Search Results (181)

Zr alloy-shaped charge liners (SCLs) offer broad application prospects due to their multiple post-penetration damage effects. However, research on these liners is still in its early stages. The mechanisms of jet formation and penetration for Zr alloys SCL remain unclear, and the specific contribution of different liner regions to the penetration process is not yet understood. This gap in knowledge has limited their structural design to a black-box correlation between global structural parameters and macroscopic penetration efficiency. To address this gap, a region-tracing Smoothed Particle Hydrodynamics (SPH) simulation was employed. Following a strategy of “wall thickness layering + axial segmentation,” the Zr alloy liner was partitioned into ten characteristic regions. This methodology facilitated the tracking of material transport from each region during jet formation and penetration into an AISI 1045 steel target. The contribution of each region to the penetration depth was then quantitatively assessed via post-processing. For the first time, the “critical region” contributing most to penetration depth was identified, and the influence of the liner’s cone angle and wall thickness on the contribution of each region was revealed. This study enhances the theoretical framework for understanding the damage effects of Zr alloy shaped charge liners. It not only advances the fundamental understanding of jet penetration mechanisms but also provides a theoretical basis for the refined design and performance optimization of these liners. Full article

The widespread commercialization of wireless chargers for electric vehicles generally suffers from one main problem, which is the perfect alignment between the two coils, leading to a decrease in mutual inductance, which causes a drop in magnetic coupling and even a failure to transfer power. To address this persistent problem, this work proposes a comprehensive and integrated method for optimizing the coils and control architecture for reliable and safe battery charging. To address the challenges of a complex, nonlinear design space and the need for misalignment-tolerant geometries, we employ a memetic algorithm (MA) that hybridizes Particle Swarm Optimization (PSO) for broad global exploration with Mesh Adaptive Direct Search (MADS) for precise local refinement. This combination effectively avoids poor local solutions—a limitation of standalone PSO or GA approaches reported in recent studies—while efficiently converging to coil geometries that maintain strong magnetic coupling under misalignment. After the coils have been designed, electromagnetic validation is tested using finite element analysis (FEA), which allows the magnetic field distribution to be evaluated, as well as the coupling coefficient under different scenarios of misalignment and variation in the air gap between the ground side and the vehicle side. At the same time, a comprehensive control strategy for the primary side of the system has been developed. This control method ensures power management on the primary side, enabling system interoperability for charging multiple types of vehicles, as well as reducing vehicle weight for greater range. All this is combined with an innovative pulsed current charging method, chosen for its advantages in terms of thermal stability, ensuring safe and efficient recharging that is mindful of battery health. Simulation and experimental validation demonstrate that the proposed framework maintains stable wireless power transfer and achieves over 87% DC–DC efficiency under lateral misalignments up to 100 mm, fully complying with SAE J2954 alignment tolerance requirements. Full article

A correct description of quarkonia production and kinematics is still one of the most challenging assignments for Quantum Chromodynamics. This document presents a study of the ${\rm Y}$(1S), (2S) and (3S) mean transverse momentum ($⟨p_{\mathrm{T}}^{{\rm Y}}⟩$) as a function of the charged particle multiplicity ($N_{\mathrm{Track}}$) in proton–proton collisions at $\sqrt{s}$ = 7 TeV generated with Pythia 8.312 CUETP8M1 tune. The comparison to real data collected by the CMS experiment indicates that the agreement is much better for the excited states than for the ground state. The observed fast increase in the $⟨p_{\mathrm{T}}^{{\rm Y}}⟩$ at small values of $N_{\mathrm{Track}}$ is mainly due to the contribution from the away region. Furthermore, when computing the $⟨p_{\mathrm{T}}^{{\rm Y}}⟩$ from jetty and isotropic events, a clear $p_{\mathrm{T}}$ hardening is observed in jetty events. Finally, analyzing the fragmentation of jets containing an ${\rm Y}$(nS), a new method is proposed to test the new quarkonia shower present in the Monte Carlo event generator. Full article

