Search Results (3,235)

Sustainable development is a central objective for contemporary firms. It involves both long-term organizational resilience and improved environmental, social, and governance (ESG) performance. Structural tools that support long-term stability and strategic continuity play a critical role in achieving these goals. However, their adoption depends on the interaction between formal and informal institutional forces. Drawing on institutional theory, this study applies fuzzy-set qualitative comparative analysis (fsQCA) to data from Chinese listed firms. We examine how four institutional dimensions jointly shape structural tool adoption: governance structure, intergenerational heterogeneity, institutional and cultural context, and market-driven and mimetic forces. Structural tools facilitate governance consolidation and leadership succession, which are essential for sustainable development. Our findings show that no single institutional condition is sufficient to trigger adoption. Instead, multiple conditions must combine to enable firms to implement structural tools. The seven configurations identified reveal diverse governance paths across different institutional contexts, including complementary, substitutive, and conflicting relationships between formal and informal institutions. We also find clear causal asymmetry: the conditions that promote adoption differ fundamentally from those that inhibit it. Structural tools provide an institutional foundation for balancing short-term pressures with long-term sustainability commitments. Firms lacking these mechanisms face greater risks of leadership succession failure and long-term instability. Additional analyses using mean difference tests and fixed-effects models further confirm that structural tool adoption significantly enhances both sustainable development capacity and ESG performance. Overall, this study advances institutional theory. It shows how the interaction between formal and informal institutions shapes governance choices. It also explains how governance structures are linked to sustainable development outcomes. Full article

The Four-Leg Three-Phase Voltage Source Inverter (4LVSI) is a versatile solution for integrating renewable energy sources (RESs) into distribution networks, as it compensates unbalanced voltages and currents while providing a path for zero-sequence components. Accurate current control is essential to ensure power quality and reliable operation under these conditions. Conventional controllers such as proportional–integral, resonant, or feedback-linearization methods achieve acceptable tracking under static dc-link conditions, but their performance degrades when dc-link voltage dynamics arise due to renewable-source fluctuations. This paper proposes a data-driven model reference neural control (MRNC) strategy for a four-leg inverter connected to RESs, explicitly accounting for dc-link voltage variations. The proposed controller reformulates the classical Model Reference Adaptive Control (MRAC) as a lightweight single-layer neural network whose adaptive weights are updated online using the Recursive Least Squares (RLS) algorithm. In this framework, the dc-link variations are not modeled explicitly but are implicitly learned through the data-driven adaptation process, as their influence is captured in the neural network regressors formed from real-time input–output measurements. This allows the controller to continuously identify the inverter dynamics and compensate the effect of dc-link fluctuations without requiring additional observers or prior modeling. The proposed approach is validated through detailed time-domain simulations and real-time Hardware-in-the-Loop (HIL) experiments implemented at a 10 kHz switching frequency. The results indicated that the RLS-based MRNC controller achieved the lowest steady-state current error, reducing it by approximately 1.85% and 1% compared to the Proportional-Resonant (PR) and One-Step-Ahead (OSAC) controllers, respectively. Moreover, under dc-link voltage variations, the proposed controller significantly reduced the current overshoot, achieving decreases of 5.9 A and 6.36 A relative to the PR controller. Full article

Exosomes and other extracellular vesicles (EVs) carry microRNAs, proteins, and lipids that reflect cardiovascular pathophysiology and can enable minimally invasive biomarker discovery. However, EV datasets are highly dimensional and heterogeneous, strongly influenced by pre-analytic variables and non-standardized isolation/characterization workflows, limiting reproducibility across studies. Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and network-based approaches, can support EV biomarker development by integrating multi-omics profiles with clinical metadata. These approaches enable feature selection, disease subtyping, and interpretable model development. Among the AI ​​approaches evaluated, ensemble methods (Random Forest, gradient boosting) demonstrate the most consistent performance for EV biomarker classification (AUC 0.80–0.92), while graph neural networks (GNNs) are particularly promising for path integration but require larger validation cohorts. Evolutionary neural networks applied to EV morphological features yield comparable discrimination but face interpretability challenges for clinical use. Current studies report promising discrimination performance for selected EV-derived panels in acute myocardial infarction and heart failure. However, most evidence remains exploratory, based on small cohorts (n < 50) and limited external validation. For clinical implementation, EV biomarkers need direct comparison against established standards (high-sensitivity troponin and natriuretic peptides), supported by locked-in assay plans, and validation in multicenter cohorts using MISEV-aligned protocols and transparent AI reporting practices. Through a comprehensive, integrative, and comparative analysis of AI methodologies for EV biomarker discovery, together with explicit criteria for reproducibility and translational readiness, this review establishes a practical framework to advance exosomal diagnostics from exploratory research toward clinical implementation. Full article

