Showing papers on "Literature survey published in 2016"
TL;DR: This paper provides an up-to-date picture of CloudIoT applications in literature, with a focus on their specific research challenges, and identifies open issues and future directions in this field, which it expects to play a leading role in the landscape of the Future Internet.
1,880 citations
TL;DR: The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/ DM for cyber security is presented, and some recommendations on when to use a given method are provided.
Abstract: This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized. Because data are so important in ML/DM approaches, some well-known cyber data sets used in ML/DM are described. The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/DM for cyber security is presented, and some recommendations on when to use a given method are provided.
1,704 citations
TL;DR: It is evident from the literature survey presented herein that modified cellulose-based adsorbents exhibit good potential for the removal of various aquatic pollutants, however, still there is a need to find out the practical utility of these adsorbent on a commercial scale, leading to the improvement of pollution control.
747 citations
TL;DR: Several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.
Abstract: In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of content-based and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.
648 citations
TL;DR: A literature survey of the latest advances in researches on machine learning for big data processing finds some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning.
Abstract: There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges. In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing. First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data. Following that, we investigate the close connections of machine learning with signal processing techniques for big data processing. Finally, we outline several open issues and research trends.
636 citations
TL;DR: In this article, the authors present a literature survey to review the literature on enterprise and supply chain resilience and provide a platform for researchers and practitioners trying to identify the existing state of the work, gaps in current research, and future directions on the topic.
604 citations
TL;DR: A systematic and standardised review of the scientific literature published from 1990 to 2013 on the effects of anthropogenic noise on wildlife, including both terrestrial and aquatic studies shows that terrestrial wildlife responses begin at noise levels of approximately 40’dBA, and 20% of papers documented impacts below 50 dBA.
Abstract: Global increases in environmental noise levels – arising from expansion of human populations, transportation networks, and resource extraction – have catalysed a recent surge of research into the effects of noise on wildlife. Synthesising a coherent understanding of the biological consequences of noise from this literature is challenging. Taxonomic groups vary in auditory capabilities. A wide range of noise sources and exposure levels occur, and many kinds of biological responses have been observed, ranging from individual behaviours to changes in ecological communities. Also, noise is one of several environmental effects generated by human activities, so researchers must contend with potentially confounding explanations for biological responses. Nonetheless, it is clear that noise presents diverse threats to species and ecosystems and salient patterns are emerging to help inform future natural resource-management decisions. We conducted a systematic and standardised review of the scientific literature published from 1990 to 2013 on the effects of anthropogenic noise on wildlife, including both terrestrial and aquatic studies. Research to date has concentrated predominantly on European and North American species that rely on vocal communication, with approximately two-thirds of the data set focussing on songbirds and marine mammals. The majority of studies documented effects from noise, including altered vocal behaviour to mitigate masking, reduced abundance in noisy habitats, changes in vigilance and foraging behaviour, and impacts on individual fitness and the structure of ecological communities. This literature survey shows that terrestrial wildlife responses begin at noise levels of approximately 40 dBA, and 20% of papers documented impacts below 50 dBA. Our analysis highlights the utility of existing scientific information concerning the effects of anthropogenic noise on wildlife for predicting potential outcomes of noise exposure and implementing meaningful mitigation measures. Future research directions that would support more comprehensive predictions regarding the magnitude and severity of noise impacts include: broadening taxonomic and geographical scope, exploring interacting stressors, conducting larger-scale studies, testing mitigation approaches, standardising reporting of acoustic metrics, and assessing the biological response to noise-source removal or mitigation. The broad volume of existing information concerning the effects of anthropogenic noise on wildlife offers a valuable resource to assist scientists, industry, and natural-resource managers in predicting potential outcomes of noise exposure.
531 citations
TL;DR: It is found that while extinction risk is highly situation dependent, genetic swamping is much more frequent than demographic swamping, and it is concluded that halting the introduction of hybridization‐prone exotics and restoring mature and diverse habitats that are resistant to hybrid establishment should be management priorities.
