Search

Dye-Sensitized Solar Cells (DSSCs) has been extensively explored as a third generation solar cell due to their simple fabrication procedures, better performance especially under low-light intensities, aesthetic features, etc. DSSCs provide an efficient and simple effectuated technology for prospective energy demands. Computing the experimental data in a high performance platform is really an appreciable task for better understanding and accessibility for photovoltaic technology. In this research paper, we fabricate the proposed DSSC, process the experimental data, as an alternative method we compute the experimental data and obtain I-V and IPCE characteristics by programming in MATLAB.

Social bots are automated social media accounts governed by software and controlled by humans at the backend. Some bots have good purposes, such as automatically posting information about news and even to provide help during emergencies. Nevertheless, bots have also been used for malicious purposes, such as for posting fake news or rumour spreading or manipulating political campaigns. There are existing mechanisms that allow for detection and removal of malicious bots automatically. However, the bot landscape changes as the bot creators use more sophisticated methods to avoid being detected. Therefore, new mechanisms for discerning between legitimate and bot accounts are much needed. Over the past few years, a few review studies contributed to the social media bot detection research by presenting a comprehensive survey on various detection methods including cutting-edge solutions like machine learning (ML)/deep learning (DL) techniques. This paper, to the best of our knowledge, is the first one to only highlight the DL techniques and compare the motivation/effectiveness of these techniques among themselves and over other methods, especially the traditional ML ones. We present here a refined taxonomy of the features used in DL studies and details about the associated pre-processing strategies required to make suitable training data for a DL model. We summarize the gaps addressed by the review papers that mentioned about DL/ML studies to provide future directions in this field. Overall, DL techniques turn out to be computation and time efficient techniques for social bot detection with better or compatible performance as traditional ML techniques.

Smart city analytics requires the harnessing and analysis of emotions and sentiments conveyed by images and video footage. In recent years, facial sentiment analysis attracted significant attention for different application areas, including marketing, gaming, political analytics, healthcare, and human computer interaction. Aiming at contributing to this area, we propose a deep learning model enabling the accurate emotion analysis of crowded scenes containing complete and partially occluded faces, with different angles, various distances from the camera, and varying resolutions. Our model consists of a sophisticated convolutional neural network (CNN) that is combined with pooling, densifying, flattening, and Softmax layers to achieve accurate sentiment and emotion analysis of facial images. The proposed model was successfully tested using 3,750 images containing 22,563 faces, collected from a large consumer electronics trade show. The model was able to correctly classify the test images which contained faces with different angles, distances, occlusion areas, facial orientation and resolutions. It achieved an average accuracy of 90.6% when distinguishing between seven emotions (Happiness, smiling, laughter, neutral, sadness, anger, and surprise) in complete faces, and 86.16% accuracy in partially occluded faces. Such model can be leveraged for the automatic analysis of attendees’ engagement level in events. Furthermore, it can open the door for many useful applications in smart cities, such as measuring employees’ satisfaction and citizens’ happiness.

Sentiment Analysis is achieved by using Natural Language Processing (NLP) techniques and finds wide applications in analyzing social media content to determine people’s opinions, attitudes, and emotions toward entities, individuals, issues, events, or topics. The accuracy of sentiment analysis depends on automatic Part-of-Speech (PoS) tagging which is required to label words according to grammatical categories. The challenge of analyzing the Arabic language has found considerable research interest, but now the challenge is amplified with the addition of social media dialects. While numerous morphological analyzers and PoS taggers were proposed for Modern Standard Arabic (MSA), we are now witnessing an increased interest in applying those techniques to the Arabic dialect that is prominent in social media. Indeed, social media texts (e.g. posts, comments, and replies) differ significantly from MSA texts in terms of vocabulary and grammatical structure. Such differences call for reviewing the PoS tagging methods to adapt social media texts. Furthermore, the lack of sufficiently large and diverse social media text corpora constitutes one of the reasons that automatic PoS tagging of social media content has been rarely studied. In this paper, we address those limitations by proposing a novel Arabic social media text corpus that is enriched with complete PoS information, including tags, lemmas, and synonyms. The proposed corpus constitutes the largest manually annotated Arabic corpus to date, with more than 5 million tokens, 238,600 MSA texts, and words from Arabic social media dialect, collected from 65,000 online users’ accounts. Furthermore, our proposed corpus was used to train a custom Long Short-Term Memory deep learning model and showed excellent performance in terms of sentiment classification accuracy and F1-score. The obtained results demonstrate that the use of a diverse corpus that is enriched with PoS information significantly enhances the performance of social media analysis techniques and opens the door for advanced features such as opinion mining and emotion intelligence.

Dye-Sensitized Solar Cells (DSSCs) is the third generation type of solar cells which is a promising solution to global energy and environmental problems because of its clean, low-cost, easy fabrication procedures and aesthetic features. In this research work, we mainly focus on the performance of DSSCs with UV filter in the presence and absence of staining additive at various light intensities.

