Search results for: raw complex data

Authors: Ferzat Anka

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Many types of problems can be solved using traditional analytical methods. However, these methods take a long time and cause inefficient use of resources. In particular, different approaches may be required in solving complex and global engineering problems that we frequently encounter in real life. The bigger and more complex a problem, the harder it is to solve. Such problems are called Nondeterministic Polynomial time (NP-hard) in the literature. The main reasons for recommending different metaheuristic algorithms for various problems are the use of simple concepts, the use of simple mathematical equations and structures, the use of non-derivative mechanisms, the avoidance of local optima, and their fast convergence. They are also flexible, as they can be applied to different problems without very specific modifications. Thanks to these features, it can be easily embedded even in many hardware devices. Accordingly, this approach can also be used in trend application areas such as IoT, big data, and parallel structures. Indeed, the metaheuristic approaches are algorithms that return near-optimal results for solving large-scale optimization problems. This study is focused on the new metaheuristic method that has been merged with the chaotic approach. It is based on the chaos theorem and helps relevant algorithms to improve the diversity of the population and fast convergence. This approach is based on Chimp Optimization Algorithm (ChOA), that is a recently introduced metaheuristic algorithm inspired by nature. This algorithm identified four types of chimpanzee groups: attacker, barrier, chaser, and driver, and proposed a suitable mathematical model for them based on the various intelligence and sexual motivations of chimpanzees. However, this algorithm is not more successful in the convergence rate and escaping of the local optimum trap in solving high-dimensional problems. Although it and some of its variants use some strategies to overcome these problems, it is observed that it is not sufficient. Therefore, in this study, a newly expanded variant is described. In the algorithm called Ex-ChOA, hybrid models are proposed for position updates of search agents, and a dynamic switching mechanism is provided for transition phases. This flexible structure solves the slow convergence problem of ChOA and improves its accuracy in multidimensional problems. Therefore, it tries to achieve success in solving global, complex, and constrained problems. The main contribution of this study is 1) It improves the accuracy and solves the slow convergence problem of the ChOA. 2) It proposes new hybrid movement strategy models for position updates of search agents. 3) It provides success in solving global, complex, and constrained problems. 4) It provides a dynamic switching mechanism between phases. The performance of the Ex-ChOA algorithm is analyzed on a total of 8 benchmark functions, as well as a total of 2 classical and constrained engineering problems. The proposed algorithm is compared with the ChoA, and several well-known variants (Weighted-ChoA, Enhanced-ChoA) are used. In addition, an Improved algorithm from the Grey Wolf Optimizer (I-GWO) method is chosen for comparison since the working model is similar. The obtained results depict that the proposed algorithm performs better or equivalently to the compared algorithms.

Keywords: optimization, metaheuristic, chimp optimization algorithm, engineering constrained problems

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Authors: Hyeon Su Kim, Sungjin Park, Hae Ryung Chang, Hae Rim Jung, Young Zoo Ahn, Yon Hui Kim, Seungyoon Nam

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Ependymoma, one of the most common parenchymal spinal cord tumor, represents 3-6% of all CNS tumor. Especially intracranial ependymomas, which are more frequent in childhood, have a more poor prognosis and more malignant than spinal ependymomas. Although there are growing needs to understand pathogenesis, detailed molecular understanding of pathogenesis remains to be explored. A cancer cell is composed of complex signaling pathway networks, and identifying interaction between genes and/or proteins are crucial for understanding these pathways. Therefore, we explored each ependymoma in terms of differential expressed genes and signaling networks. We used Microsoft Excel™ to manipulate microarray data gathered from NCBI’s GEO Database. To analyze and visualize signaling network, we used web-based PATHOME algorithm and Cytoscape. We show HOX family and NEFL are down-regulated but SCL family is up-regulated in cerebrum and posterior fossa cancers over a spinal cancer, and JAK/STAT signaling pathway and Chemokine signaling pathway are significantly different in the both intracranial ependymoma comparing to spinal ependymoma. We are considering there may be an age-dependent mechanism under different histological pathogenesis. We annotated mutation data of each gene subsequently in order to find potential target genes.

Keywords: systems biology, ependymoma, deg, network analysis

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Authors: Y. S. Lee, F. Malek, E. M. Cheng, W. W. Liu, F. H. Wee, M. N. Iqbal, Z. Liyana, B. S. Yew, F. S. Abdullah

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This paper presents the electromagnetic interference (EMI) shielding effectiveness of rice husk and carbon nanotubes (RHCNTs) composites in the X-band region (8.2-12.4 GHz). The difference weight ratio of carbon nanotubes (CNTs) were mix with the rice husk. The rectangular wave guide technique was used to measure the complex permittivity of the RHCNTs composites materials. The complex permittivity is represented in terms of both the real and imaginary parts of permittivity in X-band frequency. The conductivity of RHCNTs shows increasing when the ratio of CNTs mixture increases. The composites materials were simulated using Computer Simulation Technology (CST) Microwave Studio simulation software. The shielding effectiveness of RHCNTs and pure rice husk was compared. The highest EMI SE of 30 dB is obtained for RHCNTs composites of 10 wt % CNTs with 10 mm thick.

