This study looked at how adding augmented reality (AR) to Jordanian fast-food apps during the pandemic impacts brand identity, consumer views, and interactions. It wanted to see if AR strengthens brand connections or leads to brand dilution concerns in the industry. The research utilized a qualitative approach, employing semi-structured interviews with 52 marketing managers from diverse fast-food establishments across Jordan. The study highlighted how mobile apps, especially AR, changed brand interactions in Jordan’s fast-food market. They boosted convenience and engagement but raised worries about food quality and brand dilution due to heavy app use. It stressed the need to balance tech innovation, preserve brand identity, offer personalized experiences, understand user behavior, and tackle app development challenges for better brand loyalty. The research offers practical implications for stakeholders, recommending strategic AR integration, a user-centric approach, cultural sensitivity in tech adoption, and the preservation of emotional connections. It emphasizes the significance of maintaining a delicate balance between leveraging technological advancements and safeguarding the distinctiveness of individual brand identities within an increasingly app-centric landscape. This study uncovers AR’s influence in Jordan’s fast-food scene, highlighting its transformative power and possible drawbacks. It offers practical advice for industry players, guiding them on how to navigate the digital shift without compromising brand integrity or customer connections.

This study aims to examine and challenge the impact of local government policy governance on the oil palm plantation sector in Riau Province, Indonesia. It was discovered that 1,628 million hectares of illegal oil palm plantations are located within forest areas. Plantation area and crop harvest areas are declining due to the increase in damaged old plants, low productivity of plantation crops, inadequate facilities and infrastructure conditions, low technology application, plantation business licensing, limited downstream plantation industry and marketing, assistance in changing the attitudes, behavior, and skills of farmers. The methodology used was exploratory qualitative to explore this topic, and the determination of research topics was conducted using Biblioshiny application analysis. Then, the data was analyzed using Nvivo 12 Plus software. The results of this study discovered that the policy governance of the oil palm plantation sector as a leading commodity in Riau Province, Indonesia, is influenced by three dimensions: firstly, the actor dimension; secondly, the structural dimension; and third, the empirical dimension of governance. This research contributes as a knowledge reference to oil palm plantations.

Concession agreements (CAs) in the port sector are designed to establish mutually beneficial arrangements for involved parties. They serve as catalysts, enabling ports to attract adept private investors and secure requisite funding to enhance port infrastructure, superstructure, and service quality. Concurrently, the imperative to mitigate negative externalities and promote sustainable practices in port organization and development remains paramount. In this context, the paper explores the nuanced landscape of CAs, specifically focusing on the urgent need for an innovative framework that integrates sustainability within port organization, operations and development. Drawing from existing academic discourse and field evidence, it systematically identifies, examines, and analyzes fundamental requirements and key factors that should be considered in CAs, in line with sustainable development and proposes a reference framework for an ideal Concession Agreement model. Despite evident strengthening of sustainability implications in port concessions, significant room for improvement persists. Nevertheless, dynamics in the field create a certain optimism for the future.

The world has changed to a massive degree in the past thousands of years. Most of the time, the amount of carbon dioxide in the atmosphere remains constant. In the late 18th century, according to the sources of CDIAC and NOOA, the level of carbon dioxide began to rise, and then in the 20th century, it went through the roof, reaching levels that had not been seen in nature for millions of years. The increase in carbon in the atmosphere is the major contributing factor to climate change. The key to reversing the damage is restoring the earth’s delicate, balanced carbon cycle. As carbon cycle depicts the way carbon moves around the earth. It consists of sources that emit the carbon component into the atmosphere. The biological side of the carbon cycle is well balanced due to respiration, where carbon dioxide is released into the atmosphere, then plants, bacteria, and algae take carbon dioxide out of the atmosphere during photosynthesis and the process they use to generate chemical energy. On the other hand, oceans are the best sources and sinks; carbon dioxide is endlessly being absorbed into the ocean and released from the oceans almost exactly at the same rate, which is rapidly influencing the carbon cycle. Similarity is a methodology that has many applications in the real world. The current research article is destined to study how statistics of carbon emission metrics are alike and belong to one cluster. In the current study, the research is destined to derive a similarity analysis of several countries’ carbon emission metrics that are alike and often fall in the range of [0, 1]. And deriving the proximity of the carbon emission metrics leading to similarity or dissimilarity. In the current context of data matrices of numerical data, an Euclidian measure of distance between two data elements will yield a degree of similarity. The current research article is destined to study the similarity analysis of carbon emission metrics through fuzzy entropy clustering.

In Kazakhstan, for more than 20 years, the state policy on the formation of a single information space, aimed at reducing budgetary resources for the formation and maintenance of information resources of government agencies, as well as the creation of a unified communication environment. The relevance of the article is due to the following factors: the acceleration of digital modernization processes in Kazakhstan under the influence of global informatization and the consideration of the prospects of improving the efficiency of the Kazakh government through the introduction of information technology is not always recognized by society as an institutional advantage. As special methodological tools, the study used experimental, empirical and heuristic methods to analyze factors and identify problems in budget financing in the field of digitalization and E-Government in Kazakhstan. The main source of data is the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan. The main conclusions: there is a need for further economic and political modernization of Kazakh society through the widespread use of information technology, and in our view, the practical approach to the use of public financing to create a real e-government and the prospects for its development in Kazakhstan is interesting.

Objective: To investigate the value of differential diagnosis of hepatocellular carcinoma (HCC) and cirrhotic nodules via radiomics models based on magnetic resonance images. Background: This study is to distinguish hepatocellular carcinoma and cirrhotic nodules using MR-radiomics features extracted from four different phases of MRI images, concluded T1WI, T2WI, T2 SPIR and delay phase of contrast MRI. Methods: In this study, the four kind of magnetic resonance images of 23 patients with hepatocellular carcinoma (HCC) were collected. Among them, 12 patients with liver cirrhosis were used to obtain cirrhotic nodules (CN). The dataset was used to extract MR-radiomics features from regions of interest (ROI). The statistical methods of MRradiomics features could distinguish HCC and CN. And the ability of radiomics features between HCC and CN was estimated by receiver operating characteristic curve (ROC). Results: A total of 424 radiomics features were extracted from four kind of magnetic resonance images. 86 features in delay phase of contrast MRI，86 features in spir phase of T2WI，86 features in T1WI and 88 features in T2WI showed statistical difference (p < 0.05). Among them, the area under the curves (AUC) of these features larger than 0.85 were 58 features in delay phase of contrast MRI, 54 features in spir phase of T2WI, 62 features in T1WI and 57 features in T2WI. Conclusions: Radiomics features extracted from MRI images have the potential to distinguish HCC and CN.