Molecular docking interaction of mycobacterium tuberculosis lipb enzyme with isoniazid, pyrazinamide and a structurally altered drug 2, 6 dimethoxyisonicotinohydrazide
- Authors: Namasivayam, Muthuraman
- Date: 2015
- Type: Text , Journal article
- Relation: Computational biology and bioinformatics (Print) Vol. 3, no. 4 (2015), p. 45
- Full Text:
- Reviewed:
- Description: Tuberculosis is an infectious airborne disease caused by a bacterial infection that affects the lungs and other parts of the body. Vaccination against tuberculosis is available but proved to be unsuccessful against emerging multi drug and extensive drug resistant bacterial strains. This in turn raises the pressure to speed up the research on developing new and more efficient anti-tuberculosis drugs. Lipoate biosynthesis protein B (LipB) is found to play vital role in the lipoylation process in Mycobacterium tuberculosis and thus making it a very promising drug target. The existing first line drugs such as Isoniazid, Pyrazinamide and Rifampicin etc shows only profound binding affinity with this target protein. Therefore, new or modified drugs with better docking approach that exhibit a closer and stronger binding affinity is essential. This current study opens up a novel approach towards anti-tuberculosis agents by determining drugs that share similar structures with some of the best available first line drug and also happen to possess better binding affinity. In this article, a computational method by which, pristine as well certain first line and structurally modified drugs were docked with the LipB protein target; where, structurally modified 2, 6 Dimethoxyisonicotinohydrazide show superior target docking.
Credit scoring model based on a novel group feature selection method : the case of Chinese small-sized manufacturing enterprises
- Authors: Zhang, Zhipeng , Chi, Guotai , Colombage, Sisira , Zhou, Ying
- Date: 2022
- Type: Text , Journal article
- Relation: Journal of the Operational Research Society Vol. 73, no. 1 (2022), p. 122-138
- Full Text: false
- Reviewed:
- Description: In building a predictive credit scoring model, feature selection is an essential pre-processing step that can improve the predictive accuracy and comprehensibility of models. In this study, we select the optimal feature subset based on group feature selection in lieu of the individual feature selection method, to establish a credit scoring model for small manufacturing enterprises. In our methodology, we first select a group of features using the 0-1 programming method, with the objective function of maximising the Gini coefficient (GINI) of the credit score to identify the possibility of default. Then we introduce constraints to remove any redundant features in the same subset, provided they reflect the same information. Finally, we assign weights to different features according to the Gini coefficient, ensuring that the weight of the features reflects their discriminatory power. Our empirical results show that the selection of a set of features more effectively identifies default status than the individual feature selection approach. Moreover, a rating system with more features does not necessarily have better discriminatory power. As the number of features exceeds the optimum number of features selected, the system's discriminatory ability begins to decrease. © Operational Research Society 2022.
An efficient network intrusion detection and classification system
- Authors: Ahmad, Iftikhar , Haq, Qazi , Imran, Muhammad , Alassafi, Madini , Alghamdi, Rayed
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics Vol. 10, no. 3 (2022), p.
- Full Text:
- Reviewed:
- Description: Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Integral sliding mode control design for systems with fast sensor dynamics
- Authors: Banza, Arnold , Tan, Ying , Mareels, Iven
- Date: 2020
- Type: Text , Journal article
- Relation: Automatica Vol. 119, no. (2020), p. 109093
- Full Text: false
- Reviewed:
- Description: This paper presents a new integral sliding mode control (SMC) algorithm that can handle matched uncertainties in the presence of the given fast sensor dynamics. More precisely, by selecting the sliding surface that is sufficiently faster than the sensor dynamics, it is possible to maintain unaffected the time-scale separation between sensors and plant. A modified singular perturbation technique is used to show some semi-global practical asymptotic stability of the closed loop system. By incorporating the knowledge of sensors into the SMC design and by tuning the parameters of the proposed integral SMC appropriately, the main result shows that the closed loop system can converge to a small neighborhood of the origin (the ultimate bound) from some given domain of attraction. Both the ultimate bound and the domain of the attraction are dependent of the time-scale parameter that is related to the sensor dynamics. Simulation results are presented to show the effectiveness of the proposed approach.
