Extraction of certain heavy metals from sewage sludge using different types of acids

The removal of heavy metal from sludge before disposal or application to farmland is a necessary step to achieve a more safe sludge usage or disposal. Chemical extraction using inorganic acids (nitric, hydrochloric) and organic acids (citric, oxalic) were tested for extraction of chromium, copper, nickel, lead and zinc from contaminated sewage sludge at different pH and reaction time. Results revealed that solubilization of metals using inorganic acids achieved its maximum extraction efficiency (Cr-88%, Cu-82%, Ni-86%, Pb-94%, Zn-89%) at pH value lower than 2 and acid contact times of 1hour. while in case of organic acids oxalic acid does not show good results comparing to citric acid that at pH 2.43 citric acid seemed to be highly effective in extracting Cu (86%), Zn(88%), mostly after 1 day of extraction time, Cr (90%), Ni (96%) at 5 days leaching time, while Pb(85%) removal at the same pH was at a longer leaching time 10 days. At pH 3, citric acid seemed to be also highly effective in extracting Cr (66%), Cu(48%), Pb (66%), Zn(69%) at 1 day, while higher removal was also attained for Ni(68%) at only 4 h leaching time. Finally the extraction efficiencies of citric acid for Cr, Cu, Ni, Pb, Zn, are high enough to reduce the heavy metal content in sludge to levels below the legal standards

Industrial cyber physical systems supported by distributed advanced data analytics

The industry digitization is transforming its business models, organizational structures and operations, mainly promoted by the advances and the mass utilization of smart methods, devices and products, being leveraged by initiatives like Industrie 4.0. In this context, the data is a valuable asset that can support the smart factory features through the use of Big Data and advanced analytics approaches. In order to address such requirements and related challenges, Cyber Physical Systems (CPS) promote the development of more intelligent, adaptable and responsiveness supervisory and control systems capable to overcome the inherent complexity and dynamics of industrial environments. In this context, this work presents an agent-based industrial CPS, where agents are endowed with data analysis capabilities for distributed, collaborative and adaptive process supervision and control. Additionally, to address the different industrial levels’ requirements, this work combines two main data analysis scopes: at operational level, applying distributed data stream analysis for rapid response monitoring and control, and at supervisory level, applying big data analysis for decision-making, planning and optimization. Some experiments have been performed in the context of an electric micro grid where agents were able to perform distributed data analysis to predict the renewable energy production.info:eu-repo/semantics/publishedVersio

Implicit authentication method for smartphone users based on rank aggregation and random forest

Currently, the smartphone devices have become an essential part of our daily activities. Smartphone’ users run various essential applications (such as banking and e-health Apps), which contains very confidential information (e.g., credit card number and its PIN). Typically, the smartphone’s user authentication is achieved using mechanisms (password or security pattern) to verify the user identity. Although these mechanisms are cheap, simple, and quick enough for frequent logins, they are vulnerable to attacks such as shoulder surfing or smudge attack. This problem could be addressed by authenticating the users using their behaviour (i.e., touch behaviour) while using their smartphones. Such behaviours include finger’s pressure, size, and pressure time while tapping keys. Selecting features (from these behaviours) could play an important role in the authentication process’s performance. This paper aims to propose an efficient authentication method providing an implicit authentication for smartphone users while not imposing an additional cost of special hardware and addressing the limited smartphone capabilities. We first investigated feature selection techniques from the filter and wrapper approaches and then used the best one to propose our implicit authentication method. The random forest classifier is used to evaluate these techniques. It is also used to achieve the classification task in our authentication method. Using a public dataset, the experimental results showed that the filter-based technique (i.e., rank aggregation) is the best feature selection to build an implicit authentication method for the smartphone environment. It showed accuracy results around 97.80% using only 25 features out of 53 features (i.e., require less mobile resources (memory and processing power) to authenticate users. At the same time, the results showed that our method has less error rate: 2.03 FAR, 0.04 FRR, and 1.04 ERR, comparing to the related work. These promising results would be used to develop a mobile application that allows implicit authentication of legitimate owners while avoiding the traditional authentication problems and using fewer smartphone resources

A grey wolf-based method for mammographic mass classification

Breast cancer is one of the most prevalent cancer types with a high mortality rate in women worldwide. This devastating cancer still represents a worldwide public health concern in terms of high morbidity and mortality rates. The diagnosis of breast abnormalities is challenging due to different types of tissues and textural variations in intensity. Hence, developing an accurate computer-aided system (CAD) is very important to distinguish normal from abnormal tissues and define the abnormal tissues as benign or malignant. The present study aims to enhance the accuracy of CAD systems and to reduce its computational complexity. This paper proposes a method for extracting a set of statistical features based on curvelet and wavelet sub-bands. Then the binary grey wolf optimizer (BGWO) is used as a feature selection technique aiming to choose the best set of features giving high performance. Using public dataset, Digital Database for Screening Mammography (DDSM), different experiments have been performed with and without using the BGWO algorithm. The random forest classifier with 10-fold cross-validation is used to achieve the classification task to evaluate the selected set of features’ capability. The obtained results showed that when the BGWO algorithm is used as a feature selection technique, only 30.7% of the total features can be used to detect whether a mammogram image is normal or abnormal with ROC area reaching 1.0 when the fusion of both curvelet and wavelet features were used. In addition, in case of diagnosing the mammogram images as benign or malignant, the results showed that using BGWO algorithm as a feature selection technique, only 38.5% of the total features can be used to do so with high ROC area result at 0.871

