The present work aims to obtain an exact solution for the Lippmann-Schwinger equation considering a scalar particle in three-dimensional hyperspherical and hyper-pseudospherical spaces. We analytically solve the Lippmann-Schwinger equation for a potential of the boundary-wall type, written as Dirac delta distributions over a two-dimensional single barrier. Our Lippmann-Schwinger equation in space representation is a second-kind Fredholm integral equation, where a bilinear form of the Green's function leads to a separable kernel. The probability density for the particle, considering level surfaces in both spaces, and the differential cross-section in the hyper-pseudospherical space, are also presented graphically.
Two-dimensional semiconducting tungsten disulfide (WS2) has recently gained significant focus from researchers as a thermoelectric material because of the pronounced negative correlation between thermal and electrical conductivities, unlike the conventional 3D materials. However, high thermal conductivity and limited carrier mobility still restrict its rapid employment in the energy conversion sector. In this context, we have systematically explored the potential of substitutional doping in monolayer WS2 by isoelectronic chalcogenide counterpart selenium (Se) using density functional theory (DFT) combined with the linearized Boltzmann transport equation. The results demonstrate a nearly one-and-a-half-fold improvement in hole mobility at 60% doping concentration along with a significant lowering of thermal conductivity, caused by softening of phonon modes. The thermal conductivity was found to reduce further,up to 90% at carrier concentration similar to 1012 cm-2, emphasizing substantial importance of phonon carrier interaction on heat transport. Although, Se doping slightly reduces the Seebeck coefficient, due to the higher carrier concentration arising from band gap narrowing (while pristine WS2 exhibits the highest seebeck coefficient), the overall suppression of thermal conductivity contributes to the enhanced thermoelectric figure of merit (ZT), reaching 2.4 at room temperature for 50% Se doping. Furthermore, a machine learning-based model, trained on DFT-generated data predicts a remarkably high ZT of 3.34 for 36.2% Se doping at 885 K and a carrier concentration of similar to 1013 cm-2, highlighting the crucial role of doping in optimizing the thermoelectric performance of WS2.
We develop a quantum field theory based on random nonHermitian actions, which upon quantization lead to stochastic nonlinear Schr & ouml;dinger dynamics for the state vector. In this framework, Lorentz and spacetime translation symmetries are preserved only in a statistical sense: the probability distribution of the action remains invariant under these transformations. As a result, the theory describes ensembles of quantum-state trajectories whose probability distributions remain invariant under changes of reference frame. As a concrete example, we augment the Dirac action with a purely imaginary term coupling the fermion density operator to a universal colored noise. This noise is constructed by solving the d'Alembert equation with white noise as its source, using a generalized stochastic calculus in 1+3 dimensions. We demonstrate that the colored noise drives stochastic localization of wave packets and derive the localization length analytically. Remarkably, the localization length decreases as the size of the observable universe increases. Our model thus provides a potential framework for relativistic spontaneous wave-function collapse. While establishing consistency with Born's law remains an open challenge, the present work constitutes a step toward embedding collapse models into a Lorentz-invariant quantum field theory.
This study developed a low-cost, open-source solar cell bifocal modeling tool that integrates an Arduino-based sensor device with a Unity 3D visualization platform. In this system, real-time voltage, current, and irradiance data from the solar panel are acquired using sensors and directly visualized in a dynamic 3D/2D representation of electron movement within the cell's energy bands. The developed tool is evaluated for system functionality, reliability, and validity against the instruments' standard datasheet. The results demonstrate high accuracy, with Mean Absolute Percentage Error (MAPE) values of 0.6%, 6.43%, and 5.97% for the voltage, current, and light intensity, respectively, all within the valid category. This consistent performance enables accurate representation of current-voltage (I-V) characteristics under various illumination levels. The results show that the efficiency measurement values are close to manufacturer specifications at moderate-to-high light intensities. By combining direct experimental data with synchronized microscopic visualization, the proposed bifocal modeling tool for this solar cell concept enables students to directly connect the measured electrical output to the underlying semiconductor process and bridge macro-and microscopic perspectives in optoelectronics education.
