The government of Nepal (GoN) has developed and implemented climate policies, plans and frameworks such as NAPA, National Climate Change Policy, LAPA and currently in the process of developing National Adaptation Plans (NAPs). These policies and plans are in different states of its implementation with diverse opinions and perceptions of the stakeholders. The paper has explored these opinions and experiences of climate experts in Nepal on state of climate policies, inter-linkages, roles and responsibilities of ministries/departments, important factors and subjective indicators for effective implementation of the policies. Altogether 30 experts responded the questionnaire sent via the email, LinkedIn and Skype Interview in the 1^st phase of Delphi research technique. The experiences of these experts range from 2-30 years representing government, non-government sectors, media and independent experts. As per most of the experts interviewed, the policies and plans are progressing in strategic direction with national and local priorities. The LAPA is the pioneer framework to address the local climatic issues, originated in Nepal. However, lack of clarity on roles/responsibilities and coordination among the ministries/departments; clear mechanisms for implementation of these policies; lack of sensitization, decentralization and delegation of finance and technologies; capacity of the stakeholders are the major challenges identified.

Background: The aim of this study was to design and analyze the content validity of an instrument to assess the risk of behaviors associated with the Chemsex phenomenon since there is currently no instrument with this objective. Method: Firstly, a bank of items identified from the literature was elaborated. Secondly, 50 experts with knowledge of the Chemsex phenomenon at the national level were contacted. A Delphi group was formed with them to carry out two rounds of item evaluation (comprehension and appropriateness) using a Likert scale of 1 to 5. To assess linguistic validity, the mean score of the item comprehension scores was used. The content validity index (CVI) and Aiken's V (VdA) were calculated for each item. A minimum value of CVI and VdA of 0.6 was established to include the items in the questionnaire. Results: In the first round of evaluation, 36 experts evaluated 114 items, of which 61 were selected. In a second round, 30 experts evaluated the 61 items selected, leaving the scale finally made up of 52 items. Conclusion: A questionnaire has been designed to assess the risk of behaviors associated with the Chemsex phenomenon. The items that make up the questionnaire have shown adequate content and linguistic validity, so this instrument can be considered valid. The Delphi method proved to be a useful technique for the proposed objective.

In recent times, models like the Transformer have showcased remarkable prowess in tasks related to natural language processing. However, these models tend to be excessively intricate and demand extensive training. Additionally, while the multi-head self-attention mechanism in the Transformer model aims to capture semantic connections between words in a sequence, it encounters limitations when handling short sequences, thereby limiting its effectiveness in 5-polarity Arabic sentiment analysis tasks. The switch-transformer model has recently emerged as a high-performing alternative. Nevertheless, when these models are trained using single-task learning, they often fall short of achieving exceptional performance and struggle to generate robust latent feature representations, especially when working with compact datasets. This challenge is particularly pronounced in the case of the Arabic dialect, which is considered a low-resource language. Given these constraints, this research introduces a novel approach to sentiment analysis in Arabic text. This method leverages multitask learning in tandem with the switch-transformer shared encoder to enhance model adaptability and refine sentence representation. By introducing a mixture of expert (MoE) mechanism that break down the problem into smaller, more manageable sub-problems, the model becomes adept at handling lengthy sequences and intricate input-output relationships, benefiting both five-point and three-polarity Arabic sentiment analysis tasks. This proposed model effectively discerns sentiment in Arabic dialect sentences. The empirical results highlight the outstanding performance of the suggested model, as evidenced in evaluations on the Hotel Arabic-Reviews Dataset, the Book Reviews Arabic Dataset, and the LARB dataset.