Search Results (18,891)

This work presents Recurrence with Correlation Network(RWCNet), a novel multi-scale recurrent neural network architecture for medical image registration that integrates core principles from optical flow, including correlation volume computation and inference-time instance optimization. In evaluations on the large-displacement National Lung Screening Test (NLST) dataset, RWCNet exhibited superior performance (total registration error (TRE) of 2.11 mm) compared to other deep learning alternatives, and achieved results on par with variational optimization techniques. In contrast, on the OASIS dataset, which is characterized by smaller displacements, RWCNet achieved an average Dice similarity of 81.7%, representing only a modest improvement over other multi-scale deep learning models. Ablation experiments showed that multi-scale features consistently improved performance, whereas the correlation volume, number of recurrent steps, and inference-time instance optimization had large impacts on performance within the large-displacement NLST dataset. The performance of RWCNet compared to approaches that use instance optimization show that deep learning-based methods can find local minima that escape instance optimization methods. The results highlight the need for algorithm hyperparameter selection that adjusts with the dataset characteristics. RWCNet’s promising results may improve registration accuracy and computation efficiency, enabling many potential applications such as treatment planning, intra-procedural guidance, and longitudinal monitoring. Full article

To support the integration of high shares of renewable energy and enhance the operational flexibility of thermal power systems, thermosyphon have been considered as promising high-efficiency heat transfer components for thermal energy storage applications. In this study, a water-based thermosyphon motivated by molten-salt thermal energy storage scenarios is investigated numerically to clarify its internal heat-transfer behavior under different operating conditions. A two-dimensional CFD model is established based on the Volume-of-Fluid (VOF) multiphase approach coupled with the Lee phase-change model. The effects of heating power (3.5–5.0 kW) and liquid filling ratio (25–40%) on wall temperature distribution and thermal resistance characteristics are systematically analyzed. The results indicate that increasing the filling ratio improves the uniformity of the evaporator wall temperature, and a filling ratio of 40% leads to a relatively favorable liquid distribution and the lowest total thermal resistance within the investigated range. The evaporator thermal resistance exhibits a “decrease–increase” trend with heating power and reaches a minimum value of 1.019 × 10⁻⁴ K/W at 4.5 kW, while the condenser thermal resistance decreases monotonically with in-creasing heating power. This study provides comparative numerical insights into the coupled effects of heating power and filling ratio on thermosyphon performance, offering a reference for the component-level design and parameter selection of heat pipe heat exchangers in molten-salt-related thermal energy storage systems. Full article

The applications of nanofluids are widely beneficial in heat transmission and cooling systems. Nanofluid viscosity and thermal conductivity have a substantial effect on heat transfer applications and on devices such as solar and geothermal systems. Machine learning models enable faster, less expensive modeling of nanofluid thermophysical properties. These models are secure for future studies and in the development of nanotechnology. In this review, shape, size, temperature, and volume concentration are considered as inputs to develop several machine learning methods, such as artificial neural networks, support vector regression, decision trees, and random forests. These models were analyzed by comparing their R² values, and the results indicated that machine learning-based models generally exhibited more reliable performance than the other approaches. The observation in this review was that thermal conductivity increases with temperature and volume fractions, whereas viscosity decreases with size, temperature, and volume fractions. To determine the optimal nanoparticle type, size, and concentration for specific applications such as data center cooling and high-heat-flux electronics, future research may employ ML-based optimization techniques. Full article

A breakthrough has been achieved in shale gas exploration of the Lower Carboniferous Shale in the Yaziluo Rift Trough, Dianqiangui Basin, with Well SY-1 yielding a daily gas production of 1.1 × 10⁴ m³. To clarify the main controls and evolutionary patterns of shale pore structure, shale samples from different sedimentary environments were analyzed using TOC content, X-ray diffraction (XRD), low-pressure gas adsorption (CO₂ and N₂), and field emission-scanning electron microscopy (FE-SEM). The results show that shale from the basin sedimentary environment (BSE) exhibits the highest TOC, is dominated by siliceous minerals (quartz + feldspar), and contains minor carbonate minerals (calcite + dolomite). Shale from the upper slope sedimentary environment (USSE) has the lowest TOC and is rich in carbonate minerals. The lower slope sedimentary environment (LSSE) shows intermediate compositions. From BSE to USSE, pore volume and specific surface area decrease, while fracture development increases. A quantitative model for volumes of organic pores, clay mineral-associated pores, and brittle mineral-associated pores was established. Organic pores dominate in BSE shale (65.42%), followed by clay mineral-associated and brittle mineral-associated pores, while inorganic pores dominate in USSE shale (63%). Pore structure in BSE and LSSE is primarily controlled by TOC content, with pore volume and surface area increasing with TOC content, while mesopore development is influenced by organic matter type and mineral compositions. In USSE, pore structure is mainly governed by inorganic minerals, with clay minerals promoting pore volume and surface area development, whereas brittle minerals facilitate the preservation of macropores. Evolutionary models of pore development were established for these distinct sedimentary environments. Full article

