Russia–Ukraine crisis: The effects on the European stock market

We examine the effect of the Russia–Ukraine crisis on the European stock markets. Because of increased political uncertainty, geographic proximity and the ramifications of the fresh sanctions imposed on Russia, the European stock markets tended to react negatively to this crisis. We find that on 21 February 2022, when Russia recognized two Ukrainian states as autonomous regions, European stocks incurred a significant negative abnormal return. Moreover, the negative stock price reactions continued in the post-event period. The magnitude of the stock price reactions to this crisis exhibits considerable variation across industries, countries and size of the company.© 2022 The Authors. European Financial Management, published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.fi=vertaisarvioitu|en=peerReviewed

The impact of the Russia-Ukraine crisis on the stock market : Evidence from Australia

This paper investigates the effect of the Russia–Ukraine crisis on the Australian stock market. Using the event study methodology, we find significantly negative abnormal returns on the event date (i.e., the first trading day after Russia recognized the two Ukrainian states as autonomous regions) in the Australian stock market. However, this negative stock market reaction mostly disappeared in the post-event period. We also find that small and medium-sized firms were adversely affected during the pre-event and event periods. Interestingly, the magnitude and the direction of the abnormal returns vary across industries. We also find that high-growth, illiquid and export-oriented firms are more exposed to the Russia–Ukraine crisis.© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).fi=vertaisarvioitu|en=peerReviewed

Unveiling the Potential of Big Data Analytics for Transforming Higher Education in Bangladesh; Needs, Prospects, and Challenges

Big Data Analytics has gained tremendous momentum in many sectors worldwide. Big Data has substantial influence in the field of Learning Analytics that may allow academic institutions to better understand the learners needs and proactively address them. Hence, it is essential to understand Big Data and its application. With the capability of Big Data to find a broad understanding of the scientific decision making process, Big Data Analytics (BDA) can be a piece of the answer to accomplishing Bangladesh Higher Education (BHE) objectives. This paper reviews the capacity of BDA, considers possible applications in BHE, gives an insight into how to improve the quality of education or uncover additional values from the data generated by educational institutions, and lastly, identifies needs and difficulties, opportunities, and some frameworks to probable implications about the BDA in BHE sector. Keywords; Big Data Analytics, Learning Analytics, Quality of Education, Challenges, Higher Education, Banglades

TRA-941: EFFECTIVENESS OF VARIABLE MESSAGE SIGNS IN IMPROVING THE ROAD NETWORK THROUGH ROUTE GUIDANCE

Variable Message Signs (VMS) are a means of providing valuable information to roadway users and enhancing the performance of the road network. The main objective of this research was to analyze the effectiveness of the use of VMS in improving the efficiency of the road network. This was implemented through a number of case studies under different conditions and different designs of the VMS. As this is a unique traffic guidance method to Egypt, the different factors that may affect the effectiveness of the sign to divert drivers was studied to fully understand the benefits of implementing VMS in Egypt. Traffic data was collected from five sites which are located in Giza Governorate, Egypt. At each location, the driver had the choice of two alternative routes leading to the same destination. The VMS informed the drivers that one of these routes was congested and to use an alternative route. Three different sign types were applied to identify the most effective type of VMS on drivers in Egypt. The most effective sign type was identified during the pilot study and used in the remaining sites. For each site, Traffic counts for each route was recorded for twenty minutes without the VMS and twenty minutes with the VMS applied to obtain the diversion rate of drivers Average travel times for 30 vehicles and queue lengths were also recorded before and after the application of the VMS and Queue lengths were also recorded before and after applying the VMS

Placental pouch closure: a novel, safe and effective surgical procedure for conservative management of placenta accreta

Background: Placenta accreta spectrum (PAS) has become a global problem secondary to the high rate of cesarean delivery (CD). The current study presents an effective surgical procedure (placental pouch closure) for uterine preservation in patients with PAS. Methods: We applied this procedure in sixty cases at a tertiary university hospital between September 2017 and January 2019. We included women who were diagnosed as PAS based on preoperative ultrasound and Doppler evaluation, and who had the desire for uterine preservation. Results: The procedure was successful in almost all cases; the uterus was conserved 98.33 % of participants, with no associated severe maternal morbidities or mortality. In all cases, no additional surgical procedures were needed. The mean blood loss was 1263 ml, and the mean number of units of blood required for transfusion was 2.31 units. Conclusion: Identifying and meticulously closing the placental pouch is a novel surgical procedure for conservative management of PAS. In well-selected cases with the availability of facilities and expertise, the technique could have a place as a safe and effective surgical technique in women presenting with placenta accreta who desire uterine preservation. Trial registration number: NCT03241849. Registered on August 8, 201

