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During the COVID-19 pandemic, a ban on inspections of small businesses, including pharmacies, was imposed in Kazakhstan, which relaxed law enforcement efforts regarding the prohibition of over-the-counter antibiotic (AB) sales. This study aimed to investigate how this affected the knowledge, attitudes, and practices (KAP) related to AB and antimicrobial resistance (AMR), as well as to assess actual AB consumption at the community level. The study comprised two cross-sectional sub-studies: the first involved a KAP survey conducted in 2022 and 2024, utilizing the Special Eurobarometer questionnaire on AMR. The second sub-study analyzed AB consumption in 2021 and 2023, measured in defined daily doses per 1000 inhabitants. Results revealed an increase in the percentage of individuals reporting receipt of information about ABs and AMR in the past year (37.3% in 2022 vs. 52.9% in 2024, p < 0.001) and an increase in the percentage of individuals reporting AB use in the past year (49.0% in 2022 vs. 54.0% in 2024, p = 0.056). The most consumed ABs were from the Watch group, with azithromycin and ceftriaxone ranking highest. These findings support the hypothesis that the relaxation of law enforcement contributed to an increase in AB consumption and emphasize the need for public health policies to address this issue. Full article

This study examines the significant growth of e-commerce in Romania, which has surpassed the rates of expansion observed in other more developed countries of the European Union. Based on market analysis by sector-specific companies, the Romanian e-commerce market has reached over €6.5 billion. This rapid growth trajectory is expected to continue, driven by various factors, including the impact of the COVID-19 pandemic and the natural evolution of the market. The main purpose of this study is to assess the expansion of the e-commerce market in Romania, identify the key factors behind this growth, and project future market values. Data for this analysis has been collected from industry reports, market analysis, and relevant statistical databases. The study uses a quantitative approach, utilizing financial data and growth rates to forecast future market trends. The dataset includes financial figures from e-commerce sales, digital services such as bill payments, and airline and hotel bookings from 2018 to 2023. Projections for 2024 and beyond were derived from this historical data. In 2019, the e-commerce market in Romania was valued at €4.68 billion, representing a significant increase compared to previous years. By 2020, amid the pandemic, the market value increased to €5.5 billion, marking a 38.4% increase from the previous year. Forecasts for 2024 estimate that the market will exceed €8 billion. In addition, when related digital services are included, the total market value could exceed €10 billion, illustrating the substantial economic impact of the online sector and the growth potential. This study highlights the dynamic nature of the e-commerce landscape in Romania and underlines the significant economic opportunities it presents. Full article

Deep learning (DL) has been widely applied to forecast the sales volume of household appliances with high accuracy. Unfortunately, in small towns, due to the limited amount of historical sales data, it is difficult to forecast household appliance sales accurately. To overcome the above-mentioned challenge, we propose a novel household appliance sales forecasting algorithm based on transfer learning, temporal convolutional network (TCN), long short-term memory (LSTM), and attention mechanism (called “TransTLA”). Firstly, we combine TCN and LSTM to exploit the spatiotemporal correlation of sales data. Secondly, we utilize the attention mechanism to make full use of the features of sales data. Finally, in order to mitigate the impact of data scarcity and regional differences, a transfer learning technique is used to improve the predictive performance in small towns, with the help of the learning experience from the megacity. The experimental outcomes reveal that the proposed TransTLA model significantly outperforms traditional forecasting methods in predicting small town household appliance sales volumes. Specifically, TransTLA achieves an average mean absolute error (MAE) improvement of 27.60% over LSTM, 9.23% over convolutional neural networks (CNN), and 11.00% over the CNN-LSTM-Attention model across one to four step-ahead predictions. This study addresses the data scarcity problem in small town sales forecasting, helping businesses improve inventory management, enhance customer satisfaction, and contribute to a more efficient supply chain, benefiting the overall economy. Full article

With the rapid development of digital technology and the increasing focus on the global supply chain network, it has become a new challenge for international companies to select digital technology innovation projects in an efficient way, so as to improve their supply chain position and competitiveness. Prior works have identified the effects of digital technology adoption on companies’ supply chain positions; however, there has been limited research on the impact of digital technology innovation heterogeneity on companies’ supply chain position and the pathways through which this effect plays out. Hence, based on the global supply chain panel data from Chinese new energy vehicle companies, this study used a two-way fixed-effects model and causal stepwise regression analysis to study the impact of digital technological innovation on companies’ supply chain position and the dynamic mechanisms between them. The empirical results show that all three types of digital technology innovations, in the design and development process, the production and manufacturing process, and the sales and after-sales process, significantly enhance the company’s supply chain position. Further mechanism analysis shows that digital technology innovations enhance the company’s managerial efficiency and profitability mainly by reducing costs and increasing revenues, which ultimately improves the company’s supply chain position. This paper can provide a reference for policy makers to promote the application and development of a company’s digital technology and enhancing the supply chain position. Full article

