Search Results (535)

The proliferation of Internet of Medical Things (IoMT) devices connected to the internet poses significant challenges to data integrity, confidentiality, and patient safety due to their vulnerability to outside exploitation. Specifically, IoMT devices capture and process vast amounts of sensitive patient data but often lack adequate security mechanisms, making them susceptible to attacks that compromise data integrity—such as the injection of false or fabricated information—which imposes significant risks on the patient. To address this, we introduce a novel hybrid anomaly detection model combining a Graph Convolutional Network (GCN) with a transformer architecture. The GCN captures the structural relationships within the IoMT data, while the transformer models the sequential dependencies in the anomalies. We evaluate our approach using the novel CICIOMT24 dataset, the first of its kind to emulate real-world IoMT network traffic from over 40 devices and 18 distinct cyberattacks. Compared against several machine learning baselines (including Logistic Regress, Random Forest, and Adaptive Boosting), the hybrid model effectively captures attacks and provides early detection capabilities. This work demonstrates a scalable and robust solution to enhance the safety and security of both IoMT devices and critical patient data. Full article

Breast tissue-mimicking phantoms are essential tools for validating microwave imaging systems designed for early breast cancer detection. In this work, we report the fabrication and comprehensive characterization of oil-in-gelatin phantoms emulating normal, benign, and malignant breast tissues. The phantoms were manufactured with controlled mixtures of kerosene, safflower oil, and gelatin, and their dielectric properties were experimentally evaluated using a free-space transmission method with a Vector Network Analyzer across the 100 MHz–10 GHz range. Results demonstrated significant contrast in permittivity and conductivity among the different tissue types, consistent with values reported in the literature. Long-term stability was confirmed for up to six months under controlled storage. Additional structural and thermal characterization was performed using Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA), providing insight into molecular composition and thermal response. The proposed method enables reproducible, low-cost, and stable phantom fabrication, offering reliable tissue models to support experimental validation and optimization of microwave-based breast cancer detection systems. Full article

Optimization algorithms play a crucial role in solving complex problems in reservoir geology and engineering, particularly those involving highly non-linear, multi-parameter, and high-dimensional systems. In the context of reservoir development, accurate optimization is essential for enhancing hydrocarbon recovery, improving production efficiency, and managing subsurface uncertainties. The Moss Growth Optimization (MGO) algorithm emulates the adaptive growth and reproductive strategies of moss. It provides a robust bio-inspired framework for global optimization. However, MGO often suffers from slow convergence and difficulty in escaping local optima in highly multimodal landscapes. To address these limitations, this paper proposes a novel algorithm called Strategic Moss Growth Optimization (SMGO). SMGO integrates two enhancements: an Outpost Mechanism (OM) and an Early Stopping Strategy (ESS). The OM improves exploitation by guiding individuals through multi-stage local search with Gaussian-distributed exploration around promising regions. This helps refine the search and prevents stagnation in sub-optimal areas. In parallel, the ESS periodically reinitializes the population using a run-and-reset procedure. This diversification allows the algorithm to escape local minima and maintain population diversity. Together, these strategies enable SMGO to accelerate convergence while ensuring solution quality. Its performance is rigorously evaluated on a suite of global optimization benchmarks and compared with state-of-the-art metaheuristics. The results show that SMGO achieves superior or highly competitive outcomes, with clear improvements in accuracy and stability. To demonstrate real-world applicability, SMGO is applied to production optimization in tight reservoirs. The algorithm identifies superior production strategies, leading to significant improvements in projected economic returns. This successful application highlights the robustness and practical value of SMGO. It offers a powerful and reliable optimization tool for complex engineering problems, particularly in strategic resource management for tight reservoir development. Full article

