Search Results (616)

Limited research exists on the behavior of square CFDST slender columns, especially under the consideration of the relation global buckling and confinement effect. This study evaluates square concrete-filled double-skin steel tubular (CFDST) columns using nonlinear finite element analysis (FEA) to simulate structural behavior under axial and eccentric loads until failure. Parametric analyses of extensive specimens of square CFDST pin-ended columns evaluate various parameters, providing design insights for engineering applications. The study was conducted over a wide range of slenderness ratios. Four concrete varieties with compressive strengths were tested: normal concrete (NC), engineered cementitious composites (ECCs), high-strength concrete (HSC), and ultra-high-strength concrete (UHSC). Parametric variables included inner to outer steel tube thickness ratios, hollow ratios with a wide range, inner tube steel grades, and load eccentricities. An increasing slenderness ratio reduced the axial capacity, causing failure to change from yielding to buckling. By increasing the inner thickness, the capacity increased for intermediate columns compared to very long (i.e., slender) columns. The ideal hollow ratio is () for short columns compared to () for slender columns. UHSC improved short columns. Concrete’s performance was impacted by eccentric loading, which decreased the capacity, particularly in long columns. Designers should take into consideration the diminished efficacy of material strength enhancements under eccentric loading and prioritize stability in long, slender columns. The design formula was modified to enhance the strength estimates of square CFDST columns. Full article

Enterprise bankruptcy prediction is a critical research issue in financial risk early warning, credit evaluation, and investment decision-making. To address the limitations of traditional methods in handling high-dimensional, nonlinear, and complex financial data, including parameter sensitivity, susceptibility to local optima, and insufficient prediction stability, this study proposes a multi-strategy enhanced Beaver Behavior Optimizer and applies it to optimize kernel extreme learning machines, constructing the MEBBO KELM prediction model. Three improvement mechanisms are introduced, including an elite pool enhanced exploration strategy, a stochastic centroid reverse learning strategy, and a leader guided boundary control strategy, which improve population diversity, global search capability, boundary handling capacity, and convergence accuracy. The proposed algorithm is evaluated on CEC2017 and CEC2022 benchmark datasets and compared with EWOA, HPHHO, MELGWO, TACPSO, CFOA, ALA, AOO, RIME, and BBO. Statistical analyses are conducted using the Wilcoxon rank sum test and the Friedman test. The results demonstrate that MEBBO achieves superior solution accuracy and stability, indicating strong global optimization capability and robustness. Further experiments on the Wieslaw Corporate Bankruptcy Dataset show that MEBBO-KELM achieves strong and robust performance across multiple evaluation metrics, including ACC, MCC, Sensitivity, Specificity, Precision, Recall, and F1 score. Specifically, ACC reaches 79.7578, MCC reaches 0.6050, and F1 score reaches 78.8504, confirming its effectiveness. Full article

Cervical cancer constitutes a major global health burden with a high incidence rate. Despite its well-established role in genome stability and cell cycle regulation, its specific anti-tumor mechanism involving the induction of a senescence-like state remains unclear. To determine whether Mg²⁺ impedes cervical cancer progression through the induction of a senescence-like phenotype via the ATM/CHK2/p21 pathway, HeLa cells were used in this study. Cell proliferation, migration, and invasion were measured using CCK-8, EdU, wound-healing, and Transwell assays, while SA-β-gal staining and western blotting served to examine both senescence-related markers and pathway protein expression. A BALB/c nude mouse xenograft model was established to evaluate tumor growth and safety following intratumoral Mg²⁺ injection. The results showed that Mg²⁺ inhibited proliferation, migration, and invasion in a concentration-dependent manner. Treatment with 20 mM Mg²⁺ increased SA-β-gal positivity, decreased Lamin B1 expression, and activated the ATM/CHK2/p21 pathway; moreover, this upregulation of p21 was reversed by an ATM inhibitor. ELISA revealed that 10 mM Mg²⁺ enhanced IL-6 and TNF-α secretion, confirming effective induction of the senescence-associated secretory phenotype, while higher concentrations diminished this effect, which may be partly attributed to the reduction in cell viability. In vivo experiments showed that Mg²⁺ inhibited tumor growth without notable alterations in body weight, liver and kidney function, or serum magnesium levels. In summary, the localized high concentration of magnesium ions induces cells to enter a senescence-like state via the ATM/CHK2/p21 pathway, thereby selectively suppressing malignant cellular behaviors. Notably, its in vivo efficacy and safety profile in vivo are favorable. It is also worth noting that these findings should be interpreted within the context of a preclinical, high-dose local Mg²⁺ model. Full article

