The Influence of Culture on the Lure of Choice, Mental Accounting, and Overconfidence.

In the contemporary globalized landscape characterized by international and intercultural decision-making processes, interconnected supply chains, and diverse customer relations, susceptibility to biases and heuristics poses a substantial threat to the efficiency of decision making. This research explores the relatively understudied influence of culture on individuals' susceptibility to concepts derived from behavioral economics. Employing the Individual Cultural Values Scale (CVSCALE), we examine the impact of culture on the allure of choice, mental accounting, and overconfidence among 837 participants from Australia (AU), China (CN), Germany (GE), and the United States (US) through logistic regression analysis. At the individual level, discernible interactions between power-distance, allure of choice, and overconfidence are observed. On the national scale, power-distance (AU, US), uncertainty avoidance (US), and masculinity (CN) significantly impact the allure of choice, while overconfidence is influenced by power-distance (US) and masculinity (US). Our analysis shows that culture plays a pivotal role in shaping susceptibility to biases and heuristics, thereby influencing decision-making processes. The findings advocate for a culturally differentiated approach to behavioral economics, emphasizing the need to tailor strategies and interventions based on cultural nuances.

Review disagreements, cultural capital, and cultural discount on imported hollywood movies in China.

Within the context of mounting competition faced by Hollywood movies in the global cinema market, particularly in emerging markets, this paper examines the influence of review disagreements and cultural capital on the cultural discount of imported Hollywood blockbusters in China's movie market. Using a dataset of 652 movies from the U.S. spanning 1994-2019, we find that review disagreements between U.S. and Chinese consumers exacerbate the cultural discount on imported Hollywood movies in China. However, cultural capital, measured by the accumulated revenue of specific imported movie genres in China, effectively mitigates this discount. We also observe a reduction in the discount for movie genres with fewer language barriers. Accounting for endogeneity due to reverse causality and selection bias, we identify a significant structural break in 2012. In the post-2012 era, collaboration involving China's censorship, quota rules, and film-production laws has improved the market mechanism and cultural capital accumulation, enhancing the performance of imported Hollywood movies in China. Minority movies, characterized by being less mainstream and commercial, are more sensitive to review disagreements, while cultural capital plays a greater role in mitigating the discount for mainstream Hollywood movies. These findings have significant implications for professionals involved in the distribution and screening of Hollywood movies in China.

Polycentric agglomeration and haze pollution: evidence from China.

Polycentric agglomeration has gradually become a salient feature of rapid growth in urbanization in China. Using province-level balanced panel data over the period 2000-18, we examine the impact of polycentric agglomeration on haze pollution and its mechanism of action. The results show that the impact of polycentric agglomeration on haze pollution exhibits a significant inverted U-shaped feature. Nevertheless, except for a few provinces where polycentric agglomeration exceeds the turning point, the degree of polycentric concentration in most provinces lies to the left of the turning point. Further, a mediating effect model illustrates that industrial structure rationalization and technological progress are effective paths through which polycentric agglomeration affects haze pollution. Finally, we demonstrate that the effect of polycentric agglomeration on haze pollution is influenced by transportation and communication infrastructure; improved transportation and communication infrastructure contributes to the haze control effect of polycentric agglomeration.

Knowledge graph-based convolutional network coupled with sentiment analysis towards enhanced drug recommendation.

Recommending appropriate drugs to patients based on their history and symptoms is a complex real-world problem. Knowing whether a drug is useful without its consumption by a variety of people followed by proper evaluation is a challenge. Modern-day recommender systems can assist in this provided they receive large data to learn. Public reviews on various drugs are available for knowledge sharing. These reviews assist in recommending the best and most appropriate option to the user. The explicit feedback underpins the entire recommender system. This work develops a novel knowledge graph-based convolutional network for recommending drugs. The knowledge graph is coupled with sentiment analysis extracted from the public reviews on drugs to enhance drug recommendations. For each drug that has been used previously, sentiments have been analyzed to determine which one has the most effective reviews. The knowledge graph effectively captures user-item relatedness by mining its associated attributes. Experiments are performed on public benchmarks and a comparison is made with closely related state-of-the-art works. Based on the obtained results, the current work performs better than the past contributions by achieving up to 98.7% Area Under Curve (AUC) score.

The Impact of the "Belt and Road" Initiative on Accounting Conservatism of Energy-Intensive Enterprises under the Low-Carbon Background.