PurposeSmart city services (SCS) in contrast with other technology-based services, demand significant interaction and collaboration between the users and the service providers. This study examines the SCS delivery-channel characteristics and the users' personal (behavioral and demographic) characteristics that influence their satisfaction or dissatisfaction with the services, as well as their intention to adopt (i.e. continue using) the SCS-delivery channels.Design/methodology/approachA quantitative study using a structured questionnaire was conducted for this paper. The data-collection method was administered by emailing the survey to a list of 2,350 city/urban residents who are members of the two largest universities in the greater Dubai metropolis. A total of 600 completed responses (26 percent) were received back, while 580 useable responses (25 percent) were analyzed for this paper.FindingsOur initial findings suggest that contrary to popular belief, it is not only SCS channel factors that influence user satisfaction and continuance intention. SCS users' personal characteristics (such as their user innovativeness and control-seeking behavior) are also pivotal in determining their satisfaction and intention to continue or not continue using the SCS-delivery channels.Research limitations/implicationsThe paper argues that both SCS channel factors and SCS users' personal characteristics jointly influence the users' experience of the services and therefore jointly determine their satisfaction with the service as well as their SCS usage continuance intention. The result of our research gives important insights into users' behaviors toward the emerging SCS channels in general, and it will be of great value to architects and designers of Smart City technologies around the world.Practical implicationsThe paper argues that both SCS channel factors and SCS users' personal (behavioral and demographic) characteristics jointly influence the users' trials of the services, and therefore jointly determine their satisfaction with the service as well as their SCS usage continuance intention. The result of our research gives important insights into users' behavioral intentions toward the emerging SCS channels in general; and it will be of great value to architects and designers of Smart City technologies around the world.Originality/valueThis paper is one of the first few studies focused on investigating the antecedents of SCS usage behaviors in the Middle Eastern region.

Aiming at achieving sustainability and quality of life for citizens, future smart cities adopt a data-centric approach to decision making in which assets, people, and events are constantly monitored to inform decisions. Public opinion monitoring is of particular importance to governments and intelligence agencies, who seek to monitor extreme views and attempts of radicalizing individuals in society. While social media platforms provide increased visibility and a platform to express public views freely, such platforms can also be used to manipulate public opinion, spread hate speech, and radicalize others. Natural language processing and data mining techniques have gained popularity for the analysis of social media content and the detection of extremists and radical views expressed online. However, existing approaches simplify the concept of radicalization to a binary problem in which individuals are classified as extremists or non-extremists. Such binary approaches do not capture the radicalization process's complexity that is influenced by many aspects such as social interactions, the impact of opinion leaders, and peer pressure. Moreover, the longitudinal analysis of users' interactions and profile evolution over time is lacking in the literature. Aiming at addressing those limitations, this work proposes a sophisticated framework for the analysis of the temporal behavior of extremists on social media platforms. Far-right extremism during the Trump presidency was used as a case study, and a large dataset of over 259,000 tweets was collected to train and test our models. The results obtained are very promising and encourage the use of advanced social media analytics in the support of effective and timely decision-making.

For large, intricate, and multi-layered networks like that of Industrial IoT, an individual instance of intrusion detection system cannot efficiently work against advanced attack strategies. The reason is that it would not be aware of the overall context, environment, and relevant incidents in other networks. This necessitates a collaborative intrusion detection system that allows multiple intrusion detection systems to communicate with each other and share information on emerging cyber-attack incidents. Thus, immunizing themselves and preventing the attack from escalating. However, the main challenge here is to manage the trust among the peers, where an insider attacker may input false attack signatures to the network, thus degrading the performance. Hence, we propose a blockchain-based trustfree collaborative intrusion detection system, in which threat alert messages will only be propagated in the network after network consensus.

Crowdsensing using mobile phones is a novel addition to the Internet of Things applications suite. However, there are many challenges related to crowdsensing, including (1) the ability to manage a large number of mobile users with varying devices’ capabilities; (2) recruiting reliable users available in the location of interest at the right time; (3) handling various sensory data collected with different requirements and at different frequencies and scales; (4) brokering the relationship between data collectors and consumers in an efficient and scalable manner; and (5) automatically generating intelligence reports after processing the collected sensory data. No comprehensive end-to-end crowdsensing platform has been proposed despite a few attempts to address these challenges. In this work, we aim at filling this gap by proposing and describing the practical implementation of an end-to-end crowdsensing-as-a-service system dubbed CrowdPower. Our platform offers a standard interface for the management and brokerage of sensory data, enabling the transformation of raw sensory data into valuable smart city intelligence. Our solution includes a model for selecting participants for sensing campaigns based on the reliability and quality of sensors on users’ devices, then subsequently analysing the quality of the data provided using a clustering approach to predict user reputation and identify outliers. The platform also has an elaborate administration web portal developed to manage and visualize sensing activities. In addition to the architecture, design, and implementation of the backend platform capabilities, we also explain the creation of CrowdPower’s sensing mobile application that enables data collectors and consumers to participate in various sensing activities.