Optimal fuzzy proportional-integral-derivative control for a class of fourth-order nonlinear systems using imperialist competitive algorithms
- Authors: Hadipour, Lakmesari, S. , Safipour, Z. , Mahmoodabadi, Mohammad Javad , Ibrahim, Yousef , Mobayen, Saleh
- Date: 2022
- Type: Text , Journal article
- Relation: Complexity Vol. 2022, no. (2022), p. 1-13
- Full Text:
- Reviewed:
- Description: The proportional integral derivative (PID) controller has gained wide acceptance and use as the most useful control approach in the industry. However, the PID controller lacks robustness to uncertainties and stability under disturbances. To address this problem, this paper proposes an optimal fuzzy-PID technique for a two-degree-of-freedom cart-pole system. Fuzzy rules can be combined with controllers such as PID to tune their coefficients and allow the controller to deliver substantially improved performance. To achieve this, the fuzzy logic method is applied in conjunction with the PID approach to provide essential control inputs and improve the control algorithm efficiency. The achieved control gains are then optimized via the imperialist competitive algorithm. Consequently, the objective function for the cart-pole system is regarded as the summation of the displacement error of the cart, the angular error of the pole, and the control force. This control concept has been tested via simulation and experimental validations. Obtained results are presented to confirm the accuracy and efficiency of the suggested method. © 2022 S. Hadipour Lakmesari et al.
Reinforcement learning for adaptive optimal control of continuous-time linear periodic systems
- Authors: Pang, Bo , Jiang, Zhong-Ping , Mareels, Iven
- Date: 2020
- Type: Text , Journal article
- Relation: Automatica Vol. 118, no. (2020), p. 109035
- Full Text: false
- Reviewed:
- Description: This paper studies the infinite-horizon adaptive optimal control of continuous-time linear periodic (CTLP) systems, using reinforcement learning techniques. By means of policy iteration (PI) for CTLP systems, both on-policy and off-policy adaptive dynamic programming (ADP) algorithms are derived, such that the solution of the optimal control problem can be found without the exact knowledge of the system dynamics. Starting with initial stabilizing controllers, the proposed PI-based ADP algorithms converge to the optimal solutions under mild conditions. Application to the adaptive optimal control of the lossy Mathieu equation demonstrates the efficacy of the proposed learning-based adaptive optimal control algorithm.
Subgraph adaptive structure-aware graph contrastive learning
- Authors: Chen, Zhikui , Peng, Yin , Yu, Shuo , Cao, Chen , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics (Basel) Vol. 10, no. 17 (2022), p. 3047
- Full Text:
- Reviewed:
- Description: Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.
Fairness-aware predictive graph learning in social networks
- Authors: Wang, Lei , Yu, Shuo , Febrinanto, Falih , Alqahtani, Fayez , El-Tobely, Tarek
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics Vol. 10, no. 15 (2022), p.
- Full Text:
- Reviewed:
- Description: Predictive graph learning approaches have been bringing significant advantages in many real-life applications, such as social networks, recommender systems, and other social-related downstream tasks. For those applications, learning models should be able to produce a great prediction result to maximize the usability of their application. However, the paradigm of current graph learning methods generally neglects the differences in link strength, leading to discriminative predictive results, resulting in different performance between tasks. Based on that problem, a fairness-aware predictive learning model is needed to balance the link strength differences and not only consider how to formulate it. To address this problem, we first formally define two biases (i.e., Preference and Favoritism) that widely exist in previous representation learning models. Then, we employ modularity maximization to distinguish strong and weak links from the quantitative perspective. Eventually, we propose a novel predictive learning framework entitled ACE that first implements the link strength differentiated learning process and then integrates it with a dual propagation process. The effectiveness and fairness of our proposed ACE have been verified on four real-world social networks. Compared to nine different state-of-the-art methods, ACE and its variants show better performance. The ACE framework can better reconstruct networks, thus also providing a high possibility of resolving misinformation in graph-structured data. © 2022 by the authors.
Relational structure-aware knowledge graph representation in complex space
- Authors: Sun, Ke , Yu, Shuo , Peng, Ciyuan , Wang, Yueru , Alfarraj, Osama , Tolba, Amr , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics Vol. 10, no. 11 (2022), p.
