Papers

This paper offers a historical overview of the origins and enduring significance of gravitational particle creation, a groundbreaking discovery first formulated in Leonard Parker's 1966 doctoral thesis at Harvard University. By tracing the context in which Parker developed this idea and examining its subsequent influence, the paper highlights how the concept of gravitational particle creation advanced the study of quantum field theory in curved spacetime and profoundly shaped modern cosmology, as well as the quantum theory of black holes.

To increase the power level and energy utilization rate of injection-locked magnetron sources, a dual way 1-kW S-band magnetron microwave power combining system with high combining efficiency was proposed and validated. A waveguide magic-Tee was used to achieve power combining and to provide a pathway for the reference signal. This system utilizes the power-dividing characteristic of a magic-Tee to lock two magnetrons. Frequency tuning is applied to adjust the phase difference between the two magnetrons' signals so as to achieve a high combining efficiency. Experimental results indicate that the microwave power combining efficiency of the proposed system reaches 94.5%. The attenuation of microwave power is caused only by the waveguides and magic-Tee. Our investigation provides a guideline for future high-power microwave combining systems with low losses.

VAM (velocità ascensionale media) is a measurement that quantifies a cyclist's climbing ability. We show that to minimize the time to attain a given height gain\, – \,which is tantamount to maximizing VAM\, – \,a cyclist should climb as steep a constant-grade hill as possible. Apart from the power-to-weight ratio, the limit of steepness is imposed by such factors as the efficiency of pedalling, which is related to feasible cadence, maintaining balance, preventing lifting of the front, and skidding of the rear, wheel. In an appendix, we discuss steepness constraints due to pedalling efficiency. The article itself is focused on consequences of the power available to the cyclist, which can be viewed as a necessary condition to examine other aspects of climbing strategy. We show that\, – \,for given start and end points, and for any fixed average-power constraint\, – \,the brachistochrone, which is the trajectory of minimum ascent time, is the straight line connecting these points, covered with a constant speed, which along such a line is equivalent to a constant power. This is in contrast to the classical solution of a descent brachistochrone under gravity, which is a cycloid along which the speed is not constant.

We study the equilibrium glassy behavior of a multimode random laser model with nonlinear four-body quenched disordered interactions and a global smoothed-cubic constraint on mode intensities. This constraint, which provides a more realistic representation of gain saturation than the commonly used spherical constraint, prevents intensity condensation while preserving the dense, long-range interaction structure characteristic of many multistate random lasers. The model effective Hamiltonian is a function of mode amplitudes with random frequencies and is defined on a complete mode-locked graph. Using large-scale GPU-accelerated Monte Carlo simulations with the Parallel Tempering algorithm, we analyze systems of varying sizes to probe their thermodynamic-limit behavior. Finite-size scaling of the specific heat, of the Parisi overlap distributions, and of the inverse participation ratio's reveals a spin-glass transition, with critical exponents matching the mean-field Random Energy Model universality class. The smoothed-cubic constraint produces broad, non-condensed intensity distributions, avoiding the pseudo-condensation seen in spherical models on the same interaction graph. Our results show that more realistic gain-saturation constraints preserve spin-glass characteristics while enabling simulations of larger, more dilute systems, providing a robust framework for studying glassy random lasers with self-starting mode-locking.

Conservation of symmetries is crucial for reliable quantum simulations of molecular systems, yet compact circuit implementations of fully symmetry-adapted fermionic unitaries have remained elusive beyond the simplest excitation classes. Here we address this issue for the set of singlet spin-adapted generalized singles and doubles operators (saGSD). Using the Wei–Norman approach, we derive exact product formulas that express spin-adapted fermionic unitaries as products of elementary spin-orbital unitaries. To obtain closed-form parameters in the more challenging 28- and 84-dimensional dynamical Lie algebras, we develop a computational discovery-and-verification protocol combining numerical optimization, parameter-structure identification, closed-form inference, and exact validation against reduced Wei–Norman equations. We also introduce an algorithm for constructing closed-form fermionic unitary transformations on Krylov subspaces and extend the fermionic-excitation-based circuit formalism to generators consisting of an anti-Hermitian fermionic string multiplied by arbitrary linear combinations of number-operator products. Together, these developments yield the most compact circuits to date for exact implementation of saGSD unitaries. Finally, for non fully spin-polarized systems, we identify a compact universal symmetry-adapted subset of saGSD that further reduces the quantum resources required for chemically relevant simulations.

