Search Results (7,384)

The construction industry has been troubled by a shortage of skilled labor and safety accidents in recent years. Therefore, more and more robots are introduced to undertake dangerous and repetitive jobs, so that human workers can concentrate on higher-value and creative problem-solving tasks. Nevertheless, although human–robot collaboration (HRC) shows great potential, most existing evaluation methods still focus on the single performance of either the human or robot, and systematic indicators for a whole HRC team remain insufficient. To fill this research gap, the present study constructs a comprehensive evaluation framework for HRC team performance in construction projects. Firstly, a detailed literature review is carried out, and three theories are integrated to build 33 indicators preliminarily. Afterwards, an expert questionnaire survey (N = 15) is adopted to revise and verify the model empirically. The survey yielded a Cronbach’s alpha of 0.916, indicating excellent internal consistency. The indicators rated highest in importance were task completion time (µ = 4.53) and dynamic separation distance (µ = 4.47) on a 5-point scale. Eight indicators were excluded due to mean importance ratings falling below the 3.0 threshold. The framework is formed with five main dimensions and 25 concrete indicators. Finally, an AHP-TOPSIS method is used to evaluate the HRC team performance. The AHP analysis reveals that Safety (weight = 0.2708) is prioritized over Productivity (weight = 0.2327) by experts, establishing a safety-first principle for successful HRC deployment. The framework is demonstrated through a case study of a human–robot plastering team, whose team performance scored as fair. This shows that the framework can help practitioners find out the advantages and disadvantages of HRC team performance and provide targeted improvement strategies. Furthermore, the framework offers construction managers a scientific basis for deciding robot deployment and team assignment, thus promoting safer, more efficient, and more creative HRC in construction projects. Full article

Background: Low-glycemic index (GI) carbohydrates like isomaltulose (ISO) are known to enhance incretin release and to improve postprandial glucose control at the following meal (an effect known as second meal effect, or SME), which is particularly beneficial for individuals with metabolic syndrome (MetS). This study aimed to assess the most effective preprandial interval of ISO- or saccharose (SUC) snacks (1 h vs. 3 h preload) to enhance prandial incretin responses to a subsequent meal. Methods: In a randomized crossover design, 15 participants with MetS completed four experimental conditions on four non-consecutive days, combining two preload types (ISO or SUC) and two preload timings (Intervention A: 3 h preload; Intervention B: 1 h preload). Specifically, the four conditions were (1) ISO + Intervention A, (2) SUC + Intervention A, (3) ISO + Intervention B, and (4) SUC + Intervention B. The order of conditions was randomized and separated by a 3–7-day washout period to minimize carryover effects. On each study day, participants consumed two mixed meal tests (MMT-1 and MMT-2) with a standardized preload (50 g ISO or SUC) administered either 3 h or 1 h prior to MMT-2. Blood samples were collected over 9 h at 15 predefined time points for analysis of glucose, insulin, C-peptide, and incretin hormones (GLP-1, GIP, and PYY). Results: The unique digestion profile of ISO resulted in a blunted glucose ascent rate (ΔG/Δt: 0.28 vs. 0.53 mmol/L/min for SUC, p < 0.01), paralleled by synonyms PYY elevation over 540 min monitoring, compared with SUC. ISO also led to higher and more sustained GLP-1 and PYY levels, while SUC induced a stronger GIP response. Notably, the timing of ISO consumption significantly influenced PYY secretion, with the 3 h preload showing enhanced PYY responses and a more favorable SME compared to the 1 h preload. Conclusions: ISO, particularly when consumed 3 h before a meal (vs. 1 h), offers significant advantages over SUC by elevating PYY levels, blunting the glucose ascent rate, and sustaining GLP-1 release. This synergy enhances the second meal effect, suggesting ISO’s potential for managing postprandial glycemic excursions in MetS. Full article

