Search results for: heatmap visualization techniques

Authors: Kouzi Katia

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This paper proposes a Direct Torque Control (DTC) algorithm of dual Stator Induction Motor (DSIM) drive using two approach intelligent techniques: Artificial Neural Network (ANN) approach replaces the switching table selector block of conventional DTC and Mamdani Fuzzy Logic controller (FLC) is used for stator resistance estimation. The fuzzy estimation method is based on an online stator resistance correction through the variations of stator current estimation error and its variation. The fuzzy logic controller gives the future stator resistance increment at the output. The main advantage of suggested algorithm control is to reduce the hardware complexity of conventional selectors, to avoid the drive instability that may occur in certain situation and ensure the tracking of the actual of the stator resistance. The effectiveness of the technique and the improvement of the whole system performance are proved by results.

Keywords: artificial neural network, direct torque control, dual stator induction motor, fuzzy logic estimator, switching table

Procedia PDF Downloads 345

Authors: Sarwan Dhir, Suma Basak, Dipika Parajulee

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Alfalfa is renowned for its nutritional and biopharmaceutical value as a perennial forage legume. However, establishing a rapid plant regeneration protocol using somatic embryogenesis and efficient transformation frequency are the crucial prerequisites for gene editing in alfalfa. This study was undertaken to establish and improve the protocol for somatic embryogenesis and subsequent plant regeneration. The experiments were conducted in response to natural sensitivity using various antibiotics such as cefotaxime, carbenicillin, gentamycin, hygromycin, and kanamycin. Using 3-week-old leaf tissue, somatic embryogenesis was initiated on Gamborg’s B5 basal (B5H) medium supplemented with 3% maltose, 0.9µM Kinetin, and 4.5µM 2,4-D. Embryogenic callus (EC) obtained from the B5H medium exhibited a high rate of somatic embryo formation (97.9%) after 3 weeks when the cultures were placed in the dark. Different developmental stages of somatic embryos and cotyledonary stages were then transferred to Murashige and Skoog’s (MS) basal medium under light, resulting in a 94% regeneration rate of plantlets. Our results indicate that leaf segments can grow (tolerate) up to 450 mg/L of cefotaxime and 400 mg/L of carbenicillin in the culture medium. However, the survival threshold for hygromycin at 12.5 mg/L, kanamycin at 250 mg/L, gentamycin at 50 mg/L, and timentin (300 mg/L). The experiment to improve the protocol for achieving efficient transient gene expression in alfalfa through genetic transformation with the Agrobacterium tumefaciens pCAMBIA1304 vector was also conducted. The vector contains two reporter genes such as β-glucuronidase (GUS) and green fluorescent protein (GFP), along with a selectable hygromycin B phosphotransferase gene (HPT), all driven under the CaMV 35s promoter. Various transformation parameters were optimized using 3-week-old in vitro-grown plantlets. The different parameters such as types of explant, leaf ages, preculture days, segment sizes, wounding types, bacterial concentrations, infection periods, co-cultivation periods, different concentrations of acetosyringone, silver nitrate, and calcium chloride were optimized for transient gene expression. The transient gene expression was confirmed via histochemical GUS and GFP visualization under fluorescent microscopy. The data were analyzed based on the semi-quantitative observation of the percentage and number of blue GUS spots on different days of agro-infection. The highest percentage of GUS positivity (76.2%) was observed in 3-week-old leaf segments wounded using a scalpel blade of 11 size- after 3 days of post-incubation at a bacterial concentration of 0.6, with 2 days of preculture, 30 min of bacterial-leaf segment co-cultivation, with the addition of 150 µM acetosyringone, 4 mM calcium chloride, and 75 µM silver nitrate. Our results suggest that various factors influence T-DNA delivery in the Agrobacterium-mediated transformation of alfalfa. The stable gene expression in the putative transgenic tissue was confirmed using PCR amplification of both marker genes, indicating that gene expression in explants was not solely due to Agrobacterium, but also from transformed cells. The improved protocol could be used for generating transgenic alfalfa plants using genome editing techniques such as CRISPR/Cas9.

Keywords: Medicago sativa l. (Alfalfa), agrobacterium tumefaciens, β-glucuronidase, green fluorescent protein, transient gene

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Authors: Pawan Kumar Pant, Bhanu Gupta, S. R. Kale, S. V. Veeravalli

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In many countries, buses are the principal means of transport, of which a majority are naturally ventilated with open windows. The design of this ventilation has little scientific basis and to address this problem a study has been undertaken involving both experiments and numerical simulations. The flow pattern inside and around of an open window bus with passengers has been investigated in detail. A full scale three-dimensional numerical simulation has been used for a) a bus with closed windows and b) with open windows. In either simulation, the bus had 58 seated passengers. The bus dimensions used were 2500 mm wide × 2500 mm high (exterior) × 10500 mm long and its speed was set at 40 km/h. In both cases, the flow separates at the top front edge forming a vortex and reattaches close to the mid-length. This attached flow separates once more as it leaves the bus. However, the strength and shape of the vortices at the top front and wake region is different for both cases. The streamline pattern around the bus is also different for the two cases. For the bus with open windows, the dominant airflow inside the bus is from the rear to the front of the bus and air velocity at the face level of the passengers was found to be 1/10th of the free stream velocity. These findings are in good agreement with flow visualization experiments performed in a water channel at 10 m/s, and with smoke/tuft visualizations in a wind tunnel with a free-stream velocity of approximately 40 km/h on a 1:25 scaled Perspex model.