This study combines the Nash–Cournot competition model and the stochastic optimization model to examine the impact of electric vehicle (EV) quantity fluctuations on microgrid operations, aiming to optimize energy usage in a competitive electricity market. Integrating distributed energy resources and bidirectional charging, microgrids offer a novel approach for energy optimization, aiding in renewable energy generation, peak demand management, and emission reduction. Empirical evidence highlights benefits in Taiwan’s electricity market and net-zero emissions target by 2050, with a case study demonstrating enhanced local renewable energy generation due to EVs and microgrid integration. As the number of EVs increases, electricity sales from microgrids decline, but electricity purchases remain stable. The degree of electricity liberalization also influences the supply and demand dynamics of the electricity market. Microgrids selling electricity only to the main grid increases total power consumption by 65.55 million MWh, reducing the market share of the state-owned utility (Taipower). Conversely, allowing retailers to purchase from microgrids increases total consumption by 30.87 million MWh with a slight market share decrease for Taipower. This study contributes to providing an adaptable and flexible general model for future studies to modify and expand based on different scenarios and variables to shape energy and environmental policies. Full article

In today’s dynamic business environment, the accurate prediction of sales orders plays a critical role in optimizing Supply Chain Management (SCM) and enhancing operational efficiency. In a rapidly changing, Fast-Moving Consumer Goods (FMCG) business, it is essential to analyze the sales of the products and accordingly plan the supply. Due to low data volume and complexity, traditional forecasting methods struggle to capture intricate patterns. Domain Adversarial Neural Networks (DANNs) offer a promising solution by integrating transfer learning techniques to improve prediction accuracy across diverse datasets. This study presents a new sales order prediction framework that combines DANN-based feature extraction and various machine learning models. The DANN method generalizes the data, maintaining the data behavior’s originality. The approach addresses challenges like limited data availability and high variability in sales behavior. Using the transfer learning approach, the DANN model is trained on the training data, and this pre-trained DANN model extracts relevant features from unknown products. In contrast, Machine Learning (ML) algorithms are used to build predictive models based on it. The hyperparameter tuning of ensemble models such as Decision Tree (DT) and Random Forest (RF) is also performed. Models like the DT and RF Regressor perform better than Linear Regression and Support Vector Regressor. Notably, even without hyperparameter tuning, the Extreme Gradient Boost (XGBoost) Regressor model outperforms all the other models. This comprehensive analysis highlights the comparative benefits of various models and establishes the superiority of XGBoost in predicting sales orders effectively. Full article

Live-streaming commerce (LSC) has been adopted by an increasing number of supply-chain enterprises to enhance their market competitiveness. However, the question of who will lead live-streaming e-commerce in the supply chain (SC-LSC) is a key issue, especially when there is equity cooperation between upstream and downstream enterprises. Three main SC-LSC models are examined: independent SC-LSC run by manufacturers, independent SC-LSC run by retailers, and cooperatively run SC-LSC. Then, a novel LSC demand function composed of online popularity, price discount and sales conversion rate is proposed. Furthermore, four scenarios have been comprehensively investigated considering whether there is an online-to-offline drainage effect and whether there is equity cooperation. Regardless of the scenario, having both parties reach an agreement on a given SC-LSC model is difficult, and even equity cooperation cannot promote SC-LSC cooperation. In most cases, manufacturers tend to offset the losses caused by the drainage effect by adopting high wholesale prices, which will in turn exacerbate retailers’ resistance to SC-LSC. These findings provide insight into how LSC is modeled and how LSC can be better implemented in various types of supply chains such as that of Gree Electric. Full article

Regulator-required public disclosures of net short positions do not provide a profitable investment signal for UK stocks across a variety of portfolio formation methodologies. While long-short (zero initial outlay) portfolios based on this signal usually make a profit on average, it is rarely statistically significant in either gross or risk-adjusted terms. The issue is that the short sides of the portfolios make substantial losses. Unit initial outlay portfolios based on the disclosures do not generally significantly outperform the market, either. Where they do significantly outperform the market, this outperformance is economically modest. Full article

