The relationship between nursing groups’ organizational socialization and nurses’ perception of organizational learning subprocesses: a cross-sectional study in hospital wards

Purpose This study aims to investigate the relationship between a nursing group’s organizational socialization (OS) and the organizational learning (OL) subprocesses of information acquisition, information distribution, information interpretation, information integration and organizational memory. Design/methodology/approach A cross-sectional study, with an anonymous self-report questionnaire, was conducted at two university hospitals in Japan. OL was measured using the scale for OL subprocesses, while OS was measured using the scale for learning about the external environment. The questionnaire was administered from August to October 2018. Among the 1,077 nurses recruited from 34 wards, data from 466 nurses from 24 wards were analyzed. To verify the influence of the group’s OS on each OL subprocess, two-level hierarchical linear modeling with fixed effects was performed. Individual nurses’ OS was analyzed using centering within clusters and the group’s OS was analyzed using each ward’s average OS score by performing grand mean centering. Findings Nursing groups’ OS was positively and significantly associated with information interpretation and information integration, but not with information acquisition, information distribution and organizational memory. Originality/value This study expands OS and OL research by focusing on the relationship between the degree of OS of an entire group and the OL subprocess. When the degree of homophily of value, rule, knowledge and behavior of the entire group increases, the information understanding and the formation of new explicit knowledge may also increase in the group.

Seismic Data Inversion with Acquisition Adaptive CNN for Geological Forward-prospecting in Tunnels

Seismic forward-prospecting is essential because it can identify the velocity distribution in front of the tunnel face and provide guidance for safe excavation activities. We propose a convolutional neural network (CNN)-based method to invert forward-prospecting data recorded in tunnels for accurate and rapid estimation of seismic velocity distribution. Targeting the unusual seismic acquisition setup in tunnels, we design two separate encoders to extract features from observation data recorded on both tunnel sidewalls. Subsequently, these features are concatenated to a decoder for velocity prediction. Considering the various acquisition setups used in different tunneling projects, the deep learning inversion network must be flexible in terms of the seismic source/receiver positions for practical application. We generate two auxiliary feature maps that can be used to feed acquisition information to the proposed network. The proposed network, acquisition adaptive CNN ( A2-CNN) can be trained by defining the loss function based on the L2-norm and multiscale structural similarity (MSSIM). Compared with traditional CNNs, the proposed method shows superior performance on datasets with both fixed and random acquisition setups, and also demonstrates certain robustness when handling synthetic data with field noise. Finally, we test how the network performs when feeding the modified acquisition setup information. It turns out that the inversion result will demonstrate a shift when the provided acquisition setup information shift, which verified the validity of the network and its utilization of acquisition information.

Drones as internet of video things front-end sensors: challenges and opportunities

Internet of Video Things (IoVT) has become an emerging class of IoT systems that are equipped with visual sensors at the front end. Most of such visual sensors are fixed one whereas the drones are considered flying IoT nodes capable of capturing visual data continuously while flying over the targets of interest. With such a dynamic operational mode, we can imagine significant technical challenges in sensor data acquisition, information transmission, and knowledge extraction. This paper will begin with an analysis on some unique characteristics of IoVT systems with drones as its front end sensors. We shall then discuss several inherent technical challenges for designing drone-based IoVT systems. Furthermore, we will present major opportunities to adopt drone-based IoVT in several contemporary applications. Finally, we conclude this paper with a summary and an outlook for future research directions.

Hosting the 16th AHILA Conference in Ibadan, Nigeria: organization, achievements, challenges and lessons learnt

Attending a scientific conference offers researchers several potential benefits including opportunity to present and receive constructive feedback from professional colleagues. Organizing such conference is also beneficial to the hosts who can acquire skills for coordination, communication and networking. However, the process is fraught with many challenges. One hundred and nine professionals attended the 16th AHILA conference from 22 countries in Africa, Europe and United States of America. The conference agenda was balanced, integrating skills acquisition, information for career development, sources of evidence-based free e-resources, including databases, and e-books for libraries covering health-related topics. This article describes achievement, challenges and lessons learnt in hosting the conference and could serve as a guide for health information professionals planning a similar conference in the future.

Flipped Learning

It is known that the next generation grows intertwined with technology, can easily communicate with peers all over the world, adapt to new technological tools very quickly, and is fond of independence. For these reasons, it has become impossible to prepare the next generation for the future with the traditional education system. In a world where digital technology dominates our lives, the flipped learning model has emerged. In this model, the student performs cognitive activities on lower levels such as acquisition information and understanding before the course. The course focuses on higher level cognitive activities such as practice, analysis, evaluation, and synthesis with the support of peers and teachers. Thus, students are transformed from individuals informed by their teachers to individuals who reach information and take it to the next step. In this chapter, the flipped learning model was introduced; its benefits and limitations, researches about the model, recommendations for implementation are discussed.

Information-Based Revolution in Military Affairs

The subject of the chapter is the information-based revolution in military affairs (RMA). The concept of RMA is identified as a way to increase combat capabilities based on a synergy between three spheres: information acquisition, information processing and transfer, and making use of information in order to enhance firepower. RMA includes the following key elements: technological change, doctrinal, strategic, operational and tactical change, and transformation of military organizational structure. Paradoxically, military transformation takes place while the pace of military technology development decreases, which poses a significant inhibitor. Consequently, only the first RMA stage—computerization—might be recognized as a relatively advanced one, whereas its second stage—networking—is far from this level. The chapter's aim is to present the RMA concept and its practical application in transformation of military forces. The chapter examines to what extent expectations emerging from RMA have been fulfilled in armed conflicts over the last two decades.