- Full Text:
- Reviewed:
- Description: Relations in knowledge graphs have rich relational structures and various binary relational patterns. Various relation modelling strategies are proposed for embedding knowledge graphs, but they fail to fully capture both features of relations, rich relational structures and various binary relational patterns. To address the problem of insufficient embedding due to the complexity of the relations, we propose a novel knowledge graph representation model in complex space, namely MARS, to exploit complex relations to embed knowledge graphs. MARS takes the mechanisms of complex numbers and message-passing and then embeds triplets into relation-specific complex hyperplanes. Thus, MARS can well preserve various relation patterns, as well as structural information in knowledge graphs. In addition, we find that the scores generated from the score function approximate a Gaussian distribution. The scores in the tail cannot effectively represent triplets. To address this particular issue and improve the precision of embeddings, we use the standard deviation to limit the dispersion of the score distribution, resulting in more accurate embeddings of triplets. Comprehensive experiments on multiple benchmarks demonstrate that our model significantly outperforms existing state-of-the-art models for link prediction and triple classification. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Robust graph neural networks via ensemble learning
- Authors: Lin, Qi , Yu, Shuo , Sun, Ke , Zhao, Wenhong , Alfarraj, Osama , Tolba, Amr , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics Vol. 10, no. 8 (2022), p.
- Full Text:
- Reviewed:
- Description: Graph neural networks (GNNs) have demonstrated a remarkable ability in the task of semi-supervised node classification. However, most existing GNNs suffer from the nonrobustness issues, which poses a great challenge for applying GNNs into sensitive scenarios. Some researchers concentrate on constructing an ensemble model to mitigate the nonrobustness issues. Nevertheless, these methods ignore the interaction among base models, leading to similar graph representations. Moreover, due to the deterministic propagation applied in most existing GNNs, each node highly relies on its neighbors, leaving the nodes to be sensitive to perturbations. Therefore, in this paper, we propose a novel framework of graph ensemble learning based on knowledge passing (called GEL) to address the above issues. In order to achieve interaction, we consider the predictions of prior models as knowledge to obtain more reliable predictions. Moreover, we design a multilayer DropNode propagation strategy to reduce each node’s dependence on particular neighbors. This strategy also empowers each node to aggregate information from diverse neighbors, alleviating oversmoothing issues. We conduct experiments on three benchmark datasets, including Cora, Citeseer, and Pubmed. GEL outperforms GCN by more than 5% in terms of accuracy across all three datasets and also performs better than other state-of-the-art baselines. Extensive experimental results also show that the GEL alleviates the nonrobustness and oversmoothing issues. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Transversality, regularity and error bounds in variational analysis and optimisation
- Authors: Cuong, Nguyen
- Date: 2022
- Type: Text , Journal article
- Relation: Bulletin of the Australian Mathematical Society Vol. 106, no. 1 (2022), p. 167-169
- Full Text:
- Reviewed:
Topological transcendental fields
- Authors: Chalebgwa, Taboka , Morris, Sidney
- Date: 2022
- Type: Text , Journal article
- Relation: Axioms Vol. 11, no. 3 (2022), p.
- Full Text:
- Reviewed:
- Description: This article initiates the study of topological transcendental fields F which are subfields of the topological field C of all complex numbers such that F only consists of rational numbers and a nonempty set of transcendental numbers. F, with the topology it inherits as a subspace of C, is a topological field. Each topological transcendental field is a separable metrizable zero-dimensional space and algebraically is Q(T), the extension of the field of rational numbers by a set T of transcendental numbers. It is proven that there exist precisely 222ℵ0 of topological transcendental fields of the form ℚ(𝑇) with T a set of Liouville numbers, no two of which are homeomorphic.
