Search Results (15,164)

Urban street canyons exert a critical influence on local microclimates; however, the dynamics of mixed convective airflow under unsteady wind and thermal forcing remain poorly quantified. This study systematically investigates the spatiotemporal characteristics of airflow within symmetric and asymmetric street canyons through integrated long-term field measurements and complementary CFD simulations. Field data collected over 120 monitoring days at the Weishui Campus of Chang’an University were analyzed using the Levenberg–Marquardt nonlinear curve-fitting algorithm. The analysis demonstrates that sine functions accurately represent diurnal surface temperature variations during consecutive clear sky periods, whereas polynomial functions of varying orders are required to characterize meteorologically complex episodes, including cold-wave cooling and seasonal transitions. Ambient wind patterns outside the canyon were further classified into two characteristic variation modes: stepwise and gradual. Complementary unsteady RANS simulations, with wall boundary conditions derived directly from the fitted field data, reveal that canyon geometry and meteorological forcing jointly govern the evolution of airflow structures and thermal distributions across seasons. In the symmetric canyon, the flow transitions from complex multi-vortex activity in spring and summer to a more stable regime in autumn, with two well-defined counter-rotating vortices emerging during winter cold-wave events. In the asymmetric canyon, strong summer solar heating sustains a dominant leeward vortex with a strengthening secondary structure, whereas winter cold wave intrusion generates a hierarchically nested vortex system in which secondary and tertiary vortices progressively develop and detach. By coupling empirical surface temperature functions with CFD boundary conditions, this study advances the precision of predictive microclimate models and provides an evidence-based framework for optimizing street canyon geometry to enhance ventilation performance, energy efficiency, and outdoor thermal comfort. Full article

A sustainable alginate-based composite adsorbent was developed by valorizing soy whey wastewater for the efficient removal of copper (II) ions from aqueous solutions. Soy whey wastewater/sodium alginate/cellulose (SWWSAC) beads were fabricated via a controlled slow-release calcium ion cross-linking strategy. This strategy resulted in homogeneous gelation, effective encapsulation of wastewater-derived organics and the formation of a hierarchical mesoporous structure. Compared with pure sodium alginate (SA) and sodium alginate–cellulose (SAC) beads, the SWWSAC beads exhibited a significantly higher specific surface area (3.95 m²/g) and pore volume (0.021 cm³/g), thus having markedly enhanced copper (II) ion adsorption performance. Batch adsorption experiments demonstrate that the adsorption process was strongly dependent on solution pH, adsorbent dosage, contact time and initial metal concentration. Kinetic analysis indicates that the adsorption process followed a pseudo-second-order model, while equilibrium data were well described by the Langmuir isotherm, corresponding to monolayer chemisorption. Based on this isotherm, SWWSAC beads had a theoretical maximum adsorption capacity of 168.3 mg/g (25 °C), 190.8 mg/g (35 °C), and 204.4 mg/g (45 °C). Thermodynamic results reveal that the adsorption was spontaneous and endothermic. FTIR and XPS analyses confirm that copper (II) ion removal was governed by synergistic complexation involving carboxyl, hydroxyl, carbonyl, and protein-derived nitrogen-containing functional groups. Moreover, the SWWSAC beads had a copper (II) ion removal efficiency of (92.4 ± 0.4)% and retained 73.3% of their initial adsorption capacity after six regeneration cycles in actual electroplating wastewater treatment. In this process, the beads exhibited good anti-interference performance against coexisting cations and good structural stability. Therefore, this work demonstrates an effective and low-cost strategy for copper (II) ion removal while providing a value-added route for the sustainable utilization of soy whey wastewater. Full article

This manuscript aims at the introduction of interpolative $\mathcal{FG}$-contractions and their application to derive fixed-point results. As an application, one of the obtained results is used to analyze the fractional-order Aizawa model. Moreover, we introduce a Hutchinson–Barnsley operator defined in terms of interpolative $\mathcal{FG}$-contractions and a related iterated function system to prove the existence of a unique fractal. The theoretical findings are supported with illustrative examples and graphical demonstrations. This manuscript also sheds light on a theoretical framework for analyzing material decomposition and recycling processes. Full article

