Search Results (1,024)

Electromagnetic Railguns Face Severe Ablation and Melting Risks Due to Extremely High Transient Thermal Loads During High-Speed Launching, Directly Impacting Launch Reliability and Service Life. To address this thermal management challenge, this study proposes and validates the effectiveness of spray cooling technology. Leveraging its high heat transfer coefficient, exceptional critical heat flux (CHF) carrying capacity, and strong transient cooling characteristics, it is particularly suitable for the unsteady thermal control during the initial launch phase. An experimental platform was established, and a three-dimensional numerical model was developed to systematically analyze the dynamic influence mechanisms of nozzle inlet pressure, flow rate, spray angle, and spray distance on cooling performance. Experimental results indicate that the system achieves maximum critical heat flux (CHF) and rail temperature drop at an inlet pressure of 0.5 MPa and a spray angle of 0°. Numerical simulations further reveal that a 45° spray cone angle simultaneously achieves the maximum temperature drop and optimal wall temperature uniformity. Key parameter sensitivity analysis demonstrates that while increasing spray distance leads to larger droplet diameters, the minimal droplet velocity decay combined with a significant increase in overall momentum markedly enhances convective heat transfer efficiency. Concurrently, increasing spray distance effectively improves rail surface temperature uniformity by optimizing the spatial distribution of droplet size and velocity. Full article

Background: Breast cancer remains the most prevalent malignancy in women worldwide, characterized by remarkable genetic, molecular, and clinical heterogeneity. Traditional preclinical models have significantly advanced our understanding of tumor biology, yet consistently fall short in recapitulating the complexity of the human tumor microenvironment (TME), immune, and metastatic behavior. In recent years, breast cancer-on-a-chip (BCOC) have emerged as powerful microengineered systems that integrate patient-derived cells, stromal and immune components, and physiological stimuli such as perfusion, hypoxia, and acidic milieu within controlled three-dimensional microenvironments. Aim: To comprehensively review the BCOC development and application, encompassing fabrication materials, biological modeling of key subtypes (DCIS, luminal A, triple-negative), dynamic tumor–stroma–immune crosstalk, and organotropic metastasis to bone, liver, brain, lungs, and lymph nodes. Methods: We selected papers from academic trusted databases (PubMed, Web of Science, Google Scholar) by using Breast Cancer, Microfluidic System, and Breast Cancer on a Chip as the main search terms. Results: We critically discuss and highlight how microfluidic systems replicate essential features of disease progression—such as epithelial-to-mesenchymal transition, vascular invasion, immune evasion, and therapy resistance—with unprecedented physiological relevance. Special attention has been paid to the integration of liquid biopsy technologies within microfluidic platforms for non-invasive, real-time analysis of circulating tumor cells, cell-free nucleic acids, and exosomes. Conclusions: In light of regulatory momentum toward reducing animal use in drug development, BCOC platforms stand at the forefront of a new era in precision oncology. By bridging biological fidelity with engineering innovation, these systems hold immense potential to transform cancer research, therapy screening, and personalized medicine. Full article

Stock price prediction remains a challenging problem due to the inherent volatility and complexity of financial markets. This study proposes a multi-model machine learning framework for one-day-ahead stock price prediction using thirty-six features derived from technical indicators. Empirical analysis is conducted on data from Apple, Tesla, and NVIDIA, employing nine classification algorithms, including support vector machines, random forests, extreme gradient boosting, and logistic regression. Results indicate that momentum-based indicators are the most influential predictors. While support vector machines achieve the highest accuracy for Apple, extreme gradient boosting performed best for NVIDIA and Tesla. In addition, explainable AI techniques are applied to interpret individual model predictions, thereby enhancing transparency and trust in the results. The study contributes to financial analytics research by providing a comparative evaluation of diverse machine learning methods and highlighting key indicators critical for short-term stock price forecasting. Full article

