Search Results (162,939)

Groundwater level (GWL) in dry areas is an important parameter for understanding groundwater resources and environmental sustainability. Remote sensing data combined with machine learning algorithms have become one of the important tools for groundwater level modeling. However, the effectiveness of the above-based model in the plains of the arid zone remains underexplored. Fortunately, soil salinity and soil moisture may provide an optimized solution for GWL prediction based on the application of apparent conductivity (ECa, mS/m) using electromagnetic induction (EMI). This has not been attempted in previous studies in oases in arid regions. The study proposed two strategies to predict GWL, included an ECa-based GWL model and a remote sensing-based GWL model (RS_GWL), and then compared and explored their performances and cooperation possibilities. To this end, this study first constructed the ECa prediction model and the RS_GWL with ensemble machine learning algorithms using environmental variables and field observations (474 ECa reads and 436 groundwater level observations from a mountain–oasis–desert system, respectively). Subsequently, a strategy to improve the prediction accuracy of GWL was proposed by comparing the correlation between GWL observations and the two models. The results showed that the RS_GWL prediction model explains 30% and 90% of the spatial variability in the two value domain intervals, GWL < 10 m and GWL > 10 m, respectively. The R² of the modeling and the validation of the ECa was 79% and 73%, respectively. Careful analysis of the scatter plots between predicted ECa and GWL revealed that when ECa varies between 0–600 mS/m, 600–800 mS/m, 800–1100 mS/m, and >1100 mS/m, the fluctuation ranges of the corresponding GWL correspond to 0–31 m, 0–15 m, 0–10 m, and 0–5 m. Finally, combining the spatial variability of ECa and RS_GWL spatial distribution map, the following optimization strategies were finally established: GWL < 5 m (in natural land with ECa > 1100 mS/m), GWL < 5 m (occupied by farmland from RS_GWL) and GWL > 10 m (from RS_GWL), and 3 < GWL < 10 m (speculated). In conclusion, this study has demonstrated that the integration of EMI technology has significantly improved the precision of forecasting shallow GWL in oasis plain regions, outperforming the outcomes achieved by the use of remote sensing data alone. Full article

This study investigates the performance of DC corona discharge electrostatic precipitators (ESPs) for NO conversion to increase DeNO_x technologies’ efficiency for small-scale biomass combustion systems. Experiments were conducted using a 5 kW automatic wood pellet domestic heat source with combustion gas treated in a specially designed ESP operated in both positive and negative corona modes, resulting in a reduction in NO concentrations from 130 mg/m³ to 27/29 mg/m³ for positive/negative polarities (at 0 °C and 101.3 kPa). NO conversion efficiency was evaluated across a range of specific input energies (SIEs) from 0 to 50 J/L. The results demonstrate that DC corona ESPs can achieve up to 78% NO reduction, with positive corona demonstrating a greater energy efficiency, requiring a lower SIE (35 J/L) compared to the negative corona mode (48 J/L). A detailed analysis of reaction pathways revealed distinct conversion mechanisms between the two modes. In positive corona, dispersed active species distribution led to more uniform NO conversion, while negative corona exhibited concentrated reaction zones with about 20% higher ozone production. The reactions involving O and OH radicals were more important in positive corona, whereas ozone-mediated oxidation dominated in negative corona. The research results demonstrate that ESP technology with DC corona offers a promising, energy-efficient solution for NO_x control in small-scale combustion systems. Full article

An ultrasound-assisted dispersive liquid–liquid microextraction (DLLME) method was developed to concentrate and quantify rare earth elements (REEs) (Sc, Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu) in acidic aqueous solutions. Tetrachloroethylene (PCE) was used as the diluent, di–(2–ethyl hexyl) phosphoric acid (D2EHPA) as the extracting agent, and acetone as the dispersant solvent. The method was optimized at pH = 2.3, T = 25 °C, and V_S = 400 µL of a PCE ÷ D2EHPA mixture (10 ÷ 1) using the response surface methodology (RSM) with a Box–Behnken design. Under optimal conditions, the method proved efficient for the DLLME of most REEs (Y, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu), where the achieved recoveries were in the range of 61–109%, while relative standard deviations were in the range 11–28%. The proposed method was applied to recover REEs from real coal ash leachate samples. A greenness evaluation using the Green Analytical Procedure Index (GAPI), Analytical GREEnness (AGREE), and Analytical Eco-Scale (AES) methodologies revealed acceptable metric scores of 74, 0.61, and 26.6–79.8, respectively. Full article

