Search Results (343)

Planar multi-loop fractionated kinematic chains (FKCs)—kinematic chains that can be decomposed into two or more coupled subchains by separating joints or links—are widely used in heavy-duty manipulators, yet their large design space makes automatic synthesis and application-oriented screening challenging. The novelty of this paper is a general automated synthesis-and-screening framework for planar fractionated kinematic chains, regardless of whether multiple joints are present; multiple-joint chains are handled via an equivalent transformation to single-joint models, enabling the construction of a deduplicated topological graph atlas. In the mine scaler manipulator case study, an 18-link, 5-DOF (N18_M5) FKC with two multiple joints is taken as the target and converted into a single-joint equivalent N20_M7 model consisting of three subchains (KC1–KC3). Atlases of the required non-fractionated kinematic chains (NFKCs) for KC1 and KC3 are generated according to their link counts and DOFs. The subchains are then combined as building blocks under joint-fractionation (A-mode) and link-fractionation (B-mode) to enumerate fractionated candidates, and a WL-hash-based procedure is employed for isomorphism discrimination to obtain a non-isomorphic N20_M7 atlas. Finally, a connectivity-calculation-based screening is performed under task-driven structural and functional constraints, yielding 249 feasible configurations for the overall manipulator arm. The proposed pipeline provides standardized representations and reproducible outputs, offering a practical and transferable route from large-scale enumeration to engineering-feasible configuration sets for planar multi-loop FKCs, including those with multiple joints. Full article

This study introduces a novel framework for defining screw trajectory that utilizes PointNet—a deep neural network trained on lumbar vertebrae point clouds—to improve the manual surgical planning procedures. The conventional architecture of PointNet was modified to accommodate various vertebral orientations and predict six values, which were reconstructed into two control points that define a linear trajectory. A custom loss function was designed to align the predicted trajectory with the ground-truth trajectory. The neural networks were trained on 4284 point clouds of vertebrae, and 28 unseen point clouds were used to evaluate the model’s performance based on translational error, angular error, and clinical accuracy. For the left pedicle, the mean translational errors were 1.5 ± 0.8 mm at the entry point and 2.3 ± 1.2 mm at the target point. For the right pedicle, the mean translational errors were 1.5 ± 0.7 mm at the entry point and 2.3 ± 1.0 mm at the target point. The mean angular error was 3.5 ± 2.3° for the left pedicle and 3.9 ± 1.7° for the right pedicle. Clinically, the network generated 52 out of 56 trajectories without medial-cortical violations of the spinal canal. The trained neural network demonstrated promising technical and clinical accuracy, generating feasible screw trajectories across various vertebral orientations. Integrating a spinal segmentation network with the proposed framework could enable fully automated surgical planning in the future. Full article

The advancement of automated driving technologies offers potential safety and efficiency gains, yet safety remains the primary barrier to higher-level deployment. Failures in automated driving systems rarely result from a single technical malfunction. Instead, they emerge from coupled organizational, technical, human, and environmental factors, particularly in partial and conditional automation where human supervision and intervention remain critical. This study systematically identifies safety failures in automated driving systems and analyzes how they propagate across system layers and human–machine interactions. A qualitative case-based analytical approach is adopted by integrating the Swiss Cheese model and the SHELL model. The Swiss Cheese model is used to represent multilayer defensive structures, including governance and policy, perception, planning and decision-making, control and actuation, and human–machine interfaces. The SHELL model structures interaction failures between liveware and software, hardware, environment, and other liveware. The results reveal recurrent cross-layer failure pathways in which interface-level mismatches, such as low-salience alerts, sensor miscalibration, adverse environmental conditions, and inadequate handover communication, align with latent system weaknesses to produce unsafe outcomes. These findings demonstrate that autonomous driving safety failures are predominantly socio-technical in nature rather than purely technological. The proposed hybrid framework provides actionable insights for system designers, operators, and regulators by identifying critical intervention points for improving interface design, operational procedures, and policy-level safeguards in autonomous driving systems. Full article

