Search results for: mathematical algorithms of targeting and persecution

Authors: Thanakrit Taweesoontorn, Sarucha Yanyong, Poom Konghuayrob

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Robotic arms have gained popularity in various industries due to their accuracy and efficiency. This research proposes a method for bin-picking tasks using the Cobot, combining the YOLOv5 CNNs model for object detection and pose estimation with traditional feature detection (FAST), feature description (BRISK), and matching algorithms. By integrating these algorithms and utilizing a small-scale depth sensor camera for capturing depth and color images, the system achieves real-time object detection and accurate pose estimation, enabling the robotic arm to pick objects correctly in both position and orientation. Furthermore, the proposed method is implemented within the ROS framework to provide a seamless platform for robotic control and integration. This integration of robotics, cameras, and AI technology contributes to the development of industrial robotics, opening up new possibilities for automating challenging tasks and improving overall operational efficiency.

Keywords: robotic vision, image processing, applications of robotics, artificial intelligent

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Authors: Hirofumi Miyajima, Kazuya Kishida, Noritaka Shigei, Hiromi Miyajima

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Most of self-tuning fuzzy systems, which are automatically constructed from learning data, are based on the steepest descent method (SDM). However, this approach often requires a large convergence time and gets stuck into a shallow local minimum. One of its solutions is to use fuzzy rule modules with a small number of inputs such as DIRMs (Double-Input Rule Modules) and SIRMs (Single-Input Rule Modules). In this paper, we consider a (generalized) DIRMs model composed of double and single-input rule modules. Further, in order to reduce the redundant modules for the (generalized) DIRMs model, pruning and generative learning algorithms for the model are suggested. In order to show the effectiveness of them, numerical simulations for function approximation, Box-Jenkins and obstacle avoidance problems are performed.

Keywords: Box-Jenkins's problem, double-input rule module, fuzzy inference model, obstacle avoidance, single-input rule module

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Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Ogunrinde Roseline Bosede, Ogunrinde Rowland Rotimi

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Just as the development of ideas of early mathematics was essentially motivated by social needs, the invention of the computer was equally inspired by social needs. The early years of the twenty-first century have been remarkable in advances in mathematical and computer sciences. Mathematical and computer sciences work are fast becoming an increasingly integral and essential components of a growing catalogues of areas of interests in biology, business, military, medicine, social sciences, advanced design, advanced materials, climate, banking and finance, and many other fields of disciplines. This paper seeks to highlight the trend and impacts of the duo in the technological advancements being witnessed in our today's world.

Keywords: computer, impacts, mathematics, modern society

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Authors: Osama A. Omer, Amal Hamed

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Successful blind image Super-Resolution algorithms require the exact estimation of the Point Spread Function (PSF). In the absence of any prior information about the imagery system and the true image; this estimation is normally done by trial and error experimentation until an acceptable restored image quality is obtained. Multi-frame blind Super-Resolution algorithms often have disadvantages of slow convergence and sensitiveness to complex noises. This paper presents a Super-Resolution image reconstruction algorithm based on estimation of the PSF that yields the optimum restored image quality. The estimation of PSF is performed by the knife-edge method and it is implemented by measuring spreading of the edges in the reproduced HR image itself during the reconstruction process. The proposed image reconstruction approach is using L1 norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. A series of experiment results show that the proposed method can outperform other previous work robustly and efficiently.

Keywords: blind, PSF, super-resolution, knife-edge, blurring, bilateral, L1 norm

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Authors: Muna F. Al-Sammaraie

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The aim of this research is to present a multi spectral image enhancement methods used to achieve highly real digital image populates only a small portion of the available range of digital values. Also, a quantitative measure of image enhancement is presented. This measure is related with concepts of the Webers Low of the human visual system. For decades, several image enhancement techniques have been proposed. Although most techniques require profuse amount of advance and critical steps, the result for the perceive image are not as satisfied. This study involves changing the original values so that more of the available range is used; then increases the contrast between features and their backgrounds. It consists of reading the binary image on the basis of pixels taking them byte-wise and displaying it, calculating the statistics of an image, automatically enhancing the color of the image based on statistics calculation using algorithms and working with RGB color bands. Finally, the enhanced image is displayed along with image histogram. A number of experimental results illustrated the performance of these algorithms. Particularly the quantitative measure has helped to select optimal processing parameters: the best parameters and transform.

