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COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initiate prevention and control action, namely lockdown and qurantine. There are various mathematical and machine-learning models proposed for analyzing the spread and prediction. Each model has its own limitations and advantages for a particluar scenario. This article reviews the state-of-the art mathematical models for COVID-19, including compartment models, statistical models and machine learning models to provide more insight, so that an appropriate model can be well adopted for the disease spread analysis. Furthermore, accurate diagnose of COVID-19 is another essential process to identify the infected person and control further spreading. As the spreading is fast, there is a need for quick auotomated diagnosis mechanism to handle large population. Deep-learning and machine-learning based diagnostic mechanism will be more appropriate for this purpose. In this aspect, a comprehensive review on the deep learning models for the diagnosis of the disease is also provided in this article.

Fiber-optic communication and networks play an important role in communications technology. According to the last few decades, in this area, research development is growing rapidly. Machine learning (ML) algorithms for optical communications (OC) are certainly a hot topic in the current generation. To overcome the current limitation and different issues of fiber-optic communication and network, a machine learning (ML) algorithm is essential for us. Machine learning techniques are proof that it has superiority in solving complex problems. Machine learning algorithms generally focused on the education field, business organization, and health sectors. Currently, many researchers work in the optical communications area by using machine learning algorithms techniques. A machine learning algorithm is an emerging technology because it helps in the optical communication field for a better quality of service (QoS). According, to the first time, this work reviews machine learning for optical communication literature from a machine learning viewpoint. Only fiber-optic communication and network experts work on machine learning for optical networks, and they are not ML algorithms experts. This paper uses machine learning algorithms for calculating the quality of transmission (QoT) of light paths in optical 1 Springer Nature 2021 L A T E X template Article Title networks link. For better quality of transmission (QoT) estimation tools, it can show the performance analysis of machine learning-based algorithms by using such as bit error rate (BER), optical signal to noise ratio (OSNR), quality factor (Q-factor), blocking probability, and signal to noise ratio (SNR) data. This paper presents a novel concept of quality of transmission (QoT) based on the machine learning algorithm.

The Inter-Agency Committee on Radiation Safety (IACRS) was constituted in 1990, as a forum for collaboration and coordination between international bodies with regards to radiation safety. It consists today of representatives of eight intergovernmental member organizations (the European Commision, Food and Agriculture Organization, International Atomic Energy Agency (IAEA), International Labour Office, Organisation for Economic Co-operation and Development/ Nuclear Energy Agency (OECD/NEA), Pan American Health Organization, United Nations Scientific Committee on the Effects of Atomic Radiation and the World Health Organization) and five observer non-governmental organizations (International Commission on Radiological Protection, International Commission on Radiation Units & Measurements, International Electrotechnical Commission, International Radiation Protection Association and International Organization for Standardization). The IACRS provides a platform for interaction between these relevant international bodies to contribute to a common understanding of the scientific basis and legal framework for the application of the system of radiation protection, towards global harmonisation of radiation safety standards. The IACRS played a key role in the development of the International Basic Safety Standards (BSS) in 1996 and in its revision in 2014. Further, an IACRS specific Task Group—chaired by the IAEA—fosters the implementation of the BSS in a consistent and coherent manner in all Member States of the United Nations. The IACRS operates via a standing secretariat jointly provided by the IAEA and OECD/NEA and is chaired by one of its member organizations on a rotating basis for periods of about 18 months. This approach has proved to be effective and was the foundation for ensuring continuity of the work of the committee and at the same time allowing a rotating leadership for all member organizations. Currently, the IACRS is chaired by the WHO. The International Radiation Safety Framework under which the IACRS works is structured around four main areas: (a) science; (b) principles; (c) standards; and (d) practice. This paper presents briefly the mandates, roles and functions of the various international bodies that are relevant to the four above mentioned areas of work, discusses how these bodies coordinate their actions and complement each other to enhance radiation protection and safety worldwide and describes their contribution to the achievement of the Sustainable Development Goals. The paper also provides an overview of the main accomplishments of the IACRS since its inception 30 years ago, and an outlook on key challenges for its future activities.

The optical network plays a vital role in the current technology for providing high-speed data communication, which the networks operate at Tb/s. In this case, different modulation techniques can be used for different line rates to achieve high-speed data transmission. The light paths and various data rates such as 10, 40, and 100 Gb/s are the important parameters for mixed line rate networks. The wavelengths and line rates are powerful tools for mixed line rates networks. It can exist on different optical fibers. In this paper, advanced modulation techniques achieve a relative performance with the required Q-factor. This paper analysis for different matrix computations to achieve a superior Q-factor. It can affect the data rates and quality of transmission. This paper also proposed an algorithm that can improve the Q-factor. Q-factor is analyzed and proposes routing and wavelength assignment (RWA) techniques based on the Q-factor obtained at different line rates. This paper is a brief overview of a quality-aware path-finding algorithm for mixed line rates WDM/DWDM networks are present and discussed.

