Search results for: waste classification

Authors: Kazuyuki Takase, Yoshihisa Hiraki, Gaku Takase, Isamu Kudo

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One of the most important issue is to ensure the safety of long-term waste storage containers in which fuel debris and radioactive materials are accumulated. In this case, hydrogen generated by water decomposition by radiation is accumulated in the container for a long period of time, so it is necessary to reduce the concentration of hydrogen in the container. In addition, a condition that any power supplies from the outside of the container are unnecessary is requested. Then, radioactive waste storage containers with the passive autocatalytic recombiner (PAR) would be effective. The radioactive waste storage container with PAR was used for moving the fuel debris of the Three Mile Island Unit 2 to the storage location. However, the effect of PAR is not described in detail. Moreover, the reduction of hydrogen concentration during the long-term storage period was performed by the venting system, which was installed on the top of the container. Therefore, development of a long-term storage container with PAR was started with the aim of safely storing fuel debris picked up at the Fukushima Daiichi Nuclear Power Plant for a long period of time. A fundamental experiment for reducing the concentration of hydrogen which generates in a nuclear waste long-term storage container was carried out using a small-scale container with PAR. Moreover, the circulation flow behavior of hydrogen in the small-scale container resulting from the natural convection by the decay heat was clarified. In addition, preliminary numerical analyses were performed to predict the experimental results regarding the circulation flow behavior and the reduction of hydrogen concentration in the small-scale container. From the results of the present study, the validity of the container with PAR was experimentally confirmed on the reduction of hydrogen concentration. In addition, it was predicted numerically that the circulation flow behavior of hydrogen in the small-scale container is blocked by steam which generates by chemical reaction of hydrogen and oxygen.

Keywords: hydrogen behavior, reduction of concentration, long-term storage container, small-scale, PAR, experiment, analysis

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Authors: Ali̇ Si̇nan Soğanci

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The cement stabilization is one of the ground improvement method applied worldwide to increase the strength of clayey soils. The using of cement has got lots of advantages compared to other stabilization methods. Cement stabilization can be done quickly, the cost is low and creates a more durable structure with the soil. Cement can be used in the treatment of a wide variety of soils. The best results of the cement stabilization were seen on silts as well as coarse-grained soils. In this study, blocks of clay were taken from the Apa-Hotamış conveyance channel route which is 125km long will be built in Konya that take the water with 70m3/sec from Mavi tunnel to Hotamış storage. Firstly, the index properties of clay samples were determined according to the Unified Soil Classification System. The experimental program was carried out on compacted soil specimens with 0%, 7 %, 15% and 30 % cement additives and the results of unconfined compression strength were discussed. The results of unconfined compression tests indicated an increase in strength with increasing cement content.

Keywords: cement stabilization, unconfined compression test, clayey soils, unified soil classification system.

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Authors: Aijing Luo, Zirui Xin, Yifeng Yuan

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Background: With the rapid development of the online health community, more and more patients or families are seeking health information on the Internet. Objective: This study aimed to discuss how to fully reveal the health information needs expressed by hypertensive patients in their questions in the online environment. Methods: This study randomly selected 1,000 text records from the question data of hypertensive patients from 2008 to 2018 collected from the website www.haodf.com and constructed a classification system through literature research and content analysis. This paper identified the background characteristics and questioning the intention of each hypertensive patient based on the patient’s question and used co-occurrence network analysis to explore the features of the health information needs of hypertensive patients. Results: The classification system for health information needs of patients with hypertension is composed of 9 parts: 355 kinds of drugs, 395 kinds of symptoms and signs, 545 kinds of tests and examinations , 526 kinds of demographic data, 80 kinds of diseases, 37 kinds of risk factors, 43 kinds of emotions, 6 kinds of lifestyles, 49 kinds of questions. The characteristics of the explored online health information needs of the hypertensive patients include: i)more than 49% of patients describe the features such as drugs, symptoms and signs, tests and examinations, demographic data, diseases, etc. ii) these groups are most concerned about treatment (77.8%), followed by diagnosis (32.3%); iii) 65.8% of hypertensive patients will ask doctors online several questions at the same time. 28.3% of the patients are very concerned about how to adjust the medication, and they will ask other treatment-related questions at the same time, including drug side effects, whether to take drugs, how to treat a disease, etc.; secondly, 17.6% of the patients will consult the doctors online about the causes of the clinical findings, including the relationship between the clinical findings and a disease, the treatment of a disease, medication, and examinations. Conclusion: In the online environment, the health information needs expressed by Chinese hypertensive patients to doctors are personalized; that is, patients with different background features express their questioning intentions to doctors. The classification system constructed in this study can guide health information service providers in the construction of online health resources, to help solve the problem of information asymmetry in communication between doctors and patients.

