Publication behaviour and(dis)qualification of chief editors in Turkish national Social Sciences journals

Purpose:This study investigated the publication behaviour of 573 chief editors managing 432 Social Sciences journals in Turkey.Direct inquiries into editorial qualifications are rare,and this research aims to shed light on editors’scientific leadership capabilities.Design/methodology/approach:This study contrasts insider publication behaviour in national journals with international articles in journals indexed by the Web of Science(WOS)and Scopus.It argues that editors demonstrating a consistent ability to publish in competitive WOS and Scopus indexed journals signal high qualifications,while editors with persistent insider behaviour and strong local orientation signal low qualification.Scientific leadership capability is measured by first-authored publications.Correlation and various regression tests are conducted to identify significant determinants of publication behaviour.Findings:International publications are rare and concentrated on a few individuals,while insider publications are endemic and constitute nearly 40%of all national articles.Editors publish 3.2 insider papers and 8.1 national papers for every SSCI article.62%(58%)of the editors have no SSCI(Scopus)article,53%(63%)do not have a single lead-authored WOS(Scopus)article,and 89%publish at least one insider paper.Only a minority consistently publish in international journals;a fifth of the editors have three or more SSCI publications,and a quarter have three or more Scopus articles.Editors with foreign Ph.D.degrees are the most qualified and internationally oriented,whereas non-mobile editors are the most underqualified and underperform other editors by every measure.Illustrating the overall lack of qualification,nearly half of the professor editors and the majority of the WOS and Scopus indexed journal editors have no record of SSCI or Scopus publications.Research limitations:This research relies on local settings that encourage national publications at the expense of international journals.Findings should be evaluated in light of this setting and bearing in mind that narrow localities are more prone to peer favouritism.Practical implications:Incompetent and nepotistic editors pose an imminent threat to Turkish national literature.A lasting solution would likely include the dismissal and replacement of unqualified editors,as well as delisting and closure of dozens of journals that operate in questionable ways and serve little scientific purpose.Originality/value:To my knowledge,this is the first study to document the publication behaviour of national journal chief editors.

The Lambert-G Family:Properties,Inference,and Applications

This study proposes a new flexible family of distributions called the Lambert-G family.The Lambert family is very flexible and exhibits desirable properties.Its three-parameter special sub-models provide all significantmonotonic and non-monotonic failure rates.A special sub-model of the Lambert family called the Lambert-Lomax(LL)distribution is investigated.General expressions for the LL statistical properties are established.Characterizations of the LL distribution are addressed mathematically based on its hazard function.The estimation of the LL parameters is discussed using six estimation methods.The performance of this estimation method is explored through simulation experiments.The usefulness and flexibility of the LL distribution are demonstrated empirically using two real-life data sets.The LL model better fits the exponentiated Lomax,inverse power Lomax,Lomax-Rayleigh,power Lomax,and Lomax distributions.

Bayesian and Non-Bayesian Analysis for the Sine Generalized Linear Exponential Model under Progressively Censored Data

This article introduces a novel variant of the generalized linear exponential(GLE)distribution,known as the sine generalized linear exponential(SGLE)distribution.The SGLE distribution utilizes the sine transformation to enhance its capabilities.The updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability issues.The suggested model incorporates a hazard rate function(HRF)that may display a rising,J-shaped,or bathtub form,depending on its unique characteristics.This model includes many well-known lifespan distributions as separate sub-models.The suggested model is accompanied with a range of statistical features.The model parameters are examined using the techniques of maximum likelihood and Bayesian estimation using progressively censored data.In order to evaluate the effectiveness of these techniques,we provide a set of simulated data for testing purposes.The relevance of the newly presented model is shown via two real-world dataset applications,highlighting its superiority over other respected similar models.

Measuring Causal Effect with ARDL-BART: A Macroeconomic Application

Modeling dynamic systems with linear parametric models usually suffer limitation which affects forecasting performance and policy implications. This paper advances a non-parametric autoregressive distributed lag model that employs a Bayesian additive regression tree (BART). The performance of the BART model is compared with selection models like Lasso, Elastic Net, and Bayesian networks in simulation experiments with linear and non-linear data generating processes (DGP), and on US macroeconomic time series data. The results show that the BART model is quite competitive against the linear parametric methods when the DGP is linear, and outperforms the competing methods when the DGP is non-linear. The empirical results suggest that the BART estimators are generally more efficient than the traditional linear methods when modeling and forecasting macroeconomic time series.