As new energy integration increases, power grid load curves become steeper. Large logistics parks, with their substantial cooling load, show great peak shaving potential. Leveraging this load while maintaining staff comfort, product quality, and operational costs is a major challenge. This paper proposes a two-stage robust optimization method for large logistics parks to participate in grid peak shaving. First, a Cooling Load’s Economic Contribution (CLEC) index is introduced, integrating the Predicted Mean Vote (PMV) and Sales Pressure Index (SPI). Then, an optimization model is established, accounting for renewable energy uncertainties and maximizing large logistics parks’ participation in peak shaving. Results illustrate that the proposed method leads to a reduction in the peak shaving pressure on the distribution network. Specifically, under the scenario tolerating the maximum potential uncertainty in renewable energy output, the absolute peak-to-valley difference and fluctuation variance of the park’s net load are decreased by 45.82% and 54.59%, respectively. Furthermore, the PMV and the SPI indexes are reduced by 39.12% and 26.36%, respectively. In comparison with the determined optimization method, despite a slight cost increase of 20.06%, the proposed method significantly reduces EDR load shedding by 98.1%. Full article

The advancement of mechanization in forestry has increased productivity in the forestry sector, bringing positive and negative impacts that require a deeper understanding for sustainable forest management. This study aimed to apply a simplified instrument for assessing damage and environmental impacts in forest harvesting of commercial eucalyptus plantations, using a combination of methodologies. The methodology used combined interaction networks and impact assessment matrices, carrying out field surveys, transposing them to interaction networks and weighting them through assessment matrices, resulting in environmental indices (ES) for prioritizing actions. The study was conducted on a commercial eucalyptus plantation in the municipality of São Pedro, São Paulo, Brazil. The mechanized harvesting of the area consists of the structure of a module with a mobile unit consisting of a harvester and forwarder. The results indicated that wood transport presented the highest ES, both positive and negative. The most significant negative impacts (ES) were the depletion of water resources and erosion, while the positive impacts included regional development and job creation. The most notable changes, positive and negative, were observed in the physical and anthropic environment, with a lesser impact on the biotic environment. Full article

The booming development of e-commerce has promoted the diversified development of logistics distribution services (LDS). For LDS, e-commerce retailers (e-retailers) often choose either the outsourced logistics distribution services strategy (OLDSS) or the self-built logistics distribution services strategy (SBLDSS). Although there are problems such as products getting lost and damaged during the logistics distribution process, the high transparency and traceability characteristics of blockchain technology (BT) can help solve the problem of products being lost and damaged in the logistics distribution process. However, due to the high cost of BT, e-retailers may encounter reduced sales, which causes the supply chain corporate profits to decrease. To encourage the BT investment enthusiasm of the e-retailers and regulate corporate profits, the government implements subsidies for e-retailers’ BT, namely, the government blockchain subsidy (GBS). In addition, in recent years, environmental degradation has become increasingly severe, causing negative impacts on people’s lives. To promote sustainable development, we use variational inequality to establish an e-commerce closed-loop supply chain (E-CLSC) network equilibrium model in which the network equilibrium decisions of e-retailers choosing the OLDSS and those choosing the SBLDSS are obtained. Then, we analyze the impact of the BT input cost and the GBS quota on equilibrium decisions by studying their properties and verifying the theoretical results by performing numerical examples. Finally, we analyze the profits of the e-retailers to obtain the impact of the BT input cost and the GBS quota on e-retailers’ choice of the optimal LDS strategy; in this way, we provide a scientific basis for e-retailers to choose the optimal LDS strategy. The results show that increasing the BT input costs reduces e-retailers’ product sales under the two LDS strategies, which decreases the production rate and the recovery rate of the products. When the BT input cost is low, SBLDSS is the best choice for e-retailers. When the BT input cost is high, OLDSS is the best choice for e-retailers. Moreover, there is a positive correlation between GBS and e-retailers’ product sales; thus, GBS is conducive to expanding market demand, regulating the profits of manufacturers, increasing the e-retailers’ profits, improving the enthusiasm of the e-retailers for BT investment, and promoting the overall development of supply chain enterprises. For e-retailers, choosing the OLDSS can lead to a better development of the E-CLSC. Full article

This study delves into the effectiveness of two time integration techniques, namely the adaptive implicit–explicit (imp–exp) and explicit–explicit (exp–exp) methods, which stand as efficient formulations for tackling intricate systems characterized by multiple time scales. The imp–exp technique combines implicit and explicit procedures by employing implicit formulations for faster components and explicit calculations for slower ones, achieving high accuracy and computational efficiency. Conversely, the exp–exp method, a variation of explicit methods with sub-cycling, excels in handling locally stiff systems by employing smaller sub-steps to resolve rapid changes while maintaining stability. For both these approaches, numerical damping may be activated by adaptive time integration parameters, allowing numerical dissipation to be locally applied, if necessary, as a function of the considered discrete model and its computed responses, enabling a highly effective numerical dissipative algorithm. Furthermore, both these techniques stand as very simple and straightforward formulations as they rely solely on single-step displacement–velocity relations, describing truly self-starting procedures, and they stand as entirely automated methodologies, requiring no effort nor expertise from the user. This work provides comparative studies of the adaptive imp–exp and exp–exp approaches to assess their accuracy and efficiency across a wide range of scenarios, with emphasis on geophysical applications characterized by multiscale problems, aiming to establish under which circumstances one approach should be preferred over the other. Full article

