Autonomous Model of Software Architecture for Smart Grids

Smart grids are being deployed at global level to ensure energy efficiency. As a result, scalable smart software platforms are required which can be used to incorporate and integrate information coming from various consumers using smart meters. Smart grids are supported by smart software architectures which are supported by cloud platforms. Cloud and Internet-of-Things (IoT) platforms provide scalable resources which can be used to design software infrastructures which allow always-on applications. The report paper explores smart grid and energy efficiency, how cloud and IoT platforms are used to enhance smart software architecture for smart grids, and privacy and security issues that result from the use of clouds. © Springer International Publishing Switzerland 2015

Haptic-Enhanced Learning in Preclinical Operative Dentistry

Background: Virtual reality haptic simulators represent a new paradigm in dental education that may potentially impact the rate and efficiency of basic skill acquisition, as well as pedagogically influence the various aspects of students’ preclinical experience. However, the evidence to support their efficiency and inform their implementation is still limited. Objectives: This thesis set out to empirically examine how haptic VR simulator (Simodont®) can enhance the preclinical dental education experience particularly in the context of operative dentistry. We specify 4 distinct research themes to explore, namely: simulator validity (face, content and predictive), human factors in 3D stereoscopic display, motor skill acquisition, and curriculum integration. Methods: Chapter 3 explores the face and content validity of Simodont® haptic dental simulator among a group of postgraduate dental students. Chapter 4 examines the predictive utility of Simodont® in predicting subsequent preclinical and clinical performance. The results indicate the potential utility of the simulator in predicting future clinical dental performance among undergraduate students. Chapter 5 investigates the role of stereopsis in dentistry from two different perspectives via two studies. Chapter 6 explores the effect of qualitatively different types of pedagogical feedback on the training, transfer and retention of basic manual dexterity dental skills. The results indicate that the acquisition and retention of basic dental motor skills in novice trainees is best optimised through a combination of instructor and visualdisplay VR-driven feedback. A pedagogical model for integration of haptic dental simulator into the dental curriculum has been proposed in Chapter 7. Conclusion: The findings from this thesis provide new insights into the utility of the haptic virtual reality simulator in undergraduate preclinical dental education. Haptic simulators have promising potential as a pedagogical tool in undergraduate dentistry that complements the existing simulation methods. Integration of haptic VR simulators into the dental curriculum has to be informed by sound pedagogical principles and mapped into specific learning objectives

Urban Upgrading as A Strategic Option To Deal With Urban Deterioration Case Study: Al Shumaisi Neighborhood in Riyadh

The paper discusses the problem of urban deterioration in Saudi cities that often stand at the stage of urban removal. The research aims to develop a strategy for urban upgrading based on the optimal utilization of existing urban structures to preserves economic resources, and in line with Kingdom of Saudi Arabia 2030 Vision. The paper began with reviewing literature and previous experiences in urban upgrading and determining the foundations of social urban upgrading. The study was applied to the Al Shumaisi neighborhood in Riyadh, which is part of the Central Riyadh Development Project (CRDP). The paper followed the descriptive and the theoretical approach. Questionnaire and field survey tools were used, as well as interviewing Al Shumaisi residents and expert in the field of urban planning. The research has found that there are urban structures in good condition at a rate of 33% of the total buildings, an urban fabric that can be exploited, as well as the integration of infrastructure, services and commercial activities that represent a source of income, which strengthens the principle of urban upgrading in the Al Shumaisi neighborhood. Finally, the paper recommends reviewing the demolition decision and using urban upgrading as a strategy to deal with degraded neighborhoods within the framework of community participation

Building Towards Automated Cyberbullying Detection: A Comparative Analysis

The increased use of social media between digitally anonymous users, sharing their thoughts and opinions, can facilitate participation and collaboration. However, it’s this anonymity feature which gives users freedom of speech and allows them to conduct activities without being judged by others can also encourage cyberbullying and hate speech. Predators can hide their identity and reach a wide range of audience anytime and anywhere. According to the detrimental effect of cyberbullying, there is a growing need for cyberbullying detection approaches. In this survey paper, a comparative analysis of the automated cyberbullying techniques from different perspectives is discussed including data annotation, data pre-processing and feature engineering. In addition, the importance of emojis in expressing emotions as well as their influence on sentiment classification and text comprehension lead us to discuss the role of incorporating emojis in the process of cyberbullying detection and their influence on the detection performance. Furthermore, the different domains for using Self-Supervised Learning (SSL) as an annotation technique for cyberbullying detection is explored

NeuroPlace: categorizing urban places according to mental states

Urban spaces have a great impact on how people’s emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture

Light-Dermo: A Lightweight Pretrained Convolution Neural Network for the Diagnosis of Multiclass Skin Lesions

