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Periodicals

Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information

Citizens are increasingly becoming an important source of geographic information, sometimes entering domains that had until recently been the exclusive realm of authoritative agencies. This activity has a very diverse character as it can, amongst other things, be active or passive, involve spatial or aspatial data and the data provided can be variable in terms of key attributes such as format, description and quality. Unsurprisingly, therefore, there are a variety of terms used to describe data arising from citizens. In this article, the expressions used to describe citizen sensing of geographic information are reviewed and their use over time explored, prior to categorizing them and highlighting key issues in the current state of the subject. The latter involved a review of ~100 Internet sites with particular focus on their thematic topic, the nature of the data and issues such as incentives for contributors. This review suggests that most sites involve active rather than passive contribution, with citizens typically motivated by the desire to aid a worthy cause, often receiving little training. As such, this article provides a snapshot of the role of citizens in crowdsourcing geographic information and a guide to the current status of this rapidly emerging and evolving subject.

Architecture & Planning• 2016• Multidisciplinary Digital Publishing Institute
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Black Box and White Box Testing Techniques - A Literature Review

There are several methods for automatic test case generation has been proposed in the past. But most of these techniques are structural testing techniques that require the understanding of the internal working of the program. There is less practical coverage of all testing techniques together. In this paper we conducted a literature study on all testing techniques together that are related to both Black and White box testing techniques, moreover we assume a case situation of Insurance premium calculation for driver and we derive the test cases and test data for white box testing methods such as Branch testing, Statement testing, Condition Coverage testing, multiple condition coverage testing, in the similar way we derive the test cases and test data for the black box testing methods such as: Equivalence partitioning and Boundary value analysis.

Architecture & Planning• 2012
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Periodicals

Tailoring Formulations for Intranasal Nose-to-Brain Delivery: A Review on Architecture, Physico-Chemical Characteristics and Mucociliary Clearance of the Nasal Olfactory Mucosa

The blood-brain barrier and the blood-cerebrospinal fluid barrier are major obstacles in central nervous system (CNS) drug delivery, since they block most molecules from entering the brain. Alternative drug delivery routes like intraparenchymal or intrathecal are invasive methods with a remaining risk of infections. In contrast, nose-to-brain delivery is a minimally invasive drug administration pathway, which bypasses the blood-brain barrier as the drug is directed from the nasal cavity to the brain. In particular, the skull base located at the roof of the nasal cavity is in close vicinity to the CNS. This area is covered with olfactory mucosa. To design and tailor suitable formulations for nose-to-brain drug delivery, the architecture, structure and physico-chemical characteristics of the mucosa are important criteria. Hence, here we review the state-of-the-art knowledge about the characteristics of the nasal and, in particular, the olfactory mucosa needed for a rational design of intranasal formulations and dosage forms. Also, the information is suitable for the development of systemic or local intranasal drug delivery as well as for intranasal vaccinations.

Architecture & Planning• 2018• Multidisciplinary Digital Publishing Institute
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Inter-Individual Variation during Transcranial Direct Current Stimulation and Normalization of Dose Using MRI-Derived Computational Models

BACKGROUND: Transcranial Direct Current Stimulation (tDCS) is a non-invasive, versatile, and safe neuromodulation technology under investigation for the treatment of neuropsychiatric disorders, adjunct to rehabilitation, and cognitive enhancement in healthy adults. Despite promising results, there is variability in responsiveness. One potential source of variability is the intensity of current delivered to the brain which is a function of both the operator controlled tDCS dose (electrode montage and total applied current) and subject specific anatomy. We are interested in both the scale of this variability across anatomical typical adults and methods to normalize inter-individual variation by customizing tDCS dose. Computational FEM simulations are a standard technique to predict brain current flow during tDCS and can be based on subject specific anatomical MRI. OBJECTIVE: To investigate this variability, we modeled multiple tDCS montages across three adults (ages 34-41, one female). RESULTS: Conventional pad stimulation led to diffuse modulation with maximum current flow between the pads across all subjects. There was high current flow directly under the pad for one subject while the location of peak induced cortical current flow was variable. The High-Definition tDCS montage led to current flow restricted to within the ring perimeter across all subjects. The current flow profile across all subjects and montages was influenced by details in cortical gyri/sulci. CONCLUSION: This data suggests that subject specific modeling can facilitate consistent and more efficacious tDCS.

