Multi-static backscatter networks (BNs) are strong candidates for joint communication and localization in the ambient IoT paradigm for 6G. Enabling real-time localization in large-scale multi-static deployments with thousands of devices require highly efficient algorithms for estimating key parameters such as range and angle of arrival (AoA), and for fusing these parameters into location estimates. We propose two low-complexity algorithms, Joint Range-Angle Clustering (JRAC) and Stage-wise Range-Angle Estimation (SRAE). Both deliver range and angle estimation accuracy comparable to FFT- and subspace-based baselines while significantly reducing the computation. We then introduce two real-time localization algorithms that fuse the estimated ranges and AoAs: a maximum-likelihood (ML) method solved via gradient search and an iterative re-weighted least squares (IRLS) method. Both achieve localization accuracy comparable to ML-based brute force search albeit with far lower complexity. Experiments on a real-world large-scale multi-static testbed with 4 illuminators, 1 multi-antenna receiver, and 100 tags show that JRAC and SRAE reduce runtime by up to 40X and IRLS achieves up to 500X reduction over ML-based brute force search without degrading localization accuracy. The proposed methods achieve 3 m median localization error across all 100 tags in a sub-6GHz band with 40 MHz bandwidth. These results demonstrate that multi-static range-angle estimation and localization algorithms can make real-time, scalable backscatter localization practical for next-generation ambient IoT networks.

Advanced Aerial Mobility (AAM) operations require strategic flight planning services that predict both spatial and temporal uncertainties to safely validate flight plans against hazards such as weather cells, restricted airspaces, and CNS disruption areas. Current uncertainty estimation methods for AAM vehicles rely on conservative linear models due to limited real-world performance data. This paper presents a novel Kalman Filter-based uncertainty propagation method that models AAM Flight Management System (FMS) architectures through sigmoid-blended measurement noise covariance. Unlike existing approaches with fixed uncertainty thresholds, our method continuously adapts the filter's measurement trust based on progress toward waypoints, enabling FMS correction behavior to emerge naturally. The approach scales proportionally with control inputs and is tunable to match specific aircraft characteristics or route conditions. We validate the method using real ADS-B data from general aviation aircraft divided into training and verification sets. Uncertainty propagation parameters were tuned on the training set, achieving 76% accuracy in predicting arrival times when compared against the verification dataset, demonstrating the method's effectiveness for strategic flight plan validation in AAM operations.

This paper presents a physics-informed neural network approach for dynamic modeling of saturable synchronous machines, including cases with spatial harmonics. We introduce an architecture that incorporates gradient networks directly into the fundamental machine equations, enabling accurate modeling of the nonlinear and coupled electromagnetic constitutive relationship. By learning the gradient of the magnetic field energy, the model inherently satisfies energy balance (reciprocity conditions). The proposed architecture can universally approximate any physically feasible magnetic behavior and offers several advantages over lookup tables and standard machine learning models: it requires less training data, ensures monotonicity and reliable extrapolation, and produces smooth outputs. These properties further enable robust model inversion and optimal trajectory generation, often needed in control applications. We validate the proposed approach using measured and finite-element method (FEM) datasets from a 5.6-kW permanent-magnet (PM) synchronous reluctance machine. Results demonstrate accurate and physically consistent models, even with limited training data.

In recent times, there has been considerable interest in fault detection within electrical power systems, garnering attention from both academic researchers and industry professionals. Despite the development of numerous fault detection methods and their adaptations over the past decade, their practical application remains highly challenging. Given the probabilistic nature of fault occurrences and parameters, certain decision-making tasks could be approached from a probabilistic standpoint. Protective systems are tasked with the detection, classification, and localization of faulty voltage and current line magnitudes, culminating in the activation of circuit breakers to isolate the faulty line. An essential aspect of designing effective fault detection systems lies in obtaining reliable data for training and testing, which is often scarce. Leveraging deep learning techniques, particularly the powerful capabilities of pattern classifiers in learning, generalizing, and parallel processing, offers promising avenues for intelligent fault detection. To address this, our paper proposes an anomaly-based approach for fault detection in electrical power systems, employing deep autoencoders. Additionally, we utilize Convolutional Autoencoders (CAE) for dimensionality reduction, which, due to its fewer parameters, requires less training time compared to conventional autoencoders. The proposed method demonstrates superior performance and accuracy compared to alternative detection approaches by achieving an accuracy of 97.62% and 99.92% on simulated and publicly available datasets.

