Search Results (253)

Securing a freshwater supply for personnel engaged in remote maritime operations remains a persistent logistical challenge. This paper presents the design, hydraulic simulation, and experimental validation of a compact, manually operated seawater desalination device based on reverse osmosis (RO) coupled with an integrated hydraulic energy recovery system. The device employs a valve-commutated piston pump (cylinder bore 6 mm, rod diameter 3.7 mm, stroke 70 mm) driven by a lever-type handle mechanism. High-pressure brine rejected by the RO membrane is redirected via a two-position, three-way directional valve into the rod-end cavity of the pump cylinder, partially offsetting the filtration resistance and achieving an energy recovery ratio of 49.5%. Hydraulic circuit dynamics were analyzed using AMESim software, yielding a simulated freshwater output of approximately 0.02 L/min (1.2 L/h). A functional prototype with overall dimensions of 200 × 128 × 63 mm was fabricated and tested under 3.57% salinity conditions. Five consecutive trials produced a mean freshwater flow rate of approximately 1.15 L/h (desalination rate exceeding 95%), confirming consistency with the simulation predictions and satisfying the design requirements for individual field use in remote maritime settings. Full article

Understanding the spatiotemporal dynamics of land-use and cover change (LUCC) and ecosystem service (ES) responses is essential for assessing ecological functions in regional landscapes. However, conventional LUCC simulations often rely on historical trends and inadequately represent the spatially heterogeneous effects of top-down policy constraints. Taking the Henan section of the Yellow River Basin (HYRB) as a case study, we developed a policy-to-rule framework that translated ecological redlines, urban development boundaries, and restoration requirements into explicit spatial constraints and land-use transition rules in the PLUS model. A policy-constrained High-Quality Development Scenario (HQDS) was established, with the Natural Growth Scenario (NGS) as a reference. Five ESs were assessed using InVEST from 1985 to 2050, and the results were integrated with the Minimum Cumulative Resistance (MCR) model and circuit theory to construct an ecological security pattern (ESP). Historical reconstruction of the 2022 land-use pattern achieved an overall accuracy of 90.18% and a Kappa coefficient of 86.39%. The five ESs remained relatively stable overall: water yield, soil conservation, and the sediment-related indicator increased, whereas habitat quality and carbon storage declined slightly. Ecological source areas expanded from 7140.54 km² in 1985 to 12,039.17 km² under the HQDS in 2050, a 68.6% increase. Compared with the NGS, the HQDS increased source areas by 562.42 km² (4.9%), reduced ecological corridors from 26 to 24, and increased their total length from 1068.89 to 1099.61 km. These differences represent the projected, scenario-conditioned consequences of the specified policy constraints and provide quantitative decision support for future ecological management. Full article

The transition to a globalized, fabless semiconductor manufacturing model has integrated third-party foundries and external intellectual property (IP) vendors into the integrated circuit (IC) design cycle. While this collaborative system promotes innovation, it also exposes layouts to security threats. Protecting these designs is paramount; however, applying traditional encryption methodologies fundamentally alters the syntactic hierarchy of the industry-standard GDSII stream format, causing electronic design automation (EDA) tools to crash. Furthermore, a full encryption hinders modern system-on-chip (SoC) development, where different teams require access to specific modules of the design, without exposing the entire IP. To resolve this issue between collaborative layout sharing and zero-trust security, this paper presents a software implementing an encryption engine. By applying the NIST-standardized FF1 Format-Preserving Encryption (FPE) algorithm directly to the geometric data, the proposed software obfuscates sensitive spatial coordinates and structural nomenclature while maintaining the native GDSII format. The engine embeds multi-tiered cryptographic access control directly into the layout, utilizing native metadata properties. This framework allows proprietary logic to be securely compartmentalized, ensuring that interacting parties only view the specific structures they possess the clearance to decrypt. Full article

