Papers

In 1857, Kirchhoff published two seminal papers on the motion of electricity in wires. In that work, he was the first to derive what we now call the telegrapher`s equations, which describes the propagation of electromagnetic signals along a cable at the speed of light, in some conditions. How was Kirchhoff able to describe electromagnetic propagation as early as 1857, when the notion of displacement current which is believed to be the essential ingredient for the propagation of electric and magnetic fields was not introduced by Maxwell until 1861 and fully explained later in 1865? In this paper, we show that Kirchhoff was the first, in his 1857 paper, to introduce the continuity equation when discussing electromagnetic propagation. We argue that the continuity equation used by Kirchhoff is a more fundamental concept now as it was in 1865 than displacement current. In our view, the dynamics of charged particles can be formulated without invoking fields, but the dynamics of fields cannot be formulated without a continuity equation describing the properties of matter.

In network science, one often wants to say that a given real-world network appears to come from a particular network model. In statistical physics, the corresponding problem is about how typical a given state, representing real-world data, is in a particular statistical ensemble. One way to address this problem is to measure the distance between the data and the most typical state in the ensemble. Here, we identify the conditions that allow us to define this most typical state. These conditions hold in a wide class of grand canonical ensembles and their random mixtures. Our main contribution is a deterministic construction of a state that converges to this most typical state in the thermodynamic limit. This construction involves rounds of derandomization procedures, some of which deal with derandomizing point processes, an uncharted territory. We illustrate the construction on one particular network model, deterministic hyperbolic graphs, and its application to real-world networks, many of which we find are close to the most typical network in the model. While our main focus is on network models, our results are very general and apply to any grand canonical ensembles and their random mixtures satisfying certain niceness requirements.

A new analysis method has been developed for measurements of broadband, low-amplitude turbulent electron temperature fluctuations in fusion plasmas using individual radiometer channels of a Correlation Electron Cyclotron Emission (CECE) diagnostic. This method takes advantage of differences in the correlation time of thermal noise compared to the correlation time of plasma fluctuations in fusion reactors. The validation of this single-channel method is demonstrated using comparisons with the standard dual-channel radiometer spectral decorrelation method for measurements of turbulent electron temperature fluctuations in the core and edge of low confinement (L), improved confinement (I), and high confinement (H)-mode plasmas at the ASDEX Upgrade tokamak.

Motivated by recent breakthrough studies of wave hyperbolicity in extremely anisotropic natural materials and artificial composites, we investigate the radiation pattern of a localized emitter in a hyperbolic medium. Since the emission of a point source is associated with the Fourier transform of the iso-frequency contours of a medium, we derive and analyze the properties of the Fourier transform of hyperbolic dispersion, which sheds light into the emission properties in the presence of hyperbolic bands. Our analysis leads to a generalized form of Huygens' principle for hyperbolic waves, connecting to the emergence of negative refraction and focusing with hyperbolic media. We also highlight the occurrence of aliasing artifacts in polariton imaging. More broadly, our findings provide analytical tools to model polariton propagation in materials with extreme anisotropy, and may be applied to several other physical platforms featuring hyperbolic responses, from astrophysics to seismology.

Artificial intelligence increasingly guides decisions in safety-critical settings, yet neural networks can remain overconfident when confronted with ambiguous observations or unfamiliar inputs. Bayesian neural networks quantify such uncertainty by replacing every weight with a probability distribution, but sampling many distributions at prediction time overwhelms deterministic digital hardware. Here we show that the temporal speckle of broadband light provides a native physical representation of probabilistic weights. Random beating among the frequency components of individually amplitude-bandwidth programmed spectral channels enables optical-encoding of mean and variance in each channel independently. This way, a single broadband light source stores many probabilistic weights in parallel and delivers mutually uncorrelated samples without requiring computationally demanding random-number generation. We harness this mechanism in an ultrafast photonic processor that computes one probabilistic convolution every 37.5 ps and allows simultaneous classification and out-of-distribution detection in hybrid Bayesian neural networks. Our system classifies blood-cell images while recognizing withheld cell types as unfamiliar, and separates epistemic novelty from aleatoric ambiguity on standard uncertainty benchmarks. Programming probability distributions into the fluctuations of light removes the sampling bottleneck of Bayesian inference and opens a route to high-speed, uncertainty-aware photonic AI.

