Showing papers on "Literature survey published in 2022"

A Survey on IoT Applications in Smart Cities

Abstract: The term Internet of Things was coined a decade ago, but every day a slight part of its burgeoning ecosystem becomes an element in our lives. Industrial devices, consumer products, utility items, automobiles, sensors, and different everyday components are being combined with web connectivity and powerful information analytic capabilities that promise to remodel the lifestyle of people. The connectivity of humans, objects, and machines with the Internet is increasing, which results in the emergence of smart cities that bridge the physical and virtual world. IoT applications in smart cities such as energy management, agriculture, industrialization, transport, environment, safety, utilities, health, communities, commerce, tourism, and entertainment bring immense value into our everyday lives. This chapter is the study of various IoT applications in smart cities based on the literature survey method. The rapid growth of IoT has opened up vast new opportunities for users to look beyond their conventional industrial horizons.

User experience framework for understanding user experience in blockchain services

Abstract: Since the advent of blockchain, interest in the technology has rapidly increased, bringing the launch of new blockchain services. User experience (UX) research provides a basis for systematically understanding and improving the experience of users, and must be carried out regardless of service type. However, it seems that research on the UX of blockchain services has not kept up with the speed with which blockchain technology has been implemented. To remedy this situation, this study proposes a UX framework for understanding the nature of UX in blockchain services. An extensive literature survey related to UX, blockchain technology, and blockchain services was conducted to define UX in blockchain services and identify the elements of UX. Blockchain functions and values that had been newly introduced in blockchain services were identified to help clarify the nature of UX in blockchain services. The proposed framework consists of two parts: a general UX and a technological UX. The elements and sub-elements of the defined UX are derived with detailed explanations. The proposed framework was applied to four blockchain services as a case study. The appropriateness and applicability of the framework are supported by the results of this case study. This study is expected to inspire researchers to provide enhanced UX in blockchain services in the future.

Comparative Performance Analysis of Tanh-Apodized Fiber Bragg Grating and Gaussian-Apodized Fiber Bragg Grating as Hybrid Dispersion Compensation Model

Abstract: Fiber optic systems are used for the prolonged reach transmission systems, but by increasing the bit rate which is the main requirement of the current time, dispersion gets arisen which results in intersymbol interference. Compensation of dispersion to improve the transmission capability of the fiber optic system provides a vast field for research. From the literature survey done, use of Dispersion compensation fiber has been found as the most reliable method for compensating the dispersion, but it becomes expensive as the length of Dispersion compensation fiber is increased for long distance transmission. The Fiber Bragg Grating is also used as a dispersion compensation module as reported in previous works but has been found inefficient method. However, the Performance of the Fiber Bragg Grating can be enhanced by adapting optimum Chirping technique and Apodization profile. From the previous reported works, Tanh-Apodized Fiber Bragg grating and Gaussian-Apodized Fiber Bragg grating are found to have optimum performance characteristics in terms of side lobe suppression and maximum reflectivity, which motivates us to analyze the respective Fiber Bragg Gratings for compensating the dispersion at various chirping techniques and variable grating lengths. In this work, Tanh-Apodized Fiber Bragg grating and Gaussian-Apodized Fiber Bragg grating are analyzed and simulated in various chirping techniques individually, as well as along with the Dispersion compensation fiber, in the hybrid model of dispersion compensation for a 100 km long optical fiber link at the data rate of 10Gbps. The simulation software used is optisystem. Also, the grating length has been varied and the different performance characteristics like Q-factor, BER, and Eye diagram are analyzed and compared. It has been observed that the Gaussian-Apodized quadratic-chirped Fiber Bragg Grating at the grating length of 26.6 mm along with the 11 km long Dispersion compensation fiber makes the cheaper dispersion compensation module with the finest performance.

