Search Results (17,742)

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

Time-series machine learning models represented by long short-term memory (LSTM) networks provide an effective way to obtain high-precision sound speed profiles (SSPs) quickly and at low cost, which can meet the practical application requirements of underwater sonar systems. However, in sea areas with frequent strong internal solitary waves, the large-amplitude sound speed anomalies caused by them will seriously interfere with model learning in the form of strong outlier features, resulting in a sharp drop in SSP prediction accuracy and significant degradation of the generalization stability and robustness of the model. To address this problem, this paper proposes a time-series SSP prediction method based on a bidirectional long short-term memory (Bi-LSTM) network. First, Empirical Orthogonal Function (EOF) decomposition is used to realize the low-dimensional feature representation of SSPs, and then the bidirectional time-series feature capture capability of Bi-LSTM is used to predict the SSP sequence with large disturbances caused by strong internal solitary waves. Multiple groups of comparative experiments based on the measured temperature chain data in the continental slope area of the South China Sea show that the Bi-LSTM model has a significant improvement in prediction accuracy and robustness compared with the classical LSTM model. Among them, the Bi-LSTM model with EOF decomposition achieves a correlation coefficient of 0.995 and an average Root Mean Square Error (RMSE) as low as 0.387 m/s. Under the condition of internal solitary wave disturbance, the classical LSTM is difficult to effectively capture the large abrupt change in sound speed, while the proposed Bi-LSTM model can still achieve accurate prediction of the SSP in the disturbance section, and has both the feature recognition and evolution prediction capabilities for the strongly nonlinear internal solitary wave process. This method provides effective technical support for the rapid and large-scale reconstruction of the sound speed field under the disturbance of strong internal solitary waves. Full article

This study focuses on flame spread and merging in discrete fuel arrays composed of birch rods under dual fire source conditions. Tests were performed with five fuel spacings (nL/W = 1, 2, 3, 4, single source) and eight array spacings (S = 2 mm to 9 mm) to quantitatively evaluate the influence of these parameters on the flame merging behavior and key spread characteristics. The results indicate that the probability of flame merging decreases with increasing fuel spacing and is strongly affected by array spacing. Both the inter-fire temperature and dimensionless temperature rise were found to follow distinct power-law relationships with spacing. Flame height is governed by both spacing parameters. In contrast, the flame spread rate responded to array spacing but exhibited minimal sensitivity to fuel spacing. In this study, heat flux between the two arrays is demonstrated to be dominated by thermal radiation. A predictive model was formulated for the merged flame height, demonstrating close agreement with the experimental results. Full article

Designing high-speed wide-angle optics for large-format mirrorless cameras presents a fundamental engineering conflict between the short flange back distance and the requirement for high-resolution aberration correction. To address this challenge, this study proposes a compact 18.5 mm F/2.0 lens system utilizing a modified retrofocus architecture equipped with an internal floating-focus mechanism. The design methodology integrates glass-molded aspherical surfaces to suppress high-order aberrations and employs passive athermalization strategies to maintain stability across a temperature range of −30 °C to +70 °C. Performance was rigorously evaluated using numerical simulations in Zemax OpticStudio, alongside comprehensive Monte Carlo tolerance analysis. Simulation results demonstrate exceptional optical performance, with the Modulation Transfer Function (MTF) exceeding 0.5 at a spatial frequency of 100 lp/mm across the field. Furthermore, focus breathing is restricted to less than 1%, and optical distortion is strictly controlled within 2%. The Monte Carlo tolerance analysis predicts a manufacturing yield exceeding 80% under standard industrial precision levels. Ultimately, this work provides a theoretically sound, athermally stable, and highly manufacturable solution suitable for next-generation high-resolution mirrorless sensors. Full article

High-altitude arid regions are characterized by concurrent water scarcity, low temperatures, and intense solar radiation. However, the adaptive mechanisms of desert shrubs to these combined stressors remain poorly understood. To address this gap, we integrated large-scale field surveys with laboratory measurements of eight stem and leaf anatomical traits across six common desert shrub species in the Qaidam Basin. Principal component analysis (PCA) revealed two primary axes of trait variation. The first principal component (PC1) characterized a trade-off between leaf protective traits (e.g., cuticle and epidermal thickness) and stem hydraulic-storage traits (e.g., central cylinder, xylem, and pith diameters). The second principal component (PC2) was primarily loaded by stem cortex thickness, representing a physiological buffering mechanism. Based on PC1, species were categorized into two distinct strategic groups. Group A prioritized investment in stem conductive and storage tissues, enhancing hydraulic safety under hotter, high-evaporative demand conditions. Conversely, Group B exhibited reinforced leaf protective structures, consistent with tolerance to high radiation and low-temperature stress at higher elevations. The environmental gradients were the primary drivers of this divergence: Group A was associated with aridity, whereas Group B was correlated with elevation. Our findings demonstrate that desert shrubs in the Qaidam Basin have employed diverse adaptive strategies via the modulation of organ-specific anatomical traits to mitigate environmental stressors. These findings offer valuable insights into plant adaptive mechanisms, with implications for predicting vegetation responses and informing ecological restoration in high-altitude arid ecosystems. Full article

