Search results

Purpose – The purpose of this research is to investigate the relationship of lead (Pb) and zinc (Zn) contents in sediment of Faunus ater (F. ater) population density and to analyze the relationship between Pb and Zn accumulation in F. ater with F. ater density in Reuleng River, Leupung, Aceh Besar.

Design/Methodology/Approach – Sampling was conducted in November 2016 until January 2017. Density of F. ater was analyzed by density formula while its relationship to Pb and Zn in sediments and F. ater was conducted by correlation analysis method.

Findings – The results showed that correlation between Pb and Zn in sediments and in F. ater varies at each locations on every month of sampling. Pb and Zn content in sediments found a fluctuating relationship in each month of sampling with density of F. ater. Correlation of Pb content in sediments with F. ater density showed a medium correlation in January 2017 with r-value = 0.665. Zn in sediment has a very strong correlation to F. ater density in November 2016 with r-value = 0.891. Pb in F. ater has a medium correlation to F. ater density in January 2017 with r-value = 0.436. Furthermore, accumulation of Zn in F. ater to its density does show some apparent correlation in each month of sampling.

Research Limitation/Implications – This research gives information about the relationship of Pb and Zn contents in sediment to density of F. ater and to analyze correlation of Pb and Zn in F. ater to density of F. ater in Reuleng River, Leupung, Aceh Besar district.

Originality/Value – This is the first time research is conducted about on the correlation between lead and zinc to obtain the density of F. ater.

The purpose of this study aims to investigate the relationship between employees’ personality traits and their job performances (including task performance and contextual performance) of Taiwanese freight forwarders by using responses from a NEO Personality Inventory-Revised Form (NEO-PI-R) questionnaire survey.

One of the most popular personality trait model is the five-factor model (FFM), which includes the big five domains, namely, openness, conscientiousness, extraversion, agreeableness and neuroticism (OCEAN). Each of these five domains includes six facets. Previous researchers have used OCEAN factors to describe the relationship between human personality and job performance. NEO Personality Inventory is a professional psychological assessment instrument published by psychological assessment resources. Multivariate analysis technique and regression technique are used to analyze surveyees’ responses.

Research results reveal the following four issues. The seniority of employees in a company has a positive relationship with their conscientiousness. Employees with higher score on the facets of the neuroticism domain have a negative correlation with their task performance and contextual performance. The relationship between employees’ openness to experience and job performance (both task performance and contextual performance) is not significant. Employees’ seniority has a positive correlation with both their task performance and contextual performance. In a nutshell, freight forwarding industry in Taiwan can use the facets in the neuroticism domain to screen and recruit appropriate job applicants. In addition, retaining senior employees could increase a forwarder’s task performance and contextual performance by their high degree of conscientiousness.

FFM model is a psychological theory dealing with the personality traits and human behavior. Freight forwarding is a labor-intensive business and is one of the most important sectors in the logistics industry. According the authors’ knowledge, the application of FFM on the logistics industry is simply not existed.

This paper aims to investigate the correlation between banking sector non-performing loans (NPLs) and the level of sustainable development.

Pearson correlation test statistic was used to assess the correlation between bank NPLs and sustainable development.

There is a significant positive correlation between banking sector NPLs and the level of sustainable development measured by the sustainable development index (SDI). The significant positive correlation is evident in European countries and in countries in the region of the Americas. There is a significant negative correlation between banking sector NPLs and achieving SDG3 and SDG7 in African countries and European countries. There is also a significant negative correlation between NPLs and achieving SDG10 in European countries. There is a significant positive correlation between banking sector NPLs and achieving SDG4 and SDG7 in the region of the Americas. There is also a significant positive correlation between NPLs and achieving SDG10 in African countries and in countries in the region of the Americas.

The present study is unique and different from other studies because it used a unique SDI to capture the level of sustainable development. The analysis is also unique because it covers several regions, which have not been covered in previous studies.

Forecasting techniques improve supply chain resilience by ensuring that the correct parts are available when required. In addition, accurate forecasts conserve precious resources and money by avoiding new start contracts to produce unforeseen part requests, reducing labor intensive cannibalization actions and ensuring consistent transportation modality streams where changes incur cost. This study explores the effectiveness of the United States Air Force’s current flying hour-based demand forecast by comparing it with a sortie-based demand forecast to predict future spare part needs.

