Search Results (107)

This study focuses on the numerical analysis of a centrifugal pump’s suction capability, aiming to reliably predict its suction performance characteristics. The main emphasis of the research was placed on the influence of different turbulence models, the quality of the computational mesh, and the comparison between steady-state and unsteady numerical approaches. The results indicate that steady-state simulations provide an unreliable description of cavitation development, especially at lower flow rates where strong local pressure fluctuations are present. The unsteady k–ω SST model provides the best overall agreement with experimental NPSH₃ characteristics, as confirmed by the lowest mean deviation (within the ISO 9906 tolerance band, corresponding to an overall uncertainty of ±5.5%) and by multiple operating points falling entirely within this range. This represents one of the first detailed unsteady CFD verifications of NPSH prediction in centrifugal pumps operating at high rotational speeds (above 2900 rpm), achieving a mean deviation below ±5.5% and demonstrating improved predictive capability compared to conventional steady-state approaches. The analysis also includes an evaluation of the cavitation volume fraction and a depiction of pressure conditions on the impeller as functions of flow rate and inlet pressure. In conclusion, this study highlights the potential of advanced hybrid turbulence models (such as SAS or DES) as a promising direction for future research, which could further improve the prediction of complex cavitation phenomena in centrifugal pumps. Full article

This study investigated the impact of implant materials on Hounsfield Unit (HU) distortion and dose calculation accuracy in radiotherapy planning. Standardized cubic phantoms (1 cm³) of PEEK, titanium mesh, and titanium solid were fabricated and scanned using 120 kV and 140 kV extended CT protocols, with and without iterative metal artifact reduction (iMAR). HU profiles were analyzed to quantify imaging artifacts, and VMAT (Volumetric Modulated Arc Therapy) plans were generated for dosimetric verification. Absolute point dose differences and gamma pass rates (3%/3 mm, global normalization, 10% threshold) were compared between calculated and measured doses. PEEK exhibited HU variations within 3% and gamma pass rates consistently above 96%, confirming its suitability as a radiotherapy-compatible implant material. Titanium mesh demonstrated reduced HU distortion compared with titanium solid, with gamma pass rates of 97–98% meeting the clinical acceptance threshold (≥95%). In contrast, the titanium solid showed severe HU distortion with gamma pass rates as low as 89%, falling below the clinical criterion. The effect of iMAR varied: partial improvement for the titanium solid at 120 kV, deterioration at 140 kV, and negligible influence for PEEK and titanium mesh. Overall, PEEK demonstrated the most stable performance, while titanium mesh was superior to solid titanium. Full article

Designing UHPC beams for shear is challenging because many factors—geometry, concrete strength, fibers, and stirrups—act together. In this study, we compile a large, curated database of laboratory tests and develop machine learning models to predict shear capacity. The best models provide accurate point predictions and, importantly, a 95% prediction band that tells how much uncertainty to expect; in tests, about 95% of results fall inside this band. For day-to-day design, we also offer a short, design-oriented formula with explicit coefficients and variables that can be used in a spreadsheet. Together, these tools let engineers screen options quickly, check designs with an uncertainty margin, and choose a conservative value when needed. The approach is transparent, easy to implement, and aligned with common code variables, so it can support preliminary sizing, verification, and assessment of UHPC members. Full article

The integration of Internet of Things (IoT) technologies into public healthcare enables continuous monitoring and sustainable health management. However, conventional frameworks often depend on transmitting and storing raw personal data on centralized servers, posing challenges related to privacy, security, ethical compliance, and long-term sustainability. This study proposes a privacy-preserving framework that avoids the exposure of true health-related data. Sensor nodes encrypt collected measurements and collaborate with a secure computation core to evaluate health indicators under homomorphic encryption, maintaining confidentiality. For example, the system can determine whether a patient’s heart rate within a monitoring window falls inside clinically recommended thresholds, while the framework remains general enough to support a wide range of encrypted computations. A compliance verification client generates zero-knowledge range proofs, allowing external parties to verify whether health indicators meet predefined conditions without accessing actual values. Simulation results confirm the correctness of encrypted computation, controllability of threshold-based compliance judgments, and resistance to inference attacks. The proposed framework provides a practical solution for secure, auditable, and sustainable real-time health assessment in IoT-enabled public healthcare systems. Full article