Saline–alkaline soils are becoming prevalent across the globe, decreasing the availability of forage for animals and threatening sustainable animal production. This study evaluated the effects of a NaCl-supplemented alfalfa-based total mixed ration, simulating saline–alkaline soil conditions, on intake, the utilization of nutrients, antioxidant levels, and rumen fermentation. A 60-day feeding trial with 24 AOHU lambs (Australian White × Hu) compared a control diet (0.43% NaCl) with the NaCl-supplemented group (1.71% NaCl). Digestibility trials were conducted in metabolic cages for the collection of total feces and urine. Blood samples were taken at 0, 30, and 60 days for serum analysis, and slaughter samples (liver, kidney, rumen tissue, and rumen fluid) were taken for physiological, biochemical, and histological evaluation. The NaCl alfalfa-based TMR markedly increased liver and kidney weights. The rumen muscle layer thickened in the NaCl group. The ruminal ammonia nitrogen (NH₃-N), ruminal microbial crude protein (MCP) synthesis, and glucogenic/branched-chain VFAs increased, indicating enhanced proteolysis, microbial protein synthesis, and energetically efficient fermentation. Serum total protein and albumin also rose over time in the NaCl group, reflecting increased nitrogen retention, while superoxide dismutase and glutathione peroxidase activity rose considerably by day 60, reflecting increased antioxidant defense. Furthermore, nitrogen intake, digestibility, and retention were improved in the NaCl group along with augmented digestible and metabolizable energy (28.47 vs. 13.93 MJ/d and 24.68 vs. 11.58 MJ/d, respectively) and gross energy digestibility (78.13% vs. 67.10%). Although NaCl-based alfalfa TMR cannot fully emulate naturally salt-stressed forages, these results indicate that the NaCl alfalfa-based diets improved rumen fermentation, energy yields, and antioxidant enzyme activity without impairing electrolyte balance. These findings suggest that NaCl-supplemented alfalfa-based TMRs, with a salt content comparable to that of alfalfa hay grown under saline–alkaline conditions, could support environmentally sustainable meat production in salt-stressed regions. Full article

The accurate localization of radio frequency (RF) emitters plays a critical role in spectrum monitoring, public safety, and defense applications, particularly in environments where global navigation satellite systems are limited. This study investigates the feasibility of a single unmanned aerial vehicle (UAV) equipped with a Doppler-based software-defined radio sensor to localize modern RF sources without the need for external infrastructure or multiple UAVs. A custom-designed localization system was developed and tested using the L3Harris AN/PRC-152A tactical radio, which represents a class of real-world, dual-use emitters with lower frequency stability than laboratory signal generators. The approach was validated through both emulation studies and extensive field experiments under realistic conditions. The results show that the proposed system can localize RF emitters with an average error below 50 m in 80% of cases even when the transmitter is more than 600 m away. Performance was evaluated across different carrier frequencies and acquisition times, demonstrating the influence of signal parameters on localization accuracy. These findings confirm the practical applicability of Doppler-based single-UAV localization methods and provide a foundation for further development of lightweight, autonomous RF emitter tracking systems for critical infrastructure protection, spectrum analysis, and tactical operations. Full article

Associative learning is a fundamental neural mechanism in human memory and cognition. It has attracted considerable attention in neuromorphic system design owing to its multimodal integration, fault tolerance, and energy efficiency. However, prior studies mostly focused on single inputs, with limited attention to multibit pairs or recall under non-orthogonal input patterns. To address these issues, this study proposes a bidirectional associative learning system using paired multibit inputs. It employs a synapse–neuron structure based on spiking neural networks (SNNs) that emulate biological learning, with simple circuits supporting synaptic operations and pattern evaluation. Importantly, the update and read functions were designed by drawing inspiration from the operational characteristics of emerging synaptic devices, thereby ensuring future compatibility with device-level implementations. The proposed system was verified through Cadence-based simulations using CMOS neurons and Verilog-A synapses. The results show that all patterns are reliably recalled under intact synaptic conditions, and most patterns are still robustly recalled under biologically plausible conditions such as partial synapse loss or noisy initial synaptic weight states. Moreover, by avoiding massive data converters and relying only on basic digital gates, the proposed design achieves associative learning with a simple structure. This provides an advantage for future extension to large-scale arrays. Full article

