Search Results (291)

Background: Paediatric pelvic and acetabular fractures are rare and usually the consequence of high-energy trauma, often associated with life-threatening injuries. The majority are managed non-operatively; however, open reduction and internal fixation (ORIF) is indicated in selected, complex, or displaced, acetabular fractures. The modified Stoppa approach is well established in adults, but has been rarely reported in skeletally immature patients, and evidence guiding surgical approach and fixation in children remains limited. Methods: We report the case of an 11-year-old girl who sustained a transverse acetabular fracture following a high-energy trauma. The fracture was treated with ORIF through a modified Stoppa approach. We also performed a systematic review of the literature, focusing on ORIF of acetabular fractures in children. Results: In our patient, ORIF of the acetabular fracture was performed, achieving an anatomical reduction, 10 days after initial damage-control fixation of a concomitant open tibial plateau fracture. Postoperative management consisted of four weeks of non-weight bearing, followed by progressive weight bearing. At six months, she had returned to full daily activities and sports. The review of the literature identified 16 studies (retrospective series and case reports) describing paediatric acetabular fractures treated with ORIF using plates, screws, or flexible nails. In the literature, good to excellent clinical and radiographic outcomes were reported when anatomical reduction and stable fixation were achieved, although growth disturbance and avascular necrosis were described, particularly in cases with delayed reduction or severe triradiate cartilage injury. Conclusions: Our case illustrates the technical feasibility of the modified Stoppa approach in a skeletally immature patient with a complex acetabular fracture, with excellent mid-term outcome. Although it is not contraindicated in paediatric patients, it should be reserved for treating this type of complex fracture. The available literature supports that satisfactory results are reported after ORIF in children, but the heterogeneity and low level of evidence preclude firm recommendations on the optimal approach. Full article

Honeyberry (Lonicera caerulea L.) is a recently domesticated fruit crop, and there has been rather limited research on its phenological characteristics. In this study, we comprehensively evaluated the phenological and fruit quality traits of a honeyberry germplasm collection comprising 45 accessions. The annual growth period of the 45 accessions ranged from 153 (part of Russian accessions) to 173 days (part of Japanese accessions) in Harbin, China. The accessions of Japanese origin (‘Ri–5’, ‘Ri–68’, ‘RiM–3’, ‘Riwan’, ‘Riwandianlan’, and ‘Riwan–13’) consistently exhibited delayed flowering and ripening, as well as higher single fruit weights and fruit firmness. In contrast, the accessions of Russian and Chinese origins displayed relatively early to mid-season phenology, along with higher levels of vitamin C, anthocyanins, and soluble solids. Furthermore, there were great coefficients of variation (CVs) in total anthocyanins (CV = 35%), flavonoids (CV = 30%), phenolics (CV = 21%), and titratable acidity (CV = 19%) among the 45 accessions. Principal component analysis and hierarchical clustering revealed distinct clustering patterns among Japanese accessions. Among these accessions, ‘RiM–3’ exhibited both a relatively large fruit size and high firmness, implying its potential to overcome the inevitable trade-off between fruit size and firmness typically observed in Chinese and Russian honeyberry accessions. Our investigation and findings provide valuable information for honeyberry breeding aimed at optimizing the maturity period and enhancing fruit quality, as well as a reference for the current cultivation and management methods. Full article

Amid the normalization of flexible employment, labor dispatch, as a form of non-standard employment, has become an important component of China’s precarious labor market (PLM). Based on registration data of labor dispatch firms from 2002 to 2022, this paper analyzes the spatial distribution and evolutionary patterns of China’s PLM, using spatial autocorrelation, kernel density estimation, and Gini coefficient methods. Furthermore, it explores its driving mechanisms through a panel negative binomial regression model. The results show that (i) over the past two decades, China’s PLM has undergone four stages: initiation, acceleration, expansion, and adjustment. (ii) Spatially, it has evolved along the trend of “reinforced clustering with concurrent diffusion,” expanding from first-tier cities in eastern China to second- and third-tier cities in central and western China. (iii) Industrial upgrading, market competition, and the overall level of urban development have significantly promoted the growth of the PLM, while improvements in accessibility, proportion of migrant population, and public service provision have somewhat restrained its expansion. Overall, China’s PLM demonstrates both growth potential and structural vulnerability under institutional constraints and external shocks, offering valuable spatial insights for forging sustainable, high-quality employment and coordinated regional development. Full article