Development of spintronic and quantum computing devices increases demand for efficient, energy saving method of spin manipulation at molecular scale. Polyoxovanadate molecular magnets being susceptible to both electric and magnetic fields may serve here as a good base material. In this paper two isostructural anions [V$_{12}$As$_8$O$_{40}$(HCO$_2$)]$^{n-}$ (with $n=3,5$) featuring two different mixed-valence states with itinerant and localized valence electrons are studied. The impact of the electric field on their magnetic properties is investigated by means of two complementary methods informed by magnetic measurements: effective Hamiltonian calculations and density functional theory. It is demonstrated that the magnetoelectric effect in theses molecules is induced mostly by relocation of itinerant electrons, is highly anisotropic, depends on the valence state and can be detected even at room temperature. These findings can pave the way to practical applications in which an electric field control over spin state is required.

We present an experimental method to determine the refractive index of $Al_{x}Ga_{1-x}As$ (x = 0.0 - 0.5) from 300 K to 4 K across the 500 - 1100 nm wavelength range. The values are extracted from spectroscopically observed microcavity resonances in thin $Al_{x}Ga_{1-x}As$ membranes embedded between fully and partially reflective gold mirrors. Refined Varshni and Paessler models are used to describe temperature-dependent bandgap shifts and material composition. By tracking resonance shifts and benchmarking against finite-difference time-domain simulations, we derive the dispersive optical response with high precision. This yields a quantitatively improved analytical expression for the refractive index of $Al_{x}Ga_{1-x}As$ matching the experimental results with a coefficient of determination as high as $R^2=0.993$, enabling accurate modeling near the band edge at cryogenic temperatures. The method is straightforward and broadly applicable to other semiconductor systems, offering a valuable tool for the design of micro photonic devices such as quantum light sources.

We couple projective limits of probability measures to direct limits of their symmetry groups. We show that the direct limit group is the group of symmetries of the projective limit probability measure. If projective systems of probability measures represent point processes in increasingly larger finite regions of the same infinite space, then we show that under some additional niceness and consistency assumptions, an extension of the direct limit group is the symmetry group of the projective limit point process in the whole infinite space. The application of these results to random graph limits provides “shortest paths” to graphons and graphexes as it recovers these random graph limits as trivial corollaries. Another application example encompasses a broad class of limits of random graphs with bounded average degrees. This class includes a representative collection of paradigmatic random graph models that have attracted significant research attention in diverse areas of science. Our approach thus provides a general unified framework to study limits of very different types of random graphs.

The Integrable Optics Test Accelerator (IOTA) at Fermilab provides a versatile platform for studying the interplay of space-charge, impedance, and non-linear optics in high-intensity hadron beams within synchrotrons and storage rings. This report examines the parameters and dynamics of 2.5 MeV proton beam operations in two configurations of the bare IOTA lattice (dipoles, quadrupoles, sextupoles, and rf cavity only): one for demonstrating Non-linear Integrable Optics with the Danilov-Nagaitsev magnet, and the other for use with electron cooling. We offer order-of-magnitude estimates of the transverse emittance growth rate as a function of beam intensity, highlighting contributions from residual gas scattering, intra-beam scattering, and space-charge effects. Under nominal conditions, the beam lifetime is projected to be less than 7 minutes at low intensity with the current vacuum quality, and fewer than 100,000 turns at high intensity due to strong space-charge effects. The calculations presented here will guide strategies to mitigate emittance growth and inform future IOTA experiments.

