The Phillips Curve Under State-Dependent Pricing

This paper is related to a large recent literature studying the Phillips curve in sticky-price equilibrium models. It differs in allowing for the degree of price stickiness to be determined endogenously. A closed-form solution for short-term inflation is derived from the dynamic stochastic general equilibrium (DSGE) model with state-dependent pricing originally developed by Dotsey, King and Wolman. This generalised Phillips curve encompasses the New Keynesian Phillips curve (NKPC) based on Calvo-type price-setting as a special case. It describes current inflation as a function of lagged inflation, expected future inflation, and current and expected future real marginal costs. The paper demonstrates that inflation dynamics generated by the model for a broad class of time and state-dependent price-setting behaviours are well approximated by the popular hybrid NKPC (with one lag of inflation) in a low-inflation environment. This provides an explanation of why the hybrid NKPC performs well in describing inflation dynamics across industrial countries. It implies, however, that the reduced-form coefficients of the hybrid NKPC may not have a structural interpretationState-dependent pricing, inflation dynamics, Phillips curve.

The effect of serum vitamin D normalization in preventing recurrences of benign paroxysmal positional vertigo: A case-control study

Background: Benign paroxysmal positional vertigo (BPPV) is a condition with recurrent attacks in a significant proportion of patients. The present case- control study was conducted to assess the influence of serum vitamin D normalization on recurrent attacks of vitamin D deficient patients. Methods: Diagnosis of BPPV was made based on history and clinical examination and exclusion of other conditions. Serum 25-hydroxy vitamin D (25-OHD) was measured using ELISA method and a levels of < 20 ng/ml was considered a deficiency of vitamin D. Inclusion criteria were as follows: history of recurrent attacks and serum 25-OHD < 20.ng/ml. While the patients with history of trauma, surgery and chronic systemic diseases were excluded. The patients were classified into two groups: treatment and control, intermittently. Both groups received Epley rehabilitation therapy one session per week for 4 weeks but the treatment group received an additional supplement of 50.000 IU of vitamin D (cholecalciferol) weekly for two months to achieve serum 25-OHD � 30 ng/ml and the study patients were followed-up for 6 months. Results: Twenty-seven patients were allocated to each group. At baseline, serum 25-OHD was similar (10.7�2.3 vs 11.41�1.9, P=0.23). At month 2, serum 25-OHD in the treatment group increased significantly to � 30 ng/ml, whereas serum 25-OHD in the control group remained unchanged (34.2�3.3 vs 10.6 10.6�2.2 ng/ml, P=0.001). During the follow-up period, attacks of BPPV in the treatment group decreased significantly compared with the control group (14.8 vs 96.3 OR= 0.18, P=0.001). Conclusion: The findings of this study indicate that the normalization of serum vitamin D significantly reduces BPPV recurrences

A Novel Feature Set for Application Identification

Classifying Internet traffic into applications is vital to many areas, from quality of service (QoS) provisioning, to network management and security. The task is challenging as network applications are rather dynamic in nature, tend to use a web front-end and are typically encrypted, rendering traditional port-based and deep packet inspection (DPI) method unusable. Recent classification studies proposed two alternatives: using the statistical properties of traffic or inferring the behavioural patterns of network applications, both aiming to describe the activity within and among network flows in order to understand application usage and behaviour. The aim of this paper is to propose and investigate a novel feature to define application behaviour as seen through the generated network traffic by considering the timing and pattern of user events during application sessions, leading to an extended traffic feature set based on burstiness. The selected features were further used to train and test a supervised C5.0 machine learning classifier and led to a better characterization of network applications, with a traffic classification accuracy ranging between 90- 98%

