Search Results (170)

The objective of this study was to evaluate the effects of single versus split pre-harvest applications of aminoethoxyvinylglycine (AVG) on the concentrations of bioactive compounds and antioxidant activity in ‘Baigent’ apple fruit cultivated under anti-hail nets, assessed at harvest and after cold storage. The pre-harvest application of AVG in a single dose (125 mg L⁻¹) in ‘Baigent’ apple trees reduces the total antioxidant activity and concentration of anthocyanins and the total phenolic compound and chlorogenic acid in the fruit skin, both at harvest and after cold storage and reduces, in the skin, the concentrations of epicatechin at harvest and of quercetin after the cold storage. The parceled application of AVG (62.5 mg L⁻¹ + 62.5 mg L⁻¹) does not influence or present a less-pronounced negative effect on the total antioxidant activity and the contents of the total phenolic compound and anthocyanins in the fruit skin. Split AVG application can help maintain fruit quality during storage, providing a practical strategy for producers to optimize both marketable quality and nutritional value, potentially positively impacting commercial returns. Full article

Cattle marketed through auction market systems and/or that remain unvaccinated are considered higher risk for BRD, but impacts on host response remain unclear. We sought to identify specific genomic patterns of beef calves vaccinated against BRD viruses or not and commercially marketed or directly transported in a split-plot randomized controlled trial. Forty-one calves who remained clinically healthy from birth through backgrounding were selected (randomly stratified) from a larger cohort of cattle (n = 81). Treatment groups included VAX/DIRECT (n = 12), VAX/AUCTION (n = 11), NOVAX/DIRECT (n = 7), and NOVAX/AUCTION (n = 11). Blood RNA was acquired across five time points, sequenced, and bioinformatically processed via HISAT2 and StringTie2. Significant transcriptional changes (FDR < 0.05) were observed at backgrounding entry (T5) in NOVAX/AUCTION cattle exhibiting 2809 uniquely differentially expressed genes and relative activation of immune, inflammatory, and metabolic pathways with upregulation of interferon-stimulated genes (e.g., IFIT3, MX2, and TRIM25) and downregulation of specialized proresolving mediator (SPM) enzymes (ALOX5 and ALOX15). VAX/AUCTION cattle exhibited modulated immune activation and preserved expression of SPM-associated genes when compared to NOVAX/AUCTION cattle. Both marketing route and vaccination shape the molecular immune landscape during high-stress transitions, with preweaning vaccination potentially modulating this response. This study provides mechanistic insight into how management practices influence immunological resilience and highlights the value of integrating transcriptomics into BRD risk mitigation. Full article

Volatility modelling is a key feature of financial risk management, portfolio optimisation, and forecasting, particularly for market indices such as the JSE Top40 Index, which serves as a benchmark for the South African stock market. This study investigates volatility modelling of the JSE Top40 Index log-returns from 2011 to 2025 using a hybrid approach that integrates statistical and machine learning techniques through a two-step approach. The ARMA(3,2) model was chosen as the optimal mean model, using the auto.arima() function from the forecast package in R (version 4.4.0). Several alternative variants of GARCH models, including sGARCH(1,1), GJR-GARCH(1,1), and EGARCH(1,1), were fitted under various conditional error distributions (i.e., STD, SSTD, GED, SGED, and GHD). The choice of the model was based on AIC, BIC, HQIC, and LL evaluation criteria, and ARMA(3,2)-EGARCH(1,1) was the best model according to the lowest evaluation criteria. Residual diagnostic results indicated that the model adequately captured autocorrelation, conditional heteroskedasticity, and asymmetry in JSE Top40 log-returns. Volatility persistence was also detected, confirming the persistence attributes of financial volatility. Thereafter, the ARMA(3,2)-EGARCH(1,1) model was coupled with XGBoost using standardised residuals extracted from ARMA(3,2)-EGARCH(1,1) as lagged features. The data was split into training (60%), testing (20%), and calibration (20%) sets. Based on the lowest values of forecast accuracy measures (i.e., MASE, RMSE, MAE, MAPE, and sMAPE), along with prediction intervals and their evaluation metrics (i.e., PICP, PINAW, PICAW, and PINAD), the hybrid model captured residual nonlinearities left by the standalone ARMA(3,2)-EGARCH(1,1) and demonstrated improved forecasting accuracy. The hybrid ARMA(3,2)-EGARCH(1,1)-XGBoost model outperforms the standalone ARMA(3,2)-EGARCH(1,1) model across all forecast accuracy measures. This highlights the robustness and suitability of the hybrid ARMA(3,2)-EGARCH(1,1)-XGBoost model for financial risk management in emerging markets and signifies the strengths of integrating statistical and machine learning methods in financial time series modelling. Full article

