Search Results (1,520)

This study attempts to examine the interconnections between digital transformation, leadership, organizational culture, and organizational sustainability among Greek enterprises in the Industry 4.0 context. A quantitative research design was utilized to attain this objective, employing survey data gathered from 412 managerial-level participants across several industries. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were performed to validate the measurement model, followed by Structural Equation Modeling (SEM) to examine the proposed correlations among the constructs. The findings reveal that digital transformation, leadership, and organizational culture each have a substantial positive influence on organizational sustainability, with digital transformation exhibiting the most pronounced benefit. Furthermore, their alignment has a synergistic effect that amplifies the economic, social, and environmental aspects of sustainability. These findings validate the multifaceted character of sustainability within the Industry 4.0 framework and underscore the interrelation of technological, human, and cultural competencies. The research contributes to the field of theory by offering a comprehensive framework for sustainable organizational transformation and practical implications for managers and policymakers who are in the process of developing strategies that are oriented towards sustainability, innovation, and resilience in digitally evolving environments. Full article

As one of the key components in technetium-labeled radiopharmaceuticals, the quality of the dimercaptosuccinate (DMSA) plays a critical role in determining the safety and efficacy of the final drug product. However, due to its high polarity and susceptibility to oxidation, comprehensive characterization of the impurity profile of DMSA remains challenging. In this study, high-performance liquid chromatography and mass spectrometry were employed to achieve a systematic and thorough analysis of DMSA-related impurities. First, an HPLC-UV method was developed to enable baseline separation of DMSA and its impurities. Subsequently, a two-dimensional liquid chromatography–tandem mass spectrometry (2D-LC-MS/MS) approach was applied to identify six structurally diverse impurities present in DMSA. The developed HPLC method was rigorously validated and demonstrated to be sensitive, robust, and suitable for the accurate quantification and detection of trace impurities. Using the validated method, DMSA raw materials sourced from multiple manufacturers were analyzed, revealing significant variability in their impurity profiles. These findings underscore the importance of stringent quality control measures for DMSA in radiopharmaceutical manufacturing. This work not only establishes a reliable analytical framework for impurity profiling and structural elucidation of DMSA but also provides valuable insights for the development of quality control strategies and process optimization of radiopharmaceuticals. Full article

The impairment of neural crest cells (NCCs) plays a pivotal role in the pathogenesis of fetal alcohol spectrum disorders (FASD). Epigenetic regulators mediate ethanol-induced disruptions in NCC development and represent promising targets for nutritional interventions. Here, we developed a biologically informed machine learning framework to predict nutritional supplements that modulate five key epigenetic regulators (miR-34a, DNMT3a, HDAC, miR-125b, and miR-135a) and mitigate ethanol’s adverse effects on NCCs. The optimized models demonstrated robust predictive performance and identified a number of nutritional supplements that could attenuate ethanol-induced NCC impairment, including resveratrol, vitamin B12, emodin, quercetin, and broccoli sprout-derived compounds. Our optimized models also revealed structural features that are critical for mitigating ethanol-induced NCC impairment through specific epigenetic mechanisms. These findings support predictive modeling as a tool to prioritize nutritional supplements for further investigation and the development of dietary strategies to prevent or reduce the risk of FASD. Full article

Supply chain disruptions during the COVID-19 pandemic exposed structural vulnerabilities of centrally controlled manufacturing systems, motivating renewed interest in organizational resilience within the context of Industry 5.0 human–cyber–physical systems. This study investigates how organizational decision-making paradigms and crisis-aware artificial intelligence (AI) jointly influence performance, crisis response, and recovery. An agent-based modeling (ABM) framework is developed to compare centralized, distributed, and self-organized organizational structures across 650 simulation runs under a controlled supply side disruption. A crisis-aware Q-learning architecture enables AI agents to shift from efficiency-oriented to stability-oriented strategies when resource scarcity is detected. To avoid baseline-dependent bias, resilience is evaluated using an absolute, capacity-normalized metric. Results indicate that self-organized systems consistently outperform centralized and distributed structures in baseline performance, crisis throughput, and recovery speed. The integration of crisis-aware AI further increases absolute resilience by approximately 10.7% and enables substantially higher throughput during disruption compared to hierarchical control. Enhanced performance is primarily driven by adaptive coalition formation, proactive resource conservation, and rapid post-crisis recovery supported by preserved coordination structures. These findings provide quantitative support for Industry 5.0’s human-centric principles and show that decentralized decision-making augmented by context-adaptive AI offers a robust organizational design strategy for volatile manufacturing environments. Full article