Solid–liquid transport pumps are widely used in slurry conveying, deep-sea mining, and sediment-laden water delivery, where suspended particles substantially modify internal flow behavior, energy transfer, and operational stability. This review systematically summarizes recent progress on flow evolution and stability issues in centrifugal pumps and related particle-laden pump systems. The fundamental mechanisms of particle dynamics are first discussed, including single-particle transport and force response, particle collision and agglomeration, turbulence modulation by particle assemblies, and wake-induced local disturbances. On this basis, the review further examines particle-induced changes in global flow topology, local separation and backflow, leakage shear layers, and the evolution of representative vortex structures, with particular attention to the enhancement of flow unsteadiness. In addition, the influences of particle size, concentration, density, and shape on hydraulic performance, wear failure, and operational reliability are summarized, together with recent advances in stability evaluation and fault diagnosis. Although substantial progress has been achieved, current studies still show limitations in cross-scale correlation, unified mechanism interpretation, and life-cycle coupled analysis. This review provides a useful reference for understanding solid–liquid two-phase flow mechanisms and for improving anti-wear design and stable operation control of transport pumps. Full article

This paper presents a novel algebraic saturation-based Proportional–Integral–Derivative (ASB-PID) controller for achieving ultra-fast and well-damped dynamic response in automatic voltage regulator (AVR) systems. The proposed controller incorporates an algebraic saturation-based nonlinear transformation applied to both the error signal and its derivative, enabling adaptive control sensitivity across different operating regions. This formulation preserves high sensitivity near the equilibrium point while effectively limiting excessive control action under large transient deviations, thereby overcoming the inherent trade-off between response speed and overshoot observed in conventional PID-based controllers. To address the highly nonlinear and multimodal tuning problem, the controller parameters are optimally determined using the Animated Oat Optimization Algorithm (AOOA), which provides strong global exploration capability and stable convergence behavior. The effectiveness of AOOA is first validated through comparative analysis with widely used metaheuristic algorithms, including Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA). Furthermore, the proposed controller is benchmarked against recently developed high-performance AVR control strategies, including Gudermannian-PID (G-PID), fractional-order PID (FOPID), and higher-order PID-based controllers. Simulation results demonstrate that the proposed AOOA-optimized ASB-PID controller achieves a rise time of 0.0215 s and a settling time of 0.0383 s with zero overshoot and negligible steady-state error, significantly outperforming both competing optimization algorithms and state-of-the-art control designs. Comprehensive benchmarking further confirms that the proposed method consistently delivers superior performance in terms of speed, stability, and robustness, indicating that it provides an effective, computationally efficient, and scalable solution for high-performance AVR systems and broader nonlinear control applications. Unlike conventional nonlinear PID designs based on hyperbolic or sigmoid mappings, the proposed algebraic formulation provides a more explicit and effective saturation mechanism, enabling a superior balance between transient speed and overshoot suppression without increasing controller complexity. Full article

