Search Results (689)

In this paper, an innovative design of a silicon spiral drift detector (SDD) has been proposed. In this design, gaps under the SiO₂ layer between the cathode rings are kept constant, with a minimum value to reduce the surface leakage current. The cathode pitch profile $\mathrm{P}\left(\mathrm{r}\right)$ as a function of radius $\mathrm{r}$ is allowed to change in an arbitrary way to achieve the optimum field distribution. The concept, design considerations, modeling and electrical simulations have been carried out for this novel structure with a hexagonal spiral silicon drift detector. Using one-dimensional analyses, we obtain the exact solution of the spiral design $\mathrm{r}=\mathrm{r}\left(\mathrm{\theta }\right)$ with a near-arbitrary pitch profile $\mathrm{P}\left(\mathrm{r}\right)={\mathrm{P}}_1{\left({\displaystyle \frac{\mathrm{r}}{{\mathrm{r}}_1}}\right)}^{{\displaystyle \frac{1}{\mathrm{\eta }}}}$, with $\mathrm{\eta }$ as an arbitrary real number. We also obtained the electric potential and field profiles on both surfaces of the detector. Using a Technology Computer-Aided Design (TCAD) tool, we made 3D simulations of the detector’s electrical properties. The hexagonal spiral silicon drift detector has excellent electrical properties: a uniform electric field, smooth distribution of electric potential and electron concentration, and a clear electron drift channel. The distributions of the electric field, electric potential, and electron concentration are symmetrical and smooth, which is beneficial for applications in photon sciences (X-ray) and safeguards and homeland security (particle radiation). The theoretical work and simulation results serve as solid foundations for the detector design and the expansion of semiconductor technology. Full article

In regenerative medicine, three-dimensional (3D) printing provides precise spatial control over the fabrication of complex, biomimetic tissue constructs, enabling the production of architecturally defined and functionally tailored scaffolds. By enabling precise layer-by-layer deposition of cells, biomaterials, and bioactive compounds, 3D printing overcomes many limitations associated with conventional scaffold fabrication methods. This approach facilitates the development of tailored structures that mimic the mechanical, biological, and structural characteristics of native tissues, thereby enhancing cellular organization, proliferation, and differentiation. Extensive research in tissue engineering has led to the development of 3D-printed scaffolds for the regeneration of vascular, skin, bone, cartilage, and soft tissues. Advances in bioink formulations—including growth factor-loaded systems, decellularized extracellular matrix components, and natural and synthetic polymers—have further improved tissue-specific functionality. Moreover, multimaterial and multiscale printing strategies enable the fabrication of heterogeneous constructs with controlled porosity, mechanical gradients, and spatially regulated biological cues. Although vascularized tissue constructs remain a major challenge for clinical translation, recent bioprinting advancements have significantly accelerated progress in this area. Integration of computer-aided design with patient-specific imaging data has further strengthened the potential of 3D printing for personalized regenerative therapies. Despite these advances, challenges related to scalability, regulatory approval, and long-term functionality persist. Nevertheless, continued progress in printing technologies, biomaterials, and regulatory and standards frameworks is expected to drive the clinical adoption of 3D printing. Ultimately, 3D printing represents a transformative approach in tissue engineering, redefining strategies for functional tissue regeneration and translational regenerative medicine. Full article

This study investigates the impact of non-ideal wordline sidewall angles caused by photoresist profile variation during the wordline etching process in DRAMs employing a buried-channel array transistor (BCAT) structure. Using Technology Computer-Aided Design (TCAD), a two-dimensional (2D) BCAT-based DRAM cell was modeled to analyze the resulting variations in device characteristics as well as write and hold operations. The simulation results show that increased etch slope angles lead to degradation in device performance, including failure to meet the read pass/fail criterion and data retention during the 300 ms hold interval. To mitigate these issues, we inserted a buried oxide (BOX) layer beneath the active wordline (AWL). The incorporation of the BOX layer effectively improved overall device robustness and reduced the degradation induced by non-ideal etch slopes. Full article