Keywords: online health community, health information needs, hypertensive patients, doctor-patient communication

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Authors: Mustafa Akpolat, Baha Vural Kök

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Using a waste material such as crumb rubber (CR) obtained by waste tires has become an important issue in respect to sustainability. However, the CR modified mixture also requires high manufacture temperature as a polymer modified mixture. For this reason in this study, it is intended to produce a CR modified mixture with warm mix asphalt additives in the same mixture. Asphalt mixtures produced by pure, 10%CR, 10%CR+3% Sasobit and 10%CR+0.7% Evotherm were subjected to aging procedure in the laboratory and the field. The indirect tensile repeated tests were applied to aged and original specimens. It was concluded that the fatigue life of the mixtures increased significantly with the increase of aging time. CR+Sasobit modified mixture aged at the both field and laboratory gave the highest load cycle among the mixtures.

Keywords: crumb rubber, warm mix asphalt, aging, fatigue

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Authors: Aditi Chitharanjan Parmar, Marco Gottardo, Giulia Adele Tuci, Francesco Valentino

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The generation of urban organic waste is a significant environmental problem due to the potential release of leachate and/or methane into the environment. This contributes to climate change, discharging a valuable resource that could be used in various ways. This research addresses this issue by proposing a two-step approach by linking biowaste management to aquaculture industry via single cell proteins (SCP) production. A mixture of food waste and municipal sewage sludge (FW-MSS) was firstly subjected to a mesophilic (37°C) anaerobic fermentation to produce a liquid stream rich in short-chain fatty acids (SCFAs), which are important building blocks for the following microbial biomass growth. In the frame of stable fermentation activity (after 1 week of operation), the average value of SCFAs was 21.3  0.4 g COD/L, with a CODSCFA/CODSOL ratio of 0.77 COD/COD. This indicated the successful strategy to accumulate SCFAs from the biowaste mixture by applying short hydraulic retention time (HRT; 4 days) and medium organic loading rate (OLR; 7 – 12 g VS/L d) in the lab-scale (V = 4 L) continuous stirred tank reactor (CSTR). The SCFA-rich effluent was then utilized as feedstock for the growth of a mixed microbial consortium able to store polyhydroxyalkanoates (PHA), a class of biopolymers completely biodegradable in nature and produced as intracellular carbon/energy source. Given the demonstrated properties of the intracellular PHA as antimicrobial and immunomodulatory effect on various fish species, the PHA-producing culture was intended to be utilized as SCP in aquaculture. The growth of PHA-storing biomass was obtained in a 2-L sequencing batch reactor (SBR), fully aerobic and set at 25°C; to stimulate a certain storage response (PHA production) in the cells, the feast-famine conditions were adopted, consisting in an alternation of cycles during which the biomass was exposed to an initial abundance of substrate (feast phase) followed by a starvation period (famine phase). To avoid the proliferation of other bacteria not able to store PHA, the SBR was maintained at low HRT (2 days). Along the stable growth of the mixed microbial consortium (the growth yield was estimated to be 0.47 COD/COD), the feast-famine strategy enhanced the PHA production capacity, leading to a final PHA content in the biomass equal to 16.5 wt%, which is suitable for the use as SCP. In fact, by incorporating the waste-derived PHA-rich biomass into fish feed at 20 wt%, the final feed could contain a PHA content around 3.0 wt%, within the recommended range (0.2–5.0 wt%) for promoting fish health. Proximate analysis of the PHA-rich biomass revealed a good crude proteins level (around 51 wt%) and the presence of all the essential amino acids (EAA), together accounting for 31% of the SCP total amino acid composition. This suggested that the waste-derived SCP was a source of good quality proteins with a good nutritional value. This approach offers a sustainable solution for urban waste management, potentially establishing a sustainable waste-to-value conversion route by connecting waste management to the growing aquaculture and fish feed production sectors.

Keywords: feed supplement, nutritional value, polyhydroxyalkanoates (PHA), single cell protein (SCP), urban organic waste.

