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Improved Dye Removal Ability of Modified Rice Husk with Effluent from Alkaline Scouring Based on the Circular Economy Concept
Nina Mladenovic, Petre Makreski, Anita Tarbuk, Katia Grgic, Blazo Boev, Dejan Mirakovski, Emilija Toshikj, Vesna Dimova, Dejan Dimitrovski, Igor Jordanov
August 5, 2020 (v1)
Keywords: activation of rice husk, biosorbent, closed-loop process, colored effluent purifying, low-cost treatment, waste
To improve the ability of the rice husk to purify colored wastewater, effluent from the alkaline scouring of cotton yarn was used immediately after the scouring (without cooling and additionally added chemicals) in order to remove the non-cellulosic silicon-lignin shield from the rice husk’s surface. This rice husk, with 93.8 mg/g adsorption capacity, behaves similarly as the rice husk treated with an optimized alkaline scouring recipe consisting of 20 g/L NaOH, 2 mL/L Cotoblanc HTD-N and 1 mL/L Kemonecer NI at 70 °C for 30 min with an adsorption capacity of 88.9 mg/g of direct Congo red dye. Treating one form of waste (rice husk) with another (effluent from the alkaline scouring of cellulosic plant fibers), in an effort to produce a material able to purify colored effluent, is an elegant environment-friendly concept based on the circular economy strategy. This will result in a closed-loop energy-efficient process of the pre-treatment of cotton (alkaline scouring), modification of rice... [more]
Real-Time Optimization of Pulp Mill Operations with Wood Moisture Content Variation
Wipawadee Nuengwang, Thongchai R. Srinophakun, Matthew J. Realff
August 5, 2020 (v1)
Keywords: moisture content of wood variation, pulp mill modeling, real-time optimization, rolling horizon approach
In tropical countries, such as Thailand, the variation of tree moisture content can be significant based on seasonal variations in rainfall. Pulp mill operation optimization accounting for wood moisture variation was used to determine optimal operation conditions and minimize production cost. The optimization models were built using empirical modeling techniques with simulated data from the IDEAS software package. Three case studies were performed. First, a base case of nominal annual operation at a fixed production rate was used to calculate production cost that varies with wood moisture content. The second case is annual optimization where production was allowed to vary monthly over an annual cycle to minimize production cost. For the third case, real-time optimization (RTO) was used to determine optimal production rate with the wood moisture content varying every 3 days. The rolling horizon approach was used to schedule production to keep inventory levels within bounds and with a pe... [more]
Research on the Pressure Dropin Horizontal Pneumatic Conveying for Large Coal Particles
Daolong Yang, Yanxiang Wang, Zhengwei Hu
August 5, 2020 (v1)
Subject: Other
Keywords: DPM, Euler–Lagrange approach, large coal particles, pneumatic conveying, pressure drop
As a type of airtight conveying mode, pneumatic conveying has the advantages of environmental friendliness and conveying without dust overflow. The application of the pneumatic conveying system in the field of coal particle conveying can avoid direct contact between coal particles and the atmosphere, which helps to reduce the concentration of air dust and improve environmental quality in coal production and coal consumption enterprises. In order to predict pressure drop in the pipe during the horizontal pneumatic conveying of large coal particles, the Lagrangian coupling method and DPM (discrete particle model) simulation model was used in this paper. Based on the comparison of the experimental results, the feasibility of the simulation was verified and the pressure drop in the pipe was simulated. The simulation results show that when the flow velocity is small, the simulation results of the DPM model are quite different from that of the experiment. When the flow velocity is large, the... [more]
Progressive System: A Deep-Learning Framework for Real-Time Data in Industrial Production
Yifeng Liu, Wei Zhang, Wenhao Du
August 5, 2020 (v1)
Keywords: deep-learning, few-shot learning, image classification, real-time systems
Deep learning based on a large number of high-quality data plays an important role in many industries. However, deep learning is hard to directly embed in the real-time system, because the data accumulation of the system depends on real-time acquisitions. However, the analysis tasks of such systems need to be carried out in real time, which makes it impossible to complete the analysis tasks by accumulating data for a long time. In order to solve the problems of high-quality data accumulation, high timeliness of the data analysis, and difficulty in embedding deep-learning algorithms directly in real-time systems, this paper proposes a new progressive deep-learning framework and conducts experiments on image recognition. The experimental results show that the proposed framework is effective and performs well and can reach a conclusion similar to the deep-learning framework based on large-scale data.
