A study on Geotrichum candidum lipase stability

The lipolytic activity of Geotrichum candidum supernatant was determined before and after storage at various temperatures. The storage stability at 37°C decreased with increasing age of the culture, the activity of a 10 days old culture disappeared after 20 h. Heat treatments at 80°C for 10 min increased the storage stability of the lipase from older cultures. Mixing the supernatants of the old and young cultures revealed the existence of a transferable factor, associated with the crude lipase and more abundant in older cultures. The results support the hypothesis of the inactivation of the lipase by coexisting proteases.     

Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for Clustered IoT Driven Ubiquitous Computing System

In the Internet of Things (IoT) based system, the multi-level client’s requirements can be fulfilled by incorporating communication technologies with distributed homogeneous networks called ubiquitous computing systems (UCS). The UCS necessitates heterogeneity, management level, and data transmission for distributed users. Simultaneously, security remains a major issue in the IoT-driven UCS. Besides, energy-limited IoT devices need an effective clustering strategy for optimal energy utilization. The recent developments of explainable artificial intelligence (XAI) concepts can be employed to effectively design intrusion detection systems (IDS) for accomplishing security in UCS. In this view, this study designs a novel Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for IoT Driven Ubiquitous Computing System (BXAI-IDCUCS) model. The major intention of the BXAI-IDCUCS model is to accomplish energy efficacy and security in the IoT environment. The BXAI-IDCUCS model initially clusters the IoT nodes using an energy-aware duck swarm optimization (EADSO) algorithm to accomplish this. Besides, deep neural network (DNN) is employed for detecting and classifying intrusions in the IoT network. Lastly, blockchain technology is exploited for secure inter-cluster data transmission processes. To ensure the productive performance of the BXAI-IDCUCS model, a comprehensive experimentation study is applied, and the outcomes are assessed under different aspects. The comparison study emphasized the superiority of the BXAI-IDCUCS model over the current state-of-the-art approaches with a packet delivery ratio of 99.29%, a packet loss rate of 0.71%, a throughput of 92.95 Mbps, energy consumption of 0.0891 mJ, a lifetime of 3529 rounds, and accuracy of 99.38%.     

Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering Scheme for Cognitive Radio Wireless Sensor Networks

Cognitive radio wireless sensor networks (CRWSN) can be defined as a promising technology for developing bandwidth-limited applications. CRWSN is widely utilized by future Internet of Things (IoT) applications. Since a promising technology, Cognitive Radio (CR) can be modelled to alleviate the spectrum scarcity issue. Generally, CRWSN has cognitive radio-enabled sensor nodes (SNs), which are energy limited. Hierarchical cluster-related techniques for overall network management can be suitable for the scalability and stability of the network. This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering (MDMO-EAC) Scheme for CRWSN. The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN. Besides, the MDMO-EAC algorithm is based on the dwarf mongoose optimization (DMO) algorithm design with oppositional-based learning (OBL) concept for the clustering process, showing the novelty of the work. In addition, the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency. The presented model is validated using a comprehensive range of experiments, and the outcomes were scrutinized in varying measures. The comparison study stated the improvements of the MDMO-EAC method over other recent approaches.     

Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset’s values. In most research studies, the existence of missing values (MVs) is a vital problem. In addition, any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high. In this paper, the authors propose a novel algorithm for dealing with MVs depending on the feature selection (FS) of similarity classifier with fuzzy entropy measure. The proposed algorithm imputes MVs in cumulative order. The candidate feature to be manipulated is selected using similarity classifier with Parkash’s fuzzy entropy measure. The predictive model to predict MVs within the candidate feature is the Bayesian Ridge Regression (BRR) technique. Furthermore, any imputed features will be incorporated within the BRR equation to impute the MVs in the next chosen incomplete feature. The proposed algorithm was compared against some practical state-of-the-art imputation methods by conducting an experiment on four medical datasets which were gathered from several databases repository with MVs generated from the three missingness mechanisms. The evaluation metrics of mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R² score) were used to measure the performance. The results exhibited that performance vary depending on the size of the dataset, amount of MVs and the missingness mechanism type. Moreover, compared to other methods, the results showed that the proposed method gives better accuracy and less error in most cases.     

A dynamic programming approach for minimizing the number of drawing stages and heat treatments in cylindrical shell multistage deep drawing

Deep drawing is an important sheet metal forming process that appears in many industrial fields. It involves pressing a blank sheet against a hollow cavity that takes the form of the desired product. Due to limitations related to the properties of the blank sheet material, several drawing stages may be needed before the required shape and dimensions of the final product can be obtained. Heat treatment may also be needed during the process in order to restore the formability of the material so that failure is avoided. In this paper, the problem of minimizing the number of drawing stages and heat treatments needed for the multistage deep drawing of cylindrical shells is addressed. This problem is directly related to minimizing manufacturing costs and lead time. It is required to determine the post-drawing shell diameters along with whether heat treatment is to be conducted after each drawing stage such that the aforementioned objectives are achieved and failure is avoided. Conventional computer-aided process planning (CAPP) rules are used to define the search space for a dynamic programming (DP) approach in which both the post-drawing shell diameter and material condition are used to define the states in the problem. By discretizing the range of feasible shell diameters starting from the initial blank diameter down to the final shell diameter, the feasible transitions from state to another is represented by a directed graph, based upon which the DP functional equation is easily defined. The DP generates a set of feasible optimized process plans that are then verified by carrying out finite element analysis in which the deformation severity and the resulting strains and thickness variations are investigated. Two case studies are presented to demonstrate the effectiveness of the developed approach. The results suggest that the proposed approach is a valuable, reliable and quick computer aided process planning approach to this complicated problem.     