On Internet Traffic Classification: A Two-Phased Machine Learning Approach

Traffic classification utilizing flow measurement enables operators to perform essential network management. Flow accounting methods such as NetFlow are, however, considered inadequate for classification requiring additional packet-level information, host behaviour analysis, and specialized hardware limiting their practical adoption. This paper aims to overcome these challenges by proposing two-phased machine learning classification mechanism with NetFlow as input. The individual flow classes are derived per application through k-means and are further used to train a C5.0 decision tree classifier. As part of validation, the initial unsupervised phase used flow records of fifteen popular Internet applications that were collected and independently subjected to k-means clustering to determine unique flow classes generated per application. The derived flow classes were afterwards used to train and test a supervised C5.0 based decision tree. The resulting classifier reported an average accuracy of 92.37% on approximately 3.4 million test cases increasing to 96.67% with adaptive boosting. The classifier specificity factor which accounted for differentiating content specific from supplementary flows ranged between 98.37% and 99.57%. Furthermore, the computational performance and accuracy of the proposed methodology in comparison with similar machine learning techniques lead us to recommend its extension to other applications in achieving highly granular real-time traffic classification

Anomaly Detection in Encrypted Internet Traffic Using Hybrid Deep Learning

An increasing number of Internet application services are relying on encrypted traffic to offer adequate consumer privacy. Anomaly detection in encrypted traffic to circumvent and mitigate cyber security threats is, however, an open and ongoing research challenge due to the limitation of existing traffic classification techniques. Deep learning is emerging as a promising paradigm, allowing reduction in manual determination of feature set to increase classification accuracy. The present work develops a deep learning-based model for detection of anomalies in encrypted network traffic. Three different publicly available datasets including the NSL-KDD, UNSW-NB15, and CIC-IDS-2017 are used to comprehensively analyze encrypted attacks targeting popular protocols. Instead of relying on a single deep learning model, multiple schemes using convolutional (CNN), long short-term memory (LSTM), and recurrent neural networks (RNNs) are investigated. Our results report a hybrid combination of convolutional (CNN) and gated recurrent unit (GRU) models as outperforming others. The hybrid approach benefits from the low-latency feature derivation of the CNN, and an overall improved training dataset fitting. Additionally, the highly effective generalization offered by GRU results in optimal time-domain-related feature extraction, resulting in the CNN and GRU hybrid scheme presenting the best model.</jats:p

Perspectives on Auditing and Regulatory Compliance in Blockchain Transactions

The recent advent of blockchain technology is anticipated to revolutionize the operational processes of several industries including banking, finance, real estate, retail and benefit governmental as well as corporate information management structures. The underlying principles of information immutability, traceability, and verifiability built-in blockchain transactions may lead to greater adoption of distributed crypto-ledger applications in auditing automation, compliance monitoring, and guaranteeing high assurance. This chapter discusses the contemporary applications of blockchain technology in information auditing, exploring aspects such as data recording, accuracy, verification, transparency, and overall value of a decentralized blockchain crypto-ledger for auditors. Opportunities for timeliness, completeness, and reconciliation in appraising regulatory compliance of organizations employing blockchain-based contractual frameworks are also investigated. The chapter reviews the existing and anticipated challenges blockchain applications pose to traditional regulatory compliance models and the inherent risks for businesses and stakeholders. We highlight the impact of operational concerns such as decentralized transactions, network complexity, transaction reversals, credential management, software quality, and human resources. Finally, the chapter provides perspective on assurance complexities involved in transforming from proprietary to blockchain-based framework while adhering to IT control obligations dictated by three major auditing standards Sarbanes Oxley Act (SOX), Control Objectives for Information Technologies (COBIT), and International Standardization Organization (ISO) /International Electrotechnical Commission (IEC) 27001

