Search Results (216)

The Internet of Things increasingly relies on EDHOC (Ephemeral Diffie–Hellman Over COSE, RFC 9528) and OSCORE (Object Security for Constrained RESTful Environments, RFC 8613) for lightweight authenticated key exchange and application-layer security. On resource-constrained devices, however, the computational cost of these protocols remains prohibitive in software: a complete EDHOC handshake requires hundreds of milliseconds to several seconds on typical embedded processors. Prior evaluations of EDHOC and OSCORE focus almost exclusively on ARM Cortex-M platforms; to the best of our knowledge, no dedicated evaluation or hardware acceleration study exists for RISC-V. This paper presents the first performance characterization of EDHOC and OSCORE on a RISC-V platform. It introduces a hardware accelerator integrated as a memory-mapped peripheral within a Rocket RV32IMAC SoC. The accelerator offloads the complete EDHOC Method 3 handshake, encompassing X25519 scalar multiplication, HMAC-SHA-256 key derivation, AES-CCM-16-64-128 authenticated encryption, and all protocol state management and message construction within a single hardware boundary; OSCORE per-packet AEAD is accelerated through a dedicated post-handshake interface using the same core. By moving the entire handshake execution to dedicated hardware, the accelerator eliminates the residual overhead that remains in software, regardless of whether individual cryptographic primitives are offloaded. Implemented on a Xilinx Arty A7-100T FPGA, the design consumes 10,597 Slice LUTs, 10,421 Slice Registers, and 15 DSP slices. The accelerator completes the EDHOC handshake in 6.64 ms and 4.52 ms for the Initiator and Responder, respectively, achieving 58× and 85× speedups over the optimized Monocypher software baseline on the same platform, and delivers 37× to 56× speedups for OSCORE per-packet AEAD acceleration across payload sizes from 10 to 1000 bytes. The host firmware footprint is reduced from over 25 KB to 3.6 KB for EDHOC-only and to 5.2 KB for the combined EDHOC and OSCORE stack. Full article

Metal artifacts generated by dental implants significantly degrade cone-beam computed tomography (CBCT) volumes, obscuring critical anatomical structures and compromising diagnostic precision. To address this, an unsupervised deep learning framework has been proposed for Metal Artifact Reduction (MAR) utilizing a Cycle-Consistent Adversarial Network (CycleGAN) optimized for high-fidelity restoration. Unlike supervised methods that rely on unattainable voxel-aligned paired datasets, the proposed approach leverages an unpaired dataset of approximately 4000 images, curated from the public ToothFairy dataset. The architecture integrates U-Net-based generators and PatchGAN discriminators, specifically tuned to mitigate generative hallucinations and preserve morphological integrity. Quantitative benchmarking on a held-out test set demonstrates a 34.6% improvement in the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score, a substantial reduction in Fréchet Inception Distance (FID) from 207.03 to 157.04, and a superior Structural Similarity Index Measure (SSIM) of 0.9105. The framework achieves real-time efficiency with a 3.03 ms inference time per slice, effectively suppressing artifacts while preserving anatomical detail. Expert validation confirms high fidelity; however, to ensure reliability in extreme cases, the architecture is recommended as a clinical decision-support tool under human-in-the-loop oversight. By enhancing diagnostic clarity via a scalable software pipeline, this study provides a robust solution for high-fidelity dental implant imaging. Full article

Over the last decade, 3D printing of clay has gained attention in architecture. Yet most slicing software is designed for thermoplastics with nozzle sizes between 0.3 and 1.0 mm. Clay printing, using larger nozzles (1–30 mm), requires precise control over path arrangement, material flow, and shrinkage—capabilities not sufficiently addressed by conventional software. This paper introduces Termite, an open-source software plugin for Rhinoceros 3D Grasshopper designed specifically for Liquid Deposition Modeling (LDM) 3D printing. The novelty of this work lies in embedding slicing logic directly into a parametric design environment, enabling explicit and flexible control of printing paths tailored to the rheological behavior of clay. The plugin supports designing, simulating, optimizing, and exporting machine data within a unified workflow. In contrast to conventional slicers, it allows variable printing parameters within a single print job, controlled inrun speeds for smoother path starts, adapted material flow at path crossings, and extrusion flattening at path ends to enhance adhesion and precision. The software was evaluated through multiple architectural-scale case studies and student-based design experiments. Results demonstrate that integrating slicing operations into parametric design workflows enables new fabrication strategies and expands accessibility of clay 3D printing for architectural applications. Full article

