Search Results (145,594)

Traditional drilling methods often face difficulty meeting the demand for efficient and accurate coring under complex geological conditions. Cordless coring is an advanced technology that uses hydraulic lifting to carry out coring, which can achieve automation and automated and intelligent drilling operations. In this research, a new type of hydraulic lifting cordless coring drilling tool is designed. Moreover, a numerical simulation model of the fluid flow in the annulus between the spearhead and spool of the cordless coring drilling tool was established. Orthogonal simulation tests are carried out, and according to the orthogonal test data, a numerical prediction model of the spool annulus fluid field based on the Backpropagation Neural Network (BP neural network) is established. The prediction of the flow rate of the drilling fluid and the spool back-pressure ratio was obtained when the structural parameters of the spearhead and the spool annulus were different. A multi-objective optimization of the annulus flow structure of the cordless core drilling tool has been carried out. The optimization objectives include deciding the back pressure ratio of the spool overcoming the spring and the flow rate of the drilling fluid. According to the established nonlinear optimization model and based on the improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization algorithm, it is verified that the convergence speed and diversity of the improved algorithm are better than those before the improvement. The simulation and experimental validation are carried out. It is verified that the flow rate of drilling fluid increased by 33.56% after optimization, and the force ratio was lowered by 5.825%. Finally, based on the simulation and optimization results, the φ96 cordless core drilling tool was manufactured on a trial basis, and on-site concrete drilling, coring, and hydraulic lifting operations were conducted for smooth coring and lifting. This study could provide an important scientific basis and technical support for the application and development of hydraulic lifting cordless coring technology. Full article

Historically, pediatric liver transplantation has achieved significant milestones, yet recent innovations have predominantly occurred in adult liver transplantation due to higher caseloads and ethical barriers in pediatric studies. Emerging methods subsumed under the term artificial intelligence offer the potential to revolutionize data analysis in pediatric liver transplantation by handling complex datasets without the need for interventional studies, making them particularly suitable for pediatric research. This review provides an overview of artificial intelligence applications in pediatric liver transplantation. Despite some promising early results, artificial intelligence is still in its infancy in the field of pediatric liver transplantation, and its clinical implementation faces several challenges. These include the need for high-quality, large-scale data and ensuring the interpretability and transparency of machine and deep learning models. Ethical considerations, such as data privacy and the potential for bias, must also be addressed. Future directions for artificial intelligence in pediatric liver transplantation include improving donor–recipient matching, managing long-term complications, and integrating diverse data sources to enhance predictive accuracy. Moreover, multicenter collaborations and prospective studies are essential for validating artificial intelligence models and ensuring their generalizability. If successfully integrated, artificial intelligence could lead to substantial improvements in patient outcomes, bringing pediatric liver transplantation again to the forefront of innovation in the transplantation community. Full article

This article investigates the effects of electromagnetic field (EMF) from mobile phones on human tissues and implanted medical devices. The intensity of the electric field (E) is evaluated based on simulations and measurements of various exposure scenarios. An area of interest is the case of a person with an implanted device (heart pacemaker) who may be affected by this exposure. Due to the rapid development of communication technologies and the growing awareness of the potential health risks of radio frequency (RF) EMF, the International Commission on Non-Ionizing Radiation Protection (ICNIRP) has established exposure limits within the European Union. Our study models and analyses EMF values in human tissues in an ideal environment, in a situation where a person uses a mobile phone in the DCS (Digital Cellular System) band, including the case of a person with an implanted pacemaker. Pilot simulations were verified by experimental measurements. Based on them, specific human models with the best matching results were selected for modelling other possible interactions of exogenous EMF and cardiac pacemaker in the same situations and locations. Full article

Hydraulic tomography (HT) is a promising technique for high-resolution imaging of subsurface heterogeneity, which addresses the limitations of traditional methods, such as borehole drilling and geophysical surveys. This study focuses on the application of HT to detect and characterize fractured zones in bedrock and addresses the gap in the understanding of the role of distributed flux data in the joint inversion of hydraulic head and flux data. By conducting synthetic injection tests and using sequential successive linear estimators for inversion, the study explores the effectiveness of combining limited head data with distributed temperature sensing (A-DTS)-derived flux data. The findings highlight the fact that integrating flux data significantly enhances the accuracy of identifying fracture permeability characteristics, even when head data is sparse. This approach not only improves the resolution of hydraulic conductivity fields but also offers a cost-effective strategy for practical field applications. The results underscore the potential of HT to enhance our understanding of groundwater flow and contaminant transport in fractured media, which has important implications for carbon capture, enhanced geothermal systems, and radioactive waste disposal. Full article