This study investigates the determinants of turnover intention among real estate sales professionals in Lebanon and the United Arab Emirates (UAE), two markets that represent contrasting economic realities within the MENA region. Drawing on Herzberg’s Two-Factor Theory, Vroom’s Expectancy Theory, and March and Simon’s Push-Pull Model, this study adopts a multidimensional framework to assess the effects of compensation, job stress, career growth opportunities, and work–life balance on employee retention. A quantitative method was employed using a structured questionnaire administered to 832 respondents (425 in the UAE and 407 in Lebanon), and data were analyzed using Structural Equation Modeling. The results reveal that job stress is the most influential predictor of turnover intention, particularly in Lebanon, followed by work–life balance, compensation, and career growth opportunities. These findings underscore the importance of psychological well-being and structural incentives in talent retention. By offering empirical evidence from an underexplored regional labor market, the study contributes to the global turnover discourse and provides comparative insights into the labor dynamics of both a crisis-prone and a high-growth economy. The results carry significant practical implications for HR managers, firm owners, and policymakers, highlighting the necessity of adopting holistic and context-sensitive retention strategies that extend beyond financial rewards to include flexible work arrangements, career development frameworks, and supportive workplace cultures. Full article

This study investigates the valuation dynamics of 30 publicly listed fintech firms across six Asian economies from January 2021 to December 2025. It examines how intrinsic firm-level scale (market capitalization) and extrinsic macroeconomic conditions (GDP growth) jointly influence fintech valuation ratios, as reflected in price-to-earnings (P/E), price-to-book (P/B), and price-to-sales (P/S) measures. It also identifies significant structural heterogeneity and distributional asymmetries in valuation outcomes by implementing a multi-method empirical strategy that includes a Panel Autoregressive Distributed Lag (ARDL) framework, two-way fixed-effects models with interaction terms, and quantile regression. The findings reveal a robust, positive long-run relationship between market capitalization and valuation multiples across all ratios, confirming that firm-level scale as reflected in market capitalization is the primary driver of market value. Critically, the analysis identifies a dual-regime landscape in the Asian fintech sector: developed markets (South Korea, Japan, and Singapore) are fundamentally firm-scale driven, where intrinsic scale is the superior predictor of valuation. In contrast, developing markets (China, India, and Indonesia) are primarily macro-growth driven, exhibiting high sensitivity to GDP growth as a macroeconomic indicator of market expansion. The quantile regression results demonstrate a winner-takes-all effect, where the impact of scale on valuation is significantly more pronounced for highly valued firms in the 75th percentile. These results challenge the efficacy of universal valuation models and provide a context-dependent navigational framework for investors, analysts, and policymakers to distinguish between structural scale and cyclical growth in the rapidly evolving Asian fintech ecosystem. Full article

Driven by the national policy of total water resources control and efficiency improvement, the behavior of water resource utilization reduction by firms is widespread, which may have an impact on the value of firms. This study integrates dynamic capability theory and signaling theory to construct a dual-path analytical framework, systematically investigating the impact of water utilization reduction on firm value and its intrinsic mechanisms. Based on data from Chinese A-share listed companies spanning 2012–2023, fixed-effect models, mediation-effect tests, and heterogeneity analysis are employed for empirical verification. The results reveal that water utilization reduction exerts a significant dual-path promoting effect on firm value: it enhances financial performance (ROA) primarily through technological innovation, reflecting the process of resource orchestration and dynamic capability construction; concurrently, it boosts market performance (Tobin’s Q) mainly by improving ESG performance as a signaling channel, mirroring the capital market’s positive pricing of green signals. Further heterogeneity analysis indicates that these effects are more pronounced during the policy deepening stage, in non-water-intensive industries, and in humid/sub-humid regions. This study contributes theoretical support and empirical evidence for firms’ green transformation and the formulation of differentiated water resource policies by the government, highlighting the synergistic development of high-quality economic growth and ecological civilization construction. Full article

