This paper proposes two new AOI-based approximate encoders for data bus inversion (DBI) in on-chip data buses. Traditional accurate DBI encoders suffer from high hardware complexity, making them impractical for on-chip DBI applications. A previously proposed approximate DBI encoder, the AOI-AOI approximate majority voter (CONV-AOI), reduces hardware complexity but has low accuracy in majority condition decisions, limiting its effectiveness in minimizing switching activity. To address these limitations, we propose novel majority approximators that improve majority decision accuracy while maintaining a low-cost implementation, making them more suitable for on-chip DBI encoder designs. The conventional and proposed encoders were synthesized using Synopsys Design Compiler with a 28 nm CMOS technology under identical conditions. Synthesis results show that the proposed designs reduce power consumption by up to 63.44% compared to accurate majority voters. Additionally, our proposed approximate encoders reduce incorrect detections by up to 28.83% compared with CONV-AOI.

The Load Frequency Control (LFC) challenge in multi-source power systems increasingly complicates with the integration of renewable energy, owing to frequent nonlinearities, uncertainties, and disturbances. Conventional controllers such as proportional-integral-derivative (PID) and adaptive PI–1PD frequently exhibit constraints in terms of stability and convergence speed. This study proposes an optimal fuzzy logic two-degree-of-freedom PID controller (Optimal–FL–2DOF–PID) tuned via a metaheuristic chess optimizer (CO), which is a novel optimization algorithm introduced in this study. The performance was evaluated in MATLAB/Simulink under two scenarios: (i) a 5% Step Load Perturbation (SLP) and (ii) a Random Step Load Pattern (RSLP) increment-decrement that effectively mirrors real-world applications. The simulation results demonstrate that the Optimal–FL–2DOF–PID controller surpasses all the comparable controllers. In Scenario 1, the proposed controller attains the lowest objective function—Integral of Timeweighted Absolute Error (ITAE) of 4.6692 corresponding to reductions of 25.31%, 43.05%, and 41.92% relative to 2DOF–PID, PID, and PI–1PD, respectively (and 1.92% relative to the None–optimal–FL–2DOF– PID), overshoot (OS), undershoot (US), settling time (ST) 13–15 s, and tie-line power deviations are likewise reduced. In Scenario 2, using the corresponding optimum parameters from Scenario 1, the Optimal–FL– 2DOF–PID controller achieves ITAE = 395.72, which is lower than the None–optimal–FL–2DOF–PID controller design by 46.52%, and lower than 2DOF–PID, PID, and PI–1PD by 29.73%, 22.19%, and 9.68%, respectively. The frequency–channel ST is 150–180 s, whereas tie-line exchanges settled in the mid–150 s with visibly smoother profiles. These results indicate that Optimal–FL–2DOF–PID provides robust and efficient frequency regulation for multi-area systems under significant uncertainty, supporting deployment in future renewable energy-integrated power systems.

In this article, the design and production process of a multipurpose polarization converter with a single-layer feature has been carried out using a low-profile, ultra-thin, cost-effective metasurface for different frequency band applications such as C-band (5.8–8.2 GHz), X-band (8.2–12.4 GHz), Ku-band (12.4–18.0 GHz). The polarization converter (PC) has two different features: Linear polarization (LP) and circular polarization (CP). PC has LP characteristics in the frequency ranges 6.768–7.184 GHz, 10.432–11.856 GHz, 16.896–17.520 GHz. These frequencies (6.768–7.184, 10.432–11.856, 16.896–17.520) also exhibit CP–CP functionality. In addition, it has left-handed circular polarization (LHCP) in the 8.208–8.960 GHz range and right-handed circular polarization (RHCP) in the 13.344–15.888 GHz range. A Computer Simulation Technique (CST) Studio Suite was selected for the design process of the PC. The PC is made of FR4 material and a layer thickness of 1.6 mm. The CUBE 3D device was chosen to carry out the production process. The performance analysis of the proposed converter was implemented in the frequency range of 4–20 GHz.Considering the results, it was observed that the design and production process results of the PC were compatible with each other to the desired extent.

