Search Results (6,770)

This study evaluates the productive efficiency in the agrifood sector of 21 rural Bulgarian districts as a proxy for territorial competitiveness. Output-oriented Data Envelopment Analysis (DEA) was performed using district-level data from 2022 to 2024. The analysis incorporates five inputs related to labor, land, and capital and three economic outputs from agriculture and food processing. Results indicate substantial variation in efficiency among rural districts. Twelve districts form the efficiency frontier, with effective resource use and diverse structures; nine are inefficient due to scale or organizational/technological constraints. Bootstrap bias correction revealed standard DEA underestimates efficiency gaps. Frontier districts include large plains, mountainous regions and smaller, specialized systems, indicating diverse paths to competitiveness. A composite Territorial Competitiveness Index (TCI) showed frontier status does not guarantee efficiency, often due to underused manufacturing capital. Cluster analysis identified four performance groups needing different policy support, ranging from near-frontier territories that need knowledge transfer to deeply underperforming districts that require restructuring. No geographic clustering of efficiency was found, pointing to structural and institutional, rather than geographic, drivers. These results highlight the need for territorially tailored rural policies within the Common Agricultural Policy (CAP) and offer an empirical basis for diagnosing regional agrifood efficiency gaps. Full article

Background/Objectives: Adult spinal deformity (ASD) correction is a complex surgery to restore spinal alignment and relieve patients’ symptoms. Modern techniques and technologies allow for aggressive surgical correction in tissue-friendly ways that preserve anatomy and may enable faster recovery. Robotic-assisted posterior spinal stabilization may be used as an adjunct to complex ASD reconstruction to facilitate a minimally invasive approach, reduce perioperative morbidity and physiological insult, and allow for the performance of procedures traditionally reserved for large academic centers to be effectively performed by qualified surgeons in optimized patients at smaller hospitals with fewer resources. The objective of this study is to assess realignment, perioperative complications, and patient-reported outcomes of complex, minimally invasive, robotic-assisted adult spinal deformity correction in a surgical specialty hospital. Methods: Demographic, surgical, and perioperative data were collected from the medical record. The Oswestry Disability Index (ODI) and Numeric Rating Scale (NRS) for pain scores were collected preoperatively and at regular post-op visits. X-rays were captured preoperatively before hospital discharge and at follow-up visits. Results: Fifty consecutive deformity patients were corrected with a two-stage approach (anterior column reconstruction followed by posterior stabilization with robotic-assisted screw placement on the next day) at a 48-bed (eight operating rooms), surgeon-owned, subspecialty hospital. The average patient age was 70 years, and 64% were female. The average estimated blood loss (EBL) values for the first and second stages were 62 mL and 205 mL, respectively. The average operative time was 172 min during the first stage and 210 min for the second stage. Three interbody spacers (first stage) and 16 screws (second stage) were inserted on average in each procedure. The average length of stay (LOS) in the hospital was 5 days, and the average follow-up period was 10.6 months. No patients required a transfer to another facility with intensive care unit (ICU) capabilities, and none required a revision of hardware placement. There was an average reduction in the lumbar coronal scoliotic curve of 14.5° and an increase in lumbar lordosis of 14.8° at the latest follow-up (p < 0.01). The average mismatch between pelvic incidence and lumbar lordosis (PI-LL) preoperatively was 17.6°, which was reduced to 9.6° at the latest postoperative follow-up (p < 0.01). Mean ODI (%) and NRS scores were significantly improved by 33.8% (46.7 ± 13.3 to 30.9 ± 19.8; p < 0.01) and 55% (6.0 ± 2.2 to 2.7 ± 2.6; p < 0.01), respectively, at last follow-up. Conclusions: This study demonstrates the feasibility of performing complex, robotic-assisted ASD corrective surgery in a surgical specialty hospital, achieving significant correction of sagittal and coronal deformities, relieving patients’ symptoms, and offering efficiency and consistency to pedicle screw placement. This study demonstrates that a minimally invasive approach to complex deformity reconstruction reduces perioperative morbidity with decreased operative times, EBL, and LOS when compared to historic controls. This approach allows for the democratization of deformity care in that procedures typically reserved for large academic centers can be successfully accomplished at smaller institutions in optimized patients by qualified surgeons with appropriate perioperative support staff. Full article