Granular systems confined in a shallow box and subjected to vertical vibration provide an attractive geometry for studying fluidized granular media. In this configuration, grains acquire kinetic energy in the vertical direction through collisions with the confining walls, and this energy is subsequently transferred to the horizontal degrees of freedom via interparticle collisions. In recent years, the so-called $\mathsf{\Delta }$-model has been introduced as a simplified yet effective description of the dynamics of granular systems in such geometries. This review presents the results obtained from kinetic theory for the granular $\mathsf{\Delta }$-model. To model the energy transfer mechanism, a fixed velocity increment $\mathsf{\Delta }$ is added to the normal component of the relative velocity during collisions. In this way, the vertical motion is effectively integrated out while retaining the collisional energy injection characteristic of the confined setup. This mechanism compensates for the energy loss due to inelastic collisions and leads to stable homogeneous steady states that can be analyzed within the framework of kinetic theory. The Enskog kinetic equation is formulated for this model and first analyzed in homogeneous steady states, yielding the stationary temperature and the equation of state. The dynamics of inhomogeneous states is then investigated using the Chapman–Enskog method, from which the Navier–Stokes transport coefficients are derived. The theory is further extended to granular mixtures, in which particles may differ in mass, size, restitution coefficient, or in the value of $\mathsf{\Delta }$. In this case, the phenomenology becomes richer; for example, energy equipartition is violated even in homogeneous steady states. The mixture dynamics is studied through the corresponding Navier–Stokes equations, and the associated transport coefficients are obtained in the low-density regime. The analysis of the hydrodynamic equations shows that, in agreement with simulations, the homogeneous state is linearly stable. Moreover, the intrinsically nonequilibrium nature of the model leads to the violation of Onsager reciprocity relations in granular mixtures. The theoretical predictions exhibit in general good agreement with both molecular dynamics simulations and direct simulation Monte Carlo results. Full article

This research communication presents an analysis of dynamic supply chains (DSCs). The main goal of model-based cybertronics is to approximate, via a mathematical model from a dynamical system, the dynamics and behavior of dynamic supply chains. This considers that is at the operational level, where automation and control theory approaches take an insight —in this case, via Lyapunov stability—as a way to extend the use of mechatronic systems. Three case studies are presented: Firstly, the mathematical modeling and stability analysis of the ball-and-beam problem, as an approximation of a two echelon supply chain. Secondly, the mathematical modeling and stability analysis of a cold chain with temperature monitoring, and its relationship to inventory levels, are presented. From a theoretical perspective, applying model-based cybertronics in DSCs has direct practical implications: it improves operational control, enhances decision-making, and optimizes inventory management, particularly in cold chains. By treating high-volume supply chains as dynamical systems, managers can anticipate fluctuations and quantify efficiency. Finally, Lyapunov stability analysis ensures that models reliably reflect real-world behavior, enabling automation and predictable performance at an operational level in DSCs. Full article

Thermal accumulation is a critical constraint in robotic grinding of ZGMn13 high-manganese steel, whereas the variables that can be prescribed or monitored reliably online are often limited to the normal load F_z, spindle speed n, and feed speed ν_w. Most existing studies focus on single-pass conditions or scalar thermal indicators, while full-process near-surface transient temperature histories in multi-layer robotic grinding remains insufficiently addressed. This study presents a full-process near-surface transient temperature histories framework for multi-layer robotic grinding under fixed wheel–workpiece conditions and limited online sensing. Multi-channel near-surface thermal measurements were first reorganized into layer-resolved time-series data. A process-driven thermal surrogate was then constructed from the deployable inputs (F_z, n, ν_w), and a recursive temperature-evolution model was developed by incorporating intra-layer thermal retention and interlayer residual-heat inheritance. The proposed formulation predicts the near-surface transient temperature history over successive grinding layers. Experimental results showed clear layer-wise transience and progressive thermal accumulation during multi-layer grinding. Under representative conditions, the proposed framework reproduced the dominant transient structure of the measured full-process near-surface temperature histories, and grouped validation further showed that the recursive formulation preserved more useful history-level information than the reduced baselines within the tested domain. Within the tested operating domain, the predicted histories were further reduced to derived thermal indicators and planning-oriented peak-temperature maps. Full article