Russia’s full-scale aggression against Ukraine has led to profound disruptions in local and global agri-food markets. Since Ukraine is one of the world’s largest maize exporters, the war also contributed to considerable changes in trade reallocation, as well as an increase in the significance of the European Union in Ukrainian exports. This study analyses the effects of the Russian–Ukrainian war on horizontal maize price transmission between Ukraine and the EU countries. The panel autoregressive distributed lag model (ARDL) was applied to investigate the impact of the war on the price pass-through between those countries. The econometric analysis was performed on a weekly feed maize export price series for Ukraine and 14 selected EU countries. The time frame of research, January 2019 to December 2024, was split into pre-war and war periods. The study indicates that with the outbreak of the war, the long-term relationship between Ukraine and the EU’s maize prices has weakened. At the same time, there was an increase in the short-run maize price transmission between Ukraine and the Eastern EU countries. This proves that in the face of the conflict, market participants in these countries are increasingly guided by the market situation in Ukraine when making economic decisions. Full article

In recent years, computational intelligence techniques have significantly contributed to the automation and optimization of trading strategies. Despite the increasing sophistication of predictive models, classical technical indicators such as dual Simple Moving Averages (2-SMA) remain popular due to their simplicity and interpretability. This work proposes an adaptive trading system that combines the 2-SMA strategy with a learning-based metaheuristic optimizer known as the Learning-Based Linear Balancer ($L{B}^2$). The objective is to dynamically adjust the strategy’s parameters to maximize returns in the highly volatile cryptocurrency market. The proposed system is evaluated through simulations using historical data of the BTCUSDT futures contract from the Binance platform, incorporating real-world trading constraints such as transaction fees. The optimization process is validated over 34 training/test splits using overlapping 60-day windows. Results show that the $L{B}^2$-optimized strategy achieves an average return on investment (ROI) of 7.9% in unseen test periods, with a maximum ROI of 17.2% in the best case. Statistical analysis using the Wilcoxon Signed-Rank Test confirms that our approach significantly outperforms classical benchmarks, including Buy and Hold, Random Walk, and non-optimized 2-SMA. This study demonstrates that hybrid strategies combining classical indicators with adaptive optimization can achieve robust and consistent returns, making them a viable alternative to more complex predictive models in crypto-based financial environments. Full article

The economic efficacy of logistics infrastructure is being reshaped by the dual forces of digitalization and the labor market. However, a new-era “investment return paradox” has emerged. Digitalization and an abundant labor force are theoretically positive forces, so why does their combination, when coupled with capital investment, paradoxically engender negative emergence that suppresses growth? Conceptualizing the regional economy as a Socio-Technical System (STS), this paper unravels this paradox by identifying and theorizing an “adaptive lag trap”. Using provincial panel data from China, we first provide empirical validation for this trap, identifying a significant negative three-way interaction involving labor quantity (coef. = −0.218, p < 0.05). We then demonstrate that high-skilled labor quality is the key to mitigating this trap. While its direct interactive effects are not statistically significant, our analysis uncovers a robust and theoretically potent pattern: a higher-skilled workforce systematically reverses the negative trend of the interaction effect. The split-sample test provides the clearest evidence of this pattern, showing the coefficient pivoting from negative (−0.0572) in the low-skill subsample to positive (+0.109) in its high-skill counterpart. Our findings establish that high-skill human capital is a necessary condition to circumvent the “adaptive lag trap”, underscoring the imperative for a policy shift from investing in the scale of labor to cultivating its skill structure within a co-evolutionary framework. Full article

This study applies Gradient Boosting Machines (GBMs) and principal component regression (PCR) to forecast the closing price of the Johannesburg Stock Exchange (JSE) All-Share Index (ALSI), using daily data from 2009 to 2024, sourced from the Wall Street Journal. The models are evaluated under three training–testing split ratios to assess short-term forecasting performance. Forecast accuracy is assessed using standard error metrics: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE). Across all test splits, the GBM consistently achieves lower forecast errors than PCR, demonstrating superior predictive accuracy. To validate the significance of this performance difference, the Diebold–Mariano (DM) test is applied, confirming that the forecast errors from the GBM are statistically significantly lower than those of PCR at conventional significance levels. These findings highlight the GBM’s strength in capturing nonlinear relationships and complex interactions in financial time series, particularly when using features such as the USD/ZAR exchange rate, oil, platinum, and gold prices, the S&P 500 index, and calendar-based variables like month and day. Future research should consider integrating additional macroeconomic indicators and exploring alternative or hybrid forecasting models to improve robustness and generalisability across different market conditions. Full article

