods for assessing project resilience in existing research, the majority of assessment approaches originate from within projects and are highly subjective, which makes it difficult to objectively reflect the level of project resilience. Moreover, the availability of project samples is limited, which makes it difficult to forecast the level of project performance. In view of the fact that the Reference Class Forecasting (RCF) technique avoids subjectivity and the Radial Basis Function (RBF) neural network is known to be better at forecasting small sample datasets, this paper therefore combines the RCF technique and the RBF neural network to construct a model that forecasts the project performance of the current project after experiencing a disruption, further assessing the level of the project resilience. Specifically, this paper first presents a conceptual model of project resilience assessment; subsequently, an RBF neural network model that takes into account project budget, duration, risk level of disruption, and performance before disruption based on the RCF technique is developed to forecast project performance after experiencing disruption; and finally, the level of project resilience is assessed through calculating the ratio of recovery to loss of project performance. The model is trained and validated using 64 completed construction projects with disruptions as the datasets. The results show that the average relative errors between the forecast results of schedule performance index (SPI) and the real values are less than 5%, and the R2 of the training set and the testing set is 0.991 and 0.964, respectively, and the discrepancy between the forecasted and real values of project resilience is less than 10%. These illustrate that the model performs well and is feasible for quantifying the level of project resilience, clarifying its impact on project disruption and recovery situations, and facilitating the decision-makers of the project to make reasonable decisions.

The economic burden of diagnostic uncertainty on rare disease patients

Abstract Background It often takes a long time before a rare disease is diagnosed. Without a diagnosis, the right therapy often cannot be carried out and without the right therapy, the patients are denied the opportunity for a cure or relief from their symptoms. In addition, rare diseases can also have economic consequences for those affected. This study aimed to investigate the extent to which a rare disease affects the income and work performance of the patients concerned and whether the use of AI in diagnostics would have the potential to reduce economic losses. Methods The work performance and income of 71 patients of the outpatient clinic for rare inflammatory systemic diseases with renal involvement at Hannover Medical School were analyzed during the course of the disease. The WHO Health and Work Performance Questionnaire (HPQ) was used to collect data. During the patient interviews, the questionnaire was completed four times: at the onset of the first symptoms, when a diagnostic decision support system (DDSS) would have suggested the correct diagnosis, at the time of diagnosis and at the current status. Results With the onset of the diagnostic odyssey, the monthly net income of the patients under study dropped by an average of 5.32% due to lower work performance or work absenteeism. With the correct diagnosis, the original or even a better income of 11.92% could be achieved. Loss of income due to illness was more massive in patients with a rare disease with joint, muscle and connective tissue involvement than in patients with rare vasculitides. If a DDSS had been used, the loss of income would have been 2.66% instead of the actual 5.32%. Conclusion Rare diseases resulted in temporary or existing income losses in 28.17% of the patients. Losses in work performance and income were related to the type of disease and were more pronounced in patients with joint, muscle or connective tissue disease than in patients with rare vasculitides. The use of a DDSS may have the potential to reduce the negative income effects of patients through earlier correct diagnosis. Trial registration Retrospectively registered.

A Lightweight Cotton Field Weed Detection Model Enhanced with EfficientNet and Attention Mechanisms

Cotton is a crucial crop in the global textile industry, with major production regions including China, India, and the United States. While smart agricultural mechanization technologies, such as automated irrigation and precision pesticide systems, have improved crop management, weeds remain a significant challenge. These weeds not only compete with cotton for nutrients but can also serve as hosts for diseases, affecting both cotton yield and quality. Existing weed detection models perform poorly in the complex environment of cotton fields, where the visual features of weeds and crops are similar and often overlap, resulting in low detection accuracy. Furthermore, real-time deployment on edge devices is difficult. To address these issues, this study proposes an improved lightweight weed detection model, YOLO-WL, based on the YOLOv8 architecture. The model leverages EfficientNet to reconstruct the backbone, reducing model complexity and enhancing detection speed. To compensate for any performance loss due to backbone simplification, CA (cross-attention) is introduced into the backbone, improving feature sensitivity. Finally, AFPN (Adaptive Feature Pyramid Network) and EMA (efficient multi-scale attention) mechanisms are integrated into the neck to further strengthen feature extraction and improve weed detection accuracy. At the same time, the model maintains a lightweight design suitable for deployment on edge devices. Experiments on the CottonWeedDet12 dataset show that the YOLO-WL model achieved an mAP of 92.30%, reduced the detection time per image by 75% to 1.9 ms, and decreased the number of parameters by 30.3%. After TensorRT optimization, the video inference time was reduced from 23.134 ms to 2.443 ms per frame, enabling real-time detection in practical agricultural environments.