The increasing share of renewable energy, such as solar and wind energy, in the energy mix implies a demand for sustainable energy storage systems for the mitigation of the intermittency of these energy sources. One option, therefore, is stationary batteries based on abundant sodium, stored in hard carbon (HC) anodes. In this work, following the sustainable by design principle, HCs were synthesized from cotton-based textile waste using three different thermochemical routes: hydrothermal carbonization (HTC) followed by pyrolysis under nitrogen atmosphere (HC-250-N), HTC followed by pyrolysis under a water vapor stream (HC-250-W), and direct pyrolysis (HC-direct-N). The impact of the synthesis method on the physicochemical properties and electrochemical performance of the HCs was thoroughly investigated. X-ray diffraction, Raman spectroscopy, electron microscopy, and gas adsorption analyses revealed that the HTC pre-treatment significantly enhanced the carbon content, microporosity, and degree of structural graphitic order. HC-250-N exhibited the highest graphitic character and more uniform microstructure, while HC-250-W showed the largest specific surface area and broader micropore distribution. Electrochemical evaluation in sodium-ion half-cells indicated that HC-250-N delivered the most balanced performance, with a reversible capacity of 335 mAh g⁻¹ and good cycling stability. These findings confirm the potential of textile waste-derived HCs as promising and sustainable anode materials for sodium-ion batteries and highlight the importance of tailoring synthesis parameters—such as HTC treatment and pyrolysis conditions—to optimize their structural and electrochemical properties. Full article

The Transformer architecture drives modern Artificial Intelligence (AI), yet the physical principles that may constrain self-attention training remain poorly characterized. We develop a thermodynamic framework for attention training, drawing on the established Boltzmann correspondence between softmax attention and equilibrium statistical mechanics, and we propose a First Law analogue that decomposes the training energy budget into a heat term (the entropic cost of ordering attention) and a work term (the gain in mutual information about the target). From this framework we derive a Landauer-type bound on learning, which states that the loss reduction during training is bounded below by the entropic cost of structuring attention against thermal noise. The bound is satisfied across all configurations tested: 625 grid points spanning three datasets on a compact Vision Transformer trained from scratch (MNIST, CIFAR-10, and OrganAMNIST), and ten temperatures on a pretrained ViT-Small fine-tuned on Food-101. Reusing the same physical principles at inference time, we show that the thermodynamic work performed by each input patch provides a quantitative, energy-based measure of feature importance that outperforms standard attention weights and Integrated Gradients on ImageNet across pretrained ViT-Small, ViT-Base, and ViT-Large (22M to 304M parameters). The result is an integrated diagnostic framework that links phase structure, training-time bounds, and inference-time attribution within a single empirically falsifiable thermodynamic apparatus. Full article

This paper studies observer-based stabilization of a normalized incommensurate fractional-order discrete-time SI benchmark model for computer-virus propagation. The model is formulated with Caputo-like fractional-difference operators and allows the susceptible and infected compartments to have different memory orders. In contrast with a predictive malware-forecasting model, the proposed system is explicitly treated as a dimensionless benchmark for qualitative analysis and control design. To clarify how the benchmark can be connected to empirical cybersecurity data, the revised formulation includes a calibration and fractional-order selection procedure based on normalized infection telemetry, admissible parameter sets, and loss minimization. The incommensurate orders are therefore interpreted as identifiable modeling parameters, not as arbitrary constants. The plant, observer, and control laws are formulated on the integer update grid, and the memory terms are implemented through the equivalent Volterra-type convolution representation. A nonlinear Luenberger-type observer is proposed under infected-state measurements, which is justified as a detectability-based cyber-monitoring configuration rather than a full observability assumption. The observer gain design, the full-state feedback design, and the observer-based output-feedback design are derived from first-order linearized incommensurate fractional-order models. The resulting criteria are expressed through characteristic-root conditions associated with linear incommensurate Caputo-type fractional-order difference systems. The scope of the theoretical claims is made explicit: the results provide local linearized-design guarantees and do not establish global or semi-global nonlinear stabilization. The nonlinear residuals, measurement-noise channel, incomplete-measurement formulation, and limitations of the linearized characteristic-root approach are stated explicitly so that the numerical section can assess robustness, sensitivity, and the effective region of attraction of the nonlinear closed loop. Full article

