Coordination and integration among farms within agri-food chains are crucial to tackle the issue of fragmentation within the primary sector, both at the European and national level. The Italian agri-food system still complains about the need to aggregate supply to support market dynamics, especially for niche and quality products that characterize the Made in Italy. It is well known that the Italian agri-food sector is closely linked to the relationship between agriculture on one hand and culture/tradition on the other, which is reflected in the high number of quality products that have obtained EU PDO (Protected Designation of Origin) and PGI (Protected Geographical Indication) recognition. The development of vertical forms of coordination has found significant support in recent years from the integrated supply chain design approach, which is increasingly becoming an essential tool for implementing rural development policies. In this context, the study provides a comparison between companies that have joined the Integrated Supply Chain Projects of the Rural Development Program and those that have not applied. The aim is to highlight any differences in order to understand policy impact. The analysis is based on the Emilia-Romagna region Farm Accountancy Data Network (FADN) data, and the sample consists of more than 2 thousand farms. The statistical analysis conducted compares treated and non-treated using the Welch-t-test for independent unmatched samples. The main results show higher values for treated farms when structural variables are analyzed, like the utilized agricultural area or the agricultural work unit. In general, higher balance sheet performances emerged for treated farms. In conclusion, this study shows that the Integrated Supply Chain Projects represent a worthwhile tool both to increase cooperation, food quality, and to enhance a competitive agricultural sector.

This paper aims to provide a comprehensive view of the E-Government Development Index analysis in Southeast Asia. Through a review of the results of an annual survey of 192 United Nations (UN) member states, the study identified 11 countries with the E-Government Development Index in Southeast Asia. The findings in this study revealed that the E-Government Development Index (EGDI) in Southeast Asian countries displays different levels of development. Singapore, Malaysia, and Brunei are the countries in the region with the highest EGDI scores. Singapore leads the area with a high EGDI score. These countries have effectively implemented advanced e-government services, such as online public services, digital infrastructure, and e-participation, which have greatly improved the quality of life of their citizens and the efficiency of their government function. On the other hand, countries such as Cambodia, Laos, and Myanmar lag in their e-government development as a result of factors such as limited Internet access, inadequate digital infrastructure, and low levels of digital literacy among the populations of these countries. In addition, some moderate progress has been made in the development of e-government in mid-level countries, such as Thailand, Indonesia, the Philippines, and Vietnam. These countries continue to improve their digital infrastructure and enhance their e-service offerings to close the digital divide. Overall, EGDI in Southeast Asia reflects different levels of digital transformation in the region, with each country facing its distinct set of difficulties and opportunities when it comes to leveraging technology for better governance and public service delivery.

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.

This article uses a qualitative descriptive approach, through field visits with observations and in-depth interviews. The research location chosen was a representative village in accordance with the Tourism Village classification of the Gunung Kidul Regency Tourism Office. A tourist village is a form of integration between attractions, accommodation and supporting facilities presented in a structure of community life that is integrated with applicable procedures and traditions. In line with this, the existence of tourist villages can be an alternative strategy for increasing village original income (PADes) to support poverty alleviation. Measuring the impact of tourism village innovation on increasing Village Original Income (PADes) in supporting poverty reduction can provide a complete picture of how the implementation of tourism village innovation has a significant impact on village development through increasing PADes. Gunung Kidul Regency is one of the areas that has succeeded in developing tourist villages, this can be seen from the reduction in poverty rates in the last 10 years.

This study updates Pereira and Pereira by revisiting the macroeconomic and budgetary effects of infrastructure investment in Portugal using a dataset from the Portuguese Ministry of the Economy covering 1980–2019, thereby capturing a period of austerity and decreased investment in the 2010s. A vector-autoregressive approach re-estimates the elasticity and marginal product of twelve infrastructure types on private investment, employment, and output. The most significant long-term accumulated effects on output accrue from investments in airports, ports, health, highways, water, and railroads. In contrast, those in municipal roads, electricity and gas, and refineries are statistically insignificant. All statistically significant infrastructure investments pay for themselves over time through additional tax revenues. Compared to the previous study, highways, water, and ports have more than doubled their estimated marginal products due to a significant increase in relative scarcity over the last decade. In addition, our analysis reveals an important shift in the impacts of infrastructure investment, now producing more substantial immediate effects but weaker long-term impacts. This change offers policymakers a powerful tool for short-term economic stimulus and is particularly useful in addressing immediate economic challenges.

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.