This paper presents a frequency-selective self-quadruplexing antenna implemented using substrate-integrated waveguide (SIW) technology, specifically targeting 5G millimeter-wave (mmWave) NR bands n257, n258, and n261. The proposed methodology involves the systematic reconstruction of a single full-mode SIW cavity into four independent quarter-mode (QM) subcavities of varying dimensions. Each subcavity is individually excited using microstrip feedlines to enable independent frequency tuning and quad-band operation while maintaining high port isolation. Orthogonal arrangement of subcavities and incorporation of metallic via walls between them result in isolation exceeding 20 dB without impacting resonant frequencies. The antenna exhibits precise frequency selectivity across four distinct bands: 24.6 GHz (S11 = -27.23 dB, fractional bandwidth (FBW) = 1.06%), 25.83 GHz (S11 = -33.21 dB, FBW = 5.03%), 27.85 GHz (S11 = -27.81 dB, FBW = 7.54%), and 29.15 GHz (S11 = -40.76 dB, FBW = 10.91%). Independent frequency selectivity is achieved through geometric parameter optimization, offering an operational span of 23.5 GHz to 29.5 GHz. The proposed antenna achieves a peak realized gain of 4.66 dBi and performs well in both simulation and measurement. Compared to existing self-quadruplexing antennas, this work demonstrates compact size, broadband operation, enhanced isolation, and full support for 5G mmWave bands, marking it as the first such passive SIW-based design offering wide-range independent frequency selectivity across all ports within the 5G FR2 spectrum.
We construct a class of explicit solutions to the two-dimensional relaxed compressible viscoelastic flow with density-dependent viscosity mu = mu(rho) under rotational symmetry. By introducing the imaginary unit i and recasting the governing equations into a compact complex-valued form, the coupled viscoelastic system reduces to a single evolution equation, enabling the explicit derivation of solutions. These solutions satisfy a vacuum free-boundary condition and exhibit long-time decay: as t -> + infinity, the density, radial and angular velocities, and velocity gradients all vanish. The results extend recent spherical-symmetric constructions to genuinely rotational motion, where angular momentum and vorticity introduce a fundamentally different analytical structure.
The double pendulum is one of the simplest mechanical systems exhibiting chaotic behavior, which motivates an investigation into whether the spatial structure of its trajectory contains information about instantaneous velocity. This study introduces an epsilon-neighborhood graph framework that encodes trajectory geometry as a network, in which nodes represent spatial positions and edges connect points within an adaptive distance threshold. We analyze eight initial conditions with dynamics rigorously classified via Lyapunov exponents (lambda < 0.05: regular; 0.05 <= lambda < 0.10: transition; lambda >= 0.10: chaotic) and Poincar & eacute; sections. The connectivity threshold is determined through a physics-informed scaling analysis, with inverse and exponential models achieving R-2 > 0.68 across all cases. Validation employs genuinely topological metrics, such as shortest-path distance to temporally offset nodes and local clustering coefficient, rather than quantities trivially related to velocity by construction. Shortest-path correlations with instantaneous speed reach r = 0.79, while clustering correlations reach r = - 0.74, reflecting how slow motion creates dense local connectivity and fast motion creates sparse connectivity. The epsilon-neighborhood method outperforms k-nearest neighbor graphs by approximately 33% in mean shortest-path correlation strength and achieves comparable performance to fixed-density recurrence networks ( =0.57 versus 0.57), confirming that the performance advantage arises from the epsilon-neighborhood construction itself rather than a specific threshold selection strategy. The horizontal visibility graph, operating as a one-dimensional projection baseline, yields substantially weaker correlations. Robustness analysis confirms stability across threshold variations, with clustering coefficient variation below 0.22 and correlation standard deviations of approximately 0.03. Sensitivity analysis of the temporal offset parameter reveals three distinct regimes: trivially high correlations at small offsets, topologically meaningful correlations at moderate offsets, and regime-dependent behavior at large offsets, confirming the robustness of the chosen parameterization. These results establish epsilon-neighborhood graphs as a principled tool for extracting kinematic information from complex trajectories in nonlinear dynamical systems.