Large Language Models (LLMs) are increasingly used in higher education, but their pedagogical role in fields like cybersecurity remains under-investigated. This research explores integrating LLMs into a university cybersecurity course using a designed pedagogical approach based on active learning, problem-based learning (PBL), and computational thinking (CT). Instead of viewing LLMs as definitive sources of knowledge, the framework sees them as cognitive tools that support reasoning, clarify ideas, and assist technical problem-solving while maintaining human judgment and verification. The study uses a qualitative, practice-based case study over three semesters. It features four activities focusing on understanding concepts, installing and configuring tools, automating procedures, and clarifying terminology, all incorporating LLM use in individual and group work. Data collection involved classroom observations, team reflections, and iterative improvements guided by action research. Results show that LLMs can provide valuable, customized support when students actively engage in refining, validating, and solving problems through iteration. LLMs are especially helpful for clarifying concepts and explaining procedures during moments of doubt or failure. Still, common issues like incomplete instructions, mismatched context, and occasional errors highlight the importance of verifying LLM outputs with trusted sources. Interestingly, these limitations often act as teaching opportunities, encouraging critical thinking crucial in cybersecurity. Ultimately, this study offers empirical evidence of human–AI collaboration in education, demonstrating how LLMs can enrich active learning. Full article

With rising efforts to enable a circularity of valuable resources of lithium-ion batteries, a growing number of recycling companies in Europe are expanding their capacities and developing new recycling technologies. The European Union (EU) has set a benchmark for battery recycling by publishing recycling targets. These targets require precise mass determination of the individual battery components, making disassembly of the battery mandatory for characterization. The paper puts forth a semi-automated disassembly procedure for determining the composition of the components at the module and cell levels across a range of designs. Our analysis incorporates the introduction of TGA as a novel, direct method for determining the cathode active material with an accuracy above 99%. This approach is intended to define the recycling input for all extant recycling routes by providing quantitative experimental results with statistical significance. The results indicate a black mass proportion of 61.6% at the module level and 53–74% at the cell level. Additionally, there are significant differences in value creation, ranging from 0.80 to 1.81 USD kg⁻¹ black mass, depending on the cell chemistry. The procedure can be used for EoL and scrap material, and enables greater transparency and comparability in battery recycling, opening up new perspectives for the resource-efficient and targeted use of various recycling technologies. Full article

One of the major challenges for effective intrusion detection systems (IDSs) is continuously and efficiently incorporating changes on cyber-attack tactics, techniques, and procedures in the Internet of Things (IoT). Semi-automated cross-organizational sharing of IDS data is a potential solution. However, a major barrier to IDS data sharing is privacy. In this article, we describe the design, implementation, and evaluation of FedPrIDS: a privacy-preserving federated learning system for collaborative network intrusion detection in IoT. We performed experimental evaluation of FedPrIDS using three public network-based intrusion datasets: CIC-IDS-2017, UNSW-NB15, and Bot-IoT. Based on the labels in these datasets for attack type, we created five fictitious organizations, Financial, Technology, Healthcare, Government, and University and evaluated IDS accuracy before and after intelligence sharing. In our evaluation, FedPrIDS showed (1) a detection accuracy net gain of 8.5% to 14.4% from a comparative non-federated approach, with ranges depending on the organization type, where the organization type determines its estimated most likely attack types, privacy thresholds, and data quality measures; (2) a federated detection accuracy across attack types of 90.3% on CIC-IDS-2017, 89.7% on UNSW-NB15, and 92.1% on Bot-IoT; (3) maintained privacy of shared NIDS data via federated machine learning; and (4) reduced inter-organizational communication overhead by an average 50% and showed convergence within 20 training rounds. Full article

In both research and industrial settings, it is often necessary to expand the input/output channels of measurement instruments using relay-based multiplexer boards. In research activities in particular, the need for a highly flexible and easily configurable solution frequently leads to the development of customized systems. To address this challenge, we developed a system optimized for automated direct current (DC) measurements. The result is based on a $4\times 4$ switching platform that simplifies measurement procedures that require instrument routing. The platform is based on a custom-designed circuit board controlled by a microcontroller. We selected bistable relays to guarantee contact stability after switching. We finally developed a system architecture that allows for straightforward expansion and scalability by connecting multiple platforms. We share both the hardware design source files and the firmware source code on GitHub with the open-source community. This work presents the design and development of the proposed system, followed by the performance evaluation. Finally, we present a test of our designed system applied to a specific case study: the DC analysis of complex resistive networks through multi-point resistance measurements using only a single voltmeter and current source. Full article

The integration of Building Information Modelling (BIM), blockchain technology, and smart contracts presents a significant opportunity to fundamentally reevaluate the administration of information and contracts in construction projects. This article introduces a distributed system that amalgamates BIM models, decentralized storage via IPFS, semantic oracles, and smart contracts to automate essential procedures such as versioning, design verification, and payment issuance contingent upon execution milestones. This proof of concept, built on a Proof-of-Authority blockchain using actual IFC models, demonstrates the technical viability of the method and evaluates its performance, constraints, and operational implications. The applications of SAL automation and design review demonstrate that integrating off-chain verification with on-chain documentation can reduce uncertainty, enhance accountability, and enable hitherto unattainable forms of contract automation. The suggested framework acknowledges the need for improved information standards and Oracle governance, demonstrating that integrating BIM and distributed technologies can significantly transform the digitalisation of the construction sector. Full article

