bluuebunny/crossref_metadata_2025 huggingface dataset:
Adherence to guideline-recommended HbA1c testing frequency and better outcomes in patients with type 2 diabetes: a 5-year retrospective cohort study in Australian general practice
Authors: Imai Chisato , Li Ling , Hardie Rae-Anne , Georgiou Andrew
Published: February 2021

Background

Clinical practice guidelines emphasise the role of regular monitoring of glycated haemoglobin A1c (HbA1c) for patients with type 2 diabetes, with most recommending 6-monthly testing. Nonetheless, there are few in-depth studies evaluating the clinical impact of the recommended testing frequency for patients to underpin the significance of guideline adherence.

Objective

This study aimed to examine associations between patient outcomes and adherence to HbA1c testing frequencies recommended by Australian guidelines (6-monthly for patients with adequate glycaemic control and 3-monthly for patients with inadequate glycaemic control). The primary and secondary outcomes of interest were longitudinal changes in HbA1c values and development of ischaemic heart disease (IHD) and chronic kidney disease (CKD).

Methods

This 5-year retrospective cohort study (July 2013–June 2018) evaluated HbA1c testing frequency in a subset of patients with type 2 diabetes identified within data collected from approximately 250 Australian general practices. The study included patients who were aged ≥18 in 2013 and had a record of HbA1c testing in study practices during the study period. Each patient’s adherence rate was defined by the proportion of HbA1c tests performed within the testing intervals recommended by Australian guidelines. Based on the adherence rate, adherence level was categorised into low (≤33%), moderate (34%–66%) and high (>66%). Generalised additive mixed models were used to examine associations between adherence to the recommended HbA1c testing frequency and patient outcomes.

Results

In the 6424 patients with diabetes, the overall median HbA1c testing frequency was 1.6 tests per year with an adherence rate of 50%. The estimated HbA1c levels among patients with low adherence gradually increased or remained inadequately controlled, while HbA1c values in patients with high adherence remained controlled or improved over time. The risk of developing CKD for patients with high adherence was significantly lower than for patients with low adherence (OR: 0.42, 95% CI 0.18 to 0.99). No association between IHD and adherence to the recommended HbA1c frequency was observed.

Conclusion

Better adherence to guideline-recommended HbA1c testing frequency was associated with better glycaemic control and lower risk of CKD. These findings may provide valuable evidence to support the use of clinical guidelines for better patient outcomes in patients with type 2 diabetes.
Peripheral body temperature rhythm is associated with suicide risk in major depressive disorder: a case-control study
Authors: Ma Xin , Cao Jing , Zheng Hailin , Mei Xinchun , Wang Meijuan , Wang Haoran , Shuai Yu , Shen Yuan
Published: February 2021

Background

Patients with major depressive disorder (MDD) may have an abnormal peripheral body temperature rhythm, but its relationship with suicidal risk and the response to treatment with antidepressants remain unknown.

Aims

This study aimed to investigate the feature of peripheral body temperature in patients with MDD and its relationship with suicide risk before and after treatment with antidepressants.

Methods

This is a prospective case-control study. Patients diagnosed as MDD were enrolled into MDD group. Healthy subjects who matched in terms of gender, age and body mass index were enrolled into normal control (NC) group. The 24-hour peripheral body temperatures were monitored byTM’ Holterthe next day after assessment. Patients with MDD were re-assessed after a 2-week treatment with antidepressants. All temperature data were fitted into cosine curves byPython.

Result

There were 41 patients with MDD, and 21 NC participants enrolled and completed the baseline assessments before the treatment. Patients with MDD were further divided into subgroup of with suicide risk or without suicide risk. In patients with MDD, the mesor of peripheral body temperature rhythm was higher in both patients with (36.17 (0.30)) and without suicide risk (36.22 (0.27)) than the mesor in NC participants before treatment (35.84 (0.38), Z=11.82, p=0.003, Kruskal-Wallis test). The phase-delay of temperature before treatment was greater in patients with MDD with suicidal risk (4.71 (1.68)) in comparison with those without suicidal risk (3.05 (2.19)) and NC participants (3.19 (1.82), Z=9.68, p=0.008, Kruskal-Wallis test). Moreover, phase-delay of temperature was associated with suicide risk in patients with MDD before treatment (OR=1.046, 95% CI: 1.009 to 1.085, p=0.015, unadjusted; OR=1.080, 95% CI: 1.020 to 1.144, p=0.009, adjusted by age and sex).

Conclusion

Patients with MDD might have abnormal peripheral body temperature. The abnormal phase-delay of peripheral body temperature may indicate suicide risk in patients with MDD, depending on validation in large-scale cohorts.
123I-FP-CIT SPECT in dementia with Lewy bodies, Parkinson’s disease and Alzheimer’s disease: a new quantitative analysis of autopsy confirmed cases
Authors: Oliveira Francisco P M , Walker Zuzana , Walker Rodney W H , Attems Johannes , Castanheira Joana C , Silva Ângelo , Oliveira Carla , Vaz Sofia , Silva Mariana , Costa Durval C
Published: February 2021

Purpose

The aim of this study was to re-evaluate the differentiation of patients with dementia with Lewy bodies (DLB) from Alzheimer’s disease (AD) and Parkinson’s disease (PD) using a quantitative analysis of123I-FP-CIT SPECT scans.

