Thermal comfort prediction by applying supervised machine learning in green sidewalks of Tehran

2020 ◽  
Vol 9 (4) ◽  
pp. 361-374
Author(s):  
Nasim Eslamirad ◽  
Soheil Malekpour Kolbadinejad ◽  
Mohammadjavad Mahdavinejad ◽  
Mohammad Mehranrad
Keyword(s):  
Machine Learning ◽  
Thermal Comfort ◽  
Urban Design ◽  
Energy Modeling ◽  
Supervised Machine Learning ◽  
Outdoor Thermal Comfort ◽  
Social Implications ◽  
Design Strategies ◽  
Content Type ◽  
The Future

PurposeThis research aims to introduce a new methodology for integration between urban design strategies and supervised machine learning (SML) method – by applying both energy engineering modeling (evaluating phase) for the existing green sidewalks and statistical energy modeling (predicting phase) for the new ones – to offer algorithms that help to catch the optimum morphology of green sidewalks, in case of high quality of the outdoor thermal comfort and less errors in results.Design/methodology/approachThe tools of the study are the way of processing by SML, predicting the future based on the past. Machine learning is benefited from Python advantages. The structure of the study consisted of two main parts, as the majority of the similar studies follow: engineering energy modeling and statistical energy modeling. According to the concept of the study, at first, from 2268 models, some are randomly selected, simulated and sensitively analyzed by ENVI-met. Furthermore, the Envi-met output as the quantity of thermal comfort – predicted mean vote (PMV) and weather items are inputs of Python. Then, the formed data set is processed by SML, to reach the final reliable predicted output.FindingsThe process of SML leads the study to find thermal comfort of current models and other similar sidewalks. The results are evaluated by both PMV mathematical model and SML error evaluation functions. The results confirm that the average of the occurred error is about 1%. Then the method of study is reliable to apply in the variety of similar fields. Finding of this study can be helpful in perspective of the sustainable architecture strategies in the buildings and urban scales, to determine, monitor and control energy-based behaviors (thermal comfort, heating, cooling, lighting and ventilation) in operational phase of the systems (existed elements in buildings, and constructions) and the planning and designing phase of the future built cases – all over their life spans.Research limitations/implicationsLimitations of the study are related to the study variables and alternatives that are notable impact on the findings. Furthermore, the most trustable input data will result in the more accuracy in output. Then modeling and simulation processes are most significant part of the research to reach the exact results in the final step.Practical implicationsFinding of the study can be helpful in urban design strategies. By finding outdoor thermal comfort that resulted from machine learning method, urban and landscape designers, policymakers and architects are able to estimate the features of their designs in air quality and urban health and can be sure in catching design goals in case of thermal comfort in urban atmosphere.Social implicationsBy 2030, cities are delved as living spaces for about three out of five people. As green infrastructures influence in moderating the cities’ climate, the relationship between green spaces and habitants’ thermal comfort is deduced. Although the strategies to outside thermal comfort improvement, by design methods and applicants, are not new subject to discuss, applying machines that may be common in predicting results can be called as a new insight in applying more effective design strategies and in urban environment’s comfort preparation. Then study’s footprint in social implications stems in learning from the previous projects and developing more efficient strategies to prepare cities as the more comfortable and healthy places to live, with the more efficient models and consuming money and time.Originality/valueThe study achievements are expected to be applied not only in Tehran but also in other climate zones as the pattern in more eco-city design strategies. Although some similar studies are done in different majors, the concept of study is new vision in urban studies.

Predicting meeting participants’ note-taking from previously uttered dialogue acts

2016 ◽  
Vol 18 (2) ◽  
pp. 170-185
Author(s):  
Antje Bothin ◽  
Paul Clough
Keyword(s):  
Machine Learning ◽  
Information Systems ◽  
Speech Acts ◽  
Information Needs ◽  
Supervised Machine Learning ◽  
Social Implications ◽  
Note Taking ◽  
Content Type ◽  
Similar Work ◽  
Dialogue Acts