Background: The clinical research laboratory plays a pivotal role in the execution of clinical studies. The accurate and consistent registration of patients is dependent on the competent use of laboratory equipment and manual techniques by technicians, ensuring the reliability of the data collected. To support these activities, the Groupe de Recherche Action en Santé (GRAS) has been registered with the College of American Pathologists (CAP) and the Clinical Laboratories Services (CLS) in Johannesburg, South Africa, for external proficiency testing (EPT) of its laboratory, as part of our commitment to quality assurance. The following report details the performance achievements over the past five years. Methods: Proficiency testing (PT) samples are dispatched to GRAS Lab three times a year (quarterly) and the results are generally returned within two to three weeks. In the field of parasitology, challenge specimens were prepared as follows: thick and thin blood films were stained with Giemsa and mounted with strips to protect them for multiple uses. Photographs, also known as whole slide images (WSIs), were also taken. For the biochemistry and haematology tests, a set of five samples were received for processing. All evaluations were carried out in accordance with the GRAS laboratory’s internal procedures. Results: The CAP laboratory’s performance in terms of the diagnosis of malaria and other blood parasites from 2020 to 2024 was 97.3% accurate (ranging from 93.33% to 100%), with 93.33%, 100%, 100%, 93.33% and 100% achieved in 2020, 2021, 2022, 2023 and 2024, respectively. The number of microscopists evaluated annually has been subject to variation according to operational staff at the time of evaluation. A total of 31 microscopists were enrolled in the CLS PT scheme, of which 73.9% were classified as ‘experts’ and 19.2% as ‘reference’ microscopists. In the field of haematology, the PT demonstrated 100% accuracy over the four-year study period. This outcome is indicative of the high-performance levels exhibited by the automated systems under scrutiny and the comparable nature of the data produced by these systems. The same trend was observed in the biochemistry PT results, with an overall score of 92.12%, ranging from 78% to 100%. Conclusions: Proficiency testing has been shown to be an effective tool for quality assurance in laboratories, helping to ensure the accuracy of malaria and other blood parasite diagnoses made by microscopists, as well as the results generated by automated systems. It has been instrumental in assisting laboratories in identifying issues related to test design and performance. Full article

Since elevated temperature and humidity may occur at the prosthetic socket–skin interface, it is essential to collect thermal data from the residual limb, as this information serves as an indicator of adverse effects such as irritation, postural problems, and significant damage to health. These data are obtained non-invasively through the execution of a thermal imaging (TI) procedure. However, the precision and repeatability of a TI procedure rely significantly on its execution technique. This work presents the design and implementation of a mechatronic device that automates a thermal imaging technique. The application of the device is in lower-limb prosthetics evaluation. The proposed system improves data acquisition consistency by reducing execution time and minimizing human error, thereby enhancing the reproducibility and reliability of thermal measurements. The introduced device, Thermal Imaging Booth, proposes an automated solution for TI standardization in clinical and research settings. By minimizing inconsistencies, this system improves the diagnostic potential of thermography, facilitating its adoption in biomedical applications. Full article

Autonomous vehicle (AV) accidents introduce uncertainty in liability attribution, as responsibility is divided between humans and automated systems. The 2018 Arizona crash highlighted growing societal concerns about accountability. To address these issues, prior studies proposed investigation processes considering perception sensors, driving control systems, communication infrastructure, and cybersecurity. However, conducting such investigations requires integrating large-scale data from multiple sources, including vehicle sensors, onboard recorders, V2X communications, and road infrastructure. Raw data often lack descriptive information, limiting their use in real investigations. This study establishes a structured mapping framework linking investigation procedures, responsible entities, items, and data across accident phases. With this backdrop, an autonomous driving–specific metadata schema extending DCAT was designed, comprising 10 Classes and 76 Properties. To demonstrate its applicability, a prototype data catalog user interface (UI) was conceptualized with data discovery and visualization examples. The proposed schema strengthens accountability and interoperability by explicitly aligning responsibilities and data relationships. It enables precise event localization and effective linkage of heterogeneous data. Future work will refine the schema by incorporating DSSAD, V2X, and security log data, and develop a user-tested UI prototype as a practical support tool for AV accident investigation. Full article

