Search Results (919)

To improve the state-of-charge (SOC) estimation accuracy of sodium-ion batteries under complex operating conditions, this paper proposes a particle swarm optimization-based heterogeneous adaptive extended Kalman filter. A hardware-in-the-loop (HIL) validation platform is also established to reproduce the sampling-chain constraints of a practical battery management system. In addition, a second-order equivalent circuit model (ECM) serves to characterize battery dynamics and generate validation data. Within this framework, the degradation in estimation performance from the theoretical environment to practical hardware execution is quantitatively analyzed. The feasibility of using ECM-generated data for SOC estimation algorithm validation is also evaluated. Using measured Federal Urban Driving Schedule data at 25 °C, the proposed method achieves high estimation accuracy and stable convergence in both environments. Specifically, the mean absolute error and root-mean-square error in the theoretical environment are 0.11% and 0.25%, respectively. Under HIL conditions, the corresponding values are 0.60% and 0.63%. Additional tests under different temperatures and composite disturbance conditions further verify the adaptability and robustness of the proposed algorithm. The results also show that practical hardware constraints introduce non-negligible performance degradation. In addition, ECM-generated data remain highly consistent with measured data in terms of error-evolution trends. Therefore, ECM-generated data can serve as a feasible validation data source for SOC estimation algorithm performance evaluation and rapid validation. Full article

The increasing electrification of urban transportation has formulated a tightly coupled energy-mobility nexus. Under extreme disaster events or grid faults, rapidly restoring power supply capacity and re-dispatching shared electric vehicle (EV) fleets are critical for enhancing system resilience. Existing co-optimization methods face the curse of dimensionality when dealing with high-dimensional discrete grid reconfigurations and continuous spatio-temporal EV queuing dynamics. While multi-agent deep reinforcement learning (MADRL) offers real-time responsiveness, it inherently struggles to satisfy strict physical constraints, frequently generating infeasible and unsafe actions. To bridge this gap, this paper proposes a heuristic-guided safe multi-agent reinforcement learning (Safe-MADRL) framework for the resilient dispatch of the energy-mobility nexus. Instead of relying solely on black-box neural networks, the framework structurally embeds physical models and heuristic solvers into the learning loop. A quantum particle swarm optimization (QPSO) algorithm acts as a heuristic action refiner to ensure that grid topology actions strictly comply with non-linear power flow and voltage constraints. Simultaneously, a mixed-integer linear programming (MILP) model coupled with a single-queue multi-server (SQMS) model serves as a safety projection layer. This layer mathematically guarantees EV battery energy continuity and accurately quantifies spatio-temporal queuing delays at charging stations. Case studies on a coupled IEEE 33-node distribution system and a regional transportation network demonstrate that the proposed Safe-MADRL framework achieves zero physical violations during training and significantly outperforms traditional mathematical optimization and pure learning-based methods in computational efficiency, system power loss reduction, and overall operational economy. Full article

The field of nanoparticle-based biotechnology has undergone substantial advancement, characterized by progress in targeted drug delivery systems, the development of innovative diagnostic and imaging platforms, the expanded adoption of environmentally sustainable (“green”) synthesis approaches, and an increasing emphasis on the integration of emerging technologies such as artificial intelligence and nanorobotics. Conventional nanoparticle synthesis often involves toxic reducing agents; however, recent advances promote eco-friendly green synthesis methods utilizing biological systems such as bacteria, fungi, algae, yeast, plants, and actinomycetes. These biological approaches are safe, sustainable, cost-effective, and capable of producing highly stable Nanoparticles (NPs). The interaction of nanomaterials with biological systems is crucial for developing intracellular and subcellular drug delivery technologies with minimal toxicity, governed by nano–bio interface mechanisms such as cellular translocation, surface wrapping, embedding, and internal attachment. Key factors influencing NP behavior include morphology, size, surface area, surface charge, and ligand chemistry. Magnetic nanoparticles, particularly iron-based forms, exhibit unique superparamagnetic properties that are strongly influenced by particle size, as explained by the Néel relaxation mechanism, in which thermal energy induces flipping of magnetic moments. Nanoparticles demonstrate diverse modes of action, including antimicrobial activity, reactive oxygen species (ROS)-induced cytotoxicity, genotoxicity, and plant growth promotion. NP performance and biological effects are strongly dependent on their size, shape, dosage, and concentration. This critical review article aims to elucidate evolution, classification, preparation methods, and multifaceted applications of nanoparticles Full article