Thermal-like features in hadron production are observed in small systems such as proton–proton interactions, where conventional kinetic equilibration on sub-fm/c time scales is challenging to justify. One proposed explanation is that quantum entanglement in the incoming hadron wave functions, together with coarse-graining over unobserved degrees of freedom, can generate an entropy-like signal without requiring extensive final-state rescattering. We test whether a final-state Shannon entropy extracted from the charged-particle multiplicity distributions measured by ALICE at $\sqrt{s}=0.9$–8 TeV can be reproduced by an initial-state entanglement entropy computed from leading-order proton PDFs. In a low-x approximation where the reduced density matrix of the probed region is taken to be maximally mixed in an effective parton-number basis, the entanglement entropy reduces to $S_{EE}\simeq \ln N$, where N is obtained by integrating PDFs over an x-range mapped from the ALICE midrapidity acceptance. We include gluon and sea-quark contributions and apply correction factors accounting for the charged fraction and the limited set of measured degrees of freedom. Within the stated assumptions and PDF uncertainties, the initial- and final-state entropy become numerically compatible toward low x, supporting the interpretation that initial-state quantum entanglement can contribute to the apparent thermal-like behavior in small collision systems. Full article

We study the production of charged particles in ultrarelativistic collisions by using the string fragmentation model. To do this, we describe the $p_{\mathrm{T}}$ spectrum as the convolution of the Schwinger mechanism with string tension fluctuations that account for the stochastic nature of QCD. We found that heavy-tailed distributions are required to adequately reproduce the power-law tail of the $p_{\mathrm{T}}$ spectrum experimentally observed. Additionally, the heavy-tailed characteristic is also necessary for the KNO scale invariance of intense color interactions modeling hard processes in this framework. In this way, the initial state admits a nonextensive picture, leading to a final state out of equilibrium, in which particle production occurs in small regions at different temperatures. Applying this framework to ALICE data, we observe trends in the power-law exponent as a function of event multiplicity and collision centrality. These trends are consistent with enhanced hard-particle production in small systems and with high-$p_{\mathrm{T}}$-particle suppression in heavy-ion collisions. Full article

Background: Immunomodulatory compounds can modify or regulate the immune responses. Given that vaccine-induced immune responses can vary in magnitude and durability depending on antigen properties and adjuvant selection. Immunomodulators that enhance antigen-specific immune responses with low toxicity may complement existing adjuvant systems. Recent studies indicate that adenosine receptor–mediated signaling can modulate dendritic cell (DC) function through mechanisms distinct from classical pathogen-associated molecular pattern (PAMP)-driven Toll-like receptor pathways. Methods: In this context, the present study comparatively evaluates poly-(lactic-co-glycolic acid) (PLGA) microparticle–encapsulated β-L-adenosine (BLA MPs) alongside established FDA-approved adjuvants to assess their immunomodulatory potential under limited-antigen conditions. FDA-approved PLGA was used to encapsulate BLA in combination with multiple viral antigens, including H1N1 influenza, Zika virus, and canine coronavirus, to enable sustained delivery, antigen protection, and efficient uptake by antigen-presenting cells. Results: Physicochemical characterization demonstrated uniform particle size distribution, a low polydispersity index, and a stable negative surface charge. Release studies showed more than 50% payload release within 12 h, with release kinetics best described by the Korsmeyer–Peppas model. Cytotoxicity evaluation using DC2.4 cells confirmed that BLA MPs were non-cytotoxic at concentrations up to 250 μg/mL. Comparative in vitro immunological assessments revealed that BLA MPs induced dendritic cell activation, including upregulation of antigen-presenting and co-stimulatory molecules, at levels largely comparable to those observed with Alum- and MF59-based formulations across multiple antigen groups. Nitric oxide production remained within comparable ranges, indicating balanced immunostimulatory activity without excessive inflammatory signaling. In select conditions, co-formulation of BLA MPs with MF59 further enhanced DC activation, supporting its role as a complementary immunomodulatory component. Conclusion: These findings align with previously reported adenosine-dependent pathways involved in DC maturation and antigen presentation. Overall, this comparative study demonstrates that PLGA-encapsulated β-L-adenosine functions as an effective immunomodulatory agent, with performance comparable to that of established FDA-approved adjuvants across diverse vaccine antigens. Further in vivo studies are warranted to evaluate dose dependency, cytokine profiles, and antibody responses to define its role within combinatorial vaccine adjuvant strategies. Full article