Methods

Thirty-six patients with in vivo123I-FP-CIT SPECT and neuropathological diagnoses were included. Based on neuropathological criteria, patients were further subclassified into nine AD, eight DLB, ten PD and nine with other diagnoses. An additional 16 healthy controls (HC) scanned with123I-FP-CIT SPECT were also included. All images were visually assessed as normal versus abnormal uptake by consensus of five nuclear medicine physicians. Bihemispheric mean was calculated for caudate binding potential (CBP), putamen binding potential (PBP) and putamen-to-caudate ratio (PCR).

Results

Patients with DLB had significantly lower CBP and PBP than patients with AD and significantly higher PCR than patients with PD. Qualitative visual analysis of the images gave an accuracy of 88% in the evaluation of the status of the nigrostriatal pathway considering all individuals, and 96% considering only the patients with PD, AD and DLB. Quantitative analyses provided a balanced accuracy of 94%, 94% and 100% in binary classifications DLB versus AD, DLB versus PD and PD versus AD, respectively, and an accuracy of 93% in the differentiation among patients with DLB, AD and PD simultaneously. No statistically significant differences were observed between the AD and HC.

Conclusions

This study demonstrates a very high diagnostic accuracy of the quantitative analysis of(123I-FP-CIT SPECT data to differentiate among patients with DLB, PD and AD.
Developing the ‘checking’ discipline
Authors: Yoong Wai , Sekar Hashviniya , Nauta Maud , Yoong Helienke , Lopes Tomas
Published: February 2021

We explore how engagement with checklists and adoption of a strict ‘checking’ discipline help avoid unintentional individual, team and systemic errors. Paradoxically, this is equally important when performing repetitive mundane tasks as well as during times of high-stress workload. In this article, we aim to discuss the different types of checklists and explain how deviations from a ‘checking’ discipline can lead to never events such as wrong side or site surgery. Well-designed checklists function as mental notes and prompts in clinical situations where the combination of fatigue and stress can contribute to a decline in cognitive performance. Furthermore, the need for proactive discussion by all members of the team during the implementation of the surgical checklist also reinforces the concept of teamwork and contributes towards effective communication. Patient safety is often a product of good communication, teamwork and anticipation: a ‘checking’ mentality remains the lynchpin which links these factors.
Oxygen supplementation during exercise improves leg muscle fatigue in chronic fibrotic interstitial lung disease
Authors: Marillier Mathieu , Bernard Anne-Catherine , Verges Samuel , Moran-Mendoza Onofre , O'Donnell Denis E , Neder José Alberto
Published: February 2021

Background

Exercise-induced hypoxaemia is a hallmark of chronic fibrotic interstitial lung disease (
f
-ILD). It remains unclear whether patients’ severe hypoxaemia may exaggerate locomotor muscle fatigue and, if so, to what extent oxygen (O
2
) supplementation can ameliorate these abnormalities.

Methods

Fifteen patients (12 males, 9 with idiopathic pulmonary fibrosis) performed a constant-load (60% peak work rate) cycle test to symptom limitation (Tlim) while breathing medical air. Fifteen age-matched and sex-matched controls cycled up to patients’ Tlim. Patients repeated the exercise test on supplemental O
2
(42%±7%) for the same duration. Near-infrared spectroscopy assessed vastus lateralis oxyhaemoglobin concentration ((HbO
2
)). Pre-exercise to postexercise variation in twitch force (∆Tw) induced by femoral nerve magnetic stimulation quantified muscle fatigue.

Results

Patients showed severe hypoxaemia (lowest O
2
saturation by pulse oximetry=80.0%±7.6%) which was associated with a blunted increase in muscle (HbO
2)
during exercise vs controls (+1.3±0.3 µmol vs +4.4±0.4 µmol, respectively; p<0.001). Despite exercising at work rates ∼ one-third lower than controls (42±13 W vs 66±13 W), ∆Tw was greater in patients (∆Tw/external work performed by the leg muscles=−0.59±0.21 %/kJ vs −0.25±0.19 %/kJ; p<0.001). Reversal of exertional hypoxaemia with supplemental O
2
was associated with a significant increase in muscle (HbO
2)
, leading to a reduced decrease in ∆Tw in patients (−0.33±0.19 %/kJ; p<0.001 vs air). Supplemental O
2
significantly improved leg discomfort (p=0.005).

Conclusion

O
2
supplementation during exercise improves leg muscle oxygenation and fatigue in
f
-ILD. Lessening peripheral muscle fatigue to enhance exercise tolerance is a neglected therapeutic target that deserves clinical attention in this patient population.
Grandmaternal smoking, asthma and lung function in the offspring: the Lifelines cohort study
Authors: Mahon Gillian M , Koppelman Gerard H , Vonk Judith M
Published: February 2021

Background/objective

Limited research exists regarding the association between grandmaternal smoking during pregnancy and the risk for asthma and altered lung function in grandchildren. This study aimed to investigate this association in a three-generation design.

