Search Results (689)

T-bar penetrometers have been widely used to measure strength parameters of marine clay in laboratory and in situ tests. However, using the deep resistance factor derived from full-flow conditions to evaluate the undrained shear strength of shallow clay layers may lead to significant underestimation. Furthermore, the deep resistance factor is inherently influenced by the strain-softening behavior of clay rather than maintaining the constant value (typically 10.5) as conventionally assumed in practice. To address this issue, large-deformation finite element (LDFE) simulations incorporating an advanced exponential strain-softening constitutive model were performed to replicate the full T-bar penetration process—from shallow embedment to deeper depths below the mudline. A series of parametric studies were conducted to examine the influence of key parameters on the resistance factor and the associated failure mechanisms during penetration. Based on numerical results, empirical formulas were derived to predict critical penetration depths for both trapped cavity formation and full-flow mechanism initiation. For penetration depths shallower than the full-flow depth, an expression for the softening correction factor was developed to calibrate the shallow resistance factor. Finally, combined with global optimization algorithms, a computer-aided back-analysis procedure was established to estimate strain-softening parameters using resistance-penetration curves from initial penetration tests in marine clay. The reliability of the back-analysis procedure was validated through extensive comparisons with a series of numerical simulation results. This procedure can be applied to the interpretation of T-bar in situ test results in soft marine clay, enabling the evaluation of its strain-softening behavior. Full article

Due to the rapid advancements in coordinate measuring systems, data processing software, and additive manufacturing (AM) techniques, it has become possible to create copies of existing models through the reverse engineering (RE) process. However, the lack of precise estimates regarding the accuracy of the RE process—particularly at the measurement, reconstruction, and computer-aided design (CAD) modeling stages—poses significant challenges. Additionally, the assessment of dimensional and geometrical errors during the manufacturing stage using AM techniques limits the practical implementation of product replicas in the industry. This paper provides an estimation of the errors encountered in the RE process and the AM stage of various models. It includes examples of an electrical box, a lampshade for a standing lamp, a cover for a vacuum unit, and a battery cover. The geometry of these models was measured using a GOM Scan 1 (Carl Zeiss AG, Jena, Germany). Following the measurement process, data processing was performed, along with CAD modeling, which involved primitive detection, profile extraction, and auto-surface methods using Siemens NX 2406 software (Siemens Digital Industries, Plano, TX, USA). The models were produced using a Fortus 360-mc 3D printer (Stratasys, Eden Prairie, MN, USA) with ABS-M30 material. After fabrication, the models were scanned using a GOM Scan 1 scanner to identify any manufacturing errors. The research findings indicated that overall, 95% of the points representing reconstruction errors are within the maximum deviation range of ±0.6 mm to ±1 mm. The highest errors in CAD modeling were attributed to the auto-surfacing method, overall, 95% of the points are within the average range of ±0.9 mm. In contrast, the lowest errors occurred with the detect primitives method, averaging ±0.6 mm. Overall, 95% of the points representing the surface of a model made using the additive manufacturing technology fall within the deviation range ±0.2 mm on average. The findings provide crucial insights for designers utilizing RE and AM techniques in creating functional model replicas. Full article

Among the leading causes of bacteremia are Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus. E. coli and K. pneumoniae are increasingly exhibiting resistance to last-resort antibiotics, such as carbapenems. Rapid and accurate identification of these pathogens is critical for timely treatment and infection control. This paper aimed to develop a computer-aided bacterial morphometric technique for identifying and classifying wild-type E. coli, K. pneumoniae, and S. aureus in a field guide fashion. A 3D laser scanning confocal microscope was used to gather key parameters of each organism: length (L, µm), circular diameter (CD, µm), volume (V, µm³), surface area-to-cross-sectional area ratio (SA/CSA, unitless), surface uniformity ratio (Str), and surface texture ratio (Sdr). Microscope images and measurement results showed that S. aureus was spherical with the shortest length (1.08 µm) and smallest volume (0.52 µm³). E. coli and K. pneumoniae were rod-shaped with lengths >2.0 µm and volumes >1.0 µm³. Carbapenem-resistant (CR) strains exhibited larger volumes than their wild-type counterparts. Surface parameters further differentiated strains: wild-type E. coli had a greater surface texture or a less smooth surface (larger Sdr) than K. pneumoniae (lower Sdr) did. CR E. coli had more surface uniformity (lower Str) than CR K. pneumoniae did. A dichotomous key based on shape, circular diameter, volume, length, and surface characteristics was developed to classify the species using a series of paired, contrasting features. This morphometric analysis can aid researchers in quickly identifying bacteria, leading to faster diagnosis of life-threatening diseases and improved treatment decisions. Full article