This study employs a correlation analysis to show that demand for reparable parts on certain aircraft has a stronger correlation to the number of sorties flown than the number of flying hours. The effect of using the number of sorties flown instead of flying hours is analyzed by employing sorties in the United States Air Force (USAF)’s current reparable parts forecasting model. A comparative analysis on D200 forecasting error is conducted across F-16 and B-52 fleets.

This study finds that the USAF could improve its reparable parts forecast, and subsequently part availability, by employing a sortie-based demand rate for particular aircraft such as the F-16. Additionally, our findings indicate that forecasts for reparable parts on aircraft with low sortie count flying profiles, such as the B-52 fleet, perform better modeling demand as a function of flying hours. Thus, evidence is provided that the Air Force should employ multiple forecasting techniques across its possessed, organically supported aircraft fleets. The improvement of the forecast and subsequent decrease in forecast error will be presented in the Results and Discussion section.

This study is limited by the data-collection environment, which is only reported on an annual basis and is limited to 14 years of historical data. Furthermore, some observations were not included because significant data entry errors resulted in unusable observations.

There are few studies addressing the time measure of USAF reparable component failures. To the best of the authors’ knowledge, there are no studies that analyze spare component demand as a function of sortie numbers and compare the results of forecasts made on a sortie-based demand signal to the current flying hour-based approach to spare parts forecasting. The sortie-based forecast is a novel methodology and is shown to outperform the current flying hour-based method for some aircraft fleets.

The purpose of communicating with your esteemed magazine is your continuous support with Arab researchers and your sponsorship of research on the Arab world.

The research design was based on the APA style, and the methodology used in this research study is statistical and applied analysis to reach the desired results.

The results of this study provide an interesting perspective on the factors that influence changes in variables in Iraq: The prevailing patterns of temperature in Iraq are characterized by a clear pattern, and it is noticeable that they are linked to patterns of distance connection, but it is affected by some local influences, including the different terrain in Iraq. The highest values recorded by the correlation relationship were for the oscillation NAO and EA-WR with −0.812, −0.805, respectively. It is clear from the results that the correlation values are different between the northern and southern stations regions, and that the most influential appeared during the month of January, meaning that the effects of the remote correlation patterns are clearer during the winter season. Most of the correlation values of temperature were at the significance level of 0.5 and 0.1, in addition to that, the correlation was inverse, meaning that when the values of the distance correlation patterns rise, the temperature rates drop above the study stations and vice versa. The effect of SOI appeared during the months of December, April and May, and it recorded a strong correlation with the temperature variable. Most of the oscillations showed different effects with temperature, and it cannot be inferred that the effect is subjected and affected by the oscillation without reference to the extensions of pressure systems and their effects on the local conditions over Iraq.

There are no restrictions.

The topic of the research is one of the modern topics that researchers have not taken up in a large way, and the apparent thesis in the research is one of the effects of the newly observed climate changes with the aim of addressing and limiting the effects of these phenomena on the Arab world.

The purpose of the study is to investigate the correlation between credit supply to government and credit supply to the private sector to determine whether there is a crowding-out or crowding-in effect of credit supply to government on credit supply to the private sector.

The study used data from 43 countries during the 1980–2019 period. The study employed the Pearson correlation methodology to analyze the data.

There is a significant positive correlation between credit supply to government and credit supply to the private sector. There is also a significant positive relationship between credit supply to government and credit supply to the private sector, implying a crowding-in effect of government borrowing on private sector borrowing. The positive correlation between credit supply to government and credit supply to the private sector by banks is stronger and highly significant in the period before the Great Recession, while the positive correlation is weaker and less significant during the Great Recession, and the correlation further weakens after the Great Recession. The regional analyses show that the positive correlation between credit supply to government and credit supply to the private sector by banks is stronger and highly significant in the African region than in the Asian region and the region of the Americas.

There is no evidence on the correlation between credit supply to government and credit supply to the private sector during the Great Recession.

The purpose of this paper is to detect abnormal data of complex and sophisticated industrial equipment with sensors quickly and accurately. Due to the rapid development of the Internet of Things, more and more equipment is equipped with sensors, especially more complex and sophisticated industrial equipment is installed with a large number of sensors. A large amount of monitoring data is quickly collected to monitor the operation of the equipment. How to detect abnormal data quickly and accurately has become a challenge.