Cardiorespiratory fitness is a predictor of long-term health, traditionally assessed through structured exercise protocols that require maximal effort and controlled laboratory conditions. These protocols, while clinically validated, are often inaccessible, physically demanding, and unsuitable for unsupervised monitoring. This study proposes a non-invasive, unsupervised alternative—predicting the heart rate a person would reach after completing the step test, using wearable data collected during natural daily activities. Ground truth post-exercise heart rate was obtained through the Queens College Step Test, which is a submaximal protocol widely used in fitness settings. Separately, wearable sensors recorded heart rate (HR), blood oxygen saturation, and motion data during a protocol of lifestyle tasks spanning a range of intensities. Two machine learning models were developed—a Human Activity Recognition (HAR) model that classified daily activities from inertial data with 96.93% accuracy, and a regression model that estimated post step test HR using motion features, physiological trends, and demographic context. The regression model achieved an average root mean squared error (RMSE) of 5.13 beats per minute (bpm) and a mean absolute error (MAE) of 4.37 bpm. These findings demonstrate the potential of test-free methods to estimate standardized test outcomes from daily activity data, offering an accessible pathway to infer cardiorespiratory fitness. Full article

Accelerated industrialization and surging energy demands have led to continuously rising atmospheric CO₂ concentrations. Developing sustainable methods to reduce atmospheric CO₂ levels is crucial for achieving carbon neutrality. Concurrently, the rapid development of new energy vehicles has driven a significant increase in demand for lithium-ion batteries (LIBs), which are now approaching an end-of-life peak. Efficient recycling of valuable metals from spent LIBs represents a critical challenge. This study employs conventional hydrometallurgical processing to recover valuable metals from spent LIBs. Subsequently, Ni-doped Co₃O₄ (Ni:Co₃O₄) supported on the natural mineral attapulgite (ATP) was synthesized via a sol–gel method. The incorporation of a small amount of Ni into the Co₃O₄ lattice generates oxygen vacancies, inducing a localized surface plasmon resonance (LSPR) effect, which significantly enhances charge carrier transport and separation efficiency. During the photocatalytic reduction of CO₂, the primary product CO generated by the Ni:Co₃O₄/ATP composite achieved a high production rate of 30.1 μmol·g⁻¹·h⁻¹. Furthermore, the composite maintains robust catalytic activity even after five consecutive reaction cycles. Full article

In response to the need for separation and utilization of residual film mixtures after transformation of protaetia brevitarsis larvae, a pneumatic drum-sieve-type separator for transforming mixtures of protaetia brevitarsis larvae was designed. First, the suspension velocity of each component was determined by the suspension speed test. Secondly, the separation process of residual film, larvae, and insect sand was formulated on the basis of biological activities, shape differences, and aerodynamic response characteristics. Eventually, the main structural parameters and working parameters of the machine were determined. In order to optimize the separation effect, a single-factor experiment and a quadratic regression response surface experiment containing three factors and three levels were carried out, and the corresponding regression model was established. The experimental results showed that the effects of the air speed at the inlet, inclination angle of the sieve cylinder, and rotational speed of the sieve cylinder on the impurity rate of the residual film decreased in that order, and that the effects of the rotational speed of the sieve cylinder, inclination angle of the sieve cylinder, and air speed at the inlet on the inactivation rate of the larvae decreased in that order. Through parameter optimization, a better combination of working parameters was obtained: the rotational speed of the sieve cylinder was 24 r/min, the inclination angle of the sieve cylinder was −0.43°, and the air speed at the inlet was 5.32 m/s. The average values of residual film impurity rate and larval inactivation rate obtained from the material sieving test under these parameters were 8.74% and 3.18%, with the relative errors of the theoretically optimized values being less than 5%. The results of the study can provide a reference for the resource utilization of residual film and impurity mixtures and the development of equipment for the living body separation of protaetia brevitarsis. Full article