Methods

37 291 participants (25 747 adults and 11 544 children) were included from the Lifelines study, a prospective longitudinal three generation cohort study in The Netherlands. Spirometry was available in 69.5% and 61.1% of the included adults and children. Logistic and linear regression were used to analyse the association between grandmaternal smoking during pregnancy and (1) asthma, (2) early childhood asthma (ie, onset before 6 years) and (3) lung function level. Maternal and paternal grandmaternal smoking were studied separately and the analyses were stratified by adult/child and by gender. The analyses were adjusted for gender, current smoking, birth variables and socioeconomic status.

Results

In the adult population, maternal grandmaternal smoking during pregnancy was associated with a higher risk for asthma (OR (95% CI): 1.38 (1.06 to 1.79)), early childhood asthma (1.49 (95% CI 1.06 to 2.11)) and a lower FEV1/FVC% predicted (B (95% CI): −1.04 (−1.91 to −0.16) in men. These findings were not observed in a separate analysis of children that participated in this study. There was also no significant association between paternal grandmaternal smoking and asthma/lung function.

Conclusion

Maternal grandmaternal smoking during pregnancy is associated with higher asthma risk and lower lung function in male grandchildren and a reverse effect in male grandchildren of subsequent generations. Our study highlights the deep-rooted effects of tobacco smoking across generations.
Changes in cigarillo availability following implementation of a local flavoured tobacco sales restriction
Authors: Rogers Todd , Gammon Doris G , Coats Ellen M , Nonnemaker James M , Xu Xin
Published: February 2021

Introduction

Providence, Rhode Island (RI) was among the first US jurisdictions to enact a policy (effective 3 January 2013) restricting the retail sale of non-cigarette tobacco products with a characterising flavour other than the taste or aroma of tobacco, menthol, mint or wintergreen. We used scanner data to assess the impact of this sales restriction on retail availability of cigarillos, flavoured and otherwise, in Providence and a rest-of-state (ROS) comparison area.

Methods

Every unique cigarillo product—each indicated by a universal product code (UPC)—available for sale in RI from January 2012 to December 2016 was assigned to an exclusive flavour-name category (tobacco; explicit or concept flavour; or menthol/mint) based on characteristics in the scanner dataset and, as necessary, information from online websites. We calculated weekly unique cigarillo UPC counts and market share by flavour category and used difference-in-difference regression to assess prepolicy and postpolicy changes in counts and share in Providence relative to ROS.

Results

The prepolicy to postpolicy decrease in the number of unique cigarillo products available in Providence was 28.64 (±5.83) UPCs greater than the comparable decrease in ROS (p<0.05). The prepolicy to postpolicy increase in the number of unique concept-named flavoured cigarillo products in Providence was 6.08 (±2.31) UPCs greater than the increase in ROS (p<0.05). The postpolicy market share of concept-named flavoured cigarillos was higher in Providence (27.32%, ±1.77) than ROS (12.67%, ±1.67) (p<0.05).

Conclusions

After policy implementation, Providence consumers were exposed to fewer cigarillo UPCs but a greater variety and proportion of concept-named flavoured cigarillos in the retail marketplace.
Hybrid calculation of the MSSM Higgs boson masses using the complex THDM as EFT
Authors: Bahl Henning , Murphy Nick , Rzehak Heidi
Published: February 2021

Recently, the Higgs boson masses in the Minimal supersymmetric standard model (MSSM) and their mixing have been calculated using the complex two-Higgs-doublet model (cTHDM) as an effective field theory (EFT) of the MSSM. Here, we discuss the implementation of this calculation, which we improve in several aspects, into the hybrid framework of by combing the cTHDM-EFT calculation with the existing fixed-order calculation. This combination allows accurate predictions also in the intermediate regime where some SUSY particles are relatively light, some relatively heavy and some in between. Moreover, the implementation provides precise predictions for the Higgs decay rates and production cross-sections.
EVALUATION OF THE CORRELATION BETWEEN FACIAL MUSCLE AND BRAIN ACTIVITIES IN AUDITORY STIMULATION
Authors: SOUNDIRARAJAN MIRRA , AUGUSTYNEK MARTIN , KREJCAR ONDREJ , NAMAZI HAMIDREZA
Published: February 2021

