Papers

Dual-polarized (DP) microstrip antennas are utilized in wireless systems for efficient data transmission. However, their bandwidth is typically very limited. In this contribution, we propose to design DP microstrip antennas with enhanced bandwidth using a density-based topology optimization approach. We formulate an optimization problem that simultaneously accounts for feeding port matching, the ports' isolation, and far-field dual-polarized performance. To enhance the bandwidth, we employ an FR4 stack-up, in which the copper on two layers is optimized simultaneously. We present two antenna designs operating around 5.7 GHz, which show a compromise in performance between a high isolation (more than 40 dB) and enhanced impedance bandwidth (around 10%). The optimized designs are experimentally validated, showing an excellent agreement between the simulated and measured performance.

Nonvisual classification of ground condition based on a multimodal sensing approach was investigated for an amoeba-inspired autonomous walking robot. To classify ground condition without image sensing and processing, we implemented artificial proprioception by integrating a three-axis accelerometer, eight foot pressure sensors, and reservoir computing (RC). Even when large fluctuations in the sensor outputs are caused by dynamic motions of a four-legged robot in walking, our system can classify the ground condition, flat or rough, with high accuracy. We demonstrate on-site switching of walking gait depending on ground condition in the robot. We also discuss the contribution of each sensor to ground condition classification.

We present a case study in application-driven specialization of a five-stage soft processor, evaluated on the inner control loop of a reaction-wheel self-balancing bicycle. Starting from a custom 32-bit RISC core in the MIPS tradition, we specialize the design in two ways. First, two frequently accessed peripheral inputs are mapped directly into architectural register state, written every cycle by hardware and owned exclusively through the register file's write-port structure rather than by arbitration. Second, four periodic PWM channels are offloaded to hardware and driven continuously from four exported registers, removing periodic actuation from software entirely. Because peripheral values are addressable as ordinary register operands, all ten sensor reads in the control loop cost no dedicated instruction and no dedicated cycle, folding into arithmetic that executes anyway; the memory-mapped equivalent requires an explicit load per snapshot and costs five extra instructions and cycles. The actuation path likewise removes waveform maintenance from software entirely. We report two configurations, because the extensions and the single-cycle array multiplier they were deployed alongside are not present together in a single archived build: an archived configuration, whose worst-case loop is 91 cycles, and the integrated configuration matching the deployed system, at 43 cycles. Against a 20 ms actuation frame these are margins of roughly 7,300x and 15,000x. The deadline is met by so wide a margin in either case that the specialization was not necessary for real-time compliance; its value lies in instruction count and software simplicity, not in determinism, which an on-chip single-cycle I/O region already provides. The zero-instruction sensor read is independent of that choice: the multiplier cannot affect whether a peripheral read needs an instruction of its own.

This paper presents a discrete-time passivity-based analysis of first-order momentum-based methods for a class of functions whose gradient has lower and upper sector bounds of $0$ and $L$, respectively. Through a loop transformation, it is shown that momentum-based methods can be represented as a passive controller in negative feedback with an output strictly passive (OSP) system. The weak passivity theorem is then used to derive explicit hyperparameter conditions under which the shifted gradient asymptotically vanishes. Under an additional assumption that requires the existence of a unique stationary point and excludes arbitrarily small gradients far from that point, convergence of the iterates to the global minimizer is established.

In this paper, we address the certification of datadriven feedforward control for periodic tracking of unknown nonlinear systems under partial state measurements. To this end, we adopt an invertible neural network (INN) as a surrogate for the unknown system. This choice allows us to bypass solving a nonconvex inversion problem, eliminating the associated inversion errors and reducing tracking error certification to a surrogate modeling problem. We then apply conformal prediction to provide finite-sample probabilistic guarantees on the surrogate modeling error which, through the derived tracking error bound, yield marginal certificates on feedforward tracking error. Finally, we demonstrate the approach on a DC-motor-driven mechanical load with nonlinear friction.

