Search Results (75)

Ammonia, a zero carbon energy vector, is under consideration for decarbonising marine and energy storage applications due to its high mass-based energy density compared to many alternatives. In addition, there is widespread existing supply and transportation infrastructure due to ammonia’s use as a fertiliser. When injected in its liquid form, however, ammonia behaves quite differently to traditional fuels due to its high saturation pressure and enthalpy of vaporisation, amongst other things. This means that fundamental data on ammonia sprays need to be collected in order to understand ammonia spray behaviour and calibrate models of ammonia sprays needed for design in the virtual world. Previous work on ammonia sprays has mostly focused on spray morphology at a macroscopic level (such as liquid penetration length). However, there are fewer studies of ammonia sprays at a microscopic level. In this study, liquid ammonia was injected into a constant-volume chamber using a direct injector at two injection pressures (100 bar and 150 bar) and a range of ambient pressures from 3–13 bar. This range of ambient conditions spans regimes from flash-boiling to non-flash-boiling, thereby enabling systematic investigation of the transition between these regimes. A laser diffraction technique was used for measuring the droplet sizes of the spray at different locations (in a cylindrical volume with a diameter of 10 mm) within the spray plume at 10 kHz, and the nominal droplet sizes were quantified by the Sauter Mean Diameter (SMD). These SMD values provided, at a microscopic level, an insight of the atomisation of the spray as it left the nozzle and penetrated into an environment with different densities. It was found that the tested injector leads to a breakup dominant spray behaviour with liquid ammonia and hence the SMD values decrease as ambient pressure increases. In addition, the droplets are generally smaller at the outer edge of the spray plume compared to the inner part and both the injection pressure and injection duration have a strong effect on the droplet sizes. Full article

Background/Objectives: Thrombus composition influences the success of endovascular therapy in stroke, but conventional CT is limited in determining it. Spectral-detector-CT (SDCT) can apply material-decomposition and virtual monoenergetic (MonoE) imaging, which may provide a way to gain information on thrombus composition. This experimental study aimed to evaluate the differentiability of heterogeneous thrombi with variable red blood cell (RBC) content using SDCT. Methods: Ten thrombus entities with different compositions on RBC and plasma, thus fibrin content, were manufactured (volumetric RBC%/Plasma% = 90/10; 80/20; 70/30; 60/40; 50/50; 40/60; 30/70; 20/80; 10/90; 5/95) and scanned in an SDCT. Conventional Hounsfield-unit (HU) values, spectral electron density (ED), effective atomic number (Z-effective) and HU in MonoE maps ranging from 40– to 200 keV were evaluated for thrombus differentiation. Results: Conventional HU increased with RBC content, allowing us to differentiate the entities (p < 0.001). ED values also increased with RBC content and allowed for differentiation too (p < 0.001). Z-effective values showed no differences among the different entities (p > 0.05). Regarding the mass-attenuation curves from 40 to 200 keV the different thrombi showed a similar curve progression with highest HU values at 40 and lowest at 200 keV. The thrombi could be distinguished overall at each monoenergetic level by HU (p < 0.001 for each level). The absolute decrease in HU between 40 and 200 keV was thereby not significantly different between the different entities, but the relative decrease was, as it was more pronounced in thrombi with lower RBC content (p < 0.001). Conclusions: Spectral CT enables differentiation between thrombi with different RBC and fibrin contents by means of ED or analysis of the mass-attenuation curve. This offers alternative possibilities that go beyond characterisation based on CT-density alone. The additional inclusion of spectral parameters in thrombus diagnostics could therefore improve diagnosis and treatment. Full article

Magnetorheological (MR) fluids are composite materials composed of ferromagnetic particles, medium oils, and several types of additives. MR fluids are particularly suitable for haptic applications, because their rheological properties can be rapidly, stably, and reversibly controlled using an applied magnetic field, MR fluids are particularly suitable for haptic applications. Moreover, with recent advances in virtual reality technologies, handheld haptic interfaces that offer high portability and operability, owing to their lightweight and compact design, have become increasingly important for enhancing immersion in teleoperation systems. In this study, we design and develop a miniature MR fluid device for handheld haptic interfaces using a cylindrical rotor. The proposed device is compact and light, and exhibits a high output. We analyzed the magnetic field distribution inside the device using an analytical model and confirmed that the serpentine magnetic flux path effectively increased the magnetic flux density in the MR fluid working region. According to the experimental characterization, the device generated a maximum torque of 0.3 Nm. The resulting interface had a total mass of 122 g and provided a maximum force of 4.5 N to the user, demonstrating its suitability for teleoperation and virtual reality applications. Full article

