Search Results (18)

Big data and blockchain technology are coming together to revolutionize how decisions are made in a decentralized way across various industries. This review looks at how these technologies, along with distributed systems, can improve data security, transparency, and real-time processing, making decision-making more efficient and informed. The integration enhances data security with unchangeable records, increases transparency and traceability, and supports real-time data analysis. However, there are challenges to overcome, including scalability, data privacy, interoperability, regulatory compliance, and high costs. By examining case studies such as Estonia’s healthcare system, IBM and Walmart’s Food Trust, and the Brooklyn Microgrid project, we explore the practical applications and benefits of combining big data with blockchain. Despite these hurdles, the review finds that the ongoing advancements and innovative solutions in these technologies offer significant promise. They are set to drive the adoption and effectiveness of decentralized decision-making, ultimately leading to better efficiency and outcomes across multiple sectors. Full article

In-store grocery shopping is still widely preferred by consumers despite the rising popularity of online grocery shopping. Moreover, hardware-based in-store navigation systems and shopping list applications such as Walmart’s Store Map, Kroger’s Kroger Edge, and Amazon Go have been developed by supermarkets to address the inefficiencies in shopping. But even so, the current systems’ cost-effectiveness, optimization capability, and scalability are still an issue. In order to address the existing problems, this study investigates the optimization of grocery shopping by proposing a proximity-driven dynamic sorting algorithm with the assistance of machine learning. This research method provides us with an analysis of the impact and effectiveness of the two machine learning models or ML-DProSA variants—agglomerative hierarchical and affinity propagation clustering algorithms—in different setups and configurations on the performance of the grocery shoppers in a simulation environment patterned from the actual supermarket. The unique shopping patterns of a grocery shopper and the proximity of items based on timestamps are utilized in sorting grocery items, consequently reducing the distance traveled. Our findings reveal that both algorithms reduce dwell times for grocery shoppers compared to having an unsorted grocery shopping list. Ultimately, this research with the ML-DProSA’s optimization capabilities aims to be the foundation in providing a mobile application for grocery shopping in any grocery stores. Full article

In the present scenario, retail sales forecasting has a great significance in E-commerce companies. The precise retail sales forecasting enhances the business decision making, storage management, and product sales. Inaccurate retail sales forecasting can decrease customer satisfaction, inventory shortages, product backlog, and unsatisfied customer demands. In order to obtain a better retail sales forecasting, deep learning models are preferred. In this manuscript, an effective Bi-GRU is proposed for accurate sales forecasting related to E-commerce companies. Initially, retail sales data are acquired from two benchmark online datasets: Rossmann dataset and Walmart dataset. From the acquired datasets, the unreliable samples are eliminated by interpolating missing data, outlier’s removal, normalization, and de-normalization. Then, feature engineering is carried out by implementing the Adaptive Particle Swarm Optimization (APSO) algorithm, Recursive Feature Elimination (RFE) technique, and Minimum Redundancy Maximum Relevance (MRMR) technique. Followed by that, the optimized active features from feature engineering are given to the Bi-Directional Gated Recurrent Unit (Bi-GRU) model for precise retail sales forecasting. From the result analysis, it is seen that the proposed Bi-GRU model achieves higher results in terms of an R2 value of 0.98 and 0.99, a Mean Absolute Error (MAE) of 0.05 and 0.07, and a Mean Square Error (MSE) of 0.04 and 0.03 on the Rossmann and Walmart datasets. The proposed method supports the retail sales forecasting by achieving superior results over the conventional models. Full article

If one looks at the United States over the past sixty years, it becomes clear that religious and spiritual practices have proliferated in unexpected places and spaces. They have become thoroughly ensconced in the boardrooms, offices, shop floors, and retail spaces of business establishments. From there, they have seeped into just about every imaginable area of American life, turning schools, parks, shopping malls, sports stadiums, hospitals, gyms, health food restaurants, spas, and the very apps on our computers and cell phones into corporate spaces promising new and enticing forms of spiritual enchantment. The purpose of this essay is to document the way new forms of spirituality have become part of a much longer history of the entanglement of business and religion, a history that began in monasteries, formed the bedrock of the Puritan work ethic, and is now an established aspect of the neoliberal ideal of the privatization and corporatization of all aspects of human life. Full article