Purpose The purpose of this paper is to describe a new supervised machine learning study on the prediction of meeting participant’s personal note-taking from spoken dialogue acts uttered shortly before writing. Design/methodology/approach This novel approach of providing cues for finding important meeting events that would be worth recording in a meeting summary looks at temporal overlaps of multiple people’s note-taking. This research uses data of 124 meetings taken from the AMI meeting corpus. Findings The results show that several machine learning methods that the authors compared were able to classify the data significantly better than a random approach. The best model, decision trees with feature selection, achieved 70 per cent accuracy for the binary distinction writing for any number of participants simultaneously or no writing, whereas the performance for a more fine-grained distinction of the number of participants taking notes showed only about 30 per cent accuracy. Research limitations/implications The findings suggest that meeting participants take personal notes in accordance with the utterance of previously uttered speech acts, particularly dialogue acts about disfluencies and assessments appear to influence the note-taking activities. However, further research is necessary to examine other domains and to determine in what way this behaviour is helpful as a feature source for automatic meeting summarisation, which is useful for more efficiently satisfying people’s information needs about meeting contents. Practical implications The reader of an Information Systems (IS) journal would be interested in this paper because the work described and the findings gained could lead to the development of novel information systems that facilitate the work for businesses and individuals. Innovative meeting capture and retrieval applications, satisfying automatic summaries of important meeting points and sophisticated note-taking tools that suggest content automatically could make people’s daily lives more convenient in the future. Social implications There are wider implications in terms of productivity and efficiency. Business value is increased for the organisation, as human knowledge is built more or less automatically. There are also cognitive and social implications for individuals and possibly an impact on the society as a whole. It is also important for globalisation, social media and mobile devices. Originality/value The topic is new and original, as there has not been much research on it yet. Similar work was carried out recently (Murray, 2015; Bothin and Clough 2014). This is why it is relevant to an IS journal and interesting for the reader. In particular, dialogue acts about disfluencies and assessments appear to influence the note-taking activities. This behaviour is helpful as a feature source for automatic meeting summarisation, which is useful for more efficiently satisfying people’s information needs about meeting contents.

Evaluating disaster-related tweet credibility using content-based and user-based features

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nasser Assery ◽  
Yuan (Dorothy) Xiaohong ◽  
Qu Xiuli ◽  
Roy Kaushik ◽  
Sultan Almalki
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Emergency Response ◽  
Learning Model ◽  
Performance Comparison ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Content Type ◽  
Machine Learning Classification

Purpose This study aims to propose an unsupervised learning model to evaluate the credibility of disaster-related Twitter data and present a performance comparison with commonly used supervised machine learning models. Design/methodology/approach First historical tweets on two recent hurricane events are collected via Twitter API. Then a credibility scoring system is implemented in which the tweet features are analyzed to give a credibility score and credibility label to the tweet. After that, supervised machine learning classification is implemented using various classification algorithms and their performances are compared. Findings The proposed unsupervised learning model could enhance the emergency response by providing a fast way to determine the credibility of disaster-related tweets. Additionally, the comparison of the supervised classification models reveals that the Random Forest classifier performs significantly better than the SVM and Logistic Regression classifiers in classifying the credibility of disaster-related tweets. Originality/value In this paper, an unsupervised 10-point scoring model is proposed to evaluate the tweets’ credibility based on the user-based and content-based features. This technique could be used to evaluate the credibility of disaster-related tweets on future hurricanes and would have the potential to enhance emergency response during critical events. The comparative study of different supervised learning methods has revealed effective supervised learning methods for evaluating the credibility of Tweeter data.

Ninety days convert recruits into hosts at Edwardian Group London

2014 ◽  
Vol 22 (2) ◽  
pp. 15-18
Keyword(s):  
Crucial Role ◽  
Design Methodology ◽  
Training And Development ◽  
Social Implications ◽  
Content Type ◽  
The Future ◽  
First 90 Days ◽  
Practical Implications

Purpose – Describes the various approaches taken to training and development at Edwardian Group London, a group of hotels. Design/methodology/approach – Examines the reasons for the training, the form it takes and the results it has achieved. Findings – Emphasizes the importance the company attaches to training in the first 90 days of an employee's tenure, when recruits receive general induction training plus training specific to their area of operation. Practical implications – Outlines how the company spots and develops its managers of the future. Social implications – Highlights the crucial role of training in ensuring that hotel guests have the best possible stay. Originality/value – Provides a thorough examination of the various forms of training at Edwardian Group London.

Fast fashion and the future

2014 ◽  
Vol 22 (2) ◽  
pp. 157-160 ◽  
Author(s):  
Tom P. Abeles
Keyword(s):  
Design Methodology ◽  
Tipping Point ◽  
Social Implications ◽  
Current Effort ◽  
Fast Fashion ◽  
Content Type ◽  
Science Technology ◽  
The Future ◽  
And Mathematics ◽  
Practical Implications

Purpose – The purpose of this paper is to suggest that all of the systems, education, economic and social, are caught in an ever-increasing pace, tied in large part to a set of beliefs, largely economic, that resemble a religion and for which there appears to not be a rational option to escape. Design/methodology/approach – A study of systems. Findings – It is argued that we are at a tipping point where there are too many holes in the intellectual dike, that a shift in many dimensions may not be preventable. Practical implications – While “techno-futurists” are promoting this increasing evolution pace towards a transformational singularity, there appears to be no serious consideration that humanity may get its “wish” as did King Midas. Social implications – There is a serious question as to whether there can be, and should be, alternatives not cast into the frame of the Neo-Luddites. Originality/value – This is a contrarian view of the current effort to promote the educational focus on STEM, science, technology, engineering and mathematics, almost as a pre-cursor to being able to participate in a technology-driven societal model of the future.