This study develops a machine-learning-based framework for multiclass soil classification using Cone Penetration Test (CPT) data, aiming to overcome the limitations of traditional empirical Soil Behavior Type (SBT) charts and improve the automation, continuity, robustness, and reliability of stratigraphic interpretation. A dataset of 340 CPT soundings from 26 sites in Shanghai is compiled, and a sliding-window feature engineering strategy is introduced to transform point measurements into local pattern descriptors. An XGBoost-based multiclass classifier is then constructed using fifteen engineered features, integrating second-order optimization, regularized tree structures, and probability-based decision functions. Results demonstrate that the proposed method achieves strong classification performance across nine soil categories, with an overall classification accuracy of approximately 92.6%, an average F1-score exceeding 0.905, and a mean Average Precision (mAP) of 0.954. The confusion matrix, P–R curves, and prediction probabilities show that soil types with distinctive CPT signatures are classified with near-perfect confidence, whereas transitional clay–silt facies exhibit moderate but geologically consistent misclassification. To evaluate depth-wise prediction reliability, an Accuracy Coverage Rate (ACR) metric is proposed. Analysis of all CPTs reveals a mean ACR of 0.924, and the ACR follows a Weibull distribution. Feature importance analysis indicates that depth-dependent variables and smoothed p_s statistics are the dominant predictors governing soil behavior differentiation. The proposed XGBoost-based framework effectively captures nonlinear CPT–soil relationships, offering a practical and interpretable tool for high-resolution soil classification in subsurface investigations. Full article

For highly maneuverable aircraft, the afterburning engine serves as a core and critical component. Due to the complex structure of the afterburner and the strong coupling among parameters, mechanism-based modeling of afterburning engines remains extremely challenging. To address this problem, this paper proposes a data-driven hybrid algorithm modeling framework for a light-duty afterburning turbojet engine. Using test-bench data from the TWP220L light-duty afterburning turbojet, two hybrid algorithm models were developed: (i) PSO-DNN and (ii) NGO-LSSVM. Four models, DNN, PSO-DNN, LSSVM, and NGO-LSSVM, were compared by mapping engine input parameters (altitude, Mach number, rotor speed, and fuel flow rate) to two key performance outputs (thrust and turbine pressure ratio). Based on visual error analysis and regression evaluation metrics, it was found that the optimized algorithm significantly reduced the prediction error. The NGO-LSSVM model achieved the highest accuracy in both performance indicators, increasing R² by 5.3% for thrust, and increasing R² by 6.8% for turbine pressure ratio. This framework offers a practical and high-precision approach for light-duty afterburning engine performance prediction and lays a foundation for the development of model-based and data-driven onboard control strategies. Full article

Urban green spaces play a vital role in climate change mitigation through carbon sequestration and storage. However, accurately quantifying their carbon sink capability remains challenging due to complex vertical structures and spatial heterogeneity. This study proposes a comprehensive inventory framework integrating multi-source LiDAR (UAV and Backpack) with a phenology-based complementary strategy to quantify carbon dynamics across three nested scales: green space types, plant communities, and species. Two key indicators—Carbon Sequestration Efficiency (CSE) and Carbon Density (CD)—were used to evaluate both the dynamic and static aspects of carbon sink function. The results reveal a clear asynchrony between CSE and CD across scales. No single plant type performed best in both dimensions, indicating a trade-off between growth efficiency and biomass accumulation. Hierarchical clustering identified distinct plant groups with divergent carbon sink strategies, supporting nuanced vegetation selection. The dual-indicator and dual-platform approach proposed in this study advances our existing understanding of the carbon sequestration capacity of urban green spaces and provides a robust methodological foundation for data-driven low-carbon urban ecological planning. Full article

In the digital era, rapid urban growth and the demand for sustainable mobility are placing increasing pressure on transport systems, where congestion, energy consumption, and schedule variability complicate intermodal journey planning. This work proposes an AI-enhanced transfer-graph framework that models each transport mode as an independent subnetwork connected through explicit transfer arcs. This modular structure captures modal interactions while reducing graph complexity, enabling algorithms to operate more efficiently in time-dependent contexts. A Deep Q-Network (DQN) agent is further introduced as an exploratory alternative to exact and meta-heuristic methods for learning adaptive routing strategies. Exact (Dijkstra) and meta-heuristic (ACO, DFS, GA) algorithms were evaluated on synthetic networks reflecting Casablanca’s intermodal structure, achieving coherent routing with favorable computation and memory performance. The results demonstrate the potential of combining transfer-graph decomposition with learning-based components to support scalable intermodal routing. Full article