Image modeling algorithm for environment design based on augmented and virtual reality technologies

Abstract With the continuous development of computer vision technology, the image modeling of environmental design has also received more and more attention. This study proposed an image modeling algorithm for environment design based on augmented reality (AR) and virtual reality (VR) technologies. On the system architecture, the environment design image modeling algorithm based on AR and VR technologies has provided users with an immersive 3D simulation space environment. It also has provided users with a good interactive interface through AR technology, enabling users to interact with computers in a realistic virtual three-dimensional environment and interactive operation. This study verified the algorithm from three aspects: time, authenticity, and interactivity. When facing object A, the modeling time values of the traditional environmental design image modeling method and the environmental design image modeling method proposed in this study were 12.14 and 8.72 s, respectively. The modeling authenticity reached 87.51 and 98.57%, respectively, and the interactivity reached 79.14 and 96.45%, respectively. Some columns of data have proved that the environmental design image modeling method proposed in this study was superior to the traditional environmental design image modeling method. In addition, this study also verified the system model from three aspects: stability, running smoothness, and performance loss. When the number of image modeling in the environment design was 2, the stability, running smoothness, and performance loss of the system were 99.37, 99.25 and 0.75%, respectively. The experimental data again proved that the algorithm system model proposed in this study was feasible and worthy of further application.

Eco-friendly, stable, and high-performance biochar prepared by a twice-modification scheme: Saccharification of raw materials & thermal air oxidation of biochar

Organic pollutants, such as phenolic compounds, pose significant risks to both the environment and human health. While biochar is an effective adsorbent for removing these pollutants, its dissolved solid (DS) components can lead to the loss of functional groups, structural disintegration, unstable performance, and environmental issues. This study introduces a twice-modification scheme designed to produce a biochar (BC-M) that combines high stability with superior performance. The process begins with the preparation of a stable biochar from cellulase-treated lignocellulose. This precursor biochar is then subjected to thermal air oxidation to enhance its oxygen-containing functional groups, thereby improving its adsorption capabilities. A mathematical model was developed to explore the relationship between different thermal air oxidation conditions and the properties of BC-M, aiming to optimize both adsorption capacity and DS. The model's multi-objective optimization indicated the optimal modification conditions. Compared to unmodified biochar (BC-O), BC-M showed significant improvements: its specific surface area increased by 63.6%, pore volume by 139%, and functional groups by 50%–1271%. Notably, the DS of BC-M was reduced to just 1.08 mg/L, representing a 97.5% reduction from BC-O, with a minimal mass loss of only 0.78 ± 0.45% during modification. BC-M also demonstrated a remarkable enhancement in the adsorption of phenolic compounds, with a capacity 21%–2408% higher than BC-O. Furthermore, calculations indicated that BC-M could reduce carbon emissions by 0.70 t CO2/yr/t, outperforming activated carbon in this regard. This study offers valuable insights into biochar modification, providing a low-cost, high-stability, and high-efficiency alternative for environmental cleanup.