We numerically investigate instabilities toward bimeron crystals and multiple-Q magnetic states induced by an in-plane external magnetic field in centrosymmetric magnets with magnetic anisotropy. By focusing on the interplay between easy-plane single-ion anisotropy and bond-dependent anisotropy originating from relativistic spin–orbit coupling in crystalline environments, we construct the magnetic phase diagram of an effective spin model with competing momentum-resolved interactions using simulated annealing. Our analysis reveals that the bimeron crystal is stabilized within the regime of weak bond-dependent anisotropy, independent of its sign, whereas increasing the strength of bond-dependent anisotropy drives the system into a topologically trivial triple-Q magnetic state. The obtained bimeron crystal is characterized by finite scalar spin chirality and triple-Q modulations in both the in-plane and out-of-plane spin components. These findings demonstrate that centrosymmetric easy-plane magnets provide a fertile platform for realizing nontrivial topological spin textures without relying on Dzyaloshinskii–Moriya interactions, thereby opening new avenues for inducing emergent topological transport phenomena in centrosymmetric materials. Full article

Interferometric scattering microscopy (iSCAT) is a powerful tool for single-particle detection. However, the detection sensitivity is significantly limited by high-frequency noise. In this paper, we have proposed a novel method leveraging frequency component analysis in the Fourier domain to enhance interference patterns, thus efficiently improving the detection accuracy. The bright–dark rings momentum feather has been effectively restored by a combined filter for high-frequency noise and aperture attenuation. The value of the structural similarity index measure has been improved from 0.73 to 0.98. We validate this method on gold nanoparticle samples. The results demonstrate its great potential to advance single-particle tracking by enhancing background suppression in iSCAT applications. Full article

The transfer of momentum and kinetic energy is a key factor in turbomachinery performance, particularly influencing the efficiency of the bladeless Tesla turbine, which holds significant potential for applications such as Organic Rankine Cycle (ORC) systems and energy recovery processes. In this study, a comprehensive numerical analysis was carried out to simulate the effects of surface roughness on the flow between the co-rotating disks of a Tesla turbine, using R1234yf and n-hexane as working fluids. To capture roughness effects, a porous medium layer (PML) approach was employed, with porous material parameters adjusted to replicate real roughness behavior. The model was first validated against experimental data for water flow in a minichannel by tuning the PML parameters to match measured pressure drops. In contrast to previous studies, this work applies the PML model to a Tesla turbine operating with organic Rankine cycle (ORC) fluids, where the working medium is changed from air to low-boiling gases. Compared to the air-based cases, the gap between the co-rotating disks is rescaled to smaller dimensions, which introduces additional challenges. Under these conditions, the effective roughness thickness must also be rescaled, and this study investigates how these rescaled roughness effects influence turbine performance using the k-ω shear stress transport (SST) turbulence model combined with the proposed roughness model. Results showed that incorporating the PML roughness model enhances momentum transfer and significantly influences flow characteristics, thereby providing an effective means of simulating Tesla turbine performance under varying roughness conditions. Full article

The New Quality Productive Force (NQPF) serves as a key driver in narrowing regional economic disparities and promoting sustainable development. Clarifying the mechanism through which it affects regional economic disparities not only facilitates coordinated regional development but also provides critical insights for synergizing high-quality economic growth with ecological and environmental sustainability. Based on panel data from 30 Chinese provinces between 2012 and 2022, this study systematically examines the impact of New Quality Productive Forces on regional economic disparities, analyzing the mediating role of scientific and technological innovation as well as the nonlinear moderating effect of urbanization rate on this relationship. The findings reveal: First, NQPF significantly contributes to narrowing regional economic disparities overall, but its effects exhibit notable regional heterogeneity—widening disparities in eastern regions while demonstrating significant convergence effects in central and western regions. Second, mechanism analysis indicates that scientific and technological innovation is a critical transmission channel through which NQPF reduces regional disparities, as NQPF indirectly promotes coordinated regional development by fostering technological innovation. Third, threshold effect tests show that the convergence effect of NQPF varies nonlinearly with urbanization levels, and its enabling effect weakens once urbanization exceeds a specific threshold. Based on these findings, policy recommendations are proposed, including continuously nurturing New Quality Productive Forces, strengthening the drive of scientific and technological innovation, and coordinating urbanization with ecological civilization construction. These measures aim to provide new momentum for achieving high-quality regional economic development and sustainable transformation. Full article

