While the hybrid energy storage system (HESS) can theoretically mitigate battery degradation in electric vehicles, its practical implementation remains highly limited. To delineate the specific scenarios and application boundaries where supercapacitors remain feasible, this study proposes a multi-dimensional techno-economic feasibility evaluation framework. First, a cross-vehicle sizing method based on dynamic programming is established to quantify physical mass-volume packaging constraints and identify feasible supercapacitor candidates across different vehicle types. Building upon the optimal sizing parameters derived from the battery aging Pareto front, an expert-guided deep reinforcement learning energy management strategy is integrated to yield near-optimal online performance, ensuring a fair life-cycle economic assessment. Finally, a comprehensive feasibility matrix is constructed to systematically evaluate mass, volume, battery lifespan, additional supercapacitor costs, total cost of ownership, future energy storage prices, and the influence of emerging solid-state batteries. Results reveal that city buses remain the most promising vehicle type for HESS due to minimal additional costs and sufficient packaging space. Current mass-volume penalties and limited economic benefits hinder HESS application in passenger vehicles and heavy-duty trucks, respectively. This situation may only improve if supercapacitor prices drop significantly in the future. Beyond vehicle types, the HESS feasibility is governed by load-frequency characteristics. Furthermore, looking toward the 2030+ solid-state battery era, we highlight that integrating increasingly affordable supercapacitors can provide substantial asset protection leverage.

Vision-language-action (VLA) policies are often treated as checkpoint-defined objects: if the weights, prompt, and benchmark suite match, the deployment is assumed to be the same policy. Robot execution breaks this assumption because the same normalized model output can become a different physical action after action unnormalization and controller conventions are applied. This creates a deployment-safety gap: safety review can certify the checkpoint while missing the executable robot policy that reaches the controller. We formalize this gap as an executable policy specification problem: a VLA policy includes the learned model, action representation, metadata-selected unnormalizer, and controller-facing conventions. Under this view, identical checkpoints can be executable-inequivalent. For quantile-style action normalization, we derive a closed-form metadata mismatch transform and an ExecSpec certificate that measures action-space semantic drift without model inference or rollout. On LIBERO-Goal replay, substituting a plausible sibling metadata key yields mean drift 0.199 over six non-gripper action dimensions and reduces success from 28/28 to 2/28 under full substitution. On LIBERO-Spatial replay, the same substituted key reduces success from 26/26 to 0/26. The same full-substitution protocol gives 0/28 success for all four Object substitutions and 0/23 or 1/23 success on Long. Identity-key, replay-validity, no-op filtering, raw-vs-correct replay, mask/gripper, synthetic upper-bound, and OpenVLA-style unnormalizer interface checks rule out several simpler explanations. These results do not certify closed-loop or hardware safety. They support a narrower deployment-safety view: action-space metadata is part of the executable policy and should be checked before rollout.

Data-driven approaches for learning power flow models suffer from weak generalization across varying network topologies and limited computational scalability. Existing methods typically rely on training over a large set of grid topologies, which becomes impractical for large networks. This paper proposes a scalable and computationally efficient framework for topology-adaptive learning of power flow solutions. We propose a modular architecture consisting of bus-level Gaussian Process (GP) models, where each GP collects local features based on bus-level \textit{egonet} definition. The localized bus-level feature includes first-order power and admittance sensitivities, nodal injections and node degree. In addition to the modular architecture, we propose using Random Fourier Features (RFF) for feature reduction, which further enhances the computational scalability. We evaluate the effectiveness of the proposed method by simulations across multiple benchmark networks under N-1, N-2, and N-3 contingencies. Results for the PEGASE 1354 bus system under N-3 contingencies demonstrate high predictive quality, with an $R^2$ score of 0.983 and a voltage-magnitude RMSE of 0.0023 p.u. The framework maintains recall rates exceeding 98\% for detecting voltage limit violations across all test cases. Furthermore, the approach exhibits scalability, completing training and testing for the PEGASE 1354 system in 116.47 seconds while outperforming existing benchmarks in zero-shot generalization without requiring additional training samples.