Adherence to Advisory Committee on Immunization Practices in diabetes mellitus patients in Saudi Arabia:A multicenter retrospective study

BACKGROUND Patients with diabetes mellitus(DM)are predisposed to an increased risk of infection signifying the importance of vaccination to protect against its potentially severe complications.The Centers for Disease Control and Prevention/Advisory Committee on Immunization Practices(CDC/ACIP)issued immunization recommendations to protect this patient population.AIM To assess the adherence of patients with DM to the CDC/ACIP immunization recommendations in Saudi Arabia and to identify the factors associated with the vaccine adherence rate.METHODS An observational retrospective study conducted in 2023 was used to collect data on the vaccination records from 13 diabetes care centers in Saudi Arabia with 1000 eligible patients in phase I with data collected through chart review and 709 patients in phase II through online survey.RESULTS Among participants,10.01%(n=71)had never received any vaccine,while 85.89%(n=609)received at least one dose of the coronavirus disease 2019(COVID-19)vaccine,and 34.83%(n=247)had received the annual influenza vaccine.Only 2.96%(n=21),2.11%(n=15),and 1.12%(n=8)received herpes zoster,tetanus,diphtheria,and pertussis(Tdap),and human papillomavirus(HPV)vaccines,respectively.For patients with DM in Saudi Arabia,the rate of vaccination for annual influenza and COVID-19 vaccines was higher compared to other vaccinations such as herpes zoster,Tdap,pneumococcal,and HPV.Factors such as vaccine recommendations provided by family physicians or specialists,site of care,income level,DM-related hospitalization history,residency site,hemoglobin A1c(HbA1c)level,and health sector type can significantly influence the vaccination rate in patients with DM.Among non-vaccinated patients with DM,the most reported barriers were lack of knowledge and fear of side effects.This signifies the need for large-scale research in this area to identify additional factors that might facilitate adherence to CDC/ACIP vaccine recommendations in patients with DM.CONCLUSION In Saudi Arabia,patients with DM showed higher vaccination rates for annual influenza and COVID-19 vaccines compared to other vaccinations such as herpes zoster,Tdap,pneumococcal,and HPV.Factors such as vaccine recommendations provided by family physicians or specialists,the site of care,income level,DM-related hospitalization history,residency site,HbA1c level,and health sector type can significantly influence the vaccination rate in patients with DM.

Practical guide:Glucagon-like peptide-1 and dual glucosedependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonists in diabetes mellitus

Practical guide:Glucagon-like peptide-1 and dual glucosedependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonists in diabetes mellitus common second-line choice after metformin for treating T2DM.Various considerations can make selecting and switching between different GLP-1 RAs challenging.Our study aims to provide a comprehensive guide for the usage of GLP-1 RAs and dual GIP and GLP-1 RAs for the management of T2DM.

Editorial board publication strategy and acceptance rates in Turkish national journals

Purpose: This study takes advantage of newly released journal metrics to investigate whether local journals with more qualified boards have lower acceptance rates, based on data from 219 Turkish national journals and 2,367 editorial board members.Design/methodology/approach: This study argues that journal editors can signal their scholarly quality by publishing in reputable journals. Conversely, editors publishing inside articles in affiliated national journals would send negative signals. The research predicts that high(low) quality editorial boards will conduct more(less) selective evaluation and their journals will have lower(higher) acceptance rates. Based on the publication strategy of editors, four measures of board quality are defined: Number of board inside publications per editor(INSIDER), number of board Social Sciences Citation Index publications per editor(SSCI), inside-to-SSCI article ratio(ISRA), and board citation per editor(CITATION). Predictions are tested by correlation and regression analysis.Findings: Low-quality board proxies(INSIDER, ISRA) are positively, and high-quality board proxies(SSCI, CITATION) are negatively associated with acceptance rates. Further, we find that receiving a larger number of submissions, greater women representation on boards, and Web of Science and Scopus(WOSS) coverage are associated with lower acceptance rates. Acceptance rates for journals range from 12% to 91%, with an average of 54% and a median of 53%. Law journals have significantly higher average acceptance rate(68%) than other journals, while WOSS journals have the lowest(43%). Findings indicate some of the highest acceptance rates in Social Sciences literature, including competitive Business and Economics journals that traditionally have low acceptance rates. Limitations: Research relies on local context to define publication strategy of editors. Findings may not be generalizable to mainstream journals and core science countries where emphasis on research quality is stronger and editorial selection is based on scientific merit.Practical implications: Results offer useful insights into editorial management of national journals and allow us to make sense of local editorial practices. The importance of scientific merit for selection to national journal editorial boards is particularly highlighted for sound editorial evaluation of submitted manuscripts.Originality/value: This is the first attempt to document a significant relation between acceptance rates and editorial board publication behavior.