Evaluation of the correlation of the activities of various organs is an important area of research in physiology. In this paper, we evaluated the correlation among the brain and facial muscles’ reactions to various auditory stimuli. We played three different music (relaxing, pop, and rock music) to 13 subjects and accordingly analyzed the changes in complexities of EEG and EMG signals by calculating their fractal exponent and sample entropy. Based on the results, EEG and EMG signals experienced more significant changes by presenting relaxing, pop, and rock music, respectively. A strong correlation was observed among the alterations of the complexities of EMG and EEG signals, which indicates the coupling of the activities of facial muscles and brain. This method could be further applied to investigate the coupling of the activities of the brain and other organs of the human body.
Modifications to the Framework Regions Eliminate Chimeric Antigen Receptor Tonic Signaling
Authors: Landoni Elisa , Fucá Giovanni , Wang Jian , Chirasani Venkat R. , Yao Zhiyuan , Dukhovlinova Elena , Ferrone Soldano , Savoldo Barbara , Hong Lee K. , Shou Peishun , Musio Silvia , Padelli Francesco , Finocchiaro Gaetano , Droste Miriam , Kuhlman Brian , Shamshiev Abdijapar , Pellegatta Serena , Dokholyan Nikolay V. , Dotti Gianpietro
Published: February 2021

Chimeric antigen receptor (CAR) tonic signaling, defined as spontaneous activation and release of proinflammatory cytokines by CAR-T cells, is considered a negative attribute because it leads to impaired antitumor effects. Here, we report that CAR tonic signaling is caused by the intrinsic instability of the mAb single-chain variable fragment (scFv) to promote self-aggregation and signaling via the CD3ζ chain incorporated into the CAR construct. This phenomenon was detected in a CAR encoding either CD28 or 4-1BB costimulatory endodomains. Instability of the scFv was caused by specific amino acids within the framework regions (FWR) that can be identified by computational modeling. Substitutions of the amino acids causing instability, or humanization of the FWRs, corrected tonic signaling of the CAR, without modifying antigen specificity, and enhanced the antitumor effects of CAR-T cells. Overall, we demonstrated that tonic signaling of CAR-T cells is determined by the molecular instability of the scFv and that computational analyses of the scFv can be implemented to correct the scFv instability in CAR-T cells with either CD28 or 4-1BB costimulation.
Novel recommendation for enhancing optical properties of CP-WLEDs by Ba2Si5N8Eu2+ phosphor
Authors: Phan Van-Duc , Tran Minh , Vu Q. S.
Published: February 2021

In this paper, the Ba2Si5N8Eu2+ phosphor is proposed as the novel recommendation for enhancing the optical properties in terms of D-CCT, CRI, CQS, and LO of the CP-WLEDs. Firstly, we conducted the physical model of the CP-WLEDs in the Light Tools software with the main parameters like the real LEDs. Furthermore, the scattering process in LEDs compound of the CP-WLEDs is simulated and investigated by the Matlab software. Then the influence of the Ba2Si5N8Eu2+ concentration on the D-CCT, CRI, CQS, and LO of the CP-WLEDs is investigated. Finally, the research results showed that the Ba2Si5N8Eu2+ concentration has a considerable effect on the D-CCT, CRI, CQS, and LO of the CP-WLEDs. From the results, we can state that the Ba2Si5N8Eu2+ phosphor can be considered as the novel recommendation for enhancing the optical properties of the CP-WLEDs.
Performance analysis of real-time PSO tuned PI controller for regulating voltage and frequency in an AC microgrid
Authors: Sultana Umbrin , Qazi Sajid Hussain , Rasheed Nadia , Mustafa M. W.
Published: February 2021

In this study, a control strategy based on the self-tuning method and synchronous reference frame (SRF) with PI regulator is proposed to achieve optimum quality of power in an autonomous micro grid (MG). The MGS is based on multiple distributed generation (DG) connected with 120 kV power grid. The proposed system is first simulated with fixed gain values for PI controller which are not optimal for sudden changes in the system i.e. transition of MG to islanding mode, load variations. So, the particle swarm optimization (PSO) has been utilized for tuning of PI controller parameters which ensure flexible performance and superior quality of power. The principal parameters considered in this study are, regulation of voltage and frequency, steady-state and dynamic response and harmonic distortion, mainly when microgrid is islanded. The performance of PI and PI-PSO is compared in this study by simulating AC microgrid in the MATLAB/Simulink environment. Summarized results of the system are provided to authenticate viability of proposed arrangement. The proposed controller performs intelligently while regulating voltage and frequency of the MGS and utility system.

 
Partially isolated four port converter with combined PWM and secondary phase shift control
Authors: Ranipriya G. , Jegatheesan R. , Vijayakumar K.
Published: February 2021

A partially isolated four-port converter is proposed in this paper for interfacing two renewable sources and a storage device with an isolated load. This converter is capable of achieving high power density because of the effective sharing of devices among the input ports. Combined PWM and secondary phase shift control is employed to have a decoupled power flow management of input and output side ports. PWM control is used at the input side for maximum power tracking of renewable sources and battery power management. At the output side, secondary Phase shift control is used for controlling the output voltage. The adopted secondary phase shift control allows the primary switching legs to be operated with 1800 phase shift which results in reduced current ripple at input ports. The working principle of the converter, its output characteristics and control strategy are discussed. Working of the converter and its control strategy is verified through simulation for different input and output conditions. Further, to validate the simulation results, the experimental results of a 500W prototype are also provided.
Observer-based tracking control for single machine infinite bus system via flatness theory
Authors: Aghababa Mohammad Pourmahmood , Marinescu Bogdan , Xavier Florent
Published: February 2021

