Using multiple evaluation methods and systems give a comprehensive assessment. A computer-based multiple-choice assessment system was designed, implemented, posted online, and used to assess students as part of their final evaluation marks for a discipline. The online system of evaluation was intended to be used multiple times for evaluating the assimilation degree of a specific course at the end of the course. The data recorded for the period 2017–2023 with about 1400 distinct users were used to analyze the performance of the evaluation system. The system worked fine and a slight modification of it served well on remote evaluation during COVID-19 period. However, the upturn of mobile phone applications requires the creation of a system adapted to the new virtual reality.

This study explores the intricate relationship between emotional cues present in food delivery app reviews, normative ratings, and reader engagement. Utilizing lexicon-based unsupervised machine learning, our aim is to identify eight distinct emotional states within user reviews sourced from the Google Play Store. Our primary goal is to understand how reviewer star ratings impact reader engagement, particularly through thumbs-up reactions. By analyzing the influence of emotional expressions in user-generated content on review scores and subsequent reader engagement, we seek to provide insights into their complex interplay. Our methodology employs advanced machine learning techniques to uncover subtle emotional nuances within user-generated content, offering novel insights into their relationship. The findings reveal an inverse correlation between review length and positive sentiment, emphasizing the importance of concise feedback. Additionally, the study highlights the differential impact of emotional tones on review scores and reader engagement metrics. Surprisingly, user-assigned ratings negatively affect reader engagement, suggesting potential disparities between perceived quality and reader preferences. In summary, this study pioneers the use of advanced machine learning techniques to unravel the complex relationship between emotional cues in customer evaluations, normative ratings, and subsequent reader engagement within the food delivery app context.

The economic viability of a photovoltaic (PV) installation depends on regulations regarding administrative, technical and economic conditions associated with self-consumption and the sale of surplus production. Royal Decree (RD) 244/2019 is the Spanish legislation of reference for this case study, in which we analyse and compare PV installation offers by key suppliers. The proposals are not optimal in RD 244/2019 terms and appear not to fully contemplate power generation losses and seem to shift a representative percentage of consumption to the production period. In our case study of a residential dwelling, the best option corresponds to a 5 kWp installation with surplus sale to the market, with a payback period of 18 years and CO₂ emission reductions of 1026 kg/year. Demand-side management offers a potential improvement of 6%–21.8%. Based on the increase in electricity prices since 2020, the best option offers savings of up to €1507.74 and amortization in 4.24 years. Considering costs and savings, sale to the market could be considered as the only feasible regulatory mechanism for managing surpluses, accompanied by measures to facilitate administrative procedures and guarantees for end users.

Choosing a university is a crucial decision for each field of study, as it significantly influences the quality of graduates. An important factor in this decision is the university’s annual benchmark scores. The benchmark score represents the minimum score required for admission. This study evaluates the benchmark scores in the logistics sector for several prominent universities in Vietnam during the period 2021–2023. The research process utilized data on the benchmark scores for the years 2021, 2022, and 2023. The weights of these benchmark scores were calculated using the Rank Order Centroid (ROC) method, and the Probability method was employed to compare the benchmark scores of the universities. The analysis identified C3 as the criterion with the highest importance, while U3 emerged as the top-ranked alternative. The two-stage comprehensive sensitivity analysis revealed that universities consistently ranked high or low regardless of the method used to calculate benchmark score weights or the method employed for ranking. Additionally, the smallest weight change that affected the overall Probability ranking was 4.61%. This study provides significant guidance for students in selecting a university for logistics studies and serves as a foundational reference for universities to assess their capabilities in logistics education, thereby fostering healthy competition among institutions.

The proportion of national logistics costs to Gross Domestic Product (NLC/GDP) serve as a valuable indicator for estimating a country’s overall macro-level logistics costs. In some developing nations, policies aimed at reducing the NLC/GDP ratio have been elevated to the national agenda. Nevertheless, there is a paucity of research examining the variables that can determine this ratio. The purpose of this paper is to offer a scientific approach for investigating the primary determinants of the NLC/GDP and to advice policy for the reduction of macro-level logistics costs. This paper presents a systematic framework for identifying the essential criteria for lowering the NLC/GDP score and employs co-integration analysis and error correction models to evaluate the impact of industrial structure, logistics commodity value, and logistics supply scale on NLC/GDP using time series data from 1991 to 2022 in China. The findings suggest that the industrial structure is the primary factor influencing logistics demand and a significant determinant of the value of NLC/GDP. Whether assessing long-term or short-term effects, the industrial structure has a substantial impact on NLC/GDP compared to logistics supply scale and logistics commodity value. The research offers two policy implications: firstly, the goals of reducing NLC/GDP and boosting the logistics industry’s GDP are inherently incompatible; it is not feasible to simultaneously enhance the logistics industry’s GDP and decrease the macro logistics cost. Secondly, if China aims to lower its macro-level logistics costs, it must make corresponding adjustments to its industrial structure.