Android malware detection increasingly relies on collecting and processing sensitive user data, including device identifiers, network artifacts, and runtime traces, while privacy is too often treated as a secondary concern. Existing privacy-aware approaches typically enforce privacy after data collection, for example, through anonymization, encryption, or federated learning, yet still require access to user information and therefore demand a high level of user trust in systems that already operate with privileged access to device activity. We argue that this requirement should be removed rather than managed. Android malware detection should be privacy-aware by design, so that effective analysis does not depend on sensitive data being accessed in the first place. To this end, we first formalize a set of design requirements for privacy-by-design detection and then implement each requirement in a comprehensive pipeline. First, static analysis is performed to extract relevant data from each APK, following the Drebin representation, which is then submitted to an SVM after vectorization. The model is equipped with a dual-reject threshold rule that either commits to a confident decision or defers uncertain samples to a dynamic analysis stage within a sandboxed environment, so that genuine user information never enters the analysis loop. Results confirm that, on a temporally split dataset spanning from 2024 to 2025, the pipeline achieves an F1 score of 0.87 with the first static analysis stage, deferring only 6.7% of test samples to secondary dynamic analysis. Additionally, dynamic sandboxing helps recognize applications' maliciousness with high confidence without extracting any sensitive data. These results demonstrate that strong detection performance is achievable without sacrificing user privacy.

A publisher who releases check-in trajectories inadvertently publishes a strong predictor of every user's future locations. We address this risk by generating unlearnable trajectories, perturbed sequences that yield victim models with degraded next-Point-of-Interest (next-POI) accuracy on clean test inputs. Direct ports of image-domain unlearnable examples fail on two counts. The published data must remain geographically and semantically plausible, and the perturbation must resist purification adversaries that exploit the structure of randomized defences. We propose Ghost, a manifold-aligned framework whose perturbations look like plausible human check-in sequences yet leave no learnable signal behind. Ghost steers each substitution onto the real-trajectory manifold through a frozen trajectory language model, so a denoising-bridge adversary has nothing to invert and a context-free frequency-table adversary recovers a near-uniform distribution. Across two standard benchmarks, and four attacker postures, Ghost achieves protection-gap competitive with the strongest deterministic baseline (PGD) while attaining the lowest restored accuracy under the bigram adaptive purification adversary on both datasets, and lies within one per-cell standard deviation of PGD on the protection-versus-purification-resistance plane. Ablations confirm the manifold prior subsumes the entropy-floor knob of prior randomized defences, with the frequency-table adversary's survival gap remaining within 0.04 even when twenty percent of the pairs are leaked.

Integrated Circuits (ICs) are omnipresent, yet their globalized manufacturing process remains vulnerable to supply chain threats. Hardware Reverse Engineering (HRE) is essential for detecting such threats and re-establishing trust; however domain experts remain scarce due to a lack of educational programs. To contribute educational insights in this critical and rapidly evolving technology domain, we present our HRE course focusing on digital circuit analysis and digital circuit extraction from ICs. The course targets junior-level undergraduates at a major European research university. The curriculum has been refined over nine iterations (2017-2025), with several alumni subsequently pursuing careers in the HRE field. By reflecting on the evolution of the course organization, content, and assignments, we derive key lessons learned. We further distill these insights into actionable design priorities for educators developing courses in rapidly evolving technological domains, emphasizing iterative growth and sustainable workload management for both students and instructors.

The Formalized Process Description (FPD) according to VDI/VDE 3682 provides a standardized graphical notation for describing processes across engineering domains but lacks a standardized, tool-independent data format for machine-readable model exchange. This paper presents an AutomationML (AML) domain library that formalizes the complete set of FPD language elements, their attributes, connection semantics, and graphical representation information as class libraries based on the Computer Aided Engineering Exchange (CAEX) 3.0 metamodel. The library comprises five interrelated parts: a RoleClassLib defining the semantic roles, an InterfaceClassLib for connection types, two AttributeTypeLibs for the information model and diagram interchange, and a SystemUnitClassLib providing instantiation templates. Key design decisions regarding inheritance, diagram structure, hierarchical decomposition, and the representation of graphical information are discussed along with the alternatives that were considered. A bidirectional mapping tool demonstrates the library's applicability by converting between a web-based FPD modeler and AML. The library is proposed as a candidate for Part 3 of VDI/VDE 3682. It is available together with an example and a feedback function for community input ahead of standardization at https://aml.fpbjs.net.