Based on survey data from 320 Azerbaijani enterprises operating across the organic product value chain—including producers, sellers, and key supporting firms such as logistics, financial, and ICT service providers—this study investigates how digital platform capability influences firms’ innovation and performance outcomes and their perceived high-quality economic development within an emerging digital economy context. Four constructs—Digital Platform Capability, Enterprise Digital Transformation, Innovation and Performance Outcomes, and Perceived High-Quality Economic Development—are measured using multi-item Likert scales. Confirmatory factor analysis and Structural Equation Modeling (SEM) are employed to test the proposed relationships. The results show that Digital Platform Capability exerts a strong positive effect on Innovation and Performance Outcomes (β = 0.574) and on Perceived High-Quality Economic Development (β = 0.512). In addition, Innovation and Performance Outcomes have a direct positive impact on Perceived High-Quality Economic Development (β = 0.313). Mediation analysis further indicates that Enterprise Digital Transformation partially mediates this relationship, transmitting approximately 52% of the total effect of Innovation and Performance Outcomes on Perceived High-Quality Economic Development. These findings underscore digital transformation as a key structural mechanism through which firm-level innovation and performance contribute to broader perceptions of high-quality economic development. The study provides novel empirical evidence from Azerbaijan and offers practical implications for digital policy design and enterprise strategies aimed at promoting innovation-driven, inclusive, and sustainable growth. Full article

This study classifies corporate financial performance in countries in the Middle East and North Africa (MENA) region, addressing the critical need for accurate and early identification of high-, moderate-, and low-performance companies. The selection of the MENA region was driven by its significant economic growth, diverse market structures, and increasing attractiveness for foreign investment, which makes accurate financial performance assessment important. Despite the growing interest in AI applications for corporate financial performance, a research gap still persists. Existing studies focus primarily on bankruptcy and financial distress prediction in developed countries, with rather limited studies on multi-class financial performance classification in the MENA region. This study addresses a significant gap in the corporate financial performance evaluation literature, which is the lack of a robust, comparative evaluation of advanced DL techniques against conventional ML methods for multi-class corporate financial performance prediction using high-dimensional data. This study employs a design science research (DSR) approach by developing an evaluation analytics artifact that integrates structured preprocessing, dimensionality reduction, and comparative ML and DL modeling, following the relevance, design, and rigor cycles. By employing a design science research (DSR) methodology, the research used a dataset from the Compustat database, comprising 7971 firm-year observations from 2013 to 2024. A rigorous dimensionality reduction process, including pairwise correlation filtering, resulted in a final set of 15 key classification features. The study compared three machine learning techniques—random forests (RFs), support vector machines (SVMs), and eXtreme Gradient Boosting (XGBoost), against one deep learning technique, deep neural networks (DNNs), for classifying the corporate financial performance of MENA-region companies. The models were trained to classify corporations into three performance classes (low, moderate, and high), using the earnings per share (EPS) as the target variable. The empirical findings indicate that all four machine learning algorithms achieved meaningful predictive performance in classifying EPS-based corporate performance. Among the benchmark models, the support vector machine (SVM) and random forest (RF) classifiers produced stable and competitive results, indicating strong generalization capabilities across firms and periods. XGBoost consistently outperformed the traditional machine learning models, delivering the highest overall classification accuracy and superior discriminatory power, highlighting its effectiveness in capturing nonlinear relationships and complex feature interactions. Similarly, the deep neural network further improved classification performance relative to the benchmark models and exhibited comparable results to XGBoost, especially in modeling high-dimensional data. This superior performance can substantially enhance earnings performance classification through early performance deterioration and improvement identification, allowing more proactive strategic and operational decisions. Full article

