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Many tools are available for assessing autism in an adult population; however, few have been studied for the effects of gender on diagnostic scores. The purpose of this paper is to evaluate the Autism Diagnostic Observation Schedule (ADOS) assessment for gender bias in a clinical population, specifically whether the ADOS favours a “male-type” of autism.

The ADOS scores of patients referred to an NHS specialist autism assessment service were retrospectively examined for significant gender differences. The combined ADOS scores and diagnostic outcome were grouped by gender for each participant. The data were analysed in SPSS using independent t-tests to look for significant gender differences between combined ADOS scores and diagnostic outcomes.

A significant difference was observed in the mean combined ADOS scores for those participants who later received an autism diagnosis (male=10, female=6, t (13)=3.34, p=10; 0.005). However, no significant difference was observed between mean scores of those who did not receive an autism diagnosis (t (26)=1.21, p=0.237).

The ADOS is a popular assessment used for autism diagnosis. These results provide support for a male gender bias. This could have clinical implications for autism assessment services, whereby lower diagnostic thresholds could be considered for female patients. This could allow more females with autism to receive a diagnosis, and access support services.

Cornelia de Lange syndrome (CdLS) is a congenital disorder characterized by distinctive facial features, growth retardation, limb abnormalities, intellectual disability, and behavioral problems. Cornelia de Lange syndrome is associated with abnormalities on chromosomes 5, 10 and X. Heterozygous point mutations in three genes (NIPBL, SMC3 and SMC1A), are responsible for approximately 50-60% of CdLS cases. CdLS is characterized by autistic features, notably excessive repetitive behaviors and expressive language deficits. The prevalence of autism spectrum disorder (ASD) symptomatology is comparatively high in CdLS. However, the profile and developmental trajectories of these ASD characteristics are potentially different to those observed in individuals with idiopathic ASD. A significantly higher prevalence of self-injury are evident in CdLS. Self-injury was associated with repetitive and impulsive behavior. This study describes the behavioral phenotype of four children with Cornelia de Lange syndrome and ASDs and rehabilitative intervention that must be implemented.

The purpose of this paper is to identify a new and simple two‐end algorithm for fault location identification and characterization, in electrical distribution systems.

The developed diagnostic algorithm is based on a simple model of the network using a lumped parameters representation.

Test results have proved the approach to be efficient, allowing a precise fault identification and location while not requiring synchronized measures from the two ends.

There is a need for measurement systems at all MV/LV substations.

Applicability with limited investments is not possible where metering systems are not so diffused, although smart grids and DG units require such infrastructures. Moreover, utilities are quite interested in such issues, since the new required quality standards put severe constraints on faults management and clearance.

The paper presents a new and easier diagnostic algorithm for faults diagnosis in distribution systems.

Power converters are an integral part of the energy conversion process in solar photovoltaic (PV) systems which is used to match the solar PV generation with the load requirements. The increased penetration of renewable invokes intermittency in the generated power affecting the reliability and continuous energy supply of such converters. DC-DC converters deployed in solar PV systems impose stringent restrictions on supplied power, continuous operation and fault prediction scenarios by continuously observing state variables to ensure continuous operation of the converter.

A converter deployed for a mission-critical application has to ensure continuous regulated output for which the converter has to ensure fault-free operation. The fault diagnostic algorithm relies on the measurement of a state variable to assess the type of fault. In the same line, a predictive controller depends on the measurement of a state variable to predict the control variable of a converter system to regulate the converter output around a fixed or a variable reference. Consequently, both the fault diagnosis and the predictive control algorithms depend on the measurement of a state variable. Once measured, the available data can be used for both algorithms interchangeably.

The objective of this work is to integrate the fault diagnostic and the predictive control algorithms while sharing the measurement requirements of both these control algorithms. The integrated algorithms thus proposed could be applied to any converter with a single inductor in its energy buffer stage.

laboratory prototype is created to verify the feasibility of the integrated predictive control and fault diagnosis algorithm. As the proposed method combine the fault detection algorithm along with predictive control, a load step variation and manual fault creation methods are used to verify the feasibility of the converter as with the simulation analysis. The value for the capacitors and inductors were chosen based on the charge-second and volt-second balance equations obtained from the steady-state analysis of boost converter.

The purpose of this paper is to present the topic of control computers diagnostics. They are part of an unmanned aerial vehicle (UAV) control system implemented in a modified version of MP-2 Czajka aircraft.

The algorithms were designed as a basic version of the diagnostic system. The system is open and will be developed.

First results show that the diagnostic system works properly. The system is easy for implementation and burdens the control computers only insignificantly.

The system presented can detect only computers out of work. In its present version, it cannot detect such errors as improper calculations of control signals. After first in-flight testing, the system will be further developed.

The diagnostic system is implemented in an UAV technology demonstrator.