Embedded Subscriber Identity Modules (eSIMs) enable consumer devices to install mobile subscriptions remotely under the GSMA SGP.22 standard for Remote SIM Provisioning (RSP). Because RSP sessions rely on classical elliptic-curve cryptography and eSIM profiles can remain active for 5 to 20 years, recorded provisioning traffic faces a concrete Harvest-Now–Decrypt-Later (HNDL) threat. Upgrading the network transport to post-quantum Transport Layer Security (TLS) is often assumed to be sufficient. However, SGP.22 exchanges the keys that protect the profile across the local host-to-chip interface, beneath the transport layer. This paper presents a systematic post-quantum cryptography (PQC) migration framework for consumer RSP. We model four configurations of the SGP.22 on-card key-agreement step and determine, under a quantum key-recovery adversary, which configurations resist HNDL and what resources they require. We combine symbolic verification in ProVerif with a device-grounded evaluation that pairs provisioning and memory observations from a sysmocom C2T research embedded Universal Integrated Circuit Card (eUICC) with strict-instruction-set PQC measurements on an STM32 Nucleo-F446RE development board with an ARM Cortex-M4F core. Among the configurations studied, hybrid classical and post-quantum key exchange is the minimum configuration that resists HNDL, whereas a fully post-quantum configuration also protects authentication against signature forgery. Under the tested platform and resource assumptions, volatile Random Access Memory (RAM), rather than computation, is the binding deployment constraint. We therefore propose a capability-negotiation mechanism that would match a migration configuration to the memory advertised by each card. Full article

Hardware Trojans (HTs) threaten integrated-circuit security in a globalized semiconductor supply chain, yet how the choice of hardware description language—manual Verilog versus compiler-optimized agile languages such as Chisel—affects the synthesis quality of a Trojan-bearing design remains unexplored. This work re-implements AES-T100, the Trust-Hub leakage benchmark that exfiltrates an AES-128 key via an LFSR-driven side channel, faithfully in Chisel. It then compares the resulting netlist against a hand-written Verilog baseline under matched FPGA synthesis and examines dual-use implications. AES-T100 was rebuilt in Chisel 3.5.0 (20-bit LFSR PRNG, 1-to-8-bit key-obfuscation unit, $8\times$ flip-flop leakage circuit), compiled through FIRRTL 1.5.0, and synthesized on a Cyclone IV E FPGA via Quartus Prime 21.1. The FIRRTL-generated netlist achieved 23.8% higher $f_{\max }$ (387.60 vs. 313.19 MHz) and throughput (4.96 vs. 4.01 Gbps) at identical logic-element count (2096 LEs), with only 1.50% Trojan overhead. The gap localizes to the host AES cipher, consistent with FIRRTL optimization passes. A five-seed replication confirms statistical robustness ($t\left(8\right)=11.7$, $p<0.001$, 95% CI: 18.6–27.7%). Causal attribution—compilation flow versus single-author Verilog coding—remains open, requiring pass-ablation experiments. The parametric template supports HT benchmark generation. FIRRTL-level static analysis is proposed as a defensive direction. Full article

Field-Programmable Gate Array System-on-Chip (FPGA-SoC) platforms are increasingly adopted in modern industrial Cyber-Physical Systems (CPSs), enabling real-time control, monitoring, and automation of critical industrial processes. The increasing integration density of modern FPGA-SoC architectures introduces new thermal security challenges, where heat evolves from a reliability concern into a potential source of information leakage. Thermal Side-Channel Attacks (TSCAs) exploit runtime thermal variations to infer sensitive operational, architectural, or cryptographic information from the underlying hardware. While this study is centered on FPGA-SoC platforms, comparable thermal security challenges are increasingly reported across other densely integrated computing architectures, including Multiprocessor System-on-Chip (MPSoC), System-in-Package (SiP), and emerging Three-Dimensional Integrated Circuit (3D-IC) technologies. Consequently, the detection of thermal side-channel intrusions has become a critical hardware security challenge for next generation industrial CPS infrastructures. To address this challenge, an AI-enabled Digital Twin (DT) framework is introduced for TSCA detection in densely integrated FPGA-SoC microarchitectures. By combining thermal behavioral modeling, feature engineering, and machine learning-based anomaly detection, the proposed framework extends conventional Thermal Digital Twin (TDT) approaches beyond monitoring and mitigation toward autonomous thermal threat detection. The proposed framework is experimentally validated using an NI myRIO-1900 platform integrating a Xilinx Zynq-7010 FPGA-SoC representative of modern industrial embedded control architectures. Experimental results demonstrate the feasibility of the proposed framework, achieving an accuracy of approximately 75% with an Area Under the ROC Curve (AUC) of 0.76 using a lightweight Isolation Forest model. These results validate the capability of the proposed AI-enabled Digital Twin framework to learn normal thermal behavioral patterns and autonomously detect anomalous thermal activities potentially related to TSCAs. Full article

