Estimate Thermo-physical Parameters from Characterization of the Building Materials by Using Artificial Intelligence

2013 ◽  
Author(s):  
Thanh Nga Thai
Keyword(s):  
Artificial Intelligence ◽  
Building Materials ◽  
Physical Parameters

scholarly journals Thermal Characterization of Recycled Materials for Building Insulation

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3564
Author(s):  
Arnas Majumder ◽  
Laura Canale ◽  
Costantino Carlo Mastino ◽  
Antonio Pacitto ◽  
Andrea Frattolillo ◽  
...  
Keyword(s):  
Environmental Impact ◽  
Environmental Sustainability ◽  
Building Materials ◽  
Raw Materials ◽  
Natural Fibers ◽  
Thermal Characterization ◽  
Recycled Materials ◽  
Building Insulation ◽  
Operational Phase

The building sector is known to have a significant environmental impact, considering that it is the largest contributor to global greenhouse gas emissions of around 36% and is also responsible for about 40% of global energy consumption. Of this, about 50% takes place during the building operational phase, while around 10–20% is consumed in materials manufacturing, transport and building construction, maintenance, and demolition. Increasing the necessity of reducing the environmental impact of buildings has led to enhancing not only the thermal performances of building materials, but also the environmental sustainability of their production chains and waste prevention. As a consequence, novel thermo-insulating building materials or products have been developed by using both locally produced natural and waste/recycled materials that are able to provide good thermal performances while also having a lower environmental impact. In this context, the aim of this work is to provide a detailed analysis for the thermal characterization of recycled materials for building insulation. To this end, the thermal behavior of different materials representing industrial residual or wastes collected or recycled using Sardinian zero-km locally available raw materials was investigated, namely: (1) plasters with recycled materials; (2) plasters with natural fibers; and (3) building insulation materials with natural fibers. Results indicate that the investigated materials were able to improve not only the energy performances but also the environmental comfort in both new and in existing buildings. In particular, plasters and mortars with recycled materials and with natural fibers showed, respectively, values of thermal conductivity (at 20 °C) lower than 0.475 and 0.272 W/(m⋅K), while that of building materials with natural fibers was always lower than 0.162 W/(m⋅K) with lower values for compounds with recycled materials (0.107 W/(m⋅K)). Further developments are underway to analyze the mechanical properties of these materials.

scholarly journals Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

2021 ◽  
pp. 1-13
Author(s):  
Lamiae Benhayoun ◽  
Daniel Lang
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Academic Training ◽  
Market Requirements ◽  
Job Advertisements ◽  
The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

scholarly journals Characterization of a Novel CaCO3-Forming Alkali-Tolerant Rhodococcus erythreus S26 as a Filling Agent for Repairing Concrete Cracks

Molecules ◽  
2021 ◽  
Vol 26 (10) ◽  
pp. 2967
Author(s):  
Seunghoon Choi ◽  
Sungjin Park ◽  
Minjoo Park ◽  
Yerin Kim ◽  
Kwang Min Lee ◽  
...  
Keyword(s):  
Building Materials ◽  
Culture Fluid ◽  
Natural Phenomenon ◽  
Environment Friendly ◽  
Wide Range ◽  
Filling Agent ◽  
Calcium Source ◽  
Alkali Tolerance ◽  
High Alkali

Biomineralization, a well-known natural phenomenon associated with various microbial species, is being studied to protect and strengthen building materials such as concrete. We characterized Rhodococcus erythreus S26, a novel urease-producing bacterium exhibiting CaCO3-forming activity, and investigated its ability in repairing concrete cracks for the development of environment-friendly sealants. Strain S26 grown in solid medium formed spherical and polygonal CaCO3 crystals. The S26 cells grown in a urea-containing liquid medium caused culture fluid alkalinization and increased CaCO3 levels, indicating that ureolysis was responsible for CaCO3 formation. Urease activity and CaCO3 formation increased with incubation time, reaching a maximum of 2054 U/min/mL and 3.83 g/L, respectively, at day four. The maximum CaCO3 formation was achieved when calcium lactate was used as the calcium source, followed by calcium gluconate. Although cell growth was observed after the induction period at pH 10.5, strain S26 could grow at a wide range of pH 4–10.5, showing its high alkali tolerance. FESEM showed rhombohedral crystals of 20–60 µm in size. EDX analysis indicated the presence of calcium, carbon, and oxygen in the crystals. XRD confirmed these crystals as CaCO3 containing calcite and vaterite. Furthermore, R. erythreus S26 successfully repaired the artificially induced large cracks of 0.4–0.6 mm width.

scholarly journals Application of artificial intelligence in pancreaticobiliary diseases