Hydrocarbon fuels are a vital component of the global energy supply, owing to their excellent energy density and high burnability. It has been demonstrated that the addition of atomically precise cluster materials to hydrocarbon fuels as additives is a promising approach to achieve breakthroughs in improving their combustion performance. Though cluster materials show great potential in boosting combustion performance, their large-scale synthesis, insufficient thermochemical stability, agglomeration and deactivation have constrained their practical applications. Hence, researchers have adopted strategies such as ligand-engineered stabilization, carrier-confined encapsulation, in situ synthesis and surface functionalization to enhance their stability and dispersion in complex combustion environments. Meanwhile, studies on the compatibility of cluster materials with hydrocarbon fuels have also played a crucial role in evaluating the engineering feasibility of cluster materials, including their dissolution and dispersion behavior, interfacial interactions, and long-term storage stability. With regard to performance enhancement, it has been demonstrated through numerous studies that the addition of clusters can have a massive impact on combustion efficiency, thermal stability and ignition performance. This article reviews the ways cluster materials can improve combustion performance via molecular design and synergistic effects, extending the existing research. Full article

A within-host HIV dynamics model incorporating latent reservoirs, distributed time delays, and a B-cell-mediated humoral immune response is developed and analyzed mathematically. The model includes five compartments: uninfected CD4⁺ T cells, latently infected cells, actively infected cells, free virions, and B cells. Four distinct distributed delays are introduced to account for the periods between viral entry and the emergence of latently or actively infected cells, reactivation of latently infected cells, and intracellular virion production. For the non-delayed system, the basic reproduction number ${\mathcal{R}}_0$ is derived using the next-generation matrix method. Using Lyapunov functions and LaSalle’s Invariance Principle, a sharp threshold dynamic is proven: the infection-free equilibrium is globally asymptotically stable (GAS) when ${\mathcal{R}}_0\le 1$, whereas a unique endemic equilibrium is GAS when ${\mathcal{R}}_0>1$. For the full distributed-delay system, a delay-dependent reproduction number ${\mathcal{R}}_0^d$ is defined. The global asymptotic stability of the infection-free equilibrium is established for ${\mathcal{R}}_0^d\le 1$, and the global asymptotic stability of the endemic equilibrium is established for ${\mathcal{R}}_0^d>1$, using suitably constructed Lyapunov functionals that account for the delay history. Numerical simulations validate the analytical threshold behavior. A sensitivity analysis of ${\mathcal{R}}_0^d$ identifies the most influential parameters for potential intervention. A treatment-dependent reproduction number is derived, and the critical drug efficacy required for viral eradication is determined. The intracellular production delay is shown to act as a critical threshold for infection clearance. Full article

With the continuous expansion of the expressway network and the rapid growth of traffic demand, traditional single-source traffic detection data is limited in spatial–temporal coverage and accuracy, which can hardly support the refined operation and management of intelligent expressways. Existing data preprocessing methods often fail to fully capture global spatiotemporal features, and traditional PSO-BP neural networks are prone to local optima. To address these issues, this study investigates multi-source traffic data fusion using ETC-DSRC and RTMS microwave data from the Jiangsu section of the G50 Shanghai-Chongqing Expressway. The HaLRTC tensor completion algorithm is adopted to repair missing and abnormal data, fully mining the spatial–temporal correlation characteristics of traffic flow. The beetle antennae search (BAS) mechanism is introduced into the particle swarm optimization (PSO) process to improve particle search behavior and population diversity. On this basis, a BAS-optimized PSO-BP neural network, referred to as BSO-BP in this study, is constructed for multi-source traffic data fusion. In this model, the improved PSO algorithm is used to optimize the initial weights and thresholds of the backpropagation (BP) neural network, thereby improving the global search capability and convergence stability of the fusion model. Taking the average road speed as the fusion target, MAE, RMSE and MAPE are used for accuracy verification. The results show that the proposed model has significantly higher accuracy than single-source data methods and BP, PSO-BP, and GA-PSO-BP models, and can reflect the real traffic state of road sections more accurately. Full article