Selection of Superior Clones of Cedrela odorata L. at Early Age in an Asexual Seed Orchard for Genetic Improvement

The selection of plant age in forest species is crucial in a genetic improvement program, as time is a key factor in decision-making. This study aimed to evaluate the variation in clonal growth of Cedrela odorata L. to identify clones with superior performance at an early age. The orchard was established in September 2014, utilizing a completely randomized block design with a planting distance of 4 x 4 m. The experiment comprised 70 clones (ortets), each with 35 replicates (ramets), totaling 2,450 ramets. Selection was based on four key traits: total height (th), clear stem height (csh), normal diameter (nd), and crown diameter (cd). These variables were used to calculate the percentage gain or loss in performance for each clone relative to the population mean. A principal component analysis, analysis of variance (ANOVA), Tukey test (p≤0.05), and cluster analysis were performed to assess the data. The results identified 16 out of the 70 clones as having superior traits. The superior clones included 53-CoSFcoChan07, 2-CoGua344, 15-CoB191, 19-CoKal126, 22-CoKal142, 41-CoTux558, 35-CoTez642, 56-CoTez539, 45-CoTez549, 49-CoTez540, 50-CoTez540, 59-CoTux555, 62-CoCar5, 30-CoBac02, 48-CoTez544, and 10-CoKal16. The average traits of these superior clones were 8.0±0.4 m for total height, 2.6±0.1 m for clear stem height, 0.13±0.0 m for normal diameter, and 4.1±0.2 m for crown diameter, representing a 15% increase over the population mean.

Resting state functional magnetic resonance imaging in patients with multiple sclerosis before and after high-dose immunosuppressive therapy and autologous hemopoietic stem cell transplantation

BACKGROUND: Multiple sclerosis is a chronic autoimmune disease characterized by multifocal foci of demyelination in the central nervous system, usually affecting people of working age. The disease causes damage to the blood-brain barrier, the development of multifocal inflammation, destruction of the myelin sheath of axons and various degrees of damage. It is clinically manifested by restriction of motor activity, visual acuity, as well as other symptoms leading to loss of performance and disability of the patient. AIM: determination of changes in the functional connectivity of brain neural networks in patients with multiple sclerosis before and after high-dose immunosuppressive therapy and autologous hematopoietic stem cell transplantation by performing functional magnetic resonance imaging at rest. MATERIALS AND METHODS: The data of functional magnetic resonance imaging of patients with multiple sclerosis were analyzed in dynamics before and after the use of high-dose immunosuppressive therapy followed by autologous hematopoietic stem cell transplantation. The study involved 25 patients with a verified diagnosis of multiple sclerosis. Each underwent complex magnetic resonance imaging at two time points (before and after high-dose immunosuppressive therapy followed by autologous hematopoietic stem cell transplantation) with a difference of 12 months, which included structural magnetic resonance imaging - in order to exclude the presence of pathological foci in the brain (in addition to foci of multiple sclerosis) and functional magnetic resonance imaging.-resonance imaging at rest — to assess functional connectivity. Also, according to the method generally accepted in classical neurology, a clinical neurological examination was performed. RESULTS: At the stage of comparing data on the two groups obtained using functional magnetic resonance imaging at rest, changes in functional activity were detected in various parts of the brain, presumably responsible for clinical differences in the studied groups. CONCLUSION: Currently, the links between brain structures and morphological changes that cause cognitive impairment in multiple sclerosis are being studied. To predict the progression of the disease, the development of biomarkers, including those based on functional magnetic resonance imaging, is required. Evaluating changes in the functional connectivity of brain neural networks can help personalize therapeutic and rehabilitation approaches.

Multi-modal and multi-layer robustness analysis of the European rail and air networks

The robustness of long-distance transport services is paramount for ensuring network connectivity under disruptions. We conduct a comparative analysis of the European rail and air networks of 124 main metropolitan areas, assessing their ability to withstand successive network degradation. We undertake a multi-modal and multi-layer approach in our analysis of the robustness of long-distance transport networks. In particular, we are interested in the role of individual nodes for both air and rail networks as well as for the integrated multi-modal network. Given the hierarchical nature of long-distance transport services, we adopt a multi-layer perspective by means of clustering nodes based on their criticality in order to identify common performance profiles. Original metrics are formulated to measure the impact of nodes on network fragmentation, both at the individual level and as cluster members. Additionally, a new metric is introduced to assess cities’ reliance on air transportation, when considering the integrated multi-layer air-rail network during disruptions in air connections. Our findings indicate that the air network exhibits significantly greater robustness compared to rail, i.e. 10% versus 71% performance loss in the worst case scenario, respectively. Furthermore, primary sub-graph of nodes whose protection from attacks can greatly enhance network’s overall robustness is identified. We discuss our findings in terms of the relationship between network structure, robustness, and the role of critical nodes as well as propose potential mitigation measures.

