scholarly journals Absorption heat transformer - state-of-the-art of industrial applications

2021 ◽  
Vol 141 ◽  
pp. 110757 ◽  
Author(s):  
Falk Cudok ◽  
Niccolò Giannetti ◽  
José L. Corrales Ciganda ◽  
Jun Aoyama ◽  
P. Babu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Industrial Applications ◽  
Heat Transformer

scholarly journals Smoothing additive manufactured parts using ns-pulsed laser radiation

2021 ◽  
Author(s):  
Florian Kuisat ◽  
Fernando Lasagni ◽  
Andrés Fabián Lasagni
Keyword(s):  
Surface Roughness ◽  
Mechanical Performance ◽  
State Of The Art ◽  
Pulsed Laser ◽  
Industrial Applications ◽  
Laser Source ◽  
Pulsed Laser Radiation ◽  
Current State ◽  
The Impact

AbstractIt is well known that the surface topography of a part can affect its mechanical performance, which is typical in additive manufacturing. In this context, we report about the surface modification of additive manufactured components made of Titanium 64 (Ti64) and Scalmalloy®, using a pulsed laser, with the aim of reducing their surface roughness. In our experiments, a nanosecond-pulsed infrared laser source with variable pulse durations between 8 and 200 ns was applied. The impact of varying a large number of parameters on the surface quality of the smoothed areas was investigated. The results demonstrated a reduction of surface roughness Sa by more than 80% for Titanium 64 and by 65% for Scalmalloy® samples. This allows to extend the applicability of additive manufactured components beyond the current state of the art and break new ground for the application in various industrial applications such as in aerospace.

scholarly journals Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1459 ◽  
Author(s):  
Tamás Czimmermann ◽  
Gastone Ciuti ◽  
Mario Milazzo ◽  
Marcello Chiurazzi ◽  
Stefano Roccella ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Visual Processing ◽  
State Of The Art ◽  
Industrial Applications ◽  
The State ◽  
Shape Error ◽  
Supervised Classifiers ◽  
Processing Techniques

This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.

scholarly journals Scalable Bi-Directional SMA-Based Rotational Actuator

Actuators ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 60 ◽  
Author(s):  
Rouven Britz ◽  
Paul Motzki ◽  
Stefan Seelecke
Keyword(s):  
Modular Design ◽  
High Weight ◽  
State Of The Art ◽  
Industrial Applications ◽  
Combustion Engines ◽  
Rotational Angle ◽  
Output Torque ◽  
Rotational Actuator ◽  
Rotatory Motion ◽  
Total Angle

In industrial applications, rotatory motions and torques are often needed. State-of-the-art actuators are based on either combustion engines, electro-motors, hydraulic, or pneumatic machines. The main disadvantages are the construction space, the high weight, and a large amount of needed peripheral devices. To overcome these limitations, compact and light-weight actuator systems can be built by using shape memory alloys (SMAs), which are known for their superior energy density. In this paper, the development of a scalable bi-directional rotational actuator based on SMA wires is presented. The scalability was based on a modular design, which allowed the actuator to be adapted to various application specifications by customizing the rotational angle and the output torque. On the mechanical side, each module enabled a small rotatory motion, which added up to the total angle of the actuator. The SMA wires were arranged in an agonist-antagonist configuration to provide active rotation in both directions. The presented prototype achieved a total rotation of 100°. The modularity of the mechanical concept is also reflected in the electronics, which is discussed in this paper as well. This consideration allows the electronics to be adapted to the mechanics with minimal changes. As a result, a prototype, including the presented mechanical and electronic design, is reported in this study.

scholarly journals Mechanisms of action of ionic liquids on living cells: the state of the art

2020 ◽  
Vol 12 (5) ◽  
pp. 1187-1215 ◽  
Author(s):  
Pallavi Kumari ◽  
Visakh V.S. Pillai ◽  
Antonio Benedetto
Keyword(s):  
Ionic Liquids ◽  
Cell Membrane ◽  
State Of The Art ◽  
Industrial Applications ◽  
Mechanisms Of Action ◽  
The State ◽  
New Class ◽  
Interdisciplinary Field ◽  
Micro Organisms ◽  
Enzyme Functions

