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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.

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.

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.