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Authors: Yu-Min Chang, Kuo-Sheng Tsai, Hung-Te Tsai, Chia-Hsin Li

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This paper presents the early-warning lights classification management system for industrial parks promoted by the Taiwan Environmental Protection Administration (EPA) since 2011, including the definition of each early-warning light, objectives, action program and accomplishments. All of the 151 industrial parks in Taiwan were classified into four early-warning lights, including red, orange, yellow and green, for carrying out respective pollution management according to the monitoring data of soil and groundwater quality, regulatory compliance, and regulatory listing of control site or remediation site. The Taiwan EPA set up a priority list for high potential polluted industrial parks and investigated their soil and groundwater qualities based on the results of the light classification and pollution potential assessment. In 2011-2013, there were 44 industrial parks selected and carried out different investigation, such as the early warning groundwater well networks establishment and pollution investigation/verification for the red and orange-light industrial parks and the environmental background survey for the yellow-light industrial parks. Among them, 22 industrial parks were newly or continuously confirmed that the concentrations of pollutants exceeded those in soil or groundwater pollution control standards. Thus, the further investigation, groundwater use restriction, listing of pollution control site or remediation site, and pollutant isolation measures were implemented by the local environmental protection and industry competent authorities; the early warning lights of those industrial parks were proposed to adjust up to orange or red-light. Up to the present, the preliminary positive effect of the soil and groundwater quality management system for industrial parks has been noticed in several aspects, such as environmental background information collection, early warning of pollution risk, pollution investigation and control, information integration and application, and inter-agency collaboration. Finally, the work and goal of self-initiated quality management of industrial parks will be carried out on the basis of the inter-agency collaboration by the classified lights system of early warning and management as well as the regular announcement of the status of each industrial park.

Keywords: industrial park, soil and groundwater quality management, early-warning lights classification, SOP for reporting and treatment of monitored abnormal events

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Authors: Hafsa Tahir, Sanaullah Iqbal

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Glactooligosaccharide (GOS) is a type of prebiotic which is mainly found in human milk. GOS belongs to those bacteria which stimulates the growth of beneficial bacteria in human intestines. The aim of the present study was to develop a value-added product by producing prebiotic (GOS) in buttermilk through trans galactosylation. Buttermilk is considered as an industrial waste which is discarded after the production of butter and cream. It contains protein, minerals, vitamins and a smaller amount of fat. Raw milk was pasteurized at 100º C for butter production and then trans galactosylation process was induced in the butter milk thus obtained to produce prebiotic GOS. Results showed that the enzyme (which was obtained from bacterial strain of Esecrshia coli and has a gene of Lactobacillus reuteri L103) concentration between 400-600µl/5ml can produce GOS in 30 minutes. Chemical analysis and sensory evaluation of plain and GOS containing buttermilk showed no remarkable difference in their composition. Furthermore, the shelf-life study showed that there was non-significant (P>0.05) difference in glass and pouch packaging of buttermilk. Buttermilk in pouch packaging maintained its stability for 6 days without the addition of preservatives. Therefore it is recommended that GOS enriched buttermilk which is generally considered as a processing waste in dairy manufacturing can be turned into a cost-effective nutritional functional food product. This will not only enhance the production efficiency of butter processing but also will create a new market opportunity for dairy manufacturers all over the world.

Keywords: buttermilk, galactooligosaccharide, shelf Life, transgalactosylation

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Authors: Meeta Ratawa Tiwary

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Damaturu is the capital of Yobe State in northeastern Nigeria where civic amenities and facilities are not adequate even after 24 years of its existence. The volatile security and political situations are most significant causes for the same. The basic facility for the citizens in terms of drinking water and electricity are not available. For the drinking water, they have to rely on personal bore holes or the filtered borehole waters available in packaged sachets in the market. The present study is concerned with the environmental impact of indiscriminate disposal of drinking synthetic polythene water sachets in Damaturu. The sachet water is popularly called as ‘pure water’, but its purity is questionable. Increased production and consumption of sachet water has led to indiscriminate dumping and disposal of empty sachets leading to a serious environmental threat. The evidence of this is seen in the amount of disposed sachets littering the streets and also the drainages blocked by ‘blocks’ of water sachet waste. Sachet water gained much popularity in Nigeria because the product is convenient for use, affordable and economically viable. The present study aims to find out the solution to this environmental problem. The field-based study has found some significant factors that cause environmental and socio-economic effect due to this. Some recommendations have been made based on research findings regarding sustainable waste management, recycling and re-use of the non-biodegradable products in society.