For accurate and reliable monitoring, compliance monitoring devices (CMDs) in Port State Control must meet strict and uniform quality standards. This study evaluates how effectively CMDs, using variable fluorescence (VF) and fluorescein diacetate (FDA) technologies, detect live organisms in the 10–50 μm size range. Employing a detailed analytical framework, we analyzed key performance indicators, including accuracy, precision, sensitivity, specificity, trueness, detection limits, and reliability by comparing CMD outputs to those of traditional microscopic methods. Reliability assessments revealed that VF-type CMD and FDA-type CMD performed robustly, with a stability rate of 99% for both, surpassing the 90% verification threshold. Precision analysis indicated an average CV exceeding 0.25; however, some samples, especially those below the D-2 standard, achieved a CV of less than 0.25. Concordance evaluations revealed that VF-CMDs and FDA-CMDs achieved rates of 63% and 55%, respectively, falling short of the 80% verification standard and underscoring the need for further calibration or optimization. Structural equation modeling shows that organism density significantly influences CMD performance. These findings underscore the challenges of accurately detecting low organism concentrations, further complicated by biological diversity and environmental variability. Despite their limitations in assessing ballast water compliance, CMDs are effective initial screening tools. Full article

Face recognition technology utilizes unique facial features to analyze and compare individuals for identification and verification purposes. This technology is crucial for several reasons, such as improving security and authentication, effectively verifying identities, providing personalized user experiences, and automating various operations, including attendance monitoring, access management, and law enforcement activities. In this paper, comprehensive evaluations are conducted using different face detection and modality segmentation methods, feature extraction methods, and classifiers to improve system performance. As for face detection, four methods are proposed: OpenCV’s Haar Cascade classifier, Dlib’s HOG + SVM frontal face detector, Dlib’s CNN face detector, and Mediapipe’s face detector. Additionally, two types of feature extraction techniques are proposed: hand-crafted features (traditional methods: global local features) and deep learning features. Three global features were extracted, Scale-Invariant Feature Transform (SIFT), Speeded Robust Features (SURF), and Global Image Structure (GIST). Likewise, the following local feature methods are utilized: Local Binary Pattern (LBP), Weber local descriptor (WLD), and Histogram of Oriented Gradients (HOG). On the other hand, the deep learning-based features fall into two categories: convolutional neural networks (CNNs), including VGG16, VGG19, and VGG-Face, and Siamese neural networks (SNNs), which generate face embeddings. For classification, three methods are employed: Support Vector Machine (SVM), a one-class SVM variant, and Multilayer Perceptron (MLP). The system is evaluated on three datasets: in-house, Labelled Faces in the Wild (LFW), and the Pins dataset (sourced from Pinterest) providing comprehensive benchmark comparisons for facial recognition research. The best performance accuracy for the proposed ten-feature extraction methods applied to the in-house database in the context of the facial recognition task achieved 99.8% accuracy by using the VGG16 model combined with the SVM classifier. Full article

The sealing performance of ultra-high-pressure solenoid valves faces significant challenges, particularly under low-temperature conditions. Due to the difference in thermal expansion coefficients between the valve seat and the sealing tube, combined with material contraction at low temperatures, the bolt preload decreases, and consequently the contact force on the sealing surface and the average sealing specific pressure are reduced. This may result in an average sealing specific pressure falling below the required sealing specific pressure, causing leakage and failure of the ultra-high-pressure solenoid valve. To address this problem, this study utilizes theoretical and simulation analysis to examine the preload status in low-temperature environments and the causes of sealing failure in ultra-high-pressure solenoid valves. A corresponding optimization scheme is proposed, which involves increasing the torque from 120 N·m to 130 N·m and applying sealant to the threaded connection to enhance the sealing performance of the ultra-high-pressure solenoid valve. Following the increase in tightening torque and the application of thread sealant, the helium leakage rate at −40 °C is significantly reduced. Specifically, at a test pressure of 87.5 MPa, the helium leakage rate decreases from $1.6\times {10}^{-5}$ mbar·L/s to approximately $1.4\times {10}^{-6}$ mbar·L/s. At test pressures of 1.4 MPa and 10 MPa, the leakage rate is approximately $3.0\times {10}^{-7}$ mbar·L/s. Experimental verification shows that the proposed solution can significantly enhance the sealing reliability of ultra-high-pressure solenoid valves under extreme operating conditions. Full article