Keywords: EMI shielding effectiveness, carbon nanotube, composite materials wave guide, x-band

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Authors: Reyhane Hamed Kamran

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Early stage morphogenesis requires the execution of complex systems that direct the nearby conduct of gatherings of cells. The organization of such instruments has been, for the most part, deciphered through the recognizable proof of moderated groups of flagging pathways that spatially and transiently control cell conduct. In any case, how this data is handled to control cell shape and cell elements is an open territory of examination. The structure that rises up out of differing controls, for example, cell science, material science, and formative science, focuses to bond and cortical actin arranges as controllers of cell surface mechanics. In this specific circumstance, a scope of formative marvels can be clarified by the guideline of cell surface pressure.

Keywords: cell, tissue damage, morphogenesis, cell conduct

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Authors: Narin Salehiyan

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Early stage morphogenesis requires the execution of complex systems that direct the nearby conduct of gatherings of cells. The organization of such instruments has been, for the most part, deciphered through the recognizable proof of moderated groups of flagging pathways that spatially and transiently control cell conduct. In any case, how this data is handled to control cell shape and cell elements is an open territory of examination. The structure that rises up out of differing controls, for example, cell science, material science and formative science, focuses to bond and cortical actin arranges as controllers of cell surface mechanics. In this specific circumstance, a scope of formative marvels can be clarified by the guideline of cell surface pressure.

Keywords: cell, tissue damage, morphogenesis, cell conduct

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Authors: Avinash Kumar Ranjan, Akash Anand

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The Land Surface Temperature (LST) is an essential factor accompanying to rise urban heat and climate warming within a city in micro level. It is also playing crucial role in global change study as well as radiation budgets measuring in heat balance studies. The information of LST is very substantial to recognize the urban climatology, ecological changes, anthropological and environmental interactions etc. The Chief motivation of present study focus on time series of ANN model that taken a sequence of LST values of 2000, 2008 and 2016, realize the pattern of variation within the data set and predict the LST values for 2024 and 2032. The novelty of this study centers on evaluation of LST using series of multi-temporal MODIS (MOD 11A2) satellite data by Maximum Value Composite (MVC) techniques. The results derived from this study endorse the proficiency of Geoinformatics Technology with integration of ANN to gain knowledge, understanding and building of precise forecast from the complex physical world database. This study will also focus on influence of Land Use/ Land Cover (LU/LC) variation on Land Surface Temperature.

Keywords: LST, geoinformatics technology, ANN, MODIS satellite imagery, MVC

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Authors: Riadh Zorgati, Thomas Triboulet

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In quite diverse application areas such as astronomy, medical imaging, geophysics or nondestructive evaluation, many problems related to calibration, fitting or estimation of a large number of input parameters of a model from a small amount of output noisy data, can be cast as inverse problems. Due to noisy data corruption, insufficient data and model errors, most inverse problems are ill-posed in a Hadamard sense, i.e. existence, uniqueness and stability of the solution are not guaranteed. A wide class of inverse problems in physics relates to the Fredholm equation of the first kind. The ill-posedness of such inverse problem results, after discretization, in a very ill-conditioned linear system of equations, the condition number of the associated matrix can typically range from 109 to 1018. This condition number plays the role of an amplifier of uncertainties on data during inversion and then, renders the inverse problem difficult to handle numerically. Similar problems appear in other areas such as numerical optimization when using interior points algorithms for solving linear programs leads to face ill-conditioned systems of linear equations. Devising efficient solution approaches for such system of equations is therefore of great practical interest. Efficient iterative algorithms are proposed for solving a system of linear equations. The approach is based on a preconditioning of the initial matrix of the system with an approximation of a generalized inverse leading to a stochastic preconditioned matrix. This approach, valid for non-negative matrices, is first extended to hermitian, semi-definite positive matrices and then generalized to any complex rectangular matrices. The main results obtained are as follows: 1) We are able to build a generalized inverse of any complex rectangular matrix which satisfies the convergence condition requested in iterative algorithms for solving a system of linear equations. This completes the (short) list of generalized inverse having this property, after Kaczmarz and Cimmino matrices. Theoretical results on both the characterization of the type of generalized inverse obtained and the convergence are derived. 2) Thanks to its properties, this matrix can be efficiently used in different solving schemes as Richardson-Tanabe or preconditioned conjugate gradients. 3) By using Lp norms, we propose generalized Kaczmarz’s type matrices. We also show how Cimmino's matrix can be considered as a particular case consisting in choosing the Euclidian norm in an asymmetrical structure. 4) Regarding numerical results obtained on some pathological well-known test-cases (Hilbert, Nakasaka, …), some of the proposed algorithms are empirically shown to be more efficient on ill-conditioned problems and more robust to error propagation than the known classical techniques we have tested (Gauss, Moore-Penrose inverse, minimum residue, conjugate gradients, Kaczmarz, Cimmino). We end on a very early prospective application of our approach based on stochastic matrices aiming at computing some parameters (such as the extreme values, the mean, the variance, …) of the solution of a linear system prior to its resolution. Such an approach, if it were to be efficient, would be a source of information on the solution of a system of linear equations.

