This article analyses the case of Dubai’s smart city from a public policy perspective and demonstrates how critical it is to rely on the use of the public-private partnership (PPP) model. Effective use of this model can guarantee the building of a smart city that could potentially fulfill the vision of the political leadership in Dubai and serve as a catalyst and blueprint for other Gulf states that wish to follow Dubai’s example. This article argues that Dubai’s smart city project enjoys significant political support and has ambitious plans for sustainable growth, and that the government has invested heavily in developing the necessary institutional, legal/regulatory, and supervisory frameworks that are essential foundations for the success of any PPP project. The article also points to some important insights that the Dubai government can learn from the international experience with the delivery of smart cities through PPPs.

Fintech as a three-dimensional phenomenon reflects the rapidly changing technological, financial and business environment. The bibliometric analysis of scientific articles allowed us to identify the main themes and create a map of the field of fintech influences. Systematization of scientific articles revealed the influence of economic development and socio-demographic inequality on fintech development. Government regulatory policies can accelerate the digitisation of financial services and financial inclusion and help the fintech sector face geopolitical challenges. Fintech’s impact was divided into three areas: financial stability and sustainable development, the business ecosystem and human behaviour. The research we summarised allowed us to identify the mechanisms through which fintech influences various fields. A complex approach to the influence of fintech enables us to understand the phenomenon and make better decisions.

The cultivation of vegetables serves as a vital pillar in horticulture, offering an alternative avenue towards achieving economic sustainability. Unfortunately, farmers often lack adequate knowledge on optimizing resource utilization, which subsequently results in low productivity. Furthermore, there has been insufficient research conducted on the comparative profitability and efficient use of resources for pea cultivation. So, the present study was conducted to examine the profitability and resource use efficiency of conventional and organic pea production in Northwestern Himalayan state. Using the technique of purposive sampling, the districts and villages were selected based on the highest area. By using simple random sampling, a sample of 100 farmers was selected, out of which 50 were organic growers and 50 were inorganic growers, who were further categorized as marginal and small. The cost incurred was higher for the cultivation of inorganic vegetable crops, whereas returns and output-input ratio was higher in organic cultivation. The cultivation of peas revealed that the majority of inputs were being underutilized, and there was a need for proper reallocation of the resources, which would result in enhanced production. Further, major problems in the cultivation of vegetable crops were a high wage rate, a lack of organic certification, a shortage of skilled labour and a lack of technical knowledge.

Broccoli has been consumed around the world in various ways; either raw, blanched, frozen, dehydrated or fermented; however, functional foods and nutraceuticals are currently being designed and marketed from broccoli, through the extraction of compounds such as sulforaphane, which according to several studies and depending on its bioavailability has a protective effect on some types of cancer. Likewise, several food technologies are reported to seek to offer innovative foods to increasingly careful and critical consumers, ensuring that they retain their nutritional and sensory attributes even after processing and that they are also safe. In this sense, studies on the effect of processing on compounds of interest to health are of great relevance. Therefore, this article presents an overview on the study of traditionally consumed broccoli and the design of new products from the use of agro-industrial residues that, due to their high content of fiber and fitochemical compounds, can benefit the quality of life of the human population.

Based on 898 English documents and 363 Chinese documents citing the Rising of Network Society, it studied that the knowledge contribution of citation content analysis and citation context analysis methods, and the knowledge contribution of Chinese and foreign quotations to human geography. The study found that “mobile space” is the most quoted theoretical view in domestic and foreign literature, and the proportion of domestic research is significantly higher than foreign research; the focus of domestic and foreign research focuses on the external spatial form and its transformation, while foreign research pays more attention on the internal spatial dynamics of network society and three types of knowledge contributions, reflecting the influence of “network social theory” on human geography. Among them, critical references reveal the shortcomings of “network social theory” point out the abstraction of “spatial duality” the importance of local space, and the limitations of research data, methods, and time background, which provides new enlightenment for the future application and innovation of “network social theory” in the field of human geography.

The cost of diagnostic errors has been high in the developed world economics according to a number of recent studies and continues to rise. Up till now, a common process of performing image diagnostics for a growing number of conditions has been examination by a single human specialist (i.e., single-channel recognition and classification decision system). Such a system has natural limitations of unmitigated error that can be detected only much later in the treatment cycle, as well as resource intensity and poor ability to scale to the rising demand. At the same time Machine Intelligence (ML, AI) systems, specifically those including deep neural network and large visual domain models have made significant progress in the field of general image recognition, in many instances achieving the level of an average human and in a growing number of cases, a human specialist in the effectiveness of image recognition tasks. The objectives of the AI in Medicine (AIM) program were set to leverage the opportunities and advantages of the rapidly evolving Artificial Intelligence technology to achieve real and measurable gains in public healthcare, in quality, access, public confidence and cost efficiency. The proposal for a collaborative AI-human image diagnostics system falls directly into the scope of this program.