The COVID-19 pandemic has inflicted substantial losses on a large number of enterprises and brought about the risk of unsustainable operations across the world. However, certain enterprises still managed to grow against the trend prevailing during the epidemic and succeeded in taking risks to make profits. This study discusses how global enterprises adopt a proactive risk management approach to transform crises into sustainable business performance during the period starting from the epidemic outbreak to normalization. By mainly obtaining research data from the Internet news media and official websites of the enterprises using content analysis technique, this paper chose case studies, from December 2019 to December 2021, of eight different companies, namely: BYD (China, Asia), Mafengwo (China, Asia), Xiamen Airlines (China, Asia), Zhijiang Bio (China, Asia), The Bund (United States, America), Walmart (United States, America), Qantas Airways (Australia, Oceania), and Honotel Group (France, Europe), from different industrial sectors including manufacturing, tourism, transportation, technical services, catering, retail, airlines, and accommodation, respectively. The study results show that each enterprise specifically incorporates the method of proactive risk management, to deal with a sudden crisis and take risks to make profits during the epidemic. The study findings provide a feasible way for enterprises to cope with sudden crises and enhance their ability to maintain sustainable operations. Full article

A typical retailer carries 10,000 stock-keeping units (SKUs). However, these numbers may exceed hundreds of millions for giants such as Walmart and Amazon. Besides the volume, SKU data can also be high-dimensional, which means that SKUs can be segmented on the basis of various attributes. Given the data volumes and the multitude of potentially important dimensions to consider, it becomes computationally impossible to individually manage each SKU. Even though the application of clustering for SKU segmentation is common, previous studies do not address the problem of parametrization and model finetuning, which may be extremely tedious and time-consuming in real-world applications. Our work closes the research gap by proposing a solution that leverages automated machine learning for the automated cluster analysis of SKUs. The proposed framework for automated SKU segmentation incorporates minibatch K-means clustering, principal component analysis, and grid search for parameter tuning. It operates on top of the Apache Parquet file format, an efficient, structured, compressed, column-oriented, and big-data-friendly format. The proposed solution was tested on the basis of a real-world dataset that contained data at the pallet level. Full article

We provide theoretical and empirical insights into the impact of COVID-19 on Amazon’s financial position. A longitudinal case study of Amazon’s financial situation during the 2016–2020 period, and time-series analysis, ratio analysis, and DuPont analysis, are employed as a quantitative methodology to explore Amazon’s financial situation changes before and after the COVID-19 pandemic. As for the robustness of the in-depth analysis, we compare Amazon’s financial performance and position with Walmart. The result shows that the COVID-19 pandemic did not have a huge negative impact on the companies’ financial performance because of its promotion of their development. However, this study provides an in-depth analysis of the influence of COVID-19 on Amazon’s financial situation, which financial aspects are most affected by COVID-19, which are not, and the company’s response to COVID-19. Therefore, this study sheds light on the accounting literature to demonstrate the impact of COVID-19 on Internet companies’ financial performance and provides some reference values for subsequent academic research. Full article

Retail firms are the best representatives of a developed country’s economic condition because they sell many of the necessary goods used for daily consumption, including food, clothes, shoes, electric appliances, and office supplies. This study presents a novel framework to help retail practitioners achieve the following goals: (1) predict sales revenues by identifying significant economic indicators, (2) estimate stable equilibriums by capturing interactive dynamics between competing firms, and (3) derive operational efficiencies and indicate required improvements by conducting performance assessments. To verify the validity of the research, data pertaining to Walmart, Costco, and Kroger are collected. Specifically, the least absolute shrinkage and selection operator (Lasso) is adopted in order to identify significant economic indicators. Consumer price index and regular wage are two common indicators that affect the the three firms’ sales numbers. In sales forecasting, support vector regression (SVR) and multivariate adaptive regression splines (MARS), respectively, perform the best in the training set and the testing set. Finally, the Lotka–Volterra model (LVM) and data envelopment analysis (DEA) are used for competitive analysis and performance assessment. A relationship of economic mutualism has been identified between the three firms. Furthermore, research findings show that Kroger performs inefficiently, though it can expect to increase sales more than the others in stable equilibriums. Full article