Against the backdrop of worldwide sustainability and low-carbon development, carbon emission trading prices serve as an important signal for carbon reduction and green economic regulation. However, they are influenced by quota policies, energy markets, and macroeconomics, and exhibit pronounced non-stationary, high-noise, and nonlinear dynamics that challenge traditional forecasting models. This research aims to improve carbon price prediction accuracy by proposing a hybrid ICEEMDAN-CNN-LSTM model. The Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) method adaptively decomposes the original carbon price series, suppressing mode aliasing and noise interference, and producing stable Intrinsic Mode Function (IMF) components; each IMF is then processed by CNN-LSTM, where the Convolutional Neural Network (CNN) extracts local features and the Long Short-Term Memory (LSTM) captures long short-term dependencies, with the final results obtained by linear combination. This research uses historical closing prices of the Hubei carbon emission trading market with multiple economic indicators as inputs. Model performance is evaluated against LSTM and CNN-LSTM benchmarks. The results show that the proposed model significantly outperforms benchmarks, achieving a test-set MAE of 1.140 yuan, representing reductions of 59.1% and 65.2% compared to LSTM and CNN-LSTM, respectively, and the RMSE is reduced by 57.2% and 62.9%, respectively. At the same time, the proposed model maintains strong robustness under different data splitting ratios. Through the “decomposition–extraction–fitting” framework, the proposed model effectively handles complex carbon price dynamics, offering a reliable forecasting tool that helps stabilize carbon markets, guide emission–reduction behaviors, and advance global sustainability and low-carbon transition. Full article

Drought stress during early growth stages severely limits wheat productivity globally. Understanding varietal physiological responses to drought stress is critical for breeding climate-resilient cultivars. Two-week-old plants from two winter wheat (Triticum aestivum L.) cultivars—Katya (drought-tolerant) and Zora (drought-sensitive)—were subjected to drought for seven days, followed by rehydration. The experiments were conducted in pots in controlled conditions. The photosystem II (PSII) function was evaluated using chlorophyll a fluorescence (OJIP transients), thermoluminescence emissions and pigment content analysis. Under drought, Katya maintained functional PSII integrity with stable quantum efficiency and increased chlorophyll content, while Zora exhibited chlorophyll degradation. Fresh and dry weight declined in both genotypes but significantly only in Zora; recovery occurred after rehydration. Chlorophyll fluorescence revealed that varietal divergence was localized to the O–J phase of PSII photochemistry, indicating differences in reaction-center behavior confirmed by thermoluminescence. Katya demonstrated preserved PSII reaction-center density, balanced energy partitioning, homogeneous PSII populations, and superior recovery capacity. Conversely, Zora showed reaction-center depletion, elevated energy dissipation, impaired electron transport beyond Q_A, and persistent PSII heterogeneity even after rehydration. Drought tolerance in the studied genotypes was associated with the maintenance of PSII structural integrity, efficient photochemical function, and rapid recovery mechanisms. These physiological markers—particularly early PSII photochemistry kinetics and reaction-center stability—provide valuable selection criteria for breeding programs, targeting drought resilience under changing climate conditions. Full article