To address the limitations of existing chaotic systems such as complex structure and potential chaotic degradation, this paper proposes a novel four-dimensional discrete chaotic system (4D-DCS) and an image encryption algorithm based on it. The 4D-DCS is constructed by integrating a feedback controller and modulo operation into a linear discrete-time system, featuring a simple structure without the need for intricate matrix reconstruction or memristor circuits. Mathematical analysis confirms its chaos in the sense of Li–Yorke and numerical simulations including Lyapunov exponent (LE) analysis, 0–1 test, and NIST SP 800-22 test demonstrate its hyperchaotic characteristics and excellent pseudorandomness. Based on the 4D-DCS, the proposed encryption algorithm employs SHA-256 to generate initial states for key uniqueness, combines row–column permutation to disrupt pixel correlation, and adopts a reversible neural network for diffusion to enhance confusion capability. Comprehensive security analysis shows that the algorithm achieves an NPCR of ∼99.61% and a UACI of ∼33.46%, a key space of $2^{216}$, information entropy close to 8, and correlation coefficients of encrypted images near 0. It also exhibits strong robustness against differential, cropping, noise, and chosen-plaintext attacks. Comparative analysis with state-of-the-art algorithms validates the 4D-DCS’s advantages in structural simplicity and stability, and the encryption algorithm’s superiority in security and practicality, making it suitable for security-critical applications such as image encryption. Full article

Network intrusion detection systems (IDS) operate in high-dimensional feature spaces under evolving attack patterns and asymmetric misclassification costs, where false negatives represent a critical security risk. Reinforcement learning (RL) offers a natural mechanism for encoding domain-specific misclassification costs directly into the learning signal through reward shaping, enabling cost-sensitive policy optimization in adaptive streaming environments. However, the integration of variational quantum models into RL-based IDS remains insufficiently explored. This work investigates a variational quantum reinforcement learning (VQRL) framework for intrusion detection, in which parameterized quantum circuits are employed to model the policy function. We adopt an RL formulation primarily as a principled cost-sensitive optimization approach rather than to exploit sequential state dependencies, and we employ Instantaneous Quantum Polynomial (IQP) embedding as a quantum feature encoding strategy. The study analyzes how embedding expressivity interacts with varying levels of dimensionality reduction via principal component analysis (PCA) on the CICIDS2017 dataset. Experiments demonstrate that VQRL-IQP achieves high recall and reduces false negative rates in moderately high-dimensional feature spaces compared to a classical RL baseline. This improvement is accompanied by an increase in false positive rates, reflecting a trade-off shaped jointly by the reward structure and the structural properties of IQP encoding. Statistical validation across five independent runs confirms the consistency of these trends. Importantly, no general quantum advantage in accuracy or computational efficiency is claimed; rather, the results indicate that VQRL-IQP offers a distinct error trade-off that is operationally valuable in security-critical scenarios where minimizing missed attacks is the primary objective. Full article

The increasing penetration of distributed generation (DG) in distribution systems poses significant operational challenges, including increased power losses, voltage profile deviations, and variations in short-circuit currents. These issues may compromise network safety, reliability, and the selectivity of protection schemes under different operating scenarios. This paper proposes a hybrid optimization methodology for the optimal placement and sizing of DG, aiming to minimize active power losses while ensuring voltage regulation and keeping short-circuit currents within permissible limits. An integrated approach is proposed that combines a mesh-to-radial network reconfiguration strategy with a modified Ant Lion Optimization algorithm, known as ALO-DG, enabling the simultaneous optimization of network topology and the allocation of distributed generators at candidate buses. The problem is formulated taking into account power balance constraints, voltage limits, distribution network capacity limits, and short-circuit current limits. The proposed methodology achieved substantial reductions in active power losses in the IEEE 33-bus and 69-bus test systems, reaching 84.42% and 91.56%, respectively. These improvements were accompanied by enhanced voltage profiles while preserving the radial operating structure of the distribution networks. Furthermore, the proposed hybrid methodology serves as a tool for the planning and operation of distribution systems with high DG penetration, particularly in scenarios where grid security and protection coordination are critical considerations. Full article

