The government’s increased cigarette tariff aims to lower smoking rates and avoid adverse impacts. This study’s goal was to offer process innovation for lowering Asian’ smoking behavior. The participants were chosen by stratified random selection from a total of 738 people residing in Pathum Thani Province, Thailand. The instrument was a questionnaire. A software programmer was used to examine descriptive and inferential statistics using EFA and one-way ANOVA techniques. A strategic framework guideline using a SWOT analysis and TOWS matrix to encourage smoking reduction was proposed. The findings revealed two components: smoking behavior change and continues smoking that were based on SWOT analysis and TOWs matrix. There were nine strategies for the excise department to consider for the adjustment of the next policy in terms of reducing the number of smokers. The practical and policy suggestions could help reduce the negative impact of the cigarette industry on public health and increase government revenue while addressing weaknesses and threats in the industry.

In this paper, an improved mathematical model for flashover behavior of polluted insulators is proposed based on experimental tests. In order to determine the flashover model of polluted insulators, the relationship between conductivity and salinity of solution pollution layer of the insulator is measured. Then, the leakage of current amplitude of four common insulators versus axial, thermal conductivity and arc constants temperature was determined. The experimental tests show that top leakage distance (TLd) to bottom leakage distance (BLd) ratio of insulators has a significant effect on critical voltage and current. Therefore, critical voltage and current were modeled by TLd to BLd ratio Index (M). Also, salinity of solution pollution layer of the insulators has been applied to this model by resistance pollution parameter. On the other hand, arc constants of each insulator in new model have been identified based on experimental results. Finally, a mathematical model is intended for critical voltage against salinity of solution pollution layer of different insulators. This model depends on insulator profile. There is a good agreement between the experimental tests of pollution insulators obtained in the laboratory and values calculated from the mathematical models developed in the present study.

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.

Four alloys based on niobium and containing about 33wt.%Cr, 0.4wt.C and, in atomic content equivalent to the carbon one, Ta, Ti, Hf or Zr, were elaborated by classical foundry under inert atmosphere. Their as-cast microstructures were characterized by X-ray diffraction, electron microscopy, energy dispersion spectrometry and while their room temperature hardness was specified by Vickers indentation. The microstructures are in the four cases composed of a dendritic Nb-based solid solution and of an interdendritic NbCr₂ Laves phase. Despite the MC-former behavior of Ta, Ti, Hf and Zr usually observed in nickel or cobalt-based alloys, none of the four alloys contain MC carbides. Carbon is essentially visible as graphite flakes. These alloys are brittle at room temperature and hard to machine. Indentation shows that the Vickers hardness is very high, close to 1000HV_10kg. Indentation lead to crack propagation through the niobium phase and the Laves areas. Obviously no niobium-based alloys microstructurally similar to high performance MC-strengthened nickel-based and cobalt-based can be expected. However the high temperature mechanical and chemical properties of these alloys remain to be investigated.

This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.

The root of the problem in this research is the fact that scientific writing with a national reputation is still low and the publication of scientific writing with a national reputation is also low, thus affecting the quality of lecturers at the University. To overcome this problem, this research developed a training management model that can improve the scientific writing skills of lecturers and familiarize lecturers to actively conduct nationally reputable scientific writing. The training management model in question is called the “National Reputable Scientific Writing Training Management” model. This type of research is development research or R&D to produce a valid, practical, and effective model, as well as all devices and research instruments related to the application of the model at the University. The results showed that: (1) the National Reputable Scientific Writing Training Management model is suitable for improving the scientific writing ability of lecturers; (2) the output of the National Reputable Scientific Writing Training Management model in the model group is significantly higher than the initial group (pre-model); (3) The average value of IP/IO from experts is 4.4 with a high category, from observers at stage I test is 4.0 with a high category, at stage II test is 4.7 with a high category and stage III test is 4.77 with a high category, so it is concluded that the National Reputable Scientific Writing Training Management model meets the criteria of effectiveness, practicality and implementation; (4) The response of university managers and respondents to the implementation of the model is quite satisfactory, both regarding the concept of the model, the application in technical implementation and their perception of the National Reputable Scientific Writing Training Management model; and (5) the National Reputable Scientific Writing Training Management model can be developed as an alternative implementation in training management at the university.