Recyclability‐by‐design of Printed Electronics by Low‐Temperature Sintering of Silver Microparticles

AbstractA low‐temperature sintering mechanism of silver microparticles is established and used to enable the design‐for‐recycling of printed electronics. The formation of necks during the initial phase sintering of precipitated and atomized silver microparticles is studied. Temperature‐ and time‐dependent in‐situ analyses indicate the existence of a mobile silver species that provides efficient mass transport. The activation energy of neck formation identifies silver ion formation as the rate‐limiting step of low‐temperature silver sintering. It is demonstrated that resistivities of 271 times that of bulk silver can be attained after 40 minutes at 150°C. Low‐temperature sintering not only reduces the energy required during thermal treatment but it yields layers that are suitable for recycling, too. The resulting layers have conductive necks that are mechanically weak enough to be broken during recycling. Printed layers are redispersed and the recycled silver powder is reused without loss of the electrical performance in new prints. Their conductivities are industrially relevant, which makes this recyclability‐by‐design approach promising for manufacturing more sustainable printed electronics.

Distributed model predictive control for asynchronous multiagent systems with self‐triggered coordinator

AbstractThis paper investigates a distributed model predictive control strategy for asynchronous nonlinear multiagent systems (MASs) with external disturbances via self‐triggered generators and prediction horizon regulators. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to enhance the robustness of the system. Then, the triggering interval and the corresponding prediction horizon are determined by altering the expression of the Lyapunov function, thus achieving a trade‐off between control performance and energy loss. By implementing the proposed algorithm, the coordination objective of the MASs is achieved under asynchronous communication. Finally, the recursive feasibility and closed‐loop stability are proven successively. An illustrative example is conducted to demonstrate the merits of the presented approach.

FeBTC MOF‐Derived Fe3O4@C Nanocomposite: Controlled Synthesis and Application as Potential Adsorbent for Rhodamine Dye Elimination From Wastewater

ABSTRACTThis study explores the controlled synthesis of a novel Fe3O4@C magnetic nanocomposite derived from FeBTC metal–organic framework (MOF) and its application as an efficient adsorbent for the removal of rhodamine dye from wastewater. The FeBTC MOF was first synthesized and then thermally decomposed in a controlled oxygen atmosphere at 375°C (1 h) to form the Fe3O4@C nanocomposite with a magnetic Fe3O4 core embedded in a porous carbon matrix. A comprehensive characterization of the nanocomposite was performed using x‐ray diffraction (XRD), transmission electron microscopy (TEM), and x‐ray photoelectron spectroscopy (XPS) to confirm the successful formation and to evaluate its structural and morphological properties. The morphological investigation revealed that the particles of Fe3O4 had a spherical shape with diameter of 10–15 nm. The carbon coating appeared as a thin amorphous layer surrounding the Fe3O4 nanoparticles. The adsorption capacity of Fe3O4@C for rhodamine B (RhB) dye was assessed under various conditions, including different pH values, contact times, initial dye concentrations, and temperatures. Complete dye removal was attained in 45 min using 50 mg of the nanocomposite and 50 mL of 5.0 ppm RhB at the optimum pH of 9.1. Under the same experimental conditions, the highest adsorption capacity of 13.0 mg/g was obtained, but using 15.0 mg of the nanocomposite. Fe3O4@C nanocomposite exhibits high adsorption efficiency, with a maximum removal capacity of 100%, which is clearly superior to many conventional adsorbents. This performance can be attributed to the synergistic effects of the magnetic Fe3O4 and the large surface area of the carbon matrix. Kinetic models were employed to understand the adsorption mechanism. The adsorption kinetics followed a pseudo‐second‐order model. The reusability of the adsorbent was tested over multiple cycles and showed a minimal loss of performance (drops to 93.0% after five removal cycles). The study demonstrates that the Fe3O4@C nanocomposite is a promising candidate for the effective removal of organic dyes from wastewater, offering potential benefits for environmental remediation and sustainable water management.