(1) Background: This study examines the association between asymmetrical movements of an assembly line and machining workers and their overall well-being. The primary aim is to quantify the extent to which asymmetrical movements serve as predictors of work-related musculoskeletal disorders (WMSDs) among these workers and their overall well-being. The study emphasises the predictive relationships between asymmetry metrics and health outcomes. (2) Methods: The study included 86 employees from an automotive manufacturing plant, categorised into machining workers (MWEs) and assembly workers (AWEs). The employment duration spanned from 6 months to 40 years. Inertial motion capture technology was employed alongside the Goldberg 28-item General Health Questionnaire for a retrospective observational analysis and assessment of worker well-being. Movement dynamics were evaluated using a Motion Activity Index (MAI) to measure movement intensity, asymmetry, and quality. (3) Results: The machining group demonstrated nearly double the range of motion (median ROM: 36.6° vs. 25.5°, p = 0.019) and peak angular velocities up to eight times higher (median: 40°/s vs. 5°/s) in lumbar and thoracic rotations compared to the assembly group. Significant differences in ROM and movement speeds were observed (p < 0.001). The MAI showed higher dynamic and symmetrical movements in the machining group (36.6% vs. 25.5%, p = 0.019). No significant mental health issues were identified, aside from complaints related to somatic symptoms. (4) Conclusions: This study highlights significant occupational risks due to movement asymmetry in industrial settings, revealing substantial differences in joint angular displacements, velocities, and accelerations between machining and assembly workers. The findings emphasise the importance of targeted ergonomic interventions to enhance worker well-being and advocate for preventive health measures in occupational environments. Full article

Benthic macroinvertebrates are crucial to the health of river ecosystems. However, their conservation status is often overlooked. Due to their limited mobility and sensitivity to habitat changes, their survival is threatened. Given the current lack of research on their value composition and evaluation, this study refers to the total economic value (TEV) framework to preliminarily construct a systematic evaluation framework for freshwater benthic macroinvertebrates. Through field surveys, questionnaires, and market survey data, the value composition of benthic macroinvertebrates in the Tuojiang River Basin of Chengdu was systematically clarified. The total value was monetized using methods such as the market price method, substitute market method, and simulated market method. The main conclusions are as follows: (1) The biodiversity value of benthic macroinvertebrates in the study area ranges from 6.36 × 10⁸ to 12.76 × 10⁸ CNY/a, accounting for 0.17–0.34% of the region’s 2019 GDP. The various values in order of proportion are as follows: non-use value > direct service value > Direct Material value > indirect use value. This indicates that this biological group has enormous potential value and significant conservation importance. (2) The proportions of the value amounts of various species in the total value differ. The proportions of Viviparidae, Odonata, Ephemeroptera, and Palaemonidae are relatively high, reaching 9.8–23.8%. The proportions of Atyidae, Semisulcospiridae, and Bithyniidae are about 5%. The proportions of Unionidae, Corbiculidae, and Aillpullaridae are less than 3%. These differences are mainly influenced by factors such as species population numbers in the region, public value perception, aesthetic preferences, and dietary habits. This evaluation framework scientifically and comprehensively assesses the biodiversity value of regional benthic macroinvertebrates, providing a reference for the value assessment of other biological groups within the region and offering a scientific basis for the conservation and sustainable utilization of the target biological groups. Full article

This paper describes a constitutive model for progressive damage in carbon fibre-reinforced composites (CFRPs), developed in the framework of thermodynamics and coupled with a vector equation of state. This made the constitutive model capable of modelling shock wave propagation within orthotropic materials. Damage is incorporated in the model by using reduction in the principal material stiffness based on the effective stress concept and the hypothesis of strain energy equivalence. Damage evolution was defined in terms of a modified Tuler–Bucher criteria. The constitutive model was implemented into Lawrence Livermore National Laboratory (LLNL) DYNA3D nonlinear hydrocode. Simulation results were validated against post-impact experimental data of spherical projectile impact on an aerospace-grade woven CFRP composite panel. Two plate thicknesses were considered and a range of impact velocities above the ballistic limit of the plates, ranging from 194 m/s to 1219 m/s. Other than for the size of the delamination zone in the minor material direction, the discrepancy between the experiments and numerical results for damage and delamination in the CFRP target plates was within 8%. Full article