Architecture & Planning• 2012• Frontiers Media
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Periodicals

Designing and implementing future aerial communication networks

Providing "connectivity from the sky" is the new innovative trend in wireless communications. High and low altitude platforms, drones, aircrafts, and airships are being considered as candidates for deploying wireless communications complementing the terrestrial communication infrastructure. In this article we report the detailed account of the design and implementation challenges of an aerial network consisting of LTE-Advanced (LTE-A) base stations. In particular, we review achievements and innovations harnessed by an aerial network composed of Helikite platforms. Helikites can be raised in the sky to bring Internet access during special events and in the aftermath of an emergency. The trial phase of the system mounting LTE-A technology onboard Helikites to serve users on the ground yielded very encouraging results, and showed that such a system could offer a longer lasting solution, provided that inefficiency in powering the radio frequency equipment in the Helikite can be overcome.

Architecture & Planning• 2016• Institute of Electrical and Electronics Engineers
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The Role of Obesity in Type 2 Diabetes Mellitus—An Overview

Obesity or excessive weight gain is identified as the most important and significant risk factor in the development and progression of type 2 diabetes mellitus (DM) in all age groups. It has reached pandemic dimensions, making the treatment of obesity crucial in the prevention and management of type 2 DM worldwide. Multiple clinical studies have demonstrated that moderate and sustained weight loss can improve blood glucose levels, insulin action and reduce the need for diabetic medications. A combined approach of diet, exercise and lifestyle modifications can successfully reduce obesity and subsequently ameliorate the ill effects and deadly complications of DM. This approach also helps largely in the prevention, control and remission of DM. Obesity and DM are chronic diseases that are increasing globally, requiring new approaches to manage and prevent diabetes in obese individuals. Therefore, it is essential to understand the mechanistic link between the two and design a comprehensive approach to increase life expectancy and improve the quality of life in patients with type 2 DM and obesity. This literature review provides explicit information on the clinical definitions of obesity and type 2 DM, the incidence and prevalence of type 2 DM in obese individuals, the indispensable role of obesity in the pathophysiology of type 2 DM and their mechanistic link. It also discusses clinical studies and outlines the recent management approaches for the treatment of these associated conditions. Additionally, in vivo studies on obesity and type 2 DM are discussed here as they pave the way for more rigorous development of therapeutic approaches.

Architecture & Planning• 2024• Multidisciplinary Digital Publishing Institute
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Periodicals

Security and Privacy for Green IoT-Based Agriculture: Review, Blockchain Solutions, and Challenges

This paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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Periodicals

Fabrication of Efficient Low-Bandgap Perovskite Solar Cells by Combining Formamidinium Tin Iodide with Methylammonium Lead Iodide

Mixed tin (Sn)-lead (Pb) perovskites with high Sn content exhibit low bandgaps suitable for fabricating the bottom cell of perovskite-based tandem solar cells. In this work, we report on the fabrication of efficient mixed Sn-Pb perovskite solar cells using precursors combining formamidinium tin iodide (FASnI3) and methylammonium lead iodide (MAPbI3). The best-performing cell fabricated using a (FASnI3)0.6(MAPbI3)0.4 absorber with an absorption edge of ∼1.2 eV achieved a power conversion efficiency (PCE) of 15.08 (15.00)% with an open-circuit voltage of 0.795 (0.799) V, a short-circuit current density of 26.86(26.82) mA/cm(2), and a fill factor of 70.6(70.0)% when measured under forward (reverse) voltage scan. The average PCE of 50 cells we have fabricated is 14.39 ± 0.33%, indicating good reproducibility.

