scholarly journals Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1022
Author(s):  
Hoang T. Nguyen ◽  
Kate T. Q. Nguyen ◽  
Tu C. Le ◽  
Guomin Zhang
Keyword(s):  
Artificial Intelligence ◽  
Flame Retardancy ◽  
Material Science ◽  
Construction Materials ◽  
Engineering Problem ◽  
Future Research ◽  
Fire Performance ◽  
Structural Components ◽  
Comprehensive Overview ◽  
Fire Engineering

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

scholarly journals A Review of Auditors’ GCOs, Statistical Prediction Models and Artificial Intelligence Technology

2019 ◽  
Vol 2 (1) ◽  
pp. 19-31
Author(s):  
Bahaaeddin Alareeni
Keyword(s):  
Artificial Intelligence ◽  
Prediction Models ◽  
Professional Competence ◽  
Bankruptcy Prediction ◽  
Statistical Prediction ◽  
Future Research ◽  
Comprehensive Overview ◽  
Time Period ◽  
Artificial Intelligence Technology ◽  
Audit Program

The main aim of this study is to give an overview of literature in the accounting and finance regarding the performance of Auditors’ GCOs, Statistical Failure Prediction Models (SFPMs) and Artificial Intelligence Technology (AIT). The study reviews the accounting and finance literature regarding (SFPMs) and presents the most important types of SFPMs and AIT that have been developed to evaluate a company’s financial position from 1968 to date. The study focuses on studies that compare the relative performance of auditors’ GCOs with SFPMs and AIT. Our findings illustrated that SPFMs and AIT are better in predicting companies’ failure than auditors’ GCOs. We found that the prediction power of SFPMs is in many instances very high. Their accuracy differed from one model to another, depending on several factors such as industry, time period and economic environment. The most commonly used and accurate models are the Altman models, logit models and neural networks models, although overall the NNs models produce better results. We found that SFPMs and AIT can be very useful to users when assessing a company’s future position. Incorporating the use of SFPMs and AIT in the audit program can provide further evidence that the auditors exerted professional competence and due care. This study provides a comprehensive overview of research on Auditors’ GCOs, SFPMs and AIT. The study provides a clear picture of the best tools used in failure/bankruptcy prediction in last decades. Thus, it is an aid to future research in the area.

scholarly journals Microbial interactions with silicate glasses

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Jamie L. Weaver ◽  
Paula T. DePriest ◽  
Andrew E. Plymale ◽  
Carolyn I. Pearce ◽  
Bruce Arey ◽  
...  
Keyword(s):  
Material Science ◽  
Microbial Interactions ◽  
Silicate Glasses ◽  
Microbial Colonization ◽  
Future Research ◽  
Evidence Based ◽  
Research Needs ◽  
Comprehensive Overview ◽  
Detailed History ◽  
Alteration Processes

AbstractGlass alteration in the presence of microorganisms has been a topic of research for over 150 years. Researchers from a variety of disciplines, including material science, biology, chemistry, geology, physics, and cultural heritage materials preservation have conducted experiments in this area to try and understand when, how, and why microorganism may interact and subsequently influence the alteration of glass. The breadth and depth of these studies are the topic of this review. This review presents a detailed history and a comprehensive overview of this field of research, while maintaining focus on the terrestrial alteration of anthropogenic silicate glasses. Within this manuscript is a schema for bio-interaction with silicate glasses and an outline of an evidence-based hypothesis on how these interactions may influence glass alteration processes. Topics discussed include microbial colonization of glass, development, and interactions of biofilms with glass surface, abiotic vs. biotic alteration processes, and signatures of bio-alteration. Future research needs and a discussion of practical drivers for this research are summarized.

scholarly journals Organizational Commitment and Lean Sustainability: Literature Review and Directions for Future Research

2021 ◽  
Vol 13 (6) ◽  
pp. 3357 ◽  
Author(s):  
Amal Benkarim ◽  
Daniel Imbeau
Keyword(s):  
Organizational Commitment ◽  
Literature Review ◽  
Lean Manufacturing ◽  
Affective Commitment ◽  
Management Practices ◽  
Employee Commitment ◽  
Future Research ◽  
Comprehensive Overview ◽  
Narrative Literature Review ◽  
Key Aspects