Keywords: air flow, moving bus, open windows, vortex, wind tunnel

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Authors: Sarah A. Goodchild, Rachel Gao, Philip N. Bartlett

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A novel detection approach using immobilized DNA probes labeled with Anthraquinone (AQ) as an electrochemically active reporter moiety has been successfully developed as a new, simple, reliable method for the detection of DNA. This method represents a step forward in DNA detection as it can discriminate between multiple nucleotide polymorphisms within target DNA strands without the need for any additional reagents, reporters or processes such as melting of DNA strands. The detection approach utilizes single-stranded DNA probes immobilized on gold surfaces labeled at the distal terminus with AQ. The effective immobilization has been monitored using techniques such as AC impedance and Raman spectroscopy. Simple voltammetry techniques (Differential Pulse Voltammetry, Cyclic Voltammetry) are then used to monitor the reduction potential of the AQ before and after the addition of complementary strand of target DNA. A reliable relationship between the shift in reduction potential and the number of base pair mismatch has been established and can be used to discriminate between DNA from highly related pathogenic organisms of clinical importance. This indicates that this approach may have great potential to be exploited within biosensor kits for detection and diagnosis of pathogenic organisms in Point of Care devices.

Keywords: Anthraquinone, discrimination, DNA detection, electrochemical biosensor

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Authors: Mohsen Sanayei, Jerzy Szpunar, Sami Penttilä

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Exposure of Nickel-based superalloys to high temperature and harsh environment such as Super-Critical Water (SCW) environment leads to the formation of oxide scales composed of multiple and complex phases that are difficult to differentiate with conventional analysis techniques. In this study, we used simultaneous Electron Backscatter Diffraction (EBSD) and Energy Dispersive X-ray Spectroscopy (EDS) to analyze the complex oxide scales formed on several Nickel-based Superalloys exposed to high temperature SCW. Multi-layered structures of Iron, Nickel, Chromium and Molybdenum oxides and spinels were clearly identified using the simultaneous EBSD-EDS analysis technique. Furthermore, the orientation relationship between the oxide scales and the substrate has been investigated.

Keywords: electron backscatter diffraction, energy dispersive x-ray spectroscopy, superalloy, super-critical water

Procedia PDF Downloads 319

Authors: Pui Shan Wong, Kosuke Tashiro, Satoru Kuhara, Sachiyo Aburatani

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Functional genomics and gene regulation inference has readily expanded our knowledge and understanding of gene interactions with regards to expression regulation. With the advancement of transcriptome sequencing in time-series comes the ability to study the sequential changes of the transcriptome. This method presented here works to augment existing regulation networks accumulated in literature with transcriptome data gathered from time-series experiments to construct a sequential representation of transcription factor activity. This method is applied on a time-series RNA-Seq data set from Escherichia coli as it transitions from growth to stationary phase over five hours. Investigations are conducted on the various metabolic activities in gene regulation processes by taking advantage of the correlation between regulatory gene pairs to examine their activity on a dynamic network. Especially, the changes in metabolic activity during phase transition are analyzed with focus on the pagP gene as well as other associated transcription factors. The visualization of the sequential transcriptional activity is used to describe the change in metabolic pathway activity originating from the pagP transcription factor, phoP. The results show a shift from amino acid and nucleic acid metabolism, to energy metabolism during the transition to stationary phase in E. coli.

Keywords: Escherichia coli, gene regulation, network, time-series

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Authors: Nieto Bernal Wilson, Carmona Suarez Edgar

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The organizations have structured and unstructured information in different formats, sources, and systems. Part of these come from ERP under OLTP processing that support the information system, however these organizations in OLAP processing level, presented some deficiencies, part of this problematic lies in that does not exist interesting into extract knowledge from their data sources, as also the absence of operational capabilities to tackle with these kind of projects.  Data Warehouse and its applications are considered as non-proprietary tools, which are of great interest to business intelligence, since they are repositories basis for creating models or patterns (behavior of customers, suppliers, products, social networks and genomics) and facilitate corporate decision making and research. The following paper present a structured methodology, simple, inspired from the agile development models as Scrum, XP and AUP. Also the models object relational, spatial data models, and the base line of data modeling under UML and Big data, from this way sought to deliver an agile methodology for the developing of data warehouses, simple and of easy application. The methodology naturally take into account the application of process for the respectively information analysis, visualization and data mining, particularly for patterns generation and derived models from the objects facts structured.

Keywords: data warehouse, model data, big data, object fact, object relational fact, process developed data warehouse

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Authors: M. I. Youssef, A. E. Emam, M. Abd Elghany

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This paper evaluates the bit error rate (BER) performance of MIMO-OFDM communication system. MIMO system uses multiple transmitting and receiving antennas with different coding techniques to either enhance the transmission diversity or spatial multiplexing gain. Utilizing alamouti algorithm were the same information transmitted over multiple antennas at different time intervals and then collected again at the receivers to minimize the probability of error, combat fading and thus improve the received signal to noise ratio. While utilizing V-BLAST algorithm, the transmitted signals are divided into different transmitting channels and transferred over the channel to be received by different receiving antennas to increase the transmitted data rate and achieve higher throughput. The paper provides a study of different diversity gain coding schemes and spatial multiplexing coding for MIMO systems. A comparison of various channels' estimation and equalization techniques are given. The simulation is implemented using MATLAB, and the results had shown the performance of transmission models under different channel environments.