With the core advantages of high energy efficiency, high power density, and reliable operation, high-voltage permanent magnet motors have become the mainstream development direction of modern motor technology. However, the risk of demagnetization caused by excessive temperature increases in permanent magnets has become a key bottleneck restricting motor performance and operational reliability, which makes research on the flow and heat transfer characteristics of motor cooling systems of great engineering value. Taking the 710 kW high-voltage permanent magnet motors as the research object, this study established a global flow field mathematical model covering the internal and external air duct cooling systems of the motor based on the theories of computational fluid dynamics and numerical heat transfer, and systematically analyzed the flow characteristics and distribution laws of cooling air. The thermal–fluid coupling numerical method was employed to simulate the temperature field of the motor, and the overall temperature distribution of the motor, temperature gradient of key components, and maximum temperature value were accurately obtained. To verify the validity of the established model, a test platform for the cooling system performance was designed and built. Measuring points for wind speed, air temperature, and component temperature were arranged at key positions, such as the stator radial ventilation ducts, and experimental tests were conducted under the rated operating conditions. The results show that the flow field distribution of the internal and external air ducts of the motor is reasonable and that the cooling air flows uniformly, with the external and internal circulating air volumes reaching 1.2 m³/s and 0.6 m³/s, respectively, which meets the heat dissipation requirements. The maximum temperature of 95 °C occurs in the stator winding area, and the maximum temperature of the permanent magnets is controlled within the safe range of 65 °C. The simulation results were in good agreement with the experimental data, with an average relative error of only 4%, which fell within the engineering allowable range, thus verifying the accuracy and reliability of the established global model and thermal–fluid coupling calculation method. This study reveals the thermal–fluid coupling transfer mechanism of high-voltage permanent magnet motors and provides a theoretical basis and engineering reference for the optimal design, precise temperature rise control, and reliability improvement of motor cooling systems. Full article

Unlike conventional positioning systems that rely on multiple sensors, the positioning system proposed in this study uses a single anisotropic magnetoresistive (AMR) sensor to measure the magnetic field of a target permanent magnet. This approach significantly reduces the system hardware cost and complexity, facilitating the miniaturization of positioning systems. Leveraging a BP neural network model, which is shown to be fast and accurate, the positioning system obtains the real-time magnetic field of the target magnet using a single sensor, subsequently converting three-axis magnetic field data into coordinate information to achieve real-time tracking and localization. The results show that the root mean square errors (RMSEs) for the X and Z axes in the simulation are 0.27 mm and 0.26 mm, respectively, while the RMSEs for the X, Y, and Z axes in the actual test are 0.83 mm, 1.15 mm, and 0.85 mm, respectively. It is also observed that the positioning error correlates with variations in the magnetic field with respect to position, which originate from the strong distance-dependent nonlinearity of the magnetic field. This method not only reduces hardware costs but also maintains accuracy. It is particularly well-suited to applications requiring high-precision positioning and tracking, achieving millimeter-level accuracy within a volume of 50 × 40 × 40 mm³. It has potential applications in aerospace intelligent connectors, medical devices and automation systems, where space and signal lines are limited. Full article

Accurate and high-efficiency film metrology remains a key challenge in High-Volume Manufacturing (HVM), where conventional spectroscopic reflectometry and white light interferometry (WLI) are either limited by model dependence or throughput. In this work, we extend the measurable film-thickness range of reflectometry to at least 50 µm through a new model-free algorithm, the Linearized Reflectance Zero-Crossing (LRZ) method. The approach builds upon the previously reported Linearized Reflectance Extrema (LRE) technique but eliminates the sensitivity to spectral sampling and fringe attenuation that degrade performance in the thick-film regime. By linearizing phase response and extracting Zero-Crossing positions in wavenumber space, LRZ provides robust and repeatable thickness estimation without iterative fitting, achieving comparable accuracy with much higher computational efficiency than conventional model-based methods. Validation using more than 80 measurements on alumina films over NiFe substrates shows excellent correlation with WLI (r = 0.97) and low gauge repeatability and reproducibility (GR&R < 3%). Moreover, LRZ achieves an average Move-Acquire-Measure (MAM) time of approximately 2 s, which is about 7 times faster than WLI. The proposed method enables fast, accurate, and model-independent optical metrology for thick films, offering a practical solution for advanced HVM process control. Full article