Ni Nanoparticles Supported on Graphene-Based Materials as Highly Stable Catalysts for the Cathode of Alkaline Membrane Fuel Cells.

Ni nanoparticles supported on graphene-based materials were tested as catalysts for the oxygen reduction reaction (ORR) to be used in anion exchange membrane fuel cells (AEMFCs). The introduction of N into the graphene structure produced an enhancement of electrocatalytic activity by improving electron transfer and creating additional active sites for the ORR. Materials containing both N and S demonstrated the highest stability, showing only a 3% performance loss after a 10 h stability test and therefore achieving the best overall performance. This long-term durability is attributed to the synergetic effect of Ni nanoparticles and bi-doped (S/N)-reduced graphene oxide. The findings suggest that the strategic incorporation of both nitrogen and sulphur into the graphene structure plays a crucial role in optimising the electrocatalytic properties of Ni-based catalysts.

Simultaneous Primer Coating for Fast Drying of Battery Electrodes

Primers are used to promote adhesion and reduce electrical interface resistance. Normally, the process of applying primer and electrode coating happens in two separate, sequential steps. Herein, primer and electrode are applied simultaneously, wet‐in‐wet. For fast drying of electrode coatings, a binder‐redistribution by binder migration happens. A normally unwanted binder migration is tried to be utilized. The goal is to use less binder in the electrode coating and dry it faster without losses in adhesion and performance. By using simultaneous primer coatings incorporating LAPONITE, the adhesion can be promoted by over 200%. This allows to eliminate the styrene‐butadiene‐rubber‐binder in the electrode slurry, saving in total of 70% of the binder. For eight times faster drying up to 30% improved specific capacity at 2C can be shown. This promising approach shows potential for any materials that lack adhesion, extending it, e.g., to porous, nanostructured particles and materials used in sodium‐ion batteries.

Performance analysis of coupling vapor compression cycle to freeze and humidification-dehumidification based high-performance desalination

Although freeze desalination has a lower latent heat of ice formation (334 kJ/kg), its energy performance is still insufficient because of energy loss associated with not using the waste heat of the active cooling system. To address this challenge, this research introduces an innovative hybrid desalination system that synergistically combines freeze, humidification-dehumidification (HDH), and vapor compression cycle (VCC) technologies. The novelty of our approach lies in simultaneously leveraging the VCC's cooling and thermal energy for freeze and dehumidification processes, respectively, which greatly increases the desalination energy performance over only achieving freeze desalination. A thermodynamic model is developed to analyze the proposed system, and a series of parametric analyses are carried out to determine the system configuration that obtains the highest performance. Ultimately, a higher ice recovery rate of 20 % offers the best total desalination performance of only 63 Wh/kg. Furthermore, the HDH desalination unit can make up the for loss of freeze desalination performance at higher feed seawater temperatures, ensuring robust performance even under high-temperature conditions.

Optimizing catalytic performance: Reduction of organic dyes using synthesized Fe3O4@AC magnetic nano-catalyst