In order to determine thermally safe driving parameters of ignition coils for hydrogen internal combustion engines (ICE), a reliable estimation of internal power losses is essential. These losses include resistive winding losses, magnetic core losses due to hysteresis and eddy currents, dielectric losses in the insulation, and electronic switching losses. Direct experimental assessment is difficult because the components are inaccessible, while conventional computer-aided engineering (CAE) approaches face challenges such as the need for accurate input data, the need for detailed 3D models, long computation times, and uncertainties in loss prediction for complex structures. To address these limitations, we propose an artificial intelligence (AI)-based framework for estimating internal losses from external temperature measurements. The method relies on an artificial neural network (ANN), trained to capture the relationship between external coil temperatures and internal power losses. The trained model is then employed within an optimization process to identify losses corresponding to experimental temperature values. Validation is performed by introducing the identified power losses into a CAE thermal model to compare predicted and experimental temperatures. The results show excellent agreement, with errors below 3% across the −30 °C to 125 °C range. This demonstrates that the proposed hybrid ANN–CAE approach achieves high accuracy while reducing experimental effort and computational demand. Furthermore, the methodology allows for a straightforward determination of the coil safe operating area (SOA). Starting from estimates derived from fitted linear trends, the SOA limits can be efficiently refined through iterative verification with the CAE model. Overall, the ANN–CAE framework provides a robust and practical tool to accelerate thermal analysis and support coil development for hydrogen ICE applications. Full article

Background/Objectives: Computer-aided manufacturing techniques are divided into subtractive (milling) and additive (3D printing) techniques. The accuracy of both techniques is measured only indirectly by testing the fabricated restorations. However, the role of the fabrication technique is masked by the differences in the materials used. Hence, this study used the same printing resin to print crowns and blocks for milling. Methods: Ten maxillary first premolars were prepared for full crowns and scanned with Primescan Connect IOS, and then crown restorations were designed using Exocad. A CAD/CAM block equal to size C14 was designed in CAD software (Microsoft 3D Builder) (Version 18.0.1931.0). The designed crowns and blocks were printed using three hybrid ceramic materials, namely, Ceramic Crown (SprintRay), Varseosmile Crown plus (Bego), and P-crown (Senertek), using a SprintRay Pro95S 3D-printer. The printed blocks were then used to fabricate the designed crowns using an In-Lab MCXL milling machine. The trueness and marginal and internal gaps of the crowns were then measured using Geomagic Control X metrology software (Version 2022.1). Statistical analysis was performed using the Kruskal–Wallis test, Dunn’s test, one-way ANOVA test, and Tukey’s HSD test. Results: Generally, the milled crowns showed significantly higher trueness but lower fitness than their 3D-printed counterparts (p < 0.05). A significant reverse correlation was found between the trueness and fitness of the fabricated restorations. Conclusions: The fabrication technique significantly influenced the accuracy of the hybrid ceramic crowns. Milling offered superior trueness, whereas 3D printing resulted in better internal and marginal adaptation. Full article