This work presents the demonstration of lattice filters based on laterally excited bulk acoustic resonators (XBARs). Two filter implementations, namely direct lattice and layout-balanced lattice topologies, are designed and fabricated in periodically poled piezoelectric film (P3F) thin-film lithium niobate (TFLN). By leveraging the strong electromechanical coupling of XBARs in P3F TFLN together with the inherently wideband nature of the lattice topology, 3-dB fractional bandwidths (FBWs) of 27.42\% and 39.11\% and low insertion losses (ILs) of 0.88 dB and 0.96 dB are achieved at approximately 20 GHz for the direct and layout-balanced lattice filters, respectively, under conjugate matching. Notably, all prototypes feature compact footprints smaller than 1.3 mm\textsuperscript{2}. These results highlight the potential of XBAR-based lattice architectures to enable low-loss, wideband acoustic filters for compact, high-performance RF front ends in next-generation wireless communication and sensing systems, while also identifying key challenges and directions for further optimization.

Classical controllability and observability characterise reachability and reconstructibility of the full system state and admit equivalent geometric and eigenvalue-based Popov-Belevitch-Hautus (PBH) tests. Motivated by large-scale and networked systems where only selected linear combinations of the state are of interest, this paper studies functional generalisations of these properties. A PBH-style framework for functional system properties is developed, providing necessary and sufficient spectral characterisations. The results apply uniformly to diagonalizable and non-diagonalizable systems and recover the classical PBH tests as special cases. Two new intrinsic notions are introduced: intrinsic functional controllability, and intrinsic functional stabilizability. These intrinsic properties are formulated directly in terms of invariant subspaces associated with the functional and provide verifiable conditions for the existence of admissible augmentations required for functional controller design and observer-based functional controller design. The intrinsic framework enables the generalized separation principle at the functional level, establishing that functional controllers and functional observers can be designed independently. Illustrative examples demonstrate the theory and highlight situations where functional control and estimation are possible despite lack of full-state controllability or observability.

Large language models (LLMs) and multimodal models have become powerful general-purpose reasoning systems. However, radio-frequency (RF) signals, which underpin wireless systems, are still not natively supported by these models. Existing LLM-based approaches for telecom focus mainly on text and structured data, while conventional RF deep-learning models are built separately for specific signal-processing tasks, highlighting a clear gap between RF perception and high-level reasoning. To bridge this gap, we introduce RF-GPT, a radio-frequency language model (RFLM) that utilizes the visual encoders of multimodal LLMs to process and understand RF spectrograms. In this framework, complex in-phase/quadrature (IQ) waveforms are mapped to time-frequency spectrograms and then passed to pretrained visual encoders. The resulting representations are injected as RF tokens into a decoder-only LLM, which generates RF-grounded answers, explanations, and structured outputs. To train RF-GPT, we perform supervised instruction fine-tuning of a pretrained multimodal LLM using a fully synthetic RF corpus. Standards-compliant waveform generators produce wideband scenes for six wireless technologies, from which we derive time-frequency spectrograms, exact configuration metadata, and dense captions. A text-only LLM then converts these captions into RF-grounded instruction-answer pairs, yielding roughly 12,000 RF scenes and 0.625 million instruction examples without any manual labeling. Across benchmarks for wideband modulation classification, overlap analysis, wireless-technology recognition, WLAN user counting, and 5G NR information extraction, RF-GPT achieves strong multi-task performance, whereas general-purpose VLMs with no RF grounding largely fail.

In this paper, we present a detailed convergence analysis of a recently developed approximate Newton-type fully distributed optimization method for smooth, strongly convex local loss functions, called Network-GIANT, which has been empirically illustrated to show faster linear convergence properties while having the same communication complexity (per iteration) as its first order distributed counterparts. By using consensus based parameter updates, and a local Hessian based descent direction at the individual nodes with gradient tracking, we first explicitly characterize a global linear convergence rate for Network-GIANT, which can be computed as the spectral radius of a $3 \times 3$ matrix dependent on the Lipschitz continuity ($L$) and strong convexity ($μ$) parameters of the objective functions, and the spectral norm ($σ$) of the underlying undirected graph represented by a doubly stochastic consensus matrix. We provide an explicit bound on the step size parameter $η$, below which this spectral radius is guaranteed to be less than $1$. Furthermore, we derive a mixed linear-quadratic inequality based upper bound for the optimality gap norm, which allows us to conclude that, under small step size values, asymptotically, as the algorithm approaches the global optimum, it achieves a locally linear convergence rate of $1-η(1 -\fracγμ)$ for Network-GIANT, provided the Hessian approximation error $γ$ (between the harmonic mean of the local Hessians and the global hessian (the arithmetic mean of the local Hessians) is smaller than $μ$. This asymptotically linear convergence rate of $\approx 1-η$ explains the faster convergence rate of Network-GIANT for the first time. Numerical experiments are carried out with a reduced CovType dataset for binary logistic regression over a variety of graphs to illustrate the above theoretical results.