A key component of creating robust and sustainable businesses is the digital transformation of business operations. This study examines the impact of distributed digital technology, namely cloud computing and blockchain technology, on an auditor’s behavior, an essential component of the framework for corporate responsibility. This study also highlights the impact of digital transformation on sustainable auditing, urging auditors to improve their technological skills to build trust in evolving entities. We used a unique dataset of Chinese A-share listed companies from 2013 to 2021 to show that this time period is important because it shows the beginning and growth of these technologies in the Chinese business world. This gives us a good starting point for looking at their early-stage audit effects. Our key findings are threefold. First, we found that firms using distributed digital technologies (cloud computing and blockchain) experienced (a) higher audit fees and (b) standard audit opinions, indicating the growing complexity and the requirement that auditors acquire specialized skills in order to evaluate cyber-resilience and technological structures. Second, firms facing substantial profit fluctuations (higher risk level) following digital engagement were subject to higher audit fees and a decreased probability of standard audit outcomes, emphasizing the nuanced risks of digital transformation. Third, the main results were more pronounced in (a) non-state-owned enterprises and (b) high-tech enterprises. Our study is robust to multiple sensitivity analyses, endogeneity tests, and propensity score matching (PSM). The results show that regulators need to create and support specialized auditing regulations regarding distributed technologies. These regulations would assist auditors in evaluating cloud and blockchain engagement and make it clear to businesses what is important to be compliant. Full article

This study explored an eco-friendly coating system combining seagrass-derived cellulose fiber (SCF) from Cymodocea rotundata with marine type I collagen (MC) for tomato preservation. The SCF/MC composite was prepared through enzymatic and natural crosslinking processes and subsequently characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FESEM), and thermogravimetric analysis (TGA). The results demonstrated that SCF/MC possessed a compact morphology, strong hydrogen bonding interactions, high crystallinity, and excellent thermal stability. When applied as a coating, SCF/MC composite significantly reduced weight loss in tomatoes, preserved firmness (>39 Units), regulated acidity, maintained moisture levels (~90%), and delayed increase in pH compared to the uncoated control. Additionally, the SCF/MC coating sustained ascorbic acid and moderated lycopene accumulation, indicating delayed ripening. At 0.5% of SCF/MC composite, spoilage was limited to 22% versus ~80% in control samples, demonstrating a substantial reduction in decay. Antifungal assay showed strong inhibition of Aspergillus flavus, with the highest suppression of mycelial growth observed at 0.5% of SCF/MC. Overall, the SCF/MC coating effectively enhanced fungal safety and maintained the physicochemical quality of tomatoes, thereby extending shelf life while valorizing seagrass biomass as a sustainable postharvest resource. Full article

This study aimed to provide the first characterization of the traditional farming systems and meat quality traits of the Black Alpine pig (BAP), an ecotype under recovery originating in the Italian Central Alps, reared under real, actual conditions. Productive and reproductive data were collected through on-farm visits from six BAP farms located in Northern Italy. Longissimus dorsi (LD) muscle and backfat samples from BAPs were collected in the same farms and analyzed for color, tenderness, proximate composition, and fatty acid profile. For exploratory purposes and to provide a descriptive benchmark of commercial pork (CP) quality, loin samples were purchased from the retail market. PLS-DA served as a discriminant model between BAP and CP meat samples on a multivariate data matrix. BAP farming was characterized as an en plein air system with high outdoor access and reliance on natural vegetation. BAPs showed high rusticity and slow growth. The LD muscle was characterized by a firm texture, an intense dark-red color, and a high proportion of polyunsaturated fatty acids, reflecting traditional rustic meat features associated with the interaction between dietary, environmental, and genetic factors. Our preliminary results provided novel insights into BAP farming practices and meat quality, supporting the maintenance of this genetic resource through traditional farming and the preservation of biodiversity in alpine livestock, offering exploratory evidence of the current BAP farming system. Full article

