Search Results (34)

Field-Programmable Gate Array System-on-Chip (FPGA-SoC) platforms are increasingly adopted in modern industrial Cyber-Physical Systems (CPSs), enabling real-time control, monitoring, and automation of critical industrial processes. The increasing integration density of modern FPGA-SoC architectures introduces new thermal security challenges, where heat evolves from a reliability concern into a potential source of information leakage. Thermal Side-Channel Attacks (TSCAs) exploit runtime thermal variations to infer sensitive operational, architectural, or cryptographic information from the underlying hardware. While this study is centered on FPGA-SoC platforms, comparable thermal security challenges are increasingly reported across other densely integrated computing architectures, including Multiprocessor System-on-Chip (MPSoC), System-in-Package (SiP), and emerging Three-Dimensional Integrated Circuit (3D-IC) technologies. Consequently, the detection of thermal side-channel intrusions has become a critical hardware security challenge for next generation industrial CPS infrastructures. To address this challenge, an AI-enabled Digital Twin (DT) framework is introduced for TSCA detection in densely integrated FPGA-SoC microarchitectures. By combining thermal behavioral modeling, feature engineering, and machine learning-based anomaly detection, the proposed framework extends conventional Thermal Digital Twin (TDT) approaches beyond monitoring and mitigation toward autonomous thermal threat detection. The proposed framework is experimentally validated using an NI myRIO-1900 platform integrating a Xilinx Zynq-7010 FPGA-SoC representative of modern industrial embedded control architectures. Experimental results demonstrate the feasibility of the proposed framework, achieving an accuracy of approximately 75% with an Area Under the ROC Curve (AUC) of 0.76 using a lightweight Isolation Forest model. These results validate the capability of the proposed AI-enabled Digital Twin framework to learn normal thermal behavioral patterns and autonomously detect anomalous thermal activities potentially related to TSCAs. Full article

Melt-spun Fe_70−xPd₃₀Ga_x ribbons (x = 1 and 3 at.% Ga) were heat-treated at 1223 K for 1 h and 2 h and characterized by X-ray diffraction, scanning electron microscopy, differential scanning calorimetry, magnetometry, and magnetoelastic measurements. Increasing Ga content decreases thermodynamic equilibrium temperature from 292.0 K (1 at.% Ga) to 283.5 K (3 at.% Ga) in as-prepared ribbons. Extended heat treatment then shifts it to 288.0 K and 264.5 K, respectively, and promotes Fe-rich precipitation. Fine precipitates at 1 h preserve a large transformable matrix fraction and introduce microstructural heterogeneity that governs variant mobility and domain-wall pinning; prolonged annealing triggers coalescence, depleting the matrix and reducing both the transformation heat and the magnetoelastic response. Kissinger analysis yields apparent activation energies of 338 kJmol⁻¹ (1 at.% Ga) and 228 kJmol⁻¹ (3 at.% Ga), confirming that higher Ga content lowers the transformation energy barrier. The magnetostrictive response depends on annealing: 1 h-annealed samples exhibit field-induced variant reorientation and saturation magnetostriction of ~60 ppm at 200 K, whereas 2 h-annealed samples approach volume-conserving behavior. Coercivity scales with precipitate density, with Ga3-2h showing anomalously soft magnetic behavior following coalescence. Thermally induced precipitation thus emerges as a route to simultaneously control microstructure, transformation kinetics, magnetoelastic response, and magnetic behavior in ferromagnetic shape memory alloys. Full article