Videotaped teaching and learning methodology – an experiential learning and action research approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ghadah Al Murshidi
Keyword(s):  
Action Research ◽  
Experiential Learning ◽  
University Students ◽  
Teaching And Learning ◽  
Positive Attitude ◽  
Learning Experience ◽  
Social Implications ◽  
Content Type ◽  
The Future ◽  
Remote Learning

Purpose This paper aims to assess the effectiveness of videotaped learning when used in a university in the UAE, in terms of evaluating the experiences of the students, along with highlighting its benefits along with challenges. The research aims to analyse the challenges and benefits of videotaped story workshop method for university students in the UAE. Experiential learning theory can be the basis of this videotaped method of learning and can be used for further theory and the contribution of this research study to knowledge in international education in business. Design/methodology/approach The results got by extracting primary data (quantitative and qualitative) from a sample size of 201 students. The paper used an action research methodology within a university degree course and within the teaching profession. The research design is associated with measuring and assessing the challenges and benefits of videotaped story workshop method for university students, along with the perceptions of the students towards its use. The study primarily used participatory action research which is a community-based study, action-based enquiry and action learning. The approach mostly used to improve the conditions and practices in a range of social environments. Findings The results suggest that most students were in favour of incorporating videotaped story workshop method for the learning experience as this eradicates common linguistic and cultural barriers. Observations indicate the students found it quite challenging to learn new techniques of making videos but later on shown a positive attitude towards the adoption of technology in terms of creating videos and presenting. Technology allowed students to make videos to showcase previous experiences and stories via digital storytelling. Such methods enhance student’s knowledge and academic skills while supporting learning behaviour and inspires them to plan, organise and share their ideas and expertise. Research limitations/implications The above methodology has good potential for inclusive learning and teaching at the higher education level which was not attempted due to lack of connecting to the learners with challenges at the university and for the researchers able to connect to such people. This method can be easily extended to inclusive teaching and learning with minor adjustments as required with the disabilities noticed for the learners. Hence, while most students displayed a positive attitude towards learning from creating, sharing and viewing digital stories, it can be argued that a certain proportion of them was not able to benefit entirely from it due to lack of experience and skills in generating videos. Therefore, attention must be emphasised upon factors to minimise these challenges in multiple ways, for example, provision of training to students for easy employment of videotaping or affordable internet access, etc. Practical implications The students also stated that initially, they perceived videotape methodology quite challenging; however, with time, they started enjoying this method. Videotaped story workshops, learners easily grasp the idea/knowledge through subtitles, even if the lessons are not delivered in their native language. This aspect results in increasing student’s motivation towards learning new concepts and coordinating with other teams to share knowledge and ideas. The method creates a strong sense of achievement amongst students that serves as a motivational driver for academic performance. The videotaped story workshop method supports student’s learning rate, increases their interest and makes the whole learning process more enjoyable. Social implications The learning experience will improve as students and teachers get comfortable using this videotape learning methodology. The method will be useful in remote learning as in the COVID19 situation and has immense social implications, especially in education. It can extend to most domains and knowledge, teaching scenarios for engineering and business. The research promises to add to the knowledge of blended learning and to the experiential learning approach which is useful to the international business of education and its future. Originality/value The classroom activities videotapes stored in platforms, making it convenient for the students and teachers to browse through at their convenience and to improve on in the future. This videotape method applies to any field of learning such as music, cooking, engineering, language study, business studies as it has the advantage to be transcribed and also captions added for the learners and teachers to understand it better. It can be useful in remote learning situations, also like the current one. It promises to be a more efficient way of learning for the future in education and the education business will benefit from it.

scholarly journals Brexit associated sharp depreciation and implications for UK’s inflation and balance of payments

2018 ◽  
Vol 45 (2) ◽  
pp. 231-246 ◽  
Author(s):  
Muhammad Ali Nasir ◽  
Justine Simpson
Keyword(s):  
Exchange Rate ◽  
Trade Balance ◽  
Inflation Targeting ◽  
Social Implications ◽  
Content Type ◽  
External Balance ◽  
The Future ◽  
Trade Relationships ◽  
The Relationship ◽  
Practical Implications