Ulcerative colitis (UC) is a chronic inflammatory disorder of the colon characterized by dysregulated mucosal immunity and progressive epithelial injury. Upadacitinib (UPA), a selective Janus kinase 1 (JAK1) inhibitor, has demonstrated clinical efficacy in UC, but its therapeutic application is often constrained by adverse effects arising from systemic drug exposure. This underscores the need for advanced, site-specific delivery systems that enhance local efficacy while minimizing systemic toxicity. Here, we developed a colon-targeted natural lipid nanoparticle formulation of UPA (UPA-nLNP) to improve therapeutic performance and safety. UPA-nLNP was prepared by thin-film hydration using digalactosyldiacylglycerol (DGDG), monogalactosyldiacylglycerol (MGDG), and phosphatidic acid (PA), mimicking the lipid composition of ginger-derived exosomal particles, and was characterized for particle size, surface charge, and encapsulation efficiency. The formulation exhibited excellent mucus-penetrating capability and was evaluated in a dextran sulfate sodium (DSS)-induced acute colitis model in C57BL/6 mice following oral administration (5 mg/kg). Pharmacokinetic analysis demonstrated increased colonic accumulation with reduced systemic exposure compared to free UPA. Treatment with UPA-nLNP improved body weight recovery, reduced disease biomarkers, and suppressed key proinflammatory cytokines in the colon, with no evidence of systemic toxicity. This innovative strategy holds strong potential to enhance the clinical utility of JAK1 inhibitors by providing a safer and more effective therapeutic approach for ulcerative colitis. Full article

With the widespread adoption of electric vehicles (EVs), the deep integration of transportation and power grids has emerged as a significant trend. EV charging stations, acting as dynamic loads, present challenges to real-time power balance and economic dispatch in microgrids, while EVs serving as mobile energy storage units offer new opportunities for system flexibility. To address these issues, this paper proposes a hardware-in-the-loop (HIL) co-design method for vehicle–grid-integrated microgrid energy management systems, covering the entire workflow from simulation to embedded deployment. This method resolves the core challenges of multi-objective optimization algorithm deployment on embedded platforms (i.e., high computational complexity, strict real-time constraints, and heterogeneous communication protocol integration) via deployability analysis, hybrid code generation, real-time task restructuring, and consistency validation. A prototype microgrid system integrating photovoltaic panels, wind turbines, diesel generators, an energy storage system, and EV charging loads was built on the RK3588 embedded platform. An improved multi-objective particle swarm optimization (MOPSO) algorithm is employed to optimize operational costs. Experimental results verify the effectiveness of the proposed co-design method. Compared with traditional rule-based control strategies, the MOPSO algorithm reduces the total daily operating cost of the VGIM system by approximately 50%. After integrating vehicle-to-grid (V2G) scheduling, the operating cost is further reduced. In addition, this method ensures the consistency of algorithm functionality and performance during the migration from HIL simulation to embedded deployment, and the RK3588-based embedded system can complete a single optimization iteration within 60 s, which fully satisfies the real-time requirements of industrial applications. This work provides a feasible technical pathway for the reliable deployment of vehicle–grid-integrated microgrids in practical industrial scenarios. Full article

