The introduction of Renewable Energy Sources (RES) and Distributed Energy Resources (DERs) has led to the formulation of Microgrids (MGs) and Networks of MGs (NMGs). MGs and NMGs can operate in islanded mode, transforming the grid into a more distributed system. This has led to extensive studies in the literature on distributed hierarchical control strategies. Previous works have proposed distributed secondary level frequency and voltage regulation control schemes for Battery Energy Storage System (BESS)-based Grid-Forming (GFM) inverters with State of Charge (SoC) balancing. However, links to tertiary level control in terms of service-based reserves and local resource adequacy in MGs are largely unexplored. Therefore, this paper proposes a BESS energy reserves framework, to quantify reserves for hierarchical control operation. Additionally, to partially regulate the proposed energy reserves, we propose the formulation of a modified Distributed-Averaging Proportional-Integral (DAPI) controller with regulation energy reserve consensus. Controller Hardware-In-the-Loop (CHIL) simulation is performed on an MG topologically based on the IEEE 13 bus test feeder system in MATLAB/Simulink. The proposed scheme results illustrate effective frequency and voltage regulation along with improved power and energy sharing across droop-controlled and Virtual Synchronous Machine (VSM) controlled inverters.

Optimal experimental design provides a way of determining a-priori the best locations at which to place accelerometers in vibrations analysis experiments. However, in practice, sensors often fail during experimentation due high mechanical accelerations. There have been limited works exploring the use of robust OED in the context of vibrations analysis, where design spaces (i.e. candidate sensor locations and orientations) are high-dimensional and the finite-element models are expensive to compute. Therefore, this work considers the application of more general robust OED formulations to such a structural dynamics problem. We employ a relaxation-based approach that enables the use of efficient gradient-based optimization. Furthermore, we leverage a binary-inducing penalty during optimization to provide a binary sensor design as an alternative to leveraging post-optimization rounding heuristics. We consider performance metrics based on the log-determinant of the parameter covariance as well those based on parameter and prediction mean-squared errors. We find that although robust and classical designs are similar for the structural dynamics problem of interest, robust designs outperform classical designs on average over relevant failure scenarios of interest.

The theory of the operator $$G(x) = |\underline{x}|^2 \frac{\partial }{\partial x_0} + \underline{x} \sum_{j=1}^n x_j \frac{\partial }{\partial x_j} $$ is deeply associated with the slice monogenic function theory and has grown in recent years. In particular, for $n=3$ the quaternionic version of $G$ has been recently used to study the quaternionic slice regular function theory. This work extends the study of the $G$ operator in two senses: a) Clifford's analysis structure. The function theory induced by the operator \begin{align*}\mathcal H_a (x) = {\underline a} ( {x}) \frac{\partial }{\partial x_0} - \sum_{i=1}^n \left( \sum_{j=1}^n a_j ( {x}) \frac{\partial (a^{-1})_i}{\partial y_j}\circ a ( {x}) \right) \frac{\partial}{\partial x_i}, \end{align*} where $a$ is a function with certain properties with domain in $\mathbb R^{n+1}$ is presented extending the already known results of the $G$. Also some properties of the material derivative are presented as consequences of function theory induced by $\mathcal H_a$. b) Structure of quaternionic analysis. In particular, the case $n=3$ is approached from the point of view of quaternionic analysis.

Continuous ambulatory monitoring of peripheral vascular perfusion could enable earlier detection of vascular dysfunction in individuals with diabetes mellitus and more timely management of cardiovascular disease. Clinical imaging modalities provide high-fidelity vascular information but are impractical for ambulatory use, whereas most wearable devices are limited to single-modality sensing and do not provide imaging. Electrical bioimpedance has the potential to bridge this gap by enabling rapid spatial and temporal imaging while remaining sensitive to hemodynamic changes. Here, we introduce a wearable ring with 8 electrodes and 32-channel bioimpedance sensing for finger blood flow imaging. In 96 healthy participants measured at rest and during autonomic maneuvers, we resolve conductivity images in the digital arteries associated with pulsatile blood flow and train neural network models for continuous cuffless blood pressure waveform estimation. We demonstrate the feasibility of bioimpedance imaging in a ring form factor, supporting its potential for ambulatory cuffless hemodynamic monitoring.