Keywords: image enhancement, multi-spectral, RGB, histogram

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Authors: Mohamed Amarouayache, Badia Amrouche, Gharbi Akila, Boukadoume Mohamed

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This work is devoted to the modeling of DC-DC converter (boost) used for MPPT applications to set conditions of stability. For this, we establish a linear mathematical model of the DC-DC converter with an average small signal model. This model has allowed us to apply conventional linear methods of automation. A mathematical relationship between the duty cycle and the voltage of the panel has been set up. With this relationship we specify the conditions of the stability in closed-loop depending on the system parameters (the elements of storage capacity and inductance, PWM control).

Keywords: MPPT, PWM, stability, criterion of Routh, average small signal model

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Authors: Hina Iqbal, Mustansar Ali Ghazanfar, Sandor Szedmak

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Recommender systems uses artificial intelligence practices for filtering obscure information and can predict if a user likes a specified item. Kernel mapping Recommender systems have been proposed which are accurate and state-of-the-art algorithms and resolve recommender system’s design objectives such as; long tail, cold-start, and sparsity. The aim of research is to propose hybrid framework that can efficiently integrate different versions— namely item-based and user-based KMR— of KMR algorithm. We have proposed various heuristic algorithms that integrate different versions of KMR (into a unified framework) resulting in improved accuracy and elimination of problems associated with conventional recommender system. We have tested our system on publically available movies dataset and benchmark with KMR. The results (in terms of accuracy, precision, recall, F1 measure and ROC metrics) reveal that the proposed algorithm is quite accurate especially under cold-start and sparse scenarios.

Keywords: Kernel Mapping Recommender Systems, hybrid recommender systems, cold start, sparsity, long tail

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Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

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Authors: Zeyad Abdelmageid, Xianbin Wang

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Choosing the operational channel for a WLAN access point (AP) in WLAN networks has been a static channel assignment process initiated by the user during the deployment process of the AP, which fails to cope with the dynamic conditions of the assigned channel at the station side afterward. However, the dramatically growing number of Wi-Fi APs and stations operating in the unlicensed band has led to dynamic, distributed, and often severe interference. This highlights the urgent need for the AP to dynamically select the best overall channel of operation for the basic service set (BSS) by considering the distributed and changing channel conditions at all stations. Consequently, dynamic channel selection algorithms which consider feedback from the station side have been developed. Despite the significant performance improvement, existing channel selection algorithms suffer from very high feedback overhead. Feedback latency from the STAs, due to the high overhead, can cause the eventually selected channel to no longer be optimal for operation due to the dynamic sharing nature of the unlicensed band. This has inspired us to develop our own dynamic channel selection algorithm with reduced overhead through the proposed low-overhead, cluster-based station reporting mechanism. The main idea behind the cluster-based station reporting is the observation that STAs which are very close to each other tend to have very similar channel conditions. Instead of requesting each STA to report on every candidate channel while causing high overhead, the AP divides STAs into clusters then assigns each STA in each cluster one channel to report feedback on. With the proper design of the cluster based reporting, the AP does not lose any information about the channel conditions at the station side while reducing feedback overhead. The simulation results show equal performance and, at times, better performance with a fraction of the overhead. We believe that this algorithm has great potential in designing future dynamic channel selection algorithms with low overhead.

Keywords: channel assignment, Wi-Fi networks, clustering, DBSCAN, overhead

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Authors: Ashkan Zakaryazad, Ekrem Duman

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A typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently.

Keywords: neural network, profit-based neural network, sum of squared errors (SSE), MBO, gradient descent

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Authors: Chakib Jerry, Mounir Jerry

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In this paper, a mathematical model of infested honey bees colonies is formulated in order to investigate Colony Collapse Disorder in a honeybee colony. CCD, as it is known, is a major problem on honeybee farms because of the massive decline in colony numbers. We introduce to the model a control variable which represents forager protection. We study the controlled model to derive conditions under which the bee colony can fight off epidemic. Secondly we study the problem of minimizing prevention cost under model’s dynamics constraints.