Polyvinyl chloride (PVC) remains one of the most widely used synthetic polymers worldwide, primarily due to its versatility, cost-effectiveness, and broad applicability across construction, healthcare, automotive, and consumer goods industries. However, its production involves hazardous chemicals, particularly vinyl chloride monomer (VCM), which requires rigorous safety assessments. In this context, the present study applies the Inherent Safety Index (ISI) methodology to evaluate the safety performance of a suspension polymerization process for PVC production that incorporates direct water recycling as a sustainability measure. The integration of water reuse reduces the fractional water consumption index from 2.8 to 2.2 and achieves a recovered water purity of 99.6%, demonstrating clear environmental benefits in terms of resource conservation. Beyond water savings, the core objective is to assess how this integration influences the inherent risks associated with the process. The key operational stages—polymerization, VCM recovery, product purification, and water recirculation—were modeled and analyzed using computer-aided process engineering (CAPE) tools. The ISI analysis yielded a score of 33, surpassing the threshold typically associated with inherently safer designs, with VCM hazards alone contributing a score of 19 due to its high flammability and carcinogenicity. These findings reveal a critical trade-off between environmental performance and inherent safety, underscoring that resource integration measures, while beneficial for sustainability, may require complementary safety improvements. This study highlights the necessity of incorporating inherently safer design principles alongside process integration strategies to achieve balanced progress in operational efficiency, environmental responsibility, and risk minimization in PVC manufacturing. Full article

Mouthguards are recommended for all sports that may cause injuries to the head and oral cavity. Custom mouthguards, made conventionally in the thermoforming process from ethylene vinyl acetate (EVA), face challenges with thinning at the incisor area during the process. In contrast, additive manufacturing (AM) processes enable the precise reproduction of the dimensions specified in a computer-aided design (CAD) model. The potential use of filament extrusion materials in the fabrication of custom mouthguards has not yet been explored in comparative studies. Our research aimed to compare five commercially available filaments for the material extrusion (MEX) also known as fused deposition modelling (FDM) of custom mouthguards using a desktop 3D printer. Samples made using Copper 3D PLActive, Spectrum Medical ABS, Braskem Bio EVA, DSM Arnitel ID 2045, and NinjaFlex were compared to EVA Erkoflex, which served as a control sample. The samples underwent tests for ultimate tensile strength (UTS), split Hopkinson pressure bar (SHPB) performance, drop-ball impact, abrasion resistance, absorption, and solubility. The results showed that Copper 3D PLActive and Spectrum Medical ABS had the highest tensile strength. DSM Arnitel ID 2045 had the highest dynamic property performance, measured with the SHPB and drop-ball tests. On the other hand, NinjaFlex exhibited the lowest abrasion resistance and the highest absorption and solubility. DSM Arnitel ID 2045’s absorption and solubility levels were comparable to those of EVA, but had significantly lower abrasion resistance. Ultimately, DSM Arnitel ID 2045 is recommended as the best filament for 3D-printing mouthguards. The properties of this biocompatible material ensure high-impact energy absorption while maintaining low fluid sorption and solubility, supporting its safe intra-oral application for mouthguard fabrication. However, its low abrasion resistance indicated that mouthguards made from this material may need to be replaced more frequently. Full article

The integration of Internet of Things (IoT) systems with social networking concepts has opened new business and social opportunities, particularly by allowing smart objects to autonomously establish social relationships with each other and exchange information. However, these relations must be properly quantified and integrated with trust in order to proliferate the provisioning of IoT composite services. Therefore, this proposed work focuses on quantitatively computing social strength and trust among smart objects in IoT for the purpose of aiding efficient service composition with reasonable accuracy. In particular, we propose a trust-augmented social strength (TASS) management protocol that can cope with the heterogeneity of IoT and demonstrate high scalability and resiliency against various malicious attacks. Afterward, we show how the TASS measurements can be applied to service planning in IoT service composition. Based on the experimental results, we conclude that the proposed protocol is, in fact, capable of exhibiting the above-mentioned characteristics in real-world settings. Full article