A Novel Approach in Crude Enzyme Laccase Production and Application in Emerging Contaminant Bioremediation
Luong N. Nguyen, Minh T. Vu, Md Abu Hasan Johir, Nirenkumar Pathak, Jakub Zdarta, Teofil Jesionowski, Galilee U. Semblante, Faisal I. Hai, Hong Khanh Dieu Nguyen, Long D. Nghiem
August 5, 2020 (v1)
Keywords: crude enzyme laccase, emerging contaminants, enzymatic degradation, enzymatic membrane reactor, membrane filtration, white-rot fungi
Laccase enzyme from white-rot fungi is a potential biocatalyst for the oxidation of emerging contaminants (ECs), such as pesticides, pharmaceuticals and steroid hormones. This study aims to develop a three-step platform to treat ECs: (i) enzyme production, (ii) enzyme concentration and (iii) enzyme application. In the first step, solid culture and liquid culture were compared. The solid culture produced significantly more laccase than the liquid culture (447 vs. 74 µM/min after eight days), demonstrating that white rot fungi thrived on a solid medium. In the second step, the enzyme was concentrated 6.6 times using an ultrafiltration (UF) process, resulting in laccase activity of 2980 µM/min. No enzymatic loss due to filtration and membrane adsorption was observed, suggesting the feasibility of the UF membrane for enzyme concentration. In the third step, concentrated crude enzyme was applied in an enzymatic membrane reactor (EMR) to remove a diverse set of ECs (31 compounds in six group... [more]
Selective Photocatalytic Oxidation of 5-HMF in Water over Electrochemically Synthesized TiO2 Nanoparticles
Anna Ulyankina, Sergey Mitchenko, Nina Smirnova
August 5, 2020 (v1)
Keywords: 2,5-diformylfuran, 5-hydroxymethylfurfural, electrochemical synthesis, pulse alternating current, TiO2
TiO2 nanoparticles were prepared via an electrochemical method using pulse alternating current and applied in the photocatalytic oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-diformylfuran (DFF). Its physicochemical properties were characterized by SEM, HRTEM, XRD, and BET methods. The effect of scavenger and UVA light intensity was studied. The results revealed that electrochemically synthesized TiO2 nanoparticles exhibit higher DFF selectivity in the presence of methanol (up to 33%) compared with commercial samples.
Effect of the Chemical Composition on the Structural State and Mechanical Properties of Complex Microalloyed Steels of the Ferritic Class
Alexander Zaitsev, Anton Koldaev, Nataliya Arutyunyan, Sergey Dunaev, Dmitrii D’yakonov
August 5, 2020 (v1)
Subject: Materials
Keywords: hot rolling, low-carbon steels of the ferritic class, nanoscale phase precipitates, steel composition, strength characteristics, structure
The most promising direction for obtaining a unique combination of difficult-to-combine properties of low-carbon steels is the formation of a dispersed ferrite microstructure and a volumetric system of nanoscale phase precipitates. This study was aimed at establishing the special features of the composition influence on the characteristics of the microstructure, phase precipitates, and mechanical properties of hot-rolled steels of the ferritic class. It was carried out by transmission electron microscopy and testing the mechanical properties of metal using 8 laboratory melts of low-carbon steels microalloyed by V, Nb, Ti, and Mo in various combinations. It was found that block ferrite prevails in the structure of steel cooled after hot rolling at a rate of 10−15 °C/s. Lowering of the microalloying components content leads to a decrease in the block ferrite fraction to 20−35% and the dominance of polygonal ferrite. The presence of nanoscale carbide (carbonitride) precipitates of austeni... [more]
Effective Heavy Metals Removal from Water Using Nanomaterials: A Review
Mohamed A. Tahoon, Saifeldin M. Siddeeg, Norah Salem Alsaiari, Wissem Mnif, Faouzi Ben Rebah
August 5, 2020 (v1)
Subject: Materials
Keywords: functionalization, heavy metals, nanomaterials, water treatment
The discharge of toxic heavy metals including zinc (Zn), nickel (Ni), lead (Pb), copper (Cu), chromium (Cr), and cadmium (Cd) in water above the permissible limits causes high threat to the surrounding environment. Because of their toxicity, heavy metals greatly affect the human health and the environment. Recently, better remediation techniques were offered using the nanotechnology and nanomaterials. The attentions were directed toward cost-effective and new fabricated nanomaterials for the application in water/wastewater remediation, such as zeolite, carbonaceous, polymer based, chitosan, ferrite, magnetic, metal oxide, bimetallic, metallic, etc. This review focused on the synthesis and capacity of various nanoadsorbent materials for the elimination of different toxic ions, with discussion of the effect of their functionalization on the adsorption capacity and separation process. Additionally, the effect of various experimental physicochemical factors on heavy metals adsorption, such... [more]