We revisit the proposal to cure the negative density of states in the three-dimensional gravitational path integral by adding spinning states whose spin scales with the central charge. We show that sub-extremal and extremal spinning states below the black hole threshold can cancel the known negativities, and interpret these states as bulk spinning defects. Additionally, certain overspinning states above the black hole threshold can cure these negativities while preserving the spectral gap. Previously interpreted as classical spinning strings, we instead identify these overspinning states with overspinning BTZ geometries, which are smooth pure gravity quotients of AdS$_3$ with no fixed points. All of these spinning geometries exhibit causal pathologies in their Lorentzian continuations. Moreover, the overspinning geometries arise from mixed elliptic-hyperbolic identifications and contain a right-moving temperature and quasinormal modes. We also generalize the computation of scalar correlators to the extremal and overspinning backgrounds.

Scyphozoan jellyfish exhibit the highest locomotive efficiency in the animal kingdom making them of particular interest in fluid dynamics and bioinspired robotics. Despite this prevalent analytical models of jellyfish swimming have been based on the swimming traits of hydrozoan jellyfish which utilize jet propulsion, rather than scyphozoan jellyfish which utilize paddling propulsive methods. Additionally, while stroke frequency is a driving variable in speeds achieved by undulatory swimmers, a similar dependence has not been previously explored for jellyfish. This work investigates the relationship between stroke frequency and swimming speeds in two species of scyphozoan jellyfish, Aurelia aurita and Cassiopea xamachana. An experimental study was conducted using a biohybrid technique that controls the muscle contraction frequency of freely swimming, live jellyfish with portable, implanted microelectronics. Swimming speeds were measured from video recordings in a 2.4 m tall water tank. It was found that despite differences in their natural swimming frequencies, the Aurelia and Cassiopea displayed similar speed-frequency relationships with peak swimming speeds occurring at 0.55 +/- 0.05 Hz and 0.50 +/- 0.05 Hz respectively. The difference in natural stroke frequency displayed by scyphomedusea despite the shared relationship between swimming speed and stroke frequency in these two species, suggests that natural stroke frequency may be more related to other functions such as filter feeding, rather than locomotion. A new analytical model developed for scyphozoan, paddling jellyfish was shown to have closer agreement with the experimental results than existing models based on jet propulsion. The model demonstrated the driving factors in the relationship between swimming speed and stroke frequency to be the speed of the jellyfish bell margin and changes in body kinematics with stroke frequency.

There has been considerable interest in constructing modified gravity theories that propagate only two degrees of freedom (DOFs), corresponding to the tensorial gravitational waves of general relativity. Within the framework of spatially covariant gravity (SCG), the conditions for obtaining 2-DOF theories can be derived from Hamiltonian constraint analysis, but it is generally difficult to translate those conditions into explicit SCG Lagrangians, especially when the Lagrangian depends nonlinearly on the extrinsic curvature. In this work, we adopt an alternative perturbative approach. We consider polynomial-type SCG Lagrangians up to $d=3$, where $d$ denotes the total number of derivatives in each monomial, and expand them around a cosmological background. By requiring the scalar mode to be eliminated up to cubic order in perturbations, we derive the corresponding conditions on the coefficient functions in the Lagrangian. We find five explicit Lagrangians that propagate only 2 DOFs up to cubic order in perturbations around a cosmological background. These theories therefore provide concrete candidate 2-DOF SCG models, at least at the perturbative level up to cubic order.

We demonstrate a rapid, maskless fabrication method for superconducting terahertz Josephson plasma emitters (JPEs) based on direct ultraviolet laser micromachining of Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ (Bi-2212) single crystals. Although machining debris is formed near the processed regions, uniform stacks of intrinsic Josephson junctions are preserved inside the crystal, enabling stable terahertz emission. Devices fabricated with Ag, Cu, and Cr electrodes all exhibited terahertz radiation, with Cu electrodes showing performance comparable to Ag while offering a low-cost alternative. Spectroscopic and polarization analyses indicate that the emitted radiation is elliptically polarized and dominated by the geometrical cavity resonance mode. Structural and electrical characterizations reveal that the machining width and depth are not limited by the optical spot size but are governed by the anisotropic thermal conductivity of Bi-2212, consistent with a thermally dominated laser ablation process. This direct laser micromachining approach provides a fast and versatile fabrication technique for JPEs and is broadly applicable to superconducting electronics and terahertz devices.