This article presents a sustainable method for the catalytic reduction of both simple and binary dye systems using magnetic activated carbon (Fe3O4@AC) synthesized from almond shells, an agricultural waste biomass. The reduction of methylene blue (MB) and Congo red (CR) was investigated in the presence of NaBH4, and a series of physical-chemical experiments were conducted to elucidate the mechanism of dye conversion and its performance. The results confirmed the successful synthesis of Fe3O4 nanoparticles on the activated carbon surface. The calculated rate constants in a simple system were 0.34 min⁻1 for MB and 0.25 min⁻1 for CR, in the binary system, the Fe3O4@AC catalyst demonstrated enhanced selectivity for the cationic MB dye, attributable to the robust, attractive surface charge. The study aimed to enhance catalytic performance by employing optimization curves generated from a three-level Box-Behnken Design (BBD) simulation. Experimental results indicated that the optimal catalyst dose, dye concentration, and reaction duration were 4–7 mg, 80–120 mg/L, and 5–20 min, respectively. Response surface methodology (RSM) was developed by processing the findings from 17 replicated experiments using a two-quadratic polynomial model, establishing a functional link between the experimental parameters and MB conversion. Optimal conditions for MB conversion were determined to be 7 mg of catalyst, 80 mg/L of MB concentration, and a reaction time of 12.5 minutes, resulting in an estimated conversion rate of 99.99 %. This prediction was validated by experimental findings, with regression analysis confirming a high correlation (R2 > 0.99) between the predicted and observed values. Additionally, the Fe3O4@AC catalyst demonstrated good recyclability and stable performance over three consecutive cycles, maintaining high conversion efficiency without loss of performance. These findings demonstrate that Fe3O4@AC is a viable approach for the rapid and efficient remediation of dyes in water.

LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars

With the increasing amount of astronomical observation data, it is an inevitable trend to use artificial intelligence methods for automatic analysis and identification of light curves for full samples. However, data sets covering all known classes of variable stars that meet all research needs are not yet available. There is still a lack of standard training data sets specifically designed for any type of light-curve classification, but existing light-curve training sets or data sets cannot be directly merged into a large collection. Based on the open data sets of the All-Sky Automated Survey for SuperNovae, Gaia, and Zwicky Transient Facility, we construct a compatible light-curve data set named LEAVES for automated recognition of variable stars, which can be used for training and testing new classification algorithms. The data set contains a total of 977,953 variable and 134,592 nonvariable light curves, in which the supported variables are divided into six superclasses and nine subclasses. We validate the compatibility of the data set through experiments and employ it to train a hierarchical random forest classifier, which achieves a weighted average F1-score of 0.95 for seven-class classification and 0.93 for 10-class classification. Experimental results prove that the classifier is more compatible than the classifier established based on a single band and a single survey, and has wider applicability while ensuring classification accuracy, which means it can be directly applied to different data types with only a relatively small loss in performance compared to a dedicated model.

Modeling, assessment and characterization of soiling on PV Technologies. Toward a Better understanding of the Relation between dust deposition and performance losses

Soiling is a critical, site-specific challenge that affects the performance and economic viability of photovoltaic power plants. This study evaluates the effectiveness of an outdoor microscopy method under the specific climatic conditions and dust composition of the Mid-south region of Morocco. Semi-empirical models were developed to correlate the Soiling Coverage Index with losses in optical and electrical performance. Additionally, a comprehensive analysis of local dust, encompassing its chemical composition, morphology, and size distribution, was conducted. Results indicate quartz as the predominant mineral in Benguerir city’s dust particles, with diameters ranging from 0 μm to 26 μm. Additionally, following a 2-week exposure with no rainfall, a dust density of 2.5 g/m2 accumulated on the deployed glass coupons, resulting in a soiling ratio of 6.9 % and a relative transmittance loss of 15.19 %. The surface coverage index, as calculated by the outdoor microscope, was 9.16 %. Furthermore, the evaluation of this metric revealed strong positive correlations (r2 = 0.95 to 0.99) with key soiling indicators such as dust density, soiling ratio, and transmittance loss. These findings underscore the efficacy of the outdoor microscope as a fast, low-cost, and reliable soiling monitoring sensor, offering valuable insights for future monitoring and mitigation efforts.