Regarding the erosion–corrosion problem of 304 stainless steel, which is commonly used in the production of alumina, in high-temperature, high-pressure, and strongly alkaline aluminum ammonium solutions, a detailed study was conducted on the erosion–corrosion behavior and damage mechanism of 304 stainless steel in a sodium aluminate solution with varying S²⁻ concentrations at 65 °C and pH = 14 under the influence of key factors such as erosion speed. This study quantitatively revealed, for the first time, the flow rate threshold effect (critical point at 2 m/s) of 304 stainless steel during scouring corrosion in a strongly alkaline aluminum ammonium solution, identified its peak weight loss rate (1.892 × 10⁻³ g/m²·d), and innovatively elucidated the mechanism reversal phenomenon: below the threshold, passive film destruction and corrosion synergistically dominate, while above the threshold, high oxygen mass transfer promotes film regeneration. These findings provide a critical theoretical basis for precise flow rate control and equipment life prediction in alumina production processes. Full article

Direct urea–hydrogen peroxide fuel cells (DUHPFCs) are promising for sustainable power generation, but their performance is governed by highly nonlinear material and operating interactions. This study develops a machine-learning framework employing a multi-output artificial neural network (ANN) to predict cell voltage, power density (PD), and substrate-based energy efficiency (SEE) of DUHPFCs. The ANN exhibits excellent predictive accuracy, achieving coefficients of determination (R²) above 0.995 and normalized root mean square errors (NRMSE) below 1.75 × 10⁻² for all outputs. Model interpretability is enhanced by using Shapley additive explanations and partial dependence plots, which identify current density as the dominant factor affecting DUHPFC performance, followed by temperature and anolyte composition. The ANN is coupled with a multi-objective Pareto-search algorithm optimization (PAO) to resolve the trade-offs among competing performance metrics. Under different optimization objectives, a DUHPFC with an Ni_0.2Co_0.8/Ni-foam anode is predicted to achieve a maximum PD of 45.6 mW/cm² with a low SEE of 2.6% or a maximum SEE of 15.2% with a moderate PD of 40.9 mW/cm². Additionally, a balanced operating regime is identified, achieving a PD of 43.1 mW/cm² and an SEE of 13.9%. Overall, the proposed framework provides an effective decision-support tool for optimizing DUHPFC performance under competing objectives. Full article

Industrial drying of spunlace nonwovens (fibrous materials produced by hydroentanglement using high-pressure water jets) represents one of the most energy-intensive stages of production due to the high water content remaining after the hydroentanglement process and the large thermal energy required for water evaporation. Understanding the relationship between material structure, production parameters, and water removal intensity is therefore essential for improving process efficiency. This study investigates the drying behavior of viscose–polyester spunlace nonwovens using an integrated mass balance and statistical modeling approach based on industrial production data. Process parameters were collected from an industrial SCADA (Supervisory Control and Data Acquisition) monitoring system and combined with laboratory measurements of nonwoven mass per unit area. Experimental results show that 926–1840 kg/h of water can be removed during drying at temperatures below 100 °C, depending primarily on production speed and structural parameters of the material. A multivariate exponential regression model was developed to describe the nonlinear relationship between drying temperature, production parameters, and water removal intensity. The model demonstrated high predictive accuracy when validated with independent test data. The results indicate that mass throughput and structural characteristics dominate the drying process, while temperature variations remain limited by technological constraints. The proposed modeling framework enables predictive control of industrial drying conditions and provides a practical tool for improving energy efficiency in industrial nonwoven manufacturing. Full article

Accurate modeling of the burning rate of char particles in particle-laden flows is essential. However, because of the strong inhomogeneity and nonlinearity of the process, accurately resolving the surface burning rate of char particles remains challenging. In this study, an eXtreme Gradient Boosting (XGBoost)-based framework is developed to reformulate the conventional char oxidation rate model, namely the Baum&Street (B&S) model, resulting in a modified model referred to as the XGB-B&S model. In this model, a correction term ${\beta }_{\mathrm{turb}}$ is incorporated and formulated using the particle Reynolds number together with a dimensionless temperature. A turbulent mixing layer with char particle combustion is simulated by means of particle-resolved direct numerical simulation with three-dimensions, generating a high-fidelity dataset for model training and validation. To assess the predictive capability of the XGBoost model, its results are benchmarked against those obtained from an Artificial Neural Network model. The comparison indicates that XGBoost provides better overall accuracy, as reflected by a larger coefficient of determination (${\mathrm{R}}^2$) and smaller values of both the root mean square error and the mean absolute error. Finally, the XGB-B&S model is validated against the test dataset. The ${\mathrm{R}}^2$ between the XGB-B&S predictions and the PR-DNS results is significantly higher than that between the conventional B&S model and the PR-DNS results, confirming the strong predictive capability of XGBoost for modeling char particle oxidation rate. Full article