The grain size of metal materials has a significant impact on their macroscopic properties. However, original metallographic images often suffer from issues such as substantial noise, missing grain boundaries, low contrast, and blurred edges. These challenges hinder the accurate extraction of complete grain boundaries, limiting the precision of grain size measurement and material performance prediction. Therefore, effectively reconstructing incomplete grain boundaries is particularly crucial. This paper proposes a grain boundary reconstruction and grain size measurement method based on an improved channel attention mechanism. A generative adversarial network (GAN) serves as the backbone, with a custom-designed channel attention module embedded in the generator. Combined with a global context attention mechanism, the method captures the global contextual information of the image, enhancing the network’s semantic understanding and reconstruction accuracy for regions with missing grain boundaries. During the image reconstruction process, the method effectively leverages long-range feature correlations within the image, significantly improving network performance. To address the Mode Collapse observed during experiments, the loss function is optimized using Focal Loss, balancing the ratio of positive and negative samples and improving network robustness. Compared with other attention modules, the improved channel attention module significantly enhances the performance of the generative network. Experimental results demonstrate that the generative network based on this module outperforms comparable modules in terms of MIoU (86.25%), Accuracy (95.06%), and Precision (86.54%). The grain boundary reconstruction method based on the improved channel attention mechanism not only effectively improves the accuracy of grain boundary reconstruction but also significantly enhances the generalization ability of the network. This provides reliable technical support for the characterization of the microstructure and the performance prediction of metal materials. Full article

The investigation of wave propagation in the geological environment is warranted, and will ultimately help to provide a better understanding of the response of subsoil to excitation. Frequently utilized physical modeling represents a simplification of the global natural system for the needs of the investigation of static and dynamic phenomena with regard to the time domain. The determination of appropriate model materials is probably the most important task for physical model creation. Considering that subsoil represents a crucial medium for wave propagation, an evaluation of suitable model materials was carried out. A plate load test with a circular plate is a non-destructive method for determining the static bearing capacities of soils and aggregates, which are usually expressed by the deformation modulus E_def,2 (MPa) and the static modulus of elasticity E (MPa). A lightweight deflectometer test was used to characterize the impact modulus of deformation E_vd (MPa), which is determined based on the pressure under the load plate due to the impact load. A representative propagation of the load–settlement curve for the PLT and the acceleration–time curve for the hammer drop test were investigated. The calculated E values were found to be in the interval between 2.6 and 5.7 kPa, and depending on the load cycle, the values of E ranged from 2.6 to 3.1 kPa. The modulus E from the hammer drop test was significantly larger than the interval between 10.6 and 40.4 kPa. The values of the dynamic multiplier, as a ratio of the hammer drop value to the PLT value, of the modulus E ranged from 4.1 to 13.0. The output of the plate load testing was utilized for the calibration of the finite element method (FEM) numerical model. Both the physical and numerical models showed practically ideal linear behavior of the mass. However, the testing of gelatin-like materials is a complex process because of their viscoelastic nonlinear behavior. Full article

A novel piezoresistive cantilever microprobe (PCM) with an integrated electrothermal or piezoelectric actuator has been designed to replace current commercial PCMs, which require external actuators to perform contact-resonance imaging (CRI) of workpieces and avoid unwanted “forest of peaks” observed at large travel speed in the millimeter-per-second range. Initially, a PCM with integrated resistors for electrothermal actuation (ETA) was designed, built, and tested. Here, the ETA can be performed with a piezoresistive Wheatstone bridge, which converts mechanical strain into electrical signals by boron diffusion in order to simplify the production process. Moreover, a new substrate contact has been added in the new design for an AC voltage supply for the Wheatstone bridge to reduce parasitic signal influence via the EAM (Electromechanical Amplitude Modulation) in our homemade CRI system. Measurements on a bulk Al sample show the expected force dependence of the CR frequency. Meanwhile, fitting of the measured contact-resonance spectra was applied based on a Fano-type line shape to reveal the material-specific signature of a single harmonic resonator. However, noise is greatly increased with the bending mode and contact force increasing on viscoelastic samples. Then, to avoid unspecific peaks remaining in the spectra of soft samples, cantilevers with integrated piezoelectric actuators (PEAs) were designed. The numbers and positions of the actuators were optimized for specific CR vibration modes using analytical modeling of the cantilever bending based on the transfer-matrix method and Hertzian contact mechanics. To confirm the design of the PCM with a PEA, finite element analysis (FEA) of CR probing of a sample with a Young’s modulus of 10 GPa was performed. Close agreement was achieved by Fano-type line shape fitting of amplitude and phase of the first four vertical bending modes of the cantilever. As an important structure of the PCM with a PEA, the piezoresistive Wheatstone bridge had to have suitable doping parameters adapted to the boundary conditions of the manufacturing process of the newly designed PCM. Full article