The colloidal particles present in natural soil and groundwater systems possess distinctive properties that enable them to migrate across solid surfaces, thereby exerting a significant influence on the distribution of pollutants. While the attachment of colloidal particles to solid surfaces has been extensively investigated, the mechanisms governing their detachment under varying hydrochemical conditions remain largely unexplored. The common interaction between montmorillonite colloids and solid medium (Al₂O₃) in soil affects the fate of pollutants such as heavy metals. In our study, Al₂O₃ was used as solid medium to observe the adsorption and desorption behavior of montmorillonite colloids. It was found that the adsorption capacity of Al₂O₃ to montmorillonite colloids could reach 4.71 mg g⁻¹ (pH 5.0 and 10 mM NaCl concentration). X-ray photoelectron spectroscopy analysis shows that montmorillonite colloids react with the Al₂O₃ surface mainly through chemical groups with –O–Si bonds. Desorption experiments show that SDS drives desorption by neutralizing and reversing the surface charge of Al₂O₃, while CTAB directly modifies montmorillonite colloids and introduces steric hindrance to achieve desorption. These research data contribute to a comprehensive understanding of the migration behavior of montmorillonite colloids on solid phases. Full article

The practical application of protein hydrolysates as functional food ingredients is frequently obstructed by their inherent structural instability. To circumvent this limitation, liposomal encapsulation has emerged as a sophisticated strategy to bolster the bioactivity and integrity of cricket-derived proteins. In this study, varying concentrations (1–4% w/v) of defatted cricket protein hydrolysate (DCPH) were integrated into vesicles composed of soy lecithin and cholesterol. The highest encapsulation efficiency (EE) was observed at a 2% DCPH loading capacity, yielding a significant result of 88.18% (p < 0.05). Subsequent coating with sodium alginate (SA) at 0.1–0.3% (w/v) resulted in an increase in particle size and a more pronounced negative surface charge. When maintained at 4 °C over a 24-day duration, the SA-coated liposome (SA-L-2%DCPH) exhibited superior stability compared to its uncoated (L-2%DCPH) counterpart. Also, the digest derived from the SA-L-2%DCPH exhibited significantly enhanced transepithelial permeability across the Caco-2 cell monolayer, indicated by the higher protein content and ABTS radical scavenging activity. Thus, sodium alginate-coated liposomes serve as a promising delivery system for encapsulating DCPH both during storage stability and in the gastrointestinal digestion system. Full article

Accurate assessment of the State of Health (SOH) is critical for battery management systems in aviation. As a step towards this goal, this study presents a proof-of-concept for a novel SOH estimation method based on an Improved Particle Swarm Optimization-Extreme Learning Machine (IPSO-ELM) model, validated under controlled laboratory cycling conditions. Although traditional Extreme Learning Machines (ELM) are widely used due to their fast computation and good generalization, their random parameter initialization often leads to unstable convergence and limited accuracy. To address these limitations, this paper proposes a novel SOH estimation method based on an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the key parameters of ELM. Three health indicators (HI)—constant-current charging time, equal-voltage-drop discharge time, and average discharge voltage—were extracted from charge–discharge curves as model inputs. The IPSO algorithm dynamically adjusts the inertia weight, introduces a constriction factor and a termination counter to enhance global search capability and avoid local optima. Experimental results on open-source datasets (B005, B007, B0018) and laboratory datasets (A001, A002) demonstrate that the proposed IPSO-ELM model achieves a Root-Mean-Square Error (RMSE) below 0.7% and a Mean Absolute Percentage Error (MAPE) below 0.5%. Compared with standard ELM and PSO-ELM models, it significantly outperforms them in accuracy (e.g., for B0018, RMSE is reduced to 0.21% and MAPE to 0.14%), convergence speed, and robustness, establishing a foundation for future development of aviation-ready SOH estimators. Full article

Some windowless semiconductor photodiodes can detect not only photons but also charged particles, cover a wide spectral range including a part of the ionizing radiation region and, thus, play important roles for synchrotron radiation experiments. To understand the spectral, angular and polarizing properties of semiconductor photodiodes, complex amplitude coefficients of transmittance or reflectance are calculated based on rigorous formulation using Fresnel equations with complex optical constants of the composing materials, whose validity was verified by comparison with experiments. Concrete examples of the behavior on the complex plane are shown as a function of complex optical constants, film thickness, angle of incidence and the wavelength. The results show that the optical properties of the layered system are sensitive to its layer thickness, the angle of incidence and the wavelength in the ultraviolet region where optical indices of the composing materials steeply change. It has been shown that oblique incidence photodiodes are useful as polarization-sensitive devices, and that the graphical technique using the amplitude coefficients expressed on the complex plane is effective and powerful to search for optimal conditions for complex optical constants, film thickness and/or angle of incidence. Full article

