Search Results (5,987)

Cancer has remained a major global health challenge, with around 20 million new cases and 9.7 million fatalities recorded each year. Even though there has been recent progress in therapies such as radiotherapy, chemotherapy, immunotherapy, and gene therapy, cancer remains a major treatment challenge due to late diagnosis and difficulties in therapeutic effectiveness. Flavonoids, a substantial category of naturally occurring polyphenols, have received considerable interest in recent years for their potential involvement in cancer management and prevention, especially concerning breast cancer. These bioactive compounds, abundant in vegetables, fruits, and herbs, exhibit various therapeutic actions, including antioxidant, anti-inflammatory, and antimutagenic effects. The advanced therapeutic potential of flavonoids, when combined with FDA-approved medicines, offers synergistic effects and enhanced clinical results. Additionally, flavonoid-loaded nano-formulations, involving co-delivery systems, are being explored to increase solubility, stability, and bioavailability, enabling targeted delivery to cancer cells while reducing off-target adverse effects. This review examines the role of flavonoids in the prevention and management of breast cancer, focusing on their dietary sources, metabolism, and pharmacokinetic properties. Furthermore, we explore novel strategies, such as combination therapies with FDA-approved drugs and the application of flavonoid-based nanoformulations, which have the potential to enhance therapeutic outcomes. The clinical application of these strategies has the potential to improve breast cancer treatment and create new opportunities for the advancement of flavonoid-based therapies. Full article

Background: Nurse-manager competencies shape workforce stability, yet role-based perception gaps between managers and staff may influence staff nurses’ turnover cognitions. Objectives: To (1) compare nurse managers’ self-ratings with staff nurses’ ratings of the same managers on the Nurse Manager Competency Inventory (NMCI); (2) compare both groups’ perceptions of staff nurses’ turnover intention (EMTIS); (3) examine domain-specific links between perceived competencies and perceived turnover intention; and (4) explore demographic influences (age, education, experience) on these perceptions. Methods: Cross-sectional dual-rater study with 225 staff nurses and 171 nurse managers in two tertiary hospitals in Saudi Arabia. Data were collected from August to November 2024. Managers completed NMCI self-ratings, and staff nurses rated their managers on the same NMCI domains; both groups rated staff nurses’ turnover intention using EMTIS. Between-group differences were tested with one-way ANOVA (two-tailed α = 0.05), and associations were examined with Pearson’s r (95% CIs). Findings: Managers consistently rated themselves higher than staff rated them across all nine NMCI domains; the largest descriptive gaps were in Promoting Staff Retention, Recruit Staff, Perform Supervisory Responsibilities, and Facilitate Staff Development (e.g., overall NMCI: managers M = 3.67, SD = 0.61 vs. staff M = 3.04, SD = 0.74; F = 0.114, p = 0.73)with comparatively smaller divergence for Ensure Patient Safety and Quality. Managers and staff did not differ significantly on EMTIS (overall EMTIS: managers M = 3.16, SD = 1.28 vs. staff M = 3.00, SD = 1.15; F = 21.32, p = 0.173). Specific competency domains—retention, supervision, staff development, safety/quality leadership, and quality improvement—showed small inverse correlations with EMTIS facets (typical r ≈ −0.11 to −0.19; p < 0.05), whereas the global NMCI–overall EMTIS correlation was non-significant (r = −0.077, p = 0.124). Effect sizes were modest and should be interpreted cautiously. Conclusions: Actionable signals reside at the domain (micro-competency) level rather than in global leadership composites. Targeted, continuous, unit-embedded development in human- and development-focused competencies—tracked with dual-lens (manager–staff) measurement and linked to retention KPIs—may help nudge turnover cognitions downward. Key limitations include the cross-sectional, perception-based design and two-site setting. Findings nonetheless align with international workforce challenges and may be transferable to similar hospital contexts. Full article