Keywords: civic amenities, non-biodegradable, pure water, sustainable environment, waste disposal

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Authors: Paulos Meles Yihdego

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To address the environmental impact of the cement industry and road building waste, the use of chemical stabilizers in conjunction with recycled asphalt and cement components was investigated. The silica-based chemical stabilizers and their potential effects on the base layer stabilized by cement are discussed in this paper. Strength, moisture compaction interaction, and microstructural characteristics are all examined. According to the outcome, using this stabilizer has improved the mechanical properties. The inclusion of chemical stabilizers in the combination, which is responsible for the mixture's improved strength, raised the intensity of the C-S-H (Calcium Silicate Hydrate) gel, according to a microstructural study. The design was demonstrated to be durable by the little ettringites found in the later phases. The application of this stabilizer ensures a strong, eco-friendly, durable base layer.

Keywords: ettringites, microstructure analysis, durability properties, cement stabilized base

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Authors: Leonardo Alfredo Mendoza, Cristian Munoz, Marco Aurelio Pacheco, Manoela Kohler, Evelyn Batista, Rodrigo Moura

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Natural Language Processing (NLP) techniques are increasingly more powerful to be able to interpret the feelings and reactions of a person to a product or service. Sentiment analysis has become a fundamental tool for this interpretation but has few applications in languages other than English. This paper presents a classification of sentiment analysis in Portuguese with a base of comments from social networks in Portuguese. A word embedding's representation was used with a 50-Dimension GloVe pre-trained model, generated through a corpus completely in Portuguese. To generate this classification, the bidirectional long short-term memory and bidirectional Gated Recurrent Unit (GRU) models are used, reaching results of 99.1%.

Keywords: natural processing language, sentiment analysis, bidirectional long short-term memory, BI-LSTM, gated recurrent unit, GRU

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Authors: A. J. S. P. Nileema, S. Kulatunga , S. H. Palihawadana

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Computer aided detection (CAD) system was developed for the diagnosis of pulmonary tuberculosis using digital chest X-rays with MATLAB image processing techniques using a statistical approach. The study comprised of 200 digital chest radiographs collected from the National Hospital for Respiratory Diseases - Welisara, Sri Lanka. Pre-processing was done to remove identification details. Lung fields were segmented and then divided into four quadrants; right upper quadrant, left upper quadrant, right lower quadrant, and left lower quadrant using the image processing techniques in MATLAB. Contrast, correlation, homogeneity, energy, entropy, and maximum probability texture features were extracted using the gray level co-occurrence matrix method. Descriptive statistics and normal distribution analysis were performed using SPSS. Depending on the radiologists’ interpretation, chest radiographs were classified manually into PTB - positive (PTBP) and PTB - negative (PTBN) classes. Features with standard normal distribution were analyzed using an independent sample T-test for PTBP and PTBN chest radiographs. Among the six features tested, contrast, correlation, energy, entropy, and maximum probability features showed a statistically significant difference between the two classes at 95% confidence interval; therefore, could be used in the classification of chest radiograph for PTB diagnosis. With the resulting value ranges of the five texture features with normal distribution, a classification algorithm was then defined to recognize and classify the quadrant images; if the texture feature values of the quadrant image being tested falls within the defined region, it will be identified as a PTBP – abnormal quadrant and will be labeled as ‘Abnormal’ in red color with its border being highlighted in red color whereas if the texture feature values of the quadrant image being tested falls outside of the defined value range, it will be identified as PTBN–normal and labeled as ‘Normal’ in blue color but there will be no changes to the image outline. The developed classification algorithm has shown a high sensitivity of 92% which makes it an efficient CAD system and with a modest specificity of 70%.

Keywords: chest radiographs, computer aided detection, image processing, pulmonary tuberculosis

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Authors: A. Buruzs, M. F. Hatwágner, A. Torma, L. T. Kóczy

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Recently, an increasing number of researchers have been focusing on working out realistic solutions to sustainability problems. As sustainability issues gain higher importance for organisations, the management of such decisions becomes critical. Knowledge representation is a fundamental issue of complex knowledge based systems. Many types of sustainability problems would benefit from models based on experts’ knowledge. Cognitive maps have been used for analyzing and aiding decision making. A cognitive map can be made of almost any system or problem. A fuzzy cognitive map (FCM) can successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause and effect relationships among the concepts to model the behavior of any system. Integrated waste management systems (IWMS) are complex systems that can be decomposed to non-related and related subsystems and elements, where many factors have to be taken into consideration that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall decision process of the system. The goal of the present paper is to construct an efficient IWMS which considers various factors. The authors’ intention is to propose an expert based system design approach for implementing expert decision support in the area of IWMSs and introduces an appropriate methodology for the development and analysis of group FCM. A framework for such a methodology consisting of the development and application phases is presented.