Keywords: conditioning, generalized inverse, linear system, norms, stochastic matrix

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Authors: Remo Waser, Simon Maranda, Anastasia Stamatiou, Ludger J. Fischer, Joerg Worlitschek

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In latent heat storage, a phase change material (PCM) is used to store thermal energy. The heat transfer rate during solidification is limited and considered as a key challenge in the development of latent heat storages. Thus, finned heat exchangers (HEX) are often utilized to increase the heat transfer rate of the storage system. In this study, a new modeling approach to calculating the heat transfer rate in latent thermal energy storages with complex HEX geometries is presented. This model allows for an optimization of the HEX design in terms of costs and thermal performance of the system. Modeling solidification processes requires the calculation of time-dependent heat conduction with moving boundaries. Commonly used computational fluid dynamic (CFD) methods enable the analysis of the heat transfer in complex HEX geometries. If applied to the entire storage, the drawback of this approach is the high computational effort due to small time steps and fine computational grids required for accurate solutions. An alternative to describe the process of solidification is the so-called temperature-based approach. In order to minimize the computational effort, a quasi-stationary assumption can be applied. This approach provides highly accurate predictions for tube heat exchangers. However, it shows unsatisfactory results for more complex geometries such as finned tube heat exchangers. The presented simulation model uses a temporal and spatial discretization of heat exchanger tube. The spatial discretization is based on the smallest possible symmetric segment of the HEX. The heat flow in each segment is calculated using finite volume method. Since the heat transfer fluid temperature can be derived using energy conservation equations, the boundary conditions at the inner tube wall is dynamically updated for each time step and segment. The model allows a prediction of the thermal performance of latent thermal energy storage systems using complex HEX geometries with considerably low computational effort.

Keywords: modelling of solidification, finned tube heat exchanger, latent thermal energy storage

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Authors: Georgiana Onicescu, Yuqian Shen

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Due to the complex nature of geo-referenced data, multicollinearity of the risk factors in public health spatial studies is a commonly encountered issue, which leads to low parameter estimation accuracy because it inflates the variance in the regression analysis. To address this issue, we proposed a two-stage variable selection method by extending the least absolute shrinkage and selection operator (Lasso) to the Bayesian spatial setting, investigating the impact of risk factors to health outcomes. Specifically, in stage I, we performed the variable selection using Bayesian Lasso and several other variable selection approaches. Then, in stage II, we performed the model selection with only the selected variables from stage I and compared again the methods. To evaluate the performance of the two-stage variable selection methods, we conducted a simulation study with different distributions for the risk factors, using geo-referenced count data as the outcome and Michigan as the research region. We considered the cases when all candidate risk factors are independently normally distributed, or follow a multivariate normal distribution with different correlation levels. Two other Bayesian variable selection methods, Binary indicator, and the combination of Binary indicator and Lasso were considered and compared as alternative methods. The simulation results indicated that the proposed two-stage Bayesian Lasso variable selection method has the best performance for both independent and dependent cases considered. When compared with the one-stage approach, and the other two alternative methods, the two-stage Bayesian Lasso approach provides the highest estimation accuracy in all scenarios considered.

Keywords: Lasso, Bayesian analysis, spatial analysis, variable selection

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Authors: V. B. Bastos, M. P. Lima, P. R. G. Kurka

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This paper applies to the creation of virtual 3D environments for the study and development of mobile robot image based navigation algorithms and techniques, which need to operate robustly and efficiently. The test of these algorithms can be performed in a physical way, from conducting experiments on a prototype, or by numerical simulations. Current simulation platforms for robotic applications do not have flexible and updated models for image rendering, being unable to reproduce complex light effects and materials. Thus, it is necessary to create a test platform that integrates sophisticated simulated applications of real environments for navigation, with data and image processing. This work proposes the development of a high-level platform for building 3D model’s environments and the test of image-based navigation algorithms for mobile robots. Techniques were used for applying texture and lighting effects in order to accurately represent the generation of rendered images regarding the real world version. The application will integrate image processing scripts, trajectory control, dynamic modeling and simulation techniques for physics representation and picture rendering with the open source 3D creation suite - Blender.