This paper details the development of innovative grading techniques for 3D-printed biopolymer composites that utilize locally sourced, cellulose-based fibre streams to produce architectural-scale components. It examines the design considerations, methodologies, and fabrication strategies that are necessitated by the utilisation of biopolymers for architectural applications, and which underlie key processes of designing for and with variable materials. The presented research interrogates the methodological challenges of formulating new approaches that actively engage architects and designers with the ecological implications of their design choices. It outlines new methods for material grading that enable targeted compositional variation through three interlinked contributions: a gradable recipe, a design-interfaced specification process for grading, and an infrastructure for large-scale 3D printing of biopolymer composites. The paper presents the Rhizaerial demonstrator as an implementation of these contributions. Rhizaerial is a full-scale interior ceiling vault system, whose curved components are printed as a 3D porous lattice structure that creates an interplay of light, visual transparency, and colour, while maintaining structural integrity. We detail the gradable biopolymer composite recipe, and the residual and regenerative material streams it combines. We outline the implicit modelling pipeline, which includes methods for locally specifying lattice structures for 3D printing, as well as assigning continuous grading specifications to print paths. Finally, we describe the fabrication infrastructure and tooling for robotic printing of large-scale graded biopolymer composites. Full article

Background/Objectives: Software is now core infrastructure in biomedical science, yet fragmented workflows across subfields hinder reproducibility and delay the translation of data into actionable decisions. There is a critical need for a cross-disciplinary synthesis to bridge these silos and establish a unified framework for software maturity. This narrative review addresses this gap by synthesizing representative software ecosystems across three major pillars: bioinformatics, molecular modeling/simulations, and epidemiology/public health. Methods: A narrative review of articles indexed in PubMed/NCBI, Web of Science, and Scopus between 2000 and 2025 was conducted. Domain-specific terms related to bioinformatics, molecular modeling, docking, molecular dynamics, epidemiology, public health, and workflow management were combined with software- and algorithm-focused keywords. Studies describing, validating, or applying documented tools with biomedical relevance were included. Results: Across domains, mature data standards and reference resources (e.g., FASTQ, BAM/CRAM, VCF, mzML), widely adopted platforms (e.g., BLAST+ (v2.16.0, NCBI, Bethesda, USA), Bioconductor (v3.20, Bioconductor Foundation, Seattle, USA), AutoDock Vina (v1.2.5, Scripps Research, La Jolla, USA), GROMACS (v2024.3, GROMACS Team, Stockholm, Sweden), Epi Info (v7.2.6, CDC, Atlanta, USA), QGIS (v3.40, QGIS.org, Gossau, Switzerland), and increasing use of workflow engines were identified. Software pipelines routinely transform molecular and surveillance data into interpretable features supporting hypothesis generation. Conclusions: Integrated, standards-based, and validated software pipelines can shorten the path from measurement to decision in biomedicine and public health. Future progress depends on reproducibility practices, benchmarking, user-centered design, portable implementations, and responsible deployment of machine learning. Full article

Continuum robots (CRs) exhibit high compliance and environmental adaptability in confined, tortuous spaces, yet their inherent low stiffness and load capacity limit performance in precise positioning and stable support tasks. To solve the “soft-rigid” paradox, this study proposes and implements a three-segment tendon-driven variable-stiffness CR. Structurally, a segmented constant-curvature model directs the optimization of grid skeletons and notch parameters, enhancing bending consistency and motion predictability. Elongated flat airbag actuators, arranged in annular arrays, enable segment-level stiffness switching through the enhancement of surface properties like axial constraints and friction amplification. A time-sharing drive strategy decouples multi-segment coupling into sequential single-segment subproblems, reducing drivers and kinematic complexity while maintaining dexterity. Experimental results demonstrate that flexible-mode joints maintain near-constant curvature with stable motion (average end-effector trajectory error < 0.9 mm), and in rigid mode, stiffness increases by a factor of 5.77 (rated load: 4.0 N). Shape-locking disturbances during transitions are confined to millimeter levels (remote offset < 1.32 mm), with successful traversal of J/U/S-shaped and irregular paths confirmed in pipeline tests. This work introduces a practical, scalable system for designing variable-stiffness structures and enabling low-complexity multi-segment control, offering valuable insights for minimally invasive devices and industrial endoscopy in confined spaces. Full article