Isomerization of Glucose to Fructose in Hydrolysates from Lignocellulosic Biomass Using Hydrotalcite
David Steinbach, Andreas Klier, Andrea Kruse, Jörg Sauer, Stefan Wild, Marina Zanker
August 5, 2020 (v1)
Keywords: aldose-ketose isomerization, bioeconomy, biorefinery, fructose, glucose, hydrolyzate, hydrotalcite, hydroxymethylfurfural, lignocellulose, pretreatment
The isomerization of glucose-containing hydrolysates to fructose is a key step in the process from lignocellulosic biomass to the platform chemical hydroxymethylfurfural. We investigated the isomerization reaction of glucose to fructose in water catalyzed by hydrotalcite. Catalyst characterization was performed via IR, XRD, and SEM. Firstly, glucose solutions at pH-neutral conditions were converted under variation of the temperature, residence time, and catalyst loading, whereby a maximum of 25 wt.% fructose yield was obtained at a 38 wt.% glucose conversion. Secondly, isomerization was performed at pH = 2 using glucose solutions as well as glucose-containing hydrolysates from lignocellulosic biomass. Under acidic conditions, the hydrotalcite loses its activity for isomerization. Consequently, it is unavoidable to neutralize the acidic hydrolysate before the isomerization step with an inexpensive base. As a neutralizing agent NaOH is preferred over Ba(OH)2, since higher fructose yields... [more]
Key Challenges in Designing CHO Chassis Platforms
Anis Hamdi, Diana Széliová, David E. Ruckerbauer, Isabel Rocha, Nicole Borth, Jürgen Zanghellini
August 5, 2020 (v1)
Subject: Biosystems
Keywords: chassis cell, Chinese Hamster Ovary (CHO), modularity, recombinant proteins, systems metabolic engineering
Following the success of and the high demand for recombinant protein-based therapeutics during the last 25 years, the pharmaceutical industry has invested significantly in the development of novel treatments based on biologics. Mammalian cells are the major production systems for these complex biopharmaceuticals, with Chinese hamster ovary (CHO) cell lines as the most important players. Over the years, various engineering strategies and modeling approaches have been used to improve microbial production platforms, such as bacteria and yeasts, as well as to create pre-optimized chassis host strains. However, the complexity of mammalian cells curtailed the optimization of these host cells by metabolic engineering. Most of the improvements of titer and productivity were achieved by media optimization and large-scale screening of producer clones. The advances made in recent years now open the door to again consider the potential application of systems biology approaches and metabolic engine... [more]
Molecular Interpretation of Pharmaceuticals’ Adsorption on Carbon Nanomaterials: Theory Meets Experiments
Daniele Veclani, Marilena Tolazzi, Andrea Melchior
August 5, 2020 (v1)
Keywords: ab initio calculations, Adsorption, carbon nanomaterials, DFT, drugs, isotherms, molecular dynamics, water treatment
The ability of carbon-based nanomaterials (CNM) to interact with a variety of pharmaceutical drugs can be exploited in many applications. In particular, they have been studied both as carriers for in vivo drug delivery and as sorbents for the treatment of water polluted by pharmaceuticals. In recent years, the large number of experimental studies was also assisted by computational work as a tool to provide understanding at molecular level of structural and thermodynamic aspects of adsorption processes. Quantum mechanical methods, especially based on density functional theory (DFT) and classical molecular dynamics (MD) simulations were mainly applied to study adsorption/release of various drugs. This review aims to compare results obtained by theory and experiments, focusing on the adsorption of three classes of compounds: (i) simple organic model molecules; (ii) antimicrobials; (iii) cytostatics. Generally, a good agreement between experimental data (e.g. energies of adsorption, spectr... [more]
A Study of the Movement, Structural Stability, and Electrical Performance for Harvesting Ocean Kinetic Energy Based on IPMC Material
Vinh Nguyen Duy, Hyung-Man Kim
August 5, 2020 (v1)
Keywords: Computational Fluid Dynamics, electrochemistry, energy harvesting, engineering, ocean kinetic energy, physical sciences, Renewable and Sustainable Energy