Origins of life research investigates how life could emerge from prebiotic chemistry only. One possible explanation provides the RNA world hypothesis. It states that life could emerge from RNA strands only, storing and transferring biological information, as well as catalyzing reactions as ribozymes. Before this state could have emerged, however, the prebiotic world was probably a purely chemical pool of short RNA strands with random sequences and without biological function performing hybridization and dehybridization, as well as ligation and cleavage. In this context relevant questions are what are the conditions that allow longer RNA strands to be built and how can information carrying in RNA sequence emerge? In order to investigate such RNA reactors, efficient simulations are needed because the space of possible RNA sequences increases exponentially with the length of the strands, as well as the number of reactions between two strands. In addition, simulations have to be compared to experimental data for validation and parameter calibration. Here, we present the MoRSAIK python package for sequence motif (or k-mer) reactor simulation, analysis and inference. It enables users to simulate RNA sequence motif dynamics in the mean field approximation as well as to infer the reaction parameters from data with Bayesian methods and to analyze results by computing observables and plotting. MoRSAIK simulates an RNA reactor by following the reactions and the concentrations of all strands inside up to a certain length (of four nucleotides by default). Longer strands are followed indirectly, by tracking the concentrations of their containing sequence motifs of that maximum length.

Quantum entanglement offers powerful opportunities for enhancing measurement sensitivity beyond classical limits, with optical atomic clocks serving as a leading platform for such advances. This chapter introduces the principles of entanglement-enhanced quantum metrology and explores their applications to timekeeping. We review the theoretical framework of quantum phase estimation, comparing frequentist and Bayesian approaches, and discuss paradigmatic entangled states such as spin-squeezed and GHZ states. Particular emphasis is placed on the challenges posed by decoherence, which constrain the practical advantages that can be realized in large-scale devices. The discussion then turns to frequency estimation in atomic clocks, highlighting how experimental constraints shape the translation of abstract quantum limits into real performance gains. Finally, we outline emerging directions of contemporary quantum metrology. Together, these developments underscore the increasingly close interplay between quantum information processing and precision metrology.

We experimentally demonstrate a Mamyshev oscillator based on gain-managed nonlinearity and chirped pulse amplification. Different from other Mamyshev oscillators, the gain-managed nonlinear regime serves as a seed provider instead of a power amplifier in one arm of this laser. The output pulse energy over 300 nJ and pulse width of 739 fs has been achieved from the chirped pulsed amplification in another arm. This configuration provided a new approach to design a high-energy ultrafast laser.

Chromatic aberrations impose a fundamental barrier on optical design, confining most imaging systems to narrow spectral bands with fractional bandwidths typically limited to $\Delta\lambda/\lambda < 1$. Here we report a monolithic, inverse-designed potassium bromide (KBr) lens that achieves broadband, near-achromatic focusing from 0.45 to 14 $\mu$m, a continuous spectral span covering the visible, near-, mid-, and long-wave infrared. This corresponds to a fractional bandwidth of 1.9, approaching the theoretical limit of 2, while maintaining a nearly constant focal length across the entire range. The 19-mm-diameter, 22.5-mm-focal-length optic enables a single compact platform for hyperspectral imaging, mid-IR microscopy, super-resolution, imaging through scattering media, and simultaneous multi-band and long-range imaging. Coupling the KBr lens with a conventional refractive element further yields a hybrid telescope that extends these capabilities. By uniting inverse design with scalable manufacturing, this approach provides a route toward broadly deployable ultra-broadband imagers for biomedicine, climate and environmental monitoring, and space-based sensing.

During testing of the Stress Managed Cosine-Theta dipole mirror magnet SMCTM1, magnet quenches were observed following large voltage spikes in the half-coil voltage taps which preceded normal quench initiation. Following some recent work at CERN measuring power current converter transients in relation to flux jumps, an additional differential current probe was added to the magnet instrumentation. Measurements of this probe, as well as the half coil voltage taps show a fast and substantial current change preceding the quench, hinting at coil motion. A simple model based on relative shift of the inner and outer layer coils was developed which suggests the magnitude of coil shift events is on the estimated relative coil shift from the magnet constraint conditions. The behavior is shown to be reversible and characteristic of stick-slip.