2021 ◽  
Vol 14 ◽  
pp. 263177452199305
Author(s):  
Hemant Goyal ◽  
Rupinder Mann ◽  
Zainab Gandhi ◽  
Abhilash Perisetti ◽  
Zhongheng Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Response To Treatment ◽  
Endoscopic Retrograde ◽  
Artificial Intelligence Techniques ◽  
Computer Aided ◽  
Rapid Pace ◽  
Cystic Pancreatic Lesions ◽  
Aided Diagnosis

The role of artificial intelligence and its applications has been increasing at a rapid pace in the field of gastroenterology. The application of artificial intelligence in gastroenterology ranges from colon cancer screening and characterization of dysplastic and neoplastic polyps to the endoscopic ultrasonographic evaluation of pancreatic diseases. Artificial intelligence has been found to be useful in the evaluation and enhancement of the quality measure for endoscopic retrograde cholangiopancreatography. Similarly, artificial intelligence techniques like artificial neural networks and faster region-based convolution network are showing promising results in early and accurate diagnosis of pancreatic cancer and its differentiation from chronic pancreatitis. Other artificial intelligence techniques like radiomics-based computer-aided diagnosis systems could help to differentiate between various types of cystic pancreatic lesions. Artificial intelligence and computer-aided systems also showing promising results in the diagnosis of cholangiocarcinoma and the prediction of choledocholithiasis. In this review, we discuss the role of artificial intelligence in establishing diagnosis, prognosis, predicting response to treatment, and guiding therapeutics in the pancreaticobiliary system.

scholarly journals Acoustic Characterization of Some Steel Industry Waste Materials

2021 ◽  
Vol 11 (13) ◽  
pp. 5924
Author(s):  
Elisa Levi ◽  
Simona Sgarbi ◽  
Edoardo Alessio Piana
Keyword(s):  
Thermal Characteristic ◽  
Environmental Benefits ◽  
Acoustic Properties ◽  
Physical Parameters ◽  
Industry Waste ◽  
Acoustic Characterization ◽  
Minimization Procedure ◽  
The Difference ◽  
By Products

From a circular economy perspective, the acoustic characterization of steelwork by-products is a topic worth investigating, especially because little or no literature can be found on this subject. The possibility to reuse and add value to a large amount of this kind of waste material can lead to significant economic and environmental benefits. Once properly analyzed and optimized, these by-products can become a valuable alternative to conventional materials for noise control applications. The main acoustic properties of these materials can be investigated by means of a four-microphone impedance tube. Through an inverse technique, it is then possible to derive some non-acoustic properties of interest, useful to physically characterize the structure of the materials. The inverse method adopted in this paper is founded on the Johnson–Champoux–Allard model and uses a standard minimization procedure based on the difference between the sound absorption coefficients obtained experimentally and predicted by the Johnson–Champoux–Allard model. The results obtained are consistent with other literature data for similar materials. The knowledge of the physical parameters retrieved applying this technique (porosity, airflow resistivity, tortuosity, viscous and thermal characteristic length) is fundamental for the acoustic optimization of the porous materials in the case of future applications.

scholarly journals The Influence of Cement Substitution by Biomass Fly Ash on the Polymer–Cement Composites Properties

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3079
Author(s):  
Beata Jaworska ◽  
Dominika Stańczak ◽  
Joanna Tarańska ◽  
Jerzy Jaworski
Keyword(s):  
Fly Ash ◽  
Building Materials ◽  
Raw Materials ◽  
Combustion Process ◽  
Biomass Combustion ◽  
Cement Composites ◽  
Cement Mortars ◽  
Mineral Additive ◽  
Biomass Fly Ash

The generation of energy for the needs of the population is currently a problem. In consideration of that, the biomass combustion process has started to be implemented as a new source of energy. The dynamic increase in the use of biomass for energy generation also resulted in the formation of waste in the form of fly ash. This paper presents an efficient way to manage this troublesome material in the polymer–cement composites (PCC), which have investigated to a lesser extent. The research outlined in this article consists of the characterization of biomass fly ash (BFA) as well as PCC containing this waste. The characteristics of PCC with BFA after 3, 7, 14, and 28 days of curing were analyzed. Our main findings are that biomass fly ash is suitable as a mineral additive in polymer–cement composites. The most interesting result is that the addition of biomass fly ash did not affect the rheological properties of the polymer–cement mortars, but it especially influenced its compressive strength. Most importantly, our findings can help prevent this byproduct from being placed in landfills, prevent the mining of new raw materials, and promote the manufacture of durable building materials.

scholarly journals Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer

2020 ◽  
Vol 9 (10) ◽  
pp. 3313 ◽  
Author(s):  
Hemant Goyal ◽  
Rupinder Mann ◽  
Zainab Gandhi ◽  
Abhilash Perisetti ◽  
Aman Ali ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Narrow Band Imaging ◽  
Screening Methods ◽  
Polyp Detection ◽  
Mortality And Morbidity ◽  
Magnifying Chromoendoscopy

Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a “second look” for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.

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