In this study, the impact of principal stress states on the stress characteristics and initial failure of the rock mass surrounding a three-center arch opening was investigated using complex variable function methods and Discrete Element Method (DEM) numerical modeling. First, the mapping function of the opening was determined using the trigonometric interpolation method, and the influence of the number of terms in the mapping function on its accuracy was revealed. Based on this, the far-field stress state of the underground rock mass was characterized by the ratio of the minimum to maximum principal stress (λ) and the angle (β) between the principal stress and the vertical direction. This stress state was then converted into normal and shear stresses. Using complex variable function theory, the stress characteristics at the boundary of the opening under different stress states were analyzed. Finally, DEM numerical modeling was employed to study the initial failure characteristics at the boundary of the opening and its relationship with the stress distribution. The results indicate that the lateral pressure coefficient significantly affects the stability of the opening by influencing stress concentration around the surrounding rock. Low lateral pressure coefficients lead to tensile stress concentration at the boundary perpendicular to the maximum principal stress. As the coefficient increases, tensile stress decreases, and compressive stress areas expand. While the principal stress direction has a minor effect on stress concentration, it notably impacts stress distribution at the boundary. When λ < 1.0 and β = 45°, stress distribution asymmetry is most pronounced, with the highest compressive stress. The early failure distribution aligns with stress concentration areas, validating the use of stress analysis in predicting opening stability and failure characteristics. Full article

Innovative treatments for spinal muscular atrophy (SMA) yield the utmost advantages only within the presymptomatic phase, underlining the significance of newborn screening (NBS). We aimed to establish statewide NBS for SMA in Serbia. Our stepwise implementation process involved technical validation of a screening assay, collaboration with patient organizations and medical professionals, a feasibility study, and negotiation with public health representatives. Over 12,000 newborns were tested during the 17-month feasibility study, revealing two unrelated SMA infants and one older sibling. All three children received therapeutic interventions during the presymptomatic phase and have shown no signs of SMA. No false-negative results were found among the negative test results. As frontrunners in this field in Serbia, we established screening and diagnostic algorithms and follow-up protocols and raised awareness among stakeholders about the importance of early disease detection, leading to the incorporation of NBS for SMA into the national program on 15 September 2023. Since then, 54,393 newborns have been tested, identifying six SMA cases and enabling timely treatment. Our study demonstrates that effective collaborations between academia, non-profit organizations, and industry are crucial in bringing innovative healthcare initiatives to fruition, and highlights the potential of NBS to revolutionize healthcare outcomes for presymptomatic SMA infants and their families. Full article

Globally, building energy consumption has been rising, emphasizing the need to reduce energy usage in the building sector to lower national energy consumption and carbon dioxide emissions. This study analyzes the applicability of photovoltaic (PV) systems in enhancing the energy self-sufficiency of small-scale, low-rise apartment buildings. The analysis is based on a case study using Republic of Korea’s Zero-Energy Building Certification System. By employing the ECO2 simulation program, this research investigates the impact of PV system capacity and efficiency on the energy self-sufficiency rate (ESSR). A series of parametric analyses were carried out for various combinations of building-attached photovoltaic (BAPV) roofs and building-integrated photovoltaic (BIPV) facades, considering the initial cost of BIPV facades. The simulations demonstrate that achieving the target ESSR requires a combination of BAPV roofs and BIPV facades, due to limited roof areas for PV systems. Additionally, this study reveals that BIPV facades can be cost-effective when their unit price, relative to BAPV roofs, is below 62%. Based on the ECO2 simulations, a linear regression formula is proposed to predict the ESSR for the case study building. Verification analysis shows that the proposed formula predicts an ESSR of 74.1%, closely aligned with the official ESSR of 76.9% certified by the Korean government. Although this study focuses on the case of a specific apartment building and lacks actual field data, it provides valuable insights for future applications of PV systems to enhance energy self-sufficiency in small-scale, low-rise apartment buildings in Republic of Korea. Full article

In the mechanized harvesting of Lycium barbarum L. (L. barbarum), there are prominent problems such as low harvesting efficiency, high damage rate, incomplete separation of leaves and delayed transportation. Therefore, an integrated L. barbarum harvester was designed and developed in this study, which has the functions of picking, undertaking, transportation, winnowing and collection. The design requirements and constraints were identified by cultivation agronomy. Through simulation and physical tests, the tarpaulin was determined as the undertaking material. This machine achieved efficient picking with a vibrating picker with a multi-degree-of-freedom picking arm. The two-stage conveyor belts and the intermediate receiving plate were designed for low loss transportation of fruit. The axial flow fan and secondary buffer device were used to realize winnowing and reduce the damage rate. Through the three-factor and three-level orthogonal test, an optimal working parameter combination was determined: the vibration frequency of the picker was 20 Hz, the conveyor speed was 4 m/min, the airflow speed of the fan was 7 m/s. A field test was conducted under these parameters, and the results showed that the harvesting efficiency was about five times that of manual harvesting. The integrated L. barbarum harvester basically met the harvesting requirements and provided a new scheme for mechanized harvesting. Full article