Physiologically based pharmacokinetic (PBPK) models are widely used in the context of personalized medicine, as they allow for the evaluation of dosing schedules and routes of administration by predicting absorption, distribution, metabolism and excretion (ADME) of drugs in biological systems. Traditionally, PBPK models have been developed and applied at the population level, enabling the characterization of predefined cohorts, which remains limited in supporting true precision dosing. In this review, we explored the increasingly common shift from population-based to individual PBPK modelling, where individuals are modelled as virtual twins (VTs). Through the inclusion of additional patient-specific data, such as demographic, physiological, phenotypic and genotypic information, models can be personalized, moving beyond traditional one-size-fits-all strategies. Overall, incorporating individual patient data (e.g., septic, psychiatric, cardiac, or neonatal populations) improves model performance. Physiological parameters, particularly renal function, show strong potential given their role in drug elimination, while demographic variables enhance predictive accuracy in certain studies. In contrast, the benefits of including cytochrome P450 (CYP) phenotypic and genotypic data remain inconsistent. We further emphasize methodologies used to evaluate model performance, with a focus on clinical validation through comparisons between predicted and observed concentration-time profiles. Key challenges, including limited sample sizes and data availability, that may compromise predictive precision, are also discussed. Finally, we highlight the potential integration of PBPK-based VTs into broader digital twin frameworks as a promising path toward clinical translation, while acknowledging the critical barriers that must be addressed to enable routine clinical implementation. Full article

Building information modeling (BIM)-based interior design automation remains constrained by a semantic mismatch: engineering constraints are explicit and categorical, whereas aesthetic style is implicit, contextual, and difficult to formalize. As a result, existing systems often overfit local visual similarity or rely on rigid rules, producing recommendations that drift stylistically at the scene level or conflict with professional design logic. This paper proposes KsDesign, a neuro-symbolic framework for interpretable, retrieval-based BIM scene completion that unifies visual style perception with explicit design knowledge. Offline, KsDesign mines category-level co-occurrence and compatibility patterns from curated designer-quality interiors and encodes them as a weighted Furniture-Matching Knowledge Graph (FMKG). Online, it learns style representations exclusively from BIM-derived 2D renderings/projections of 3D family models and BIM scenes, and applies a knowledge-guided attention mechanism to weight contextual furniture cues, synthesizing a global scene-style representation for candidate ranking and retrieval. In a Top-3 (K = 3) evaluation on 10 BIM test scenes with a 20-expert consensus ground truth, KsDesign consistently outperforms single-modal baselines, achieving 86.7% precision in complex scenes and improving average precision by 23.5% (up to 40%), with a 15.5% average recall increase. These results suggest that global semantic constraints can serve as a logical regularizer, mitigating the local biases of purely visual matching and yielding configurations that are both aesthetically coherent and logically valid. We further implement in-authoring explainability within Revit, exposing KG-derived influence weights and evidence paths to support rationale inspection and immediate family insertion. Finally, the knowledge priors and traceable intermediate representations provide a robust substrate for integration with LLM-driven conversational design agents, enabling constraint-aware, verifiable generation and interactive iteration. Full article