- Description: This article initiates the study of topological transcendental fields F which are subfields of the topological field C of all complex numbers such that F only consists of rational numbers and a nonempty set of transcendental numbers. F, with the topology it inherits as a subspace of C, is a topological field. Each topological transcendental field is a separable metrizable zero-dimensional space and algebraically is Q(T), the extension of the field of rational numbers by a set T of transcendental numbers. It is proven that there exist precisely 2
The effects of vaporisation, condensation and diffusion of water inside the tissue during saline-infused radiofrequency ablation of the liver: A computational study
- Authors: Kho, Antony , Ooi, Ean , Foo, Ji , Ooi, Ean Tat
- Date: 2022
- Type: Text , Journal article
- Relation: International journal of heat and mass transfer Vol. 194, no. (2022), p. 123062
- Full Text: false
- Reviewed:
- Description: •Effect of vaporisation, condensation & diffusion on saline-infused RFA was studied.•Condensation significantly affects the prediction of the RFA treatment outcome.•Water diffusion is insignificant when compared to condensation.•The model serves as benchmark for the accurate modelling of saline-infused RFA. [Display omitted] Saline-infused radiofrequency ablation (RFA) is a thermal ablation technique that combines saline infusion and Joule heating to destroy cancer tissues. During treatment, the intense heat generated can cause water from the infused saline and inside the tissue to vaporise. Conventionally, the effects of vaporisation have been modelled by adopting the apparent heat capacity method. However, this approach does not account for the loss of water content during vaporisation, which raises questions on its accuracy, primarily because of the large water content present during saline-infused RFA. To address this, the present study proposes an alternative approach to model vaporisation effects during saline-infused RFA. The approach adopts and modifies the water fraction method to account for the effects of vaporisation, condensation and diffusion of water inside the tissue during saline-infused RFA. The framework was compared against the commonly used apparent heat capacity method through numerical simulations carried out on 3D finite element models of the liver. Results indicated that unlike condensation, the role of diffusion of water during saline-infused RFA was not as significant as condensation, where the latter was found to affect the ablation process. With the water fraction method, there was a trend of exponential decrease in tissue electrical conductivity with time, which ultimately led to the prediction of smaller coagulation volume than that of the apparent heat capacity method.
A reliable image quality assessment metric : evaluation using camera impacts
- Authors: Kaur, Roopdeep , Karmakar, Gour , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Pattern Recognition and Image Analysis Vol. 32, no. 3 (2022), p. 551-560
- Full Text:
- Reviewed:
- Description: Abstract: Image analysis is being applied in many applications including industrial automation with the Industrial Internet of Things and machine vision. The images captured by cameras, especially from the outdoor environment are impacted by various parameters such as lens blur, dirty lens, and lens distortion (barrel distortion). There exist many approaches that assess the impact of camera parameters on the quality of the images. However, most of these techniques do not use important quality assessment metrics such as oriented FAST and rotated BRIEF, and structural content. None of these techniques objectively evaluate the impact of barrel distortion on the image quality using quality assessment metrics such as mean square error, peak signal-to-noise ratio, structural content, oriented FAST, and rotated BRIEF, and structural similarity index. In this paper, besides lens dirtiness and blurring, we also examine the impact of barrel distortion using various types of datasets having different levels of barrel distortion. Analysis shows none of the existing metrics produces quality values consistent with intuitively defined impact levels for lens blur, dirtiness, and barrel distortion. To address the loopholes of existing metrics and make the quality assessment metric more reliable, we propose a new image quality assessment metric that fuses the quality values obtained from different metrics using a decision fusion technique known as the Dempster–Shafer theory. Our proposed metric produces quality values that are more consistent and conform with the perceptually defined camera parameter impact levels. For all the above-mentioned camera impacts, our proposed metric exhibits 100% assessment reliability, which includes an enormous improvement over other metrics. © 2022, Pleiades Publishing, Ltd.
Mechanistic modelling of bubble growth in sodium pool boiling
- Authors: Iyer, Siddharth , Kumar, Apurv , Coventry, Joe , Lipiński, Wojciech
- Date: 2023
- Type: Text , Journal article
- Relation: Applied Mathematical Modelling Vol. 117, no. (2023), p. 336-358
- Full Text: false
- Reviewed:
- Description: This work presents a mechanistic model to simulate the growth of a sodium bubble from nucleation to departure in sodium pool boiling. A previously developed and validated heat transfer sub-model is coupled to a force balance sub-model to predict the growth rate and departure radius of a sodium bubble. The model accounts for the change in the contact angle of a bubble as it grows, and the shrinkage of the bubble base prior to departure. The developed model is used to quantify and analyse the heat transfer from different regions, i.e. the microlayer, the macrolayer, the thermal boundary layer and the bulk liquid surrounding the bubble. In addition, bubble growth rate and departure radius are calculated for different values of wall superheat, rate of change of contact angle and bulk liquid temperature. It is found that the departure radius of a sodium bubble is on the order of a few centimetres and the wall superheat has a significant influence on the shape of a sodium bubble at departure. © 2022 Elsevier Inc.