Keywords: MIMO communication, BER, space codes, channels, alamouti, V-BLAST

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Authors: Temitope Olumide Olugbade

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Low resistance and delayed fracture to corrosion, especially in harsh environmental conditions, often limit the wide application of high-manganese (high-Mn) steels. To address this issue, the present work investigates the influence of microalloying on the corrosion properties of high-Mn steels. Microalloyed and base high-Mn steels were synthesized through an arc melting process under an argon atmosphere. To generate different microstructures, the temperature and duration were varied via thermal homogenization treatments. The electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization techniques were used to determine the corrosion properties in 0.6 M NaCl aqueous solution at room temperature. The relationship between the microstructures and corrosion properties was investigated via Scanning Kelvin Probe Microscopy (SKPFM), energy dispersive X-ray spectroscopy (EDX), and Scanning electron microscopy (SEM) techniques. The local corrosion properties were investigated via in situ atomic force spectroscopy (AFM), considering the homogenization treatments. The results indicate that microalloying is a successful technique for enhancing the corrosion behavior of high-Mn steels. Compared to other alloying elements, Vanadium has shown improvement in corrosion properties for both general and local corrosion in chloride environments.

Keywords: corrosion, high-manganese steel, homogenization, microalloying, vanadium

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Authors: Abisuga Oluwayemisi Adebola, Kerri Akiwowo, Deon de Beer, Kobus Van Der Walt

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The convergence of additive manufacturing (AM) and traditional textile dyeing techniques has initiated innovative possibilities for improving the visual application and customization potential of 3D-printed polymer objects. Textile dyeing techniques have progressed to transform fabrics with vibrant colours and complex patterns over centuries. The layer-by-layer deposition characteristic of AM necessitates adaptations in dye application methods to ensure even colour penetration across complex surfaces. Compatibility between dye formulations and polymer matrices influences colour uptake and stability, demanding careful selection and testing of dyes for optimal results. This study investigates the development interaction between these areas, revealing the challenges and opportunities of applying textile dyeing methods to colour 3D-printed polymer materials. The method explores three innovative approaches to colour the 3D-printed polymer object: (a) Additive Manufacturing of a Prototype, (b) the traditional dyebath method, and (c) the contemporary digital sublimation technique. The results show that the layer lines inherent to AM interact with dyes differently and affect the visual outcome compared to traditional textile fibers. Skillful manipulation of textile dyeing methods and dye type used for this research reduced the appearance of these lines to achieve consistency and desirable colour outcomes. In conclusion, integrating textile dyeing techniques into colouring 3D-printed polymer materials connects historical craftsmanship with innovative manufacturing. Overcoming challenges of colour distribution, compatibility, and layer line management requires a holistic approach that blends the technical consistency of AM with the artistic sensitivity of textile dyeing. Hence, applying textile dyeing methods to 3D-printed polymers opens new dimensions of aesthetic and functional possibilities.

Keywords: polymer, 3D-printing, sublimation, textile, dyeing, additive manufacturing

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Authors: Raphael Tuor, Denis Lalanne

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The ATM research community is missing suitable tools to design, test, and validate new UI prototypes. Important stakes underline the implementation of both DSS and XAI methods into current systems. ML-based DSS are gaining in relevance as ATFM becomes increasingly complex. However, these systems only prove useful if a human can understand them, and thus new XAI methods are needed. The human-machine dyad should work as a team and should understand each other. We present xSky, a configurable benchmark tool that allows us to compare different versions of an ATC interface in conflict detection tasks. Our main contributions to the ATC research community are (1) a conflict detection task simulator (xSky) that allows to test the applicability of visual prototypes on scenarios of varying difficulty and outputting relevant operational metrics (2) a theoretical approach to the explanations of AI-driven trajectory predictions. xSky addresses several issues that were identified within available research tools. Researchers can configure the dimensions affecting scenario difficulty with a simple CSV file. Both the content and appearance of the XAI elements can be customized in a few steps. As a proof-of-concept, we implemented an XAI prototype inspired by the maritime field.

Keywords: air traffic control, air traffic simulation, conflict detection, explainable artificial intelligence, explainability, human-automation collaboration, human factors, information visualization, interpretability, trajectory prediction

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Authors: Izadpanh Shaghayegh, Adli Fateme

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Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms, including tremors, bradykinesia, rigidity, and freezing of gait (FOG), which arise from degeneration of the basal ganglia. While pharmacological treatments, particularly dopaminergic therapies, remain the primary approach for managing PD, their long-term effectiveness diminishes due to complications such as dyskinesia and motor fluctuations. Deep brain stimulation (DBS) has emerged as an alternative for symptom management but remains invasive, costly, and associated with significant risks. In light of these challenges, non-invasive brain stimulation (NIBS) techniques are gaining attention as promising alternatives for treating PD. These methods, including transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), and microwave brain stimulation (MBS), offer advantages such as reduced risk and non-invasiveness while providing targeted modulation of brain activity. Recent innovations, such as hemispherical antenna arrays for focused stimulation and advanced signal patterns like high-frequency prime harmonics and temporal interference (TI), have further enhanced the precision and efficacy of NIBS. These techniques have shown potential in modulating neuronal excitability, improving gait, and reducing motor symptoms in PD patients, with some approaches demonstrating effectiveness in treating FOG. Despite promising results, continued research is necessary to refine these technologies, optimize treatment protocols, and evaluate their long-term impact on PD progression. This review highlights recent advances in non-invasive brain stimulation for PD and discusses their potential as adjunctive therapies for managing motor symptoms and improving quality of life in PD patients.