Architecture & Planning• 2016• American Chemical Society
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Periodicals

Tocopherols and Tocotrienols in Common and Emerging Dietary Sources: Occurrence, Applications, and Health Benefits

Edible oils are the major natural dietary sources of tocopherols and tocotrienols, collectively known as tocols. Plant foods with low lipid content usually have negligible quantities of tocols. However, seeds and other plant food processing by-products may serve as alternative sources of edible oils with considerable contents of tocopherols and tocotrienols. Tocopherols are among the most important lipid-soluble antioxidants in food as well as in human and animal tissues. Tocopherols are found in lipid-rich regions of cells (e.g., mitochondrial membranes), fat depots, and lipoproteins such as low-density lipoprotein cholesterol. Their health benefits may also be explained by regulation of gene expression, signal transduction, and modulation of cell functions. Potential health benefits of tocols include prevention of certain types of cancer, heart disease, and other chronic ailments. Although deficiencies of tocopherol are uncommon, a continuous intake from common and novel dietary sources of tocopherols and tocotrienols is advantageous. Thus, this contribution will focus on the relevant literature on common and emerging edible oils as a source of tocols. Potential application and health effects as well as the impact of new cultivars as sources of edible oils and their processing discards are presented. Future trends and drawbacks are also briefly covered.

Architecture & Planning• 2016• Multidisciplinary Digital Publishing Institute
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Periodicals

IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges

The growth of the global population coupled with a decline in natural resources, farmland, and the increase in unpredictable environmental conditions leads to food security is becoming a major concern for all nations worldwide. These problems are motivators that are driving the agricultural industry to transition to smart agriculture with the application of the Internet of Things (IoT) and big data solutions to improve operational efficiency and productivity. The IoT integrates a series of existing state-of-the-art solutions and technologies, such as wireless sensor networks, cognitive radio ad hoc networks, cloud computing, big data, and end-user applications. This study presents a survey of IoT solutions and demonstrates how IoT can be integrated into the smart agriculture sector. To achieve this objective, we discuss the vision of IoT-enabled smart agriculture ecosystems by evaluating their architecture (IoT devices, communication technologies, big data storage, and processing), their applications, and research timeline. In addition, we discuss trends and opportunities of IoT applications for smart agriculture and also indicate the open issues and challenges of IoT application in smart agriculture. We hope that the findings of this study will constitute important guidelines in research and promotion of IoT solutions aiming to improve the productivity and quality of the agriculture sector as well as facilitating the transition towards a future sustainable environment with an agroecological approach.

Architecture & Planning• 2022• Multidisciplinary Digital Publishing Institute
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Periodicals

Moving the Customer Experience Field Forward: Introducing the Touchpoints, Context, Qualities (TCQ) Nomenclature

In response to initial voices that put the customer experience (management) (CX(M)) movement into question, this article aims to introduce a formal nomenclature to push the CX(M) field toward a more mature state. First, drawing from an inductive analysis of 143 CX(M) papers, the authors identify 12 basic CX components that aggregate into three overarching building blocks—touchpoints (T, i.e., points of interaction between the customer and brand/firm), context (C, i.e., situationally available resources internal and/or external to the customer), and qualities (Q, i.e., attributes that reflect the nature of customer responses and reactions to interactions with the brand/firm). The TCQ nomenclature offers a language to make CX actionable, moving beyond the breadth of the current definition and frameworks by disentangling CX into small bite-sized chunks (i.e., the CX components) that any academic and practitioner, regardless of their discipline, may understand and use to discuss and manage CX. Second, using the TCQ nomenclature, the authors assess the current state of the CX(M) literature and identify mature (e.g., firm-controlled touchpoints and cognitive and emotional qualities associated with CX) and underdeveloped (e.g., nonfirm controlled touchpoints and the market and environmental context in which CX emerges) areas ripe for future research. In addition, they also provide a set of recommendations to strengthen the methodological rigor of the field. Third, the TCQ nomenclature may support managers in auditing their current CXM practices and/or serve as a basis for CX design and innovation.

Architecture & Planning• 2020• SAGE Publishing
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Periodicals

Enhancing the rate of genetic gain in public-sector plant breeding programs: lessons from the breeder’s equation

KEY MESSAGE: The integration of new technologies into public plant breeding programs can make a powerful step change in agricultural productivity when aligned with principles of quantitative and Mendelian genetics. The breeder's equation is the foundational application of quantitative genetics to crop improvement. Guided by the variables that describe response to selection, emerging breeding technologies can make a powerful step change in the effectiveness of public breeding programs. The most promising innovations for increasing the rate of genetic gain without greatly increasing program size appear to be related to reducing breeding cycle time, which is likely to require the implementation of parent selection on non-inbred progeny, rapid generation advance, and genomic selection. These are complex processes and will require breeding organizations to adopt a culture of continuous optimization and improvement. To enable this, research managers will need to consider and proactively manage the, accountability, strategy, and resource allocations of breeding teams. This must be combined with thoughtful management of elite genetic variation and a clear separation between the parental selection process and product development and advancement process. With an abundance of new technologies available, breeding teams need to evaluate carefully the impact of any new technology on selection intensity, selection accuracy, and breeding cycle length relative to its cost of deployment. Finally breeding data management systems need to be well designed to support selection decisions and novel approaches to accelerate breeding cycles need to be routinely evaluated and deployed.