Abelmoschus esculentus (L.) Moench, commonly known as okra or lady’s finger, is an annual flowering plant belonging to the Malvaceae family. Okra is a native plant in Africa as well as a traditional medicine in Africa and India for treating different diseases and conditions. Today, okra is widely consumed as a vegetable and is increasingly recognized as a superfood due to its rich nutritional profile and potential pharmacological benefits. Research indicates that okra exhibits a range of biological activities, including antidiabetic, antihyperlipidemic, antifatigue, vasoprotective, hepatoprotective, antitumor, anti-inflammatory, and antimicrobial effects. Despite its promising therapeutic potential, research on the active compounds in okra and evaluating efficacy in clinical settings remains limited. This review aims to consolidate existing scientific knowledge on the biological and pharmacological properties of okra, thereby encouraging further investigation into its health benefits. Ultimately, this could pave the way for the development of functional foods or health supplements that leverage okra as a key ingredient to prevent chronic diseases and enhance overall health outcomes. Full article

Power plants remain major contributors to air pollution, and while their impact on air quality and atmospheric chemistry have been extensively studied, there are still uncertainties in quantifying their precise contributions to PM_2.5 and O₃ formation under varying environmental conditions. This study employs the WRF/CMAQ modeling system to quantify the impact of power plant emissions on PM2.5 and O₃ levels across eastern China in June 2019. We investigate the spatial and temporal patterns of pollutant formation, analyze contributions to secondary PM2.5 components, and assess process-specific influences on O₃ concentrations. Results show that power plant emissions contribute up to 2.5–3.0 μg m⁻³ to PM_2.5 levels in central and eastern regions, with lower impacts in coastal and southern areas. O₃ contributions exhibit a more complex pattern, ranging from −4 to +4 ppb, reflecting regional variations in NOx saturation. Among secondary PM_2.5 components, nitrate formation is most significantly influenced by power plant emissions, emphasizing the critical role of NOx. Diurnal O₃ patterns reveal a transition from widespread morning suppression to afternoon enhancement, particularly in southern regions. Process analysis indicates that vertical transport is the primary mechanism enhancing surface O₃ from power plant emissions, while dry deposition acts as the main removal process. This comprehensive assessment provides crucial insights for developing targeted air quality management strategies, highlighting the need for region-specific approaches and prioritized NOx emission controls in the power sector. Our findings contribute to a deeper understanding of the complex relationships between power plant emissions and regional air quality, offering a foundation for more effective pollution mitigation policies. Full article

The calcium requirements of dairy cows increase dramatically soon after calving, and many cows have subclinical hypocalcemia, adversely affecting health and performance. Traditional laboratory tests for calcium are complex and not easily adapted to rapid point-of-care applications. The objectives were to evaluate a portable iCa testing device, Horiba LAQUAtwin Ca-11C, for measuring ionized calcium (iCa) in the whole blood of dairy cows and to investigate the iCa-to-total-calcium (tCa) ratio in blood collected from dairy cows within 9 days after calving. This study was conducted on two large dairies in northern China. First, blood samples were collected from 246 cows within 3 days after calving, and whole-blood iCa concentrations were measured with a Horiba LAQUAtwin Ca-11C and Abbott i-STAT 1. In addition, 885 blood samples were collected from 102 dairy cows between calving and 9 days postpartum, with iCa concentrations measured using a Horiba LAQUAtwin Ca-11C and tCa concentrations determined by a fully automated biochemical analyzer. The Horiba LAQUAtwin Ca-11C and Abbott i-STAT 1 had significant consistency in the measurement of ionized calcium concentration in whole blood (the frequency of differences between the measured values was within ±20% of the average, reaching 95.53%), and the measurement results of the two instruments were correlated (Deming regression analysis R² = 0.87). This implied the potential application of the Horiba LAQUAtwin Ca-11C as a simplified device for measuring iCa in dairy farms for on-site testing. Within the first 9 days after calving, the ratio of ionized calcium to total calcium fluctuated but overall remained within the range of 44.2 to 47.22%. Furthermore, significant variations in the ratio among individual cows indicated that individual differences and physiological states of cows affected calcium metabolism. This study identified the potential to use the Horiba LAQUAtwin Ca-11C for point-of-care testing on dairy farms. Nevertheless, the impacts of factors such as health status and individual cow differences on the ratio of ionized calcium to total calcium still require further investigation. Full article

