Search Results (890)

In deep-water environments, the combined effects of hydrostatic pressure, explosion-induced shock waves, and bubble pulsation can produce complex nonlinear dynamic responses and instability in stiffened cylindrical shells. Clarifying these response mechanisms is critical for the safety assessment and blast-resistant design of deep-sea equipment. In this study, an acoustic–structural coupled numerical method was developed for stiffened cylindrical shells subjected to underwater explosion loading and validated using deep-water explosion tests conducted in a pressure vessel. The numerical results show that the relative error between the numerical and experimental wall-pressure impulses on the blast-facing surface is 3.6%, while the relative error in the maximum compressive strain at a representative measurement point on the blast-facing surface is 15.6%, indicating that the established numerical model can reasonably reproduce the pressure impulse and the primary dynamic response of the structure. Based on this validated model, a systematic investigation was conducted to evaluate the effects of hydrostatic pressure, stand-off distance, shell-plate thickness, and stiffener number on the deep-water explosion response of stiffened cylindrical shells. With increasing water depth, the structural deformation mode transitions from localized plastic indentation to global instability and crushing. The findings provide practical guidance for blast-resistant design and parameter optimization of deep-water stiffened cylindrical shells. Full article

Streptococcus thermophilus is widely used as a starter culture for Swiss and cured Mozzarella cheeses. A large volume of perishable but nutritive cheese whey by-product is generated. This study investigates S. thermophilus inactivation kinetics in sweet cheese whey under thermal and ultrasound-assisted pasteurization treatments (10 W/mL thermosonication, TS) at 55.0–70.6 °C, operated in pulse mode. For thermal pasteurization alone, no inactivation was observed at 55 °C, while first-order linear kinetics was found at 60 °C (D-value = 22 min). At 65 °C and 70 °C, non-linear inactivation behavior was observed, with a first-order biphasic model providing the best fit. The susceptible ($D_s$ of 2.7–0.29 min) and the resistant ($D_r$ of 54–5.7 min) microbial fractions decreased with an increase in temperature from 65 to 70 °C. A first-order biphasic model also fitted well for TS. The D_s-values decreased from 4.4 to 0.27 min, while $D_r$-values decreased from 23.5 to 2.9 min when increasing TS temperatures within 55.5–65.3 °C. Overall, TS enhanced inactivation compared to thermal treatment, reducing both microbial fractions faster. Storage validation tests showed that refrigeration effectively limited microbial growth, ensuring whey stability/preservation for further uses. The whey preservation may facilitate the further use of whey by the food industry, supporting the circular economy and zero-waste strategies in the dairy sector. Full article

This study develops an integrated electromechanical model of a seed-removing device used in a saw-type cotton gin. The modeled machine unit comprises a squirrel-cage induction motor, an elastic-dissipative belt transmission, a seed-removing tube rigidly connected to a ring gear, planet gears mounted on a fixed carrier, and an auger rigidly connected to the sun gear. The equations of motion were derived using Lagrange’s equations of the second kind. The induction motor was represented by the dynamic characteristic proposed by A.E. Levin, which was selected as a reduced-order model that captures the transient electromagnetic torque response during start-up without requiring the additional electrical parameters of a full direct–quadrature (d–q) axis model, while providing a more realistic transient representation than a static torque–speed characteristic. The moments of inertia of the rotating components were identified experimentally by the acceleration method, and the resulting nonlinear ordinary differential equations were solved by a fourth-order Runge-Kutta scheme. The model reproduces the start-up, transient, and steady-state stages and enables the evaluation of angular velocities, torques, angular accelerations, power demand, and rotational irregularity. Experimental validation was performed for the steady-state rotational speeds of the seed-removing tube and auger and for motor power, whereas the reported transient peak torque and angular acceleration were obtained from the numerical simulation. For the 3 kW, 735 rpm induction motor, the rated torque was 38.98 $\mathrm{N}·\mathrm{m}$, whereas the calculated peak starting torque reached 101.63 $\mathrm{N}·\mathrm{m}$, corresponding to a starting-torque ratio of 2.61. The transient process lasted approximately 3.5 s, and the maximum motor angular acceleration reached 2988.6 $\mathrm{rad}/{\mathrm{s}}^2$ at t = 2.25 s. Within the investigated parameter ranges, the OFAT sensitivity analysis showed that the resistance moment of the seed-removing tube and the inertia of the auger exert the strongest influence on rotational irregularity, whereas the inertia and resistance of the planet gears have a comparatively weak effect. A reduction in the effective torsional stiffness of the belt drive from 17.2 to approximately 10.3 $\mathrm{N}·\mathrm{m}$/rad reduced the start-up rotational irregularity of the auger, evaluated over t = 2–4 s, from 0.435 to 0.420 and decreased motor power consumption from about 2.55 to 2.50 kW. The proposed model provides a system-level framework for selecting drive parameters and limiting torsional oscillations in planetary-driven cotton-processing machinery. Full article

