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In the crowd intelligence networking era, the smart connections of human, machines and things enable point-to-point trustable transactions and distributed efficient collaboration; the smart connections among government, enterprises, organizations and the public would enable active participation of the public in society management and decision-making and improve the efficiency of government management and services. All interactions among various agents can be viewed as the transaction activity. The social division of labor system drives the evolution of transaction. The transaction mode also differentiated into different patterns with the development of human society. What will be the intelligent transaction in the crowd intelligence networking era? What will be the transactions modes and rules in the crowd intelligence networking era? The answers to these questions are of great importance to the future development of transactions.

The authors review the evolution of traditional transaction and transaction modes and analyze the driving forces of it. They attempt to give the definitions of intelligent transaction and intelligent transaction mode. They also review the traditional transaction modes and rules, analyze the characteristics of the intelligent transaction and classify the intelligent transaction modes.

The authors find the intelligent transaction is mainly reflected in the intellectualization of transaction subject, transaction object and transaction process. They summarize the characteristics of intelligent transaction and develop four modes for the intelligent transactions based on the modularization level of the transaction objects and the quantity of transaction subjects, including the demand side and the supply side. The authors also show representative examples to further illustrate rules and features of these transaction modes and point out the potential research directions.

This study is among the first to analyze the characteristics of the intelligent transaction, and the proposed division framework of the intelligent transaction modes could not only add value to the future research of intelligent transaction modes and rules but also help to guide the transactions in the crowd intelligence network.

Building on the routine dynamics literature, this paper aims to expand our philosophical, practical and infrastructural understanding of implementing lean production. The authors provide a process view on the interplay between lean operating routines and continuous improvement (CI) routines and the roles of different actors in initiating and establishing these routines.

Using data from interviews, observations and document analysis, retrospective comparative analyses of three embedded case studies on lean implementations provide a process understanding of enacting and patterning lean operating and CI routines in manufacturing SMEs.

Incorporating the “who” and “how” next to the “what” of practices and routines helps explain that rather than being implemented in isolation or even in conjunction with each other, sustainable lean practices and routines come about through team leader and employee enactment of the CI practices and routines. Neglecting these patterns aligned with unsustainable implementations.

The proposed process model provides a valuable way to integrate variance and process streams of literature to better understand lean production implementations.

The process model helps manufacturing managers, policy makers, consultants and educators to reconsider their approach to implementing lean production or teaching how to do so.

Nuancing the existing lean implementation literature, the proposed process model shows that CI routines do not stem from implementing lean operating routines. Rather, the model highlights the importance of active engagement of actors at multiple organizational levels and strong connections between and across levels to change routines and work practices for implementing lean production.

The purpose of this paper is to propose and evaluate the method for grasping a defined set of objects in an unstructured environment. To this end, the authors propose the method of integrating convolutional neural network (CNN)-based object detection and the category-free grasping method. The considered scenario is related to mobile manipulating platforms that move freely between workstations and manipulate defined objects. In this application, the robot is not positioned with respect to the table and manipulated objects. The robot detects objects in the environment and uses grasping methods to determine the reference pose of the gripper.

The authors implemented the whole pipeline which includes object detection, grasp planning and motion execution on the real robot. The selected grasping method uses raw depth images to find the configuration of the gripper. The authors compared the proposed approach with a representative grasping method that uses a 3D point cloud as an input to determine the grasp for the robotic arm equipped with a two-fingered gripper. To measure and compare the efficiency of these methods, the authors measured the success rate in various scenarios. Additionally, they evaluated the accuracy of object detection and pose estimation modules.

The performed experiments revealed that the CNN-based object detection and the category-free grasping methods can be integrated to obtain the system which allows grasping defined objects in the unstructured environment. The authors also identified the specific limitations of neural-based and point cloud-based methods. They show how the determined properties influence the performance of the whole system.

The authors identified the limitations of the proposed methods and the improvements are envisioned as part of future research.

The evaluation of the grasping and object detection methods on the mobile manipulating robot may be useful for all researchers working on the autonomy of similar platforms in various applications.

