Search Results (2,352)

Open-hole compression (OHC) tests were carried out on non-crimp fabric (NCF) laminates with varied open-hole orientation (angle to the loading direction) and inter-hole spacing. Failure modes were documented by scanning electron microscopy (SEM), and the compressive strength was quantified. Finite element simulations in Abaqus were developed to replicate the tests, establishing a progressive-damage model for open-hole laminates under compression. Intralaminar failure was described using the three-dimensional Hashin failure criterion and a modified matrix compression criterion incorporating shear coupling effects, while interlaminar delamination was modeled with cohesive elements, enabling the simulation of damage initiation, growth, delamination, and final collapse. The results show that hole orientation and spacing have a pronounced effect on open-hole compression (OHC) strength. A spacing threshold is observed, beyond which further increases in spacing provide little additional benefit. In contrast, the apparent elastic stiffness is essentially insensitive to hole spacing and orientation. The combined intralaminar and interlaminar model successfully reproduces the characteristic mechanical response—linear elasticity followed by catastrophic failure—in good agreement with the experiments. Full article

Research on road transport infrastructures is rapidly evolving as electrification, automation, and digital connectivity reshape how systems are designed, operated, and managed. This study presents a combined bibliometric, scientometric, and network analysis of 2755 publications published between 2021 and 2025 to map the intellectual structure, main contributors, and dominant technological themes shaping contemporary road transport research. Using data from the Web of Science Core Collection, co-occurrence mapping, thematic analysis, and collaboration networks were generated using Bibliometrix and VOSviewer. The results reveal strong growth in research output, with China, the United States, and Europe forming the core of high-impact publication and collaboration networks. Six bibliometric clusters were identified and consolidated into three overarching domains: road transport systems, emphasizing vehicle dynamics, control, and real-time computational frameworks; energy and efficiency-oriented mobility research, focusing on electrification, optimization, and infrastructure integration; and emerging digital technologies, including IoT, AI, and autonomous vehicles. The analysis highlights persistent research gaps related to interoperability, cybersecurity, large-scale deployment, and governance of intelligent transport infrastructures. Overall, the findings provide a data-driven overview of current research priorities and structural patterns shaping next-generation road transport systems. Full article

The rapid growth of precision agriculture has accelerated the deployment of plant protection unmanned aerial vehicles (UAVs). However, reliable data resources for vision-based intelligent supervision of operational states, such as whether a UAV is currently spraying, remain limited. Most publicly available UAV detection datasets target urban security and surveillance scenarios, where annotations emphasize object localization rather than agricultural operation state recognition, making them insufficient for farmland spraying supervision. Therefore, agricultural-oriented data resources are needed to cover diverse backgrounds and include operation state labels, thereby supporting both academic research and practical deployment. In this study, we construct and release the first multi-background dataset dedicated to agricultural UAV spraying behavior recognition. The dataset contains 9548 high-quality annotated images spanning the following six typical backgrounds: green cropland, bare farmland, orchard, woodland, mountainous terrain, and sky. For each UAV instance, we provide both a bounding box and a binary operation state label, namely spraying and flying without spraying. We further conduct systematic benchmark evaluations of mainstream object detection algorithms on this dataset. The dataset captures agriculture-specific challenges, including a high proportion of small objects, substantial scale variation, motion blur, and complex dynamic backgrounds, and can be used to assess algorithm robustness in real-world agricultural settings. Benchmark results show that YOLOv5n achieves the best overall performance, with an accuracy of 97.86% and an mAP@50 of 98.30%. This dataset provides critical data support for automated supervision of plant protection UAV spraying operations and precision agriculture monitoring platforms. Full article

Organizational culture is widely recognized as an important contextual factor shaping career development and long-term human resource sustainability. Although prior research has examined organizational culture, career development, and sustainable HRM, these constructs have often been studied separately and predominantly within Western contexts. This study addresses this gap by analyzing their interrelationships within a transitional economy. Grounded in sustainable human resource management and sustainable careers perspectives, the study examines how organizational culture typologies influence career development and HR sustainability. Career development is operationalized through organizational career growth and career competences. Survey data were collected from 542 employees across 23 IT and manufacturing companies in Serbia and analyzed using factor analysis and multiple regression. The findings show that organizational culture significantly shapes career growth opportunities and career competences and is also directly related to HR sustainability. Person-oriented cultures are associated with more favorable career development conditions and higher levels of HR sustainability, while power- and role-oriented cultures are linked to weaker outcomes. Career growth and career competences further emerge as key mechanisms supporting long-term workforce sustainability. This study contributes to the literature by integrating organizational culture, career development, and HR sustainability into a single analytical framework within a transitional economy context and provides practical insights for managers aiming to foster sustainable careers and long-term HR sustainability. Full article

