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The risk-return relationship of real estate equities is of particular interest for investors, practitioners and researchers. The purpose of this paper is to examine, in an asset pricing framework, whether the systematic risk factors play a significantly different role in explaining the returns of listed real estate companies, compared to general equities.

Running the difference test of the Fama-French three-factor and the liquidity-augmented asset pricing model, the authors analyze the effect of the systematic risk factors related to market, size, BE/ME and liquidity in a time-series setting over the period July 1992 to June 2012. By applying the propensity score matching (PSM) algorithm, the authors bypass the “curse of dimensionality” of traditional matching techniques and identify a comparable control sample of general equities, in terms of the relevant firm characteristics of size, BE/ME and liquidity.

The empirical results indicate that European real estate equity returns load significantly differently on the size, value and liquidity factor, while the influence of the market factor seems to be equivalent. In addition, the authors find an economically and statistically significant underperformance of European real estate equities, after accounting for the diverging role of systematic risk factors. Running the conditional time-series regression, the authors further reveal that these findings are predominately caused by the divergent risk-return behavior of real estate equities in economic downturns.

Due to the diverging role of the systematic risk factors in pricing real estate equities, the authors provide evidence of potential diversification benefits for investors and portfolio managers.

This is the first real estate asset pricing study to dissect the unique risk-return relationship of real estate equities by employing propensity score matching.

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

In model‐based recognition the 3D models of objects are stored in a model library during an offline phase. During the online recognition phase, a view of the scene is matched with the model library to identify the location and pose of certain library objects in the scene. Aims to focus on the process of 3D modeling and model‐based recognition.

This paper discusses the process of 3D modeling and model‐based recognition along with their potential applications in industry with a particular emphasis on robot grasp analysis. The paper also emphasises the main challenges in these areas and give a brief literature review.

In order to develop an automatic 3D model‐based object recognition system it is necessary to automate the process of 3D modeling and recognition. The challenge in automating the 3D modeling process is to develop an automatic correspondence technique. The core of recognition is the representation scheme. Recognition is an online process. Therefore, representation and matching must be very fast in order to facilitate real time recognition.

There are numerous applications of 3D modeling in a variety of areas ranging from the entertainment industry to industrial automation. Some of its applications include computer graphics, virtual reality, medical imaging, reverse engineering, and 3D terrain construction.

Provides information on 3D modeling which constitutes an important part of computer vision or robot vision.

In this paper, we review the process of “3D modeling” and “model‐based recognition” along with their potential industrial applications. We put a particular emphasis on the case scenario of robot grasp analysis for which 3D model‐based object recognition seems to be a more palpable choice compared with the conventional tactile sensors solutions. We also put a particular emphasis on the main challenges in the areas of 3D modeling and model‐based recognition and give a brief literature review of the latest research that was carried out to respond to these challenges.

Various types of short training programmes exist in Germany. This article aims to evaluate short training courses for welfare recipients and to detect which programme type works best with respect to different outcome indicators.

The author measures the impact of six short training programmes on the participants. She uses propensity score matching and large‐scale administrative data to find suitable comparison groups. She compares treatment to non‐participation as well as participation in different training types directly.

The results demonstrate that in‐firm training has large positive effects on individual employment prospects and stability. Furthermore, classroom skill training is more effective than other classroom types while application training is rather ineffective. Treating some of the application training participants with an alternative sub‐programme would improve the effectiveness.

The results show which training types work best. However, the results do not imply macro or cost‐benefit effects.

Several of the short training courses, mostly occupation‐specific sub‐programmes, are short and relatively inexpensive options to activate welfare recipients.

The paper analyses the effects of six short training programmes for welfare recipients that have not been analysed before. The sensitivity of the results is examined using a wide range of estimators and different outcomes.

The purpose of this paper is to develop a cost model by the variable sampling interval and optimization of the average cost per unit of time. The paper considers an economic–statistical design of the X̅ control charts under the Burr shock model and multiple assignable causes were considered and compared with three types of prior distribution for the mean shift parameter.

The design of the modified X̅ chart is based on the two new concepts of adjusted average time to signal and average number of false alarms for X̅ control chart under Burr XII shock model with multiple assignable causes.

The cost model was examined through a numerical example, with the same cost and time parameters, so the optimal of design parameters were obtained under uniform and non-uniform sampling schemes. Furthermore, a sensitivity analysis was conducted in a way that the variability of loss cost and design parameters was evaluated supporting the changes of cost, time and Burr XII distribution parameters.

The economic–statistical model scheme of X̅ chart was developed for the Burr XII distributed with multiple assignable causes. The correlated data are among the assumptions to be examined. Moreover, the optimal schemes for the economic-statistic chart can be expanded for correlated observation and continuous process.

The economic–statistical design of control charts depends on the process shock model distribution and due to difficulties from both theoretical and practical aspects; one of the proper alternatives may be the Burr XII distribution which is quite flexible. Yet, in Burr distribution context, only one assignable cause model was considered where more realistic approach may be to consider multiple assignable causes.

This study presents an advanced theoretical model for cost model that improved the shock model that presented in the literature. The study obviously indicates important evidence to justify the implementation of cost models in a real-life industry.