Gas phase ultrafast electron diffraction (GUED) has become a powerful technique to directly observe the structural dynamics of photoexcited molecules. GUED reveals information about the nuclear motions that is complementary to the information on the electronic states provided by spectroscopic measurements. GUED experiments so far have utilized a single laser pulse to excite the molecules and an electron pulse to probe the dynamics. This limits the excited states which can be studied to only those that can be reached by absorption of a photon from the ground state or in some cases simultaneous absorption of multiple photons. A broader class of experiments and dynamics can be accessed using two time-delayed laser pulses to access unexplored regions of the potential energy surfaces. As a proof-of-principle experiment using a double excitation, we studied the photodissociation of trifluoroiodomethane molecules that are pre-excited with an infrared (800 nm) femtosecond laser pulse before photo-dissociation is triggered with an ultraviolet (266 nm) femtosecond laser pulse. We have observed significant differences in the dissociation dynamics, with pre-excitation resulting in a slower dissociation process. This new capability can offer new insights on the evolution of nuclear wavepackets in regions of the excited potential energy surface which are inaccessible in single photon excitation. We present a methodology to carry out the measurement, analyze and interpret the data that could be applied to a broad class of experiments.

Photon-number resolved detection with superconducting nanowire single-photon detectors (SNSPDs) attracts increasing interest, but lacks a systematic framework for interpreting and benchmarking this capability. In this work, we combine principal component analysis (PCA) with a new readout technique to explore the photon-number resolving capabilities of SNSPDs and find that the information of the photon number is contained in a single principal component which approximates the time derivative of the average response trace. We introduce a new confidence metric based on the Bhattacharyya coefficient to quantify the photon-number-resolving capabilities of a detector system and show that this metric can be used to compare different systems. Our analysis and interpretation of the principal components imply that photon-number resolution in SNSPDs can be achieved with moderate hardware requirements in terms of both sample rate (5 GSample/sec) and analog bandwidth (3 GHz) and could be implemented in an FPGA, giving a highly scalable solution for real-time photon counting.

Understanding how group interactions influence opinion dynamics is fundamental to the study of collective behavior. In this work, we propose and study a model of opinion dynamics on $d$-uniform hypergraphs, where individuals interact through group-based (higher-order) structures rather than simple pairwise connections. Each one of the two opinions $A$ and $B$ is characterized by a quality, $Q_A$ and $Q_B$, and agents update their opinions according to a general mechanism that takes into account the weighted fraction of agents supporting either opinion and the pooling error, $\alpha$, a proxy for the information lost during the interaction. Through bifurcation analysis of the mean-field model, we identify two critical thresholds, $\alpha_{\text{crit}}^{(1)}$ and $\alpha_{\text{crit}}^{(2)}$, which delimit stability regimes for the consensus states. These analytical predictions are validated through extensive agent-based simulations on both random and scale-free hypergraphs. Moreover, the analytical framework demonstrates that the bifurcation structure and critical thresholds are independent of the underlying topology of the higher-order network, depending solely on the parameters $d$, i.e., the size of the interaction groups, and the quality ratio. Finally, we bring to the fore a nontrivial effect: the large sizes of the interaction groups, could drive the system toward the adoption of the worst option.

Flow networks are fundamental for understanding systems such as animal and plant vasculature or power distribution grids. These networks can encode, transmit, and transform information embodied in the spatial and temporal distribution of their flows. In this work, we focus on a minimal yet physically grounded system that allows us to isolate the fundamental mechanisms by which active flow networks generate and regulate emergent dynamics capable of supporting information transmission. The system is composed of active units that pump fluid and elastic units that store volume. From first principles, we derive a discrete model – an active flow network – that enables the simulation of large systems with many interacting units. Numerically, we show that the pressure field can develop solitary waves, resulting in the spontaneous creation and transmission of localized packets of information stored in the physical properties of the flow. We characterize how these solitary waves emerge from disordered initial conditions in a one-dimensional network, and how their size and propagation speed depend on key system parameters. Finally, when the elastic units are coupled to their neighbors, the solitary waves exhibit even richer dynamics, with diverse shapes and finite lifetimes that display power-law behaviors that we can predict analytically. Together, these results show how simple fluidic elements can collectively create, shape and transport information, laying the foundations for understanding – and ultimately engineering – information processing in active flow systems.