Objective: This study investigates the influence of selecting three thermoplastics as raw materials (PLA, PETG, and ABS) on dimensional accuracy, defect formation, and slicer-based mass prediction reliability in FFF 3D-printed UAV micro-frames. Methods: A factorial experimental design combining three materials, two micro-frame geometries, and two infill levels was implemented. Print quality was assessed through structured visual inspection of common FFF defects, while manufacturing reliability was evaluated by comparing slicer-predicted and experimentally measured mass. Dimensional fidelity was quantified at critical motor mount features using repeated micrometric measurements and dedicated accuracy and uniformity indices. Results: The results reveal strong material-dependent behaviour. PLA exhibited the highest dimensional consistency and near-zero mean mass prediction error, PETG showed intermediate performance, and ABS presented significant warping, together with a pronounced positive mass prediction bias. These findings indicate systematic discrepancies between predicted and measured mass values and highlight the need for material-dependent calibration of slicing software. Conclusions: Material selection and process calibration strongly affect dimensional fidelity and manufacturing reliability in FFF-printed UAV micro-frames. The findings provide practical guidance for material choice and slicing parameter adjustment in UAV fabrication and similar small-scale FFF applications. Full article

Background/Objectives: Young women with breast cancer (aged ≤ 40 years) have distinct prognostic characteristics, yet little is known about how modifiable body composition factors influence outcomes in this age group. This study examined whether CT-derived body composition measures could identify thresholds that predict overall survival (OS). Methods: This was a single-center, 10-year, matched case–control study nested within a cohort, utilizing retrospectively collected data. Using an institutional database (2009–2018) and the initial cohort of 112 patients, we performed a subset analysis of patients with stage I–III breast cancer at diagnosis who had available pretreatment CT scans to estimate associations with body composition metrics and OS. The final analytic dataset included 89 individuals (49 survivors and 40 deceased). CT scans at the L3 level were analyzed using Slice-O-Matic software to quantify visceral (VAT), subcutaneous (SAT), intermuscular (IMAT), total adipose tissue (TAT), skeletal muscle density (SMD), skeletal muscle gauge (SMG), and skeletal muscle index (SMI). Cox proportional hazard models determined optimal cutpoints for OS. Multivariable models included adjustments for disease stage and hormone receptor status. Results: The median age was 35 (IQR, 32–38); 47% were White and 37% were Black. The majority (78%) were not Hispanic or Latina. Most (67%) were overweight/obese. Specific thresholds for IMAT index (>2.57), VAT (>31.38), and SMG (<2419.89) were associated with worse survival (all p < 0.05), while no cutpoints were identified for other variables. Conclusions: These findings show that muscle fat infiltration and reduced muscle quality have important prognostic value in young women with breast cancer. Exploratory cutpoints derived from routine staging CT scans may help inform risk stratification and generate hypotheses for targeted nutritional or exercise interventions, but require validation in larger, independent cohorts before clinical application. Full article

To address over-extrusion and forming defects at path corners caused by path overlap in additive manufacturing, this paper proposes an angle-adaptive look-ahead compensation algorithm based on a Sech attenuation curve. This method establishes a mapping model between the path angle and the adaptive look-ahead distance of the overlapping area, aiming to eliminate the material accumulation at the corner by precisely identifying the overlapping area and modulating the flow rate. By building a Beckhoff five-axis 3D-printing device and relying on the TwinCAT control platform, the compensation triggering logic based on PLC real-time Euclidean distance calculation was realized, and a slicing software with dynamic bias compensation was also developed. Experiments were conducted on triangular samples with extreme acute angles of 5°, universal acute angles of 85°, and 90° straight angles for printing verification. The results show that this algorithm can effectively suppress the material over-extrusion and accumulation at the path overlap in multiple angles, achieving a smooth transition of the sharp corners in the printed contour. The research confirms that the algorithm proposed in this study, together with the integrated software and hardware system, can ensure the forming accuracy of extreme and conventional geometric features while also guaranteeing the printing efficiency, providing an important reference for ensuring the quality coordination control of the formation process of extreme geometric features in additive manufacturing. Full article