In recent years, the "Belt and Road" Initiative (BRI) has paid more and more attention to practicing the concept of green development. The concept of green development in the BRI will help promote the active response to climate change in the regions along the route and maintain global ecological security and is of great significance to the green transformation and development of energy-intensive enterprises. Using company-level data from China over the 2011-2020 period, we provide a comprehensive analysis of the impact of the BRI on energy-intensive enterprise accounting conservatism. We find that BRI has decreased the energy-intensive enterprise accounting conservatism, and this result continues to hold after a series of robustness tests. We also examine the effect of the RBI on accounting conservatism across company types and ages and find that the BRI is beneficial to energy-intensive state-owned enterprises (SOEs) and young companies. Furthermore, analysis reveals that BRI changes the accounting conservatism of energy-intensive enterprises mainly through debt financing, tax burden, and legal environment channels.

Genotypic and phenotypic profiles of antibiotic-resistant bacteria isolated from hospitalised patients in Bangladesh.

OBJECTIVES: Characterisation of resistance phenotype and genotype is crucial to understanding the burden and transmission of antimicrobial resistance (AMR). This study aims to determine the spectrum of AMR and associated genes encoding aminoglycoside, macrolide and ß-lactam classes of antimicrobials in bacteria isolated from hospitalised patients in Bangladesh. METHODS: 430 bacterial isolates from patients with respiratory, intestinal, wound infections and typhoid fever, presenting to clinical care from 2015 to 2019, were examined. They included Escherichia coli (n = 85); Staphylococcus aureus (n = 84); Salmonella typhi (n = 82); Klebsiella pneumoniae (n = 42); Streptococcus pneumoniae (n = 36); coagulase-negative staphylococci (n = 28); Enterococcus faecalis (n = 27); Pseudomonas aeruginosa (n = 26); and Acinetobacter baumannii (n = 20). Reconfirmation of these clinical isolates and antimicrobial susceptibility tests was performed. PCR amplification using resistance gene-specific primers was done, and the amplified products were confirmed by Sanger sequencing. RESULTS: 53% of isolates were multidrug-resistant (MDR), including 97% of Escherichia coli. There was a year-wise gradual increase in MDR isolates from 2015 to 2018, and there was an almost twofold increase in the number of MDR strains isolated in 2019 (P = 0.00058). Among the 5 extended-spectrum ß-lactamases investigated, CTX-M-1 was the most prevalent (63%) followed by NDM-1 (22%); Escherichia coli was the major reservoir of these genes. The ermB (55%) and aac(6')-Ib (35%) genes were the most frequently detected macrolide and aminoglycoside resistance genes, respectively. CONCLUSION: MDR pathogens are highly prevalent in hospital settings of Bangladesh.

Supply chain carbon emission reductions and coordination when consumers have a strong preference for low-carbon products.

Owing to the rising concerns about environmental degradation worldwide, firms in several developed and developing countries are pursuing carbon emission reduction targets. In addition, in recent years, there is evidence of a shift in consumer preferences in favour of low-carbon products. Using a theoretical model, where the shift in consumer preferences is explicitly incorporated, we evaluate the impact of carbon emission reduction cost-sharing on supply chain profit. In our model, consumers are willing to pay a higher price for low-carbon products and hence the retailer considers sharing the cost of carbon emission reduction with the manufacturer. Our model also includes a carbon trading mechanism. We identify a range of carbon emission reduction cost-sharing such that both supply chain enterprises are better-off. We find that, while achieving the aim of carbon emission reduction, consumer preference for low-carbon products can benefit both supply chain enterprises. Numerical simulations show that carbon emission reduction cost-sharing increases the retailer's order quantity as well as the profit and hence there is an incentive for the two supply chain enterprises to cooperate.

COVID-19 Patient Count Prediction Using LSTM.

In December 2019, a pandemic named COVID-19 broke out in Wuhan, China, and in a few weeks, it spread to more than 200 countries worldwide. Every country infected with the disease started taking necessary measures to stop the spread and provide the best possible medical facilities to infected patients and take precautionary measures to control the spread. As the infection spread was exponential, there arose a need to model infection spread patterns to estimate the patient volume computationally. Such patients' estimation is the key to the necessary actions that local governments may take to counter the spread, control hospital load, and resource allocations. This article has used long short-term memory (LSTM) to predict the volume of COVID-19 patients in Pakistan. LSTM is a particular type of recurrent neural network (RNN) used for classification, prediction, and regression tasks. We have trained the RNN model on Covid-19 data (March 2020 to May 2020) of Pakistan and predict the Covid-19 Percentage of Positive Patients for June 2020. Finally, we have calculated the mean absolute percentage error (MAPE) to find the model's prediction effectiveness on different LSTM units, batch size, and epochs. Predicted patients are also compared with a prediction model for the same duration, and results revealed that the predicted patients' count of the proposed model is much closer to the actual patient count.