Perovskite solar cells (PSCs) continue to be constrained by interfacial defects and trap-assisted nonradiative recombination, which hinder further improvements in power conversion efficiency (PCE) and long-term operational stability. To address these challenges, we propose a synergistic dual-passivation strategy that simultaneously incorporates phenethylammonium iodide (PEAI) and 1,3-diaminopropane dihydroiodide (PDADI) onto the surface of perovskite films. This approach effectively suppresses interfacial defects, enhances crystal quality, and modulates interfacial charge dynamics at the perovskite/electron transport layer interface. The dual-passivated perovskite films exhibit significantly improved morphology and crystallinity, with grain sizes increasing from 228.64 nm to 355.19 nm, trap-state density decreasing from 1.00 & times; 1016 cm-3 to 8.27 & times; 1015 cm-3, and average carrier lifetime extending from 323.42 ns to 775.41 ns. Consequently, inverted PSCs based on this treatment display reduced dark current density, enhanced radiative recombination, and achieve a champion PCE of 21.72% (average 20.63%), with a high fill factor of 82.3%. Moreover, unencapsulated devices retain 93% of their initial efficiency after 547 h of ambient storage, outperforming control devices. These results demonstrate that PEAI-PDADI synergistic passivation provides an effective and generalizable pathway toward efficient and stable inverted PSCs.
Reconstructing attractors from nonlinear time series is central to nonlinear dynamics, particularly when only a single observable is available. Although Takens' embedding theorem guarantees diffeomorphic reconstruction in theory, practical issues such as the role of sampling frequency and missing data still remain unexplored. In this work, we study how these factors affect reconstruction quality from a topological perspective. Hence, in our work, we consider two scenarios: a) We vary the sampling frequency of the time series data b) Time series data with varied percentages of missing points (scanty data). For each case, we reconstruct the attractor using optimal embedding parameters and evaluate the topology via persistent homology (PH). Their fidelity is quantified through various measures, such as the behavior of optimal parameters, the persistence of true features with respect to topological noise, the area under the curve (AUC) analysis of Betti curves extracted from PH, and comparing all these measures using the original phase space as a reference. We tested our methodology on two widely studied systems: Duffing and Lorenz. Our results show that sequential sampling (with different sampling frequencies) has a negligible impact on the disruption of the topological invariants. In contrast, scanty data (random removals) leads to strong variability in AUC of Betti curves, severe degradation of reconstruction quality, and disruption of topological invariants (true features), making it lose the dynamical information. This study highlights a crucial insight: even minimal data loss can severely hamper the reconstruction fidelity. These findings underscore the importance of reliable and continuous sensor recordings in experimental settings, as seen in data-driven studies in nonlinear physics, physiology, and climate science.
Physical hydrodynamics in realistic environments-including supernovae, plasma fusion, aerodynamics, aeronautics, materials processing and nanofabrication-involves a complex interplay of processes and scales. In this work we introduce a scale ratio number to quantify an interplay of a turbulence and an acceleration in complex hydrodynamic processes. We use the number to describe dynamics of turbulent flows influenced by accelerations and that of Rayleigh-Taylor and Richtmyer-Meshkov mixing driven by accelerations. The number is established to be in conformity with group theory and with experiments and simulations on Rayleigh-Taylor dynamics. The number is found to be consistent with the historic studies on accelerated turbulent boundary layers, to explain the long-standing challenges in Rayleigh-Taylor mixing, and to reveal the effect of variable accelerations on canonical turbulence. The latter predicts new regimes in which the flow may laminarize when it is accelerated, and in which it may remain turbulent even if accelerated.