The movement of water in the oceans generates a vast store of kinetic energy, which has led to the development of a wide variety of offshore energy harvesters all over the world. In our energy harvester, we used ionic polymer-metal composites (IPMCs) to convert the ocean energy into electricity. This paper presents a simulated model of an IPMC-based electrochemical kinetic energy harvesting system installed in the ocean and produced using the computational fluid dynamics (CFD) method. The simulation processes focused on the movement and structural stability of the system design in the ocean for the protection of the IPMC module against possible damage, which would directly affect the power output. Furthermore, the experimental tests under real marine conditions were also studied to analyze the electrical harvesting performance of the IPMC system. These results showed that the use of IPMC materials has many advantages as they are soft and durable; as a result, they can respond faster to... [more]
Mechanism, Thermodynamics and Kinetics of Rutile Leaching Process by Sulfuric Acid Reactions
Anastasiia V. Dubenko, Mykola V. Nikolenko, Eugene V. Aksenenko, Andrii Kostyniuk, Blaž Likozar
August 5, 2020 (v1)
Keywords: altered ilmenite, chemical kinetics, equilibrium Gibbs free energy, fluoride ions, homogeneous–heterogeneous catalysis, leaching, rutile, sulfuric acid, thermodynamic parameter evaluation, titanium dioxide
Rutile decomposition by sulfuric acid, including the formation of two salts, Ti(SO4)2/TiOSO4, is thermodynamically modelled. It is shown that TiO2 can spontaneously dissolve in H2SO4 solutions. However, titania is considered as an inert (ballast) phase component of titanium-containing raw materials due to the decelerated separate nature of such chemical transformations. It is concluded that the hampered related kinetics of dissolution can be attributed to the lability of Ti(IV) cations/the specific engineered features of the hierarchical crystalline structure. It is suggested that the breaking of Ti−O−Ti bonds without additional mechanical strains in crystal lattice geometry becomes more advantageous when smaller negative anions/fluoride ions can be used. The analysis of sulfate-fluoride extraction leaching of titanium confirmed that a decrease in the Gibbs energy in the presence of F occurs. It is indicated by kinetic research studies that the addition of corrosive sodium reagent (NaF... [more]
Active Control Parameters Monitoring for Freight Trains, Using Wireless Sensor Network Platform and Internet of Things
Adrian Brezulianu, Cristian Aghion, Marius Hagan, Oana Geman, Iuliana Chiuchisan, Alexandra-Ligia Balan, Doru-Gabriel Balan, Valentina Emilia Balas
August 5, 2020 (v1)
Keywords: connected train, Internet of Things (IoT), monitoring web server application, railway transport management, sensor networks
Operating in a dynamic and competitive global market, railway companies have realized many years ago that better management of their logistical operations will enhance their strategic positions on the market. The financial component of daily operations is of utmost importance these days and many companies concluded that maximizing the profit relies on the integration of logistical activities with better income management. This paper presents a system consisting of three components: Ferodata BOX, Ferodata MOBILE, and Ferodata SYS, used to transmit to a web-server the status and operating information of an electric or diesel train. Train information includes data from locomotives, wagons, train driver, route, direction, fuel or electric consumption, speed, etc. All this information is processed in real-time and can be viewed in the web-server application. Additionally, the web-server application could manage and report details that are coming from the wagons, such as valuable information... [more]
Measuring Performance Metrics of Machine Learning Algorithms for Detecting and Classifying Transposable Elements
Simon Orozco-Arias, Johan S. Piña, Reinel Tabares-Soto, Luis F. Castillo-Ossa, Romain Guyot, Gustavo Isaza
August 5, 2020 (v1)
Keywords: classification, deep learning, detection, Machine Learning, metrics, transposable elements
Because of the promising results obtained by machine learning (ML) approaches in several fields, every day is more common, the utilization of ML to solve problems in bioinformatics. In genomics, a current issue is to detect and classify transposable elements (TEs) because of the tedious tasks involved in bioinformatics methods. Thus, ML was recently evaluated for TE datasets, demonstrating better results than bioinformatics applications. A crucial step for ML approaches is the selection of metrics that measure the realistic performance of algorithms. Each metric has specific characteristics and measures properties that may be different from the predicted results. Although the most commonly used way to compare measures is by using empirical analysis, a non-result-based methodology has been proposed, called measure invariance properties. These properties are calculated on the basis of whether a given measure changes its value under certain modifications in the confusion matrix, giving co... [more]