Radiometric consistency across satellite platforms is fundamental to producing high-quality Climate Data Records (CDRs). Because different cross-calibration methods have distinct advantages and limitations, comparative evaluation is necessary to ensure record accuracy. This study presents a comparative assessment of two widely applied calibration approaches—Simultaneous Nadir Overpass (SNO) and Double Difference (DD)—for the thermal infrared (TIR) bands of FY-3D MERSI. MODIS/Aqua serves as the reference sensor, while radiative transfer simulations driven by ERA5 inputs are generated with the Advanced Radiative Transfer Modeling System (ARMS) to support the analysis. The results show that SNO performs effectively when matchup samples are sufficiently large and globally representative but is less applicable under sparse temporal sampling or orbital drift. In contrast, the DD method consistently achieves higher calibration accuracy for MERSI Bands 24 and 25 under clear-sky conditions. It reduces mean biases from ~−0.5 K to within ±0.1 K and lowers RMSE from ~0.6 K to 0.3–0.4 K during 2021–2022. Under cloudy conditions, DD tends to overcorrect because coefficients derived from clear-sky simulations are not directly transferable to cloud-covered scenes, whereas SNO remains more stable though less precise. Overall, the results suggest that the two methods exhibit complementary strengths, with DD being preferable for high-accuracy calibration in clear-sky scenarios and SNO offering greater stability across variable atmospheric conditions. Future work will validate both methods under varied surface and atmospheric conditions and extend their use to additional sensors and spectral bands. Full article

Cancer, a global health burden, is characterized by uncontrolled cell growth and metastasis, often resulting in debilitating treatments and mortality. While conventional therapeutic strategies have improved survival rates, they are limited by challenges such as off-target toxicity and drug resistance. With their design to enable targeted drug delivery, nanoparticles have presented a promising avenue to overcome these limitations. Protein-based nanoparticles, particularly those based on albumin, are notable for their biocompatibility, stability, and ease of modification. The approval of Abraxane, an albumin-based nanoparticle formulation of paclitaxel, for metastatic breast cancer marked a significant milestone. However, further approvals have been slow to materialize until the recent approval of Fyarro^® in 2021. This focused review highlights the potential of albumin-based nanoparticles, emphasizing their advantages, current state, and progress in clinical use as anticancer therapeutics. We also discuss challenges impeding new approvals and future directions for unlocking the full potential of this technology. Full article

As water pollution from dyes, pharmaceuticals, heavy metals, and other emerging contaminants continues to rise at an alarming rate, ensuring access to clean and safe water has become a pressing global challenge. Conventional water treatment methods, though widely used, often fall short in effectively addressing these complex pollutants. In response, researchers have turned to Advanced Functional Membranes (AFMs) as promising alternatives, owing to their customizable structures and enhanced performance. Among the most explored AFMs are those based on metal–organic frameworks (MOFs), carbon nanotubes (CNTs), and electro–catalytic systems, each offering unique advantages such as high permeability, selective pollutant removal, and compatibility with advanced oxidation processes (AOPs). Notably, hybrid systems combining AFMs with electrochemical or photocatalytic technologies have demonstrated remarkable efficiency in laboratory settings. However, translating these successes to real-world applications remains a challenge due to issues related to cost, scalability, and long-term stability. This review explores the recent progress in AFM development, particularly MOF-based, CNT-based, and electro-Fenton (EF)-based membranes, highlighting their material aspects, pollutant filtration mechanisms, benefits, and limitations. It also offers insights into how these next-generation materials can contribute to more sustainable, practical, and economically viable water purification solutions in the near future. Full article