Keywords: factors, fuzzy cognitive map, group decision, integrated waste management system

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Authors: M. Sari Yilmaz, N. Karamahmut Mermer

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Coal fly ash is formed as a solid waste product from the combustion of coal in coal fired power stations. Huge amounts of fly ash are produced globally every year and are predicted to increase. Nowadays, less than half of the fly ash is used as a raw material for cement manufacturing, construction and the rest of it is disposed as a waste causing yet another environmental concern. For this reason, the recycling of this kind of slurries into useful materials is quite important in terms of economical and environmental aspects. The purpose of this study is to evaluate the Orhaneli and Tuncbilek coal fly ashes for utilization in some industrial applications. Therefore the mineralogical and chemical compositions of these fly ashes were analyzed by X-ray fluorescence (XRF) spectroscopy and X-ray diffraction (XRD). The silicon (Si) and aluminum (Al) in the fly ashes were activated by alkali fusion technique with sodium hydroxide. The obtained extracts were analyzed for Si and Al content by inductively coupled plasma optical emission spectrometry (ICP-OES).

Keywords: extraction, fly ash, fusion, XRD

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Authors: A. Buruzs, M. F. Hatwágner, A. Torma, L. T. Kóczy

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The development, operation and maintenance of Integrated Waste Management Systems (IWMS) affects essentially the sustainable concern of every region. The features of such systems have great influence on all of the components of sustainability. In order to reach the optimal way of processes, a comprehensive mapping of the variables affecting the future efficiency of the system is needed such as analysis of the interconnections among the components and modelling of their interactions. The planning of a IWMS is based fundamentally on technical and economical opportunities and the legal framework. Modelling the sustainability and operation effectiveness of a certain IWMS is not in the scope of the present research. The complexity of the systems and the large number of the variables require the utilization of a complex approach to model the outcomes and future risks. This complex method should be able to evaluate the logical framework of the factors composing the system and the interconnections between them. The authors of this paper studied the usability of the Fuzzy Cognitive Map (FCM) approach modelling the future operation of IWMS’s. The approach requires two input data set. One is the connection matrix containing all the factors affecting the system in focus with all the interconnections. The other input data set is the time series, a retrospective reconstruction of the weights and roles of the factors. This paper introduces a novel method to develop time series by content analysis.

Keywords: content analysis, factors, integrated waste management system, time series

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Authors: Raghavi C. Janaswamy

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In the healthcare sector, heterogenous data elements such as patients, diagnosis, symptoms, conditions, observation text from physician notes, and prescriptions form the essentials of the Electronic Health Record (EHR). The data in the form of clear text and images are stored or processed in a relational format in most systems. However, the intrinsic structure restrictions and complex joins of relational databases limit the widespread utility. In this regard, the design and development of realistic mapping and deep connections as real-time objects offer unparallel advantages. Herein, a graph neural network-based classification of EHR data has been developed. The patient conditions have been predicted as a node classification task using a graph-based open source EHR data, Synthea Database, stored in Tigergraph. The Synthea DB dataset is leveraged due to its closer representation of the real-time data and being voluminous. The graph model is built from the EHR heterogeneous data using python modules, namely, pyTigerGraph to get nodes and edges from the Tigergraph database, PyTorch to tensorize the nodes and edges, PyTorch-Geometric (PyG) to train the Graph Neural Network (GNN) and adopt the self-supervised learning techniques with the AutoEncoders to generate the node embeddings and eventually perform the node classifications using the node embeddings. The model predicts patient conditions ranging from common to rare situations. The outcome is deemed to open up opportunities for data querying toward better predictions and accuracy.

