Search Results (27)

Across the world’s electrical substations, water-treatment plants, and manufacturing lines, a single engineer with valid credentials and a laptop can today push new control logic to a programmable logic controller (PLC) and change the physical behaviors of safety-critical equipment within minutes. Firmware and ladder-logic updates on SCADA and industrial IoT systems are privileged operations: an attacker installing a malicious update controls the physical process. Existing protections concentrate install authority in a single party with no externally verifiable record; compromise of the vendor key, the engineering workstation, or any operator credential suffices to deliver an attacker-chosen payload to a PLC. CertiFlash binds every update to four independent approvals: a vendor signature, a FROST-Ed25519 threshold signature from an operator quorum, a per-session nonce from the PLC, and a monotonic counter. Every decision is recorded in an append-only Merkle transparency log. The PLC verifies the aggregate with a standard RFC 8032 Ed25519 verifier, requiring no FROST-specific device code. Four security properties (authenticity, authorization, rollback resistance, auditability) are machine-checked in Tamarin under a Dolev–Yao adversary with up to t − 1 compromised operators and corroborated through ten attack scenarios. The implementation runs with concurrent Modbus TCP and Siemens S7 traffic, blocks all attacks, signs in 27–192 ms (k = 3–10), keeps ML-DSA-65 within 6% of Ed25519 from 1 KiB to 10 MiB, and sustains 30.1 updates/s on 100 PLCs. The operator-quorum signature remains FROST-Ed25519: the design is partially post-quantum in the evaluated version. The framework maps to IEC 62443-3-3 SR 3.4 and NIS2 Article 21(2)(d–e). Full article

The deployment of object detection models on resource-constrained edge devices remains a substantial challenge, primarily because conventional static networks expend the same worst-case computational cost on every input, regardless of intrinsic difficulty. This paper proposes an input-adaptive dynamic neural network architecture for object detection in embedded environments. The present study investigates two orthogonal axes of input-adaptive inference for embedded object detection: The system demonstrates depth adaptivity through the implementation of Early Exit, and width adaptivity via group-wise Adaptive Routing. The proposed framework is constructed on a frozen Ultralytics YOLO26s backbone and incorporates two YOLO-style early-exit heads positioned at approximately 33% and 66% of the backbone depth. Furthermore, the framework incorporates two Straight-Through Gumbel-Softmax routers, which are appended after Layers 4 and 8 with group-wise hard gating. Both axes additionally accept an explicit external control signal that allows the host system to override the input-conditional policy at inference time. The dual-mode design facilitates the functionality of the trained checkpoint as either an input-adaptive policy, in which the depth and width are determined per sample from the input distribution, or an externally controlled policy. The experimental findings demonstrate two strongly asymmetric input-adaptive policies on a frozen YOLO26s backbone. The early-exit profile reduces the compute per sample from 12.739 to 10.532 GFLOPs—a 17.32% reduction according to our in-house Conv2d/Linear MAC-based GFLOPs estimator—while preserving baseline accuracy (mAP50 = 0.1545 vs. baseline = 0.1528; ΔmAP50 = +0.0017, within evaluation noise; mAP50–95 = −0.0033). Evaluating the router-only profile in the same validator pipeline with a sparsity penalty of γ = 0.05 results in a 12.3% decrease in logical GFLOPs (from 12.739 to 11.172), while maintaining an accuracy level that is at or above the PEFT baseline (mAP50 = 0.2324 and mAP50–95 = 0.1040). In our small-domain PEFT setup, training the dynamic-policy modules yields per-checkpoint mAP shifts in this magnitude. Therefore, we interpret the width-axis accuracy result as preservation of the baseline rather than an improvement. Our contribution on the width axis is reducing computing power while maintaining baseline accuracy. Importantly, the router profile’s logical GFLOP savings are not currently reflected in wall-clock latency under our dense-kernel PyTorch implementation. Achieving practical speedup requires sparse-kernel deployment, such as structured-sparse kernels, TensorRT, TVM, or Triton paths. We will address this in future deployment-level work. Our results indicate that the depth axis can yield genuine end-to-end speedup today, while the width axis offers deployment-pending compute reduction. Full article

