Search Results (380)

Forest fires cause societal damage, including injuries, burns, and the development and exacerbation of cardiorespiratory diseases. One of the damaging factors of forest fires is carbonaceous particles heated up to high temperatures. These particles are carried from the forest fire front and can interact with human tissue. Three scenarios for the interaction of a heated carbonaceous particle with human tissue are considered. The first scenario involves particle impact on the skin. The second scenario involves particle impact on the nasopharyngeal mucosa. The third scenario involves the impact on the tissues of the upper airways. A two-dimensional mathematical statement is considered in the “carbonaceous particle–human tissue” system. Mathematically, the heat transfer process is described by non-stationary parabolic partial differential equations with corresponding initial and boundary conditions. The problem is solved using locally one-dimensional and finite-difference methods. Difference analogs of the differential equations are solved using the marching method. Temperature distributions for particles of varying sizes and initial heat contents were obtained. The software realization was implemented using the high-level Object Pascal programming language in the RAD Studio environment. Conclusions were drawn regarding the potential practical applications of the developed software in healthcare and environmental protection. Full article

This work presents the realization of a compact and embedded impedance-based sensor system for the characterization of lithium-ion batteries by means of electrical impedance spectroscopy (EIS). The analog magnitude-ratio and phase-difference detection (MRPDD) method is implemented and extended through a generalized formulation that models the shunt element as a frequency-dependent impedance and compensates the parasitic contributions of the printed circuit board. This reformulation corrects magnitude and phase errors introduced by the measurement hardware without increasing the overall complexity. The prototype comprises two main functional blocks: current-mode excitation and voltage-mode measurement. The excitation stage uses an operational transconductance amplifier and a power MOSFET to generate a voltage-controlled current source, whereas the sinusoidal voltage signal is generated by means of a direct digital synthesizer. The measurement chain relies on differential acquisition using instrumentation amplifiers and analog magnitude/phase detection based on the AD8302 vector detector under microcontroller control. The proposed method has been first validated by simulations using both a linear RC equivalent model and an extended Randles-type battery-equivalent model, and then experimentally characterized using a linear RC equivalent model of the device under test. Measurements show that the generalized formulation recovers the ideal impedance response in the presence of parasitic effects, both in the shunt device and in the printed circuit board. In the experimental validation with the RC model, a magnitude error of 1.65% is obtained at 1 kHz, which is adopted as the upper frequency limit for battery characterization, even though operation up to 10 kHz is possible. Phase measurements revealed that the input capacitive coupling of the vector detector, conceived for operation in the RF range, requires an adaptation for appropriate operation in the intended frequency range. The prototype has been also applied to the characterization of a commercial lithium-ion 18650 cell, enabling the measurement of battery impedance and the analysis of its dependence on the state-of-charge and on the discharge current. Full article

In the context of Kolmogorov complexity, the complexity of an object can be characterized by the length of the shortest algorithm required to describe or compute it. In condensed matter systems under equilibrium and nonequilibrium conditions, macroscopic properties distinct from elementary ones can emerge from large numbers of particles. Such a large system size allows system properties to change as control parameters change, producing phase transitions. Inspired by this analogy, it is natural to consider that an optimized computing system may undergo a transition from an initially random organization to a functionally organized state. In this paper, by adopting a specific multi-task setting based on a typical dynamical system, we propose an evolutionary echo state network that realizes the functional differentiation of a random neural network into two subnetworks—one specialized for memory and the other for program execution. The model suggests a minimal neural mechanism for dynamic processes that extract rules embedded in input sequences and store information over short or long time scales. Because the proposed evolutionary model is driven by constraints that jointly reduce task errors and structural redundancy, the resulting network can be regarded as an optimized descriptor of memory and program functions. To clarify the relationship between the proposed model and algorithmic complexity, we introduce Lempel–Ziv-based complexity indices for both network structure and node-wise reservoir dynamics. Although neither density, effective spectral radius, nor the Lempel–Ziv-based complexity indices were prescribed or optimized in the eESN, the evolved network exhibited structural complexity comparable to conventional ESNs in the corresponding region and slightly higher dynamical complexity. These results suggest that the advantage of eESN is not attributable to a direct maximization of complexity itself, but rather to the evolutionary organization of complexity into a functionally differentiated reservoir that supports both memory-like retention and program-like rule extraction. Full article