Heart Rate Variability (HRV) refers to the capability of the heart rhythm to vary at different times, typically reflecting the regulation of the heart by the autonomic nervous system. In recent years, with advancements in Electrocardiogram (ECG) signal processing technology, HRV features reflect various aspects of cardiac activity, such as variability in heart rate, cardiac health status, and responses. We extracted key features of HRV and used them to develop and evaluate an automatic recognition model for cardiac diseases. Consequently, we proposed the HRV Heart Disease Recognition (HHDR) method, employing the Spectral Magnitude Quantification (SMQ) technique for feature extraction. Firstly, the HRV signals are extracted through electrocardiogram signal processing. Then, by analyzing parts of the HRV signal within various frequency ranges, the SMQ method extracts rich features of partial information. Finally, the Random Forest (RF) classification computational method is employed to classify the extracted information, achieving efficient and accurate cardiac disease recognition. Experimental results indicate that this method surpasses current technologies in recognizing cardiac diseases, with an average accuracy rate of 95.1% for normal/diseased classification, and an average accuracy of 84.8% in classifying five different disease categories. Thus, the proposed HHDR method effectively utilizes the local information of HRV signals for efficient and accurate cardiac disease recognition, providing strong support for cardiac disease research in the medical field. Full article

There is a growing need for precise and rapid detection methods in fields such as biomedical diagnostics, environmental monitoring, and chemical analysis. Surface plasmon resonance (SPR) sensors have been used for the detection and quantification of a wide range of analytes, including biomolecules, chemicals, and gases, in real-time. Despite the promising capabilities of SPR sensors, there remains a gap in creating a balance between having a large enough area to capture a significant number of analytes for detection and being small enough to ensure high sensitivity. This research aims to explore the design of a D-shaped SPR-based optical fiber sensor, focusing on the use of copper, gold, and silver thin films at optimized width and thickness of 10 µm and 45 nm, respectively, to improve the sensor’s performance. Employing a computational approach, this study examines the influence of the optimized width and refractive indices of metallic films on the sensor’s characteristics. The 10 µm width of the metallic thin film has been found to produce an optimal balance between the sensitivity and the dynamic range of the sensor. Leveraging on the ratio of the real and imaginary parts of the dielectric constant of the thin film metal provides insight into the optical properties and sensitivity at certain wavelengths. Within an analyte refractive index range of 1.37–1.42 and a wavelength range of 650–1200 nm, results indicate that silver outperforms gold and copper at the optimized width with a wavelength sensitivity, and detection accuracy of 12,300 nmRIU⁻¹, and 3.075, respectively. By optimizing the width of the metal thin film at 10 µm, a highly sensitive D-SPR is designed, allowing for enhanced sensor detection capabilities for a wide range of bioanalytes. Full article

This paper introduces a novel approach to neural networks: a Generalized Liquid Neural Network (GLNN) framework. This design excels at handling both sequential and non-sequential tasks. By leveraging the Runge Kutta DOPRI method, the GLNN enables dynamic simulation of complex systems across diverse fields. Our research demonstrates the framework’s capabilities through three key applications. In predicting damped sinusoidal trajectories, the Generalized LNN outperforms the neural ODE by approximately 46.03% and the conventional LNN by 57.88%. Modelling non-linear RLC circuits shows a 20% improvement in precision. Finally, in medical diagnosis through Optical Coherence Tomography (OCT) image analysis, our approach achieves an F1 score of 0.98, surpassing the classical LNN by 10%. These advancements signify a significant shift, opening new possibilities for neural networks in complex system modelling and healthcare diagnostics. This research advances the field by introducing a versatile and reliable neural network architecture. Full article