Background: The majority of parotid gland tumors are benign, e.g., pleomorphic adenoma (PA) and Warthin’s tumor (WT). From a biomedical point of view, oxidative stress is of significant importance due to its established association with the initiation and progression of various types of cancer, including parotid gland cancers. This study aimed to assess whether blood prooxidant–antioxidant markers could aid in diagnosing and guiding surgery for recurrent malignancies after parotid tumor treatment. Methods: We examined patients (n = 20) diagnosed with WT (n = 14) and PA (n = 6) using histopathological verification and computed tomography (CT) who qualified for surgical treatment. Blood samples were taken before the surgery and again 10 days later for biochemical analysis. The activities of the antioxidant enzymes (SOD, CAT and GPx), the non-enzymatic antioxidants (GSH and UA) and oxidative stress markers (MDA and TOS) were determined in the blood. The activities of CK and LDH and the concentrations of Cor and TAS were measured in the serum. Hb and Ht were determined in whole blood. Results: The patients’ SOD, CAT, and GPx activities after surgery did not differ significantly from their preoperative levels. However, following surgery, their serum TOS levels were significantly elevated in all the patients compared to baseline. In contrast, the plasma MDA concentrations were markedly reduced after surgery. Similarly, the GSH concentrations showed a significant decrease postoperatively. No significant changes were observed in the CK and LDH activities, TAS concentrations, or levels of Hb, Ht and Cor following surgery. Conclusions: The surgical removal of salivary gland tumors did not result in a reduction in oxidative stress at 10 days after surgery. Therefore, further studies are needed to determine the effectiveness of endogenous defense mechanisms in counteracting the oxidative stress induced by salivary gland tumors. Full article

The mechanisms underlying reduced deformability of red blood cells (RBCs) in Plasmodium falciparum remain unclear. The decrease in RBC deformability associated with malarial infection was measured using ektacytometry, and only mean values were evaluated. In this study, we report the development of a microfluidic sensing device that can evaluate decreased RBC deformability at the single-cell level by measuring ionic current waveforms through micropores. Using an in vitro culture system, we found that when RBC deformability was reduced by P. falciparum infection, ionic current waveforms changed. As RBC deformability decreased, waveforms became asymmetric. Computer simulations suggested that these waveform parameters are largely independent of RBC size and may represent a reliable indicator of diminished deformability. This novel microfluidic RBC deformability sensor allows for detailed single-cell analysis of malaria-associated deformability reduction, potentially aiding in elucidating its pathology. Full article

Orthognathic surgery (OS) is a complex procedure commonly used to treat dentofacial deformities (DFDs). These conditions, related to jaw position or size and often involving malocclusion, affect approximately 15% of the population. Due to the complexity of OS, accurate planning is essential. Digital assessment using computer-aided design (CAD) and computer-aided manufacturing (CAM) tools enhances surgical predictability. However, limitations in soft tissue simulation often require surgeon input to optimize aesthetic results and minimize surgical impact. This study aimed to evaluate the accuracy of virtual surgery planning (VSP) by analyzing the relationship between planning deviations and surgical satisfaction. A single-center, retrospective study was conducted on 16 patients who underwent OS at San Pedro University Hospital of La Rioja. VSP was based on CT scans using Dolphin Imaging software (v12.0, Patterson Dental, St. Paul, MN, USA) and surgeries were guided by VSP-designed occlusal splints. Outcomes were assessed using the Orthognathic Quality of Life (OQOL) questionnaire and deviations were measured through pre- and postoperative imaging. The results showed high satisfaction scores and good overall outcomes, despite moderate deviations from the virtual plan in many cases, particularly among Class II patients. A total of 63% of patients required VSP modifications due to poor soft tissue fitting, with 72% of these being Class II DFDs. Most deviations involved less maxillary advancement than planned, while maintaining optimal occlusion. This suggests that VSP may overestimate advancement needs, especially in Class II cases. No significant differences in satisfaction were observed between patients with low (<2 mm) and high (>2 mm) deviations. These findings support the use of VSP as a valuable planning tool for OS. However, surgeon experience remains essential, especially in managing soft tissue behavior. Improvements in soft tissue prediction are needed to enhance accuracy, particularly for Class II DFDs. Full article

The mixing of liquid–solid systems still poses a challenge in modern engineering. Numerical models often struggle to reliably describe the complex hydrodynamics in many aspects, such as the fundamental drag force model. In this article, an established experimental method is revisited. The method is newly modified through computer-aided image analysis for increased objectivity and repurposed for comparative experiments with numerical results to aid in model validation in practical engineering cases without the need for expensive equipment. The original method consists of measuring patterns forming in settled particles at impeller speeds below the just off-bottom suspension speed in a mixing tank with a flat transparent bottom. The use of mathematical p-norms to fully capture the emerging shapes is introduced here for the first time. Using this methodology, LES CFD results with different drag force models are quantitatively compared with the experimental findings. Full article