Public opinion is of critical importance to businesses and governments. It represents the collective opinion and prevalent views about a certain topic, policy, or issue. Extreme public opinion consists of extreme views held by individuals that advocate and spread radical ideas for the purpose of radicalizing others. while the proliferation of social media gives unprecedented reach and visibility and a platform for freely expressing public opinion, social media fora can also be used for spreading extreme views, manipulating public opinions, and radicalizing others. In this work, we leverage data mining and analytics techniques to study extreme public opinion expressed using social medial. A dataset of 259,904 tweets posted between 21/02/2016 and 01/05/2021 was collected in relation to extreme nationalism, hate speech, and supremacy. The collected data was analyzed using a variety to techniques, including sentiment analysis, named entity recognition, social circle analysis, and opinion leaders' identification, and results related to an American politician and an American right-wing activist were presented. The results obtained are very promising and open the door to the ability to monitor the evolution of extreme views and public opinion online.

Social media platforms have changed the way extremist groups recruit, influence and potentially radicalize users online. Radicalization has drawn the attention of many researchers worldwide with its increasing presence on social media. Therefore, the ability to identify and classify radical views online and their potential role in radicalizing individuals is of critical importance. In this work, we address this challenge by combining a number of machine learning and natural language processing techniques such as sentiment analysis, named entity recognition, clustering, opinion leader identification, and social circle analysis to gain insights about radical views related to extreme nationalism, and understand the social dynamics underpinning extremists' related interactions. The proposed approach was implemented and tested on more than 259,000 tweets collected over a five-year span. The different analysis steps were presented and the results obtained were analyzed.

With the advent of mobile crowdsensing, we now have the possibility of tapping into the sensing capabilities of smartphones carried by citizens every day for the collection of information and intelligence about cities and events. Finding the best group of crowdsensing participants that can satisfy a sensing task in terms of data types required, while satisfying the quality, time, and budget constraints is a complex problem. Indeed, the time-constrained and location-based nature of crowdsensing tasks, combined with participants’ mobility, render the task of participants’ selection, a difficult task. In this paper, we propose a comprehensive and practical mobile crowdsensing recruitment model that offers reliability and quality-based approach for selecting the most reliable group of participants able to provide the best quality possible for the required sensory data. In our model, we adopt a group-based approach for the selection, in which a group of participants (gathered into sites) collaborate to achieve the sensing task using the combined capabilities of their smartphones. Our model was implemented using MATLAB and configured using realistic inputs such as benchmarked sensors’ quality scores, most widely used phone brands in different countries, and sensory data types associated with various events. Extensive testing was conducted to study the impact of various parameters on participants’ selection and gain an understanding of the compromises involved when deploying such process in practical MCS environments. The results obtained are very promising and provide important insights into the different aspects impacting the quality and reliability of the process of mobile crowdsensing participants’ selection.

With the advent of mobile crowdsensing, we now have the possibility of tapping into the sensing capabilities of smartphones carried by citizens every day for the collection of information and intelligence about cities and events. Finding the best group of crowdsensing participants that can satisfy a sensing task in terms of data types required, while satisfying the quality, time, and budget constraints is a complex problem. Indeed, the time-constrained and location-based nature of crowdsensing tasks, combined with participants' mobility, render the task of participants' selection, a difficult task. In this paper, we propose a comprehensive and practical mobile crowdsensing recruitment model that offers reliability and quality-based approach for selecting the most reliable group of participants able to provide the best quality possible for the required sensory data. In our model, we adopt a group-based approach for the selection, in which a group of participants (gathered into sites) collaborate to achieve the sensing task using the combined capabilities of their smartphones. Our model was implemented using MATLAB and configured using realistic inputs such as benchmarked sensors' quality scores, most widely used phone brands in different countries, and sensory data types associated with various events. Extensive testing was conducted to study the impact of various parameters on participants' selection and gain an understanding of the compromises involved when deploying such process in practical MCS environments. The results obtained are very promising and provide important insights into the different aspects impacting the quality and reliability of the process of mobile crowdsensing participants' selection.

