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This paper uses two competing machine learning models, namely the Support Vector Regression (SVR) and the eXtreme Gradient Boosting (XGBoost) against the Autoregressive Integrated Moving Average ARIMAX (p,d,q) model to identify their predictive performance of the crude oil volatility index before and during COVID-19. In terms of accuracy, forecasting results reveal that the SVR model dominates the XGBoost and ARIMAX models in predicting the crude oil volatility index before COVID-19. However, the XGBoost model provides more accurate predictions of the crude oil volatility index than the SVR and ARIMAX models during the pandemic. The inverse cumulative distribution of residuals suggests that both ML models produce good results in terms of convergence. Findings also indicate that there is a fast convergence to the optimal solution when using the XGBoost model. When analyzing the feature importance, the Shapley Additive Explanation Method reveals that the SVR performs significantly better than the XGBoost in terms of feature importance. During the pandemic, the predictive power of the CBOE Volatility Index and Economic Policy Uncertainty index for forecasting the crude oil volatility index is improved compared to the pre-COVID-19 period. These findings imply that investor fear-induced uncertainty in the financial market and economic policy uncertainty are the most significant features and hence represent substantial sources of uncertainty in the oil market.

This study examines the forecasting power of the gas price and uncertainty indices for crude oil prices. The complex characteristics of crude oil price such as a non-linear structure, time-varying, and non-stationarity motivate us to use a newly proposed approach of machine learning tools called XGBoost Modelling. This intelligent tool is applied against the SVM and ARIMAX (p,d,q) models to assess the complex relationships between crude oil prices and their forecasters. Empirical evidence shows that machine learning models, such as the SVM and XGBoost models, dominate traditional models, such as ARIMAX, to provide accurate forecasts of crude oil prices. Performance assessment reveals that the XGBoost model displays superior prediction capacity over the SVM model in terms of accuracy and convergence. The superior performance of XGBoost is due to its lower complexity and costs, high accuracy, and rapid processing times. The feature importance analysis conducted by the Shapley additive explanation method (SHAP) highlights that the different uncertainty indexes and the gas price display a significant ability to forecast future WTI crude prices. Additionally, the SHAP values suggest that the oil implied volatility captures valuable forecasting information of gas prices and other uncertainty indices that affect the WTI crude oil price.

Background Remnant cholesterol (RC) is an independent predictor of cardiovascular events in type 2 diabetes mellitus (T2DM). Concurrently, vitamin D deficiency is a recognized risk factor for developing T2DM. However, the association between serum 25-hydroxyvitamin D (25(OH)D) levels and RC in patients with established T2DM remains incompletely elucidated. Specifically, potential non-linear relationships and modifications of this association by age and sex are unclear. This study investigates the relationship between 25(OH)D and RC in a cohort of 380 patients with T2DM. Methods A total of 380 T2DM patients (283 men and 97 women) were evaluated. Demographic data were analyzed descriptively. Statistical tests assessed the association between 25(OH)D levels and RC, and piecewise linear regression was utilized to explore potential threshold effects. Results Spearman correlation analysis revealed that female gender was significantly associated with higher RC levels (ρ = 0.163, p  = 0.002). Piecewise linear regression identified a threshold effect at 18.8 ng/mL: below this threshold, each 1 ng/mL increase in 25(OH)D was associated with a decrease in RC of 0.01 mmol/L (β = -0.01, 95% CI: -0.02 to -0.00); above this threshold, it was associated with an increase of 0.02 mmol/L (β = 0.02, 95% CI: 0.00 to 0.03).Age significantly modified this association (interaction p  < 0.05), suggesting an age-dependent inversion of the effect of vitamin D on RC. Conclusion This study demonstrates a complex, non-linear relationship between 25(OH)D levels and Remnant cholesterol in patients with type 2 diabetes. Age significantly modifies this relationship, suggesting that tailored interventions based on vitamin D status may be warranted to inform future interventional studies targeting RC modulation.