A Reliable Automated Sampling System for On-Line and Real-Time Monitoring of CHO Cultures
Alexandra Hofer, Paul Kroll, Matthias Barmettler, Christoph Herwig
August 5, 2020 (v1)
Keywords: amino acids, automated sampling, bioprocess, CHO, process analytical technology, vitamins
Timely monitoring and control of critical process parameters and product attributes are still the basic tasks in bioprocess development. The current trend of automation and digitization in bioprocess technology targets an improvement of these tasks by reducing human error and increasing through-put. The gaps in such automation procedures are still the sampling procedure, sample preparation, sample transfer to analyzers, and the alignment of process and sample data. In this study, an automated sampling system and the respective data management software were evaluated for system performance; applicability with HPLC for measurement of vitamins, product and amino acids; and applicability with a biochemical analyzer. The focus was especially directed towards the adaptation and assessment of an appropriate amino acid method, as these substances are critical in cell culture processes. Application of automated sampling in a CHO fed-batch revealed its potential with regard to data evaluation. T... [more]
The Effect of Temperature on the Biosorption of Dyes from Aqueous Solutions
Lech Smoczyński, Bogusław Pierożyński, Tomasz Mikołajczyk
August 5, 2020 (v1)
Subject: Other
Keywords: biosorption, dye, temperature, Wastewater
This work is a review of scientific papers on the influence of temperature (T) on the biosorption of various dyes from aqueous solutions and wastewaters. The dyeing process of textiles is usually carried out at high temperatures, and therefore, the wastewater generated there when entering the treatment plant may still be hot. Hence, depending on the climatic conditions of a given region, the biosorption method used for their purification may occur at various temperatures. Most of the papers clearly stated the positive influence of T on biosorption, generally indicating the chemical nature of this process. At the same time, substantial number of authors confirmed the positive effect of T on the biosorption with an initial T-rise from approximately 20 °C to about 30−40 °C range; conversely, at higher temperatures, they indicated a decrease in the biosorption efficiency. Additionally, many authors clearly implied the negative impact of T on the biosorption parameters. They generally envis... [more]
Electro-Discharge Machining of Zr67Cu11Ni10Ti9Be3: An Investigation on Hydroxyapatite Deposition and Surface Roughness
Abdul’Azeez Abdu Aliyu, Ahmad Majdi Abdul-Rani, Saeed Rubaiee, Mohd Danish, Michael Bryant, Sri Hastuty, Muhammad Al’Hapis Razak, Sadaqat Ali
August 5, 2020 (v1)
Keywords: coating, deposition rate, elecro-discharge, hydroxyapatite, machining, metallic glass, Optimization, RSM, surface roughness
This study attempts to simultaneously machine and synthesize a biomimetic nanoporous hydroxyapatite coating on the Zr67Cu11Ni10Ti9Be3 bulk metallic glass (BMG) surface. The aim is to investigate and optimize the hydroxyapatite deposition rate and the surface roughness during the electro-discharge coating of Zr67Cu11Ni10Ti9Be3 BMG. Scanning Electron Microscopy (SEM), X-ray powder Diffraction (XRD) and Energy-dispersive X-ray Spectroscopy (EDS) were employed to characterize and analyze the results. Response Surface Methodology using D-optimum custom design approach was utilized to generate the models and optimize the input parameters. A globule nanostructured and nanoporous coating of about 25.2 µm thick, containing mainly Ca, O, and K were ascertained. Further XRD analysis confirmed the deposition of biocompatible oxides (HA, CaZrO3, and ZrO2) and hard ZrC coating on the Zr67Cu11Ni10Ti9Be3 BMG surface. A significant improvement in cell viability was observed in the HA electro-discharge... [more]
Quality 4.0 in Action: Smart Hybrid Fault Diagnosis System in Plaster Production
Javaneh Ramezani, Javad Jassbi
August 5, 2020 (v1)
Keywords: construction industry, control chart pattern, decision support systems, discriminant analysis, disruption management, disruptions, expert systems, failure mode and effects analysis (FMEA), fault diagnosis, Industry 4.0, neural networks, plaster production, statistical process control
