scholarly journals Development of a Data-Driven Predictive Model of Clothing Thermal Insulation Estimation by Using Advanced Computational Approaches

2019 ◽  
Vol 11 (20) ◽  
pp. 5702 ◽  
Author(s):  
Lee ◽  
Choi ◽  
Choi ◽  
Kim
Keyword(s):  
Predictive Model ◽  
Thermal Conditions ◽  
Data Driven ◽  
Training Dataset ◽  
Relevant Parameter ◽  
Practical Application ◽  
Additional Test ◽  
Clothing Insulation ◽  
Climate Conditions ◽  
Study Results

Clothing condition was selected as a key human-subject-relevant parameter which is dynamically changed depending on the user’s preferences and also on climate conditions. While the environmental components are relatively easier to measure using sensor devices, clothing value (clo) is almost impossible to visually estimate because it varies across building occupants even though they share constant thermal conditions in the same room. Therefore, in this study we developed a data-driven model to estimate the clothing insulation value as a function of skin and clothing surface temperatures. We adopted a series of environmental chamber tests with 20 participants. A portion of the collected data was used as a training dataset to establish a data-driven model based on the use of advanced computational algorithms. To consider a practical application, in this study we minimized the number of sensing points for data collection while adopting a wearable device for the user’s convenience. The study results revealed that the developed predictive model generated an accuracy of 88.04%, and the accuracy became higher in the prediction of a high clo value than in that of a low value. In addition, the accuracy was affected by the user’s body mass index. Therefore, this research confirms that it is possible to develop a data-driven predictive model of a user’s clo value based on the use of his/her physiological and ambient environmental information, and an additional study with a larger dataset via using chamber experiments with additional test participants is required for better performance in terms of prediction accuracy.

scholarly journals Future Thermal Assessment for the Phenological Development of Potato [Solanum tuberosum (L.)] in Cuba

2020 ◽  
Vol 4 (1) ◽  
pp. 6
Author(s):  
Alexis Augusto Hernández-Mansilla ◽  
Francisco Estrada-Porrúa ◽  
Oscar Calderón-Bustamante ◽  
Graciela Lucía Binimelis de Raga
Keyword(s):  
Solanum Tuberosum ◽  
Climate Model ◽  
Solanum Tuberosum L ◽  
Regional Climate ◽  
Thermal Conditions ◽  
Climate Change Scenarios ◽  
Thermal Index ◽  
Climate Conditions ◽  
Time Space ◽  
Biological Development

Current changes in climate conditions due to global warming affect the phenological behavior of economically important cultivable plant species, with consequences for the food security of many countries, particularly in small vulnerable islands. Thus, the objective of this study was to evaluate the thermal viability of Solanum tuberosum (L.) through the behavior of the Thermal Index of Biological Development (ITDB) of two cultivation areas in Cuba under different climate change scenarios. For the analysis, we elaborated bioclimatic scenarios by calculating the ITDB through a grounded and parameterized stochastic function based on the thermal values established for the phenological development of the species. We used the mean temperature values from the period 1980 to 2010 (historical reference period) of the Meteorological Stations: 78320 “Güira de Melena” and 78346 “Venezuela”, located at the western and central of Cuba respectively. We also used modeled data from RCP 2.6 scenarios; 4.5 and 8.5 from the PRECIS-CARIBE Regional Climate Model, which used global outputs from the ECHAM5 MCG for the period 2010 to 2100. As result, the scenarios showed that the annual average ITDB ranges from 0.7 to 0.8, which indicates that until 2010 there were temporary spaces with favorable thermal conditions for the species, but not for the period from 2010 to 2100 in RCP 4.5 and 8.5. In these scenarios, there was a progressive decrease in the indicator that warned of a marked loss of Viability of S. tuberosum, reduction of the time-space to cultivate this species (particularly the month of April is the most inappropriate for the ripening of the tuber). These results showed that Cuba requires the establishment of an adaptation program with adjustments in the sowing and production calendar, the use of short-cycle varieties of less than 120 days, the management of genotypes adaptable to high temperatures, and the application of “Agriculture Climate Smart”, to reduce risks in food safety.

scholarly journals Data-Driven Wildfire Risk Prediction in Northern California

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 109
Author(s):  
Ashima Malik ◽  
Megha Rajam Rao ◽  
Nandini Puppala ◽  
Prathusha Koouri ◽  
Venkata Anil Kumar Thota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Curves ◽  
Data Driven ◽  
Northern California ◽  
Combined Model ◽  
Wildfire Risk ◽  
Study Results ◽  
Forest Models ◽  
Random Forest Models

Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the wildfire risk at areas near Monticello and Winters, California. This study demonstrated how the models were developed and applied with comprehensive data parameters such as powerlines, terrain, and vegetation in different perspectives that improved the spatial and temporal accuracy in predicting the risk of wildfire including fire ignition. The combined model uses the spatial and the temporal parameters as a single combined dataset to train and predict the fire risk, whereas the ensemble model was fed separate parameters that were later stacked to work as a single model. Our experiment shows that the combined model produced better results compared to the ensemble of random forest models on separate spatial data in terms of accuracy. The models were validated with Receiver Operating Characteristic (ROC) curves, learning curves, and evaluation metrics such as: accuracy, confusion matrices, and classification report. The study results showed and achieved cutting-edge accuracy of 92% in predicting the wildfire risks, including ignition by utilizing the regional spatial and temporal data along with standard data parameters in Northern California.

Online versus offline: preferred retail choice for premium brand purchase

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rituparna Basu ◽  
Neena Sondhi
Keyword(s):  
Predictive Model ◽  
Logit Analysis ◽  
Chi Square ◽  
Content Type ◽  
Sales Personnel ◽  
Study Results ◽  
Shopping Experience ◽  
Brick And Mortar ◽  
Chi Square Analysis ◽  
Retail Format

PurposeThis exploratory study aims to examine the prevalent triggers that motivate a premium brand purchase in an online vs offline retail format.Design/methodology/approachA binary logit analysis is used to build a predictive model to assess the likelihood of the premium brand consumer seeking an online or an offline platform. Demographic and usage-based profile of the two set of consumers is established through a chi-square analysis.FindingsThree hundred and forty six urban consumers of premium branded apparels residing in two Indian Metros were studied. A predictive model with 89.6% accuracy was validated for distinguishing premium brand buyers who shop at brick-and-mortar store or online platforms. Quality and finish were factors sought by the online buyer, whereas autotelic need, pleasurable shopping experience and social approval were important triggers for an in-store purchase.Research limitations/implicationsThe study posits divergent demographics and motivational drivers that led to an online vs offline purchase. Though interesting and directional, the study results need to be examined across geographies and categories for establishing the generalizability of the findings.Practical implicationsThe study findings indicate that premium brand manufacturers can devise an omni-channel strategy that is largely tilted toward the online platform, as the quality conscious and brand aware consumer is confident and thus open to an online purchase. The implication for the physical outlet on the other hand is to ensure exclusive store atmospherics and knowledgeable but non-intrusive sales personnel.Originality/valueThe study is unique as it successfully builds a predictive model to forecast online vs offline purchase decisions among urban millennials.

scholarly journals Corpus-Informed Pedagogy in a Language Course

2020 ◽  
pp. 305-334 ◽  
Author(s):  
Nina Vyatkina
Keyword(s):  
Teaching And Learning ◽  
Turn Of The Century ◽  
Data Driven ◽  
Research Approach ◽  
Methodological Framework ◽  
Practical Application ◽  
Design Based Research ◽  
Regular Classrooms ◽  
Language Courses ◽  
Pedagogical Frameworks

Data-Driven Learning (DDL), or a corpus-based method of language teaching and learning, has been developing rapidly since the turn of the century and has been shown to be effective and efficient. Nevertheless, DDL is still not widely used in regular classrooms for a number of reasons. One of them is that few workable pedagogical frameworks have been suggested for integrating DDL into language courses and curricula. This chapter describes an exemplar of a practical application of such a pedagogical framework to a high-intermediate university-level German as a foreign language course with a significant DDL component. The Design-Based Research approach is used as the main methodological framework. The chapter concludes with a discussion of wider pedagogical implications.

scholarly journals Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Tenghui Han ◽  
Jun Zhu ◽  
Xiaoping Chen ◽  
Rujie Chen ◽  
Yu Jiang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Predictive Model ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Subsequent Treatment ◽  
Training Dataset ◽  
Discriminative Ability ◽  
Fundamental Parameters

Abstract Background Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically. Methods We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model. Results A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956). Conclusion We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.

