Search Results (2,394)

This paper presents the design, modeling and experimental validation of an on-grid photovoltaic system with self-consumption, sized for the sustainable supply of a water pumping station. The system, composed of 68 photovoltaic panels, uses an architecture based on a Boost DC-DC converter controlled by the Perturb and Observe algorithm, raising the operating voltage to a high-voltage DC bus to maximize the conversion efficiency. The study integrates dynamic performance analysis through simulations in the Simulink environment, testing the stability of the DC bus under sudden irradiance shocks, with rigorous experimental validation based on field production data. The simulation results, which indicate a peak DC power of approximately 34 kW, are confirmed by real monitoring data that records a maximum of 35 kW, the error being justified by the high efficiency of the panels and system losses. Long-term validation, carried out over three years of operation (2023–2025), demonstrates the reliability of the technical solution, with the system generating a total of 124.68 MWh. The analysis of energy flows highlights a degree of self-consumption of 60.08%, while the absence of chemical storage is compensated for by injecting the surplus of 49.78 MWh into the national grid, which is used as an energy buffer. The paper demonstrates that using the grid to balance night-time or meteorological deficits, in combination with a stabilized DC bus, represents an optimal technical-economic solution for critical pumping infrastructures, eliminating the maintenance costs of the accumulators and ensuring continuous operation. Full article

Acute kidney injury (AKI) affects thousands of hospitalized patients annually, yet early detection remains challenging as serum creatinine elevation lags behind clinical deterioration. Decreased urine output (UO) represents a key diagnostic criterion of AKI, sometimes manifesting hours before biochemical changes; however, current manual monitoring methods are labor-intensive and prone to error. Here, we developed and validated a simple, cost-effective automated urine flow meter using non-contact optical sensors, a peristaltic pump, and microcontroller-based automation for precise, real-time monitoring of urine output in clinical settings, named P-meter. Three successive prototypes (V1, V2, V3) were validated against gold-standard gravimetric measurements over 285 h of testing during animal experiments that required bladder catheterization. Iterative refinement addressed miniaturization challenges, fluid dynamics optimization, and sensor positioning to achieve progressively improved accuracy. The optimized V3 prototype demonstrated further enhanced volumetric precision, stability, and flow accuracy with near-unity linearity vs. reference method (R² = 0.9889), minimal bias (mean error −0.1 mL), and 94.18% agreement within confidence limits (n = 86), outperforming the initial V1 prototype (R² = 0.9971, mean error −1.69 mL, n = 207) and intermediate V2 design (R² = 0.9941, mean error 3.63 mL, n = 390), primarily in terms of reduced bias and improved agreement. The P-meter offers accurate urine output monitoring at a lower cost than commercial systems, facilitating its use in early AKI detection and thereby improving patient outcomes. Full article

Modern, electrically operated heat pumps are characterized by a high degree of efficiency and represent an attractive alternative to conventional heating systems. However, the noise emissions from heat pumps installed outside can lead to increasing noise pollution in densely populated residential areas, which represents an obstacle to widespread use. As part of a research project, a heat pump mock-up was built based on an outdoor unit in the Fraunhofer IBP. With this mock-up, investigations have now been carried out with a prototypical silencer–resonator concept. The aim was to reduce the sound power on the outlet side of the heat pump mock-up. To estimate the effect of this silencer–resonator concept for heat pumps, FEM simulations were first carried out using COMSOL Multiphysics^® with a simplified model. The simulation results validated the silencer–resonator concept for heat pumps and indicated the considerable potential for sound reduction. A measurement was then set up, with which different silencer lengths and absorber thicknesses in the silencer were tested. The measured sound attenuation was higher than the simulated values. The results showed that porous absorbers with sufficient thickness can achieve effective performance in the mid-frequency range. A maximum sound power reduction of 5.7 dB was achieved with the 0.15 m absorber. Additionally, Helmholtz resonators were implemented to attenuate the low-frequency range and tonal peaks. With these resonators sound attenuation was increased to 7.7 dB. Full article