Keywords: honey bee, disease transmission model, disease control honeybees, optimal control

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Authors: K. Sathishkumar, V. Thiagarasu

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Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

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Authors: Amal Alshahrani, Allan Ramsay

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Paraphrasing is one of the important tasks in natural language processing; i.e. alternative ways to express the same concept by using different words or phrases. Paraphrases can be used in many natural language applications, such as Information Retrieval, Machine Translation, Question Answering, Text Summarization, or Information Extraction. To obtain pairs of sentences that are paraphrases we create a system that automatically extracts paraphrases from a corpus, which is built from different sources of news article since these are likely to contain paraphrases when they report the same event on the same day. There are existing simple standard approaches (e.g. TF-IDF vector space, cosine similarity) and alignment technique (e.g. Dynamic Time Warping (DTW)) for extracting paraphrase which have been applied to the English. However, the performance of these approaches could be affected when they are applied to another language, for instance Arabic language, due to the presence of phenomena which are not present in English, such as Free Word Order, Zero copula, and Pro-dropping. These phenomena will affect the performance of these algorithms. Thus, if we can analysis how the existing algorithms for English fail for Arabic then we can find a solution for Arabic. The results are promising.

Keywords: natural language processing, TF-IDF, cosine similarity, dynamic time warping (DTW)

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Authors: Rolivhuwa Bishop Ramagoma1*, Lynn Cairncross1, , Saartjie Roux1

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According to the world health organisation, colon cancer is among the most common cancers diagnosed in both men and women. Specifically, it is the second leading cause of cancer related deaths accounting for over 860 000 deaths worldwide in 2018. Currently, chemotherapy has become an essential component of most cancer treatments. Despite progress in cancer drug development over the previous years, traditional chemotherapeutic drugs still have low selectivity for targeting tumour tissues and are frequently constrained by dose-limiting toxicity. The creation of nanoscale delivery vehicles capable of directly directing treatment into cancer cells has recently caught the interest of researchers. Herein, the development of peptide-functionalized polyethylene glycol gold nanoparticles (Peptide-PEG-AuNPs) as a cellular probe and delivery agent is described, with the higher aim to develop a specific diagnostic prototype and assess their specificity not only against cell lines but primary human cells as well. Gold nanoparticles (AuNPs) were synthesized and stabilized through chemical conjugation. The synthesized AuNPs were characterized, stability in physiological solutions was assessed, their cytotoxicity against colon carcinoma and non-carcinoma skin fibroblasts was also studied. Furthermore, genetic effect through real-time polymerase chain reaction (RT-PCR), localization and uptake, peptide specificity were also determined. In this study, different peptide-AuNPs were found to have preferential toxicity at higher concentrations, as revealed by cell viability assays, however, all AuNPs presented immaculate stability for over 3 months following the method of synthesis. The final obtained peptide-PEG-AuNP conjugates showed good biocompatibility in the presence of high ionic solutions and biological media and good cellular uptake. Formulation of colon cancer specific targeting peptide was successful, additionally, the genes/pathways affected by the treatments were determined through RT-PCR. Primary cells study is still on going with promising results thus far.

Keywords: nanotechnology, cancer, diagnosis, therapeutics, gold nanoparticles.

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Authors: H. S. Saini, K. Vijayalakshmi, Rishi Sayal

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Network-based education has been growing rapidly in size and quality. Knowledge clustering becomes more important in personalized information retrieval for web-learning. A personalized-Learning service after the learners’ knowledge has been classified with clustering. Through automatic analysis of learners’ behaviors, their partition with similar data level and interests may be discovered so as to produce learners with contents that best match educational needs for collaborative learning. We present a specific mining tool and a recommender engine that we have integrated in the online learning in order to help the teacher to carry out the whole e-learning process. We propose to use sequential pattern mining algorithms to discover the most used path by the students and from this information can recommend links to the new students automatically meanwhile they browse in the course. We have Developed a specific author tool in order to help the teacher to apply all the data mining process. We tend to report on many experiments with real knowledge so as to indicate the quality of using both clustering and sequential pattern mining algorithms together for discovering personalized e-learning systems.