Architecture & Planning• 2019• Springer Science+Business Media
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Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications

Predicting crop yield based on the environmental, soil, water and crop parameters has been a potential research topic. Deep-learning-based models are broadly used to extract significant crop features for prediction. Though these methods could resolve the yield prediction problem there exist the following inadequacies: Unable to create a direct non-linear or linear mapping between the raw data and crop yield values; and the performance of those models highly relies on the quality of the extracted features. Deep reinforcement learning provides direction and motivation for the aforementioned shortcomings. Combining the intelligence of reinforcement learning and deep learning, deep reinforcement learning builds a complete crop yield prediction framework that can map the raw data to the crop prediction values. The proposed work constructs a Deep Recurrent Q-Network model which is a Recurrent Neural Network deep learning algorithm over the Q-Learning reinforcement learning algorithm to forecast the crop yield. The sequentially stacked layers of Recurrent Neural network is fed by the data parameters. The Q- learning network constructs a crop yield prediction environment based on the input parameters. A linear layer maps the Recurrent Neural Network output values to the Q-values. The reinforcement learning agent incorporates a combination of parametric features with the threshold that assist in predicting crop yield. Finally, the agent receives an aggregate score for the actions performed by minimizing the error and maximizing the forecast accuracy. The proposed model efficiently predicts the crop yield outperforming existing models by preserving the original data distribution with an accuracy of 93.7%.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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Periodicals

New Generation Sensor Web Enablement

Many sensor networks have been deployed to monitor Earth's environment, and more will follow in the future. Environmental sensors have improved continuously by becoming smaller, cheaper, and more intelligent. Due to the large number of sensor manufacturers and differing accompanying protocols, integrating diverse sensors into observation systems is not straightforward. A coherent infrastructure is needed to treat sensors in an interoperable, platform-independent and uniform way. The concept of the Sensor Web reflects such a kind of infrastructure for sharing, finding, and accessing sensors and their data across different applications. It hides the heterogeneous sensor hardware and communication protocols from the applications built on top of it. The Sensor Web Enablement initiative of the Open Geospatial Consortium standardizes web service interfaces and data encodings which can be used as building blocks for a Sensor Web. This article illustrates and analyzes the recent developments of the new generation of the Sensor Web Enablement specification framework. Further, we relate the Sensor Web to other emerging concepts such as the Web of Things and point out challenges and resulting future work topics for research on Sensor Web Enablement.

Architecture & Planning• 2011• Multidisciplinary Digital Publishing Institute
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Periodicals

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

Nature computing has evolved with exciting performance to solve complex real-world combinatorial optimization problems. These problems span across engineering, medical sciences, and sciences generally. The Ebola virus has a propagation strategy that allows individuals in a population to move among susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population groups. Motivated by the effectiveness of this strategy of propagation of the disease, a new bio-inspired and population-based optimization algorithm is proposed. This study presents a novel metaheuristic algorithm named Ebola Optimization Search Algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. First, we designed an improved SIR model of the disease, namely SEIR-HVQD: Susceptible (S), Exposed (E), Infected (I), Recovered (R), Hospitalized (H), Vaccinated (V), Quarantine (Q), and Death or Dead (D). Secondly, we represented the new model using a mathematical model based on a system of first-order differential equations. A combination of the propagation and mathematical models was adapted for developing the new metaheuristic algorithm. To evaluate the performance and capability of the proposed method in comparison with other optimization methods, two sets of benchmark functions consisting of forty-seven (47) classical and thirty (30) constrained IEEE-CEC benchmark functions were investigated. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability, convergence, and sensitivity analyses. Extensive simulation results show that the EOSA outperforms popular metaheuristic algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC). Also, the algorithm was applied to address the complex problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classification of digital mammography. Results obtained showed the optimized CNN architecture successfully detected breast cancer from digital images at an accuracy of 96.0%. The source code of EOSA is publicly available athttps://github.com/NathanielOy/EOSA_Metaheuristic.