Industry 4.0 (I4.0) represents the Fourth Industrial Revolution in manufacturing, expressing the digital transformation of industrial companies employing emerging technologies. Factories of the future will enjoy hybrid solutions, while quality is the heart of all manufacturing systems regardless of the type of production and products. Quality 4.0 is a branch of I4.0 with the aim of boosting quality by employing smart solutions and intelligent algorithms. There are many conceptual frameworks and models, while the main challenge is to have the experience of Quality 4.0 in action at the workshop level. In this paper, a hybrid model based on a neural network (NN) and expert system (ES) is proposed for dealing with control chart patterns (CCPs). The idea is to have, instead of a passive descriptive model, a smart predictive model to recommend corrective actions. A construction plaster-producing company was used to present and evaluate the advantages of this novel approach, while the result... [more]
Comparison of Steel Manufacturing Off-Gas Utilization Methods via Life Cycle Analysis
July 31, 2020 (v1)
Subject: Other
Keywords: blast furnace gas, coke oven gas, combined cycle power plant, Life Cycle Analysis, methanol production
This study utilizes life cycle analysis to compare three steel manufacturing off-gas utilization systems: a status quo system, which produces electricity via a low-pressure steam turbine; a combined cycle power plant (CCPP) system, which produces electricity using gas and steam turbines; and a methanol (MeOH) system, which converts coke oven gas (COG) and blast furnace gas (BFG) into MeOH (CBMeOH). This research seeks to compare the environmental impacts of each system based on equivalent raw material inputs. Since the systems have different products, system expansion is used to ensure that they have the same outputs and are therefore comparable. The system boundary consists of a combination of cradle-to-gate and gate-to-gate boundaries. The environmental effects of each system are compared at five locations—Ontario, the USA, Finland, Mexico, and China—using TRACI, CML-IA baseline, ReCiPe2016, and IMPACT2002+ in SimaPro v9. The results show that in Ontario, Finland, and China, CBMeOH s... [more]
Supply Chain Monitoring Using Principal Component Analysis
Jing Wang, Christopher Swartz, Brandon Corbett, Kai Huang
July 16, 2020 (v1)
Keywords: monitoring, Multivariate Statistics, Supply Chain
Various types of risks exist in a supply chain, and disruptions could lead to economic loss or even breakdown of a supply chain without an effective mitigation strategy. The ability to detect disruptions early can help improve the resilience of the supply chain. In this paper, the application of principal component analysis (PCA) and dynamic PCA (DPCA) in fault detection and diagnosis of a supply chain system is investigated. In order to monitor the supply chain, data such as inventory levels, market demands and amount of products in transit are collected. PCA and DPCA are used to model the normal operating conditions (NOC). Two monitoring statistics, the Hotelling's T-squared and the squared prediction error (SPE), are used to detect abnormal operation of the supply chain. The confidence limits of these two statistics are estimated from the training data based on the $\chi^2$- distributions. The contribution plots are used to identify the variables with abnormal behavior when at le... [more]
MobileNetV2 Ensemble for Cervical Precancerous Lesions Classification
Cătălin Buiu, Vlad-Rareş Dănăilă, Cristina Nicoleta Răduţă
July 17, 2020 (v1)
Keywords: biomedical image processing, cervical cancer, computer-aided diagnosis, deep learning, ensemble, machine learning algorithms, MobileNetV2, transfer learning
Women’s cancers remain a major challenge for many health systems. Between 1991 and 2017, the death rate for all major cancers fell continuously in the United States, excluding uterine cervix and uterine corpus cancers. Together with HPV (Human Papillomavirus) testing and cytology, colposcopy has played a central role in cervical cancer screening. This medical procedure allows physicians to view the cervix at a magnification of up to 10%. This paper presents an automated colposcopy image analysis framework for the classification of precancerous and cancerous lesions of the uterine cervix. This framework is based on an ensemble of MobileNetV2 networks. Our experimental results show that this method achieves accuracies of 83.33% and 91.66% on the four-class and binary classification tasks, respectively. These results are promising for the future use of automatic classification methods based on deep learning as tools to support medical doctors.