Designing a speech quality assessment (SQA) system for estimating mean-opinion-score (MOS) of multi-rate speech with varying sampling frequency (16-48 kHz) is a challenging task. The challenge arises due to the limited availability of a MOS-labeled training dataset comprising multi-rate speech samples. While self-supervised learning (SSL) models have been widely adopted in SQA to boost performance, a key limitation is that they are pretrained on 16 kHz speech and therefore discard high-frequency information present in higher sampling rates. To address this issue, we propose a spectrogram-augmented SSL method that incorporates high-frequency features (up to 48 kHz sampling rate) through a parallel-branch architecture. We further introduce a two-step training scheme: the model is first pre-trained on a large 48 kHz dataset and then fine-tuned on a smaller multi-rate dataset. Experimental results show that leveraging high-frequency information overlooked by SSL features is crucial for accurate multi-rate SQA, and that the proposed two-step training substantially improves generalization when multi-rate data is limited.

With the ongoing transition of electricity markets worldwide from hourly to intra-hourly bidding, market participants--especially Renewable Energy Sources (RES)--gain improved opportunities to adjust energy and reserve schedules and to benefit from more accurate higher-resolution forecasts. However, this shift requires participants to update decision-making frameworks and to strengthen uncertainty management in order to fully exploit the new market potential. In particular, Renewable-Based Virtual Power Plants (RVPPs) aggregating dispatchable and non-dispatchable RES must account for these changes through market-oriented scheduling methods that efficiently address multiple uncertainties, including electricity prices, RES generation, and demand consumption. In this vein, this paper proposes a multi-bound robust optimization framework to simultaneously capture these uncertainties, explicitly incorporate intra-hourly variability, and differentiate the deviation levels (frequent, moderate deviations and rare, extreme ones) of uncertain parameters. The proposed approach yields less conservative and more implementable bidding and scheduling decisions, thus improving RVPP profitability in both energy and reserve markets. Simulation studies compare the proposed method with standard robust optimization and evaluate the operational, market-strategy, and economic impacts of quarter-hourly versus hourly market resolution. Results indicate that the normalized absolute differences, across different uncertainty-handling strategies, between hourly and 15-minute schedules are 18.0--34.2% for day-ahead traded energy, and 28.7--65.6% and 10.1--16.3% for upward and downward reserve traded in the secondary reserve market, respectively. Furthermore, relative to classic robust optimization, the proposed multi-bound approach increases profit by 24.9--49.2% across the considered strategies.

We propose a parameter-minimal neural architecture for solving differential equations by restricting the hypothesis class to Horner-factorized polynomials, yielding an implicit, differentiable trial solution with only a small set of learnable coefficients. Initial conditions are enforced exactly by construction by fixing the low-order polynomial degrees of freedom, so training focuses solely on matching the differential-equation residual at collocation points. To reduce approximation error without abandoning the low-parameter regime, we introduce a piecewise ("spline-like") extension that trains multiple small Horner models on subintervals while enforcing continuity (and first-derivative continuity) at segment boundaries. On illustrative ODE benchmarks and a heat-equation example, Horner networks with tens (or fewer) parameters accurately match the solution and its derivatives and outperform small MLP and sinusoidal-representation baselines under the same training settings, demonstrating a practical accuracy-parameter trade-off for resource-efficient scientific modeling.

This paper investigates a cyber-physical DC microgrid employing a nonlinear distributed consensus-based control scheme for coordinated integration and management of distributed generating units within an expandable framework. Relying on nested primary andsecondary control loops; a (distributed) outer-loop and a (decentralized) inner-loop, the controller achieves proportional current sharing among all distributed generation units, while dynamically operating within predefined voltage limits. A rigorous Lyapunov-based stability analysis establishes a scalable global exponential stability certificate under some tuning conditions and sufficient time-scale separation between the control loops, based on singular perturbation theory. An optimization-based tuning strategy is then formulated to identify and subsequently diminish unstable operating conditions. In turn, various practical tuning strategies are introduced to provide stable operations while facilitating near-optimal proportional current sharing. The effectiveness of the proposed control framework and tuning approaches are finally supported through time-domain simulations of a case-specific low-voltage DC microgrid.