The proposed method increases the autonomy of robots in applications in the small industry which is related to repetitive tasks in a noisy and potentially risky environment. This allows reducing the human workload in these types of environments.

The main contribution of this research is the integration of the state-of-the-art methods for grasping objects with object detection methods and evaluation of the whole system on the industrial robot. Moreover, the properties of each subsystem are identified and measured.

The purpose of this paper is to introduce translational mobilization theory (TMT) and explore its application for healthcare quality improvement purposes.

TMT is a generic sociological theory that explains how projects of collective action are progressed in complex organizational contexts. This paper introduces TMT, outlines its ontological assumptions and core components, and explores its potential value for quality improvement using rescue trajectories as an illustrative case.

TMT has value for understanding coordination and collaboration in healthcare. Inviting a radical reconceptualization of healthcare organization, its potential applications include: mapping healthcare processes, understanding the role of artifacts in healthcare work, analyzing the relationship between content, context and implementation, program theory development and providing a comparative framework for supporting cross-sector learning.

Poor coordination and collaboration are well-recognized weaknesses in modern healthcare systems and represent important risks to quality and safety. While the organization and delivery of healthcare has been widely studied, and there is an extensive literature on team and inter-professional working, we lack readily accessible theoretical frameworks for analyzing collaborative work practices. TMT addresses this gap in understanding.

At present numerical simulation of seismicity, used in mining and hydraulic fracturing practice, is quite time expensive what hampers its combined employing with observed seismicity in real time. The purpose of this paper is to suggest a mean for drastic speeding up numerical modeling seismic and aseismic events.

The authors propose the means to radically decrease the time expense for the bottleneck stage of simulation: calculations of stresses, induced by a large group of already activated flaws (sources of events), at locations of flaws of another large group, which may be activated by the stresses. This is achieved by building a hierarchical tree and properly accounting for the sizes of activated flaws, excluding check of their influence on flaws, which are beyond strictly defined near-regions of strong interaction.

Comparative simulations of seismicity by conventional and improved methods demonstrate high efficiency of the means developed. When applied to practical mining and hydrofracturing problems, it requires some two orders less time to obtain practically the same output results as those of conventional methods.

The proposed improvement provides a means for simulation of seismicity in real time of mining steps and hydrofracture propagation. It can be also used in other applications involving seismic and aseismic events and acoustic emission.

At airport security checkpoints, baggage screening is aimed to prevent transportation of prohibited and potentially dangerous items. Observing the projection images generated by X-rays scanner is a critical method. However, when multiple objects are stacked on top of each other, distinguishing objects only by a two-dimensional picture is difficult, which prompts the demand for more precise imaging technology to be investigated for use. Reconstructing from 2D X-ray images to 3D-computed tomography (CT) volumes is a reliable solution.

To more accurately distinguish the specific contour shape of items when stacked, multi-information fusion network (MFCT-GAN) based on generative adversarial network (GAN) and U-like network (U-NET) is proposed to reconstruct from two biplanar orthogonal X-ray projections into 3D CT volumes. The authors use three modules to enhance the reconstruction qualitative and quantitative effects, compared with the original network. The skip connection modification (SCM) and multi-channels residual dense block (MRDB) enable the network to extract more feature information and learn deeper with high efficiency; the introduction of subjective loss enables the network to focus on the structural similarity (SSIM) of images during training.

On account of the fusion of multiple information, MFCT-GAN can significantly improve the value of quantitative indexes and distinguish contour explicitly between different targets. In particular, SCM enables features more reasonable and accurate when expanded into three dimensions. The appliance of MRDB can alleviate problem of slow optimization during the late training period, as well as reduce the computational cost. The introduction of subjective loss guides network to retain more high-frequency information, which makes the rendered CT volumes clearer in details.

The authors' proposed MFCT-GAN is able to restore the 3D shapes of different objects greatly based on biplanar projections. This is helpful in security check places, where X-ray images of stacked objects need to be distinguished from the presence of prohibited objects. The authors adopt three new modules, SCM, MRDB and subjective loss, as well as analyze the role the modules play in 3D reconstruction. Results show a significant improvement on the reconstruction both in objective and subjective effects.