Keywords: electronic health record, graph neural network, heterogeneous data, prediction

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Authors: Mohamed Mounir Tellache, Hiroyuki Kambara, Yasuharu Koike, Makoto Miyakoshi, Natsue Yoshimura

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This study explores the practicality of using electroencephalographic (EEG) independent components to predict eight-direction finger movements in pseudo-real-time. Six healthy participants with individual-head MRI images performed finger movements in eight directions with two different arm configurations. The analysis was performed in two stages. The first stage consisted of using independent component analysis (ICA) to separate the signals representing brain activity from non-brain activity signals and to obtain the unmixing matrix. The resulting independent components (ICs) were checked, and those reflecting brain-activity were selected. Finally, the time series of the selected ICs were used to predict eight finger-movement directions using Sparse Logistic Regression (SLR). The second stage consisted of using the previously obtained unmixing matrix, the selected ICs, and the model obtained by applying SLR to classify a different EEG dataset. This method was applied to two different settings, namely the single-participant level and the group-level. For the single-participant level, the EEG dataset used in the first stage and the EEG dataset used in the second stage originated from the same participant. For the group-level, the EEG datasets used in the first stage were constructed by temporally concatenating each combination without repetition of the EEG datasets of five participants out of six, whereas the EEG dataset used in the second stage originated from the remaining participants. The average test classification results across datasets (mean ± S.D.) were 38.62 ± 8.36% for the single-participant, which was significantly higher than the chance level (12.50 ± 0.01%), and 27.26 ± 4.39% for the group-level which was also significantly higher than the chance level (12.49% ± 0.01%). The classification accuracy within [–45°, 45°] of the true direction is 70.03 ± 8.14% for single-participant and 62.63 ± 6.07% for group-level which may be promising for some real-life applications. Clustering and contribution analyses further revealed the brain regions involved in finger movement and the temporal aspect of their contribution to the classification. These results showed the possibility of using the ICA-based method in combination with other methods to build a real-time system to control prostheses.

Keywords: brain-computer interface, electroencephalography, finger motion decoding, independent component analysis, pseudo real-time motion decoding

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Authors: L. S. Chathurika

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Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

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Authors: Mohammed Riyas Pp

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This study aimed at investigating major issues in translating the Arabic Hadith terminologies into English, focusing on choosing the most appropriate translation for each, reviewing major Hadith works in English. This study is confined to twenty terminologies with regard to classification of Hadith based on authority, strength, number of transmitters and connections in Isnad. Almost all available translations are collected and analyzed to find the most proper translation based on linguistic and translational values. To the researcher, many translations lack precise understanding of either Hadith terminologies or English language and varieties of methodologies have influence on varieties of translations. This study provides a classification of translational and conceptual issues. Translational issues are related to translatability of these terminologies and their equivalence. Conceptual issues provide a list of misunderstandings due to wrong translations of terminologies. This study ends with a suggestion for unification in translating terminologies based on convention of Muslim scholars having good understanding of Hadith terminologies and English language.

Keywords: english language, hadith terminologies, equivalence in translation, problems in translation

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Authors: Sónia Ribeiro, Diana Farinha, Elsa Pereira, Hélia Sales, Filipa Figueiredo, Rita Pontes, João Nunes

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Conventional plastic impacts on Planet, natural resources contamination, human health as well as animals are the most attractive environmental and health attention. The lack of treatment in the end-of-life (EOL) phase and uncontrolled discard allows plastic to be found everywhere in the world. Food waste is increasing significantly, with a final destination to landfills. To face these difficulties, new packaging solutions are needed with the objective of prolonging the shelf-life of products as well as equipment solutions for the development of the mentioned packaging. FLUI project thus presents relevance and innovation to reach a new level of knowledge and industrial development focused in Ecodesign. Industrial equipment field for the manufacture of new packaging solutions based on biodegradable plastics films to apply in the food sector. With lesser environmental impacts and new solutions that make it possible to prevent food waste, reduce the production e consequent poor disposal of plastic of fossil origin. It will be a paradigm shift at different levels, from industry to waste treatment stations, passing through commercial agents and consumers. It can be achieved through the life cycle assessment (LCA) and ecodesign of the products, which integrates the environmental concerns in the design of the product as well as through the entire life cycle. The FLUI project aims to build a piece of new bio-PLA extrusion equipment with the incorporation of bioactive extracts through the production of flexible mono- and multi-layer functional films (FLUI systems). The biofunctional and biodegradable films will prompt the extension of packaged products’ shelf-life, reduce food waste and contribute to reducing the consumption of non-degradable fossil plastics, as well as the use of raw material from renewable sources.