With millions of images and videos uploaded on social media every day, facial sentiment analysis has gained significant attention as means of gaining large scale insights into people’s emotions and sentiments. While several models have been proposed for sentiment and emotion analysis of complete, camera-facing pictures, the analysis of images appearing in natural settings and crowded scenes poses more challenges. In such settings, images typically contain a mix of complete and partially occluded faces (i.e. obstructed faces) presented with different angles, resolutions and distances from the camera. In this paper, we propose a hybrid machine learning model combining convolutional neural networks (CNNs) and support vector machines (SVMs) to achieve accurate facial sentiment and emotion analysis of incomplete and partially occluded facial images. The proposed model was successfully tested using 4,690 images containing 25,400 faces, collected from a large-scale public event. The model was able to correctly classify the test dataset containing faces with different angles, camera distances, occlusion areas, and image resolutions. The results show a classification accuracy of 89.9% for facial sentiment analysis, and an accuracy of 87.4% when distinguishing between seven emotions in partially occluded faces. This makes our model suitable for real-life practical applications.

Recently, considerable advancement has been made in the progress of fundamental and applied facets of solar cells, especially in light weight materials and inexpensive electrode materials for the development of energy conversion devices. Among these devices, the 3rd generation type of solar cells i.e., Dye-Sensitized Solar Cells (DSSCs) gained a remarkable attention owing to their distinctive features like ease of fabrication, fascinating appearance, etc. Herein, we predominantly concentrate on the performance and stability of the proposed DSSC at indoor and outdoor light intensities.

Collaborative Intrusion Detection Systems are considered an effective defense mechanism for large, intricate, and multilayered Industrial Internet of Things against many cyberattacks. However, while a Collaborative Intrusion Detection System successfully detects and prevents various attacks, it is possible that an inside attacker performs a malicious act and compromises an Intrusion Detection System node. A compromised node can inflict considerable damage on the whole collaborative network. For instance, when a malicious node gives a false alert of an attack, the other nodes will unnecessarily increase their security and close all of their services, thus, degrading the system’s performance. On the contrary, if the spurious node approves malicious traffic into the system, the other nodes would also be compromised. Therefore, to detect a compromised node in the network, this article introduces a device integrity check mechanism based on “Digital Genome.” In medical science, a genome refers to a set that contains all of the information needed to build and maintain an organism. Based on the same concept, the digital genome is computed over a device’s vital hardware, software, and other components. Hence, if an attacker makes any change in a node’s hardware and software components, the digital genome will change, and the compromised node will be easily detected. It is envisaged that the proposed integrity attestation protocol can be used in diverse Internet of Things and other information technology applications to ensure the legitimate operation of end devices. This study also proffers a comprehensive security and performance analysis of the proposed framework.

Emotional and sentiment analysis of social media content is essential for smart city analytics. In the past few years, researchers have relied on online content’s sentiment analysis to capture public opinion about current events. Despite their merits, the existing solutions take a retrospective coarse-grained approach that analyses millions of social media posts to study public opinion about past events (e.g., presidential elections). Such models give late insights, which makes it difficult to intervene or adjust strategies based on the evolution of public opinion over time. Moreover, such approaches lack efficiency and scalability since they require the analysis of millions of posts to obtain accurate results. In this work, we address those limitations by proposing a framework for the real-time monitoring of the evolution of public opinion over time. To ensure efficiency and scalability, we focus on the analysis of high impact social media content generated by opinion leaders and their followers. To build our framework, we leveraged our opinion leaders’ identification algorithm, along with text mining and text classification techniques, to capture and analyze the evolution of the sentiments and emotions of 34 opinion leaders concerning the topic of global warming. The results obtained are very promising and open the door to advanced social media analytics to monitor public opinion in real-time.

Sentiment analysis of user-generated online content is crucial for smart city analytics and relevant social services. Researchers have relied mainly on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their feelings and share emotions. Sentiment analysis of such large-scale visual content, such as those in image tweets, helps to obtain user sentiments toward events or topics and therefore complement textual sentiment analysis. Motivated by the need to leverage large scale yet noisy training data to solve the extremely challenging problem of face sentiment analysis, we employ Convolutional Neural Networks (CNN). We designed a suitable CNN architecture to classify facial emotions and analyze sentiments. We have conducted extensive experiments on labeled images. The results show that the proposed CNN achieved a very good performance in face sentiment analysis with 89.9% of F1-measure

Cyber-security is indispensable as malicious incidents are ubiquitous on the Internet. Intrusion Detection Systems have an important role in detecting and thwarting cyber-attacks. However, it is more effective in a centralized system but not in peer-to-peer networks which makes it subject to central point failure, especially in collaborated intrusion detection systems. The novel blockchain technology assures a fully distributed security system through its powerful features of transparency, immutability, decentralization, and provenance. Therefore, in this paper, we investigate and demonstrate several methods of collaborative intrusion detection with blockchain to analyze the suitability and security of blockchain for collaborative intrusion detection systems. We also studied the difference between the existing means of the integration of intrusion detection systems with blockchain and categorized the major vulnerabilities of blockchain with their potential losses and current enhancements for mitigation.

