Search Results (4,157)

To address the lack of traversable region awareness in conventional path planning algorithms for obstacle-crossing robots, an adaptive path planning method is proposed. First, a traversal-aware environment model is constructed by introducing graded traversable regions with associated physical traversal costs. To effectively navigate this complex model, a hybrid Ant Colony Optimization (ACO) framework integrating Jump Point Search (JPS) and the Genetic Algorithm (GA) is developed. Specifically, a JPS-inspired pruning strategy is incorporated into the state transition process to significantly reduce redundant node expansion. Crucially, genetic operators—namely crossover and mutation—are embedded within the main ACO iterative loop to dynamically sustain population diversity and effectively mitigate stagnation in local optima. Correspondingly, the pheromone initialization, state transition mechanisms, and update rules are redesigned to incorporate the robot’s obstacle traversal capabilities. The framework is further complemented by path optimization operations that reduce unnecessary turning points. Extensive simulation experiments demonstrate that the proposed method outperforms conventional ACO-based and classical path planning algorithms. In particular, it achieves an average reduction of 11.1% in path length and 65.5% in the number of waypoints, while ensuring effective coordination with the robot’s physical traversal capabilities. These results validate the superior search efficiency, robustness, and practical applicability of the proposed approach. Full article

Industrial chains in agriculture are currently fragmented and do not support developing resource-based competitive advantages. This is true under the Big Food Framework’s strategic orientation. This research seeks to develop a new analytical framework for evaluating pathways to the integration of agricultural industrial chains and their impact on the performance of companies engaged in food processing in Xinjiang. A mixed-method approach, employing both an exploratory and sequential design, will be used to do this. The primary method of data collection for this study is the case study method, along with the questionnaire method involving 145 agricultural enterprises. From these data, structural equation modeling (SEM) will be used to test the paths of causation among cognitive managers of firms who have implemented the BFF. Evidence will be presented to demonstrate the relationship among three types of integration (vertical, horizontal, and lateral) in the agricultural industrial chain, dynamic capabilities, and company performance. Additionally, network topology and optimization simulations will be conducted to determine how effectively structures are organized in training the respective companies. Important findings revealed in this research include the following: The managerial cognition constructs offered by BFFs play a key role in enhancing the depth and structural balance of industry chain integration. There were complementary performance effects found, and they are related to vertical integration achieving operational efficiency and financial efficiency; horizontal integration improving market competitiveness and brand competitiveness; and lateral integration facilitating innovative growth. Dynamic capabilities are a significant mediating mechanism linking institutional support and digital capability with the depth of integration across different modes of integration. The findings from network optimization suggest that there is a positive effect of balanced connectivity across the different dimensions of integration on overall system efficiency and reduced structural inefficiencies. Based on these findings, the authors recommend that organizations establish governance mechanisms that facilitate coordinated connectivity; strengthen adaptive capabilities within the firm; and promote balanced integration across industrial networks. Future researchers should consider applying these findings to conducting longitudinal studies on network evolution; integrating sustainability measures as part of their analysis; and conducting comparative validation studies across regions or industry systems. Full article

As natural nanoscale intercellular messengers, exosomes exhibit considerable potential in modulating inflammation, angiogenesis, immunoregulation, and tissue remodeling, making them attractive candidates for regenerative medicine. However, their clinical translation remains limited by rapid systemic clearance, nonspecific biodistribution, insufficient lesion retention, and functional attenuation in hostile pathological microenvironments. In this review, we propose that biomaterial engineering should evolve from providing passive exosome carriers to constructing active regulatory platforms capable of precise spatiotemporal control. We summarize engineering strategies along two complementary dimensions. In the temporal dimension, biomaterials can enable sustained, sequential, or microenvironment-responsive release to match the dynamic phases of tissue repair. In the spatial dimension, biomaterials can improve local retention, tissue anchoring, structural guidance, endogenous cell recruitment, and lesion-specific delivery. Using cutaneous wound healing, osteochondral regeneration, myocardial repair, and neural regeneration as representative examples, we further analyze these strategies through a “clinical challenge–engineering strategy–biological mechanism” framework, with particular attention to how engineered systems influence key signaling pathways such as PI3K/Akt, Wnt/β-catenin, NF-κB, and PTEN/PI3K/Akt/mTOR. We also discuss translational barriers, including exosome heterogeneity, safety concerns inherited from parental cells, large-scale GMP-compliant manufacturing, product standardization, storage stability, and regulatory classification of exosome–biomaterial hybrids. Finally, we highlight emerging directions, including multi-mechanism combinational systems, closed-loop responsive platforms, and artificial intelligence-assisted design for personalized exosome therapeutics. This review provides a design-oriented framework to accelerate the bench-to-bedside development of biomaterial-enabled precision exosome therapy. Full article