Extracellular vesicles (EVs) derived from microbial sources, including beer yeast (Saccharomyces cerevisiae), have recently attracted increasing attention as bioactive nanostructures with potential biomedical and cosmetic applications. In this study, EVs were isolated from Saccharomyces cerevisiae (beer yeast) using an electrokinetic ion-binding filtration system, followed by tangential flow filtration (TFF)-based buffer exchange. Their physicochemical characteristics and hair follicle-related biological activities were systematically evaluated. Nanoparticle tracking analysis demonstrated a mean particle size within the typical EV range, and zeta potential analysis confirmed a negatively charged surface. Cryo-transmission electron microscopy further verified the presence of lipid bilayer-enclosed nanovesicles. Biological activity was assessed in human dermal papilla cells, keratinocytes, and dermal fibroblasts, which collectively represent key components of the hair follicle microenvironment. At non-cytotoxic concentrations, yeast-derived EVs enhanced dermal papilla cell proliferation and promoted keratinocyte migration. The EVs attenuated pro-inflammatory cytokine expression under stimulated conditions and upregulated collagen-related gene expression in dermal fibroblasts. In addition, measurable antioxidant activity was observed. Collectively, these findings indicate that S. cerevisiae-derived extracellular vesicles exhibit multifunctional bioactivity relevant to the regulation of hair follicle-associated cellular processes. This study supports the potential of microbial EVs as scalable bioactive platforms for modulating hair follicle microenvironmental homeostasis. Full article

This review surveys how nanoparticles and biomolecular nanosized structures interact with model membrane systems, and how these interfacial processes govern their performance in drug and gene delivery, antimicrobial strategies, biosensing, and nanotoxicology. The nanostructures covered include polymeric nanoparticles, lipid-based carriers, peptide nanostructures, dendrimers, and multifunctional hybrids. Model membranes span Langmuir monolayers, supported lipid bilayers, vesicles/liposomes across sizes, and emerging hybrid or asymmetric constructs that better approximate native complexity. Mechanistically, interactions follow recurrent routes—surface adsorption, bilayer insertion, pore formation, and lipid extraction/reorganization—regulated by particle size, morphology, charge, ligand architecture, and lipophilicity, in conjunction with membrane composition, phase state, curvature, and asymmetry. A multiscale toolkit links structure, mechanics, and dynamics: Langmuir troughs and Brewster Angle Microscopy map thermodynamics and mesoscale morphology; atomic force microscopy and quartz crystal microbalance with dissipation resolve nanoscale topography and viscoelasticity; fluorescence microscopy/spectroscopy reports on localization and packing; neutron and X-ray reflectometry quantify vertical structure; molecular dynamics provides atomistic pathways and design hypotheses. Historically, the field advanced from early monolayers and bilayers, through the fluid mosaic model, to raft microdomains and modern biomimetic systems, enabling increasingly realistic experiments. Key advances include cross-method integration linking experimental observations with image-based computational models; persistent debates concern the translation from simplified models to living membranes, the role of dynamic coronas, and scale/force-field limits in simulations. Future efforts should prioritize hybrid models incorporating proteins and asymmetric lipidomes, standardized reporting and reference systems, rigorous coupling of experiments with calibrated simulations and machine learning, and alignment with safety-by-design and regulatory expectations, thereby shifting interfacial measurements from descriptive observation to predictive design rules. Full article

The ionizations of the 1S state of hydrogenic systems with a nuclear charge of Z = 2 and 3 have been carried out using the hybrid theory. This is a continuation of the work started earlier. The present results are compared with the published cross-sections for Z = 1 [Bhatia, A.K. 2025]. The distortion of the orbit is considered irrespective of the position of the incident particle, whether it is outside or inside the orbit. Only the distortion of the target orbit in the initial state is considered, but the distortion in the final state is not considered. Cross-sections decrease as the nuclear charge increases. Full article