In this paper, the authors propose an approach called Multiple Group Correlation-based Anomaly Detection (MGCAD), which can detect equipment anomaly quickly and accurately. The single-point anomaly degree of equipment and the correlation of each kind of data sequence are modeled by using multi-group correlation probability model (a probability distribution model which is helpful to the anomaly detection of equipment), and the anomaly detection of equipment is realized.

The simulation data set experiments based on real data show that MGCAD has better performance than existing methods in processing multiple monitoring data sequences.

The MGCAD method can detect abnormal data quickly and accurately, promote the intelligent level of smart articles and ultimately help to project the real world into cyber space in CrowdIntell Network.

This paper examines the correlation of economic policy uncertainty (EPU) with nonperforming loans and loan loss provisions for 22 major developed countries over the 2008–2017 period.

The study used the Pearson correlation methodology to assess the correlation between EPU, bank nonperforming loans and loan loss provisions.

The findings reveal that EPU is negatively correlated with nonperforming loans and loan loss provisions in the banking sector of EU countries but not for non-EU countries. Also, EPU is negatively correlated with nonperforming loans in the banking sector of the most advanced economies – the G7 countries, while loan loss provisions are more responsive to changes in EPU than NPLs in EU countries.

The implication of the findings is that the correlation of EPU with loan loss provisions and nonperforming loans is influenced by regional characteristics.

This study is the first to analyze the association of EPU with bank nonperforming loans and loan loss provisions under regional classifications such as the EU, non-EU and the G7 countries. This study provides insights on how regional differences might explain the co-movement of EPU with bank nonperforming loans and loan loss provisions.

The previous theoretical studies on default correlations analyze them only when the firm value moves continuously. Unlike these researches, this paper examines them when the firm value is exposed to jump risks and these jump risks between firms are correlated. Under these conditions, the effects of the correlated jump risks on default correlations are the followings. First, as stated in the previous study, this paper also states that the default correlations increase and then decrease with time. Second, there is a big difference between the existing study and this paper in the aspect of the firms' credit qualities. In the previous study, over a long horizon, default correlations of lower credit qualities of firms are lower than those of higher credit qualities of firms. However, under the jump-diffusion model we are able to obtain the opposite result to the previous study, which is that the jump-diffusion model is consistent with the empirical study in the case of lower credit qualities of firms. Third, on default correlations between the speculative graded firms over a long horizon, this paper is more consistent with the empirical results rather than the previous theoretical study. On contrary, on default correlations between the investment graded firms over a short horizon, the result is completely the opposite. Finally, in contrast to the diffusion model of Merton (1974) where default correlations over a short period are insignificant, default correlations under the jump-diffusion model may be consistent with the empirical results due to correlated jump risks.

One popular method to assess discriminant validity in structural equation modeling is the heterotrait-monotrait ratio of correlations (HTMT). However, the HTMT assumes tau-equivalent measurement models, which are unlikely to hold for most empirical studies. To relax this assumption, the authors modify the original HTMT and introduce a new consistent measure for congeneric measurement models: the HTMT2.

The HTMT2 is designed in analogy to the HTMT but relies on the geometric mean instead of the arithmetic mean. A Monte Carlo simulation compares the performance of the HTMT and the HTMT2. In the simulation, several design factors are varied such as loading patterns, sample sizes and inter-construct correlations in order to compare the estimation bias of the two criteria.

The HTMT2 provides less biased estimations of the correlations among the latent variables compared to the HTMT, in particular if indicators loading patterns are heterogeneous. Consequently, the HTMT2 should be preferred over the HTMT to assess discriminant validity in case of congeneric measurement models.

However, the HTMT2 can only be determined if all correlations between involved observable variables are positive.

This paper introduces the HTMT2 as an improved version of the traditional HTMT. Compared to other approaches assessing discriminant validity, the HTMT2 provides two advantages: (1) the ease of its computation, since HTMT2 is only based on the indicator correlations, and (2) the relaxed assumption of tau-equivalence. The authors highly recommend the HTMT2 criterion over the traditional HTMT for assessing discriminant validity in empirical studies.