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This paper aims to present a generic lifecycle framework model for guiding architects, engineers, contractors and facilities managers (AEC/FM) practitioners on the effective implementation of adaptive reuse projects.

A mixed approach of qualitative and quantitative techniques was followed in the development of the framework model. A literature review was conducted to comprehend the processes involved in adaptive reuse projects. In total, 90 AEC/FM practitioners were surveyed to identify the current practices in these projects. A generic framework model was then developed to standardize the processes involved, using integration definition for function modeling process modeling methodology. Face-to-face interviews with a targeted group of 30 AEC/FM practitioners were conducted, to validate the developed framework model, by assessing the importance and the frequency of implementing each function in the developed framework model.

The framework model consisted of four sequential processes, namely, assess the feasibility of the adaptive reuse project, design the adaptive reuse project, construct the adaptive reuse project and operate and maintain the adaptive reuse project. The validation confirmed the importance of all the framework functions and the frequency of their implementation.

This research contributes to the literature and the AEC/FM professions, through developing a lifecycle and knowledge-oriented framework model for building adaptive reuse. The framework presents clear documentation of adaptive reuse processes. Thus, it holds the potential of endeavoring on adaptive reuse projects to be more efficient.

This paper aims to present a novel adaptive sliding mode control (ASMC) method based on the predefined performance barrier function for reusable launch vehicle under attitude constraints and mismatched disturbances.

A novel ASMC based on barrier function is adopted to deal with matched and mismatched disturbances. The upper bounds of the disturbances are not required to be known in advance. Meanwhile, a predefined performance function (PPF) with prescribed convergence time is used to adjust the boundary of the barrier function. The transient performance, including the overshoot, convergence rate and settling time, as well as the steady-state performance of the attitude tracking error are retained in the predetermined region under the barrier function and PPF. The stability of the proposed control method is analyzed via Lyapunov method.

In contrast to conventional adaptive back-stepping methods, the proposed method is comparatively simple and effective which does not need to disassemble the control system into multiple first-order systems. The proposed barrier function based on PPF can adjust not only the switching gain in an adaptive way but also the convergence time and steady-state error. And the efficiency of the proposed method is illustrated by conducting numerical simulations.

A novel barrier function based ASMC method is proposed to fit in the amplitude of the mismatched and matched disturbances. The transient and steady-state performance of attitude tracking error can be selected as prior control parameters.

A chaotic home environment, marked by disorganization, noise and a lack of routine, has negative associations with language development, social competence and executive functioning. This study aims to investigate the mediating effect of chaotic homes on adaptive behaviors, or behaviors that allow independent functioning, in children diagnosed with autism spectrum disorder (ASD) compared to neurotypical (NT) children. Children with ASD have difficulties with adaptive functioning in their environment, and identifying factors in the home that may exacerbate these behaviors will help in understanding the larger family dynamics that may affect behavior.

In total, 251 primary caregivers completed questionnaires about their children’s adaptive behavior and the structure of the home environment.

The results of a mediation analysis found a significant indirect effect of ASD status leading to lower adaptive behaviors through home chaos. This suggests those with ASD experienced more household chaos than NT children, which influenced their lower adaptive behavior scores.

This research provides insights into the complex relationship between the home environment and child behavior in children with ASD.

The decline of the motoric and cognitive functions of the elderly and the high risk of changes in their vital signs lead to some disabilities that inconvenience them. This paper aims to assist the elderly in their daily lives through personalized and seamless technologies.

The authors developed a personalized adaptive system for elderly care in a smart home using a fuzzy inference system (FIS), which consists of a predictive positioning system, reflexive alert system and adaptive conditioning system. Reflexive sensing is obtained from a body sensor and environmental sensor networks. Three methods comprising the FIS generation algorithm – fuzzy subtractive clustering (FSC), grid partitioning and fuzzy c-means clustering (FCM) – were compared to obtain the best prediction accuracy.

The results of the experiment showed that FSC produced the best F₁-score (96 per cent positioning accuracy, 94 per cent reflexive alert accuracy, 96 per cent air conditioning accuracy and 95 per cent lighting conditioning accuracy), whereas others failed to predict some classes and had lower validation accuracy results. Therefore, it is concluded that FSC is the best FIS generation method for our proposed system.

Personalized and seamless technologies for elderly implies life-share awareness, stakeholder awareness and community awareness.

This paper presents a model of personalized adaptive system based on their preferences and medical reference, which consists of a predictive positioning system, reflexive alert system and adaptive conditioning system.

