The number of accidents at level railway crossings, especially crossings without gate barriers/attendants, is still very high due to technical problems, driving culture, and human error. The aim of this research is to provide road maps application based on ergonomic visual displays design that can increase awareness level for drivers before crossing railway crossings. The double awareness driving (DAD) map information system was built based on the waterfall method, which has 4 steps: defining requirements, system and software design, unit testing, and implementation. User needs to include origin-destination location, geolocation, distance & travel time, directions, crossing information, and crossing notifications. The DAD map application was tested using a usability test to determine the ease of using the application used the System Usability Scale (SUS) questionnaire and an Electroencephalogram (EEG) test to determine the increase in concentration in drivers before and immediately crossing a railway crossing. Periodically, the application provides information on the driving zone being passed; green zone for driving distances > 500 m to the crossing, the yellow zone for distances 500m to 100m, and the red zone for distances < 100 m. The DAD map also provides information on the position and speed of the nearest train that will cross the railway crossing. The usability test for 10 respondents giving SUS score = 97.5 (satisfaction category) with a time-based efficiency value = 0.29 goals/s, error rate = 0%, and a success rate of 93.33%. The cognitive ergonomic testing via Electroencephalogram (EEG) produced a focus level of 21.66%. Based on the results of DAD map testing can be implemented to improve the safety of level railroad crossings in an effort to reduce the number of driving accidents.

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.

This academic paper explores the impact of multi-entity cooperation on the effectiveness of public service provision in China. It examines the social governance pattern proposed by the 19th National Congress of the CCP and the emphasis on co-building, co-governing, and sharing. The paper highlights the need for collaboration among various entities and the transition from sole government provision to improve urban public services. It aims to investigate the moderating effects of institutions, policies, and public participation. The study will involve quantitative and qualitative phases in three cities in Guangdong Province and target governmental departments, commercial organizations, non-profit social organizations, and local residents. The research aims to provide policy recommendations, innovate institutional policies, enhance public engagement, and improve multi-party cooperation and urban public services. It seeks to contribute practical models and measures for effective government public management and service implementation.

By carrying out a laboratory experiment, the influence of priming methods, including ZnSO4, BSN, and hydropriming was evaluated on the seed germination of hybrid AS71 corn. Then, the main and interaction effects of the priming methods, planting dates, and weed interference levels were surveyed on the vegetative growth traits, yield, and yield components of corn in a field experiment. Based on the lab experiment, although the maximum germination percentage (100%) was observed in the treated plots by hydropriming 22 h after treatment (HAT), the greatest seedling vigor index (122.99) was recorded with treated seeds by ZnSO4 (0.03 mg L–1) at 8 HAT. The greatest emergence index was observed in the treated plots by hydropriming on both planting dates of June 1 and 11. The interaction of planting dates and weed interference levels revealed that the highest emergence index (14%–17%) occurred in the weed-free plots on both planting dates. BSN recorded the greatest corn 1000-grain weight that was significantly higher than the control plots by 28%. Furthermore, BSN enhanced the corn grain yield compared with the control plots by 63% and 24.9% on the planting dates of June 1 and 11, respectively. BSN, as a nutri-priming approach, by displaying the highest positive effects in boosting the corn grain yield in both weedy and weed-free plots as well as both planting dates, could be a recommendable option for growers to improve the crop yield production.

The hospital is a complex system, which evolving practices, knowledge, tools, and risks. This study aims to assess the level of knowledge about risks at Hassan II Hospital among healthcare workers (HCWs) working in three COVID-19 units. The action-research method was adopted to address occupational risks associated with the pandemic. The study involved 82 healthcare professionals in the three COVID-19 units mentioned above. All participants stated they were familiar with hospital risks. Seventy-four HCPs reported no knowledge of how to calculate risk criticality, while eight mentioned the Occurrence rating, Severity rating, and Detection rating (OSD) method, considering Occurrence rating, Severity rating, and Detection rating as key elements for risk classification. Staff indicated that managing COVID-19 patients differs from other pathologies due to the pandemic’s evolving protocols. There is a significant lack of information among healthcare professionals about risks associated with COVID-19, highlighting the need for a hospital risk management plan at a subsequent stage.