Skin cancer develops due to the unusual growth of skin cells. Early detection is critical for the recognition of multiclass pigmented skin lesions (PSLs). At an early stage, the manual work by ophthalmologists takes time to recognize the PSLs. Therefore, several “computer-aided diagnosis (CAD)” systems are developed by using image processing, machine learning (ML), and deep learning (DL) techniques. Deep-CNN models outperformed traditional ML approaches in extracting complex features from PSLs. In this study, a special transfer learning (TL)-based CNN model is suggested for the diagnosis of seven classes of PSLs. A novel approach (Light-Dermo) is developed that is based on a lightweight CNN model and applies the channelwise attention (CA) mechanism with a focus on computational efficiency. The ShuffleNet architecture is chosen as the backbone, and squeeze-and-excitation (SE) blocks are incorporated as the technique to enhance the original ShuffleNet architecture. Initially, an accessible dataset with 14,000 images of PSLs from seven classes is used to validate the Light-Dermo model. To increase the size of the dataset and control its imbalance, we have applied data augmentation techniques to seven classes of PSLs. By applying this technique, we collected 28,000 images from the HAM10000, ISIS-2019, and ISIC-2020 datasets. The outcomes of the experiments show that the suggested approach outperforms compared techniques in many cases. The most accurately trained model has an accuracy of 99.14%, a specificity of 98.20%, a sensitivity of 97.45%, and an F1-score of 98.1%, with fewer parameters compared to state-of-the-art DL models. The experimental results show that Light-Dermo assists the dermatologist in the better diagnosis of PSLs. The Light-Dermo code is available to the public on GitHub so that researchers can use it and improve it

Near-fatal presentation of bilateral pneumothorax in cutis laxa patient: Case report, and review of the literature

Cutis laxa (CL) is a rare connective tissue disease characterized by a loose, wrinkled, and inelastic skin. Here, we report an unusual presentation in a 15-year-old male patient who is a known patient of CL who presented with bilateral pneumothorax. He was successfully managed initially by chest tube insertion and then he was treated surgically with bilateral staged thoracoscopy, apical bullectomy, and pleurodesis with full uneventful recovery

Bioethanol Production from Lignocellulosic Biomass Using <i>Aspergillus niger</i> and <i>Aspergillus flavus</i> Hydrolysis Enzymes through Immobilized <i>S. cerevisiae</i>

Lignocellulose, the main component of a plant cell wall, is a potential renewable bioenergy source. It is composed of cellulose, hemicellulose, and lignin structures. Cellulose is a linear polysaccharide that is hydrolyzed chemically or enzymatically by cellulase. The addition of lignocellulosic biomass, such as wheat bran and coffee pulp, into the fermentation culture, induces the production of cellulases. Cellulose accounts for 20% of the enzyme market worldwide, demonstrating benefits in diverse applications, especially bioethanol and biogas generation. The aim is to evaluate the optimal condition for bioethanol production by previously isolated fungal species from different soil types in the eastern region of the Kingdom of Saudi Arabia. This study attempts to evaluate and optimize the culture conditions of lignocellulosic biomass under SSF using the highest cellulases-producer strains in the region: Aspergillus niger and Aspergillus flavus (GenBank Accession No. MT328516 and MT328429, respectively) to produce raw sugar that consequently is used in the next step of bioethanol production. This process has two parts: (1) hydrolyze lignocellulosic biomass to obtain raw sugar using A. niger and A. flavus that produce cellulase, and (2) produce bioethanol through the conversion of the raw sugar produced from the cellulolysis into ethanol using Saccharomyces cerevisiae. The optimal conditions under SSF were seven days of incubation, 5% glucose as a carbon source, 1% ammonium sulfate as a nitrogen source, and 80% moisture for both isolates. Biochemical characterization showed stability for the immobilized enzyme in all temperature ranges (from 20 °C to 70 °C), while the free enzyme exhibited its maximum at 20 °C of 1.14 IU/mL. CMCase production was the highest at pH 4.0 (1.26 IU/mL) for free enzyme and at pH 5.0 (2.09 IU/mL) for the immobilized form. The CMCase activity increased steadily with an increase in water level and attained a maximum of 80% moisture content. The maximum enzyme activity was with coffee pulp as a substrate of 7.37 IU/mL and 6.38 IU/mL for A. niger and A. flavus after seven days of incubation, respectively. The Carboxymethyl Cellulase (CMCase) activity in immobilized enzymes showed good storage stability under SSF for six weeks, maintaining 90% of its initial activity, while the free enzyme retained only 59% of its original activity. As a carbon source, glucose was the best inducer of CMCase activity with coffee pulp substrate (7.41 IU/mL and 6.33 IU/mL for A. niger and A. flavus, respectively). In both fungal strains, ammonium sulfate caused maximum CMCase activities with coffee pulp as substrate (7.62 IU/mL and 6.47 IU/mL for A. niger and A. flavus, respectively). Immobilized S. cerevisiae showed an increase in ethanol production compared to free cells. In the case of immobilized S. cerevisiae cells, the concentration of ethanol was increased steadily with increasing fermentation time and attained a maximum of 71.39 mg/mL (A. niger) and 11.73 mg/mL (A. flavus) after 72 h of fermentation