Understanding when and where to shift land from agriculture to forestry is essential for designing sustainable land use strategies that align with climate, biodiversity, and rural development goals. However, traditional profitability comparisons rely on long-term discounting, which is highly sensitive to assumptions and often misaligned with the shorter-term decision-making horizons that are relevant for policymakers. This study presents a deposit-based framework that interprets annual timber biomass growth as accumulating economic value, enabling direct, per-hectare comparisons with yearly agricultural profits. The framework integrates parcel-level spatial data, land quality indicators, national statistics, and expert inputs to produce high-resolution maps of annual profitability for both agriculture and forestry. Applied to the case of Latvia, the results show strong spatial variation in agricultural returns, particularly in low-quality areas where profits are marginal or negative. By contrast, forestry provides more stable, though modest, economic gains across a wide range of biophysical conditions. These insights help identify where afforestation becomes a financially viable land use alternative. The framework is designed to be transferable to other regions by substituting local data on land quality, prices and growth. It complements policy instruments such as performance-based CAP payments and afforestation support, offering a future-oriented tool for spatially explicit and economically grounded land use planning. Full article

Digitalisation is reshaping commercial systems in Europe, yet the joint evolution of national digital capabilities, e-commerce and macroeconomic performance remains imperfectly understood. This article develops a parsimonious Digital Maturity Index for the EU-27 over 2015–2023 and examines its association with the share of enterprise turnover generated through e-commerce using a systems-oriented econometric design. Two-way fixed-effects and dynamic panel models show that e-commerce turnover is strongly persistent within countries and systematically higher in more trade-open economies and in labour markets with slightly higher unemployment, after controlling for income and unobserved heterogeneity. The marginal effect of digital maturity on e-commerce intensity is small and statistically fragile, suggesting that digital capabilities act more as a slow-moving state variable than as a direct short-run driver of online sales. The marginal within-country effect of digital maturity on e-commerce intensity is small and statistically fragile once unobserved heterogeneity is controlled for, whereas trade openness and labour-market conditions remain robust correlates. The PVAR results suggest a stable system with strong persistence in e-commerce and digital maturity, limited spillovers to growth and a pronounced temporary contraction in output during the COVID-19 shock. Full article

Metal pollution from metallurgical emissions poses serious environmental and public health risks in Kazakhstan. A replicated pot-culture experiment (n = 4) in a completely randomized design under controlled phytotron conditions evaluated biomass production and metal accumulation in six crop and forage species, alfalfa (Medicago sativa), amaranth (Amaranthus spp.), corn (Zea mays), mustard (Brassica juncea), rapeseed (Brassica napus), and sunflower (Helianthus annuus); three ornamental species, purple coneflower (Echinacea purpurea), marigold (Tagetes spp., ‘Tiger Eyes’), and sweet alyssum (Lobularia maritima); and three native wild plants, greater burdock (Arctium lappa), horse sorrel (Rumex confertus), and mug wort (Artemisia vulgaris). Plants were grown in soils collected from the Qarmet industrial zone in Temirtau, central Kazakhstan. Initial soil analysis revealed substantial mixed-metal contamination, ranked as Mn > Ba > Zn > Sr > Cr > Pb > Cu > Ni > B > Co. Mn reached 1059 mg·kg⁻¹, ~50-fold higher than B (22.7 mg·kg⁻¹). Ba (620 mg·kg⁻¹) exceeded FAO/WHO limits sixfold, Zn (204 mg·kg⁻¹) surpassed the lower threshold, and Pb (41.6 mg·kg⁻¹) approached permissible levels, while Cr, Cu, Ni, Co, and Sr were lower. Biomass production varied markedly among species: corn and sunflower produced the highest shoot biomass (126.8 and 60.9 g·plant⁻¹), whereas horse sorrel had the greatest root biomass (54.4 g·plant⁻¹). Root-to-shoot ratios indicated shoot-oriented growth (>1–8) in most species, except horse sorrel and burdock (<1). Metal accumulation was strongly species-specific. Corn and marigold accumulated Co, Pb, Cr, Mn, Ni, Cu, B, and Ba but showed limited translocation (transfer function, TF < 0.5), whereas sunflower, amaranth, and mug wort exhibited moderate to high translocation (TF > 0.8 to <1) for selected metals. Corn is recommended for high-biomass metal removal, marigold for stabilization, sunflower, horse sorrel, and mug wort for multi-metal extraction, and amaranth and coneflower for targeted Co, Ni, and Cu translocation, supporting sustainable remediation of industrially contaminated soils. Full article