This paper presents research in the field of computer-aided 3D clothing design, focusing on an investigation of three methods for determining the mechanical properties of woven fabrics and their impact on 3D clothing simulations in the context of sustainable apparel development. Five mechanical parameters were analyzed: tensile elongation in the warp and weft directions, shear stiffness, bending stiffness, specific weight, and fabric thickness. These parameters were integrated into the CLO3D CAD software v.2025.0.408, using data obtained via the KES-FB system, the Fabric Kit protocol, and the AI-based tool, SEDDI Textura 2024. Simulations of women’s blouse and trousers were evaluated using dynamic tests and validated by real prototypes measured with the ARAMIS optical 3D system. Results show average differences between digital and real prototype deformation data up to 6% with an 8% standard deviation, confirming the high accuracy of 3D simulations based on the determined mechanical parameters of the real fabric sample. Notably, the AI-based method demonstrated excellent simulation results compared with real garments, highlighting its potential for accessible, sustainable, and scalable fabric digitization. Presented research is entirely in line with the current trends of digitization and sustainability in the textile industry. It contributes to the advancement of efficient digital prototyping workflows and emphasizes the importance of reliable mechanical characterization for predictive garment modeling. Full article

In the last two decades, there has been a considerable surge in the development of artificial intelligence. Imaging is most frequently employed for the diagnostic evaluation of patients, as it is regarded as one of the most precise methods for identifying the presence of a disease. However, a study indicates that approximately 800,000 individuals in the USA die or incur permanent disability because of misdiagnosis. The present study is based on the use of computer-aided diagnosis of colorectal cancer. The objective of this study is to develop a practical, low-cost, AI-based decision-support tool that integrates clinical test data (blood/stool) and, if needed, colonoscopy images to help reduce misdiagnosis and improve early detection of colorectal cancer for clinicians. Convolutional neural networks (CNNs) and artificial neural networks (ANNs) are utilized in conjunction with a graphical user interface (GUI), which caters to individuals lacking programming expertise. The performance of the artificial neural network (ANN) is measured using the mean squared error (MSE) metric, and the obtained performance is 7.38. For CNN, two distinct cases are under consideration: one with two outputs and one with three outputs. The precision of the models is 97.2% for RGB and 96.7% for grayscale, respectively, in the first instance, and 83% for RGB and 82% for grayscale in the second instance. However, using a pretrained network yielded superior performance with 99.5% for 2-output models and 93% for 3-output models. The GUI is composed of two panels, with the best ANN model and the best CNN model being utilized in each. The primary function of the tool is to assist medical personnel in reducing the time required to make decisions and the probability of misdiagnosis. Full article

This paper presents a visual active SLAM method considering measurement and state uncertainty for space exploration in urban search and rescue environments. An uncertainty evaluation method based on the Fisher Information Matrix (FIM) is studied from the perspective of evaluating the localization uncertainty of SLAM systems. With the aid of the Fisher Information Matrix, the Cramér–Rao Lower Bound (CRLB) of the pose uncertainty in the stereo visual SLAM system is derived to describe the boundary of the pose uncertainty. Optimality criteria are introduced to quantitatively evaluate the localization uncertainty. The odometry information selection method and the local bundle adjustment information selection method based on Fisher Information are proposed to find out the measurements with low uncertainty for localization and mapping in the search and rescue process. By adopting the method above, the computing efficiency of the system is improved while the localization accuracy is equivalent to the classical ORB-SLAM2. Moreover, by the quantified uncertainty of local poses and map points, the generalized unary node and generalized unary edge are defined to improve the computational efficiency in computing local state uncertainty. In addition, an active loop closing planner considering local state uncertainty is proposed to make use of uncertainty in assisting the space exploration and decision-making of MAV, which is beneficial to the improvement of MAV localization performance in search and rescue environments. Simulations and field tests in different challenging scenarios are conducted to verify the effectiveness of the proposed method. Full article