Socio-Organisational Approach to Online Banking Transaction Risk Communication inside Banks in Jordan

This study aims to investigate the innovation of Online Banking Transaction (OBT) risk communication issues inside banks in Jordan from the socioorganisational point-of-view through studying the effects of national and organisational cultures on the risk communication process. Although risk communication issue has been approved to be one of the success reasons of Online Banking (OB) usage, the risk communication approaches that have been developed during past years tend to offer narrow technically oriented solutions, and they have not paid enough attention to the social aspects of risks and the informal structures of organizations. Using the previous research findings, this study presents a socioorganisational approach to the OBT risk communication innovation process inside banks in Jordan, which enrich the in depth understanding for practical projects and empirical research contexts

UTJECAJ FINANCIRANJA DEFICITA NA EKONOMSKU STABILNOST: SLUČAJ JORDANA

This study examines the effect of defi cit fi nancing on economic stability in Jordan during the period 2005-2017, using quarterly data by employing the Vector Error Correction Model (VECM) after seasonally adjusting the variables. This paper is unique as it is the fi rst of its kind that tackles the issue of stability in Jordan. It provides empirical evidence that external borrowing (EBDT) and domestic bank fi nancing (BANK) negatively affect economic stability in Jordan. The bank effect is due to crowding out the private sector. External borrowing negative impact is driven by the current high level of outstanding public debt, 98 percent of GDP. Public debt is mainly channeled to fi nance current expenditures at the expense of capital expenditures, which has a minimal impact on growth. Interest rate (REPO) effect is in line with the fi nance theory as higher rates lead to lower growth. Nonbank fi nancing (NonBank), although not statistically signifi cant, exhibits the right sign as it has a positive effect. Future research may extend this work by including other macroeconomic variables such as current account defi cit, money supply and direct foreign investment.U ovom se radu ispituje utjecaj financiranja deficita na ekonomsku stabilnost u Jordanu u razdoblju od 2005. do 2017. godine, temeljem tromjesečnih podataka, korištenjem vektorskog modela korekcije pogreške (VECM) nakon sezonskog prilagođavanja varijabli. Ovaj je rad jedinstven jer je prvi takve vrste koji se bavi pitanjem stabilnosti u Jordanu. Rad pruža empirijske dokaze da vanjsko zaduživanje (EBDT) i financiranje domaćih banaka (BANK) negativno utječu na ekonomsku stabilnost u Jordanu. Učinak banke posljedica je istiskivanja privatnog sektora. Negativni utjecaj vanjskog zaduživanja utječe na trenutačno visoku razinu nepodmirenog javnog duga od 98 posto BDP-a. Javni dug uglavnom se usmjerava na financiranje tekućih rashoda na teret kapitalnih rashoda, što ima minimalan utjecaj na rast. Učinak kamatnih stopa (REPO) u skladu je s teorijom financija jer veće stope dovode do nižeg rasta. Nebankarsko financiranje (NonBank), iako nije statistički značajno, pokazuje pravi predznak, jer ima pozitivan učinak. Buduća istraživanja mogu proširiti ovaj rad uključivanjem ostalih makroekonomskih varijabli poput deficita tekućeg računa, novčane mase i izravnih stranih ulaganja

ODNOS INVESTICIJA I ŠTEDNJE U MENA ZEMLJAMA: RAZDVAJANJE BRUTO ŠTEDNJE

The paper disentangles gross savings into government and private savings and investigate their impact on gross investment. Our methodology is based on a balanced panel of four MENA countries (Tunisia, Jordan, Egypt and Lebanon) for the period 2000-2017 by employing the Panel Vector Autoregressive Model (PVAR). Our findings show that government savings as a ratio of GDP does not have any impact on investment while private savings as a ratio of GDP does. Both variables exhibit the correct signs. The results also show that mobility of private saving is high and seemingly statistically inconsistent with the Fielstein and Horioka (1980) puzzle. Our paper also reveals that even though OECD countries are more open than our sample countries, the higher capital mobility of our sample is driven by the economic and political instability in the region.Rad razgraničava bruto štednju na državnu i privatnu štednju te istražuje njihov utjecaj na bruto investicije. Metodologija se temelji na uravnoteženom panelu četiri zemlje Bliskog istoka i Afrike (Tunis, Jordan, Egipat i Libanon) za razdoblje od 2000. do 2017. primjenom panel vektorskog autoregreijskog modela (PVAR). Rezultati pokazuju da državna štednja kao omjer BDP-a nema utjecaja na investicije, dok privatna štednja kao omjer BDP -a ima. Obje varijable potvrđuju očekivani predznak. Rezultati također pokazuju da je mobilnost privatne štednje velika i naizgled statistički neusklađena s zagonetkom Fielstein i Horioka (1980.). Ovaj članak također otkriva da, iako su zemlje OECD-a otvorenije od zemalja promatranog uzorka, veća mobilnost kapitala promatranog uzorka je posljedica ekonomske i političke nestabilnosti u regiji

