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1 – 4 of 4Ibrahim Yitmen, Amjad Al-Musaed and Fikri Yücelgazi
Decisions taken during the early design of adaptive façades involving kinetic, active and responsive envelope for complex commercial buildings have a substantial effect on…
Abstract
Purpose
Decisions taken during the early design of adaptive façades involving kinetic, active and responsive envelope for complex commercial buildings have a substantial effect on inclusive building functioning and the comfort level of inhabitants. This study aims to present the application of an analytic network process (ANP) model indicating the order of priority for high performance criteria that must be taken into account in the assessment of the performance of adaptive façade systems for complex commercial buildings.
Design/methodology/approach
The nominal group technique (NGT) stimulating and refining group judgments are used to find and categorize relevant high performance attributes of the adaptive façade systems and their relative pair-wise significance scores. An ANP model is applied to prioritize these high performance objectives and criteria for the adaptive façade systems.
Findings
Embodied energy and CO2 emission, sustainability, energy saving, daylight and operation maintenance were as the most likely and crucial high performance criteria. The criteria and the weights presented in this study could be used as guidelines for evaluating the performance of adaptive façade systems for commercial buildings in planning and design phases.
Practical implications
This research primarily provides the required actions and evaluations for design managers in accomplishing a high performance adaptive façade system, with the support of an ANP method. Before beginning the adaptive façade system of a building design process, the design manager must determine the significance of each of these attributes as high performance primacies will affect the results all through the entire design process.
Originality/value
In this research, a relatively innovative, systematic and practical approach is proposed to sustain the decision-making procedure for evaluation of the high performance criteria of adaptive façade systems in complex commercial buildings.
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Wang Zengqing, Zheng Yu Xie and Jiang Yiling
With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene…
Abstract
Purpose
With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection.
Design/methodology/approach
This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure.
Findings
This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs.
Research limitations/implications
The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate.
Social implications
The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security.
Originality/value
This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.
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Nairana Radtke Caneppele, Fernando Antonio Ribeiro Serra, Luis Hernan Contreras Pinochet and Izabela Martina Ramos Ribeiro
The purpose of this study is to understand how neuroscientific tools are used and discussed in ongoing research on strategy in organizations.
Abstract
Purpose
The purpose of this study is to understand how neuroscientific tools are used and discussed in ongoing research on strategy in organizations.
Design/methodology/approach
The authors used a bibliometric study of bibliographic pairing to answer the research question. They collected data from the Web of Science and Scopus databases using the keywords “neuroscience*,” “neurostrategy*” and “neuroscientific*.”
Findings
This study presents a framework that relates fundamental aspects discussed in current research using neuroscientific tools: Neuroscience and its research tools in organizations; emotions and information processing; interdisciplinary application of neuroscientific tools; and moral and ethical influences in the leaders' decision-making process.
Research limitations/implications
The inclusion of neuroscientific tools in Strategic Management research is still under development. There are criticisms and challenges related to the limitations and potential to support future research.
Practical implications
Despite recognizing the potential of neuroscientific tools in the mind and brain relationship, this study suggests that at this stage, because of criticisms and challenges, they should be used as support and in addition to other traditional research techniques to assess constructs and mechanisms related to strategic decisions and choices in organizations.
Social implications
Neuroscientific methods in organizational studies can provide insights into individual reactions to ethical issues and raise challenging normative questions about the nature of moral responsibility, autonomy, intention and free will, offering multiple perspectives in the field of business ethics.
Originality/value
In addition to presenting the potential and challenges of using scientific tools in strategic management studies, this study helps create methodological paths for studies in strategic management.
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Jiemin Zhong, Haoran Xie and Fu Lee Wang
A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic…
Abstract
Purpose
A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic review of recommendation systems by collecting related journal articles from the last five years (i.e. from 2014 to 2018). This paper aims to study the correlations between recommendation technologies and e-learning systems.
Design/methodology/approach
The paper reviews the relevant articles using five assessment aspects. A coding scheme was put forward that includes the following: the metrics for the e-learning system, the evaluation metrics for the recommendation algorithms, the recommendation filtering technology, the phases of the recommendation process and the learning outcomes of the system.
Findings
The research indicates that most e-learning systems will adopt the adaptive mechanism as a primary metric, and accuracy is a vital evaluation indicator for recommendation algorithms. In existing e-learning recommender systems, the most common recommendation filtering technology is hybrid filtering. The information collection phase is an important process recognized by most studies. Finally, the learning outcomes of the recommender system can be achieved through two key indicators: affections and correlations.
Originality/value
The recommendation technology works effectively in closing the gap between the information producer and the information consumer. This technology could help learners find the information they are interested in as well as send them a valuable message. The opportunities and challenges of the current study are discussed; the results of this study could provide a guideline for future research.
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