The intrinsic limitations of Ti₃C₂T_x electrodes, specifically low interfacial charge-transfer efficiency and structural degradation in strongly acidic environments, hinder their performance in high-rate aqueous supercapacitors. Herein, we report a synergistic strategy combining nitrogen plasma surface engineering with a redox-active ascorbic acid electrolyte to optimize the electrode/electrolyte interfacial kinetics. By systematic investigation, the Ti₃C₂T_x supercapacitor obtained by a 10-min plasma duration (N10P-AA) achieved the optimal balance between activating surface sites and preserving the conductive Ti–C framework integrity. The ascorbic acid electrolyte broadened the potential window to approximately 0.7 V, and N10P-AA exhibited the lowest charge-transfer impedance and superior rate capability, retaining a relatively high Coulombic efficiency (>72%) even at a high scan rate of 10,000 mV·s⁻¹. The EIS results and kinetics analysis (b values) confirmed that the moderate plasma activation effectively promoted more surface-dominated charge storage kinetics and mitigated diffusion limitation, consistent with reduced charge-transfer resistance and a smaller Warburg slope. The XPS results revealed that the 10-min treatment suppressed detrimental oxidation during cyclings and facilitated the formation of electrochemically favorable hydroxylated surface functional groups. This work demonstrates a feasible surface electrolyte co-engineering strategy for modulating the interfacial behavior of MXene, which is of great significance for future high-efficiency aqueous electrochemical energy storage and potential biosensing applications. Full article

The increasing generation of biomass-derived waste has accelerated the development of sustainable strategies for its valorization into functional materials. Activated carbon (AC), due to its high surface area, tunable porosity, and chemical versatility, has emerged as a key product for applications in adsorption, catalysis, energy storage, and biosensing, among others. Recent studies have highlighted the importance of symmetry and asymmetry in determining the structural and functional performance of AC. Symmetric architectures, typically generated via templating methods, yield ordered pore networks, whereas asymmetric structures, commonly produced through direct chemical activation or heteroatom doping, exhibit hierarchical porosity and heterogeneous surface functionalities. This work critically examines the fundamentals of symmetry and asymmetry in AC materials, as well as their influence on design and use. It discusses synthesis strategies, characterization techniques, and recent approaches that enable the rational engineering of carbon structures. Application-specific case studies are presented, along with current challenges related to feedstock variability, scalability, and regulatory integration. By highlighting the interplay between structural order and functional diversity, this work provides a conceptual framework for guiding future research in the development on symmetrical and asymmetrical carbonaceous materials for sustainable waste valorization. Full article

Against the backdrop of economic recovery and ongoing market restructuring, China’s hotel industry is undergoing a significant shift from being service-based to becoming strategy-driven and competitive. This study aims to identify the key factors driving hotel competitiveness utilizing a multidimensional quantitative perspective. Based on a structured questionnaire survey administered to hotel managers across provinces and cities in China, primary data was collected from a total of 727 valid responses. As such, exploratory factor analysis, comprising 31 ranked items, and multiple regression analysis were performed using SPSS, identifying four dimensions that significantly affect hotel competitiveness: namely, operational characteristics, service quality, customer satisfaction, and competitive performance. The results show that operational characteristics are the primary determinants of hotel competitiveness. Specifically, sustainable development strategy, digital technology adoption, product and service innovation, and pricing strategies were the most significant factors leading to competitive advantage. Unlike previous studies that emphasized service quality and customer satisfaction as the dominant drivers of competitiveness, this study finds that a hotel’s competitive advantage relies heavily on developing strategic operational innovation and resource allocation capabilities. This study’s novelty lies in its use of national empirical data to validate the multidimensional composition and inherent logical relationships for competitive advantage. It also proposes a multidimensional framework for analyzing hotel competitiveness, specifically designed for the developmental characteristics of China’s hotel industry, highlighting the need to transform from a service-oriented to a strategy-driven operational approach. The findings of this paper offer empirical evidence for hotel enterprises to refine management structures, foster innovation, and thereby develop strong capabilities for a sustainable competitive advantage. Full article