Aiming at cemented tailings backfill (CTB) damage and collapse induced by secondary stope blasting in the sublevel open stoping with a subsequent filling method, a new CTB damage control technology termed “Synergistic Utilization of Cumulative Blasting Damage (SU-CBD)” is proposed. First, theoretical analysis is conducted to reveal the influence mechanism of rock mass damage accumulation on its blastability, verifying the feasibility of the SU-CBD technology. Subsequently, based on the LS-DYNA R11.1 software and RHT material model, a numerical model is established, and the small restart technique is adopted to realize the continuous simulation of multi-row blasting. By comparing the rock mass fragmentation ratio, energy distribution, and CTB damage degree among different charge structure schemes, the optimal charge structure combination is obtained. To address the issues of retained rock mass damage and overbreak caused by multiple blasting operations, a dynamic adjustment method for blasthole row spacing is proposed, with the optimal row spacing increment determined as 1.0 m. To verify the technical effectiveness, field industrial tests are carried out in Stope No. 5 of the 4500 m–4550 m mining level in the Bangzhong Zinc-Copper Mine. The results show that the optimized blasting scheme keeps the CTB intact without collapse and achieves uniform ore fragmentation, and the oversize ore ratio (particle size > 50 cm) is only 2.4%, with the numerical simulation results in good agreement with the field test results. The research indicates that the SU-CBD technology can effectively reduce the powder factor and CTB blasting damage while ensuring the blasting fragmentation effect, providing reliable blasting design support for the secondary stope. Full article

In underground construction, the drilling and blasting method is widely used due to its advantages, such as low cost, simple implementation, and applicability under various geological and hydrogeological conditions. One parameter that significantly affects the effectiveness of drilling and blasting is the post-blast tunnel cross-sectional area. In this study, multiple linear regression analysis (MLRA) and multiple nonlinear regression (MNLR) models were used to predict the area of a tunnel face after blasting, utilizing 136 datasets containing parameters measured from the tunnel face area after blasting during the Deo Ca tunnel construction project. Three deep learning models, an artificial neural network (ANN) and two hybrid models combining an ANN with the particle swarm optimization (PSO) algorithm and an ANN with a genetic algorithm (GA), were then developed to predict the tunnel face area after blasting. The input variables for the calculation and prediction models included the designed tunnel face area (S_d), the specific charge (SC) of the explosion, the average borehole length (L), and the rock mass rating (RMR) of the rock mass on the tunnel face. The GA-ANN model’s results, including determination coefficient (R²) and mean square error (MSE) values of R²_train = 0.9562, R²_testing = 0.94, MSE_training = 0.0156, and MSE_testing = 0.0302, indicate that it provides a better prediction of the tunnel face area after blasting than the other models. Full article

Co/Fe layered double hydroxides (LDHs) are among the most promising materials for advanced industrial and energy applications. Controlling the synthesis conditions of LDH materials is thus crucial to precisely tailoring cation composition and distribution, thereby regulating surface charge, ion sorption, and electron transfer required for optimal chemical and electrochemical performance. Therefore, characterizing Co/Fe precipitates (chemical composition, purity, morphology, and crystallinity) is also required to further exploit their controlled properties. Thus, solids with Co/Fe cation ratios between 2/2 and 10/2 were synthesized under an air atmosphere, at pH 8 or 11.5. For the first time, multiscale physicochemical techniques (FTIR, TEM-EELS, SEM, AAS, TGA, CHN elemental analysis, and XRD complemented by Rietveld refinement) were used to provide a fully documented characterization of the structure, texture, purity, chemical composition, and thermal properties of Co/Fe LDH-based materials. The combined interpretation of data from these complementary techniques enabled the precise identification and chemical characterization of the mineralogical phases formed. Both acid–base and redox reactions govern the overall Co^II/Fe^III LDH formation process. Well-crystallized LDHs were synthesized, except for the 2/2 ratio at pH 11.5, which led to the formation of α-Co(OH)₂, γ-Fe₂O₃, and Co₃O₄ byproducts. A pH value of 8.0 provides valuable LDH materials made of quasi-hexagonal particles with diagonal lengths between 200 and 500 nm. Rietveld refining showed the presence of LDH phases in the range of 95–98%. Multiple local chemical analyses using EDX on chosen particles demonstrated pure 4/2 and 6/2 LDHs. For the 2/2 ratio, the cumulative mass fraction of two LDH-type products consistently reached 97%, distributed between Co/Fe 1.5/2 (71%) and Co/Fe 4/2 (29%). For the 10/2 ratio, only partial Co precipitation was observed, forming 95% Co/Fe LDH phases distributed between Co/Fe 10/2 (72%) and 7/2 (28%). Full article