Objectives: To assess the role of a real-time therapeutic drug monitoring (TDM)-guided expert clinical pharmacological advice (ECPA) program of isavuconazole in preventing under- or overexposure with the intent of improving efficacy and safety outcomes in the critically ill patients. Methods: This retrospective study included critical patients receiving intravenous isavuconazole for prophylaxis or treatment of invasive fungal infections (IFI) and undergoing at least one TDM-guided ECPA in the period 1 March 2021–31 March 2025. Desired isavuconazole exposure was defined as trough concentrations (C_min) of 1.0–5.1 mg/L. Efficacy outcome was assessed by means of bronchoalveolar (BAL) galactomannan (GM) index, breakthrough IFI, and 30-day mortality rate, whereas safety was assessed by means of hepatic test disturbances (HTD). Univariate analysis was carried out for assessing potential variables associated with isavuconazole under- or overexposure and for comparing features of solid organ transplant (SOT) recipients vs. non-SOT patients. Proportions of isavuconazole C_min underexposure, desired exposure, and overexposure were assessed at different timepoints from starting therapy. Trends over time of HTD in relation to isavuconazole exposure were assessed separately in patients having HTD or not at baseline. Results: Overall, 32 critical patients were included. A total of 166 TDM-guided ECPAs were provided. Median (IQR) average isavuconazole C_min was 3.5 mg/L (2.1–4.6 mg/L). Proportions of ECPAs with isavuconazole C_min under- and overexposure were 4.2% (7/166) and 16.3% (27/166), respectively. Patients experiencing underexposure had higher body mass index (30.1 vs. 25.5 kg/m²; p < 0.001). Trends of isavuconazole C_min under- and overexposure changed over time, significantly decreasing the former (10.5% <7 days vs. 4.3% 7–28 days vs. 0.0% >28 days; p < 0.001) and increasing the latter (5.3% <7 days vs. 12.8% 7–28 days vs. 29.3% >28 days; p < 0.001). HTD occurred in 15/32 patients, most of whom (10/15) were affected just at baseline. Patients with transient or persistent overexposure trended toward a higher risk of HTD compared to those without (33.3% vs. 8.3%; p = 0.11). Conclusions: A real-time TDM-guided approach could be a valuable tool for optimizing isavuconazole exposure, especially whenever dealing with obese patients or with prolonged treatment. Full article

Rapid screening of foodborne pathogens is critical for food safety, yet current detection techniques often suffer from low efficiency and complexity. In this study, we developed a sliding microfluidic colorimetric biosensor for the fast, sensitive, and multiplex detection of Salmonella. First, the target bacteria were specifically captured by antibody-functionalized magnetic nanoparticles in the microfluidic chip, forming magnetic bead–bacteria complexes. Then, through motor-assisted sliding of the chip, manganese dioxide (MnO₂) nanoflowers conjugated with secondary antibodies were introduced to bind the captured bacteria, generating a dual-antibody sandwich structure. Finally, a second sliding step brought the complexes into contact with a chromogenic substrate, where the MnO₂ nanoflowers catalyzed a colorimetric reaction, and the resulting signal was used to quantify the Salmonella concentration. Under optimized conditions, the biosensor achieved a detection limit of 10 CFU/mL within 20 min. In spiked pork samples, the average recovery rate of Salmonella ranged from 94.9% to 125.4%, with a coefficient of variation between 4.0% and 6.8%. By integrating mixing, separation, washing, catalysis, and detection into a single chip, this microfluidic biosensor offers a user-friendly, time-efficient, and highly sensitive platform, showing great potential for the on-site detection of foodborne pathogens. Full article

Currently, the primary random access protocol for satellite communications is Irregular Repetition Slotted ALOHA (IRSA). This protocol leverages interference cancellation and burst repetition based on probabilistic distributions, achieving up to 80% channel utilization in practical use. However, it faces three significant issues: (1) low channel utilization with smaller frame sizes; (2) drastic performance degradation under heavy load, where channel utilization can be lower than that of traditional Slotted ALOHA; and (3) even under optimal load and frame sizes, up to 20% of the valuable satellite channel resources are still wasted despite reaching up to 80% channel utilization. In this paper, we propose the Separated Transmission and Control ALOHA (STCA) protocol, which introduces a space–air–ground layered network and separates the access control process from the satellite to an airborne platform, thus preventing collisions in satellite channels. Additionally, the airborne-platform estimates the load to ensure maximum access rates. Simulation results demonstrate that the STCA protocol significantly outperforms the IRSA protocol in terms of channel utilization. Full article