Learning-based and physics-informed methods are increasingly used for inverse estimation in controlled nonlinear dynamical systems. However, in many such approaches, the theoretic requirements that make unknown-input reconstruction meaningful, namely well-posedness in the sense of Hadamard, are often disregarded or weakly addressed through generic regularization terms with no explicit guarantees. In this work, we adopt a complementary viewpoint in which these control-theoretic and structural conditions inform the estimator design and constrain its training. We thus develop a physics-informed input-state neural estimator for joint unknown-input and state estimation in nonlinear controlled systems with partial measurements. In the present work, this general framework is instantiated on a model of autonomic cardiac regulation, provides a concrete study case. The estimator is formulated as an inverse neural map conditioned on time and measured outputs, and is trained under data fidelity and dynamical consistency constraints. To ensure it complies with the same structural requirements imposed in robust estimation, we derive left-invertibility conditions by differential-algebraic elimination and embed the resulting constraints directly into the training objective. We further analyze a priori the stability of the inverse mapping to output perturbations and derive a conservative Lipschitz bound that guides the tuning of cost functional hyper-parameters. The framework is evaluated on simulated data, where ground truth data is available, and on two distinct datasets of real cardiovascular recordings. The results show that incorporating control-theoretic solvability constraints into physics-informed learning improves the reliability of inverse inference beyond forward consistency alone.

In this paper, we provide the first deterministic algorithms with sublogarithmic round complexity for spanners and approximate shortest paths in various MPC models. Moreover, we significantly improve upon the state of the art in the deterministic Congested Clique. In particular, we obtain the following four results on undirected graphs: 1. In both linear MPC and Congested Clique, we obtain an $O(k)$ stretch-spanner of a weighted graph of size $O(n^{1+1/k})$ in $O(1)$ rounds, for some parameter $k\ge 0$. For $k=O(\log{n})$, this leads to an $O(\log n)$ approximation of APSP in constant rounds in both models. 2. In sublinear MPC, we obtain an $O(k^{1+\varepsilon})$-stretch spanner of a weighted graph of size $O(n^{1+1/k})$ in $O(\log k)$ rounds, for any fixed constant $\varepsilon>0$. 3. In Congested Clique, we obtain $O(1)$-approximate APSP for weighted graphs in $O(\log \log \log n)$ rounds. 4. In near-linear MPC, we obtain $(1+\varepsilon)$-approximate single-source shortest paths and $O(1)$-approximate all-pairs shortest paths for unweighted graphs in $\textsf{poly}\log \log n$ rounds. Our algorithm only requires a single near-linear memory machine, where the rest can have sublinear memory. Our deterministic algorithms obtain similar guarantees to the state of the art randomized algorithms without incurring additional factors in the round complexity. To obtain these results, we inspect the randomized algorithms and isolate a randomized sampling routine. Then we derandomize these sampling routines by using a deterministic hitting set. Hereto, we develop a versatile deterministic hitting set algorithm, which we hope will have further derandomization applications.

Much recent press claims that developers no longer need to read code. We disagree, at least within the domain of tabular software-engineering (SE) optimization tasks: rows of $x$ and $y$ values where the $y$ values are expensive to obtain. As evidence we present 400 lines of EZR.py, a Python toolkit (no heavy dependencies) that implements Naive Bayes, $k$-means clustering, classification and regression trees, simulated annealing, local search, active learning, and complementary-Bayes text-mining relevance filtering for tabular SE data. EZR was built by repeatedly reading and refactoring AI tools to simplify and unify them. The result demonstrates that many seemingly different learning algorithms are nearly the same once stripped back to their core: classical algorithms collapse to a few lines each, and a state-of-the-art active learner fits in roughly 80 lines. Tested on the 120+ tabular SE optimization tasks in the MOOT repository, these tiny tools perform as well as or better than state-of-the-art explanation tools (SHAP, LIME), the SMAC3 optimizer, and SVM-based text-mining filters (FASTREAD), while running 500$\times$ faster than SMAC3, using orders of magnitude less labelled data, and building trees from fewer than ten variables even when thousands are available. We conclude that, within the scope of tabular SE optimization, reading and refactoring code is a useful method of generating insight, and small unified toolkits can rival large libraries. EZR is available under an open-source license. Install via \textsf{pip install ezr}; example data at \textsf{github.com/timm/moot}.

