Search Results (1,984)

Knowledge sharing (KS) among healthcare professionals is essential for sustaining organisational learning and facilitating the transfer of expertise between experienced and less experienced professionals, thereby supporting workforce stability and retention in healthcare organisations (HCOs). However, despite its importance, high turnover among healthcare professionals remains a significant and persistent challenge in public HCOs, indicating a potential gap in understanding the mechanisms that support workforce stability. To address this gap, this study examines the interplay between work performance (WP), satisfaction with co-workers (CW), KS and turnover intention (TI) among healthcare professionals. Data from 220 respondents were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) within the Input–Process–Output (IPO) framework. The results indicate that CW positively influences KS, while KS has a negative effect on TI, thereby reducing TI. In contrast, WP does not have a statistically significant effect on KS, nor does it indirectly influence TI through KS. Furthermore, although both WP and CW were hypothesised to be predictors of KS, only CW demonstrates a significant indirect effect on TI through KS. Grounded in Social Exchange Theory (SET) and the Knowledge-Based View (KBV), the results highlight the role of KS and interpersonal relationships in supporting sustainable human resource management (SHRM). Although sustainability-related dimensions were not directly measured, the results suggest potential implications for the Sustainable Development Goals (SDGs), particularly SDG 3, SDG 8, and SDG 9. Full article

We analyze the evolution of atmospheric and surface physical properties over the region of the Earth observed by the Meteosat Second Generation (MSG) satellites during the period 2005–2024. Long-term changes are detected in the observed radiances, with a decrease in the solar domain (−1.3%) and an increase in the thermal infrared domain (+0.4%), consistent with trends reported by independent broadband radiometers such as CERES. The outgoing solar radiance (OSR) exhibits a marked decline, which we associate with a reduction in low-level cloud cover within the nominal Meteosat field of view (MFoV) centered at 0° longitude. Changes in atmospheric CO₂ concentration also contribute to the observed radiative imbalance at the top of the atmosphere (TOA). Instrument calibration stability and inter-satellite homogenization across the MSG series are explicitly addressed, enabling the detection of robust interdecadal signals. By subdividing the MFoV into 60 regional sectors, we characterize spatial variations in cloud amount at low and high atmospheric levels and relate these changes to regional TOA radiative imbalances and concurrent variations in Atlantic sea surface temperature (SSTs). The spectral information provided by SEVIRI allows a more detailed attribution of radiative changes than broadband observations alone from other instruments. In particular, radiances measured in the atmospheric split-window region near 11 µm are shown to be sensitive to variations in low-tropospheric humidity, which exhibits a widespread decadal-scale increase. The results indicate a close coupling between cloud-cover changes, radiative fluxes, and SST evolution on the recent interdecadal time scale. The observed decrease in low-level total cloud cover is independently in line with ECMWF ERA5 reanalysis data. These findings highlight the value of long, stable geostationary observations for investigating atmosphere–ocean interactions and their role in regional climate variability. Full article

In this work, we report the fabrication of SnO₂-based composite nanostructures in view of their application as a sensor element toward N₂O gas exposure. The samples were produced by laser ablation of a composite SnO₂-TiO₂ target performed in air at atmospheric pressure (in open air). We examined how the structure, morphology, composition, and physical properties of the samples change with the TiO₂ content being introduced into the SnO₂ target. The laser ablation of SnO₂-based targets in open air produced samples with a structure in which SnO₂ and SnO crystal phases co-existed, as the crystal phases were distinguished in separate nanoparticles. The nanoparticles formed a complex porous structure with oxygen-related defects. We investigated the gas-sensing properties of composite SnO₂-based sensor elements working under UV irradiation. The highest response to N₂O exposure and the fastest response/recovery times were demonstrated by the sensor element produced by the laser ablation of a composite target prepared by 10 wt% TiO₂ in SnO₂. Additionally, we found that a small amount (below 0.1 wt%) of noble metal (Pt) added to the sensor element substantially improved the gas sensor performance without inducing significant structural and/or morphological changes. Further, we explored how simultaneous irradiation of the sensor surface with UV and visible light changes the sensor properties. The best sensor performance toward N₂O exposure was achieved by irradiating the Pt-doped SnO₂-TiO₂ sensor surface simultaneously with UV and red lights. Full article