Recently, the application of blockchain to the setting, management, and trading of the energy system has formed an innovative technology and has attracted a lot of attention from industry, academia, and research. In this study, we use patent analysis technology to explore the development trends of the energy system with blockchain technology. During the patent analysis process, this study makes corresponding analysis charts, such as patent application numbers over time, patent application numbers for main leading countries, applicants, patent citations, international patent classification (IPC), and life cycle. Relative research and design (R&D) capability of the top ten applicants is estimated and the cluster map of the technology is obtained. The technical features of the top five IPC patent applications are related to the cluster map to show the development of energy blockchain technology. Through this paper, first, the basics of the blockchain and patent analysis are illustrated and, moreover, the reason why and how blockchain technology can be combined with the energy system is also briefly described and analyzed. The results of the patent analysis of energy blockchain technology indicate that the United States leads the way, accounting for more than half of the global total. It is also interesting to note that the participants are not from traditional specific fields, but included electric power manufacturers, computer software companies, e-commerce companies, and even many new companies devoted to blockchain technology. Walmart Apollo, LLC and International Business Machines Corporation (IBM) have the highest number of patent applications. However, Walmart Apollo, LLC ranks first with a greater number of inventors of 36, an activity year of 2 years, and a relative R&D capability of 100%. IBM ranks second with an activity year of 3 years and a research and development capability of 91%. Among various applicants, IBM and LO3 energy started earlier in this field, and their patent output is also more prominent. The IPC is mainly concentrated in G06Q 50/06, which belongs to the technical field of the setting and management of the energy system including electricity, gas, or water supply. Currently, most projects are in the early development stages, and research on key areas is still ongoing to improve the required scalability, decentralization, and security. Thus, energy blockchain technology is still in the growth period, and there is still considerable room for development of the patent in the later period. Moreover, it is suggested that the novel communication module such as the combination of the consortium blockchain and the private blockchain cold also provide their own advantages to achieve the purpose of improving system performance and efficiency. Full article

To contribute to global sustainability, many manufacturers are starting to implement green product development and trying to provide environmentally friendly products. Although green products are environmentally beneficial to our society, the performance of green product development remains poor because of cannibalization from traditional alternatives at lower prices. This is particularly the case in the current unforgiving marketing reality in which many brand retailers, such as Wal-Mart, Tesco, and Carrefour, offer their own store brands as traditional alternatives. Although a large stream of research has studied the effects of competition on manufacturers’ green design, to the best of our knowledge, there is a dearth of research on the effects of competition from retailers’ store brands on manufacturers’ green design. To fill this gap, we present two models in which the manufacturer has an incentive to design for the environment, and the retailer has the flexibility to sell store brands (Model S), or it does not (Model N). Surprisingly, our analysis indicates that the presence of store brands may stimulate the manufacturer to release a new greener version of the national brand. Moreover, we find that although the presence of store brands is beneficial to the retailer and industry, it always hurts the manufacturer’s profitability. To incentivize the manufacturer to support Model S, we propose a two-part tariff contract. Full article

In recent years, the research into automatic aided detection systems for pulmonary nodules has been extremely active. Most of the existing studies are based on 2D convolution neural networks, which cannot make full use of computed tomography’s (CT) 3D spatial information. To address this problem, a computer-aided detection (CAD) system for lung nodules based on a 3D residual network (3D-ResNet) inspired by cognitive science is proposed in this paper. In this system, we feed the slice information extracted from three different axis planes into the U-NET network set, and make the joint decision to generate a candidate nodule set, which is the input of the proposed 3D residual network after extraction. We extracted 3D samples with 40, 44, 48, 52, and 56 mm sides from each candidate nodule in the candidate set and feed them into the trained residual network to get the probability of positive nodule after re-sampling the 3D sample to $48\times 48\times 48$ mm ${}^3$ . Finally, a joint judgment is made based on the probabilities of five 3D samples of different sizes to obtain the final result. Random rotation and translation and data amplification technology are used to prevent overfitting during network training. The detection intensity on the largest public data set (i.e., the Lung Image Database Consortium and Image Database Resource Initiative—LIDC-IDRI) reached 86.5% and 92.3% at 1 frame per second (FPs) and 4 FPs respectively using our algorithm, which is better than most CAD systems using 2D convolutional neural networks. In addition, a 3D residual network and a multi-section 2D convolution neural network were tested on the unrelated Tianchi dataset. The results indicate that 3D-ResNet has better feature extraction ability than multi-section 2D-ConvNet and is more suitable for reduction of false positive nodules. Full article