I distinguish two senses in which one can take a given physical theory to be `complete'. On the first, a complete physical theory is one that, in principle, completely describes physical reality. On the second, a complete physical theory is one that provides all of the conceptual resources one needs to describe any (in general probabilistic) physical phenomenon to any level of detail one likes, in principle. I argue that while the (neo-)Everettian approach to interpreting quantum mechanics aims to show that it is complete in the first sense, the (neo-)Bohrian approach begins from an understanding of quantum mechanics as being complete in the second sense. I then discuss some of the essential differences between how classical and quantum theory describe phenomena, and the way in which the quantum description can be thought of as a “natural generalisation” (to use Bohr's phrase) of the classical description. Finally, I elaborate upon the two visions of physics from which one can motivate the first and the second sense of completeness, respectively: metaphysical realism, on the one hand, and what I will call methodological realism, on the other – and discuss what one can say about the significance of the differences between quantum and classical description from each of these points of view. I suggest that there is a sense in which the views of (neo-)Everett and the views of (neo-)Bohr can be understood to be mutually supporting positions, from their respective perspectives, even though they are diametrically opposed.

It has been documented that Spread-Error equality and a flat rank histogram are necessary but insufficient for demonstrating ensemble forecast reliability. Nevertheless, these metrics are heavily relied upon, both in the literature and at operational numerical weather prediction centers. In this study, we demonstrate theoretically why the Spread-Error relationship is necessary but insufficient for diagnosing reliability up to second order, even when mean bias is absent or accounted for. Assuming joint normality between ensemble members and the reference truth, we further show with idealized experiments that the same covariance structure responsible for this insufficiency also produces false diagnoses of reliability with the rank histogram and the reliability component of the continuous rank probability score. Under this structure and when the ensemble mean is meaningfully different from climatology, the truth lies among the least (most) extreme members when climatological variance is excessive (deficient) in each member. Importantly, this behavior is also shown to be plausible in operational ensemble weather forecasts. Combining these results with calibration principles from statistical postprocessing leads us to conclude that both perfect dispersion and underdispersion are ill-defined. When diagnostics are misinterpreted as indicating the latter, improper tuning can lead to further deterioration of forecast quality, even while improving Spread-Error and rank histogram behavior. To address these issues, we propose a new reliability diagnostic based on three easily computed statistics, motivated by the structure of the joint distribution of ensemble members and the reference truth up to second order. The diagnostic separates contributions to unreliability originating from climatology and predictability, enabling a more precise and robust characterization of ensemble behavior.

We present a thermodynamically consistent theoretical framework for lyotropic liquid crystals (LCs) based on the GENERIC (General Equation for the Non-Equilibrium Reversible-Irreversible Coupling) formalism. This formalism ensures conservation of energy and production of entropy, while coupling concentration, momentum balance, and liquid crystalline order. Starting from a binary nematic-isotropic mixture, we derive a theory for these key variables, which is then extended to multi-component systems. The binary equations are solved numerically using a Julia-based solver that relies on an upwind finite-difference scheme, enabling stable and efficient simulations capable of handling multiple time scales while satisfying fundamental mathematical constraints. The results of simulations are consistent with experimental observations of topological core defects in chromonic LCs, as well as flow-driven droplet shape transitions under Couette and Poiseuille flows. This work provides a platform for simulations of multi-component lyotropic LCs that can be extended to systems with multiple interfaces, active materials, and materials subject to external fields.

