Since its inception in 2013, “The Belt and Road Initiative” has become an important engine driving global economic growth. The initiative has not only promoted infrastructure construction in countries along the Belt and Road but also strengthened financial integration, unimpeded trade, people-to-people exchanges, and policy communication. In this context, higher education, as an important avenue for talent training and scientific and technological innovation, is of great significance to promoting the economic and social development of countries along the Belt and Road. By strengthening academic cooperation with Chinese universities, Kyrgyzstan can enhance its curriculum, adopt advanced teaching methods, and integrate cutting-edge research to foster more skilled labor. In addition, innovation and technology transfer through higher education partnerships can drive sustainable economic growth and diversification. This paper explores the strategic path of integrating higher education into the Belt and Road. Initiative, focusing on academic collaboration, enhancing R&D capabilities, and fostering an entrepreneurial ecosystem.

Kampar Regency, as the largest pineapple producer in Riau Province, has yet to provide significant added value for the surrounding SMEs. The limitations in technology and innovation, infrastructure support, and market access have prevented this potential from being optimally utilized. A Technopark can provide the necessary facilities and infrastructure to enhance production efficiency, innovation, and product quality, thus driving local economic growth. The objective of this study is to identify and determine potential locations for the development of a pineapple-based Technopark in Kampar Regency. This study is crucial as a fundamental consideration in selecting the technopark location and assessing the effectiveness and success of the technopark area. The method used in this study is AHP-GIS to analyze relevant parameters in the site selection process for the technopark area. Parameters considered in this study include slope, land use, availability of raw materials, accessibility of roads, access to water resources, proximity to universities, market access, population density, and landfill. The analysis results indicate that the percentage of land highly suitable for the technopark location is 0.78%, covering an area of 8943 hectares. Based on the analysis, it is recommended that potential locations for the development of a pineapple SMEs-based technopark in Kampar Regency are dispersed in Tambang District, encompassing three villages: Rimbo Panjang, Kualu Nenas and Tarai Bangun. The findings of this study align with the spatial planning of Kampar Regency.

Modern agricultural production technologies based on the widespread use of pesticides and mineral fertilizers have largely solved the problem of providing the population with food, and at the same time have generated multiple ecological, medical and environmental problems, problems of environmentally friendly and biologically valuable food products, land rehabilitation, restoration of their fertility, etc. Therefore, the emergence of new classes of pesticides with different mechanisms of action, high selectivity and low toxicity for warm-blooded animals is very modern. Currently, the development and application of new plant protection products that are not toxic to humans and animals is of global importance. Priority is given to research aimed at creating plant protection products based on microorganisms and their metabolites, as well as the search for plant substances with potential pesticide activity. In this regard, the question arose of finding new safe fertilizers that can also be economically profitable for production on an industrial scale. One of the current trends in this industry is the use of green microalgae. In this regard, the purpose of our research is the possibility of cultivating green microalgae on phosphorus production waste. During the work, traditional and modern research methods in biology were used. As a result of the work, several problems can be solved, such as the disposal of industrial waste and the production of safe biological fertilizer.

Industrial zones require careful and meticulous planning because industry can have a major impact on the surrounding environment. The research location is the northern part of West Java Province which is a gold triangle area named Rebana Triangle Area. The purpose of this study is to measure the weight of the research variables in determining industrial zones from the results of fuzzy analytical hierarchy process (F-AHP) analysis, assessing the location of industrial zones in the research area based on important variables in determining industrial zones. The result of this study is the weight of the research variables in determining the industrial zone from the results of the fuzzy analytical hierarchy process (F-AHP) analysis obtained is the availability of electrical infrastructure with an influence weight of 15.00%. The second most influential factor is the availability of telecommunications infrastructure with an effect of 13.02%, the distance of land to roads and access of 11.76%, land use of 11.21%, distance of land to public facilities of 9.99%, labour cost work is 9.60%, the distance of land to the river is 8.19%, the price of land is 7.97%, the slope is 6.79%, and the type of soil is 6.43%. This GIS analysis model can be a reference model for the government in determining the potential of industrial zones in other regions in Indonesia. A total of 4822.41 Ha or the equivalent of 3.50% of the total area of 6 (six) regencies/cities research areas which are very suitable to be used as industrial zones. The district that has the largest area of potential industrial zone is Majalengka, while Cirebon does not have a location that has the potential for industrial zone locations. Based on the results of the analysis of 10 (ten) variables for determining industrial zones from expert opinion, a draft policy proposal for the government can be proposed, among others. These 10 (ten) variables are variables that are expected to be mandatory variables in planning and determining the location of potential industrial areas.

Earnings disparities in South Africa, and specifically the Eastern Cape region are influenced by a complex interplay of historical, socio-economic, and demographic factors. Despite significant progress since the end of apartheid, persistent disparities in earnings continue to raise questions about the effectiveness of policies aimed at reducing inequality and promoting equitable social system. Individual-level dataset from the 2021 South African general household survey were subjected to exploratory analysis, while Heckman selection model was used to investigate the determinants of earnings disparities in the study area. The results showed that majority of the population are not working for a wage, commission or salary, which also pointed to the gravity of unemployment situation in the area of study. Most of the working population (both male and female) are lowest earners (R ≤ 10,000), and this also cuts across all age-group categories. Majority of working population have no formal education, are drop out, or have less than grade-12 certificate, and very few working populations with higher education status were found in the moderate and relatively high earnings categories. While many of the working population are engaged in the informal sector, those in the formal sector are in the lowest earners group. Compared to any other race, the Black African group constituted the majority of non-wage earners, and most in this group were found in the lowest earners group. Some of the working population who were beneficiaries of social grants and medical aids scheme were found in the lowest, low, and moderate earnings categories. The findings significantly isolated the earnings-effect of age, marital status, gender, race, education, geographic indicators, employment sector, and index of health conditions and disabilities. The study recommends interventions addressing racial, gender, and geographic wage gaps, while also emphasizing the importance of equitable access to education, health infrastructure, and skills development.

Outsourcing logistics operations is a common trend as businesses prioritize core activities. Establishing a sustainable partnership between businesses and logistics service providers requires a systematic approach. This study is needed to develop a more effective and adaptive framework for logistics service provider selection by integrating diverse criteria and decision-making methodologies, ultimately enhancing the precision and sustainability of procurement processes. This study advocate for leveraging industry-based knowledge in procurement, emphasizing the need to define decision-making elements. The research analyzes nearly 300 logistics procurement projects, using a neural network-based methodology to propose a model that aids businesses in identifying optimal criteria for evaluating logistics service providers based on extensive industry knowledge. The goal of this study is to develop and test a practical model that would support businesses in choosing most suitable criteria for selection of logistics service providers based on cumulative market patterns. The results of this study are as follows. It introduces novel elements by gathering and systematizing unique market data using developed data processing methodology. It innovatively classifies decision-making elements, allocating them into distinct groups for use as features in a neural network. The study further contributes by developing and training a predictive model based on a prepared dataset, addressing pre-defined goals, expectations related to green logistics, and specific requirements in the tendering process for selecting logistics service providers. Study is concluded by summarizing suggestions for future research in area of adopting neural networks for selection of logistics service providers.