This paper deals with security allocation challenges for networked control systems represented by positive-weighted digraphs under stealthy false data injection attacks. These systems consist of interconnected subsystems, referred to as nodes in the underlying digraph, where an adversary aims to maximize network performance loss by stealthily attacking specific nodes. Meanwhile, a defender monitors several nodes to impose stealthiness constraints on the adversary's actions, thereby minimizing the network performance loss. We analyze the worst-case network performance loss of these stealthy attacks and make the following contributions: we (i) show that the worst-case network performance loss is upper-bounded by a tractable semi-definite programming (SDP) problem; (ii) establish the relationship between the SDP problem and the Katz centrality measure of the underlying digraph under a sufficient condition, resulting in a network-size-independent optimization problem; and (iii) provide a heuristic search based on the Katz centrality measure of the underlying digraph for selecting sub-optimal monitor nodes against all admissible attack scenarios without solving optimization problems. These results offer practical insights for safeguarding large-scale networked control systems against stealthy false data injection attacks. The obtained results are validated via extensive simulations on Erdos-Renyi random graphs with different network sizes.

Optimizing for both low Age of Information (AoI) and high throughput is critical for remote sensing applications that rely on multichannel wireless networks. However, jointly optimizing these two metrics is an analytically challenging problem, particularly in systems with heterogeneous and unreliable channels. To address this challenge, we propose TSDM, a Two-Stage Deficit Matching scheduling framework. TSDM is based on a second-order approach that characterizes the performance of each data flow by its mean and temporal variance. In the first stage, TSDM translates the high-level utility maximization objective into a concrete set of target mean and temporal variance statistics for transmissions over each node-channel pair. In the second stage, a low-complexity Weighted Matching Deficit (WMD) rule performs real-time channel assignment. We theoretically prove that TSDM achieves the desired mean and temporal variance for each flow. Furthermore, we conduct extensive simulations on two open joint throughput-AoI optimization problems. In both cases, TSDM significantly outperforms existing scheduling policies.

This work studies model-free reinforcement learning for co-safe linear temporal logic (sc-LTL) objectives in finite Markov decision processes, which can be reduced to maximal reachability objectives via the standard product construction. For this problem, direct sample-based bootstrap methods (e.g., TD or Q-learning) may fail to converge to optimal policies due to the noncontractive nature and nonuniqueness of solutions to the Bellman equation. We develop a new two-step model-free reinforcement learning method that first uses a discounted surrogate to identify a clamp set that resolves this nonuniqueness, and then applies undiscounted policy evaluation and greedy policy improvement with guarantees of finding an optimal solution. We prove almost-sure convergence of the policy evaluation step and finite termination of the policy iteration algorithm at an optimal policy. These theoretical results are validated through numerical experiments on a stochastic grid world.

We study the synthesis of optimal policies for planning problems on Markov decision processes with both objectives and safety constraints specified in co-safe linear temporal logic (sc-LTL). Our problems are inherently non-Markovian due to the complexity of the sc-LTL specification and may require policy randomization to balance the objective and constraint. We propose a novel approach that reduces the constrained sc-LTL planning problem to a constrained reachability problem on an extended model. We then show that a class of switching policies constructed from stationary policies for the individual sc-LTL specifications is sufficient for optimality for the constrained reachability problem. Our finding enables a tractable linear program to compute the optimal policy. A grid world case study demonstrates that our switching policies can achieve the optimal trade-off between the objective and the safety constraint and validates both optimality and tractability.

Analog circuit design automation using reinforcement learning (RL) has emerged as a promising approach for reducing manual effort. However, many existing RL-based methods focus on single-objective optimization. Even methods designed for multi-objective (MO) problems often reduce multiple design specifications to a single scalar reward. This simplification limits the ability to capture the true Pareto trade-off among competing objectives and often leads to suboptimal designs. Moreover, requiring the model to be retrained from scratch whenever the desired MO specifications change remains a key limitation. To address these challenges, we present ORACLE, an open-source RL-based framework for MO analog circuit design optimization that replaces scalar reward optimization with vector-valued learning and preference-aware conditioning. ORACLE represents a true MO analog circuit design optimizer that uses a preference vector to specify the relative weights of multiple objectives, enabling a single trained model to generate designs across diverse trade-off settings without retraining. We further propose two preference-guidance strategies, namely normalized-weight guidance and cosine-aligned guidance, to improve convergence. In addition, we incorporate a large language model (LLM)-guided action selection mechanism to filter actions that are likely to lead to suboptimal designs or increased runtime. Our results show that, on multiple circuit topologies with 2,000 test cases, ORACLE reduces runtime by 20.4x - 104.4x compared to state-of-the-art approaches. It also meets 99.9% of the 2,000 target specifications, and achieves 5.1x - 318.6x better figure of merit in the resulting output specs.

