The spiritual dimensions contained in New Quality Productive Forces are not only the core driving force for its vigorous development but also the key to leading it towards high-quality development. These spiritual dimensions include the scientific spirit of daring to innovate and continuous exploration, the fighting spirit of being unyielding and enterprising in the face of challenges, the collaborative spirit of being innovative and entrepreneurial in the face of challenges, and the spiritual spirit of being a good teacher. These spiritual dimensions include the scientific spirit of daring to innovate and continuous exploration, the fighting spirit of being unyielding and enterprising in the face of challenges, the collaborative spirit of advocating These spiritual dimensions include the scientific spirit of daring to innovate and continuous exploration, the fighting spirit of being unyielding and enterprising in the face of challenges, the collaborative spirit of advocating cross-disciplinary cooperation and open sharing, and the dedication of serving society and benefiting humanity. the concept of green development ensures that New Quality Productive Forces, while pursuing economic benefits, is also committed to achieving These spiritual dimensions are interwoven and complement each other, jointly constructing the spiritual core of New Quality Productive Forces. These spiritual dimensions are interwoven and complement each other, jointly constructing the spiritual core of New Quality Productive Forces and injecting it with profound moral depth and a continuous source of development momentum.

This study aimed to analyze the effect of training programs on entrepreneurial self-efficacy (ESE) and the Optimism of micro, small, and medium enterprises (MSMEs). The research was conducted at Babakan Madang MSMEs, Bogor Regency, assisted by Human Resources Education and Training Center (P2SDM) under the Community Service Institution (LPPM) at IPB University (IPB). The sample size was set at 100 SMEs with a purposive sampling method. Data was obtained by distributing questionnaires and analyzed using Structural Equation Modeling (SEM). The results of the study were as follows: 1) Reactions in the training program did not affect the ESE of MSME actors, 2) Learning in the training program affected the ESE of MSME actors, 3) Behavior in the training program did not affect the ESE of MSME actors, 4) Results in the training program does not affect the ESE of MSME actors, and 5) ESE affects the Optimism of MSME actors. The effect of ESE on the Optimism of MSME actors is greater than the effect of learning in training programs on the Optimism of MSME owners.

Foodborne diseases are a global health problem. Every year, millions of people die worldwide from these diseases. It has been determined that the high prevalence of these diseases is related to unfavorable socioeconomic conditions of the population. In this study, the relationship between foodborne diseases and socioeconomic conditions of the population was determined using principal component analysis as a multivariate statistical analysis technique. In this study, the socioeconomic variables of each Ecuador province and the prevalence of foodborne diseases (hepatitis A, salmonella, shigellosis and typhoid fever) during the years 2018 and 2019 were considered. The results show the relationship between foodborne diseases and the socioeconomic conditions of the population, as well as identifying regions more vulnerable to present high levels of prevalence of foodborne diseases, thus facilitating the implementation of social investment programs to reduce the prevalence of these diseases.

The use of artificial intelligence (AI) is related to the dynamic development of digital skills. This article focuses on the impact of AI on the work of non-profit organizations that aim to help those around them. Based on 10 semi-structured interviews, it is presented here how it is possible to work with AI and in which areas it can be used—in social marketing, project management, routine bureaucracy. At the same time, workers and volunteers need to be educated in critical and logical thinking more than ever before. These days, AI is becoming more and more present in almost all the activities, bringing several benefits to those making use of it. On the one hand, by using AI in the day-to-day activities, the entities are able to substantially decrease their costs and have the advantage of being able to have, in most cases, a better and faster job done. On the other hand, those individuals that are more creative and more innovative in their line of work should not feel threatened by those situations in which organizations decide to use more AI technologies rather than human beings for the routine activities, since they will get the opportunity to perform tasks that truly require their intellectual capital and decision making abilities.

The safeguarding of agricultural land is rooted in national land surveys and remote sensing data, which are enhanced by contemporary information technology. This framework facilitates the monitoring and regulation of unauthorized alterations in cultivated land usage. This paper aims to analyze land policies at the national, provincial, and local levels, investigate the cultivated land protection strategies implemented within the research region, where the policies have gained societal acceptance, and propose recommendations and countermeasures to enhance the development and utilization of land resources. The central issue of this study is to identify the challenges in achieving a balance between human activities and natural ecosystems. To address this issue, the research employs a combination of literature review, semi-structured interviews, text analysis, and content analysis, emphasizing the integration of empirical fieldwork and theoretical frameworks. Key areas of focus include: (a) the current state of the farmland protection system, (b) the legal foundations for local enforcement, (c) the systematic mechanisms for implementing arable land protection, and (d) the coordinated oversight system involving both the Party and government. Notably, the practice of cultivated land protection faces several challenges, primarily stemming from two factors. Firstly, there exists a disconnect between the economic interests of certain illegal land users and the objectives of land management, which hinders effective enforcement. Secondly, environmental repercussions arise from misinterpretations of land policy or non-compliant land development practices aimed at profit, which contradict the goals of ecological sustainability. The study examines two approaches to address the issue: the distribution and effective use of land resources, and the capacity for monitoring and early warning systems. Findings indicate that Dongtai City in Jiangsu Province has rigorously implemented all national land management policies, while also preserving the adaptability of local townships in practical applications, thereby ensuring the consistency of both the quality and quantity of arable land.

The telecommunications services market faces essential challenges in an increasingly flexible and customer-adaptable environment. Research has highlighted that the monopolization of the spectrum by one operator reduces competition and negatively impacts users and the general dynamics of the sector. This article aims to present a proposal to predict the number of users, the level of traffic, and the operators’ income in the telecommunications market using artificial intelligence. Deep Learning (DL) is implemented through a Long-Short Term Memory (LSTM) as a prediction technique. The database used corresponds to the users, revenues, and traffic of 15 network operators obtained from the Communications Regulation Commission of the Republic of Colombia. The ability of LSTMs to handle temporal sequences, long-term dependencies, adaptability to changes, and complex data management makes them an excellent strategy for predicting and forecasting the telecom market. Various works involve LSTM and telecommunications. However, many questions remain in prediction. Various strategies can be proposed, and continued research should focus on providing cognitive engines to address further challenges. MATLAB is used for the design and subsequent implementation. The low Root Mean Squared Error (RMSE) values and the acceptable levels of Mean Absolute Percentage Error (MAPE), especially in an environment characterized by high variability in the number of users, support the conclusion that the implemented model exhibits excellent performance in terms of precision in the prediction process in both open-loop and closed-loop.