The number of domestic studies on "variational pragmatics" (Ren Yuxin, Chen Xinren, 2012) is very limited. The research scopes are also relatively limited, which has not yet attracted the attention of more researchers. Therefore, based on this book The Routledge Handbook of Second Language Acquisition and Pragmatics, this paper aims to sort out and summarize the development trend of pragmatics from the meaning, goal and theory of variational pragmatics, and then put forward suggestions for future research.

It is critical for urban and regional planners to examine spatial relationships and interactions between a port and its surrounding urban areas within a region’s spatial structure. This paper seeks to develop a targeted framework of causal relationships influencing the spatial structure changes in the Bushehr port-city. Hence, the study utilizes Fuzzy Cognitive Maps (FCMs), a computational technique adept at analyzing complex decision-making processes. FCMs are employed to identify concepts that act as drivers or barriers in the spatial structure changes of Bushehr port-city, thereby elucidating the causal relationships within this context. Additionally, the study evaluates these concepts’ relative significance and interrelationships. Data was collected through interviews with ten experts from diverse backgrounds, including specialists, academics, policymakers, and urban managers. The insights from these experts were analyzed using FCMapper and Pajek software to construct a collective FCM, which depicts the influential and affected concepts within the system. The resulting collective FCM consists of 16 concepts, representing the varied perspectives and expertise of the participants. Among these, the concepts of management and planning reform, economic growth of the city-port, and port development emerged as the three most central concepts. Moreover, the effects of all influential concepts on the spatial structure change in Bushehr port-city were evaluated through simulations conducted across four different scenarios. The analysis demonstrated that the system experiences the most significant impact under the fourth scenario, where the most substantial changes are observed in commercial and industrial growth and the planning of port-city separation policies.

Fruits are a source of vitamins. Mango is one of the abundantly nutritional fruits. Vitamin B9, or folic acid, is one of the important vital amines due to its role in preventing neural deficiency. Several beneficial micro-organisms are used for the synthesis of folic acid. In this study, Lactobacillus acidophilus, Leuconostoc mesenteroides, Streptococcus thermophilus, and Saccharomyces cerevisiae were used. Saccharomyces cerevisiae synthesized folic acid as compared to other organisms. There were five different concentrations of mango pulp that were analyzed for folic acid synthesis (5%, 10%, 15%, 20%, and 30%). The initial concentration of pulp was 133.37 mg kg⁻¹, but after fermentation with four micro-organisms it got reduced. As compared to the other three organisms, Saccharomyces cerevisiae synthesizes 17.15 mg kg⁻¹, 30.14 mg kg⁻¹, 28.62 mg kg⁻¹, 21.70 mg kg⁻¹, and 21.78 mg kg⁻¹, respectively, at different pulp concentrations of 5%, 10%, 15, 20%, and 30%. Vitamin C increased to 320 mg as compared to the control, and there was no significant difference between the four micro-organisms. Antioxidants also showed positive results at different concentrations of pulp. There was an increase in titratable acidity and a decrease in pH recorded for the 24 h fermentation period. In this variety, the color of mango pulp slightly changes to yellow shades due to the breakdown of pigments, so this effects the *b value in between the pulp concentrations. Data supports the enrichment of folic acid, which will further support the utilization of beneficial micro-organisms in food beverages. 

The present study focuses on improving Cognitive Radio Networks (CRNs) based on applying machine learning to spectrum sensing in remote learning scenarios. Remote education requires connection dependability and continuity that can be affected by the scarcity of the amount of usable spectrum and suboptimal spectrum usage. The solution for the proposed problem utilizes deep learning approaches, namely CNN and LSTM networks, to enhance the spectrum detection probability (92% detection accuracy) and consequently reduce the number of false alarms (5% false alarm rate) to maximize spectrum utilization efficiency. By developing the cooperative spectrum sensing where many users share their data, the system makes detection more reliable and energy-saving (achieving 92% energy efficiency) which is crucial for sustaining stable connections in educational scenarios. This approach addresses critical challenges in remote education by ensuring scalability across diverse network conditions and maintaining performance on resource-constrained devices like tablets and IoT sensors. Combining CRNs with new technologies like IoT and 5G improves their capabilities and allows these networks to meet the constantly changing loads of distant educational systems. This approach presents another prospect to spectrum management dilemmas in that education delivery needs are met optimally from any STI irrespective of the availability of resources in the locale. The results show that together with machine learning, CRNs can be considered a viable path to improving the networks' performance in the context of remote learning and advancing the future of education in the digital environment. This work also focuses on how machine learning has enabled the enhancement of CRNs for education and provides robust solutions that can meet the increasing needs of online learning.

This article evaluates the Didactic Strategies for Teaching Mathematics (DSTM) program, designed to enhance the teaching of mathematical content in primary and secondary education in a hybrid modality. In alignment with SENACYT’s Gender-STEM-2040 Policy, which emphasizes gender equality as a foundational principle of education, this study aims to assess whether initial teacher training aligns with this policy through the use of mathematical strategies promoting gender equality. A descriptive-correlational approach was applied to a sample of 64 educators, selected based on their responses during the training, with the goal of improving teaching and data collection methodologies. Findings indicate that, although most teachers actively engage in training, an androcentric approach persists, with sexist language and a curriculum that renders girls invisible, hindering the fulfillment of the National Gender Equality Policy in Science, Technology, and Innovation of Panama (Gender-STEM Policy 2040). Additionally, through a serendipitous finding, a significant gap in student activity levels, especially in secondary school, was discovered. While in primary school, activity levels were similar between genders, a decline in active participation among girls in secondary school was observed. This discovery, not initially contemplated in the study’s objectives, provides valuable insights into gender differences in active participation, particularly in higher educational stages. The serendipity suggests the need for further exploration of social, environmental, and family factors that may influence this decrease in girls’ active participation. The article concludes with a preliminary diagnosis and a call to deepen gender equality training and the effective implementation of coeducation in Panama’s educational system.