Search Results (46)

The study aimed to identify the variants of SARS-CoV-2 (Severe Acute Respiratory Syndrome related coronavirus-2) virus isolates within the window of March 2021 to February 2022 in Bangladesh and investigate their comparative mutational profiles, preferences and phylogenetics. After the collection of the sample specimen and RNA extraction, the genome was sequenced using Illumina COVID Seq, and NGS data analysis was performed in DRAGEN COVID Lineage software (version 3.5.9). Among the 96 virus isolates, 24 (25%) were from Delta (clade 21A (n = 21) and 21J (n = 3)) and 72 (75%) were from Omicron (clade 20A (n = 6) and 20B (n = 66)). In Omicron and Delta, substitutions were much higher than deletions and insertions. High-frequency nucleotide change patterns were similar (for C > T, and A > G) in both of the variants, but different in some (i.e., G > T, G > A). Preferences for specific amino acids over the other amino acids in substitutions and deletions were observed to vary in different proteins of these variants. Phylogenetic analysis showed that the most ancestral variants were from clade 21A and clade 20A, and then the other variants emerged. The study demonstrates noteworthy variations of Omicron and Delta in mutational pattern and preferences for amino acids and protein, and further study on their biological functional impact might unveil the reason behind their mutational strategies and behavioral changes. Full article

Significant vulnerabilities in traditional authentication systems have been demonstrated due to the high dependence on smartphone hardware devices to execute many different and complicated tasks. PINs, passwords, and static biometric techniques have been shown to be subjected to various serious attacks, such as environmental limitations, spoofing, and brute force attacks, and this in turn mitigates the security level of the entire system. In this study, a robust framework for smartphone authentication is presented. Touch dynamic pattern recognitions, including trajectory curvature, touch pressure, acceleration, two-dimensional spatial coordinates, and velocity, have been extracted and assessed as behavioral biometric features. The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methodology has also been incorporated to obtain the most affected and valuable features, which are then fed as input to three different Machine Learning (ML) algorithms: Random Forest (RF), Gradient Boosting Machines (GBM), and K-Nearest Neighbors (KNN). Our analysis, supported by experimental results, ensure that the RF model outperforms the two other ML algorithms by getting F1-Score, accuracy, recall, and precision of 95.1%, 95.2%, 95.5%, and 94.8%, respectively. In order to further increase the resiliency of the proposed technique, the data perturbation approach, including temporal scaling and noise insertion, has been augmented. Also, the proposal has been shown to be resilient against both environmental variation-based attacks by achieving accuracy above 93% and spoofing attacks by obtaining a detection rate of 96%. This emphasizes that the proposed technique provides a promising solution to many authentication issues and offers a user-friendly and scalable method to improve the security of the smartphone against cybersecurity attacks. Full article

The convergence of 3D printing and auxetic materials is paving the way for a new era of adaptive structures. Auxetic materials, known for their unique mechanical properties, such as a negative Poisson’s ratio, can be integrated into 3D-printed objects to enable them to morph or deform in a controlled manner, leading to the creation of 4D-printed structures. Since the first introduction of 4D printing, scientific interest has spiked in exploring its potential implementation in a wide range of applications, from deployable structures for space exploration to shape-adaptive biomechanical implants. In this context, the current paper aimed to develop 4D-printed arterial stents utilizing bioinspired architected auxetic materials made from biocompatible and biodegradable polymeric material. Specifically, three different auxetic materials were experimentally examined at different relative densities, under tensile and compression testing, to determine their mechanical behavior. Based on the extracted experimental data, non-linear hyperelastic finite element material models were developed in order to simulate the insertion of the stent into a catheter and its deployment in the aorta. The results demonstrated that among the three examined structures, the ‘square mode 3’ structure revealed the best performance in terms of strength, at the same time offering the necessary compressibility (diameter reduction) to allow insertion into a typical catheter for stent procedures. Full article

In the damage tolerance analysis of aircraft panels, it was necessary to frequently apply complex boundaries and loads to simulate crack propagation. It was difficult to simulate crack propagation with the conventional finite element method. In this paper, a convenient method for crack propagation simulation in thin-walled structures was proposed. This method combined the extended finite element method (XFEM) and level-set method (LSM). Crack insertion, analysis, result extraction and automatic propagation were realized through the secondary development script in the ABAQUS platform. A specimen with a single center crack was simulated to study the crack propagation behavior under tensile and bending conditions. Also, multiple-crack propagation was simulated. The present work shows that the developed method can not only take the complex loading and boundary conditions into account, but also can give good predictions on the crack analysis. This method provided a convenient and effective secondary development of ABAQUS to solve complex crack problems. Full article