Variable-stiffness tactile interfaces are of interest for applications including medical simulation, rehabilitation, and human–machine interaction, where controllable mechanical resistance is required during physical contact. Granular jamming provides a mechanically simple approach for modulating stiffness by transitioning a particle-filled compliant structure from a deformable to a load-bearing state through vacuum-induced confinement. This study presents the design, fabrication, and quasi-static mechanical characterization of a 25 mm diameter vacuum-controlled granular-jamming tactile nodule. The device consisted of a compliant membrane chamber containing 8 g of 0.6–0.8 mm plastic microbeads and was actuated using a syringe-based vacuum circuit. Compression testing was performed using a finger-like rubber indenter across eleven vacuum levels from 0 to −68.9 kPa. Global and depth-dependent local stiffness were calculated from repeated force-displacement measurements. Vacuum actuation increased effective global stiffness from 1.168 N/mm at atmospheric pressure to a maximum of 9.808 N/mm at −62.1 kPa, representing an approximately 8.4-fold increase. Local stiffness exhibited nonlinear pressure- and depth-dependent behavior, reaching 15.06 N/mm at 2.00 mm indentation and −68.9 kPa. These results provide quantitative design guidance for future granular-jamming tactile interfaces requiring controllable compliance and realistic mechanical feedback. Full article

This study investigates the effect of natural pumice filler content on the physical and mechanical performance of flexible polyester-based polyurethane (PU) composites. Composites containing 0, 2, 5, 10, and 20 wt.% pumice were produced using a constant PU matrix formulation and identical processing conditions. Microstructural and structural characteristics were examined using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and Fourier transform infrared spectroscopy (FTIR), while density, water absorption, tensile behavior, abrasion resistance, and cyclic flexural durability were evaluated. Increasing pumice content resulted in greater local microstructural heterogeneity and increased density and water absorption. Tensile strength exhibited a non-linear numerical response to filler content, with the highest mean value obtained at 5 wt.% pumice, although the differences among formulations were not statistically significant. In contrast, elongation decreased significantly overall with increasing filler content, while mean abrasion mass loss progressively increased with filler loading. Cyclic flexural testing revealed that the unfilled PU and 2 wt.% composite completed 30,000 cycles without measurable crack propagation at 20 and 0 °C, whereas higher filler contents exhibited severe crack growth or complete fracture. At −20 °C, all formulations fractured completely. Overall, pumice content produced a property-dependent response, revealing a trade-off among tensile response, deformability, abrasion resistance, and cyclic durability. These findings provide a basis for tailoring flexible PU composites for applications involving repeated deformation and mechanical contact, including footwear and cushioning components. Full article