The vast majority of works published on Lean focus on the evaluation of tools and/or the strategies needed for its implementation. Although many authors highlight the degree of employee commitment as one of the key aspects of Lean, what has gone largely unnoticed in the literature, is that few studies have examined in-depth the concept of organizational commitment in connection with Lean. With this narrative literature review article, our main objective is (1) to identify and analyze an extensive body of literature that addresses the Lean Manufacturing approach and how it relates to employee commitment, emphasizing affective commitment as the main type of organizational commitment positively associated with Lean, and (2) to highlight the management practices required to encourage this kind of commitment and promote the success and sustainability of Lean. This paper aims to provide a comprehensive overview that can help researchers and practitioners interested in Lean better understand the importance of employee commitment in this type of approach, and as well, to identify related research questions.

scholarly journals Artificial intelligence in hospitals: providing a status quo of ethical considerations in academia to guide future research

AI & Society ◽  
2021 ◽  
Author(s):  
Milad Mirbabaie ◽  
Lennart Hofeditz ◽  
Nicholas R. J. Frick ◽  
Stefan Stieglitz
Keyword(s):  
Artificial Intelligence ◽  
Citation Network ◽  
Status Quo ◽  
Biomedical Ethics ◽  
Future Research ◽  
Ethical Considerations ◽  
Ethical Discourse ◽  
The Status ◽  
Clinical Environments ◽  
Research Questions

AbstractThe application of artificial intelligence (AI) in hospitals yields many advantages but also confronts healthcare with ethical questions and challenges. While various disciplines have conducted specific research on the ethical considerations of AI in hospitals, the literature still requires a holistic overview. By conducting a systematic discourse approach highlighted by expert interviews with healthcare specialists, we identified the status quo of interdisciplinary research in academia on ethical considerations and dimensions of AI in hospitals. We found 15 fundamental manuscripts by constructing a citation network for the ethical discourse, and we extracted actionable principles and their relationships. We provide an agenda to guide academia, framed under the principles of biomedical ethics. We provide an understanding of the current ethical discourse of AI in clinical environments, identify where further research is pressingly needed, and discuss additional research questions that should be addressed. We also guide practitioners to acknowledge AI-related benefits in hospitals and to understand the related ethical concerns.

scholarly journals Dual UV-Thermal Curing of Biobased Resorcinol Epoxy Resin-Diatomite Composites with Improved Acoustic Performance and Attractive Flame Retardancy Behavior

2021 ◽  
Vol 2 (1) ◽  
pp. 24-48
Author(s):  
Quoc-Bao Nguyen ◽  
Henri Vahabi ◽  
Agustín Rios de Anda ◽  
Davy-Louis Versace ◽  
Valérie Langlois ◽  
...  
Keyword(s):  
Heat Release ◽  
Flame Retardant ◽  
Epoxy Resin ◽  
Flame Retardancy ◽  
Sound Absorption ◽  
Flexural Modulus ◽  
Construction Materials ◽  
Acoustic Properties ◽  
Low Frequencies ◽  
Compacting Pressure

This study has developed novel fully bio-based resorcinol epoxy resin–diatomite composites by a green two-stage process based on the living character of the cationic polymerization. This process comprises the photoinitiation and subsequently the thermal dark curing, enabling the obtaining of thick and non-transparent epoxy-diatomite composites without any solvent and amine-based hardeners. The effects of the diatomite content and the compacting pressure on microstructural, thermal, mechanical, acoustic properties, as well as the flame behavior of such composites have been thoroughly investigated. Towards the development of sound absorbing and flame-retardant construction materials, a compromise among mechanical, acoustic and flame-retardant properties was considered. Consequently, the composite obtained with 50 wt.% diatomite and 3.9 MPa compacting pressure is considered the optimal composite in the present work. Such composite exhibits the enhanced flexural modulus of 2.9 MPa, a satisfying sound absorption performance at low frequencies with Modified Sound Absorption Average (MSAA) of 0.08 (for a sample thickness of only 5 mm), and an outstanding flame retardancy behavior with the peak of heat release rate (pHRR) of 109 W/g and the total heat release of 5 kJ/g in the pyrolysis combustion flow calorimeter (PCFC) analysis.

scholarly journals Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

2021 ◽  
Author(s):  
Christian Horn ◽  
Oscar Ivarsson ◽  
Cecilia Lindhé ◽  
Rich Potter ◽  
Ashely Green ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bronze Age ◽  
Data Augmentation ◽  
Rock Art ◽  
Training Data ◽  
Future Research ◽  
Rock Surface ◽  
Southern Scandinavia ◽  
Bounding Boxes