Background: Oral lesions, particularly enanthema, may accompany chickenpox and represent an important but often underrecognized component of the clinical presentation. Their timely identification is especially relevant in dental practice, as oral manifestations may be more frequent in patients with a more severe clinical course. This study aimed to describe characteristic oral cavity changes in hospitalized and outpatient patients with chickenpox, to identify patterns in the occurrence of oral findings in relation to disease severity, and to support clinical assessment in practice. Methods: A retrospective review of medical records was conducted for patients diagnosed with chickenpox in Bulgaria between December 2023 and May 2025. Data from hospitalized patients and outpatient cases were analyzed and compared to evaluate the distribution of oral manifestations and their association with clinical severity. Results: A total of 144 patients were included, of whom 32.6% required hospitalization. Oral enanthema was more frequently observed among hospitalized patients (48.8%). In univariate analyses, oral enanthema and tongue changes were associated with hospitalization. Multivariable logistic regression identified age and body temperature as independent factors associated with hospitalization, while oral manifestations did not retain independent predictive significance. Conclusion: Oral enanthema was more frequently observed among hospitalized patients and was associated with a more severe clinical presentation in univariate analyses. Although oral findings should not be interpreted as independent predictors of disease severity, their recognition may support clinical assessment, dental treatment planning, and appropriate infection control measures. Full article

Accurate prediction of transient melt pool thermal fields in Laser Powder Bed Fusion (LPBF) is essential for understanding melt pool geometry and defect formation mechanisms, yet conventional finite element methods (FEM) impose prohibitive computational costs for parametric process exploration. A variational physics-informed neural network (VPINN) framework is presented for 3D transient thermal modeling of a GH3536 single-track LPBF scan. The framework incorporates a continuously differentiable Goldak double-ellipsoid moving heat source, temperature-dependent thermophysical property surrogates, and an effective heat-capacity treatment of latent heat associated with solid–liquid phase change and vaporization. These components are embedded in a weak-form residual-minimization scheme with octree-adaptive domain decomposition, hierarchical Legendre test functions, and sequential sliding-window time marching. Effective absorptivity is inferred jointly with the network parameters, using sparse experimental melt pool profiles as supervision. Within a parametric study covering laser powers from 100 to 140 W and scan speeds from 1000 to 1500 mm/s, the predicted melt pool width, depth, and aspect ratio agree closely with FEM benchmarks and cross-sectional optical micrograph measurements across both supervised and held-out interpolation conditions, with total relative L₂ nodal temperature errors ranging from 3.23% to 6.75%. Following a one-time offline training investment of 15,323 s that simultaneously resolves the full parametric space, surrogate inference reduces per-condition query time from 3000–4000 s (FEM) to merely 4–5 s, delivering a speedup of two to three orders of magnitude and making the framework increasingly cost-effective for high-throughput parametric studies and digital-twin integration as the number of queried conditions grows. Full article

To investigate the aging behavior of high-temperature-vulcanized silicone rubber (HTV-SiR) used in composite insulator sheds under coupled electrical, thermal, humidity, and mechanical stresses, accelerated aging tests were conducted to emulate the service conditions of ±800 kV ultra-high-voltage direct current (UHVDC) systems in Guangzhou, China. The physicochemical, mechanical, and electrical properties of the specimens were systematically characterized. The results show simultaneous degradation of both electrical and mechanical performance. In particular, the tensile strength exhibits a significant monotonic decrease and drops to 49.52% of its initial value under the most severe condition (0.5 kV·mm⁻¹ and 5% tensile strain) after 75 days. In contrast, the DC breakdown strength shows a non-monotonic “rise-then-fall” trend and decreases more markedly with increasing tensile strain. To address the one-shot and destructive nature of tensile testing and the associated statistical uncertainties, a lifetime prediction framework was developed by integrating a generalized Eyring acceleration relation with a stochastic degradation process. Under representative service conditions of 0.09 kV·mm⁻¹ and 0.2% tensile strain, the predicted lifetimes corresponding to failure probabilities of 10%, 75%, and 90% are 1.77, 9.08, and 17.90 years, respectively. The applicability of the model is supported by field-aged specimens. These findings provide a mechanistically grounded and reliability-oriented basis for condition assessment, lifetime-margin evaluation, material screening, and maintenance planning of UHVDC composite insulators operating in hot–humid environments. Full article