We introduce a system capable of faithfully modifying the perceptual voice quality of creak while preserving the speaker's perceived identity. While it is well known that high creak probability is typically correlated with low pitch, it is important to note that this is a property observed on a population of speakers but does not necessarily hold across all situations. Disentanglement of pitch from creak is achieved by augmentation of the training dataset of a speech synthesis system with a speaker manipulation block based on conditional continuous normalizing flow. The experiments show greatly improved speaker verification performance over a range of creak manipulation strengths.

Proximity operations of rigid bodies, such as spacecraft rendezvous and docking, require precise tracking of both position and attitude over finite time intervals. These operations are often repeated under uncertain conditions, with unknown but repeatable parameters and disturbances. Adaptive iterative learning control (ILC) is well suited to such tasks, as it can track desired trajectories while learning unknown, iteration-invariant signals or parameters. However, conventional adaptive ILC faces two challenges: (i) the coupling between rotational and translational dynamics complicates the design of the two coordinated learning loops for position and attitude, and (ii) standard adaptive ILC designs cannot guarantee bounded control inputs. To address these issues, we propose a dual-number-based, segment-based two-loop adaptive ILC framework for simultaneous high-precision position and attitude tracking. The framework employs two learning loops that interact through a dual-number representation of tracking errors, combining position and attitude errors into a single mathematical object for unified control design. A segment-based dynamic projection mechanism ensures that both parameter estimates and control inputs remain bounded without prior knowledge of uncertainties. Mathematical analysis and numerical simulations demonstrate that the proposed framework significantly enhances tracking performance under unknown but repeatable uncertainties and strong rotational-translational coupling.

We investigate the optimal transport (OT) problem over networks, wherein supply and demand are conceptualized as temporal marginals governing departure rates of particles from source nodes and arrival rates at sink nodes. This setting extends the classical OT framework, where all mass is conventionally assumed to depart at $t = 0$ and arrive at $t = t_f$. Our generalization accommodates departures and arrivals at specified times, referred as departure--arrival(DA) constraints. In particular, we impose nodal-temporal flux constraints at source and sink nodes, characterizing two distinct scenarios: (i) Independent DA constraints, where departure and arrival rates are prescribed independently, and (ii) Coupled DA constraints, where each particle's transportation time span is explicitly specified. We establish that OT with independent DA constraints admits a multi-marginal optimal transport formulation, while the coupled DA case aligns with the unequal-dimensional OT framework. For line graphs, we analyze the existence and uniqueness of the solution path. For general graphs, we use a constructive path-based reduction and optimize over a prescribed set of paths. From a computational perspective, we consider entropic regularization of the original problem to efficiently provide solutions based on multi-marginal Sinkhorn method, making use of the graphical structure of the cost to further improve scalability. Our numerical simulation further illustrates the linear convergence rate in terms of marginal violation.

While synthetic aperture radar is widely adopted to provide high-resolution imaging at long distances using small arrays, the concept of coherent synthetic aperture communication (SAC) has not yet been explored. This article introduces the principles of SAC for direct satellite-to-device uplink, showcasing precise direction-of-arrival estimation for user equipment (UE) devices, facilitating spatial signal separation, localization, and easing link budget constraints. Simulations for a low Earth orbit satellite at 600 km orbit and two UE devices performing orthogonal frequency-division multiplexing-based transmission with polar coding at 3.5 GHz demonstrate block error rates below 0.1 with transmission powers as low as -10 dBm, even under strong interference when UE devices are resolved but fall on each other's strongest angular sidelobe. These results validate the ability of the proposed scheme to address mutual interference and stringent power limitations, paving the way for massive Internet of Things connectivity in non-terrestrial networks.