Small aerial object detection under adverse weather conditions, such as rain, fog, snow, and frost, remains a major challenge in computer vision. Although existing methods perform well for conventional object detection under adverse weather, a gap persists due to the lack of suitable public datasets targeting small aerial objects. Developing a deep learning model capable of restoring weather-degraded images and accurately detecting small objects is difficult because of their low resolution and the background noise introduced by weather effects. To address this limitation, we construct DOTA-Wx, a synthesized weather-augmented dataset derived from DOTA-v1.0, incorporating multiple weather effects specifically for small aerial object detection. To the best of our knowledge, DOTA-Wx is the first dataset developed for this task. In addition, we propose WAGAN-FPN (Weather-Adaptive GAN-based Feature Pyramid Network), an end-to-end framework with a two-stage (restoration and detection) pipeline, which introduces a Weather-Adaptive Generator (WAG) integrating convolutional layers with adaptive attention blocks to restore degraded images while preserving features essential for small-object detection. We evaluate DOTA-Wx using 17 deep learning models published between 2020 and 2025, and compare their performance with WAGAN-FPN. Results show that WAGAN-FPN achieves 68.52% mAP on DOTA-Wx and 82.23% mAP on DOTA-v1.0, outperforming existing methods by effectively handling weather distortions and maintaining higher detection accuracy.
Ensuring seafood safety from microplastics (MPs) contamination has emerged as a significant concern in environmental and public health monitoring. Different SPR techniques have been used for MPs detection but fiber-based designs with advanced sensing materials are not widely developed for real time detection. To address this need, we propose a highly sensitive Surface Plasmon Resonance (SPR)-based optical fiber sensor incorporating a novel WCrO3 sensing layer for the detection of microplastics. The chosen sensing layer offers exceptional dielectric properties, a tunable bandgap, structural stability, and strong adsorption characteristics which make it suitable for MPs detection. Finite Element Method (FEM)-based simulations were performed to analyze the SPR spectrum by considering the essential performance metrics such as electric field distribution, modal loss, resonance wavelength shift, and sensitivity. The proposed sensor demonstrates a high refractive index sensitivity of 7112 nm RIU-1. This study also provides a first-principles (DFT) investigation to examine the optical properties and adsorption behavior of ethylene (C2H4) which is the fundamental unit of polyethylene microplastics as a representative probe molecule on the WCrO3 (110) surface. Finding reveals that the structural stability of the WCrO3 monolayer was maintained after adsorption of microplastic with no significant distortions observed, validating WCrO3 as a promising biosensing layer for MPs detection. This work establishes WCrO3-coated SPR fibers as a scalable platform for seafood safety monitoring.
This work presents a numerical investigation of a CdS/WSe2/CdTe dual-absorber heterostructure using SCAPS-1D to evaluate the role of WSe2 as an intermediate optoelectronic layer. Intermediate absorber, particularly, WSe2 contributes to both photon absorption and favorable band alignment at heterointerfaces. The influence of key parameters, including WSe2 thickness and bandgap, is systematically analyzed. The optimized configuration, with a WSe2 thickness of 2.0-2.5 mu m, and a bandgap of 1.3-1.4 eV, achieves a maximum power conversion efficiency of similar to 26.47%, compared to 22.53% for the baseline CdS/CdTe device. The improvement under the upper limit, optimized condition, is primarily driven by enhanced open-circuit voltage and fill factor due to reduced recombination enabled by spike-like band alignment at the interfaces. These findings highlight the potential of WSe2-based dual-absorber architectures for advancing high-performance thin-film photovoltaic technologies.
In this study, we show that a neutral Dyson shell enclosing a charged compact object described by the Reissner-Nordstr & ouml;m spacetime can attain a stable equilibrium configuration. This is in contrast to Dyson shells surrounding uncharged compact objects, which are in general unstable. We analytically derive the conditions for stability, determine the equilibrium radius and the corresponding minimum asymptotic energy, and show that small perturbations about this equilibrium lead to a stable oscillatory motion of the shell. The oscillation frequency is obtained explicitly and shown to increase with the shell mass and decrease with the charge of the central object. When the shell itself carries charge, its stability depends on the sign of this charge. Shells with the same sign as the central charge become progressively less stable, while oppositely charged shells exhibit enhanced stability due to the electrostatic attraction. These findings highlight the stabilizing role of electromagnetic interactions in Dyson-type thin-shell configurations within general relativity.