This study analyzes the feasibility of increasing the energy and economic efficiency of a residential heating and domestic hot water (DHW) preparation system with a solar-assisted air-to-water heat pump (AWHP), implemented in southeast Romania. The following options are evaluated from the sustainability point of view (energy, economic and CO₂ emissions): renovation of the building and modernization of the system by integrating an electric accumulator, increasing the capacity of photovoltaic panels (PV) and solar thermal collectors (STCs), and the option of replacing the AWHP with a ground-source heat pump (GSHP) with a vertical loop (GSHP-VL) and a GSHP with a horizontal loop (GSHP-HL). The energy performance of heating systems was simulated using GeoT*SOL software. The results show that by renovating a home, the energy requirement for heating decreased by about 58%; therefore, following the current financial rules applied to prosumers, the GSHP-VL system has the best energy performance (electricity consumption and solar coverage rate of this consumption), economic performance (investment recovery period and annual operating cost) and environmental performance (lowest CO₂ emissions) and that through a government program that promotes energy efficiency and the use of renewable energy sources in homes, capital costs can be reduced by (43–57)% in the case of systems with HP, PV and electric storage. This study shows that a 5 kW PV system combined with 5 kWh battery cannot cover the full heat demand of a medium-to-large house during the winter, and for full energy independence, a larger PV array paired with a higher-capacity battery is necessary. Generous government subsidies amounting to 50% can reduce the payback period for such investments from (11.26–14.68) years to (5.86–7.26) years. Full article

Grounded in the knowledge-based view and the emerging logic of digital knowledge governance, this study investigates how blockchain adoption strategies reshape inter-firm knowledge-sharing and sustainable innovation. A game theory and decision-optimization model is developed to capture the interplay among blockchain cost, knowledge trust, and collaboration incentives under four adoption scenarios between knowledge creators and users. The results uncover a double-threshold mechanism: when blockchain costs are high, the technology suppresses collaboration by increasing coordination frictions; yet as costs fall below a critical level, blockchain shifts from a trust-reinforcing tool to a catalyst for co-creation efficiency and joint environmental performance. Interestingly, partial adoption can yield a trust paradox-enhancing local reliability but diminish system-wide innovation synergy. As adoption diffuses, the equilibrium dynamically evolves from non-adoption to asymmetry and eventually bilateral digital trust, producing higher social welfare and resilience. Among asymmetric modes, creator-led adoption consistently outperforms user-led adoption, underscoring the strategic value of upstream knowledge transparency. The findings extend the knowledge-based view to the context of digital trust architecture and provide actionable insights for policymakers and firms seeking to build trust-based, knowledge-driven, and digitally sustainable innovation ecosystems. Full article

Measurement of material thermodynamic parameters plays a crucial role in understanding the interactions between host materials and guest species. Therefore, developing a general-purpose system for thermodynamic parameter measurement is of great significance. In this work, a complete gas–solid interface thermodynamic parameter measurement platform was developed based on isothermal adsorption and a resonant microcantilever testing platform. Unlike conventional adsorption measurement systems that rely on manual, multi-cycle adsorption–desorption processes, the proposed platform integrates an automated hardware–software architecture together with a stepwise concentration-gradient protocol and on-chip thermal desorption, enabling continuous and efficient acquisition of adsorption isotherms. The study includes: (i) construction of an improved thermodynamic parameter extraction model based on the Sips model, (ii) development of an integrated resonant microcantilever control and acquisition module using a modified Fourier algorithm, and (iii) implementation of an automated testing and data analysis software framework developed in LabVIEW based on the Queued Message Handler (QMH) architecture. The system was validated from both hardware performance and material testing perspectives using CO₂ adsorption on H-SSZ-13 as a representative case. The results show that the system achieves a maximum sampling rate of 10,000 pts (points per second), with minimum root-mean-square (RMS) noise levels of 0.0083 Hz for frequency and 0.0109 °C for temperature. The PID temperature-control settling time (0.1%) is 24.9 ms, and the frequency-response settling time (0.01%) is 9.6 ms. Thermodynamic parameters including entropy change (ΔS), enthalpy change (ΔH), and Gibbs free energy change (ΔG) were successfully extracted during CO₂ adsorption at 294.15 K under different relative uptakes. Reproducibility was verified across three independent samples, yielding a standard deviation of 9.1 J·mol⁻¹ for ΔS at 2% relative uptake and relative standard deviations of 6.85% and 8.12% for ΔH and ΔG, respectively. These results demonstrate that the proposed thermodynamic measurement platform features a simple architecture, superior performance, and high reproducibility in gas–solid interface thermodynamic studies, showing strong potential for future commercialization. Full article