The rapid transition toward a converter-dominated power system, driven by high penetration of inverter-based resources (IBRs), the explosive growth of artificial intelligence (AI) technologies, and large power electronic loads, is fundamentally altering grid dynamics and exposing critical limitations in conventional stability, protection, and planning frameworks. Traditional metrics, such as the short-circuit ratio (SCR), have been shown to be insufficient for capturing impedance interactions, control coupling, and multi-timescale dynamics in such systems. This paper develops a unified, control-aware, and impedance-based modeling framework that accurately represents both grid-following and grid-forming behaviors. It highlights the increasingly active role of large-scale loads as grid-interactive resources with significant impacts on frequency and voltage stability, particularly in weak grids. In addition, battery energy storage systems (BESSs) are identified as a key enabler for providing fast dynamic support and mitigating variability across multiple timescales. A hierarchical assessment methodology combining system-strength screening, impedance-based stability analysis, Nyquist evaluation, and EMT-oriented validation is proposed to bridge conventional planning studies and converter-dominated system assessment. Key findings demonstrate that the reliable operation of future grids requires moving beyond steady-state and phasor-domain assumptions toward EMT-based validation, adaptive protection schemes, and coordinated grid-forming control strategies. The study further emphasizes the need for harmonized, performance-based grid codes to ensure the consistent integration of both generation and large loads. Overall, this work provides a comprehensive framework for the modeling, analysis, and control of inverter-dominated power systems, addressing critical gaps in current methodologies and supporting the secure evolution of modern power grids. Full article

Memtransistors, as multi-terminal devices with gate-tunable memristive behavior, offer new opportunities for nonlinear circuit design beyond conventional two-terminal memristors. The paper proposes a novel four-dimensional chaotic oscillator by integrating a three-terminal memtransistor model with a memristor. The mathematical models of both devices are established, and their equivalent circuits are presented. Based on the memtransistor model, a chaotic circuit is constructed, and its dynamical behavior is investigated by the Lyapunov exponent spectrum, bifurcation diagram, dynamical map, and other tools. It is found that the chaotic circuit has complex nonlinear characteristics and that the phenomenon of attractor coexistence exists. Furthermore, the chaotic system is discretized by the Euler approach, and experiments on an STM32-based circuit confirm the reliability of the theoretical analysis. This work provides a hardware-validated platform for studying memtransistor-based nonlinear circuits and may find applications in chaos-based secure communication and neuromorphic computing. Full article

This paper presents a comprehensive review of voltage stability challenges in South Africa’s constrained power grid, particularly in the context of rising hybrid renewable energy integration. With the growing deployment of inverter-based resources (IBRs) like solar PV, wind, and battery energy storage systems (BESS), especially under programmes through the Independent Power Procurement Office, voltage stability has emerged as a key concern, particularly in weak grid areas like the Northern Cape Province. We highlight how weak grids characterized by low short-circuit capacity, long transmission lines, and limited reactive power support are more susceptible to voltage instability, especially with high penetration of non-synchronous generation. Using a modified IEEE 14-bus system with hybrid generation, the study simulates a weak grid scenario. Findings point to significant reactive power losses and capacitive over-voltages in long and lightly loaded lines, mirroring some of the weak-grid-transmission challenges experiences in an area of the South African power grid. The study underscores the importance of dynamic load modelling (e.g., ZIP and exponential models) and inverter behaviour in stability analysis. It concludes that hybrid systems, when optimally designed and integrated with storage, can help support grid stability. However, proactive planning, advanced modelling, and compliance with evolving grid codes remain essential for securing reliable renewable integration. Full article