Construction suffers from “peculiarities” that concern the temporary natures of the construction site, project teams and unique product design. Considering the digital transformation of construction, new solutions are being investigated that can provide consistent data between changing projects. One such source of data manifests in the tracking of logistics activities across the supply chain. Construction logistics is traditionally considered a site management activity focused solely on the “back end” of projects, but an expanded logistics focus can unlock new avenues of improvement. This study aims to understand the requirements and benefits of such a consistent thread of data.

From a research project with one of Australia’s largest contracting companies, this paper details a series of construction tracking tests as an empirical case study in using Bluetooth low energy aware tracking technology to capture data across the manufacture, delivery and assembly of a cross-laminated timber structural prototyping project.

The findings affirm the tracking of expanded logistics data can improve back-end performance in subsequent projects while also demonstrating the opportunity to inform a project’s unique front-end design phase. The case study demonstrates that as the reliability, range and battery life of tracking technologies improve, their incorporation into a broader range of construction activities provides invaluable data for improvement across projects.

As a live case study, this research offers unique insights into the potential of construction tracking to close the data loop from final site assembly back to the early project design phase, thus driving continual improvement from a holistic perspective.

Effective use of data across public health organisations (PHOs) is essential for the provision of health services. While health technology and data use in clinical practice have been investigated, interactions with data in non-clinical practice have been largely neglected. The purpose of this paper is to consider what constitutes data, and how people in non-clinical roles in a PHO interact with data in their practice.

This mixed methods study involved a qualitative exploration of how employees of a large PHO interact with data in their non-clinical work roles. A quantitative survey was administered to complement insights gained through qualitative investigation.

Organisational boundaries emerged as a defining issue in interactions with data. The results explain how data work happens through observing, spanning and shifting of boundaries. The paper identifies five key issues that shape data work in relation to boundaries. Boundary objects and processes are considered, as well as the roles of boundary spanners and shifters.

The study was conducted in a large Australian PHO, which is not completely representative of the unique contexts of similar organisations. The study has implications for research in information and organisational studies, opening fields of inquiry for further investigation.

Effective systems-wide data use can improve health service efficiencies and outcomes. There are also implications for the provision of services by other health and public sectors.

The study contributes to closing a significant research gap in understanding interactions with data in the workplace, particularly in non-clinical roles in health. Research analysis connects concepts of knowledge boundaries, boundary spanning and boundary objects with insights into information behaviours in the health workplace. Boundary processes emerge as an important concept to understand interactions with data. The result is a novel typology of interactions with data in relation to organisational boundaries.

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.

The purpose of this paper is to explore a formative intervention project that was developed for the Municipal Secretariat of Education in São Paulo, Brazil for the broad development of all levels of educational management (teacher educators, coordinators, principals, teachers and students). Thus, the creative chain of activities is a key theoretical framework for promoting critical collaboration in order to cross the boundaries of educational management organization.

The authors use data from the Management in Creative Chains Project (Liberali, 2012), as a way to enable the wide development of all levels of educational management. Data comprise formative meetings in which different educational managers system take part in two settings, the regional board with 25 schools and one of the participating schools. The analysis is based on thematic content and argumentative organization, and on critical situations and the potentials they entailed.

The study guides to the conclusions of the process of creative chain as a possibility to expand management in the educational system and its community.

Every time there is a change in the mayors, there are changes in the way of addressing school management in the city. However, after the project, considerations about the needs of the communities became part of the public policy regardless of who is in charge of the city and its educational system.

This study can be used for transformation in the management and teaching and learning activities and improvement of the school-community relation.

Socially this study can lead to improvement in the quality of life in the community and at school.

Differently from a top down educational management, which enables a reproductive chain, educational management in a creative chain, considering the community needs, enables subjects to become interdependent to expand and transform the activities in the educational system and hence the communities’ reality.