This paper presents a series of enhancements to a previously proposed mixed-integer linear programming (MILP) model for investment decisions and operational planning in distributed generation (DG) systems. The main contribution of this study consists of integrating a wind generation system and multiple loads at different buses in a network. The model considers dynamic weather data, energy prices, costs related to photovoltaic and wind systems, storage systems, operational and maintenance costs, and other pertinent factors, such as efficiencies, geographical locations, resource availability, and different load profiles. The simulation results obtained through implementation in Julia’s programming language illustrate that the MILP formulation maximizes the net present value, and four configurations for hybrid power generation systems in Mexico are analyzed. The objective is to enable profitability assessment for investments in large-capacity DG systems in two strategic zones of Mexico. The results show that the configurations in the NE zone, especially in Tamaulipas, are the most cost-effective. Case 1 stands out for its highest net present value and shortest payback time, while Case 2 offers the highest energy savings. In addition, Cases 3 and 4, which incorporate storage systems, exhibit the longest payback periods and the lowest savings, indicating less favorable economic performance compared with Cases 1 and 2. Moreover, the sales of two case studies, one without a storage system and the other with a storage system, are shown. The model also incorporates instruments for buying or selling energy in the wholesale electricity market, including variables that depict the injected energy into the electrical grid. This comprehensive approach provides a detailed overview of optimal energy management. Full article

Misreporting is prevalent in supply chain characterized by asymmetric information, and its impact on the supply chain is substantial and cannot be overlooked. In order to explore the impact of fresh supplier’s misreporting decisions on fresh supply chain, this paper takes the fresh supply chain with a single fresh supplier and a single e-commerce enterprise as the research object, and constructs five Stackelberg game models based on the differences of supply chain information transparency and power structure. Particularly, the effect of fresh-keeping level on the after-sales rate and market demand of the product is incorporated into the model, and the following conclusions are drawn by solving and analyzing the decision results of the different models: (1) When the supplier has the decision advantage, it will not choose to misreport. But when it loses the decision advantage, it will produce the misreporting behavior. Supplier misreporting is detrimental to the retailer and the supply chain; specifically, it can lead to lower fresh-keeping level and higher after-sales rates. (2) In the decentralized decision-making model, it is more beneficial for the supply chain that the supplier has the leading right of decision-making. In the absence of misreporting, the leader’s profit is always higher than that of the follower. When there is misreporting, even if the retailer is the dominant player, its profit is still lower than the manufacturer’s. (3) Both supply chain profit and fresh-keeping level are positively correlated with the coefficient of consumer perception of freshness and the coefficient of sensitivity to fresh-keeping technology, and are more significant under the centralized decision-making model. Furthermore, in response to supplier misreporting behavior, this paper achieves coordination in the fresh supply chain by designing a joint contract and confirms the effectiveness of this contract through an arithmetic analysis. Full article

The integration of renewable energy sources in the European power system is one of the main goals set by the European Union. In order to ease this integration, in recent years, Renewable Energy Communities (RECs) have been introduced that aim to increase the exploitation of renewable energy at the local level. This paper presents an Energy Management System (EMS) for an industrial microgrid owned and operated by a greentech company located in the north of Italy. The company is a member of an REC. The microgrid is made of interconnected busbars, integrating photovoltaic power plants, a fleet of electric vehicles, including company cars and delivery trucks supporting Vehicle-to-Grid (V2G), dedicated charging stations, and a centralized battery energy storage system. The industrial site includes two warehouses, an office building, and a connection to the external medium-voltage network. The EMS is designed to optimize the operation of the microgrid and minimize the operating costs related to the sale and purchase of energy from the external network. Furthermore, as the company is a member of an REC, the EMS must try to follow a desired power exchange profile with the grid, suggested by the REC manager, with the purpose of maximizing the energy that is shared within the community and incentivized. The results demonstrate that, when minimizing only costs, local self-consumption is favored, leading to a Self-Sufficiency Rate (SSR) of 65.37%. On the other hand, when only the adherence to the REC manager’s desired power exchange profile is considered in the objective function, the SSR decreases to 56.43%, net operating costs increase, and the energy shared within the REC is maximized. Full article