Guided by principles of symmetry to achieve a proper balance among model consistency, accuracy, and complexity, this paper proposes a new approach for identifying the unknown parameters of nonlinear one-degree-of-freedom mechanical systems using nonlinear regression methods. To this end, the steps followed in this study can be summarized as follows. Firstly, given a proper set of input time histories and a virtual model with all parameters known, the dynamic response of the mechanical system of interest, used as output data, is evaluated using a numerical integration scheme, such as the classical explicit fixed-step fourth-order Runge–Kutta method. Secondly, the numerical values of the unknown parameters are estimated using the Levenberg–Marquardt nonlinear regression algorithm based on these inputs and outputs. To demonstrate the effectiveness of the proposed approach through numerical experiments, two benchmark problems are considered, namely a mass-spring-damper system and a simple pendulum-damper system. In both mechanical systems, viscous damping is included at the kinematic joints, whereas dry friction between the bodies and the ground is accounted for and modeled using the Coulomb friction force model. While the source of nonlinearity is the frictional interaction alone in the first benchmark problem, the finite rotation of the pendulum introduces geometric nonlinearity, in addition to the frictional interaction, in the second benchmark problem. To ensure symmetry in explaining model behavior and the interpretability of numerical results, the analysis presented in this paper utilizes five different input functions to validate the proposed method, representing the initial phase of ongoing research aimed at applying this identification procedure to more complex mechanical systems, such as multibody and robotic systems. The numerical results from this research demonstrate that the proposed approach effectively identifies the unknown parameters in both benchmark problems, even in the presence of nonlinear, time-varying external input actions. Full article

This study addresses the inverse problem of identifying adiabatic temperature rise (ATR) characteristics for mass concrete using the Physics-Informed Neural Network (PINN). The characteristics are defined by parameters representing the maximum ATR and temperature increasing rate. The PINN-based identification of these parameters was conducted using virtual experimental data generated through numerical simulation with three different ATR models. To assess the robustness of the PINN in the identification process, noise was introduced into the data. The observation period and noise condition of the data were used as variables to evaluate the performance of PINN-based parameter identification. In addition, 10 independent PINN training sessions were conducted, and the results were statistically analyzed. The identification performance of the unknown parameters was influenced by the observation period. The PINN accurately identified the parameters used in the virtual experiments, even with short-term observation data, regardless of the noise. Statistical analysis indicates that the PINN demonstrates significant reliability and consistency in parameter identification. Full article

Existing research has preliminarily achieved stable walking in humanoid robots; however, natural human-like leg motion and adaptive capabilities in dynamic environments remain unattained. This paper proposes a bionic central pattern generator (CPG) gait generation method based on Kimura neurons. The method maps the CPG output to the spatial motion patterns of the robot’s center of mass (CoM) and foot trajectory, modulated by 22 undetermined parameters. To address the vague physical interpretation of CPG parameters, the strong neuronal coupling, and the difficulty of decoupling, this research systematically optimized the CPG parameters by defining an objective function that integrates dynamic balance performance with step constraints, thereby enhancing the naturalness and coordination of gait generation. To further enhance the walking stability of the robot under varying road curvatures, a vestibular reflex mechanism was designed based on the Tegotae theory, enabling real-time posture adjustment during slope walking. To validate the proposed approach, a virtual simulation platform and a physical humanoid robot system were constructed to comparatively evaluate motion performance on flat terrain and slopes with different gradients. The results show that the energy consumption characteristics of robot-coordinated gait are highly consistent with the energy-saving mechanism of human natural motion. In addition, the established reflection mechanism significantly improves the motion stability of the robot in slope transition, and its excellent stability margin and environmental adaptability are verified by simulation and experiment. Full article