In maritime navigation, precise and stable ship course control is critical for operational efficiency and maritime safety, yet it is severely challenged by unpredictable marine disturbances (e.g., waves, wind, and currents) that consist of slowly varying and stochastic components. Traditional control methods, though effective under specific operating conditions, exhibit limited adaptability to the nonlinear, time-varying characteristics of marine systems and inherent dependence on accurate ship mathematical models, which easily leads to suboptimal performance and elevated navigation risks, especially under sudden and intense disturbances. To address these limitations, this study proposes a novel control strategy, namely, model-free control integrated with super-twisting sliding mode control (MFSTSMC) with a linear extended state observer (LESO), for enhanced ship course control. Derived from the ultra-local model, the proposed method integrates the simplicity and practicality of model-free control, the real-time disturbance estimation and compensation capability of LESO, and the strong robustness of STSMC. The Lyapunov stability theory is rigorously employed to prove the stability of the entire control system, ensuring that the steady-state error converges to zero. Comparative analyses are conducted on an ocean ship verification platform, with strictly unified parameters for fairness. The comparative results evaluate the proposed method under three typical scenarios: course-keeping (small ±10° and large ±70° maneuvers), course tracking (low/high-frequency sinusoidal trajectories and high-frequency 20° abrupt change trajectory), and resistance to sudden escalating disturbances. The results demonstrate that the proposed MFSTSMC with LESO significantly outperforms existing controllers in terms of tracking accuracy, response speed, stability, and disturbance rejection capability. Its superior performance originates from the synergistic effect of real-time disturbance compensation and robust sliding mode compensation, which effectively mitigates the impact of model deviations and complex marine disturbances. This study provides valuable insights for the development of advanced marine navigation control strategies, and the proposed method exhibits promising engineering application prospects for ocean ship navigation in complex dynamic marine environments. Full article

Climate change is reshaping veterinary parasite transmission by altering thermal and hydrological suitability, environmental stage persistence, vector and intermediate host ecology, and contact across livestock–wildlife–companion animal interfaces. These effects are nonlinear; while warming may extend transmission in some systems, heat, desiccation, habitat loss, or disrupted hydrology can reduce the risk or concentrate transmission in local refugia. This critical narrative review compares pasture-transmitted helminths, snail-borne trematodes, environmentally transmitted protozoa, vector-borne parasites, and multi-host cycles. We propose an attribution framework that classifies observed changes across four dimensions (geographic range, seasonal timing, transmission intensity, and host-interface structure) and evaluates them through five analytical filters: suitability, parasite life-cycle response, vector or intermediate-host response, host-interface change, and surveillance artifacts. This framework prevents improved detection, land-use change, animal movement, management shifts and improved detection from being mistaken for climate-driven emergence. We also propose a climate–refugia paradox hypothesis, requiring empirical validation, in which drought or heat may reduce unselected parasite refugia and intensify selection for anthelmintic resistance. Finally, we connect a tiered diagnostic approach from field tools to reference molecular surveillance to support attribution-aware, risk-based One Health strategies that protect animal production, biodiversity, and public health. Full article