Thermo-hydrodynamic (THD) lubrication is a key mechanism in injection pumps, where frictional heating and heat transfer strongly influence lubrication performance. Accurate numerical modeling remains challenging due to the nonlinear coupling of temperature- and pressure-dependent fluid properties and the high computational cost of iterative solvers. The rising relevance of bio-hybrid fuels, however, demands the investigation of a great number of fuel mixtures and conditions, which is currently infeasible with traditional solvers. Physics-informed neural networks (PINNs) have recently been applied to lubrication problems; existing approaches are typically restricted to stationary cases or rely on data to improve training. This work presents a novel, purely physics-based PINN framework for solving coupled, transient THD lubrication problems in injection pumps. By embedding the Reynolds equation, energy conservation laws, and temperature- and pressure-dependent fluid models directly into the loss function, the proposed approach eliminates the need for any simulation or experimental data. The PINN is trained solely on physical laws and validated against an iterative solver for 16 transient test cases across two fuels and eight operating scenarios. The good agreement of PINN and iterative solver demonstrates the strong potential of PINNs as efficient, scalable surrogate models for transient THD lubrication and iterative design applications. Full article

The strong heterogeneity of karst aquifers limits the applicability of traditional pumping test parameter inversion methods, and karst conduits are the key factor causing this heterogeneity. To reveal how karst conduits influence the inversion of hydrogeological parameters, this study established a series of s numerical models in FEFLOW, based on the Lianhuashan mining area in Jingmen. These models was used to systematically analyze the effects of conduit characteristics (hydraulic conductivity, diameter, length, burial depth) and pumping test conditions (pumping rate and distance from the well) on the flow field, drawdown behavior, and parameter inversion results. Results indicate that the well-conduit distance R is the most critical factor: inversion errors exceeded 60% when R < 25 m; the larger the deviation between the conduit permeability coefficient (Kp) and the aquifer permeability coefficient, the larger the inversion error; the conduit length (L) and diameter (D) determine the catchment area and the cross-sectional area for flow, respectively, and are positively correlated with the inversion error; the conduit burial depth (Z) and the pumping rate (Q) affect the lag in vertical recharge and the magnitude of the drawdown, respectively, and have a small impact on the inversion error. The findings provide a theoretical basis for improving parameter estimation and well-field design in karst terrains. Full article

Coalbed methane, an abundant clean energy resource in China, is gaining significant attention. Electric submersible progressive cavity pumps, ideal for downhole extraction with high solids content, are vital in coalbed methane operations. Current fault diagnosis research for these pumps mainly relies on machine learning algorithms to identify fault features, but complex working conditions and imbalanced sample distributions challenge these models’ ability to perceive multi-scale and multi-dimensional features. To enhance the model’s perception of deep abnormal data in complex multi-case industrial datasets, this study proposes a deep learning model based on a multi-scale extraction and residual module convolutional neural network. Innovatively, a cross-attention module using global autocorrelation and local cross-correlation is introduced to constrain the multi-scale feature extraction process, making the model better suited to specific and differentiated data environments. Post feature extraction, the model employs Borderline-SMOTE to augment minority class samples and uses Tomek Links for noise removal. These enhancements improve the comprehensive perception of fault types with significant differences in period, amplitude, and dimension, as well as the learning capability for rare faults. Based on field-collected fault data and using enhanced and cleaned features for classifier training, tests on a real industrial dataset show the proposed model achieves an F1 Measure of 90.7%—an improvement of 13.38% over the unimproved model and 9.15–31.64% over other common fault diagnosis models. Experimental results confirm the method’s effectiveness in adapting to extremely imbalanced sample distributions and complex, variable field data characteristics. Full article