It is well known that there is a significant conflict of interest between high-speed rail (HSR) operators and logistics companies. This study proposes an HSR freight pricing strategy based on a multi-objective optimization framework and a freight mode splitting model based on the Logit model. A utility function was constructed to quantify the comprehensive utility of different modes of transportation by integrating five key influencing factors: economy, speed, convenience, stability, and environmental sustainability. A bi-objective optimization model was developed to balance the cost of the logistics with the benefits of high-speed rail operators to achieve a win–win situation. The model is solved by the TOPSIS method, and its effectiveness is verified by the freight case of the Zhengzhou–Chongqing high-speed railway in China. The results of this study showed that (1) HSR has advantages in medium-distance freight transportation; (2) increasing government subsidies can help improve the market competitiveness of high-speed rail in freight transportation. This research provides theoretical foundations and methodological support for optimizing HSR freight pricing mechanisms and improving multimodal transport efficiency. Full article

The Coffea canephora market has grown significantly, driven by its economic relevance and improvements in beverage quality. Developing varieties adapted to local edaphoclimatic conditions is essential for supporting smallholder farmers, increasing productivity, and ensuring quality in the face of environmental challenges. This study evaluated 27 genotypes under two irrigation regimes using a split-plot design in a completely randomized block arrangement, with four replicates and three plants per plot. Growth and yield parameters were assessed. Genotypes 102, 103, 105, 106, 202, 209, 301, 303, and 305 showed significantly higher yields under full irrigation—up to 60% greater than under minimal irrigation—demonstrating strong responsiveness to water availability. In contrast, genotypes 203 and 206 performed better under minimal irrigation, with 29% higher yields, suggesting lower water requirements or greater drought tolerance. These findings highlight the potential for selecting genotypes suited to transitional altitudes that can benefit from targeted irrigation strategies. The combined use of irrigation and altitude-specific cultivation represents a viable and necessary approach to maximizing genetic potential, optimizing water use, and enhancing the sustainability of C. canephora cultivation in regions facing climate variability. Full article

This study explores how small family wineries in the Croatian Split-Dalmatia County integrate social media into their marketing and business strategies, focusing on the interplay between tradition, family identity, and digital innovation. Utilising a qualitative methodology, semi-structured interviews were conducted with winery owners to explore their use of social media platforms, their sales and distribution strategies, and their perceptions of Croatia’s EU membership. The results show that social media, particularly Facebook and Instagram, serve as highly personalised, low-cost marketing tools, predominantly managed by younger family members and used to convey authenticity, family heritage, and local identity. Despite limited resources and professional marketing expertise, these wineries take an intuitive, do-it-yourself approach and rely on direct customer relationships, storytelling, and experiential offerings to drive loyalty. While EU membership is generally seen as beneficial for tourism and funding opportunities, bureaucratic complexity remains a significant obstacle. This study highlights the importance of leveraging cultural heritage for digital content and emphasises the need for targeted policy support to improve digital competencies and reduce administrative barriers. These findings contribute to a deeper understanding of how family-run SMEs can gain and sustain competitive advantage by blending tradition with digital marketing practices in a rapidly evolving business environment. Full article

Organic labeling is a potentially influential factor in shaping consumer behavior toward beef products. However, limited research has examined consumer responses about perceptions of organic beef. This research thus explores the relationship between organic perception of beef, price fairness, and revisit intention. This research also investigates the moderating role of freshness in the impact of organic perception of beef on price fairness using information overload as a theoretical underpinning. An online survey targeted American consumers, with 415 responses collected via Clickworker. All participants were based in the United States regarding the consumption amount in the market. The Hayes Process Macro Model 7 was employed to test the research hypotheses. This research performed a median split analysis to scrutinize the moderating effect of freshness on the relationship between organic perception and price fairness. The findings indicate that the perception of organically produced beef positively affects price fairness and revisit intention. Furthermore, price fairness was found to influence revisit intention. The study also revealed a significant moderating effect of freshness on the relationship between organic perception of beef and price fairness. These outcomes contribute to the literature by clarifying the interrelationships among these four attributes within the context of beef products. Full article

Most transmission system operators (TSOs) use seasonally static models considering extreme weather conditions, serving as a reference for computing the transmission capacity of power lines. The use of dynamic line rating (DLR) models can avoid the construction of new lines, market splitting, false congestions and the degradation of lines in a cost-effective way. The operation of power systems is planned based on market results, which consider transactions hours ahead of real-time operation using forecasts with errors. The same is true for the DLR. So, during real-time operation TSOs should rapidly compute the DLR of overhead lines to avoid considering an ampacity above their lines’ design, reflecting the real-time weather conditions. Considering that the DLR of the lines can affect the power flow of an entire region, the use of the complete indirect DLR methodology has a high computation burden for all sectors and lines in a region. So, this article presents and tests three pre-solve methodologies able to rapidly identify the critical sector of each line. These methodologies solve the problem of the high computation burden of the CIGRÉ thermodynamic model of overhead lines. They have been tested by using real data of the transmission grid and the weather conditions for two different regions in Portugal, leading to errors in the computation of the DLR lower than 1% in relation to the complete CIGRÉ model, identifying the critical sector in significantly less time. Full article