Continuous time (CT) and discrete time (DT) linear time invariant (LTI) systems are commonly introduced through distinct mathematical formalisms, which can obscure their underlying dynamical equivalence. This tutorial presents a unified treatment of firstorder CT and DT systems, emphasizing their shared modal structure and stability properties. Beginning with transfer functions and pole zero representations in the Laplace domain, canonical first order low pass and high-pass dynamics are examined from an operational perspective. The discussion then transitions to discrete-time sequences and the Z transform, highlighting geometric sequences as eigenfunctions of DT systems and establishing the correspondence between the left half of the s plane and the interior of the unit circle in the z plane. Practical discretization and sampled data implementations are analyzed to illustrate how continuous time dynamics are reinterpreted through recursion and accumulation in discrete time realizations. By maintaining structural symmetry between domains, the manuscript consolidates established concepts into a coherent framework linking mathematical representation, physical realizability, and implementation.

As the standardization of sixth generation (6G) wireless systems accelerates, there is a growing consensus in favor of evolutionary waveforms that offer new features while maximizing compatibility with orthogonal frequency division multiplexing (OFDM), which underpins the 4G and 5G systems. This article presents affine frequency division multiplexing (AFDM) as a premier candidate for 6G, offering intrinsic robustness for both high-mobility communications and integrated sensing and communication (ISAC) in doubly dispersive channels, while maintaining a high degree of synergy with the legacy OFDM. To this end, we provide a comprehensive analysis of AFDM, starting with a generalized fractional-delay-fractional-Doppler (FDFD) channel model that accounts for practical pulse shaping filters and inter-sample coupling. We then detail the AFDM transceiver architecture, demonstrating that it reuses nearly the entire OFDM pipeline and requires only lightweight digital pre- and post-processing. We also analyze the impact of hardware impairments, such as phase noise and carrier frequency offset, and explore advanced functionalities enabled by the chirp-parameter domain, including index modulation and physical-layer security. By evaluating the reusability across the radio-frequency, physical, and higher layers, the article demonstrates that AFDM provides a low-risk, feature-rich, and efficient path toward achieving high-fidelity communications in the later versions of 6G and beyond (6G+).

Traffic Steering (TS) dynamically allocates user traffic across cells to enhance Quality of Experience (QoE), load balance, and spectrum efficiency in 5G networks. However, TS algorithms remain vulnerable to adversarial conditions such as interference spikes, handover storms, and localized outages. To address this, an AI-driven fuzz testing framework based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to systematically expose hidden vulnerabilities. Using NVIDIA Sionna, five TS algorithms are evaluated across six scenarios. Results show that AI-driven fuzzing detects 34.2% more total vulnerabilities and 5.8% more critical failures than traditional testing, achieving superior diversity and edge-case discovery. The observed variance in critical failure detection underscores the stochastic nature of rare vulnerabilities. These findings demonstrate that AI-driven fuzzing offers an effective and scalable validation approach for improving TS algorithm robustness and ensuring resilient 6G-ready networks.

Millimeter-wave (mmWave) radar has emerged as a compact and powerful sensing modality for advanced perception tasks that leverage machine learning. It is particularly effective in scenarios where vision-based sensors fail to capture reliable information, such as detecting occluded objects or distinguishing between different surface materials in indoor environments. Due to the nonlinear characteristics of mmWave radar signals, deep learning-based methods are well suited for extracting relevant information from in-phase and quadrature (IQ) data. However, the current state of the art in IQ signal-based occluded-object and material classification still offers substantial potential for further improvement. In this paper, we propose a bidirectional cross-attention fusion network that combines IQ signal and FFT-transformed radar features obtained by distinct complex-valued convolutional neural networks (CNNs). In our experiments, we achieve a material classification accuracy of 99.92% on samples collected at the same sensor distances used during training, and an accuracy of 65.56% on samples measured at previously unseen distances, demonstrating improved generalization across varying measurement conditions. Furthermore, our approach improves occluded object classification to 94.20%, outperforming all comparison and ablation models and underscoring the benefit of the proposed fusion strategy.

We consider a time-slotted job-assignment system with a central server, N users and a machine which changes its state according to a Markov chain (hence called a Markov machine). The users submit their jobs to the central server according to a stochastic job arrival process. For each user, the server has a dedicated job queue. Upon receiving a job from a user, the server stores that job in the corresponding queue. When the machine is not working on a job assigned by the server, the machine can be either in internally busy or in free state, and the dynamics of these states follow a binary symmetric Markov chain. Upon sampling the state information of the machine, if the server identifies that the machine is in the free state, it schedules a user and submits a job to the machine from the job queue of the scheduled user. To maximize the number of jobs completed per unit time, we introduce a new metric, referred to as the age of job completion. To minimize the age of job completion and the sampling cost, we propose two policies and numerically evaluate their performance. For both of these policies, we find sufficient conditions under which the job queues will remain stable.