We consider the problem of time-series forecasting via statistical reconstruction of the coefficients of the Itô representation of the underlying stochastic process $X\left(t\right)$. The reconstructed coefficients are obtained using techniques that account for their dependence on the current value of the process. We augmented the basic linear autoregressive model with the estimated Itô drift coefficients: 1st order $\hat{a}(t,X_t)$ and 2nd order $\hat{\hat{a}}(t,\hat{a}(t,X_t))$ that can be treated as the 1st and 2nd quasi-derivatives of the original time series that is assumed to be a realization of $X\left(t\right)$. The predictive techniques used in this paper are based on a kind of statistical analog of the Taylor expansion for the time series. The proposed predictive algorithms demonstrate higher accuracy as compared to other autoregressive algorithms applied to forecasting a big set of time series. Full article

Ultrafast physical random bit generators (PRBGs) are essential components for modern applications in secure communication, quantum cryptography, encrypted optical fiber sensing and artificial intelligence. While optical chaos-based PRBGs offer high-speed capabilities, conventional systems often rely on discrete components that suffer from system complexity and environmental instability. This paper proposes and experimentally demonstrates a robust, integrated solution using a two-section mutual injection DFB laser. The device was fabricated using the reconstruction equivalent chirp (REC) technique, which provides precise control over grating phase variation while utilizing low-cost, high-volume fabrication methods. The laser sections, each measuring 450 μm in length, were designed with a free-running wavelength difference of 0.3 nm to ensure a flat optical spectrum and enhanced chaotic dynamics. By optimizing the bias currents, we achieved a chaos RF bandwidth of 20.1 GHz. Notably, the resulting chaotic signal lacks time-delayed signatures, which simplifies the randomness extraction process. To generate random bits, the chaotic waveform was sampled by an 8-bit analog-to-digital converter at 100 GSa/s. Following post-processing through delay-subtracting and the extraction of the four least significant bits (4-LSBs), we realized a total physical random bit rate of 400 Gb/s. The randomness of the generated sequence was successfully verified using the NIST SP 800-22 statistical test suite. This approach offers a compact, energy-efficient, and high-performance integrated chaotic source suitable for secure communication and high-performance computation. Full article

Memristors, as programmable resistive switching devices, offer two fundamental computational modalities for artificial intelligence: static conductance for parallel data processing and dynamic switching for temporal, logical, and stochastic operations. This Review systematically distinguishes these two modalities and evaluates their respective hardware implementations. In terms of our review scope, we first examine how static conductance modality is exploited in analog matrix computing, which encompasses matrix–vector multiplication and matrix equation solving, and discuss how these primitives enable efficient neural network inference and training. Second, we survey dynamic switching modality and its algorithmic applications, including stateful logic for digital in-memory acceleration, attractor networks for associative memory, reservoir computing and spatiotemporal signal processing using transient device dynamics, biologically inspired spike-timing-dependent plasticity, and stochastic computation. In addition, we discuss key challenges such as device variability, stochastic switching, interconnect parasitics, peripheral circuit overhead, and endurance limitations. We also highlight opportunities for future development, emphasizing algorithm–hardware co-design to leverage application-specific error tolerance and mitigate device non-idealities. Finally, we outline promising research directions aimed at realizing robust, scalable, and energy-efficient memristor-based AI systems. Full article

Our concept of where life can thrive on Earth has advanced over the past 70 years to include extreme ionizing radiation, high temperatures, the deep subsurface, hydrothermal vents on the deep ocean floor, extreme arid deserts, and the ice-covered lakes and high mountain valleys of Antarctica. This expanding understanding of the biosphere has coincided with the development of space exploration programs, and it has informed those programs with regard to the search for life on other water worlds in our Solar System—especially Mars, Europa, and Enceladus. Titan presents a reverse of this approach. The interesting organic solids and fluids on that world have no analog in Earth habitability but have inspired suggestions of possible biological systems unlike any on Earth. If realized, the discovery of life on Titan would stretch the concept of habitability just as it stretches the concept of life as we know it. Habitability studies on exoplanets may follow both of these paths: we will look for habitability on exoplanets based on observed habitats on Earth, and we will also use observations of exoplanets as grist for contemplation of lifestyles different from anything we know on Earth. Full article

