With the progress of information technology, especially the widespread use of artificial intelligence technology, it has shown an important role in promoting economic and social development. Art and design in universities is a new discipline that combines modern technology with humanities and art. Only by emphasizing the development of science and technology, adapting to the requirements of the times, and closely integrating humanities and art with science and technology, can we gradually expand the educational channels for cultivating composite and innovative talents. Effectively organizing different types of scientific research activities, building a sound and comprehensive education system, plays an important role in adjusting teaching relationships, innovating teaching models, enhancing students' professional and comprehensive qualities, and improving their academic performance and employment competitiveness.

Abrupt changes in environmental temperature, wind and humidity can lead to great threats to human life safety. The Gansu marathon disaster of China highlights the importance of early warning of hypothermia from extremely low apparent temperature (AT). Here a deep convolutional neural network model together with a statistical downscaling framework is developed to forecast environmental factors for 1 to 12 h in advance to evaluate the effectiveness of deep learning for AT prediction at 1 km resolution. The experiments use data for temperature, wind speed and relative humidity in ERA-5 and the results show that the developed deep learning model can predict the upcoming extreme low temperature AT event in the Gansu marathon region several hours in advance with better accuracy than climatological and persistence forecasting methods. The hypothermia time estimated by the deep learning method with a heat loss model agrees well with the observed estimation at 3-hour lead. Therefore, the developed deep learning forecasting method is effective for short-term AT prediction and hypothermia warnings at local areas.

In this paper, we modeled and simulated two tandem solar cell structures (a) and (b), in a two-terminal configuration based on inorganic and lead-free absorber materials. The structures are composed of sub-cells already studied in our previous work, where we simulated the impact of defect density and recombination rate at the interfaces, as well as that of the thicknesses of the charge transport and absorber layers, on the photovoltaic performance. We also studied the performance resulting from the use of different materials for the electron and hole transport layers. The two structures studied include a bottom cell based on the perovskite material CsSnI₃ with a band gap energy of 1.3 eV and a thickness of 1.5 µm. The first structure has an upper sub-cell based on the CsSnGeI₃ material with an energy of 1.5 eV, while the second has an upper sub-cell made of Cs₂TiBr₆ with a band gap energy of 1.6 eV. The theoretical model used to evaluate the photocurrent density, current-voltage characteristic, and photovoltaic parameters of the constituent sub-cells and the tandem device was described. Current matching analysis was performed to find the ideal combination of absorber thicknesses that allows the same current density to be shared. An efficiency of 29.8% was obtained with a short circuit current density J_sc = 19.92 mA/cm², an open circuit potential V_oc = 1.46 V and a form factor FF = 91.5% with the first structure (a), for a top absorber thickness of CsSnGeI₃ of 190 nm, while an efficiency of 26.8% with J_sc = 16.74, V_oc = 1.50 V and FF = 91.4% was obtained with the second structure (b), for a top absorber thickness of Cs₂TiBr₆ of 300 nm. The objective of this study is to develop efficient, low-cost, stable and non-toxic tandem devices based on lead-free and inorganic perovskite.

The study focused on investigating the effects of varying levels of HA (HA₁ = 0, HA₂ = 25, HA₃ = 50, HA₄ = 75, and HA₅ = 100) on Red Dragon, Red Prince, and Red Meat varieties of red radish. This analysis aimed to unravel the relationship between different levels of HA and their impact on the growth and productivity of red radish genotypes. The findings revealed that the Red Prince genotype attained the utmost plant height of 24.00 cm, an average of 7.50 leaves per plant, a leaf area of 23.11 cm², a canopy cover of 26.76%, a leaf chlorophyll content of 54.60%, a leaf fresh weight of 41.16 g, a leaf dry weight of 8.20 g, a root length measuring 9.73 cm, a root diameter of 3.19 mm, a root fresh weight of 27.60 g, a root dry weight of 6.75 g, and a remarkable total yield of 17.93 tons per hectare. The implications of this study are poised to benefit farmers within the Dera Ismail Khan Region, specifically in the plain areas of Pakistan, by promoting the cultivation of the Red Prince variety.

The idea of a smart city has evolved in recent years from limiting the city’s physical growth to a comprehensive idea that includes physical, social, information, and knowledge infrastructure. As of right now, many studies indicate the potential advantages of smart cities in the fields of education, transportation, and entertainment to achieve more sustainability, efficiency, optimization, collaboration, and creativity. So, it is necessary to survey some technical knowledge and technology to establish the smart city and digitize its services. Traffic and transportation management, together with other subsystems, is one of the key components of creating a smart city. We specify this research by exploring digital twin (DT) technologies and 3D model information in the context of traffic management as well as the need to acquire them in the modern world. Despite the abundance of research in this field, the majority of them concentrate on the technical aspects of its design in diverse sectors. More details are required on the application of DTs in the creation of intelligent transportation systems. Results from the literature indicate that implementing the Internet of Things (IoT) to the scope of traffic addresses the traffic management issues in densely populated cities and somewhat affects the air pollution reduction caused by transportation systems. Leading countries are moving towards integrated systems and platforms using Building Information Modelling (BIM), IoT, and Spatial Data Infrastructure (SDI) to make cities smarter. There has been limited research on the application of digital twin technology in traffic control. One reason for this could be the complexity of the traffic system, which involves multiple variables and interactions between different components. Developing an accurate digital twin model for traffic control would require a significant amount of data collection and analysis, as well as advanced modeling techniques to account for the dynamic nature of traffic flow. We explore the requirements for the implementation of the digital twin in the traffic control industry and a proper architecture based on 6 main layers is investigated for the deployment of this system. In addition, an emphasis on the particular function of DT in simulating high traffic flow, keeping track of accidents, and choosing the optimal path for vehicles has been reviewed. Furthermore, incorporating user-generated content and volunteered geographic information (VGI), considering the idea of the human as a sensor, together with IoT can be a future direction to provide a more accurate and up-to-date representation of the physical environment, especially for traffic control, according to the literature review. The results show there are some limitations in digital twins for traffic control. The current digital twins are only a 3D representation of the real world. The difficulty of synchronizing real and virtual world information is another challenge. Eventually, in order to employ this technology as effectively as feasible in urban management, the researchers must address these drawbacks.