Keywords: food packing, bioplastics, ecodesign, circular economy

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Authors: Liliana Crăc, Nicolae Giorgi, Gheorghe Fometescu, Mihai Cruceru

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Power plants represent the main source of air pollution, through combustion processes, both by releasing large amounts of dust, greenhouse gases and acidifying, and large quantities of waste, slag and ash disposed in landfills covering significant areas. SC Turceni S.A. is one of the largest power generating unit from Romania. Their policy is focused on the production and delivery of electricity in order to increase energy efficiency and to reduce the environmental impact. The paper presents environmental impact produced by slag and ash storage, while pointing out that the recovery of this waste significant improves the air quality in the area. An important aspect is the proprieties of the ash and slag evacuated by Turceni power plant in order to use them for building materials manufacturing.

Keywords: ash and slag properties, air pollution, building materials industry, power plants

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Authors: Oumaima Lahmar

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This paper aims to define a structured topography for finance researchers seeking to navigate the body of knowledge in their extrapolation of finance phenomena. To make sense of the body of knowledge in finance, a probabilistic topic modeling approach is applied on 6000 abstracts of academic articles published in three top journals in finance between 1976 and 2020. This approach combines both machine learning techniques and natural language processing to statistically identify the conjunctions between research articles and their shared topics described each by relevant keywords. The topic modeling analysis reveals 35 coherent topics that can well depict finance literature and provide a comprehensive structure for the ongoing research themes. Comparing the extracted topics to the Journal of Economic Literature (JEL) classification system, a significant similarity was highlighted between the characterizing keywords. On the other hand, we identify other topics that do not match the JEL classification despite being relevant in the finance literature.

Keywords: finance literature, textual analysis, topic modeling, perplexity

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Authors: Gabriel Sanjo Aruwajoye, E. B. Gueguim Kana

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Agricultural residues present a low-cost feedstock for bioenergy production around the world. Cassava peels waste are rich in organic molecules that can be readily converted to value added products such as biomaterials and biofuels. However, due to the presence of high proportion of structural carbohydrates and lignin, the hydrolysis of this feedstock is imperative to achieve maximum substrate utilization and energy yield. This study model and optimises the release of Fermentable Sugar (FS) from cassava peels waste using the Response Surface Methodology. The investigated pretreatment input parameters consisted of soaking temperature (oC), soaking time (hours), autoclave duration (minutes), acid concentration (% v/v), substrate solid loading (% w/v) within the range of 30 to 70, 0 to 24, 5 to 20, 0 to 5 and 2 to 10 respectively. The Box-Behnken design was used to generate 46 experimental runs which were investigated for FS release. The obtained data were used to fit a quadratic model. A coefficient of determination of 0.87 and F value of 8.73 was obtained indicating the good fitness of the model. The predicted optimum pretreatment conditions were 69.62 oC soaking temperature, 2.57 hours soaking duration, 5 minutes autoclave duration, 3.68 % v/v HCl and 9.65 % w/v solid loading corresponding to FS yield of 91.83g/l (0.92 g/g cassava peels) thus 58% improvement on the non-optimised pretreatment. Our findings demonstrate an efficient pretreatment model for fermentable sugar release from cassava peels waste for various bioprocesses.

Keywords: feedstock pretreatment, cassava peels, fermentable sugar, response surface methodology

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Authors: Asim Olgun, Ali Kara, Vural Butun, Pelin Sevinc, Merve Gungor, Orhan Ornek

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In this study, we investigated the possibility of using waste containing boron impurity (BW) as an adsorbent for the removal of Orange 16 from aqueous solution. Surface properties of the BW, heat treated BW, and diblock copolymer coated BW were examined by using Zeta Meter and scanning electron microscopy (SEM). The polymer modified sample having the highest positive zeta potential was used as an adsorbent. Batch adsorption studies were carried out. The operating variables studied were the initial dye concentration, contact time, solution pH, and adsorbent dosage. It was found that the dye adsorption largely depends on the initial pH of the solution with maximum uptake occurring at pH 3. The adsorption followed pseudo-second-order kinetics and the isotherm fit well to the Langmuir model.