Keywords: e-learning, cluster, personalization, sequence, pattern

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Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

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Authors: Saeed Anwar

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Foreign aid has long been a prominent tool in the pursuit of economic development in recipient countries. This research paper aims to analyze the impact of foreign aid on economic development and explore the effectiveness of aid in promoting sustainable growth, poverty reduction, and improvements in human development indicators. Drawing upon a comprehensive review of existing literature, both theoretical frameworks and empirical evidence are synthesized to provide insights into the complex relationship between foreign aid and economic development. The paper examines various channels through which foreign aid influences economic development, including infrastructure development, education and healthcare investments, technology transfer, and institutional capacity building. It explores the potential positive effects of aid in stimulating economic growth, reducing poverty, and enhancing human capital formation. Additionally, it investigates the potential challenges and limitations associated with aid, such as aid dependency, governance issues, and the potential crowding out of domestic resources. Furthermore, the study assesses the heterogeneity of aid effectiveness across different types of aid modalities, recipient country characteristics, and aid allocation mechanisms. It considers the role of aid conditionality, aid fragmentation, and aid targeting in influencing the effectiveness of aid in promoting economic development. The findings of this research contribute to the ongoing discourse on foreign aid and economic development by providing a comprehensive analysis of the existing literature. The study highlights the importance of context-specific factors, recipient country policies, and aid effectiveness frameworks in determining the impact of foreign aid on economic development outcomes. The insights derived from this research can inform policymakers, donor agencies, and practitioners in designing and implementing effective aid strategies to maximize the positive impact of foreign aid on economic development.

Keywords: foreign aid, economic development, sustainable growth, poverty reduction, human development indicators, infrastructure development, education, healthcare, technology transfer, institutional capacity building, aid effectiveness, aid dependency, governance, crowding out, aid conditionality, aid fragmentation, aid targeting, recipient country policies, aid strategies, donor agencies, policymaking

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Authors: Yuhanis Yusof, Zuriani Mustaffa, Siti Sakira Kamaruddin

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Data mining is a fundamental technique in identifying patterns from large data sets. The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical. Prior to that, data are consolidated so that the resulting mining process may be more efficient. This study investigates the effect of different data normalization techniques, which are Min-max, Z-score, and decimal scaling, on Swarm-based forecasting models. Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC). Forecasting models are later developed to predict the daily spot price of crude oil and gasoline. Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max. Nevertheless, the GWO is more superior that ABC as its model generates the highest accuracy for both crude oil and gasoline price. Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.

Keywords: artificial bee colony, data normalization, forecasting, Grey Wolf optimizer

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Authors: Amira Abdelrasoul, Huu Doan, Ali Lohi

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An efficient and well-planned ultrafiltration process is becoming a necessity for monetary returns in the industrial settings. The aim of the present study was to develop a mathematical model for an accurate prediction of ultrafiltration membrane fouling of latex effluent applied to homogeneous and heterogeneous membranes with uniform and non-uniform pore sizes, respectively. The models were also developed for an accurate prediction of power consumption that can handle the large-scale purposes. The model incorporated the fouling attachments as well as chemical and physical factors in membrane fouling for accurate prediction and scale-up application. Both Polycarbonate and Polysulfone flat membranes, with pore sizes of 0.05 µm and a molecular weight cut-off of 60,000, respectively, were used under a constant feed flow rate and a cross-flow mode in ultrafiltration of the simulated paint effluent. Furthermore, hydrophilic ultrafilic and hydrophobic PVDF membranes with MWCO of 100,000 were used to test the reliability of the models. Monodisperse particles of 50 nm and 100 nm in diameter, and a latex effluent with a wide range of particle size distributions were utilized to validate the models. The aggregation and the sphericity of the particles indicated a significant effect on membrane fouling.

Keywords: membrane fouling, mathematical modeling, power consumption, attachments, ultrafiltration

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Authors: M. Khoshab, M. J. Sedigh

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Dynamic systems, which in mathematical point of view are those governed by differential equations, are much more difficult to study and to predict their behavior in comparison with static systems which are governed by algebraic equations. Economical systems such as market are among complicated dynamic systems. This paper tries to adopt a very simple mathematical model for market and to study effect of supply and demand function on behavior of the market while the supply side experiences a lag due to production restrictions.

