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Apart from, the smart edge computing (EC) robot (SECR) provides the tools to manage Internet of things (IoT) services in the edge landscape by means of real-world test-bed designed in ECR. Eventually, based on the results from two experiments held in little constrained condition, such as the maximum data size is 2GB, the performance of the proposed techniques demonstrate the effectiveness, scalability and performance efficiency of the proposed IoT model.

Certainly, the proposed SECR is trying primarily to take over other traditional static robots in a centralized or distributed cloud environment. One aspect of representation of the proposed edge computing algorithms is due to challenge to slow down the consumption of time which happened in an artificial intelligence (AI) robot system. Thus, the developed SECR trained by tiny machine learning (TinyML) techniques to develop a decentralized and dynamic software environment.

Specifically, the waste time of SECR has actually slowed down when it is embedded with Edge Computing devices in the demonstration of data transmission within different paths. The TinyML is applied to train with image data sets for generating a framework running in the SECR for the recognition which has also proved with a second complete experiment.

The work presented in this paper is the first research effort, and which is focusing on resource allocation and dynamic path selection for edge computing. The developed platform using a decoupled resource management model that manages the allocation of micro node resources independent of the service provisioning performed at the cloud and manager nodes. Besides, the algorithm of the edge computing management is established with different path and pass large data to cloud and receive it. In this work which considered the SECR framework is able to perform the same function as that supports to the multi-dimensional scaling (MDS).

The purpose of this paper is to set up a new framework to enable direct modifications of volume meshes enriched with semantic information associated to multiple partitions. An instance of filleting operator is prototyped under this framework and presented in the paper.

In this paper, a generic mesh modification operator has been designed and a new instance of this operator for filleting finite element (FE) sharp edges of tetrahedral multi-partitioned meshes is also pro-posed. The filleting operator works in two main steps. The outer skin of the tetrahedral mesh is first deformed to round user-specified sharp edges while satisfying constraints relative to the shape of the so-called Virtual Group Boundaries. Then, in the filleting area, the positions of the inner nodes are relaxed to improve the aspect ratio of the mesh elements.

The classical mainstream methodology for product behaviour optimization involves the repetition of four steps: CAD modelling, meshing of CAD models, enrichment of models with FE simulation semantics and FEA. This paper highlights how this methodology could be simplified by two steps: simulation model modification and FEA. The authors set up a new framework to enable direct modifications of volume meshes enriched with semantic information associated to multiple partitions and the corresponding fillet operator is devised.

The proposed framework shows only a paradigm of direct modifications of semantic enriched meshes. It could be further more improved by adding or changing the modules inside. The fillet operator does not take into account the exact radius imposed by user. With this proposed fillet operator the mesh element density may not be enough high to obtain wished smoothness.

This paper fulfils an identified industry need to speed up the product behaviour analysis process by directly modifying the simulation semantic enriched meshes.

The intelligence in the Internet of Things (IoT) can be embedded by analyzing the huge volumes of data generated by it in an ultralow latency environment. The computational latency incurred by the cloud-only solution can be significantly brought down by the fog computing layer, which offers a computing infrastructure to minimize the latency in service delivery and execution. For this purpose, a task scheduling policy based on reinforcement learning (RL) is developed that can achieve the optimal resource utilization as well as minimum time to execute tasks and significantly reduce the communication costs during distributed execution.

To realize this, the authors proposed a two-level neural network (NN)-based task scheduling system, where the first-level NN (feed-forward neural network/convolutional neural network [FFNN/CNN]) determines whether the data stream could be analyzed (executed) in the resource-constrained environment (edge/fog) or be directly forwarded to the cloud. The second-level NN ( RL module) schedules all the tasks sent by level 1 NN to fog layer, among the available fog devices. This real-time task assignment policy is used to minimize the total computational latency (makespan) as well as communication costs.

Experimental results indicated that the RL technique works better than the computationally infeasible greedy approach for task scheduling and the combination of RL and task clustering algorithm reduces the communication costs significantly.

The proposed algorithm fundamentally solves the problem of task scheduling in real-time fog-based IoT with best resource utilization, minimum makespan and minimum communication cost between the tasks.

The real-time generation of feature descriptors for object recognition is a challenging problem. In this research, the purpose of this paper is to provide a hardware friendly framework to generate sparse features that can be useful for key feature point selection, feature extraction, and descriptor construction. The inspiration is drawn from feature formation processes of the human brain, taking into account the sparse, modular, and hierarchical processing of visual information.