Abstract: Hybridization may drive rare taxa to extinction through genetic swamping, where the rare form is replaced by hybrids, or by demographic swamping, where population growth rates are reduced due to the wasteful production of maladaptive hybrids. Conversely, hybridization may rescue the viability of small, inbred populations. Understanding the factors that contribute to destructive versus constructive outcomes of hybridization is key to managing conservation concerns. Here, we survey the literature for studies of hybridization and extinction to identify the ecological, evolutionary, and genetic factors that critically affect extinction risk through hybridization. We find that while extinction risk is highly situation dependent, genetic swamping is much more frequent than demographic swamping. In addition, human involvement is associated with increased risk and high reproductive isolation with reduced risk. Although climate change is predicted to increase the risk of hybridization-induced extinction, we find little empirical support for this prediction. Similarly, theoretical and experimental studies imply that genetic rescue through hybridization may be equally or more probable than demographic swamping, but our literature survey failed to support this claim. We conclude that halting the introduction of hybridization-prone exotics and restoring mature and diverse habitats that are resistant to hybrid establishment should be management priorities.
490 citations
TL;DR: In this article, the authors focused on the classification of various paraffins and salt hydrates, and provided an understanding on how to maximize thermal utilization of PCM and how to improve the phase transition rate, thermal conductivity, latent heat storage capacity and thermo-physical stability.
484 citations
TL;DR: In this article, a review of emerging micropollutants in the environment is presented, with emphasis on their occurrences, effects, environmental fates, and potential risk of exposure in water, soil or sediment.
Abstract: The presence of emerging micropollutants such as pharmaceuticals, endocrine disruptors, personal care products, nanomaterials and perfluorinated substances in the environment remains a great threat to the health and safety of humans and aquatic species. These micropollutants enter the environment via anthropogenic activities and have been detected in surface water, groundwater and even drinking water at nanogram per litre to microgram per litre concentration. To date, limited information exists on the fate, behaviours, and pathways of these micropollutants in the environment. The potential ecotoxicological effects on the receptors due to exposure to individual or mixture of these chemicals still remain unknown. This review provides an overview on pharmaceuticals, endocrine disrupting compounds, personal care products, nanomaterials and perfluorinated pollutants, with emphasis on their occurrences, effects, environmental fates, and potential risk of exposure in water, soil or sediment. Based on the literature survey, it was found that in spite of an extensive research and different developmental efforts on the challenges of emerging micropollutants, the solution to the problem of emerging micropollutants in the environment is far from being solved. The needs for behavioural change among citizens, strong political will and policy formulation on the part of government are identified as possible panacea for combating the growing influence of these potential damaging substances. Suggestions on proactive and precautionary measures that must be taken to protect the environment as well as guarantee the health and safety of humans and aquatic species are provided. Future research should concentrate on the development of a risk based screening models and framework that can predict the sources, fate and behaviours of emerging contaminants in the environment is recommended.
313 citations
TL;DR: In this article, the impact of supplementary cementitious materials (SCMs) on the pore solution composition of blended cements is reviewed, leading to a set of practical guidelines and recommendations.
Abstract: This paper is the work of working group 3 of the RILEM Technical Committee on Hydration and Microstructure of Concrete with SCM (TC 238-SCM). The pore solution is an essential but often overlooked part of hydrated cements. The composition of the cement pore solution reflects the ongoing hydration processes and determines which solid phases are stable and may precipitate, and which phases are unstable and may dissolve. The study of the cement pore solution therefore contributes to the understanding of the mechanisms as well as of the kinetics of cement hydration. This paper reviews the impact of supplementary cementitious materials (SCMs) on the pore solution composition of blended cements. In a first part, the extraction and analysis methods of cement pore solutions are reviewed, leading to a set of practical guidelines and recommendations. In a second part, an extensive literature survey is used to document the effect of the addition of SCMs (blast furnace slag, fly ash and silica fume) on the pore solution. Finally, in a third part the collected literature data are compared to thermodynamic simulations. The performance and current limitations of thermodynamic modelling of blended cement hydration are demonstrated and discussed in view of future progress.
TL;DR: The relations between the trend of big data era and that of the new generation green revolution are discovered through a comprehensive and panoramic literature survey in big data technologies toward various green objectives and a discussion on relevant challenges and future directions.
Abstract: Big data are widely recognized as being one of the most powerful drivers to promote productivity, improve efficiency, and support innovation. It is highly expected to explore the power of big data and turn big data into big values. To answer the interesting question whether there are inherent correlations between the two tendencies of big data and green challenges, a recent study has investigated the issues on greening the whole life cycle of big data systems. This paper would like to discover the relations between the trend of big data era and that of the new generation green revolution through a comprehensive and panoramic literature survey in big data technologies toward various green objectives and a discussion on relevant challenges and future directions.
TL;DR: In this paper, the authors review the concept of hybrid energy storage system, hybridization principles and proposed topologies, power electronics interface architectures, control and energy management strategies, and application arenas.