The rapid advancement of Artificial Intelligence (AI) technologies is driving a profound transformation in higher education, shifting traditional learning toward digital, remote, and AI-mediated environments. This shift—accelerated by the COVID-19 pandemic—has made computer-supported collaborative learning (CSCL) a central pedagogical model for engaging students in virtual, interactive, and peer-based learning. However, while these environments enhance access and flexibility, they also introduce new emotional, social, and intercultural challenges that students must navigate without the benefit of face-to-face interaction. In this evolving context, Social and Emotional Learning (SEL) has become increasingly essential—not only for supporting student well-being but also for fostering the self-efficacy, adaptability, and interpersonal competencies required for success in AI-enhanced academic settings. Despite its importance, the role of SEL in higher education—particularly within CSCL frameworks—remains underexplored. This study investigates how SEL, and specifically cultural empathy, influences students’ learning experiences in multicultural CSCL environments. Grounded in Bandura’s social cognitive theory and Allport’s Contact Theory, this study builds on theoretical insights that position emotional stability, social competence, and cultural empathy as critical SEL dimensions for promoting equity, collaboration, and effective participation in diverse, AI-supported learning settings. A quantitative study was conducted with 258 bachelor’s and master’s students on a multicultural campus. Using the Multicultural Social and Emotional Learning (SEL CASTLE) model, the research examined the relationships among SEL competencies and self-efficacy in CSCL environments. Findings reveal that cultural empathy plays a mediating role between emotional and social competencies and academic self-efficacy, emphasizing its importance in enhancing collaborative learning experiences within AI-driven environments. The results highlight the urgent need to cultivate cultural empathy to support inclusive, effective digital learning across diverse educational settings. This study contributes to the fields of intercultural education and digital pedagogy by presenting the SEL CASTLE model and demonstrating the significance of integrating SEL into AI-supported collaborative learning. Strengthening these competencies is essential for preparing students to thrive in a globally interconnected academic and professional landscape. Full article

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article

Accurate and reliable vehicle localization is essential for autonomous driving in complex outdoor environments. Traditional feature-based visual–inertial odometry (VIO) suffers from sparse features and sensitivity to illumination, limiting robustness in outdoor scenes. Deep learning-based optical flow offers dense and illumination-robust motion cues. However, existing methods rely on simple bidirectional consistency checks that yield unreliable flow in low-texture or ambiguous regions. Global navigation satellite system (GNSS) measurements can complement VIO, but often degrade in urban areas due to multipath interference. This paper proposes a multi-sensor fusion system that integrates monocular VIO with GNSS measurements to achieve robust and drift-free localization. The proposed approach employs a hybrid VIO framework that utilizes a deep learning-based optical flow network, with an enhanced consistency constraint that incorporates local structure and motion coherence to extract robust flow measurements. The extracted optical flow serves as visual measurements, which are then fused with inertial measurements to improve localization accuracy. GNSS updates further enhance global localization stability by mitigating long-term drift. The proposed method is evaluated on the publicly available KITTI dataset. Extensive experiments demonstrate its superior localization performance compared to previous similar methods. The results show that the filter-based multi-sensor fusion framework with optical flow refined by the enhanced consistency constraint ensures accurate and reliable localization in large-scale outdoor environments. Full article

This paper’s goal was to determine the state of green financing and reveal the main aspects of its regulation and influence on environmental risk management in the conditions of the growth of global uncertainty. Based on the sample that contains the top 10 countries of the world with a higher level of green economic capabilities in 2024, by the assessment for developed and developing countries in isolation, we performed regression analysis of the following: (1) Dependence of environmental costs of GDP on the volume of green investments; (2) Dependence of the volume of green investments on the application of the measures of state regulation of green finance. As a result, we proved that in developed countries, the growth of the activity of green investing in the economy leads to a reduction in the environmental costs of GDP, and in developing countries, an increase in the environmental costs of GDP. Unlike developed countries, in which green investments are not determined by the influence of the factors of state regulation, the implementation of the measures of state regulation of green finance in developing countries ensures the inflow of green investments into the economy. This paper’s novelty, compared to the existing literature, is that it discloses previously unknown differences in the character of the influence of the factors of state regulation of green finance on green investments in the economy and differences in the consequences of the activity of investing for environmental risks in different categories of countries (in particular, differences between developed and developing countries) and at different phases of the economic cycle (in the conditions of relative stability and in the conditions of global instability). The established regularities of the development of green finance under the influence of state regulation measures in developed and developing countries will raise the precision of forecasting and planning of this development in support of green economic growth and decarbonization. The revealed differences between developed and developing countries will allow forming a strategy of development of green finance in each category of countries, given their specifics, and thus, achieving the growth of these strategies’ effectiveness. The proposed policy implications for the reduction in environmental risks through the improvement of state regulation of green finance in developed and developing countries, given their revealed specifics, have practical significance. Full article