Do synchronized units have higher probabilities of being available when needed than non-synchronized units? Power system operation implicitly relies on the qualitative belief that synchronized units are more likely to be available when needed because they are already synchronized to the grid, whereas non-synchronized units must first start and synchronize before becoming available. However, this distinction is rarely expressed through an explicit quantitative measure. To quantify this distinction, we propose failure probabilities for synchronized and non-synchronized generating units, denoted by SynFORd and NonSynFORd, by accounting for their different initial operating states. The complements of the proposed probabilities directly represent the corresponding availability probabilities when the units are needed. Closed-form analytical expressions are derived, revealing the dominant failure mechanisms of the two unit types. Case studies using generating-unit data from the New England system show that non-synchronized units generally exhibit higher and more dispersed failure probabilities than synchronized units.

With growing concerns about resource scarcity and environmental degradation, remanufacturing of end-of-life (EoL) products within the circular economy is attracting increasing attention. Remanufacturing can preserve most of the original manufacturing value and materials while transforming EoL products into like-new condition. However, the variability and uncertainty of EoL products make remanufacturing highly dependent on human expertise. Recently, large language models (LLMs) have demonstrated remarkable capabilities in learning from massive, unstructured datasets, generating expert-level output across various tasks, and communicating with humans in natural language for interpretation. These advantages can align closely with the complex demands of remanufacturing, thereby mitigating the reliance on specialized expertise. However, their roles and research progress in this domain remain underexplored. In this paper, we present a forward-looking review and analysis of the role of LLMs in remanufacturing automation, grounded in a brief critical review of existing LLM-related studies relevant to remanufacturing. Building on this foundation, we introduce ReManGPT as a conceptual framework and use three representative case studies to illustrate selected modules of the framework in practical remanufacturing scenarios. We also analyze three representative remanufacturing applications, electric vehicle batteries, electronic waste, and electric motors, to illustrate how the proposed framework could address their domain-specific challenges. Finally, we discuss the current barriers to deploying this framework in practice and outline future research directions, including LLM-assisted human operation and language-action models for robotic automation.

This paper develops a prescribed performance leader-following consensus protocol for directed networks under event-based broadcasting. Each node schedules its transmissions using only its current state and its last broadcast value, while followers use held neighbor and leader samples between broadcasts. To accommodate jumps in sampled neighborhood errors caused by asynchronous receptions, a decaying receiver-side performance correction term temporarily modifies the prescribed performance function. For a directed network containing a spanning tree rooted at a time-varying leader with bounded trajectory and derivative, we prove existence and uniqueness of complete non-Zeno solutions, boundedness of all closed-loop signals, and explicit prescribed bounds for the sampled and continuous neighborhood disagreements and the leader-following error. These bounds quantify the effects of the communication topology and broadcast thresholds. Simulations verify the prescribed performance bounds and illustrate the event-based communication mechanism.

The limited overcurrent capability of grid-forming (GFM) inverters makes current limiting essential during large disturbances. Activation of a circular current limiter (CCL) does not always cause the operating equilibrium to disappear. This paper develops an analytical framework to predict the grid-voltage boundaries at which the CCL is activated, determine whether the operating equilibrium persists as a saturated stable equilibrium point (satSEP), and identify the voltage at which it is lost. The CCL-based GFM inverter with frozen anti-windup is formulated as a piecewise-smooth system comprising normal-control and current-limited modes, so limiter activation is interpreted as a boundary-equilibrium bifurcation (BEB). A continuation formulation that switches to a reduced current-limited model when the CCL is activated is introduced to avoid the rank deficiency caused by frozen integrator states. An equivalent circuit that includes the filter capacitor yields closed-form expressions for the lower and upper boundary voltages at which the CCL is activated. A positive lower-boundary slope predicts that a satSEP persists in the current-limited mode and is lost at a later saddle-node, whereas a nonpositive slope predicts a non-smooth fold and equilibrium loss at the BEB. The upper-boundary slope is always negative under the assumed parameter conditions. Power-angle analysis, dynamic-model continuation in single-inverter and modified 9-bus systems, and time-domain simulations validate these predictions, showing that CCL activation can either cause immediate equilibrium loss through a non-smooth fold or allow a satSEP to persist until it is lost at a later saddle-node.