The exchange of food commodities significantly contributes to alleviating the strain on land used for agricultural production by linking areas rich in land with those facing resource limitations. This study employs the entropy weight–TOPSIS method to measure the water–land–food system, utilizes a two-way fixed-effects model to examine the impact of food import competition on the coordination of the water–land–food system, and applies a spatial Durbin model to explore the spatial spillover effects of this impact. The findings indicate the following: (1) The average coordination level of the WLF system in China stands at 0.317, showing considerable variability. The WLF system coordination in all regions of China initially decreased and then increased in the period studied, with the northeast region exhibiting the highest level of coordination. (2) The competitive effect of domestic and foreign food costs driven by food imports has a positive impact on the coordination of the WLF system. For every 100,000 hectares of land saved through the competition effect, the coordination of China’s WLF system increases by 0.002. However, once the saved land exceeds 1.5 million hectares, the impact of import competition on the importing country’s food market becomes excessive and starts to have a negative effect. (3) Split-sample regression revealed that the positive effect of food import competition on the coordination of the WLF system is stronger in the southern region compared to the northern region. Additionally, the increase in the competition effect has a more pronounced impact on the coordination of the WLF system in major food production areas than in non-major production areas. (4) Based on the results of the spatial econometric model, the increase in the competitive effect of food imports in a region not only increases the coordination of the WLF system within that region but also positively impacts the coordination of the system in neighboring regions. (5) The land use efficiency of food imports acts as a conduit for the impact of food import competition on the coordination of the WLF system. Full article

Accurate Day-Ahead Energy Price (DAEP) forecasting is essential for optimizing energy market operations. This study introduces a machine learning framework to predict the DAEP with a 24 h lead time, leveraging historical data and forecasts available at the prediction time. Hourly DAEP data from the California Independent System Operator (January 2017 to July 2023) were integrated with exogenous and engineered endogenous features. A custom rolling window cross-validation, with 24 h validation blocks sliding daily across 2372 folds, evaluates an Extreme Gradient Boosting (XGBoost) model’s performance under diverse market conditions, achieving a median mean absolute error of 6.26 USD/MWh and root mean squared error of 8.27 USD/MWh, with variability reflecting market volatility. The feature importance analysis using Shapley additive explanations highlighted the dominance of engineered endogenous features in driving the 24 h lead time forecasts under relatively stable market conditions. Forecasting the DAEP at a runtime of 10 AM on the prior day was used to assess model uncertainty. This involved training random forest, support vector regression, XGBoost, and feed forward neural network models, followed by stacking and voting ensembles. The results indicate the need for ensemble forecasting and evaluation beyond a static train–test split to ensure the practical utility of machine learning for DAEP forecasting across varied market dynamics. Finally, operationalizing the forecast model for bidding decisions by forecasting the DAEP and real-time prices at runtime is presented and discussed. Full article

Accurate predictions of Initial Public Offerings (IPOs) aftermarket performance are essential for making informed investment decisions in the financial sector. This paper attempts to predict IPO short-term underperformance during a month post-listing. The current research landscape lacks modern models that address the needs of small and imbalanced datasets relevant to emerging markets, as well as the risk preferences of investors. To fill this gap, we present a practical framework utilizing tree-based ensemble learning, including Bagging Classifier (BC), Random Forest (RF), AdaBoost (Ada), Gradient Boosting (GB), XGBoost (XG), Stacking Classifier (SC), and Extra Trees (ET), with Decision Tree (DT) as a base estimator. The framework leverages data-driven methodologies to optimize decision-making in complex financial systems, integrating ANOVA F-value for feature selection, Randomized Search for hyperparameter optimization, and SMOTE for class balance. The framework’s effectiveness is assessed using a hand-collected dataset that includes features from both pre-IPO prospectus and firm-specific financial data. We thoroughly evaluate the results using single-split evaluation and 10-fold cross-validation analysis. For the single-split validation, ET achieves the highest accuracy of 86%, while for the 10-fold validation, BC achieves the highest accuracy of 70%. Additionally, we compare the results of the proposed framework with deep-learning models such as MLP, TabNet, and ANN to assess their effectiveness in handling IPO underperformance predictions. These results demonstrate the framework’s capability to enable robust data-driven decision-making processes in complex and dynamic financial environments, even with limited and imbalanced datasets. The framework also proposes a dynamic methodology named Investor Preference Prediction Framework (IPPF) to match tree-based ensemble models to investors’ risk preferences when predicting IPO underperformance. It concludes that different models may be suitable for various risk profiles. For the dataset at hand, ET and Ada are more appropriate for risk-averse investors, while BC is suitable for risk-tolerant investors. The results underscore the framework’s importance in improving IPO underperformance predictions, which can better inform investment strategies and decision-making processes. Full article