Digital Twins (DTs) are emerging as a cornerstone of the 6G vision, enabling real-time cyber-physical mirroring for smart manufacturing, autonomous vehicles, and remote healthcare. However, maintaining high-fidelity synchronization at scale demands an enormous and sustained uplink bandwidth, threatening both the feasibility and the energy efficiency of large deployments. We propose a Semantic-Aware DT Synchronization (SA-DTS) framework that radically redefines the synchronization pipeline: instead of streaming raw sensor or video data, a lightweight neural semantic encoder at the physical-world source extracts only task-relevant features and transmits compact semantic descriptors over the 6G air interface. At the DT replica, a paired decoder coupled with a dynamic Knowledge Graph (KG) reconstructs the full contextual state. A hierarchical KG partitioning strategy with an adaptive partition count $G = \lceil N / \log_2 N \rceil$ ensures that aggregate update overhead scales as $O(N \log N)$ rather than $O(N^2)$, making the framework viable for deployments with hundreds of simultaneously twinned entities. Extensive simulations on three canonical DT workloads -- industrial robot control, patient-monitoring, and vehicular platooning -- demonstrate bandwidth savings of up to 94%, end-to-end synchronization latency reductions of 87%, and KG-assisted state-reconstruction accuracy exceeding 97%, all under realistic 6G channel conditions. Empirical correlation confirms that the proposed Semantic Fidelity Score tracks standard task metrics (collision accuracy, alarm F1, spacing deviation) with Pearson $r > 0.97$ (95% CI: [0.961, 0.982]). Our results reveal that semantic communication is not merely a compression tool but a fundamental enabler for truly real-time, scalable DT ecosystems.

Long-video Omni assistants often fail not by inventing content, but by misbinding real evidence: they hear the right utterance and see the right event, yet attach it to the wrong speaker, moment, or modality. These \emph{almost-true} errors evade standard video QA because local evidence remains valid, so item-level scoring can reward both a supported claim and its near-counterfactual. We introduce a counterfactual event-binding protocol that constructs paired supported/counterfactual claims from the same audio-visual event evidence and evaluates them by strict-pair accuracy. We instantiate it as \bench, a benchmark for long-video Omni hallucination, with 3{,}600 single-claim QA items from 638 long-form videos averaging 24.16 minutes and covering 256.87 hours. Under this protocol, open-weight Omni models remain weak at pair-level binding: Qwen2.5-Omni-7B reaches 32.06\% and Qwen3-Omni-Instruct reaches 41.55\%, versus 76.54\% for a closed-source reference. To narrow this gap without updating the backbone, we propose \method, Modality-Perturbation Reliability Calibration, a frozen-backbone framework that selects audio-negative probes within video-level folds and fuses their response shifts with native audio-visual confidence into per-claim support estimates. \method lifts Qwen2.5-Omni-7B to 36.22\% and Qwen3 to 51.09\% on \bench, and improves target-adapted MCQ accuracy on OmniVideoBench ($+$2.20) and WorldSense ($+$1.51) with Qwen3.

Sixth-generation (6G) wireless networks will underpin ultra-dense Industrial IoT (IIoT) ecosystems in which resource-constrained Far-Edge devices -- autonomous mobile robots, industrial actuators, connected vehicles -- must simultaneously satisfy sub-millisecond latency, $10^{-7}$-class reliability, and decades-long cryptographic security. Current architectures delegate Digital Twin (DT) computation to centralised cloud or Mobile Edge Computing (MEC) servers, incurring prohibitive round-trip latency, and rely on classical public-key cryptography vulnerable to quantum attacks under the harvest-now, decrypt-later (HNDL) threat model. We propose Q-FE, a Quantum-Native 6G Far-Edge architecture integrating three co-designed components: (i) Micro-Digital Twins ($μ$DTs) co-located with 6G base stations and high-capability endpoints; (ii) a Cross-Layer Post-Quantum Key Exchange module embedding CSIDH-512 isogeny key material directly within MAC-layer control frames, exploiting the scheme's uniquely compact keys ($\le 64$ bytes) to avoid packet fragmentation; and (iii) an Asynchronous Federated Learning (AFL) protocol governed by lightweight DAG smart contracts at MEC nodes, eliminating straggler bottlenecks and preventing model-poisoning and Sybil attacks without exposing raw data. End-to-end simulations (NS-3 + PySyft) demonstrate that Q-FE reduces MAC-layer overhead by 62% versus ML-KEM/Kyber-1024, maintains P99.9 URLLC latency at 0.78 ms, and accelerates global-model convergence by 31% over synchronous Federated Learning. Protocol complexity analysis confirms $O(N \log R)$ per aggregation round, and $μ$DT handover migration completes in $1.9 \pm 0.3$ ms across $10^4$ simulated events. A formal threat model confirms resilience against quantum eavesdropping, model-poisoning, and Sybil attacks.