Accurate and spatially resolved yield estimation is a critical requirement for precision agriculture and orchard management. This paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental factor-graph optimization. Camera poses are obtained using ZED GNSS–VIO fusion and subsequently refined using an iSAM2-based nonlinear smoothing approach that incorporates strong relative-motion constraints and soft global ENU (East-North-Up) translation priors. Apples are detected using a YOLO-based model and associated across frames via CoTracker3, enabling robust multi-view landmark reconstruction. Reprojection factors and landmark priors are incorporated into a unified nonlinear factor graph to jointly optimize camera trajectories and 3D apple positions. The reconstructed apples are spatially aggregated into a grid-based mass map, where individual fruit volumes are estimated assuming spherical geometry and converted to mass using density models. The resulting ENU-referenced yield plot provides a structured representation of orchard production variability. Experimental results demonstrate significant reductions in reprojection error after optimization and improved global consistency of the trajectory, leading to stable and spatially coherent 3D reconstructions. The proposed pipeline bridges perception, geometry, and optimization, providing a scalable solution for orchard-scale yield mapping and decision support in precision agriculture. Full article

The dynamic process through which latent differences in business models of partners escalate into daily operational conflicts within exploitation alliances remains insufficiently explained. This study examines how alignment in partner business models influences operational conflicts, a key determinant of exploitation alliance sustainability. Questionnaire data from 110 experts in Chinese residential joint ventures (JVs) were used to test the proposed hypotheses. The findings indicate that key resources (KRs) and profit formula (PF) indirectly affect operational conflicts through jointly established core business standards (CBSs). Counterintuitively, these standards significantly increase operational conflict risks (OCRs) when they institutionalize underlying misalignments, thereby acting as a full mediator. The results advance theory by clarifying the micro-process of institutionalized misalignment and refining the Resource-Based View (RBV) in alliance contexts. Practically, the study highlights the importance of conducting thorough ex-ante business model analysis, co-creating operational standards, and undertaking continuous alignment reviews to mitigate conflict and enhance JV viability. Full article

Neutrophils are among the early responders of the innate immune system and play a key role in host defense against viral infections. Beyond their classical antimicrobial functions, neutrophils can engage in a specialized defense mechanism by releasing web-like extracellular DNA known as neutrophil extracellular traps (NETs). These extracellular traps are a mesh-like network of chromatin DNA decorated with cellular components, including histones, proteases, and antimicrobial enzymes, that function to contain and limit the spread of pathogens. While NET formation contributes to antiviral immunity, accumulating evidence indicates that excessive or dysregulated NET formation can significantly contribute to immunopathology during viral infections. Thus, depending on the context and outcome, NET formation may be viewed as a double-edged sword. Therefore, understanding the regulatory mechanisms governing NET formation and its harmful effects is critical for developing therapeutic strategies that enhance antiviral defense while minimizing tissue damage. In this review, we provide a comprehensive overview of the molecular mechanisms that drive NET formation and clearance, with a particular focus on how viruses modulate these processes to influence disease outcome. We also discuss the pathways underlying NET formation and subsequent neutrophil cell death (NETosis), including canonical and non-canonical pathways, and highlight key signaling axes involving SYK, MAPKs, and NF-κB. Using SARS-CoV-2 and hepatitis B virus as representative models, we examine how different viral components trigger, exploit, or evade NET targeting and how persistent accumulation of NETs can contribute to hyperinflammation, progressive tissue injury, and post-viral syndromes. We further explore emerging evidence linking impaired NET clearance and neutrophil heterogeneity, particularly low-density neutrophils (LDNs), to chronic inflammation and post-viral sequelae such as long COVID and autoimmune hepatitis. Finally, we summarize current and emerging therapeutic strategies aimed at modulating NET formation or enhancing NET clearance. Altogether, this review underscores the dual nature of NETs in viral infections, highlighting their potential roles in antiviral defense and tissue injury, and provides a framework for the development of targeted interventions to limit virus-induced immunopathology. Full article