Background: Non-small cell lung cancer (NSCLC), a malignant tumor with high global incidence and mortality rates, urgently requires more effective targeted drug delivery systems for its treatment. As an EGFR tyrosine kinase inhibitor, gefitinib has its clinical efficacy limited by poor solubility and low bioavailability. This study aimed to develop a gefitinib–salicylic acid salt (Gef-Sa) and its nano-formulation (Gef-Sa-NPs) via a combined strategy of crystal engineering and nanotechnology to improve its pharmaceutical properties. Methods: Gef-Sa was prepared using a suspension method, and its salt formation and thermal stability were predicted by the ΔpKa rule and confirmed by various solid-state characterization techniques, including single crystal/powder X-ray diffraction, thermal analysis, and infrared spectroscopy. Gef-Sa-NPs were prepared via an ultrasound-assisted anti-solvent precipitation method. Their performance was evaluated through in vitro dissolution tests, pharmacokinetic studies, and in vitro antitumor experiments. Results: Gef-Sa-NPs with a particle size of 31 nm (PDI = 0.15) were successfully prepared. In vitro dissolution tests demonstrated that the nano-formulation exhibited a significantly higher dissolution rate in pH 1.2, pH 4.5, pH 6.8 and pure water when compared with the raw drug (p < 0.01). Pharmacokinetic studies revealed that Gef-Sa and Gef-Sa-NPs increased the oral bioavailability in rats to 1.5-fold and 1.9-fold that of the raw drug, respectively. In vitro antitumor experiments confirmed that the Gef-Sa-NPs increased the inhibition rate against A549 cells compared with the Gef. Conclusions: This study innovatively combines salt formation and nanonization technologies to systematically address the key issue of the poor solubility of Gef. The resulting nano-formulation demonstrates excellent dissolution characteristics, pharmacokinetic behavior, and antitumor efficacy. This strategy not only provides a novel drug delivery system with translational potential for NSCLC treatment but also offers a paradigm for the formulation design of poorly soluble drugs. Subsequent research will focus on scaling up production and evaluating pre-clinical safety. Full article

Large-scale renewable energy bases increasingly employ automatic voltage control (AVC) to coordinate heterogeneous reactive-power resources. The resulting voltage regulation process inherently involves sampling, communication delay, and nonlinear device characteristics, which may induce nontraditional voltage oscillations and stability degradation that cannot be adequately captured by conventional continuous-time or small-signal analysis. This paper proposes a discrete-time nonlinear voltage stability analysis framework for renewable energy bases with multi-reactive-power-resource coupling under AVC-based coordinated control. The voltage regulation dynamics are formulated as a discrete-time nonlinear closed-loop system by incorporating sampled AVC actions, delayed voltage feedback, and nonlinear voltage–reactive-power coupling. An incremental system representation is constructed, and a strong-contraction-based stability criterion is derived using sector-bounded nonlinearity descriptions and linear matrix inequalities, providing a sufficient condition for global voltage convergence without local linearization. Extensive numerical studies are conducted on a representative renewable energy base with parallel and series coupling topologies. A total of 2916 randomized configurations are evaluated. The proposed criterion achieves consistency rates exceeding 96% for the parallel topology and 99% for the series topology when compared with time-domain simulations, while the probability of dangerous misjudgment remains below 1%. Scenario-based simulations further demonstrate that coupling topology plays a critical role in shaping voltage stability behaviors, and state-space analysis further supports the observed stability behaviors. These results indicate that nonlinear strong contraction offers an effective and practical stability notion for AVC-based voltage regulation in renewable energy bases. Full article

Research on dishonest behavior within organizational contexts has expanded rapidly in recent years. However, the structural organization of dominant explanatory mechanisms within this literature remains insufficiently clarified. This study provides a theory-informed bibliometric analysis focusing on a deliberately selective segment of the workplace dishonesty literature. Rather than attempting an exhaustive census, the study maps a corpus centered on dominant socio-cognitive and organizational explanatory frameworks in order to examine how these mechanisms are positioned, interconnected, and evolving within this theory-filtered segment. To ensure a transparent and reproducible review process, the study was conducted in accordance with the PRISMA 2020 guidelines, which guided the identification, screening, and eligibility assessment of the literature. Drawing on a systematically constructed corpus retrieved from Web of Science and Scopus and covering the period 1989–2025, the bibliometric analysis was conducted using Biblioshiny 4.5.2 on a final dataset of 679 documents. The analysis integrates performance indicators with science-mapping techniques, including keyword co-occurrence networks, thematic mapping, multiple correspondence analysis, thematic evolution, and global citation analysis. The findings indicate that this theory-based subset of the literature has developed steadily over time alongside a clearer structuring of publication outlets. Conceptually, it remains largely organized around a small number of recurring mechanisms, most notably ethical climate and moral disengagement. Thematic analyses suggest a degree of theoretical stabilization alongside diversification within this selected corpus, while factorial mapping suggests recurring contrasts between cognitive, normative, and organizational explanatory logics. From a longitudinal dynamic perspective, the mapped patterns suggest a possible movement toward more context-sensitive and governance-oriented perspectives; however, this should be interpreted as an inferential reading of this selected corpus. Overall, the findings suggest that, within this corpus, unethical workplace behavior is increasingly conceptualized as a context-dependent socio-cognitive phenomenon shaped by justificatory mechanisms, organizational environments, and performance-related pressures. This review contributes to the fields of behavioral ethics and organizational behavior by providing a structured reading of this specific body of work, clarifying its conceptual organization, identifying its main developmental trajectories, and outlining a theoretically grounded future research agenda for this selected body of literature. Full article