Since data are gradually enriched over time, knowledge graphs are inherently imperfect. Thus, knowledge graph completion is proposed to perfect knowledge graph by completing triples. Currently, a family of translation models has become the most effective method for knowledge graph completion. These translation models are modeled to solve the complexity and diversity of entities, such as one-to-many, many-to-one, and many-to-many, which ignores the diversity of relations themselves, such as multiple relations between a pair of entities. As a result, with current translation models, it is difficult to effectively extract the semantic information of entities and relations. To effectively extract the semantic information of the knowledge graph, this paper fundamentally analyzes the complex relationships of the knowledge graph. Then, considering the diversity of relations themselves, the complex relationships are refined as one-to-one-to-many, many-to-one-to-one, one-to-many-to-one, many-to-one-to-many, many-to-many-to-one, one-to-many-to-many, and many-to-many-to-many. By analyzing the complex relationships, a novel knowledge graph completion model, entity and relation double embedding on relation hyperplanes and relation projection hyperplanes (ERDERP), is proposed to extract the semantic information of entities and relations. First, ERDERP establishes a relation hyperplane for each relation and projects the relation embedding into the relation hyperplane. Thus, the semantic information of the relations is extracted effectively. Second, ERDERP establishes a relation projection hyperplane for each relation projection and projects entities into relation projection hyperplane. Thus, the semantic information of the entities is extracted effectively. Moreover, it is theoretically proved that ERDERP can solve antisymmetric problems. Finally, the proposed ERDERP are compared with several typical knowledge graph completion models. The experimental results show that ERDERP is significantly effective in link prediction, especially in relation prediction. For instance, on FB15k and FB15k-237, Hits@1 of ERDERP outperforms TransH at least 30%.

Several previous studies examined the variables of public-transit-related walking and privately owned vehicles (POVs) to go to work. However, most studies neglect the possible non-linear relationships between these variables and other potential variables. Using the 2017 U.S. National Household Travel Survey, we employ the Bayesian Network algorithm to evaluate the non-linear and interaction impacts of health condition attributes, work trip attributes, work attributes, and individual and household attributes on walking and privately owned vehicles to reach public transit stations to go to work in California. The authors found that the trip time to public transit stations is the most important factor in individuals’ walking decision to reach public transit stations. Additionally, it was found that this factor was mediated by population density. For the POV model, the population density was identified as the most important factor and was mediated by travel time to work. These findings suggest that encouraging individuals to walk to public transit stations to go to work in California may be accomplished by adopting planning practices that support dense urban growth and, as a result, reduce trip times to transit stations.

The aim of this study is to assess the impact of rice-based food security on water, energy, land, and CO2 emissions from a holistic point of view using the Nexus approach, which analyzes tradeoffs between water, energy, and food management. In Japan, both rice consumption and the area harvested for rice have decreased. Maintaining a high self-sufficiency ratio (SSR) in rice production is an important aspect of food security in Japan, impacting the management of key resources, such as water, energy, and land. This study has, therefore, assessed the impact of various SSRs on rice production, focusing on consumption and land-use trends. First, the rice production SSR is predicted to drop to 87% by 2025 within the logarithmic trend of rice consumption and the polynomial trend line of the harvested area of rice. This reflects the fact that rice production is expected to decline more steeply than consumption between 2016 and 2025. Second, this study sets the SSRs for rice in 2025 between 80% and 100%, reflecting a range of low-to-high food security levels. In comparison with the 2016 baseline, about 0.70 × 10 6 additional tons of rice will be produced. Achieving a rice production SSR of 100% will require 10,195 × 10 6 m3 more of water and 23.31 × 10 6 GJ more of energy. Furthermore, an additional 283,000 tons of CO2 will be emitted in 2025, as more energy is used. By contrast, an 80% rice production SSR scenario would save 1482 × 10 6 m3 of water and 3.39 × 10 6 GJ of energy, as well as making a 398,000-ton reduction in CO2 emissions in 2015. A lower SSR would have a positive impact on resource management but a negative impact on food security. It would also reduce the income and economic status of farmers. It is, therefore, important to consider the tradeoffs between food security and resource savings in order to achieve sustainable water, energy, food, and land management in Japan.

The casting processes are characterized by complex relationships between predictors and responses. It is the fundamental understanding of these complex relationships that often involves hundreds of factors, which improves quality without losing productivity and raising cost. In this work, cast solid solution strengthened ferritic spheroidal graphite irons GJS-500-14 and GJS-600-10 (EN 1563:2012) have been evaluated. These materials offer stronger components with good machinability owing to their even hardness properties. In this case the predictors are chemical composition, gating layout, foundry set-up, testing procedure and equipment etc. and the responses are the tensile properties (Rp0.2, Rm, A5). Here 200 tensile specimens compiled from industrial foundry melts from over 30 years of research have created a state-of-the-art platform for statistical engineering in order to perform Exploratory Data Analysis (EDA) and data visualization. This statistical platform has provided new insight on how foundries should treat complex relationships between predictors and responses in order to identify sources of variation and interaction effects.