Abstract: The idea of Hybrid Energy Storage System (HESS) lies on the fact that heterogeneous Energy Storage System (ESS) technologies have complementary characteristics in terms of power and energy density, life cycle, response rate, and so on. In other words, high power ESS devices possess fast response rate while in the contrary, high energy ESS devices possess slow response rate. Therefore, it may be beneficial to hybridize ESS technologies in the way that synergize functional advantages of two heterogeneous existing ESS technologies As a consequence, this hybridization provides excellent characteristics not offered by a single ESS unit. This new technology has been proposed and investigated by several researchers in the literature particularly in the fields of renewable energy and electrified transport sector. In this context and according to an extensive literature survey, this paper is to review the concept of the HESS, hybridization principles and proposed topologies, power electronics interface architectures, control and energy management strategies, and application arenas.
TL;DR: In this article, an extended analysis is presented which has been carried out with the final aim of identifying the most effective simulation settings to ensure a reliable fully-unsteady, two-dimensional simulation of an H-type Darrieus turbine.
Book•
01 Jan 2016
TL;DR: Ghosh as mentioned in this paper argues that the extreme nature of today's climate events make them peculiarly resistant to contemporary modes of thinking and imagining, particularly true literary fiction: hundred-year storms and freakish tornadoes simply feel too improbable for the novel; they are automatically consigned to other genres.
Abstract: "Are we deranged? The acclaimed Indian novelist Amitav Ghosh argues that future generations may well think so. How else to explain our imaginative failure in the face of global warming? In his first major book of nonfiction since In an Antique Land, Ghosh examines our inability at the level of literature, history, and politics to grasp the scale and violence of climate change. The extreme nature of today's climate events, Ghosh asserts, make them peculiarly resistant to contemporary modes of thinking and imagining. This is particularly true of serious literary fiction: hundred-year storms and freakish tornadoes simply feel too improbable for the novel; they are automatically consigned to other genres. In the writing of history, too, the climate crisis has sometimes led to gross simplifications; Ghosh shows that the history of the carbon economy is a tangled global story with many contradictory and counter-intuitive elements. Ghosh ends by suggesting that politics, much like literature, has become a matter of personal moral reckoning rather than an arena of collective action. But to limit fiction and politics to individual moral adventure comes at a great cost. The climate crisis asks us to imagine other forms of human existence a task to which fiction, Ghosh argues, is the best suited of all cultural forms. His book serves as a great writer's summons to confront the most urgent task of our time."
01 Sep 2016
TL;DR: There is not a single ecological niche for electroactive microorganisms and microbial resource mining based on ecological knowledge bears a great potential for broadening the foundation of microbial electrochemistry as well as for future developments of primary METs.
Abstract: The core of primary microbial electrochemical technologies (METs) is the ability of the electroactive microorganisms to interact with electrodes via extracellular electron transfer (EET), allowing wiring of current flow and microbial metabolism. Geobacter sulfurreducens and Shewanella oneidensis are the model organisms for understanding and engineering EET. Many other microorganisms are reported being electroactive but are often sparsely characterized. Based on a literature survey 94 species are ascribed as electroactive. Their apparent diversity raises questions on the natural importance and distribution of the EET capacity, that is, of the ecological niche of microbial electroactivity. To identify this potential niche the environmental preferences and natural habitat characteristics of all electroactive species were combined with their metabolic, growth and EET characteristics and an extensive meta-analysis performed. The results indicate that there is not a single ecological niche for electroactive microorganisms. Significantly more electroactive species presumably exist in nature as well as already existing strain collections but due to current cultivation techniques their EET potential is not leveraged. Thus, in the light of specific traits required for industrial application, microbial resource mining based on ecological knowledge bears a great potential for broadening the foundation of microbial electrochemistry as well as for future developments of primary METs.
TL;DR: The knowledge of thermodynamic and transport properties of CO2-mixtures is important for designing and operating different processes in carbon capture and storage systems as mentioned in this paper, and a literature survey is presented in this paper.
TL;DR: This study applies a network clustering method to group the literature through a citation network established from the DEA literature over the period 2000 to 2014, and presents the research fronts, a coherent topic or issue addressed by a group of research articles in recent years.