Architecture & Planning• 2022• Institute of Electrical and Electronics Engineers
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Beyond the Barriers of Light Penetration: Strategies, Perspectives and Possibilities for Photodynamic Therapy

Photodynamic therapy (PDT) is a photochemistry based treatment modality that involves the generation of cytotoxic species through the interactions of a photosensitizer molecule with light irradiation of an appropriate wavelength. PDT is an approved therapeutic modality for several cancers globally and in several cases has proved to be effective where traditional treatments have failed. The key parameters that determine PDT efficacy are 1. the photosensitizer (nature of the molecules, selectivity, and macroscopic and microscopic localization etc.), 2. light application (wavelength, fluence, fluence rate, irradiation regimes etc.) and 3. the microenvironment (vascularity, hypoxic regions, stromal tissue density, molecular heterogeneity etc.). Over the years, several groups aimed to monitor and manipulate the components of these critical parameters to improve the effectiveness of PDT treatments. However, PDT is still misconstrued to be a surface treatment primarily due to the limited depths of light penetration. In this review, we present the recent advances, strategies and perspectives in PDT approaches, particularly in cancer treatment, that focus on increasing the 'damage zone' beyond the reach of light in the body. This is enabled by a spectrum of approaches that range from innovative photosensitizer excitation strategies, increased specificity of phototoxicity, and biomodulatory approaches that amplify the biotherapeutic effects induced by photodynamic action. Along with the increasing depth of understanding of the underlying physical, chemical and physiological mechanisms, it is anticipated that with the convergence of these strategies, the clinical utility of PDT will be expanded to a powerful modality in the armamentarium for the management of cancer.

Architecture & Planning• 2016• Ivyspring International Publisher
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Blood-Based Cancer Biomarkers in Liquid Biopsy: A Promising Non-Invasive Alternative to Tissue Biopsy

Cancer is one of the greatest threats facing our society, being the second leading cause of death globally. Currents strategies for cancer diagnosis consist of the extraction of a solid tissue from the affected area. This sample enables the study of specific biomarkers and the genetic nature of the tumor. However, the tissue extraction is risky and painful for the patient and in some cases is unavailable in inaccessible tumors. Moreover, a solid biopsy is expensive and time consuming and cannot be applied repeatedly. New alternatives that overcome these drawbacks are rising up nowadays, such as liquid biopsy. A liquid biopsy is the analysis of biomarkers in a non-solid biological tissue, mainly blood, which has remarkable advantages over the traditional method; it has no risk, it is non-invasive and painless, it does not require surgery and reduces cost and diagnosis time. The most studied cancer non-invasive biomarkers are circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and exosomes. These circulating biomarkers play a key role in the understanding of metastasis and tumorigenesis, which could provide a better insight into the evolution of the tumor dynamics during treatment and disease progression. Improvements in isolation technologies, based on a higher grade of purification of CTCs, exosomes, and ctDNA, will provide a better characterization of biomarkers and give rise to a wide range of clinical applications, such as early detection of diseases, and the prediction of treatment responses due to the discovery of personalized tumor-related biomarkers.

Architecture & Planning• 2018• Multidisciplinary Digital Publishing Institute
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The Neotoma Paleoecology Database, a multiproxy, international, community-curated data resource

Abstract The Neotoma Paleoecology Database is a community-curated data resource that supports interdisciplinary global change research by enabling broad-scale studies of taxon and community diversity, distributions, and dynamics during the large environmental changes of the past. By consolidating many kinds of data into a common repository, Neotoma lowers costs of paleodata management, makes paleoecological data openly available, and offers a high-quality, curated resource. Neotoma’s distributed scientific governance model is flexible and scalable, with many open pathways for participation by new members, data contributors, stewards, and research communities. The Neotoma data model supports, or can be extended to support, any kind of paleoecological or paleoenvironmental data from sedimentary archives. Data additions to Neotoma are growing and now include >3.8 million observations, >17,000 datasets, and >9200 sites. Dataset types currently include fossil pollen, vertebrates, diatoms, ostracodes, macroinvertebrates, plant macrofossils, insects, testate amoebae, geochronological data, and the recently added organic biomarkers, stable isotopes, and specimen-level data. Multiple avenues exist to obtain Neotoma data, including the Explorer map-based interface, an application programming interface, the neotoma R package, and digital object identifiers. As the volume and variety of scientific data grow, community-curated data resources such as Neotoma have become foundational infrastructure for big data science.