Keywords: Parkinson’s disease, non-invasive brain stimulation, deep brain stimulation, transcranial magnetic stimulation, transcranial direct current stimulation, freezing of gait, microwave brain stimulation, neuromodulation

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Authors: G. Anjan Babu, G. Sumana, M. Rajasekhar

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Many inherited diseases and non-hereditary disorders are common in the development of renal cystic diseases. Polycystic kidney disease (PKD) is a disorder developed within the kidneys in which grouping of cysts filled with water like fluid. PKD is responsible for 5-10% of end-stage renal failure treated by dialysis or transplantation. New experimental models, application of molecular biology techniques have provided new insights into the pathogenesis of PKD. Researchers are showing keen interest for developing an automated system by applying computer aided techniques for the diagnosis of diseases. In this paper a multi-layered feed forward neural network with one hidden layer is constructed, trained and tested by applying back propagation learning rule for the diagnosis of PKD based on physical symptoms and test results of urinanalysis collected from the individual patients. The data collected from 50 patients are used to train and test the network. Among these samples, 75% of the data used for training and remaining 25% of the data are used for testing purpose. Furthermore, this trained network is used to implement for new samples. The output results in normality and abnormality of the patient.

Keywords: dialysis, hereditary, transplantation, polycystic, pathogenesis

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Authors: Chaghoub Soraya, Zhang Xiaoyan

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This research presents the first constant approximation algorithm to the p-median network design problem with multiple cable types. This problem was addressed with a single cable type and there is a bifactor approximation algorithm for the problem. To the best of our knowledge, the algorithm proposed in this paper is the first constant approximation algorithm for the p-median network design with multiple cable types. The addressed problem is a combination of two well studied problems which are p-median problem and network design problem. The introduced algorithm is a random sampling approximation algorithm of constant factor which is conceived by using some random sampling techniques form the literature. It is based on a redistribution Lemma from the literature and a steiner tree problem as a subproblem. This algorithm is simple, and it relies on the notions of random sampling and probability. The proposed approach gives an approximation solution with one constant ratio without violating any of the constraints, in contrast to the one proposed in the literature. This paper provides a (21 + 2)-approximation algorithm for the p-median network design problem with multiple cable types using random sampling techniques.

Keywords: approximation algorithms, buy-at-bulk, combinatorial optimization, network design, p-median

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Authors: Ruzanna Muradyan

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Medical data mining is a crucial field in contemporary healthcare that offers cutting-edge tactics with enormous potential to transform patient care. This abstract examines how sophisticated data mining techniques could transform the healthcare industry, with a special focus on how they might improve patient outcomes. Healthcare data repositories have dynamically evolved, producing a rich tapestry of different, multi-dimensional information that includes genetic profiles, lifestyle markers, electronic health records, and more. By utilizing data mining techniques inside this vast library, a variety of prospects for precision medicine, predictive analytics, and insight production become visible. Predictive modeling for illness prediction, risk stratification, and therapy efficacy evaluations are important points of focus. Healthcare providers may use this abundance of data to tailor treatment plans, identify high-risk patient populations, and forecast disease trajectories by applying machine learning algorithms and predictive analytics. Better patient outcomes, more efficient use of resources, and early treatments are made possible by this proactive strategy. Furthermore, data mining techniques act as catalysts to reveal complex relationships between apparently unrelated data pieces, providing enhanced insights into the cause of disease, genetic susceptibilities, and environmental factors. Healthcare practitioners can get practical insights that guide disease prevention, customized patient counseling, and focused therapies by analyzing these associations. The abstract explores the problems and ethical issues that come with using data mining techniques in the healthcare industry. In order to properly use these approaches, it is essential to find a balance between data privacy, security issues, and the interpretability of complex models. Finally, this abstract demonstrates the revolutionary power of modern data mining methodologies in transforming the healthcare sector. Healthcare practitioners and researchers can uncover unique insights, enhance clinical decision-making, and ultimately elevate patient care to unprecedented levels of precision and efficacy by employing cutting-edge methodologies.

Keywords: data mining, healthcare, patient care, predictive analytics, precision medicine, electronic health records, machine learning, predictive modeling, disease prognosis, risk stratification, treatment efficacy, genetic profiles, precision health

Procedia PDF Downloads 63

Authors: Ahmed Al-Adaileh

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Renewable energy sources, domestic storage systems, controllable loads and machine learning technologies will be key components of future smart homes management systems. An energy management scheme that uses a Deep Learning (DL) approach to support the smart home management systems, which consist of a standalone photovoltaic system, storage unit, heating ventilation air-conditioning system and a set of conventional and smart appliances, is presented. The objective of the proposed scheme is to apply DL-based machine learning to predict various running parameters within a smart home's environment to achieve maximum comfort levels for occupants, reduced electricity bills, and less dependency on the public grid. The problem is using Reinforcement learning, where decisions are taken based on applying the Continuous-time Markov Decision Process. The main contribution of this research is the proposed framework that applies DL to enhance the system's supervised dataset to offer unlimited chances to effectively support smart home systems. A case study involving a set of conventional and smart appliances with dedicated processing units in an inhabited building can demonstrate the validity of the proposed framework. A visualization graph can show "before" and "after" results.