Purpose The purpose of this paper is to analyse the implications of exchange rate depreciation for inflation targeting and trade balance of UK in the context of the Brexit epoch. Design/methodology/approach The study employed a time-varying structural vector auto-regression (TVSVAR) model framework in which the sources of time variation were both the coefficients and variance-covariance matrix of the innovations on the data from January 1989 to September 2016. Findings The findings suggest that the depreciation of the Stirling has significant effects on inflation and trade balance in UK in context of Brexit epoch. It also showed that such a depreciation can be helpful in the improvement of external balance as well as steering the inflation to its statutory target. Despite, the inflation targeting, there is strong evidence of a pass-through. Research limitations/implications Research has profound implications in terms of the sharp depreciation of GBP associated with the Brexit outcome. The study is very topical and could be very interesting to the readership of JES as well as wider audience. The study has limitations in a context that the significance of the results and association of the under analysis entities is contingent on the future trade relationships and Channel between UK and EU. Therefore, although there is a lot of uncertainty about the future of Britain trade relationships, this study provides guidance on the importance of exchange rate channel if the similar trade arrangements prevails in the post-Brexit era. Practical implications The research has profound practical implications, using a TVSVAR model in which the relationship among the entities varies over time; it has shown the importance of exchange rate in terms of external balance and inflation targeting. Hence, it has appeal for the practitioners as well as academics. Social implications The research has great social implications. The Brexit is the biggest political and economic event of this era for UK and EU. There are big questions about the relationship between UK and EU in the post-Brexit epoch as well as questions about the future of the European integration. In this context, this study has shown that how the exchange rate could play an important role for the UK economy when its contemporary trade channels prevail. Concomitantly, it has social implications particularly for the European society. Originality/value The research is an original piece of work. It has contributed to the debate on the exchange rate deprecation, external balance and inflation targeting in context of the Brexit associated sharp depreciation of Stirling. It has used a framework, i.e. TVSVAR, which also have unique features in terms of testing the associations among under analysis entities against time.

Study on Design Strategies for Improving Outdoor Thermal Comfort in the Cold Regions of China

2011 ◽  
Vol 250-253 ◽  
pp. 3798-3801 ◽  
Author(s):  
Jing Li ◽  
Yu Liu
Keyword(s):  
Thermal Comfort ◽  
North China ◽  
Outdoor Environment ◽  
Outdoor Thermal Comfort ◽  
Design Strategies ◽  
Cold Regions ◽  
Winter Climate ◽  
Thermal Environments ◽  
The North ◽  
Xi’An City

Along with the improvement of dwelling quality, the length of time and frequency of outdoor activities in winter greatly increase in the north China area, although the outdoor thermal environments are still unsatisfactory. It is necessary for both planners and architects to improve outdoor thermal environments in the cold regions of north China. This paper firstly introduces the general winter climate features in some north China cities. Then, it takes Xi'an city as an example to show the shortage of uncomfortable outdoor environment. Thirdly, the design strategies to improve outdoors thermal comfort include wind and snow protection, sunlight usage, environmental zones and recreational facilities, etc. are discussed.

scholarly journals Combined free energy calculation and machine learning methods for understanding ligand unbinding kinetics

2021 ◽  
Author(s):  
Magd Badaoui ◽  
Pedro J Buigues ◽  
Dénes Berta ◽  
Gaurav M. Mandana ◽  
Hankang Gu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Drug Discovery ◽  
Energy Barrier ◽  
Structural Features ◽  
Supervised Machine Learning ◽  
Energy Calculation ◽  
Free Energy Barrier ◽  
Residence Times ◽  
Design Strategies

ABSTRACTThe determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental pharmacokinetics measurements are, however, expensive, and time-consuming. In this work, we aimed to obtain drug residence times computationally. Furthermore, we propose a novel algorithm to identify molecular design objectives based on ligand unbinding kinetics. We designed an enhanced sampling technique to accurately predict the free energy profiles of the ligand unbinding process, focusing on the free energy barrier for unbinding. Our method first identifies unbinding paths determining a corresponding set of internal coordinates (IC) that form contacts between the protein and the ligand, then iteratively updates these interactions during a series of biased molecular-dynamics (MD) simulations to reveal the ICs important for the whole of the unbinding process. Subsequently, we performed finite temperature string simulations to obtain the free energy barrier for unbinding using the set of ICs as a complex reaction coordinate. Importantly, we also aimed to enable further design of drugs focusing on improved residence times. To this end, we developed a supervised machine learning (ML) approach that uses as input unbiased “downhill” trajectories from the transition state (TS) ensemble of the string unbinding path. We demonstrate that our ML method can identify key ligand-protein interactions driving the system through the TS. Some of the most important drugs for cancer treatment are kinase inhibitors. One of these kinase targets is Cyclin Dependent Kinase 2 (CDK2), an appealing target for anticancer drug development. Here, we tested our method using three different CDK2 inhibitors for potential further development of these compounds. We compared the free energy barriers obtained from our calculations with those observed in available experimental data. We highlighted important interactions at the distal ends of the ligands that can be targeted for improved residence times. Our method provides a new tool to determine unbinding rates, and to identify key structural features of the inhibitors that can be used as starting points for novel design strategies in drug discovery.

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