Blockchain technology‑based FinTech banking sector involvement using adaptive neuro‑fuzzy‑based K‑nearest neighbors algorithm

The study aims to investigate the financial technology(FinTech)factors influencing Chinese banking performance.Financial expectations and global realities may be changed by FinTech’s multidimensional scope,which is lacking in the traditional financial sector.The use of technology to automate financial services is becoming more important for economic organizations and industries because the digital age has seen a period of transition in terms of consumers and personalization.The future of FinTech will be shaped by technologies like the Internet of Things,blockchain,and artificial intelligence.The involvement of these platforms in financial services is a major concern for global business growth.FinTech is becoming more popular with customers because of such benefits.FinTech has driven a fundamental change within the financial services industry,placing the client at the center of everything.Protection has become a primary focus since data are a component of FinTech transactions.The task of consolidating research reports for consensus is very manual,as there is no standardized format.Although existing research has proposed certain methods,they have certain drawbacks in FinTech payment systems(including cryptocurrencies),credit markets(including peer-to-peer lending),and insurance systems.This paper implements blockchainbased financial technology for the banking sector to overcome these transition issues.In this study,we have proposed an adaptive neuro-fuzzy-based K-nearest neighbors’algorithm.The chaotic improved foraging optimization algorithm is used to optimize the proposed method.The rolling window autoregressive lag modeling approach analyzes FinTech growth.The proposed algorithm is compared with existing approaches to demonstrate its efficiency.The findings showed that it achieved 91%accuracy,90%privacy,96%robustness,and 25%cyber-risk performance.Compared with traditional approaches,the recommended strategy will be more convenient,safe,and effective in the transition period.

Artificial neural network analysis of the day of the week anomaly in cryptocurrencies

Anomalies,which are incompatible with the efficient market hypothesis and mean a deviation from normality,have attracted the attention of both financial investors and researchers.A salient research topic is the existence of anomalies in cryptocurrencies,which have a different financial structure from that of traditional financial markets.This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market,which is hard to predict.It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods.An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies.On October 6,2021,Bitcoin(BTC),Ethereum(ETH),and Cardano(ADA),which are the top three cryptocurrencies in terms of market value,were selected for this study.The data for the analysis,consisting of the daily closing prices for BTC,ETH,and ADA,were obtained from the Coinmarket.com website from January 1,2018 to May 31,2022.The effectiveness of the established models was tested with mean squared error,root mean squared error,mean absolute error,and Theil’s U1,and R2 OOS was used for out-of-sample.The Diebold–Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models.When the models created with feedforward artificial neural networks are examined,the existence of the day-of-the-week anomaly is established for BTC,but no day-of-the-week anomaly for ETH and ADA was found.

Economic complexity and environmental sustainability in eastern European economies:Evidence from novel Fourier approach

Globally,economies have become complex and new technologies have transformed and facilitated the modernization of economies.In the previous literature,economic complexity approach has become one of the popular tools in the development and innovation studies of economic geography.Researchers have found that green technology and eco-innovation approaches should be used to decisively reduce the effects of carbon emissions on the environment.However,debates about the impact of economic complexity on environment remain unsettled since some emerging production technologies have far-reaching pollution effects.This study explored the impacts of economic complexity on environmental sustainability in Turkey using the novel Fourier-based approaches,namely:Fourier Augmented Dickey-Fuller(FADF)and Fourier Autoregressive-Distributed Lag(FARDL)models.The Fourier-based approaches indicated that all variables(economic complexity index(ECI),GDP,energy consumption,and CO_(2)emission(CO_(2)E))are cointegrated in the long run.Additionally,the FARDL model implied that(i)in the long run,the effect of ECI(as a proxy for economic complexity),GDP(as a proxy for economic growth),and energy consumption on CO_(2)E(as a proxy for environmental quality)are important;(ii)economic complexity decreases environmental degradation in Turkey;and(iii)economic growth and energy consumption negatively affect environmental quality.The results also showed that economic complexity could be used as a policy tool to tackle environmental degradation.The findings also revealed that the fossil fuelbased economy will continue to expand and undermine Turkey’s efforts to meet its net zero emission target by 2053.Therefore,policy-makers should take actions and establish diversified economic,environmental,and energy strategies.For policy insights,the Turkish governments can use the combination of tax exemptions and technical support systems to support knowledge creation and the diffusion of environmentally friendly technologies The governments can also impose strict environmental regulations on the knowledge development phases.

