Search Results (153,460)

This study developed and validated simplified, individualized heart rate (HR)-based regression models to predict energy expenditure (EE) during treadmill exercise without direct VO2 calibration, addressing the need for more practical and accurate methods that overcome limitations of existing predictions and facilitate precise EE estimation outside specialized laboratory conditions. Energy expenditure was measured by assessing oxygen uptake (VO₂) using a portable gas analyzer and predicted across three treadmill protocols: Bruce, Modified Bruce, and Progressive Speed. These protocols were selected to capture a wide range of exercise intensities and improve the accuracy of heart rate-based EE predictions. The six models combined heart rate, heart rate reserve (HRres), and demographic variables (sex, age, BMI, resting HR) using the Enter method of multiple regression, where all variables were included simultaneously to enhance the real-world applicability of the energy expenditure predictions. All models showed high accuracy with R² values between 0.80 and 0.89, and there were no significant differences between measured and predicted energy expenditure (p ≥ 0.05). HRres-based models outperformed others at submaximal intensities and remained consistent across sex, weight, BMI, and resting HR variations. By incorporating individual resting and maximal HR values, HRres models offer a personalized, physiologically relevant estimation method. These results support integrating HRres-based EE prediction into wearable devices to improve accessible and precise monitoring of physiological energy metabolism. Full article

In this paper, we proposed a novel method for grinding trajectory planning on large-curved forgings to improve grinding performance and grinding efficiency. Our method consists of four main steps. Firstly, we conducted simulations and analyses on the contact state and contact pressure between the grinding tool and curved workpieces, and explored different grinding methods. Based on the Preston equation, a material removal model was established to analyze the grinding force. Secondly, we proposed an adaptive impedance control method based on grinding force analysis, which can control the contact force indirectly by adjusting the end position of the robot. To address the inability of impedance control to adjust impedance parameters in real time, a control strategy involving online estimation of environmental position and stiffness is adopted. Based on the Lyapunov asymptotic stability principle, an adaptive impedance control model is established, and the effectiveness of the adaptive algorithm is verified through simulation. Thirdly, Position correction is realized through gravity compensation of the grinding force and discretization of the impedance control model. Subsequently, a dynamic trajectory adjustment strategy is proposed, which integrates position correction for the current grinding point and position compensation for the next grinding point, to achieve the force control objective in the grinding process. Finally, a constant force grinding experiment was conducted on large-curvature blades using a robotic automatic grinding system. The grinding system effectively removed the knife marks on the blade surface, resulting in a surface roughness of 0.5146 µm and a grinding efficiency of approximately 0.89 cm²/s. The simulation and experimental results indicate that the smoothness and grinding efficiency of the blades are superior to the enterprise’s existing grinding technology, verifying the feasibility and effectiveness of our proposed method. Full article

Biomass gasification, a key waste-to-energy technology, is a complex thermochemical process with many input variables influencing the yield and quality of syngas. In this study, data-driven machine learning models are developed to capture the nonlinear relationships between feedstock properties, operating conditions, and syngas composition, in order to optimize process performance. Random Forest (RF), CatBoost (Categorical Boosting), and an Artificial Neural Network (ANN) were trained to predict key syngas outputs (syngas composition and syngas yield) from process inputs. The best-performing model (ANN) was then integrated into a multi-objective optimization framework using the open-source Optimization & Machine Learning Toolkit (OMLT) in Pyomo. An optimization problem was formulated with two objectives—maximizing the hydrogen-to-carbon monoxide (H₂/CO) ratio and maximizing the syngas yield simultaneously, subject to operational constraints. The trade-off between these competing objectives was resolved by generating a Pareto frontier, which identifies optimal operating points for different priority weightings of syngas quality vs. quantity. To interpret the ML models and validate domain knowledge, SHapley Additive exPlanations (SHAP) were applied, revealing that parameters such as equivalence ratio, steam-to-biomass ratio, feedstock lower heating value, and fixed carbon content significantly influence syngas outputs. Our results highlight a clear trade-off between maximizing hydrogen content and total gas yield and pinpoint optimal conditions for balancing this trade-off. This integrated approach, combining advanced ML predictions, explainability, and rigorous multi-objective optimization, is novel for biomass gasification and provides actionable insights to improve syngas production efficiency, demonstrating the value of data-driven optimization in sustainable waste-to-energy conversion processes. Full article