Survey on Internet of Things Based Intelligent Wireless Sensor Network for Fire Detection System in Building

Abstract: Disaster caused by fire in various residential, commercial and industrial places is a major concern as it may result in huge damage of infrastructure as well as human life. Thus, an early detection of fire and notify the appropriate authority for prompt extinguishing to protect valuable lives and properties is a very important task. A real-time automatic intelligent fire detection system integrated with wireless sensor network (WSN), artificial intelligent (AI), and internet of things (IoT) and can solve this problem. In this paper, a literature survey on wireless sensor network capabilities through use of different intelligent algorithm using IoT aimed at fire detection has been presented. A schematic block diagram of IoT-based intelligent WSN for fire detection system (FDS) is also proposed for real-time automatic early detection of fire and disaster management.

A synthetical methodology for identifying priority pollutants in reclaimed water based on meta-analysis

Abstract: Wastewater reclamation and reuse is an increasing global project, while the reclamation treatment on wastewater does not completely remove all pollutants in water. The residual pollutants in reclaimed water would cause potential risk on human health and ecosystem safety during the long-term use. It is impossible to analyze and control all pollutants one by one in practice, therefore, identification and control of priority pollutants will be efficient strategy to ensure the safe use of reclaimed water. An integrated three-step methodology for identifying priority pollutants in reclaimed water was proposed in this study. First, a comprehensive literature survey on the occurrence of pollutants in reclaimed water was conducted, and a dataset DPR for pollutants occurrence in reclaimed water was established, containing 1,113 pollutants. Second, 611 chemicals that had been recommended as hazardous pollutants for various water bodies in previous literatures were summarized, and a dataset DHP for hazardous pollutants in water was obtained. Third, meta-analysis on these two datasets (DPR and DHP) was performed, a new dataset DHPR for hazardous pollutants in reclaimed water was established, including 265 candidates. Finally, 59 substances out of dataset DHPR were identified as priority pollutants for reclaimed water based on their recommendation frequency. It is expected that this synthetical methodology will provide powerful support for scientific evaluating and managing water pollution and ensuring safe use of reclaimed water.

Application of bacterial tyrosinases in organic synthesis.

Abstract: Bacterial tyrosinases, as in the case of other bacterial oxidative enzymes, have been found to possess biochemical characteristics that typically make them more suited to applications requiring special operational conditions such as alkaline pH, high or low temperature, the presence of organic solvents, and the presence of inhibitors. Even though a great deal is known about fungal tyrosinases, bacterial tyrosinases still vastly remain underexplored for their potential application in organic synthesis. A literature survey in particular highlights the gaps in our knowledge pertaining to their biochemical properties. Bacterial tyrosinases have not only shown promise in the synthesis of medically important compounds such as L-3,4-dihydroxyphenylalanine (L-DOPA) and melanin but have also seen application in cross-linking reactions of proteins and the polymerization of environmental pollutants. Their ability to catalyse o-hydroxylation reactions have shown some degree of promise in the biocatalytic conversion of resveratrol to piceatannol, tyrosol to hydroxytyrosol, and many more. In this review, we will explore the world of bacterial tyrosinases, their current applications, and future perspectives for the application of these enzymes in organic synthesis.

Currículo e práticas na educação superior no contexto da pandemia da COVID-19

Abstract: The contagion by the Sars-CoV-2 virus brought consequences to all spheres of social life, and Higher Education also needed to respond to the health emergency. The response speed of Higher Education Institutions to maintain academic activities in this scenario was related to the technical and technological capabilities and the mastery of the distance and/or online education model already installed. The discussion about digital accessibility and connectivity also permeated the construction of these responses. This study aims to analyze university pedagogical practices and the need for curricular reinvention imposed by the global crisis of the COVID-19 pandemic. It is a qualitative and quantitative research. A literature survey was conducted and an online semi-structured questionnaire was applied. The sample is composed of 106 professors who work in Brazilian public and private Higher Education Institutions. The research findings show that the pandemic context demanded an emerging pedagogical model, called emergency remote teaching. It was found that there was an effort by professors to qualify for the use of digital technologies in the mediation of the teaching and learning process, curriculum, and pedagogical practices readjustment, enabling students to have new learning experiences.