Keywords: dynamic system, lag on supply demand, market stability, supply demand model

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Authors: Ernest Moles, Patricia Urbán, María Belén Jiménez-Díaz, Sara Viera-Morilla, Iñigo Angulo-Barturen, Maria Antònia Busquets, Xavier Fernàndez-Busquets

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Bearing in mind the absence of an effective vaccine against malaria and its severe clinical manifestations causing nearly half a million deaths every year, this disease represents nowadays a major threat to life. Besides, the basic rationale followed by currently marketed antimalarial approaches is based on the administration of drugs on their own, promoting the emergence of drug-resistant parasites owing to the limitation in delivering drug payloads into the parasitized erythrocyte high enough to kill the intracellular pathogen while minimizing the risk of causing toxic side effects to the patient. Such dichotomy has been successfully addressed through the specific delivery of immunoliposome (iLP)-encapsulated antimalarials to Plasmodium falciparum-infected red blood cells (pRBCs). Unfortunately, this strategy has not progressed towards clinical applications, whereas in vitro assays rarely reach drug efficacy improvements above 10-fold. Here, we show that encapsulation efficiencies reaching >96% can be achieved for the weakly basic drugs chloroquine (CQ) and primaquine using the pH gradient active loading method in liposomes composed of neutrally charged, saturated phospholipids. Targeting antibodies are best conjugated through their primary amino groups, adjusting chemical crosslinker concentration to retain significant antigen recognition. Antigens from non-parasitized RBCs have also been considered as targets for the intracellular delivery of drugs not affecting the erythrocytic metabolism. Using this strategy, we have obtained unprecedented nanocarrier targeting to early intraerythrocytic stages of the malaria parasite for which there is a lack of specific extracellular molecular tags. Polyethylene glycol-coated liposomes conjugated with monoclonal antibodies specific for the erythrocyte surface protein glycophorin A (anti-GPA iLP) were capable of targeting 100% RBCs and pRBCs at the low concentration of 0.5 μM total lipid in the culture, with >95% of added iLPs retained into the cells. When exposed for only 15 min to P. falciparum in vitro cultures synchronized at early stages, free CQ had no significant effect over parasite viability up to 200 nM drug, whereas iLP-encapsulated 50 nM CQ completely arrested its growth. Furthermore, when assayed in vivo in P. falciparum-infected humanized mice, anti-GPA iLPs cleared the pathogen below detectable levels at a CQ dose of 0.5 mg/kg. In comparison, free CQ administered at 1.75 mg/kg was, at most, 40-fold less efficient. Our data suggest that this significant improvement in drug antimalarial efficacy is in part due to a prophylactic effect of CQ found by the pathogen in its host cell right at the very moment of invasion.

Keywords: immunoliposomal nanoparticles, malaria, prophylactic-therapeutic polyvalent activity, targeted drug delivery

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Authors: Mike Omilusi

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Since the Buhari administration came to power, it has gained the people’s confidence with its anti-corruption efforts. Making culprits account for their past unlawful deeds, in a very determined and aggressive manner ever witnessed in the nation’s political history, generates different reactions among Nigerians. However, some questions remain pertinent to this study: Are Nigerians really advocating persecution or prosecution in respect of the graft suspects? Do they want conviction without being convinced? Is their outburst propelled by emotions and revengeful anticipation of having suspected looters of the nation’s commonwealth behind bars? Can the war be successfully fought without resorting to impunity? Relying extensively on secondary sources with the aid of descriptive and narrative tools, this study seeks to interrogate the claim of fundamental human rights in the face of wanton looting of the nation’s resources. If, as opined by President Buhari, corruption is a crime against humanity, then it is argued that those who commit such crime should be subjected to penalties prescribed by law. Such crime -as corruption in this study- deprives the citizens of welfare, social amenities and good things of life. In this instance, it also poses threats to national security, having misappropriated funds meant for the war against the Boko Haram terrorism as revealed by the anti-corruption agency in the country. A theoretically-driven investigation, this essay raises some expectations within the context of good governance-propelled anti-corruption crusade, making modest recommendations as to how corruption should be prevented and combated within the confine of rule of law.

Keywords: corruption, rule of law, human rights, prosecution, commonwealth

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Authors: Watcharapan Sukkerd, Teeradej Wuttipornpun

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This paper aims to present non-population search algorithms called tabu search (TS), simulated annealing (SA) and variable neighborhood search (VNS) to minimize the total cost of capacitated MRP problem in multi-stage assembly flow shop with two alternative machines. There are three main steps for the algorithm. Firstly, an initial sequence of orders is constructed by a simple due date-based dispatching rule. Secondly, the sequence of orders is repeatedly improved to reduce the total cost by applying TS, SA and VNS separately. Finally, the total cost is further reduced by optimizing the start time of each operation using the linear programming (LP) model. Parameters of the algorithm are tuned by using real data from automotive companies. The result shows that VNS significantly outperforms TS, SA and the existing algorithm.

