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Abstract In pursuit of reducing dependence on oil, the Kingdom of Saudi Arabia has made significant efforts to advance sustainability under the strategic framework known as Saudi Vision. This initiative encompasses a range of activities aimed at promoting sustainability within the energy sector. This paper aims to analyze the existing sustainable practices of companies, employees, and the broader community in the supply chain. The objective is to evaluate current progress toward the sustainability goals of the Kingdom and assess the competitiveness of firms in the energy sector based on the effectiveness of sustainability management in their supply chain. Based on data collected from Saudi energy companies, structural equation modeling (SEM) techniques are utilized to investigate sustainable supply chain management (SSCM) practices and their influence on CP in Saudi Arabian energy companies. This study examines the influence of several practices on competitiveness (CP), including environmental practices (EMPs), social practices for employees (SPEs), social practices for the community (SPCs), operational practices (OPs), and supply chain integration practices (SCIs). The study incorporates five distinct independent variables, namely, EMP, SPE, SPC, OP, and SCI, where the dependent variable under investigation is denoted as CP. The findings from the SEM analysis show that three variables—SPE, SPC, and OP—demonstrate statistical significance, while EMP and SCI do not. These findings enhance the continuing academic discussion about balancing economic growth and environmental preservation through sustainable practices in the energy sector.

This research examines the intricate correlation between speed variation (celeration), a metric of driver behavior associated with vehicle control, and occurrences of running red lights. The study is based on a thorough analysis of a large dataset that includes a variety of parameters, such as exceeding speed limits, driver age, passenger count, weather, road condition, and temporal factors. Using cutting-edge machine learning methods like AdaBoost and Bagging, predictive models for red-light violations are painstakingly built, achieving remarkable validation accuracies of 90.4% and 90.1%, respectively. The study acknowledges the dataset’s limitations in capturing real-world traffic complexities while focusing on the effectiveness and trade-offs inherent in these methodologies. This emphasizes how important it is to have synchronized and thorough data sources to guarantee accurate representation. The research field is enhancing predictive modeling techniques and improving transportation safety by connecting celebration, speed variation patterns over time, with instances of red-light violations.

Iron ore has had a highly global market since setting a new pricing mechanism in 2008. With current dollar values, iron ore concentrate for sale price, which was $39 per tonne (62% Fe) in December 2015, reached $218 per tonne (62% Fe) in mid-2021. It is hovering around $120 in October 2023 (cf. https://tradingeconomics.com/commodity/iron-ore). The uncertainty associated with these fluctuations creates hardship for iron ore mine operators and steelmakers in planning mine development and making future sale agreements. Therefore, iron ore price forecasting is of special importance. This paper proposes a cutting-edge multi-echelon tandem learning (METL) model to forecast iron ore prices. This model comprises variational mode decomposition (VMD), multi-head convolutional neural network (MCNN), stacked long short-term-memory (SLSTM) network, and attention mechanism (AT). In the proposed METL (i.e., the combination of VMD, MCNN, SLSTM, AT) model, the VMD decomposes the time series data into sub-sequential modes for better measuring volatility. Then, the MCNN is applied as an encoder to extract spatial features from the decomposed sub-sequential modes. The SLSTM network is adopted as a decoder to extract temporal features. Finally, the AT is employed to capture spatial–temporal features to obtain the complete forecasting process. Extensive computational experiments are conducted based on daily-based and weekly-based iron ore price datasets with different time scales. It was validated that the proposed METL model outperformed its single-echelon and other categorized models by 10–65% in range. The proposed METL model can improve the prediction accuracy of iron ore prices and thus help mining and steelmaking enterprises to determine their sale or purchase strategies. [ABSTRACT FROM AUTHOR]

Background: Many observational studies have examined the association between statins and the incidence of Parkinson's disease (PD) in high‐risk populations. On the other hand, clinical trials as well as other observational studies investigated the safety and efficacy of statins in slowing disease progression in PD patients. However, the evidence has been inconclusive in both questions. To that end, we conducted this systematic review and meta‐analysis to synthesize evidence on the role of statins in decreasing the risk of PD among high‐risk populations and as a possible disease‐modifying agent for patients with PD. Methods: A comprehensive literature search of electronic databases including PubMed, Scopus, Cochrane, and Web of Science has been performed. Relevant studies were chosen and data were extracted and analyzed using RevMan software version 5.4.1. Results: Twenty‐five studies (14 cohort, 9 case–control, and 2 randomized controlled trials) have been included in the present systematic review. Of them, 21 studies reported the association between statins and PD risk. Statins were found to significantly reduce the risk of developing PD (pooled RR 0.86, 95% CI [0.77–0.95], p < 0.005). Four studies investigated statins as a disease‐modifying agent. The pooled mean difference (MD) in the UPDRS‐III from baseline to endpoint did not differ significantly between the statin and control groups (MD −1.34 points, 95% CI [−3.81 to 1.14], p = 0.29). Conclusion: Although epidemiological observational studies showed that statin use was associated with a reduced risk of PD, current evidence is insufficient to support the role of statins in slowing the progression of PD. These findings are limited by the fact that most of the included studies are observational studies which carry a high risk of confounding bias which highlights the need for future well‐designed RCTs. [ABSTRACT FROM AUTHOR]