Smart city analytics involves tracking, interpreting, and evaluating the sentiments and emotions that are shared via online social media channels. Sentiment analysis of social media posts has become increasingly prominent in recent years as a means of gaining insights into how members of the public perceive current affairs. The ongoing research in this domain has leveraged multiple types of sentiment analysis. However, although the existing approaches enable researchers to acquire retrospective insights into public opinion, they do not enable a real-time evaluation. In addition, they are not readily scalable and necessitate the analysis of a significant amount of posts (in the millions) to facilitate a more in-depth evaluation. The study outlined in this paper was designed to address these shortcomings by presenting a framework that facilitates a real-time evaluation of the evolution of opinions shared by prominent public features and their respective followers; that is, high-impact posts. The developed solution encompasses a sophisticated Bi-directional LSTM classifier that was implemented and tested using a dataset consisting of 278,000 tweets related to the topic of climate change. The outcomes reveal that the proposed classifier achieved the following accuracies: 88.41% for discrimination; 89.66% for anger; 87.01% for inspiration; and 87.52% for joy - with negative emotions being more accurately classified than positive emotions. Similarly, the sentiment classification performance yielded accuracies of 89.32% for support and 89.80% for strong support, as well as 88.14% for opposition and 87.52% for strong opposition. In addition, the findings of the study indicated that geographic location, chosen topic, cultural sensitivities, and posting frequency all play a critical role in public reactions to posts and the ensuing perspectives they adopt. The solution stands out from existing retrospective analysis methods because it does not rely on the analysis of vast quantities of data records; rather, it can perform real-time, high-impact content analysis in a resource-efficient and sustainable manner. This framework can be used to generate insights into how public opinion is developing on a real-time basis. As such, it can have meaningful application within social media analysis efforts.

In order to map the thoron prone areas of the coastal region of Kollam district, a well known HBRA of south India, comparative study of radon and thoron exhalation rate was conducted. The in situ measurement of radon and thoron exhalation has been taken. These studies were correlated with the gamma radiation level. The average value of thoron exhalation is found to 5.55 ± 1.35 Bq m−2 s−1 along the coastal areas and the radon exhalation rate is found to 107.6 ± 32 Bq m−2 h−1. The value of thoron exhalation was found 12 times greater than the global values in Neendakara and Chavara region and about 6 times greater in the Alappad region.

Facial emotion detection and expressions are vital for applications that require credibility assessment, evaluating truthfulness, and detection of deception. However, most of the research reveal low accuracy in emotion detection mainly due to the low quality of images under consideration. Conducting intensive pre-processing activities and using artificial intelligence especially deep learning techniques are increasing accuracy in computational predictions. Our research focuses on emotion detection using deep learning techniques and combined preprocessing activities. We propose a solution that applies and compares four deep learning models for image pre-processing with the main objective to improve emotion recognition accuracy. Our methodology includes three major stages in the data value chain, pre-processing, deep learning and post-processing. We evaluate the proposed scheme on a real facial data set, namely Facial Image Data of Indian Film Stars for our study. The experimentation compares the performance of various deep learning techniques on the facial image data and confirms that our approach enhanced significantly the image quality using intensive pre-processing and deep-learning, improves accuracy in emotion prediction.

The single-beam gradient force optical tweezers have transformed various fields of scientific research by enabling manipulation and characterization of single molecules. Conventional optical tweezers pose limitations in trapping particles in the sub-Rayleigh regime. These limitations have been overcome with the help of plasmonic nanoapertures like the double-nanohole aperture. A modified colloidal lithography technique has been used in fabrication of double-nanohole apertures achieving dimensions appropriate for trapping single molecules in this regime. This paper demonstrates optical trapping of a single 10 nm enzyme, rubisco, using double-nanohole apertures fabricated using the modified colloidal lithography technique as well as presents the results from transmission characterization of different double-nanohole apertures carried out using the finite-difference time-domain (FDTD) simulations.

Institutions in higher education generate terabytes of data that has great value to shape future of nations. This Big Data is in heterogeneous formats, very current, and in large volumes. We propose a framework to collect, scope and verify this large amount of data. The analysis of the data is used to evaluate the institution against a standard set by an accreditation body, for the purpose of the academic accreditation of higher education programs. Therefore, the framework reduces human involvement in accreditation. The paper provides the detailed design of the process of aligning assessment with student learning outcomes.

The data in higher educational institutions come from the interaction of students with the various online systems, such as learning management, registration, advising and email. Research in the field of educational data mining is concerned with the collection and analysis of such data to discover new insights about student behavior, learning style and success factors. Since different departments in an institution manage different IT systems, collecting data from all of these departments requires collaboration. The data has to be extracted from many systems, which uses different data formats. Therefore, a typical research work in this field analyzes data extracted from one system. This paper, on the other hand, analyzes the network traffic that students generate while on-campus. This approach provides us with a better view of the student interaction with the educational systems, compared to the single view achieved by analyzing data from one system. We anonymize student personally identifiable information to protect student privacy. Further, we propose the use of fog computing to enhance student privacy and reduce network load.