Modeling concept drift: A probabilistic graphical model based approach

An often used approach for detecting and adapting to concept drift when doing classi cation is to treat the data as i.i.d. and use changes in classi cation accuracy as an indication of concept drift. In this paper, we take a different perspective and propose a framework, based on probabilistic graphical models, that explicitly represents concept drift using latent variables. To ensure effcient inference and learning, we resort to a variational Bayes inference scheme. As a proof of concept, we demonstrate and analyze the proposed framework using synthetic data sets as well as a real fi nancial data set from a Spanish bank

An Application of Using Support Vector Machine Based on Classification Technique for Predicting Medical Data Sets

© 2019, Springer Nature Switzerland AG. This paper illustrates the utilise of various kind of machine learning approaches based on support vector machines for classifying Sickle Cell Disease data set. It has demonstrated that support vector machines generate an essential enhancement when applied for the pre-processing of clinical time-series data set. In this aspect, the objective of this study is to present discoveries for a number of classes of approaches for therapeutically associated problems in the purpose of acquiring high accuracy and performance. The primary case in this study includes classifying the dosage necessary for each patient individually. We applied a number of support vector machines to examine sickle cell data set based on the performance evaluation metrics. The result collected from a number of models have indicated that, support vector Classifier demonstrated inferior outcomes in comparison to Radial Basis Support Vector Classifier. For our Sickle cell data sets, it was found that the Parzen Kernel Support Vector Classifier produced the highest levels of performance and accuracy during training procedure accuracy 0.89733, AUC 0.94267. Where the testing set process, accuracy 0.81778, the area under the curve with 0.86556

A Framework for Online Conformance Checking

Conformance checking – a branch of process mining – focuses on establishing to what extent actual executions of a process are in line with the expected behavior of a reference model. Current conformance checking techniques only allow for a-posteriori analysis: the amount of (non-)conformant behavior is quantified after the completion of the process instance. In this paper we propose a framework for online conformance checking: not only do we quantify (non-)conformant behavior as the execution is running, we also restrict the computation to constant time complexity per event analyzed, thus enabling the online analysis of a stream of events. The framework is instantiated with ideas coming from the theory of regions, and state similarity. An implementation is available in ProM and promising results have been obtained.Peer ReviewedPostprint (author's final draft

The ligational behavior of a phenolic quinolyl hydrazone towards copper(II)- ions

<p>Abstract</p> <p>Background</p> <p>The heterocyclic hydrazones constitute an important class of biologically active drug molecules. The hydrazones have also been used as herbicides, insecticides, nematocides, redenticides, and plant growth regulators as well as plasticizers and stabilizers for polymers. The importance of the phenolic quinolyl hydrazones arises from incorporating the quinoline ring with the phenolic compound; 2,4-dihydroxy benzaldehyde. Quinoline ring has therapeutic and biological activities whereas, phenols have antiseptic and disinfectants activities and are used in the preparation of dyes, bakelite and drugs. The present study is planned to check the effect of the counter anions on the type and geometry of the isolated copper(II)- complexes as well as the ligational behavior of the phenolic hydrazone; 4-[(2-(4,8-dimethylquinolin-2-yl)hydrazono)methyl] benzene-1,3-diol; (H₂L).</p> <p>Results</p> <p>A phenolic quinolyl hydrazone (H₂L) was allowed to react with various copper(II)- salts (Cl‾, Br‾, NO₃‾, ClO₄‾, AcO‾, SO₄^2-). The reactions afforded dimeric complexes (ClO₄‾, AcO‾ ), a binuclear complex (NO₃‾ ) and mononuclear complexes (the others; Cl‾, Br‾, SO₄^2-). The isolated copper(II)- complexes have octahedral, square pyramid and square planar geometries. Also, they reflect the strong coordinating ability of NO₃‾, Cl‾, Br‾, AcO‾ and SO₄^2-anions. Depending on the type of the anion, the ligand showed three different modes of bonding <it>viz</it>. (NN)⁰for the mononuclear complexes (<b>3, 4, 6</b>), (NO)^-with O- bridging for the dimeric complexes (<b>1, 5</b>) and a mixed mode [(NN)⁰+ (NO)^-with O- bridging] for the binuclear nitrato- complex (<b>2</b>).</p> <p>Conclusion</p> <p>The ligational behavior of the phenolic hydrazone (H₂L) is highly affected by the type of the anion. The isolated copper(II)- complexes reflect the strong coordinating power of the SO₄^2-, AcO‾, Br‾, Cl‾ and NO₃‾ anions. Also, they reflect the structural diversity (octahedral, square pyramid and square planar) depending on the type of the counter anion.</p

A SOM-based Chan–Vese model for unsupervised image segmentation

Active Contour Models (ACMs) constitute an efficient energy-based image segmentation framework. They usually deal with the segmentation problem as an optimization problem, formulated in terms of a suitable functional, constructed in such a way that its minimum is achieved in correspondence with a contour that is a close approximation of the actual object boundary. However, for existing ACMs, handling images that contain objects characterized by many different intensities still represents a challenge. In this paper, we propose a novel ACM that combines—in a global and unsupervised way—the advantages of the Self-Organizing Map (SOM) within the level set framework of a state-of-the-art unsupervised global ACM, the Chan–Vese (C–V) model. We term our proposed model SOM-based Chan– Vese (SOMCV) active contourmodel. It works by explicitly integrating the global information coming from the weights (prototypes) of the neurons in a trained SOM to help choosing whether to shrink or expand the current contour during the optimization process, which is performed in an iterative way. The proposed model can handle images that contain objects characterized by complex intensity distributions, and is at the same time robust to the additive noise. Experimental results show the high accuracy of the segmentation results obtained by the SOMCV model on several synthetic and real images, when compared to the Chan–Vese model and other image segmentation models