We analyze subliminal transfer in Transformer models, where a teacher embeds hidden traits that can be linearly decoded by a student without degrading main-task performance. Prior work often attributes transferability to global representational similarity, typically quantified with Centered Kernel Alignment (CKA). Using synthetic corpora with disentangled public and private labels, we distill students under matched and independent random initializations. We find that transfer strength hinges on alignment within a trait-discriminative subspace: same-seed students inherit this alignment and show higher leakage {τ\approx} 0.24, whereas different-seed students -- despite global CKA > 0.9 -- exhibit substantially reduced excess accuracy {τ\approx} 0.12 - 0.13. We formalize this with subspace-level CKA diagnostic and residualized probes, showing that leakage tracks alignment within the trait-discriminative subspace rather than global representational similarity. Security controls (projection penalty, adversarial reversal, right-for-the-wrong-reasons regularization) reduce leakage in same-base models without impairing public-task fidelity. These results establish seed-induced uniqueness as a resilience property and argue for subspace-aware diagnostics for secure multi-model deployments.

We introduce Model-Bound Latent Exchange (MoBLE), a decoder-binding property in Transformer autoencoders formalized as Zero-Shot Decoder Non-Transferability (ZSDN). In identity tasks using iso-architectural models trained on identical data but differing in seeds, self-decoding achieves more than 0.91 exact match and 0.98 token accuracy, while zero-shot cross-decoding collapses to chance without exact matches. This separation arises without injected secrets or adversarial training, and is corroborated by weight-space distances and attention-divergence diagnostics. We interpret ZSDN as model binding, a latent-based authentication and access-control mechanism, even when the architecture and training recipe are public: encoder's hidden state representation deterministically reveals the plaintext, yet only the correctly keyed decoder reproduces it in zero-shot. We formally define ZSDN, a decoder-binding advantage metric, and outline deployment considerations for secure artificial intelligence (AI) pipelines. Finally, we discuss learnability risks (e.g., adapter alignment) and outline mitigations. MoBLE offers a lightweight, accelerator-friendly approach to secure AI deployment in safety-critical domains, including aviation and cyber-physical systems.

Let $A \in \mathbb{R}^{m \times n}$ be an arbitrary, known matrix and $e$ a $q$-sparse adversarial vector. Given $y = A x^\star + e$ and $q$, we seek the smallest robust solution set containing $x^\star$ that is uniformly recoverable from $y$ without knowing $e$. While exact recovery of $x^\star$ via strong (and often impractical) structural assumptions on $A$ or $x^\star$ (e.g., restricted isometry, sparsity) is well studied, recoverability for arbitrary $A$ and $x^\star$ remains open. Our main result shows that the smallest robust solution set is $x^\star + \ker(U)$, where $U$ is the unique projection matrix onto the intersection of rowspaces of all possible submatrices of $A$ obtained by deleting $2q$ rows. Moreover, we prove that every $x$ that minimizes the $\ell_0$-norm of $y - A x$ lies in $x^\star + \ker(U)$, which then gives a constructive approach to recover this set.

This paper presents a comparative study of context management strategies for end-to-end Spoken Dialog State Tracking using Speech-LLMs. We systematically evaluate traditional multimodal context (combining text history and spoken current turn), full spoken history, and compressed spoken history approaches. Our experiments on the SpokenWOZ corpus demonstrate that providing the full spoken conversation as input yields the highest performance among models of similar size, significantly surpassing prior methods. Furthermore, we show that attention-pooling-based compression of the spoken history offers a strong trade-off, maintaining competitive accuracy with reduced context size. Detailed analysis confirms that improvements stem from more effective context utilization.

4D live fluorescence microscopy is often compromised by prolonged high intensity illumination which induces photobleaching and phototoxic effects that generate photo-induced artifacts and severely impair image continuity and detail recovery. To address this challenge, we propose the CellINR framework, a case-specific optimization approach based on implicit neural representation. The method employs blind convolution and structure amplification strategies to map 3D spatial coordinates into the high frequency domain, enabling precise modeling and high-accuracy reconstruction of cellular structures while effectively distinguishing true signals from artifacts. Experimental results demonstrate that CellINR significantly outperforms existing techniques in artifact removal and restoration of structural continuity, and for the first time, a paired 4D live cell imaging dataset is provided for evaluating reconstruction performance, thereby offering a solid foundation for subsequent quantitative analyses and biological research. The code and dataset will be public.