As the provision of public services in many advanced welfare states has increasingly come to be marked by competition, social enterprises have actively been encouraged by governments to become involved in the delivery of public services. While the evaluation of complex public health interventions has arguably become increasingly more sophisticated, this has not been the case where social enterprise is concerned: evaluation of the actual impacts of social enterprises remains significantly underdeveloped by comparison. This study aims to support the establishment of a robust evidence base for the use of social enterprise as a policy instrument.

This paper assesses the potential of three methodological approaches common in the evaluation of complex public health interventions and applies them to the complex realm of community-led social enterprise.

Only through the involvement of different comparator groups, based on the research questions addressed, would it be possible to disentangle the embedded characteristics of organisations such as social enterprises. Each of the methods adopted in this research is time-consuming and resource-intensive and requires the researcher to possess advanced skills. Public officials should recognise the complexity and resource-intensive nature of such evaluation and resource it accordingly. If the aim of policymakers is to understand the added value of social enterprise organisations, an integrative research approach combining different research methods and design should be implemented to improve generalisability.

This study applies a range of favoured approaches to evaluate “complex” public health interventions include systematic reviews, realist evaluation and quasi-experimental investigation. However, such evaluation approaches have rarely been applied before in the context of social enterprise.

Statistical modeling has been successfully applied to integrated circuit (IC) solder joint inspection. However, there are some inherent problems in previous statistical modeling methods. This paper aims to propose an adaptive statistical modeling method to further improve the inspection performance for IC solder joints.

First, different pixels in the IC solder joint image were modeled by different templates, each of which was composed of the hue value of the pixel and a proposed template significance factor. Then, the potential defect image was obtained by adaptive template matching and the potential defect threshold for each pixel. It was noted that the number of templates, matching distance threshold, potential defect threshold and updating rate were adaptively updated during model training. Finally, the trained statistical model was used to inspect the IC solder joints by means of defect degree.

Experimental results indicated that the proposed adaptive schemes greatly contributed to the inspection performance of statistical modeling. Also, the proposed inspection method achieved better performance compared with some state-of-the-art inspection methods.

The proposed method offers a promising approach for IC solder joint inspection, which establishes different numbers of templates constructed by pixel values and template significance factors for different pixels. Also, some important parameters were adaptively updated with the updating of the model, which contributed to the inspection performance of the model.

The purpose of this paper is to find a real-time parking location for a four-wheeler.

Real-time parking availability using specific infrastructure requires a high cost of installation and maintenance cost, which is not affordable to all urban cities. The authors present statistical block matching algorithm (SBMA) for real-time parking management in small-town cities such as Bhavnagar using an in-built surveillance CCTV system, which is not installed for parking application. In particular, data from a camera situated in a mall was used to detect the parking status of some specific parking places using a region of interest (ROI). The method proposed computes the mean value of the pixels inside the ROI using blocks of different sizes (8 × 10 and 20 × 35), and the values were compared among different frames. When the difference between frames is more significant than a threshold, the process generates “no parking space for that place.” Otherwise, the method yields “parking place available.” Then, this information is used to print a bounding box on the parking places with the color green/red to show the availability of the parking place.

The real-time feedback loop (car parking positions) helps the presented model and dynamically refines the parking strategy and parking position to the users. A whole-day experiment/validation is shown in this paper, where the evaluation of the method is performed using pattern recognition metrics for classification: precision, recall and F1 score.

The authors found real-time parking availability for Himalaya Mall situated in Bhavnagar, Gujarat, for 18th June 2018 video using the SBMA method with accountable computational time for finding parking slots. The limitations of the presented method with future implementation are discussed at the end of this paper.

Based on the examination of the roles of message framing and evidence type, this study made an analysis of the promotion methods of intention and information need towards HPV vaccination.

The study conducted a 2 (gain-framed messages vs loss-framed messages) × 2 (statistical evidence vs narrative evidence) quasi-experimental design built upon theories of message framing and evidence type. This experiment recruited college students who were not vaccinated against HPV as participants. The analysis of variance (ANOVA), the analysis of covariance (ANCOVA), and the independent sample T-test were used to test the hypotheses.

The results (N = 300) indicate that (1) Loss-framed messages will lead to a more favorable intention towards HPV vaccination than gain-framed messages. (2) Statistical evidence will lead to a more explicit information need than narrative evidence. (3) Message framing and evidence type will interact and (a) for statistical evidence, loss-framed messages will lead to a more favorable intention towards HPV vaccination than gain-framed messages and (b) for narrative evidence, gain-framed messages will lead to a more favorable intention towards HPV vaccination than loss-framed messages. (4) Message framing and evidence type will interact and (a) for loss-framed messages, statistical evidence will stimulate more explicit information need of HPV vaccination than narrative evidence and (b) for gain-framed messages, narrative evidence will stimulate more explicit information need of HPV vaccination than statistical evidence.

This paper can help to further understand the important roles of message framing and evidence type in health behavior promotion. The study contributes to the literature on how health information can be well organized to serve the public health communication and further enhance the health information service.