Abstract Ionic liquids (ILs) are a relatively new class of organic electrolytes composed of an organic cation and either an organic or inorganic anion, whose melting temperature falls around room-temperature. In the last 20 years, the toxicity of ILs towards cells and micro-organisms has been heavily investigated with the main aim to assess the risks associated with their potential use in (industrial) applications, and to develop strategies to design greener ILs. Toxicity, however, is synonym with affinity, and this has stimulated, in turn, a series of biophysical and chemical-physical investigations as well as few biochemical studies focused on the mechanisms of action (MoAs) of ILs, key step in the development of applications in bio-nanomedicine and bio-nanotechnology. This review has the intent to present an overview of the state of the art of the MoAs of ILs, which have been the focus of a limited number of studies but still sufficient enough to provide a first glimpse on the subject. The overall picture that emerges is quite intriguing and shows that ILs interact with cells in a variety of different mechanisms, including alteration of lipid distribution and cell membrane viscoelasticity, disruption of cell and nuclear membranes, mitochondrial permeabilization and dysfunction, generation of reactive oxygen species, chloroplast damage (in plants), alteration of transmembrane and cytoplasmatic proteins/enzyme functions, alteration of signaling pathways, and DNA fragmentation. Together with our earlier review work on the biophysics and chemical-physics of IL-cell membrane interactions (Biophys. Rev. 9:309, 2017), we hope that the present review, focused instead on the biochemical aspects, will stimulate a series of new investigations and discoveries in the still new and interdisciplinary field of “ILs, biomolecules, and cells.”

scholarly journals Microalgal Enzymes with Biotechnological Applications

Marine Drugs ◽  
2019 ◽  
Vol 17 (8) ◽  
pp. 459 ◽  
Author(s):  
Giorgio Maria Vingiani ◽  
Pasquale De Luca ◽  
Adrianna Ianora ◽  
Alan D.W. Dobson ◽  
Chiara Lauritano
Keyword(s):  
State Of The Art ◽  
Industrial Applications ◽  
Biofuel Production ◽  
Biotechnological Applications ◽  
Efficient Manner ◽  
Environmentally Sustainable ◽  
Essential Components ◽  
Cost Efficient ◽  
Number Of Publications

Enzymes are essential components of biological reactions and play important roles in the scaling and optimization of many industrial processes. Due to the growing commercial demand for new and more efficient enzymes to help further optimize these processes, many studies are now focusing their attention on more renewable and environmentally sustainable sources for the production of these enzymes. Microalgae are very promising from this perspective since they can be cultivated in photobioreactors, allowing the production of high biomass levels in a cost-efficient manner. This is reflected in the increased number of publications in this area, especially in the use of microalgae as a source of novel enzymes. In particular, various microalgal enzymes with different industrial applications (e.g., lipids and biofuel production, healthcare, and bioremediation) have been studied to date, and the modification of enzymatic sequences involved in lipid and carotenoid production has resulted in promising results. However, the entire biosynthetic pathways/systems leading to synthesis of potentially important bioactive compounds have in many cases yet to be fully characterized (e.g., for the synthesis of polyketides). Nonetheless, with recent advances in microalgal genomics and transcriptomic approaches, it is becoming easier to identify sequences encoding targeted enzymes, increasing the likelihood of the identification, heterologous expression, and characterization of these enzymes of interest. This review provides an overview of the state of the art in marine and freshwater microalgal enzymes with potential biotechnological applications and provides future perspectives for this field.

scholarly journals Deep Session Interest Network for Click-Through Rate Prediction

2019 ◽  
Author(s):  
Yufei Feng ◽  
Fuyu Lv ◽  
Weichen Shen ◽  
Menghan Wang ◽  
Fei Sun ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Target Item ◽  
State Of The Art ◽  
Online Advertising ◽  
Industrial Applications ◽  
Research Topic ◽  
Intrinsic Structure ◽  
User Behaviors ◽  
Click Through Rate ◽  
Behavior Sequences

Click-Through Rate (CTR) prediction plays an important role in many industrial applications, such as online advertising and recommender systems. How to capture users' dynamic and evolving interests from their behavior sequences remains a continuous research topic in the CTR prediction. However, most existing studies overlook the intrinsic structure of the sequences: the sequences are composed of sessions, where sessions are user behaviors separated by their occurring time. We observe that user behaviors are highly homogeneous in each session, and heterogeneous cross sessions. Based on this observation, we propose a novel CTR model named Deep Session Interest Network (DSIN) that leverages users' multiple historical sessions in their behavior sequences. We first use self-attention mechanism with bias encoding to extract users' interests in each session. Then we apply Bi-LSTM to model how users' interests evolve and interact among sessions. Finally, we employ the local activation unit to adaptively learn the influences of various session interests on the target item. Experiments are conducted on both advertising and production recommender datasets and DSIN outperforms other state-of-the-art models on both datasets.