Keywords: capacitated MRP, tabu search, simulated annealing, variable neighborhood search, linear programming, assembly flow shop, application in industry

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Authors: Vrushali Guhe, Shailza Singh

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Cutaneous leishmaniasis is neglected tropical disease present worldwide caused by the protozoan parasite Leishmania major, the therapeutic armamentarium for leishmaniasis are showing several limitations as drugs are showing toxic effects with increasing resistance by a parasite. Thus identification of novel therapeutic targets is of paramount importance. Previous studies have shown that autophagy, a cellular process, can either facilitate infection or aid in the elimination of the parasite, depending on the specific parasite species and host background in leishmaniasis. In the present study, our objective was to target the essential autophagy protein ATG8, which plays a crucial role in the survival, infection dynamics, and differentiation of the Leishmania parasite. ATG8 in Leishmania major and its homologue, LC3, in Homo sapiens, act as autophagic markers. Present study manifested the crucial role of ATG8 protein as a potential target for combating Leishmania major infection. Through bioinformatics analysis, we identified non-conserved motifs within the ATG8 protein of Leishmania major, which are not present in LC3 of Homo sapiens. Against these two non-conserved motifs, we generated a peptide library of 60 peptides on the basis of physicochemical properties. These peptides underwent a filtering process based on various parameters, including feasibility of synthesis and purification, compatibility with Selective Reaction Monitoring (SRM)/Multiple reaction monitoring (MRM), hydrophobicity, hydropathy index, average molecular weight (Mw average), monoisotopic molecular weight (Mw monoisotopic), theoretical isoelectric point (pI), and half-life. Further filtering criterion shortlisted three peptides by using molecular docking and molecular dynamics simulations. The direct interaction between ATG8 and the shortlisted peptides was confirmed through Surface Plasmon Resonance (SPR) experiments. Notably, these peptides exhibited the remarkable ability to penetrate the parasite membrane and exert profound effects on Leishmania major. The treatment with these peptides significantly impacted parasite survival, leading to alterations in the cell cycle and morphology. Furthermore, the peptides were found to modulate autophagosome formation, particularly under starved conditions, suggesting their involvement in disrupting the regulation of autophagy within Leishmania major. In vitro, studies demonstrated that the selected peptides effectively reduced the parasite load within infected host cells. Encouragingly, these findings were corroborated by in vivo experiments, which showed a reduction in parasite burden upon peptide administration. Additionally, the peptides were observed to affect the levels of LC3II within host cells. In conclusion, our findings highlight the efficacy of these novel peptides in targeting Leishmania major’s ATG8 and disrupting parasite survival. These results provide valuable insights into the development of innovative therapeutic strategies against leishmaniasis via targeting autophagy protein ATG8 of Leishmania major.

Keywords: ATG8, leishmaniasis, surface plasmon resonance, MD simulation, molecular docking, peptide designing, therapeutics

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Authors: Jitendra Singh, Vivek Kumar

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In this paper, a susceptible infective susceptible mathematical model is proposed and analyzed where the migration of human population is given by migration function. It is assumed that the disease is transmitted by direct contact of susceptible and infective populations with constant contact rate. The equilibria and their stability are studied by using the stability theory of ordinary differential equations and computer simulation. The model analysis shows that the spread of infectious disease increases when human population immigration increases in the habitat but it decreases if emigration increases.

Keywords: SIS (Susceptible Infective Susceptible) model, migration function, susceptible, stability

Procedia PDF Downloads 255

Authors: M. Maatar, M. Mekadem, M. Medale, B. Hadjed, B. Imine

Abstract:

In this paper, numerical simulations have been carried out to study the performances of a flapping wing used as an energy collector. Metamodeling and genetic algorithms are used to detect the optimal configuration, improving power coefficient and/or efficiency. Radial basis functions and genetic algorithms have been applied to solve this problem. Three optimization factors are controlled, namely dimensionless heave amplitude h₀, pitch amplitude θ₀ and flapping frequency f. ANSYS FLUENT software has been used to solve the principal equations at a Reynolds number of 1100, while the heave and pitch motion of a NACA0015 airfoil has been realized using a developed function (UDF). The results reveal an average power coefficient and efficiency of 0.78 and 0.338 with an inexpensive low-fidelity model and a total relative error of 4.1% versus the simulation. The performances of the simulated optimum RBF-NSGA-II have been improved by 1.2% compared with the validated model.