The modern power system, increasingly composed of Inverter-Based Resources (IBR) from multiple manufacturers, requires new study and design techniques that balance accuracy with the need to protect the Intellectual Property (IP) of various stakeholders. One possible method to support detailed electromagnetic transient (EMT) simulations is to convert the original equipment manufacturers (OEM) models into shareable black-box versions using dynamic link libraries (DLLs). This technique prevents IP violations while potentially maintaining simulation accuracy by embedding the original components within the shareable DLL. Thereby, this work aims explicitly to enhance simulation fidelity by translating full-switching models of offshore wind turbines (OWTs). In this context, the paper offers valuable recommendations, including how to convert interpolation-based elements, preserve simulation speed, recognize limitations, and outline future improvements

Many proof-of-stake protocols finance validator rewards from two sources: transaction fees and a finite reserve of tokens. This creates a dynamic hand-off problem. Early in the life of the system, fees may be too small to fund the target level of security; later, fees may become sufficient. The central question is whether the reserve provides enough runway for the protocol to remain secure until this fee-only region is reached. We study this problem in a discrete-time stochastic model of validator participation. Token price and transaction demand fluctuate over time, while validators choose participation strategically. We solve the validator entry game and derive an exact state-dependent reserve threshold, i.e., the minimal reserve stock necessary and sufficient to sustain a target security level. This threshold separates three regions: infeasibility, reserve-dependent security, and fee-only security. Security fails if the reserve first falls below the state-dependent threshold, and a successful hand-off occurs exactly if the fee-only region is reached before that failure time. We derive stress-test guarantees that convert lower confidence bands for token price and demand into reserve requirements, and obtain explicit failure-probability and expected hand-off-time bounds. Finally, we extend the model to forward-looking validators and derive the Markov participation condition that captures how current participation affects future reserve-funded rewards. The main implication is that reserve policy should not be evaluated by nominal depletion dates or steady-state reward ratios alone. A protocol can have a large nominal reserve and still be close to security failure after adverse price or demand shocks. Conversely, once demand crosses the fee-only threshold, the reserve becomes redundant for security. This paper provides a tractable equilibrium framework for stress-testing this transition.

Channel charting enables location-based services (LBSs) without requiring explicit position information by using pseudo-locations from the channel chart. While this property implies inherent privacy advantages, it does not provide formal privacy guarantees. In this work, we address location privacy in channel charting referred to as chart location indistinguishability (CLI), which extends geo-indistinguishability (GI) to channel charting representations. In order to achieve CLI, a standard planar Laplace mechanism is investigated and a geometry-aware Mahalanobis norm planar Laplace (MNPL) mechanism is devised. The proposed MNPL mechanism perturbs the channel chart by injecting noise aligned with the local structure of the chart. In the CLI framework with MNPL, privacy is defined in latent channel chart manifolds using locally adaptive covariance derived from chart neighborhoods, while preserving manifold topology under privacy constraints. In addition, differential privacy is considered as a privacy baseline. The proposed approach is evaluated across multiple channel charting schemes. The performance is assessed using utility metrics such as quality loss (QL) and range query error (RQE), as well as geometry-aware metrics including trustworthiness (TW) and continuity (CT). Numerical results demonstrate that the proposed privacy mechanism provides strong privacy guarantees while preserving the channel chart for LBSs tasks.

This paper introduces a systematic gray-box identification framework for voltage-source converter models based solely on terminal time-series data. The proposed approach combines a physically informed white-box standard model with iterative time-domain calibration to estimate controller parameters that mimic the behavior of the black-box model in electromagnetic transient (EMT) simulations. Unlike conventional frequency-domain identification methods, the framework leverages time-domain data more effectively to better constrain the surrogate model across a broader operating range and capture reference-signal dynamics. To evaluate the accuracy of the identified model, the paper presents additional frequency-domain validation metrics based on Nyquist analysis and singular value decomposition, allowing for both quantitative assessment of model divergence and qualitative classification of mismatch types. The methodology is tested on cases with increasing structural uncertainty, from exact parametric recovery to an actual detailed EMT black-box model. Results demonstrate that the proposed framework can accurately recover parameters when the internal structure is known, adjust for moderate structural mismatch with extra degrees of freedom, and offer a reliability measure for small-signal stability analysis of converter models protected by intellectual property