The paper presents a comprehensive evaluation of certain main parameters and the performance of microjet series models from the same engine manufacturer, AMT Netherlands, under various operating regimes. The study was performed through a percentage-based analysis of a series of actual values extracted from a set of charts, from which a specific database was created. The database comprised data sourced from official specification sheets issued by the manufacturer. The studied engines shared the same technical turbomachinery design, comprising a single shaft, one centrifugal compressor rotor, one axial turbine rotor stage, and a convergent jet nozzle, but differed in thrust class, ranging from 167 to 1569 N. Parameter and performance ratios were calculated to analyze the variation patterns within each engine and across different engines. The study refers to the variation analysis of thrust, fuel flow, exhaust gas temperature, and specific fuel consumption relative to engine speed, from idle to maximum regime. It presents the actual percentage values alongside polynomial functions that characterize the variations in engine parameters through which the analysis can be conducted. Full article

Background. The existing literature highlights a lack of comparative studies between subtypes of cholangiocarcinoma (CC) and the impact of misclassification on the epidemiological parameters. Methods. A retrospective study was conducted to evaluate the surgical outcomes. The authors used Poisson regression with modified errors to calculate the risk ratios (RR) and reported post-estimation marginal effects. Coefficient estimates, variance inflation factors, and Pearson’s goodness-of-fit test statistics were used to check for multicollinearity and model fit, respectively. We also performed a reclassification analysis by modeling Klatskin tumors (PCC) as extrahepatic (ECC), reclassifying them as intrahepatic (ICC), and comparing the corresponding changes in estimates. Results. Regression analysis revealed an increased risk of death in patients with ICC (RR = 2.05, 95% CI 1.11–3.78) and PCC (RR = 2.03, 95% CI 0.97–4.24) compared to those with DCC. When PCC was analyzed as an ECC, the ICC revealed an RR of 1.52 (95% CI 0.84–2.73). Further reclassification of PCC showed an RR of 2.04 for ICC (95% CI: 1.53–3.53). The adjusted marginal effects saw a reduction in the death probability for both ICC and ECC. However, post hoc analyses revealed insufficient evidence for differences between the reclassified models. Conclusions. Patients with DCC had slightly better prognosis compared to ICC and PCC. We found no differences in survival between ICC and ECC (combining DCC and PCC). The decrease in mortality risk due to reclassification in both groups was not confirmed statistically. Future studies should focus on statistical evidence when referring to the Will Rogers phenomenon, instead of inferring from raw comparisons. Full article

Information announcement is the process of propagating and synchronizing the information of Computing Resource Nodes (CRNs) within the system of the Computing Networks. Accurate and timely acquisition of information is crucial to ensuring the efficiency and quality of subsequent task scheduling. However, existing announcement mechanisms primarily focus on reducing communication overhead, often neglecting the direct impact of information freshness on scheduling accuracy and service quality. To address this issue, this paper proposes a hierarchical and clustering-based announcement mechanism for the wide-area Computing Networks. The mechanism first categorizes the Computing Network Nodes (CNNs) into different layers based on the type of CRNs they interconnect to, and a top-down cross-layer announcement strategy is introduced during this process; within each layer, CNNs are further divided into several domains according to the round-trip time (RTT) to each other; and in each domain, inspired by the “Six Degrees of Separation” concept from social propagation, a RTT-aware fast clustering algorithm canopy is employed to partition CNNs into multiple overlap clusters. Intra-cluster announcements are modeled as a Traveling Salesman Problem (TSP) and optimized to accelerate updates, while inter-cluster propagation leverages overlapping nodes for global dissemination. Experimental results demonstrate that, by exploiting shortest path optimization within clusters and overlapping-node-based inter-cluster transmission, the mechanism is significantly superior to the comparison scheme in key indicators such as convergence time, Age of Information (AoI), and communication data volume per hop. The mechanism exhibits strong scalability and adaptability in large-scale network environments, providing robust support for efficient and rapid information synchronization in the Computing Networks. Full article