As cybersecurity threats become more sophisticated, the integration of Large Language Models (LLMs) into defensive and analytical systems is transforming the field. This paper presents a PRISMA-guided bibliometric and thematic review of 149 studies published between 2015 and 2025, including 117 peer-reviewed journal and conference articles, examining publication trends and dominant research themes in LLM-enabled cybersecurity, organized around five research questions: (i) secure incorporation of LLMs into cyber threat intelligence workflows; (ii) hybrid architectures for privacy-preserving and real-time threat detection; (iii) LLM-enabled secure code remediation; (iv) adversarial misuse and dual-use risks; and (v) multi-layer defense strategies addressing prompt injection, model inversion, and data poisoning. Drawing on over 100 primary studies, the analysis highlights key trends, methodological innovations, and recurring vulnerabilities. Notable developments include decentralized trust-enhanced frameworks, context-aware remediation systems, and simulation-based red teaming. However, gaps persist in adversarial robustness, standardization of evaluation, and ethical governance. By mapping research across technical, operational, and policy dimensions, this review provides a structured basis for advancing trustworthy, resilient, and secure LLMs deployments in high-stakes cybersecurity contexts.

Protecting lightweight networked devices against manipulation or cloning is an important aspect in critical infrastructure, especially when processing and transmitting sensitive data such as in industry, medical devices, or smart home systems. However, these devices often have a lightweight structure and limited resource capacity, which makes it difficult or impossible to implement complex security measures. Physical unclonable functions (PUFs) can be used as lightweight security primitives to generate unique signals for device identification within a network or for key generation without relying on memory components. However, internal and external factors can influence the behavior of PUFs, which can affect their performance and, consequently, their security. To reduce this problem, error-correction code (ECC) algorithms are used in addition to PUFs to correct bit errors. In this paper, we combine PUFs with standardized low-density parity-check (LDPC) codes to improve reliability. For this purpose, we present a statistical model based on our previously implemented double arbiter PUF (DAPUF) designs in order to simulate different conditions and test scenarios. Our experimental results show that under normal operating conditions, reliability can be improved to the ideal value of 100% using LDPC codes. However, under extreme conditions, by adding more instabilities and bit errors, reliability is compromised, making the PUF unsuitable for security applications. Our experiments show that in order to increase reliability, larger LDPC codes with low code rates must be used however, this increases the complexity, processing time, and resource requirements of the hardware.

In the face of growing challenges in the electrical sector, such as demand variability and climate change, understanding and forecasting electrical variables become critical for distribuition companies. This work presents a set of methodologies for calculating equivalent temperatures in large geographic areas, which can be used in forecasting models to understand the behavior of electrical variables such as demand and load, thus assisting energy distributors. For this purpose, six calculation methodologies were developed, with emphasis on the one based on linear regression. With this parameter, the electric company improves communication about consumption variations with stakeholders, in addition to avoiding the problem of curse of dimensionality in the development of consumption forecasting models. The case study, related to a distribution company in Brazil, involves forecasting both own load and total load. The study shows that using this temperature data can improve the forecast’s performance, whether using statistical or machine learning models. The best results indicate a MAPE of 2.0% for the Arima model and 2.4% for the Random Forest Regressor. Finally, it is essential to mention that the developed methodologies are applicable to other weather variables, such as precipitation, solar radiation, air humidity, and wind speed.