Safe actor-critic control often treats barrier filtering, uncertainty estimation, and experience replay as separate modules, even though each changes the data used for learning and control. We develop an integrated architecture in which the uncertainty estimate updates the obstacle geometry used by a control barrier function, filter interventions and estimation residuals determine replay priority, and the critic learns from the executed rather than nominal action. We instantiate the architecture on a two-dimensional robot-navigation task with corrupted obstacle measurements and compare six component-matched configurations under common training budgets, random seeds, sensor streams, exploration, and disturbances. Evaluation includes a moderate post-training test, an eleven-level perception-noise sweep, and an exploratory extreme-stress test at multiplier $6.0$. In the extreme test, the integrated configuration recorded no contacts and reached the goal in all five evaluation seeds. Its mean cost was $7.63\pm0.44$ and its obstacle-belief root-mean-square error was $3.52\pm0.55$ cm. The uncertainty-estimation ablation also recorded no contacts but reached the goal in four of five seeds, with mean cost $8.96\pm2.08$ and belief error $11.08\pm1.23$ cm. A finite-training bound clarifies replay exposure, and a robust barrier condition states the required estimation-error and feasibility assumptions. The results support coupling estimation, safety filtering, and replay on this benchmark; broader safety and convergence claims require further study.

Contingencies can alter power-system dynamics and introduce prediction mismatch in model predictive control (MPC)-based load frequency control (LFC). Although such events may be detected or cleared by protection systems, the corresponding post-contingency dynamic model may not be available to the MPC controller on the LFC time scale. This paper proposes a contingency detection-integrated MPC (CDI-MPC) framework that combines disturbance-aware contingency detection with predictive frequency regulation. Contingencies are modeled as stochastic discrete events of a stochastic hybrid system (SHS), and a disturbance-aware residual formulation is developed to jointly identify the active mode and estimate unknown disturbances. The detected mode is then used to update the MPC prediction model, reducing contingency-induced prediction mismatch under changing operating conditions. Simulation results demonstrate accurate contingency detection and substantial improvements in closed-loop LFC performance under multiple contingency scenarios and unknown disturbances.

By incorporating integrated sensing and communication (ISAC) into vehicle-to-infrastructure (V2I) networks, roadside units (RSUs) can support data transmission while providing additional sensing capabilities, thereby enabling intelligent transportation services. By deploying large-scale antenna arrays at RSU, the V2I network can realize more reliable connectivity and more accurate vehicle tracking by harnessing the significant beamforming gains provided by the enlarged antenna aperture. However, it is not energy efficient to realize such arrays using conventional phased arrays, which rely on numerous power-hungry phase shifters. To address this, this demo presents the first reconfigurable intelligent surface (RIS)-based ISAC-empowered vehicular network prototype, which operates at sub-6 GHz band to be compatible with existing vehicular systems. The RIS has a low power consumption of 6.8W. Experimental results show that compared with the system without RIS, the proposed RIS-based prototype enables more accurate vehicle trajectory tracking with an average localization error of 0.11m and supports more robust data transmission, as evidenced by a 41.9% reduction in error vector magnitude (EVM). These results validate the effectiveness of the RIS-based ISAC system for supporting vehicular networks.

LEO satellite networks are an attractive solution to support farm automation in Agriculture 4.0 because of their ubiquitous coverage. However, LEO networks often suffer from high latency volatility, which can limit their utility in mission-critical farm operations such as remote control. This paper studies highly-reliable remote control over LEO networks by (i) casting highly-reliable remote control as a quantile forecasting problem and (ii) proposing a high-quantile estimator that can predict latency spikes at a given reliability level. Our results, drawn from a real-world OneWeb dataset collected in a major agricultural hub in the US, show that the proposed estimator can support highly-reliable remote control on the farm by meeting reliability requirements while allowing the remote-controlled vehicle to operate at speeds up to 138.6% higher than what would be possible otherwise.

Gastrointestinal (GI) motility disorders impair the coordinated movement of food through the digestive tract. The Wireless Motility Capsule was developed to indirectly assess GI motility by recording pressure, pH, and temperature along the GI tract. Although an inertial measurement unit (IMU) enables precise and direct motion tracking, a single-IMU system is highly susceptible to artifacts caused by body movement and respiration. To address this limitation, we propose a dual-IMU system designed to distinguish intrinsic GI motility from extrinsic body motion. One IMU is integrated into an ingestible capsule to monitor internal movement, while a second IMU is worn externally to capture whole-body dynamics. Experimental validation was conducted using a turntable to simulate random body movements and a Stewart platform oscillating at 0.1 Hz to emulate GI motility. Three sensor fusion algorithms, including the Extended Kalman Filter, Madgwick Filter, and Mahony Filter, were benchmarked to calculate the real-time relative orientation between the two IMUs using quaternion analysis. The results show that the dual-IMU system effectively differentiates platform motion from turntable motion, demonstrating its potential to isolate GI-specific motility. Pearson correlation coefficients greater than 0.8 between the baseline and noise-cancelled motility signals indicate strong agreement and effective noise suppression.