Background and Objectives: This study aimed to compare coronary plaque characteristics between Asian and European populations undergoing coronary CT angiography and to examine associations between cardiovascular risk factors and coronary artery calcification. Materials and Methods: In this retrospective, two-center, cross-sectional observational study, 1591 adult patients (1203 of Asian and 388 of European descent) referred for coronary computed tomography angiography (CCTA) due to suspected coronary artery disease between 2008 and 2025 were included. Demographic, clinical characteristics, and laboratory data were obtained from medical records. Computed tomography (CT) was performed on different CT scanners, including a 64-slice Siemens SOMATOM Definition AS, a 250-slice Siemens SOMATOM, a 640-slice multi-detector Canon Aquilion ONE, and a 128-slice multi-detector GE Revolution scanner with prospective cardiac synchronization and 0.6 mm slice reconstruction. Coronary artery calcium (CAC) scores were quantified using automated software “Vitrea”. Associations between ethnicity, cardiovascular risk factors, and CAC were assessed using non-parametric analyses and multivariable regression models. Stata 18 software was used for all statistical analyses. Results: European participants demonstrated a higher prevalence of obesity, hypertension, tobacco use, and alcohol consumption compared with Asian participants. The prevalence of CAC > 0 was higher in Europeans than in Asians (60.6% vs. 50.3%, p < 0.01). European individuals were independently associated with CAC presence in multivariable analysis. Multivessel (≥2-vessel) stenosis and calcified plaques were more frequently observed in Europeans, whereas non-calcified and low-density plaques predominated among Asians. Conclusions: Within this referral-based cohort, differences in coronary plaque characteristics were observed between the studied groups within this clinical CCTA cohort. The European group was associated with a higher prevalence of calcified plaques, whereas non-calcified and low-density plaques were more frequently observed among Asian participants. These findings show associations between ethnicity and plaque characteristics within a clinical cohort and require confirmation in prospective studies. Full article

In this study, CT scanning technology was combined with ImageJ 1.54r and Avizo 3D 2022 professional image analysis software to quantify porosity. The aim was to reveal the intrinsic correlation between the pore structure characteristics and the macroscopic properties of vegetated concrete. A combination of 3D reconstruction, fractal analysis and multi-parameter regression modelling techniques was utilised to quantify the association between pore parameters and material properties. The mechanistic role of pore structure in regulating the strength–permeability trade-off relationship was elucidated. The results show that: (1) aggregate particle size and porosity are significantly negatively correlated with the compressive strength of vegetated concrete and strongly positively correlated with the water permeability coefficient, while the effects of both of them on the pH value of the material are negligible; (2) the porosity obtained by the image analysis method meets the design requirements of the target porosity, and the deviation between the computed 3D porosity from CT scanning and the 2D sliced porosity is less than 1%. The image analysis porosity is slightly lower than the measured value, a deviation within a reasonable range. (3) There is a robust positive correlation between the fractal dimension of the vegetated concrete structural surface and porosity. With increasing aggregate size, porosity gradually increases, pore network connectivity is significantly enhanced, and the fractal dimension increases correspondingly. (4) Function fitting analysis confirms that the correlation between the connected porosity and the compressive strength and permeability coefficient is more significant than that of the cross-sectional porosity. Specifically, compressive strength is significantly negatively correlated with equivalent pore size and fractal dimension, and the water permeability coefficient is strongly positively correlated with these two parameters. This study can provide important theoretical support and engineering reference for the optimization of the mix proportion and performance control of vegetated concrete. Full article

This paper presents the design, construction, and calibration of a modular low-cost 3D printer based on open-source technologies, developed as part of an academic research project. The printer utilises fused filament fabrication (FFF) and is built using locally available materials and components, including a T-slot aluminium frame, NEMA 23 stepper motors, and an Arduino Mega 2560 with RAMPS 1.4 control board. The system integrates Marlin firmware and CURA slicing software, enabling autonomous operation via an LCD panel and encoder interface. A detailed methodology is provided for mechanical assembly, electronic integration, firmware configuration, and calibration procedures. Special attention is given to the challenges encountered during the initial testing phase, including filament feeding issues, thermal inconsistencies, and mechanical misalignments. Solutions such as replacing inadequate components (e.g., fibreglass bushings with PTFE), adjusting spring tension, and refining firmware parameters are discussed. The results demonstrate successful printing of complex geometries after iterative calibration, validating the printer’s performance and replicability. This work contributes to the democratisation of additive manufacturing by offering a replicable, open-source solution for educational and prototyping purposes. The findings are relevant to machine design, automation, and robotics communities seeking practical insights into low-cost fabrication systems. Full article