ENAS-B: Combining ENAS with Bayesian Optimisation for Automatic Design of Optimal CNN Architectures for Breast Lesion Classification from Ultrasound Images

Efficient Neural Architecture Search (ENAS) is a recent development in searching for optimal cell structures for Convolutional Neural Network (CNN) design. It has been successfully used in various applications including ultrasound image classification for breast lesions. However, the existing ENAS approach only optimises cell structures rather than the whole CNN architecture nor its trainable hyperparameters. This paper presents a novel framework for automatic design of CNN architectures by combining strengths of ENAS and Bayesian Optimisation in two folds. Firstly, we use ENAS to search for optimal normal and reduction cells. Secondly, with the optimal cells and a suitable hyperparameter search space, we adopt Bayesian Optimisation to find the optimal depth of the network and optimal configuration of the trainable hyperparameters. To test the validity of the proposed framework, a dataset of 1,522 breast lesion ultrasound images is used for the searching and modelling. We then evaluate the robustness of the proposed approach by testing the optimized CNN model on three external datasets consisting of 727 benign and 506 malignant lesion images. We further compare the CNN model with the default ENAS-based CNN model, and then with CNN models based on the state-of-the-art architectures. The results (error rate of no more than 20.6% on internal tests and 17.3% on average of external tests) showed that the proposed framework generates robust and light CNN models

Vascular endothelial growth factor receptor inhibition enhances chronic obstructive pulmonary disease picture in mice exposed to waterpipe smoke

Background: Chronic obstructive pulmonary disease (COPD) is marked by destruction of alveolar architecture. Preclinical modelling for COPD is challenging. Chronic cigarette smoke exposure, the reference animal model of COPD, is time-inefficient, while exposure to waterpipe smoke (WPS), a surging smoking modality, was not fully tested for its histopathological pulmonary consequences. Since alveolar damage and pulmonary vascular endothelial dysfunction are integral to COPD pathology, lung histopathological effects of WPS were temporally evaluated, alone or in combination with vascular endothelial growth factor receptor (VEGFR) inhibition in mice.Materials and methods: Mice were exposed to WPS, 3 hours/day, 5 days/week, for 1, 2, 3, or 4 months. Another group of mice was exposed to WPS for 1 month, while being subjected to injections with the VEGFR blocker Sugen5416 (SU, 20 mg/kg) 3 times weekly. Control mice were exposed to fresh air in a matching inhalation chamber. Histopathological assessment of COPD was performed. Alveolar destructive index (DI) was counted as the percentage of abnormally enlarged alveoli with damaged septa per all alveoli counted. Mean linear intercept (MLI) was calculated as a measure of airspace enlargement.Results: Exposure to WPS resulted in significant increases in alveolar DI and MLI only after 4 months. Lung inflammatory score was minimal across all time-points. Importantly, combination of WPS and SU resulted in significantly increased DI, MLI, and inflammatory scores as early as 1 month post exposure.Conclusions: Combined exposure to WPS and SU results in COPD picture, highlighting the role of pulmonary vascular endothelial dysfunction in the disease

A Probabilistic Data Fusion Modeling Approach for Extracting True Values from Uncertain and Conflicting Attributes