Does country risk impact the banking sectors’non‑performing loans?Evidence from BRICS emerging economies

This study aims to fill the gap in the literature by specifically investigating the impact of country risk on the credit risk of the banking sectors operating in Brazil,Russia,India,China,and South Africa(BRICS),emerging countries.More specifically,we explore whether the country-specific risks,namely financial,economic,and political risks significantly impact the BRICS banking sectors’non-performing loans and also probe which risk has the most outstanding effect on credit risk.To do so,we perform panel data analysis using the quantile estimation approach covering the period 2004–2020.The empirical results reveal that the country risk significantly leads to increasing the banking sector’s credit risk and this effect is prominent in the banking sector of countries with a higher degree of non-performing loans(Q.25=−0.105,Q.50=−0.131,Q.75=−0.153,Q.95=−0.175).Furthermore,the results underscore that an emerging country’s political,economic,and financial instabilities are strongly associated with increasing the banking sector’s credit risk and a rise in political risk in particular has the most positive prominent impact on the banking sector of countries with a higher degree of non-performing loans(Q.25=−0.122,Q.50=−0.141,Q.75=−0.163,Q.95=−0.172).Moreover,the results suggest that,in addition to the banking sectorspecific determinants,credit risk is significantly impacted by the financial market development,lending interest rate,and global risk.The results are robust and have significant policy suggestions for many policymakers,bank executives,researchers,and analysts.

An Optimized Method for Accounting Information in Logistic Systems

In the era of rapid information development,with the popularity of computers,the advancement of science and technology,and the ongoing expansion of IT technology and business,the enterprise resource planning(ERP)system has evolved into a platform and a guarantee for the fulfilment of company management procedures after long-term operations.Because of developments in information technology,most manual accounting procedures are being replaced by computerized Accounting Information Systems(AIS),which are quicker and more accurate.The primary factors influencing the decisions of logistics firm trading parties are investigated in order to enhance the design of decision-supporting modules and to improve the performance of logistics enterprises through AIS.This paper proposed a novel approach to calculate the weights of each information element in order to establish their important degree.The main purpose of this research is to present a quantitative analytic approach for determining the important information of logistics business collaboration response.Furthermore,the idea of total orders and the significant degrees stated above are used to identify the optimal order of all information elements.Using the three ways of marginal revenue,marginal cost,and business matching degree,the information with cumulative weights is which is deployed to form the data from the intersection of the best order.It has the ability to drastically reduce the time and effort required to create a logistics business control/decision-making system.

Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 protocol.Due to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate users.Moreover,the security design of the wireless structure is vulnerable to versatile attacks.For example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false messages.Althoughmany security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world applications.This paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks effectively.The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack.The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in theWLAN.The proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related work.The experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

Impact of ICTs on Core Competences of SMEs in Bukavu, DRC

Currently, it has been noticed a strong use of ICTs in several companies. The objective of this study was to analyze and measure the impact of the implementation of ICTs on the strategy and structure of management of core competence in SMEs in Bukavu, DRC. The scale used shows the good psychometric properties (χ2 = 75.045;P = 0.16;χ2/ddl = 1.471;CFI = 0.92;IFI = 0.92;RMSEA = 0.049;NFI = 0.79;GFI = 0.94;AGFI = 0.91. This study analyzed primary data collected from 195 SMEs in the city of Bukavu. Data processing was achieved using exploratory and confirmatory factor analysis and flat sorting. The results indicate that the use of ICTs by SME managers in the city of Bukavu has a positive impact on all dimensions of core competence selected in this study. They also reveal that the views of these leaders on the importance attached to the adoption of ICTs were divided and the level of ICT use was above average.