We present a comprehensive study of spin relaxation dynamics at cryogenic temperatures in a rare-earth-doped crystal used for quantum memory applications: $^{171}$Yb:Y$_2$SiO$_5$. Spin relaxation is indeed a major limiting factor for both the efficiency and storage time of quantum memory protocols based on atomic frequency combs in rare-earth materials. The relaxation dynamics among the four ground-state hyperfine levels were simultaneously investigated by optically perturbing the spin population distribution and monitoring its return to thermal equilibrium through optical absorption spectroscopy. By applying different types of perturbations, we were also able to distinguish between two types of relaxation processes, induced by spin-phonon and spin-spin interactions. Below 1 K, we observed that the re-thermalization of the Yb$^{3+}$ ion population takes several hours, driven solely by direct phonon absorption or emission. However, the effective lifetime of individual spin states is much shorter - on the order of several seconds in low-doped (2 ppm) samples and of milliseconds in 10 ppm samples - due to spin-spin interactions. These findings provide valuable guidelines for optimizing doping levels and operating temperatures in rare-earth-doped crystals for quantum applications. Notably, they suggest that atomic frequency combs with lifetimes of several hours could be realized using $^{171}$Yb:Y$_2$SiO$_5$ crystals with slightly less than 2 ppm doping and operating near 1 K.

Accurate short-range prediction of extreme air temperature events remains a fundamental challenge in operational climate-risk management. We present Multi-Modal Weather State Transition Model with Anomaly-Driven Recurrent Attention Network Plus (MMWSTM-ADRAN+), a dual-stream deep learning architecture that couples a regime-aware dynamics model with an anomaly-focused attention mechanism to forecast daily maximum temperature and its extremes. The first stream, MMWSTM, combines bidirectional Long Short-Term Memory (BiLSTM) units with a learnable Markov state transition matrix to capture synoptic-scale weather regime changes. The second stream, ADRAN, integrates bidirectional Gated Recurrent Units (BiGRUs), multi-head self-attention, and a novel anomaly amplification layer to enhance sensitivity to low-probability signals. A lightweight attentive fusion gate adaptively determines the contribution of each stream to the final prediction. Model optimization employs a custom ExtremeWeatherLoss function that up-weights errors on the upper 5% and lower 5% of the temperature distribution, and a time-series data augmentation suite (jittering, scaling, time/magnitude warping) that effectively quadruples the training data

Quantum cryptography remains a topic of enduring scientific and educational interest. Here, we present a clear and accessible framework for exploring the six-state quantum key distribution protocol, an enhanced three-basis extension of the BB84 scheme that combines optical experiments with computational analysis. Designed for testing quantum communication protocols through emulation, this approach provides a robust and cost-effective platform that highlights the fundamental principles of multi-basis encoding and demonstrates how experimental measurements connect directly to theoretical expectations in a controlled tabletop setting.

Electrical isolation is critical to ensure safety and minimize electromagnetic interference (EMI), yet existing methods struggle to simultaneously transmit power and signals through a unified channel. Here we demonstrate a mechanically-isolated gate driver based on microwave-frequency surface acoustic wave (SAW) device on lithium niobate that achieves galvanic isolation of 2.75 kV with ultralow isolation capacitance (0.032 pF) over 1.25 mm mechanical propagation length, delivering 13.4 V open-circuit voltage and 44.4 mA short-circuit current. We demonstrate isolated gate driving for a gallium nitride (GaN) high-electron-mobility transistor, achieving a turn-on time of 108.8 ns comparable to commercial drivers and validate its operation in a buck converter. In addition, our SAW device operates over an ultrawide temperature range from 0.5 K (-272.6 {\deg}C) to 544 K (271 {\deg}C). The microwave-frequency SAW devices offer inherent EMI immunity and potential for heterogeneous integration on multiple semiconductor platforms, enabling compact, high-performance isolated power and signal transmission in advanced power electronics.

K-shell ionization and characteristic x-ray radiation (CXR) by high-energy electrons and positrons in oriented silicon crystals are studied using computer simulation. A method for this simulation has been developed and is described in detail. The evolution of the angular distribution of CXR from the upstream surface of the crystal with changes in the angle between the incident particle momentum and the crystal <100> axis or (100) plane, as well as with changes in particle energy over a wide range (1-1000 GeV), is investigated. It is shown that in most cases this evolution is non-monotonic. The physical mechanisms underlying this behavior are discussed. In particular, the impact of the dechanneling process on CXR produced by electrons is analyzed.