The spread of infectious disease is strongly influenced by social dynamics. In addition to infection risk, individuals vaccination decisions depend on prevailing social behavior: high infection levels and widespread vaccination can increase vaccine uptake, which in turn suppresses infection. This feedback can generate sustained oscillations in disease prevalence and vaccination behavior. Here, we study two such populations undergoing the same behavioral epidemiological limit cycle and introduce weak coupling between them through social influence. We show that coupling leads to synchronization of disease dynamics between the two groups. Moreover, we find that different payoff sensitivity may lead to synchronization or anti synchronization.

We establish a connection between analytic number theory and computational learning theory by showing that the Möbius function belongs to a class of functions that is statistically hard to learn from random samples. Let $μ_R$ denote the restriction of the Möbius function to the squarefree integers in $\{1,\dots,R\}$. Using a recent lower bound of Pandey and Radziwiłł for the $L^1$ norm of exponential sums with Möbius coefficients, we prove that \[ \FR(μ_R) \gg R^{-1/4-ε} \] for every $ε>0$. We then show that, for a suitable absolute constant $c_0>0$, the class of $\{-1,1\}$-valued functions on the squarefree integers with Fourier Ratio at least $c_0$ has Vapnik--Chervonenkis dimension at least $cR$. It follows that any distribution-independent learning algorithm that succeeds uniformly on the class $\mathcal{H}_R(η_R)$ containing $μ_R$, where $η_R \to 0$, requires at least $Ω(R)$ samples. We also discuss a conditional improvement under a strong uniform bound for additive twists of the Möbius function, and we note that the same method applies to the Liouville function.

Many body gravity (MBG) is a novel modified theory of gravity formulated in a 5-D space-time-temperature framework, in which the variation in temperature is recast as a variation in the 5-D metric. Previous work on MBG has shown that it can reproduce galaxy rotation curves, radial acceleration relation and the weak gravitational lensing of the bullet cluster, without the inclusion of dark matter. In this work we show that MBG can reproduce cosmic inflation, and in the process, analyze fundamental relations between interaction, time and gravity. To analyze cosmic inflation using interacting massless scalar fields, we first analyze theoretically a hypothetical universe with a single massive particle, or a collection of non-interacting massive particles. A quantitative relation between time and interaction is developed using Quantum Field Theory (QFT), which suggests that the notion of time becomes ill-defined for such a universe. The mass terms in MBG and General Relativity cause a discrepancy with the QFT results. An interacting massless scalar field then becomes a necessity to resolve the issue at the onset of inflation. However, the entropic terms in the MBG field equations are seen to be consistent with the QFT results and further accelerate inflation. The slow-roll condition is shown to be a natural consequence of the Euler-Lagrange equations of motion governing the massless scalar field in 5-D space-time-temperature, during the early phase of inflation. Finally, the MBG field equations are solved in the context of a Friedmann metric, leading to inflation. The matter era is also investigated.

Ultralight dark matter searches widely assume that signals are monochromatic, with a single frequency set by the mass. This assumption is generally violated in the presence of field mixing, even when the constituent fields have similar frequencies. Instead, dark matter signals can exhibit a two-timescale structure with intrinsic slow modulation. We demonstrate that mixing between ultralight wave dark matter fields induces a parametric structure, leading to a scenario we refer to as wave-envelope dark matter, in which a slow-beating envelope emerges alongside the primary oscillation. This results in distinctive features such as slow modulation and characteristic sideband structures in the frequency spectrum, beyond the conventional monochromatic expectation. As a representative example, we briefly discuss implications for neutrino observables.

Angle-of-arrival (AoA) estimation is a crucial function in wireless communications used for localization, beam-forming, interference management, and other applications. Deep learning (DL) solutions have been proposed for AoA to mitigate limitations of traditional AoA estimation techniques such as sensitivity to noise and the inability to generalize across different array characteristics. A challenge, however, of DL-based approaches is their reliance on large data collection campaigns and model training. This paper proposes the application of Prototypical Networks (PN) to address this challenge and utilizes a real-world dataset collected on a software defined radio (SDR) testbed to validate the effectiveness of the proposed solution. Prototypical Networks excel in extracting representative embeddings from unstructured input data, establishing class prototypes during training that can be few-shot trained on unseen classes. We demonstrate the efficacy of PNs for AoA classification using complex IQ samples, focusing on its ability to correctly classify new, unseen angles that the model was not trained on previously. Our results show that training our proposed ProtoAoA on only 23% of the AoA dataset classes can attain a mean absolute error (MAE) of 3 degrees with only 4-shots of training on the unseen angles - and an MAE of 2 degrees with 32-shots of training data. These results demonstrate that the developed prototypical network architecture requires remarkably few data samples to achieve reliable AoA estimation - and highlights its potential for other wireless applications where data availability is limited.