Extracting standardized digital design patterns from real knitted fabric images is critical for textile reverse engineering and digital archiving. Unlike smooth graphics, knitted fabrics exhibit high-frequency textures from yarn loop interlacing, introducing significant grayscale variations within same-color regions. Existing algorithms struggle to distinguish these from pattern edges, causing color quantization and segmentation failures. To suppress yarn texture while preserving edges between color blocks, we propose an adaptive pattern extraction method using Bayesian-optimized bilateral filtering. The primary contribution lies in providing a domain-specific, application-focused integrated framework. Specifically, (1) a knitting-texture-aware multidimensional evaluation parameter is constructed by integrating physical-cause-based texture features (gray-level co-occurrence matrix (GLCM) contrast, homogeneity, and Laplacian variance) with perception-based edge preservation metrics (the Sobel operator and the structural similarity index (SSIM)), enabling accurate discrimination between yarn-level texture noise and pattern-level color block boundaries—a distinction that generic image quality metrics cannot make. (2) Then, this domain-specific objective function is embedded within a Bayesian optimization framework to achieve automatic, zero-shot, per-image parameter adaptation across different knitting processes, without requiring any external training data. K-means color quantization maps in continuous tones to discrete classes, generating standardized patterns meeting knitting requirements. Experiments on 316 samples covering six processes show our method outperforms standard denoising and advanced algorithms like relative total variation (RTV), achieving an average SSIM of 0.83 and PSNR of 26.92 dB, reducing processing time from 15–30 min to 21 s per image, providing efficient automation for knitted Computer-Aided Design (CAD) systems. Full article

Human civilization embodies a rich cultural heritage shaped over long historical periods by numerous ethnic groups, each employing distinctive motifs and patterns in religious spaces, architecture, clothing, utensils, and other artifacts. Such motifs commonly originate from elementary geometric primitives that are organized through symmetric or asymmetric compositions to convey symbolic and esthetic meaning. This study focuses on Mongolian patterns derived from the nomadic heritage of Mongolia and still prevalent in contemporary design. These patterns draw inspiration from nature, geometry, animals, plants, and symbolic forms. This article proposes a mathematical modeling-driven digitization framework for the systematic analysis and digitization of Mongolian patterns, with the objective of generating accurate digital representations in the form of computer-aided design (CAD) models. A concise review of related work is first presented, followed by a structured digitization framework and a taxonomy of representative Mongolian motifs. A case study demonstrates that, when combined through distance-preserving and shape-preserving geometric operations such as translation, rotation, and reflection, four fundamental geometric entities, namely the circle, circular arc, spiral, and astroid, are sufficient to retain the intrinsic symmetry and compositional coherence of complex patterns observed in selected artifacts. Furthermore, the proposed analytical modeling approach enables the generation of vector-based line drawings that support precise CAD model construction. Accordingly, this study establishes a computational design workflow that integrates cultural heritage patterns into CAD-based modeling environments, thereby supporting digital preservation and fabrication with high geometric fidelity. Full article

Accurate segmentation of breast cancer regions in histopathological images is critical for advancing computer-aided diagnostic systems, yet challenges persist due to heterogeneous tissue structures, staining variations, and the need to capture features across multiple scales. This study introduces MSB-UNet, a novel Multi-Scale Bifurcated U-Net architecture designed to address these challenges through a dual-pathway encoder–decoder framework that processes images at multiple resolutions simultaneously. By integrating a bifurcated encoder with a Feature Fusion Module, MSB-UNet effectively captures fine-grained cellular details and broader tissue-level patterns. MSB-UNet is formulated as a binary segmentation framework (tumor vs. outside region of interest), producing a two-channel probability map via a channel-wise Softmax output. Evaluated on a publicly available breast cancer histopathology dataset, MSB-UNet achieves a Dice Similarity Coefficient (DSC) of 91.3% and a mean Intersection over Union (mIoU) of 84.4%, outperforming state-of-the-art segmentation models. The architecture demonstrates better results compared to other baseline methods and has the potential to enhance automated diagnostic tools for breast cancer histopathology. Full article

The investigation of the emergency situation of an aircraft landing on water is mandatory for the certification of novel aircraft. In this context, computer-aided methods are becoming more relevant to support physical testing and to extend the analysis to further impact conditions. In this work, the meshless Lagrangian Finite Pointset Method was integrated into an aircraft pre-design process chain and used for the simulation of the interaction between the water and the structure during ditching. To assess the applicability of the method, results from simulations were compared with experimental data from water impact tests of curved panels and scaled models. In addition, the method was implemented in ditching simulations using a generic mid-range aircraft model. Results are analyzed in terms of accuracy, flexibility, and performance. Full article

Background/Objectives: The stimulator of interferon genes (STING) pathway plays a central role in innate immune signaling and represents an attractive therapeutic target for cancer immunotherapy. Amidobenzimidazole (ABZI) derivatives have emerged as promising non-nucleotide STING agonists with improved drug-like properties compared to cyclic dinucleotides. However, current ABZI compounds still exhibit limited oral bioavailability and cross-species potency discrepancies. In addition, potential systemic toxicity remains a concern, indicating the need for further structural optimization. Methods: In this study, a comprehensive computer-aided drug design strategy was employed to systematically investigate ABZI derivatives and identify novel STING agonists with enhanced activity and favorable pharmacokinetic profiles. A 3D quantitative structure–activity relationship (3D-QSAR) model was constructed using the Topomer CoMFA approach based on a dataset of 109 reported ABZI compounds. Guided by the contour map analysis, new chemical groups were introduced through a fragment growth method, generating a large virtual library. The library was subsequently filtered via molecular docking, molecular dynamics simulations, and MM-PBSA binding free energy calculations. Results: Among the newly designed ABZI compounds, five compounds displayed lower binding free energies than D59, with M13 and M44 showing reductions exceeding 6.7 kcal/mol. This work demonstrates the effectiveness of an integrated in silico design strategy for the discovery of novel STING agonists. Conclusions: The identified compounds represent promising candidates for subsequent experimental validation and may support the development of nitrogen heterocycle-based STING agonists for antitumor applications. Full article