Accurate estimation of the State of Charge (SOC) for lithium-ion batteries is a core function of the Battery Management System (BMS). However, LiFePO₄ batteries present specific challenges for SOC estimation due to the characteristic plateau in their open-circuit voltage (OCV) versus SOC relationship. Moreover, data-driven estimation approaches often face significant difficulties stemming from measurement noise and interference, the highly nonlinear internal dynamics of the battery, and the time-varying nature of key battery parameters. To address these issues, this paper proposes a Long Short-Term Memory (LSTM) model integrated with feature engineering, physical constraints, and the Extended Kalman Filter (EKF). First, the model’s temporal perception of the historical charge–discharge states of the battery is enhanced through the fusion of temporal voltage information. Second, a post-processing strategy based on physical laws is designed, utilizing the Particle Swarm Optimization (PSO) algorithm to search for optimal correction factors. Finally, the SOC obtained from the previous steps serves as the observation input to EKF filtering, enabling a probabilistically weighted fusion of the data-driven model output and the EKF to improve the model’s dynamic tracking performance. When applied to SOC estimation of LiFePO₄ batteries under various operating conditions and temperatures ranging from 0 °C to 50 °C, the proposed model achieves average Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as low as 0.46% and 0.56%, respectively. These results demonstrate the model’s excellent robustness, adaptability, and dynamic tracking capability. Additionally, the proposed approach only requires derived features from existing input data without the need for additional sensors, and the model exhibits low memory usage, showing considerable potential for practical BMS implementation. Furthermore, this study offers an effective technical pathway for state estimation under a “physical information–data-driven–filter fusion” framework, enabling accurate SOC estimation of lithium-ion batteries across multiple operating scenarios. Full article

Accurate state estimation is a key technology for improving battery utilization and ensuring operational safety in electric vehicles. The joint estimation of the state of charge (SOC) and the state of power (SOP) over a wide temperature range is therefore essential for intelligent battery management systems. To address modeling uncertainties and estimation accuracy degradation induced by ambient temperature variations, a dual-polarization equivalent circuit thermal model incorporating temperature bias is proposed, and online parameter updating is achieved using the forgetting factor recursive least squares (FFRLS) algorithm. Furthermore, an unscented particle filter (UPF) is constructed by employing the unscented Kalman filter (UKF) as the proposal density function of the particle filter, thereby improving the estimation accuracy and convergence speed of SOC under wide temperature conditions. Based on the coupling relationship between SOC and SOP, a stepwise progressive strategy is then developed to predict the peak power state under multiple constraints, enhancing the robustness of SOP estimation. Simulation and experimental results demonstrate that the proposed method can accurately estimate SOC and SOP under complex operating conditions over a wide temperature range from −5 °C to 45 °C, exhibiting favorable convergence performance and estimation accuracy, which contributes to the safe operation and performance optimization of electric vehicle battery systems. Full article

Background: The tumor microenvironment (TME) poses significant challenges to effective therapy, with cancer-associated fibroblasts (CAFs) playing a key role in tumor progression and drug resistance in triple-negative breast cancer (TNBC). Herein, a TME responsive nanocapsule, NPC-ABS/FDS, was developed utilizing baicalein, a CAFs modulator, and the cytotoxic drug doxorubicin to selectively target CAFs and tumor cells, respectively, in a stepwise manner. Methods: NPC-ABS/FDS was designed with CD13-mediated primary targeting for tumor accumulation and secondary targeting via σ-receptor binding (ABS nanoparticles) for CAFs and folate modification (FDS nanoparticles) for cancer cells. Physicochemical properties were assessed using TEM, particle size, and ζ-potential analyses. Fluorescence imaging evaluated tumor retention, while cellular uptake and TME modulation were analyzed in vitro and in vivo. Results: The successful preparation of NPC-ABS/FDS was demonstrated by its uniform morphology, stable characteristics, charge reversal, and increased particle size. Fluorescence imaging confirmed prolonged peritumoral retention. Cellular uptake increased 2.5-fold for baicalein in CAFs and 4.3-fold for doxorubicin in cancer cells. NPC-ABS/FDS downregulated α-SMA and FAP, reducing CAFs activation, improving intratumoral drug penetration, and enhancing CD8⁺ and CD4⁺ T cell infiltration while decreasing regulatory T cells. Conclusions: NPC-ABS/FDS effectively modulates multiple TME components, including CAFs and immune cells, and improves drug delivery in TNBC. These findings may support the development of improved therapeutic approaches for TNBC. Full article