\textit{Ab initio} pseudo-atomic orbital (PAO) Hamiltonians express the electronic structure of a solid in a compact, localized basis that spans the same Hilbert space as a conventional Slater--Koster tight-binding model, thereby providing an exact \textit{ab initio} representation without any loss of accuracy. Building on this correspondence, we develop an environment-dependent tight-binding (EDTB) framework in which Slater--Koster hopping integrals are augmented with bond-screening functions that capture the local coordination environment. All parameters are determined by fitting to the PAO eigenvalue spectrum across multiple atomic configurations simultaneously, which breaks the degeneracy between screening and hopping parameters and yields physically meaningful, transferable models capable of generating Hamiltonians for large systems with \textit{ab initio} precision. We demonstrate the efficiency and accuracy of the approach on four prototypical systems: bulk platinum, silicon surfaces, Si/Ge~[001] superlattices, and twisted bilayer graphene with up to $4{,}324$ atoms. The method is implemented in the \paoflow{} code and integrates seamlessly with its full post-processing suite, enabling the evaluation of a broad range of electronic, optical, and transport properties.

Self-propelled particles can navigate complex environments, including viscous fluid interfaces with curved geometries. In this work, we study the emergent dynamics of a suspension of self-propelled particles confined to a stationary curved viscous interface. The evolution of the particle configurations is modeled using the Fokker-Planck equation on the curved surface, formulated using Cartan's moving frame method, and coupled to the bulk and surface Stokes equations with flows driven by an interfacial nematic active stress. Specifically, for a spherical vesicle, the flow field and the distribution of the particles are analyzed theoretically and numerically within the framework of spin-weighted functions and spin-weighted spherical harmonics, which provide a natural geometric description of the probability distribution function on the sphere. A linear stability analysis about the uniform, isotropic state is performed and predicts a finite-wavelength instability, with mode selection arising from the competition between the vesicle radius and the Saffman-Delbrück length. This instability and the associated mode-selection mechanism are also confirmed in nonlinear numerical simulations using a pseudo-spectral method based on spin-weighted spherical harmonics.

This paper studies the 2021 U.S. inflation forecasting failure. I show that the failure was primarily driven by sample composition rather than functional-form misspecification: estimation samples dominated by the Great Moderation underweight supply-shock regimes, and expectations anchored to that regime were slow to recognize the shift. Three historically informed adjustments, an intercept correction, a similarity re-estimation on 1970s data, and a kernel-weighted estimator, substantially close the forecast gap, and the gains extend to eight additional U.S. price indices. Household survey respondents over 60, whose lifetime includes the 1970s, reported higher inflation expectations from early 2021, consistent with experience-based learning; younger cohorts remained anchored to the prevailing regime. A controlled experiment with large language models conditioned on ``experienced'' and ``young'' professional personas confirms that experiential priors generate significant forecast differences under a common training leakage assumption. Across all three exercises, the source of the prior mattered more than the sophistication of the model.

This paper presents the experimental verification of a planar guided-wave terahertz (THz) spoof surface plasmon polariton (SSPP) bandpass filter (BPF) using a coplanar stripline (CPS) with internal grooves and periodic gaps. The proposed BPF operates by combining the low-pass behavior from the SSPP's band edge and the high-pass behavior from the gaps that act as series capacitors. The higher and lower cut-off frequencies can be tailored by the appropriate selection of the unit cell geometry. For demonstration, a BPF with a center frequency of approximately 1 THz and a bandwidth of 0.25 THz was designed, fabricated, and experimentally validated. The passband around 1 THz is observed in the measurements, along with the lower and higher cut-off frequencies at approximately 0.91 THz and 1.16 THz, respectively, in agreement with simulation results.