Analyzing the Barriers for the Implementation of Lean and Green Closed-Loop Supply Chain in Indian SMEs

Abstract: The dynamics of the supply chain has been changing since the past two decades. The different view of the supply chain is proposed by the researchers to achieve sustainability in the system. With consideration of the environment and cleaner production concerns, lean and green closed-loop supply chain aspect is crucial to enhance the sustainability of an organization. In this context, this chapter analyzes the barriers for the implementation of lean and green closed-loop supply chain in the Indian small and medium enterprises (SMEs). A total of 35 barriers have been identified from the literature survey; out of which, 15 potential barriers have been shortlisted by using the best–worst method (BWM). After finalizing the top 15 barriers, interpretive structural modeling (ISM) has been adopted to analyze the relationship among the barriers. The proposed framework provides systematic approach for analyzing the barriers, and also creates roadmaps for the implementation of lean and green closed-loop supply chain in the SMEs.

A Comparative Study on Augmented Analytics Using Deep Learning Techniques

Abstract: Image augmentation is the most recognized type of data augmentation and intrinsic development for transforming image diversities in the training dataset that belongs to a similar class as the novel image. In the area of image augmentation handling, a collection of operations is shifting, flipping, zooming, cropping, rotation, and transformation in color space. A wide range of applications frequently used the aspects of deep learning are industry, science, and government domain, namely adaptive testing, image classification, computer vision, object detection, and face recognition and has achieved substantial development and accomplishment of deep learning. This study concentrates on the most important challenges present in the image estimation level that have a significant effect on dimension reduction, pooling, and edge detection. The deep learning methods involved here are convolution neural network (CNN), generative adversarial network (GAN), and deep convolution neural network (DCNN). Finally, a comparative study has performed a massive literature survey on various deep learning models.

Non-local dispersion and the reassessment of Richardson's t3-scaling law

Abstract: The Richardson-scaling law states that the mean square separation of a fluid particle pair grows according to t3 within the inertial range and at intermediate times. The theories predicting this scaling regime assume that the pair separation is within the inertial range and that the dispersion is local, which means that only eddies at the scale of the separation contribute. These assumptions ignore the structural organization of the turbulent flow into large-scale shear layers, where the intense small-scale motions are bounded by the large-scale energetic motions. Therefore, the large scales contribute to the velocity difference across the small-scale structures. It is shown that, indeed, the pair dispersion inside these layers is highly non-local and approaches Taylor dispersion in a way that is fundamentally different from the Richardson-scaling law. Also, the layer's contribution to the overall mean square separation remains significant as the Reynolds number increases. This calls into question the validity of the theoretical assumptions. Moreover, a literature survey reveals that, so far, t3 scaling is not observed for initial separations within the inertial range. We propose that the intermediate pair dispersion regime is a transition region that connects the initial Batchelor- with the final Taylor-dispersion regime. Such a simple interpretation is shown to be consistent with observations and is able to explain why t3 scaling is found only for one specific initial separation outside the inertial range. Moreover, the model incorporates the observed non-local contribution to the dispersion, because it requires only small-time-scale properties and large-scale properties.

Survey for Electroencephalography EEG Signal Classification Approaches

Abstract: This paper presents a literature survey for electroencephalogram (EEG) signal classification approaches based on machine learning algorithms. EEG classification plays a vital role in many health applications using machine learning algorithms. Mainly, they group and classify patient signals based on learning and developing specific features and metrics. In this paper, 32 highly reputed research publications are presented focusing on the designed and implemented approach, applied dataset, their obtained results and applied evaluation. Furthermore, a critical analysis and statement are provided for the surveyed papers and an overall analysis in order to have all the papers under an evaluation comparison. SVM, ANN, KNN, CNN, LDA, multi-classifier and more other classification approaches are analyzed and investigated. All classification approaches have shown potential accuracy in classifying EEG signals. Evidently, ANN has shown higher persistency and performance than all other models with 97.6% average accuracy.