Deep learning technology can automatically learn features from large amounts of data, with powerful feature extraction and pattern recognition capabilities, thereby improving the accuracy and efficiency of object detection. [The objective of this study]: In order to improve the accuracy and speed of mask wearing deep learning detection models in the post pandemic era, the [Problem this study aimed to resolve] was based on the fact that no research work has been reported on standardized detection models for mask wearing with detecting nose targets specially. [The topic and method of this study]: A mask wearing normalization detection model (towards the wearing style exposing the nose to outside, which is the most obvious characteristic of non-normalized style) based on improved YOLOv5s (You Only Look Once v5s is an object detection network model) was proposed. [The improved method of the proposed model]: The improvement design work of the detection model mainly includes (1) the BottleneckCSP (abbreviation of Bottleneck Cross Stage Partial) module was improved to a BottleneckCSP-MASK (abbreviation of Bottleneck Cross Stage Partial-MASK) module, which was utilized to replace the BottleneckCSP module in the backbone architecture of the original YOLOv5s model, which reduced the weight parameters’ number of the YOLOv5s model while ensuring the feature extraction effect of the bonding fusion module. (2) An SE module was inserted into the proposed improved model, and the bonding fusion layer in the original YOLOv5s model was improved for better extraction of the features of mask and nose targets. [Results and validation]: The experimental results indicated that, towards different people and complex backgrounds, the proposed mask wearing normalization detection model can effectively detect whether people are wearing masks and whether they are wearing masks in a normalized manner. The overall detection accuracy was 99.3% and the average detection speed was 0.014 s/pic. Contrasted with original YOLOv5s, v5m, and v5l models, the detection results for two types of target objects on the test set indicated that the mAP of the improved model increased by 0.5%, 0.49%, and 0.52%, respectively, and the size of the proposed model compressed by 10% compared to original v5s model. The designed model can achieve precise identification for mask wearing behaviors of people, including not wearing a mask, normalized wearing, and wearing a mask non-normalized. Full article

The efficient testing and validation of the high-voltage (HV) insulation of small-outline integrated circuit (SOIC) packages presents numerous challenges when trying to achieve faster and more accurate processes. The complex behavior these packages when submerged in diverse physical media with varying densities requires a detailed analysis to understand the factors influencing their behavior. We propose a systematic and scalable mathematical model based on trapezoidal motion patterns and a deterministic analysis of hydrodynamic forces to predict SOIC package misalignment during automated high-voltage testing in a dielectric fluid. Our model incorporates factors known to cause misalignment during the maneuvering of packages, such as surface tension forces, sloshing, cavity formation, surface waves, and bubbles during the insertion, extraction, and displacement of devices while optimizing test speed for minimum testing time. Our model was validated via a full-factorial statistical experiment for different SOIC package sizes on a pick-and-place (PNP) machine with preprogrammed software and a zero-insertion force socket immersed in different dielectric fluids under controlled thermal conditions. Results indicate the model achieves 99.64% reliability with a margin of error of less than 4.78%. Our research deepens the knowledge and understanding of the physical and hydrodynamic factors that impact the automated testing processes of high-voltage insulator SOIC packages of different sizes for different dielectric fluids. It enables improved testing times and higher reliability than traditional trial-and-error methods for high-voltage SOIC packages, leading to more efficient and accurate processes in the electronics industry. Full article

Due to their anisotropic behavior, composite structures are weak in transverse direction loading. produces transverse cracks, which for a laminated composite, may lead to delamination and total failure. The transition from transverse crack to delamination failure is important and the subject of recent studies. In this paper, a simulation of transverse crack and its transition to delamination on cross-ply laminate was studied extensively using a cohesive element Finite Element Method (FEM). A pre-cracked [0/90] composite laminate made of bamboo was modeled using ABAQUS/CAE. The specimen was in a three-point bending configuration. Cohesive elements were inserted in the middle of the 90° layer and in the interface between the 0° and 90° layer to simulate transverse crack propagation and its transition to delamination. A load–displacement graph was extracted from the simulation and analyzed. As the loading was given to the specimen, stress occurred in the laminates, concentrating near the pre-cracked region. When the stress reached the tensile transverse strength of the bamboo, transverse crack propagation initiated, indicated by the failure of transverse cohesive elements. The crack then propagated towards the interface of the [0/90] laminates. Soon after the crack reached the interface, delamination propagated along the interface, represented by the failure of the longitudinal cohesive elements. The result of the numerical study in the form of load–displacement graph shows a consistent pattern compared with the data found in the literature. The graph showed a linear path as the load increased and the crack propagated until a point where there was a load-drop in the graph, which showed that the crack was unstable and propagated quickly before it turned into delamination between the 0^o and 90° plies. Full article