Graphene-Based Hydrogen Gas Sensors: A Review
Anna Ilnicka, Jerzy P. Lukaszewicz
July 17, 2020 (v1)
Keywords: functionalized graphene, gas sensor, graphene, graphene oxide, hydrogen sensor, metal, metal oxide, polymer, reduced graphene oxide, semiconductor
Graphene is a material gaining attention as a candidate for new application fields such as chemical sensing. In this review, we discuss recent advancements in the field of hydrogen gas sensors based on graphene. Accordingly, the main part of the paper focuses on hydrogen gas sensors and examines the influence of different manufacturing scenarios on the applicability of graphene and its derivatives as key components of sensing layers. An overview of pristine graphene customization methods is presented such as heteroatom doping, insertion of metal/metal oxide nanosized domains, as well as creation of graphene-polymer blends. Volumetric structuring of graphene sheets (single layered and stacked forms) is also considered as an important modifier of its effective use. Finally, a discussion of the possible advantages and weaknesses of graphene as sensing material for hydrogen detection is provided.
Experimental and Statistical Validation of Data on Mesh-Coupled Annular Distributor Design for Swirling Fluidized Beds
Shazia Shukrullah, Muhammad Yasin Naz, Abdul Ghaffar, Yasin Khan, Abdulrehman Ali Al-Arainy, Rashed Meer
July 17, 2020 (v1)
Keywords: annular distributor, NCSS software, particle image velocimetry, statistical analysis, swirling fluidized bed
In this study, velocimetry and statistical analyses were conducted on a swirling fluidized bed. A bed of spherical particles (4 mm) was fluidized by using an annular distributor covered with mesh. The angles of rectangular blades in the distributor were set at 30°, 45°, 60°, 75° and 90°, and the cell size of the mesh cover was 2.5 × 2.5 mm2. The weight was varied from 500 to 1250 g and the effect of each variable on bed velocity response was quantified through velocimetry and statistical analysis. The statistical analysis was conducted using NCSS statistical software. The blade angle, bed weight and superficial velocity for 4 mm particles were statistically optimized at 750 g, 58.26° and 1.45 m/s, respectively. On the experimental side, these parameters have been optimized at 750 g, 60° and 1.41 m/s, respectively. A small difference of 1.74° was noticed in experimental and statistical predictions for the blade angle. The bed weights and superficial velocities were found to be same in b... [more]
Industrial Processes Management for a Sustainable Society: Global Research Analysis
Emilio Abad-Segura, Manuel E. Morales, Francisco Joaquín Cortés-García, Luis Jesús Belmonte-Ureña
July 17, 2020 (v1)
Keywords: effective management, green technology, industry, scientific research, sustainable process, sustainable society
Few decades ago, the development of the industrial sector was disconnected from society’s protection. Negative effects awareness emerges from the current industrial processes through the Sustainable Development Goals (SDGs), considering the causal implications to build up a more sustainable society. The aim of this study is to analyze the state of the art in industrial processes management to obtain positive and sustainable effects on society. Thus, a bibliometric analysis of 1911 articles was set up during the 1988−2019 period, bringing up the authors’ productivity indicators in the scientific field, that is, journals, authors, research institutions, and countries. We have identified environmental management; the impact assessments of industrial processes on the environment and its relation with a more sustainable society; as well as the study of the sustainable management of water resources as the related axes in the study of environmental protection with political, economic, and edu... [more]
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