Keywords: smart homes systems, machine learning, deep learning, Markov Decision Process

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Authors: Somayeh Khanjani, Hamideh Abolghasemi, Hadi Shirzad, Samaneh Nabavi

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At world market can be currently encountered a wide range of gemological objects that are incorrectly declared, treated, or it concerns completely different materials that try to copy precious objects more or less successfully. Counterfeiting of precious commodities is a problem faced by governments in most countries. Police have seized many counterfeit coins that looked like the real coins and because the feeling to the touch and the weight were very similar to those of real coins. Most people were fooled and believed that the counterfeit coins were real ones. These counterfeit coins may have been made by big criminal organizations. To elucidate the manufacturing process, not only the quantitative analysis of the coins but also the comparison of their morphological characteristics was necessary. Several modern techniques have been applied to prevent counterfeiting of coins. The objective of this study was to demonstrate the potential of X-ray Fluorescence (XRF) technique and the other analytical techniques for example SEM/EDX/WDX, FT-IR/ATR and Raman Spectroscopy. Using four elements (Cu, Ag, Au and Zn) and obtaining XRF for several samples, they could be discriminated. XRF technique and SEM/EDX/WDX are used for study of chemical composition. XRF analyzers provide a fast, accurate, nondestructive method to test the purity and chemistry of all precious metals. XRF is a very promising technique for rapid and non destructive counterfeit coins identification in forensic science.

Keywords: counterfeit coins, X-ray fluorescence, forensic, FT-IR

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Authors: Brwa Abdulrahman Abubaker

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Recently, there has been a lot of interest in the difficult task of applying reinforcement learning to autonomous mobile robots. Conventional reinforcement learning (TRL) techniques have many drawbacks, such as lengthy computation times, intricate control frameworks, a great deal of trial and error searching, and sluggish convergence. In this paper, a modified Spiking Neural Network (SNN) is used to offer a distinct method for autonomous mobile robot learning and control in unexpected surroundings. As a learning algorithm, the suggested model combines dopamine modulation with spike-timing-dependent plasticity (STDP). In order to create more computationally efficient, biologically inspired control systems that are adaptable to changing settings, this work uses the effective and physiologically credible Izhikevich neuron model. This study is primarily focused on creating an algorithm for target tracking in the presence of obstacles. Results show that the SNN trained with three obstacles yielded an impressive 96% success rate for our proposal, with collisions happening in about 4% of the 214 simulated seconds.

Keywords: spiking neural network, spike-timing-dependent plasticity, dopamine modulation, reinforcement learning

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Authors: Olanike A. Ojo, Akindele M. Ojo, Jacob H. Tsado, Ramatu U. Kutigi

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The study was carried out on analysis of the effect of farmers’ socio-economic factors on the net farm income of catfish farmers in Kwara State, Nigeria. Primary data were collected from selected catfish farmers with the aid of well-structured questionnaire and a multistage sampling technique was used to select 102 catfish farmers in the area. The analytical techniques involved the use of descriptive statistics and multiple regression analysis. The findings of the analysis of socio-economic characteristics of catfish farmers reveal that 60% of the catfish farmers in the study area were male gender which implied the existence of gender inequality in the area. The mean age of 47 years was an indication that they were at their economically productive age and could contribute positively to increased production of catfish in the area. Also, the mean household size was five while the mean year of experience was five. The latter implied that the farmers were experienced in fishing techniques, breeding and fish culture which would assist in generating more revenue, reduce cost of production and eventual increase in profit levels of the farmers. The result also revealed that stock capacity (X3), accessibility to credit (X7) and labour (X4) were the main determinants of catfish production in the area. In addition, farmer’s sex, household size, no of ponds, distance of the farm from market, access to credit were the main socio-economic factors influencing the net farm income of the catfish farmers in the area. The most serious constraints militating against catfish production in the study area were high mortality rate, insufficient market, inadequate credit facilities/ finance and inadequate skilled labour needed for daily production routine. Based on the findings, it is therefore recommended that, to reduce the mortality rate of catfish extension agents should organize training workshops on improved methods and techniques of raising catfish right from juvenile to market size.

Keywords: credit, income, stock, mortality

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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: Hadj Sahraoui Omar, Kebir Lahcen Wahib, Bennia Ahmed

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Interferometric Synthetic Aperture Radar (InSAR) coherence is a crucial parameter for accurately monitoring ground deformation and environmental changes. However, coherence can be degraded by various factors such as temporal decorrelation, atmospheric disturbances, and geometric misalignments, limiting the reliability of InSAR measurements (Omar Hadj‐Sahraoui and al. 2019). To address this challenge, we propose an innovative hybrid approach that combines artificial intelligence (AI) with advanced filtering techniques to optimize interferometric coherence in InSAR data. Specifically, we introduce a Convolutional Neural Network (CNN) integrated with the Lee filter to enhance the performance of radar interferometry. This hybrid method leverages the strength of CNNs to automatically identify and mitigate the primary sources of decorrelation, while the Lee filter effectively reduces speckle noise, improving the overall quality of interferograms. We develop a deep learning-based model trained on multi-temporal and multi-frequency SAR datasets, enabling it to predict coherence patterns and enhance low-coherence regions. This hybrid CNN-SAR with Lee filtering significantly reduces noise and phase unwrapping errors, leading to more precise deformation maps. Experimental results demonstrate that our approach improves coherence by up to 30% compared to traditional filtering techniques, making it a robust solution for challenging scenarios such as urban environments, vegetated areas, and rapidly changing landscapes. Our method has potential applications in geohazard monitoring, urban planning, and environmental studies, offering a new avenue for enhancing InSAR data reliability through AI-powered optimization combined with robust filtering techniques.

