Urbanization plays a crucial role in facilitating the integration of population growth, industrial development, economic expansion, and energy consumption. In this paper, we aim to examine the relationships between CO2 emissions and various factors including economic growth, urbanization, financial development, and energy consumption within Pakistan’s building sector. The study utilizes annual data spanning from 1990 to 2020. To analyze the cointegration relationship between these variables, we employ the quantile autoregressive distributed lag error correction model (QARDL-ECM). The findings of this research provide evidence supporting the presence of an asymmetric and nonlinear long-term relationship between the variables under investigation. Based on these results, we suggest the implementation of tariffs on nonrenewable energy sources and the formulation of policies that promote sustainable energy practices. By doing so, policymakers and architects can effectively contribute to minimising environmental damage. Overall, this study offers valuable insights that can assist policymakers and architects in making informed decisions to mitigate environmental harm while fostering sustainable development.

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.

This paper revisits the analysis on the effect of cross-cultural awareness and self-efficacy, which are both significant constructs in today’s globalized world. People are expected to have both a high level of self-efficacy and a strong sense of cross-cultural awareness due to the growing frequency of cross-cultural interactions. For fields like education, psychology, and cross-cultural communication, it can be very crucial to comprehend how cross-cultural awareness affects self-efficacy. 60 relevant articles were found after a thorough assessment of the literature on the subject using thematic analysis of the CNKI and Google Scholar databases. Ten major themes were found in the review: 1) the cultivation of cross-cultural awareness, 2) the current situation of students’ cross-cultural awareness, 3) the importance of cross-cultural awareness, 4) the relationship between self-efficacy and academic achievement, 5) the relationship between learning self-efficacy and influencing factors, 6) the relationship between cross-cultural awareness and self-efficacy, 7) the relationship between self-efficacy and cross-cultural adaptation, 8) cultural factors affecting learning self-efficacy, 9) the effect of social environment on individual self-efficacy, and 10) the relationship between cultural expectations and self-efficacy. The findings of this review demonstrate how crucial cross-cultural understanding is to the growth of self-efficacy. The design of educational and training programs aiming at boosting cross-cultural knowledge and self-efficacy will also be significantly impacted by this review.

This article delves into the controversial practice of utilizing a student’s first language (L1) as a teaching resource in second language (L2) learning environments. Initially, strategies such as code-switching/code-mixing and translanguaging were considered signs of poor linguistic ability. There was a strong push towards using only the target language in foreign language education, aiming to limit the first language’s interference and foster a deeper immersion in the new language. However, later research has shown the benefits of incorporating the first language in bilingual education and language learning processes. It’s argued that a student’s knowledge in their native language can actually support their comprehension of a second language, suggesting that transferring certain linguistic or conceptual knowledge from L1 to L2 can be advantageous. This perspective encourages the strategic use of this knowledge transfer in teaching methods. Moreover, the text points to positive results from various studies on the positive impact of L1 usage in L2 classrooms. These insights pave the way for further exploration into the application of the first language in adult English as a Second Language (ESL)/English as a Foreign Language (EFL) education, particularly regarding providing corrective feedback.

Nanoparticle V₂O₅ is prepared by the measurement of X-ray diffraction (XRD) and atomic force microscopy (AFM) analyses. The crystallite size = 19.59 nm, optical energy gap = 2.6 eV, an average particle size of 29.58 nm and, RMS roughness of ~6.8 nm. Also, Fourier transformer infrared spectrophotometer (FTIR) showed a porous free morphology with homogeneity and uniformity on the sample surface. The film surface exhibited no apparent cracking and, the grains exhibited large nicely separated conical columnar growth combined grains throughout the surface with coalescence of some columnar grains at a few places. The fabrication of a thin film of V₂O₅ NPs/PSi heterojunction photodetector was characterized and investigated.