Given a thin torus $T_K$ around a knot $K\subset \mathbb{R}^3$, we construct Morse models of cord algebra $Cord(T_K)$ with $\mathbb{Z}$ and loop space coefficients. Using the Multiple time scale dynamics we identify $Cord(T_K; \mathbb{Z})$ with $Cord(K; \mathbb{Z})$. In combination with the works of Cieliebak-Ekholm-Latschev-Ng and Petrak this indirectly relates $Cord(T_K)$ to $0$-th degree Legendrian contact homology $LCH_0(\mathcal{L}^\ast_+ T_K)$ of one component of the unit conormal bundle over $T_K$. Our definition of $Cord(T_K)$ is motivated by $J$-holomorphic curves with boundary on the Lagrangian submanifold $L^\ast_+ T_K\cup\mathbb{R}^3$ with an arboreal singularity along the torus $T_K$.

For each finite configuration of distinct points in the plane, there is an associated lattice of noncrossing partitions. When these points form the vertices of a convex polygon, the result is the classical noncrossing partition lattice, which is enumerated by the Catalan numbers and satisfies many other useful properties. In this article, we examine three variations of this lattice which arise when the starting configuration is allowed to have points on the sides of a convex polygon rather than just the vertex set.

Each finite configuration of points in the plane determines a corresponding lattice of noncrossing partitions. When these points form the vertex set of a convex polygon, the associated lattice is the classical noncrossing partition lattice (introduced by Kreweras in 1972), which makes many appearances in combinatorics and geometric group theory. If, on the other hand, all points of the configuration lie on a common line segment, the result is a Boolean lattice. In this article, we examine the more general class of hull configurations, which we define to be those which lie either on a line segment or on the boundary of a convex polygon. We prove that the corresponding lattices of noncrossing partitions are unions of maximal Boolean subposets and, under certain circumstances, have symmetric chain decompositions.

Deep neural networks (DNNs) remain largely opaque at inference time, limiting our ability to detect and diagnose malicious input manipulations such as adversarial examples. Existing detection methods predominantly rely on layer-local signals (e.g., activations or attribution scores), leaving cross-layer information flow and execution structure under-explored. We introduce NeuroTrace, a framework and open dataset for analyzing inference provenance through Inference Provenance Graphs (IPGs). IPGs are heterogeneous graphs that capture both activation behavior and parameter-induced dataflow during a model's forward pass, providing a structured representation of how information propagates through the network. NeuroTrace includes (i) a reproducible extraction engine that instruments model execution, (ii) a standardized graph representation compatible with heterogeneous GNNs, and (iii) a benchmark suite spanning multiple adversarial attack families across vision and malware domains. Using this framework, we evaluate IPG-based detectors for adversarial example detection under intra-attack, multi-attack, and cross-threat transfer settings. Our results show that inference provenance provides a strong and transferable signal for distinguishing adversarial and benign inputs, achieving consistently high detection performance and improving over prior graph-based baselines. We further analyze the conditions under which provenance-based detection generalizes across attack types, as well as the associated runtime and storage trade-offs. By releasing the dataset, extraction pipeline, and evaluation protocol, NeuroTrace enables systematic study of inference-time behavior and establishes inference provenance as a practical foundation for building more transparent and auditable machine learning systems.

We investigate the behaviour of quantum fields in null-shifted Rindler wedges and analyse the particle spectra perceived by accelerated observers associated with these null deformations. Unlike the standard Unruh effect, our analysis compares two accelerated frames connected by a null displacement. We consider both massive scalar and Dirac fields, constructing their corresponding mode solutions in Rindler coordinates. Using normalised field expansions, we compute the Bogoliubov transformations between modes defined in the two null-shifted wedges. Our results demonstrate a fundamental breakdown of thermality: the presence of mass modifies the mode structure, rendering the characteristic exponential mixing of frequencies absent. This suggests that the massive field remains unexcited on this background, leading to a manifestly nonthermal response. These findings highlight that thermality in accelerated frames depends sensitively on the conformal symmetry of the field, which is broken by the introduction of a mass term.

