This study explores the determinants of control loss in eating behaviors, employing decision tree regression analysis on a sample of 558 participants. Guided by Self-Determination Theory, the findings highlight amotivation (β = 0.48, p < 0.001) and external regulation (β = 0.36, p < 0.01) as primary predictors of control loss, with introjected regulation also playing a significant role (β = 0.24, p < 0.05). Consistent with Self-Determination Theory, the results emphasize the critical role of autonomous motivation and its deficits in shaping self-regulation. Physical characteristics, such as age and weight, exhibited limited predictive power (β = 0.12, p = 0.08). The decision tree model demonstrated reliability in explaining eating behavior patterns, achieving an R² value of 0.39, with a standard deviation of 0.11. These results underline the importance of addressing motivational deficits in designing interventions aimed at improving self-regulation and promoting healthier eating behaviors.

Adopting electric vehicles (E.V.) is crucial for promoting sustainable mobility in metropolitan areas such as Medan, Indonesia. To achieve this, it is essential to comprehend the factors that influence E.V. adoption, with a particular focus on the impact of media. This study examines the adoption of electric vehicles in Medan and evaluates the influence of the media on the public’s perception and policy decisions. Opinions, concerns, and recommendations surrounding electric vehicles were examined through surveys and interviews with 35 stakeholders, including students, lawmakers, industry experts, business owners, and media professionals. The findings indicate a strong knowledge and favorable perception of electric vehicles in Medan. However, there are worries regarding the expenses associated with E.V.s and the availability of charging infrastructure. Notably, 60% of the respondents identified media as their primary source of information, highlighting its significant influence. Encouraging cooperation between media, professionals, and stakeholders is advisable to achieve accurate and balanced reporting. This can be done by employing techniques like showcasing success stories and emphasizing the environmental advantages to encourage acceptance and implementation. This study provides valuable insights into improving the adoption of electric vehicles in Medan. It emphasizes the significance of implementing effective media strategies and supportive policies to achieve sustainable transportation solutions.

Malaria is a mosquito-borne infectious disease that affects humans and poses a severe public health problem. Nigeria has the highest number of global cases. Geospatial technology has been widely used to study the risks and factors associated with malaria hazards. The present study is conducted in Ibadan, Oyo State, Nigeria. The objective of this study is to map out areas that are at high risk of the prevalence of malaria by considering a good number of factors as criteria that determine the spread of malaria within Ibadan using open-source and Landsat remote sensing data and further analysis in GIS-based multi-criteria evaluation (MCE). This study considered factors like climate, environmental, socio-economic, and proximity to health centers as criteria for mapping malaria risk. The MCE used a weighted overlay of the factors to produce an element at-risk map, a malaria hazard map, and a vulnerability map. These maps were overlaid to produce the final malaria risk map, which showed that 72% of Ibadan has a risk of malaria prevalence. Identification and delineation of risk areas in Ibadan would help policymakers and decision-makers mitigate the hazards and improve the health status of the state.

There are several methods in the literature to find the fuzzy optimal solution of fully fuzzy linear programming (FFLP) problems. However, in all these methods, it is assumed that the product of two trapezoidal (triangular) fuzzy numbers will also be a trapezoidal (triangular) fuzzy number. Fan et al. (“Generalized fuzzy linear programming for decision making under uncertainty: Feasibility of fuzzy solutions and solving approach”, Information Sciences, Vol. 241, pp. 12–27, 2013) proposed a method for finding the fuzzy optimal solution of FFLP problems without considering this assumption. In this paper, it is shown that the method proposed by Fan et al. (2013) suffer from errors and to overcome these errors, a new method (named as Mehar method) is proposed for solving FFLP problems by modifying the method proposed by Fan et al. (2013) . To illustrate the proposed method, some numerical problems are solved.

In many cases, the expected efficiency advantages of public-private partnership (PPP) projects as a specific form of infrastructure provision did not materialize ex post. From a Public Choice perspective, one simple explanation for many of the problems surrounded by the governance of PPPs is that the public decision-makers being involved in the process of initiating and implementing PPP projects (namely, politicians and public bureaucrats) in many situations make low- cost decisions in the sense of Kirchgässner (1948–2017). That is, their decisions may have a high impact on the wealth of the jurisdiction in which the PPP is located (most notably, on the welfare of citizen-taxpayers in this jurisdiction) but, at the same time, these decisions often only have a low impact on the private welfare of the individual decision-makers in politics and bureaucracy. The latter, for example, in many settings often have a low economic incentive to monitor/control what the private-sector partners are doing (or not doing) within a PPP arrangement. The purpose of this paper is to draw greater attention to the problems created by low-cost decisions for the governance of PPPs. Moreover, the paper discusses potential remedies arising from the viewpoint of Public Choice and Constitutional Political Economy.

In recent times, there has been a surge of interest in the transformative potential of artificial intelligence (AI), particularly within the realm of online advertising. This research focuses on the critical examination of AI’s role in enhancing customer experience (CX) across diverse business applications. The aim is to identify key themes, assess the impact of AI-powered CX initiatives, and highlight directions for future research. Employing a systematic and comprehensive approach, the study analyzes academic publications, industry reports, and case studies to extract theoretical frameworks, empirical findings, and practical insights. The findings underscore a significant transformation catalyzed by AI integration into Customer Relationship Management (CRM). AI enables personalized interactions, fortifies customer engagement through interactive agents, provides data-driven insights, and empowers informed decision-making throughout the customer journey. Four central themes emerge: personalized service, enhanced engagement, data-driven strategy, and intelligent decision-making. However, challenges such as data privacy concerns, ethical considerations, and potential negative experiences with poorly implemented AI persist. This article contributes significantly to the discourse on AI in CRM by synthesizing the current state, exploring key themes, and suggesting research avenues. It advocates for responsible AI implementation, emphasizing ethical considerations and guiding organizations in navigating opportunities and challenges.