The objective of this research is to investigate various neural network (NN) observer techniques for sensors fault identification and diagnosis of nonlinear system in consideration of numerous faults, failures, uncertainties and disturbances. For the importunity of increasing the faults diagnosis and reconstruction preciseness, a new technique is used for modifying the weight parameters of NNs without enhancement of computational complexities.

Various techniques such as adaptive radial basis functions (ARBF), conventional radial basis functions, adaptive multi-layer perceptron, conventional multi-layer perceptron and extended state observer are presented. For increasing the fault detection preciseness, a new technique is used for updating the weight parameters of radial basis functions and multi-layer perceptron (MLP) without enhancement of computational complexities. Lyapunov stability theory and sliding-mode surface concepts are used for the weight-updating parameters. Based on the combination of these two concepts, the weight parameters of NNs are updated adaptively. The key purpose of utilization of adaptive weight is to enhance the detection of faults with high accuracy. Because of the online adaptation, the ARBF can detect various kinds of faults and failures such as simultaneous, incipient, intermittent and abrupt faults effectively. Results depict that the suggested algorithm (ARBF) demonstrates more confrontation to unknown disturbances, faults and system dynamics compared with other investigated techniques and techniques used in the literature. The proposed algorithms are investigated by the utilization of quadrotor unmanned aerial vehicle dynamics, which authenticate the efficiency of the suggested algorithm.

The proposed Lyapunov function theory and sliding-mode surface-based strategy are studied, which shows more efficiency to unknown faults, failures, uncertainties and disturbances compared with conventional approaches as well as techniques used in the literature.

For improvement of the system safety and for avoiding failure and damage, the rapid fault detection and isolation has a great significance; the proposed approaches in this research work guarantee the detection and reconstruction of unknown faults, which has a great significance for practical life.

In this research, two strategies such Lyapunov function theory and sliding-mode surface concept are used in combination for tuning the weight parameters of NNs adaptively. The main purpose of these strategies is the fault diagnosis and reconstruction with high accuracy in terms of shape as well as the magnitude of unknown faults. Results depict that the proposed strategy is more effective compared with techniques used in the literature.

The debate as to whether intellectual disability (ID) should be conceptualized as a health condition or as a disability has intensified as the revision of World Health Organization’s (WHO’s) International Classification of Diseases (ICD) is being finalized. Defining ID as a health condition is central to retaining it in ICD, with significant implications for health policy and access to health services. The purpose of this paper is to include some reflections on the consensus document produced by the first WHO Working Group on the Classification of MR (WHO WG-MR) and on the process that was followed to realize it. The consensus report was the basis for the development of official recommendations sent to the WHO Advisory Group for ICD-11.

A mixed qualitative approach was followed in a series of meetings leading to the final consensus report submitted to the WHO Advisory group. These recommendations combined prior expert knowledge with available evidence; a nominal approach was followed throughout with face-to-face conferences.

The WG recommended a synonym set (“synset”) ontological approach to the conceptualisation of this health condition underlying a clinical rationale for its diagnosis. It proposed replacing MR with Intellectual Developmental Disorders (IDD) in ICD-11, defined as “a group of developmental conditions characterized by a significant impairment of cognitive functions, which are associated with limitations of learning, adaptive behaviour and skills”. The WG further advised that IDD be included under the parent category of neurodevelopmental disorders, that current distinctions (mild, moderate, severe and profound) be continued as severity qualifiers, and that problem behaviours removed from its core classification structure and instead described as associated features.

Within the ID/IDD synset two different names combine distinct aspects under a single construct that describes its clinical as well as social, educational and policy utilities. The single construct incorporates IDD as a clinical meta-syndrome, and ID as its functioning and disability counterpart. IDD and ID are not synonymous or mirror concepts as they have different scientific, social and policy applications. New diagnostic criteria for IDD should be based on a developmental approach, which accounts for the complex causal factors known to impact the acquisition of specific cognitive abilities and adaptive behaviours. The paper focuses on a new clinical framework for the diagnosis of IDD that also includes and complements the existing social, educational and policy components inherent in ID.

This paper aims to explore a new wavelet adaptive threshold de-noising method to resolve the shortcomings of wavelet hard-threshold method and wavelet soft-threshold method, which are usually used in gear fault diagnosis.

A new threshold function and a new determined method of threshold for each layer are proposed. The principle and the implementation of the algorithm are given. The simulated signal and the measured gear fault signal are analyzed, and the obtained results are compared with those from wavelet soft-threshold method, wavelet hard-threshold method and wavelet modulus maximum method.