We present global 3D GRMHD simulations of black hole (BH) accretion disks designed to investigate how MRI-driven dynamo action regulates jet formation and evolution. Unlike standard SANE/MAD setups that impose a coherent large-scale poloidal loop, our "sub-SANE" initial conditions use multiple same-polarity small-scale magnetic loops. Rapid reconnection erases magnetic memory and enables large-scale dynamo to emerge early from MRI turbulence. We perform two such sub-SANE simulations at different BH spins ($a = 0.5, 0.9375$) and compare them with conventional SANE runs. The sub-SANE disks show regular large-scale dynamo cycles with periods of about ten orbits. Decomposition of the induction equation shows that the turbulent dynamo term is stronger in 3D compared to 2.5D and balances advection in the saturated state, confirming sustained large-scale field generation. These dynamo-generated fields are advected inward with minimal time lag, producing correlated peaks in both poloidal and toroidal field strengths from $r_{\rm max}$ to the horizon. Early in the evolution, these peaks imprint directly onto the jet's electromagnetic energy flux, indicating that the jet mirrors the dynamo wave. Though jets form at early times, the sub-SANE runs eventually undergo jet shutdown. We show that this occurs when the magnetic field at the horizon loses coherence, as quantified by a decline in the signed-to-unsigned flux ratio $\mathcal{C}_{\rm BH}$ below $\approx 0.6$. In contrast, the SANE reference case with similar accretion rate and horizon magnetic flux maintains high magnetic coherence because its initial large-scale field persists, allowing its jet to survive. Our results show that both dynamo-driven field evolution and horizon magnetic-field coherence critically regulate jet longevity, establishing a direct dynamo-jet connection in GRMHD disks.

We investigate the capabilities of cloud computing for large-scale,tightly-coupled simulations of biological fluids in complex geometries, traditionally performed in supercomputing centers. We demonstrate scalable and efficient simulations in the public cloud. We perform meso-scale simulations of blood flow in image-reconstructed capillaries, and examine targeted drug delivery by artificial bacterial flagella (ABFs). The simulations deploy dissipative particle dynamics (DPD) with two software frameworks, Mirheo (developed by our team) and LAMMPS. Mirheo exhibits remarkable weak scalability for up to 512 GPUs. Similarly, LAMMPS demonstrated excellent weak scalability for pure solvent as well as for blood suspensions and ABFs in reconstructed retinal capillaries. In particular, LAMMPS maintained weak scaling above 90% on the cloud for up to 2,000 cores. Our findings demonstrate that cloud computing can support tightly coupled, large-scale scientific simulations with competitive performance.

The link between information and energy introduces the observer and their knowledge into the understanding of a fundamental quantity of physics. Two approaches compete to account for this link, Brillouin's negentropy law of information and Landauer's law on data erasure, which are often confused. The first, based on the Clausius' inequality and Shannon's mathematical results is very robust, while the second, based on the simple idea that information needs a material embodiment (data-bits) is today perceived as more physical and prevails. In this paper, we show that Landauer's idea results from a confusion between information (a global emergent concept) and data (a local material object). This confusion leads to many inconsistencies and is incompatible with thermodynamics and information theory. The reason it prevails is interpreted to be due to a frequent tendency of materialism towards reductionism, neglecting emergence and seeking to eliminate the role of the observer. A paradoxical trend given that it is often accompanied by the materialist idea that all scientific knowledge nevertheless originates from observation. Information and entropy are actually emergent quantities introduced in the theory by convention.

We investigate the LiF spectrum up to $7800$ cm$^{-1}$ above the first dissociation limit. The ab initio calculations of the adiabatic potentials and other molecular functions are performed in a wide range of interatomic separations, $r=1$-17 bohr. We consider the model of two interacting electronic states including both the bound states and the resonances of two kinds, the tunneling resonances and the predissociative ones. The Born-Oppenheimer potentials are modeled with use of two functions imitating the diabatic terms whereas the coupling between them was set constant equal to the half of the minimum separation between the adiabatic terms, and then we define the final diabatic terms and the final diabatic coupling via the adiabatic potentials and the angle of the adiabatic-to-diabatic basis rotation obtained by integration of the nonadiabatic coupling matrix element. The energies of the bound states, as well as the positions and widths of the resonances are calculated. The observed transition frequencies are reproduced with the standard deviation of 0.0009 cm$^{-1}$ for $^7$LiF, 0.0006 cm$^{-1}$ for $^6$LiF, and within the experimental uncertainties for the most of the lines. The line lists for the bound-bound $X$-$X$ rovibrational transitions are calculated for quantum numbers $v\le50,\Delta v\le15,J\le170$ ($J\le200$ for the 0-0 and 1-0 bands).