In this research, we aim to use the flatness control theory to develop a useful control scheme for a single machine connected to an infinite bus (SMIB) system taking into account input magnitude and rate saturation constraints. We adopt a fourth-order nonlinear SMIB model along an exciter and a turbine governor as actuators. According to the flatness-based control strategy, first we show that the adopted nominal SMIB model is a flat system. Then, we develop a full linearizing state feedback as well as an outer integral-type loop to ensure suitable tracking performances for the power and voltage as well as the angular velocity outputs. We assume that only the angular velocity of the generator is available to be measured. So, we provide a linear Luenberger observer to estimate the remaining states of the system. Also, the saturation nonlinearities are transferred to the linear part of the system and they are canceled out using their estimations. The efficiency and usefulness of the proposed observer-controller against faults are illustrated using simulation tests in Eurostag and Matlab. The results show that the clearing critical time of the introduced methodology is larger than the classical control approaches and the proposed observer-based flatness controller exhibits over much less control energy compared to the classic IEEE controllers.
Automatic segmentation of plantar thermograms using adaptive C means technique
Authors: Prabhu S. Madhava , Verma Seema
Published: February 2021

Diabetic foot ulcer (DFU) is one of the major concern of diabetes and it is rapidly increasing, in worst case scenario this may lead to amputation. The DFU can be avoided by the early detection and proper diagnosis. Many of the studies carried out highlights that, thermography is the most useful technique to measure the changes in the temperature of plantar surface and alerts to indicate the risk associated with DFU. The distribution of temperature does not have a fixed pattern across the patients, hence it makes the difficulty in measuring the appropriate changes. This gap will provide a scope to improve the analysis technique so as to measure the plantar surface temperature effectively and identify any abnormal changes. In this paper, the segmentation algorithm namely adaptive C means (ACM) for the image segmentation is discussed. ACM is based on the spatial information and this method includes the two stage implementation. In the first stage, nonlocal spatial information is added and in the second stage, spatial shape information is used in order to refine the constraint of local spatial. Outcome of the proposed method shows that ACM is very much effective and it outperforms the other existing methods.
Illumination-invariant vegetation detection for a vision sensor-based agricultural applications
Authors: Choi Keun Ha , Kim SooHyun
Published: February 2021

In this paper, we propose a novel method, illumination-invariant vegetation detection (IVD), to improve many aspects of agriculture for vision-based autonomous machines or robots. The proposed method derives new color feature functions from simultaneously modeling the spectral properties of the color camera and scene illumination. An experiment in which an image sample dataset was acquired under nature illumination, including various intensities, weather conditions, shadows and reflections, was performed. The results show that the proposed method (IVD) yields the highest performance with the lowest error and standard deviation and is superior to six typical methods. Our method has multiple strengths, including computational simplicity and uniformly high-accuracy image segmentation.
A proposed model for frequency tuned antennas used in mobile communication systems
Authors: Al Ka'bi Amin H. , Rady Magid M.
Published: February 2021

The antenna is considered as one of the most fundamental elements in wireless communication systems, especially in mobile devices. Desirable specifications of antennas include covering wide range of operating frequencies, while maintaining high quality of system performance over the whole range of operating frequencies. Therefore, the ability of tuning the resonant frequency of the antenna without altering its physical dimensions would be highly recommended in up-and-coming designs of antennas in mobile devices. This research work proposes a model for tuning the operating frequency of the inverted F-antenna over a reasonably wide range of frequencies, via altering the electromagnetic properties of its ferrite material. In this proposed model, it will be shown that the electronic control of the permeability of the ferrite material of the antenna leads effectively to a significant shift in its resonant frequency, and hence to an overall improvement in the performance of the communication system.
A trust-based authentication framework for security of WPAN using network slicing
Authors: Parvin Sazia , Gawanmeh Amjad , Venkatraman Sitalakshmi , Alwadi Ali , Al-Karaki Jamal N. , Yoo Paul D.
Published: February 2021

New technologies bring along many challenges including security and privacy. Wireless personal area networks (WPANs) are characterized by limited energy resources and computing power that calls for lightweight security mechanisms in these networks as a mandatory requirement. In this paper, a lightweight trust-based framework for node authentication in WPAN is proposed. Our main objective is to minimise the effort in distinguishing valid requests of trustworthy nodes from invalid requests of malicious nodes that can result in network compromises. We achieve this through network slicing which divides the network into virtually primary and secondary networks. The proposed framework has three-fold benefits. First, it authenticates nodes’ requests based on a novel method of trust value calculation. Second, the framework maintains energy efficiency while authenticating nodes’ requests to access WPAN resources. Finally, the framework provides a solution for biasing problem that can arise due to unexpected behaviour of malicious users in WPANs. The framework efficacy is illustrated on a case study to shown how it can accurately capture trust relations among nodes while preventing malicious behavior.
Improvements on the performance of subcarrier multiplexing/wavelength division multiplexing based radio over fiber system
Authors: Tran Duc-Tan , Bui Ninh Trung
Published: February 2021