Attribute-based encryption (ABE) allows users to encrypt and decrypt data based on attributes. It realizes fine-grained access control and can effectively solve the one-to-many encryption and decryption problem in open cloud application. Linear secret sharing scheme (LSSS) is the common access structure with a matrix on the attributes in ABE schemes, which may depict AND, OR, threshold operations, etc. However, LSSS access structure does not depict the complex and dynamic access policy of attributes, such as the complicated relationship of different attributes and the generation of dynamic attributes. It severely restricts the expansion of the practical application of ABE. Besides, there exists another problem; attribute authority (AA) in traditional ABE has a concentration of power and easily suffers from single-point failure or privacy leakage for being attacked or corrupted. Blockchain is a decentralized, tamper-free, traceable, and multi-party distributed database technology. Consortium blockchain (CB) is a partially centralized blockchain, whose openness is between the public blockchain and the private blockchain. In this paper, an ABE scheme on LSSS access structure with expressive dynamic attributes (EDA) based on CB (LSSS-EDA-ABE-CB) was proposed to resolve the above issues. EDA can construct the comprehensive attribute calculation expressions by conducting various operations, such as arithmetic operations, relational operations, and string operations. In virtue of the application of EDA, the proposed scheme can reconstruct new composite attributes to realize the dynamic adjustment of attributes. A partitioning method of EDA avoids one attribute appearing in two different EDA expressions. The CB technology enhanced the authority and trustworthiness of AA by openly recording AA’s attribute key distributions in CB transactions. The scheme in the paper was proven CPA-secure under the decision q-PBDHE assumption in standard model in the CB application environment. The scheme provides a more general data access policy and maintains the fine-grained character of ABE simultaneously. Finally, the security and performance analysis shows that the proposed scheme is secure and highly efficient.

The factors influencing the incidence of COVID‐19, including the impact of the vaccination programs, have been studied in the literature. Most studies focus on one or two factors, without considering their interactions, which is not enough to assess a vaccination program in a statistically robust manner. We examine the impact of the U.S. vaccination program on the SARS‐CoV‐2 positivity rate while simultaneously considering a large number of factors involved in the spread of the virus and the feedbacks among them. We consider the effects of the following sets of factors: socioeconomic factors, public policy factors, environmental factors, and non‐observable factors. A time series Error Correction Model (ECM) was used to estimate the impact of the vaccination program at the national level on the positivity rate. Additionally, state‐level ECMs with panel data were combined with machine learning techniques to assess the impact of the program and identify relevant factors to build the best‐fitting models. We find that the vaccination program reduced the virus positivity rate. However, the program was partially undermined by a feedback loop in which increased vaccination led to increased mobility. Although some external factors reduced the positivity rate, the emergence of new variants increased the positivity rate. The positivity rate was associated with several forces acting simultaneously in opposite directions such as the number of vaccine doses administered and mobility. The existence of complex interactions, between the factors studied, implies that there is a need to combine different public policies to strengthen the impact of the vaccination program. Plain Language Summary When vaccines against COVID‐19 became available, governments around the world implemented vaccination policies to contain the COVID‐19 pandemic. Many factors such as socioeconomic factors, environmental factors, and public policy factors affect the positivity rate. Thus, we used a multivariate framework to assess the impact of the vaccination program and isolate the effects of the many the factors and their interactions impacting positivity rate in US. A robust statistical analysis yielded that the vaccination program reduced the positivity rate. However, the positive effect of the vaccination program was reduced by the other factors and their interactions. For example, an interesting finding is that the vaccination rate and mobility have a complex relationship with COVID‐19 positivity rate because when vaccination rate increases, mobility increases (people go out because they are vaccinated) but mobility causes the number of infections to increase. Therefore, this needs to be considered when designing policy to contain the pandemic and reduce the positivity rate. Key Points Mobility and vaccination are key factors in reducing COVID‐19 positivity rate, they both have a feedback effect on each other Socioeconomic factors, public policy factors, environmental factors, and non‐observable factors and their interactions affect positivity rate A multivariate model shows complex relationships between vaccination and positivity rate

Purpose Many firms engage in co-opetitive projects during which they have simultaneously competitive and collaborative relationships with many rivals in a complex network. A co-opetitive network offers them access to a large volume of resources and knowledge, for example, to support new markets and/or territories. So, does the network grow with the scope of the co-opetition project? The paper aims to discuss this issue. Design/methodology/approach An empirical study of 106 French boating intermediate-sized enterprises (ETIs) and small and medium enterprises provides a descriptive and explanatory analysis of co-opetitive networks. Findings The results support this definition of a complex co-opetitive network only when the objectives of a firm are part of the geographical expansion of its activities. In contrast, these relations remain simple (dyadic) when a firm favours a strategy of diversifying its activities while maintaining its unique local geographical market. Research limitations/implications First, the work is based on a quantitative methodology, so is static. It would be interesting to analyze the process of the building of co-opetitive relationships and opportunism between rival firms, for example, through a qualitative study. Second, this work focusses on boating companies in France. It may be appropriate to consider the sanctions placed on the opportunism of foreign firms in co-opetition. Third, future work could increase understanding, not only of the nature of reprisals inflicted on individualistic co-opetitors, but also on the structure, objectives and results of these reprisals. Originality/value The study deepens our knowledge of the definition, composition and determinants of co-opetitive networks.