In this study, a novel arbitrary order Virtual Element Method (p-VEM) for free torsional vibration analysis of beams with negligible warping is presented. This method can serve as an equivalent beam model for slender aerospace structural components. The proposed formulation utilizes a spatial discretization of the primary variable with implicit virtual functions that are approximated with polynomials of arbitrary order p by employing a suitably defined projection operator and degrees of freedom. From the spatial discretization of the weak form of the equations of motion, the semi-discrete equations of motion are obtained, from which stiffness and mass matrices are derived without the need for additional stabilization. The developed formulation is validated through several case studies, which demonstrate that the p-VEM offers higher accuracy and faster convergence rate compared to traditional modeling approaches. Full article

Background and Clinical Significance: Prostate cancer (PCa) is among the most commonly diagnosed malignancies in men worldwide. While bone and lymph nodes are the most frequent metastatic sites, prostate cancer cells have the potential to spread to virtually any organ, including the pleura, which is an exceedingly rare initial site of presentation that can mimic mesothelioma or primary lung cancer. Case Presentation: We describe a 77-year-old man who presented with exertional dyspnea and intermittent cough, initially suggesting a cardiopulmonary etiology. Imaging revealed multiple pleural nodules and an extensive right-sided pleural effusion. Despite a borderline serum prostate-specific antigen (PSA) level of 2.91 ng/mL, histopathology and immunohistochemistry of pleural biopsies confirmed metastatic prostate adenocarcinoma. Subsequent imaging identified a PIRADS 5 lesion in the prostate, and a biopsy confirmed ISUP Grade Group 5 disease (Gleason score 4 + 5 = 9). A bone scan showed no skeletal metastases, and a contrast-enhanced CT of the abdomen found no additional metastatic lesions. The patient was started on androgen deprivation therapy followed by abiraterone. This case underscores the diagnostic challenge posed by atypical metastatic presentations of prostate cancer. Low or moderately elevated PSA can obscure suspicion of prostate origin, especially with pleural-based lesions suggestive of mesothelioma. Immunohistochemical markers, including androgen receptors, AMACR, and Prostein, are critical for accurate diagnosis. Conclusions: Clinicians must maintain a high index of suspicion for prostate cancer in older men with unexplained pleural effusions, nodules, or masses, even with low-normal PSA levels. Early recognition and prompt treatment can improve outcomes, despite the rarity and aggressiveness of pleural metastases. Full article

The rapid and accurate annotation of the complex compounds and metabolites in natural products remains a significant challenge. In this study, we developed an integrated strategy to efficiently and accurately profile both the prototypes and metabolites of natural products in vivo. And this was achieved by establishing a gypenosides constituent database and utilizing R programming language to combine sample selection, virtual metabolite database construction, polygon mass defect filtering, and Kendrick mass defect filtering. In addition, the annotation strategy was successfully applied to identify the prototypes and metabolites of gypenosides in mice serum. As a case study, gypenoside LXXV was used to validate the feasibility of this approach. The results demonstrated 36 prototypes and 108 metabolites were annotated from the serum by the established annotation strategy. The prototype and eight metabolites of gypenoside LXXV were further confirmed, indicating that the proposed strategy is available. This study provides a novel approach for the rapid and accurate identification of prototypes and metabolites of natural products and offers new insights into the metabolic processes of gypenosides in vivo. Full article

Bacterial virulence plays an important role in infection. Antibacterial virulence factors are effective for preventing crop bacterial diseases. Resin acid copper salt as an effective inhibitor exhibited excellent anti-Xanthomonas oryzae pv. oryzae (Xoo) activity with an EC₅₀ of 50.0 μg mL⁻¹. Resin acid copper salt (RACS) can reduce extracellular polysaccharides’ (EPS’s) biosynthesis by down-regulating gumB relative expression. RACS can also effectively inhibit the bio-mass of Xoo biofilm. It can reduce the activity of Xoo extracellular amylase at a concentration of 100 μg mL⁻¹. Meanwhile, the results of virtual computing suggested that RACS is an enzyme inhibitor. RACS displayed good curative activity with a control effect of 38.5%. Furthermore, the result of the phytotoxicity assessment revealed that RACS exhibited slight toxicity compared with the control at a concentration of 200 μg mL⁻¹. The curative effect was increased to 45.0% using an additional antimicrobial agent like orange peel essential oil. RACS markedly inhibited bacterial pathogenicity at a concentration of 100 μg mL⁻¹ in vivo. Full article