Cellular renewing active matter - assemblies of proliferating and apoptotic cells - underlies tissue homeostasis, morphogenesis, and clonal competition. Previous work in one-dimensional periodic systems identified a fitness advantage associated with rapid dead-cell clearance, an "opportunistic" competition mechanism. Extending this framework, we study two-dimensional cellular aggregates and show that dimensionality modifies the interplay between competition mechanisms for clones with different clearance rates: in 2D, opportunistic and homeostatic-pressure-based competition jointly shape clonal selection, to varying degrees. We then introduce an explicit circular confinement to probe how boundaries modulate this interplay. While opportunistic competition persists, distinct timescale-dependent behaviors emerge through weakened homeostatic-pressure-based competition near boundaries. Structural analysis reveals that confinement promotes tangential alignment and spatially heterogeneous homeostatic pressure, thereby reshaping competitive outcomes at tissue edges. Our study connects newly discovered competition mechanisms with more realistic biological contexts, highlighting how dimensionality and spatial constraints influence tissue structures and modulate competition in heterogeneous cell populations, with implications for tumor growth dynamics and tissue development.

We present a seven-pronged no-go result for quantum mechanics: a "heptalemma". It shows that seven initially plausible theses about physical reality are jointly inconsistent with the predictions of quantum mechanics, while any six are jointly consistent. We must then decide which theses to retain and which to give up. Since different interpretations of quantum mechanics entail different responses to the heptalemma, we get a novel taxonomy of such interpretations. Beyond the application to quantum mechanics, the heptalemma offers a general diagnostic criterion for determining whether a given scientific domain should count as classical or not, and if not, how it departs from classicality.

Growth and shape formation in crowded multicellular assemblies arise from the interplay of chemical gradients, single-cell expansion and mechanical interactions, making it essential to understand how these processes jointly shape collective organization. Using a particle-based model that resolves nutrient fields as well as cellular orientations and their inheritance, we investigate how orientational order emerges within expanding fronts whose morphology is set by nutrient limitation. We identify a transition in nematic order controlled by front morphology, with orientational memory influencing alignment only on one side of this transition. Under strong inheritance, orientational order varies non-monotonically: both thin active layers (fingering morphologies) and thick active layers (flat fronts) produce strong alignment, whereas intermediate cases are less ordered. Analysis of velocities, reorientation statistics, and stress anisotropies shows that this behavior reflects a shift from inheritance-dominated to mechanically driven alignment that overrides lineage memory. The resulting differences in front speed produce a fitness advantage of orientational memory only in the diffusion-limited, memory-dominated regime. These findings elucidate how nutrient supply, mechanical interactions, and single-cell expansion together shape self-organization during growth.

Femtosecond laser writing in glass was harnessed to fabricate photonic circuits comprised of waveguides, micro-mirrors, and fiber sockets, in an optical interposer chip. The compact design enabled ultra-dense routing of 40 optical channels from six multi-core fibers, in a two-layer array, vertically coupling onto a 2D grid of silicon photonic grating couplers. Various photonic circuit designs were explored and tested, achieving an average single-pass insertion loss of 5.0 dB for the fully packaged system, with a minimum loss of 3.2 dB. The reasonably low loss and low-profile, compact package make such interposers an attractive option for densifying interconnects to address bottlenecks in optical networks, datacenters, and optical computing applications.