Heat transport in heterogeneous materials can deviate markedly from classical Fourier behavior when microstructural disorder, trapping effects, nonlinear mobility, and long-range temporal correlations interact across multiple spatial and temporal scales. These mechanisms may produce delayed relaxation, persistent thermal footprints, front deformation, and non-classical spreading patterns that are not adequately represented by conventional integer-order diffusion models. In this study, a modeling and simulation framework is developed for anomalous heat transport in heterogeneous media using a two-dimensional time-fractional porous medium equation. The model combines a Caputo fractional time derivative, which represents thermal memory, with nonlinear degenerate porous-medium diffusion, spatially heterogeneous conductivity, localized volumetric heating, and Robin-type convective boundary exchange. A conservative fully discrete numerical scheme is constructed using flux-based finite differences for the heterogeneous nonlinear diffusion operator and an $L1$ approximation for the Caputo derivative. The nonlinear algebraic system at each time level is solved using an under-relaxed Picard frozen-coefficient iteration with non-negativity enforcement and sparse direct solution of the resulting linear systems. The numerical implementation is verified through a manufactured-solution convergence study, and additional analyses are performed to examine computational cost, Picard iteration behavior, coefficient-regularization sensitivity, strong-source effects, heterogeneous conductivity structures, and long-time thermal-footprint persistence. The results show that heterogeneous conductivity mainly redirects heat through preferential pathways and enlarges the spatial footprint while producing negligible changes in global heat content. Stronger fractional memory, represented by smaller fractional order, increases the persistence and spatial reach of moderate heating, whereas larger porous-medium exponents confine heat near the source and preserve higher local peaks. Source amplitude increases the thermal burden and footprint monotonically over the tested range, including strong forcing, without producing an abrupt localization-spreading transition. Boundary exchange remains secondary in the short-time interior-heating regime considered. These findings demonstrate that the proposed two-dimensional time-fractional porous medium framework provides a verified and physically interpretable model for non-Fourier heat transport in heterogeneous materials, where local intensity, global heat retention, and spatial thermal exposure must be assessed jointly. Full article

To investigate the influence mechanism of Yellow River silt powder on the hydration process, microstructure, and strength development of shotcrete, and to promote the resource utilization of Yellow River sediment, this study systematically investigated the effects of different silt powder replacement levels (0%, 10%, 30%, and 50%) on a cement–accelerator system. A combination of setting time tests, isothermal calorimetry, and mechanical strength measurements was employed, together with microstructural characterization techniques including X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), differential thermal analysis (DTA), and scanning electron microscopy (SEM). The results indicate that the silt powder content exerts a two-stage effect on the setting behavior of shotcrete. At low replacement levels (0–30%), both initial and final setting times are significantly prolonged, whereas at higher replacement levels (>30%), the setting time is anomalously shortened, approaching that of the reference mixture. The incorporation of silt powder delays the onset of the pre-induction period, prolongs the induction stage, and reduces the cumulative heat release, with the reduction exhibiting a staged trend characterized by gradual, pronounced, and then moderate changes as the replacement level increases. With increasing silt powder content, both compressive strength and splitting tensile strength decrease continuously. At a 50% replacement level, the 28-day compressive strength loss reaches 48.35%, while the splitting tensile strength loss reaches 43.30%, with more pronounced deterioration observed at early ages. The tensile-to-compressive strength ratio increases with silt powder content at early ages, while converging to similar values among all mixtures at later ages. Microstructural analysis indicates that silt powder primarily affects hydration through physical dilution and ion adsorption. At low dosages, nucleation effects slightly promote early hydration, whereas at high dosages, the hydration of calcium silicate phases is inhibited, resulting in reduced C–S–H gel formation and increased porosity. Additionally, AFt morphology transitions from dense prismatic crystals to loosely distributed needle-like structures. This study provides a systematic understanding of the role of silt powder in shotcrete and offers theoretical guidance for mix design optimization and the sustainable utilization of Yellow River sediment. Full article

This paper presents a novel proportional–integral–derivative (PID) control framework for first $\alpha$-order systems evolving in fractal time. The main contribution is the extension of classical control theory to systems exhibiting anomalous temporal scaling by employing local fractal derivatives. In contrast to fractional-order PID (FOPID) approaches, which primarily model memory effects, the proposed fractal PID framework captures time-scaling behavior arising in non-smooth environments, such as viscoelastic friction and irregular contact surfaces. The closed-loop dynamics are formulated as a second $\alpha$-order fractal differential equation, from which a characteristic equation is derived to establish conditions for asymptotic stability. It is shown that, for a constant reference input and positive controller gains, the tracking error converges to zero as $t\to \infty$. In addition, a quantitative performance analysis demonstrates that the fractal-order $\alpha$ governs temporal stretching: smaller values of $\alpha$ lead to increased rise and settling times and reduced oscillation frequency. The effectiveness of the proposed approach is illustrated through applications to a thermal system with fractal heat input and robotic actuators operating in irregular environments. These results highlight the potential of fractal-time control as a systematic framework for modeling and controlling dynamical systems with non-integer temporal structure. Full article