Computational and statistical modeling for parameters optimization of electrochemical decontamination of synozol red dye wastewater.

In this study, computational and statistical models were applied to optimize the inherent parameters of an electrochemical decontamination of synozol red. The effect of various experimental variables such as current density, initial pH and concentration of electrolyte on degradation were assessed at Ti/RuO0·3TiO0·7O2 anode. Response surface methodology (RSM) based central composite design was applied to investigate interdependency of studied variables and train an artificial neural network (ANN) to envisage the experimental training data. The presence of fifteen neurons proved to have optimum performance based on maximum R2, mean absolute error, absolute average deviation and minimum mean square error. In comparison to RSM and empirical kinetics models, better prediction and interpretation of the experimental results were observed by ANN model. The sensitive analysis revealed the comparative significance of experimental variables are pH = 61.03%>current density = 17.29%>molar concentration of NaCl = 12.7%>time = 8.98%. The optimized process parameters obtained from genetic algorithm showed 98.6% discolorization of dye at pH 2.95, current density = 5.95 mA cm-2, NaCl of 0.075 M in 29.83 min of electrolysis. The obtained results revealed that the use of statistical and computational modeling is an adequate approach to optimize the process variables of electrochemical treatment.

Climate Change and Infectious Diseases: Evidence from Highly Vulnerable Countries.

BACKGROUND: Climate change is an alarming challenge for humanity at large due to its mediating role in emergence and spread of infectious diseases like cholera and malaria. This study was conducted to examine the effect of climate change and some socio-economic factors on incidence of infectious diseases. METHODS: We used country level panel data over the 1990-2017 period using panel ARDL-PMG technique on highly affected countries from climate change. RESULTS: There is a long run co-integrating relationship among climate change, socio-economic factors and prevalence of infectious diseases. Climate change, as measured by the temperature, is contributing to the spread of infectious diseases. CONCLUSION: This is the first study giving evidence of the impact of climate change on incidence of infectious diseases as can be seen from highly vulnerable countries to climate change. It is recommended to improve the level of education along with public health and town planning to reduce the incidence of infectious diseases.

The environmental Kuznets curve relationship: a case study of the Gulf Cooperation Council region.

By using panel data over the 1980-2017 period and two alternative indicators of environmental pollution (carbon dioxide (CO2) and sulfur dioxide (SO2) emissions), this paper investigates the validity of the environmental Kuznets curve (EKC) hypothesis in the Gulf Cooperation Council (GCC) region. Using three alternative econometric estimation techniques, we find strong evidence of a long-run inverted U-shaped relationship between real GDP per capita and both environmental indicators in the GCC region. Country-level short-run analysis indicates that the EKC hypothesis holds for Bahrain, Kuwait, Qatar, Saudi Arabia, and the United Arab Emirates (UAE) when CO2 emissions are used as a measure of environmental pollution. However, when SO2 emissions are used as a measure of environmental pollution, the EKC hypothesis holds for Oman, Qatar, Saudi Arabia, and the UAE. The results of a recently developed panel causality test reveal one-way causality from real GDP per capita to CO2 emissions and from real GDP per capita to SO2 emissions.

A novel rules based approach for estimating software birthmark.

Software birthmark is a unique quality of software to detect software theft. Comparing birthmarks of software can tell us whether a program or software is a copy of another. Software theft and piracy are rapidly increasing problems of copying, stealing, and misusing the software without proper permission, as mentioned in the desired license agreement. The estimation of birthmark can play a key role in understanding the effectiveness of a birthmark. In this paper, a new technique is presented to evaluate and estimate software birthmark based on the two most sought-after properties of birthmarks, that is, credibility and resilience. For this purpose, the concept of soft computing such as probabilistic and fuzzy computing has been taken into account and fuzzy logic is used to estimate properties of birthmark. The proposed fuzzy rule based technique is validated through a case study and the results show that the technique is successful in assessing the specified properties of the birthmark, its resilience and credibility. This, in turn, shows how much effort will be required to detect the originality of the software based on its birthmark.