Global Climate Models (GCMs) are critical for simulating large-scale climate dynamics, but their coarse spatial resolution limits their applicability in regional studies. Regional Climate Models (RCMs) address this limitation through dynamical downscaling, albeit at considerable computational cost and with limited flexibility. Deep learning has emerged as an efficient data-driven alternative; however, most existing approaches focus on single-variable models that downscale one variable at a time. This paradigm can lead to redundant computation, limited contextual awareness, and weak cross-variable interactions.To address these limitations, we propose a multi-variable Vision Transformer (ViT) architecture with a shared encoder and variable-specific decoders (1EMD). The proposed model jointly predicts six key climate variables: surface temperature, wind speed, 500 hPa geopotential height, total precipitation, surface downwelling shortwave radiation, and surface downwelling longwave radiation, directly from GCM-resolution inputs, emulating RCM-scale downscaling over Europe. Compared to single-variable ViT models, the 1EMD architecture improves performance across all six variables, achieving an average MSE reduction of approximately 5.5% under a fair and controlled comparison. It also consistently outperforms alternative multi-variable baselines, including a single-decoder ViT and a multi-variable U-Net. Moreover, multi-variable models substantially reduce computational cost, yielding a 29-32% lower inference time per variable compared to single-variable approaches. Overall, our results demonstrate that multi-variable modeling provides systematic advantages for high-resolution climate downscaling in terms of both accuracy and efficiency. Among the evaluated architectures, the proposed 1EMD ViT achieves the most favorable trade-off between predictive performance and computational cost.

In this paper, we present an on-chip antenna at 290 GHz that achieves a maximum efficiency of 42\% on a low-resistivity silicon substrate for sub-terahertz integrated transceivers. The proposed antenna is based on a dual-slot structure to accommodate a limited ground plane and maintain desired radiation and impedance characteristics across the target frequency range. The antenna impedance bandwidth reaches 39\% with compact physical dimensions of 0.24$λ_0\times$0.42$λ_0$. Simulation and measurement results confirm its promising antenna performance for potential transceiver and radar applications.

Cost-effectiveness analyses, based on decision-analytic models of disease progression and treatment, are routinely used to assess the economic value of a new intervention and consequently inform reimbursement decisions for the intervention. Many decision-analytic models developed to assess the economic value of highly effective directly acting antiviral (DAA) treatments for the hepatitis C virus (HCV) infection do not incorporate the transmission dynamics of HCV, accounting for which is required to estimate the number of downstream infections prevented by curing an infection. In this study, we develop and validate a comprehensive agent-based simulation (ABS) model of HCV transmission dynamics in the Indian context and use it to: (a) quantify the extent to which the cost-effectiveness of a DAA is underestimated - as a function of its uptake rate - if disease transmission dynamics are not considered in a cost-effectiveness analysis model; and (b) quantify the impact of the frequency and timing of treatment with DAAs, also as a function of their uptake rate, within a disease surveillance period on its cost-effectiveness.

Transformers are crucial for reliable and efficient power system operations, particularly in supporting the integration of renewable energy. Effective monitoring of transformer health is critical to maintain grid stability and performance. Thermal insulation ageing is a key transformer failure mode, which is generally tracked by monitoring the hotspot temperature (HST). However, HST measurement is complex, costly, and often estimated from indirect measurements. Existing HST models focus on space-agnostic thermal models, providing worst-case HST estimates. This article introduces a spatio-temporal model for transformer winding temperature and ageing estimation, which leverages physics-based partial differential equations (PDEs) with data-driven Neural Networks (NN) in a Physics Informed Neural Networks (PINNs) configuration to improve prediction accuracy and acquire spatio-temporal resolution. The computational accuracy of the PINN model is improved through the implementation of the Residual-Based Attention (PINN-RBA) scheme that accelerates the PINN model convergence. The PINN-RBA model is benchmarked against self-adaptive attention schemes and classical vanilla PINN configurations. For the first time, PINN based oil temperature predictions are used to estimate spatio-temporal transformer winding temperature values, validated through PDE numerical solution and fiber optic sensor measurements. Furthermore, the spatio-temporal transformer ageing model is inferred, which supports transformer health management decision-making. Results are validated with a distribution transformer operating on a floating photovoltaic power plant.