The agricultural sector is undergoing a profound digital transformation driven by artificial intelligence, the Internet of Things, remote sensing, robotics, blockchain, and edge computing, which are being integrated into crop monitoring, irrigation management, disease detection, and supply chain transparency systems. This study employs systematic evidence mapping to characterize the applications of emerging digital technologies in sustainable agriculture; it delineates technological trajectories, areas of application, implementation gaps, and opportunities for improvement. Adhering to the PRISMA 2020 reporting protocol, 101 peer-reviewed articles indexed in Scopus and Web of Science (2020–2025) were identified, screened, and subjected to integrated thematic and bibliometric synthesis, using RStudio Version: 2026.01.1+403 and VOSviewer 1.6.20 for data mining on keywords and technological evolution patterns. Results show that deep learning and computer vision models achieved diagnostic accuracies of 90–99%, smart irrigation systems reduced water consumption by 10–30%, predictive yield models frequently reported R² values above 0.80, and greenhouse automation reduced energy consumption by approximately 20–30%. Blockchain-based architectures improved traceability and secure data transmission by 15–20%, while remote sensing integration enhanced spatial estimation accuracy up to R² = 0.92. The findings demonstrate a measurable transition toward data-driven, resource-efficient agricultural ecosystems supported by validated digital architectures. However, interoperability limitations, lack of standardized performance metrics, scalability challenges, and uneven geographical implementation—identified in nearly 40% of studies—highlight the need for harmonized evaluation frameworks, cross-platform integration standards, and long-term field validation to ensure sustainable and scalable digital transformation. Full article

This study examines institutional processes in digital justice through a mixed conceptual approach that integrates bibliometric analysis and technology-adoption modeling, incorporating artificial intelligence (AI) as a projected component rather than an implemented system. A corpus of approximately 200 Scopus-indexed documents (2003–2024) was analyzed, identifying five dominant thematic clusters: advanced technologies, institutional justice, digital government, judicial information management, and digital criminal justice. The results reveal persistent gaps in the literature, particularly in rural and underserved communities, where connectivity barriers and the limited application of adoption models hinder inclusive digital transformation. As an institutional contribution, the study presents the conceptual design of the digital solution “Travel Permits—Accessible Justice”, developed under a Service-Oriented Architecture (SOA) and projected for future integration with AI-supported components to automate judicial authorizations through biometric validation, electronic signatures, and digital delivery. To evaluate its potential acceptance, the Technology Acceptance Model (TAM) is analytically adapted and extended to the community-based judicial context, framing institutional learning processes as a prospective form of learning analytics focused on user interaction, perceived usefulness, perceived ease of use, and behavioral intention. Taken together, the integration of bibliometric evidence with an extended TAM, along with the projected incorporation of AI-supported institutional learning processes, offers a coherent foundation for future studies on inclusive digital innovation in justice environments. Full article

New quality productive forces are increasingly recognized as important drivers of coordinated regional development, with urban agglomerations acting as key vehicles for their spatial implementation. Based on the theory of new quality productive forces, this study takes the 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration as its research subjects, spanning the period from 2005 to 2023, and constructs a four-dimensional evaluation index system for new quality productive forces covering economic, social, ecological, and technological dimensions. It employs the entropy method to determine indicator weights and calculate development indices for each dimension and utilizes a coupling coordination model to measure the overall and subsystem-level coordination by analyzing their spatiotemporal evolution characteristics. The results indicate a steady upward trend in the overall coordination level, progressing from a low level to an intermediate level, with the state of coordination continuously improving; spatial differentiation is significant, forming a gradient development pattern centered on Beijing, with marked disparities in coordination levels among cities. Subsystem analysis reveals an imbalanced synergy structure: while economic and ecological synergy levels are relatively high, the coupling and synergy between science and technology and the economy and society remain prominent weaknesses. Most cities in Hebei Province lack sufficient scientific and technological innovation capabilities, resulting in a weak supportive role for economic and social development. Based on these findings, this study proposes policy recommendations such as establishing a regional innovation community, promoting the integration of factor markets, and strengthening collaborative governance of the ecological environment, with the aim of leveraging new quality productive forces to drive a qualitative leap in the coordinated development of the BTH urban agglomeration. Full article

