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This study aims to improve the efficiency of the assessment process within a Step4 Psychological Therapies Service by identifying factors related to assessment non-attendance and service suitability for referred clients.

Referral and assessment information was accessed between October 2019 and March 2020 from Step4 routine service data, electronic client records where necessary and Step4 staff self-report questionnaires.

All clients offered an assessment during this time attended. Findings indicated several factors could influence service suitability in meeting client need. These included individual differences such as readiness to change, which was not necessarily identified at referral or prior to assessment, and potential systemic factors, such as the opt-in procedure, which possibly impeded access. Though the necessity for assessment in clarifying client needs and treatment was indicated, an assessment (from referral to assessment appointment) that led to discharge could take an excess of one working day of service time, associated with considerable opportunity cost to other clients awaiting assessment. Recommendations are made for improving assessment efficiency.

With a high prevalence of poor mental health in the UK, efforts must be made to identify and reduce additional demand upon service time and resources within mental health services to effectively meet people’s needs. Recommendations to improve assessment process efficiency include the use of a standardised referral form, offer of follow-up support procedures, increased client involvement, a streamlined opt-in battery and ongoing monitoring to ensure shared practice between clinicians. These are transferable to other mental health services, with implications for subsequent quality and timeliness of care.

In any critical system, high‐availability of software components like web services has so far been achieved through replication. Three replication strategies known as active, passive, and hybrid, describe for example how many replicas are needed, where to locate replicas, and how replicas interact with the original web service and among themselves if needed. The purpose of this paper is to show how replicates could be substituted with components that are similarly functional to the component that needs back‐up in case of failure.

After examination of the different existing replication strategies, it was decided to test the suitability of the proposed web services high‐availability approach based on communities for each strategy. To this end, the specification of web services using two behaviors, namely control and operational, was deemed appropriate.

The active replication strategy is the only strategy that could support the development of a web services high‐availability approach based on communities of web services.

The proposed approach has been validated in practice by deploying a JXTA‐based testbed. The experimental work has implemented the active replication strategy.

Software component high‐availability could be achieved by components that are similarly functional to this component, which permits the common limitations of existing replication strategies to be addressed.

For the purpose of reducing the incidence of hazardous materials transport accident, eliminating the potential threats and ensuring their safety, aiming at the shortcomings in the process of current hazardous materials transportation management, this paper aims to construct the framework of hazardous materials transportation safety management system under the vehicle-infrastructure connected environment.

The system takes the intelligent connected vehicle as the main supporter, integrating GIS, GPS, eye location, GSM, networks and database technology.

By analyzing the transportation characteristics of hazardous materials, this system consists of five subsystems, which are vehicle and driver management subsystem, dangerous sources and hazardous materials management subsystem, route analysis and optimization subsystem, early warning and emergency rescue management subsystem, and basic information query subsystem.

Hazardous materials transportation safety management system includes omnibearing real-time monitoring, timely updating of system database, real-time generation and optimization of emergency rescue route. The system can reduce the transportation cost and improve the ability of accident prevention and emergency rescue of hazardous materials.

This paper aims to propose a 3D-map aided tightly coupled positioning solution for land vehicles to reduce the errors caused by non-line-of-sight (NLOS) and multipath interference in urban canyons.

First, a simple but efficient 3D-map is created by adding the building height information to the existing 2D-map. Then, through a designed effective satellite selection method, the distinct NLOS pseudo-range measurements can be excluded. Further, an enhanced extended Kalman particle filter algorithm is proposed to fuse the information from dual-constellation Global Navigation Satellite Systems and reduced inertial sensor system. The dependable degree of each selected satellite is adjusted through fuzzy logic to further mitigate the effect of misjudged LOS and multipath.

The proposed solution can improve positioning accuracy in urban canyons. The experimental results evaluate the effectiveness of the proposed solution and indicate that the proposed solution outperforms all the compared counterparts.

The effect of NLOS and multipath is addressed from both the observation level and fusion level. To the authors’ knowledge, mitigating the effect of misjudged LOS and multipath in the fusion algorithm of tightly coupled integration is seldom considered in existing literature.

Sewing is one of the most commonly used manufacturing processes in the world. Millions of parts are sewn every day ranging from cloths, shoes, furniture, to automobile seat covers. However, it is also one of the least understood processes. In fact, according to literature survey, few know how to calculate the sewing force or the fabric deformation during the sewing. This paper presents our research on using finite element model (FEM) to study the sewing process. The model is developed using ANSYS software system. In the model, the fabric is approximated by a number of perpendicular beam elements with elastic and plastic capabilities. On the other hand, the needle is modeled by a simple elastic beam. The contact between the two parts is modeled by contact elements. The variations of the needle geometry and the fabric material properties as well as the sewing conditions are also included in the model. The model can simulate the needle piercing through a material, and calculates the sewing forces as well as the fabric deformation forming a hole. It has been verified experimentally and can be used to study the effects of the key sewing parameters such as the fabric material properties and the needle geometry.

This study aims to investigate the relationship between earnings quality and corporate voluntary disclosure among Malaysian listed companies. Moreover, it examines the moderating effect of the ownership structure on the relationship between earnings quality and corporate voluntary disclosure.

This study covers 300 companies listed on Bursa Malaysia. It has used strategic, financial and non-financial information to measure voluntary disclosure; earnings management, persistence and smoothness to measure earnings quality; and institutional and managerial shareholders to measure ownership structure. Hierarchical regression analysis was used to investigate if ownership structure moderates the relationship between earnings quality and corporate voluntary disclosure.

The results in this work imply that companies with high earnings quality are more likely to disclose voluntary information to help stakeholders. Furthermore, this study provides original evidence that institutional ownership and managerial ownership play a main role as moderating variables that influence management motives toward practices of voluntary disclosure and earnings quality.