Large language models (LLMs) now match or exceed human performance on many open-ended language tasks, yet they continue to produce fluent but incorrect statements, which is a failure mode widely referred to as hallucination. In low-stakes settings this may be tolerable; in regulated or safety-critical domains such as financial services, compliance review, and client decision support, it is not. Motivated by these realities, we develop an integrated mitigation framework that layers complementary controls rather than relying on any single technique. The framework combines structured prompt design, retrieval-augmented generation (RAG) with verifiable evidence sources, and targeted fine-tuning aligned with domain truth constraints. Our interest in this problem is practical. Individual mitigation techniques have matured quickly, yet teams deploying LLMs in production routinely report difficulty stitching them together in a coherent, maintainable pipeline. Decisions about when to ground a response in retrieved data, when to escalate uncertainty, how to capture provenance, and how to evaluate fidelity are often made ad hoc. Drawing on experience from financial technology implementations, where even rare hallucinations can carry material cost, regulatory exposure, or loss of customer trust, we aim to provide clearer guidance in the form of an easy-to-follow tutorial. This paper makes four contributions. First, we introduce a three-layer reference architecture that organizes mitigation activities across input governance, evidence-grounded generation, and post-response verification. Second, we describe a lightweight supervisory agent that manages uncertainty signals and triggers escalation (to humans, alternate models, or constrained workflows) when confidence falls below policy thresholds. Third, we analyze common but under-addressed security surfaces relevant to hallucination mitigation, including prompt injection, retrieval poisoning, and policy evasion attacks. Finally, we outline an implementation playbook for production deployment, including evaluation metrics, operational trade-offs, and lessons learned from early financial-services pilots. Full article

Given that the traditional optimization algorithm (GWO) often encounters problems like local optimum, and the convergence efficiency is not satisfactory in the path planning task of multiple mobile robots, an improved grey wolf optimization algorithm (LAPGWO) based on the combination of the logistic chaotic mapping and the artificial potential field method (APF) is proposed. Firstly, the LAPGWO algorithm uses logistic chaotic mapping to initialize the scale of grey wolves, improving the diversity of the population distribution. Secondly, the potential field function of APF is introduced to guide the individual grey wolves to move towards the low potential energy area. By adjusting the angle between the resultant force direction of the possible field and the movement direction, the global search ability is enhanced, and the algorithm is prevented from falling into the local optimum. At the same time, in the later iterations, it gradually decreases to increase the local search ability and accelerate the search efficiency. Finally, a repulsive force correction term function is proposed to solve the problem of unreachable targets. An independent potential field is constructed for each robot during the driving process to reduce path conflicts. To verify the performance of the improved algorithm, this paper will verify and analyze two different improved grey wolf algorithms based on the warehouse environment. The results show that, compared with the GWO algorithm, the shortest path and calculation time of the LAPGWO algorithm is shortened by 22.09%, 34.12%, and 47.75%, respectively. It has better convergence and stability. A physical verification platform is built to verify the practical effectiveness of the method proposed in this paper. Full article

Code-to-code translation, a critical domain in software engineering, increasingly utilizes trans-compilers to translate between high-level languages. Traditionally, the fidelity of such translations has been evaluated using the BLEU score, which predominantly measures token similarity between the generated output and the ground truth. However, this metric falls short of assessing the methodologies underlying the translation processes and only evaluates the translations that are tested. To bridge this gap, this paper introduces an innovative architecture, “TC-Verifier”, to formally employ the Uppaal Model-checker to verify trans-compiler-based code translators. We applied the proposed architecture to a trans-compiler translating between Swift and Java, providing insights into the verified and unverified aspects of the translation process. Our findings illuminate the strengths and limitations of using Model-checking for formal verification in code translation. Notably, the examined trans-compiler reached a verification success rate of 50.74% for the grammar rules and productions modeled. This study underscores the gaps in trans-compiler-based translations and suggests that these gaps could potentially be addressed by integrating Large Language Models (LLMs) in future work. Full article

This article introduces a high-precision vertical pendulum tiltmeter with rapid deployment capability to improve the observation efficiency, practicality, and reliability of geophysical site tilt observation instruments. The system consists of a pendulum body, a triangular platform, a locking pendulum motor, a sealed cover, a ratio measurement bridge, a high-precision ADC, and an embedded data acquisition unit. The sensing unit adopts a vertical pendulum system suspended by a cross spring and a differential capacitance bridge measurement circuit, which can simultaneously measure two orthogonal directions of ground tilt. The pendulum is installed on a short baseline triangular platform, sealed as a whole with the platform, and equipped with a locking pendulum motor. When the pendulum is locked and packaged, it can withstand a 2 m free fall impact, with high reliability and easy use. It can be quickly deployed without the need for professional technicians. This article analyzes its various performance and technical indicators based on its application in the rapid deployment of the Zeketai seismic station in Xinjiang. It is of great significance for emergency response, mobile observation, base detection, anomaly verification, and other applications of ground tilt. Full article

Due to the soft mechanical properties of soft rock strata, roof fall accidents are frequent, causing great hazards to production. In order to eliminate hazards in the actual mining process, a new type of bag-filling scheme was designed by analyzing the mechanisms of roof falls in soft rock strata. By testing the filling material, the optimal ratio of foam filling material was determined, and the corresponding filling process was formulated. Through the field verification of this filling process, better support was achieved in the roof fall area, providing useful guidance and support for mines with similar conditions. Full article

