The analysis of the accumulation and export of nutrients by the cowpea crop is fundamental for a more sustainable fertilization program, because the definition of the doses of organic fertilizers based only on the estimated maximum yield does not guarantee the maintenance of soil fertility. The objective of this study was to evaluate the effect of fertilization with chicken manure on the productivity, accumulation and exportation of nutrients by the pods of cowpea. A randomized block design was used, with five doses of chicken manure (0; 5; 10; 20 and 40 t ha^-1) and four repetitions. The highest levels of P and Mg were found in the leaves with the application of 40 t ha^-1 of manure. The maximum pod length was 14.47 cm, estimated with the dose of 33.33 t ha^-1 of manure. The highest values of diameter, number of pods per plant and pod productivity were observed at the highest dose of manure applied. In relative terms, that is, total exported in relation to the total extracted by the aerial part, phosphorus is the nutrient most exported by the pods, on average 58%, followed by N (55%), K (43%), Mg (40%), S (38%) and Ca (17%). At the highest dose, although Ca accumulation occurred in large quantities (31.3 kg ha^-1), only 13% of it was exported by the pods. Fertilizing cowpea with chicken manure supplied essential nutrients and increased pod yield from 7.2 (no fertilization) to 16.3 t ha^-1 (fertilization with 40 t ha^-1 of chicken manure). The plant remains of the cowpea constitute an important source of nutrients, being obtained at the highest dose of manure applied (40 t ha^-1) the following amounts of macronutrients (kg ha^-1): N (51.4); P (5.1); K (27.6); Ca (27.1); Mg (8.2); S (5.1), which may return to the soil, with the incorporation of the plants.

This study aims to investigate the phenomenon of non-disclosure of personal information among male individuals, employing the Communication Privacy Management Theory as a guiding framework. The objectives of the study encompass identifying the specific types of personal information male students refrain from disclosing, examining the underlying reasons for their non-disclosure practices, and assessing the impact of non-disclosure on their interpersonal relationships. Qualitative research methods, primarily in-depth interviews, were employed to gather insights, with six male students from Sultan Idris Education University (UPSI) participating in the interviews. The findings reveal that male students at UPSI do engage in non-disclosure of personal information, albeit to a certain extent. Specifically, the findings discovered four types of personal information—secrets, traumas, dark history, and family matters—that these students commonly choose not to disclose. Notably, there are four categories of personal information they tend to withhold, namely secrets, traumas, dark history, and family matters. The reluctance to disclose stems from factors such as insecure attachment, a reluctance to worry about their parents, and strained relationships with their family members. Furthermore, the study highlights that non-disclosure of personal information has both negative and positive repercussions on the participants’ relationships with others. Moreover, the study underscores that non-disclosure of personal information can have both negative and positive effects on the participants’ relationships, shedding light on the complexities of navigating personal privacy choices in the university and job-seeking context. The study contributes valuable insights into the challenges of employability dilemmas faced by male university students concerning the management of personal information.

Corporate finance courses are increasingly adopting data-driven teaching methods. Modern corporate finance courses are focusing more on students' career development. Through simulation practice and career planning guidance, students are better prepared to face challenges in the workplace after graduation. Students need to learn how to utilize data analysis tools and techniques to extract useful information from large datasets and make more accurate decisions. Data-driven teaching is a significant innovation in current curriculum reforms. In recent years, with the development of technology and the emergence of financial innovation, corporate finance courses have been undergoing continuous changes and innovations. These courses have started to emphasize emerging areas such as digital finance, blockchain technology, and sustainable development. Taking the example of corporate finance, this paper integrates the demands of skill development in the era of digital finance, focusing on aspects like teaching methods, reform methodologies, practical experiments, feedback mechanisms, and data analysis.

The purpose of this study is to identify the effects of multidimensional (fuzzy) inequalities and marginal changes on the Gini coefficients of various factors. This allows a range of social policies to be specifically targeted to reduce broader inequalities, but these policies are focused primarily on health, education, housing, sanitation, energy and drinking water. It is necessary to target policy areas that are unequally distributed, such as those with access to unevenly distributed drinking water policies. The data are from the Household and Consumption Survey of 6695 households in 2003 and 9259 households in 2011. This paper uses Lerman and Yitzhaki’s method. The results revealed that the main contributors to inequalities over the two periods were health and education. These sources have a potentially significant effect on total inequality. Health increases overall inequalities, but sources such as housing, sanitation and energy reduce them. This article provides resources to disadvantaged and vulnerable target groups. Multiple inequalities are analyzed for different subgroups of households, such as place of residence and the gender of the head of household. Analyzing fuzzy poverty inequalities makes it possible to develop targeted measures to combat poverty and inequality. This study is the first to investigate the sources of Gini’s fuzzy inequality in Chad via data analysis techniques, and in general, it is one of the few studies in Saharan Africa to be interested in this subject. Some development policies in sub-Saharan Africa should therefore focus on different sources (negative effect), sources (positive effect) and the equalization effect.

With the gradual penetration of artificial intelligence technology into various fields of society, it has brought many deeper and broader impacts, gradually improving the status of artificial intelligence in talent cultivation and education to adapt to the current development of social intelligence technology. Therefore, as the core course of artificial intelligence education in universities, machine learning needs to deeply analyze and explore the main factors that affect its development, in order to better mobilize students' learning enthusiasm and teachers' educational innovation, enhance the teaching and learning effectiveness of the course, and maximize the exploration of the educational achievements of artificial intelligence.