Accurate measurement of plant height in leafy vegetables is challenging due to their short stature, high planting density, and severe canopy occlusion during later growth stages. These factors often limit the reliability of single-plant monitoring across the full growth cycle in open-field environments. To address this, we propose a multi-temporal point cloud alignment method for accurate plant height measurement, focusing on Choy Sum (Brassica rapa var. parachinensis). The method estimates plant height by calculating the vertical distance between the canopy and the ground. Multi-temporal point cloud maps are reconstructed using an enhanced Oriented FAST and Rotated BRIEF–Simultaneous Localization and Mapping (ORB-SLAM3) algorithm. A fixed checkerboard calibration board, leveled using a spirit level, ensures proper vertical alignment of the Z-axis and unifies coordinate systems across growth stages. Ground and plant points are separated using the Excess Green (ExG) index. During early growth stages, when the soil is minimally occluded, ground point clouds are extracted and used to construct a high-precision reference ground model through Cloth Simulation Filtering (CSF) and Kriging interpolation, compensating for canopy occlusion and noise. In later growth stages, plant point cloud data are spatially aligned with this reconstructed ground surface. Individual plants are identified using an improved Euclidean clustering algorithm, and consistent measurement regions are defined. Within each region, a ground plane is fitted using the Random Sample Consensus (RANSAC) algorithm to ensure alignment with the X–Y plane. Plant height is then determined by the elevation difference between the canopy and the interpolated ground surface. Experimental results show mean absolute errors (MAEs) of 7.19 mm and 18.45 mm for early and late growth stages, respectively, with coefficients of determination (R²) exceeding 0.85. These findings demonstrate that the proposed method provides reliable and continuous plant height monitoring across the full growth cycle, offering a robust solution for high-throughput phenotyping of leafy vegetables in field environments. Full article

Employing textual analysis of the “short-term vision” vocabulary in annual reports, we investigate the impact of managerial myopia on firm innovation and performance. Our results indicate that managerial myopia hampers innovation, and this result remains robust across a battery of robustness checks. Managerial myopia also weakens the positive impact of innovation on firm growth, and value in the long run. We find that state ownership and good corporate governance mitigate the negative impact of managerial myopia. The evidence supports the upper echelon theory and time orientation theoretical framework. This paper enriches the research on the influencing factors of corporate innovation, by providing evidence that people’s perception of time affects decision making and provides support for government ownership and strong corporate governance practices in alleviating the negative consequences of managerial myopia. Full article

This study examines the dynamics of economic freedom in nine advanced democracies in comparison to China over the 1970–2022 period. Using data from the Fraser Institute and the Manifesto Project Database, we apply a Bayesian time-series methodology to identify three key patterns. First, economic freedom in China has exhibited a sustained increase since the 1980s. Second, by contrast, liberal democracies in advanced economies show a decline in economic freedom since the turn of the millennium. Third, evidence from party manifestos indicates a rising prevalence of de-growth-oriented political preferences in democratic economies over the past decade. As a potential avenue for future research, we propose framing economic freedom as a public good, in line with Hayekian principles. Overall, the study provides a descriptive foundation of the relationship between economic freedom, political preferences, and economic performance. Full article

Developing high-quality regional integration requires a good-quality water environment. In this study, the impact of formal and informal environmental regulation (FIER) on water environment governance performance (WEP) is examined using a fixed-effects model and spatial Durbin model with a panel data sample of 281 cities from 2011 to 2022. It is found that (i) there is an inverted U-shaped relationship between FIER and WEP, which is first promoted and then inhibited and remains significant after endogeneity exploration and multiple robustness tests; (ii) the pressure of economic growth has weakened this elationship, while the digital economy has strengthened it; and (iii) further analysis reveals that there is an inverted U-shaped relationship between the local and spillover effects of FIER on WEP. Therefore, WEP can be improved by dynamically adjusting the intensity of FIER, optimizing the appraisal orientation of local governments, and accelerating the integration of digital economy and environmental governance Full article

Exploring the spatiotemporal evolution of building stock in African countries is of great significance for understanding the urbanization process, regional development disparities, and sustainable development pathways in the Global South. Integrating long-term (1975–2020), 100 m resolution building stock data for Tanzania with multi-source environmental and socioeconomic datasets, this study employed GIS spatial analysis techniques—including optimized hotspot analysis, standard deviational ellipse, and geographical detector—to investigate the spatiotemporal evolution characteristics and influencing factors of building differentiation. The results indicate that over the 45-year period, Tanzania’s building stock underwent rapid expansion, with a 3.83-fold increase in volume and a 4.93-fold increase in area, while the average height decreased continuously by 1.04 m. This growth was predominantly driven by the expansion of residential buildings. The spatial distribution of buildings exhibited a “north-dense, south-sparse” pattern with agglomeration along traffic axes. During 1975–1990, building growth hotspots were concentrated in western and southern regions, shifting to areas surrounding Lake Victoria and central administrative centers during 2005–2020. In contrast, coldspots expanded progressively from northern, northeastern regions and Zanzibar Island to parts of the southern and eastern coasts. The building distribution consistently maintained a northwest–southeast spatial orientation, with increasingly prominent directional characteristics; the centroid of building distribution moved more than 90 km northwestward, and the agglomeration intensity continued to increase. Socioeconomic factors—including population density, road network density, and GDP density—have a significantly stronger influence on building distribution than natural factors. Among natural factors, only river network density exhibits a significant effect, while constraints such as slope and terrain relief are relatively insignificant. Full article