Temperature alters antioxidant status and induces cell damage in the Amazonian fish tambaqui

Since Amazonian fish live close to their maximum thermal limits, this makes them vulnerable to the effects of global warming. The aim of this study was to evaluate the oxidative stress and antioxidant enzymatic and biochemical responses of the plasma, liver and muscle of tambaqui (Colossoma macropomum) exposed to a rising gradient of water temperature. One hundred and twenty (N=120) juvenile tambaqui were exposed to four temperature levels, these being: the environmental temperature of the season (Tenv – 25.7-30 ºC), 31 ºC, 34 ºC and 37 ºC, following a completely randomized design with three replicates for a period of 60 days. Liver and muscle samples were used to determine the levels of the enzymes superoxide dismutase (SOD), catalase (CAT) glutathione peroxidase (GPx) and lipid peroxidation (LPO). Plasma levels of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were measured. A histopathological damage assessment (HAI) was performed using liver samples and the results showed an increase in lipid peroxidation in the muscle and liver of animals kept at 37 ºC in relation to other temperatures. Enzyme responses were tissue-specific in the liver and muscle. In the liver, the reduction of CAT, SOD and GPx levels of the animals was observed at 37 ºC compared to those maintained at Tenv and SOD and GPx in relation to animals maintained at 31 and 34 ºC. The GPx enzyme showed higher activity at 34 and 37 ºC compared to the other evaluated temperatures. At 37 ºC, plasma levels of ALT and AST were higher than the other temperatures evaluated, as well as an increase in histopathological damage. In this way, in a scenario of warming of the waters of the Amazon or even of the systems used for rearing of the species, the tambaqui will be able to cope with temperatures of up to 34 ºC, without affecting its antioxidant capacity. However, at 37 ºC, oxidative stress levels and increased liver damage suggest a reduction in antioxidant capacity due to tissue impairment of the organ and general loss of animal performance as it approaches the upper thermal limit of the species.

Heat transfer evaluation of return-flow impingement cooling considering inclined sidewall and conical return hole

Impingement cooling techniques are extensively utilized on the turbine blade leading edge to mitigate thermal load. This study is an extend of an anti-crossflow return-flow impingement cooling scheme. In this study, the effects of inclined sidewalls and return holes are numerically studied with jet Reynolds number varying from 5,000 to 15,000. Three sidewall schemes (vertical, inward inclined, and outward inclined) and two return hole shapes (constant cross section and conical cross section) are investigated and compared. Numerical results show that by inclining the sidewalls, the interaction and scrape effects from return flow on the sidewalls are changed, causing heat transfer performance and pressure loss vary. To be specific, as the sidewalls are inclined inwards/outwards, the heat transfer is enhanced/weakened accordingly, making a maximum increase by 12.9% and maximum decrease by 14.3% compared to the origin design. The introduction of conical return holes contributes to improving upwards velocity of return flow and subsequently enhancing scraping effects on sidewalls. For case with vertical sidewalls, inward-inclined sidewalls, and outward-inclined sidewalls, the area-averaged Nusselt number is maximum increased by 47.2%, 61.9% and 25.7% with the application of conical return holes.

Antidehydration and Stable Mechanical Properties during the Phase Transition of the PNIPAM-Based Hydrogel for Body-Temperature-Monitoring Sensors.

Poly(N-isopropylacrylamide) (PNIPAM) enhances the reversibility and responsiveness of wearable temperature-sensitive devices. However, an open question is whether and how the hydrogel design can prevent adhesive performance loss caused by phase-transition-induced dehydration and unstable mechanical properties between devices and human skin and reduce interfacial failure. Herein, a gelatin-mesh scaffold-based hydrogel (NAGP-Gel) is constructed to inhibit dehydration and volume change, leading to stable mechanical properties, superior adhesiveness, and thermal sensing sensitivity during the phase transition. NAGP-Gel enhances the polymer chains-water interaction and weakens the degree of aggregation of polymer chains-chains, improving antidehydration properties under 45 °C conditions that are higher than the lower critical solution temperature (LCST; i.e., ∼32 °C). The mesh scaffold greatly restricts the phase-transition-induced polymer chain movement and maintains the mechanical performance. In a 60 °C environment, the maximum water loss and volume retention ratio of NAGP-Gel are only 3.58% and 97.3%, respectively. Additionally, NAGP-Gel serves as a temperature sensor, producing a stable thermal-electrical signal within the LCST range. It also can be assembled into an electronic device enabling the transmission of information and recognition of sign language via Morse code. This work broadens the application of PNIPAM in constructing intelligent hydrogels and opens the door to exploring emerging hydrogels for temperature-monitoring applications.