Background and Objectives: This study aimed to evaluate and compare the effects of immersion and brushing on resins used for temporary crowns, including two 3D-printed resins (Nanolab and PrintaX) and one self-curing resin (Duralay), with different surface finishing protocols. Materials and Methods: Printed specimens were designed using specialized software, followed by slicing and printing. Self-curing resin samples were fabricated using silicone matrices, with the printed specimens serving as references. Square samples (7.0 × 7.0 × 2.0 mm, n = 90) were divided into three groups based on surface finishing: extrinsic pigment with glaze, glaze only, and polish only. The samples were immersed in 15 mL of cola soft drink, energy drink, or distilled water for six days at 37 °C in a dark environment before undergoing a brushing test (180 cycles/minute, 65,700 cycles, 2 N, 37 °C). Color alterations, surface roughness, and Knoop microhardness were then analyzed. Results: Statistical analyses revealed that all factors significantly influenced the tested properties (p < 0.05). Nanolab exhibited the most pronounced color alterations, with ∆E₀₀ values reaching up to 22.21 ± 3.13 in specific conditions (e.g., Glaze, Cola soft drink). It also presented increased surface roughness, particularly when compared to PrintaX. Conversely, Duralay consistently displayed the highest Knoop microhardness changes (e.g., ranging from −1.84 ± 0.36 to 0.47 ± 0.45 in different conditions) across most experimental groups. Polishing consistently provided better outcomes in terms of color stability, surface roughness, and microhardness compared to extrinsic pigment + glaze or glaze-only treatments. The first immersion generally led to the greatest color change. Conclusions: The acidic challenge promoted significant changes in the optical and surface properties of the evaluated resins, increasing ∆E₀₀ and roughness and reducing microhardness to different extents depending on the material. Clinically, these findings highlight the relevance of material selection and limiting exposure to acidic beverages during provisional use. Full article

Background: One of the main tools for investigating pulmonary disorders is computed tomography. Starting with a CT, analyses can be qualitative (e.g., direct interpretation of 2D slices, virtual bronchoscopy) or quantitative (e.g., fibrosis score). Qualitative analyses can be performed without segmentation, but quantitative analyses require lung segmentation. Methods: We present the concepts for a class of lung segmentation methods that use region-growing algorithms, the implementation and testing details, and the results obtained in our software platform. Accurate segmentation of lung regions from medical images is a crucial step in computer-aided diagnosis (CAD) systems for pulmonary diseases such as chronic obstructive pulmonary disease (COPD), pneumonia, and lung cancer. Manual segmentation is time-consuming and subjective, while fully automated methods may fail under challenging imaging conditions. Results: This article presents a semi-automated lung segmentation approach, based on region-growing methods, that balances automation with user control. Conclusions: The proposed technique effectively delineates lung boundaries in computed tomography (CT), minimizing computational complexity and manual effort. Full article

Background: We aimed to determine paranasal sinus volumes using 3D volumetric measurements and to evaluate the effect of anatomical variations on these volumes, ensuring balanced age and sex distribution during childhood. Methods: Thirteen age groups (0–16 years), each including 10 males and 10 females, were formed. After excluding sinus pathologies, a total of 260 subjects were randomly selected from CT head examinations. Right and left frontal, maxillary, and sphenoid sinus volumes were calculated using 3D Slicer software (version 5.6.2) following manual segmentation of axial CT slices. Also, the presence of right and left Agger Nasi cells, Haller cells, Onodi cells, and concha bullosa were recorded. Results: No significant difference was found between males and females in sinus volumes (p > 0.05). Mean right and left maxillary sinus volumes were 6.23 cm³ and 6.27 cm³ (p = 0.551); frontal sinuses were 0.79 cm³ and 0.86 cm³ (p = 0.170); and sphenoid sinuses were 1.64 cm³ and 1.85 cm³ (p = 0.041). Sphenoid sinus pneumatization appeared in 30% of the 0–6-month group and in over 75% of older groups. Frontal pneumatization began at age 2–3 and exceeded 50% after age 4. Agger Nasi, Haller, Onodi cells, and concha bullosa were detected in 58.8%, 31.2%, 10%, and 22.3% of cases, respectively. Anatomical variations showed no significant effect on sinus volumes (p > 0.05). Conclusions: We developed a paranasal sinus volume chart applicable to routine practice, showing that anatomical variations had no significant impact on the development. This is the first study to investigate the impact of anatomical variations on sinus development and volume, along with the age at which variations emerge, with a balanced distribution of age and sex. Full article