This paper presents a hybrid switched-capacitor converter (HSCC) with a novel multi-mode modulation (3M) scheme for wide-range voltage regulation in 48-V automotive power systems. By introducing a three-state operating sequence beyond the conventional 2:1 resonant operation, the proposed converter achieves variable step-down conversion ratios while preserving soft-switching operation in most transitions. The proposed modulation supports both zero-current switching (ZCS) and zero-voltage switching (ZVS) modes, enabling efficient operation over a broad range of load and conversion conditions. To enable voltage regulation, a closed-loop control configuration is proposed with a linear proportional-integral (PI) controller, with gain tuning assisted by reinforcement learning (RL) to address the converter's nonlinear and variable-frequency nature while maintaining good transient performance. A hardware prototype was built to validate the proposed modulation scheme. The measured results verify ZCS operation over voltage conversion ratios of 0.2--0.4, with a peak efficiency exceeding 92\% at 100~W, and efficiency above 88\% over a wide operating range for 3:1 conversion. The feasibility of both ZCS and ZVS operation is also experimentally demonstrated. These results show that the proposed HSCC significantly extends the practical regulation range of resonant switched-capacitor converters while maintaining high efficiency.

Probabilistic computers offer promising solutions for computationally hard problems in domains such as combinatorial optimization and machine learning. A key building block in these systems is the probabilistic bit (p-bit), which relies on superparamagnetic tunnel junctions (sMTJs) as its source of randomness. A challenging threshold to cross for scaling sMTJ-based p-bit systems is integration of sMTJs with CMOS technology. In this work, we present experimental results of a p-bit unit cell using sMTJs integrated with 130 nm CMOS technology and demonstrate that the sMTJ's resistance fluctuations can generate a corresponding fluctuating digital output voltage which is tunable via the input voltage. These findings establish the feasibility of CMOS-compatible, sMTJ-based probabilistic circuits and mark a key step toward scalable hardware for real-world probabilistic computing applications.

The growing volume of data in scientific domains has made spatial query processing increasingly challenging due to high data transfer costs across the memory hierarchy and limited memory bandwidth. To address these bottlenecks and reduce the energy consumed on data movement, this work explores Processing-in-Memory (PIM) systems by executing range queries directly inside memory chips. Unlike prior PIM studies centered on linear scans or hash-based queries, this work is the first to map R-tree range queries onto commercial PIM hardware. The proposed broadcast-based method constructs the R-tree bottom-up on the CPU, broadcasts top levels to UPMEM DPUs (DRAM Processing Units) for global filtering, and distributes lower levels for parallel batched queries in a CPU-DPU system. We evaluate our approach on two real spatial datasets, Sports (999K rectangles) and Lakes (8.4M rectangles), and assess scalability using a synthetic dataset with up to 16M rectangles and 3.9M queries on a commercial UPMEM PIM system with up to 2,540 DPUs. Across all datasets, broadcast-based execution consistently outperforms subtree partitioning by preventing communication from dominating execution. On the Lakes dataset, strong scaling from 512 to 2,540 DPUs reduces kernel time from 64.9 s to 17.6 s, yielding up to 3.66x kernel and 2.70x end-to-end speedup relative to the CPU R-tree search on the same system. The PIM kernel also consumes approximately 3.4x less energy than the corresponding CPU search (e.g., 59.6 kJ vs. 167.0 kJ on Lakes), demonstrating scalable and energy-efficient hierarchical spatial range queries.

The large-area synthesis of high-crystalline-quality two-dimensional (2D) materials is at the core of novel material integration for semiconductor technology. This effort relies on developing fabrication and characterization techniques that can uncover the material's intrinsic properties by preserving its pristine conditions. In this article, we present an all ultra-high-vacuum cluster for the growth using molecular beam epitaxy of 2D semiconductors that are unstable under ambient conditions and optical spectroscopy using low temperature (20 K) photoluminescence and Raman scattering. The optical chamber of the setup provides micrometer scale spatial resolution and the ability to scan the entire wafer. The performance of its setup regarding spatial resolution, temperature control over a temperature range of 20-300 K using a closed-cycle cryostat and long-term preservation are demonstrated using as-grown post-transition metal monochalcogenides. Furthermore, we introduce a deconvolution-based algorithm to recover spatial information under vibration using a system-specific point-spread function. This enables in situ analysis of the structural and optoelectronic properties of as-grown materials in their pristine form, providing rich and reproducible feedback for both fundamental studies and the optimization of scalable 2D material growth toward integration in advanced devices.