Although Generative AI (GenAI) improves task efficiency in the short term, it creates competitive pressures that perpetuate individuals' fear of being eliminated, thereby increasing the risk of problematic use. Existing research has focused on the perspective of individual psychological vulnerability, but has neglected the social comparison context caused by GenAI. This study examines the direct effects of social comparison orientation on problematic GenAI use and explores their indirect effects via emotional and cognitive mechanisms, grounded in the Person-Affect-Cognition-Execution (I-PACE) model. The research analyzed data from 396 Chinese GenAI users using SEM and bootstrap methods. Findings show that social comparison orientation has a significant direct impact on problematic GenAI use and can additionally influence AI flow and perceived irreplaceability through fear of missing out (FoMO), finally leading to problematic GenAI use.

We study the robustness of AMM-based on-chain price oracles to strategic manipulation. An attacker trades against constant product automated market makers (CPMMs) to distort an on-chain oracle, arbitrageurs restore cross-pool and cross-venue consistency, and an oracle designer chooses how to aggregate pool quotes. Taking an efficient-market-hypothesis (EMH) view of the off-chain "true" price, we define the \emph{cost of manipulation} as the minimal mark-to-market loss that an attacker must incur to move the oracle by a given multiplicative factor. For independent CPMMs, we derive closed-form single-pool manipulation formulas and solve the attacker-designer game for weighted means and weighted medians, showing that liquidity weights maximize the minimum cost of manipulation within these classes for weighted medians (for any distortion level) and, for weighted means, locally as the distortion tends to zero. For larger distortions, weighted means become more fragile: optimal weights can depend on the target distortion and no single choice is uniformly optimal across distortion levels. In a frictionless CPMM model with cross-pool arbitrage, the manipulation cost depends only on the total quote depth and coincides across symmetric aggregators. We extend this framework to multi-asset star architectures, confirming that liquidity weights remain optimal in the same sense. Finally, we bridge theory and practice by incorporating dwell times and rate limits, providing a quantitative yardstick to size oracles against the explicit economic costs of attack.

Computationally-intensive research (CIR) takes place on a wide variety of topics including AI. Its environmental impact is potentially significant yet it does not always fall clearly within the scope of organisational ethics review policy on its own merits. Many academic institutions have ethics oversight bodies (e.g. Research Ethics Committees or Institutional Review Boards) that occupy a potentially powerful position to encourage recognition of these issues and seek reflexive practice in researchers. However, policies are often poorly-defined in respect of environmental issues and thus research is not reviewed, reviewers have little guidance for legitimate critique, and researchers are not challenged to consider planetary limits on computing resources and the interaction of these with their research. This paper aims to address these problems by proposing scoping criteria for institutional ethics policy to bring CIR within the scope of ethics review on its own merits, framing evidential criteria for reviewers to apply in ethics review, and presenting a method by which CIR researchers can reflect on their proposed research in relation to environmental factors, and assess its potential value in the light of planetary limits.

Electronic health records (EHRs) are central to clinical prediction, but existing methods either rely on correlation-driven deep models or use single large language models (LLMs), making it difficult to support multidisciplinary clinical reasoning. Recent multi-agent systems (MAS) provide a promising alternative, yet current EHR-grounded MAS methods still suffer from weak evidence differentiation across agents and redundant multi-round interaction. We propose D2MDT, a Department-aware MultiDisciplinary Team Consultation with Deliberation for Efficient clinical prediction. D2MDT first constructs structured EHR evidence and consultation-ready semantic evidence for multi-agent consultation. It then assigns patient-specific department perspectives to doctor agents and retrieves complementary evidence for collaborative consultation. To improve efficiency, D2MDT further introduces residual deliberation, which updates only unresolved consensus rather than replaying the full discussion history. Finally, D2MDT fuses the refined consensus report with structured EHR representations for prediction. Experiments on mortality prediction show that D2MDT improves both predictive performance and consultation efficiency. We release the code online to ease the reproducibility of this paper.