Keywords: simulation, visual navigation, mobile robot, data visualization

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Authors: Sohail Ahmed, Ke Xing

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This research theoretically explored the evolution mechanism of enterprise technological innovation capability system (ETICS) from the perspective of complex adaptive systems (CAS). Starting from CAS theory, this study proposed an analytical framework for ETICS, its concepts and theory by integrating CAS methodology into the management of technological innovation capability of enterprises and discusses how to use the principles of complexity to analyze the composition, evolution and realization of the technological innovation capabilities in complex dynamic environment. This paper introduces the concept and interaction of multi-agent, the theoretical background of CAS and summarizes the sources of technological innovation, the elements of each subject and the main clusters of adaptive interactions and innovation activities. The concept of multi-agents is applied through the linkages of enterprises, research institutions and government agencies with the leading enterprises in industrial settings. The study was exploratory based on CAS theory. Theoretical model is built by considering technological and innovation literature from foundational to state of the art projects of technological enterprises. On this basis, the theoretical model is developed to measure the evolution mechanism of enterprise technological innovation capability system. This paper concludes that the main characteristics for evolution in technological systems are based on enterprise’s research and development personal, investments in technological processes and innovation resources are responsible for the evolution of enterprise technological innovation performance. The research specifically enriched the application process of technological innovation in institutional networks related to enterprises.

Keywords: complex adaptive system, echo model, enterprise knowledge system, research institutions, multi-agents.

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Authors: Luvisa Kusuma, Panji Yudha Prakasa

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Cloud storage, as a subservice of infrastructure as a service (IaaS) in Cloud Computing, is the model of nerworked storage where data can be stored in server. In this paper, we propose a secure cloud storage system consisting of two main components; client as a user who uses the cloud storage service and server who provides the cloud storage service. In this system, we propose the protocol schemes to guarantee against security attacks in the data transmission. The protocols are login protocol, upload data protocol, download protocol, and push data protocol, which implement hybrid cryptographic mechanism based on data encryption before it is sent to the cloud, so cloud storage provider does not know the user's data and cannot analysis user’s data, because there is no correspondence between data and user.

Keywords: cloud storage, security, cryptographic protocol, artificial intelligence

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Authors: Meshari Aljohani, Stephan Olariu, Ravi Mukkamala

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Data is essential for enhancing the quality of life. Its value creates chances for users to profit from data sales and purchases. Users in data marketplaces, however, must share and trade data in a secure and trusted environment while maintaining their privacy. The first main contribution of this paper is to identify enabling technologies and challenges facing the development of decentralized data marketplaces. The second main contribution is to propose a decentralized data marketplace framework based on blockchain technology. The proposed framework enables sellers and buyers to transact with more confidence. Using a security deposit, the system implements a unique approach for enforcing honesty in data exchange among anonymous individuals. Before the transaction is considered complete, the system has a time frame. As a result, users can submit disputes to the arbitrators which will review them and respond with their decision. Use cases are presented to demonstrate how these technologies help data marketplaces handle issues and challenges.

Keywords: blockchain, data, data marketplace, smart contract, reputation system

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Authors: Camilo Cerda Sarabia, Fernanda Bravo Cornejo, Diego Santibanez Oyarce, Hugo Osses Prado, Esteban Gómez Terán, Belén Diaz Diaz, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Antimicrobial resistance (AMR) represents a significant and rapidly escalating global health threat. Projections estimate that by 2050, AMR infections could claim up to 10 million lives annually. Respiratory infections, in particular, pose a severe risk not only to individual patients but also to the broader public health system. Despite the alarming rise in resistant respiratory infections, AMR within the lung microbiome (microbial community) remains underexplored and poorly characterized. The lungs, as a complex and dynamic microbial environment, host diverse communities of microorganisms whose interactions and resistance mechanisms are not fully understood. Unlike studies that focus on individual genomes, analyzing the entire microbiome provides a comprehensive perspective on microbial interactions, resistance gene transfer, and community dynamics, which are crucial for understanding AMR. However, this holistic approach introduces significant computational challenges and exposes the limitations of traditional analytical methods such as the difficulty of identifying the AMR. Machine learning has emerged as a powerful tool to overcome these challenges, offering the ability to analyze complex genomic data and uncover novel insights into AMR that might be overlooked by conventional approaches. This study investigates microbial resistance within the lung microbiome using unsupervised machine learning approaches to uncover resistance patterns and potential clinical associations. it downloaded and selected lung microbiome data from HumanMetagenomeDB based on metadata characteristics such as relevant clinical information, patient demographics, environmental factors, and sample collection methods. The metadata was further complemented by details on antibiotic usage, disease status, and other relevant descriptions. The sequencing data underwent stringent quality control, followed by a functional profiling focus on identifying resistance genes through specialized databases like Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. Subsequent analyses employed unsupervised machine learning techniques to unravel the structure and diversity of resistomes in the microbial community. Some of the methods employed were clustering methods such as K-Means and Hierarchical Clustering enabled the identification of sample groups based on their resistance gene profiles. The work was implemented in python, leveraging a range of libraries such as biopython for biological sequence manipulation, NumPy for numerical operations, Scikit-learn for machine learning, Matplotlib for data visualization and Pandas for data manipulation. The findings from this study provide insights into the distribution and dynamics of antimicrobial resistance within the lung microbiome. By leveraging unsupervised machine learning, we identified novel resistance patterns and potential drivers within the microbial community.