The construction industry’s dependence on ordinary Portland cement (OPC) makes low-carbon binder systems an urgent priority; yet, the nonlinear interactions among multiple supplementary cementitious materials (SCMs) and nanomaterials complicate rational mix design. This study fuses explainable artificial intelligence (XAI) with a hierarchy of closed-form mathematical formalisms and an experimental durability programme for a quaternary sustainable concrete in which OPC is partially replaced by sugarcane bagasse ash (SCBA, 40 kg/m³), ground granulated blast furnace slag (GGBS, 60 kg/m³), natural zeolite (20 or 40 kg/m³) and nano-silica (0–20 kg/m³) at a constant water–binder ratio of 0.45. Thirteen mixes were tested for compressive and flexural strength, rapid chloride penetration (RCPT) and sulfuric acid resistance at 7, 28 and 56 days. The optimum blend (12 kg/m³ nano-silica) reached 45.0 MPa at 28 days, 49.5% above the control, while reducing chloride charge by 70% and acid mass loss by 65%. Information theoretic discrimination among three competing hydration kinetics laws selects the hyperbolic rate model with an Akaike weight of 1.000 (ΔAIC_c > 32), showing the blend raises the ultimate strength ceiling by 46% while delaying half-strength by only two days. Within this mix series, effective binder (k-value) analysis indicates that, at low dosage, one kilogram of nano-silica contributes 28-day strength broadly comparable to that of several tens of kilograms of OPC (a dataset-specific, dose-dependent estimate rather than a general mass equivalence), and three independent estimators—the experimental peak, the response surface stationary point (12.8 kg/m³) and the marginal efficiency zero (13.2 kg/m³)—converge on an optimum nano-silica dosage of 3.0–3.3% of binder. Principal component analysis compresses the six-dimensional strength–durability response into a single latent statistical axis (interpreted as an indicator of pore connectivity) carrying 91.5% of the variance, and a Fickian error function solution seeded by Berke–Hicks conversion of RCPT charge projects a 3.4-fold extension of the chloride-initiation service life (36.7 versus 10.8 years at 50 mm cover). Six machine learning models were benchmarked; extremely randomized trees performed best (R² = 0.9905, RMSE = 0.920 MPa; leave-one-out R² = 0.986; bootstrap 95% CI on R² [0.981, 0.996]), and SHAP force plot attributions were triangulated with Sobol global sensitivity indices (curing age 75.4%, nano-silica 23.1% of output variance) and response surface significance tests. The optimized mixes cut embodied CO₂ by 26–32% and improve eco-strength efficiency 2.1-fold; grey relational analysis over six strength, durability and carbon criteria ranks the 12 kg/m³ nano-silica mixes first. The framework demonstrates how interpretable machine learning, information theoretic model selection, diffusion theoretic service-life projection and experimental durability evidence can be unified into a transparent, physically validated basis for sustainable concrete mix design. Full article

To address the limited endurance of unmanned underwater vehicles (UUVs) during long-duration missions, this study proposes a foldable and retractable wave energy converter (WEC) conformally integrated with the UUV hull. A two-degrees-of-freedom heave-coupled dynamic model of the float–UUV system is established, and parameter-matching relationships are derived using complex dynamic stiffness and impedance-matching theory. A bidirectionally coupled STAR-CCM+-AMESim co-simulation framework resolves the nonlinear viscous flow field, relative motion, and PTO dynamic response in closed loop. Under regular wave conditions defined based on a representative Bohai Sea state, the effects of the transmission ratio and spring stiffness on the coupled motion and equivalent resistive load power output are systematically investigated. Under the specified wave condition, average electrical power varies unimodally with both parameters, reaching 70.8 W at a transmission ratio of 15 and a spring stiffness of 4642 N/m; the corresponding peak power is 161.2 W. The system is more sensitive to increases than decreases in transmission ratio, suggesting a value slightly below the theoretical optimum for engineering design. The instantaneous power shows an asymmetric double-peak pattern, indicating a shift in dominance between direct float-driven generation and spring-mediated energy release. Agreement between theory and co-simulation provides numerical cross-validation and offers a theoretical basis and numerical methodology for designing and optimizing WECs on mobile UUV platforms. Full article

The effects of wide-ranging service temperatures on interfacial bond transfer and slip evolution in high-strength concrete-filled steel tube members have not yet been systematically characterized. A test program involving 15 circular specimens was carried out at temperatures between −60 °C and 60 °C with steel tube wall thicknesses of 2 mm, 3 mm, and 4 mm. Particular attention was given to interface failure patterns, slip development, and stress transfer during loading. Cooling markedly enhanced the interfacial resistance, whereas heating produced a moderate reduction. The increase observed at subzero temperatures was smaller than that commonly reported for conventional concrete-filled steel tubes. Bond capacity also declined as the diameter-to-thickness ratio increased because of reduced confinement from the steel tube. Regression of the experimental data produced expressions for ultimate bond strength and the associated slip. A piecewise constitutive relation was then formulated to represent the complete bond–slip process. The proposed relation was implemented in ABAQUS through distributed nonlinear connector elements and temperature-dependent material properties. Numerical predictions showed close agreement with the measured load–slip responses, characteristic loads, and stress distributions. These results provide a modeling basis for HSCFST members exposed to severe cold and large temperature fluctuations. Full article