Multi-locus sequence type analysis of Shigellas pp. Isolates from Tehran, Iran

Background and Objectives: Strains of Shigella spp. can cause shigellosis, or bacillary dysentery. That is a public health problem worldwide. The aim of this study was to describe the population structure and genetic relatedness of multidrug resistant S. sonnei and S. flexneri isolated during a one year period from children with diarrhea in Tehran, Iran. Materials and Methods: A total of 70 Shigella spp. were detected during the study period. Twenty MDR isolates of Shigella spp. were randomly selected and used in this study. Bacterial identification was performed by conventional biochemical and serological and confirmed by molecular method. After antimicrobial susceptibility testing, we used Multilocus sequence typing (MLST) for subtyping isolates. Results: We found 14 Shigella sonnei and 6 Shigella flexneri isolates. Results of MLST showed five sequence types (ST) (145, 152, 241, 245, 1502) and BURST analysis revealed the largest number of single locus variant (SLV) and highest frequency (FREQ) for ST152. ST 152 with nine members was predicted as the founder by BURST. Frequency for ST 1502 and ST 245 was four isolates and the least frequency was seen for ST 241 and 145 with one and two members, respectively. ST 145 and ST 245 were described as singletons in BURST. All isolates with ST145 and ST245 were identified as Shigella flexneri. Conclusion: Annual Multi locus sequence typing of MDR Shigella would help us in better understanding of dominant species and comparing our results with the same studies in other countries especially our neighbor countries in source tracking purposes. © Tehran University of Medical Science. All rights reserved

Evaluation of the inertia force in compressive impact loading on steel fiber-reinforced concrete

First Online: 05 September 2021Steel-fibre-reinforced concrete (SFRC) is a strain rate sensitive material and, therefore, its dynamic and static compressive behaviour can be significantly different. In the present study, the effect of loading rate on the compressive behaviour of SFRC with 1% hooked end steel fibres is experimentally investigated. During impact loading, an inertia force is created due to acceleration along the specimen, whose effect in the range of impact is studied for a comprehensive assessment of the dynamic analysis of SFRC structures. For the evaluation of the inertia force, an instrumented drop-weight test setup is used, which includes two fast response loadcells with capacities of 1000 and 2000 kN on top (impact force) and bottom (reaction force) of specimen. The drop-weight impact tests were performed with three different drop heights, corresponding to maximum strain rates that ranged from 1 to 50 s−1. Two high-capacity accelerometers (5000 g) were mounted in the middle of the cylindrical specimens to obtain the cylinder acceleration response. The results show that, by increasing the strain rates, compressive strength, maximum acceleration at the middle of cylinder, and inertia force are increased. The results in terms of the ratio between inertia and impact load of specimens are presented and discussed.The study reported in this paper is part of the project “PufProtec - Prefabricated Urban Furniture Made by Advanced Materials for Protecting Public Built” with the reference of (POCI-01-0145-FEDER-028256) supported by FEDER and FCT funds. The first author gratefully acknowledges the financial support of FCT-Fundação para a Ciência e Tecnologia for the Ph.D. Grant SFRH/BD/149246/2019

An analytical approach for evaluating the impact response of steel fiber reinforced concrete beam

In this paper, a new approach is proposed for predicting reaction force in simply supported steel fiber reinforced concrete (SFRC) beams under impact loading (drop weight test) considering the energy conservation approach. If SFRC beams completely fail under impact load, it can be found that the total reaction force is equal to force capacity of SFRC beams. The force-deflection relationship can show the peak force that the SFRC beam can carry under impact load. Since concrete is a material sensitive to loading rates, the strain rate of loading and also the volume fraction of steel fiber will influence the beam´s response. The force-deflection relationship of the SFRC beam under impact loading is obtained using the proposed model. This model considers the effect of volume fraction of steel fiber and also the strain rate on the concrete properties. The model is then verified with the results collected from the literature that include 189 SFRC beams tested under drop-weight impacts and included in a database. The results obtained show that this method can estimate the maximum impact force with acceptable accuracy.The study reported in this paper is part of the project “PufProtec - Prefabricated Urban Furniture Made by Advanced Materials for Protecting Public Built” with the reference of (POCI-01-0145-FEDER-028256) supported by FEDER and FCT funds. The third author also acknowledges the support provided by FEDER and FCT funds within the scope of the project StreColesf (POCI-01-0145-FEDER-029485)