The authors have requested that this preprint be removed from Research Square.

To overcome the lack of rural electricity in Indonesia vortex pico-hydro turbines are used as an option solution. This is due to the ability of the vortex turbine to work in low head conditions effectively. This study is conducted with comparison of curved and straight blade to obtain a more optimum turbine performance. Two methods are carried out in this study, analytical and computational method. Analytical methods are used to determine blade geometry and its performance while computational methods are used to analyse internal flow of turbine. As a result, the study concludes that hydraulic efficiency of vortex turbine in this study doesn’t affect much between straight and curved blades. The hydraulic efficiency for those blades is around 0.63. In addition, the study continued by analysing the optimum location of the blade in the basin. The results of the study show that the optimum ratio of depth and diameter of the blade is 0.33 with turbine efficiency is 0.84. Thus, the location of the blades is more important than the type of blades.

The 5G and beyond wireless networks will be more dynamic and heterogeneous, which needs to work on multistrand waveforms. One of the most significant challenges in such a dynamic network, especially non cooperated cases, is the identification of particular modulation type, which the transmitter uses at the given time to decode the data successfully. This research proposes a modulation classification algorithm using the combination architectures of modified convolutional neural network. The proposed deep learning architecture is developed by combining the convolutional neural network, dense network, and long short-term memory network (LSTM), which is named as convolutional LSTM dense neural network (CLDNN). Moreover, the mean cumulative sum metric (MCS) is introduced in the pooling layer for improved classification accuracy. Dimensionality reduction through Principal Component Analysis is also applied to minimize the training time, so that the proposed architecture can be adopted for its practical usage. The simulation results prove that the presented CLDNN outperforms an ordinary CNN, while taking less training time.

Smarthealth care system is required to handle the large volume of data produced by the Massive Internet of Medical things. Smart healthcare system uses information and communication technology of IoT, data analytic, machine learning, deep learning, augmented reality, and cloud technologies to realize efficient, personalized, convenient health care systems. This chapter intended to give an overview, and the case of the above said ICT technologies for smart health applications. Smart healthcare systems are more patient-centric and enable them to get anywhere any health care service at an affordable cost. Machine learning and deep learning allow auto diagnosis of disease and predict the disease in an earlier stage in a systematic way from the acquired data set from the patient. Prediction of the disease increases the chance of curing the disease and reduces the Mortality rate. IoT enables smart continuous remote patient monitoring and medical data acquisition via an intelligent sensor system. Augmented Reality (AR) helps the surgeons to diagnose the disease accurately, performs surgery precisely with the help of real-time data of the patient quickly. AR also make surgeons precisely study patients’ anatomy through overlaying AR set augmentation of their scanned image on top of their body. Doctors able to visualize the internal part of the human body and organs without cutting the body. Cloud platform enables the patient data storage and retrieval of them anywhere at any time. Cloud computing facilitates run diagnosis and decision support software on ad hoc fashion and enables remote healthcare services. mHealth system utilizes mobile applications to easily reach the community of patients to provide health care services.

The International Commission on Radiological Protection (ICRP) has embarked on a review and revision of the system of Radiological Protection that will update the 2007 general recommendations in ICRP Publication 103. This is the beginning of a process that will take several years, involving open and transparent engagement with organisations and individuals around the world. While the system is robust and has performed well, it must adapt to address changes in science and society to remain fit for purpose. The aim of this paper is to encourage discussions on which areas of the system might gain the greatest benefit from review, and to initiate collaborative efforts. Increased clarity and consistency are high priorities. The better the system is understood, the more effectively it can be applied, resulting in improved protection and increased harmonisation. Many areas are identified for potential review including: classification of effects, with particular focus on tissue reactions; reformulation of detriment, potentially including non-cancer diseases; re-evaluation of the relationship between detriment and effective dose, and the possibility of defining detriments for males and females of different ages; individual variation in the response to radiation exposure; heritable effects; and effects and risks in non-human biota and ecosystems. Some of the basic concepts are also being considered, including the framework for bringing together protection of people and the environment, incremental improvements to the fundamental principles of justification and optimisation, a broader approach to protection of individuals, and clarification of the exposure situations introduced in 2007. In addition, ICRP is considering identifying where explicit incorporation of the ethical basis of the system would be beneficial, how to better reflect the importance of communications and stakeholder involvement, and further advice on education and training. ICRP invites responses on these and other areas relating to the review of the System of Radiological Protection.