Keywords: antibiotic resistance, microbial community, unsupervised machine learning., sequences of AMR gene

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Authors: Jing Yu, Hongyang Liu, Dong Hao

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On-Orbit Refueling is of great significance in extending space crafts' lifetime. The problem of minimum-fuel, time-fixed, Peer-to-Peer On-Orbit Refueling mission planning is addressed here with the particular aim of assigning fuel-insufficient satellites to the fuel-sufficient satellites and optimizing each rendezvous trajectory. Constraints including perturbation, communication link, sun illumination, hold points for different rendezvous phases, and sensor switching are considered. A planning model has established as well as a two-level solution method. The upper level deals with target assignment based on fuel equilibrium criterion, while the lower level solves constrained trajectory optimization using special maneuver strategies. Simulations show that the developed method could effectively resolve the Peer-to-Peer On-Orbit Refueling mission planning problem and deal with complex constraints.

Keywords: mission planning, orbital rendezvous, on-orbit refueling, space mission

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Authors: Mehran Dizbadi, Daniyal Safarzadeh, Behrooz Arastoo, Ansgar Brunn

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Rapid and uncontrolled expansion of cities has led to unplanned aerial development. In this way, modeling and predicting the urban growth of a city helps decision-makers. In this study, the aspect of sustainable urban development has been studied for the city of Mashhad. In general, the prediction of urban aerial development is one of the most important topics of modern town management. In this research, using the Cellular Automaton (CA) model developed for geo data of Geographic Information Systems (GIS) and presenting a simple and powerful model, a simulation of complex urban processes has been done.

Keywords: urban modeling, sustainable development, fuzzy cellular automaton, geo-information system

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Authors: Collins C. Chiemeke, I. B. Osazuwa, S. O. Ibe, G. N. Egwuonwu, C. D. Ani, E. C. Chii

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The study area is Zaria, located in the basement complex of northern Nigeria. The rock type forming the major part of the Zaria batholith is granite. This research work was carried out to compare the responses of seismic refraction tomography and resistivity tomography in the same geologic environment and under the same conditions. Hence, the choice of the site that has a visible granitic outcrop that extends across a narrow stream channel and is flanked by unconsolidated overburden, a neutral profile that was covered by plain overburden and a site with thick lateritic cover became necessary. The results of the seismic and resistivity tomography models reveals that seismic velocity and resistivity does not always simultaneously increase with depth, but their responses in any geologic environment are determined by changes in the mechanical and chemical content of the rock types rather than depth.

Keywords: environment, resistivity, response, seismic, velocity

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Authors: Tshipeng Mwenze, Charles Okujeni, Abdi Siad, Russel Bailie, Dirk Frei, Marcelene Voigt, Petrus Le Roux

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The Platreef is a platinum group element (PGE) deposit in the northern limb of the Bushveld Igneous Complex (BIC) which was emplaced as a series of mafic and ultramafic sills between the Main Zone (MZ) and the country rocks. The PGE mineralisation in the Platreef is hosted in different rock types, and its distribution and style vary with depth and along strike. This study contributes towards understanding the processes involved in the genesis of the Platreef. Twenty-four Platreef (2 harzburgites, 4 olivine pyroxenites, 17 feldspathic pyroxenites and 1 gabbronorite) and few MZ (1 gabbronorite and 1 leucogabbronorite) quarter core samples were collected from four drill cores (e.g., TN754, TN200, SS339, and OY482) and analysed for whole-rock Sr-Nd isotope data. The results show positive ɛNd values (+3.53 to +7.51) for harzburgites suggesting their parental magmas derived from the depleted Mantle. The remaining Platreef rocks have negative ɛNd values (-2.91 to -22.88) and show significant variations in Sr-Nd isotopic compositions. The first group of Platreef samples has relatively high isotopic compositions (ɛNd= -2.91 to -5.68; ⁸⁷Sr/⁸⁶Sri= 0.709177 - 0.711998). The second group of Platreef samples has Sr ratios (⁸⁷Sr/⁸⁶Sri= 0.709816-0.712106) overlapping with samples of the first group but slightly lower ɛNd values (-7.44 to -8.39). Lastly, the third group of Platreef samples has low ɛNd values (-10.82 to -14.32) and low Sr ratios (⁸⁷Sr/⁸⁶Sri= 0.707545-0.710042) than those from samples of the two Platreef groups mentioned above. There is, however, a Platreef sample with ɛNd value (-5.26) in range with the Platreef samples of the first group, but its Sr ratio (0.707281) is the lowest even when compared to samples of the third Platreef group. There are also five other Platreef samples which have either anomalous ɛNd or Sr ratios which make it difficult to assess their isotopic compositions relative to other samples. These isotopic variations for the Platreef samples indicate both multiple sources and multiple magma chambers where varying crustal contamination styles have operated during the evolution of these magmas prior their emplacements into the Platreef setting as sills. Furthermore, the MZ rocks have different Sr-Nd isotopic compositions (For OY482 gabbronorite [ɛNd= +0.65; ⁸⁷Sr/⁸⁶Sri= 0.711746]; for TN754 leucogabbronorite [ɛNd= -7.44; ⁸⁷Sr/⁸⁶Sri= 0.709322]) which do not only indicate different MZ magma chambers, but also different magmas from those of the Platreef. Although the Platreef is still considered a single stratigraphic unit in the northern limb of the BIC, its genesis involved multiple magmatic processes which evolved independently from each other.