The integration of offshore wind power plants (OW-PPs) into weak grids can pose stability challenges due to the interaction between inverter-based resources (IBRs), Flexible AC Transmission Systems (FACTS) and the grid. In this context, long HVAC transmission systems, relatively common for OWPPs, can exacerbate the stability challenges. Therefore, this paper introduces a novel methodology for estimating the equivalent short-circuit ratio (ESCR) at the offshore point of connection (PoC), combining analytical two-port network (TPN) modeling with electromagnetic transient (EMT) simulations. The approach derives the Thevenin equivalent impedance for passive and active components, enabling accurate ESCR computations without complex derivations. Limitations of traditional SCR metrics are addressed by incorporating the dynamics of the converters, such as static synchronous compensators (STATCOMs), into a hybrid EMT-TPN method for synthesizing equivalent impedances. The process is then verified on the CIGRE OWPP benchmark and is found to capture ESCR variations with cable lengths, shunt reactors, and grid strength. Additionally, the results emphasize the correlation between the ESCR and voltage stability, highlighting the role of STATCOMs in supporting voltage stability in weak grids. This modular framework aids in OWPP design and stability analysis for converter-dominated systems.

To meet the stringent requirements of future motion systems exhibiting time-varying and/or position-dependent behavior, online data must be leveraged to improve control performance. This paper presents a recursive algorithm for simultaneous learning of feedforward and compliance compensation parameters. A multivariate regression formulation is proposed that jointly estimates friction, mass, jerk, and compliance compensation parameters while mitigating parameter coupling. Experimental results on a high-tech semiconductor metrology and inspection system demonstrate an order-of-magnitude improvement in servo performance.

AI-based intrusion detection systems (IDS) have shown promise in detecting attacks on IoT systems. In this work, we explore the use of foundation models to detect and identify attacks, with a specific focus on RPL-based IoT networks. We study multiple attack types, attack variations, and network configurations, and provide insights into the performance of foundation models for attack identification. Specifically, we fine-tune the MOMENT foundation model for multi-class attack identification. Our evaluation is based on a dataset containing RPL-related statistics collected under normal operation and under Blackhole, DIS flooding, Worst Parent, and Local Repair attacks, generated in a Cooja simulation environment. The initial results are promising. The approach achieves attack-detection performance comparable to state-of-the-art methods, while also demonstrating strong performance in distinguishing between different attack types.

This paper addresses distributed throughput optimization for dense multi-AP IEEE P802.11bq networks. We develop a packet-level model that jointly captures cross-link carrier-sense multiple access with collision avoidance (CSMA/CA), sub-7GHz RTS/CTS exchange, beam-training overhead, directional mmWave interference, signal-to-interference-plus-noise-ratio (SINR)-based MCS selection, and retransmissions. The resulting configuration problem is formulated as a multi-group combinatorial multi-armed bandit (CMAB), where each AP selects its contention window, clear-channel assessment threshold, beamwidth, and MCS reservation margin from finite candidate sets. Inspired by combinatorial successive accept-reject methods, we propose a group-wise feasible CSAR variant that uses Hadamard-guided feasible exploration to estimate empirical ranking scores and eliminate low-performing candidates within each parameter group. Simulations show that the proposed scheme improves aggregate and per-AP throughput over the considered Thompson-sampling baseline across most AP densities and reduces throughput stabilization time by approximately 49$\%$ under the evaluated settings. The learned configurations reveal that high throughput requires a balance among control-channel aggressiveness, mmWave spatial reuse, beam-training cost, and MCS robustness, rather than simply minimizing collisions or maximizing the PHY rate.

Advertisements often strategically disclose information to consumers who make decisions on further information acquisition and eventual purchase. Anderson and Renault (2006) model this problem using an information design framework, where the advertiser acts as a sender and the consumer as a receiver. We extend this model to a competitive setting with horizontally differentiated senders competing for a unit-demand receiver. Under costly inspection, the receiver's optimal sequential search action is given by Weitzman's Index Algorithm. We give a method, based on duality arguments, to verify whether a sender's given information strategy constitutes a best response against his competitors (other senders). We establish the existence of an equilibrium in the game among senders when the prior distributions have no mass; we also illustrate that such equilibria may exhibit intricate behaviors. Finally, we meticulously characterize symmetric equilibria played by the senders for cases when the prior distributions have monotone increasing densities, while offering economic intuitions behind the insightful equilibrium structure.