The quality and integrity of aluminothermic rail welds are strongly governed by the thermal conditions involved during preheating, pouring and cooling stages of the process. In this study, a simplified numerical framework is presented, based on the finite volume method and implemented in the open-source software OpenFOAM^® version 7, to predict the heat transfer and solidification processes. Within this framework, the preheating stage is simulated by employing a heat flux profile derived from experimental measurements, while the mould filling stage is neglected under the assumption of instantaneous pouring of the molten metal. The steel–slag multiphase system is treated using the Volume of Fluid method, whereas melting and solidification are captured using the enthalpy-porosity approach on a fixed Eulerian grid. The numerical framework is validated for a rapid welding process with short preheating procedure, consistent with typical industrial practice for rail welding. The predicted temperature histories during the preheating stage show sufficiently good agreement with the experimental measurements. Subsequently, the cooling stage is validated for a molten metal temperature of 2200${}^{\circ }\mathrm{C}$ (≈$2200+273$$\mathrm{K}$). The predicted width of the fusion zone is compared with experimental data, showing reasonably good agreement in the railhead region, while an underestimation is observed in the rail web and rail foot regions. Furthermore, a systematic parametric investigation is conducted by varying two key process parameters, namely the molten metal temperature examined at four distinct levels ranging from 1800${}^{\circ }\mathrm{C}$ (≈$1800+273$$\mathrm{K}$) to 2400${}^{\circ }\mathrm{C}$ (≈$2400+273$$\mathrm{K}$), and the active preheating duration, varied across six values ranging from 90$\mathrm{s}$ ( $90/60$$\min$)– 390$\mathrm{s}$ ( $390/60$$\min$), in order to assess their influence on the cooling stage. The numerical results provide detailed insight into the temporal evolution of the thermal field and its influence on the formation and extent of the fusion zone and heat-affected zone. The results demonstrate that, despite simplifications, the model captures the dominant thermal phenomena of the process and offers a computationally efficient tool for parameter studies and process optimisation. Full article

Soft pneumatic robotic joints driven by low-cost on/off solenoid valves are attractive for lightweight and compliant robotic systems, but precise control remains challenging because continuous actuation commands must be realized through discrete valve states subject to minimum pulse-width constraints. This paper presents a model-based constrained equivalent-control PWM (C-EC) framework for a dual-chamber bellows actuator driven by four on/off valves. An ideal duty ratio is derived so that the averaged differential pressure rate matches the desired value required to impose first-order inner-loop error dynamics. To make this law physically implementable, the ideal duty is projected onto the feasible duty set determined by the minimum reliable pulse width of the valves. The resulting duty projection error is explicitly incorporated into a Lyapunov-based analysis, yielding a uniform ultimate boundedness result for the closed-loop system under the proposed implementation and an analytical comparison with conventional discrete sliding-mode control (D-SMC). The valve flow model is parameterized through PWM step-test-based sonic conductance identification. The proposed framework is implemented on a custom 1-DOF rotary joint based on an aluminum-film spiral-duct bellows actuator. Experiments show that C-EC does not uniformly dominate D-SMC over all operating conditions, but it improves $e_{\mathrm{RMS}}$ and $R_{\Delta P}$ in the medium-to-large positive-step regime and in long-hold regulation. In the representative 45${}^{\circ }$–65${}^{\circ }$–45${}^{\circ }$ step-hold test, C-EC reduced the RMS tracking error by 39.3% and the differential pressure ripple by 34.5% relative to D-SMC. In the 65${}^{\circ }$ long-hold test, the RMS tracking error and pressure ripple were further reduced by 35.4% and 37.9%, respectively. A loop-period comparison also showed that a 10 ms control period reduced duty projection and pressure ripple relative to 5 ms without degrading tracking accuracy. Full article