New data mining technique to enhance IDS alarms quality

The intrusion detection systems (IDSs) generate large number of alarms most of which are false positives. Fortunately, there are reasons for triggering alarms where most of these reasons are not attacks. In this paper, a new data mining technique has been developed to group alarms and to produce clusters. Hereafter, each cluster abstracted as a generalized alarm. The generalized alarms related to root causes are converted to filters to reduce future alarms load. The proposed algorithm makes use of nearest neighboring and generalization concepts to cluster alarms. As a clustering algorithm, the proposed algorithm uses a new measure to compute distances between alarms features values. This measure depends on background knowledge of the monitored network, making it robust and meaningful. The new data mining technique was verified with many datasets, and the averaged reduction ratio was about 82% of the total alarms. Application of the new technique to alarms log greatly helps the security analyst in identifying the root causes; and then reduces the alarm load in the future.     

Factors affecting the solubility of gypsum. The effect of lime and temperature in different media

The effect of lime on the solubility of gypsum in water and in sodium hydroxide solution at 30, 60 and 100°C has been studied turbidimetrically and by means of X-ray diffraction analysis. Lime depressed the solubility of gypsum in water only slightly at 30°C and a rise of temperature minimised its effect until its complete elimination. In the absence of alkali the minimum sulphate ion concentration in a supersaturated lime solution and at temperatures in the range 30–100°C is about 1.2gdm⁻³. In sodium hydroxide solution neither lime nor temperature played a significant role and the solubility of gypsum was clearly promoted. At temperatures in the range 30–100°C, 0.5g of gypsum dissolved in 100cm³ of 0.2MNaOH solution.     

Pattern‐based prognostic methodology for condition‐based maintenance using selected and weighted survival curves

This paper proposes a pattern‐based prognostic methodology that combines logical analysis of data (LAD) as an event‐driven diagnostic technique, and Kaplan–Meier (KM) estimator as a time‐driven technique. LAD captures the effect of the instantaneous conditions on the health state of a monitored system, while KM estimates the baseline reliability curve that reflects the effect of aging, based on the observed historical failure times. LAD is used to generate a set of patterns from the observed values of covariates that represent the operating conditions and condition indicators. A pattern selection procedure is carried out to select the set of significant patterns from all the generated patterns. A survival curve is estimated, for each subset of observations covered by each selected pattern. A weight that reflects the coverage of each pattern is assigned to its survival curve. Given a recently collected observation, the survival curve of a monitored system is updated on the basis of the patterns covering that observation. The updated curve is then used to predict the remaining useful life of the monitored system. The proposed methodology is validated using a common dataset in prognostics: the turbofan degradation dataset that is available at NASA prognostic repository. Copyright © 2017 John Wiley & Sons, Ltd.     

Efficient SVD speech watermarking with encrypted images

This paper presents a new approach for speech signal watermarking using encrypted watermarks. The objective of this approach is to increase the degree of security of speech watermarking. In the proposed framework, watermark embedding is implemented with singular value decomposition due to its robustness to attacks. Moreover, two encryption schemes are tested for watermark image encryption; chaotic encryption due to its permutation nature and data encryption standard (DES) due to its diffusion nature. Overall and block-by-block watermarking scenarios are tested and compared for speech signal watermarking. Different modes of operation of the DES are investigated for watermark image encryption. These modes are the electronic code book, cipher block chaining, cipher feedback, and output feedback (OFB) modes. Simulation results reveal that the DES with OFB mode and the chaotic Baker map encryption make the system less sensitive to attacks with good quality of extracted watermarks.     

Face recognition using multi-class Logical Analysis of Data

This paper addresses the applicability of multi-class Logical Analysis of Data (LAD) as a face recognition technique (FRT). This new classification technique has already been applied in the field of biomedical and mechanical engineering as a diagnostic technique; however, it has never been used in the face recognition literature. We explore how Eigenfaces and Fisherfaces merged to multi-class LAD can be leveraged as a proposed FRT, and how it might be useful compared to other approaches. The aim is to build a single multi-class LAD decision model that recognizes images of the face of different persons. We show that our proposed FRT can effectively deal with multiple changes in the pose and facial expression, which constitute critical challenges in the literature. Comparisons are made both from analytical and practical point of views. The proposed model improves the classification of Eigenfaces and Fisherfaces with minimum error rate.