Virtual networks have emerged as a promising solution for enabling diverse users to efficiently share bandwidth resources over optical network infrastructures. Despite the invention of various schemes aimed at ensuring secure isolation among virtual networks, the security of data transfer in virtual networks remains a challenging problem. To address this challenge, the concept of evolving traditional optical networks into key programmable optical networks (KPONs) has been proposed. Inspired by this, this paper delves into the establishment of secure virtual networks over KPONs, in which the information-theoretically secure keys can be supplied for ensuring the information-theoretic security of data transfer within virtual networks. A layered architecture for secure virtual network provisioning over KPONs is proposed, which leverages software-defined networking to realize the programmable control of optical-layer resources. With this architecture, a heuristic algorithm, i.e., the key adaptation-based secure virtual network provisioning (KA-SVNP) algorithm, is designed to dynamically allocate key resources based on the adaption between the key supply and key demand. To evaluate the proposed solutions, an emulation testbed is established, achieving millisecond latencies for secure virtual network establishment and deletion. Moreover, numerical simulations indicate that the designed KA-SVNP algorithm performs superior to the benchmark algorithm in terms of the success probability of secure virtual network requests. Full article

We address surrogate-assisted multi-objective optimization for computationally expensive structural designs. The testbed is an axisymmetric laminated composite shell whose geometry, ply angles, and plywise materials are optimized to simultaneously (i) maximize separation of selected natural frequencies from a known excitation and (ii) minimize material cost. To reduce high-fidelity (HF) finite element evaluations, we develop a deep neural network surrogate framework with three variants: an HF-only baseline; a multi-fidelity (MF) pipeline using an auxiliary refinement network to convert abundant low-fidelity (LF) data into pseudo-HF labels for a single-fidelity evaluator; and a cascaded ensemble that emulates HF responses and then maps them to pseudo-experimental targets. During optimization, only surrogates are queried—no FEM calls—while final designs are verified by FEM. Pareto-front quality is quantified primarily by a normalized relative hypervolume indicator computed against an envelope approximation of the True Pareto Front, complemented where appropriate by standard indicators. A controlled training protocol and common validation regime isolate the effect of fidelity strategy from architectural choices. Results show that MF variants markedly reduce HF data requirements and improve Pareto-front quality over the HF-only baseline, offering a practical route to scalable, accurate design under strict computational budgets. Full article

The recent growing adversarial activity against critical systems, such as the power grid, has raised attention on the necessity of appropriate measures to manage the related risks. In this setting, our research focuses on developing tools for early detection of adversarial activities, taking into account the specificities of the energy sector. We developed a framework to design and deploy AI-based detection models, and since one cannot risk disrupting regular operation with on-site tests, we also included a testbed for evaluation and fine-tuning. In the test environment, adversarial activity that produces realistic artifacts can be injected and monitored, and evidence analyzed by the detection models. In this paper we concentrate on the emulation of attacks inside our framework: A tool called SecuriDN is used to define, through a graphical interface, the network in terms of devices, applications, and protection mechanisms. Using this information, SecuriDN produces sequences of attack steps (based on the MITRE ATT&CK project) that are interpreted and executed by software called Netsploit. A case study related to Distributed Energy Resources is presented in order to show the process stages, highlight the possibilities given by our framework, and discuss possible limitations and future improvements. Full article