Keywords: CNN-SAR, Lee Filter, hybrid optimization, coherence, InSAR phase unwrapping, speckle noise reduction

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Authors: Magid Maatallah

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The problem is, investment management in modern conditions boils down to risk management which is very underdeveloped in Islamic financial theory and practice. Add to this the fact that, in Islamic perception, this is one of the areas of conventional finance in need of drastic reforms. This need was recently underlined by the story of Long Term Capital Management (LTCM ), ( told by Roger Lowenstein in his book, When Genius Failed, Random House, 2000 ). So we face a double challenge, to develop Islamic techniques of risk management and to see that these new techniques are free from the ills with which conventional methods are suffering. This is different from the challenge faced in the middle of twentieth century, to develop a method of financial intermediation free of interest.Risk was always there, especially in business. But industrialization brought risks unknown in trade and agriculture. Industrial production often involves long periods of time .The longer the period of production the more the uncertainty. The scope of the market has expanded to cover the whole world, introducing new kinds of risk. More than a thousand years ago, when Islamic laws were being written, the nature and scope of risk and uncertainty was different. However, something can still be learnt which, in combination with the modern experience, should enable us to realize the Shariah objectives of justice, fairness and efficiency.

Keywords: financial markets, Islamic framework, risk management, investment

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Authors: Drashti G. Daraji, Rosa E. Moo-Puc, Hitesh D. Patel

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Substituted 2-Thioimidazole analogues have been synthesized and confirmed by advanced spectroscopic techniques. Among them, ten compounds have been selected and evaluated for their in vitro anti-cancer activity at the National Cancer Institute (NCI) for testing against a panel of 60 different human tumor cell lines derived from nine neoplastic cancer types. Furthermore, synthesized compounds were tested for their in vitro antiprotozoal activity, and none of them exhibited significant potency against antiprotozoans. It was observed that the tested all compounds seem effective on the UACC-62 melanoma cancer cell line as compared to other cancer cell lines and also exhibited the least potent in the Non-Small Cell Lung Cancer cell line in one-dose screening. In silico studies of these derivatives were carried out by molecular docking techniques and Absorption, Distribution, Metabolism, and Excretion (ADME) using Schrödinger software to find potent B-Raf kinase inhibitor (PDB ID: 3OG7). All the compounds have been performed for docking study; Compound D4 has a good docking score for melanoma cancer as compared with other.

Keywords: anticancer activity, cancer cell line, 2-thio imidazole, one-dose assay, molecular docking

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Authors: Babak Bashari Rad

Abstract:

A metamorphic computer virus uses different code transformation techniques to mutate its body in duplicated instances. Characteristics and function of new instances are mostly similar to their parents, but they cannot be easily detected by the majority of antivirus in market, as they depend on string signature-based detection techniques. The purpose of this research is to propose a Hidden Markov Model for classification of metamorphic viruses in executable files. In the proposed solution, portable executable files are inspected to extract the instructions opcodes needed for the examination of code. A Hidden Markov Model trained on portable executable files is employed to classify the metamorphic viruses of the same family. The proposed model is able to generate and recognize common statistical features of mutated code. The model has been evaluated by examining the model on a test data set. The performance of the model has been practically tested and evaluated based on False Positive Rate, Detection Rate and Overall Accuracy. The result showed an acceptable performance with high average of 99.7% Detection Rate.

Keywords: malware classification, computer virus classification, metamorphic virus, metamorphic malware, Hidden Markov Model

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Authors: Efstratios Karavasilis, Foteini Christidi, Georgios Velonakis, Agapi Plousi, Kalliopi Platoni, Nikolaos Kelekis, Ioannis Evdokimidis, Efstathios Efstathopoulos

Abstract:

Introduction: Advanced structural and functional neuroimaging techniques contribute to the study of anatomical and functional brain connectivity and its role in the pathophysiology and symptoms’ heterogeneity in several neurodegenerative disorders, including multiple sclerosis (MS). Aim: In the present study, we applied multiparametric neuroimaging techniques to investigate the structural and functional cortico-cerebellar changes in MS patients. Material: We included 51 MS patients (28 with clinically isolated syndrome [CIS], 31 with relapsing-remitting MS [RRMS]) and 51 age- and gender-matched healthy controls (HC) who underwent MRI in a 3.0T MRI scanner. Methodology: The acquisition protocol included high-resolution 3D T1 weighted, diffusion-weighted imaging and echo planar imaging sequences for the analysis of volumetric, tractography and functional resting state data, respectively. We performed between-group comparisons (CIS, RRMS, HC) using CAT12 and CONN16 MATLAB toolboxes for the analysis of volumetric (cerebellar gray matter density) and functional (cortico-cerebellar resting-state functional connectivity) data, respectively. Brainance suite was used for the analysis of tractography data (cortico-cerebellar white matter integrity; fractional anisotropy [FA]; axial and radial diffusivity [AD; RD]) to reconstruct the cerebellum tracts. Results: Patients with CIS did not show significant gray matter (GM) density differences compared with HC. However, they showed decreased FA and increased diffusivity measures in cortico-cerebellar tracts, and increased cortico-cerebellar functional connectivity. Patients with RRMS showed decreased GM density in cerebellar regions, decreased FA and increased diffusivity measures in cortico-cerebellar WM tracts, as well as a pattern of increased and mostly decreased functional cortico-cerebellar connectivity compared to HC. The comparison between CIS and RRMS patients revealed significant GM density difference, reduced FA and increased diffusivity measures in WM cortico-cerebellar tracts and increased/decreased functional connectivity. The identification of decreased WM integrity and increased functional cortico-cerebellar connectivity without GM changes in CIS and the pattern of decreased GM density decreased WM integrity and mostly decreased functional connectivity in RRMS patients emphasizes the role of compensatory mechanisms in early disease stages and the disintegration of structural and functional networks with disease progression. Conclusions: In conclusion, our study highlights the added value of multimodal neuroimaging techniques for the in vivo investigation of cortico-cerebellar brain changes in neurodegenerative disorders. An extension and future opportunity to leverage multimodal neuroimaging data inevitably remain the integration of such data in the recently-applied mathematical approaches of machine learning algorithms to more accurately classify and predict patients’ disease course.

Keywords: advanced neuroimaging techniques, cerebellum, MRI, multiple sclerosis

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Authors: Vadanasundari Vedarethinam, Kun Qian

Abstract:

The metabolic analysis is more distal over proteomics and genomics engaging in clinics and needs rationally distinct techniques, designed materials, and device for clinical diagnosis. Conventional techniques such as spectroscopic techniques, biochemical analyzers, and electrochemical have been used for metabolic diagnosis. Currently, there are four major challenges including (I) long-term process in sample pretreatment; (II) difficulties in direct metabolic analysis of biosamples due to complexity (III) low molecular weight metabolite detection with accuracy and (IV) construction of diagnostic tools by materials and device-based platforms for real case application in biomedical applications. Development of chips with nanomaterial is promising to address these critical issues. Mass spectroscopy (MS) has displayed high sensitivity and accuracy, throughput, reproducibility, and resolution for molecular analysis. Particularly laser desorption/ ionization mass spectrometry (LDI MS) combined with devices affords desirable speed for mass measurement in seconds and high sensitivity with low cost towards large scale uses. We developed a plasmonic chip for clinical metabolic fingerprinting as a hot carrier in LDI MS by series of chips with gold nanoshells on the surface through controlled particle synthesis, dip-coating, and gold sputtering for mass production. We integrated the optimized chip with microarrays for laboratory automation and nanoscaled experiments, which afforded direct high-performance metabolic fingerprinting by LDI MS using 500 nL of serum, urine, cerebrospinal fluids (CSF) and exosomes. Further, we demonstrated on-chip direct in-vitro metabolic diagnosis of early-stage lung cancer patients using serum and exosomes without any pretreatment or purifications. To our best knowledge, this work initiates a bionanotechnology based platform for advanced metabolic analysis toward large-scale diagnostic use.

Keywords: plasmonic chip, metabolic fingerprinting, LDI MS, in-vitro diagnostics

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Authors: Sean Yao Zu Kong, Khong Yik Chew

Abstract:

Introduction: We described a case report of the successful use of advanced surgical techniques in a patient with recurrent breast cancer who underwent a wide resection including the hemi-sternum, clavicle, multiple ribs, and a lobe of the lung due to tumor involvement. This extensive resection exposed critical structures, requiring a creative approach to reconstruction. To address this complex chest wall reconstruction, a free fibula flap and a 4-zone rectus abdominis musculocutaneous flap were successfully utilized. The use of a free vascularized bone flap allowed for rapid osteointegration and resistance against osteoradionecrosis after adjuvant radiation, while a four-zone tram flap allowed for reconstruction of both the chest wall and breast mound. Although limited recipient vessels made free flaps challenging, the free fibula flap served as both a bony reconstruction and vascular conduit, supercharged with the distal peroneal artery and veins of the peroneal artery from the fibula graft. Our approach highlights the potential of advanced surgical techniques to improve outcomes in complex cases of chest wall reconstruction in patients with recurrent breast cancer, which is becoming increasingly relevant as breast cancer incidence rates increases. Case presentation: This report describes a successful reconstruction of a patient with recurrent breast cancer who required extensive resection, including the anterior chest wall, clavicle, and sternoclavicular joint. Challenges arose due to the loss of accessory muscles and the non-rigid rib cage, which could lead to compromised ventilation and instability. A free fibula osteocutaneous flap and a four-zone TRAM flap with vascular supercharging were utilized to achieve long-term stability and function. The patient has since fully recovered, and during the review, both flaps remained viable, and chest mound reconstruction was satisfactory. A planned nipple/areolar reconstruction was offered pending the patient’s decision after adjuvant radiotherapy. Conclusion: In conclusion, this case report highlights the successful use of innovative surgical techniques in addressing a complex case of recurrent breast cancer requiring extensive resection and radical reconstruction. Our approach, utilized a combination of a free fibula flap and a 4-zone rectus abdominis musculocutaneous flap, demonstrates the potential for advanced techniques in chest wall reconstruction to minimize complications and ensure long-term stability and function. As the incidence of breast cancer continues to rise, it is crucial that healthcare professionals explore and utilize innovative techniques to improve patient outcomes and quality of life.