Against the background of the global “dual carbon” strategic goal, low-carbon upgrading of road engineering and efficient recycling of waste plastics have become critical approaches to relieve the shortage of natural aggregates and control plastic pollution. Most existing studies only focus on the optimization of single mechanical indicators, while lacking collaborative analysis of mechanical performances and carbon reduction benefits, meaning they cannot provide sufficient scientific support for the design of low-carbon and sustainable road materials. In this study, recycled plastic aggregate (PA) was used to partially replace natural coarse aggregate, and its influence on the mechanical characteristics of cement-stabilized macadam (CSM) was systematically investigated. Combined with life cycle assessment (LCA), the carbon emission reduction potential was quantitatively evaluated, aiming to improve the toughness of road base materials and promote low-carbon sustainable development. The results demonstrate that when the PA content increases from 0% to 20%, the mechanical strength of CSM gradually decreases, while the toughness presents a steady upward trend, and the maximum carbon emission reduction rate reaches 50.8%. The optimal toughness improvement of 28.39% is obtained at the PA content of 16%. This study clarifies the internal correlation between mechanical behaviors and low-carbon benefits of recycled plastic aggregate, provides reliable technical support for the high-value utilization of waste plastics and the optimization of sustainable road materials, and offers important references for the green and low-carbon transformation of transportation infrastructure. Full article

Wireless Sensor Networks (WSNs) enable energy-efficient data collection in dynamic environments but continue to face the dual challenges of severely constrained node energy and the spatiotemporal heterogeneity of data traffic. Inspired by honeybee foraging behavior, this paper proposes a hybrid optimization framework that integrates mixed-integer linear programming (MILP) and Markov decision processes (MDP), utilizing Q-learning for adaptive decision-making. The proposed framework systematically maps the dual-layer decision-making mechanism of honeybee foraging onto a synergistic architecture combining MILP-based global planning and MDP-based local adaptation, offering a novel bio-inspired solution for mobile sink trajectory planning and adaptive routing. Specifically, the upper-level MILP module simulates a colony-level global assessment of distant nectar sources, generating an initial global trajectory by determining the optimal access sequence of cluster heads to minimize the movement cost of the mobile sink. The lower-level Q-learning module simulates the individual-level local adaptation, where bees adjust harvesting behavior in real-time based on nectar quality and distance. This module continuously optimizes routing parameters based on real-time network states, including residual energy, the ratio of surviving nodes, data queue lengths, and cluster head density. The algorithm employs an $ϵ$-greedy strategy to balance exploration and exploitation, while a periodic decision-update mechanism is introduced to harmonize computational efficiency with learning stability. Furthermore, a multi-objective reward function is designed to jointly optimize energy efficiency, network lifetime, end-to-end latency, and path length. Extensive simulation results demonstrate that the proposed MILP-MDP hybrid framework significantly outperforms several representative baseline algorithms in terms of network lifetime extension and energy balance. These findings validate that the integration of bio-inspired foraging strategies and reinforcement learning provides an efficient and robust solution for trajectory planning and adaptive routing in dynamic WSNs. Full article