This paper presents two classic analog oscillators: a relaxation oscillator and a Wien bridge one, where a memristor replaces a resistor. The circuits are simulated in TopSPICE 7.12 using a memristor emulation circuit and commercially available components to evaluate the memristor’s impact. In the case of the relaxation oscillator, which includes the memristor, a notable increase in oscillation frequency was observed compared to the classical circuit, with a nearly 10-fold increase from 790 Hz to 7.78 kHz while maintaining a constant amplitude. This confirms the influence of the memristor’s dynamic resistance on the circuit time constant. On the other hand, the Wien-bridge oscillator exhibits variations in specific parameters, such as peak voltage, amplitude, and frequency. In this case, the oscillation frequency decreased from 405 Hz to 146 Hz with the addition of the memristor, a characteristic introduced by the proposed memristive element’s nonlinear interactions. Experimental results confirm the feasibility of incorporating memristors into classical oscillator circuits, enabling frequency changes while maintaining stable oscillations, allowing reconfigurable and adaptable analog designs that leverage the properties of memristive devices. Full article

Extreme ultraviolet (EUV) pellicles must withstand intense thermal stress during exposure due to their limited heat dissipation, which results from their ultrathin geometry and the vacuum environment within EUV scanners. To address this challenge, we investigated the crystalline phase-dependent emissivity of nanometer-thick molybdenum disilicide (MoSi₂) membranes. Membranes exhibiting amorphous, hexagonal, and tetragonal phases were independently prepared via controlled annealing, and their thermal radiation properties were evaluated using heat-load testing under emulated EUV scanner conditions. The Hall effect measurements revealed distinct variations in carrier density and mobility across phases, which were theoretically correlated with emissivity using the Lorentz–Drude model. The results demonstrate that emissivity increases in the hexagonal phase due to increased carrier density and reduced scattering, offering improved thermal radiation performance. These findings establish the phase engineering of conductive silicides as a viable strategy for enhancing radiative cooling in EUV pellicles and offer a theoretical framework applicable to other high-temperature nanomaterials. Full article

The virtual synchronous generator (VSG) technique plays a crucial role in power systems with high penetration of power electronics, as it can provide virtual inertia and damping performance by emulating the swing characteristics of a synchronous generator (SG). However, the VSG faces challenges due to its inherent limitations, such as vulnerability to disturbances and instability in strong grid conditions. To address these issues, this article proposes an exchanged VSG (eVSG) control strategy. In this approach, the phase information (θ) is derived from reactive power (Q), while the voltage information (E) is derived from active power (P). Furthermore, a Magnitude-Phase Motion Equation (MPME) is introduced to analyze the eVSG system from a physical perspective. Additionally, this article is the first to illustrate the oscillation propagation effect between P and frequency (f) in both VSG and eVSG systems. Finally, the advantages of the eVSG strategy are comprehensively demonstrated through three aspects: (1) comparing the motion trajectory of f using the MPME model, (2) evaluating the oscillation propagation effect between VSG and eVSG systems, and (3) conducting simulations and experiments. Full article

This study evaluates the forecast performance of four radiation parameterization schemes—the Rapid Radiative Transfer Model for General Circulation Models (RRTMG), its improved version RRTMG-K, the infrequently applied variant, RRTMG-K_60x, and the neural network emulator, RRTMG-K_NN, within a high-resolution numerical weather prediction (NWP) model. The evaluation uses satellite-derived observations of Outgoing Longwave Radiation (OLR) and Outgoing Shortwave Radiation (OSR) from the Clouds and the Earth’s Radiant Energy System (CERES) over the Korean Peninsula during 2020, including an extreme case study of Typhoon Haishen. Results show that RRTMG-K reduces RMSEs by 4.8% for OLR and 17.5% for OSR relative to RRTMG, primarily due to substantial bias reduction (42.3% for OLR, 60.4% for OSR). The RRTMG-K_NN scheme achieves approximately 60-fold computational speedup while maintaining similar or slightly better accuracy than RRTMG-K; specifically, it reduces OLR errors by 1.2% and OSR errors by 1.6% compared to the infrequently applied RRTMG-K_60x. In contrast, the infrequent application of RRTMG-K (RRTMG-K_60x) slightly increases errors, underscoring the trade-off between computational efficiency and accuracy. These findings demonstrate the value of integrating advanced satellite flux observations and machine learning techniques into the evaluation and optimization of radiation schemes, providing a robust framework for improving cloud–radiation interaction representation in NWP models. Full article