With the evolution of social structure and the intensification of population aging, traditional community service centers struggle to meet residents’ complex needs due to their functional singularity and spatial rigidity. In response to the continuously evolving social structure and functional requirements, this research proposes a strategy based on the “Separation of Support and Infill,” distinguishing between the building’s permanent Support Structure and its replaceable Infill Components. These two parts are combined with modularization to achieve long-term spatial adaptability and sustainability throughout the entire life cycle. In terms of functional space, through the combination of vertical stratification, horizontal staggering and spatial permeability, a three-dimensional composite space system is constructed, which not only enhances the functional flexibility but also improves the environmental performance. Taking a design case in Yicheng District, Zhumadian City as an example, through a comparative analysis with the traditional building model, the comparative analysis demonstrates that this framework increases the Floor Area Ratio (FAR) by approximately 0.15 compared to traditional models. Furthermore, the modular characteristics significantly enhance demountability and reusability, reducing construction and demolition waste while lowering life-cycle costs by an estimated 15% to 25%. These studies show that the support structure and the composite functional space system can not only promote social interaction and community cohesion but also reduce the life-cycle cost and carbon emissions. The framework proposed in this paper constructs a theoretical and practical system for sustainable community buildings from the perspectives of functional compounding and low-carbon community development. Its innovation lies in its flexible spatial organization mode and the enhancement of the sustainability of community buildings. Full article

In this research, we establish a precise correspondence between the theory of logarithmic connections on principal G-bundles over compact Riemann surfaces and the geometric formulation of control systems on curved manifolds, providing a novel differential–geometric framework for analyzing optimal control problems with non-holonomic constraints. By characterizing control systems through the geometric structure of flat connections with logarithmic singularities at marked points, we demonstrate that optimal trajectories correspond precisely to horizontal lifts with respect to the connection. These horizontal lifts project onto geodesics on the punctured surface, which is equipped with a Riemannian metric uniquely determined by the monodromy representation around the singularities. The main geometric result proves that the isomonodromic deformation condition translates into a compatibility condition for the control system. This condition preserves the conjugacy classes of monodromy transformations under variations of the marked points, and ensures the existence and uniqueness of optimal trajectories satisfying prescribed boundary conditions. Furthermore, we analyze systems with non-holonomic constraints by relating the constraint distribution to the kernel of the connection form, showing how the degree of non-holonomy can be measured through the failure of integrability of the associated horizontal distribution on the principal bundle. As an application, we provide computational implementations for $\mathrm{SL}(2,\mathbb{C})$ connections over hyperbolic Riemann surfaces with genus $g\ge 2$, explicitly constructing the monodromy-induced metric via the Poincaré uniformization theorem and deriving closed-form expressions for optimal control strategies that exhibit robust performance characteristics under perturbations of initial conditions and system parameters. Full article

Fretting wear due to flow-induced vibration (FIV) remains a primary cause of fuel failure in light water nuclear reactors. In the study of axial FIV, i.e., FIV caused by axial flows, three vibration characteristics, namely natural frequency, damping ratio, and root-mean-square (RMS) amplitude, are critical for mitigating fretting wear by avoiding resonance, maximising overdamping, and preventing large-amplitude instability motion, respectively. This paper presents a set of best practices for simulating axial FIV with a focus on predicting these parameters based on a URANS-FSI numerical framework, utilising high-Reynolds-number Unsteady Reynolds-Averaged Navier–Stokes (URANS) turbulence modelling and two-way fluid–structure interaction (FSI) coupling. This strategy enables accurate and efficient prediction of vibration parameters and offers promising scalability for full-scale nuclear fuel assembly applications. Validation is performed against a semi-empirical model to predict RMS amplitude and experimental benchmarking. The validation experiments involve two setups: vibration of a square beam with fixed and roller-supported ends in annular flow tested at Vattenfall AB, and self-excited vibration of a cantilever beam in annular flow tested at the University of Manchester. The study recommends best practices for numerical schemes, mesh strategies, and convergence criteria, tailored to improve the accuracy and efficiency for each validated parameter. Full article

Electro-hydraulic servo systems are characterized by significant nonlinearities. Reinforcement learning (RL), known for its model-free nature and adaptive learning capabilities, presents a promising approach for handling uncertainties inherent in such systems. This paper proposes a predefined-time tracking control scheme based on RL, which achieves fast and accurate tracking performance. The proposed design employs an actor–critic neural network strategy to actively compensate for system uncertainties. Within a conventional backstepping framework, a command-filtering technique is integrated to construct a predefined-time control structure. This not only circumvents the issue of differential explosion but also guarantees system convergence within a predefined time, which can be specified independently by the designer. Simulation results and comparisons validate the enhanced control performance of the proposed controller. Full article