Abstract: Research activities relating to data envelopment analysis (DEA) have grown at a fast rate recently. Exactly what activities have been carrying the research momentum forward is a question of particular interest to the research community. The purpose of this study is to find these research activities, or research fronts, in DEA. A research front refers to a coherent topic or issue addressed by a group of research articles in recent years. The large amount of DEA literature makes it difficult to use any traditional qualitative methodology to sort out the matter. Thus, this study applies a network clustering method to group the literature through a citation network established from the DEA literature over the period 2000 to 2014. The keywords of the articles in each discovered group help pinpoint its research focus. The four research fronts identified are “bootstrapping and two-stage analysis”, “undesirable factors”, “cross-efficiency and ranking”, and “network DEA, dynamic DEA, and SBM”. Each research front is then examined with key-route main path analysis to uncover the elements in its core. In addition to presenting the research fronts, this study also updates the main paths and author statistics of DEA development since its inception and compares them with those reported in a previous study.
TL;DR: This article reviewed the recent high-frequency trader literature to single out the economic channels by which HFTs affect market quality and came to a data-weighted judgement on the economic value of HFT.
TL;DR: This literature survey presents a method of qualitative measurement that is widely used by researchers in the area of stereo vision disparity mappings and notes the implementation of previous software-based and hardware-based algorithms.
Abstract: This paper presents a literature survey on existing disparity map algorithms. It focuses on four main stages of processing as proposed by Scharstein and Szeliski in a taxonomy and evaluation of dense two-frame stereo correspondence algorithms performed in 2002. To assist future researchers in developing their own stereo matching algorithms, a summary of the existing algorithms developed for every stage of processing is also provided. The survey also notes the implementation of previous software-based and hardware-based algorithms. Generally, the main processing module for a software-based implementation uses only a central processing unit. By contrast, a hardware-based implementation requires one or more additional processors for its processing module, such as graphical processing unit or a field programmable gate array. This literature survey also presents a method of qualitative measurement that is widely used by researchers in the area of stereo vision disparity mappings.
TL;DR: A literature survey reveals that ES and ESM in their natural as well as chemically modified forms have provided excellent results for the removal of various classes of dyes, oxalic acid, phenol, pesticides, humic acid and pharmaceutics, surfactants and PAHs as mentioned in this paper.
TL;DR: In this paper, a comprehensive review of thermography NDT techniques for composites inspection was conducted based on an orderly and concise literature survey and detailed analysis, and some research trends were predicted.
TL;DR: This work critically assessed existing knowledge and research on the uptake of nickel by various adsorbents such as activated carbon, non-conventional low-cost materials, nanomaterials, composites and nanocomposites to assemble the scattered available enlightenment on a wide range of potentially effective adsorbent for nickel(II) ions removal.
TL;DR: This paper studies the relevance between big data and green metrics and proposes two new metrics, effective energy efficiency and effective resource efficiency in order to bring new views and potentials of green metrics for the future times of big data.
Abstract: Nowadays, there are two significant tendencies, how to process the enormous amount of data, big data, and how to deal with the green issues related to sustainability and environmental concerns. An interesting question is whether there are inherent correlations between the two tendencies in general. To answer this question, this paper firstly makes a comprehensive literature survey on how to green big data systems in terms of the whole life cycle of big data processing, and then this paper studies the relevance between big data and green metrics and proposes two new metrics, effective energy efficiency and effective resource efficiency in order to bring new views and potentials of green metrics for the future times of big data.
TL;DR: This review article provides comprehensive information on the botany, phytochemistry, pharmacology and nutritional importance of P. amboinicus essential oil and its various solvent extracts to further explore the further potential of this multi-utility herb for various biomedical applications.
Abstract: Plectranthus amboinicus (Lour.) Spreng. is a perennial herb belonging to the family Lamiaceae which occurs naturally throughout the tropics and warm regions of Africa, Asia and Australia. This herb has therapeutic and nutritional properties attributed to its natural phytochemical compounds which are highly valued in the pharmaceutical industry. Besides, it has horticultural properties due to its aromatic nature and essential oil producing capability. It is widely used in folk medicine to treat conditions like cold, asthma, constipation, headache, cough, fever and skin diseases. The leaves of the plant are often eaten raw or used as flavoring agents, or incorporated as ingredients in the preparation of traditional food. The literature survey revealed the occurrence 76 volatiles and 30 non-volatile compounds belonging to different classes of phytochemicals such as monoterpenoids, diterpenoids, triterpenoids, sesquiterpenoids, phenolics, flavonoids, esters, alcohols and aldehydes. Studies have cited numerous pharmacological properties including antimicrobial, antiinflammatory, antitumor, wound healing, anti-epileptic, larvicidal, antioxidant and analgesic activities. Also, it has been found to be effective against respiratory, cardiovascular, oral, skin, digestive and urinary diseases. Yet, scientific validation of many other traditional uses would be appreciated, mainly to discover and authenticate novel bioactive compounds from this herb. This review article provides comprehensive information on the botany, phytochemistry, pharmacology and nutritional importance of P. amboinicus essential oil and its various solvent extracts. This article allows researchers to further explore the further potential of this multi-utility herb for various biomedical applications.