Climate Change Perceptions , Impacts and Adaptation Strategies of F arm Households in Potohar Region of Punjab, Pakistan

Climate change has become a global phenomenon and is adversely affecting agricultural development across the globe.Developing countries like Pakistan where 18.9%of the GDP(gross domestic product)came from the agriculture sector and also 42%of the labor force involved in agriculture.They are directly and indirectly affected by climate change due to an increase in the frequency and intensity of climatic extreme events such as floods,droughts and extreme weather events.In this paper,we have focused on the impact of climate change on farm households and their adaptation strategies to cope up the climatic extremes.For this purpose,we have selected farm households by using multistage stratified random sampling from four districts of the Potohar region i.e.Attock,Rawalpindi,Jhelum and Chakwal.These districts were selected by dividing the Potohar region into rain-fed areas.We have employed logistic regression to assess the determinants of adaptation to climate change and its impact.We have also calculated the marginal effect of each independent variable of the logistic regression to measure the immediate rate of change in the model.In order to check the significance of our suggested model,we have used hypothesis testing.

Linking Competitors’ Knowledge and Developing Innovative Products Using Data Mining Techniques

In this article, the relationship between the knowledge of competitors and the development of new products in the field of capital medical equipment has been investigated. In order to identify the criteria for measuring competitors’ knowledge and developing new capital medical equipment products, marketing experts were interviewed and then a researcher-made questionnaire was compiled and distributed among the statistical sample of the research. Also, in order to achieve the goals of the research, a questionnaire among 100 members of the statistical community was selected, distributed and collected. To analyze the gathered data, the structural equation modeling (SEM) method was used in the SMART PLS 2 software to estimate the model and then the K-MEAN approach was used to cluster the capital medical equipment market based on the knowledge of actual and potential competitors. The results have shown that the knowledge of potential and actual competitors has a positive and significant effect on the development of new products in the capital medical equipment market. From the point of view of the knowledge of actual competitors, the market of “MRI”, “Ultrasound” and “SPECT” is grouped in the low knowledge cluster;“Pet MRI”, “CT Scan”, “Mammography”, “Radiography, Fluoroscopy and CRM”, “Pet CT”, “SPECT CT” and “Gamma Camera” markets are clustered in the medium knowledge. Finally, “Angiography” and “CBCT” markets are located in the knowledge cluster. From the perspective of knowledge of potential competitors, the market of “angiography”, “mammography”, “SPECT” and “SPECT CT” in the low knowledge cluster, “CT scan”, “radiography, fluoroscopy and CRM”, “pet CT”, “CBCT” markets in the medium knowledge cluster and “MRI”, “pet MRI”, “ultrasound” and “gamma camera” markets in the high knowledge cluster are located.

Pulmonary involvement in inflammatory bowel disease

AIM:To determine the relationship of pulmonary abnormalities and bowel disease activity in inflammatory bowel disease(IBD).METHODS:Thirty ulcerative colitis(UC)and nine Crohn's disease patients,and 20 control subjects were enrolled in this prospective study.Detailed clinical information was obtained.Extent and activity of the bowel disease were established endoscopically.Each patient underwent pulmonary function tests and high-resolution computed tomography(HRCT).Blood samples for measurement of C-reactive protein(CRP),erythrocyte sedimentation rate(ESR),angiotensin converting enzyme and total IgE were delivered by the patients.RESULTS:Ten(25.6%)patients had respiratory symptoms.A pulmonary function abnormality was present in 22 of 39 patients.Among all patients,the most prevalent abnormalities in lung functions were a decrease in forced expiratory volume in 1 s(FEV1),FEV1/forced vital capacity(FVC),forced expiratory flow(FEF)25%-75%,transfer coefficient for carbon monoxide(DLCO),DLCO/alveolar volume.Increased respiratory symptoms score was associated with high endoscopic activity index in UC patients.Endoscopic and clinical activities in UC patients were correlated with FEV1,FEV1/FVC,and FEF 25%-75%.Smoking status,duration of disease and medication were not correlated with pulmonary physiological test results,HRCT abnormalities,clinical/endoscopic disease activity,CRP,ESR or total IgE level or body mass index.CONCLUSION:It is important that respiratory manifestations are recognized and treated early in IBD.Otherwise,they can lead to destructive and irreversible changes in the airway wall.