We present a structured large-language-model (LLM) architecture for zero-shot human–robot coordination in a cooperative construction task with private goal views. Guided by a Dec-POMDP formulation, the architecture decomposes decision-making into (i) action-conditioned Theory-of-Mind (ToM) inference, (ii) hierarchical planning, (iii) conversation interpretation, (iv) action verification, and (v) feedback-based replanning. We compare the proposed method with an ablation without ToM inference and a multi-agent reinforcement-learning policy trained offline over many goal pairs. In human-participant experiments, the proposed method required fewer interaction steps and yielded higher post-interaction trust ratings than both baselines. These results suggest that systematically decomposing the team decision problem, using LLMs as tractable surrogates for otherwise intractable inference and planning computations, and retaining conventional verification for physical feasibility can improve both task coordination and the human experience.

LLM-agent evaluations commonly measure task success or agreement with a declared plan. In strategic cyber-physical systems, an architecture must also remain appropriate after autonomous participants respond and physics constrains outcomes. We introduce a controlled benchmark of planning-induced control trajectories: ordered planning operations and directives linking execution architecture to strategic response and physical consequences. Four coded executors (predefined, sequential, hierarchical, and search) control demand response for 40 prosumers on a radial feeder. The LLM declares or advises typed policies and mediates communication; schedules, base prosumer dynamics, stochastic actions, and power flow remain explicit code. Paired forced-mode counterfactuals, exact-prompt caching, common response draws with separate randomness streams, critic isolation, and event-level feasibility isolate comparisons. The Llama-3.3-70B experiments on this feeder distinguish three properties. First, forced search is the oracle in all five baseline seeds under the specified objective. Second, injected objective substitution preserves mode agreement at 1.0 while increasing cumulative voltage shortfall by 2.68x. Third, the 144-scenario, 576-episode factorial bank, using three repeated seeds, contains feasible oracles from predefined, sequential, and search. The prespecified stress-held-out ridge has mean regret 90.7 and no observed value over fixed sequential. A post-hoc constraint-aware analysis reduces regret to 29.0; a simple deadline rule attains 28.7, so this gain does not establish a learning advantage. An all-feasible ablation does not improve over fixed search. These are simulation-internal, descriptive comparisons. A five-model, 300-declaration extension tests interface behaviour, not cross-backbone physical rankings; shared-endpoint latency tails motivate probabilistic live feasibility.

The rapid expansion of distributed energy resources is heightening uncertainty and variability in distribution system operations, potentially leading to power quality challenges such as voltage magnitude violations and excessive voltage unbalance. Ensuring the dependable and secure operation of distribution grids requires system real-time assessment. However, constraints in sensing, measurement, and communication capabilities within distribution grids result in limited awareness of the system's state. To achieve better real-time estimates of distribution system security, we propose a real-time security assessment based on data from smart meters, which are already prevalent in most distribution grids. Assuming that it is possible to obtain a limited number of voltage magnitude measurements in real time, we design an iterative algorithm to adaptively identify a subset of smart meters whose real-time measurements allow us to certify that all voltage magnitudes remain within bounds. This algorithm iterates between (1) solving optimization problems to determine the worst possible voltage magnitudes, given a limited set of voltage magnitude measurements, and (2) leveraging the solutions and sensitivity information from these problems to update the measurement set. Numerical tests on the IEEE 123 distribution feeder demonstrate that the proposed algorithm consistently identifies and tracks the nodes with the highest and lowest voltage magnitude, even as the load changes over time.