Highly Hazardous Pesticides (HHPs) pose severe risks to human health and the environment, making it essential to understand their molecular stability and degradation pathways. In this study, the Quantum Theory of Atoms in Molecules (QTAIM) was applied to four representative organophosphate pesticides, allowing the identification of electronically weak bonds as intrinsic sites of lability. These findings are consistent with reported hydrolytic, oxidative, enzymatic, and microbial degradation routes. Importantly, QTAIM descriptors proved largely insensitive to solvation, confirming their intrinsic character within the molecular electronic structure. To complement QTAIM, conceptual DFT (Density Functional Theory) reactivity indices were analyzed, revealing that solvent effects induce more noticeable variations in global and local descriptors than in topological parameters. In addition, a Topological Analysis of the Fukui Function (TAFF) was performed, which mapped nucleophilic, electrophilic, and radical susceptibilities directly onto QTAIM basins. The TAFF analysis confirmed that bonds identified as weak by QTAIM (notably P–O, P–S, and P–N linkages) also coincide with the most reactive sites, thereby reinforcing their mechanistic role in degradation pathways. This integrated framework highlights the robustness of QTAIM, the sensitivity of global and local reactivity descriptors to solvation revealed by conceptual DFT, and the complementary insights provided by TAFF, contributing to risk assessment, remediation strategies, and the rational design of safer pesticides. Full article

In field exploration, disaster rescue, and complex terrain operations, the accuracy of ground type recognition directly affects the walking stability and task execution efficiency of legged robots. To address the problem of terrain recognition in complex ground environments, this paper proposes a high-precision classification method based on single-leg triaxial force signals. The method first employs a one-dimensional convolutional neural network (1D-CNN) module to extract local temporal features, then introduces a long short-term memory (LSTM) network to model long-term and short-term dependencies during ground contact, and incorporates a convolutional block attention module (CBAM) to adaptively enhance the feature responses of critical channels and time steps, thereby improving discriminative capability. In addition, an improved whale optimization algorithm (iBWOA) is adopted to automatically perform global search and optimization of key hyperparameters, including the number of convolution kernels, the number of LSTM units, and the dropout rate, to achieve the optimal training configuration. Experimental results demonstrate that the proposed method achieves excellent classification performance on five typical ground types—grass, cement, gravel, soil, and sand—under varying slope and force conditions, with an overall classification accuracy of 96.94%. Notably, it maintains high recognition accuracy even between ground types with similar contact mechanical properties, such as soil vs. grass and gravel vs. sand. This study provides a reliable perception foundation and technical support for terrain-adaptive control and motion strategy optimization of legged robots in real-world environments. Full article

With the rapid development of renewable energy and the continuous pursuit of efficient energy utilization, distributed photovoltaic power generation has been widely used in village-level microgrids. As a key platform connecting distributed photovoltaics with users, energy storage systems play an important role in alleviating the imbalance between supply and demand in VMG. However, current energy storage systems rely heavily on lithium batteries, and their frequent charging and discharging processes lead to rapid lifespan decay. To solve this problem, this study proposes a hybrid energy storage system combining supercapacitors and lithium batteries for VMG, and designs a hybrid energy storage scheduling strategy to coordinate the “source–load–storage” resources in the microgrid, effectively cope with power supply fluctuations and slow down the life degradation of lithium batteries. In order to give full play to the active support ability of supercapacitors in suppressing grid voltage and frequency fluctuations, the scheduling optimization goal is set to maximize the sum of the virtual inertia time constants of the supercapacitor. In addition, in order to efficiently solve the high-complexity model, the reason for choosing the snow goose algorithm is that compared with the traditional mathematical programming methods, which are difficult to deal with large-scale uncertain systems, particle swarm optimization, and other meta-heuristic algorithms have insufficient convergence stability in complex nonlinear problems, SGA can balance global exploration and local development capabilities by simulating the migration behavior of snow geese. By improving the convergence effect of SGA and constructing a multi-objective SGA, the effectiveness of the new algorithm, strategy and model is finally verified through three cases, and the loss is reduced by 58.09%, VMG carbon emissions are reduced by 45.56%, and the loss of lithium battery is reduced by 40.49% after active support optimization, and the virtual energy inertia obtained by VMG from supercapacitors during the scheduling cycle reaches a total of 0.1931 s. Full article