Remote sensing scene classification (RSSC) plays a crucial role in Earth observation. Current deep learning methods, while accurate, tend to focus on high-level semantic features and overlook complementary shallow details such as edges and textures. Moreover, conventional CNNs are limited by fixed receptive fields, whereas transformers incur high computational costs. To address these limitations, we propose the Lie Group lightweight multi-scale network (LGLMNet), a lightweight multi-scale network that integrates Lie Group covariance features. It employs a dual-branch architecture combining Lie Group machine learning (LGML) for shallow feature extraction and a deep learning branch for high-level semantics. In the deep branch, we design a parallel depthwise separable convolution block (PDSCB) for multi-scale perception and a spatial-channel collaborative attention mechanism (SCCA) for efficient global–local modeling. A cross-layer feature fusion block (CLFFB) effectively merges the two branches. Compared with state-of-the-art methods, the proposed LGLMNet achieves accuracy improvements of 2.14%, 2.32%, and 1.12% on UCM-21, AID, and NWPU-45 datasets, respectively, while maintaining a lightweight structure with only 2.6 M parameters. Full article

This study analyzed the low-frequency noise characteristics of nanosheet field-effect transistors (NSFETs) using technology computer-aided design (TCAD) simulations. In particular, the effects of shallow trench isolation (STI) depth and gate–insulator interface trap density on the device’s flicker noise power spectral density (PSD) were systematically evaluated. The simulation results show that as STI depth increases, excessive trap charges formed in the STI oxide can deplete or invert the substrate beneath the STI layer, reducing the threshold voltage of parasitic transistors and thereby increasing flicker noise. In contrast, the shallow STI structure’s trapped charge density was found to be lower than in thicker structures. Additionally, when an HfO₂–ZrO₂ (HZO)-based ferroelectric insulator is applied, improved gate–field control and reduced trap-induced noise are observed compared to HfO₂. Optimization results indicate that the optimal noise performance is achieved with an STI depth of 3 nm and a SiO₂/silicon interface trap density of 1 × 10¹⁰ eV⁻¹cm⁻². This study provides a design direction for low-noise NSFETs through structural (STI) and material (interface traps and HZO) optimization and is expected to contribute to the development of next-generation low-power, high-reliability logic devices. Full article

As the digitalization of construction standards accelerates, the integration of structural analysis results into Building Information Modeling (BIM) environments has become a critical prerequisite for effective BIM-based Automated Code Checking (ACC), particularly for structural code compliance. In current practice, structural analysis results generated by Computer-Aided Engineering (CAE) tools are often manually transferred into IFC-based BIM models, leading to inefficiencies and increased risk of human error. To address this limitation, this study proposes an extended IFC-based representation, termed IFC-KR-Structure, designed to systematically organize and manage section-wise and load combination-dependent structural analysis results required for code compliance within the IFC environment. Based on the proposed schema, an automated CAE-to-BIM integration module was implemented within the IFC-KR Toolkit to enable direct integration of analysis results generated by a commercial CAE tool (midas Civil NX) into IFC models. The approach establishes consistent element correspondence between structural and BIM models through coordinate alignment and spatial mapping procedures and represents multidimensional analysis results using a schema-compliant, tabular data structure embedded within IFC models. The applicability of the proposed framework was validated using a prestressed concrete girder bridge case, confirming that structural analysis results were accurately mapped, stored, visualized, and subsequently utilized within a BIM-based ACC workflow. The results demonstrate that the proposed approach enables systematic reintegration of CAE-generated analysis results into BIM models and significantly improves the efficiency, consistency, and reliability of BIM-based code compliance processes. Full article