The Clausius–Mossotti (CM) factor underpins the theoretical description of dielectrophoresis (DEP) and is widely used in micro- and nano-scale systems for frequency-dependent particle and cell manipulation. It has further been proposed as an “electrophysiology Rosetta Stone” capable of linking DEP spectra to intrinsic cellular electrical properties. In this paper, the mathematical foundations and interpretive limits of this proposal are critically examined. By analyzing contrast factors derived from Laplace’s equation across multiple physical domains, it is shown that the CM functional form is a universal consequence of geometry, material contrast, and boundary conditions in linear Laplacian fields, rather than a feature unique to biological systems. Key modelling assumptions relevant to DEP are reassessed. Deviations from spherical symmetry lead naturally to tensorial contrast factors through geometry-dependent depolarisation coefficients. Complex, frequency-dependent CM factors and associated relaxation times are shown to inevitably arise from the coexistence of dissipative and storage mechanisms under time-varying forcing, independent of particle composition. Membrane surface charge influences DEP response through modified interfacial boundary conditions and effective transport parameters, rather than by introducing an independent driving mechanism. These results indicate that DEP spectra primarily reflect boundary-controlled field–particle coupling. From an inverse-problem perspective, this places fundamental constraints on parameter identifiability in DEP-based characterization. The CM factor remains a powerful and general modelling tool for micromachines and microfluidic systems, but its interpretive scope must be understood within the limits imposed by Laplacian field theory. Full article

Precisely assessing electric vehicle hosting capacity (EVHC) is critical for ensuring the secure integration of EVs and optimizing the use of distribution network resources. Although optimization-based methods such as Particle Swarm Optimization (PSO) can identify a high theoretical HC under steady-state voltage constraints, these static formulations fail to capture short-term dynamics such as photovoltaic (PV) intermittency and uncoordinated EV arrivals. As a result, the hosting capacity that can actually be used in practice is often reduced to a much lower capacity to keep the system operating safely. This study compares optimization-based and simulation-based HC assessments and introduces a Weighted Average Power Estimator (WAPE)-based dynamic control framework to preserve the higher HC identified by optimization under real-world conditions. Case studies on a modified IEEE 13-bus system show PV drops of 90% during a 4-s cloud event. Studies also demonstrate that a sudden clustering of multiple EVs would significantly lower effective HC. With WAPE control, the system maintains stable operation at full HC, holding the bus voltage within an acceptable range (400–430 V) during the two events, representing a 2–3% voltage improvement. In addition, WAPE allows the EV to continue charging at a lower rate during disturbances, reducing the total charging time by almost 10% compared with completely stopping the charging process. Overall, the proposed WAPE substantially improves the usable and sustainable HC of distribution networks, ensuring reliable EV integration under dynamic and uncertain operating conditions. Full article

With respect to battery management and safe operation and maintenance scheduling of electric vehicles (EVs), it is very important to predict the remaining useful life (RUL) of lithium-ion batteries (LIBs). Accurate prediction of RUL can bring secure working conditions, avert internal and external failure, and, last, avoid any undesirable consequences. However, achieving accurate prediction of RUL is complicated for EV applications due to various reasons such as the complex operational characteristics, dynamic changes in the model parameters during the aging process, extraction of battery parameters, data preparation, and hyper-parameter tuning of the predictive model. This research proposes a novel approach that integrates Particle Swarm Optimization (PSO) with a multi-model technique for RUL prediction. The framework integrates many machine learning (ML) models and deep learning (DL) models. Combining domain knowledge, advanced optimization techniques, and learning models to make high-accuracy RUL predictions reduces maintenance costs and improves battery management systems. This study uses domain-driven feature engineering to extract battery-specific indicators, including voltage drops, charging time, and temperature fluctuations, to increase model accuracy. Among the evaluated models, LSTM demonstrates superior performance, achieving a mean absolute error (MAE) of 0.34, a root mean square error (RMSE) of 0.76, and an R² of 0.93, providing the best results in RUL prediction. The proposed research uniquely integrates PSO-based optimization with domain-driven feature engineering across multiple machine learning and deep learning models, demonstrating a unified and novel approach that significantly improves the prediction accuracy of RUL in LIBs. Full article