A novel acid-free oxygen pressure leaching for the extraction of zinc from hemimorphite was proposed in this study. Green vitriol (FeSO₄·7H₂O), as one of the important industrial by-products, was used as the leaching reagent to separate zinc from silicon and iron. The effect of leaching conditions, including Fe/Zn molar ratio, leaching temperature, pressure, and reaction time, on the leaching efficiency of zinc, Fe, and Si was investigated systematically. The results showed that the molar ratio of Fe/Zn and leaching temperature play a pivotal role in determining the leaching efficiency rate of Zn. Under the optimized leaching conditions (Fe/Zn molar ratio = 6:1, 150 °C, 1.8 × 10⁶ Pa, and leaching time of 2 h), the leaching efficiency of Zn reached 98.80% and the leaching efficiencies of Fe and Si were 0.76% and 16.80%, respectively. In addition, the shrinking core model was established to represent the relationship between the rate control step and the leaching conditions. The leaching process was controlled by chemical reaction and diffusion, and the activation energy of the leaching process is 97.14 kJ/mol. Full article

Focusing on the problem of insufficient detection accuracy caused by blurred target boundaries, variable scales, and severe noise interference in side-scan sonar images, this paper proposes a high-precision detection network named ESL-YOLO, which integrates edge perception and adaptive quality assessment. Firstly, an Edge Fusion Module (EFM) is designed, which integrates the Sobel operator into depthwise separable convolution. Through a dual-branch structure, it realizes effective fusion of edge features and spatial features, significantly enhancing the ability to recognize targets with blurred boundaries. Secondly, a Self-Calibrated Dual Attention (SCDA) Module is constructed. By means of feature cross-calibration and multi-scale channel attention fusion mechanisms, it achieves adaptive fusion of shallow details and deep-rooted semantic content, improving the detection accuracy for small-sized targets and targets with elaborate shapes. Finally, a Location Quality Estimator (LQE) is introduced, which quantifies localization quality using the statistical characteristics of bounding box distribution, effectively reducing false detections and missed detections. Experiments on the SIMD dataset show that the mAP@0.5 of ESL-YOLO reaches 84.65%. The precision and recall rate reach 87.67% and 75.63%, respectively. Generalization experiments on additional sonar datasets further validate the effectiveness of the proposed method across different data distributions and target types, providing an effective technical solution for side-scan sonar image target detection. Full article

Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree separately. We focus on segmenting individual apple trees as the main task in this context. Segmenting individual apple trees in dense orchard rows is challenging because of the complexity of outdoor illumination and intertwined branches. Traditional methods rely on supervised learning, which requires a large amount of annotated data. In this study, we explore an alternative approach using prompt engineering with the Segment Anything Model and its variants in a zero-shot setting. Specifically, we first detect the trunk and then position a prompt (five points in a diamond shape) located above the detected trunk to feed to the Segment Anything Model. We evaluate our method on the apple REFPOP, a new large-scale European apple tree dataset and on another publicly available dataset. On these datasets, our trunk detector, which utilizes a trained YOLOv11 model, achieves a good detection rate of $97\%$ based on the prompt located above the detected trunk, achieving a Dice score of 70% without training on the REFPOP dataset and 84% without training on the publicly available dataset.We demonstrate that our method equals or even outperforms purely supervised segmentation approaches or non-prompted foundation models. These results underscore the potential of foundational models guided by well-designed prompts as scalable and annotation-efficient solutions for plant segmentation in complex agricultural environments. Full article

The constitutive model is widely employed to characterize the rheological properties of metallic materials under high-temperature conditions. It is typically derived from a series of high-temperature tests conducted at varying deformation temperatures, strain rates, and strains, including hot stretching, hot compression, separated Hopkinson pressure bar testing, and hot torsion. The original experimental data used for establishing the constitutive model serves as the foundation for developing phenomenological models such as Arrhenius and Johnson–Cook models, as well as physical-based models like Zerilli–Armstrong or machine learning-based constitutive models. The resulting constitutive equations are integrated into finite element analysis software such as Abaqus, Ansys, and Deform to create custom programs that predict the distributions of stress, strain rate, and temperature in materials during processes such as cutting, stamping, forging, and others. By adhering to these methodologies, we can optimize parameters related to metal processing technology; this helps to prevent forming defects while minimizing the waste of consumables and reducing costs. This study provides a comprehensive overview of commonly utilized experimental equipment and methods for developing constitutive models. It discusses various types of constitutive models along with their modifications and applications. Additionally, it reviews recent research advancements in this field while anticipating future trends concerning the development of constitutive models for high-temperature deformation processes involving metallic materials. Full article