This study introduces a systematic methodology for modelling the radius of curvature of the arc-shaped section $\stackrel{⌢}{\mathrm{BC}}$ in a Michell–Banki cross-flow turbine blade. The method combines geometric modeling in polar coordinates with nonlinear regression, using both two- and three-parameter formulations estimated through the Ordinary Least Squares (OLS) method. Model performance is assessed through two complementary criteria: the coefficient of determination ($R^2$) and the computed arc length, ensuring that statistical accuracy aligns with geometric fidelity. The methodology was validated on digital measurements obtained from CATIA, using datasets with $N=187$ and a reduced subset of $N=48$ points. Results demonstrate that even with fewer data points, the regression model maintains high predictive accuracy and geometric consistency. The best-performing three-parameter model achieved $R^2=0.958$, with a five-point Gauss–Legendre quadrature yielding an arc length of approximately $145\mathrm{mm}$, representing 98.8% agreement with the reference value of $146.78\mathrm{mm}$. By representing the arc as a single smooth exponential function rather than a piecewise mapping, the approach simplifies analysis and enhances reproducibility. Coupling regression precision with arc-length verification provides a robust and reproducible basis for curvature modeling. This methodology supports turbine blade design, manufacturing, and quality control by ensuring that the blade geometry is validated with high statistical confidence and physical accuracy. Future research will focus on deriving analytical arc-length integrals and integrating the procedure into automated design and inspection workflows. Full article

Off-site hydrogen refueling stations (HRS) handle large volumes of high-pressure hydrogen, requiring precise and systematic risk-reduction strategies. In this study, a Hazard and Operability (HAZOP) analysis was performed for an off-site HRS, and Layer of Protection Analysis (LOPA) was conducted for four risk-level-4 events using two different approaches. The Functional Safety only LOPA, based on IEC 61511, and the All Safeguards LOPA, developed according to the Center for Chemical Process Safety (CCPS) guideline, were both applied. The Functional Safety only approach, which considers only automated protection layers, required Safety Integrity Level (SIL) ratings of 1 and 2, whereas the All Safeguards approach, accounting for mechanical and procedural protection layers, achieved the Target Mitigated Event Likelihood (TMEL) in all scenarios without additional SIL requirements. Consequently, it was confirmed that the definition of protection layer scope significantly influences the required SIL, design cost, and system complexity. This study proposes a hybrid approach in which all safeguards are considered during the early design stage, while in the final design stage, protection measures are evaluated from a functional safety perspective in accordance with IEC 61511 to ensure both design efficiency and safety integrity. Full article

This research proposes a heterogeneous graph neural network (GNN) framework to attribute advanced persistent threat (APT) activity using enriched cyber threat intelligence (CTI). We construct a tripartite graph linking APT groups, contextualised Tactics, Techniques, and Procedures (TTPs), and their Cyber Kill Chain (CKC) stages. TTP nodes are embedded with Sentence-BERT (SBERT) vectors for semantic similarity, while CKC stages provide procedural context. This design captures both behavioural semantics and attack-stage relationships, enabling robust and interpretable attribution. Empirical evaluation on the APTNotes corpus achieves a Macro-F1 score of 0.84 and 85% accuracy, addressing limitations in baselines such as DeepOP (technique prediction without CKC integration) and APT-MMF (no procedural or temporal TTP modelling). The framework is suitable for Security Operations Centres (SOCs), enabling faster and more accurate decision-making during incident response. Overall, the study advances automated and explainable APT attribution for practical SOC deployment. Full article

Sinter-based additive manufacturing (SBAM) processes, such as Cold Metal Fusion (CMF), combine the geometric freedom of additive manufacturing with the scalability of powder metallurgy, but part distortion and collapse during debinding and sintering remain critical design challenges. This study presents a revised stress-based optimization framework to address these issues by integrating sintering-specific load cases into topology optimization. In contrast to earlier approaches, the revised workflow applies all load cases to the upscaled green-part geometry. This adjustment mitigates the non-linear scaling effects of dead load-induced stresses. A Case study, including a steering bracket for a Formula Student racing car, demonstrates that the revised method improves not only sinterability but also application-related performance compared to earlier approaches. In addition, a semi-automated procedure for generating sinter supports is introduced, allowing stable processing of geometries without planar bearing surfaces. Experimental validation confirms that optimized supports effectively prevent part failure during post-processing, though challenges remain in separating complex freeform geometries. Finally, the influence of stiffness on sintering-induced deformations is investigated, showing that higher stiffness configurations significantly reduce dimensional errors. Together, these results highlight stress- and stiffness-based optimization as tools to enhance the reliability, efficiency, and design freedom of SBAM. Full article