Solid amine adsorbents can capture CO₂ at indoor-relevant concentrations (~1000 ppm), but many high-capacity adsorbents rely on granular or powdery supports that are difficult to integrate directly into air purification systems. Here, we applied three amination strategies to commercial fibrous substrates: bridge-grafting on viscose (TEPA-AMVF), direct grafting on polyacrylonitrile (TEPA-PAN), and physical impregnation on pore-engineered activated carbon fibre felt (PEI-ACF). These adsorbents were systematically screened under simulated indoor conditions (1000 ppm CO₂, 27 °C, 50% RH). A significant capacity difference was observed: TEPA-AMVF (24.8 mg g⁻¹) < TEPA-PAN (35.8 mg g⁻¹) ≪ PEI-ACF (97.0 mg g⁻¹). The superior performance of PEI-ACF was attributed to KOH activation, which produced a mesopore-rich structure (average pore diameter 26.1 nm at an optimal KOH/carbon ratio of 1.25) and enabled high nominal amine utilisation (0.19 mmol CO₂ mmol N⁻¹). PEI-ACF maintained high breakthrough-derived CO₂ uptake across realistic indoor conditions (64.2–118.6 mg g⁻¹ over 0%–100% RH; 71.6–124.5 mg g⁻¹ over 400–5000 ppm CO₂), exhibited rapid kinetics (pseudo-first-order rate constant k = 1.77 h⁻¹; 81.7% of equilibrium uptake within 1 h), and showed stable but partial regeneration over four adsorption–desorption cycles at 60–70 °C under N₂. Compared with granular or resin-based amine sorbents, the self-supporting PEI-ACF felt is expected to offer practical advantages for filter-integrated CO₂ removal, including mechanical integrity under airflow, reduced risk of particle leakage, and compatibility with HVAC filter slots. Remaining challenges include direct pressure-drop validation, operation in O₂-containing indoor air, long-term cycling, and management of CO₂ released during regeneration. Full article

Contemporary vulnerability management relies on the Common Vulnerability Scoring System (CVSS) and the Exploit Prediction Scoring System (EPSS), both of which evaluate Common Vulnerabilities and Exposures (CVE) entry in isolation, disregarding the network topology in which vulnerable components operate. We present the Dynamic Security Resistance Distance (DSRD) framework, which parses Docker Compose, GNS3, and Containerlab configuration files into weighted attack graphs where edge conductance reflects EPSS exploitability. A version-aware filtering stage matches discovered CVEs against the software versions declared in container image tags, reducing version-irrelevant CVE matches by up to 97%. Kirchhoff effective resistance, computed via the Moore-Penrose pseudoinverse of the graph Laplacian, yields a structural compromise affinity—a monotone score guaranteed not to increase upon patching. Four algorithms—Ant Colony Optimization, Physarum, Fungal Network Growth, and Greedy Kirchhoff-rank vulnerabilities by their structural impact on network-wide risk. Evaluation on nine representative topologies derived from public IaC artifacts, spanning six Docker Compose and three GNS3 deployments, with 895 version-relevant vulnerability nodes from cvelistV5 shows that graph-aware prioritization reduces structural risk by up to $5.62\times {10}^{-2}$ after ten patches, whereas EPSS-only ordering achieves at most $1.28\times {10}^{-2}$ on the same topology. EPSS-only targets high-probability CVEs on entry points that do not lie on critical paths; graph-aware methods instead prioritize CVEs on high-resistance paths toward critical assets. The advantage depends on infrastructure heterogeneity and topology structure: topologies with diverse vendors and well-defined structural bottlenecks benefit most, while densely connected or homogeneous environments show marginal improvement. We release the full pipeline as open-source software. Full article

Generalization of the ordinary hyperbolic functions called hyperbolic Ateb-functions is considered. They are the inverse of incomplete Beta-function. These functions are solutions of differential equations, which describe the aperiodic vibration motion. It is shown that hyperbolic Ateb-functions have different dependence levels on their parameters. Investigation of the domain of hyperbolic Ateb-functions is conducted. It is shown that the minimum value of the domain can be expressed in terms of the lemniscate constant. The formulas for derivatives of hyperbolic Ateb-functions are proved and the structure of the higher-order derivatives is obtained. Some other properties connected with symmetry are considered. Taylor expansions of Ateb-cosine and Ateb-sine are taken out. Based on the mathematical induction principle, the corresponding theorems are proven. Examples of Taylor expansions of Ateb-cosine and Ateb-sine for different parameters are presented. The comparison of Ateb-function calculation using Taylor expansion and numerical methods shows the advantage of the Taylor series approach. Full article

