Search results for: genetic algorithms

Authors: Elif Tugce Aksun Tumerkan

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Although seafood is an important part of human diet and categorized highly traded food industry internationally, it is remain overlooked generally in the global food security aspect. Food product authentication is the main interest in the aim of both avoids commercial fraud and to consider the risks that might be harmful to human health safety. In recent years, with increasing consumer demand for regarding food content and it's transparency, there are some instrumental analyses emerging for determining food fraud depend on some analytical methodologies such as proteomic and metabolomics. While, fish and seafood consumed as fresh previously, within advanced technology, processed or packaged seafood consumption have increased. After processing or packaging seafood, morphological identification is impossible when some of the external features have been removed. The main fish and seafood quality-related issues are the authentications of seafood contents such as mislabelling products which may be contaminated and replacement partly or completely, by lower quality or cheaper ones. For all mentioned reasons, truthful consistent and easily applicable analytical methods are needed for assurance the correct labelling and verifying of seafood products. DNA-barcoding methods become popular robust that used in taxonomic research for endangered or cryptic species in recent years; they are used for determining food traceability also. In this review, when comparing the other proteomic and metabolic analysis, DNA-based methods are allowing a chance to identification all type of food even as raw, spiced and processed products. This privilege caused by DNA is a comparatively stable molecule than protein and other molecules. Furthermore showing variations in sequence based on different species and founding in all organisms, make DNA-based analysis more preferable. This review was performed to clarify the main advantages of using DNA-barcoding for determining seafood fraud among other techniques.

Keywords: DNA-barcoding, genetic analysis, food fraud, mislabelling, packaged seafood

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Authors: Mark Andrew

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Geopolitical tensions are at a thirty-year high, and the pace of technological innovation is driving asymmetry in force capabilities between nation states and between non-state actors. Technology futures are a vital component of defence capability growth, and investments in technology futures need to be informed by accurate and reliable forecasts of the options for ‘systems of systems’ innovation, development, and deployment. This paper describes a method for forecasting technology futures developed through an analysis of four key systems’ development stages, namely: technology domain categorisation, scanning results examining novel systems’ signals and signs, potential system-of systems’ implications in warfare theatres, and political ramifications in terms of funding and development priorities. The method has been applied to several technology domains, including physical systems (e.g., nano weapons, loitering munitions, inflight charging, and hypersonic missiles), biological systems (e.g., molecular virus weaponry, genetic engineering, brain-computer interfaces, and trans-human augmentation), and information systems (e.g., sensor technologies supporting situation awareness, cyber-driven social attacks, and goal-specification challenges to proliferation and alliance testing). Although the current application of the method has been team-centred using paper-based rapid prototyping and iteration, the application of autonomous language models (such as GPT-3) is anticipated as a next-stage operating platform. The importance of forecasting accuracy and reliability is considered a vital element in guiding technology development to afford stronger contingencies as ideological changes are forecast to expand threats to ecology and earth systems, possibly eclipsing the traditional vulnerabilities of nation states. The early results from the method will be subjected to ground truthing using longitudinal investigation.

Keywords: forecasting, technology futures, uncertainty, complexity

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Authors: Hilal Naimi, Amelbahahouda Adamou-Mitiche, Lahcene Mitiche

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The main problem in the area of medical imaging has been image denoising. The most defying for image denoising is to secure data carrying structures like surfaces and edges in order to achieve good visual quality. Different algorithms with different denoising performances have been proposed in previous decades. More recently, models focused on deep learning have shown a great promise to outperform all traditional approaches. However, these techniques are limited to the necessity of large sample size training and high computational costs. This research proposes a denoising approach basing on LDTCWT (Lifting Dual Tree Complex Wavelet Transform) using Hybrid Thresholding with Wiener filter to enhance the quality image. This research describes the LDTCWT as a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). To develop this approach, a hybrid thresholding function is modeled by integrating the Wiener filter into the thresholding function.

Keywords: lifting wavelet transform, image denoising, dual tree complex wavelet transform, wavelet shrinkage, wiener filter

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Authors: Chao-Ton Su, Li-Fei Chen