Rapid urbanization has intensified wetland fragmentation and ecological connectivity degradation, threatening the structural stability and functional sustainability of urban wetland ecosystems. Constructing resilient wetland ecological networks is therefore essential for maintaining regional ecological security and supporting sustainable urban development. Taking Wuhan as a case study, multi-temporal land-use data from 2004, 2014, and 2024, together with land-use transition matrices, were used to analyze urban expansion and wetland landscape transformation. Morphological Spatial Pattern Analysis (MSPA), the Minimum Cumulative Resistance (MCR) model, and circuit theory were integrated to identify ecological sources, construct ecological corridors, and evaluate the structural connectivity of the wetland ecological network. Ecological source importance was quantified using the Probability of Connectivity (PC) and dPC indices. In addition, robustness analysis based on the sequential removal of high-dPC ecological source patches was conducted to assess network stability under disturbance scenarios. The results identified 20 core ecological source areas and 45 ecological corridors, forming a relatively interconnected wetland ecological network centered around major lake clusters and key ecological hubs. High-current corridors and pinch points were mainly distributed in ecologically sensitive transition zones and urban expansion boundaries. Robustness analysis showed that sequential removal of high-dPC ecological hubs resulted in continuous declines in EC(PC) and corridor number, while corridor length increased substantially. Although overall connectivity was maintained through alternative ecological pathways, ecological movement efficiency decreased significantly under disturbance scenarios, indicating increasing dispersal costs and reduced structural stability. These findings suggest that the wetland ecological network possesses moderate structural connectivity through pathway redundancy but remains highly dependent on several dominant ecological hubs. This study extends traditional static connectivity assessment by incorporating robustness and disturbance response analysis into wetland ecological network evaluation. The proposed framework provides scientific support for resilient wetland conservation, ecological restoration, and sustainable spatial planning in rapidly urbanizing metropolitan regions. Full article

As hardware Trojans (HTs) are becoming increasingly stealthy in global semiconductor supply chains, the need for both robust and explainable detection methods is pressing. The use of deep learning models (e.g., Siamese networks, Transformer models) in side-channel signals has shown promising detection accuracy. Yet, they are black-box, data-intensive, and do not expose the causal, structural, and temporal relationships that indicate the presence of HTs. In this paper, we present a causality-focused and explainable detection framework that goes beyond pattern matching. We develop a Self-Supervised Spatio-Temporal Graph Neural Network (SST-GNN) that embeds spatio-temporal side-channel information. Our approach builds a graph that models gate-level components as nodes with temporal power and electromagnetic (EM) features, and functional and physical connections as edges. To address label scarcity, a common problem in real-world applications, we leverage a self-supervised pretraining approach. In particular, a context-aware contrastive loss allows the model to differentiate valid augmentations of benign subgraphs and their side-channel signatures, thus capturing general representations of benign components without Trojan labels. This involves a Causality-Aware GNN (CA-GNN) layer, which embeds differentiable causal discovery into graph learning. This process decouples correlation from causation, identifying the pathways potentially affected by HT trigger and payload. To explain decision making, a gradient-based graph explainer localizes minimal decisive subcircuits and pivotal time windows, generating intuitive detection reports. We evaluated our method on the IEEE Hardware Trojan Side-Channel Dataset (with netlist data), achieving state-of-the-art results (F1 > 0.98). In particular, the model achieves over 60% improvement in Trojan localization precision and false-positive rate, compared to Transformer-based approaches, with high label efficiency and adversarial robustness. Full article

Ensuring global food security under accelerating climate change requires transformative approaches to crop improvement that extend beyond the limits of traditional breeding and gene editing. While domestication and modern agriculture have delivered substantial gains in productivity, these advances often came at the cost of genetic diversity, stress resilience, and developmental plasticity. Plants, however, inherently exhibit remarkable flexibility in their morphology and development, as evidenced by the vast diversity of organ shapes, cell types, and adaptive responses that have evolved across lineages. This natural design space provides a foundation for reimagining plant architecture using synthetic biology. Recent advances in plant synthetic biology, including programmable transcription factors, CRISPR-based regulatory systems, synthetic gene circuits, orthogonal signalling pathways, and plant artificial chromosomes, now enable precise, modular, and environmentally responsive manipulation of developmental processes. These tools allow researchers to rewire hormone pathways, tune quantitative gene expression, integrate multiple environmental signals, and create novel regulatory modules that operate independently of endogenous networks. Beyond understanding plant development, these capabilities open avenues for engineering crops with dynamic architectures, enhanced plasticity, and improved resilience to complex and fluctuating stresses. In this review, we synthesise insights from natural diversity, developmental biology, and synthetic regulatory engineering to outline how plant architecture can be rationally redesigned. We argue that integrating synthetic biology with modern breeding and modelling frameworks will be essential for generating the next generation of programmable crops; i.e., varieties capable of sustaining productivity and stability in an era of unprecedented environmental and geopolitical changes. Full article