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Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

This review aims to focus on recent reported research work on the construction and function of different electrochemical DNA biosensors. It also describes different sensing materials, chemistries of immobilization probes, conditions of hybridization and principles of transducing and amplification strategies.

The human disease-related mutated genes or DNA sequence detection at low cost can be verified by the electrochemical-based biosensor. A range of different chemistries is used by the DNA-based electrochemical biosensors, out of which the interactions of nanoscale material with recognition layer and a solid electrode surface are most interesting. A diversity of advancements has been made in the field of electrochemical detection.

Some important aspects are also highlighted in this review, which can contribute in the creation of successful biosensing devices in the future.

This paper provides an updated review of construction and sensing technologies in the field of biosensing.

Innovation is the process of turning knowledge and ideas into value. An “urban innovation engine” is a system which can trigger, generate, foster and catalyze innovation in the city. This paper describes the concept of the “urban innovation engine”, provides some historical and contemporary examples, and suggests a set of guidelines for turning ordinary urban institutions into innovation engines. The paper has two purposes: to trigger further theoretic and action research and exploration in the domain of urban innovation. In recent years there has been intensive research about the conditions (“ecology”) which enable and catalyze knowledge development and innovation in the business world. A second new focus area in the research of knowledge development is the role of the city as a hub for intensive flows and exchanges of knowledge among its habitants and additional stakeholders. We suggest weaving the learning from the business and urban worlds by attempting to apply the dimensions of innovation ecology models to knowledge cities. More specifically, we look at multiple traditional urban constructs, and show how they might act as significant drivers for creativity and renewal. Typically an urban innovation engine is a complex system that includes people, relationships, values, processes, tools and technological, physical and financial infrastructure. We suggest that what innovation engines really do is to create conversations – which are the foundation of most innovations. We bring some examples and snap‐shots from several urban innovation engines such as the museum, the library, the stock exchange, the café, the brownfield, the grand fair, the outlook tower, and the industrial district. The paper conceptualizes the notion of “urban innovation engines”. Based on this concept, it provides a set of guidelines for creating a knowledge city using innovation engines as its building blocks, and innovation ecology elements as an important part of its operating system.

The desire to improve efficiency has led both academics and practitioners to embrace various technologies to aid managers to discharge their functions. Recently, there has been a growing interest amongst construction researchers on the use of computer vision and image‐processing techniques to automatically capture work in progress. Reported findings are promising; however, those previous studies fall short of providing a reporting mechanism to aid decision making. The purpose of this paper is to develop a reporting model based on progress captured using computer vision.

The paper first presents trends in research relating to use of computer vision in the monitoring of work in progress. It then employs the unified modelling language to present the conceptual development of the model. The computerised reporting model is developed using the visual basic programming language.

The key elements of the model are computations of cost‐schedule variances, payments and cash flows. Results of a test on a hypothetical case show that the model accurately computes the metrics.

The reporting model serves to provide managers with a quick and easy means of interpreting work progress captured using computer vision. It reinforces the value of already existing work on the application of computer vision techniques to the measurement of work in progress on construction sites.

The UK regime for financial reporting and auditing was radically altered in 1990 and 1991 by two separate developments. When removing sole responsibility for setting accounting standards from the accounting profession, the opportunity was taken to establish a monitoring body, the Financial Reporting Review Panel (FRRP), to oversee compliance with company law and accounting standards, and with powers to apply to the courts for rectification. In addition, a new regulatory system for auditors was set up. This paper considers the problems arising between the regulatory responsibilities of the Institute of Chartered Accountants in England and Wales (ICAEW) and FRRP. In 50 cases (up to September 1999) FRRP found defects in accounts, 49 of which were audited by firms regulated by ICAEW, but no disciplinary action was taken by ICAEW until 1999. The way in which the new audit regulations were grafted onto the existing ICAEW disciplinary regime is considered, and the anomalies arising from that explored. The cases resulting in ICAEW's disciplinary action are compared with the other cases together with some evidence from finance directors and audit partners with experience of dealing both with FRRP and an ICAEW disciplinary investigation. The relevant theories relating to professional bodies and regulation are also reviewed. Finally, the authors review the problems identified in this study and make suggestions as to how they may be addressed.

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.

The purpose of this paper is to elaborate the picture of strategies and tactics for information seeking and searching by focusing on the heuristic elements of such strategies and tactics.

A conceptual analysis of a sample of 31 pertinent investigations was conducted to find out how researchers have approached heuristics in the above context since the 1970s. To achieve this, the study draws on the ideas produced within the research programmes on Heuristics and Biases, and Fast and Frugal Heuristics.

Researchers have approached the heuristic elements in three major ways. First, these elements are defined as general level constituents of browsing strategies in particular. Second, heuristics are approached as search tips. Third, there are examples of conceptualizations of individual heuristics. Familiarity heuristic suggests that people tend to prefer sources that have worked well in similar situations in the past. Recognition heuristic draws on an all-or-none distinction of the information objects, based on cues such as information scent. Finally, representativeness heuristic is based on recalling similar instances of events or objects and judging their typicality in terms of genres, for example.