Smart Production Planning and Control Model

Abstract: Industry 4.0 concept is based on digitalization, networking, and value-creation. The Industry 4.0 efforts comprise sensing, connectiveness, systems integration, advanced automation, manufacturing data-driven, and cyber-physical production system in the manufacturing systems-context. The digital capabilities and resources provided by Industry 4.0 need to be coordinating for value-creation. For this purpose, the manufacturing systems concentrate these efforts around Production Planning and Control Function (PPC) and their systems. PPC function is considered the “brain” of manufacturing. PC function is considered the “brain” of manufacturing. Therefore, the digitalization and efforts toward Industry 4.0 are mandatory when PPC’s role is providing to manufacturing companies their performance goals and competitive advantage to cope with digital business strategies. We carry out a literature survey of over 50 journal articles to determine the core Industry 4.0 technologies, key-factors and resources, and input/output capabilities, which support a smart PPC status. Therefore, our results found the main five Industry 4.0 ecosystems for Smart PPC: Industrial Internet of Things, industrial big data and analytics/artificial intelligence, cloud manufacturing, ICTs and accessory technologies, and cyber-physical production systems. We establish 36 resources/key-factors and ten input/output capabilities along with a digital thread that links these Industry 4.0 technologies’ ecosystems. In last, we develop a technological relationship model for smart PPC based on these resources and capabilities provided by the five Industry 4.0 technologies spread over ten activities in three PPC levels: aggregate planning, detailed planning, and production control.

Mycogenic nanoparticles: Synthesis, characterizations and applications

Abstract: Nanotechnology is an emergent field that is relevant to the diversified arena of science and technology. Owing to the disadvantages over the non-biological systems, several researchers turned their focus on the biological system for nanoparticle (NP’s) synthesis. Among the different microorganisms used for nanoparticle synthesis, fungi are the effective candidate to produce nanostructures both intracellularly and extracellularly with required shape and size. Also, the mycogenesis of nanoparticles found to be proficient in fabricating, manipulating and utilizing the materials in nanosize and the nanoparticle size as well shape depends on the microorganisms used and the experimental factors. The literature survey documented that NaADH-dependent nitrate reductase from the fungi plays a significant role in the change of mycogenic metal ions to mycogenic metal NP’s. Though the mycogenic nanoparticle is eco-friendly, it has several uses in the field of agriculture, medicine, textile industries and also in drug delivery systems. In this chapter, we have discussed a brief outline of the emergent part of fungi in mycogenic nanoparticle synthesis, mechanism, characterization and their applications.

5G Modulation Techniques—A Systematic Literature Survey

Abstract: In the recent trend, data consumption goes on increasing day by day. In this way, the present 3G and 4G advancements cannot bolster those expansions in data usage, and the speed should be improved to accomplish better experience while at the same time accessing the data services. 5G technologies have a higher information rate and a better coverage area. It expends less power and has greater security, better spectral efficiency and energy efficiency. The speed of 5G technology reaches from roughly 50 mbps to 2G and even to 1000 Gbps which is much quicker than the 4G technology. Modulation is a process of influencing the data to a signal transmitted over radio carrier, which is the backbone of wireless communication system. Most remote transmissions today are computerized, and with the restricted range accessible, the modulation is more critical than it has ever been. We live in a digital era where wires are not needed to connect with loved ones. Messages, information and signals are sent across the globe within minutes. The modulation process is an important technique in the fast transmission of signals. The fundamental objective of modulation process is squeezing as much of data into a smaller possible spectrum is known as the spectral efficiency. It is used to measures how rapidly the information is transmitted in a single bandwidth. Its unit is b/s/Hz (bits per second per Hz). Various methods have risen to accomplish high spectral efficiency in different modulation techniques. Thus, in this paper, the various methods for modulation techniques are utilized for the development of the next generation communication system are contemplated.

Anxiety Performance Among Athlete in Response to Theories and Standard Instruments: A Systematic Review

Abstract: Successful sports performance is determined on how skillful the athlete perceives the anxiety into positive performance. This study purposely assesses the background of recent studies on anxiety theory, assessment instrument and map the anxiety literature survey into a simple taxonomy. Current research intends to identify the vital aspects that affect athlete performance in the sports field that remain a challenge nowadays. Most related articles on (1) anxiety theory (2) athlete (3) sports performance through three (3) popular databases are searched on Clarivate Analytics, Scopus, and PubMed. These databases are deemed broad to cover both relevant theoretical and technical literature. Thirty-three studies (N = 33) were selected after screening process and biddable to the inclusion criteria mentioned. Background data of studies provided varying samples, sports, and conditions. The study finding has highlighted the functional of anxiety theory to measure anxiety performance assisted with an anxiety assessment. The ability of the anxiety theories getting robust toward athlete performance by integrating with anxiety assessment.