Many compressed air devices are energy inefficient. One example is using air nozzles above pastry lines to remove flour and cool products. These nozzles consume excessive energy, particularly when mounted too high, requiring stronger airflow. Adjustable nozzle height and energy-efficient nozzles should be used with careful control of air pressure, flow rate, and activation time, ensuring efficient and adaptive control. Additionally, sensor-based control should activate airflow only when pastries are present and until the correct amount of powder material has been blown out, as the nozzles often operate unnecessarily. Accurate measurement of powder volume after blow-off remains a challenge. With the use of computer vision methodology, the system would continuously read the measured values and determine not only the optimal moment to interrupt device operation but also dynamically adjust key parameters. This paper demonstrates that computer vision can estimate powder volume using two non-contact 3D methods: a depth camera, and a structured light scanner. Their accuracy, reliability, advantages, and limitations are analyzed. The results show that the structured light scanner can be used in the case of a static model (the conveyor belt with products stops at the moment when it is necessary to perform a 3D measurement). This approach shows higher repeatability and gives a more accurate 3D model. On the other hand, for the dynamic model (the conveyor belt with products moves while the 3D measurement device is fixed), the depth camera can be used because, at minimum rotation speeds of the substrate, it shows higher accuracy and enables faster adaptive modeling and creation of the necessary data. Full article

Sulphatic evaporites represent a critical challenge for underground engineering due to their high solubility, swelling potential, and sensitivity to changing hydraulic and thermal conditions. In this study, we investigate the temperature-dependent dissolution behavior and microstructural evolution of Triassic sulphate rocks consisting of anhydrite and minor portions of gypsum from the Western Alps. Twelve cylindrical samples were immersed in CaSO₄-saturated water solutions at 15 °C, 40 °C, and 60 °C for six months. Periodic mass and volume measurements were combined with Scanner Electron Microscope (SEM) imaging to quantify dissolution and document mineralogical transformations. All samples experienced progressive mass loss, whereas volumetric changes remained below measurement resolution. Dissolution pathways varied strongly with temperature. At 15 °C, dissolution occurred mainly along anhydrite grain boundaries, producing rounded crystal edges, while less effect was observed in the gypsum veins, leaving the intergranular layers preserved. In contrast, at 40–60 °C, gypsum was preferentially dissolved, generating porosity around comparatively unaltered anhydrite grains. These results qualitatively reproduce the temperature-controlled solubility inversion between gypsum and anhydrite predicted by thermodynamic models. No secondary gypsum precipitation or swelling features were observed. The experimental evidence highlights the role of temperature and hydraulic regime in controlling the stability of sulphate rocks and provides insights relevant to tunnel excavation, underground storage facilities, and geomechanical modeling in evaporitic settings. Full article

The punching shear capacity of reinforced ultra-high-performance concrete (UHPC) two-way slabs in applications such as floor slabs and bridge decks has attracted increasing attention. However, due to the insufficient consideration of the internal force transmission path and failure mechanism, existing empirical formulas exhibit limited accuracy for predicting the punching shear capacity of reinforced UHPC slabs. Therefore, based on the critical shear crack theory (CSCT), this study proposes a specific theoretical model where the tensile strain-hardening behavior and tensile strength of UHPC, the punching shear-span ratio, and the reinforcement ratio are comprehensively considered. In the proposed model, the steel fiber bridging contribution is derived via the variable engagement method (VEM), for which an equation describing the bond strength between steel fibers and UHPC matrix was developed. The feasibility of the proposed model was validated through an established experimental database. Furthermore, the effects of several key parameters on the punching shear behavior of reinforced UHPC slabs were analyzed. The results show that the proposed models can accurately predict the punching shear capacity and ultimate rotation angle of reinforced UHPC slabs. With increasing slab thickness, UHPC strength, and reinforcement ratio, the punching shear capacity increases, whereas the corresponding ultimate rotation angle and steel fiber contribution ratio decrease. Increasing the fiber volume fraction enhances both the fiber contribution and the punching shear capacity. For slabs with higher UHPC strength, the reinforcing effect of a higher reinforcement ratio is more pronounced. Full article