This study proposes an interdisciplinary approach that integrates bioclimatology, agronomy, environmental education, and information and communication technologies (ICT) to analyze their potential to support sustainable land management in the context of climate change. The research focuses on the application of bioclimatic indices, the continentality index (Ic), the ombrothermic index (Io), and the thermicity index (It/Itc), combined with the use of a virtual herbarium as a didactic resource for interpreting ecological indicators associated with vegetation. The study was conducted using a pretest–posttest design aimed at assessing students’ self-reported understanding of ecological concepts, bioclimatology, geobotany, and the use of digital tools for learning plant species. The results show a significant improvement in students’ perceived understanding following the educational intervention, with the mean questionnaire score increasing from 21.99 (SD = 5.03) in the pretest to 31.33 (SD = 5.06) in the posttest (t(69) = 37.13, p < 0.001). The normalized gain (g = 0.42) indicates a moderate improvement in students’ self-reported comprehension of bioclimatic and ecological concepts. These findings highlight the potential of ICT to strengthen environmental education and to foster the development of competencies related to sustainable agricultural and forest land management. Full article

Background/Objectives: Compared to conventional test methods, point-of-care tests (POCTs) offer advantages for optimising care in patient groups at risk of vitamin D deficiency. However, their diagnostic accuracy in clinical settings has not previously been systematically assessed. We conducted a systematic review to assess the diagnostic accuracy of current point-of-care technology (POCT) for diagnosing vitamin D deficiency in adults and children. Methods: We searched Embase, MEDLINE and Web of Science on 3 December 2024 and also conducted forward and backward citation searching. We included studies from all patient groups and clinical settings where the index test had been conducted and processed at point of care, with a comparator of any laboratory reference standard test. We assessed risk of bias and applicability concerns for the included studies using published tools. The review was registered in advance (PROSPERO reference CRD42024618338). Results: After screening, five articles relating to four studies were included. These assessed five index POCTs against reference standard laboratory tests (liquid chromatography tandem mass spectrometry in three of the four included studies). The number of samples per comparison ranged from 6 to 20. There was variation in the level of agreement between POCT and laboratory reference standard tests. We also identified incomplete reporting of key study features, which prevented definitive assessment of several domains of the risk of bias and applicability tools. Conclusions: There is currently insufficient peer-reviewed evidence from clinical evaluations to recommend any particular POCT for vitamin D. Future studies should recruit adequate sample size and complete reporting of study design features and diagnostic accuracy measures. Full article

Peptaibols, predominantly secreted by Trichoderma species, are a class of linear peptides composed of five to twenty amino acid residues, synthesized non-ribosomally and enriched with α-amino isobutyric acid. These unique peptides appear to be highly effective in mediating the interactions between Trichoderma and plant pathogenic fungi. In this study, Ultra-Performance Liquid Chromatography–Quadrupole Time-Of-Flight Mass Spectrometry/Mass Spectrometry (UPLC-QTOF-MS/MS) technology was used to detect peptaibols profiles of Trichoderma strains during their interactions with the pathogen Fusarium graminearum. MS investigations of crude extracts derived from in vitro confrontations of Trichoderma atroviride T23 and its genetically modified counterparts, dual-culture assays of Mlae1, Mvel1, OElae1, and OEvel1 with F. graminearum were performed to shed light on the regulatory role of the velvet complex composed of LAE1&VEL1 in the synthesis of peptaibols during the microbial interaction. These results revealed intriguing variations in the total peptaibols produced during the interactions, as well as some differences in the specific peptaibol profiles between the confrontation and control tests. The overexpression strains, OElae1 and OEvel1, distinguished themselves by their proficiency in inducing long-residue peptaibols synthesis, attaining an impressive biocontrol index of up to 76%. The crude extracts containing peptaibols of OElae1 and OEvel1 demonstrated a capability to enhance cell membrane permeability and decrease DON toxin production in F. graminearum, and the crude extracts of OElae1 strains exhibited more effectiveness in reducing DON toxin production. In conclusion, the interaction with F. graminearum significantly impacted the peptaibol production in the examined Trichoderma strain, emphasizing the intricate interplay and reciprocal influence of genetic factors and environmental stimuli. Full article

