During and after the Covid-19 outbreak, people’s precautionary measures of not visiting public venues like cinema halls or multiplexes were replaced by watching treasured videos or films in private settings. People are able to watch their favourite video contents on a variety of internet-connected gadgets thanks to advanced technologies. As a result, it appears that the Covid-19 outbreak has had a substantial impact on people’s inclination to continue using video streaming services. This study attempted to establish an integrated framework that describes how people change their health behaviours during pandemic conditions using the health belief model (HBM), as well as the mediating effect of HBM constructs over ECM constructs such as continuous intention to subscribe to OTT video streaming services among subscribers. The study looked at the impact of three perceived constructs, susceptibility, severity, and self-efficacy, on the confirmation/adoption of over-the-top (OTT) video streaming services during the lethal pandemic (Covid-19). The study focused on new OTT video streaming service subscribers, and 473 valid replies were collected. Path analysis and multivariate analytical methods, such as structural equation modelling (SEM), were used to estimate construct linkages in the integrated framework. Perceived severity has been identified as the most influential factor in confirmation/adoption, followed by perceived susceptibility. The results also showed that satisfied users/subscribers are more likely to use OTT video streaming services. The mediators, confirmation/adoption, perceived usefulness, and satisfaction were used to validate the influence of perceived susceptibility on continuance intention. Furthermore, contactless entertainment enhances security for users/subscribers by allowing them to be amused across several internet-based venues while adhering to social distance norms.

The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant interest in modern agriculture. The appeal of AI arises from its ability to rapidly and precisely analyze extensive and complex information, allowing farmers and agricultural experts to quickly identify plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant attention in the world of agriculture and agronomy. By harnessing the power of AI to identify and diagnose plant diseases, it is expected that farmers and agricultural experts will have improved capabilities to tackle the challenges posed by these diseases. This will lead to increased effectiveness and efficiency, ultimately resulting in higher agricultural productivity and reduced losses caused by plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has resulted in significant benefits in the field of agriculture. By using AI technology, farmers and agricultural professionals can quickly and accurately identify illnesses affecting their crops. This allows for the prompt adoption of appropriate preventative and corrective actions, therefore reducing losses caused by plant diseases.

The article is devoted to formulation of theoretical principles and practical recommendations regarding organization and planning of the investigation of criminal offenses in the field of economic activity, which are committed with the participation (assistance) of law enforcement officers. The methodology for the article is chosen taking into account the purpose and tasks, object and subject matter of the study. The research results were obtained with the help of the following methods: dialectical; formal and logical; formal and legal; comparative and legal; historical and legal, complex analysis; analysis and synthesis; axiomatic; system and structural method. The obtained results of the study indicated that organization and planning of the investigation of criminal acts under consideration is a purposeful activity of the authorized bodies, which is carried out under the guidance of the investigator, detective of the pre-trial investigation body. These activities require systematic, comprehensive approach and must take into account a wide range of circumstances that can affect the process and results of the investigation: the nature of the criminal offense, access to the necessary financial, human and technical resources; the competence of the investigator, the detective; terms and deadlines for investigation and presenting materials to the court, establishing effective cooperation between competent authorities. The study highlights the peculiarities of the organization and planning of the investigation of criminal offenses in economic activities, when law enforcement officers are involved, and suggests directions for improving the effectiveness of their implementation.

Introduction: Food well-being of the population is one of the priorities of the Togolese government, which relies on the agricultural investment and food security Programme to increase national food production. In addition, the country relies on food imports to make up the shortfall. At the same time undernourishment and malnutrition remain high among the country’s population. This research analyzes food supply and its implications for household consumption in Grand Lomé, Togo. [Methods] The methodology used documents, a survey of 963 heads of household randomly sampled households and semi-structured interviews with 10 households and with Togolese food safety agency (ANSAT). Quantitative data were processed and analyzed using Excel spreadsheets R and R-Studio, while content analysis was applied to the verbal applied to the verbal statements collected. Results: Firstly, the results show that domestic agricultural production contributed an average of 91% of food supply between 2014–2017. The deficit is made up by food imports, which rose from 13.5% in 2014 to 15.4% in 2017. This translated into an acceptable food energy consumption of 2337 Kcal/head/day in 2017. Secondly, 81% of respondents recognize a strong food presence at consumer markets, except that the chi-square test applied to the data at the 5% threshold shows (p-value < 2.2 × 10⁻¹⁶), indicates that this satisfaction is a function of place of residence. Despite this, persistent shortages affect more staple crops, livestock and dairy products, leading households to deprive themselves and buy food at affordable prices. Finally, we observe non-diversified diets marked by regular consumption of “cereals/legumes”, vegetables and beverages to the detriment of “tubers/roots”, “meat/fish”, “fruit” and “dairy products”. Conclusion: This research shows that food supply, although adequate, is not sufficient to ensure balanced, nutritious and culturally appropriate food consumption by urban households. Recommendations: To meet these challenges, the central government, in collaboration with urban communes and consumer advocates, must mobilize resources to create urban agricultural farms, strengthen food protection systems, distribute staple products directly to households and limit the importation of food that is hazardous to health.