Radio over fiber (RoF) techniques are good candidates to create the backbone of the next generation of wireless networks. Many parameters affect RoF communications such as amplified spontaneous emission noise (ASE), four-wave mixing nonlinearity (FWM), the modulation, channel spacing, switching voltage, and phase shifter. In this paper, we propose an improved model of RoF communication systems using subcarrier multiplexing/wavelength division multiplexing (SCM/WDM) technique with unequal channel spacing and 1-km Erbium-doped fiber amplifier (EDFA). Simulation results confirmed that we could obtain the lowest bit error rate and noises when the EDFA is placed at 1 km from the transmitter by using optical single-sideband (OSSB) modulation at frequencies 193.1 THz, 193.2 THz, 193.35 THz, and 193.6 THz.
A comprehensive review on hybrid network traffic prediction model
Authors: Shi Jinmei , Leau Yu Beng , Li Kun , Obit Joe Henry
Published: February 2021

Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes with a summary of the strengths and limitations of existing hybrid network prediction models which use optimization and decomposition techniques, respectively. These two techniques have been identified as major contributing factors in constructing a more accurate and fast response hybrid network traffic prediction.
Design and implementation of dual-core MIPS processor for LU decomposition based on FPGA
Authors: Saad Rusul Khalil , Omran Safaa S.
Published: February 2021

Many systems like the control systems and in communication systems, there is usually a demand for matrix inversion solution. This solution requires many operations, which makes it not possible or very hard to meet the needs for real-time constraints. Methods were exists to solve this kind of problems, one of these methods by using the LU decomposition of matrix which is a good alternative to matrix inversion. The LU matrices are two matrices, the L matrix, which is a lower triangular matrix, and the U matrix, which is an upper triangular matrix. In this paper, a design of dual-core processor is used as the hardware of the work and certain software was written to enable the two cores of the dual-core processor to work simultaneously in computing the value of the L matrix and U matrix. The result of this work are compared with other works that using single-core processor, and the results found that the time required in the cores of the dual-core is more less than using single-core. The designed dual-core processor is invoked using the VHDL language.
Forging a deep learning neural network intrusion detection framework to curb the distributed denial of service attack
Authors: Ojugo Arnold Adimabua , Yoro Rume Elizabeth
Published: February 2021

Today’s popularity of the internet has since proven an effective and efficient means of information sharing. However, this has consequently advanced the proliferation of adversaries who aim at unauthorized access to information being shared over the internet medium. These are achieved via various means one of which is the distributed denial of service attacks-which has become a major threat to the electronic society. These are carefully crafted attacks of large magnitude that possess the capability to wreak havoc at very high levels and national infrastructures. This study posits intelligent systems via the use of machine learning frameworks to detect such. We employ the deep learning approach to distinguish between benign exchange of data and malicious attacks from data traffic. Results shows consequent success in the employment of deep learning neural network to effectively differentiate between acceptable and non-acceptable data packets (intrusion) on a network data traffic.
Real-time human detection for electricity conservation using pruned-SSD and arduino
Authors: S. Ushasukhanya , S. Jothilakshmi
Published: February 2021

Electricity conservation techniques have gained more importance in recent years. Many smart techniques are invented to save electricity with the help of assisted devices like sensors. Though it saves electricity, it adds an additional sensor cost to the system. This work aims to develop a system that manages the electric power supply, only when it is actually needed i.e., the system enables the power supply when a human is present in the location and disables it otherwise. The system avoids any additional costs by using the closed circuit television, which is installed in most of the places for security reasons. Human detection is done by a Modified-single shot detection with a specific hyperparameter tuning method. Further the model is pruned to reduce the computational cost of the framework which in turn reduces the processing speed of the network drastically. The model yields the output to the Arduino micro-controller to enable the power supply in and around the location only when a human is detected and disables it when the human exits. The model is evaluated on CHOKEPOINT dataset and real-time video surveillance footage. Experimental results have shown an average accuracy of 85.82% with 2.1 seconds of processing time per frame.
Help through demonstration and automation for interactive computing systems: A survey of recent works
Authors: Rodrigues Pedro , Silva Jose Luis
Published: February 2021

Usability is very important however, it is still difficult to develop interactive computing systems that meet all user’s specificities. Help systems should be a way of bridging this gap. This paper presents a general survey on recent works (building upon previous surveys) related to improving applications’ help through demonstration and automation and, identifies which technologies are acting as enablers. The main contributions are, identifying (i) which are the recent existing solutions; (ii) which aspects must be investigated further; and (iii) which are the main difficulties that are preventing a faster progress.
A hybrid method of genetic algorithm and support vector machine for DNS tunneling detection
Authors: Al-Ibraheemi Fuqdan A. , AL-Ibraheemi Sattar , Amintoosi Haleh
Published: February 2021

