Robot-mediated therapies for autism spectrum disorder (ASD) have shown promising results in the past. We have proposed a novel mathematical model based on an adaptive multi-robot therapy of ASD children focusing on two main impairments in autism: 1) joint attention and 2) imitation. Joint attention intervention is based on three different least-to-most (LTM) cues, whereas the adaptive imitation module uses joint attention for activation of the robot. The proposed model uses a multi-robot system as a therapist without any external stimuli (from the environment) to improve the skills of the ASD child. Another novel aspect of this paper is the deployment of a multi-robot system for introducing the ASD child to the concept of multi-person communication. This is particularly useful as, unlike humans, robots can be more consistent and relatively immune to fatigue. Two different therapies of human-robot interaction (i.e., with and without interrobot communication) have been conducted. The model has been tested on 12 ASD children, eight sessions for each intervention over a period of six months. The effectiveness of the model is validated by analyzing the cognitive state of the brain before and after the intervention with electroencephalogram (EEG) neuroheadsets. Moreover, results obtained using the childhood autism rating scale (CARS) to measure the effectiveness of therapy also support the conclusions firmly. The statistical results with the p-value = 3.79E-07 < 0.05 and the F value = 23.93>3.28 show reliability and significance of the data. The results strongly indicate significant improvements in both modules, along with a notable improvement in multi-communication skills of the participating children.

Recent research has shown reliability in robotic therapies for improvement in core impairments of autism. To improve the efficiency of communication using robots, this study evaluates the effectiveness of three different stimuli in a robotic intervention for children with autism spectrum disorder. Three different reinforcement stimuli presented in least-to-most (LTM) order introduced in this therapy using NAO robot are: visual (color variation), auditory and motion cues. The therapy was tested on 12 ASD children, 4 out of 12 children fall under mild category whereas 8 fall under the minimal category of autism. The experimentation was conducted for 2 months. Total 8 experiments were conducted with 1 trial per week. Total 12 cues were given per trial, 4 cues corresponding to each category. In total 96 cues were given per subject, 32 cues from each category. The results indicate a general trend for linking a particular autism category with the most effective stimulus for that category. It can be concluded that visual cue (color variation) is the most effective reinforcement stimulus for children with minimal autism as 8 out of 8 i.e., 100% were more responsive to visual cues whereas for children with mild autism category, 3 out of 4 i.e., 75% are more receptive towards the motion stimulus. The parameters used for assessment were joint attention and the time eye contact is maintained. Single factor ANOVA was used for the statistical analysis of results with alpha is 0.05 and p-value 0.0342, F value is 3.7456 and F critical value is 3.2834. The test was performed on 96 (8x12) trails in total, therefore ensuring the significance and reliability of our results. INDEX TERMS Autism spectrum disorder (ASD), reinforcement stimulus, robotic therapy, joint attention.

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This paper proposes an accurate High Impedance Fault (HIF) detection and isolation scheme in a power distribution network. The proposed schemes utilize the data available from voltage and current sensors. The technique employs multiple algorithms consisting of Principal Component Analysis, Fisher Discriminant Analysis, Binary and Multiclass Support Vector Machine for detection and identification of the high impedance fault. These data driven techniques have been tested on IEEE 13-node distribution network for detection and identification of high impedance faults with broken and unbroken conductor. Further, the robustness of machine learning techniques has also been analysed by examining their performance with variation in loads for different faults. Simulation results for different faults at various locations have shown that proposed methods are fast and accurate in diagnosing high impedance faults. Multiclass Support Vector Machine gives the best result to detect and locate High Impedance Fault accurately. It ensures reliability, security and dependability of the distribution network.

Recently, the World Economic Forum (WEF) highlighted mission-critical Internet of Things (MC-IoT) applications as one of the six enablers of sustainable development of smart cities. MC-IoT refers to systems which exacerbate properties like availability, reliability, safety, and security in an application environment of heterogeneously connected physical things and virtual things whose failure could lead to severe consequences such as life loss. The sole characteristic of the mission-critical system is its compliance with real-time behavior. As a result of the critical nature of these systems, it is essential to design the system with sufficient clarity so that none of the requirements is misinterpreted. For this, the involvement of non-technical stakeholders and policymakers is crucial. Previous studies on mission-critical structures mainly focus on the communication overheads, and overlook the design and planning of them. Therefore, in this paper, we present an architecture which enables mission planning on a do-it-yourself plane. We present a task–object mapping and deployment model where different tasks are mapped onto virtual objects and deployed on physical hardware in a task–object pair. The system uses semantic knowledge for autonomous task mapping and suggestions to further aid the orchestration of the process. The tasks are autonomously mapped onto the devices based on the correlation index; this is computed based on the attribute similarities, thus making the system flexible. The performance of the proposed architecture is evaluated with different key performance indicators under different load conditions and the response time is found to be under a few seconds even at peak load conditions.