Background/Objectives: It is of the utmost importance to study environmental bacteria, as these microorganisms remain poorly characterized regarding their diversity, antimicrobial resistance, and impact on the global ecosystem. This knowledge gap is particularly pronounced for marine bacteria. In this study, we aimed to isolate bacteria from different marine samples and to gain insights into the environmental bacterial resistome, an aspect that remains largely neglected. Methods: Bacteria were isolated from several marine sources using two different culture media, and their identification was based on 16S rRNA gene analysis. Whole-genome sequencing was performed for selected isolates belonging to novel taxa. Antimicrobial susceptibility to seven antibiotics was evaluated using the disk diffusion method. Results: A total of 171 bacterial isolates belonging to the phyla Pseudomonadota, Bacteroidota, Planctomycetota, Actinomycetota, and Bacillota were obtained from diverse marine samples. The most abundant group belonged to the class Alphaproteobacteria. Thirty isolates represented novel taxa, comprising 16 new species and one new genus. Despite the challenges associated with determining antibiotic resistance profiles in environmental bacteria, only one isolate (1.8%) was pan-susceptible, whereas 54 (98.2%) showed resistance to at least one of the tested antibiotics. Moreover, 33 isolates exhibited a multidrug-resistant phenotype. Genome analysis of four novel taxa revealed the presence of an incomplete AdeFGH efflux pump. Conclusions: This study highlights the high bacterial diversity in marine environments, the striking prevalence of antibiotic resistance, and the major methodological challenges in studying environmental bacteria. Importantly, it emphasizes the relevance of culturomics-based approaches for uncovering hidden microbial diversity and characterizing environmental resistomes. Full article

Methicillin-resistant coagulase-negative staphylococci (MRCoNS) are a major cause of infectious diseases, owing to their ability to form biofilms and colonize community and hospital environments. MRCoNS strains were identified using biochemical tests, an MALDI-TOF MS analyzer, and PCR-based 16S rRNA gene confirmation. This study was designed to assess antibiotic resistance and biofilm-forming capacity and to determine the presence of the mecA, mecC, agrA, srtA, icaABCD, bap, fnbAB, and clfAB genes in MRCoNS isolates. From patients undergoing random screening during hospitalization in the Orthopedics Clinic in Slovakia, 28 strains of MRCoNS were identified: S. epidermidis (n = 10), S. hominis (n = 8), S. haemolyticus (n = 4), S. lugdunensis (n = 3), while S. simulans, S. pasteuri, and S. warneri were detected only once. The highest rates of resistance were observed for ampicillin, oxacillin, rifampicin, trimethoprim (100%), and erythromycin (62%). The mecA gene was detected in 12 analyzed isolates. In 12 isolates, MDR, strong efflux pump activity, and strong or moderate biofilm formation were simultaneously detected. Our findings highlight the problems posed by biofilm-forming, resistant CoNS in hospitalized patients and the importance of diagnostics, separation, rapid treatment, and proper hospital hygiene. Full article

An accurate in situ stress field is a prerequisite for evaluating the stability of surrounding rock in underground caverns of a pumped-storage power station (PSPS) and ensuring the long-term safe operation of underground powerhouses. However, in situ stress measurements in the field are typically characterized by a limited number of measurement points, strong data randomness, and high testing costs. Meanwhile, conventional regression inversion methods often yield stress fields with insufficient accuracy or unstable spatial distributions. To address these issues, this paper proposes an in situ stress field inversion method based on the particle swarm optimization–support vector regression (PSO-SVR) algorithm. Stress boundary conditions are formulated in terms of lateral stress coefficients combined with shear stresses, and PSO is employed to optimize the hyperparameters of the SVR model. The stress boundary conditions predicted by the PSO-SVR algorithm are then imposed on a numerical model to compute the stresses at the measurement points, and the optimal boundary conditions are identified by minimizing the root mean square error (RMSE) between the inverted and measured in situ stresses. On this basis, the stress components at the measurement points and the in situ stress field in the study area are obtained. The results demonstrate that the inverted in situ stresses agree well with the field measurements, exhibiting good consistency and spatial regularity. Specifically, compared with the traditional multiple linear regression (MLR) method, the PSO-SVR algorithm reduces the RMSE and mean absolute error (MAE) of the in situ stress measurement data by 48.21% and 47.01%, respectively, and produces inversion results with higher accuracy, more stable spatial patterns, and markedly fewer anomalous zones. Consequently, the PSO-SVR algorithm is well suited for in situ stress inversion in PSPSs and provides a reliable stress-field basis for subsequent optimization of underground cavern excavation and support. Full article