The discrete element method (DEM) has been widely applied as a vital auxiliary technique in the design and optimization processes of agricultural equipment, especially for simulating the behavior of granular materials. In this study, the focus is placed on accurately calibrating the simulation contact parameters necessary for the V7 potato minituber seed DEM simulation. Firstly, three mechanical tests are conducted, and through a combination of actual tests and simulation tests, the collision recovery coefficient between the seed and rubber material is determined to be 0.469, the static friction coefficient is 0.474, and the rolling friction coefficient is 0.0062. Subsequently, two repose angle tests are carried out by employing the box side plates lifting method and the cylinder lifting method. With the application of the response surface method and a search algorithm based on Matlab 2019, the optimal combination of seed-to-seed contact parameters, namely, the collision recovery coefficient, static friction coefficient, and rolling friction coefficient, is obtained, which are 0.500, 0.476, and 0.043, respectively. Finally, the calibration results are verified by a seed-falling device that combines collisions and accumulation, and it is shown that the relative error between the simulation result and the actual result in the verification test is small. Thus, the calibration results can provide assistance for the design and optimization of the potato minituber seed planter. Full article

The UV–Visible Working Group of the Network for the Detection of Atmospheric Composition Changes (NDACC) focuses on the monitoring of air-quality-related stratospheric and tropospheric trace gases in support of trend analysis, satellite validation and model studies. Tropospheric measurements are based on MAX-DOAS-type instruments that progressively emerged in the years 2010 onward. In the interest of improving the overall consistency of the NDACC MAX-DOAS network and facilitating its further extension to the benefit of satellite validation, the ESA initiated, in late 2016, the FRM4DOAS project, which aimed to set up the first centralised data processing system for MAX-DOAS-type instruments. Developed by a consortium of European scientists with proven expertise in measurements, data extraction algorithms and software design specialities, the system has now reached pre-operational status and has demonstrated its ability to deliver a set of quality-controlled atmospheric composition data products with a latency of one day. The processing system has been designed using a highly modular approach, making it easy to integrate new tools or processing updates. It incorporates advanced algorithms selected by community consensus for the retrieval of total ozone, lower tropospheric and stratospheric NO₂ vertical profiles and formaldehyde profiles. The ozone and NO₂ products are currently generated from a total of 22 stations and delivered daily to the NDACC rapid delivery (RD) repository, with an additional mirroring to the ESA Validation Data Centre (EVDC). Although it is still operated in a pre-operational/demonstrational mode, FRM4DOAS was already used for several validation and science studies, and it was also deployed in support of field campaigns for the validation of the TROPOMI and GEMS satellite missions. It recently went through a CEOS-FRM self-assessment process aiming at assessing the level of maturity of the service in terms of instrumentation, operations, data sampling, metrology and verification. Based on this evaluation, it falls under class C, which is a good rating but also implies that further improvements are needed to reach full compliance with FRM standards, i.e., class A. Full article

The spread of invasive pests exacerbates the direct damage to host plants and the potential threat to the environment. Silicon has the potential to enhance host plant resistance to insects while also increasing plant yield. This study evaluated changes in Italian ryegrass biological yield and resistance to fall armyworm (Spodoptera frugiperda) larvae after silicon supplementation (sodium silicate and potassium silicate at 6 mmol·L⁻¹ were denoted as groups T1 and T2, respectively). Silicon supplementation significantly increased the shoot biological yield (T1 by 30.26%, T2 by 23.05%) and silicon content (T1 by 22.61% and T2 by 12.43%) of Italian ryegrass. At the same time, silicon supplementation increased the protein, soluble sugar, and vitamin contents of Italian ryegrass, while also stimulating the improvement of its physical and chemical defenses. Therefore, even though the nutrient intake of fall armyworm increased, the synergistic physical-chemical defense formed by silica deposition, flavonoid content, and increased protease inhibitor activity in the Italian ryegrass still weakened the antioxidant capacity of the larvae and inhibited larval feeding and protein accumulation. The larval body weight of the T1 and T2 groups decreased by 20.32% and 15.16%, respectively. The comprehensive scores showed that sodium silicate and potassium silicate of the same concentration had similar effects on the growth and insect resistance of Italian ryegrass. These findings suggest that both sodium and potassium silicate are effective silicon supplements for host plants. Therefore, reasonable supplementation of silicon fertilizer may become an important alternative plan for optimizing the comprehensive pest control strategy in agricultural production areas in the future, but this still needs further field research verification. Full article