By leveraging the complementary advantages of free-space optical (FSO) and radio frequency (RF) links, hybrid FSO/RF systems exhibit broad application prospects. However, maintaining robustness while performing trade-off optimization between reliability and transmission efficiency under dynamic conditions with power constraints remains challenging. To address this, we propose a weather-aware multi-objective adaptive power allocation approach for hybrid FSO/RF systems based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a deep neural network (DNN). Closed-form expressions for the average bit-error rate (ABER) and average channel capacity (ACAP) are derived and used to evaluate NSGA-II objectives, generating a labeled optimal allocation dataset across diverse scenarios. A DNN is then trained on the dataset to learn adaptive power allocation strategies for dynamic environments. Numerical results demonstrate that the proposed scheme effectively achieves adaptive power allocation and significantly outperforms existing benchmark schemes. In dynamic scenarios, it reduces the ABER by 1–2 orders of magnitude and substantially lowers the outage probability (OP), while improving the overall ACAP by more than 0.5 Gbps under the same transmit power. Full article

The training of physics-informed neural networks (PINNs) for nonlinear multiphase flow in porous media is hampered by gradient conflicts between the individual components of the composite loss function. To address this problem, we propose a weighted gradient consistency metric that jointly accounts for the magnitudes and directions of the gradients of each loss term. Theoretical estimates of the convergence rate are derived, relating the proposed metric to the spectral properties of the preconditioner. The method is evaluated through a comparative study of optimizers—Adam, L-BFGS, and self-scaled Broyden—applied to three formulations of increasing complexity: a linear Buckley–Leverett model, a compressible two-phase model, and a fully nonlinear model with non-Newtonian rheology. The experiments demonstrate that self-scaled methods consistently achieve higher gradient alignment, faster loss reduction, and improved approximation accuracy compared to standard quasi-Newton and first-order baselines. Full article

This paper presents a method of data-driven parametric dynamic mode decomposition (p-DMD) to derive a linear parameter-varying reduced-order model (LPV-ROM) and predictive control for the nonlinear aeroelasticity of highly flexible aircraft. It directly uses the data snapshots obtained at varying flight conditions and encodes a nonlinear model’s polynomial dependency on flight conditions to produce a polynomial-dependent LPV-ROM. The modeling method can handle not only equilibrium flight conditions but also continuously varying flight conditions. In numerical studies, the proposed data-driven p-DMD modeling is applied to a highly flexible cantilever wing perturbed around equilibrium conditions and a flexible aircraft with time-varying angles of attack in dynamic maneuvers. The numerical results demonstrate that the current p-DMD model can capture the non-equilibrium (or transient) aeroelastic and flight dynamic behaviors of highly flexible aircraft in both time and frequency domains with over 95% accuracy in the simulated representative cases. Accuracy is quantified by the normalized root mean square error (NRMSE) in the time domain and the normalized error between the frequency responses over the frequency range of interest. The data-driven reduced-order model is further implemented in predictive control to suppress the vibrations excited by Dryden gust disturbances. The simulation results demonstrate that for a Dryden gust profile, data-driven predictive control can suppress the strains by 18.34% as quantified by the reduction in the root mean square of strains compared to the uncontrolled case. Full article

The temporal dynamics of nitrogen (N) fractions and ammonia (NH₃) volatilization were investigated over a 56-day storage period using a laboratory-scale pig slurry pit simulator. A detailed N mass balance, encompassing total N (TN), total ammonium N (TAN), organic N, and nitrate N (NO₃⁻-N) fractions, yielded a N mass recovery of 96.5%, despite uncertainties associated with discrete emission measurements, with a TN reduction of 28.3 g vessel⁻¹ closely matched by cumulative NH₃-N emissions of 27.3 g. The NH₃ emission profile exhibited a distinct two-phase pattern. During Phase I (days 1–28), emissions remained stable at 16.7–19.5 g m⁻² d⁻¹, accounting for approximately 58% of total cumulative NH₃-N loss (518.6 g m⁻²), consistent with zero-order kinetics. Phase II (days 29–56) was characterized by first-order exponential decay (k = 0.0293 d⁻¹, R² = 0.982), coinciding with progressive TAN depletion. Measured emission rates were strongly correlated with theoretical free ammonia N (FAN) concentrations derived from pH and temperature (R² = 0.74), confirming that theoretical FAN provides a useful upper bound for emission potential, although the actual gaseous flux is restricted by mass-transfer limitations at the slurry–air interface. These results demonstrate that continuous pH and temperature monitoring provides a practical basis for tracking emission dynamics and informing the timing of mitigation interventions, particularly during the high-flux initial storage phase. Full article