Calcium is an essential macronutrient for plant growth and development, and there is an optimal calcium concentration for plant growth. Calcium ion concentration changes create “calcium signals” that regulate plant growth through perception, decoding, transduction, and response processes. However, the mechanisms by which calcium signaling regulates photosynthesis are still not fully understood. In this study, Quercus acutissima seedlings were used to investigate the inhibitory effects of different concentrations of the calcium channel blocker lanthanum chloride (LaCl₃) on photosynthesis and the underlying mechanisms. The results show that increasing LaCl₃ concentration significantly decreased photosynthetic parameters, photosynthetic pigment contents, and photosynthetic product accumulation. Long-term water use efficiency decreased with increasing LaCl₃ concentration, while instantaneous water use efficiency initially increased and then decreased. Structural equation modeling analysis indicated that LaCl₃ concentration was significantly positively correlated with leaf calcium concentration in Quercus acutissima seedlings, while it was significantly negatively correlated with stomatal conductance, carotenoids, and soluble sugar content. The study concludes that LaCl₃ directly inhibits the photosynthetic physiological processes of Quercus acutissima seedlings by blocking calcium signaling, providing insights into the regulatory mechanisms of calcium signaling in plant photosynthesis and a theoretical basis for the cultivation and application of Quercus acutissima under varying environmental conditions. Full article

Background/Objectives: Multiple chronic conditions (MCCs) pose global health and social challenges, with caregiving often relying on family members, especially in low- and middle-income countries (LMICs). However, limited evidence exists regarding the factors influencing caregiver contribution (CC) to patient self-care among older adults with MCCs in these settings. Aim: The aim of this study was to examine the associations between caregivers’ and patients’ socio-demographic characteristics and patients’ clinical variables and the CC to patient self-care behaviors in adults with MCCs in an LMIC context. Methods: This multicenter, cross-sectional study included patient–caregiver dyads recruited from outpatient and community settings across Albania, between August 2020 and April 2021. CC was assessed using the Caregiver Contribution to Self-Care of Chronic Illness Inventory scale (CC-SCCII). Three multivariable linear regression models were used to explore associations with the three dimensions of CC to self-care maintenance, monitoring, and management. Results: Caregivers were mostly female, children, or spouses with a high level of education and employed. Patients were primarily female and had low education. Hypertension and diabetes were the most prevalent. Older and employed caregivers contributed less to CC to self-care maintenance, while higher education and caregiving experience increased it. Living with the patient and being a spouse reduced CC to self-care monitoring, whereas more caregiving hours and experience improved it. CC to self-care management was negatively influenced by cohabitation, presence of a second caregiver, and being a spouse, but improved with more caregiving hours. Conclusions: Socio-demographic and caregiving factors differently influence CC to self-care dimensions in older adults with MCCs in an LMIC. Tailored caregiver support programs are essential to enhance caregiver involvement and improve MCC patient outcomes in LMICs. Full article

The widespread use of antimicrobial drugs (AMDs) in livestock production contributes to antimicrobial resistance (AMR), a global One Health concern affecting humans, animals, and the environment. This study analyzed AMD residues and the AMR profiles in Escherichia coli and Enterococcus spp./Streptococcus spp. (ES) isolated from lagoon water samples collected from nine California dairies. Antimicrobial susceptibility testing was performed using the microbroth dilution method, and enzyme-linked immunosorbent assay (ELISA) kits were used to detect AMD residues in lagoon water. Overall, residues of florfenicol and tilmicosin were detected in more than 90% of the samples, while tetracycline was detected in 74.2 ± 4.6% of the samples. In contrast, penicillin and sulfamethazone residues were low, observed in only 3.4 ± 1.9% and 32.3 ± 5.0% of samples, respectively. The very low prevalence of penicillin was likely due to limited use in dairy cattle, given its prolonged withdrawal period. Prevalence estimates for AMR in the lagoon samples showed 100% resistance of E. coli to tiamulin, tilmicosin or tylosin and high prevalence against florfenicol (96.0% ± 2.0) or gamithromycin (92.0% ± 1.9). However, low AMR estimates (less than 10%) were observed against other AMDs tested. Similarly, the prevalence estimates for AMR of ES isolates in the studied lagoon were high against florfenicol (95.1% ± 2.0), tildipirosin (97.6% ± 1.7), or tilmicosin (98.8% ± 1.2), but low against ampicillin (4.9% ± 1.9) and penicillin (8.5% ± 2.4). Despite numerical differences in AMR prevalence by season, region, and sampling point, these variations were not statistically significant. Logistic regression models were applied to explore associations between AMD residues and AMR phenotypes where appropriate. Tilmicosin residues were significantly associated with reduced resistance to danofloxacin, enrofloxacin, and tildipirosin in E. coli isolates, while sulfamethoxazole residues were linked to increased tetracycline resistance in Enterococcus spp. The presence of florfenicol residues, potentially originating from treated calves and heifers, helps explain the high prevalence of resistance to this drug in both bacterial species. However, not all AMD residues were associated with AMR, underscoring the complex ecological and genetic factors involved in the development and maintenance of resistance in dairy environments. These findings underscore the importance of integrating AMR surveillance and prudent AMD use practices across all segments of dairy production systems. Full article