Decision-focused learning (DfL) trains forecasting models to align downstream decision consequences, such as power-system operating costs. However, its application to realistic power networks is limited by the need to repeatedly solve and differentiate large optimization problems during training. This paper presents DiffAPQP, a solver-flexible framework and open-source Python package for scalable DfL with affine-parametric quadratic programs. To accelerate the forward pass, DiffAPQP automatically canonicalizes quadratic power-system models written in CVXPY into a differentiation-ready representation and takes advantage of the repetitive solving structure through solver warm-start and solver-data update during training. For the backward pass acceleration, we establish the equivalence between differentiation through the full KKT system and a reduced system obtained by eliminating inactive inequality constraints. For training losses depending solely on the optimal value, we further derive an envelope-theorem-based gradient that avoids solving an adjoint KKT system, resulting in eligible backward time. To our knowledge, this work presents the first solver-based end-to-end DfL demonstration on the IEEE 118-bus system with a 24-hour coupled economic-dispatch and redispatch horizon. Under matched SCS and Clarabel backends on a Linux machine, DiffAPQP achieves $2.27\times$–$3.58\times$ closed-loop and $3.62\times$–$4.38\times$ counterfactual end-to-end DfL training speedups over CvxpyLayers. The best solver configurations increase these speedups to $3.91\times$ (from 38.65 to 9.55 min/epoch) and $6.39\times$ (from 10.73 to 1.68 min/epoch), respectively. Additionally, DiffAPQP reduces peak memory usage by approximately $50\%$, while keeping similar operating costs as CvxpyLayers.

This work presents an intrinsic stochastic successive convexification method formulated on the Special Euclidean group SE(3) for six degrees of freedom spacecraft rendezvous trajectory optimization. The proposed approach extends stochastic successive convexification, originally developed for Euclidean state spaces, to the nonlinear manifold of SE(3), thereby enabling a consistent covariance steering and chance constrained optimization of rigid body pose trajectories. While conventional trajectory optimization methods often treat position and attitude separately, or account for stochastic dispersion only after a deterministic reference trajectory has been generated, the proposed SE(3)-based formulation captures the intrinsic coupling between translational and rotational motion uncertainty. This coupling is especially important for rendezvous problems with safety constraints that depend on the full relative pose, including collision avoidance, docking corridor, camera field of view, and probabilistic force and torque bounds. Numerical simulations show that jointly optimizing the nominal trajectory, covariance, and feedback law shapes the closed loop dispersion and improves probabilistic constraint satisfaction relative to tracking a deterministic reference with a feedback linearization controller.

Artificial intelligence (AI) data centers are driving rapid electricity load growth across all U.S. ISO/RTO regions, raising both system costs and carbon exposure. This study develops a three-level Stackelberg–Bayesian game in which a regulator (leader) sets carbon penalties and subsidies, a single ISO capacity market clears against an energy balance modeled as a classical generation-expansion problem, and technology-specific investors (followers) decide capacity and operation under incomplete information, yielding a Bayesian Nash equilibrium. The AI impact is captured parsimoniously as an additional load-growth factor on a greenfield-incremental expansion, isolating how much new capacity the growth pulls in and which technology fills it. Within this framework, we consider second-life battery (SLB) storage competing against new/first-life storage for capacity-market revenue. We quantify how a carbon tax, a renewable subsidy, and an SLB subsidy reshape the equilibrium investment mix, carbon emissions, and profit. Different scenarios are compared at the end based on cost-effectiveness and reduced carbon emissions.

Bimanual manipulation policies trained with imitation learning are typically evaluated on workstation or datacenter-class GPUs, leaving the cost of deploying them on embedded hardware largely uncharacterized. We present a bimanual SO-101 system running entirely on an NVIDIA Jetson Orin Nano Super (8 GB), the entry-level tier of NVIDIA's embedded line, using a desktop GPU (RTX 3070) only for offline training, evaluated on pick-and-place of a deformable beanbag. First, we build a GStreamer capture pipeline backed by NVMM buffers that removes redundant host-device copies from three-camera sensing. Contrary to expectation, the conventional path fit the memory budget and dropped no frames; what zero-copy sensing recovers is CPU headroom (peak single-core utilization 98.0% to 77.0%) and worst-case latency (117.31 ms to 101.52 ms). Second, we train ACT and Diffusion Policy on identical demonstrations, each at its own reference budget (100k gradient steps for ACT, 200k for Diffusion Policy). ACT converges to a task-competent policy (19/20 trials) while Diffusion Policy does not converge to a usable one (0/10) even at twice the step count, which we attribute to differing convergence costs rather than an accuracy ceiling. Third, we convert ACT to TensorRT. FP16 reduces mean inference latency from 114.02 ms to 17.93 ms (6.4x) and INT8 to 12.65 ms (9.0x), with task success preserved at all three precisions (19/20, 18/20, 19/20). We report two findings not previously documented for ACT: TensorRT's general-purpose INT8 calibration quantizes the ResNet18 backbone but accepts zero of 145 transformer layers, explaining INT8's negligible size reduction over FP16 (0.9%) despite a further 28% latency gain; and the need for quantization is conditional on ACT's action-chunking configuration, feasible in full precision at n_action_steps = 100 but not at the per-step re-prediction temporal ensembling requires.

Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications. Recent synthetic grid generation methods have improved structural realism and operational feasibility by incorporating engineering knowledge through post-generation validation, optimization, or physics-aware generation. However, generated scenarios may still exhibit low AC feasibility and robustness, limiting their practical value for downstream power-system studies. This paper proposes a feasibility-aware distribution-learning framework that learns the AC-operable joint distribution of network topology, branch electrical parameters, and time-varying load profiles. Instead of enforcing feasibility after generation, the proposed framework incorporates AC power-flow convergence and operational constraints into hierarchical diffusion-based distribution learning. This enables the generator itself to produce operationally feasible grid scenarios through efficient diffusion sampling. The hierarchical architecture decomposes the high-dimensional generation task into three engineering-motivated stages: topology and bus-attribute generation, branch-parameter generation conditioned on the generated structure, and load-profile generation conditioned on both network structure and electrical characteristics. Experiments on benchmark systems demonstrate that the proposed framework significantly improves operational feasibility and contingency robustness while maintaining strong statistical fidelity and eliminating optimization-based post-processing.

This white paper presents ADMITBench, a reference framework for evaluating industrial LLM advisories at the level of the proposed action. The framework implements a versioned, safety-governed evaluation contract that checks whether a recommendation is supported by the available evidence, permitted under the stated authority and procedure, and acceptable under the plant-specific consequence checks encoded in the selected evaluation profile. In this report, safety-governed means that eligibility is determined through explicit, non-compensatory checks derived from a versioned plant profile; it does not mean that the evaluator, model, or plant has been safety-certified. Release 0.1.0 is a public reference implementation for technical and research evaluation, not an authorisation for physical execution.

This work presents a stability analysis of a hybrid control system integrating a reset controller (RC) featuring a single reset state with an integral sliding-mode controller (ISMC). It is shown that the reachability of the sliding surface is decoupled from the nominal reset mechanism. This decoupling property enables a Lyapunov-based stability analysis, demonstrating that the closed-loop RC-ISMC system achieves input-to-state stability (ISS) and uniform ultimate boundedness (UUB) in the presence of exogenous references and disturbances. Furthermore, global asymptotic stability (GAS) is recovered in the unperturbed regulation scenario. A numerical case study illustrates the theoretical results.

Anxiety and depressive disorders are increasingly viewed as dysregulations along continuous stress-regulatory dimensions. However, existing computational approaches seldom connect interpretable circuit level mechanisms to autonomic physiology. Methods: This study develops a mechanistic framework that links amygdala dysregulation to cardiovascular stress responses for digital phenotyping and clinical interpretation. We formulated a compact, nine equation, conductance based model of the amygdala hypothalamus cardiovascular pathway. The framework extends Hodgkin Huxley formalism with three clinically grounded modulators: coping capacity, perceived stress load, and prefrontal regulatory strength. A slow, history-dependent internal state, adaptive thresholding, graded threat acknowledgement, and baroreflex coupled hypothalamic integration were used to generate heart rate and blood pressure trajectories. Results: Distinct strong, moderate, and weak regulatory regimes emerged as stable operating states of a single closed-loop system. Robust analyses showed that stochastic variability and parameter perturbation preserved regime separation, while ablation studies identified the adaptive threshold as the principal mechanism driving quantitative regime separation. Simulated cardiovascular responses remained within reported stress physiology ranges. Furthermore, external evaluation across three independent datasets supported robust agreement with real world, stress related autonomic patterns. Conclusion: A compact, mechanistic model can jointly link psychometric modulators, amygdala excitability, and downstream cardiovascular output within a single, interpretable framework. Significance: This work provides a computationally tractable basis for mechanism informed digital phenotyping, patient specific stress monitoring, and future digital twin approaches for mental health decision support.