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Purpose – Evaluating the economic effects of climate change is a pivotal step for planning adaptation in developing countries. For Bangladesh, global warming has put it among the most vulnerable countries in the world to climate change, with increasing temperatures and sea-level rise. Hence, the purpose of this paper is to examine how climate change impacts the economy in Bangladesh in the case of climate scenarios. Design/methodology/approach – Using a dynamic computable general equilibrium (CGE) model and three climate change scenarios, this paper assesses the economy-wide implications of climate change on Bangladesh’s economy and agriculture. It is clear from the examination of the CGE model that the impacts of climate change on agricultural sectors were felt more sharply, reducing output by −3.25% and −3.70%, respectively, and increasing imports by 1.22% and 1.53% in 2030 and 2050, compared to the baseline. Findings – The findings reveal that, relative to baseline, agricultural output will decline by a range of −3.1% to −3.6% under the high climate scenario (higher temperatures and lower yields). A decrease in agricultural output results in declines in agricultural labor and household income. Household income falls in all categories, although it drops the most in urban less educated households with a range of −3.1% to −3.4%. On the other hand, consumption of commodities will fall by −0.11% to −0.13%, according to the findings. Although climate change impacts had a relatively small effect on gross domestic product, reducing it by −0.059% and −0.098% in 2030 and 2050, respectively. Practical implications – As agricultural output, household consumption and income decline, it will impact the majority of the population’s health in Bangladesh by increasing malnutrition, hidden hunger, poverty, changing food environment, changing physical and mental health status and a changing health-care environment. Therefore, population health and food security will be a top socioeconomic and political concern for Bangladesh Government. Originality/value – The examination of the dynamic CGE model is its originality. In conclusion, the evidence generated here can provide important information to policymakers and guide government policies that contribute to national development and the achievement of food security targets. It is also necessary to put more emphasis on climate change issues and address potential risks in the following years.

Climate change is widely acknowledged as a serious threat to global development, and Bangladesh is no exception; without a doubt, global warming has placed Bangladesh among the most vulnerable countries. The motivation behind this paper is to examine the effects of climate change by taking into account the changes in temperature and precipitation over time. This paper first evaluates the climate change impact on crop production by using a crop modeling framework and then constructs a dynamic computable general equilibrium (CGE) model. The result of this study finds that average gross domestic product (GDP) decreases by −6.17% and investment declines by −7.76% due to the climate change impact. The impacts of climate change on rice sectors were felt more intensely, increasing prices by 5.82 and 8.11%, reducing output by −3.08 and −3.7% collectively in 2030 and 2050. The agricultural sectors’ output declined more compared to the manufacturing, mining and gas, construction, service and transportation sectors, which indicates declines in agricultural labor and household income. In conclusion, the impact of climate change by analyzing the computable general equilibrium model in Bangladesh had been paid little attention in the past and this paper tried to fill the gaps and provide policy-makers with crucial information and guide government policies. HIGHLIGHTS Develops a dynamic computable general equilibrium (DCGE) model to evaluate how climate change may influence Bangladesh's economy by and large.; Significant decline observed in GDP, consumption, investment, savings, household income, export, and output.; Agricultural sectors shrank more compared to other sectors in the economy.;

Intelligent mobile robots have been gradually used in various fields, including logistics, healthcare, service, and maintenance. Path planning is a crucial aspect of intelligent mobile robot research, which aims to empower robots to create optimal trajectories within complex and dynamic environments autonomously. This study introduces an improved A* algorithm to address the challenges faced by the preliminary A* pathfinding algorithm, which include limited efficiency, inadequate robustness, and excessive node traversal. Firstly, the node storage structure is optimized using a minimum heap to decrease node traversal time. In addition, the heuristic function is improved by adding an adaptive weight function and a turn penalty function. The original 8-neighbor is expanded to a 16-neighbor within the search strategy, followed by the elimination of invalid search neighbor to refine it into a new 8-neighbor according to the principle of symmetry, thereby enhancing the directionality of the A* algorithm and improving search efficiency. Furthermore, a bidirectional search mechanism is implemented to further reduce search time. Finally, trajectory optimization is performed on the planned paths using path node elimination and cubic Bezier curves, which aligns the optimized paths more closely with the kinematic constraints of the robot derivable trajectories. In simulation experiments on grid maps of different sizes, it was demonstrated that the proposed improved A* algorithm outperforms the preliminary A* Algorithm in various metrics, such as search efficiency, node traversal count, path length, and inflection points. The improved algorithm provides substantial value for practical applications by efficiently planning optimal paths in complex environments and ensuring robot drivability. [ABSTRACT FROM AUTHOR]