Keywords: crustal contamination styles, magma chambers, magma sources, multiple sills emplacement

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Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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Authors: Jamaludin Tarkim, Yoga Teguh Guntara, Maftuhah

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Islamic leadership style is model of leadership style applied by the Prophet Muhammad SAW. Islamic leadership style is applied, namely Syura (deliberation), ‘Adl bil qisth (justice, with equality), and Hurriyyah al-kalam (freedom of expression) and along with the values of Islam in the Islamic leadership style. This research aims to gain an overview of the experience of Head Nurse in the implementation of Islamic leadership style. This research is a qualitative one with descriptive phenomenology design through in-depth interviews. Participants were occupied as Head Nurse at the Hospital room Syarif Hidayatullah, set directly (purposive) with the principle of suitability (appropriateness) and sufficiency (adequacy). Retrieval of data and research conducted during the month of June 2014. Data collected in the form of recording in-depth interviews and analysis with Collazi method. This research identified four themes Syura (deliberation);‘Adl bil qisth (justice, with equality); Hurriyyah al-kalam (freedom of expression) and along with the values of Islam in the Islamic leadership style. The results of this research can provide a review of the Head Room experience in the application of Islamic leadership style at Syarif Hidayatullah Hospital already skilled leadership during the process, but the application is still not maximized. Required further research on in-depth exploration of how to get more comprehensive results from room Head Nurse experience in the application of Islamic leadership style, as well as subsequent researchers can choose a wider scope and complex so get more complete data.

Keywords: experience, Islamic leadership style, head nurse, nursing management

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Authors: Innocent Uzougbo Onwuegbuzie, Siene Elizabeth Eke

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This study investigates cybersecurity challenges and solutions in managing Information and Communication Technology (ICT) at the Federal Polytechnic, Ado-Ekiti, South-West Nigeria. The rapid evolution of ICT has revolutionized organizational operations and impacted various sectors, including education, healthcare, and finance. While ICT advancements facilitate seamless communication, complex data analytics, and strategic decision-making, they also introduce significant cybersecurity risks such as data breaches, ransomware, and other malicious attacks. These threats jeopardize the confidentiality, integrity, and availability of information systems, necessitating robust cybersecurity measures. The primary aim of this research is to identify prevalent cybersecurity challenges in ICT management, evaluate their impact on the institution's operations, and assess the effectiveness of current cybersecurity solutions. Adopting a quantitative research approach, data was collected through surveys and structured questionnaires from students, staff, and IT professionals at the Federal Polytechnic, Ado-Ekiti. The findings underscore the critical need for continuous investment in cybersecurity technologies, employee and student training, and regulatory compliance to mitigate evolving cyber threats. This research contributes to bridging the knowledge gap in cybersecurity management and provides valuable insights into effective strategies and technologies for safeguarding ICT systems in educational institutions. The study's objectives are to enhance the security posture of the Federal Polytechnic, Ado-Ekiti, in an increasingly digital world by identifying and addressing the cybersecurity challenges faced by its ICT management.

Keywords: cybersecurity challenges, cyber threat mitigation, federal polytechnic Ado-Ekiti, ICT management

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Authors: Mais Haj Qasem, Maen M. Al Assaf, Ali Rodan

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Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%.