Understanding how water transforms into ice at complex surfaces is central to a wide range of natural and technological processes, yet molecular simulations of this transformation have largely been restricted to idealized surfaces or used models with limited predictive power. Here, we employ advanced molecular simulation tools to study ice nucleation on feldspar, the most abundant mineral in Earth's crust and one of the main ice nucleating particles in the atmosphere. We develop a machine learning interatomic potential trained on ab initio electronic structure calculations, achieving quantum accuracy across a broad range of feldspar-water interfaces. Molecular simulations driven by this potential reveal that the feldspar (110) surface uniquely templates interfacial water into an arrangement resembling the structure of water over ice. Combining this approach with enhanced-sampling and seeded methods, we directly observe nucleation of cubic ice at the (110) feldspar surface, characterize the critical nucleus, and demonstrate that its orientation relative to the mineral surface is consistent with experimental observations. On this basis, we identify the (110) surface as the dominant active site, overturning the currently accepted mechanism, which attributes feldspar's exceptional ice nucleation ability to the (100) surface exposed at defects. These results provide new insight into a principal pathway for atmospheric ice formation. More broadly, they demonstrate the power of ab initio machine learning simulation for the in silico prediction of ice nucleation at surfaces, and clarify the connection between interfacial water structure and the ice-nucleation ability of realistic surfaces.

Macroscopic modeling of a non-crimp fabric's (NCF's) forming behavior is challenging as it strongly depends on the textile architecture, fiber type, and stitching type. While shear is the main deformation mode of woven fabrics, membrane modeling approaches for NCFs should also consider stitching deformation and roving slippage. However, for 0{\textdegree}/90{\textdegree} biaxial NCFs (Biax-NCF) with a symmetrical stitching pattern and high stitch pretension, deviations from a pure-shear assumption in coupon tests are only observed at higher shear angles due to limited roving slippage. In this work, a hyperelastic approach initially proposed for unidirectional NCFs is adopted for a tricot stitched 0{\textdegree}/90{\textdegree} Biax-NCF based on a pure-shear assumption. The shear behavior is experimentally characterized through 45{\textdegree} off-axis-tension tests, and the parameterization is derived from energetic approaches originally developed for woven fabrics. This approach efficiently and adequately describes the general behavior in forming simulations of different geometries. Fiber orientation and location of areas with high shear angles are predicted well, but the peak shear angles are overestimated due to the neglected roving slippage.

Climate change is one of the 21st centurys major challenges. However, the progress in reducing greenhouse gas emissions is perceived as being too slow. Hence, more radical technologies such as stratospheric aerosol injection are entering discussions to limit climate change. This study presents a methodology for evaluating the effects of injecting 20Mt of SO$_2$ into the atmosphere annually on the global radiative balance, photovoltaic potentials, and renewable energy systems under a targeted temperature reduction of 2{\deg}C. Results show that the average annual reduction of PV potentials ranges from 0.25% to 4% up to 12% in Northern Europe during summer. The modeled renewable energy systems largely absorb these reductions resulting in minor capacity shifts with larger changes confined to a few systems. The results show that the inherent flexibility of large scale renewable energy systems helps mitigating changes in cost, but understanding this flexibility is crucial to avoid errors in design.

The heterogeneity of Point of Interest (POI) taxonomies is a persistent challenge for the integration of urban datasets and the development of location-based services. OpenStreetMap (OSM) adopts a flexible, community-driven tagging system, while Foursquare (FS) relies on a curated hierarchical structure. Here we present an openly available benchmark and mapping framework that aligns OSM tags with the FS taxonomy. This resource integrates the richness of community-driven OSM data with the hierarchical structure of FS, enabling reproducible and interoperable urban analytics. The dataset is complemented by an evaluation of embedding and LLM-based alignment strategies and a pipeline that supports scalable updates as OSM evolves. Together, these elements provide both a robust reference resource and a practical tool for the community. Our approach is structured around three components: the construction of a manually curated benchmark as a gold standard, the evaluation of pretrained text embedding models for semantic alignment between OSM tags and FS categories, and an LLM-based refinement stage that enhances robustness and adaptability. The proposed methodology provides a scalable and reproducible solution for taxonomy unification, with direct applications to urban analytics, mobility studies, and smart city services.