This paper proposes a novel hierarchical control architecture that combines Model Predictive Control (MPC) with Explicit Model-Following Control (EMFC) to enable accurate and efficient trajectory tracking for quadrotor electric Vertical Takeoff and Landing (eVTOL) aircraft operating in urban environments. The approach addresses the challenges of strong nonlinear dynamics, multi-axis coupling, and stringent safety constraints by separating the planning task from the fast-response control task. The MPC layer generates constrained velocity and yaw rate commands based on a simplified inertial prediction model, effectively reducing computational complexity while accounting for physical and operational limits. The EMFC layer then compensates for dynamic couplings and ensures the rapid execution of commands. A high-fidelity simulation model, incorporating rotor flapping dynamics, differential collective pitch control, and enhanced aerodynamic interference effects, is developed to validate the controller. Four representative ADS-33E-PRF tasks—Hover, Hovering Turn, Pirouette, and Vertical Maneuver—are simulated. Results demonstrate that the proposed controller achieves accurate trajectory tracking, stable flight performance, and full compliance with ADS-33E-PRF criteria, highlighting its potential for autonomous urban air mobility applications. Full article

The deformation behavior of polymers is influenced not only by service conditions such as temperature and the strain rate but also significantly by the formation process. However, existing simulation frameworks typically treat injection molding and the in-service mechanical response separately, making it difficult to capture the impact of the thermal history on large deformation behavior. In this study, the deformation behavior of injection-molded polycarbonate (PC) was investigated by accounting for its thermal history during formation, achieved through combined experimental characterization and constitutive modeling. PC specimens were prepared via injection molding followed by annealing at different molding/annealing temperatures and durations. Uniaxial tensile tests were conducted using a Zwick universal testing machine at strain rates of 10⁻³–10⁻¹ s⁻¹ and temperatures ranging from 293 K to 353 K to obtain stress–strain curves. The effects of the strain rate, testing temperature, and annealing conditions were thoroughly examined. Building upon a previously proposed phenomenological model, a new constitutive framework incorporating thermal history effects during formation was developed to characterize the large deformation behavior of PC. This model was implemented in ABAQUS/Explicit using a user-defined material subroutine. Predicted stress–strain curves exhibit excellent agreement with the experimental data, accurately reproducing elastic behavior, yield phenomena, and strain-softening and strain-hardening stages. Full article

The vast majority of photocatalysts find it difficult to consistently and stably exhibit high performance due to the variability of sunlight intensity within a day, as well as the high energy consumption of artificial light sources. In this study, mechanoluminescent Sr₂MgSi₂O₇:Eu,Dy phosphors is combined with NiS@g-C₃N₄ composite to construct a ternary heterogeneous photocatalytic system, denoted as NCS. In addition to the enhanced separation efficiency of photogenerated charge carriers by the formation of a heterojunction, the introduction of Sr₂MgSi₂O₇:Eu,Dy provides an ultra-driving force for the photocatalytic reactions owing to its mechanoluminescence-induced excitation. Results show that the degradation rate of RhB increased significantly in comparison with pristine g-C₃N₄ and NiS@g-C₃N₄, indicating the obvious advantages of the ternary system for charge separation and migration. Moreover, the additional photocatalytic activity of NCS under ultrasound stimulation makes it a promising all-weather photocatalyst even in dark environments. This novel strategy opens up new horizons for the synergistic combination of light-driven and ultrasound-driven heterogeneous photocatalytic systems, and it also has important reference significance for the design and application of high-performance photocatalysts. Full article