Injection mold design is a repetitive and time-consuming process with common individual tasks related to each other. This study presents the development of an automatic computer-aided design (CAD) tool for basic injection molds with complete modeling and no other interaction by the user after inserting the part, built on the SolidWorks Application Programming Interface 2022 (API) and Visual Basic for Applications 7.1 2012(VBA). The tool combines user input forms and supplier catalog data as inputs in an algorithm to automatically generate mold structures, cavity blocks, runner system, ejection system and straight drilled cooling channels without further manual modeling. Three case studies with one-, two-, and four-cavity molds demonstrate the approach. The results show that complete mold assemblies can be produced in less than 10 min rather than hours while maintaining standard component dimensions. Although the present version applies to rule-based geometric placement rather than thermal or injection process optimization, it provides a framework for future integration of more complex mold structures and functions such as slides, hot runner system, unscrewing geometries, conformal cooling, heat-transfer-based design, family molds and machine selection. This work demonstrates how API-driven automation can reduce design time, standardize layouts, and lay the groundwork for next-generation injection mold development. Full article

Three-dimensional (3D) anatomic modeling derived from high-resolution medical imaging, such as computed tomography (CT) and magnetic resonance imaging (MRI), has been increasingly adopted in preclinical testing and device development. This white paper describes a cardiac-specific workflow that integrates 3D printing and silicone molding for support device development and procedural simulation. Patient-derived computed tomography angiography data were segmented using FDA-cleared medical modeling software to isolate the left ventricular anatomy and were further processed in computer-aided design (CAD) to ensure accurate physiological wall thickness and structural fidelity. Material jetting 3D printing was performed on a Stratasys J750 using material distributions designed to mimic the mechanical properties of myocardium, thereby approximating myocardial compliance. In parallel, stereolithography apparatus molds were designed from the left ventricle CAD model to cast transparent, pliable left ventricular models in Sorta-Clear™ 18 silicone. The 3D-printed models preserved intricate morphological detail and were suitable for mechanical manipulation and device deployment studies, whereas silicone models offered tunable mechanical properties, transparency for visualization, and durability for repeated use. Together, these complementary modalities provided rapid manufacturing capability and application-relevant physical representation. Case-specific parameters, strengths, and limitations of both models in enhancing patient care and device testing are highlighted, with relevance to heart failure applications. Current knowledge gaps, workflow and integration challenges, and future opportunities are identified, positioning this work as a reference framework for continued innovation in anatomic modeling. Within the collaborative framework of Mayo Clinic’s Anatomic Modeling Unit and Simulation Center, this integrated modeling workflow demonstrates the value of multidisciplinary collaboration between engineers and clinicians. Clinically, these patient-specific left ventricular models may enable pre-procedural device sizing and positioning and may support simulation of mechanical circulatory support (MCS) deployment while identifying possible anatomic constraints prior to intervention. This workflow has direct applicability in advanced heart failure patients undergoing MCS support, such as the Impella axillary MCS device or the durable LVAD, with potential to reduce procedural uncertainty while reducing complications and improving peri-procedural outcomes. Additionally, these models also serve as high-accuracy educational tools, enabling trainees and multidisciplinary care teams to visualize and possibly rehearse procedural steps while gaining hands-on experience in a risk-free environment. Full article

Let G be a graph with n vertices and m edges. A vertex magic total labeling of G is a bijection $f:V\left(G\right)\cup E\left(G\right)\to \{1,2,\dots ,n+m\}$ such that, for each vertex $u\in V\left(G\right)$, the sum of the label of u and the labels of all edges incident to u is equal to a fixed constant, referred to as the magic constant. A vertex magic total labeling is said to be even if the labels assigned to the vertices are exactly even numbers $\{2,4,6,\dots ,2n\}$. These labelings, along with related variations, have theoretical significance and practical applications, such as resource allocation, fault tolerance, and network design. Structured labelings aid channel assignment, address computation, and reduce collisions in networks. In this paper, we investigate wheel-related graphs that either admit or do not admit an even vertex magic total labeling. Furthermore, we introduce a new class of wheel-related graph, referred to as the plus wheel $W_n+r$, that can have such labelings, and we also establish a necessary and sufficient condition for such graphs to possess this property. Full article

Design decisions made in the early phases of the design process have a significant impact on the eventual performance of the completed building. Currently, computer-assisted methods offer limited support during the crucial stages of creating, assessing and refining design variants. This paper presents an integrated framework combining computer-aided design processes and performance-based evaluation methods to support a non-linear, iterative design process. The framework integrates spatial metrics (e.g., layout and design similarity), structural metrics (e.g., feasible systems and material quantities) and environmental–energy metrics (e.g., heating demand) to provide transparent, quantitative and qualitative feedback for early-stage decision-making. Applying the framework allowed for the incorporation of structural, environmental and spatial assessments early in the process. The design assistance framework is graphically represented using business process model and notation (BPMN), which facilitates communication between process design and implementation. A real-world, mixed-use building scenario illustrates how the individual methods interact to streamline the decision-making process for architects. The framework guides the entire process, from design decisions to structural and performance-specific features, offering inspirational support for architects and practical assistance for structural engineers, sustainability experts and other professionals involved in early-stage building design. Full article