Keywords: free fibula flap, rectus abdominis musculocutaneous flap, post-adjuvant radiotherapy, reconstructive surgery, malignancy

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Authors: Lewis Wallace, William Dempster, David Nash, Alexandros Boukis, Craig Maclean

Abstract:

This paper, through traditional test methods and close adherence to international standards, presents a characterization study of a woven Polyethylene Terephthalate (PET). Testing is undergone in the axial, shear, and out-of-plane (bend) directions, and the results are fitted to the *FABRIC material model with ABAQUS FEA. The non-linear behaviors of the fabric in the axial and shear directions and behaviors on the macro scale are explored at the meso scale level. The medical grade bio-fabric is tested in untreated and heat-treated forms, and deviations are closely analyzed at the micro, meso, and macro scales to determine the effects of the process. The heat-treatment process was found to increase the stiffness of the fabric during axial and bending stiffness testing but had a negligible effect on the shear response. The ability of *FABRIC to capture behaviors unique to fabric deformation is discussed, whereby the unique phenomenological input can accurately represent the experimentally derived inputs.

Keywords: experimental techniques, FEA modelling, materials characterization, post-processing techniques

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Authors: Navajyoti Panda, R. S. Pawar

Abstract:

In this study, process parameters like punch angle, die opening, grain direction, and pre-bend condition of the strip for deep draw of high strength low alloy steel HSLA 420 are investigated. The finite element method (FEM) in association with the Taguchi and the analysis of variance (ANOVA) techniques are carried out to investigate the degree of importance of process parameters in V-bending process for HSLA 420&ST12 grade material. From results, it is observed that punch angle had a major influence on the spring-back. Die opening also showed very significant role on spring back. On the other hand, it is revealed that grain direction had the least impact on spring back; however, if strip from flat sheet is taken, then it is less prone to spring back as compared to the strip from sheet metal coil. HyperForm software is used for FEM simulation and experiments are designed using Taguchi method. Percentage contribution of the parameters is obtained through the ANOVA techniques.

Keywords: bending, spring-back, v-bending, FEM, Taguchi, HSLA 420 and St12 materials, HyperForm, profile projector

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Authors: Isobel M. Thompson, Dorian Audot, Dominic Hudson, Martin Warner, Joseph Banks

Abstract:

Underwater fly kick is an essential skill in swimming, which can have a considerable impact upon overall race performance in competition, especially in sprint events. Reduced wave drags acting upon the body under the surface means that the underwater fly kick will potentially be the fastest the swimmer is travelling throughout the race. It is therefore critical to understand fly kicking techniques and determining biomechanical factors involved in the performance. Most previous studies assessing fly kick kinematics have focused on two-dimensional analysis; therefore, the three-dimensional elements of the underwater fly kick techniques are not well understood. Those studies that have investigated fly kicking techniques using three-dimensional methodologies have not reported full three-dimensional kinematics for the techniques observed, choosing to focus on one or two joints. There has not been a direct comparison completed on the results obtained using two-dimensional and three-dimensional analysis, and how these different approaches might affect the interpretation of subsequent results. The aim of this research is to quantify the differences in kinematics observed in underwater fly kicks obtained from both two and three-dimensional analyses of the same test conditions. In order to achieve this, a six-camera underwater Qualisys system was used to develop an experimental methodology suitable for assessing the kinematics of swimmer’s starts and turns. The cameras, capturing at a frequency of 100Hz, were arranged along the side of the pool spaced equally up to 20m creating a capture volume of 7m x 2m x 1.5m. Within the measurement volume, error levels were estimated at 0.8%. Prior to pool trials, participants completed a landside calibration in order to define joint center locations, as certain markers became occluded once the swimmer assumed the underwater fly kick position in the pool. Thirty-four reflective markers were placed on key anatomical landmarks, 9 of which were then removed for the pool-based trials. The fly-kick swimming conditions included in the analysis are as follows: maximum effort prone, 100m pace prone, 200m pace prone, 400m pace prone, and maximum pace supine. All trials were completed from a push start to 15m to ensure consistent kick cycles were captured. Both two-dimensional and three-dimensional kinematics are calculated from joint locations, and the results are compared. Key variables reported include kick frequency and kick amplitude, as well as full angular kinematics of the lower body. Key differences in these variables obtained from two-dimensional and three-dimensional analysis are identified. Internal rotation (up to 15º) and external rotation (up to -28º) were observed using three-dimensional methods. Abduction (5º) and adduction (15º) were also reported. These motions are not observed in the two-dimensional analysis. Results also give an indication of different techniques adopted by swimmers at various paces and orientations. The results of this research provide evidence of the strengths of both two dimensional and three dimensional motion capture methods in underwater fly kick, highlighting limitations which could affect the interpretation of results from both methods.

Keywords: swimming, underwater fly kick, performance, motion capture

Procedia PDF Downloads 136