Diapause is a vital overwintering strategy for many insects, yet its comprehensive molecular architecture remains elusive. In the polyphagous pest Helicoverpa armigera, facultative pupal diapause is key to its ecological success. To elucidate the complex diapause regulatory network, we conducted a transcriptomic analysis of diapause (DP) versus non-diapause (NP) pupal brains across early pupal development (days 2, 5, and 10). Integrated analyses, including differential expression, persistent gene identification, weighted gene co-expression network analysis (WGCNA), and multiscale embedded network analysis (MEGENA), were employed to define core regulatory modules and hubs. The number of differentially expressed genes (DEGs) increased over time, with 1781 genes persistently regulated across all time points, enriched in mitochondrial metabolism, hormone signaling, and chromatin remodeling. WGCNA revealed a diapause-associated module (red) linked to RNA processing/transcription and a development-associated module (blue) enriched for translation and mitochondrial metabolism. Network analyses pinpointed three central hub genes: DDX5 and PLK4 (downregulated in diapause, upregulated upon 20E treatment) and TAF5L (upregulated in diapause, downregulated after 20E). This study provides a systems-level view of the transcriptional landscape governing pupal diapause in H. armigera and identifies novel candidate regulators for future functional studies. Full article

Fine particulate matter (PM2.5) and ozone (O₃) are the key factors restricting the continuous improvement of air quality in the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZT). Due to the potential correlation between variations in urban PM2.5–O₃ concentration, the analysis of its composite pollution characteristics is helpful in formulating accurate and thorough air control policies. Based on the long-term concentration change in PM2.5 and O₃, this study analyzed the features and synergistic factors of PM2.5–O₃ pollution in the CZT by using spatial autocorrelation and a linear driving model of PM2.5–O₃. The results showed that from 2017 to 2023, under the current Chinese atmospheric environment standard, the CZT saw four combined pollution days. However, if the daily limit values were viewed in line with Grade II of the WHO transition period (O₃: 120 μg/m³, PM2.5: 50 μg/m³), the combined pollution days would reach 111. The concentration of O₃ in Zhuzhou and Xiangtan was about 10 μg/m³ lower than that in Changsha. Lower SO₂ levels in Changsha might influence the partitioning of OH radicals and reactive nitrogen species, potentially affecting local O₃ formation efficiency. NO₂ and meteorological conditions jointly influence the co-variation in PM2.5 and O₃, with NO₂ playing a more significant role in PM2.5 formation. The long-term time series and daily concentrations of PM2.5 and O₃ in the CZT showed opposing values, but there were short-term synergistic events on the scale of daily concentrations, and the time period was typically 3–10 days. Low humidity and strong sunlight may cause antagonistic events in which the concentration of O₃ rises rapidly. Under static and stable weather conditions with low wind speed, no rainfall and moderate humidity, the concentration of PM2.5 and O₃ rose alternately on sunny and cloudy days, demonstrating synergistic growth. Full article

In unmanned ground vehicle (UGV) swarm systems, comprehensive environmental awareness is critical for coordinated operations. Yet they are frequently deployed in occlusion-rich, constrained environments where multi-agent visual fusion is essential. However, existing methods are critically limited by offline-calibrated extrinsic parameters, hindering flexible deployment, and by a strong co-visibility assumption, which fails under severe occlusion. To overcome these constraints, we introduce an end-to-end, calibration-free framework for the joint registration of cameras and subjects. Our approach begins with a single-view module that estimates subjects’ poses and appearance features. Subsequently, a novel graph-based pose propagation module (GPPM) treats UGVs’ cameras as nodes in a graph, connecting them with edges when they share co-visible subjects identified via appearance matching. Breadth-first search (BFS) then finds the shortest registration path from any camera to a designated root camera, enabling pose propagation via local co-visibility links and global alignment of all subjects into a unified bird’s-eye-view (BEV) space. This strategy relaxes the stringent requirement of full co-visibility with the root node. A multi-task loss function is proposed to jointly optimize pose estimation and feature matching. Trained and evaluated on a synthetic dataset with occlusions (CSRD-O) collected by a UGV swarm system, our framework achieves mean camera pose errors of 1.57 m/8.70° and mean subject pose errors of 1.40 m/9.14°. Furthermore, we demonstrate a scene monitoring task using a UGV swarm system. Experiments show that the proposed method generates robust BEV estimates even under severe occlusion and low inter-view overlap. This work presents a purely visual, self-calibrating multi-view fusion perception scheme, demonstrating its potential to support cooperative perception, task-oriented monitoring, and collective situational awareness in UGV swarm systems. Full article