The results show that the numerical simulation error based on the RPI two-phase boiling heat transfer model is less than 5%, which is in good agreement with the test results. Compared with the original engine, the temperature near the spark plugs’ position of improvement in scheme 2 decreased by 8.4 K, and the maximum temperature difference between the cylinder head intake and exhaust decreased by 14 K. Moreover, the overheating degree of the water jacket wall is the lowest, avoiding the occurrence of film boiling, and the local maximum vaporization rate is less than 50%. The prototype tests also confirmed that the improvement scheme effectively enhanced the heat transfer performance of the water jacket. The inlet flow rate and temperature of the coolant have significant and complex effects on two-phase boiling heat transfer. Both too low a flow rate and too high a temperature will lead to local film boiling, deteriorating heat transfer. Too high a flow rate will blow away bubbles, while too low an inlet temperature will not cause boiling, both of which can only enforce convective heat transfer. Appropriately reducing the flow rate and increasing the temperature can effectively utilize the enhanced heat transfer potential of subcooled boiling, while also save pump power consumption and improving engine fuel economy. The average heat flux density of boiling heat transfer in this paper is 13.9% higher than that of the forced convective heat transfer. When designing a water jacket with boiling heat transfer, attention should be paid to the transport effect of convective motion on bubbles, controlling subcooled boiling in the high-temperature zone and preventing film boiling. Full article

Background and Objectives: Clinically meaningful drug–drug interactions may be overlooked in oncology. Proton pump inhibitors (PPIs) may modulate outcomes with immune checkpoint inhibitors (ICIs) by altering the gut microbiome, altering the immune milieu, and affecting transporter interactions. We evaluated whether concomitant PPI use affects survival among patients with metastatic non-small cell lung cancer (NSCLC) treated with nivolumab. Materials and Methods: We retrospectively included patients with metastatic NSCLC who received second-line nivolumab across five oncology centers (January 2020–June 2023). Patients were grouped as concomitant PPI users vs. non-users. Overall survival (OS) and progression-free survival (PFS) were estimated by the Kaplan–Meier method and compared with the log-rank test; multivariable Cox models assessed independent associations. Results: A total of 194 patients were screened, of whom 30 were excluded according to predefined criteria. The final analysis included 164 patients—85 PPI users and 79 non-users. Median OS was 26.1 months (95% CI 15.5–36.7) in PPI users and 29.3 months (22.2–36.4) in non-users; this difference was not statistically significant (p = 0.54). Median PFS was 6.2 months (3.7–8.6) in PPI users vs. 10.2 months (7.1–13.2) in non-users (p = 0.04). In multivariable analysis, absence of concomitant PPI use (No vs. Yes) was independently associated with longer PFS (HR = 0.52, 95% CI 0.24–0.89, p = 0.03), whereas PPI use was not associated with OS (HR = 0.96, 95% CI 0.67–1.61, p = 0.83). Conclusions: Concomitant PPI use during nivolumab therapy was associated with significantly shorter PFS and a numerical reduction in OS in real-world metastatic NSCLC. Where clinically feasible, the need for PPIs should be re-evaluated before and during ICI therapy. Full article

By the year 2050, the United States aims to achieve net-zero carbon emissions. To achieve this target, the licensing of the Light Water Reactor (LWR) fleet has been extended for 20 more years. To stay economically competitive with other power sources such as renewable and fossil-fuel power plants, the U.S. Department of Energy has introduced a plan to modernize the existing LWR fleet and diversify the revenue stream. One of the plans is to dispatch thermal energy to endothermic industrial processes. SMART valves will play an important role in this initiative by efficiently balancing the load by regulating valves in a coordinated manner while monitoring the thermal-hydraulic systems to enhance safety and maintain the integrity of the power plant. This research aims to develop a facility to test the coordinated control algorithm and produce various test results for training the monitoring system. The constructed facility is capable of simulating various operational and accidental scenarios by coordinating all the valves (positions) and pump (flowrate). The facility is developed with an Internet of Things (IoT)-based custom system and a python-based valve position control and coordination mechanism. It has achieved stable sensor outputs, pump control, and coordinated valve regulation in all three valves with minimum obstruction in the system. Full article