The rapid digitalization of the media and publishing industry has deepened systemic asymmetries in resources, power, and institutional rights. These asymmetries create fundamental barriers to the economic–institutional sustainability of digital content dissemination. Existing governance frameworks have not yet comprehensively addressed the resulting competitive and informational imbalances. Adopting China’s publishing and media industry as a focal case, this study draws on symmetry theory to develop an integrated analytical framework. It reconceptualizes government regulation as a multi-dimensional governance mechanism operating across three dimensions: resource allocation, technological innovation, and rights protection. We test this framework empirically using Xinbang Index data covering the top 10 publishing and media enterprises from 24 January 2025 to 7 December 2025. Multiple regression analysis and Spearman rank correlation are applied to assess each dimension’s differential impact on content dissemination efficiency. The results yield four key findings. First, all three regulatory dimensions contribute positively to content dissemination efficiency. Second, technological innovation is the most potent symmetry-restoring lever, exerting a statistically robust direct effect on dissemination outcomes. Third, resource allocation provides a necessary foundational contribution, while rights protection operates conditionally—its effect is fully realized only alongside adequate technological and resource inputs. Fourth, an integrated multivariate regression confirms the cross-dimensional hierarchy: the standardized Beta coefficient for technological innovation (β = 0.394) exceeds those for rights protection (β = 0.294) and resource allocation (β = 0.125). No single regulatory instrument is sufficient to achieve dynamic equilibrium. A synergistic, technology-centered combination of all three dimensions is required. This study proposes a tripartite symmetry-based governance strategy for media platform ecosystems. The symmetry framework developed here offers an analytical template for diagnosing analogous asymmetries in other platform-dependent sectors. Empirical validation beyond the Chinese publishing and media context is recommended as a priority for future research. Full article

As critical rare earth elements (REEs), the industrial demand for neodymium (Nd) and dysprosium (Dy) increases rapidly due to their specific physical and chemical properties. Recycling these REEs from secondary resources such as spent NdFeB magnetic materials is an efficient approach for sustainable production. However, the separation of neodymium and dysprosium in aqueous solutions is an arduous task because of their close chemical properties. Recovering and purifying neodymium and dysprosium from a simulated leaching solution of spent NdFeB magnets were conducted by employing selective ion exchange resins. It was found that Purolite S950 PLUS resin functionalized with aminophosphonic groups demonstrated selective adsorption toward Nd³⁺ and Dy³⁺ while maintaining low affinity for Fe(II) at low pH (i.e., 0.65), which could realize efficient iron removal from the solution. Purolite MTX7010 resin impregnated with di-(2-ethylhexyl) phosphoric acid (D2EHPA) had a strong adsorption preference for Dy³⁺ over Nd³⁺, which is highly suitable for Dy separation from their mixed solutions under optimized conditions. By employing a multistage adsorption–elution process analogous to distillation, a prospective purity of 98.51% for Dy and a purity over 99.90% for Nd were realized with high metal recoveries from the synthetic leaching solution of spent NdFeB magnets. This research demonstrates that recovery and purification of single REEs from leaching solutions containing mixed REEs and other metals can be achieved with selective resin adsorption processes analogous to distillation despite large concentration differences in the metals in the solutions, which presents a new approach. Full article

In this study, the task of integrating capture and conversion of CO₂ into a single material platform is realized by developing bifunctional membranes based on polymer ionic liquids (PILs). The novelty of this work lies in the fabrication and comprehensive evaluation of PIL-based membrane materials that combine catalytic activity toward CO₂ conversion with gas separation performance within one material system. In contrast to most previously reported imidazolium-based PILs, which have mainly been considered either as catalysts or as membrane materials, the present approach focuses on their dual functionality under both catalytic and gas transport conditions. A series of imidazolium-based PILs, including homopolymers and block copolymers with polystyrene, were synthesized. The materials were characterized to determine their catalytic activity during the cycloaddition of CO₂ to epichlorohydrin and to determine their gas transport properties using pure gases (N₂, O₂, CO₂) and a simulated dry flue gas mixture; membrane morphology was studied by scanning electron microscopy. Block copolymers exhibited higher catalytic conversions (up to 82.7%) than homopolymers, with selectivities above 93%. Chloride-containing block copolymers gave the best combination of CO₂ permeability (up to 7.5 Barrer) and CO₂/N₂ selectivity (18–22) under mixed-gas conditions. Iodide-containing analogs demonstrated higher selectivity (up to 30) but lower CO₂ permeability. Morphological analysis confirmed the presence of dense, defect-free structures in materials with the chloride anion, while materials with the iodide anion showed increased free volume and microheterogeneity. These results indicate that by altering the polymer and anion architecture, PIL-based membranes can effectively combine catalytic activity with selective CO₂ transport, providing a promising avenue for enhancing carbon capture and utilization processes. Full article