Passive fluid control has mostly been used for valves, pumps, and mixers in microfluidic systems. The basic principle is to generate localized losses in special channel structures, such as branches, grooves, or spirals. The flow field in two-dimensional space can be easily calculated using the typical Stokes formula, but it is challenging in three-dimensional space. Moreover, the flow field with periodic variable cross-sections channeled of polyhedral units has been neglected in this research field due to previous limitations in manufacturing technology. With the continuous progress of 3D printing technology, the field of microfluidic devices ushered in a new era of manufacturing three-dimensional irregular channels. In this study, we present finite analysis results for a periodic nodular-like channel. The experiments involve variations in the Reynold number (Re), periodic frequency, and comparative analyses with conventional structures. The findings indicate that this variable 3D cross-section structure can readily achieve performance comparable to other passive fluid control methods in valve applications. A 3D model of the periodic tetrahedron channel was fabricated using 3D printing to validate these conclusions. This research has the potential to significantly enhance the performance of passive fluid control units that have long been constrained by manufacturing dimensions. Full article

In this paper, we introduce SHSnet, an advanced deep learning model designed for the efficient end-to-end segmentation of complex cracks, including thin, tortuous, and densely distributed ones. SHSnet features a non-uniform attention mechanism, a large receptive field, and boundary refinement to enhance segmentation performance while maintaining computational efficiency. To further optimize the model’s learning capability with highly imbalanced datasets, we employ a loss function (LP) based on the focal Tversky function. SHSnet shows very high performance, with values of 0.85, 0.83, 0.81, and 0.84 for precision, recall, intersection over union (IOU), and F-score, respectively. It achieves this with 10x fewer parameters than other models in the literature. Complementing SHSnet, we also present the post-processing unit (PPU), which analyzes crack morphological parameters through fracture mechanics and geometric properties. The PPU generates scanning lines to accurately compute these parameters, ensuring reliable results. The PPU shows a relative error of 0.4%, 1.2%, and 5.6% for crack number, length, and width, respectively. The methodology was benchmarked on complex ECC crack datasets as well as on multiple online datasets. In both of these cases, our results confirm that SHSnet consistently delivers superior performance and efficiency across various scenarios as compared to the methods in the literature. Full article

An electrical motors, together with its appropriate drive system, is one of the most important elements of electromobility. In recent years, there has been a particular interest by academic researchers and engineers in permanent-magnet motors (PMSMs) in various applications, such as electric vehicles, Unmanned Aerial Vehicles (UAVs), elevator systems, etc., as the main source of drive transmission. Nowadays, the elevator industry, with the evolution of magnetic materials, has turned to gearless PMSMs over geared induction motors (IMs). One of the most important elements that is given special emphasis in these applications is proper motor design in consideration of the weight and speed of the chamber to be served during operation. This paper presents a design of a high-efficiency PMSM, in which finite elements analysis (FEA) and the study of the lift operating cycle provided useful conclusions on the magnetic field of the machine in different operating states. In addition, a simulated model was compared with experimental results of test operations. Furthermore, the drive system also required the use of appropriate electrical power and controls to drive the PMSM. Especially in elevator applications, the control of the motor speed by the variable voltage variable frequency technique (VVVF) is the most common technology used to avoid endangering the safety of the passengers. Thus, suitable speed and current controllers were used for this purpose. In our research, we focused on studying different control techniques using a suitable inverter to compare the system operation in each case studied. Full article

The development of fast-responding electrically conductive polymers as actuators activated by low electrical current is now regarded as an urgent matter. Due to their limited electrical conductivity, actuators based on polymeric hydrogels must be activated using a high voltage (up to 200 V) and frequency (up to 500 Hz) when employing AC power. In this work, to improve the electrical conductivity of the hydrogel and decrease the required activation voltage of the hydrogel actuators, lithium chloride (LiCL) was added as a conductive filler to the polymer matrix of polyvinyl alcohol (PVA). In order to ascertain the deformation of actuators, activation and relaxation times, actuator efficiencies, and generated force under the conditions of activation, linear actuators that can be activated by extension/contraction (swelling/shrinking) cycles were prepared and investigated depending on the LiCl content, applied voltage, and frequency. Under a load of approximately 20 kPa and using a 90 V AC power at a 50 Hz frequency with a 30 wt.% LiCl content, it was found that the actuators’ total contraction, reinforced by a woven mesh braided material, was about 20% with a ~2.2 s activation time, while the actuators’ total extension, reinforced by a spiral weave material, was about 52% with a ~2.5 s activation time, after applying a 110 V AC at a 50 Hz frequency with a 10 wt.% LiCl content. Furthermore, as the lowest voltage, a 20 V AC power can operate these actuators by increasing the LiCl weight content to the same PVA mass content. Moreover, the PVA/LiCl hydrogels’ activation force can be greater than 0.5 MPa. The actuators that have been developed have broad applications in soft robotics, artificial muscles, medicine, and aerospace fields. Full article