Background: Mechanical circulatory support (MCS) has revolutionized advanced heart failure and cardiogenic shock management, yet randomized controlled trials have failed to demonstrate consistent mortality benefits with temporary devices, and outcomes remain highly variable across institutions. Methods: This narrative review examines contemporary MCS devices, analyzing their hemodynamic principles, clinical outcomes, complications, and selection strategies. The published literature addressing MCS clinical applications and outcomes was reviewed, with reference lists examined to identify additional sources. Results: Temporary MCS devices demonstrate a persistent hemodynamic-survival paradox where improved hemodynamics fail to translate into mortality benefits in randomized trials. This disconnect reflects delayed intervention after irreversible organ damage, device complications offsetting hemodynamic gains, heterogeneous patient selection without phenotyping, timing challenges, and inadequate statistical power. Landmark trials provide definitive evidence against routine early VA-ECMO use, showing no survival advantage while significantly increasing complications. Optimal device selection requires integrating hemodynamic phenotyping with shock stage to match devices to pathophysiology, while biventricular failure presents the greatest challenge with substantially lower survival. For durable devices, third-generation systems demonstrate superior outcomes with dramatically reduced pump thrombosis and improved survival. Critically, multidisciplinary shock teams employing standardized protocols significantly reduce mortality beyond what devices alone achieve, with structured programs showing substantially improved survival compared to trials using similar devices without organized care systems. Conclusions: Mechanical circulatory support has transformed heart failure management, but optimal outcomes require integrating devices within structured care delivery systems. Success depends on comprehensive hemodynamic assessment, multidisciplinary team activation, protocolized device selection, standardized escalation and weaning strategies, and regionalized networks. The future lies in shifting focus from device innovation to implementation science, establishing quality metrics, developing precision medicine approaches, and conducting trials in phenotype-selected populations with protocolized care. This systems-of-care paradigm offers the most promising path toward translating technological advances into sustained mortality reduction. Full article

Steering angle control in self-driving cars is usually organized in layers combining trajectory planning, path tracking, and low-level actuator control. The steering controller converts the planned path into a desired steering angle and then ensures accurate tracking by the electric power steering (EPS). However, automotive electric power steering (AEPS) systems face many problems caused by model uncertainties, disturbances, and unknown system dynamics. In this paper, a robust finite-time control strategy based on an adaptive backstepping scheme is proposed to handle these problems. First, radial basis function neural networks (NNs) are designed to approximate the unknown system dynamics. Then, an adaptive sliding-mode disturbance observer (ASMDO) is introduced to address the impacts of the lumped disturbance. Enhanced control performance for the AEPS system is implemented using a combination of the above technologies. Numerical simulations and a hardware-in-the-loop (HIL) experimental verification are performed to demonstrate the significant improvement in performance achieved using the proposed strategy. Full article

Under the background that urban–rural integrated development continuously deepens and the common prosperity goal continuously advances, systematically identifying the actual results of urban–rural integrated development and its influence mechanism on common prosperity holds important significance for understanding regional development differences and optimizing policy implementation paths. Based on land use data, NTL data, and POI facility data from 2013 to 2025, this study comprehensively employs spatial analysis and deep learning methods to conduct an empirical analysis on the spatiotemporal evolution characteristics and coupling relationship of urban–rural integrated development and common prosperity levels from dimensions including urban–rural spatial form evolution, economic activity intensity, and public service facility diversity. The research results indicate that urban–rural integration significantly promotes urban spatial expansion and the improvement in overall economic activity levels during the study period, but the difference in development magnitude among different regions remains obvious. The common prosperity level generally presents a rising trend, but it highly concentrates in the Pearl River Delta and city–county center areas in space, and the promotion effect of urban–rural integration on common prosperity exhibits obvious characteristics of regional heterogeneity, stages, time lags, and diminishing marginal effects. This study considers that urban–rural integration does not inevitably and synchronously transform into an elevation in common prosperity levels. Combining regional development basis and structural conditions to optimize urban–rural integration development paths by region and by stage and to improve the realization quality of common prosperity possesses important practical reference value. Full article

Accurate path planning was the prerequisite for autonomous navigation of agricultural vehicles. An Unmanned Aerial Vehicle (UAV)-based coverage path planning was developed in this research for automating guidance of agricultural vehicles and reducing the operator maneuver in the creation of navigation maps. High-resolution orthophoto maps of the field were constructed by using low-altitude UAV photogrammetry to obtain spatial information. Travel paths and working paths were automatically generated from anchor points selected by the operator under the image coordinate domain. The navigation path for unmanned agricultural vehicles was generated by Mercator projection-based conversion for the anchor pixel coordinates into latitude and longitude geographic coordinates. A Graphical User Interface (GUI) was developed for path generation, visualization, and performance evaluation, through which the proposed path planning method was implemented for autonomous agricultural vehicle navigation. Calculation accuracy tests demonstrated the mean planar coordinate error was 2.23 cm and the maximum error was 3.37 cm for path planning. Field tests showed that lateral navigation errors remained within ±5.5 cm for the unmanned high-clearance sprayer, which indicated that the developed UAV-based coverage path planning method was feasible and featured high accuracy. It provided an effective solution for achieving fully autonomous agricultural vehicle operations. Full article