Visible light communication (VLC) employs light-emitting diodes (LEDs) for simultaneous illumination and wireless data transmission, offering advantages such as unlicensed spectrum, immunity to electromagnetic interference, and intrinsic security. Conventional PAM-VLC transmitters generally rely on a single high-power LED driven by analog front-end components, such as digital-to-analog converters and power amplifiers, which increase hardware complexity, power consumption, and thermal burden. To address these limitations, this paper proposes an energy-efficient spatial-combining VLC transmitter in which multiple LEDs are directly driven by FPGA GPIO ports, without using DACs or power amplifiers. Multilevel PAM is digitally realized by controlling the number of activated LEDs, and the emitted optical signals are spatially combined through an optical lens. Experimental results demonstrate reliable 1 m free-space transmission. At a bit-error rate (BER) of 3.8 × 10⁻³, the proposed scheme achieves SNR gains of 0.75 dB for PAM-4 and 0.8 dB for PAM-8 over the conventional pulse amplitude modulation (PAM)-VLC architecture. Moreover, the proposed transmitter reduces power consumption by 38.7%. These results confirm that digitally driven multi-LED spatial combining is a promising solution for low-cost and energy-efficient VLC systems. Full article

As social infrastructure intensively developed during the high economic growth period of the 1970s faces simultaneous aging, there is an urgent need to transition from conventional reactive maintenance to preventive maintenance utilizing various data (data-driven asset management. However, the greatest barrier in practice is that inspection data is unevenly distributed in analog formats such as paper and unstructured files, and heavily relies on the subjective visual evaluation of expert engineers (e.g., discrete graded evaluations from A to D). The intervention of this “Assessor Bias” makes it difficult to ensure the robustness required for direct statistical analysis. This paper serves as a bridge between this analog expert knowledge and quantitative data science. It formulates human cognitive conflicts (true state, peer pressure, avoidance of cognitive load) using the distance-decay model of the Analytic Hierarchy Process (AHP) and the Softmax function, constructing a micro-macro link model accompanied by stochastic variations. Through large-scale multi-agent simulations ($N={10}^7$) validating the model’s convergence, it was demonstrated that in long-tail distributions formed under peer pressure, macroscopic statistical distance metrics such as the Kullback-Leibler (KL) divergence ignore the fact that a small number of true signals are non-linearly suppressed, causing a statistical misinterpretation that “the error is within an acceptable range”. This implies that as long as macroscopic statistical indicators are over-trusted, signs of critical deterioration (minorities) will be structurally marginalized. Returning to the debate on “Homogeneity (Homogenität)” in German social statistics, this paper advocates that in order to realize objective “Micro-segmentation of Homogeneous Statistical Populations,” a paradigm shift from qualitative methods relying on human intuition to quantitative methods incorporating multi-criteria decision making is essential, rather than simply expanding the sample size. Full article