Hydraulic thrust cylinders in hard-rock tunnel boring machines (TBMs) often exhibit slow response and sluggish acceleration during start-up, which degrades early-stage tracking performance and limits overall operational accuracy. Most existing studies primarily enhance start-up behavior through advanced control algorithms, yet the achievable improvement is ultimately constrained by the system’s flow–pressure capacity. Meanwhile, reported system-level optimization approaches are either difficult to implement under practical TBM operating conditions or fail to consistently deliver high-accuracy tracking. To address these limitations, this paper proposes a “dual-pump–single-cylinder” control framework for the TBM thrust system, where a large-displacement pump serves as the main supply and a parallel small-displacement pump provides auxiliary flow compensation to mitigate the start-up flow deficit. Building on this architecture, an adaptive compensation algorithm is developed for the auxiliary pump, with its output updated online according to the system’s dynamic states, including displacement error and velocity-related error components. Comparative simulations and test-bench experiments show that, compared with a single-pump scheme, the proposed method notably accelerates cylinder start-up while effectively suppressing overshoot and oscillations, thereby improving both transient smoothness and tracking accuracy. This study provides a feasible and engineering-oriented solution for achieving “rapid and smooth start-up” of TBM hydraulic cylinders. Full article

Large Language Models (LLMs) are increasingly used in industrial monitoring and decision support, yet they remain prone to process-control hallucinations—diagnoses and explanations that sound plausible but conflict with physical constraints, sensor data, or plant dynamics. This paper investigates hallucination as a failure of abductive reasoning, where missing premises, weak mechanistic support, or counter-evidence lead an LLM to propose incorrect causal narratives for faults such as pump restriction, valve stiction, fouling, or reactor runaway. We develop a neuro-symbolic framework in which Abductive Logic Programming (ALP) evaluates the coherence of model-generated explanations, counter-abduction generates rival hypotheses that test whether the explanation can be defeated, and Discourse-weighted ALP (D-ALP) incorporates nucleus–satellite structure from operator notes and alarm logs to weight competing explanations. Using our 500-scenario Process-Control Hallucination Dataset, we assess LLM reasoning across mechanistic, evidential, and contrastive dimensions. Results show that abductive and counter-abductive operators substantially reduce explanation-level hallucinations and improve alignment with physical process behavior, particularly in “easy-but-wrong’’ cases where a superficially attractive explanation contradicts historian trends or counter-evidence. These findings demonstrate that abductive reasoning provides a practical and verifiable foundation for improving LLM reliability in safety-critical process-control environments. Full article

External gear pumps are widely used in industrial hydraulic systems, but their volumetric efficiency can deteriorate significantly because of internal leakage, especially under high-pressure operating conditions. Conventional lumped parameter models typically assume fixed clearances and therefore cannot accurately capture the leakage behavior associated with pressure-induced deformation and wear. In this study, a dynamic clearance model for an external gear pump is developed and experimentally validated. Radial and axial clearances are measured in situ using eddy-current gap sensors over a range of operating conditions, and empirical correlation equations are identified as functions of pressure and rotational speed. These correlations are embedded into a tooth-space-volume-based lumped parameter model so that the leakage flow is updated at each time step according to the instantaneous dynamic clearances. The proposed model is validated against experimental measurements of volumetric efficiency obtained from a dedicated test bench. At 800 rev/min, the average prediction error of volumetric efficiency is reduced to 1.98% with the proposed dynamic clearance model, compared with 9.43% for a nominal static-clearance model and 3.35% for a model considering only static wear. These results demonstrate that explicitly accounting for dynamic clearance variations significantly improves the predictive accuracy of volumetric efficiency, and the proposed model can be used as a design tool for optimizing leakage paths and enhancing the energy efficiency of external gear pumps. Full article