We study a discrete-time multi-period portfolio optimization problem under an explicit constraint on the Deviation Conditional Value-at-Risk (DCVaR), defined as the excess of Conditional Value-at-Risk over expected terminal wealth. The objective is to maximize expected return subject to a global tail-risk constraint, leading to a time-inconsistent precommitment problem. We propose a recurrent neural-network-based approach to approximate the optimal precommitment policy, which accommodates path-dependent risk constraints and highdimensional state dynamics without relying on dynamic programming. The explicit constraint formulation allows for exact penalty methods and provides a transparent notion of feasibility. The methodology is validated in a classical complete-market financial model and extended to a multi-period portfolio allocation problem in (re)insurance, capturing the long-term risk dynamics of insurance liabilities.

In this article, we mathematically justify (globally in time) a Baer-Nunziato type system from the non-isentropic compressible Navier-Sokes equations for heat conducting ideal gases posed over the torus and in one space dimension. The breakthrough in this paper is to define and prove the global existence of solutions in a framework intermediate between weak and strong solutions and then to derive the system through homogenization and Young measures characterization. Note that the main difficulty is to derive a priori uniform bounds on appropriate unknowns in the presence of piecewise constant coefficients (viscosity and adiabatic constants) exhibiting rapid oscillations between two positive values.

The Variational Quantum Linear Solver (VQLS), a hybrid quantum-classical algorithm for solving linear systems, faces a practical scalability bottleneck: the Linear Combination of Unitaries (LCU) decomposition requires O(L^2) circuit evaluations per optimizer iteration, where $L$ can grow as 4^n for n-qubit systems for the worst case scenario. We address this computational bottleneck through two complementary strategies. First, we present a distributed VQLS (D-VQLS) framework, built on NVIDIA CUDA-Q, that enables asynchronous, scalable distribution of the O(L^2) cost-function evaluations. Second, a fast Walsh--Hadamard transform (FWHT)-based Pauli decomposition with 1% coefficient thresholding curbs the exponential growth of LCU terms, reducing L from O}(2^n) to O(1) for n > 6 qubits and compressing the per-iteration circuit complexity from O(n * 4^n) to O(n) for sparse, structured matrices. For a 10-qubit tridiagonal Toeplitz system, this yields a 256x reduction, from 23 million to 90,112 circuits per iteration, while preserving over $99.99\%$ solution fidelity. Additionally, to inform feasibility on early fault-tolerant QPUs, the paper provides resource estimates -- gate counts, qubit requirements, and circuit evaluations per iteration -- for VQLS applied to arbitrary matrices. The D-VQLS framework is validated on the NERSC Perlmutter supercomputer using multi-node, multi-GPU ideal state-vector simulations, achieving over 99.99% fidelity against classical solutions on tridiagonal Toeplitz and Hele--Shaw flow benchmarks, with near-ideal strong scaling up to 24 GPUs and 95.3% weak scaling efficiency at 96 GPUs processing 360,448 circuits per iteration for a 10-qubit system. Systematic profiling identifies the optimal resource allocation for distributed quantum circuit workloads, yielding a 2.52x speedup for the configurations studied.

Stereolithography (SLA) and Tailored Fiber Placement (TFP) were combined to fabricate shape memory alloy hybrid composites (SMAHC) featuring a three-layer structure and exhibiting out of plane bending deformation when activated, in a fully integrated, additive manufacturing process. SMA wires as active elements were attached to a textile reinforcement layer, which then was embedded within a UV-curable polymer matrix and combined with a geometrically tailored toplayer, featuring the re-entrant honeycomb architecture. Exploiting the design freedom of SLA, the overall mechanical response of the SMAHC can be systematically adjusted, enabling controlled out-of-plane bending during thermal activation. Two different SMA integration strategies - manual embedding and automated TFP were investigated to assess their influence on actuation behavior, reproducibility, and deformation behaviour. A total of eight geometric configurations were manufactured and experimentally characterized using synchronized optical measurements. The results demonstrate that the combination of SLA-based fabrication and textile-mediated SMA integration enables precise control over the actuation response, while the use of re-entrant honeycomb structures provides an effective approach to tailor stiffness and deformation characteristics. In particular, the automated TFP integration yields improved reproducibility and more symmetric deformation behavior compared to manual fabrication. The presented approach establishes a fully additive manufacturing route for SMAHCs, enabling the realization of structurally integrated, morphing composite systems with programmable mechanical properties.