Entrepreneurship is recognised globally as the vehicle for economic development and poverty eradication, yet in developing economies, it is not receiving the support it deserves. Based on the institutional framework, this study explores its role in fostering the development of an entrepreneurial mindset in Masvingo Province, Zimbabwe. Being grounded in the interpretivist research philosophy and following an inductive qualitative research design, the study adopted a case study strategy. Data were collected through in- depth interviews with 12 participants, purposively selected from industry leaders and entrepreneurs. Thematic analysis was used to inductively generate contextual insights from the interaction between the regulatory, socio-economic, and cultural pillars of the institutional framework and individual capabilities. The findings show that entrepreneurship development in Masvingo Province, Zimbabwe, is influenced to a greater extent by the institutional framework, which is characterised by economic volatility, infrastructure gaps, and evolving regulatory demands. The formal institutional framework was noted to confer legitimacy while, at the same time, imposing obligations on institutions; informal institutional frameworks rooted in communal values, social capital, and professional bodies helped fill gaps in the formal framework. The study also demonstrates that entrepreneurial mindset development is an integrated output of continuous learning, strategic networking, and individual capability. In reinforcing the normative dimensions of institutional theory, it was noted that entrepreneurs do not only have profit-maximisation goals but also long-term sustainability and survival targets. The study contributes to scarce empirical research on the nexus between institutional framework and entrepreneurship development in emerging economies. The findings reinforce the need for an integrated approach that streamlines the regulatory process, strengthens infrastructure, supports capacity building, and recognises the role of the informal institutional network in enhancing entrepreneurship development. Even though the qualitative, cross-sectional design limits the generalizability of the study’s findings, the study offers insights into fostering entrepreneurship development in emerging markets. Full article

With the large-scale integration of wind power, modern power systems are facing reduced equivalent inertia, weakened primary frequency regulation capability, and insufficient coordination between wind turbines and energy storage during joint frequency support. To address these issues, this paper investigates a wind–storage hybrid system composed of doubly fed induction generators (DFIG) and supercapacitor energy storage and proposes a coordinated primary frequency regulation strategy combining fuzzy logic control (FLC) and model predictive control (MPC). Considering the variations in rotor kinetic energy reserve and frequency support capability under different wind speed regions, a coordinated regulation mechanism is developed for multiple operating conditions. In addition, a variable-coefficient synthetic inertia control scheme with rotor speed safety constraints is designed to adaptively adjust the turbine regulation coefficients, while an SOC-feedback-based adaptive virtual droop strategy is introduced to improve the sustained support capability of the energy storage unit. On this basis, a multi-objective model predictive control framework is established to optimize the reference power allocation between the wind turbine and the energy storage unit in a rolling manner. The proposed method is characterized by three coordinated features, namely, multi-region wind–storage frequency regulation, rotor-speed-safe adaptive support of the wind turbine and SOC-aware adaptive support of the storage unit, as well as MPC-based rolling power allocation. Simulation results show that the proposed strategy improves the frequency nadir, reduces the steady-state frequency deviation, and enhances coordinated power sharing, thereby improving the primary frequency regulation performance and overall frequency stability of the wind–storage hybrid system. Full article