New technological advances in user mobility and context immersion are enabling novel adaptive and pervasive learning models in ambient environments. These advances allow physical learning spaces with embedded computing capabilities to provide an augmented self-aware learning experience. In this paper, we aim at developing a novel ubiquitous learning model within a pervasive smart campus environment. The goal of our research consists of identifying the steps towards building such an environment and the involved learning processes. We define a model of a smart campus, and advocate learning practices in the light of new paradigms such as context-awareness, ubiquitous learning, pervasive environment, resource virtualization, autonomic computing and adaptive learning. We reveal a comprehensive architecture that defines the various components and their inter-operations in a smart educational environment. The smart campus approach is presented as a composition of ambient learning spaces, which are environments where physical learning resources are augmented with digital and social services. We present a model of these spaces to harness future ubiquitous learning environments. One of the distinguished features of this model is the ability to unleash the instructional value of surrounding physical structures. Another one is the provision of a personalized learning agenda when moving across these ambient learning environments. To achieve these goals, we profile learners and augment physical campus structures to advocate context-aware learning processes. We suggest a social community platform for knowledge sharing which involves peer learners, domain experts as well as campus physical resources. Within this pervasive social scope, learners are continuously immersed in a pedagogically supported experiential learning loop as a persuasive approach to learning. A learning path, which responds to learners’ goals and qualifications, autonomously guides learners in achieving their objectives in the proposed smart campus. We evaluated our ubiquitous learning approach to assert the performance of these building blocks in the proposed smart campus model. The results show interesting tradeoffs and promising insights.

Double-nanoholes fabricated by colloidal lithography were used for trapping single colloidal particles and single proteins. A gap separation of 60 nm between the cusps of the double-nanohole was achieved in a gold film of 70 nm thickness sputter coated onglass. The cusp separation was reduced steadily down to 10 nm by plasma etching the colloidal particles prior to sputter coating. Scanning electron microscopy was used to locate a particular double-nanohole and it was registered for later microscopy experiments. 30 nm polystyrene particles, the rubisco protein and bovine serum albumin were trapped using a laser focused through the aperture. Compared to other methods that require top-down nanofabrication, this approach is inexpensive and produces high-quality samples.

Peak-time traffic woes create considerable amount of stress and environmental pollution resulting in an economic loss. Research innovations in areas such as the Web of Things are able to curtail some of these issues by creating scalable and sustainable environments like parking lots, which provide motorists with access to convenient parking spots. We present a scalable parking lot network infrastructure that exposes parking management operations through a judicious mashup of physical things’ services within a parking lot. Our system uses service-oriented architecture, allowing motorists to reserve parking spots in advance. In doing so, our proposed system leverages the use of HTTP and Wi-Fi for the Web enablement and interoperability of things within a parking spot and elevates it as a Smart Parking Spot on the Web. Our suggested semantic Web-based structure for representing things makes it possible to query physical things’ states and services depending on their capabilities and other relevant parking-related parameters. Our performance evaluation reveals that a maximum of 40 % time is saved to find parking spots and also 40 % reduction in air pollution is observed.

With diabetes patients doubling every year especially in the UAE there is a need to curb this epidemic and help those who are affected to live an active life. Continuous monitoring of health indicators ensures prompt medical attention and reduction in fatalities. The primary challenge to continuously monitor diabetes is that glucose level measurement requires invasive methods. Moreover, continuous monitoring must happen remotely and therefore would require computing and networking technologies that is seamless, real-time, high speed, and with large storage capacities. With the increasing penetration of smartphones, especially in the UAE, we propose a framework for continuous monitoring of diabetes on the smartphone platform. We also survey the state-of-the-art in continuous health monitoring.

Institutions in higher education generate terabytesof data that has great value to shape future of nations. This Big Data is in heterogeneous formats, very current, and in large volumes. We propose a framework to collect, scope and verify this large amount of data. Although the framework is explained in the context of institution accreditation in higher education, the framework can be applied in the fields of health-care, finance, marketing etc. Our framework is used to reduce human involvement in the collection and analysis of data, for the purpose of accreditation. The framework extends the scope of data collected to target the heterogeneous nature of the data. Finally, our framework helps to verify the data against a standard set by an accreditation body.

Sensing as a Service (SenaaS) is emerging as a prominent element in the middleware linking together the Internet of Things (IoT) and the Web of Things (WoT) layers of future ubiquitous systems. An architecture framework is discussed in this paper whereby things are abstracted into services via embedded sensors which expose a thing as a service. The architecture acts as a blueprint to guide software architects realizing WoT applications. Web-enabled things are eventually appended into Web platforms such as Social Web platforms to drive data and services that are exposed by these things to interact with both other things and people, in order to materialize further the future social Web of Things. Research directions are discussed to illustrate the integration of SenaaS into the proposed WoT architectural framework.