Explainable artificial intelligence (XAI) is increasingly important in scientific and engineering applications where predictive performance alone is insufficient and model outputs must also be physically credible, transparent, and reliable under unseen conditions. This study proposes a domain-informed XAI framework for predicting total suction in xanthan gum-treated clays using 139 experimental observations covering different mineralogical, moisture, polymer-dosage, and curing conditions. Eleven linear, kernel-based, ensemble, boosting, and physics-guided algorithms were evaluated using leakage-resistant five-fold grouped cross-validation, including a matched constrained–unconstrained HGB comparison with identical model settings. The methodological contribution is an evidence-linked XAI validation protocol in which model explanations are not accepted from feature attribution alone, but are audited through their agreement with leakage-resistant grouped generalization, physically constrained response directions, matched experimental contrasts, residual behavior, predictive uncertainty, and applicability-domain support. Selective monotonic constraints, physics-guided residual learning, SHAP explanations, and nonlinear response visualization are integrated within this protocol as complementary sources of evidence rather than treated as independent indicators of interpretability. The unconstrained histogram–gradient-boosting model achieved the highest out-of-fold predictive performance (R² = 0.958, RMSE = 0.098, and MAE = 0.070 in log10(MPa)). The corresponding monotonic model produced R² = 0.935, RMSE = 0.121, and MAE = 0.094 but eliminated the directional violations detected in the unconstrained response, revealing a measurable trade-off between predictive accuracy and guaranteed physical consistency. Explanations identified moisture content as the dominant negative control and revealed that xanthan-gum effects were non-monotonic and dependent on curing, moisture, and mineralogy. The residual model remained interpretable but underperformed the leading ensembles. Overall, the framework validates explanations against experimental contrasts, physical directions, grouped generalization, residual behavior, uncertainty, and domain support, offering a transferable strategy for trustworthy XAI in structured scientific datasets. Full article

This work focuses on studying the magnetohydrodynamic flow and heat transfer mechanism of a Casson–Maxwell nanofluid due to a stretching surface through a porous medium, using a physics-informed neural network (PINNs) approach as the main tool for the solution of the physical problem. The mathematical model describes the phenomena of viscosity variation with temperature, viscous dissipation, thermal slip, Brownian motion, thermophoresis, and drag force due to a porous medium, which give a realistic physical scenario of the coupled transport phenomena of momentum, heat, and nanoparticles. First, the nonlinear partial differential equations are converted into a dimensionless boundary layer model using similarity transformations. Then, the yielded system is solved via the PINNs approach, which integrates physical law within the optimization procedure. The proposed technique does not require a significant number of labeled datasets and provides accurate and stable predictions of the strongly nonlinear flow. A comprehensive parametric analysis was performed to explore the impact of the dimensionless controlling factors on the velocity, temperature, and nanoparticle concentration distributions. It is found that the interaction of magnetic field effects, porous media resistivity, thermal and concentration slip, viscosity variation, and viscous heating significantly modifies the transport features for the studied model of the Casson–Maxwell nanofluid, which can be used effectively to control the rate of heat and mass transfer. This study proves the efficiency of the PINN technique in solving this type of model, and it also provides useful insights for designing thermal systems, energy conversion devices, and electrically conducting viscoelastic nanofluid transport problems. The close concordance between the present findings and established data from the literature validates the precision and dependability of the developed PINN-based framework. Full article

This paper presents a technique for enhancing the cracking resistance of reinforced concrete (RC) beams through the external application of liquid rubber. To evaluate the influence of this coating on flexural performance, four-point bending tests were conducted on seven RC beams, comprising six coated beams and one uncoated control beam. The effects of coating position (beam soffit, beam sides, or both) and number of coating layers (3 or 6) on the flexural response were investigated. The results indicate that the external application of liquid rubber improves the cracking load and ductility of the RC beams to some extent, and the degree of improvement is related to the number of coating layers. Moreover, the application of liquid rubber to the tensile zone of the RC beams can moderately restrain tensile deformation of the concrete. When the coating was applied only to the beam soffit, the concrete strain at the soffit was lower than that in the tensile zone on the beam sides, resulting in a non-linear distribution of tensile strain along the section height within the pure bending region. Full article