Pelvis and trunk counter-rotation are key factors known to effect throwing arm kinematics in baseball pitching, where energy or momentum is transferred from the lower extremities through to the trunk during the pitching cycle. The purpose of this study was to retrospectively analyze previously recorded motion capture data of 19 skilled competitive pitchers to test the a priori hypothesis whether different stride lengths affect transverse pelvis and trunk biomechanics. A blinded randomized crossover design was used where pitchers threw two simulated games at ±25% from desired stride length (DSL), respective of overstride (OS) and under-stride (US). Variables of interest included pelvic–trunk separation (PTS) angle or degree of uncoupling and proximal plyometric effect (PPE) or ratio between trunk–pelvis angular velocities, as surrogate measures of rotational and elastic energy transfer. Paired t-tests were used to compare across stride conditions. A one-way ANOVA with a Bonferroni post hoc analysis demonstrated stride lengths differed statistically, (DSL vs. OS p = 0.006), (DSL vs. US, p < 0.001), and (US vs. OS, p < 0.001). Despite the statistically different stride lengths, fastball velocities tracked with radar were consistent. No significant differences within and across innings pitched between OS and OS conditions were found. The ±25% stride length changes influenced temporal parameters within the pitching cycle. Shorter stride elicited by early SFC reduced time during the Generation phase and extended the Brace-Transfer duration (p < 0.001). Statistically different transverse pelvis and trunk kinematics at hallmark events and phases consequently influenced pelvic–trunk separation and proximal plyometrics. During the Generation (PKH-SFC) and Brace-Transfer (SFC-MER) phases, the pelvis and trunk were significantly more externally rotated (p < 0.001) with shorter strides, concomitant with less separation at the instant of SFC and the Generation phase with greater peak proximal plyometrics effect ratios peak during throwing arm acceleration, indicative of greater contribution of trunk angular velocity (p < 0.05). Greater transverse trunk angular velocities relative to the pelvis late in double support necessitates the throwing arm to “catch up” from a position of greater arm lag, which compromises the dynamic and passive stabilizers. In conclusion, stride length alters pitching biomechanics and timing of peak pelvic–trunk separation and trunk angular velocity relative to the pelvis. Increased shoulder and elbow tensile stress is to be expected, consequently increasing risk for injury. Full article

Moons tidally interact with their host planets and stars. A close moon is quickly synchronised by the planet or becomes captured in a higher spin–orbit resonance. However, the planet requires much more time to significantly alter its rotation rate under the influence of moon-generated tides. The situation becomes more complex for close-in planets, as star-generated tides come into play and compete with moon-generated tides. The synchronisation of the planet by its moon changes the tidal dynamics of the entire star–planet–moon system and can lead to long-term stable configurations. In this paper, we demonstrate that a certain initial condition must be met for this to occur. Based on the angular momentum conservation, the derived condition is universal and bears no dependence upon the planet’s internal structure or tidal dissipation model. It is applicable to dwindling systems as well as to tidally expanding orbits and cases of initially retrograde motion. We present calculations for specific planet–moon systems (Earth and the Moon; Neptune and Triton; Venus and its hypothetical presently extinct moon Neith; Mars, Phobos, and Deimos; and Pluto and Charon) to constrain dynamically plausible formation and evolution scenarios. Among other things, our analysis prompts the question of whether Pluto and Charon evolved into their current state from an initially more compact configuration (as is commonly assumed) or from a wider orbit—a topic that will be discussed at length elsewhere. Our results are equally applicable to exoplanets. For example, if asynchronous close-in exoplanets are detected, the possibility of tidal synchronisation by an exomoon should be considered. Full article