Depleted p-channel field effect transistor detectors with repetitive-non-destructive readout (RNDR-DEPFETs) achieve a deep sub-electron noise by averaging several independent measurements of one single event. During the repetitive readout collected electrons are transferred between two readout nodes within each pixel to enable electron number-resolved measurements. The pixels serve as a unit cell of an active pixel sensor to achieve a high level of parallelization and fast readout. These properties are exploited in the DANAE experiment, which aims for the direct detection of light dark matter based with the event signature of electron recoils. We present the experimental characterization of an $64\times64$ RNDR-DEPFET pixel detector with a focus on the charge carrier generation rate. This technology achieves a high time resolution, which increases its sensitivity on rare events with a signal of two or more electrons due to the Poisson distribution of thermal generated electrons.

A major challenge in nonadiabatic molecular dynamics is to automatically and objectively identify the key reaction coordinates that drive molecules toward distinct excited-state decay channels. Traditional manual analyses are inefficient and rely heavily on expert intuition, creating a bottleneck for interpreting complex photochemical processes. To overcome this, we introduce a fully automated machine-learning framework that directly extracts these coordinates from on-the-fly trajectory surface hopping data. By combining an Autoencoder for nonlinear dimensionality reduction with clustering and information entropy analysis, our method autonomously maps reaction channels and pinpoints their governing structural motions. When applied to keto isocytosine and the methaniminium cation, the framework objectively revealed invovled reaction channels and corresponding active coordinates with high efficiency and accuracy. This work establishes an effective paradigm for mechanistic insight in excited-state dynamics, transforming raw trajectory data into clear, interpretable reaction mechanisms.

One challenge in developing a statistical field theory of turbulence is the analysis of the functional equations that govern the complete statistics of the flow field. Simplified models of turbulence may help to develop such a statistical framework. Here, we consider the advection and stretching of an incompressible passive vector field by a spatially linear stochastic field as a model for small-scale turbulence. The model encompasses non-Gaussian statistics due to an intermittent energy flux from large scales to small scales, thereby displaying hallmark features of turbulence. We explore this model using the Hopf functional formalism, which naturally leads to a decomposition of the complex non-Gaussian statistics into Gaussian sub-ensembles based on different realizations of the advecting field. We then characterize intermittency of the model using a numerical implementation, which takes advantage of this statistical decomposition.

In many large metropolitan areas, cars cruising for parking significantly contribute to congestion and pollution. At the same time, parking restrictions are contemplated to encourage the use of greener transport alternatives. We give a short overview of a versatile modelling framework for parking search, which can be solved by both numerical simulations and theoretical developments, and we illustrate its applicability in Lyon, France. Then, we show how this framework can be leveraged to assess the environmental impact of parking restrictions, weighing the antagonistic effects of the thus-induced excess cruising vs. mode shift.

The projection-based wave function (WF)-in-DFT embedding enables an efficient description of both the energetics and properties of large and complex chemical systems, with accuracy exceeding that of pure DFT. Recently, we have proposed using the density matrix renormalization group (DMRG) as the WF method for molecules containing strongly correlated fragments [Beran, P. et al. J. Phys. Chem. Lett. 2023, 14, 3, 716-722]. In this work, we demonstrate that the accuracy of the DMRG-in-DFT approach is primarily limited by the approximate treatment of the coupling between the active component and its environment through nonadditive exchange-correlation functionals. To address this issue, we combine exact exchange to reduce the nonadditive exchange error with a multireference adiabatic connection (AC) scheme to recover nonadditive correlation. The performance of the improved DMRG-in-DFT embedding is illustrated on two prototypical strongly correlated systems: the dissociation of the H20 chain and the cleavage of a triple CN bond in propionitrile.