Intrusion detection systems face challenges in processing high-volume network traffic while maintaining accuracy across diverse low volume attack types. This study presents a hybrid approach combining ARIMA time series forecasting with Decision Tree classification to detect attacks in Zeek network flow data labeled with MITRE ATT&CK tactics, leveraging PySpark for scalability. ARIMA identifies temporal anomalies which Decision Trees then classify by attack type. The ARIMA model was evaluated across 13 MITRE ATT&CK tactics, though only 7 maintained sufficient class balance for valid assessment. Results are reported at three evaluation levels: Baseline (Decision Tree only), ARIMA-DT (Decision Tree tested on ARIMA-filtered anomalies), and End-to-End (pipeline performance measured against the original test population). The hybrid model demonstrated two distinct benefits: performance improvement for detectable attacks and detection enablement for previously undetectable attacks. For high-volume attacks with existing baseline detection, ARIMA preprocessing substantially improved performance, for example, Reconnaissance achieved an ARIMA-DT F1 score of 99.71% (from a baseline of 80.88%) with End-to-End metrics confirming this improvement at 97.59% F1-score. Credential Access reached a perfect 100% precision and recall on the ARIMA-filtered subset (from a baseline recall of 7.48%); however, End-to-End evaluation revealed that ARIMA filtering removed the vast majority of Credential Access attacks, resulting in a 1.28% End-to-End F1-score—worse than the baseline F1-score of 7.41%—demonstrating that the hybrid pipeline is counterproductive for attack types whose flow characteristics closely resemble legitimate traffic. More significantly, ARIMA preprocessing enabled detection where traditional Decision Trees completely failed (0% recall) for four stealthy attack types: Defense Evasion (ARIMA-DT recall of 93.22%, End-to-End 67.83%), Discovery (ARIMA-DT recall of 100%, End-to-End 63.43%), Persistence (ARIMA-DT recall of 86.92%, End-to-End 73.38%), and Privilege Escalation (ARIMA-DT recall of 89.93%, End-to-End 64.68%). These results demonstrate that ARIMA-based statistical anomaly detection is particularly effective for attacks involving subtle, low-volume activities that blend with legitimate operations, while also improving classification accuracy for high-volume reconnaissance activities. Full article

This study developed a combined computational-experimental approach to investigate crack kinking in thick adhesively bonded Glass Fibre Reinforced Polymer (GFRP) composite joints, focusing on the adhesive joints found at wind turbine blade trailing edges. Double Cantilever Beam (DCB) tests were performed on composite joints with a 10-mm thick epoxy adhesive, representative of trailing-edge joints. Finite Element (FE) models included cross-ply GFRP composites and an adhesive layer. Subsequently, both the composite/adhesive interfaces and voids were explicitly modelled, allowing separate and combined evaluations of their effects on crack kinking. A cohesive zone model was used to capture the fracture along the composite/adhesive interfaces, while a Drucker-Prager plasticity model combined with a ductile damage model was used for the adhesive. The numerical findings indicated that crack kinking in FE simulations with explicit interfaces was primarily governed by the lower fracture resistance of the composite/adhesive interface relative to that of the bulk adhesive. Voids with a total volume fraction of approximately 1% were modelled by randomly deleting cubic 1 mm C3D8R elements in the adhesive layer to reproduce the voids typically observed in thick adhesive joints. The predicted crack paths closely matched experimental results. Simulations with voids revealed that voids above or below the adhesive midplane caused crack deflection toward the nearest interface. In models combining both features, cracks were consistently redirected toward the composite/adhesive boundary near voids, reproducing experimental observations. These results provide new insights into trailing-edge adhesive joint failure and establish a foundation for better modelling and design. Full article