Ulcerative colitis (UC) is a chronic inflammatory condition that is variable in both extent and severity of disease as well as response to therapy. Corticosteroids (CSs) were the first drugs used in the management of UC and are still used for induction of remission. However, because of their extensive side-effect profile, they are not utilized for maintenance of remission. In view of this, CS-free remission has become an important end point while evaluating therapeutic agents used in the management of UC. This review highlights the results of various studies conducted to evaluate the efficacy of different medications to attain CS-free remission in the setting of active UC. The drugs reviewed include established agents such as thiopurines, methotrexate, infliximab, adalimumab, vedolizumab, golimumab, and newer experimental agents, and if all else fails, colectomy will be performed. The efficacy of these drugs is evaluated individually. Our aim is to provide a synopsis of the work done in this field to date.

Internet of Things (IoT) is considered one of the future disruptive technologies and has attracted lots of research attention in the recent past. IoT devices are tiny sensing or actuating devices attached to daily life objects, capable of sending sensing data and receiving commands. Cloud computing technology provides tremendous computing and storage capacity over the Internet to overcome limited resources of IoT devices. Many studies are conducted on IoT device virtualization in the cloud environment to facilitate remote access and control. In the future, IoT devices will be accessed through its corresponding virtual objects. Just like the network of physical devices, there needs to be a network of virtual objects in the cyber world. In this paper, we present a concept of building a dynamic virtual network in the cloud environment among connected IoT devices. The key idea is to provide a mechanism for building a virtual network among connected IoT devices from different domains through their corresponding virtual objects in the cloud environment. This will facilitate the sharing of resources and the rapid development of diverse applications on top of the virtualization layer by establishing a dynamic end-to-end connection between IoT devices. In this study, we present a detailed design of the proposed system for building a virtual IoT network. We have also implemented three application layers protocols in OMNET++ for simulation of a virtual objects network to conduct performance analysis of the proposed IoT network virtualization.

Αβστραχτ: Τηισ ωορκ φοχυσεσ ον Dιστριβυτεδ Σεχονδαρψ Χοντρολ (DΣΧ) τεχηνιθυε, φορ φρεθυενχψ ρεγυλατιον ανδ Εχονοmιχ Λοαδ Dισπατχη (ΕΛD) οφ Μιχρογριδ (ΜΓ). Τηε φλυχτυατινγ νατυρε ανδ λαργε θυαντιτψ οφ Dιστριβυτεδ Ενεργψ Ρεσουρχεσ (DΕΡ) ιν αυτονοmουσ ΜΓ ρεσυλτ ιν χοmπλεξ χοντρολ ρεθυιρεmεντσ, δεmανδινγ φαστ ανδ ροβυστ ρεσπονσε. Τηε χοντεmποραρψ DΣΧ σχηεmεσ αρε mοστλψ βασεδ ον Dιστριβυτεδ Αϖεραγινγ Ιντεγρατιον τεχηνιθυε, οωινγ σλοω ρεσπονσε. Τηε παπερ προποσεσ, Dιστριβυτεδ Μοδελ Πρεδιχτιϖε βασεδ Σεχονδαρψ Χοντρολ (DΜΠΣΧ) ωηιχη εφφεχτιϖελψ χοmπλψ ωιτη τηε χοντρολ ρεθυιρεmεντσ οφ ΜΓ. DΜΠΣΧ ρεθυιρεσ εαχη DΕΡ−νοδε το σολϖε α λοχαλ οπτιmιζατιον προβλεm ωιτη τηε χοστ φυνχτιον πεναλιζινγ τηε δεϖιατιον οφ στατεσ φροm τηειρ δεσιρεδ ϖαλυεσ ανδ διφφερενχε βετωεεν τηε ασσυmεδ ανδ πρεδιχτεδ ϖαλυεσ. Τηε δεσιρεδ−στατεσ φορ νον− λινεαρ δψναmιχσ οφ DΕΡ−νοδεσ, αρε βασεδ ον λοχαλ ιντερmεδιατε−οπτιmυm ϖαλυεσ, χοmπυτεδ υσινγ λοχαλ ανδ νειγηβουρινγ ινφορmατιον. Εθυαλιτψ βασεδ τερmιναλ χονστραιντσ αρε ιντροδυχεδ το ενσυρε τηε σταβιλιτψ, ωηερε εαχη νοδε ισ φορχεδ το ρεαχη τηε δεσιρεδ−στατε ϖαλυε ατ τηε ενδ οφ πρεδιχτιον ηοριζον. Τηε τερmιναλ−χονσενσυσ οφ τηε νετωορκ αφφιρmσ χονϖεργενχε οφ δεσιρεδ−στατεσ το α γλοβαλ οπτιmαλ ποιντ οφ τηε νετωορκ. Τηε ασψmπτοτιχ σταβιλιτψ οφ προποσεδ χοντρολ ισ προϖεδ βψ υσινγ τηε συm οφ λοχαλ χοστ−φυνχτιονσ ασ Λψαπυνοϖ χανδιδατε φυνχτιον. Σιmυλατιον ρεσυλτσ ϖαλιδατε τηε εφφεχτιϖενεσσ οφ τηε προποσεδ χοντρολ σχηεmε.