Querying a Static and Dynamic Learner Corpus

2018 ◽  
pp. 494-511
Author(s):  
Trude Heift ◽  
Catherine Caws
Keyword(s):  
Language Learners ◽  
Data Driven ◽  
User Group ◽  
Proficiency Levels ◽  
Learner Corpus ◽  
Study Results ◽  
Call System ◽  
L2 Learners ◽  
Activity Types ◽  
Cyclical Process

This chapter discusses the cyclical process of collecting and recycling learner data within the E-Tutor CALL system and presents a study on student usage of its data-driven learning (DDL) tool. E-Tutor consists of a static and dynamic learner corpus for L2 learners of German. The static learner corpus has been constructed from approximately 5000 learners who used the system over a period of five years. These learners provided millions of submissions from a variety of activity types. In addition, all concurrent E-Tutor users contribute data to a dynamic corpus, which allows them to compare and examine their ongoing system submissions to those contained in the static corpus. The authors conducted a study with 84 learners and recorded their interaction with the DDL tool of E-Tutor over one semester. Study results on student usage suggest that investigating sample input of a large, unknown user group might be less informative and of less interest to language learners than their own data. For the DDL tool to be useful for all proficiency levels, training and scaffolding must also be provided.

scholarly journals The Priority of Factors of Building Government as a Platform with Analytic Hierarchy Process Analysis

2020 ◽  
Vol 12 (14) ◽  
pp. 5615 ◽  
Author(s):  
Hyungjun Seo ◽  
Seunghwan Myeong
Keyword(s):  
Public Sector ◽  
Analytic Hierarchy Process ◽  
Process Analysis ◽  
Electronic Government ◽  
Data Driven ◽  
Analytic Hierarchy ◽  
The Public ◽  
Study Results ◽  
The Government ◽  
Hierarchy Process

Nowadays, the Government as a Platform (GaaP) based on cloud computing and network, has come to be considered a new structure to manage efficiently data-driven administration in the public sector. When the GaaP concept was first introduced, the ICT infrastructures that could underpin GaaP were not sufficiently developed. However, the recent digital transformation has transformed the previous electronic government, which was system- and architecture-oriented. As part of the next generation of government models, GaaP may reinvent the government at a lower cost but with better performance, similar to the case of electronic government two decades ago. This study attempted to determine the priority of factors of GaaP by using the analytic hierarchy process (AHP) methodology. Because of the GaaP characteristics, we drew the main components for building GaaP from previous studies and a group interview with experts. The study results show that experts tend to prefer publicness in terms of building GaaP. Most of the factors that the experts weighed with the highest importance are related to the public sector, which revealed that governments should focus on their primary duty, regardless of the origin and characteristics of the platform in GaaP. However, since GaaP allows governments to be more horizontal and innovative, the platform approach can fundamentally shift the existing processes and culture of the public sector. The enhanced activity of citizens with ICT can also accelerate the introduction of GaaP. Finally, the study showed that a data-driven GaaP is necessary to efficiently handle big data, contract services, and multiple levels of on-line and off-line channels. In this public platform, government, citizens, and private sector organizations can work cooperatively as partners to seamlessly govern the hyper-connected society.

scholarly journals Concepts and New Implements for Modified Physiologically Equivalent Temperature

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 694 ◽  
Author(s):  
Yung-Chang Chen ◽  
Wei-Nai Chen ◽  
Charles Chou ◽  
Andreas Matzarakis
Keyword(s):  
Urban Climate ◽  
Thermal Conditions ◽  
Physiological Model ◽  
Physiologically Equivalent Temperature ◽  
Slight Variation ◽  
Equivalent Temperature ◽  
Thermal Perception ◽  
Thermal Indices ◽  
Clothing Insulation ◽  
Thermal Risk

Different kinds of thermal indices have been applied in several decades as essential tools to investigate thermal perception, environmentally thermal conditions, occupant thermal risk, public health, tourist attractiveness, and urban climate. Physiologically equivalent temperature (PET) has been proved as a relatively wide applicable thermal indicator above other thermal indices. However, the current practical PET performs a slight variation influenced by changing the humidity and clothing insulation. The improvement of the PET has potentiality for further multi-application as a general and consistent standard to estimate thermal perception and tolerance for different studies. To achieve the above purpose, modified physiologically equivalent temperature (mPET) is proposed as an appropriate indicator according to the new structure and requirements of the thermally environmental ergonomics. The modifications to formulate the mPET are considerably interpreted in the principle of the heat transfer inside body, thermo-physiological model, clothing model, and human-environmental interaction in this study. Specifically, the mPET-model has adopted a semi-steady-state approach to calculate an equivalent temperature refer to an indoor condition as the mPET. Finally, the sensitivity test of the biometeorological variables and clothing impact proves that the mPET has better performance on the humidity and clothing insulation than the original PET.

Sign in / Sign up

Export Citation Format

Share Document