This study focuses on understanding the impact of different material compositions and printing parameters on the structural integrity of hybrid curved composite beams. Using the continuous filament fabrication technique, which is an advanced fused deposition modelling process, composite curved beams made of short carbon and various continuous fibre-reinforced nylon laminae were fabricated and subjected to four-point bending tests to assess their delamination characteristics. The results show that the presence of five flat zones in the curved region of a curved beam achieves 10% and 6% increases in maximum load and delamination strength, respectively, against a smooth curved region. The delamination response of a curved composite beam design consisting of unidirectional carbon/nylon laminae is superior to that of a curved beam made of glass fibre/nylon laminae, while the existence of highly strengthened glass fibre bundles is alternatively quite competitive. Doubling the number of continuous fibre-reinforced laminae results in an increase of up to 36% in strength by achieving a total increase in the beam thickness of 50%, although increases in mass and material cost are serious concerns. The hybrid curved beam design has a decrease in the maximum load and the strength by 11% and 13%, respectively, when compared with a non-hybrid design, which consists of some type of stronger and stiffer nylon laminae instead of short carbon fibre-reinforced conventional nylon laminae. Two-dimensional surface-based cohesive finite element models, which have a good agreement with experimental results, were also established for searching for the availability of useful virtual testing. The results from this study will greatly contribute to the design and numerical modelling of additively manufactured hybrid composite curved beams, brackets, and fittings. Full article

Introduction. Three-dimensional (3D) speckle-tracking echocardiography (STE) combines the advantages of STE and volumetric 3D echocardiography, which shows the left ventricle (LV) in 3D during the cardiac cycle and is also suitable for accurate strain measurements in addition to volumetric assessments using the same virtual 3D LV cast. The present study aimed to confirm the prognostic impact of 3DSTE-derived LV global longitudinal strain (GLS) in healthy adults during a 12-year follow-up period. Patients and methods. The current study comprised 124 healthy individuals with a mean age of 31.0 ± 11.7 years (64 males) at the time of complete two-dimensional Doppler echocardiography (2DE) and 3DSTE. Results. During a mean follow-up of 8.01 ± 4.12 years, 10 healthy individuals suffered cardiovascular events, including 2 cardiac deaths. Using ROC analysis, 3DSTE-derived LV-GLS ≥ 14.77% was found to be a significant predictor for cardiovascular event-free survival (sensitivity 70%, specificity 71%, area under the curve 76%, p = 0.007). Using 2DE, higher LV end-diastolic and end-systolic volumes, a larger LV end-systolic diameter and a lower LV ejection fraction could be detected in subjects with LV-GLS < 14.77% as compared to cases with LV-GLS ≥ 14.77%. Subjects with events had thicker interventricular septa, a larger LV mass and lower 3DSTE-derived LV-GLS and a higher ratio of cases had LV-GLS < 14.77%. From subjects with LV-GLS < 14.77%, seven individuals (18%) had events. Multivariate regression analysis identified age and LV-GLS as independent predictors of event-free survival. Conclusions. 3DSTE-derived LV-GLS is a strong independent predictor of cardiovascular survival in healthy adults. Full article