Over recent years, driven by intertwined economic, social, environmental, and technological factors, urbanization has accelerated at an unprecedented pace, posing complex challenges to metropolitan transport systems. This has intensified the demand for innovative mobility solutions, notably Mobility as a Service (MaaS), which promotes a paradigm shift from private vehicle ownership to mobility consumed as a service. With rapid advances in digital technologies, MaaS has gained substantial momentum, attracting significant scholarly attention for its potential to enable intelligent and sustainable transportation systems. This study aims to provide a comprehensive conceptual foundation of MaaS and its components, and to systematically examine how artificial intelligence (AI), machine learning (ML), and big data techniques are applied in this domain. Following PRISMA guidelines, a bibliometric and systematic review was conducted on peer-reviewed articles published between 2020 and 2024 and indexed in the Scopus and Web of Science databases. The analysis classifies AI applications across four MaaS integration levels: basic, intermediate, advanced, and full integration. The results show that machine learning and basic optimization dominate at the basic level; blockchain and big data are most prominent at the advanced and full levels; and deep learning is applied across all levels, with a particularly strong presence at the advanced stage for real-time, personalized mobility solutions. The findings also indicate that while most implementations focus on developed countries, there is substantial potential for adaptation in emerging markets. The paper concludes by discussing key challenges in regulatory compliance, inclusivity, and the protection of sensitive user data, and outlines future research avenues for building socially equitable, intelligent, and sustainable MaaS ecosystems. Full article

The integration of the industrial Internet and big data analytics is reshaping enterprise decision-making models and providing new momentum for the transformation and upgrading of traditional manufacturing industries. In this study, a decision support system based on multi-source heterogeneous data fusion was established. The system carries out data collection, storage, and processing, as well as visualization analysis. The system also performs time-series data feature extraction and unstructured data processing in a three-layer architecture model to train models and generate decision-making. In case studies, the effectiveness of the system in predictive maintenance of equipment, dynamic optimization of supply chains, and product quality traceability was verified. A fault prediction model was developed based on an improved random forest algorithm, and it showed a high level of accuracy. Optimization strategies, such as modular system design, dynamic knowledge base updating, and human–machine collaborative decision-making, can be formulated using the system. To evaluate the system, a three-dimensional evaluation index system was built, including technology maturity, application adaptability, and benefit–output ratio. The developed system effectively improved the efficiency of enterprise resource allocation, shortened abnormality response times, and enhanced market adaptability. By using edge computing and digital twin technologies, a more flexible distributed decision-making architecture can be created in the system, promoting data-driven and intelligent decision-making in manufacturing industry. Full article

This study presents a three-dimensional numerical analysis of natural convection during the postharvest storage of corn and wheat in a galvanized steel silo with a conical roof and floor, measuring 3 m in radius and 18.7 m in height, located in the Bajío region of Mexico. Simulations were carried out specifically for December, a period characterized by cold ambient temperatures (10–20 °C) and comparatively lower solar radiation than in warmer months, yet still sufficient to induce significant heating of the silo’s metallic surfaces. The governing conservation equations of mass, momentum, energy, and species were solved using the finite volume method under the Boussinesq approximation. The model included grain–air sorption equilibrium via sorption isotherms, as well as metabolic heat generation: for wheat, a constant respiration rate was assumed due to limited biochemical data, whereas for corn, respiration heat was modeled as a function of grain temperature and moisture, thereby more realistically representing metabolic activity. The results, obtained for December storage conditions, reveal distinct thermal and hygroscopic responses between the two grains. Corn, with higher thermal diffusivity, developed a central thermal core reaching 32 °C, whereas wheat, with lower diffusivity, retained heat in the upper region, peaking at 29 °C. Radial temperature profiles showed progressive transitions: the silo core exhibited a delayed response relative to ambient temperature fluctuations, reflecting the insulating effect of grain. In contrast, grain at 1 m from the wall displayed intermediate amplitudes. In contrast, zones adjacent to the wall reached 40–41 °C during solar exposure. In comparison, shaded regions exhibited minimum temperatures close to 15 °C, confirming that wall heating is governed primarily by solar radiation and metal conductivity. Axial gradients further emphasized critical zones, as roof-adjacent grain heated rapidly to 38–40 °C during midday before cooling sharply at night. Relative humidity levels exceeded 70% along roof and wall surfaces, leading to condensation risks, while core moisture remained stable (~14.0% for corn and ~13.9% for wheat). Despite the cold ambient temperatures typical of December, neither temperature nor relative humidity remained within recommended safe storage ranges (10–15 °C; 65–75%). These findings demonstrate that external climatic conditions and solar radiation, even at reduced levels in December, dominate the thermal and hygroscopic behavior of the silo, independent of grain type. The identification of unstable zones near the roof and walls underscores the need for passive conservation strategies, such as grain redistribution and selective ventilation, to mitigate fungal proliferation and storage losses under non-aerated conditions. Full article