Frequent Itemset Mining Algorithms—A Literature Survey

Abstract: A prominent subfield of data mining is Frequent Itemset Mining which explores mysterious and hidden patterns in the transaction database. However, as the volume of data increases, the mining of hidden patterns of the frequent itemset is more time-consuming. Moreover, dominant memory consumption is required in mining where the hidden pattern of the frequent itemset computation is complicated through the algorithm. Therefore, a powerful algorithm is needed to mine the hidden patterns of the frequent itemset within a more precise execution time and with lower consumption of memory while the size of data increases over the period. This study article focuses on the pros and cons of FP-growth, LP-growth, FIU-tree, IFP-growth algorithm for frequent pattern discovery, and more efficient frequent pattern mining algorithms can be further carried out.

Polyethylene glycol and its derivatives as environmental sustainable corrosion inhibitors: A literature survey

Abstract: Increasing ecological awareness is escalating pressure on the synthesis, development, and use of nonhazardous alternatives to traditional toxic corrosion inhibitors. Polyethylene glycol (PEG) and PEG derivatives represent a new class of environmental sustainable alternatives. They have several beneficial properties, such as being nonvolatile and having a relatively higher vapor density. PEG and its derivatives also possess biodegradability and low flammability that offer them as environmentally sustainable alternatives for use in different industrial and biological applications, including as corrosion inhibitors. PEG and its derivatives are relatively highly stable in acids and bases, and therefore they can be used as corrosion inhibitors at various pH ranges of electrolytes without degradation. They can also be used as high-temperature corrosion inhibitors as they are thermally stable. PEG and most of the PEG derivatives are highly stable for oxidation by H2O2 and hydride reduction (NaBH4). One of the greatest advantages of using PEG and its derivatives as corrosion inhibitors is that they can be recovered from aqueous media through an extraction process using a suitable solvent or by the direct distillation method. A literature study shows that several reports are available on PEG and its derivatives as green corrosion inhibitors. This chapter features the collection of some major reports on PEG and PEG derivatives as corrosion inhibitors.

Performance Improvement in Hot Rolling Process with Novel Neural Architectural Search

Abstract: State-of-the-art infrastructure, excellent computational facilities and ubiquitous connectivity across the industries have led to the generation of large amounts of heterogeneous process data. At the same time, the applicability of machine learning and artificial intelligence is witnessing a significant rise in academics and engineering, leading to the development of a large number of resources and tools. However, the number of research works and applications aimed at implementing data sciences to problems in process industries is far less. The proposed work aims to fill the niche by proposing Artificial Neural Network (ANN)-based surrogate construction using extremely nonlinear, static, high dimensional (32 features) noisy data sampled irregularly from inlet and outlet streams of hot rolling process in iron and steel making industry. Though ANNs are used extensively for modelling nonlinear data, literature survey has shown that their modelling is governed by heuristics thus making them inefficient for use in process industries. This aspect is of high relevance in contemporary times as hyper-parameter optimization, automated machine learning and neural architecture search (NAS) constitute a major share of current research in data sciences. We propose a novel multi-objective evolutionary NAS algorithm to optimally design multi-layered feed-forward ANNs by balancing the aspects of parsimony and accuracy. The integer nonlinear programming problem of ANN design is solved using binary coded Non-Dominated Sorting Genetic Algorithm (NSGA-II). ANNs designed for the hot rolling process are found to demonstrate an accuracy of 0.98 (averaged on three outputs) measured in terms of correlation coefficient R2 on the test set. The successful construction of accurate and optimal ANNs provides a first-of-its-kind model for the hot rolling process in the iron and steel making industry. The proposed method can minimize the chances of over-fitting in ANNs and provides a generic method applicable to any kind of data/model from process industries.