Architecture & Planning• 2018• Cambridge University Press
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Periodicals

Big Data in Natural Disaster Management: A Review

Undoubtedly, the age of big data has opened new options for natural disaster management, primarily because of the varied possibilities it provides in visualizing, analyzing, and predicting natural disasters. From this perspective, big data has radically changed the ways through which human societies adopt natural disaster management strategies to reduce human suffering and economic losses. In a world that is now heavily dependent on information technology, the prime objective of computer experts and policy makers is to make the best of big data by sourcing information from varied formats and storing it in ways that it can be effectively used during different stages of natural disaster management. This paper aimed at making a systematic review of the literature in analyzing the role of big data in natural disaster management and highlighting the present status of the technology in providing meaningful and effective solutions in natural disaster management. The paper has presented the findings of several researchers on varied scientific and technological perspectives that have a bearing on the efficacy of big data in facilitating natural disaster management. In this context, this paper reviews the major big data sources, the associated achievements in different disaster management phases, and emerging technological topics associated with leveraging this new ecosystem of Big Data to monitor and detect natural hazards, mitigate their effects, assist in relief efforts, and contribute to the recovery and reconstruction processes.

Architecture & Planning• 2018• Multidisciplinary Digital Publishing Institute
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Design and implementation of the infrastructure of HadGEM3: the next-generation Met Office climate modelling system

Abstract. This paper describes the development of a technically robust climate modelling system, HadGEM3, which couples the Met Office Unified Model atmosphere component, the NEMO ocean model and the Los Alamos sea ice model (CICE) using the OASIS coupler. Details of the coupling and technical solutions of the physical model (HadGEM3-AO) are documented, in addition to a description of the configurations of the individual submodels. The paper demonstrates that the implementation of the model has resulted in accurate conservation of heat and freshwater across the model components. The model performance in early versions of this climate model is briefly described to demonstrate that the results are scientifically credible. HadGEM3-AO is the basis for a number of modelling efforts outside of the Met Office, both within the UK and internationally. This documentation of the HadGEM3-AO system provides a detailed reference for developers of HadGEM3-based climate configurations.

Architecture & Planning• 2011• Copernicus Publications
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Advantages and Disadvantages of Using e-Learning in University Education: Analyzing Students’ Perspectives

: The architecture of a learning system implies a heavy task for e-learning to be integrated into a complicated system that is flexible, time scalable, and capable of lasting, even though there are many diverse tools. Currently, higher education in United Arab Emirates is experiencing a major transformation, considering increased accessibility. Therefore, the study aims to identify the advantages and disadvantages of e-learning in university education in United Arab Emirates. A descriptive study design was used to randomly select students from Ajman university, who were enrolled in 2018/2019 academic year. A close-ended structured questionnaire was constructed to collect data from students. Frequencies and percentages were used to analyse the data collected. 81% students stated that e-learning provides scientific material in an interesting way. Similarly, 80% students have responded that e-learning increases the possibility of contact between students among themselves and between the students and the teacher. 73% students indicated that due to increasing social isolation, they spend more time in front of the technical means of social interaction account and face-to-face with others. 70% students have indicated that there is a presence of electronic illiteracy among parents, which reduces their ability to follow their children electronically. It is essential for potential e-learners to understand the differences between an e-learning classroom setting and a conventional classroom setting as there are both advantages and disadvantages of e-learning to both environments that can probably influence their overall performance as a student.

Architecture & Planning• 2021• Academic Conferences and Publishing International
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Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review

Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the number of related publications almost doubling each year since 2015 is an attest to that. The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, seems to be a great candidate for exploiting the potentials of such complex massive data. However, there are some challenges related to the ground-truth, resolution, and the nature of data that strongly impact the performance of classification. In this paper, we review the use of deep learning in land use and land cover classification based on multispectral and hyperspectral images and we introduce the available data sources and datasets used by literature studies; we provide the readers with a framework to interpret the-state-of-the-art of deep learning in this context and offer a platform to approach methodologies, data, and challenges of the field.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
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