The presented wavelet adaptive threshold method overcomes the defects of the traditional wavelet threshold method, and it can effectively eliminate the noise hidden in the gear fault signal at different decomposition scales. It provides more accurate information for the further fault diagnosis.

A new threshold function is adopted and the multi-resolution unbiased risk estimation is used to determine the adaptive threshold, which overcomes the defect of the traditional wavelet method.

The purpose of this paper is to explore the profiles of behaviours and adaptive functioning in the UK children and young people with fetal alcohol spectrum disorders.

Data of 106 participants registered from 2005 to 2015 were extracted from a clinic database. In total, 99 individuals with confirmed prenatal alcohol exposure (PAE), aged from 5 to 25 years, were analysed using scaled scores of the Vineland Adaptive Behavior Scales-Second Edition (VABS-II), and the Developmental Behaviour Checklist-Primary Carer Version (DBC-P) and Teacher Version (DBC-T). Differences due to age, gender, IQ and family structure (adopted/living with birth parents) were also explored.

The mean composite adaptive behaviour score on the VABS-II was classified as “low” at 68.2 (SD=8.5), with the socialisation domain being the most impaired. Significantly lower VABS-II composite scores were found in individuals with lower IQ’s, older ages and in males. Disruptive behaviours were the most commonly observed on the DBCs, whereas primary carers scored significantly higher than teachers across all subscales. IQ, age and gender were not associated with the total percentile scores of both DBCs. Adoption made no differences compared to living with birth parents.

Future studies would replicate these findings in a larger sample size including individuals without PAE and those living with birth parents.

This study is the first UK report that examines this issue.

Assessing the failure probability of engineering structures is still a challenging task in the presence of various uncertainties due to the involvement of expensive-to-evaluate computational models. The traditional simulation-based approaches require tremendous computational effort, especially when the failure probability is small. Thus, the use of more efficient surrogate modeling techniques to emulate the true performance function has gained increasingly more attention and application in recent years. In this paper, an active learning method based on a Kriging model is proposed to estimate the failure probability with high efficiency and accuracy.

To effectively identify informative samples for the enrichment of the design of experiments, a set of new learning functions is proposed. These learning functions are successfully incorporated into a sampling scheme, where the candidate samples for the enrichment are uniformly distributed in the n-dimensional hypersphere with an iteratively updated radius. To further improve the computational efficiency, a parallelization strategy that enables the proposed algorithm to select multiple sample points in each iteration is presented by introducing the K-means clustering algorithm. Hence, the proposed method is referred to as the adaptive Kriging method based on K-means clustering and sampling in n-Ball (AK-KBⁿ).

The performance of AK-KBⁿ is evaluated through several numerical examples. According to the generated results, all the proposed learning functions are capable of guiding the search toward sample points close to the LSS in the critical region and result in a converged Kriging model that perfectly matches the true one in the regions of interest. The AK-KBⁿ method is demonstrated to be well suited for structural reliability analysis and a very good performance is observed in the investigated examples.

In this study, the statistical information of Kriging prediction, the relative contribution of the sample points to the failure probability and the distances between the candidate samples and the existing ones are all integrated into the proposed learning functions, which enables effective selection of informative samples for updating the Kriging model. Moreover, the number of required iterations is reduced by introducing the parallel computing strategy, which can dramatically alleviate the computation cost when time demanding numerical models are involved in the analysis.

The purpose of this paper is to study a class of issues that in spite of recognised needs and explicit managerial demands have proven hard to have “stick” in organisations (information security is used as an example). It offers a theory-driven rationale why superficially different issue areas can indeed be considered as instances of the identified class, and builds on complexity leadership theory (CLT) to explain how the related strategic challenges can be explained and possibly alleviated.

A. Kenneth Rice’s notion of organisations’ “primary task” is used to home in on its opposite that is here labelled “peripherality”. Existing strands of organisation research that can be related to this notion are then revisited to ground the fundamental concept theoretically. The CLT is finally used to provide a detailed understanding of the underlying dynamics.

The paper explains how and why certain issue areas seem resistant to common managerial intervention methods even though it would seem that organisational members are in fact favouring proposed changes (a state that would normally increase the chances of success). It also offers ideas how these challenges may fruitfully be approached.

Problems related to the suggested “peripherality” class of issues have thus far been approached as wholly unrelated (and for that reason as idiosyncratic). The proposed framework offers a hitherto never attempted way systematically to link these challenges – and so structure and concentrate discussion about possibly common remedies.