Metal–Insulator–Metal (MIM) tunneling diodes are among the most promising rectifying devices for long-wave infrared (LWIR) rectenna systems due to their ultrafast response and zero-bias operation. However, their performance is strongly dependent on the choice of electrode and dielectric materials, making the identification of optimal material combinations a key challenge. To address this issue, this theoretical study presents a numerical investigation of a new class of MIM diodes based on a quantum-mechanical tunneling framework. Novel combinations of transition-metal dichalcogenides (NbS₂, VSe₂, and TaS₂) as anode materials (M1), conductive carbides and nitrides (Mo₂C, VN, and V) as cathode materials (M2), and rare-earth oxide and oxyhalide compounds (Sc₂O₃, LaOF, and LaOBr) as tunnel barriers (I) were selected through an extensive literature survey. These materials were combined to design previously unexplored MIM architectures for LWIR rectification. The electrical transport and rectification properties were evaluated using the Simmons tunneling model by calculating the current density–voltage (J–V) and current–voltage (I–V) characteristics, together with key figures of merit (FOMs), including zero-bias resistance, asymmetry factor, nonlinearity, and responsivity, at room temperature (300 K). The effects of tunnel barrier height and dielectric properties on device performance were systematically investigated. Among all the investigated architectures, the TaS₂/LaOBr/V MIM diode exhibited the most promising overall performance, achieving an asymmetry factor exceeding 2.5 × 10⁵, a nonlinearity factor of 1, and a zero-bias responsivity of 10 V⁻¹ at 300 K. Furthermore, this structure demonstrated the highest current density and the most favorable I–V characteristics among the proposed material combinations. These results identify the TaS₂/LaOBr/V material system as a promising candidate for high-performance LWIR energy harvesting applications, owing to its optimized tunnel barrier height, which promotes efficient electron tunneling while maintaining excellent rectification properties. Full article

Zn-Al-Mg (ZAM) coatings have attracted significant attention in the field of corrosion protection owing to their combination of excellent corrosion resistance and cost-effectiveness. However, the corrosion behavior of ZAM coatings was governed by the synergistic coupling effects of multiple factors, including alloy composition, coating thickness, corrosive medium, and multiphase microstructure, making it challenging for traditional empirical analysis to systematically reveal the underlying mechanisms. To address this challenge, we constructed a multidimensional corrosion dataset comprising alloy composition, corrosive medium, coating thickness, and phase composition, based on literature data from the past three decades combined with self-measured potentiodynamic polarization experimental results. After data normalization and correlation analysis, we introduced phase structure features—including the Al-rich phase, MgZn₂ phase, Mg₂Si phase, and eutectic microstructures—to enhance the model’s capability in representing microstructural factors. On this basis, we established random forest (RF), support vector regression (SVR), and artificial neural network (ANN) models to predict the corrosion current density, and subsequently conducted an interpretability analysis using the SHapley Additive exPlanations (SHAP) method. The results demonstrated that the expanded feature set significantly improved the prediction performance of the models. Among them, the RF model exhibited the best performance, achieving a determination coefficient (R²) of 0.7363 on the test set, which represented a substantial improvement over the baseline dataset. Feature importance analysis revealed that coating thickness, Mg content, NaCl concentration, and Zn content were the primary factors influencing the corrosion current density. Further SHAP analysis showed that the marginal contribution of the eutectic phase was more prominent in local samples. Meanwhile, the Mg element exhibited distinct non-linear regulation characteristics, exerting varying impacts on the corrosion behavior across different concentration ranges. This study demonstrated that the interpretable machine learning models constructed via feature engineering not only improved the prediction accuracy of the corrosion performance of ZAM coatings, but also provided a novel data-driven approach to revealing the intrinsic correlations among alloy composition, phase structure, and corrosion response. Full article