Collective dynamics in proliferating anisotropic particle systems arise from an interplay between growth, division, and mechanical interactions, often mediated by particle shape. In classical models of prolate, rod-like growth, flow-induced alignment and division geometry reinforce one another, leading to robust nematic order under confinement. Here we introduce a complementary regime by considering smooth convex particles whose geometry can be oblate for part or all of their growth cycle, creating a tunable competition between these two alignment mechanisms. Using agent-based simulations of elliptical and rounded-rectangular particles in both channel and open-domain geometries, we systematically vary the division aspect ratio to span regimes of cooperation and competition between ordering cues. We find that oblate growth can reverse classical flow-alignment, destabilize microdomain formation in intermediate regimes, and open up new regimes with modified microdomain dynamics in free expansion and sustained orientation dynamics in channel geometry. These findings are explained by an order- and shape-dependent mechanical relaxation interpretation that is supported by explicit measurements. This sheds new light on the available relaxation pathways and therefore provides key ingredients for effective descriptions of collective anisotropic proliferation dynamics.

This study compares calibration strategies for predicting particle velocity in granular sugar subjected to weak shock loading, using measurements from flyer-plate impact experiments as a benchmark. Two computational approaches are evaluated: a continuum-based P-alpha Menikoff model requiring calibration of effective constitutive parameters, and mesoscale simulations that explicitly resolve grain geometry and porosity. Both models can match the measured particle-velocity histories, but only through fundamentally different calibration mechanisms. In the P-alpha model, a pressure-dependent yield strength is essential and the response remains highly sensitive to parameter choices such as the crush-out pressure. In contrast, mesoscale simulations are far less sensitive to parameter tuning and instead depend primarily on the physical state variable of porosity, represented in 2D through an equivalent mapping of the 3D specimen. These results show that continuum parameters act as effective surrogates for underlying grain-scale processes, whereas mesoscale modeling identifies porosity as the dominant control on macroscopic wave onset, highlighting distinct calibration pathways and interpretive implications for each modeling approach.

The Transfer Matrix Method is a powerful numerical tool for simulating wave propagation in layered media. It has been widely applied in many fields, although its use is typically restricted to passive media. In this paper, we develop the transfer method to simulate optical nonlinear generation and give examples for processes such as difference frequency generation, spontaneous parametric down conversion, and four-wave mixing including generation of counter propagating waves. This framework enables accurate simulation of complex multilayer structures and resonant cavities, providing a versatile tool for designing nonlinear photonic devices.

This work presents a hands-on molecular communication (MC) testbed developed for the undergraduate Communication Engineering lab course at the Institute for Communications Technology (IfN), TU~Braunschweig. The goal of the experiment is to provide students with an intuitive and reproducible introduction to MC concepts using a low-cost and accessible fluidic setup. The system employs a background water flow into which three dye colors are injected and symbols are detected by a multi-wavelength photosensor. A zero-forcing–based estimator is used to separate the spectral components and reliably identify the transmitted colors. The experiment is designed to be completed independently by students within a single laboratory session and requires only basic prior knowledge from introductory communication engineering courses. A detailed script accompanies the experiment, guiding students through channel characterization, color detection, pseudoinverse computation, and simple data transmission using on-off keying. In pilot trials, students successfully reproduced the entire communication chain and achieved stable data rates of up to 0.5~bit/s over a 15~cm channel. The proposed testbed demonstrates that fundamental principles of MC can be taught effectively using a compact and inexpensive experimental setup. The experiment will be integrated into an undergraduate lab course.

We introduce a computational framework for generating realistic transition paths between distinct conformations of large bio-molecular systems. The method is built on a stochastic integro-differential formulation derived from the Langevin bridge formalism, which constrains molecular trajectories to reach a prescribed final state within a finite time and yields an efficient low-temperature approximation of the exact bridge equation. To obtain physically meaningful protein transitions, we couple this formulation to a new coarse-grained potential combining a Go-like term that preserves native backbone geometry with a Rouse-type elastic energy term from polymer physics; we refer to the resulting approach as SIDE. We evaluate SIDE on several proteins undergoing large-scale conformational changes and compare its performance with established methods such as MinActionPath and EBDIMS. SIDE generates smooth, low-energy trajectories that maintain molecular geometry and frequently recover experimentally supported intermediate states. Although challenges remain for highly complex motions-largely due to the simplified coarse-grained potential-our results demonstrate that SIDE offers a powerful and computationally efficient strategy for modeling bio-molecular conformational transitions.