Vortex-induced energy dissipation is critical, yet its influence is frequently neglected in potential-flow analysis of wave interaction with thin-walled structures. This study revisits the parametrization of vortex-induced energy dissipation in potential-flow analysis, particularly for wave interaction with vertical, surface-piercing plates. The parametrization is derived by conceptually appending a short perforated region to the vortex-shedding edge of the plate. The underlying physical principle relies on the similarity between vortex shedding from a sharp edge and from an orifice. Two parameters are identified as important: the length of the perforated region and the quadratic loss coefficient associated with the pressure change. For practical applications, the value of the quadratic loss coefficient that is invariant of wave conditions is recommended for a given optimal length of the perforated region. The parametrization is validated using published results for a single plate, and its robustness is further demonstrated through applications involving two surface-piercing vertical plates with varying spacings. The findings of this study can find applications in using potential-flow theory to model plate-type wave breakwaters and wave interaction with thin-walled oscillating water column devices. Full article

Power-system planning is being reshaped by rapid decarbonisation, electrification, and digitalisation, which collectively amplify uncertainty in demand, generation, technology adoption, and policy pathways. This review critically synthesises three principal optimisation paradigms used to plan under uncertainty in power systems: scenario-based stochastic optimisation, set-based robust optimisation (including adaptive and distributionally robust variants), and minimax-regret decision models. The review is positioned to address a recurrent limitation of many uncertainty-planning surveys, namely the separation between “method reviews” and “technology reviews”, and the consequent lack of decision-operational guidance for planners and system operators. The central contribution is a decision-centric framework that operationalises method selection through two explicit dimensions. The first is an information posture, which formalises what uncertainty information is credible and usable in practice (probabilistic, set-based, or probability-free scenario representations). The second is a flexibility posture, which formalises the availability, controllability, and timing of operational recourse enabled by smart-grid technologies. These postures are connected to modelling templates, data requirements, tractability implications, and validation/stress-testing needs. Smart-grid technologies are integrated not as an appended catalogue but as explicit sources of recourse that change the economics of uncertainty and, in turn, shift the relative attractiveness of stochastic, robust, and regret-based planning. Soft Open Points, Coordinated Voltage Control, and Vehicle-to-Grid/Vehicle-to-Building are treated uniformly under this recourse lens, highlighting how device capabilities, control timescales, and implementation constraints map into each paradigm. The paper also increases methodological transparency by describing literature-search, screening, and inclusion principles consistent with a structured narrative review. Practical guidance is provided on modelling choices, uncertainty governance, computational scalability, and institutional adoption constraints, alongside revised comparative tables that embed data credibility, regulatory interpretability, and implementation maturity. Full article

Botnet attacks on Internet of Things (IoT) devices are escalating at the 5G/6G multi-access edge, yet most federated learning frameworks for IoT malware detection (FL-IMD) still hinge on a central aggregator, enlarging the attack surface, weakening privacy, and creating a single point of failure. We propose a two-tier, fully decentralized FL architecture aligned with MEC’s Proximal Edge Server (PES)/Supplementary Edge Server (SES) hierarchy. PES nodes train locally and encrypt updates with the Cheon–Kim–Kim–Song (CKKS) scheme; SES nodes verify ECDSA-signed provenance, homomorphically aggregate ciphertexts, and finalize each round via an Algorand-style committee that writes a compact, tamper-evident record (update digests/URIs and a global-model hash) to an append-only ledger. Using the N-BaIoT benchmark with an unsupervised autoencoder, we evaluate known-device and leave-one-device-out regimes against a classical centralized baseline and a cryptographically hardened but server-centric variant. With the heavier CKKS profile, attack sensitivity is preserved (TPR $\ge 0.99$), and specificity (TNR) declines by only 0.20 percentage points relative to plaintext in both regimes; a lighter profile maintains TPR while trading 3.5–4.8 percentage points of TNR for about 71% smaller payloads. Decentralization adds only a negligible per-round overhead for committee finality, while homomorphic aggregation dominates latency. Overall, our FL-IMD design removes the trusted aggregator and provides verifiable, ledger-backed provenance suitable for trustless MEC deployments. Full article