TL;DR: At present, CA125 remains the most important biomarker for epithelial ovarian cancer, excluding tumors of mucinous origin.
Abstract: Objective To present an update of the European Group on Tumor Markers guidelines for serum markers in epithelial ovarian cancer. Methods Systematic literature survey from 2008 to 2013. The articles were evaluated by level of evidence and strength of recommendation. Results Because of its low sensitivity (50–62% for early stage epithelial ovarian cancer) and limited specificity (94–98.5%), cancer antigen (CA) 125 (CA125) is not recommended as a screening test in asymptomatic women. The Risk of Malignancy Index, which includes CA125, transvaginal ultrasound, and menopausal status, is recommended for the differential diagnosis of a pelvic mass. Because human epididymis protein 4 has been reported to have superior specificity to CA125, especially in premenopausal women, it may be considered either alone or as part of the risk of ovarian malignancy algorithm, in the differential diagnosis of pelvic masses, especially in such women. CA125 should be used to monitor response to first-line chemotherapy using the previously published criteria of the Gynecological Cancer Intergroup, that is, at least a 50% reduction of a pretreatment sample of 70 kU/L or greater. The value of CA125 in posttherapy surveillance is less clear. Although a prospective randomized trial concluded that early administration of chemotherapy based on increasing CA125 levels had no effect on survival, European Group on Tumor Markers state that monitoring with CA125 in this situation should occur, especially if the patient is a candidate for secondary cytoreductive surgery. Conclusions At present, CA125 remains the most important biomarker for epithelial ovarian cancer, excluding tumors of mucinous origin.
TL;DR: In this article, an overview based on ammonia and phenol recovery, biochemical treatments and advanced treatment technologies is presented with the purpose of unveiling the reasons behind problems of high concentration of persistent organic pollutants being added in the environment from coal gasification wastewater operations.
TL;DR: In this article, the authors provide a comprehensive review of the factors affecting adaptive reuse decision-making and develop a holistic model for adaptive reuse strategies for heritage buildings, which can be used to evaluate the appropriateness of the new use for re-functioned heritage buildings and to define the problems in the decision making.
TL;DR: This literature survey focuses on identifying recent advances in research on digital technology in the field of mathematics education and discusses some of the implications that these digital technologies may have for mathematics education research and practice as well as making some recommendations for future research.
Abstract: In this literature survey we focus on identifying recent advances in research on digital technology in the field of mathematics education. To conduct the survey we have used internet search engines with keywords related to mathematics education and digital technology and have reviewed some of the main international journals, including the ones in Portuguese and Spanish. We identify five sub-areas of research, important trends of development, and illustrate them using case studies: mobile technologies, massive open online courses (MOOCs), digital libraries and designing learning objects, collaborative learning using digital technology, and teacher training using blended learning. These examples of case studies may help the reader to understand how recent developments in this area of research have evolved in the last few years. We conclude the report discussing some of the implications that these digital technologies may have for mathematics education research and practice as well as making some recommendations for future research in this area.
TL;DR: Textured grinding wheels (TGWs) as mentioned in this paper are wheels that have both specially-designed active and passive grinding areas on their geometrically active surfaces, which allow TGWs to perform the intermittent grinding process such that total wheel-workpiece contact time, average grinding forces and temperature in the cutting zone can be reduced.
Abstract: Textured grinding wheels (TGWs) are wheels that have both specially-designed active and passive grinding areas on their geometrically active surfaces. The active area allows TGWs to perform the intermittent grinding process such that total wheel-workpiece contact time, average grinding forces and temperature in the cutting zone can be reduced. The passive area (or textures) refers to the non-grinding area where no grain is located at and the main functions of it include serving as reservoirs to transport more coolants/lubricants into the grinding zone and providing larger chip disposal space. With the increasingly demanding requirements from industries, the continuous evolution of TGWs has been enforced. However, to the best of the authors' knowledge, no comprehensive review on TGWs has been reported yet. To address this gap in the literature, this paper aims to present an informative literature survey of research and engineering developments in relation to TGWs, define and categorise TGW concepts, explain basic principles, briefly review the concept developments, discuss key challenges, and further provide new insights into understanding of TGWs for their advanced future engineering applications.