This paper analyzes the characteristics and influence mechanisms of financial support for China’s strategic emerging industries. Using a sample of 356 listed companies across nine major industries, we conduct an in-depth analysis of the efficiency of financial support and its influencing factors. In addition, this paper analyzes the influence mechanism of financial support for strategic emerging industries based on the relevant theory of financial support for industry development. It clarifies the internal and external influencing factors. Based on the theoretical analysis, a two-stage empirical investigation was conducted: The data of 356 listed companies in strategic emerging industries from 2010 to 2022 were selected as a sample, and the data envelopment analysis (DEA) method was applied to measure efficiency. The influencing factors were then analyzed using a Tobit regression and an intermediate effects test.

The research aims to map environmental protection strategies and the related control tools and to identify the links among companies with the largest number of employees and sites in Hungary. The research questions were answered using a questionnaire survey method. The authors used cluster analysis to classify the 205 company strategies into the identified strategy clusters: Leaders, Awakeners, and Laggards. Then, the examined 21 environmental management control tools in the sample were divided into four groups: strategic, administrative, methodological and economic. Economic and strategic methods were the most common in the sample. The authors used cross-tabulation analysis to examine whether there is a statistically proven relationship between belonging to environmental strategy clusters and specific control tools. The analysis showed significant but weak to moderate relationships. According to Cramer's V and the contingency coefficient, the closest relationship between the tested environmental management control tools and membership in environmental strategy clusters is shown by evaluating investments, assessing the economic viability of environmental strategies, and running an environmental training program for employees. In case of the robust lambda indicator, a significant relationship was found by examining the economics of environmental strategies and identifying environmental success factors and eco-balances. It can be concluded that the companies under examination follow a set of environmental goals, which they have incorporated into their strategic objectives. They use the available environmental management control toolbox to develop their strategies and to monitor their implementation to varying degrees.

A Detailed geophysical investigation was conducted on Knossos territory of Crete Island. Main scope was the detection of underground archaeological settlements. Geophysical prospecting applied by an experienced geophysical team. According to area dimensions in relation to geological and structural conditions, the team designed specific geophysical techniques, by adopted non-catastrophic methods. Three different types of geophysical techniques performed gradually. Geophysical investigation consisted of the application of geoelectric mapping and geomagnetic prospecting. Electric mapping focusses on recording soil resistance distribution. Geomagnetic survey was performed by using two different types of magnetometers. Firstly, recorded distribution of geomagnetic intensity and secondly alteration of vertical gradient. Measured stations laid along the south-north axis with intervals equal to one meter. Both magnetometers were adjusted on a quiet magnetic station. Values were stored in files readable by geophysical interpretation software in XYZ format. Oasis Montaj was adopted for interpretation of measured physical properties distribution. Interpretation results were illustrated as color scale maps. Further processing applied on magnetic measurements. Results are confirmed by overlaying results from three different techniques. Geoelectric mapping contributed to detection of a few archaeological targets. Most of them were recorded by geomagnetic technique. Total intensity aimed to report the existence of magnetized bodies. Vertical gradient detected subsurface targets with clearly geometrical characteristics.

This study examines factors associated with an increasingly poor perception of the novel coronavirus in Africa using a designed electronic questionnaire to collect perception-based information from participants across Africa from twenty-one African countries (and from all five regions of Africa) between 1 and 25 February 2022. The study received 66.7% of responses from West Africa, 12.7% from Central Africa, 4.6% from Southern Africa, 15% from East Africa, and 1% from North Africa. The majority of the participants are Nigerians (56%), 14.1% are Cameroonians, 8.7% are Ghanaians, 9.3% are Kenyans, 2% are South Africans, 2.1% are DR-Congolese, 1.6% are Tanzanians, 1.2% are Rwandans, 0.4% are Burundians, and others are Botswana’s, Chadians, Comoros, Congolese, Gambians, Malawians, South Sudanese, Sierra Leoneans, Ugandans, Zambians, and Zimbabweans. All responses were coded on a five-point Likert scale. The study adopts descriptive statistics, principal component analysis, and binary logistic regression analysis for the data analysis. The descriptive analysis of the study shows that the level of ignorance or poor “perception” of COVID-19 in Africa is very high (87% of individuals sampled). It leads to skepticism towards complying with preventive measures as advised by the WHO and directed by the national government across Africa. We adopted logistic regression analysis to identify the factors associated with a poor perception of the virus in Africa. The study finds that religion (belief or faith) and media misinformation are the two leading significant causes of ignorance or poor “perception” of COVID-19 in Africa, with log odd of 0.4775 (resulting in 1.6120 odd ratios) and 1.3155 (resulting in 3.7265 odd ratios), respectively. The study concludes that if the poor attitude or perception towards complying with the preventive measures continues, COVID-19 cases in Africa may increase beyond the current spread.

The detection of urban expansion through digital processing of satellite images provides valuable information for understanding the dynamics of land use change and its spatial relationship with environmental factors. In order to apply or generate effective land-use planning policies, it is essential to have a historical record of the regional distribution of human settlements, an element that is practically non-existent in our country. For this reason, this text aims to determine the urban growth rate during the period 2000–2014 in the state of Hidalgo, Mexico, and to identify potential expansion zones from Landsat images. Six Landsat scenes were used for the spatial analysis of the state urban coverage and their relationship with the road influence area was evaluated. Two maps were obtained as cartographic products: one of urban coverage distribution and another of the municipalities with the greatest expansion, whose areas are located in the Valle del Mezquital region. However, Mineral de la Reforma, Tetepango, Tizayuca and Pachuca de Soto stand out for their growth rates during the study period: 183.44%, 102%, 94% and 68.5%, respectively. In total, the state urban area in-creased 72.3 km² from 2000 to 2014 with an average growth rate of 1.8% per year. Such growth was associated with the areas of influence of important road infrastructure, such as the Libramiento Arco Norte in Hidalgo. Therefore, the Mezquital Valley and the Mexico Basin are considered as potential regions for urban expansion in the state.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.