In this paper, the in vivo behavior of orthopedic implants covered with thin films obtained by matrix-assisted pulsed laser evaporation and containing bioactive glass, a polymer, and natural plant extract was evaluated. In vivo testing was performed by carrying out a study on guinea pigs who had coated metallic screws inserted in them and also controls, following the regulations of European laws regarding the use of animals in scientific studies. After 26 weeks from implantation, the guinea pigs were subjected to X-ray analyses to observe the evolution of osteointegration over time; the guinea pigs’ blood was collected for the detection of enzymatic activity and to measure values for urea, creatinine, blood glucose, alkaline phosphatase, pancreatic amylase, total protein, and glutamate pyruvate transaminase to see the extent to which the body was affected by the introduction of the implant. Moreover, a histopathological assessment of the following vital organs was carried out: heart, brain, liver, and spleen. We also assessed implanted bone with adjacent tissue. Our studies did not find significant variations in biochemical and histological results compared to the control group or significant adverse effects caused by the implant coating in terms of tissue compatibility, inflammatory reactions, and systemic effects. Full article

An electrical connector is an important component for achieving the interconnection of electric equipment. However, the degradation of contacting parts within the electrical connector under repetitive mechanical insertion and extraction operations causes a decrease in the contact reliability level, resulting in considerable safety hazards. The coating quality, determining the degree of degradation of contact pairs, is considered a critical factor in fabricating more reliable and safer electrical connectors. In this paper, a gold and nickel coating is deposited onto the surface of a pin within an electrical connector using magnetron sputtering and is compared to an electroplated pin, and the effects of different processing techniques on the microstructure, mechanical properties, and wear behavior are systematically investigated. The measurement results indicate that the surface quality (uniformity and defect density) and mechanical properties (hardness and elastic modulus) of the gold/nickel coating based on magnetron sputtering are significantly better than those achieved using electroplating, showing excellent wear properties and electrical contact stability after repetitive insertion–extraction operations. This study is critical for the development of advanced coatings using a novel deposition technique. Full article

This paper presents an advanced method that combines coupling-of-modes (COM) theory and the finite element method (FEM), which enables the quick extraction of COM parameters and the accurate prediction of the electroacoustic and temperature behavior of surface acoustic wave (SAW) devices. For validation, firstly, the proposed method is performed for a normal SAW resonator. Then, the validated method is applied to analysis of an I.H.P. SAW resonator based on a 29°YX−LT/SiO₂/SiC structure. Via optimization, the electromechanical coupling coefficient (K²) is increased up to 13.92% and a high quality (Q) value of 1265 is obtained; meanwhile, the corresponding temperature coefficient of frequency (TCF) is −10.67 ppm/°C. Furthermore, a double-mode SAW (DMS) filter with low insertion loss and excellent temperature stability is also produced. It is demonstrated that the proposed method is effective even for SAW devices with complex structures, providing a useful tool for the design of SAW devices with improved performance. Full article

Lithium–titanium–sulfur cathodes have gained attention because of their unique properties and have been studied for their application in lithium-ion batteries. They offer different advantages such as lower cost, higher safety, and higher energy density with respect to commonly adopted transition metal oxides. Moreover, this family of compounds is free from critical raw materials such as cobalt and nickel. For cathode materials, a crucial aspect is evaluating the evolution and behavior of the structure and properties during the cycling process, which means simulating the system under lithium extraction and insertion. Structural optimization, electronic band structures, density of states, and Raman spectra were simulated, looking for fingerprints and peculiar aspects related to the delithiation and overlithiation process. Lithium transport properties were also investigated through the nudged elastic band methodology. This allowed us to evaluate the diffusion coefficient of lithium, which is a crucial parameter for cathode performance evaluation. Full article