As the study focuses on three heuristics only, the findings cannot be generalized to describe the use of all heuristic elements of strategies and tactics for information seeking and searching.

The study pioneers by providing an in-depth analysis of the ways in which the heuristic elements are conceptualized in the context of information seeking and searching. The findings contribute to the elaboration of the conceptual issues of information behavior research.

The purpose of this paper is to study the participation behaviors in the context of crowdsourcing projects from the perspective of gamification.

This paper first proposed a model to depict the effect of four categories of game elements on three types of motivation based upon several motivation theories, which may, in turn, influence user participation. Then, 5 × 2 between-subject Web experiments were designed for collecting data and validating this model.

Game elements which provide participants with rewards and recognitions or remind participants of the completion progress of their tasks may positively influence the extrinsic motivation, whereas game elements which can help create a fantasy scene may strengthen intrinsic motivation. Besides, recognition-kind and progress-kind game elements may trigger the internalization of extrinsic motivation. In addition, when a task is of high complexity, the effects from game elements on extrinsic motivation and intrinsic motivation will be less prominent, whereas the internalization of extrinsic motivation may benefit from the increase of task complexity.

This study may uncover the motivation mechanism of several different kinds of game elements, which may help to find which game elements are more effective in enhancing engagement and participation in crowdsourcing projects. Besides, as task complexity is used as a moderator, one may be able to identify whether task complexity is able to influence the effects from game elements on motivations. Last, but not the least, this study will indicate the interrelationship between game elements, individual motivation and user participation, which can be adapted by other scholars.

This paper examines how managerial discretion and judgment in revenue recognition affect earnings and revenue value relevance. Specifically, the purpose of this paper is to assess the impact of lifting the objective-price constraint in revenue recognition on the value relevance of earnings and revenue by examining firms’ contemporaneous returns-earnings/revenue relation before and after the implementation of Accounting Standards Update (ASU) 2009-13. In addition, this paper examines how the change in earnings value relevance is conditioned by agency costs, corporate governance, information environment, and audit quality. This paper further examines whether earnings, revenue, and accruals quality change after the objective-price constraint is lifted.

This paper employs a difference-in-differences research design to examine whether earnings and revenue value relevance are enhanced or lowered more for a list of 107 US firms that applied selling price estimates in revenue recognition under ASU 2009-13 than for a list of 107 matched US firms that did not apply selling price estimates. Sub-sample analyses are employed to examine how agency costs, corporate governance, information environment, and audit quality condition the change in value relevance. Additional analyses examine the changes in earnings, revenue, and accruals quality using accruals, revenue accruals, discretionary revenue, absolute abnormal accruals, earnings/revenue predictability, and smoothness.

The empirical results suggest that lifting the objective-price constraint in revenue recognition improves earnings and revenue value relevance for positive earnings and that the effect of information usefulness dominates that of managerial opportunism. Change in the earnings value relevance is conditioned by the level of corporate governance, information environment, and audit quality. Evidence of no significant reduction in the earnings/revenue/accruals quality corroborates the main findings.

The findings lend support to the new revenue standard (ASU 2014-09) that continues the use of the estimates of selling price in revenue recognition.

This study provides some of the first evidence that managerial judgment exercised in revenue recognition through the use of selling price estimates (i.e. lifting the objective-price constraint in revenue recognition) enhances earnings and revenue value relevance while such benefit does not come at a cost of reduced earnings/revenue/accruals quality.

This paper seeks to detail an exploratory examination of a multidimensional, creativity‐based theoretical model of opportunity recognition originally proposed by Hills et al. and later refined by Lumpkin et al., but never empirically tested. The paper also aims to examine the relationship between individual dimensions of the model and creativity.

Analyses were conducted using AMOS software on a sample of 145 entrepreneurs. One structural equation model (SEM) and three confirmatory factor analysis models were tested.

The five‐dimensional model – consisting of preparation, incubation, insight, evaluation, and elaboration – was determined to be the best fitting model. The SEM model also indicated that incubation and elaboration were significantly related to creativity. Overall, a multidimensional, creativity‐based approach to modeling opportunity recognition is supported by this study.

Cross‐sectional data do not allow for testing of the process aspect of the model; however, they do provide evidence that the model can stand up to empirical tests of the five elements of the model. Future research should examine opportunity using multiple dimensions and a creativity perspective. Additional research is needed to examine the process aspects of opportunity recognition.

Fostering opportunity recognition processes that are iterative and involve multiple stages is likely to promote more creative entrepreneurial outcomes.

This study provides one of the few examples of a multidimensional perspective on opportunity recognition as well as an empirical examination of a creativity‐based theoretical model of opportunity recognition.