This study presents new developments in novel drag reduction devices for road vehicles, focusing on the use of inflatable and alternative material rear drag reduction devices that employ both a single- and multi-cavity approach. The effectiveness of these devices is assessed through on-road testing using constant power measurements to evaluate the resulting drag reductions. Surface pressure measurements collected during testing are compared with CFD predictions, using both the RANS and HLES methods to evaluate how accurately pressure changes are modelled when the devices are fitted to the test vehicles. A novel method for analysing vehicle surface flow in real-world conditions is also introduced, involving the capture and processing of video-recorded tuft imagery to determine appropriate means and standard deviations for the surface flow behaviour. Additionally, the study presents the latest advancements in multi-cavity drag reduction device design, along with considerations on how such devices can significantly enhance the benefits of vehicle platooning. Full article

Agri-food safety issues have received widespread attention globally. The emergence of blockchain technology (BCT) effectively addresses trust issues in the agri-food supply chain traceability system (AFSCTS). However, the append-only feature of blockchain has led to continuous linear data growth in BCT-based AFSCTSs, which increases the equipment requirements and has become a bottleneck for BCT-based AFSCTS applications. The storage capacity required by BCT-based AFSCTSs can be effectively reduced by deleting expired data, thereby reducing the storage pressure on blockchain devices and lowering the device requirements. In this paper, we propose an AFSCTS architecture that incorporates redactable blockchain and InterPlanetary file system (IPFS) technologies to achieve traceability with low storage pressure, using the wheat supply chain as a proof of concept. Firstly, the key links were analyzed in agri-food traceability and the demand was proposed for agri-food blockchain traceability based on the timeliness of traceability data. Secondly, a lightweight accountable parallel blockchain architecture called LAP-chain is proposed. This architecture utilizes redactable blockchain technology to offload expired agri-food traceability data to IPFS, thereby reducing the storage pressure on blockchain devices and ensuring data accountability through IPFS. Finally, we evaluate the correctness, collision resistance, and storage performance of the LAP-chain built on the Ethereum private chain. The results show that when expired agri-food traceability data are permanently retained, the storage capacity of the proposed architecture is only 52.38% of that of the traditional blockchain traceability architecture, after running continuously for 36 months. When traceability data of expired agri-food are deleted in accordance with the food laws and regulations of various countries, the storage capacity of the proposed architecture can be reduced from a linear level to a constant level compared to the traditional blockchain traceability architecture. The proposed architecture has the potential to contribute to improving the safety and quality of agri-food. Full article

The integration of external devices and network connectivity into autonomous vehicles has raised significant concerns about in-vehicle security vulnerabilities. Existing security mechanisms for in-vehicle bus systems, which mainly rely on appending authentication codes and data encryption, have been extensively studied in the context of CAN and CAN-FD buses. However, these approaches are not directly applicable to Ethernet buses due to the much higher data transmission rates of Ethernet buses compared to other buses. The real-time encryption and decryption required by Ethernet buses cannot be achieved with conventional methods, necessitating an acceleration in the speed of cryptographic operations to match the demands of Ethernet communication. In response to these challenges, our paper introduces a range of cryptographic solutions specifically designed for in-vehicle Ethernet networks. We employ an AES-ECC hybrid algorithm for critical vehicle control signals, combining the efficiency of AES with the security of ECC. For multimedia signals, we propose an improved AES-128 (IAES-128) and an improved MD5 (IMD), which improve encryption time by 15.77%. Our proposed security mechanisms have been rigorously tested through attack simulations on the CANoe (version 10) platform. These tests cover both in-vehicle control signals, such as braking and throttle control, and non-critical systems like multimedia entertainment. The experimental results convincingly demonstrate that our optimized algorithms and security mechanisms ensure the secure and reliable operation of real-time communication in autonomous vehicles. Full article

In this study, novel phosphorescent dipyrido[3,2-a;2′3′-c]phenazine (dppz)–platinum(II)–phenylacetylide complexes were developed to fabricate non-doped organic light-emitting diodes (OLED) by solution-processing. To facilitate the charge carrier injection into the emitting layer (EML), 3,6-di-tert-butylcarbazole-functinalized phenylacetylides were employed. As for the dppz ligand, 9,9-dihexylfluoren-2-yl and 4-hexylthiophen-2-yl side-arms were introduced to the 2,7-positions, which led to reddish orange and red photoluminescence (PL), respectively, in solution and film states (PL wavelength: ca. 600 and ca. 625 nm, respectively). The carbazole-appended phenylacetylide ligands hardly affected the emission color, although unsubstituted phenylacetylides gave rise to aggregate- or excimer-based near-infrared PL with a low quantum yield. Two types of non-doped OLEDs were fabricated: single-layer and multilayer devices. In both devices, the organic layers were fabricated by spin-coating, and the EML consisted of a neat film of the corresponding platinum(II) complex. Therein, electroluminescence spectra corresponding to those of PL were observed. The single-layer devices exhibited low device efficiencies due to a deteriorated charge carrier balance. The multilayer devices possessed hole- and electron-transporting layers on the anode and cathode sides of the EML, respectively. Owing to an improved charge carrier balance, the multilayer devices exhibited higher device performance, affording considerably improved values of luminance and external quantum efficiency. Full article