The FT-IR and FT-Raman spectra of 5-chloro-2-(3-chlorophenylcarbamoyl)phenylacetate were studied. The vibrational wave numbers and corresponding vibrational assignments were examined theoretically using the Gaussian 03 set of quantum chemistry codes and the normal modes are assigned by Potential Energy Distribution calculations. The synthesis, elemental analysis and NMR values are presented. The red shift of the NH stretching wave number in the infrared spectrum from the computed wave number indicates the weakening of the NH bond resulting in proton transfer to the neighboring oxygen atom. The first hyperpolarizability, infrared intensities and Raman activities are reported. The calculated first hyperpolarizability is comparable with the reported values of similar derivatives and is an attractive object for future studies of nonlinear optics. The geometrical parameters of the title compound are in agreement with that of similar reported derivatives. (C) 2011 Elsevier B.V. All rights reserved.

The infrared and Raman spectra of 3‐{[(4‐methoxyphenyl)methylene]amino}‐2‐ phenylquinazolin‐4(3H)‐one have been recorded and analysed. Geo metry and harmonic vibrational wavenumbers were calculated theoretically using Gaussian 03 set of quantum chemistry codes. Calculations were performed at the Hartree‐Fock and DFT (B3LYP) levels of theory using the standard 6‐31G* basis. The calculated wavenumbers (B3LYP) agree well with the observed wavenumbers. The proposed assignments of normal modes are based on potential energy distribution (PED) analysis. Calculated infrar ed intensities and first hyperpolarizability are reported. The prepared compound was identified by NMR and mass spectra. The phenyl C‐C stretching modes are equally active as strong bands in both IR and Raman spectra, which are responsible for hyperpolarizability enhancement leading to nonlinear optical activity. The calculated first hyperpolarizability is comparable with the

Introduction: Dengue virus infection is a major cause of morbidity, mortality, and economic hardship in the tropics and subtropics. We undertook this study to see the level of awareness of symptoms and the treatment options for dengue fever. Materials and Methods: This was a cross-sectional study done in the Department of Medicine at a tertiary medical care in central Kerala during July 2017. The patients and their attendants visiting outpatient department were interviewed by trained social workers after taking a written consent. The interview was conducted based on a structured questionnaire. Distribution of various aspects of awareness and sociodemographic characteristics are presented. The association of the awareness of treatment options with sociodemographic characteristics was estimated. Results: Among the 522 subjects interviewed 509 (97.51%) had heard about dengue. Majority of them (89.59%) were aware that this is a preventable disease. Newspaper (80.94%) and television (74.85%) were the main sources of information. Most (85.85%) of them were aware of the infectious nature of the disease and that mosquitoes are the responsible vector. The most common symptoms of this disease which they were aware of were fever (64.83%), body ache (62.48%) and headache (50.68%). Almost half (48.13%) of them incorrectly expressed the need for antibiotics for this viral illness and 46.37% felt the need for platelet transfusion to hasten the recovery. Only education was associated with better awareness regarding antibiotic use (odds ratio [OR] [95% confidence interval [CI]: 1.55 [1.03–2.34]) and platelet transfusion (OR [95% CI]: 2.40 [1.17–4.91]). Conclusions: They had a reasonably good awareness of the vector that promotes dengue and its breeding sites. However, there were misconceptions regarding the treatment options.

FT-Raman and FT-IR spectra of 3-amino-2-phenyl quinazolin-4(3H)-one were recorded and analyzed. The vibrational wavenumbers of the title compound have been computed using the HartreeFock/6-31G* and B3LYP/6-31G* basis sets and compared with the experimental data. The prepared compound was identified by NMR and mass spectra. The red shift of the NH stretching wavenumber indicates the weakening of the NH bond. The simultaneous IR and Raman activation of the Cdouble bond; length as m-dashO stretching mode gives the charge transfer interaction through a π-conjugated path. The first hyperpolarizability, Infrared intensities and Raman activities are reported. The calculated first hyperpolarizability is comparable with the reported values of similar derivatives and is an attractive object for future studies of non-linear optics. The assignments of the normal modes are done by potential energy distribution (PED) calculations. Optimized geometrical parameters of the title compound are in agreement with reported similar structures.

The Fourier transform Raman (FT-Raman) and Fourier transform infrared (FT-IR) spectra of 2-[acetyl(4-bromophenyl)carbamoyl]-4-chlorophenyl acetate were studied. The vibrational wavenumbers were examined theoretically using the Gaussian03 set of quantum chemistry codes, and the normal modes were assigned by potential energy distribution (PED) calculations. The simultaneous Raman and infrared (IR) activations of the CO stretching mode in the carbamoyl moiety show a charge transfer interaction through a π-conjugated path. From the optimized structure, it is clear that the hydrogen bonding decreases the double bond character of the CO bond and increases the double bond character of the CN bonds. The first hyperpolarizability and predicted IR intensities are reported. The calculated first hyperpolarizability is comparable with the reported values of similar structures, which makes this compound an attractive object for future studies of nonlinear optics. Optimized geometrical parameters of the compound are in agreement with similar reported structures. Copyright © 2009 John Wiley & Sons, Ltd.