In this paper, we examine a possible application of ordered weighted average (OWA for short) aggregation operators in the insurance industry. Aggregation operators are essential tools in decision-making when a single value is needed instead of a couple of features. Information aggregation necessarily leads to information loss, at least to a specific extent. Whether we concentrate on extreme values or middle terms, there can be cases when the most important piece of the puzzle is missing. Although the simple or weighted mean considers all the values there is a drawback: the values get the same weight regardless of their magnitude. One possible solution to this issue is the application of the so-called Ordered Weighted Averaging (OWA) operators. This is a broad class of aggregation methods, including the previously mentioned average as a special case. Moreover, using a proper parameter (the so-called orness) one can express the risk awareness of the decision-maker. Using real-life statistical data, we provide a simple model of the decision-making process of insurance companies. The model offers a decision-supporting tool for companies.

This paper is the third in a series focused on bridging the gap between secondary and higher education. Our primary objective is to develop a robust theoretical framework for an innovative e-business model called the Undergraduate Study Programme Search System (USPSS). This system considers multiple criteria to reduce the likelihood of exam failure or the need for multiple retakes, while maximizing the chances of successful program completion. Testing of the proposed algorithm demonstrated that the Stochastic Gradient Boosted Regression Trees method outperforms the current method used in Lithuania for admitting applicants to 47 educational programs. Specifically, it is more accurate than the Probabilistic Neural Network for 25 programs, the Ensemble of Regression Trees for 24 programs, the Single Regression Tree for 18 programs, the Random Forest Regression for 16 programs, the Bayesian Additive Regression Trees for 13 programs, and the Regression by Discretization for 10 programs.

This study investigates the factors influencing the adoption of telehealth among consumers in Malaysia, aiming to understand the impact of effort expectancy, performance expectancy, computer self-efficacy, and trust on the intention to use telehealth, building on the Unified Theory of Acceptance and Use of Technology (UTAUT). A quantitative descriptive methodology was used, collecting data from 390 Malaysian consumers via an online survey. The data were analyzed using IBM SPSS software to evaluate the relationships between the variables. The analysis revealed significant positive relationships between all examined factors and the adoption of telehealth. Performance expectancy was the most influential factor, followed by trust, effort expectancy, and computer self-efficacy. The multiple regression model indicated that these variables collectively explain 82.1% of the variance in telehealth adoption intention. The findings provide valuable insights for providers and marketers, suggesting that telehealth platforms should focus on performance expectancy, trust, and ease of use. Additionally, the study emphasizes the need for supportive policies from the Malaysian government to enhance telehealth adoption. The results contribute to the literature on healthcare technology adoption, offering practical implications for improving telehealth implementation in Malaysia.

The aim of this study was to analyze the perceived self and collective efficacy, individual and social norms and feelings related to environmental health concern among a sample of Pakistanis who are (or are not) engage in pro- environment behaviors in their daily lives. An ad hoc questionnaire with scales on pro-environmental behavior, self and collective efficacy, individual and social norms, and environmental health concerns was administered to adults in Lahore, Pakistan, and 833 respondents (62% males and 38% females) responded. Analysis of our research data shows that among those who engaged in daily pro-environmental behaviors, perceptions of individual and social norms and individual and collective efficacy were positively associated with concern for the environment and health. This study offers some interesting ideas that could be useful in developing federal, regional, local and community policies to promote daily pro-environmental behaviors. For example, in addition to advocating for environmental health and reducing one’s ecological footprint, social communication could explain that caring about environmental health (and thus adopting daily pro-environmental behaviors) is a way to manage one’s mental health. In this way, circular behavior is encouraged, which not only benefits the environment and the community, but also brings personal benefits.