Federated learning (FL) has emerged as a pivotal paradigm for training machine learning models across decentralized devices while maintaining data privacy. In the healthcare domain, FL enables collaborative training among diverse medical devices and institutions, enhancing model robustness and generalizability without compromising patient privacy. In this paper, we propose DPS-GAT, a novel approach integrating graph attention networks (GATs) with differentially private client selection and resource allocation strategies in FL. Our methodology addresses the challenges of data heterogeneity and limited communication resources inherent in medical applications. By employing graph neural networks (GNNs), we effectively capture the relational structures among clients, optimizing the selection process and ensuring efficient resource distribution. Differential privacy mechanisms are incorporated, to safeguard sensitive information throughout the training process. Our extensive experiments, based on the Regensburg pediatric appendicitis open dataset, demonstrated the superiority of our approach, in terms of model accuracy, privacy preservation, and resource efficiency, compared to traditional FL methods. The ability of DPS-GAT to maintain a high and stable number of client selections across various rounds and differential privacy budgets has significant practical implications, indicating that FL systems can achieve strong privacy guarantees without compromising client engagement and model performance. This balance is essential for real-world applications where both privacy and performance are paramount. This study suggests a promising direction for more secure and efficient FL medical applications, which could improve patient care through enhanced predictive models and collaborative data utilization. Full article

Background: Airway management in Duchenne patients can be challenging. We present a case of an anticipated difficult airway in a 24-year-old Duchenne patient that was managed by planning different suitable strategies based on the unanticipated difficult airway algorithm of the Difficult Airway Society (DAS). Case presentation: The patient initially presented with appendicitis, requiring a laparoscopic appendectomy within 6 h. Due to the underlying condition and a known difficult airway, we anticipated potential airway problems and successfully managed the “can ventilate but cannot intubate” situation using the algorithm. The difficult airway was attributed to reduced mandibular mobility, limited inclination or reclination, a large tongue, prominent incisors, and a posteriorly positioned epiglottis. Despite thorough preparation and team briefing, we experienced three failed intubation attempts. Considering limited nighttime resources, the urgency of the surgery, the need for a tube for laparoscopy, and the risk of exacerbating airway issues, we made the decision to awaken the patient and wait for a second attempt after the epiglottis swelling had subsided. We used reversible, short-acting agents for induction, enabling us to continue with the algorithm within the allotted timeframe. In a second stage, we successfully performed fiberoptic-guided intubation via a supraglottic airway device using the Aintree intubation catheter, utilizing more favorable resources. Conclusions: For a patient with Duchenne muscular dystrophy and a difficult airway, advanced expertise is critical. Detailed anesthesia planning, clear team communication, and the use of reversible, short-acting agents are crucial. Adherence to the Difficult Airway Society guidelines is essential for safe airway management. Full article

This paper investigates the impact of contemporary lightbars on vehicle fuel efficiency with a focus on quantifying their effects on fuel consumption and exploring strategies to improve drag performance through modifications. Simulations showed an 8–11% increase in drag for square-back vehicles, with greater penalties outlined for vehicles with rear-slanting roofs. Given the moderate drag increase, the impact on the driving range, especially for electric vehicles, remains minimal, supporting the continued use of external lightbars. Positioning experiments suggest marginal drag reductions when lowering the lightbar to its lowest position due to additional drag effects that can be caused by the mounting mechanism in its condensed form. Angling the lightbar showed negligible drag increases up to an angle of 2.5 degrees, but beyond that, a 4% increase in drag was observed for every additional 2.5 degrees. Additionally, fitting drag-reducing ramps ahead of the lightbar yielded no significant drag savings. Noise analysis identified that the lightbar’s wake and rear surfaces were responsible for the largest production of noise. The optimal lightbar design was found to incorporate overflow rather than underflow and rear tapering in sync with roof curvature. Appendable clip-on devices for the lightbar, particularly rear clip-ons, demonstrated appreciable drag reductions of up to 2.5%. A final optimised lightbar design produced a minimal 2.8% drag increase when fitted onto an unmarked vehicle, representing a threefold improvement compared with the current generation of lightbars. This study advances the field of lightbar aerodynamics by precisely quantifying drag effects by using highly detailed geometry and examines the significance of optimal positioning, angle adjustment, and appendable clip-on devices in greater depth than any existing published work. Full article