Amphiphilic cationic lipids based on the 1,4-dihydropyridine (1,4-DHP) scaffold represent a versatile platform for the development of self-assembling delivery systems. In this work, a series of ten new amphiphilic 1,4-DHP derivatives bearing branched ester substituents at the 3,5-positions and quaternized cationic groups at the 2,6-positions were designed and synthesized. The effect of branched ester chain length and branching on nanoparticle formation was investigated. The self-assembling properties of the synthesized amphiphiles were evaluated by dynamic light scattering using an ethanol injection method. All compounds formed positively charged nanoparticles with hydrodynamic diameters ranging from 52 to 439 nm and polydispersity index from 0.194 to 0.452. Amphiphiles 14b–17b with 2-hexyldecyl substituents formed smaller particles, with an average diameter below 100 nm. Several derivatives exhibited good stability over a 14-day storage period at room temperature. To clarify structure–property relationships, lipophilicity (AlogP), polar surface area (PSA), and pK_a values were calculated using Schrödinger computational tools. The compounds displayed high lipophilicity AlogP 8.98–19.32, while PSA values remained within a narrow range. The calculated pK_a values ranged from 7.20 to 10.99. The results demonstrate that both the length and architecture of branched ester chains significantly influence nanoparticle size, homogeneity, and stability, highlighting branched-chain 1,4-DHP amphiphiles as promising synthetic lipid candidates for further development of delivery systems after evaluation of biological properties. Full article

As global oil and gas exploration extends to deep and ultra-deep formations, high-temperature and high-salt environments have become major challenges for drilling fluid viscosifiers. In this study, a hydrophobic associative polymer viscosifier, HATA, was synthesized via free-radical copolymerization using acrylamide (AM), 2-acrylamido-2-methylpropanesulfonic acid (AMPS), sodium styrene sulfonate (SSS), and stearyl methacrylate (SMA) as monomers, and its structure was systematically characterized, while its performance and action mechanism in a 4 wt% bentonite base slurry were evaluated. The results show that the base slurry modified with 3 wt% HATA maintains an apparent viscosity retention ratio of 69.20% following 16 h of hot rolling at 180 °C, with an API filtration loss of only 7.2 mL, and its HTHP filtration loss is 73.72% lower than that of the blank bentonite slurry system; this viscosifier sustains effective viscosity and yield point of the drilling fluid system at 200 °C and in 36 wt% NaCl brine. HATA achieves viscosity enhancement and filtration control by regulating surface charges of bentonite particles, constructing stable three-dimensional networks, and stabilizing clay hydration layers, thus presenting a high-performance viscosifier formulation for high-temperature and high-salinity water-based drilling fluids with important theoretical and engineering application values. Full article

To address the challenges of difficult quantitative design and potential coil mismatch in auxiliary coils within wireless power transfer systems, a data-driven parameter optimization method based on multi-objective particle swarm optimization (MOPSO) was proposed. First, based on the inductor–capacitor–capacitor series (LCC-S) compensation topology, a mechanism-based analysis was conducted, establishing coil side length A and number of turns N as core optimization variables. Subsequently, a collaborative optimization framework integrating “parametric simulation–surrogate modeling–active learning” was established. An offline fingerprint database was constructed via finite element simulation, and a high-accuracy surrogate model was developed using a kernel ridge regression ensemble approach. Active learning strategies were employed to adaptively augment data points and mitigate uncertainty. Finally, the multi-objective particle swarm optimization (MOPSO) algorithm was applied to identify the Pareto-optimal solution set. Experimental results reveal that the optimized auxiliary coil parameters achieved positioning errors below 8 mm at all test points. The maximum positioning error was significantly reduced by approximately 80% compared to the traditional empirical approach, providing a useful parameter-selection reference for high-precision wireless charging alignment systems under the investigated static operating conditions. Full article