In the rubidium-87 atomic fountain clock, the laser collimating and beam-expanding telescope plays a key role in atomic cooling and manipulation, as well as in realizing the cold-atom fountain. To address the bulkiness of conventional laser collimating and beam-expanding telescopes, which limits system integration and miniaturization, we design and implement a compact laser collimating and beam-expanding telescope. The design employs a Galilean beam-expanding optical path to shorten the optical path length. Combined with optical modeling and optimization, this approach reduces the mechanical length of the telescope by approximately 50%. We present the mechanical structure of a five-degree-of-freedom (5-DOF) adjustment mechanism for the light source and the associated optical elements and specify the corresponding tolerance ranges to ensure their precise alignment and mounting. Based on this 5-DOF adjustment mechanism, we further propose a method for tuning the output beam characteristics, enabling precise and reproducible control of the emitted beam. The experimental results demonstrate that, after adjustment, the divergence angle of the output beam is better than 0.25 mrad, the coaxiality is better than 0.3 mrad, the centroid offset relative to the mechanical axis is less than 0.1 mm, and the output beam diameter is approximately 35 mm. Furthermore, long-term monitoring over 45 days verified the system’s robustness, maintaining fractional power fluctuations within ±1.2% without manual realignment. Compared with the original telescope, all of these beam characteristics are significantly improved. The proposed telescope therefore has broad application prospects in integrated atomic fountain clocks, atomic gravimeters, and cold-atom interferometric gyroscopes. Full article

Microplastic (MP) pollution poses an emerging environmental concern, yet current methods for isolation and quantification are often time-consuming, costly, and poorly adapted to real-world variability. In this study, a workflow for the preparation, filtration, and quantification of MP standards, emphasizing environmental relevance and methodological efficiency, was developed and evaluated. To address the scarcity of irregularly shaped MP standards, low-cost, environmentally representative standards were lab-prepared by grinding and sieving plastic sheets. These MPs were successfully categorized according to sizes up to ~250 μm and dyed for enhanced visibility. The filtration efficiency for two systems, a long-circuit pump (LC-pump) and a short-circuit vacuum (SC-vacuum), was compared. The SC-vacuum method demonstrated a more than 11-fold increase in filtration speed and higher MP recovery rates for both polystyrene and polypropylene standards. Ethanol-based solvents significantly improved MP dispersion and recovery for irregular shapes of the MPs, including polystyrene and polypropylene. Finally, a user-guided machine learning tool (Ilastik) was implemented for automated MP quantification. Ilastik showed a strong correlation with manual counting (r = 0.824) and reduced variability, offering a reproducible and time-efficient alternative. By cutting down cost, time, and technical complexity relative to existing MP analysis techniques, this workflow provides a more accessible path toward consistent and scalable environmental MP assessments. Full article

Expressway networks represent evolving complex systems whose topological properties significantly impact regional development. This paper presents a decision support framework for addressing the expressway infrastructure sequencing problem using computational intelligence. We develop a novel framework that models expressways as L-space networks and evaluates how construction sequences create path-dependent evolutionary trajectories, introducing network science principles into infrastructure planning decisions. Our decision support framework quantifies project impacts on accessibility, connectivity, and reliability using nine topological metrics and a hybrid weighting mechanism that combines domain expertise with entropy-based uncertainty quantification. The system employs a hybrid TOPSIS algorithm that relies on geometric symmetry to simulate network evolution, capturing emergent properties in which each decision restructures possibilities for subsequent choices—a computational challenge that conventional planning approaches have not addressed. The system was validated with real-world Chongqing expressway planning data, demonstrating its ability to identify sequences that maximize synergistic network effects. Results reveal how topologically equivalent projects produce dramatically different system-wide outcomes depending on implementation order. Analysis shows that network science-informed sequencing substantially enhances system performance by exploiting structural synergies. This research advances decision support frameworks by bridging complex network theory with computational decision-making, creating a novel analytical tool that enables transportation authorities to implement evidence-based infrastructure sequencing strategies beyond the reach of conventional planning methods. Full article