This study presents a VR-based teleoperation framework enhancing collaborative robot stability and manipulability via hand-tracking, adaptive control, and dual-modality haptic feedback. It addresses critical synchronization challenges (singularity avoidance, tracking responsiveness, and workspace constraints), which are especially problematic in first-person VR where kinematic limits are not directly perceivable. The framework employs Adaptive Damped Least Squares (A-DLS) to maintain manipulability near singular configurations, workspace impedance control to enforce boundary constraints, and vibrotactile feedback delivered through a haptic glove to convey both workspace limits (fingertip vibration) and path deviation information (wrist vibration) to operators. Key features include real-time hand-tracking, workspace calibration, and adaptive controls to ensure seamless coordination between virtual and real robot workspaces. Experimental validation through two complementary studies demonstrates the system’s effectiveness. Experiment 1 evaluated singularity management and workspace stability, showing that the A-DLS algorithm maintained manipulability above critical thresholds for 92% of operational time versus 78% without adaptive damping. Experiment 2 assessed trajectory tracking accuracy through a path-following task with 10 participants. Results demonstrate that haptic-enabled control achieves a 24.7% reduction in mean path-following error (from 10.03 mm to 7.55 mm, p = 0.001) compared to haptic-disabled conditions, indicating improvements in both accuracy and consistency. Although haptic guidance modestly increases task time due to higher precision focus, the resulting gains in accuracy and stability make this framework ideal for precision-critical tasks. By ensuring stability near workspace boundaries, the system effectively facilitates VR-based teleoperation for applications like painting, polishing, and contour-following.

Accurate calibration of instrumentation transmitters is critical for ensuring operational safety, process efficiency, and regulatory compliance in the oil and gas industry. Traditional calibration methods and existing machine learning approaches treat calibration as a purely data-driven regression problem, ignoring the underlying sensor physics and failing to adapt to evolving drift patterns. This paper proposes a novel Physics-Informed Adaptive Drift Compensation (PI-ADC) framework that integrates first-principles sensor models with data-driven machine learning for automated calibration of pressure, temperature, flow, and level transmitters. The PI-ADC framework incorporates three key innovations: (1) a physics-constrained loss function that enforces thermodynamic consistency, (2) an adaptive drift detection mechanism using statistical process control, and (3) a selective retraining strategy that updates models only when significant drift is detected. We conduct comprehensive experiments comparing nine machine learning algorithms—SVR, RF, XGBoost, KNN, ANN, LSTM, 1D-CNN, GPR, and LSTM-SVM hybrid—enhanced with the proposed PI-ADC framework using industrial datasets from 45 instrumentation transmitters across three petroleum facilities over 18 months. Results demonstrate that the PI-ADC-enhanced XGBoost achieves 52.3% RMSE reduction compared to traditional polynomial fitting (p < 0.001), with 34.7% improvement in long-term stability over standalone ML approaches. The framework reduces required retraining frequency by 67% while maintaining calibration accuracy within ±0.1% of full scale. A pilot deployment at an offshore platform validates the practical applicability of the proposed approach.

We present a single-city case study (Seaside, Oregon) that ranks cross-layer infrastructure nodes (transport, power, and water) by directly optimizing early service-loss AUC. Rather than use proxy centralities, we optimize the evaluation metric directly: the normalized area under the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i>-removal curve (AUC) on a fixed grid (α ∈ {0.10, 0.20, 0.30}). A two-stage, CPU-only pipeline plans a short prefix with Monte Carlo Tree Search (MCTS; shortlist <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i>, rollouts, iterations fixed for fairness) and completes the order with a lazy greedy (CELF) tail using exact bitset coverage. On Seaside (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i>=1176 infrastructure nodes; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</i>=4679 buildings), our method attains AUC ≈ 0.714 at α=0.20, outperforming Degree (0.231), PageRank (0.239), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i>-core (0.263), Collective Influence (0.223), approximate Betweenness (0.247; sampling budget), and an attachment-count heuristic (0.528). Robustness evidence—α-sensitivity (0.10/0.20/0.30), dependency-rule variants (single vs. multi-attachment; AND/OR), demand weights, attachment jitter, and missing buildings—shows that the observed advantages are not an artifact of a single modeling choice. We also report a fair-compute comparison using wall-clock time and method-specific budgets. For k ∈ {5, 10}, we certify small-<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> optima via exact MILP (SciPy milp) and report optimality gaps and solver statuses. Overall, this single-city case study suggests that directly optimizing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i>-removal AUC yields a simple, CPU-only, auditable ranking that aligns with building-level service loss; establishing external validity will require multi-city assessment.