Keywords: hybrid storage system, data mining, recurrent neural network, support vector machine

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Authors: Arindam Pramanik, Parimal Karmakar

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We have explored the synergistic anti-cancer activity of copper ion and acetylacetone complex containing 1,3 diketone group (like curcumin) in metallorganic compound “Copper acetylacetonate” (CuAA). The cytotoxicity mechanism of CuAA complex was evaluated on various cancer cell lines in vitro. Among these, reactive oxygen species (ROS), glutathione level (GSH) in the cell was found to increase. Further mitochondrial membrane damage was observed. The fate of cell death was found to be induced by apoptosis. For application purpose, we have developed a novel biodegradable, non-toxic polymer-based nanoparticle which has hydrophobically modified core for loading of the CuAA. Folic acid is conjugated on the surface of the polymer (chitosan) nanoparticle for targeting to cancer cells for minimizing toxicity to normal cells in-vivo. Thus, this novel drug CuAA has an efficient anticancer activity which has been targeted specifically to cancer cells through polymer nanoparticle.

Keywords: anticancer, apoptosis, copper nanoparticle, targeted drug delivery

Procedia PDF Downloads 478

Authors: Areeg Shermaddo, Abedulgader Baktheer

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Solar updraft power plants (SUPPs) made from reinforced concrete (RC) are an innovative technology to generate solar electricity. An up to 1000 m high chimney represents the major part of each SUPP ensuring the updraft of the warmed air from the ground. Numerical simulation of nonlinear behavior of such large mega shell concrete structures is a challenging task, and computationally expensive. A general finite element approach to simulate reinforced concrete bearing behavior is presented and verified on a simply supported beam, as well as the technique of submodeling. The verified numerical approach is extended and consecutively transferred to a more complex chimney structure of a SUPP. The obtained results proved the reliability of submodeling technique in analyzing critical regions of simple and complex mega concrete structures with high accuracy and dramatic decrease in the computation time.

Keywords: ABAQUS, nonlinear analysis, submodeling, SUPP

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Authors: Lucrezia Crescenzi-Lanna, Silvia Cataldi, Williams Contreras, Valerio Pieri

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According to the European Commission, gender-based violence (GBV) is a violation of human rights and a form of discrimination against women in five areas: physical violence, sexual violence, psychological violence, mistreatment of women, and economic violence (henceforth EV). The TESORO project "Treatment, ExpoSition, and ORigin of economic viOlence: An innovation and internationalization project between Italy and Spain" focuses on this last dimension of gender-based violence, the least studied and the one that has received least media coverage. In Spain, 12% (2,350,684) of women over fifteen years of age have suffered economic violence from their partner or ex-partner during their lives. In Italy, another country participating in the project, many women who are welcomed in refuges and who report cases of psychological violence (79%) and/or physical violence (61%) are also victims of economic violence (34%), according to the D.i.Re. Thermometer: "Donne in Rete contro la Violenza", the association that brings together more than eighty refuges against violence in Italy. At the social level, this form of violence is incorporated into practices of inequality that manifest themselves in both the daily management of couples and families and the workplace and institutional settings. As for the mechanisms related to EV, the literature argues that it is a complex and multidimensional phenomenon that has socioeconomic and cultural roots. EV manifests itself through various strategies, which represent forms of power and control aimed at preventing women's financial independence. To analyse the issue of EV we use a multidisciplinary approach and a mixed design that includes: 1) a questionnaire administered to a stratified sample of more than a thousand Italian and Spanish citizens to study the cultural and socio-relational mechanisms and the origin of EV in family and couple contexts; and 2) interviews with those running refuges as part of the struggle against gender violence, to understand how mechanisms and educational activities in the field of economic violence are manifested in the respective region and are supportive of women. The decision to use this strategy responds to the need to combine an exploratory perspective with an explanatory one in order to understand some of the relevant concepts related to the complex phenomena of EV and the interventions dedicated to its prevention. The data will be finalized in June 2022 and presented at the ICWS conference. Among TESORO’s contributions, its collection of qualitative and quantitative data on EV in Italy and Spain stands out, deepening its origin, prevention, and treatment beyond its incidence, which has already been studied in the Macro-Survey on Violence against Women.

Keywords: gender-based violence, economic violence, economic harm, gender inequality, workplace and family contexts

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Authors: Chala Elhassen

Abstract:

City planning and urban management seem more complex our days compared to past times. The interaction of many factors both endogenous and exogenous made more difficult the urban fact. The city has changed status with the demographic bulge. It passed the primary status meeting limited requirements to a multidisciplinary status marked by the diversity of needs. These increase with the increase in population and living standard. Our era is marked by urbanization, complex phenomenon that develops both in industrialized countries in those of the third world. Human concentrations increasingly have significant multiplier effects on the social and economic structure of a region or a country. On the whole, the issue of urban planning revolved around questions related firstly to the understanding of the phenomena of urbanization; and also in search of the most appropriate ways to ensure control, the efficiency and consistency of the urbanization process. Urban planning remains an ambiguous area that mixes scientific contributions, technical, artistic, administrative and legal in varying proportions. What is the founder of specificity is that it always presupposes the existence of a will to act, itself supported by a thorough knowledge of will.