With the expansion of the business over the internet, corporations nowadays are investing numerous amounts of money in the web applications. However, there are different threats could make the corporations vulnerable for potential attacks. One of these threats is harnessing the domain name protocol for passing harmful information, this kind of threats is known as DNS tunneling. As a result, confidential information would be exposed and violated. Several studies have investigated the machine learning in order to propose a detection approach. In their approaches, authors have used different and numerous types of features such as domain length, number of bytes, content, volume of DNS traffic, number of hostnames per domain, geographic location and domain history. Apparently, there is a vital demand to accommodate feature selection task in order to identify the best features. This paper proposes a hybrid method of genetic algorithm feature selection approach with the support vector machine classifier for the sake of identifying the best features that have the ability to optimize the detection of DNS tunneling. To evaluate the proposed method, a benchmark dataset of DNS tunneling has been used. Results showed that the proposed method has outperformed the conventional SVM by achieving 0.946 of f-measure
Machine learning model for clinical named entity recognition
Authors: J. Ravikumar , Kumar P. Ramakanth
Published: February 2021

To extract important concepts (named entities) from clinical notes, most widely used NLP task is named entity recognition (NER). It is found from the literature that several researchers have extensively used machine learning models for clinical NER.The most fundamental tasks among the medical data mining tasks are medical named entity recognition and normalization. Medical named entity recognition is different from general NER in various ways. Huge number of alternate spellings and synonyms create explosion of word vocabulary sizes. This reduces the medicine dictionary efficiency. Entities often consist of long sequences of tokens, making harder to detect boundaries exactly. The notes written by clinicians written notes are less structured and are in minimal grammatical form with cryptic short hand. Because of this, it poses challenges in named entity recognition. Generally, NER systems are either rule based or pattern based. The rules and patterns are not generalizable because of the diverse writing style of clinicians. The systems that use machine learning based approach to resolve these issues focus on choosing effective features for classifier building. In this work, machine learning based approach has been used to extract the clinical data in a required manner
Monitoring of solenoid parameters based on neural networks and optical fiber squeezer for solenoid valves diagnosis
Authors: Zahidi Abdallah , Amrane Said , Azami Nawfel , Nasser Naoual
Published: February 2021

As crucial parts of various engineering systems, solenoid valves (SVs) operated by electromagnetic solenoid (EMS) are of great importance and their failure may lead to cause unexpected casualties. This failure, characterized by a degradation of the performances of the SVs, could be due to a fluctuations in the EMS parameters. These fluctuations are essentially attributed to the changes in the spring constant, coefficient of friction, inductance, and the resistance of the coil. Preventive maintenance by controlling and monitoring these parameters is necessary to avoid eventual failure of these actuators. The authors propose a new methodology for the functional diagnosis of electromagnetic solenoids (EMS) used in hydraulic systems. The proposed method monitors online the electrical and mechanical parameters varying over time by using artificial neural networks algorithm coupled with an optical fiber polarization squeezer based on EMS for polarization scrambling. First, the MATLAB/Simulink model is proposed to analyze the effect of the parameters on the dynamic EMS model. The result of this simulation is used for training the neural network, then a simulation is proposed using the neural net fitting toolbox to determine the solenoid parameters (Resistance of the coil R, stiffness K and coefficient of friction B of the spring) from the coefficients of the transfer function, established from the model step response. Future work will include not only diagnosing failure modes, but also predicting the remaining life based on the results of monitoring.
Detection of citrus leaf diseases using a deep learning technique
Authors: Luaibi Ahmed R. , Salman Tariq M. , Miry Abbas Hussein
Published: February 2021

The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is very important. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be completed without a human by utilizing plant leaf pictures. There are many methods employed for the classification and detection in machine learning (ML) models, but the combination of increasing advances in computer vision appears the deep learning (DL) area research to achieve a great potential in terms of increasing accuracy. In this paper, two ways of conventional neural networks are used named Alex Net and Res Net models with and without data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small datasets. A self-dataset of 200 images of diseases and healthy citrus leaves are collected. The trained models with data augmentation give the best results with 95.83% and 97.92% for Res Net and Alex Net respectively.
Smart automatic petrol pump system based on internet of things
Authors: Baqir Zahra'a M. , Motlak Hassan J.
Published: February 2021

IoT is that a rapid expanding program presently for blend all equipment things like (sensors, gadgets, hardware and so on.) assemble and embed those with programming creating our own gadgets use The petroleum pump is these days running physically. it's an activity that fundamentally a drawnout time and requires more workforce. Additionally, put fuel stations in away zones is extermely costly. So achievement an automatic fuel filling system using web technology to solve these problems. There are dense proposed systems which goal to improve the fueling operation so as to form it less difficulty and more dependabl and more-safe, guarinte that the purchaser gets the same quantity of fuel in interchange for what he/she pays, so assist to end fraud at different fuel stations. these systems take human-software interaction by the web-enabeled procedure, thus keep off all errors made by people. The fundamental objective of this review paper is to survey of recent projects in design protype of smart petro pump based on RFID as payment tool and control on it remotely with high security level and concluded with future potential direction in design of smart petrol pump system.
A review on orchestration distributed systems for IoT smart services in fog computing
Authors: Mohd Pakhrudin Nor Syazwani , Kassim Murizah , Idris Azlina
Published: February 2021