CAFFEINEAS A PROMISING ANTIFBROTIC AGENT AGAINST CCL4-INDUCED LIVER FIBROSIS

Objective: Hepatic fibrosis is a wound-healing process in the liver with chronic injury and is characterized by an excess production and deposition of extracellular matrix (ECM) components. Our aim is to investigate the hepatoprotective effects of caffeine against CCL4-induced liver fibrosis in rats and to be compared with the reference standard N-acetylcysteine (NAC).Methods: Rats were divided into 5 groups (n=8), the 1st group served as normal control, the 2nd group received corn oil, the 3rd group is a fibrosis control and the remaining two groups received in addition to CCL4, NAC (150 mg/kg/day) as a reference treatment and caffeine (15 mg/kg/day). At the end of experimental period the following parameters were measured, specific fibrosis biomarkers [hepatic transforming growth factor β1(TGF-β1) and hepatic hydroxyproline (HYP)], liver function biomarkers [serum alanine transaminase (ALT), aspartate transaminase (AST), total bilirubin and albumin], lipid profile [serum triglycerides, total cholesterol, low density lipoprotein cholesterol (LDL-Ch) and high density lipoprotein cholesterol (HDL-Ch)], inflammatory biomarkers [serum tumor necrosis factor alpha (TNF-α), hepatic myeloperoxidase (MPO)], oxidative stress biomarkers [hepatic malondialdehyde (MDA), glutathione (GSH) and catalase (CAT)], relative liver body weight and liver histopathological study.Results: Caffeine significantly improved all the aforementioned biochemical parameters and liver sections obtained from this group showed prominent histopathological improvement.Conclusion: Caffeine hepatoprotective effects may be due to modulation of the fibrous scar formation, Improvement of liver function, Anti-inflammatory and antioxidant potentials

Some properties for certain class of analytic functions defined by convolution

In this paper, we introduce a new class H_{T}(f,g;\alpha ,k) of analytic functions in the open unit disc U={z\in \mathbb{C}: left\vert z \right\vert <1} defined by convolution. The object of the present paper is to determine coefficient estimates, extreme points, distortion theorems, partial sums and integral means for functions belonging to the class H_{T}(f,g;\alpha ,k). We also obtain several results for the neighborhood of functions belonging to this class

Interaction of a three-level atom and a field with a time-varying frequency in the context of triangular well potentials: an exact treatment

We introduce a model of interaction between a three-level atom (3LA) and a one-mode field whose frequency evolves with the time in the context of triangular potentials. We consider a new class of cat states described as a superposition of the coherent states that are associated with these kinds of potentials. Mathematical and physical consequences of the obtained results are analyzed and discussed in detail by using the exact analytical treatment of the quantum system-state at subsequent times. We investigate the nonlocal and nonclassical properties of different system states in terms of the main parameters of the model. Interestingly, we present the dynamical behavior of the entanglement, second order correlation function, quantum Fisher information, and geometric phase of the considered bipartite quantum system. We show that the physical quantities for the proposed scheme are very sensitive through the choice of the time-varying frequency of the fields, at either coherent states or their superposition, and we compare the results to the case of fields that are associated to harmonic well potentials. Finally, we explore the relationship and dependence of the physical quantifiers on the main parameters of the model

SLSNet: Skin lesion segmentation using a lightweight generativeadversarial network

The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time and memory. Consequently, running such segmentation algorithms requires a powerful GPU and high bandwidth memory, which are not available in dermoscopy devices. Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model. The 1-D kernel factorized network reduces the computational cost of 2D filtering. The position and channel attention modules enhance the discriminative ability between the lesion and non-lesion feature representations in spatial and channel dimensions, respectively. A multiscale block is also used to aggregate the coarse-to-fine features of input skin images and reduce the effect of the artifacts. SLSNet is evaluated on two publicly available datasets: ISBI 2017 and the ISIC 2018. Although SLSNet has only 2.35 million parameters, the experimental results demonstrate that it achieves segmentation results on a par with the state-of-the-art skin lesion segmentation methods with an accuracy of 97.61%, and Dice and Jaccard similarity coefficients of 90.63% and 81.98%, respectively. SLSNet can run at more than 110 frames per second (FPS) in a single GTX1080Ti GPU, which is faster than well-known deep learning-based image segmentation models, such as FCN. Therefore, SLSNet can be used for practical dermoscopic applications