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

In today’s energy landscape, defined by the growing demand for sustainable energy generation technologies and the parallel need to advance internal combustion engine (ICE) architectures toward cleaner and more efficient solutions, the adoption of Free-Piston Linear Generator (FPLG) engines emerges as a highly promising approach. This innovative system enables the direct conversion of combustion-induced piston motion into electrical energy, eliminating the need for traditional crankshaft and connecting rod mechanisms. The FPLG concept facilitates efficient utilization of a broad spectrum of fuels—including methane, ethanol, LPG, gasoline, biodiesel, and hydrogen—by supporting variable compression ratio operation. This feature enhances operational flexibility and fuel adaptability, positioning the technology as a viable candidate for future energy transition scenarios. The absence of rotating mechanical components significantly reduces frictional losses, contributing to an overall increase in system efficiency. To accurately characterize and optimize engine performance, an extensive series of one-dimensional (1D) numerical simulations was performed under both free and controlled operating conditions. The resulting data enabled the development of semi-empirical models capable of predicting the dynamic behavior of the engine across a wide range of working scenarios. Finally, through a detailed parametric analysis, the optimal operating conditions were identified to maximize both net electric efficiency and electrical power output. These findings provide a solid ground for the design and implementation of FPLG engine systems in advanced power generation applications. Full article

In a context marked by the Anthropocene, the climate crisis, and the contemporary blockage of political and projective imagination, utopias and dystopias re-emerge as fundamental critical instruments for architecture. Far from constituting evasive or unrealizable exercises, these constructions operate as epistemological and projective devices capable of exploring possible futures, revealing latent tensions, and questioning the ideological frameworks that shape the built environment. This article examines speculative design as a contemporary updating of the utopian and dystopian tradition in architecture, understood not as a normative model but as a critical method for imagining radical transformations of dwelling in response to the current ecological, social, and geopolitical urgencies. Drawing on a series of projects developed within the university context, it analyses how architectural speculation, enhanced by artificial intelligence tools, enables the exploration of alternative scenarios of urbanization, adaptive habitats, and new relationships between architecture, territory, and nature. The cases analysed show that the combination of utopia, dystopia, and emerging technologies fosters an understanding of architecture as an open, dynamic, and relational system capable of responding to contexts of high uncertainty. The article argues that the return of utopian imagination, now mediated by speculative practices and digital tools, constitutes a relevant contribution to the contemporary debate on new forms of urbanization, flexible megastructures, and sustainable architectural futures. Full article

Enhancing the resilience of real-economy firms is essential to sustainable development because firms must not only absorb shocks but also maintain long-term adaptive and renewal capacity. Against this background, this study examines whether insurance institutional ownership, as a form of patient capital, is systematically associated with corporate resilience. Using panel data for Chinese A-share listed firms from 2008 to 2024, we construct a multidimensional corporate resilience index based on risk resistance, adaptive recovery, and renewal and development and estimate two-way fixed-effects models. The results show that insurance ownership is positively associated with the baseline corporate resilience index, and this pattern remains qualitatively similar when we examine stock-return volatility, financial performance growth, and a stricter capability-oriented resilience index. The positive association is stronger for state-owned enterprises, small firms, non-manufacturing firms, and firms located in northern China. Channel analysis suggests that insurance ownership is associated with lower agency costs, stronger internal controls, greater external scrutiny, and lower financing constraints, patterns that are consistent with the proposed channels linking insurance ownership to corporate resilience. Further analyses show that higher insurance ownership and increases in insurance holdings are associated with stronger resilience, whereas decreases in holdings are associated with weaker resilience. Long holding duration is negatively associated with resilience, suggesting that performance-evaluation pressure may weaken the long-term governance role of insurance capital. Overall, the findings suggest that insurance investors may support corporate resilience and, when governance incentives and evaluation mechanisms are appropriately aligned, contribute to the sustainable development of the real economy. Full article