Keywords: urbanization, urban planning, management, industrialized countries

Procedia PDF Downloads 467

Authors: Aroonsri Priprem, Sucharat Limsitthichaikoon, Suttasinee Thappasarapong

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Anthocyanins are natural pigments with effective UV protection but their topical use could be limited due to their physicochemical characteristics. An attempt to overcome such limitations by complexation of 2 major anthocyanin-rich sources, C. ternatea, and Z. mays, for investigation on potential use as topical anti-inflammatory. Cell studies indicate no cytotoxicity of the anthocyanin complex (AC) up to 1 mg/ml tested in HaCaT and human forehead fibroblasts by MTT. Croton oil-induced ear edema in Wistar rats suggests an effective dose of 5 mg/cm2 of AC as a topical anti-inflammatory in comparison to 0.5 mg/cm2 of fluocinolone acetonide. Niosomal encapsulation of the AC significantly prolonged the anti-inflammatory activity particularly at 8 h after topical application (p = 0.0001). The AC was not cytotoxic and its anti-inflammatory and activity was dose-dependent and prolonged by niosomal encapsulation. It has also shown to promote collagen type 1 production in cell culture. Thus, AC could be a potential candidate for topical anti-inflammatory agent from natural resources.

Keywords: anthocyanin complex, ear edema, inflammation, niosomes, skin

Procedia PDF Downloads 322

Authors: Fulya Gokalp Yavuz, Mark Daniel Ward

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This study focuses on a contemporary and inevitable topic of Data Science and its exemplary application for early career building: Big Data and Leaving Learning Community (LLC). ‘Academia’ and ‘Industry’ have a common sense on the importance of Big Data. However, both of them are in a threat of missing the training on this interdisciplinary area. Some traditional teaching doctrines are far away being effective on Data Science. Practitioners needs some intuition and real-life examples how to apply new methods to data in size of terabytes. We simply explain the scope of Data Science training and exemplified its early stage application with LLC, which is a National Science Foundation (NSF) founded project under the supervision of Prof. Ward since 2014. Essentially, we aim to give some intuition for professors, researchers and practitioners to combine data science tools for comprehensive real-life examples with the guides of mentees’ feedback. As a result of discussing mentoring methods and computational challenges of Big Data, we intend to underline its potential with some more realization.

Keywords: Big Data, computation, mentoring, training

Procedia PDF Downloads 352

Authors: Kunal Ahluwalia, Ampara Aramcharoen, Chan Wai Luen, Swee Hock Yeo

Abstract:

The different process engineering applications of vibratory finishing technology have led to its versatile use in the development of aviation components. The most noteworthy applications of vibratory finishing include deburring and imparting the required surface finish. In this paper, vibratory finishing has been used to study its effectiveness in removal of laser shock peened (LSP) layers from Titanium workpieces. A vibratory trough operating at a frequency of 25 Hz, amplitude 3.5 mm and titanium specimens (Ti-6Al-4V, Grade 5) of dimensions 50 x 50 x 10 mm³ were utilized for the experiments. A vibrating fixture operating at 200 Hz was used to provide vibration to the test piece and was immersed in the vibratory trough. It was evident that there is an increase in efficiency of removal of the complex featured layer and smoother surface finish with the introduction of the vibrating fixture in the vibratory finishing setup as compared to the conventional vibratory finishing setup wherein the fixture is not vibrating.

Keywords: laser shock peening, material removal, surface roughness, vibrating fixture, vibratory finishing

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Authors: Jugal Bhandari, K. Hari Priya

Abstract:

The ECG signal provides important clinical information which could be used to pretend the diseases related to heart. Accordingly, delineation of ECG signal is an important task. Whereas delineation of P and T waves is a complex task. This paper deals with the Study of ECG signal and analysis of signal by means of Verilog Design of efficient filters and MATLAB tool effectively. It includes generation and simulation of ECG signal, by means of real time ECG data, ECG signal filtering and processing by analysis of different algorithms and techniques. In this paper, we design a basic particle filter which generates a dynamic model depending on the present and past input samples and then produces the desired output. Afterwards, the output will be processed by MATLAB to get the actual shape and accurate values of the ranges of P-wave and T-wave of ECG signal. In this paper, Questasim is a tool of mentor graphics which is being used for simulation and functional verification. The same design is again verified using Xilinx ISE which will be also used for synthesis, mapping and bit file generation. Xilinx FPGA board will be used for implementation of system. The final results of FPGA shall be verified with ChipScope Pro where the output data can be observed.

Keywords: ECG, MATLAB, Bayesian filtering, particle filter, Verilog hardware descriptive language

Procedia PDF Downloads 363