This study presents a quantum electrodynamics (QED) framework that explains the anomalous behavior of liquid water. The theory posits that water consists of two coexisting phases: a coherent phase, in which molecules form phase-locked coherence domains (CDs), and an incoherent phase that behaves like a dense van der Waals fluid. By solving polynomial-type equations, we derive key thermodynamic properties, including the minima in the isobaric heat capacity per particle (IHCP) and the isothermal compressibility, as well as the divergent behavior observed near 228 K. The theory also accounts for water’s high static dielectric constant. These results emerge from first-principles QED, integrating quantum coherence with macroscopic thermodynamics. The framework offers a unified explanation for water’s anomalies and has implications for biological systems, materials science, and fundamental physics. Future work will extend the theory to include phase transitions, solute interactions, and the freezing process. Full article

The corrosion behavior of an additively manufactured Mg-1Zn alloy was investigated in both the transverse and longitudinal directions relative to the build direction, in the as-built condition and after annealing at 350 °C for 24 h under high vacuum. Microstructural characterization using XRD and SEM revealed the presence of magnesium oxide (MgO) and the absence of intermetallic second-phase particles. Optical microscopy (OM) images and Electron Backscatter Diffraction (EBSD) maps showed a highly complex grain morphology with anomalous, anisotropic shapes and a heterogeneous grain size distribution. The microstructure includes grains with a pronounced columnar morphology aligned along the build direction and is therefore characterized by a strong crystallographic texture. Electrochemical techniques, including PDP and EIS, along with gravimetric H₂ collection, concluded that the transverse plane exhibited greater corrosion resistance compared to the longitudinal plane. Additionally, an increase in cathodic kinetics was observed when comparing as-built with heat-treated samples. Full article

Thermal infrared (TIR) ground observations are a well-established method for investigating surface temperature variations in thermally anomalous areas. However, commercially available technical solutions are currently limited, often offering proprietary products with minimal customization options for establishing a permanent TIR monitoring network. This work presents the comprehensive development of a thermal infrared monitoring network, detailing everything from the hardware schematics of the remote monitoring station (RMS) to the code for the final data processing software. The procedures implemented in the RMS for managing TIR sensor operations, acquiring environmental data, and transmitting data remotely are thoroughly discussed, along with the technical solutions adopted. The processing of TIR imagery is carried out using ASIRA (Automated System of InfraRed Analysis), a free software package, now developed for GNU Octave. ASIRA performs quality filtering and co-registration, and applies various seasonal correction methodologies to extract time series of deseasoned surface temperatures, estimate heat fluxes, and track variations in thermally anomalous areas. Processed outputs include binary, Excel, and CSV formats, with interactive HTML plots for visualization. The system’s effectiveness has been validated in active volcanic areas of southern Italy, demonstrating high reliability in detecting anomalous thermal behavior and distinguishing endogenous geophysical processes. The aim of this work is to enable readers to easily replicate and deploy this open-source, low-cost system for the continuous, automated thermal monitoring of active volcanic and geothermal areas and environmental pollution, thereby supporting hazard assessment and scientific research. Full article

Nanofluids—engineered suspensions of nanometer-sized particles—have attracted significant attention due to their reportedly enhanced thermal properties, making them promising candidates for advanced heat transfer applications. However, despite extensive studies, uncertainties remain regarding the magnitude and origin of these effects, limiting their practical implementation. To address this, we present a comprehensive study on nanofluid formulations based on the commercial refrigerant HFE-7500, incorporating surfactant-stabilized dispersions of several metal oxide and nitride nanoparticles. We measured key physicochemical properties, including zeta potential, particle size, viscosity, and thermal conductivity. Our results show that while the nanofluids exhibited high stability, their particle sizes in suspension were significantly larger than the primary nanoparticle sizes measured by TEM. Notably, alumina-based suspensions demonstrated the greatest enhancement, exhibiting approximately 10–15% increases in thermal conductivity as a function of volume percentage. These surpassed the 5–10% improvements observed with other metal oxides, an effect that may be linked to their comparatively larger particle sizes. However, the observed enhancements were lower than some previously reported values that claimed anomalously high thermal conductivity increases. Furthermore, steady shear viscosity increased with particle concentration, showing enhancements of 10–20%, which suggests a potential trade-off for practical implementation. Our findings refine the understanding of nanofluid behavior in refrigerants and establish a foundation for optimizing their performance in thermal management applications. However, viscosity increases must be carefully considered when designing next-generation nanofluids for real-world use. Full article