This study presents a comprehensive bibliometric and science mapping analysis of research on co-creation and co-production in public administration, based on 819 publications indexed in the Web of Science (WoS). The analysis of scientific production in this field shows sparse early contributions before 2005, followed by steady growth after 2010 and accelerated expansion from 2016 onward, driven primarily by European and United States research communities. In terms of scholarly influence, the results identify a stable core of highly productive and influential authors, institutions, and countries, with strong concentration in Northern and Western Europe and Anglo-Saxon contexts. To address the intellectual structure of the field, science mapping identifies four dominant thematic clusters: (1) co-production and value creation, (2) participation and public engagement, (3) governance and policy, and (4) knowledge development, lessons learned, and evaluative insights. Examining thematic and keyword evolution over time, the findings indicate a shift from early conceptual and normative discussions toward more applied and implementation-oriented research, with increasing attention to barriers, challenges, and enabling conditions in recent years. Overall, the findings show that research on co-creation and co-production has evolved from conceptual fragmentation toward greater thematic consolidation and analytical maturity, while persistent implementation challenges remain. By systematically mapping these developments, the study provides a structured overview that supports future conceptual integration and informs both research agendas and practice-oriented discussions on co-creation and co-production in public administration. Full article

Despite substantial growth in eco-innovation (EI) research, most studies rely on cross-sectional data, limiting understanding of the temporal dynamics of EI and its determinants under varying macroeconomic conditions. This study addresses this gap by analysing panel data from Spanish manufacturing firms across three phases of the business cycle: pre-crisis expansion (2004–2007), the global financial crisis (2008–2013), and recovery (2014–2016). We investigate the drivers of two distinct types of eco-innovation: efficiency EI (energy and material savings) and environmental EI (reducing environmental harm), focusing on the role of regulation, institutional interventions, and firm-level innovation capacities. Using a random-effects panel probit model that accounts for unobserved firm heterogeneity, we examine how these drivers operate across different macroeconomic contexts. Our findings reveal that regulation consistently fosters EI, while the influence of subsidies, R&D capacity, and collaborative networks is more context-dependent, particularly during economic downturns. The results highlight the cumulative, path-dependent, and cyclical nature of EI, providing novel insights into the conditions that enable firms to sustain green innovation over time. Drivers of eco-innovation differ systematically between efficiency- and environment-oriented strategies, and these differences remain stable over the business cycle, implying distinct underlying mechanisms and policy implications. Accordingly, policy design—particularly during economic downturns—should distinguish between reinforcing incentives for internal efficiency improvements and sustaining regulatory and financial support for environmental EI. Full article

The exponential growth of data generated by modern digital applications, including systems inspired by Industrial Internet of Things (IIoT) requirements, has accelerated the adoption of NoSQL databases due to their scalability, flexibility, and performance advantages over traditional relational systems. Among document-oriented solutions, MongoDB and RavenDB stand out due to their architectural features and their ability to manage dynamic, large-scale datasets. This paper presents a comparative analysis of MongoDB and RavenDB, focusing on the performance of fundamental CRUD (Create, Read, Update, Delete) operations. To ensure a controlled performance evaluation, a mobile and web application for managing product orders was implemented as a case study inspired by IIoT data characteristics, such as high data volume and frequent transactional operations, with experiments conducted on datasets ranging from 1000 to 1,000,000 records. Beyond the core CRUD evaluation, the study also investigates advanced operational scenarios, including joint processing strategies (lookup versus document inclusion), bulk data ingestion techniques, aggregation performance, and full-text search capabilities. These complementary tests provide deeper insight into the systems’ architectural strengths and their behavior under more complex and data-intensive workloads. The experimental results highlight MongoDB’s consistent performance advantage in terms of response time, particularly with large data volumes, while RavenDB demonstrates competitive behavior and offers additional benefits such as built-in ACID compliance, automatic indexing, and optimized mechanisms for relational retrieval and bulk ingestion. The analysis does not propose a new benchmarking methodology but provides practical insights for selecting an appropriate document-oriented database for data intensive mobile and web application contexts, including IIoT-inspired data characteristics, based on a controlled single-node experimental setting, while acknowledging the limitations of a single-host experimental environment. Full article