Gulf Cooperation Council (GCC) economies are investing heavily in digital infrastructure to diversify beyond hydrocarbons, yet the productivity returns from these investments remain uncertain. This study examines whether digital adoption enhances labor productivity in GCC economies (2000–2023). We construct a Composite Digital Index (CDI) from broadband subscriptions, internet use, and mobile penetration. Interpreting the Gulf economies as socio-technical systems, we frame digital adoption, productivity, and investment (measured by GCF) as a reinforcing loop, with government effectiveness amplifying the cycle and oil rents dampening it. Using panel data methods, including fixed-effects and long-run estimators, we find that digital adoption yields persistent productivity gains. In the long run, a one-point increase in CDI is associated with a 12.6 percentage point rise in labor productivity growth (p < 0.05). This effect triples—to approximately 38.5 percentage points—when moderated by strong government effectiveness (CDI × Governance interaction: +26.3; p < 0.01). Conversely, the productivity payoff declines significantly with oil-rent dependence: for every 10 percentage-point rise in oil rents, the marginal effect of digital adoption drops by 3.4 points. These gains are significantly larger where government effectiveness is stronger, while oil dependence weakens them. The findings imply that infrastructure adoption alone is insufficient: institutions and fiscal structures condition whether digital adoption translate into sustained productivity growth. Policy priorities should focus on institutional reform, fiscal diversification, and enabling firm-level digital absorption—particularly in high-rent economies—so that adoption translates into broad-based productivity dividends. Full article

Sweet cherry (Prunus avium L.) cultivation is rapidly expanding in Northern Italy, where excessive vegetative vigor often limits fruit set and quality. This study aimed to evaluate the effects of Prohexadione-calcium (Pro-Ca) on the vegetative growth, productivity, and fruit quality of cv. ‘Tip Top’ sweet cherries grafted onto Gisela 6 and MaxMa 14 rootstocks. The growth regulator was applied twice between the flower bud and petal fall stages. Pro-Ca significantly reduced vigor and increased the fruit setting by 10%, resulting in an yield average of +3 kg per plant. Also preharvest treatment increased average cherry size compared with the control, particularly in plants grafted onto Gisela 6. Moreover, Pro-Ca-treated fruits exhibited a +20% red overcolor extension of the skin, improved skin firmness (+12%), and led to higher nutraceutical properties. In conclusion, Pro-Ca improved plant yield and fruit quality in ‘Tip Top’ sweet cherry, likely through the combined effects on hormonal balance, assimilate allocation, and canopy light distribution, supporting its potential as a valuable growth regulator in high-density sweet cherry orchards. Full article

The use of sustainable and efficient practices is important for high crop yields. This study aimed to determine the effects of microorganisms and fertilizers on the growth, yield, and fruit quality of two strawberry cultivars in Cañete, Peru. The experiment was set up in a randomized complete block design with a split-plot arrangement, where the main plots were the fertilizer doses (0, 50, 100, and 150%) and the subplots were arranged in a factorial scheme of 2 × 4, with two strawberry varieties, three microorganisms (Azospirillum brasilense, Rhizophagus spp., and Trichoderma sp.), and the control. Growth variables included the number of leaves, crowns, and flowers; petiole length and diameter; foliar area; number of fruits; and yield. Nutrient concentrations of nitrogen, phosphorus, potassium, calcium, and magnesium were determined. For fruit quality, the variables of pH, Brix grade, and acidity were measured. The results indicated that the Sabrina cultivar had higher growth and yield (+15%). All fertilizer doses promoted yield and firmness, especially the 50% dose. All microorganisms promoted growth, yield (+60%), and fruit quality, demonstrating their importance in improving fruit production in this crop in Peru. Full article

According to the U.S. Department of Commerce, there is strong evidence of a significant bounce in global e-commerce sales following the paradigm shift caused by COVID-19 in everyday life. The global e-commerce market size is projected to reach $6.33 trillion in 2024, with the top five countries being China, the United States, the United Kingdom, Japan, and the Republic of Korea (hereafter, Korea). Among the top five global players, two countries, Korea and Japan, are particularly noteworthy because Korea has the lowest retail e-commerce sales among the top five countries despite a high penetration rate while Japan shows the lowest penetration rate while maintaining a similar level of retail e-commerce sales. The e-commerce industry in Korea and Japan is facing a new inflection point regarding sustainable growth and survival. However, the sustainability of e-commerce firms in Korea and Japan has not yet been investigated from a comparative perspective. In the context of an extremely challenging global business environment, this study focuses on a representative e-commerce firm from each country: Coupang in Korea and Rakuten in Japan. By examining these cases, this study provides important insights into sustainable management practices in the e-commerce industry from a comparative perspective. Full article