Distributed Graph Neural Network (GNN) training depends critically on how the underlying graph is partitioned across compute resources. Existing graph partitioners focus either on vertex partitioning or edge partitioning and typically optimize only a single communication objective (edge cut or vertex cut) under a single balance constraint (vertex balance or edge balance). We present SIGMA (Streaming Integrated Graph Partitioning with Multi-objective Awareness), a versatile streaming graph partitioner that supports both vertex and edge partitioning within a unified multi-objective, multi-constraint framework. Depending on the target distributed GNN system, SIGMA can be configured for edgecut-oriented vertex partitioning or vertex-cut-oriented edge partitioning while simultaneously accounting for both vertex and edge balancing. A clustering-based preprocessing stage incorporates global graph structure to improve partition quality while preserving the efficiency and scalability advantages of streaming partitioning. We evaluate SIGMA on six benchmark graphs spanning diverse domains and scales using two distributed GNN training systems: Dist-GNN (edge-partitioned) and DistDGL (vertex-partitioned). Across both settings, SIGMA consistently achieves strong performance, showing its ability to navigate complex trade-offs between partition quality, training efficiency, and memory consumption, frequently outperforming streaming baselines while remaining competitive with high-quality in-memory partitioners such as METIS, KaHIP and HEP. These results demonstrate that a unified streaming partitioner can effectively address the communication, compute, and memory challenges of distributed GNN training across fundamentally different system architectures.

Quantum computing presents a promising method to overcome the efficiency and memory constraints in large-scale mechanical problems, with numerous successful applications demonstrated in fluid mechanics. However, solid mechanics problems usually require irregular grids for spatial discretization, due to the Lagrange formulations and complex boundaries, which makes the quantum simulation of the system matrix, e.g., the mass or stiffness matrix which is often referred to as the Hamiltonian in quantum computing, difficult to be effectively conducted. This study proposes a voxel-based quantum computing method (VBQC) for the quantum simulation of Hamiltonians in solid mechanics. VBQC applies voxel grids to discretize the spatial domain, thereby enabling the system matrix to exhibit the tridiagonal fractal property. Based on this property, the system matrix can be decomposed into three groups of fundamental matrices, $\mathbf{k}_{n}$, $\mathbf{c}_{n}$, and $\mathbf{q}_{n}$. This decomposition process is referred to as the KCQ decomposition. By integrating the KCQ decomposition with the quantum Fourier transform and the quantum multiplexer, VBQC enables efficient quantum simulation of Hamiltonians in solid mechanics. Three specific solid problems with different dimensions and numbers of variables are applied to preliminarily verify the correctness of the proposed VBQC for solid mechanics problems.

Recent small-signal stability studies of AC grids have shifted towards analysing power systems as interconnections of subsystems and leveraging their input-output properties to derive scalable stability certificates. Two subsystem representations appear frequently in the literature: the PQ model, coupling powers to phase angle and voltage magnitude, and the IV model, coupling currents to voltages. In this paper, we derive both models without simplifying the bus or line dynamics and show that a loop transformation relates the two. One of the main results in the paper is to then show analytically that each representation may exhibit unstable poles depending primarily on the operating point (IV model) or the presence of high-frequency passive dynamics (PQ model). In particular, such unstable poles in the subsystems can occur even when the aggregate interconnection is stable and well-behaved. These effects are validated numerically, including a case study using the full-order dynamics of a synchronous generator with an exciter and transformer. Our results highlight that care must be taken when choosing a subsystem representation, as neglecting high-frequency dynamics or device operating points may obscure unstable poles that must be stabilised by the network interconnection and must be accounted for in system identification.

With growing emphasis on privacy protection, homomorphic encryption (HE) has emerged as a core method for privacy-preserving image processing, as it enables operations directly on encrypted data. However, existing research predominantly focuses on low-resolution image processing, and techniques for privacy-preserving high-resolution image processing remain underexplored. As the image size increases, the HE parameters must be adjusted accordingly, and directly applying existing methods can lead to significant computational overhead. In this work, we propose a multi-ciphertext privacy-preserving framework for large images, enabling efficient image encryption and computation under the semi-honest model. Specifically, we divide the large image into multiple sub-images, which allows us to maintain smaller HE parameters and reduce key size. By parallel processing the sub-image ciphertexts and introducing a new bootstrapping placement strategy, we significantly reduce encryption overhead and enhance user experience. On the server side, we optimize the large image convolution operation through a repeated packing technique and implement the Sobel operator computation based on HE. To improve gradient direction calculation for the Sobel operator, we introduce a new polynomial approximation method for the reciprocal function based on the sign function, which can be applied to other HE-based protocols.