Group management is practiced to deploy access control and to ease multicast and broadcast communication. However, the devices that constitute the Internet of Things (IoT) are resource-constrained, and the network of IoT is heterogeneous with variable topologies interconnected. Hence, to tackle heterogeneity, SDN-aided centralized group management as a service framework is proposed to provide a global network perspective and administration. Group management as a service includes a group key management function, which can be either centralized or decentralized. Decentralized approaches use complex cryptographic primitives, making centralized techniques the optimal option for the IoT ecosystem. It is also necessary to use a safe, scalable approach that addresses dynamic membership changes with minimal overhead to provide a centralized group key management service. A group key management strategy called a one-way Function Tree (OFT) was put forth to lower communication costs in sizable dynamic groups. The technique, however, is vulnerable to collusion attacks in which an appending and withdrawing device colludes and conspires to obtain unauthorized keys for an unauthorized timeline. Several collusion-deprived improvements to the OFT method are suggested; however, they come at an increased cost for both communication and computation. The Modified One-Way Function Tree (MOFT), a novel technique, is suggested in this proposed work. The collusion resistance of the proposed MOFT system was demonstrated via security analysis. According to performance studies, MOFT lowers communication costs when compared to the original OFT scheme. In comparison to the OFT’s collusion-deprived upgrades, the computation cost is smaller. Full article

This article presents a study on the aerodynamic drag of a generic dual-axle flatbed trailer and explores ways to reduce the drag using appendable drag-reducing devices. The primary sources of drag originated from the van and trailer’s rear, along with the trailer’s wheels. The most-effective initial device for reducing drag was a full trailer underside cover, which offered a 7% drag reduction. Additionally, ladder racks, dropsides, and rear gates were studied, and it was found that protruding ladder racks significantly increased drag. Rear gates added large amounts of drag and should be removed and stored when not needed. The study also explored novel mid-section devices that increased the van’s base pressure and reduced drag. An axle test revealed that drag for single-, dual-, and triple-axle trailers was very similar in direct flow, but different in yawed flow. A drawbar length test showed a near-linear relationship between drawbar length and drag, manifesting as a 1.7% change in drag per 250 mm change in drawbar length. Several novel modifications were made to the trailer, including fitting six unique appendable devices, which offered a total 7.3% drag reduction. A novel rear van device known as the multi-stage converging cavity was introduced, which reduced drag by nearly 18%. When all the devices were used together, a total 25% drag reduction was observed for the van–trailer combination. Full article

This comprehensive study focused on the standard taxi sign used in Ireland and its impact on drag production, fuel expenses, and CO${}_2$ emissions. Experimental analysis revealed that the conventional taxi sign significantly increased drag, especially when mounted on streamlined vehicles such as saloon cars, due to flow separation issues on the rear roof and rear windshield. Longitudinal reorientation of the sign offered a 14-fold reduction in drag increase compared to the traditional placement. It was found that positioning the sign in the middle of the roof offered the greatest fuel efficiency. Furthermore, the study estimated that implementing longitudinal repositioning on all Irish taxi signs could save drivers approximately EUR 832 per year and reduce national CO${}_2$ emissions by a substantial 22,464 tonnes annually. Comparative analyses with international taxi signs demonstrated that the Irish sign had significantly larger drag contributions, emphasizing the need for improved aerodynamics. To address the inherent drag issue, the study explored novel appendable devices and proposed alternative taxi sign designs. Among the tested solutions, a magnet-mounted front ramp proved the most effective, reducing total drag by nearly 30%. Additionally, a motorized flip-up taxi sign design demonstrated a remarkable 40% reduction in drag. Finally, a newly proposed taxi sign design, featuring longitudinal positioning and pointed triangular front and rear faces, resulted in a minimal 4.3% increase in vehicle drag compared to the baseline car. Full article