Real-world data obtained from integrating heterogeneous data sources are often multi-valued, uncertain, imprecise, error-prone, outdated, and have different degrees of accuracy and correctness. It is critical to resolve data uncertainty and conflicts to present quality data that reflect actual world values. This task is called data fusion. In this paper, we deal with the problem of data fusion based on probabilistic entity linkage and uncertainty management in conflict data. Data fusion has been widely explored in the research community. However, concerns such as explicit uncertainty management and on-demand data fusion, which can cope with dynamic data sources, have not been studied well. This paper proposes a new probabilistic data fusion modeling approach that attempts to find true data values under conditions of uncertain or conflicted multi-valued attributes. These attributes are generated from the probabilistic linkage and merging alternatives of multi-corresponding entities. Consequently, the paper identifies and formulates several data fusion cases and sample spaces that require further conditional computation using our computational fusion method. The identification is established to fit with a real-world data fusion problem. In the real world, there is always the possibility of heterogeneous data sources, the integration of probabilistic entities, single or multiple truth values for certain attributes, and different combinations of attribute values as alternatives for each generated entity. We validate our probabilistic data fusion approach through mathematical representation based on three data sources with different reliability scores. The validity of the approach was assessed via implementation into our probabilistic integration system to show how it can manage and resolve different cases of data conflicts and inconsistencies. The outcome showed improved accuracy in identifying true values due to the association of constructive evidence

Diagnosis of COVID-19 from X-rays Using Recurrent Neural Network

Nearly two years ago, the COVID-19 pandemic caused by the SARS-CoV-2 virus has caused drastic changes in many aspects of life at many levels in the world, and this has affected peoples lifestyles. This impact was particularly significant and impactful on the health sectors, among many others. The COVID-19 virus has essentially increased the demand for treatment, diagnosis and testing. The definitive test for diagnosing COVID-19 is reverse transcriptase polymerase chain reaction (RT-PCR); nevertheless, chest x-ray is a quick, effective and inexpensive diagnosis to detect possible pneumonia associated with COVID-19. In this study, the feasibility of using a deep learning-based Recurrent Neural Network (RNN) classifier to detect COVID-19 from CXR images is investigated. The proposed classifier consists of an RNN, trained by a deep learning model. The RNN identifies abnormal images that contain signs of COVID-19. The experiment used in the study employed 286 COVID-19 samples from the Kaggle Repository. The proposed technique is compared with the decision tree algorithm in order to prove the efficiency of the proposed one. The results revealed that the accuracy of the RNN was 97.90%, with a low data loss rate of 2.10%, while the decision tree accuracy was 75.8741%, and a relatively high data loss rate of 24.1259%. These results support the usefulness of the proposed deep learning-based RNN classifier in pre-screening patients for triage and decision-making before RT-PCR data are available

The Determinants Of Capital Flight: Evidence From MENA Countries

This paper examines the determinants of capital flight in seven Middle East and North Africa (MENA) countries during the period of 1981-2008. The results are robust to four econometrics techniques: Ordinary least Squares, Fixed effects, Random Effects, and Seemingly Unrelated Regression Model. The empirical findings indicate that the capital flight in MENA countries is driven mainly by lag capital flight, external debt, foreign direct investment, real GDP growth rate and uncertainty. Based on these results, the paper recommends that governments in these countries should manage their external debt efficiently, and stabilize their monetary and macroeconomic policies in order to staunch capital flight

The Impact of Managers Efficiency on Quality of Strategic Decision-making under Crisis Management: An Empirical Study on Private Hospitals in Baghdad-Iraq

Managers have essential role in considering the foundation in organization, to avoid risks and crises, their efficiency and ability to minimize risks if it should occur, also they should make the right decision at crisis management, at a high qualities as a results of good experienced, education, skills, and best practice. The main objective of this study is to explore the impact of managers’ efficiency on quality of strategic decision-making directly and indirectly through crisis management in private hospitals in Baghdad/ Iraq, the study population was private hospitals in Baghdad/ Iraq, and a sample was chosen randomly which consists of (100) managers (administrative and physicians), and a questionnaire was designed consisting of (44) phrases to gather the primary data from the study sample. Data were analyzed using relevant statistical methods like regression analysis and path analysis. The study came to show a high level of importance for all study variables, and showed there is a significant positive direct impact of managers’ efficiency on quality of strategic decision making also there is indirect impact (through crisis management), beside there is a significant positive direct impact of managers efficiency on crisis management rather than a significant impact of crisis management on quality of strategic decision making, in private hospitals in Baghdad/ Iraq. Key words: Decision-making, Quality of Strategic Decision-making, Crisis Management, Efficiency.