In the multi-variety and small batch manufacturing workshop, digital twin model is mostly established for specific scenarios. Due to its lack of adaptive ability under working conditions, the prediction accuracy of machining quality is insufficient. To solve this problem, an adaptive transferring method of the digital twin model is proposed. By building the transferable digital twin model, the online prediction of machining quality based on the fusion of mechanism and algorithm model is realized. The transferring process and strategy of the digital twin model are proposed. Based on the analysis and calculation of characteristic data, the source model to be transferred is selected. At the same time, in combination with the transfer learning theory, the transfer of digital twin models is realized under simple and complex changing conditions. Taking drilling as an example, the drilling experiment platform is built and the feasibility of digital twin model transfer is verified. The results show that the model can keep the mean absolute error of prediction less than 1.5% under changing working conditions. This method provides a new idea to improve the adaptive ability of digital twin models.

A review of the relevant research reveals that the concept of “professional growth” is understood to refer to an all-encompassing developmental process that occurs throughout a person’s career as well as their entire life, and it is acknowledged as a phrase that is connected to other ideas. In addition, we can say that human attributes and circumstances are not the only factors that play a role in the process of professional development, but social and institutional experience also play a role in it. After that, this research is shown, which has personal dimensions for beliefs (B), co-workers (CW), culture inheritance (CI), customs (C), information and communications technology (ICT), and learning practice (LP). The research is formed to illustrate that promotion of professional growth in workplaces is connected to the mentioned variables. This study was based on the working experience of different backgrounds people. We tried to check how their attributes could influence their working practices and learning practices. A semi-structured questionnaire was adopted from existing literature and modified as per the study area. B > LP (β = 0.022; p 0.010), CI- > LP (β = 0.368; p 0.000), C- > LP (β = 0.210; p 0.000), and ICT- > LP (β = 0.410; p 0.000) were found to be significant using PLS-SEM direct path analysis.These results offer support to hypotheses H1, H3-H6 accepted and H2 rejected. As control variables, we looked at the respondents’ age, education, and gender. Gender (β = 0.006; p 0.846) showed insignificant relationships with LP, whereas age (β = −0.103; p 0.001) and education (β = −0.072; p 0.034) showed significant relationships with the dependent variable. gender does not have any relationship with learning practices (LP), but age and education can be favorable control variables, and an aged and experienced person can have more impacts on LP and impacts on independent variables.

In the present study, a bibliometric analysis of the published literature on Ecological Niche Modelling (ENM) topic from 1992 to 2023 was carried out using Web of Science (WoS) as a data source. Analysis of the data was carried out using bibliometrix tool in R software. Additionally, VOS Viewer software was used to provide visualization of the bibliometric analysis through network maps. The results of the study revealed that a total of 3, 595 scholarly documents in the English language were published on ENM from 1992-2023, originating from 564 sources. Furthermore, a significant increase in publications was observed over the years. A total of 13,122 authors have contributed to the field, with "Peterson AT" from the University of Kansas being the most prolific author. Journal of Biogeography emerges as the most relevant source with 290 documents published, while Ecography as the most cited source with 8,485 citations. Collaboration analysis revealed that only 109 documents were single-authored, with an average of 5.07 co-authors per document and an international co-authorship rate of 50.96%. Additionally, our analysis shows that the USA leads in Multiple Country Publications (MCP). The development of sophisticated software tools and algorithms coupled with remote sensing data has democratized ENM research, enabling scientists from diverse backgrounds to contribute to the field's growth. However, like any other model ENMs have some limitations and thus the entire procedure used to create an ENM must be reliable, transparent, and repeatable in order for it to be used effectively in conservation, management and decision-making. [ABSTRACT FROM AUTHOR]