Keywords: zeta potential, adsorption, Orange 16, isotherms

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Authors: Debarati Bhaumik, Diptish Dey

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Multilevel models, increasingly deployed in industries such as insurance, food production, and entertainment within functions such as marketing and supply chain management, need to be transparent and ethical. Applications usually result in binary classification within groups or hierarchies based on a set of input features. Using open-source datasets, we demonstrate that popular explainability methods, such as SHAP and LIME, consistently underperform inaccuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution to the outcome). Besides accuracy, the computational intractability of SHAP for binomial classification is a cause of concern. For transparent and ethical applications of these hierarchical statistical models, sound audit frameworks need to be developed. In this paper, we propose an audit framework for technical assessment of multilevel regression models focusing on three aspects: (i) model assumptions & statistical properties, (ii) model transparency using different explainability methods, and (iii) discrimination assessment. To this end, we undertake a quantitative approach and compare intrinsic model methods with SHAP and LIME. The framework comprises a shortlist of KPIs, such as PoCE (Percentage of Correct Explanations) and MDG (Mean Discriminatory Gap) per feature, for each of these three aspects. A traffic light risk assessment method is furthermore coupled to these KPIs. The audit framework will assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying multilevel models to be future-proof and aligned with the European Commission’s proposed Regulation on Artificial Intelligence.

Keywords: audit, multilevel model, model transparency, model explainability, discrimination, ethics

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Authors: Christoph Linse, Thomas Martinetz

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Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.

Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets

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Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas

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The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.

Keywords: Artificial Neural network, Competitive dynamics, Logistic Regression, Text classification, Text mining

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Authors: Özlem Turan, Şule Turhan

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About 1 billion people in the world are suffering from hunger. Approximately 1.3 billion tons of produced food is wasted each year as well. While the waste of industrialized countries is 670 million tons per year, the waste per year in developing countries is estimated as 630 million tons. When evaluated in this respect, the importance of sustainability and food security can be seen clearly. Food safety is defined as taking the necessary measures and eliminating all risk arising from food. The goal of sustainable food security is, protection of consumer health, development of safe food and beverages trade nationally and internationally and to ensure reliable fair trade schemes. In this study, this study will focus on sustainable food systems and food security, by examining the food wastage and losses from environmental and economic point of views and the precautions that need to be taken will be discussed.

Keywords: food, food safety, food systems, sustainability

Procedia PDF Downloads 376

Authors: Wenbo Du, Xiaomei Ma

Abstract:

With the empowerment of Cognitive Diagnostic Assessment (CDA), various domains of language testing and assessment have been investigated to dig out more diagnostic information. What is noticeable is that most of the extant empirical CDA-based research puts much emphasis on individual-level diagnostic purpose with very few concerned about learners’ group-level performance. Even though the personalized diagnostic feedback is the unique feature that differentiates CDA from other assessment tools, group-level diagnostic information cannot be overlooked in that it might be more practical in classroom setting. Additionally, the group-level diagnostic information obtained via current CDA always results in a “flat pattern”, that is, the mastery/non-mastery of all tested skills accounts for the two highest proportion. In that case, the outcome does not bring too much benefits than the original total score. To address these issues, the present study attempts to apply cluster analysis for group classification and quantile regression analysis to pinpoint learners’ performance at different proficiency levels (beginner, intermediate and advanced) thus to enhance the interpretation of the CDA results extracted from a group of EFL learners’ reading performance on a diagnostic reading test designed by PELDiaG research team from a key university in China. The results show that EM method in cluster analysis yield more appropriate classification results than that of CDA, and quantile regression analysis does picture more insightful characteristics of learners with different reading proficiencies. The findings are helpful and practical for instructors to refine EFL reading curriculum and instructional plan tailored based on the group classification results and quantile regression analysis. Meanwhile, these innovative statistical methods could also make up the deficiencies of CDA and push forward the development of language testing and assessment in the future.

Keywords: cognitive diagnostic assessment, diagnostic feedback, EFL reading, quantile regression

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

Abstract:

Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement

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Authors: Joao A. Labrincha, Rui M. Novais, L. Senff, J. Carvalheiras

Abstract:

The development of multifunctional materials to tackle the energy consumption and improve the hygrothermal performance of buildings is very relevant. This work reports the development of porous geopolymers or bi-layered composites, composed by a highly porous top-layer and a dense bottom-layer, showing high ability to reduce the temperature swings inside buildings and simultaneously buffer the humidity levels. The use of phase change materials (PCM) strongly reduces the indoor thermal fluctuation (up to 5 °C). The potential to modulate indoor humidity is demonstrated by the very high practical MBV (2.71 g/m2 Δ%HR). Since geopolymer matrixes are produced from wastes (biomass fly ash, red mud) the developed solutions contribute to sustainable and energy efficient and healthy building.

Keywords: waste-based geopolymers, thermal insulation, temperature regulation, moisture buffer

Procedia PDF Downloads 56