The designed system is the part of an UAV control system, designed for ground observation. Such technology demonstrator and flying laboratory enable different type of research in the area of aviation.

A multi-load available, response reliable and product-friendly method is in urgent need to diagnose the signs of incipient arcing. This paper aims to propose a novel algorithm that originates the application of correlativity analysis of wavelet high-frequency component in state discrimination and further in arcing detection.

The proposed method calculates the correlation coefficient between the extraction by wavelet transform of arcing series current and that of normal, compares it with a predefined threshold and outputs a trip signal when eight qualified arcing half cycles within a period of 0.5 s are detected.

Typical appliances are selected in laboratory for arc detection to test the method which carries on independently of impedance type. The algorithm could be optimized to identify arcing for different kinds of loads, including resistive, inductive, capacitive and switching power supply loads, with a same correlation coefficient threshold.

The arithmetic operations of the method are addition and multiplication, which contribute to efficient data computation and transmission for micro-processor to undertake. The reference optimal sampling rate recommended for the algorithm helps to reduce the processed data volume and shows its promising prospect for portable product development.

This proposed correlativity analysis of wavelet transform component algorithm could classify the tested signal into two categories, which benefits the discrimination of normal and fault states in condition monitoring. Laboratory tests prove that it works effectively in arc detection for the commonly used impedance types of loads and needs no offline self-learning or training of samples.

Organizations involved in facility management (FM) can use building information modeling (BIM) as a knowledge repository to document evolving facility information and to support decisions made by the facility managers during the operational life of a facility. Despite ongoing advances in FM technologies, FM practices in most facilities are still labor intensive, time consuming and often rely on unreliable and outdated information. To address these shortcomings, the purpose of this study is to propose an automated approach that demonstrates the potential of using BIM to develop algorithms that automate decision-making for FM applications.

A BIM plug-in tool is developed that uses a fault detection and diagnostics (FDD) algorithm to automate the process of detecting malfunctioning heating, ventilation, and air conditioning (HVAC) equipment. The algorithm connects to a complaint ticket database and automates BIM to determine potentially damaged HVAC system components and develops a plan of action for the facility inspectors accordingly. The approach has been implemented as a case study in an operating facility to improve the process of HVAC system diagnosis and repair.

By implementing the proposed application in a case study, the authors found that automated BIM approaches such as the one developed in this study, can be highly beneficial in FM practices by increasing productivity and lowering costs associated with decision-making.

This study introduces an innovative approach that leverages BIM for automated fault detection in operational buildings. FM personnel in charge of HVAC inspection and repair can highly benefit from the proposed approach, as it eliminates the time required to locate HVAC equipment at fault manually.

Vehicle fault diagnosis is a key factor in ensuring the safe and efficient operation of the railway system. Due to the numerous vehicle categories and different fault mechanisms, there is an unbalanced fault category problem. Most of the current methods to solve this problem have complex algorithm structures, low efficiency and require prior knowledge. This study aims to propose a new method which has a simple structure and does not require any prior knowledge to achieve a fast diagnosis of unbalanced vehicle faults.

This study proposes a novel K-means with feature learning based on the feature learning K-means-improved cluster-centers selection (FKM-ICS) method, which includes the ICS and the FKM. Specifically, this study defines cluster centers approximation to select the initialized cluster centers in the ICS. This study uses improved term frequency-inverse document frequency to measure and adjust the feature word weights in each cluster, retaining the top τ feature words with the highest weight in each cluster and perform the clustering process again in the FKM. With the FKM-ICS method, clustering performance for unbalanced vehicle fault diagnosis can be significantly enhanced.

This study finds that the FKM-ICS can achieve a fast diagnosis of vehicle faults on the vehicle fault text (VFT) data set from a railway station in the 2017 (VFT) data set. The experimental results on VFT indicate the proposed method in this paper, outperforms several state-of-the-art methods.

This is the first effort to address the vehicle fault diagnostic problem and the proposed method performs effectively and efficiently. The ICS enables the FKM-ICS method to exclude the effect of outliers, solves the disadvantages of the fault text data contained a certain amount of noisy data, which effectively enhanced the method stability. The FKM enhances the distribution of feature words that discriminate between different fault categories and reduces the number of feature words to make the FKM-ICS method faster and better cluster for unbalanced vehicle fault diagnostic.

Concern has been expressed that the introduction of a non‐invasive test as a further step in the diagnostic pathway for deep venous thrombosis (DVT) can only increase the expense of detection in this difficult condition. Presents a study and cost analysis of strain gauge plethysmography in the diagnosis of DVT in 119 symptomatic patients. Shows that non‐invasive screening is highly cost‐effective and does not result in reduced efficacy. Examined and costed a number of diagnostic algorithms and found a single strain gauge test to be the most sensitive and cost‐effective selection procedure for venography. The cost of diagnosing a single DVT can be reduced from £691 to £266 by the use of this technique.