Purpose: To develop prognostic models integrating delta radiomics from prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA-PET/CT) and dosiomics with clinical variables to predict metastasis-free survival (MFS) in patients with localized prostate adenocarcinoma treated with androgen deprivation therapy and external-beam radiotherapy. Materials/Methods: Delta-radiomics analysis included 43 patients. Radiomics features were extracted from the primary tumor on pre- and post-treatment PSMA-PET/CT, and delta features were calculated as relative changes. Eight high-variance features were selected and combined with clinical variables (age, Gleason score, initial PSA, and a binary variable, indicating the occurrence of PSA relapse). Data was split 70:30 with training-set imbalance correction. Predictors that were significant in univariate Cox regression (p < 0.05) were entered into multivariate Cox models, and five-year MFS was classified using a quadratic support vector machine. Dosiomics analysis included 48 patients. Dosiomics features were extracted from the planning target volume receiving 86 Gy and combined with pre-treatment radiomics and clinical variables using the same framework. Results: For delta radiomics, Model 1 (delta radiomics + pre-treatment radiomics + clinical) achieved the best performance (test c-score 0.58; AUC 0.70), exceeding Model 2 (pre-treatment radiomics + clinical; c-score 0.56; AUC 0.65) and Model 3 (clinical only; c-score 0.51; AUC 0.56). For dosiomics, Model 1 showed the highest performance (test c-score 0.56; AUC 0.67) compared with Model 2 (c-score 0.55; AUC 0.62) and Model 3 (c-score 0.50; AUC 0.54). Conclusions: Integrating delta radiomics or dosiomics with pre-treatment imaging and clinical variables improves MFS prediction and supports their role as non-invasive biomarkers for individualized radiotherapy in localized prostate cancer. Full article

Brucellosis diagnosis remains challenging in resource-limited endemic settings. This retrospective case–control study evaluated the diagnostic utility of mean platelet volume (MPV) and hematological inflammatory ratios in brucellosis. Fifty patients with confirmed brucellosis and 50 age-matched healthy controls were included at a university hospital in Turkey (2015–2018). Complete blood count parameters, hematological ratios (neutrophil-to-lymphocyte ratio [NLR], platelet-to-lymphocyte ratio [PLR], lymphocyte-to-monocyte ratio [LMR]), erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) were measured at diagnosis. Receiver operating characteristic (ROC) curve analysis evaluated diagnostic performance; multivariate logistic regression developed a combined model. Brucellosis patients showed significantly lower MPV (8.04 ± 0.95 vs. 8.56 ± 0.69 fL, p = 0.002), higher platelet counts (305.0 ± 116.0 vs. 246.0 ± 55.2 × 10³/μL, p = 0.002), lower NLR (median: 1.69 vs. 2.07, p = 0.013), and higher LMR (median: 5.28 vs. 4.12, p = 0.008). ESR demonstrated the best individual diagnostic performance (area under the curve [AUC] = 0.842). The combined model (MPV + ESR + CRP) achieved superior performance (AUC = 0.891, sensitivity 84%, specificity 86%). Limitations include the single-center retrospective design, lack of internal validation, and comparison with healthy controls only. Notably, healthy controls were deliberately selected to establish baseline hematological profiles associated with brucellosis rather than to differentiate it from other infections. Brucellosis presents a unique hematological profile with decreased MPV and altered inflammatory ratios. The combined model offers a potentially cost-effective screening approach for endemic settings, pending external validation. Full article

Background: Very Low Birth Weight preterm infants are at elevated risk for disrupted brain growth and later neurodevelopmental impairment. Bedside-accessible tools for monitoring cerebral development remain limited. Methods: In this retrospective pilot cohort study, 153 Very Low Birth Weight infants (<32 weeks gestational age and/or <1500 g) from two Level III Neonatal Intensive Care Units underwent serial cranial ultrasound assessments. Total brain volume was estimated using an ellipsoid formula derived from standardized imaging planes. Growth trajectories were analysed via linear mixed-effects modelling. Associations with clinical predictors—including invasive ventilation, sepsis, and somatic growth—were evaluated. Results: A total of 976 brain volume measurements were collected. Median cerebral volume increased from 164 cm³ to 275 cm³ across the hospital stay, corresponding to a median growth rate of 2.3 cm³/day (95% CI: 1.5–3.1). Duration of invasive mechanical ventilation was associated with reduced cerebral growth (p < 0.01, R² = 0.26). Cerebral volume growth showed a weak but statistically significant correlation with head circumference percentile progression (p < 0.05, ρ = 0.16). Conclusions: Sonographic brain volumetry is a feasible and non-invasive method for tracking cerebral development in Very Low Birth Weight infants. These findings confirm significant associations between cerebral growth and head growth and identify prolonged invasive ventilation as a risk factor for impaired cerebral development. Full article