An ongoing challenge in pelagic oceanography and limnology is to quantify and understand the distribution of suspended particles and particle aggregates with sufficient temporal and spatial fidelity to understand their dynamics. These particles include biotic (mesoplankton, organic fragments, fecal pellets, etc.) and abiotic (dusts, precipitates, sediments and flocks, anthropogenic materials, etc.) matter and their aggregates (i.e., marine snow), which form a large part of the total particulate matter > 200 μm in size in the ocean. The transport of organic material from surface waters to the deep-sea floor is of particular interest, as it is recognized as a key factor controlling the global carbon cycle and hence, a critical process influencing the sequestration of carbon dioxide from the atmosphere. Here we describe the development of an oceanographic instrument, the Pelagic Laser Tomographer (PLT), that uses high-resolution optical technology, coupled with post-processing analysis, to scan the 3D content of the water column to detect and quantify 3D distributions of small particles. Existing optical instruments typically trade sampling volume for spatial resolution or require large, complex platforms. The PLT addresses this gap by combining high-resolution laser-sheet imaging with large effective sampling volumes in a compact, deployable system. The PLT can generate spatial distributions of small particles (~100 µm and larger) across large water volumes (order 100–1000 m³) during a typical deployment, and allow measurements of particle patchiness over spatial scales to less than 1 mm. The instrument’s small size (6 kg), high resolution (~100 µm in each 3000 cm² tomographic image slice), and analysis software provide a tool for pelagic studies that have typically been limited by high cost, data storage, resolution, and mechanical constraints, all usually necessitating bulky instrumentation and infrequent deployment, typically requiring a large research vessel. Full article

Background: Computed tomography (CT)-derived body composition parameters, including skeletal muscle and fat indices, are prognosticators in oncology. Most studies focus on baseline body-composition parameters; however, changes during treatment may provide better prognostic value. Standardized methods for measuring/reporting these parameters remain limited. Methods: This retrospective study included patients who were treated with immunotherapy for non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC), or melanoma between 2017 and 2024 and had technically adequate baseline and follow-up CT scans. Body composition was analyzed using a novel, fully automated software (CompoCT) for L3 slice selection and segmentation. Body composition indices (e.g., skeletal muscle index [SMI]) were calculated by dividing the cross-sectional area by the patient’s height squared. Results: The cohort included 376 patients (mean [SD] age 66.4 [11.4] years, 67.3% male, 72.6% NSCLC, 14.6% RCC, and 12.8% melanoma). During a median follow-up of 21 months, 220 (58.5%) died. Baseline body composition parameters were not associated with mortality, except for a weak protective effect of higher SMI (HR = 0.98, p = 0.043). In contrast, longitudinal decreases were strongly associated with increased mortality. Relative decreases in SMI (HR, 1.17; 95% CI, 1.07–1.27) or subcutaneous fat index (SFI) (HR, 1.11; 95% CI, 1.07–1.15) significantly increased mortality risk. Multivariate models showed similar concordance (0.65) and identified older age, NSCLC tumor type, and relative decreases in SMI and SFI (per 5% units) as independent predictors of mortality. Conclusions: Longitudinal decreases in skeletal muscle and subcutaneous fat were independent predictors of mortality in immunotherapy-treated patients. Automated CT-based body composition analysis may support treatment decisions during immunotherapy. Full article

Connecting distributed applications across multiple cloud-native domains is growing in complexity. Applications have become containerized and fragmented across heterogeneous infrastructures, such as public clouds, edge nodes, and private data centers, including emerging IoT-driven environments. Existing networking solutions like CNI plugins and service meshes have proven insufficient for providing isolated, low-latency and secure multi-cluster communication. By combining SDN control with Kubernetes abstractions, we present L2S-CES, a Kubernetes-native solution for multi-cluster layer-2 network slicing that offers flexible isolated connectivity for microservices while maintaining performance and automation. In this work, we detail the design and implementation of L2S-CES, outlining its architecture and operational workflow. We experimentally validate against state-of-the-art alternatives and show superior isolation, reduced setup time, native support for broadcast and multicast, and minimal performance overhead. By addressing the current lack of native link-layer networking capabilities across multiple Kubernetes domains, L2S-CES provides a unified and practical foundation for deploying scalable, multi-tenant, and latency-sensitive cloud-native applications. Full article