Tourist loyalty is vital for destination success, fostering repeat visits and positive word-of-mouth. This study explores the psychological and safety-related factors driving tourist loyalty to natural attractions in Oman, a rising destination known for its stability and safety. Using Social Cognitive Theory as a foundation, the research incorporates perceived risk and novelty seeking as key moderating variables. Data were collected via an online survey of 165 international tourists and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings show that attachment, satisfaction, and novelty seeking significantly affect both attitudinal and behavioral loyalty. While perceived value strongly influences behavioral loyalty, its impact on attitudinal loyalty appears more complex, suggesting possible unobserved mediators. Additionally, risk perception and novelty seeking moderate the link between destination familiarity and loyalty, underscoring the role of tourists’ internal evaluations of safety and desire for new experiences. This study advances the limited literature on tourist loyalty in developing countries by integrating psychological and risk-related dimensions. It offers actionable insights for tourism planners and marketers in Oman: emphasizing the country’s safety reputation, improving satisfaction levels, and crafting experiences that blend familiarity with novelty can enhance tourist loyalty and ensure sustained competitiveness in the global tourism market. Full article

The global transition toward renewable energy sources has intensified in response to escalating environmental challenges. Nevertheless, the inherent intermittency and instability of renewable energy necessitate the development of reliable energy storage technologies. Supercapacitors are particularly notable for their high specific capacitance, rapid charge and discharge capability, and exceptional cycling stability. Concurrently, the increasing demand for efficient and sustainable energy storage systems has stimulated interest in multifunctional electrode materials that integrate electrocatalytic activity with electrochemical energy storage. Two-dimensional transition metal dichalcogenides (TMDs), owing to their distinctive layered structures, large surface areas, phase state, energy band structure, and intrinsic electrocatalytic properties, have emerged as promising candidates to achieve dual functionality in electrocatalysis and electrochemical energy storage for asymmetric supercapacitors (ASCs). Specifically, their unique electronic properties and catalytic characteristics promote reversible Faradaic reactions and accelerate charge transfer kinetics, thus markedly enhancing charge storage efficiency and energy density. This review highlights recent advances in TMD-based multifunctional electrodes. It elucidates mechanistic correlations between intrinsic electronic properties and electrocatalytic reactions that influence charge storage processes, guiding the rational design of high-performance ASC systems. Full article

With the integration of the global economy and the rapid development of port logistics, the port dangerous goods transportation system faces complex risk-coupling problems, and the probability of accidents keeps climbing. However, the existing research on the system risk-coupling mechanism and dynamic control mechanism is still insufficient, and there is an urgent need to construct a scientific risk analysis and control model. This study takes the port dangerous goods transportation system as the object, based on the four-factor framework of “personnel-machine-environment-management,” uses the N-K model to quantify the degree of risk coupling, analyzes the dynamic evolution mechanism of risk under the action of a single factor, and uses Dufferin’s oscillation and bifurcation response equation to reveal the interaction between the system’s internal defenses and the external influences. It is found that the coupled risk value of personnel–machine factors is the highest, and the sudden change in system state is characterized by a sudden jump and lag. The system stability can be significantly improved by enhancing internal damping control and optimizing external excitation regulation. This study provides a quantitative tool for the risk assessment of dangerous goods transportation in ports and theoretical support for the development of the “damping-excitation” synergistic control strategy, which is of great practical significance for the improvement of the port safety management system. Full article