The increasing deployment of internet of things(IoT) systems across critical domains has broadened the threat landscape, and being the catalyst for a variety of security concerns, including very stealthy slow denial of service (slow DoS) attacks. These exploit the hypertext transfer protocol’s (HTTP) application-layer protocol to either close down service requests or degrade responsiveness while closely mimicking legitimate traffic. Current available datasets fail to capture the more stealthy operational profiles of slow DoS attacks or account for the presence of genuine slow nodes (SN), which are devices experiencing high latency. These can significantly degrade detection accuracy since slow DoS attacks closely emulate SN. This paper addresses these problems by synthesising a realistic HTTP slow DoS dataset derived from a live IoT network, that incorporates both stealth-tuned slow DoS traffic and legitimate SN traffic, with the three main slow DoS variants of slow GET, slow Read, and slow POST being critically evaluated under these network conditions. A limited packet capture (LPC) strategy is adopted which focuses on just two metadata attributes, namely packet length ($lp$) and packet inter-arrival time ($\mathrm{\Delta }t$). Using a resource lightweight decision tree classifier, the proposed model achieves over 96% accuracy while incurring minimal computational overheads. Experimental results in a live IoT network reveal the negative classification impact of including SN traffic, thereby underscoring the importance of modelling stealthy attacks and SN latency in any slow DoS detection framework. Finally, a MPerf (Modelling Performance) is presented which quantifies and balances detection accuracy against processing costs to facilitate scalable deployment of low-cost detection models in resource-constrained IoT networks. This represents a practical solution to improving IoT resilience against stealthy slow DoS attacks whilst pragmatically balancing the resource-constraints of IoT nodes. By analysing the impact of SN on detection performance, a robust reliable model has been developed which can both measure and fine tune the accuracy-efficiency nexus. Full article

Gas-phase chemistry has been identified as a major computational bottleneck in both the forward and adjoint modes of chemical transport models (CTMs). Although previous studies have demonstrated the potential of deep learning models to simulate and accelerate this process, few studies have examined the applicability and performance of these models in adjoint sensitivity analysis. In this study, a deep learning emulator for gas-phase chemistry is developed and trained on a diverse set of forward-mode simulations from the Community Multiscale Air Quality (CMAQ) model. The emulator employs a residual neural network (ResNet) architecture referred to as FiLM-ResNet, which integrates Feature-wise Linear Modulation (FiLM) layers to explicitly account for photochemical and non-photochemical conditions. Validation within a single timestep indicates that the emulator accurately predicts concentration changes for 74% of gas-phase species with coefficient of determination (R²) exceeding 0.999. After embedding the emulator into the CTM, multi-timestep simulation over one week shows close agreement with the numerical model. For the adjoint mode, we compute the sensitivities of ozone (O₃) with respect to O₃, nitric oxide (NO), nitrogen dioxide (NO₂), hydroxyl radical (OH) and isoprene (ISOP) using automatic differentiation, with the emulator-based adjoint results achieving a maximum R² of 0.995 in single timestep evaluations compared to the numerical adjoint sensitivities. A 24 h adjoint simulation reveals that the emulator maintains spatially consistent adjoint sensitivity distributions compared to the numerical model across most grid cells. In terms of computational efficiency, the emulator achieves speed-ups of 80×–130× in the forward mode and 45×–102× in the adjoint mode, depending on whether inference is executed on Central Processing Unit (CPU) or Graphics Processing Unit (GPU). These findings demonstrate that, once the emulator is accurately trained to reproduce forward-mode gas-phase chemistry, it can be effectively applied in adjoint sensitivity analysis. This approach offers a promising alternative approach to numerical adjoint frameworks in CTMs. Full article