AbstractRock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.

scholarly journals Endocrine Disrupting Chemicals and Thyroid Cancer: An Overview

Toxics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
Mathilda Alsen ◽  
Catherine Sinclair ◽  
Peter Cooke ◽  
Kimia Ziadkhanpour ◽  
Eric Genden ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Flame Retardants ◽  
Endocrine Disrupting Chemicals ◽  
National Institutes Of Health ◽  
Future Research ◽  
National Library ◽  
Comprehensive Overview ◽  
Carcinogenic Effect ◽  
Increased Risk ◽  
Endocrine Disrupting

Endocrine disruptive chemicals (EDC) are known to alter thyroid function and have been associated with increased risk of certain cancers. The present study aims to provide a comprehensive overview of available studies on the association between EDC exposure and thyroid cancer. Relevant studies were identified via a literature search in the National Library of Medicine and National Institutes of Health PubMed as well as a review of reference lists of all retrieved articles and of previously published relevant reviews. Overall, the current literature suggests that exposure to certain congeners of flame retardants, polychlorinated biphenyls (PCBs), and phthalates as well as certain pesticides may potentially be associated with an increased risk of thyroid cancer. However, future research is urgently needed to evaluate the different EDCs and their potential carcinogenic effect on the thyroid gland in humans as most EDCs have been studied sporadically and results are not consistent.

SNF1-related protein kinase 1: the many-faced signaling hub regulating developmental plasticity in plants

2021 ◽  
Author(s):  
Muhammed Jamsheer K ◽  
Manoj Kumar ◽  
Vibha Srivastava
Keyword(s):  
Protein Kinase ◽  
Environmental Conditions ◽  
Protein Trafficking ◽  
Developmental Plasticity ◽  
Inositol Phosphate ◽  
Future Research ◽  
Primary Metabolism ◽  
Related Protein ◽  
Comprehensive Overview ◽  
Cellular Processes

AbstractThe Snf1-related protein kinase 1 (SnRK1) is the plant homolog of the heterotrimeric AMP-activated protein kinase/sucrose non-fermenting 1 (AMPK/Snf1), which works as a major regulator of growth under nutrient-limiting conditions in eukaryotes. Along with its conserved role as a master regulator of sugar starvation responses, SnRK1 is involved in controlling the developmental plasticity and resilience under diverse environmental conditions in plants. In this review, through mining and analyzing the interactome and phosphoproteome data of SnRK1, we are highlighting its role in fundamental cellular processes such as gene regulation, protein synthesis, primary metabolism, protein trafficking, nutrient homeostasis, and autophagy. Along with the well-characterized molecular interaction in SnRK1 signaling, our analysis highlights several unchartered regions of SnRK1 signaling in plants such as its possible communication with chromatin remodelers, histone modifiers, and inositol phosphate signaling. We also discuss potential reciprocal interactions of SnRK1 signaling with other signaling pathways and cellular processes, which could be involved in maintaining flexibility and homeostasis under different environmental conditions. Overall, this review provides a comprehensive overview of the SnRK1 signaling network in plants and suggests many novel directions for future research.

Hierarchical Reinforcement Learning

2021 ◽  
Vol 54 (5) ◽  
pp. 1-35
Author(s):  
Shubham Pateria ◽  
Budhitama Subagdja ◽  
Ah-hwee Tan ◽  
Chai Quek
Keyword(s):  
Reinforcement Learning ◽  
Future Research ◽  
Comprehensive Overview ◽  
Open Problems ◽  
Practical Applications ◽  
Hierarchical Reinforcement Learning ◽  
The Past ◽  
Agent Learning ◽  
Multi Agent ◽  
Supplementary Material

Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of challenging long-horizon decision-making tasks into simpler subtasks. During the past years, the landscape of HRL research has grown profoundly, resulting in copious approaches. A comprehensive overview of this vast landscape is necessary to study HRL in an organized manner. We provide a survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL. The survey is presented according to a novel taxonomy of the approaches. Based on the survey, a set of important open problems is proposed to motivate the future research in HRL. Furthermore, we outline a few suitable task domains for evaluating the HRL approaches and a few interesting examples of the practical applications of HRL in the Supplementary Material.

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