Anti-PD-1 therapies have transformed cancer treatment by restoring antitumor T cell activity. Despite their broad clinical use, variability in treatment response and immune-related adverse events underscore the need for therapeutic optimization. This article provides an integrative overview of the pharmacokinetics (PKs) of anti-PD-1 antibodies—such as nivolumab, pembrolizumab, and cemiplimab—and examines pharmacokinetic–pharmacodynamic (PK-PD) relationships, highlighting the impact of clearance variability on drug exposure, efficacy, and safety. Baseline clearance and its reduction during therapy, together with interindividual variability, emerge as important dynamic biomarkers with potential applicability across different cancer types for guiding individualized dosing strategies. The review also discusses established biomarkers for anti-PD-1 therapies, including tumor PD-L1 expression and immune cell signatures, and their relevance for patient stratification. The evidence supports a shift from traditional weight-based dosing toward adaptive dosing and therapeutic drug monitoring (TDM), especially in long-term responders and cost-containment contexts. Notably, the inclusion of clearance-based biomarkers—such as baseline clearance and its reduction—into therapeutic models represents a key step toward individualized, dynamic immunotherapy. In conclusion, optimizing anti-PD-1 therapy through PK-PD insights and biomarker integration holds promise for improving outcomes and reducing toxicity. Future research should focus on validating PK-based approaches and developing robust algorithms (machine learning models incorporating clearance, tumor burden, and other validated biomarkers) for tailored cancer treatment. Full article

Climate change is not only a technical and environmental challenge but also an emotional and psychological one that affects public engagement, policy acceptance, and long-term sustainability. This study presents a conceptual framework based on the Kübler-Ross model from psychotherapy to explore emotional responses to climate change: denial, anger, bargaining, depression, and acceptance. Based on a thematic analysis of the interdisciplinary secondary literature and illustrative cases, we analyse how these emotional dynamics influence climate mitigation and adaptation efforts. Each stage reveals specific psychological barriers and entry points for communication, resilience-building, and policy design. We argue that emotional readiness is a critical yet underacknowledged factor in sustainable development and societal transformation. Addressing emotional dimensions can support mental health, increase acceptance of climate measures, and improve the alignment between sustainability strategies and public responses. Our findings emphasise the importance of tailoring sustainability communication and policies to different emotional stages to foster inclusive, effective, and lasting climate action. Full article

As a common engineering building material, concrete material is widely used in buildings, bridges, and protective structures. Concrete load-bearing columns are one of the main load-bearing components in buildings. In order to analyze the change rule of strength of plain concrete column under small size impact damage, the impact concrete test of 11 mm prefabricated tungsten alloy spherical fragment at different speeds was carried out, and the damage parameters of concrete were obtained. The numerical simulation was carried out with the concrete material model under the experimental strength. Based on the obtained material parameters, five initial variables of load (10–30 MPa), column side length (0.1–0.3 m), fragment velocity (500–1500 m/s), impact angle (0–45°), and position height (200–400 mm) were numerically simulated. Based on the action law of each variable on the concrete column, a comprehensive numerical calculation of orthogonal optimization with five variables and five levels was carried out. The calculation results show that the structural strength of concrete is mainly affected by the side length of the column, and the initial velocity of the fragment determines the size of the loss mass. The greater the load on the concrete column, the greater the height of the position, and the more easily the column collapses; when the side length of the concrete column reaches more than 250 mm, the fragment has little effect on the overall strength of the concrete column. Through the results obtained in this paper, it can be further extended to the evaluation of damage of building components under different loads, so as to obtain whether the bearing level of damaged concrete components can meet the requirements. Full article