We construct and analyze a family of support-defined Brauer-type configurations canonically associated with the Boolean geometry underlying the Grassmann algebra. The construction is governed by an x-support map on monomial labels, which identifies the vertex set with the Boolean lattice $\mathcal{P}\left(\right[n\left]\right)$. This identification yields a Boolean support quiver isomorphic to the directed Hasse diagram of $\mathcal{P}\left(\right[n\left]\right)$, equivalently, to an oriented hypercube. We then equip the family with a canonical cyclic ordering at each vertex and obtain a genuine connected reduced Brauer configuration in the standard sense, together with its associated Brauer configuration algebra and its standard Brauer quiver. A ghost-variable mechanism is introduced to obtain a connected realization without altering any support-controlled invariants. We prove that polygon membership, valencies, multiplicities, Boolean stratification, and the support quiver are invariant under support-preserving ghost relabelings. We also give an explicit description of the standard Brauer quiver and show that it is different from the Boolean support quiver. On the algebraic side, we derive closed formulas for the center dimension, the algebra dimension, and the normalization constant of the induced weighted distribution. On the probabilistic side, we distinguish the vertex entropy from the layer entropy, establish an exact decomposition of the former by Hamming layers, and show that the layer distribution is asymptotically concentrated on the middle layers, while extremal vertices and any fixed maximal path contribute a negligible fraction of the total weight. As a consequence, the layer entropy satisfies a logarithmic asymptotic law. We also investigate geometric consequences of the Boolean model transported through the support identification. Coordinate projections produce a rigidity phenomenon for antipodal pairs, providing a combinatorial analogue of Greenberger–Horne–Zeilinger (GHZ)-type fragility, whereas the first Boolean layer exhibits a persistence property analogous to W-type robustness. Together, these results exhibit a concrete bridge between Grassmann combinatorics, Brauer configuration theory, hypercube geometry, and entropy asymptotics. Full article

Surface Acoustic Wave (SAW) technology, long central to analog signal processing and RF filtering, is undergoing a major renewal. Driven by advances that decouple SAWs from traditional piezoelectric materials and fixed-function devices, the field is gaining unprecedented control over acoustic, optical, and electronic interactions at the micro and nanoscale. This review synthesizes these developments across four fronts: new physical mechanisms for SAW manipulation, emerging material platforms, ranging from thin films to 2D systems, along with reconfigurable device architectures and circuits, and the expanding landscape of applications they enable. Optical methods are reshaping how SAWs are generated and controlled, bypassing the limits of conventional electromechanical coupling. Coherent optical excitation of high-Q SAW cavities via Brillouin-like optomechanical interactions now grants access to modes in non-piezoelectric substrates such as diamond and silicon, while on-chip SAW excitation in photonic waveguides through backward stimulated Brillouin scattering opens new integrated sensing routes. In parallel, magneto-acoustic experiments have revealed nonreciprocal SAW diffraction from resonant scattering in magnetoelastic gratings. On the device side, ZnO thin-film transistors integrated on LiNbO₃ exploit acoustoelectric coupling to realize voltage-tunable phase shifters; UHF Z-shaped delay lines achieve high sensitivity in a compact footprint; and parametric synthesis of wideband, multi-stage lattice filters targets 5G-class performance. Atomistic simulations show that SAW propagation in 2D MXene films can be engineered via surface terminations, while aerosol jet printing and SAW-assisted particle patterning provide agile, cleanroom-light fabrication of microfluidic and magnetic components. These advances enable applications ranging from hybrid quantum systems and quantum links to lab-on-a-chip particle control, SBS-based and UHF sensing, reconfigurable RF front-ends, and soft robotic actuators based on patterned magnetic composites. At the same time, optical techniques offer non-contact probes of dissipation, and MXenes and other emerging materials open new regimes of acoustic control. Conclusively, they are transforming SAW technology into a versatile, programmable platform for mediating complex interactions in next-generation electronic, photonic, and quantum systems. Full article

This paper presents a novel voltage differencing transconductance amplifier (VDTA)-based mixed-mode inverse filter capable of operating in voltage mode, transadmittance mode, transimpedance mode, and current mode using a single topology. The proposed configuration employs only three VDTAs with two resistors and three capacitors, offering low component count, high input/output impedance flexibility, and no requirement for component matching. It simultaneously realizes first-order inverse lowpass and highpass, as well as second-order inverse bandpass responses. A comprehensive non-ideal analysis, which includes the effects of VDTA parasitic impedances, determines the practical operating frequency range. The design is validated through PSPICE simulations using 0.18 μm CMOS technology, showing close alignment between theoretical predictions and simulation results, with cutoff frequencies of approximately 1.60 MHz and low power consumption of 0.972 mW. Further analyses confirm orthogonal tuning capability, acceptable temperature stability, and robustness against component tolerances. In a practical application, the proposed inverse filter is employed to implement a mixed-mode PID controller, which significantly improves transient response characteristics by reducing rise time, settling time, and steady-state error. These findings highlight the effectiveness and versatility of the proposed design for analog signal processing and control system applications. Full article