scholarly journals Membrane transporters in the bioproduction of organic acids: state of the art and future perspectives for industrial applications

2020 ◽  
Vol 367 (15) ◽  
Author(s):  
I Soares-Silva ◽  
D Ribas ◽  
M Sousa-Silva ◽  
J Azevedo-Silva ◽  
T Rendulić ◽  
...  
Keyword(s):  
Plasma Membrane ◽  
Organic Acids ◽  
State Of The Art ◽  
Biomass Conversion ◽  
Current Trend ◽  
Dicarboxylic Acids ◽  
Cost Effective ◽  
Industrial Applications ◽  
Industrial Biotechnology ◽  
Extracellular Medium

ABSTRACT Organic acids such as monocarboxylic acids, dicarboxylic acids or even more complex molecules such as sugar acids, have displayed great applicability in the industry as these compounds are used as platform chemicals for polymer, food, agricultural and pharmaceutical sectors. Chemical synthesis of these compounds from petroleum derivatives is currently their major source of production. However, increasing environmental concerns have prompted the production of organic acids by microorganisms. The current trend is the exploitation of industrial biowastes to sustain microbial cell growth and valorize biomass conversion into organic acids. One of the major bottlenecks for the efficient and cost-effective bioproduction is the export of organic acids through the microbial plasma membrane. Membrane transporter proteins are crucial elements for the optimization of substrate import and final product export. Several transporters have been expressed in organic acid-producing species, resulting in increased final product titers in the extracellular medium and higher productivity levels. In this review, the state of the art of plasma membrane transport of organic acids is presented, along with the implications for industrial biotechnology.

Performance Analysis of Anisotropic Diffusion Based Colour Texture Descriptors in Industrial Applications

2017 ◽  
Vol 7 (2) ◽  
pp. 50-63 ◽  
Author(s):  
Prakash S. Hiremath ◽  
Rohini A. Bhusnurmath
Keyword(s):  
Performance Analysis ◽  
Anisotropic Diffusion ◽  
State Of The Art ◽  
Texture Classification ◽  
Industrial Applications ◽  
Statistical Features ◽  
Linear Discriminant ◽  
Class Separability ◽  
Novel Method

A novel method of colour texture analysis based on anisotropic diffusion for industrial applications is proposed and the performance analysis of colour texture descriptors is examined. The objective of the study is to explore different colour spaces for their suitability in automatic classification of certain textures in industrial applications, namely, granite tiles and wood textures, using computer vision. The directional subbands of digital image of material samples obtained using wavelet transform are subjected to anisotropic diffusion to obtain the texture components. Further, statistical features are extracted from the texture components. The linear discriminant analysis is employed to achieve class separability. The texture descriptors are evaluated on RGB, HSV, YCbCr, Lab colour spaces and compared with gray scale texture descriptors. The k-NN classifier is used for texture classification. For the experimentation, benchmark databases, namely, MondialMarmi and Parquet are considered. The experimental results are encouraging as compared to the state-of-the-art-methods.

Interpreting Model Predictions with Constrained Perturbation and Counterfactual Instances

2021 ◽  
Author(s):  
Jun-Peng Fang ◽  
Jun Zhou ◽  
Qing Cui ◽  
Cai-Zhi Tang ◽  
Long-Fei Li
Keyword(s):  
Financial Markets ◽  
State Of The Art ◽  
Industrial Applications ◽  
Importance Value ◽  
Art Interpretation ◽  
Critical Areas ◽  
Black Boxes ◽  
Model Predictions ◽  
Interpretation Method ◽  
Made In

In recent years, machine learning models have achieved magnificent success in many industrial applications, but most of them are black boxes. It is crucial to understand why such predictions are made in many critical areas such as medicine, financial markets, and auto driving. In this paper, we propose Coco, a novel interpretation method which can interpret any binary classifier by assigning each feature an importance value for a particular prediction. We first adopt MixUp method to generate reasonable perturbations, then apply these perturbations with constraints to obtain counterfactual instances and finally compute a comprehensive metric on these instances to estimate the importance of each feature. To demonstrate the effectiveness of Coco, we conduct extensive experiments on several datasets. The results show our method achieves better performance in identifying the most important features compared with the state-of-the-art interpretation methods, including Shap and Lime.

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