Amid Europe’s demographic decline and the intensifying global “war for talent,” migration is increasingly viewed as a critical source of human capital capable of sustaining economic growth and welfare systems. Nevertheless, the literature on Macro-Talent Management (MTM) has primarily focused on the attraction of highly skilled expatriates, paying limited attention to how national integration systems shape the broader capacity of countries to attract and retain migrant talent. Addressing this gap, the present study conceptualizes migrant integration as a strategic component of macro-level talent management and evaluates the “talent attractiveness” of different European welfare and migration regimes. Methodologically, the study develops a multi-criteria evaluation framework based on the PROMETHEE II (Preference Ranking Organization Method for Enrichment of Evaluations) outranking method, enabling the simultaneous assessment of institutional, socio-economic, and administrative dimensions of migration governance. The model integrates nine indicators combining policy inclusiveness (e.g., Migrant Integration Policy Index—MIPEX (Migrant Integration Policy Index), citizenship accessibility), labor market outcomes (employment and gender gaps), and systemic pressures on migration management (asylum applications). By integrating policy indicators with real-world labor market performance and administrative capacity, the proposed framework offers a novel analytical tool for comparative migration policy evaluation and decision support. The empirical application covers six European countries representing distinct migration regimes: Portugal, Sweden, France, Poland, Greece, and Germany. The results challenge the conventional assumption that economic strength alone determines migrant attractiveness. Portugal emerges as the most attractive destination, demonstrating that inclusive rights-based integration policies can offset lower GDP levels. In contrast, Germany ranks last in the sample, revealing signs of systemic overextension due to extreme administrative pressure, while Greece occupies the fifth position characterized by structural integration deficits. The study contributes to the literature by linking migration governance, integration policy effectiveness, and macro-level talent management and by introducing a multi-criteria decision-analytic approach for evaluating national migration systems in Europe. The study offers a reassessment of the ‘talent attractiveness’ of European welfare models in a post-pandemic context (2023). Full article

The continuous and reliable monitoring of soil organic carbon and soil respiration is vital for sustainable agricultural and environmental management. However, current manual methods are labor-intensive and time-consuming. This work focuses on the development of a fully automated robotic platform for in situ measurement of Soil Organic Carbon (SOC) and Soil Respiration (${\mathbf{R}}_s$). The system consists of a four-wheeled mobile platform, equipped with a robotic arm, and custom sampling and measurement tools. The platform is designed with a protected central opening that houses an on-board laboratory, enabling automated surface cleaning, soil drilling, sample collection and homogenization, SOC spectroscopy analysis, and chamber-based soil respiration measurement. The platform is equipped with a high-force mechanical insertion mechanism capable of operating a range of tools designed for soil treatment and penetration. These tools include a soil surface scraper, a soil respiration chamber, and a soil drilling unit. The mobile robotic laboratory system enables the sequential deployment of these tools in any desired order, providing flexible and efficient in-field operation. Full article

Background/Objectives: Healthcare facilities are among the most energy-intensive public buildings, yet hospital decision-support models rarely integrate energy-related performance indicators alongside operational metrics. This study aims to address this gap by developing a discrete-event simulation framework capable of jointly evaluating clinical efficiency and energy consumption in elective orthopedic surgical pathways. Methods: A comprehensive discrete-event simulation model was developed to represent the diagnostic imaging and orthopedic surgical process. The model was parameterized using a hybrid data-collection approach that combined clinical activity data, scientific literature, and expert judgment. Energy consumption was modeled by differentiating fixed loads, such as heating, ventilation, and air-conditioning systems and lighting, from activity-dependent loads associated with diagnostic and surgical equipment. Baseline performance was assessed and compared with alternative scenarios for organizational and technological improvements. Results: The analysis showed that fixed infrastructural loads, particularly HVAC systems, were the main drivers of per-patient energy consumption, with inefficient space utilization and prolonged idle times. Scenario analysis demonstrated that organizational interventions, such as increasing operating room throughput and optimizing MRI scheduling, can substantially reduce energy intensity by diluting fixed loads and decreasing idle consumption. Technological interventions, such as replacing conventional surgical lamps with LED systems, produced smaller but still beneficial reductions. The combined implementation of organizational and technological strategies yielded the greatest overall improvement. Conclusions: Integrating energy metrics into discrete-event simulation provides effective support for hospital decision-making by revealing the interaction between workflow design, resource utilization, and environmental performance. The findings indicate that organizational redesign, particularly when combined with technological upgrades, can significantly improve both operational efficiency and sustainability in hospital settings. This study highlights discrete-event simulation as a promising tool for energy-aware healthcare planning. Full article