In recent years, when solving MOPs, especially discrete path optimization problems, MOACOs concerning other meta-heuristic algorithms have been used and improved often, and they have become a hot research topic. This article will start from the basic process of ant colony algorithms for solving MOPs to illustrate the differences between each step. Secondly, we provide a relatively complete classification of algorithms from different aspects, in order to more clearly reflect the characteristics of different algorithms. After that, considering the classification result, we have carried out a comparison of some typical algorithms which are from different categories on different sizes TSP (traveling salesman problem) instances and analyzed the results from the perspective of solution quality and convergence rate. Finally, we give some guidance about the selection of these MOACOs to solve problem and some research works for the future. Full article

Existing indoor semantic recognition schemes are mostly capable of discovering patterns through smartphone sensing, but it is hard to recognize rich enough high-level indoor semantics for map enhancement. In this work we present DeepMap+, an automatical inference system for recognizing high-level indoor semantics using complex human activities with wrist-worn sensing. DeepMap+ is the first deep computation system using deep learning (DL) based on a multi-length window framework to enrich the data source. Furthermore, we propose novel methods of increasing virtual features and virtual samples for DeepMap+ to better discover hidden patterns of complex hand gestures. We have performed 23 high-level indoor semantics (including public facilities and functional zones) and collected wrist-worn data at a Wal-Mart supermarket. The experimental results show that our proposed methods can effectively improve the classification accuracy. Full article

In this paper, the Humane Society of the United States (HSUS) farm animal protection work over the preceding decade is described from the perspective of the organization. Prior to 2002, there were few legal protections for animals on the farm, and in 2005, a new campaign at the HSUS began to advance state ballot initiatives throughout the country, with a decisive advancement in California (Proposition 2) that paved the way for further progress. Combining legislative work with undercover farm and slaughterhouse investigations, litigation and corporate engagement, the HSUS and fellow animal protection organizations have made substantial progress in transitioning the veal, pork and egg industries away from intensive confinement systems that keep the animals in cages and crates. Investigations have become an important tool for demonstrating widespread inhumane practices, building public support and convincing the retail sector to publish meaningful animal welfare policies. While federal legislation protecting animals on the farm stalled, there has been steady state-by-state progress, and this is complemented by major brands such as McDonald’s and Walmart pledging to purchase only from suppliers using cage-free and crate-free animal housing systems. The evolution of societal expectations regarding animals has helped propel the recent wave of progress and may also be driven, in part, by the work of animal protection organizations. Full article

This paper challenges the notion that the incorporation of actors from civil society into hybrid governance arrangements improves outcomes and legitimacy. Multi-stakeholder collaborations are a popular hybrid governance approach to development, including NGOs’ work to integrate smallholder farmers into supermarket supply chains. As a result, NGOs’ service provision role has expanded to include market facilitation, often necessitating NGOs act as market intermediaries. This paper explores how this new role may jeopardize NGOs’ organizational legitimacy in the eyes of their constituents, other development organizations, and supermarket partners, and therefore ultimately affect their ability to represent civil society in hybrid governance arrangements. Drawing on qualitative data collected in the Central American country of Honduras, this paper focuses on NGOs’ role organizing producer associations to facilitate access to Wal-Mart supermarkets. Findings suggest that a lack of supply chain transparency, NGOs’ negotiation between commercial and aid-oriented goals, and the potential to exclude producers from development projects threaten NGOs’ legitimacy. These findings illustrate the difficulties of embedding philanthropic activities in market-based systems, and demonstrate how multi-stakeholder collaborations may be influenced more by commercial priorities than the elements of a partnership. Ultimately, development NGOs are products of neoliberal, hybrid governance, even as their activities are expected to ease the transition of small-scale producers into this system. Full article