Exploring the attributes of collaborative working in construction industry

Due to the increased level of uncertainty of construction market and the variety of building functions, the practitioners in construction need work together more closely, which means a higher degree of collaborative working is often necessary. There is evidence that higher degree of collaborative working can produce more successful projects, but there has been only limited research to examine the definition of collaborative working. The lack of understanding of collaborative working resulted in confusion of application of more collaborative approaches e.g. partnering or alliancing. The work presented here is part of an ongoing PhD study which aims to explore the impact of collaborative working on construction project performance. The aim of this paper is to identify a spectrum of attributes of collaborative working, which will facilitate the understanding what collaborative working is, why collaborative working is needed and how to work together. In order to identify those attributes of collaborative working, the method of ‘identification test’ will be adopted, which is based on the recent related literature

Measuring the associations between collaborative working and project performance

There is evidence that higher degrees of collaborative working can produce more successful project performance, but there is only limited research to systematically examine the specific associations between collaborative working and project performance. In particular, there is a lack of exploration of appropriate approaches to test these associations. In order to test these associations in an appropriate approach, the concepts of collaborative working and project performance in this research are transformed into a measurable form in terms of the philosophy of AHP (analytic hierarchy process). In the process of measurement design for collaborative working and project performance, a Likert Scale is adopted. After refining the final measures through unidimensionality and reliability testing, as a part of PhD study, this paper presents the results of the association exploration between collaborative working and project performance. The produced conclusion is strongly supporting that there is a strong positive linear relationship between collaborative working and project performance

The hierarchy of higher order solutions of the derivative nonlinear Schr\"odinger equation

In this paper, we provide a simple method to generate higher order position solutions and rogue wave solutions for the derivative nonlinear Schr\"odinger equation. The formulae of these higher order solutions are given in terms of determinants. The dynamics and structures of solutions generated by this method are studied

The higher order Rogue Wave solutions of the Gerdjikov-Ivanov equation

We construct higher order rogue wave solutions for the Gerdjikov-Ivanov equation explicitly in term of determinant expression. Dynamics of both soliton and non-soliton solutions is discussed. A family of solutions with distinct structures are presented, which are new to the Gerdjikov-Ivanov equation

Synaptic Behavior in Metal Oxide-Based Memristors

With the end of Moore’s law in sight, new computing paradigms are needed to fulfill the increasing demands on data and processing potentials. Inspired by the operation of the human brain, from the dimensionality, energy and underlying functionalities, neuromorphic computing systems that are building upon circuit elements to mimic the neurobiological activities are good concepts to meet the challenge. As an important factor in a neuromorphic computer, electronic synapse has been intensively studied. The utilization of transistors, atomic switches and memristors has been proposed to perform synaptic functions. Memristors, with several unique properties, are exceptional candidates for emulating artificial synapses and thus for building artificial neural networks. In this paper, metal oxide-based memristor synapses are reviewed, from materials, properties, mechanisms, to architecture. The synaptic plasticity and learning rules are described. The electrical switching characteristics of a variety of metal oxide-based memristors are discussed, with a focus on their application as biological synapses

The effect of abusive supervision variability on work–family conflict: The role of psychological detachment and optimism

Although a number of studies have examined the effects of abusive supervision variability, which refers to leaders engaging in differential abuse toward different subordinates within the team on work-related outcomes, scant research has investigated whether and how abusive supervision variability affects non-work outcomes. Drawing on the conservation of resources theory, the current study explores how abusive supervision variability affects work–family conflict through psychological detachment, as well as the moderating role of optimism. Results based on a survey of 260 employees from nine companies show that abusive supervision variability is significantly and positively related to work–family conflict. Psychological detachment mediates the effect of abusive supervision variability on work–family conflict. Optimism moderates the relationship between abusive supervision variability and psychological detachment and the indirect effects of abusive supervision variability on work–family conflict through psychological detachment. This study extends the literature on the effects of abusive supervision variability and provides several important practical implications

NDDepth: Normal-Distance Assisted Monocular Depth Estimation

Monocular depth estimation has drawn widespread attention from the vision community due to its broad applications. In this paper, we propose a novel physics (geometry)-driven deep learning framework for monocular depth estimation by assuming that 3D scenes are constituted by piece-wise planes. Particularly, we introduce a new normal-distance head that outputs pixel-level surface normal and plane-to-origin distance for deriving depth at each position. Meanwhile, the normal and distance are regularized by a developed plane-aware consistency constraint. We further integrate an additional depth head to improve the robustness of the proposed framework. To fully exploit the strengths of these two heads, we develop an effective contrastive iterative refinement module that refines depth in a complementary manner according to the depth uncertainty. Extensive experiments indicate that the proposed method exceeds previous state-of-the-art competitors on the NYU-Depth-v2, KITTI and SUN RGB-D datasets. Notably, it ranks 1st among all submissions on the KITTI depth prediction online benchmark at the submission time.Comment: Accepted by ICCV 2023 (Oral

IEBins: Iterative Elastic Bins for Monocular Depth Estimation

Monocular depth estimation (MDE) is a fundamental topic of geometric computer vision and a core technique for many downstream applications. Recently, several methods reframe the MDE as a classification-regression problem where a linear combination of probabilistic distribution and bin centers is used to predict depth. In this paper, we propose a novel concept of iterative elastic bins (IEBins) for the classification-regression-based MDE. The proposed IEBins aims to search for high-quality depth by progressively optimizing the search range, which involves multiple stages and each stage performs a finer-grained depth search in the target bin on top of its previous stage. To alleviate the possible error accumulation during the iterative process, we utilize a novel elastic target bin to replace the original target bin, the width of which is adjusted elastically based on the depth uncertainty. Furthermore, we develop a dedicated framework composed of a feature extractor and an iterative optimizer that has powerful temporal context modeling capabilities benefiting from the GRU-based architecture. Extensive experiments on the KITTI, NYU-Depth-v2 and SUN RGB-D datasets demonstrate that the proposed method surpasses prior state-of-the-art competitors. The source code is publicly available at https://github.com/ShuweiShao/IEBins.Comment: Accepted by NeurIPS 202