This study examines the onset of rotating magnetoconvection under an axially imposed magnetic field in the presence of partial thermal stable stratification. Three stratification models-fully unstable, weakly stable, and strongly stable-are analyzed across Ekman numbers $E = 10^{-3}, 10^{-4}$, and $10^{-5}$ (rotation rates) and Roberts numbers $q = 0.01, 1$ and $10$ (diffusivity ratios). Magnetic back-reaction is explored by varying the Elsasser number ($\Lambda$) from 0 to 10. Symmetry-breaking effects of stable layer are assessed via an asymmetry index. Additionally, local scaling laws are derived for onset parameters and convective penetration is quantified numerically. Results show that stable stratification promotes earlier onset and smaller-scale flows, with stronger effects in weak field regimes-hallmarks of penetrative convection. In weak magnetic fields, symmetry breaking is pronounced but weakens for strong field regime. Convective roll thickening peaks at $\Lambda = 1$, while columnarity persists in both weak and strong field regimes due to rotational constraints and elongation effects along imposed field direction, respectively. Magnetic stabilization is most effective at low to moderate values of $q$ but weakens in high $q$ regimes. Penetration depth is inversely related to the magnetic field strength and rotation rates, particularly under strong stratification, but varies non-monotonically with rotation in weakly stratified cases. In the non-magnetic limit, the critical Ekman number $E_c$, exhibiting maximum penetration effects, is obtained as $E_c = 10^{-4}$ for weak stable stratification and $E_c = 10^{-3}$ for strong stable stratification. Obtained results can provide insights into the complex interplay of various geophysical effects on planetary interiors.

To explore the combined effects of partial thermal stable stratification and magnetic back-reaction within Earth's tangent cylinder, we study the onset of magnetoconvection in an infinite plane layer subject to horizontal magnetic field imposed perpendicular to the rotation axis. Three stratification models-fully unstable, weakly stable, and strongly stable-are considered to examine their influence on convective onset. A broad range of rotation rates and diffusivity ratios captures the effects of rotation and thermal-to-magnetic diffusivity contrast, while magnetic back-reaction is analyzed by varying the imposed magnetic field strength. To assess the impact of stratification on convection threshold and flow structure, we derive local scaling laws for critical onset parameters and compute penetration percentages to quantify convective intrusion into the stable layer. Results show that stable stratification promotes earlier onset and smaller-scale flows, with stronger effects in rotation-dominated regimes-hallmarks of penetrative convection. In weak magnetic fields, faster rotation enhances columnarity and intensifies stratification effects while delaying onset. Under strong magnetic fields, thicker rolls persist even at rapid rotation, with limited but noticeable penetration into the stable layer. Magnetic stabilization is more effective at low to moderate diffusivity ratios but weakens at high diffusivity ratio. Penetration decreases with stronger magnetic fields and rotation, especially under strong stratification, but varies non-monotonically with rotation in weak stratification and magnetic regimes. These findings highlight the complex interplay among stratification, rotation, and magnetic field strength in setting the onset and structure of rotating convection relevant to planetary interiors.

This article presents an analytical account of the capacity limits and operational challenges of the main airports in the São Paulo Macrometropolis. Drawing on international examples, such as London Heathrow, it discusses how large hubs combine high traffic generation with severe physical constraints, highlighting how saturation intensifies delays, operating costs, and pressures for expansion. It analyzes capacity scarcity as a central economic problem, in which runways, aprons, boarding gates, and terminals become critical resources whose use requires administrative and market mechanisms, including slot coordination, prioritization rules, and regulatory incentives. It discusses the limitations imposed by high earthmoving costs, environmental impacts, and expropriation costs, which restrict the physical expansion of central airports such as Congonhas and increase dependence on efficiency gains. Demand projections indicate that the combined capacity of Congonhas, Guarulhos, and Viracopos is likely to be exceeded, even in conservative scenarios, reinforcing the urgency of integrated planning. The effects of regulatory restrictions on Congonhas, the challenges of expanding Guarulhos, and the structural difficulties of Viracopos are evaluated, highlighting the strategic relevance of this multi-airport system for sustaining national connectivity.

Computed tomography perfusion (CTP) and magnetic resonance perfusion (MRP) are widely used in acute ischemic stroke assessment and other cerebrovascular conditions to generate quantitative maps of cerebral hemodynamics. While commercial perfusion analysis software exists, it is often costly, closed source, and lacks customizability. This work introduces PyPeT, an openly available Python Perfusion Tool for head CTP and MRP processing. PyPeT is capable of producing cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), time-to-peak (TTP), and time-to-maximum (Tmax) maps from raw four-dimensional perfusion data. PyPeT aims to make perfusion research as accessible and customizable as possible. This is achieved through a unified framework in which both CTP and MRP data can be processed, with a strong focus on modularity, low computational burden, and significant inline documentation. PyPeT's outputs can be validated through an extensive debug mode in which every step of the process is visualized. Additional validation was performed via visual and quantitative comparison with reference perfusion maps generated by three FDA-approved commercial perfusion tools and a research tool. These comparisons show a mean SSIM around 0.8 for all comparisons, indicating a good and stable correlation with FDA-approved tools. The code for PyPeT is openly available at our GitHub https://github.com/Marijn311/CT-and-MR-Perfusion-Tool