This study examines how digitalization and institutional quality jointly influence the size and dynamics of the shadow economy across EU member states. It adopts an integrated framework in which digital capacity is treated as an operational extension of state capacity that can either complement strong institutions or compensate for institutional weaknesses. The empirical analysis is based on a two-dataset panel covering 27 EU countries over the periods 2013–2022 and 2017–2022. Institutional quality is measured using the Worldwide Governance Indicators, while digitalization is captured through detailed indicators from the Digital Economy and Society Index. Fixed-Effects models with Driscoll–Kraay standard errors are employed, alongside interaction and nonlinear specifications. Results show that institutional quality is consistently associated with lower levels of the shadow economy, but its effect exhibits diminishing returns at higher levels of governance, indicating institutional saturation. Digitalization effects are domain-specific. In isolation, both citizen- and business-oriented digital services show a positive association with the shadow economy, a finding termed the Digitalization Paradox, reflecting a phase where technological facilitation of informal activity outpaces regulatory adaptation. However, their interaction with institutional quality reveals divergent mechanisms. Citizen-oriented services tend to substitute for weaker governance, while business-oriented services complement strong institutional frameworks. The findings indicate that digitalization serves as an institutional amplifier whose final impact on the shadow economy, whether formalizing or facilitating, is dictated by the maturity of the host institution. Full article

The rapid expansion of the digital economy is reshaping the global production and trade system, bringing new opportunities for developing economies seeking to enhance their international competitiveness, while also posing structural challenges. This study focuses on China, a typical emerging economy, and uses provincial panel data from 2015 to 2024 to empirically examine how digital transformation and technological innovation jointly affect foreign trade competitiveness. The core variables are measured as follows: The digitalization level is constructed using principal component analysis (PCA) based on three dimensions: digital infrastructure, digital industrialization, and industrial digitization; technological innovation is proxied by the logarithm of technology market transaction volume. This study employs a fixed-effects model with interaction terms to estimate the independent effects of digitalization and technological innovation and to explore their interaction within the framework of the digital economy. The empirical results show that both digital transformation and technological innovation have a significant positive impact on foreign trade competitiveness. Specifically, a 10-point increase in the digitalization index is associated with an approximately 0.10-unit increase in the trade competitiveness index, and a 1% increase in technological innovation input is associated with an increase of 0.032–0.042 units. However, their interaction coefficient is significantly negative (−0.001, p < 0.01), indicating a substitution effect: an increase in technological innovation investment weakens the marginal contribution of digitalization to export competitiveness, and vice versa. Further heterogeneity analysis shows that the direct effects of digital transformation and technological innovation are more significant in less developed regions, while the substitution effect is stronger in economically developed regions. The findings suggest that policies promoting digital transformation and technological innovation should avoid a uniform approach and instead adopt coordinated and phased strategies that are suitable for regional development conditions. By providing new empirical evidence on the interaction between digital economy development and innovation investment, this study enriches the existing literature and offers policy implications for emerging economies seeking to achieve sustainable foreign trade development under increasing global trade uncertainty. Full article

As the world’s largest carbon emitter and one of the countries facing severe air pollution challenges, China is under growing pressure to promote coordinated carbon reduction and air pollution control in support of sustainable development. From the perspective of interprovincial trade-embedded emissions, this study examines the spatiotemporal evolution, regional heterogeneity, and driving mechanisms of the synergy between embodied carbon emissions and air pollution emissions across 30 provincial-level regions in China in the 2012–2017 period. The multi-regional input–output (MRIO) model and coupling coordination degree (CCD) model are used to measure embodied emissions and the synergy effect, while the stochastic impacts by regression on population, affluence, and technology (STIRPAT) and geographically and temporally weighted regression (GTWR) models are employed to identify the main driving factors and their spatiotemporal heterogeneity. The results show that the overall synergy index of embodied carbon and air pollution emissions in China showed an increasing trend, and provinces with high-quality coordination shifted southward. Low-carbon policy and technology development mainly acted as positive drivers, whereas air pollution reduction policy and energy intensity tended to exert inhibitory effects; the role of energy consumption was more conditional and stage-specific. These findings provide useful evidence for differentiated governance, coordinated air pollution and carbon reduction, and the green and low-carbon transition. Full article