The increasing demand for effective flow control in supersonic boundary layers, particularly for mitigating shock-wave boundary-layer interactions, underscores the need to explore optimized micro-vortex generator (MVG) configurations. This study investigates the aerodynamic performance of two different MVG configurations: a two-MVG setup with a pair of close parallel-positioned MVGs and a three-MVG arrangement that includes an additional upstream unit. Both are examined within a Mach 2.5 flow regime, aiming to improve mixing and energize the boundary layer. Large Eddy Simulations (LES) were performed using high-order numerical schemes. A newly developed vortex identification method was utilized to characterize vortex structures, while turbulent kinetic energy (TKE) metrics were integrated to quantify turbulence. Findings reveal that the two-MVG configuration produces regular, symmetric vortex pairs with limited interaction. This results in a steady increase in TKE and a thickened momentum boundary layer—indicative of notable energy loss. In contrast, the three-MVG setup generates more intricate and interactive vortex formations that significantly elevate TKE levels, rapidly expand the turbulent region, and reduce energy loss downstream. The peak TKE occurs before tapering slightly. Instantaneous flow analysis further highlights chaotic, hairpin-dominated vortex structures in the three-MVG case, compared to the more orderly ones observed in the two-MVG case. Overall, the three-MVG configuration demonstrates superior mixing and boundary-layer energization potential, albeit with greater structural complexity. Full article

This study develops a comprehensive analytical framework to examine how nations secure core positions in global supply chains (GSCs) for key industries. It combines a comparative analysis of advanced economies—Los Angeles (aerospace), Munich (high-end manufacturing), London (biopharmaceuticals), and Tokyo (automotive)—with a survey-based empirical assessment of Chinese industry practitioners. Using the Analytic Hierarchy Process (AHP), factor analysis and the Delphi method, an evaluation framework is constructed across five dimensions: technology, value, governance, resilience, and sustainability. The findings show that developed economies sustain their leadership through upstream innovation and standard-setting, coordination of high-value activities, integrated industrial ecosystems, and risk-buffering mechanisms. Empirical results reveal that while China demonstrates relative strengths in governance and value creation, it continues to lag in frontier technologies, resilience, and sustainability. Building on both comparative and empirical evidence, the study proposes strategic pathways for China’s key industries, including technological breakthroughs, innovation-driven clusters, governance reforms, digital resilience, and green cooperation. These insights provide actionable guidance for policymakers and highlight how latecomer economies can transform structural disadvantages into innovation momentum, evolving from participants to rule-setters in global supply chains. Full article

This study explores the critical role of the flow deflector in suppressing boundary layer separation and enhancing aerodynamic efficiency through systematic geometric parameterization and computational analysis. By defining eight key design variables, this research identifies optimal configurations that significantly delay flow separation at high angles of attack. Computational Fluid Dynamics (CFD) simulations reveal that optimized deflector geometries enhance suction peaks near the airfoil leading edge, redirect separated flow toward the upper surface, and inject momentum into the boundary layer to generate a more positive lift coefficient. The numerical results demonstrate that the optimized design achieves a 58.4% increase in lift coefficient and an 83.3% improvement in the lift–drag ratio by effectively mitigating large-scale vortical structures inherent in baseline configurations. Sensitivity analyses further highlight threshold-dependent “sudden-jump” behaviors in lift coefficients for parameters such as element spacing and deflection angles, while thickness exhibits minimal influence. Additionally, pre-stall optimizations show that strategically aligned deflectors preserve baseline performance with a 0.4% lift gain, whereas misaligned configurations degrade aerodynamic efficiency by up to 9.1%. These findings establish a direct correlation between deflector-induced flow redirection and separation suppression, offering actionable insights for passive flow control in stalled regimes. This research advances fundamental understanding of flow deflector-based separation management and provides practical guidelines for enhancing aerodynamic performance in aerospace applications. Full article