This paper reviews the growing demand for and importance of fast and ultra-fast charging in lithium-ion batteries (LIBs) for electric vehicles (EVs). Fast charging is critical to improving EV performance and is crucial in reducing range concerns to make EVs more attractive to consumers. We focused on the design aspects of fast- and ultra-fast-charging LIBs at different levels, from internal cell architecture, through cell design, to complete system integration within the vehicle chassis. This paper explores battery internal cell architecture, including how the design of electrodes, electrolytes, and other factors may impact battery performance. Then, we provide a detailed review of different cell format characteristics in cylindrical, prismatic, pouch, and blade shapes. Recent trends, technological advancements in tab design and placement, and shape factors are discussed with a focus on reducing ion transport resistance and enhancing energy density. In addition to cell-level modifications, pack and chassis design must be implemented across aspects such as safety, mechanical integrity, and thermal management. Considering the requirements and challenges of high-power charging systems, we examined how modules, packs, and the vehicle chassis should be adapted to provide fast and ultra-fast charging. In this way, we explored the potential of fast and ultra-fast charging by investigating the required modification of individual cells up to their integration into the EV system through pack and chassis design. Full article

Background: Vulvar carcinoma is an uncommon gynecological tumor primarily affecting older women. Its treatment significantly impacts the quality of life and, not least, aesthetics because of the mutilating surgery it requires. Objectives: The management requires a multidisciplinary team of specialists who know how to care for the patient in her entirety, not neglecting psychological aspects and reconstructive surgery. How do the guidelines address multidisciplinarity, team surgical management, passing through preoperative diagnosis, and follow-up in such a challenging rare tumor to treat? Methods: To answer these questions, we compared the main scientific recommendations to identify similarities and differences in diagnostic and therapeutic management to provide an overview of the gaps that there are currently in European and American international recommendations in providing management guidance in a cancer that is both among the rarest and most difficult to manage. In this way, we aim to encourage an update in practices based on the latest scientific evidence. Results: A review of various international guidelines, some dating back to 2014, shows significant variation in approaches, ranging from initial diagnostic procedures to managing relapses. The most recent guidelines also lacked references to the latest literature, indicating that more robust scientific evidence is needed before new treatments, such as electrochemotherapy for palliation and reconstructive surgery post exenteration, can be widely adopted. Conclusions: From the systematic comparison of the main international guidelines, a strong heterogeneity emerged in the diagnostic and therapeutic recommendations as well as for the multidisciplinary approach that today is essential. Our work certainly stimulated an update of the main guidelines. Full article

Artificial intelligence (AI) is increasingly applied in a wide range of healthcare and Intensive Care Unit (ICU) areas to serve—among others—as a tool for disease detection and prediction, as well as for healthcare resources’ management. Since sepsis is a high mortality and rapidly developing organ dysfunction disease afflicting millions in ICUs and costing huge amounts to treat, the area can benefit from the use of AI tools for early and informed diagnosis and antibiotic administration. Additionally, resource allocation plays a crucial role when patient flow is increased, and resources are limited. At the same time, sensitive data use raises the need for ethical guidelines and reflective datasets. Additionally, explainable AI is applied to handle AI opaqueness. This study aims to present existing clinical approaches for infection assessment in terms of scoring systems and diagnostic biomarkers, along with their limitations, and an extensive overview of AI applications in healthcare and ICUs in terms of (a) sepsis detection/prediction and sepsis mortality prediction, (b) length of ICU/hospital stay prediction, and (c) ICU admission/hospitalization prediction after Emergency Department admission, each constituting an important factor towards either prompt interventions and improved patient wellbeing or efficient resource management. Challenges of AI applications in ICU are addressed, along with useful recommendations to mitigate them. Explainable AI applications in ICU are described, and their value in validating, and translating predictions in the clinical setting is highlighted. The most important findings and future directions including multimodal data use and Transformer-based models are discussed. The goal is to make research in AI advances in ICU and particularly sepsis prediction more accessible and provide useful directions on future work. Full article