Keywords: numerical simulation, flapping wing, energy extraction, power coefficient, efficiency, RBF, NSGA-II

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Authors: Amanbek Jainakov, Abdikerim Kurbanaliev

Abstract:

The work presents the results of mathematical modeling of large-scale flows in areas with a complex topographic relief. The Reynolds-averaged Navier—Stokes equations constitute the basis of the three-dimensional unsteady modeling. The well-known Volume of Fluid method implemented in the solver interFoam of the open package OpenFOAM 2.3 is used to track the free-boundary location. The mathematical model adequacy is checked by comparing with experimental data. The efficiency of the applied technology is illustrated by the example of modeling the breakthrough of the dams of the Andijan (Uzbekistan) and Papan (near the Osh town, Kyrgyzstan) reservoir.

Keywords: three-dimensional modeling, free boundary, the volume-of-fluid method, dam break, flood, OpenFOAM

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Authors: Zahid Ullah, Atlas Khan

Abstract:

This research endeavors to explore the regularity properties associated with a specific class of equations, namely extremely degenerate elliptic equations. These equations hold significance in understanding complex physical systems like porous media flow, with applications spanning various branches of mathematics. The focus is on unraveling and analyzing regularity results to gain insights into the smoothness of solutions for these highly degenerate equations. Elliptic equations, fundamental in expressing and understanding diverse physical phenomena through partial differential equations (PDEs), are particularly adept at modeling steady-state and equilibrium behaviors. However, within the realm of elliptic equations, the subset of extremely degenerate cases presents a level of complexity that challenges traditional analytical methods, necessitating a deeper exploration of mathematical theory. While elliptic equations are celebrated for their versatility in capturing smooth and continuous behaviors across different disciplines, the introduction of degeneracy adds a layer of intricacy. Extremely degenerate elliptic equations are characterized by coefficients approaching singular behavior, posing non-trivial challenges in establishing classical solutions. Still, the exploration of extremely degenerate cases remains uncharted territory, requiring a profound understanding of mathematical structures and their implications. The motivation behind this research lies in addressing gaps in the current understanding of regularity properties within solutions to extremely degenerate elliptic equations. The study of extreme degeneracy is prompted by its prevalence in real-world applications, where physical phenomena often exhibit characteristics defying conventional mathematical modeling. Whether examining porous media flow or highly anisotropic materials, comprehending the regularity of solutions becomes crucial. Through this research, the aim is to contribute not only to the theoretical foundations of mathematics but also to the practical applicability of mathematical models in diverse scientific fields.

Keywords: elliptic equations, extremely degenerate, regularity results, partial differential equations, mathematical modeling, porous media flow

Procedia PDF Downloads 65

Authors: Murat Yazici

Abstract:

Anomalies are irregularities found in data that do not adhere to a well-defined standard of normal behavior. The identification of outliers or anomalies in data has been a subject of study within the statistics field since the 1800s. Over time, a variety of anomaly detection techniques have been developed in several research communities. The cluster analysis can be used to detect anomalies. It is the process of associating data with clusters that are as similar as possible while dissimilar clusters are associated with each other. Many of the traditional cluster algorithms have limitations in dealing with data sets containing categorical properties. To detect anomalies in categorical data, fuzzy clustering approach can be used with its advantages. The fuzzy k-Mode (FKM) clustering algorithm, which is one of the fuzzy clustering approaches, by extension to the k-means algorithm, is reported for clustering datasets with categorical values. It is a form of clustering: each point can be associated with more than one cluster. In this paper, anomaly detection is performed on two simulated data by using the FKM cluster algorithm. As a significance of the study, the FKM cluster algorithm allows to determine anomalies with their abnormality degree in contrast to numerous anomaly detection algorithms. According to the results, the FKM cluster algorithm illustrated good performance in the anomaly detection of data, including both one anomaly and more than one anomaly.

Keywords: fuzzy k-mode clustering, anomaly detection, noise, categorical data

Procedia PDF Downloads 48