Accounting for 30.8% of the total economic volume of the western region by 2021, the "fourth pole" of China's economic development is progressively being established as the Chengdu-Chongqing twin-city(CCTC) Zone. Therefore, increasing Total Factor Energy Efficiency (TFEE) and reducing the disparities in energy efficiency amongst cities in the area are important for the economic development of the CCTC Economic Zone. Accordingly, this study employs an output-oriented, super-efficient DEA model with constant returns to scale to measure the total factor energy efficiency of 16 prefecture-level cities in the CCTC economic circle from 2006 to 2020. It also examines the spatial distribution and variation patterns of each city's prefecture-level total factor energy efficiency. Then, spatial autocorrelation and random forest were used to explore the interrelationship among the indicators of the drivers, and the variables were screened according to Gini importance, finally, PCA-GWR models and spatial panel regression models were constructed to dissect the key drivers. The empirical findings indicate that: (1) the average energy efficiency of Chongqing and Chengdu is only 0.708 and 0.788, and the overall efficiency shows a steady increase from 2009 to 2018. (2) The spatial distribution is mainly as follows: H-H agglomerations are distributed in the northeastern cities of the CCTC Economic Zone, L-L agglomerations are distributed in the south, H-L agglomerations and L-H agglomerations are distributed in the north-central part of Chengdu-Chongqing Economic Zone. (3) Industrial structure, population structure, and governmental behavior have significant effects on energy efficiency, with a population mortality rate as the inhibiting factor and the proportion of tertiary industry and policy behavior as the contributing factors. Based on the empirical findings, it is recommended to accelerate the industrial structure adjustment, implement the CCTC economic circle's core cities' target of energy-saving, and build a "community" bridge to improve energy efficiency and promote economic development.

The advent of metaverses, NFTs, blockchain, and similar growth topics influence the future of businesses, and the industry is looking at a transformation. SAP has been a pioneer in innovation and enables some of the best businesses around the World. With its own Cloud infrastructure in place and expertise in areas of Blockchain etc., it’s time to take a leap and give the existing and new customers an edge over the others. The solution proposed here is to address how the future could look for retail, for example, in sportswear. It is possible to provide an NFT Marketplace for sportswear manufacturers like Nike, where the subscribers can sell their NFTs (digital shoes, wearables, etc.) or buy the ones available in the marketplace with the help of Crypto Wallets/Payments, etc. The idea proposed in this paper helps sellers’ who design products to have a review process, where the company can evaluate if they want to introduce those products in their physical form. Or, if a digital product sells well in the NFT marketplace, the company can decide to make a physical launch of the product as well, sharing some royalty.

Customer segmentation is not only limited to the identification of user groups but searching and determining the attitude of individual customer groups toward a particular product or service aside helping organization in developing better marketing strategies. Many studies have proposed different techniques for customer segmentation, but some of these studies failed to examine individual customer’s needs in the cluster. In a customer segmentation, when customers are grouped into various cluster based on their common needs, there may be customers that have other needs that differ from the general needs of the group. In a situation where the needs of individual were not captured, organizations may find it difficult to control the rendering of their services. The aim of this study is to extract the individual customer’ needs to enhance organizations’ services that meet the needs of customers, as well as increase organization profits. This study, therefore, proposes the use of an associative rules mining algorithm augmented with assignment optimization to properly examine the needs of individual customers in the group. This approach enhances the cross-segmentation of customers for better marketing strategies and the assignment technique also improved the segmentation processing speed. The degree of accuracy of the system developed was tested with about 9,500 customers’ dataset that was obtained from goggle multi category online store dataset. Both customer transaction history dataset and customer purchasing behavior dataset were obtained for segmentation which achieved 94.5% customer segmentation accuracy. The implementation was done using Python programming language.

Systems for registering vehicles are essential for keeping track of ownership changes. However, severe flaws in the current systems permit vehicles that have been stolen or illegally sold to be registered. Inefficient verification techniques, drawn-out administrative processes, and dishonest employees cause these problems. This paper introduces a transparent system to prevent denial, alteration, or unauthorized manipulation. The proposed method employs hybrid blockchain architecture, distinguishing between confidential and non-confidential data. Personal information is stored privately, while vehicle-related data is maintained as public information. The adoption of blockchain technology is driven by its robust security features, transparency, and traceability, as well as its immutability and ability to handle many users effectively.

The impact of technology must be addressed by the educational methods themselves and their perspectives in the new paradigm of citizenship in this intelligent environment. Learning environments have changed dramatically in the last 50 years, in large part due to information and communications technologies. The study uses a qualitative descriptive. Thomas S. Kuhn, fully Thomas Samuel Kuhn, (born 18 July 1922 in Cincinnati, Ohio, USA-17 June 1996 in Cambridge, Massachusetts), best known for The Structure of the Scientific. Kuhn explains that in every scientific discipline, there are some identified and natural phenomena that are then investigated experimentally and explained theoretically. Pre-paradigm as the basis of normal science, the formation of smart learning is started when primitive cave. Normal science for learning is a traditional teaching method, teaching takes place within the four walls of a classroom. Anomalies are surprising discoveries that cannot be defined through a paradigm, together with discoveries of troubles that cannot be solved through a paradigm. The lock down following the COVID-19 pandemic has made us extrude in a single day from mastering withinside the bodily international to mastering withinside the virtual one. Model crises are the third phase of the Kuhn cycle. Triggering the Model Crisis movement Blended learning can be the brand new normal – “Blended learning”. The smart learning cognizance and traits has emerged as a brand-new fashion withinside the international academic field. different smart technologies, consisting of cloud computing, learning analytics, huge information, Internet of things (IoT) and wearable generation.