Experimental and theoretical design on thermal and structural properties of Cu-Zn-Al alloys are established. As such, from an experimental point of view, differential thermal analysis has been performed with the help of a DSC Netzsch STA 449 F1 Jupiter calorimeter with high levels of sensitivity, and the structural analysis has been accomplished through X-ray diffraction and SEM analysis. An unusual specific property for a metallic material has been discovered, which is known as “rubber-type behavior”, a characteristic determined by micro-structural changes. From the theoretical point of view, the thermal transfer in Cu-Zn-Al is presented by assimilating this alloy, both structurally and functionally, with a multifractal, situation in which the order–disorder transitions assimilated with thermal “dynamics” of Cu-Zn-Al, are mimed through transitions from non-multifractal to multifractal curves. In such a context, the thermal expansion velocity contains both the propagation speed of the phase transformation (be it a direct one: austenitic–martensitic transformation, or an indirect one: martensitic–austenitic transformation) and the thermal diffusion speed. Then, through self-modulations of the thermal field, the Cu-Zn-Al alloy will self-structure in channel-type or cellular-type thermal patterns, which can be linked to obtained experimental data. Consequently, since the thermal conductivity becomes a function of the observation scale, and heat transfer is modified to reflect the multifractal, non-differentiable paths in the material, it leads to anomalous diffusion and complex thermal behaviors. Full article

The simulation of the transport properties of confined, low-dimensional fluids can be performed efficiently by means of multi-particle collision (MPC) dynamics with suitable thermal-wall boundary conditions. We illustrate the effectiveness of the method by studying the dimensionality effects and size-dependence of thermal conduction, since these properties are of crucial importance for understanding heat transfer at the micro–nanoscale. We provide a sound numerical evidence that the simple MPC fluid displays the features previously predicted from hydrodynamics of lattice systems: (1) in 1D, the thermal conductivity $\kappa$ diverges with the system size L as $\kappa \sim L^{1/3}$ and its total heat current autocorrelation function $C\left(t\right)$ decays with the time t as $C\left(t\right)\sim t^{-2/3}$; (2) in 2D, $\kappa$ diverges with L as $\kappa \sim \ln \left(L\right)$ and its $C\left(t\right)$ decays with t as $C\left(t\right)\sim t^{-1}$; (3) in 3D, its $\kappa$ is independent with L and its $C\left(t\right)$ decays with t as $C\left(t\right)\sim t^{-3/2}$. For weak interaction (the nearly integrable case) in 1D and 2D, there exists an intermediate regime of sizes where kinetic effects dominate and transport is diffusive before crossing over to the expected anomalous regime. The crossover can be studied by decomposing the heat current in two contributions, which allows for a very accurate test of the predictions. In addition, we also show that, upon increasing the aspect ratio of the system, there exists a dimensional crossover from 2D or 3D dimensional behavior to the 1D one. Finally, we show that an applied magnetic field renders the transport normal, indicating that pseudomomentum conservation is not sufficient for the anomalous heat conduction behavior to occur. Full article

Rare-earth tetraborides RB₄ are of great interest due to the occurrence of geometric magnetic frustration and corresponding unusual magnetic properties. While the Gd³⁺ spins in GdB₄ align along the ab plane, Er³⁺ spins in the isomorphic ErB₄ are confined to the c–axis. The magnetization in the latter exhibits a plateau at the midpoint of the saturation magnetization. Therefore, solid solutions of (Gd, Er)B₄ provide an excellent playground for exploring the intricate magnetic behavior in these compounds. Single crystals of Gd_1−xEr_xB₄ (x = 0, 0.2, and 0.4) were grown in aluminum flux. X-ray diffraction scans revealed single-phase materials, and a drop in the unit cell volume with increasing Er content, suggesting the partial substitution of Er at the Gd sites. Heat capacity measurements indicated a systematic decrease of the Néel temperature (T_N) with increasing Er content. The effective magnetic moment determined from the magnetization measurement agreed with the calculated free ion values for Gd³⁺ and Er³⁺, providing further evidence for the successful substitution of Er for Gd. The partial substitution resulted in an anomalous ferromagnetic phase below T_N, exhibiting significant anisotropy, predominantly along the c-axis. This intriguing behavior merits further studies of the magnetism in the Gd_1−xEr_xB₄ borides. Full article