Although a growing number of exoskeletons have been developed for occupational applications, wrist exoskeletons remain relatively rare. However, in the meat processing industry, elbow and hand-wrist musculoskeletal disorders are particularly common. The aim of this study was to assess the potential effectiveness and risks of a 670g wrist exoskeleton prototype designed to assist operators during meat cutting tasks. Six professional butchers performed three standardized tasks reproducing meat cutting gestures in foam, in three randomized experimental conditions: (1) without exoskeleton, (2) wearing the exoskeleton passive, with brakes off and (3) using it with brakes activated, locked in a static position. Cutting forces were recorded using an instrumented table, joint angles using an optoelectronic motion capture system, muscle activity using surface EMG and user experience was assessed using questionnaires. The cutting inaccuracy was defined as the area between the prescribed task and the actual cut on the foam surface. Joint torques were estimated by inverse dynamics with and without taking the exoskeleton's mass into account, to isolate its effect. Linear mixed-effects statistical models were fitted. With the exoskeleton active during tasks, EMG activity was decreased of up to 18.7% (p<0.01 to p<0.001) in the wrist flexors and increased of up to 61.7% (not significant to p<0.05) in the upper trapezius. Shoulder elevation joint torques were increased of up to 39.7% (p<0.001), mainly due to the exoskeleton mass. The proposed multi-criteria exoskeleton evaluation has provided guidance for following prototyping stages. Too heavy wrist exoskeletons could increase the risk of shoulder tendinitis for such tasks.

Electrolyte design plays an important role in the development of lithium-ion batteries and sodium-ion batteries. Battery electrolytes feature a large design space composed of different solvents, additives, and salts, which is difficult to explore experimentally. High-fidelity molecular simulation can accurately predict the bulk properties of electrolytes by employing accurate potential energy surfaces, thus guiding the molecule and formula engineering. At present, the overly simplified classic force fields rely heavily on experimental data for fine-tuning, thus its predictive power on microscopic level is under question. In contrast, the newly emerged machine learning interatomic potential (MLIP) can accurately reproduce the ab initio data, demonstrating excellent fitting ability. However, it is still haunted by problems such as low transferrability, insufficient stability in the prediction of bulk properties, and poor training cost scaling. Therefore, it cannot yet be used as a robust and universal tool for the exploration of electrolyte design space. In this work, we introduce a highly scalable and fully bottom-up force field construction strategy called PhyNEO-Electrolyte. It adopts a hybrid physics-driven and data-driven method that relies only on monomer and dimer EDA (energy deomposition analysis) data. With a careful separation of long/short-range and non-bonding/bonding interactions, we rigorously restore the long-range asymptotic behavior, which is critical in the description of electrolyte systems. Through this approach, we significantly improve the data efficiency of MLIP training, allowing us to achieve much larger chemical space coverage using much less data while retaining reliable quantitative prediction power in bulk phase calculations. PhyNEO-electrolyte thus serves as an important tool for future electrolyte optimization.

The general-purpose programmable photonic processors offer a scalable and reconfigurable solution for a wide range of RF and optical applications. Therefore, implementing photonic Ising machines using programmable processors leverages the advantages of high speed and parallelism, enabling efficient hardware acceleration for finding ground-state solutions to combinatorial optimization problems. In this work, we demonstrate a novel programmable photonic Ising solver based on a hexagonal mesh general-purpose programmable photonic platform. The integrated system allows reconfigurable matrix multiplication and computes the Hamiltonian iteratively using an annealing algorithm that facilitates spin updates and effectively searches for the ground state. As a proof of concept, we experimentally solve two benchmark optimization problems, a fundamental three-node ferromagnetic coupling problem with external bias that demonstrates nontrivial spin interactions, and a four-node Max-Cut problem with arbitrary coupling matrices. Furthermore, to establish a large-scale capability, we emulated Ising problems with sizes up to N = 50, achieving success probabilities exceeding 80%. Additionally, we examined the impact of errors, such as phase and coupling, on the performance of the programmable photonic Ising machine. Our general-purpose photonic Ising machine paves the way for implementing large-scale, programmable architectures for solving optimization problems.