中国胃癌死亡率趋势（2003–2015）：一项基于人群的研究

背景与目的在过去几十年里,世界上多数国家的胃癌死亡率大幅下降。本研究旨在评估中国胃癌死亡率的最新趋势,并分析中国农村和城市地区胃癌死亡率的差异。方法胃癌按性别、年龄组和地域划分的粗死亡率数据来源于覆盖中国10%人口的《中国卫生统计年鉴(2003–2015)》。采用2010年中国人口普查按年龄、性别和地区划分的人群,估算城乡胃癌的年龄标准化死亡率(age-standardized rates of mortality,ASRM)。采用Joinpoint分析法评估胃癌死亡率趋势。结果在13年间,农村地区的ASRM从2003年的31.5/100,000降至2015年的20.9/100,000,城市地区从2003年的18.9/100,000降至2015年的14.5/100,000。在男性中,城市地区的死亡率年度百分比变化为-2.2%[95%置信区间(CI):-3.8%–-0.6%;P<0.001],农村地区的死亡率年度百分比变化为-3.4%(95%CI:-5.1%–-1.8%;P<0.001)。在女性中,城市地区的死亡率年度百分比变化为-2.7%(95%CI:-4.2%–-1.2%;P<0.001),农村地区的死亡率年度百分比变化为-4.6%(95%CI:-5.5%–-3.7%;P<0.001)。结论2003年至2015年,我国农村和城市的胃癌死亡率均呈下降趋势。中国农村地区胃癌死亡率的下降幅度大于城市地区。

Response of Lakes to Climate Change in Xainza Basin Tibetan Plateau Using Multi-Mission Satellite Data from 1976 to 2008

Changes in the lake areas of Xainza basin in the past 33 years （1976 to 2008） were studied using Landsat data from Multispectral Scanners （1973- 1977）, Thematic Mapper （1989-1992, 2007-2009）, and Enhanced Thematic Mapper Plus （1999-2002）. The results indicated that lakes in the study area evidently expanded from 1976 to 2008, with total expansion of 1512.64km2. The mean annual air temperature presented an upward trend with certain fluctuations from 1966 to 2008. The air temperature rise rates in the cold season （o.31~C/loa） were higher than those in the hot season （0.24℃/1oa）, in the Xainza station example. Precipitation exhibited evident seasonal differences. Mean annual precipitation in hot season is 281.48 mm and cold season is 32.66 mm from 1966 to 2008 in study area. Precipitation in the hot season was the major contributor to the increase in annual precipitation. Grey relational analysis （GRA） was used to study the response of lake areas to climatic factors. The mean air temperature and precipitation were selected as comoared series, and the lake areas were regarded as the reference series. The grey relational grade （GRG） between compared series and reference series were calculated through GRA. The results indicated that changes in lake areas were mainly affected by climatic factors in the hot season. Lakes in this region were classified into three grades, namely, Grades I, II, and III according to the recharge source and elevation. The GRGs of each series varied for different grade lakes： the area of Grade III lakes were the most relevant to the hot season factors, the GRGs of precipitation and air temperature were 0.7570 and 0.6606; followed by the Grade II lakes; Grade I lakes were more sensitive to the air temperature.

The chaotic behavior among the oil prices, expectation of investors and stock returns: TAR-TR-GARCH copula and TAR-TR-TGARCH copula

This paper has two aims. The first one is to investigate the existence of chaotic structures in the oil prices, expectations of investors and stock returns by combining the Lyapunov exponent and Kolmogorov entropy, and the second one is to analyze the dependence behavior of oil prices, expectations of investors and stock returns from January 02, 1990, to June06, 2017. Lyapunov exponents and Kolmogorov entropy determined that the oil price and the stock return series exhibited chaotic behavior. TAR-TR-GARCH and TAR-TR-TGARCH copula methods were applied to study the co-movement among the selected variables. The results showed significant evidence of nonlinear tail dependence between the volatility of the oil prices, the expectations of investors and the stock returns. Further, upper and lower tail dependence and comovement between the analyzed series could not be rejected. Moreover, the TAR-TR-GARCH and TAR-TR-TGARCH copula methods revealed that the volatility of oil price had crucial effects on the stock returns and on the expectations of investors in the long run.