This paper provides a review of orchestration distributed systems for IoT smart services in fog computing. The cloud infrastructure alone cannot handle the flow of information with the abundance of data, devices and interactions. Thus, fog computing becomes a new paradigm to overcome the problem. One of the first challenges was to build the orchestration systems to activate the clouds and to execute tasks throughout the whole system that has to be considered to the situation in the large scale of geographical distance, heterogeneity and low latency to support the limitation of cloud computing. Some problems exist for orchestration distributed in fog computing are to fulfil with high reliability and low-delay requirements in the IoT applications system and to form a larger computer network like a fog network, at different geographic sites. This paper reviewed approximately 68 articles on orchestration distributed system for fog computing. The result shows the orchestration distribute system and some of the evaluation criteria for fog computing that have been compared in terms of Borg, Kubernetes, Swarm, Mesos, Aurora, heterogeneity, QoS management, scalability, mobility, federation, and interoperability. The significance of this study is to support the researcher in developing orchestration distributed systems for IoT smart services in fog computing focus on IR4.0 national agenda
Best strategy to control data on internet-of-robotic-things in heterogeneous networks
Authors: Lafta Wisam Mahmood , A. Alkadhmawee Ahmed , A. Altaha Mohammed
Published: February 2021

The control and transmission of huge data constitute an immense challenge in various types of networks (wired and wireless). Congestion caused by the high traffic and low throughput of huge data continues to be major problems in a heterogeneous platforms such as internet of things (IoT) technology and internet-of-robotic-things (IoRT). The heterogeneous network requires new models and mechanisms to deal with the increased challenges posed by IoT and IoRT. Accordingly, eliminating the issues that emerge has compelled finding improved solutions as a new strategy. This study proposed a new strategy called routing information and distance vector (RIDV) to create the best improvement of a heterogeneous network. The RIDV strategy activates the routing information protocol (RIPv2) on a router in wire network parallel with the ad-hoc on-demand distance vector (AODV) protocol on the wireless network. The RIDV strategy is used to solve the problems of the diversity of heterogeneous networks as the basis of the infrastructure IoRT technology. Hence, this strategy can reduce or avoid congestion through the use of enhanced and effective best routing protocols. Simulation results using OPNET show that the proposed method improved the quality of service (QoS) compared with other related strategies and AODV and RIPv1 protocols in terms of data drop, traffic drop, queue delay, and throughput.
A systematic mapping study of performance analysis and modelling of cloud systems and applications
Authors: Odun-Ayo Isaac , Williams Toro-Abasi , Odusami Modupe , Yahaya Jamaiah
Published: February 2021

Cloud computing is a paradigm that uses utility-driven models in providing dynamic services to clients at all levels. Performance analysis and modelling is essential because of service level agreement guarantees. Studies on performance analysis and modelling are increasing in a productive manner on the cloud landscape on issues like virtual machines and data storage. The objective of this study is to conduct a systematic mapping study of performance analysis and modelling of cloud systems and applications. A systematic mapping study is useful in visualization and summarizing the research carried in an area of interest. The systematic study provided an overview of studies on this subject by using a structure, based on categorization. The results are presented in terms of research such as evaluation and solution, and contribution such as tools and method utilized. The results showed that there were more discussions on optimization in relation to tool, method and process with 6.42%, 14.29% and 7.62% respectively. In addition, analysis based on designs in terms of model had 14.29% and publication relating to optimization in terms of evaluation research had 9.77%, validation 7.52%, experience 3.01%, and solution 10.51%. Research gaps were identified and should motivate researchers in pursuing further research directions
Hybrid energy storage system control analogous to power quality enhancement operation of interlinking converters
Authors: Hajiaghasi Salman , Salemnia Ahmad , Hamzeh Mohsen
Published: February 2021

Increasing nonlinear loads and power electronic converters lead to various power quality issues in microgrids (MGs). The interlinking converters (ILCs) can participate in these systems to harmonic control and power quality enhancement. However, ILC participation deteriorates the dc link voltage, system stability, and storage lifetime due to oscillatory current phenomena. To address these problems, a new control strategy for a hybrid energy storage system (HESS) is proposed to eliminate the adverse effects of the harmonic control operation of ILC. Specifically, battery and super-capacitor (SC) are used as HESSs that provide low and high power frequency load, respectively. The proposed strategy tries to compensate the current oscillation imposed by ILC with fuzzy control of HESS. In this method, a proportional-resonant (PR) controller integrated with harmonic compensator (HC) is employed to control the ILC for power quality enhancement and oscillatory current elimination. The main advantages of the proposed strategy are to reduce DGs power ﬂuctuations, precise DC bus voltage regulation for generation and load disturbances, improved grid power quality under nonlinear load and transition conditions. The performance of the proposed method for isolated and grid-connected modes is verified using simulation studies in the MATLAB software environment.