Corporate tax avoidance has become a major governance and fiscal sustainability concern, particularly in developing economies where corporate tax revenues constitute a critical source of public financing. While prior research suggests that board gender diversity (BGD) enhances ethical oversight and monitoring, its effectiveness in constraining aggressive tax planning may depend on firms’ informational and technological environments. This study examines whether artificial intelligence (AI) capability strengthens the governance role of BGD in reducing corporate tax avoidance. Using a balanced panel of 1586 non-financial firms from developing economies over the period 2009–2023, the analysis employs firm FE models and dynamic two-step System GMM estimations to address unobserved heterogeneity, endogeneity, and the persistence of corporate tax behavior. The results indicate that BGD is positively associated with effective tax rates, implying lower levels of corporate tax avoidance. Furthermore, AI capability—measured using a lagged specification—significantly strengthens this relationship, suggesting that firms with higher AI adoption exhibit a stronger governance effect of gender-diverse boards on tax compliance. Additional robustness tests—including alternative tax avoidance measures, alternative BGD specifications, heterogeneity analysis, and selection-bias corrections using Heckman, propensity score matching (PSM), and instrumental variable (2SLS) approaches—confirm the stability of the findings. Overall, the results highlight the complementary role of technological capability and board diversity in strengthening corporate governance (CG) and fiscal discipline in developing economies. Full article

The dual circulation strategy represents China’s new-era national development paradigm aimed at achieving sustainable economic growth while promoting global value chain (GVC) upgrading. This study develops an integrated analytical framework that combines a production–demand-based theoretical model with firm-level empirical analysis to examine how domestic and international circulation jointly influence value chain upgrading. Using multiple datasets, including the World Input–Output Database (WIOD), the China Industrial Enterprise Database, and the China Customs Database, the study provides empirical evidence on the mechanisms underlying GVC upgrading. The results show that within the domestic circulation framework, firms positioned further upstream in the value chain experience significantly slower upgrading, reflecting structural constraints such as limited domestic circulation depth and weaker feedback effects. The findings further indicate that dual circulation plays a critical role in overcoming these constraints. Improvements in domestic technological capabilities enhance firms’ competitiveness in global markets, facilitating their transition into higher value-added segments of GVCs. Domestic and international circulation thus operate in a mutually reinforcing manner. Overall, the study highlights the importance of strengthening domestic circulation while deepening international engagement to achieve sustainable and coordinated value chain upgrading. Full article

Anticipating the socio-environmental impacts of spatial planning strategies is a prerequisite for sustainable development pathways. Land change models are increasingly employed to evaluate the impacts of spatial planning on land use and land cover, and their subsequent effects on ecosystem services and environmental resources. Nevertheless, modelling land use and land cover changes, and their interactions, at a fine scale to preserve future landscape patterns has been identified as a key challenge in the land change science community. This paper presents an innovative process-based model—the FORecasting landscapE SCEnarios Model (FORESCEM)—designed to spatially simulate fine-scale future land use and land cover changes (LUCC) based on narratives developed through participatory or expert-driven approaches. By clearly distinguishing land covers and land uses as two different but related inputs, its conception and architecture enable the assessment of interactions among LUCC within human-managed landscapes. It relies on conventional functions and properties of LUCC models, and aims at completing the existing land change models. Applied on a French case study, the validation results demonstrate the model’s capability to replicate LUCC dynamics, effectively simulating trend-based and trend-breaking LUCC trajectories under contrasting scenarios. More broadly, this paper questions and discusses the validation of land change models used for simulating future LUCC. Full article