This review highlights recent advances in renal cell carcinoma (RCC) imaging. It begins with dual-energy computed tomography (DECT), which has demonstrated a high diagnostic accuracy in the evaluation of renal masses. Several studies have suggested the potential benefits of iodine quantification, particularly for distinguishing low-attenuation, true enhancing solid masses from hyperdense cysts. By determining whether or not a renal mass is present, DECT could avoid the need for additional imaging studies, thereby reducing healthcare costs. DECT can also provide virtual unenhanced images, helping to reduce radiation exposure. The review then provides an update focusing on the advantages of multiparametric magnetic resonance (MR) imaging performance in the histological subtyping of RCC and in the differentiation of benign from malignant renal masses. A proposed standardized stepwise reading of images helps to identify clear cell RCC and papillary RCC with a high accuracy. Contrast-enhanced ultrasound may represent a promising diagnostic tool for the characterization of solid and cystic renal masses. Several combined pharmaceutical imaging strategies using both sestamibi and PSMA offer new opportunities in the diagnosis and staging of RCC, but their role in risk stratification needs to be evaluated. Although radiomics and tumor texture analysis are hampered by poor reproducibility and need standardization, they show promise in identifying new biomarkers for predicting tumor histology, clinical outcomes, overall survival, and the response to therapy. They have a wide range of potential applications but are still in the research phase. Artificial intelligence (AI) has shown encouraging results in tumor classification, grade, and prognosis. It is expected to play an important role in assessing the treatment response and advancing personalized medicine. The review then focuses on recently updated algorithms and guidelines. The Bosniak classification version 2019 incorporates MRI, precisely defines previously vague imaging terms, and allows a greater proportion of masses to be placed in lower-risk classes. Recent studies have reported an improved specificity of the higher-risk categories and better inter-reader agreement. The clear cell likelihood score, which adds standardization to the characterization of solid renal masses on MRI, has been validated in recent studies with high interobserver agreement. Finally, the review discusses the key imaging implications of the 2017 AUA guidelines for renal masses and localized renal cancer. Full article

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death. Recent studies have underlined the importance of non-contrast-enhanced chest CT scans not only for emphysema progression quantification, but for correlation with clinical outcomes as well. As about 40 percent of the 300 million CT scans per year are contrast-enhanced, no proper emphysema quantification is available in a one-stop-shop approach for patients with known or newly diagnosed COPD. Since the introduction of spectral imaging (e.g., dual-energy CT scanners), it has been possible to create virtual non-contrast-enhanced images (VNC) from contrast-enhanced images, making it theoretically possible to offer proper COPD imaging despite contrast enhancing. This study is aimed towards investigating whether these VNC images are comparable to true non-contrast-enhanced images (TNC), thereby reducing the radiation exposure of patients and usage of resources in hospitals. In total, 100 COPD patients with two scans, one with (VNC) and one without contrast media (TNC), within 8 weeks or less obtained by a spectral CT using dual-layer technology, were included in this retrospective study. TNC and VNC were compared according to their voxel-density histograms. While the comparison showed significant differences in the low attenuated volumes (LAVs) of TNC and VNC regarding the emphysema threshold of −950 Houndsfield Units (HU), the 15th and 10th percentiles of the LAVs used as a proxy for pre-emphysema were comparable. Upon further investigation, the threshold-based LAVs (−950 HU) of TNC and VNC were comparable in patients with a water equivalent diameter (DW) below 270 mm. The study concludes that VNC imaging may be a viable option for assessing emphysema progression in COPD patients, particularly those with a normal body mass index (BMI). Further, pre-emphysema was generally comparable between TNC and VNC. This approach could potentially reduce radiation exposure and hospital resources by making additional TNC scans obsolete. Full article

The Internet of Things (IoT) was born from the fusion of virtual and physical space and became the initiator of many scientific fields. Economic sustainability is the key to further development and progress. To keep up with the changes, it is necessary to adapt economic models and concepts to meet the requirements of future smart environments. Today, the need for electronic commerce (e-commerce) has become an economic priority during the transition between Industry 4.0 and Industry 5.0. Unlike mass production in Industry 4.0, customized production in Industry 5.0 should gain additional benefits in vertical management and decision-making concepts. The authors’ research is focused on e-commerce in a three-layer vertical IoT environment. The vertical IoT concept is composed of edge, fog, and cloud layers. Given the ubiquity of artificial intelligence in data processing, economic analysis, and predictions, this paper presents a few state-of-the-art machine learning (ML) algorithms facilitating the transition from a flat to a vertical e-commerce concept. The authors also propose hands-on ML algorithms for a few e-commerce types: consumer–consumer and consumer–company–consumer relationships. These algorithms are mainly composed of convolutional neural networks (CNNs), natural language understanding (NLU), sequential pattern mining (SPM), reinforcement learning (RL for agent training), algorithms for clicking on the item prediction, consumer behavior learning, etc. All presented concepts, algorithms, and models are described in detail. Full article