Achieving high-level self-reliance in science and technology requires a science and technology finance ecosystem that is aligned with the needs of technological innovation. To overcome bottlenecks in core technologies, firms must accelerate R&D, strengthen their core competitiveness, and pursue innovation-led, quality-oriented development. Using provincial-level data for 2013–2023, this paper constructs an index system for China’s science and technology finance ecosystem from four dimensions: science and technology financial services, science and technology capital markets, science and technology financial organizations, and government guidance for science and technology. We then measure the development level of this ecosystem and employ a panel data model to examine its impact on innovation in Specialized and Sophisticated SMEs. The results show that a more developed science and technology finance ecosystem significantly promotes innovation in these firms, with a stronger effect on substantive innovation than on strategic innovation. These findings remain robust across a series of robustness checks. Further analysis reveals significant heterogeneity across regions and levels of government intervention: the positive effect is stronger in eastern China and in regions with weaker government intervention. Mechanism tests indicate that the science and technology finance ecosystem promotes innovation by facilitating the accumulation of R&D capital and the agglomeration of scientific and technological talent. This study enriches the literature on science and technology finance ecosystems and SME innovation, and provides policy-relevant evidence for ecosystem development and the cultivation of Specialized and Sophisticated SMEs. Full article

Faced with increasingly severe resource shortages and environmental pressures, exploring the impact of the digital economy on green and low-carbon development and its potential mechanisms is of great significance. Drawing on a comprehensive panel dataset spanning the decade from 2014 to 2023, this study examines 11 provincial administrative regions situated within the Yangtze River Economic Belt in China, systematically examining the effects and underlying pathways of the digital economy on green and low-carbon development. We construct an evaluation index system for the digital economy and green and low-carbon development, and use a two-way fixed effects model, a moderating effect model, and a threshold regression model for empirical analysis. Empirical results show that the digital economy significantly promotes green and low-carbon development, and this conclusion remains robust after a series of robustness tests. Mechanism analysis indicates that green technology innovation plays a significant moderating role, amplifying the environmental benefits of the digital economy; industrial structure upgrading exhibits a double threshold effect, with the promoting effect of the digital economy on green and low-carbon development increasing as the threshold is exceeded. Heterogeneity analysis shows that the ecological effects of the digital economy are significant in the midstream and southwest cluster and in areas with high factor allocation efficiency. We conclude that optimizing the environment for digital economic development, emphasizing innovation in digital green technologies, and implementing differentiated regional and structural policies can achieve a coordinated advancement of digital transformation and green and low-carbon development, providing valuable empirical evidence and policy implications for regional sustainable development. Full article

To evaluate the effectiveness of an adaptive physical education (APE) program using VR technologies, we studied the physical development and vitality indicators of college female students in two groups: those with and without functional health limitations (N = 70 each). Students with disabilities were randomly divided into experimental and control groups of 35 people each. The experimental group participated in physical education classes using VR. The health assessment included heart rate, blood pressure, and subjective health assessments. Physical development was assessed by the biological age index (BAI) by Voitenko, static balance duration, and breath-holding time. Psychological activity was assessed using the Mindfulness Attention Awareness Scale (MAAS), the Rezapkina Vitality Test, and the Subjective Vitality Scale (SVS). The delayed effect was assessed using a questionnaire. Students with functional impairments initially demonstrated a significant decrease in overall vitality, physical activity duration, and more negative health self-perception. After the virtual reality sessions, they showed a restoration of physical development and vitality indicators to levels close to healthy, as well as a decrease in BAI. The delayed effect was confirmed after three months. The use of virtual reality technologies in the APE program effectively improves the physical development and activity (vitality) of students with functional health impairments. Full article

In the context of global efforts to achieve carbon neutrality, understanding how digital–intelligent integration influences carbon emissions is crucial for advancing the ecological transition. Using panel data from 30 provincial-level regions in China (2014–2023), a digital–intelligent integration index was constructed via entropy weighting and a coupling coordination model. Employing fixed-effects, mediation, and spatial Durbin models, the analysis shows that digital–intelligent integration is significantly associated with lower carbon intensity, a result that is robust to endogeneity concerns and alternative specifications. Industrial structure upgrading and green technology innovation were identified as mediating pathways. Furthermore, digital–intelligent integration generates positive spatial spillovers, reducing carbon intensity in neighboring provinces. Notably, these spillovers are geographically constrained and vary significantly across the regions. These findings indicate the need to formulate regionally differentiated strategies to harness the specific mechanisms through which digital–intelligent integration operates in different contexts. Full article