A sparse set of neurons referred as active neurons determines the feature points necessary for high-level vision applications such as object recognition. A psycho-physical mechanism of human low-level vision relates edge detection to noticeable local spatial stimuli, representing this set of active neurons. A cognitive memory cell array-based implementation of low-level vision is proposed. Applications of memory cell in edge detection are used for realizing human vision inspired feature selection and leading to feature vector construction for high-level vision applications.

True parallel architecture and faster response of cognitive circuits avoid time costly and redundant feature extraction steps. Validation of proposed feature vector toward high-level computer vision applications is demonstrated using standard object recognition databases. The comparison against existing state-of-the-art object recognition features and methods shows an accuracy of 97, 95, 69 percent for Columbia Object Image Library-100, ALOI, and PASCAL VOC 2007 databases indicating an increase from benchmark methods by 5, 3 and 10 percent, respectively.

A hardware friendly low-level sparse edge feature processing system is proposed for recognizing objects. The edge features are developed based on threshold logic of neurons, and the sparse selection of the features applies a modular and hierarchical processing inspired from the human neural system.

The purpose of this paper is to investigate the effect of surface protrusions on the flow unsteadiness of NACA 0012 at a Reynolds number of 100,000.

Effect of protrusions is investigated through numerical simulation of two-dimensional Navier–Stokes equations using a finite volume solver. Turbulent stresses are resolved through the transition Shear stress transport (four-equation) turbulence model.

The small protrusion located at 0.05c and 0.1c significantly improve the lift coefficient by up to 36% in the post-stall regime. It also alleviates the leading edge stall. The larger protrusions increase the drag significantly along with significant degradation of lift characteristics in the pre-stall regime as well. The smaller protrusions also increase the frequency of the vortex shedding.

The effect of macroscopic protrusions or deposits in rarely investigated. The delay in stall shown by smaller protrusions can be beneficial to micro aerial vehicles. The smaller protrusions increase the frequency of the vortex shedding, and hence, can be used as a tool to enhance energy production for energy harvesters based on vortex-induced vibrations and oscillating wing philosophy.

Several decades of mental health research in the UK repeatedly report that people of African-Caribbean origin are more likely than other ethnic minorities, including the White majority, to be diagnosed with schizophrenia and related psychoses. Race-based inequalities in mental healthcare persist despite numerous initiatives such as the UK’s ‘Delivering Race Equality’ policy, which sought to reduce the fear of mainstream services and promote more timely access to care. Community-level engagement with members of African-Caribbean communities highlighted the need to develop culturally relevant psychosocial treatments. Family Intervention (FI) is a ‘talking treatment’ with a strong evidence-base for clinical-effectiveness in the management of psychoses. Benefits of FI include improved self-care, problem-solving and coping for both service users and carers, reducing the risk of relapse and re-hospitalisation. Working collaboratively with African-Caribbeans as ‘experts-by-experience’ enabled co-production, implementation and evaluation of Culturally adapted Family Intervention (CaFI). Our findings suggests that a community frequently labelled ‘hard-to-reach’ can be highly motivated to engage in solutions-focussed research to improve engagement, experiences and outcomes in mental health. This underscores the UK’s Mental Health Task Force’s message that ‘new ways of working’ are required to reduce the inequalities faced by African-Caribbeans and other marginalised groups in accessing mental healthcare. Although conducted in the UK (a high-income multi-cultural country), co-production of more culturally appropriate psychosocial interventions may have wider implications in the global health context. Interventions like CaFI could, for example, contribute to reducing the 75% ‘mental health gap’ between High and Low-and-Middle-Income counties reported by the World Health Organization.

This paper explores how individual and collective burnout has become an organizational concern for school leaders, why burnout matters and what might be done to address the problems individual and organizational burnout generates.

This paper presents an analysis of the current literature regarding individual and collective burnout, identifies contributing factors and explores the impacts of each. Following a discussion and synthesis of the research literature, implications for practice are presented.

Highlighting exhaustion as a factor in burnout and as a significant consequence of stress, the paper proposes specific individual teacher and leader actions focused on addressing broad organizational responses with the potential to address the consequences of burnout including depersonalization, cynicism, emotional and compassion fatigue, and a loss of individual and collective efficacy. The authors argue that for burnout to be successfully mitigated, urgent strategic and focused organizational responses are essential to identify, track, and counter individual and collective burnout.