Formative assessment is widely seen as a key teaching strategy to support student learning; however, evidence about its connection with self-regulated learning and the alignment between teachers’ and students’ perceptions remains mixed. This study explored the role of formative assessment in promoting self-regulated learning in lower secondary mathematics by incorporating both students’ and teachers’ viewpoints. From a co-regulatory perspective, formative assessment is considered a process developed through ongoing interactions between teachers and students and shared views of assessment practices. The sample included 305 students from Grades 5–9 and 39 mathematics teachers. Students reported their perceptions of formative assessment practices and self-regulated learning, while teachers reported their own practices. Analyses included Pearson correlation and multiple regression at the student level, along with class-level comparisons of teacher–student perceptions and analyses of perceptual agreement. Results revealed that students’ perceptions of formative assessment were positively linked to cognitive, metacognitive, behavioral, and motivational dimensions of self-regulated learning. Multiple regression results showed that different aspects of formative assessment significantly predicted students’ self-regulation, with the greatest explained variance in behavioral self-regulation. Teachers believed they used more formative assessment practices than students perceived. Additionally, higher levels of perceptual agreement between teachers and students, especially in clarifying learning goals and gathering evidence of learning, were associated with increased behavioral regulation and motivational independence among students. These findings emphasize formative assessment in mathematics as a relational and co-regulatory process that relies on shared understanding between teachers and students. Full article

This research addresses a structural cybernetic anomaly within strategic management precipitated by the integration of artificial intelligence into the organizational core. Traditional paradigms, specifically the resource-based view and the dynamic capabilities framework, operate under closed-system, first-order cybernetic assumptions that fail to capture the dissipative nature of algorithmic agents. By conceptualizing the enterprise as a complex adaptive system operating far from thermodynamic equilibrium, this study introduces the theory of dynamic cognitive advantage. Grounded in second-order cybernetics, the framework posits that competitive differentiation emerges from the historical, recursive, structural coupling of human semantic intent and machine syntactic processing. This research formalizes this co-evolutionary dynamic utilizing coupled non-linear differential equations and time decay integrals. Furthermore, it operationalizes the central mechanism of this capability—the cognitive flywheel—and proposes a fractal governance architecture to mitigate systemic vulnerabilities such as automation bias. To transition these propositions into management science, a proposed mixed-methods empirical research agenda is presented. It outlines a future partial least squares–structural equation modeling (PLS-SEM) approach to test the mediating role of the cognitive flywheel and the moderating effect of fractal governance on organizational resilience. This research provides a mathematically formalized, empirically testable architecture for navigating the artificial intelligence economy. Full article

Drought stress severely restricts the growth of pear trees. As a widely used drought-tolerant rootstock, Pyrus betulifolia exhibits stable growth performance; however, the molecular mechanisms underlying its drought tolerance remain to be elucidated. In this study, we investigated the molecular responses of P. betulifolia leaves to osmotic stress induced by 20% PEG-4000 using time-series RNA-seq technology. A total of 3745 differentially expressed genes were identified, with transcriptional changes peaking at 6 h, indicating a critical phase of transcriptional reprogramming during drought response. Genes associated with osmotic adjustment (e.g., P5CS) and oxidative stress responses (e.g., SOD and POD) were significantly upregulated between 6 and 12 h. Weighted gene co-expression network analysis (WGCNA) identified three distinct temporal modules and screened out NF-Y, RVE1, COL9, COL6, C2C2 zinc finger proteins, and Pseudo ARR-B as putative key regulators, whose expression patterns were validated using qRT-PCR. Collectively, these results provide a comprehensive view of the temporal transcriptional dynamics of drought response in P. betulifolia and offer valuable candidate gene resources for further functional studies and drought tolerance breeding. Full article