Because of the limited number of empirical studies on the relationship between voluntary disclosure and earnings quality, this study fills a gap in the literature by investigating the relationship between them. In addition, a lack of research exists on the effects of ownership structure on the relationship between voluntary disclosure and the earnings quality. Therefore, this study makes an original contribution to the literature by using institutional and managerial ownership as moderating variables to investigate the effects of the ownership structure on the relationship between voluntary disclosure and earnings quality in Malaysian companies.

The Indian hospitality and tourism industries, major economic growth drivers and employment generators, have been greatly affected by the outbreak of the COVID-19 pandemic. In FY 2020, the Indian tourism sector created 39 million jobs and contributed nearly US$194.3bn, or 6.8%, to India’s gross domestic product. The purpose of this study is to focus on ranking 22 listed hotels and 9 listed travel agencies in India based on their performance across 14 selected financial parameters in both the pre-COVID-19 year ending in March 2019 and the post-COVID-19 year ending in March 2021 to understand how the pandemic affected their businesses.

This research proposes to analyze the impact of the COVID-19 pandemic on the financial performance of 22 listed Indian hotels and 9 listed travel agencies evaluated over 14 financial parameters using a pipeline of two recently developed multicriteria decision-making techniques, method based on the removal effects of criteria (MEREC) and grey-based combined compromised solution (CoCoSo). First, the criteria weights are objectively determined using MEREC, and then the financial performances of the selected companies in both the hospitality and tourism industries are separately assessed using CoCoSo to get their overall performance score, based on which the companies are ranked in order of preference.

It was observed that Westlife Development, Lemon Tree Hotels, Indian Tourism Development Corporation, Royal Orchid and Country Club performed significantly poorer than their peers in the aftermath of the pandemic, whereas EIH, Advani Hotels and Resorts and TGB Banquets performed relatively better. Travel agencies Easy Trip and International Travel House performed particularly poorly because of the pandemic, but VMV Holidays performed relatively better in FY 2021.

The findings of the analysis will aid portfolio construction, corporate investment decisions, competition research, government policymaking and industrial analysis.

The proposed model is novel because it fills the research gap in the application of the integrated MEREC–CoCoSo method to study the impact of COVID-19 on the hospitality and tourism sectors in India.

Based on virtual maintenance, this paper aims to propose a time prediction method of assembly and disassembly (A&D) actions of product maintenance process to enhance existing methods’ prediction accuracy, applicability and efficiency.

First, a framework of A&D time prediction model is constructed, which describes the time prediction process in detail. Then, basic maintenance motions which can comprise a whole A&D process are classified into five categories: body movement, working posture change, upper limb movement, operation and grasp/placement. A standard posture library is developed based on the classification. Next, according to motion characteristics, different time prediction methods for each motion category are proposed based on virtual maintenance simulation, modular arrangement of predetermined time standard theory and the statistics acquired from motion experiment. Finally, time correction based on the quantitative evaluation method of motion time influence factors is studied so that A&D time could be predicted with more accuracy.

Case study of time prediction of products’ various A&D processes is conducted by implementing the proposed method. The prediction process of diesel cooling fan disassemble time is presented in detail. Through comparison, the advantages and effectiveness of the method are demonstrated.

This paper proposes a more accurate, efficient and applicable product A&D time prediction method. It can help designers predict A&D time of a product maintenance accurately in early design phases without a physical prototype. It can also provide basis for the verification of maintainability, the balance of the design of product structure and system layout.

The purpose of this paper is to find out the significant influence of assembly predicate consideration on optimal assembly sequence generation (ASG) in terms of search space, computational time and possibility of resulting practically not feasible assembly sequences. An appropriate assembly sequence results in minimal lead time and low cost of assembly. ASG is a complex combinatorial optimisation problem which deals with several assembly predicates to result an optimal assembly sequence. The consideration of each assembly predicate highly influences the search space and thereby computational time to achieve valid assembly sequence. Often, the ignoring an assembly predicate leads to inappropriate assembly sequence, which may not be physically possible, sometimes predicate assumption drastic ally raises the search space with high computational time.

The influence of assuming and considering different assembly predicates on optimal assembly sequence generation have been clearly illustrated with examples using part concatenation method.

The presence of physical attachments and type of assembly liaisons decide the consideration of assembly predicate to reduce the complexity of the problem formulation and overall computational time.

Most of the times, assembly predicates are ignored to reduce the computational time without considering their impact on the assembly sequence problem irrespective of assembly attributes. The current research proposes direction towards predicate considerations based on the assembly configurations for effective and efficient ASG.

The purpose of this paper is to show how shape analysis and quantitative characterization of fiber cross sections, with the aid of image analysis techniques, provide a quick, powerful approach to automated profiled fiber identification.

In this paper, an effective method of cross‐sectional shape characterization for profiled fiber identification is reported with extraction of the distance fluctuation curve of fiber cross‐sectional boundary to the centroid. By calculating their cross‐correlations using signal processing techniques, the authors tackle the problem of calibrating the starting points of fiber objects orientated arbitrarily in image successfully, which are difficult to deal with by means of image processing, to finish the normalization of distance fluctuation curves. For two fiber cross‐sections, the similarity degree of their boundary fluctuation curves normalized can effectively reflect the similarity degree of themselves.

Based on this, the method presented extracts the curves of all fiber cross‐sections in one sample, compares the similarity degrees between each other, and creates clusters to identify profiled fiber.

Experimental results validate that this curve can effectively characterize profiled fiber cross‐sectional contour for profiled fiber identification and the normalization method is feasible.