Fourier transform infrared (FT-IR) and Fourier transform (FT) Raman spectra of 3-{[(2-hydroxyphenyl)methylene]amino}-2phenylquinazolin-4(3H)-onewererecordedandanalyzed.Thevibrationalwavenumbersof thetitlecompound werecomputed usingHF/6-31G ∗ and6-311G ∗ basissetsandcomparedwithexperimentaldata.Theassignmentsofthenormalmodesaredone by potential energy distribution (PED)calculations. The prepared compound was identified by nuclear magnetic resonance (NMR) and mass spectra. Optimized geometrical parameters of the title compound are in agreement with reported structures. ShorteningofCNbondlengthsrevealtheeffectofresonance.ThesimultaneousIRandRamanactivationsoftheC Ostretching mode shows a charge transfer interaction through a π-conjugated path. The first hyperpolarizability, infrared intensities and Raman activities are reported. The phenyl C‐C stretching modes are equally active as strong bands in both IR and Raman spectra, which are responsible for hyperpolarizability enhancement leading to nonlinear optical activity. Copyright c

Fourier transform (FT)-Raman and Fourier transform infrared (FT-IR) spectra of 3-{[(4-fluorophenyl)methylene]amino}-2-phenylquinazolin-4(3H)-one were recorded and analyzed. The vibrational wavenumbers of the title compound were computed using the B3LYP/6-31G* basis and compared with the experimental data. The prepared compound was identified by NMR and mass spectra. The simultaneous IR and Raman activation of the CO stretching mode shows a charge transfer interaction through a π-conjugated path. The first hyperpolarizability and infrared intensities are reported. The assignments of the normal modes are done by potential energy distribution (PED) calculations. Copyright © 2008 John Wiley & Sons, Ltd.

Real-world things are increasingly becoming fully qualified members of the Web. From, pacemakers and medical records to children’s toys and sneakers, things are connected over the Web and publish information that is available for the whole world to see. It is crucial that there is secure access to this Web of Things (WoT) and to the related information published by things on the Web. In this paper, we introduce an architecture that encompasses Web-enabled things in a secure and scalable manner. Our architecture utilizes the features of the well-known role-based access control (RBAC) to specify the access control policies to the WoT, and we use cryptographic keys to enforce such policies. This approach enables prescribers to WoT services to control who can access what things and how access can continue or should terminate, thereby enabling privacy and security of large amount of data that these things are poised to flood the future Web with. Springer Cham

Little research effort contributed to help learners reflect on their current cognitive skills. A storehouse of this valuable information could provide valuable indications to experienced mentors to intervene and guide learners improving their learning capabilities. Learning how to learn using the proposed cognitive apprenticeship approach in this paper goes beyond the given classroom knowledge to reflect on how to maximize learning readiness under the supervision of experienced mentors. In this paper, we promote an autonomous inquiry-based learning cycle to support formal education processes, whereby students learn from individual discoveries and from peers. At the completion of each inquiry-based learning cycle, a cognitive apprenticeship cycle is triggered to assess the improvements in learning dispositions and guide apprentices in developing their own cognitive skills. We propose an online instrument for collecting self-assessment data and communicating with mentors, to produce willing, confident and creative lifelong learners. The provided instrument presents a storehouse view of learning disposition through an integrated portal which captures self-stated learning experiences and converts them into analytical results to diagnose deficiencies and prescribe improvement recommendations. In doing so, we provide "learning disposition" dimensions along which, we analytically measure learning aptitudes of individual learners. The proposed tool in this project generates an inventory of learning capabilities to guide learners reorganizing their learning agenda. Collected data out of the proposed tool reflects learners' perception at a given point of time. The tool is applied again at a later point of time to assess the changes and move the apprentice to the next level of his cognitive skills.

New innovations in the Web of Things (WoT) and the use of the social Web in business scenarios are paving the path for 'real-world commodities' or things to inherently sell themselves to potential customers on the Web. An important challenge to realize this vision of personalized sales initiated by things is the adoption of massive instances of potential things into the virtual world. We show in this paper how this issue is alleviated by creating community of things. We describe how the presentation of things on the Web as Web Smart things supports such synergies. We also describe how a cluster of things represented as Ambient Spaces (AS) increases the possibility of identifying potential customers on the social Web. The community framework is used to drive things into populating the Web to reach relevant customer profiles leading to friendships with things and people, which could nurture future adoptions and purchase opportunities. Hence, these social relationships between people and physical commodities are exploited to elevate their value, promote adoption and induce an opportunistic commercial transaction.