In this paper, we extenuate the effect of malicious cognitive sensors (MCSs) from the optimal combining of cooperative spectrum sensing scheme. We consider the cognitive radio network (CRN) that includes honest cognitive sensors (HCS) and MCSs; the former one, report accurate sensing data to the combining centre (CC), while the latter, due to its malfunctioning, reports CC a false data of dissimilar statistical distribution. Our sensing scheme includes the combined two-stage test at CC, namely Shapiro–Wilk’s normality test and log-likelihood ratio test. The former is used to identify MCS and to exclude them from the latter, which optimally combines the sensing information of HCS. Simulation results show that detection performance of proposed method bests fault-tolerant cooperative spectrum sensing (FTCSS) method by 44.9%, when 4 MCS exist in a CRN. Further, the receiver operating characteristics show that, the proposed method does not increase the event of false alarm, unlike equal gain combining scheme and FTCSS, when MCS increases the false alarm.

This research proposes a composition process of method chunks to form a new software development method using Essence as a standard notation (we call it an essence-based method). A tool has also been built to support the process. Quality of the product and the process of software engineering are influenced by the method used. When the problem and the complexity of the solution increase, it is more difficult to choose the appropriate method. Some existing methods are harder to be modified to meet the need of a certain software project situations. Situational method engineering (SME) is an approach to compose a method from several method parts (or chunks) to meet the need of developer team according to the project situations. Some existing research have promoted several process models. One of such process is assembly process model. When Essence is used as a standard notation, the assembly process model must be adapted. Thus, we modify the process model to support composition of essence-based method chunks. A composition tool that support the process has also been built. Essence is a standard released by the Object Management Group (OMG) to define software engineering methods. Our tool has been evaluated by composing essence-based method chunks to form a new (essence-based) method.

In this paper we propose three novel ideas for Spatial Modualtion(SM) based Millimeter-Wave(mmWave) Multiple-Input Multiple-Output(MIMO) systems. Firstly, we propose an Hemispherical-Conformal Antenna Array(H-CAA) and Cylindrical Conformal Array Antenna(C-CAA), for which we develop the framework for SM scheme known as Conformal Antenna Array-Spatial Modulation(CAA-SM). The proposed system comprises of a single RF-chain like Conventional SM system. With the aid of this single RF chain we activate multiple antenna elements and make all the receive antenna aim at the transmitter. Whilst maintaining the advantages of the Conventional SM scheme it has been proved that the system under study has much better performance when compared with traditional SM systems.

Cognitive radio is emerging as one of the most promising aspects regarding the efficient usage of the radio spectrum and also on a non-interference basis. However the most challenging part is the effective detection of primary users (PUs). Nowadays there are a lot of threats from attackers who use techniques like data falsification, primary user emulations to cause harm to the users, so we need to address them with proper and efficient solutions. So in this survey we address the various threats and the challenges faced in cognitive radio environments and also we are here to discuss the various sampling techniques that could be used for the purpose of proper detection.

In this paper, we propose a robust spectrum sensing (SS) technique for cognitive radio receivers (CRRs) when a malicious CR user (MCRU) coexists in the network. The primary users (PUs) that we consider are the television (TV) broadcasters, who operate in an ultrahigh-frequency (UHF) band. Using multiple antennas of CRR, as a first step, we locate a narrower subband-within an UHF-TV range-at which the interference due to MCRU is least. Then, SS is implemented only at the chosen band, so that the detection of PU is not affected by the presence of MCRU. The finding of the apt band for SS is implemented through a table, which needs to know only the angle of arrival of signal from MCRU. We have shown the technique to estimate this angle of arrival as well. The simulation results show that the proposed method does not compromise the detection performance, unlike the energy detection scheme, which compromises severely when MCRU coexists.

In the recent past, the energy-efficient feature of cognitive radio networks has drawn the attention of many researchers. This is due to rapid growing demand of green environment standards and escalation in the growth of energy consumption costs. The work reported in the literature was developed for the sensing environment where the noise variance remains constant, which is not true in real life. This paper proposes a robust energy-efficient cooperative spectrum sensing strategy, which is immune to noise uncertainty. Coarse and fine sensing schemes are employed in the proposed method to achieve energy-efficient feature. Simulation upshots show that energy consumption is reduced significantly with revamped detection performance against noise uncertainty in the proposed technique.

The biological effects on humans of low-dose and low-dose-rate exposures to ionizing radiation have always been of major interest. The most recent concept as suggested by the International Commission on Radiological Protection (ICRP) is to extrapolate existing epidemiological data at high doses and dose rates down to low doses and low dose rates relevant to radiological protection, using the so-called dose and dose-rate effectiveness factor (DDREF). The present paper summarizes what was presented and discussed by experts from ICRP and Japan at a dedicated workshop on this topic held in May 2015 in Kyoto, Japan. This paper describes the historical development of the DDREF concept in light of emerging scientific evidence on dose and dose-rate effects, summarizes the conclusions recently drawn by a number of international organizations (e.g., BEIR VII, ICRP, SSK, UNSCEAR, and WHO), mentions current scientific efforts to obtain more data on low-dose and low-dose-rate effects at molecular, cellular, animal and human levels, and discusses future options that could be useful to improve and optimize the DDREF concept for the purpose of radiological protection.