As an important mechanical property, tensile behavior has been regarded as an indicator for the creep and thermal mechanical fatigue properties of Ni-based superalloys. The tensile property of Ni-based superalloys is closely related to the amount, size, and distribution of ${\gamma }^{\prime }$-phase and carbides. To further clarify the tensile deformation mechanism of the CM247LC alloy, this study investigated its solidification characteristics and directionally-solidified and heat-treated microstructure. The dependence of tensile properties on the varied temperature ranging between 650 and 950 °C is discussed in detail. It was found that the deformation mechanism at 650 °C is dominated by the shearing of dislocations into the ${\gamma }^{\prime }$-precipitates to form the superlattice stacking faults. At 800 °C, the K-W lock leads to the anomalous yield effects. At 950 °C, the deformation mechanism is dominated by the dislocations bypassing the ${\gamma }^{\prime }$-precipiates. The results provide a comprehensive understanding of the CM247LC alloy and are beneficial for the development of corrosion-resistant Ni-based superalloys. Full article

We used the Taylor–Ulitovsky technique to prepare nanocrystalline Co₂MnGe Heusler alloy glass-coated microwires with a metallic nucleus diameter of 18 ± 0.1 µm and a total diameter of 27.2 ± 0.1 µm. Magnetic and structural studies were carried out to determine the fundamental magneto-structural characteristics of Co₂MnGe glass-coated microwires. XRD revealed a well-defined nanocrystalline structure with an average grain size of about 63 nm, lattice parameter a = 5.62 and a unique mixture of L2₁ and B2 phases. The hysteresis loops measured at different temperatures indicated a well-known ferromagnetic behavior for the reduced remanent, where a monotonic increasing in the reduced remanent and saturation magnetization occurs. The coercivity shows anomalous behavior compared to the Co2Mn-based glass-coated microwires. The magnetization curves for field cooling and field heating (FC–FH) demonstrate a considerable dependence on the applied magnetic field, ranging from 50 Oe to 20 kOe. Internal stresses, originated by the production process, resulted in various magnetic phases, which were responsible for the notable difference of FC and FH curves on magnetization dependence versus temperature. Furthermore, the ferromagnetic behavior and expected high Curie temperature, together with high degree of the L2₁ order, make it a promising candidate for many applications. Full article

Faults and anomalous behavior affect the operation of Heating, Ventilation and Air Conditioning (HVAC) systems. This causes performance loss, energy waste, noncompliance with regulations and discomfort among occupants. To prevent damage, automated, fast identification of faults in HVAC systems is needed. Fault Detection and Diagnosis (FDD) techniques are very effective for these purposes. The best FDD methods, in terms of cost effectiveness and data exploitation, are based on process history; i.e., on sensor data from automation systems. In this work, supervised and semi-supervised models were developed. Other than with regard to outdoor temperature and humidity, the input parameters of an HVAC system have few internal variables. Performance of traditional methods (e.g., VAR, Random Forest) is low, so Artificial Neural Networks (ANNs) were selected, since they can capture nonlinear relationships among features and are easily optimized. ANNs can detect simultaneous faults from different classes. ANN metrics are easily evaluated. The ground truth is obtained from process history (supervised case) and from a mix of deterministic methods and clustering (semi-supervised case). The derivation of the ground truth in the semi-supervised case, and extensive comparison with advanced supervised models, set this work apart from previous studies. The Mean Absolute Error (MAE) of the best supervised model was 0.032 over 15 min and 0.034 over 30 min. The Balanced Accuracy Score (BAS) of the best semi-supervised model was 86%. Full article