Thanks to recent technological and IT advances, there have been rapid developments in biomedical and health research applications of computational fluid dynamics. This is a methodology of computer-based simulation that uses numerical solutions of the governing equations to simulate real fluid flows. The aim of this study is to investigate, using a patient-specific computational fluid dynamics analysis, the hemodynamic behavior of two arterial cannulae, with two different geometries, used in clinical practice during cardiopulmonary bypass. A realistic 3D model of the aorta is extracted from a subject’s CT images using segmentation and reverse engineering techniques. The two cannulae, with similar geometry except for the distal end (straight or curved tip), are modeled and inserted at the specific position in the ascending aorta. The assumption of equal boundary conditions is adopted for the two simulations in order to analyze only the effects of a cannula’s geometry on hemodynamic behavior. Simulation results showed a greater percentage of the total output directed towards the supra-aortic vessels with the curved tip cannula (66% vs. 54%), demonstrating that the different cannula tips geometry produces specific advantages during cardiopulmonary bypass. Indeed, the straight one seems to generate a steadier flow pattern with good recirculation in the ascending aorta. Full article

This study focuses on the measurement and analysis of the complex permittivities of polymer blends using the field enhancement method (FEM). The blends, consisting of air-powder or solvent–solute mixtures, are placed in a Teflon holder and inserted into the FEM cavity to determine the complex permittivity. The resonant frequency and quality factor of the FEM cavity coupled with the samples provide information on the blends’ dielectric constant and loss tangents. To extract the complex permittivities of three specific samples of DC-840, MCL-805, and MCL-Siloxane, we employ effective medium theories and the high-frequency structure simulator (HFSS) together with the measured data. The results reveal that when the volume fraction of the DC-840 solute in the xylene solvent surpasses a specific threshold, the dielectric constants and the loss tangents experience a notable increase. This phenomenon, known as percolation, strongly correlates with the viscosity of polymer blends. The observed percolation effect on the dielectric behavior is further elucidated using the generalized dielectric constant and the Debye model. By employing these models, the percolation effect and its impact on the dielectric properties of the blends can be explained. Full article

Deep learning has experienced an increased demand for its capabilities to categorize and optimize operations and provide higher-accuracy information. For this purpose, the implication of deep learning procedures has been described as a vital tool for the optimization of supply chain firms’ transportation operations, among others. Concerning the indexes of transportation operations of supply chain firms, it has been found that the contribution of big data analytics could be crucial to their optimization. Due to big data analytics’ variety and availability, supply chain firms should investigate their impact on their key transportation indexes in their effort to comprehend the variation of the referred indexes. The authors proceeded with the gathering of the required big data analytics from the most established supply chain firms’ websites, based on their (ROPA), revenue growth, and inventory turn values, and performed correlation and linear regression analyses to extract valuable insights for the next stages of the research. Then, these insights, in the form of statistical coefficients, were inserted into the development of a Hybrid Model (Agent-Based and System Dynamics modeling), with the application of the feedforward neural network (FNN) method for the estimation of specific agents’ behavioral analytical metrics, to produce accurate simulations of the selected key performance transportation indexes of supply chain firms. An increase in the number of website visitors to supply chain firms leads to a 60% enhancement of their key transportation performance indexes, mostly related to transportation expenditure. Moreover, it has been found that increased supply chain firms’ website visibility tends to decrease all of the selected transportation performance indexes (TPIs) by an average amount of 87.7%. The implications of the research outcomes highlight the role of increased website visibility and search engine ranking as a cost-efficient means for reducing specific transportation costs (Freight Expenditure, Inferred Rates, and Truckload Line Haul), thus achieving enhanced operational efficiency and transportation capacity. Full article

Purpose: Triple-negative breast cancer (TNBC) is a molecularly complex and heterogeneous breast cancer subtype with distinct biological features and clinical behavior. Although TNBC is associated with an increased risk of metastasis and recurrence, the molecular mechanisms underlying TNBC metastasis remain unclear. We performed whole-exome sequencing (WES) analysis of primary TNBC and paired recurrent tumors to investigate the genetic profile of TNBC. Methods: Genomic DNA extracted from 35 formalin-fixed paraffin-embedded tissue samples from 26 TNBC patients was subjected to WES. Of these, 15 were primary tumors that did not have recurrence, and 11 were primary tumors that had recurrence (nine paired primary and recurrent tumors). Tumors were analyzed for single-nucleotide variants and insertions/deletions. Results: The tumor mutational burden (TMB) was 7.6 variants/megabase in primary tumors that recurred (n = 9); 8.2 variants/megabase in corresponding recurrent tumors (n = 9); and 7.3 variants/megabase in primary tumors that did not recur (n = 15). MUC3A was the most frequently mutated gene in all groups. Mutations in MAP3K1 and MUC16 were more common in our dataset. No alterations in PI3KCA were detected in our dataset. Conclusions: We found similar mutational profiles between primary and paired recurrent tumors, suggesting that genomic features may be retained during local recurrence. Full article