Much of the existing burnout literature focuses on the individual as the locus of experience and inquiry. The authors contend that this predominant focus on individual experience is insufficient to address systemic organizational issues, problems and concerns facing educational organizations that perpetuates and accelerates the experience of individuals. This paper contribution elevates conceptions of and discussions about burnout to the organizational level and reframes the conversation by focusing on organizational responses.

Perinatal mental illness is an important public health issue. Conditions such as postnatal depression increase mothers' risk of suicide and can herald onset of recurrent and chronic mental health problems. Maternal mental illness can also adversely impact the cognitive, physical, and psychological health and development of children. In light of known psychosocial risks, there is concern that fewer than expected women from black and minority ethnic (BME) backgrounds access care and treatment. This paper aims to address this issue.

Responding to persistent reports of patchy service provision across the UK more generally and particular concerns about potentially unmet needs among BME women, mixed‐method research was undertaken between September 2009 and March 2010. Using survey questionnaires and telephone interviews, the study sought to explore professional stakeholders' perspectives on current perinatal mental health provision and the extent to which it meets the needs of BME women. Findings from the study were intended to inform policy and plans to improve provision by establishing managed care networks (MCNs) for perinatal mental healthcare.

In total, 45 questionnaires were returned from the national survey. One‐third of respondents (n=14) consented to follow‐up telephone interviews. There was evidence of multi‐agency working among the 27 professional groups which respondents reported as being directly involved in delivering perinatal mental healthcare across the country. However, there was also evidence of disjuncture and poor communication – particularly between statutory and voluntary sectors and NHS primary and secondary care. Some respondents had difficulty defining “BME” or identifying the women to whom the acronym might be applied. They also questioned the validity of providing “BME‐specific” services. Instead, they endorsed more ethnically “inclusive models” of provision and “signposting” women to appropriate “community” services.

Taken together, these findings suggests that whilst there might be a theoretical argument for perinatal mental health MCNs, considerable effort is required if policy‐makers' aspirations for more “joined‐up” services capable of meeting the needs of all women are to be fully realised. Furthermore, current proposals for public sector reform coupled with reduction in voluntary sector provision is likely to disproportionately affect women from BME and other marginalised communities as they provide significant amounts of “below the radar” care and support.

This paper is of particular relevance to policy makers and practitioners. Findings suggest that women from BME backgrounds might be particularly vulnerable to perinatal mental illness. Contraction of voluntary sector provision increases the likelihood that the needs of BME women will remain unmet with deleterious consequences for their health and wellbeing of their families. This has potentially serious public health implications. MCNs/clinical networks have the potential to reduce inequalities by providing more “joined up” care for all women. However, the evidence base for levels of need and appropriate service response to perinatal mental illness among BME women is weak. Further research is required to bridge the evidence gap and to evaluate the impact of health and social care reform on vulnerable groups.

In recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of knowledge graph triples when obtaining the entity and relationship representations. In contrast, the integration of the entity description and the knowledge graph network structure has been ignored. This paper aims to investigate how to leverage both the entity description and the network structure to enhance the knowledge graph completion with a high generalization ability among different datasets.

The authors propose an entity-description augmented knowledge graph completion model (EDA-KGC), which incorporates the entity description and network structure. It consists of three modules, i.e. representation initialization, deep interaction and reasoning. The representation initialization module utilizes entity descriptions to obtain the pre-trained representation of entities. The deep interaction module acquires the features of the deep interaction between entities and relationships. The reasoning component performs matrix manipulations with the deep interaction feature vector and entity representation matrix, thus obtaining the probability distribution of target entities. The authors conduct intensive experiments on the FB15K, WN18, FB15K-237 and WN18RR data sets to validate the effect of the proposed model.

The experiments demonstrate that the proposed model outperforms the traditional structure-based knowledge graph completion model and the entity-description-enhanced knowledge graph completion model. The experiments also suggest that the model has greater feasibility in different scenarios such as sparse data, dynamic entities and limited training epochs. The study shows that the integration of entity description and network structure can significantly increase the effect of the knowledge graph completion task.

The research has a significant reference for completing the missing information in the knowledge graph and improving the application effect of the knowledge graph in information retrieval, question answering and other fields.