Objectives Innate immunity significantly contributes to SSc pathogenesis. TLR8 is an important innate immune mediator that is implicated in autoimmunity and fibrosis. However, the expression, mechanism of action, and pathogenic role of TLR8 in SSc remain unclear. The aim of this study was to explore the roles and underlying mechanisms of TLR8 in SSc. Methods The expression of TLR8 was analysed, based on a public dataset, and then verified in skin tissues and skin fibroblasts of SSc patients. The role of TLR8 in inflammation and fibrosis was investigated using a TLR8-overexpression vector, activator (VTX-2337), inhibitor (cu-cpt-8m), and TLR8 siRNA in skin fibroblasts. The pathogenic role of TLR8 in skin inflammation and fibrosis was further validated in a bleomycin (BLM)-induced mouse skin inflammation and fibrosis model. Results TLR8 levels were significantly elevated in SSc skin tissues and myofibroblasts, along with significant activation of the TLR8 pathway. In vitro studies showed that overexpression or activation of TLR8 by a recombinant plasmid or VTX-2337 upregulated IL-6, IL-1β, COL I, COL III and α-SMA in skin fibroblasts. Consistently, both TLR8-siRNA and cu-cpt-8m reversed the phenotypes observed in TLR8-activating fibroblasts. Mechanistically, TLR8 induces skin fibrosis and inflammation in a manner dependent on the MAPK, NF-κB and SMAD2/3 pathways. Subcutaneous injection of cu-cpt-8m significantly alleviated BLM-induced skin inflammation and fibrosis in vivo. Conclusion TLR8 might be a promising therapeutic target for improving the treatment strategy for skin inflammation and fibrosis in SSc. [ABSTRACT FROM AUTHOR]

Purpose: Climate change and its imminent threat to human survival adversely impact the agriculture sector. In an impoverished country like The Gambia, economic costs of climate change are colossal. This study aims to establish a computable general equilibrium (CGE) model for The Gambia's agriculture sector to examine the effects of climate change on crops, livestock and sea-level rise. Design/methodology/approach: This study used a CGE model with other climate change impact models to compute the impacts of climate change on The Gambia's agriculture sector. The social accounting matrix calibrates the results from the various models, thereby generating the baseline results which exemplify a "steady-state" and policy shock results illustrating the medium- and long-term effects of climate change on the country's agriculture sector. Findings: The baseline results indicate the status quo showing the neglect of the agriculture sector due to limited investment in the sector. Hence, the sector is the "hardest hit" sector as a result of climate change. When the model factored in climate change in the medium term (2055) and long term (2085), the macroeconomic indicators of gross domestic product, national savings, wages, disposable income and consumer price index deteriorated, elucidating the vulnerability of the economy to climate change. The consumption of groundnuts, cattle and fish will decline by 5%, 5% and 4%, respectively, in the long term. However, the production of all agricultural commodities will decline by an average of 35% for the same period. The results for international trade show that exportation would decline while importation will increase over time. The general price level for agricultural commodities would increase by 3% in 2055 and 5% in 2085. Generally, the results manifest the severity of climate change in the agriculture sector which will have a multiplier effect on the economy. The impact of climate change would result in agriculture and economic decline causing hunger, poverty and human misery. Originality/value: The caveat of this study revealed the nuances not captured by previous Gambian climate change studies, thus the novelty of the study. [ABSTRACT FROM AUTHOR]

This study is an effort to investigate the asymmetric effects of privatization and the digital economy on renewable energy consumption. The nonlinear quantile autoregressive distributed lag (QARDL) technique is used to estimate short and long-run analysis. Findings of the nonlinear QARDL model posit that the long-run positive shock in privatization promotes renewable energy consumption by increasing renewable energy consumption, while the long-run negative shock in privatization demotes renewable energy consumption by reducing renewable energy consumption. In the short run, the positive shock of privatization does not significantly impact renewable energy consumption, while the negative shock of privatization reduces renewable energy consumption. Moreover, information and communications technology (ICT), economic development, and financial development increase renewable energy consumption in the long run; however, in the short-run only financial development helps increase renewable energy consumption. The Wald test confirms the asymmetric impact of privatization on renewable energy consumption only in the long run. Based on these results, policymakers should thus take into account both positive and negative shocks in privatization when developing policies to encourage pro-environmental behavior. [ABSTRACT FROM AUTHOR]

Process monitoring of full mass production phase of multistage manufacturing processes (MMPs) has been successfully implemented in many applications; however, monitoring of ramp-up phase of MMPs is often more difficult to conduct due to the limited information to establish valid process control parameters (such as mean and variance). This paper focuses on the estimation of the process control parameters used for monitoring scheme design of ramp-up phase of MMPs. An engineering model of variation propagation of an MMP is developed and reconstructed to a linear model, establishing a relationship between the error sources and the variation of product characteristics. Based on the developed linear model, a two-step Bayesian method is proposed to estimate the process control parameters. The performance of the proposed Bayesian method is validated with simulation data and real-world data, and the results demonstrate that the proposed method can effectively estimate process parameters during ramp-up phase of MMP. [ABSTRACT FROM AUTHOR]