Underwater wireless optical communication (UWOC) is a key technology in ocean resource development, and its link stability is often limited by the difficulty of optical alignment in complex underwater environments. In response to this difficulty, this study has focused on improving the Real-Time Detection Transformer v2 (RT-DETRv2) model. We have improved the underwater light source detection model by collaboratively designing a lightweight backbone network and deformable convolution, constructing a cross-stage local attention mechanism to reduce the number of network parameters, and introducing geometrically adaptive convolution kernels that dynamically adjust the distribution of sampling points, enhance the representation of spot-deformation features, and improve positioning accuracy under optical interference. To verify the effectiveness of the model, we have constructed an underwater light-emitting diode (LED) light-spot detection dataset containing 11,390 images was constructed, covering a transmission distance of 15–40 m, a ±45° deflection angle, and three different light-intensity conditions (noon, evening, and late night). Experiments show that the improved model achieves an average precision at an intersection-over-union threshold of 0.50 (AP50) value of 97.4% on the test set, which is 12.7% higher than the benchmark model. The UWOC system built based on the improved model achieves zero-bit-error-rate communication within a distance of 30 m after assisted alignment (an initial lateral offset angle of 0°–60°), and the bit-error rate remains stable in the ${10}^{-7}$–${10}^{-6}$ range at a distance of 40 m, which is three orders of magnitude lower than the traditional Remotely Operated Vehicle (ROV) underwater optical communication system (a bit-error rate of ${10}^{-6}$–${10}^{-3}$), verifying the strong adaptability of the improved model to complex underwater environments. Full article

This paper considers a heterogeneous queuing system with an unlimited number of servers, where the parameters are determined by a random environment. A distinctive feature is that the parameters of the exponential distribution of the request processing time do not change their values until the end of service. Thus, the devices in the system under consideration are heterogeneous. For the study, a method of asymptotic analysis is proposed under the condition of extremely rare changes in the states of the random environment. We consider the following problem. Cloud node accepts requests of one type that have a similar intensity of arrival and duration of processing. Sometimes an input scheduler switches to accept requests of another type with other intensity and duration of processing. We model the system as an infinite-server queue in a random environment, which influences the arrival intensity and service time of new requests. The random environment is modeled by a Markov chain with a finite number of states. Arrivals are modeled as a Poisson process with intensity dependent on the state of the random environment. Service times are exponentially distributed with rates also dependent on the state of the random environment at the time moment when the request arrived. When the environment changes its state, requests that are already in the system do not change their service times. So, we have requests of different types (serviced with different rates) present in the system at the same time. For the study, we consider a situation where changes of the random environment are made rarely. The method of asymptotic analysis is used for the study. The asymptotic condition of a rarely changing random environment (entries of the generator of the corresponding Markov chain tend to zero) is used. A multi-dimensional joint steady-state probability distribution of the number of requests of different types present in the system is obtained. Several numerical examples illustrate the comparisons of asymptotic results to simulations. Full article

Partial Discharge (PD) measurement is one of the effective methods for assessing the internal insulation condition of power transformers in factories and substations. The pulse current signals generated by PD within transformer windings are significantly influenced by the winding structure during their propagation from the discharge source to the external measurement system. This influence may lead to misinterpretation of the insulation status, particularly in the analysis of PD measurement results. Such effects are closely related to the signal transmission path and distance and exhibit a strong correlation with the winding transfer function, manifesting as attenuation, distortion, or delay of the measured signals compared to the original PD waveforms. Therefore, it is essential to investigate the impact of the discharge path on the propagation characteristics of transformer windings and its effect on PD waveforms. This paper establishes a simplified distributed parameter model of a 180-turn single-winding multi-conductor transmission line using the finite element method and mathematical modeling, deriving the transfer functions between the winding head or winding end and various internal discharge positions. By injecting different types of PD waveforms collected in the laboratory at various discharge locations within the winding, the alterations of PD signals propagated to the winding head and winding end are simulated, and clustering analysis is performed on the propagated PD signals of different types. Full article