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The parameter design is crucial to improving the uniformity of a product or process. In the product design stage, parameter design aims to determine the optimal settings for the parameters of each element in the system, thereby minimizing the functional deviations of the product. In the process design stage, parameter design aims to determine the operating settings of the manufacturing processes so that non-uniformity in manufacturing processes can be minimized. The parameter design, trying to minimize the influence of noise on the manufacturing system, plays an important role in the high-tech companies. Taiwan has many well-known high-tech companies, which show key roles in the global economy. Quality remains the most important factor that enables these companies to sustain their competitive advantage. In Taiwan however, many high-tech companies face various quality problems. A common challenge is related to root causes and defect patterns. In the R&D stage, root causes are often unknown, and defect patterns are difficult to classify. Additionally, data collection is not easy. Even when high-volume data can be collected, data interpretation is difficult. To overcome these challenges, high-tech companies in Taiwan use more advanced quality improvement tools. In addition to traditional statistical methods and quality tools, the new trend is the application of powerful tools, such as neural network, fuzzy theory, data mining, industrial engineering, operations research, and innovation skills. In this study, several examples of optimizing the parameter settings for the manufacturing process in Taiwan’s tech companies will be presented to illustrate proposed approach’s effectiveness. Finally, a discussion of using traditional experimental design versus the proposed approach for process optimization will be made.

Keywords: quality engineering, parameter design, neural network, genetic algorithm, experimental design

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Authors: Foteini Zagklavara, Peter Jimack, Nikil Kapur, Ozz Querin, Harvey Thompson

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The invention and development of the Polymerase Chain Reaction (PCR) technology have revolutionised molecular biology and molecular diagnostics. There is an urgent need to optimise their performance of those devices while reducing the total construction and operation costs. The present study proposes a CFD-enabled optimisation methodology for continuous flow (CF) PCR devices with serpentine-channel structure, which enables the trade-offs between competing objectives of DNA amplification efficiency and pressure drop to be explored. This is achieved by using a surrogate-enabled optimisation approach accounting for the geometrical features of a CF μPCR device by performing a series of simulations at a relatively small number of Design of Experiments (DoE) points, with the use of COMSOL Multiphysics 5.4. The values of the objectives are extracted from the CFD solutions, and response surfaces created using the polyharmonic splines and neural networks. After creating the respective response surfaces, genetic algorithm, and a multi-level coordinate search optimisation function are used to locate the optimum design parameters. Both optimisation methods produced similar results for both the neural network and the polyharmonic spline response surfaces. The results indicate that there is the possibility of improving the DNA efficiency by ∼2% in one PCR cycle when doubling the width of the microchannel to 400 μm while maintaining the height at the value of the original design (50μm). Moreover, the increase in the width of the serpentine microchannel is combined with a decrease in its total length in order to obtain the same residence times in all the simulations, resulting in a smaller total substrate volume (32.94% decrease). A multi-objective optimisation is also performed with the use of a Pareto Front plot. Such knowledge will enable designers to maximise the amount of DNA amplified or to minimise the time taken throughout thermal cycling in such devices.

Keywords: PCR, optimisation, microfluidics, COMSOL

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Authors: Lidia Kowalska, Edward Arseniuk

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Glume and leaf blotch is a disease of wheat caused by necrotrophic fungus Parastagonospora nodorum. It is a serious pathogen in many wheat-growing areas throughout the world. Use of resistant cultivars is the most effective and economical means to control the above-mentioned disease. Plant breeders and pathologists have worked intensively to incorporate resistance to the pathogen in new cultivars. Conventional methods of breeding for resistance can be supported by using the biotechnological ones, i.e., somatic embryogenesis and androgenesis. Therefore, an effort was undertaken to compare genetic variation in P. nodorum resistance among winter wheat somaclones, dihaploids and conventional varieties. For the purpose, a population of 16 somaclonal and 4 dihaploid wheat lines from six crosses were used to assess their resistance to P. nodorum under field conditions. Lines were grown in disease-free (fungicide protected) and inoculated micro plots in 2 replications of a split-plot design in a single environment. The plant leaves were inoculated with a mixture of P. nodorum isolates three times. Spore concentrations were adjusted to 4 x 10⁶ of viable spores per one milliliter. The disease severity was rated on a scale, where > 90% – susceptible, < 10% - resistant. Disease ratings of plant leaves showed statistically significant differences among all lines tested. Higher resistance to P. nodorum was observed more often on leaves of somaclonal lines than on dihaploid ones. On average, disease, severity reached 15% on leaves of somaclones and 30% on leaves of dihaploids. Some of the genotypes were showing low leaf infection, e.g. dihaploid D-33 (disease severity 4%) and a somaclone S-1 (disease severity 2%). The results from this study prove that dihaploid and somaclonal variation might be successfully used as an additional source of wheat resistance to the pathogen and it could be recommended to use in commercial breeding programs. The reported results prove that biotechnological methods may effectively be used in breeding for disease resistance of wheat to fungal necrotrophic pathogens.

Keywords: glume and leaf blotch, somaclonal, androgenic variation, wheat, resistance breeding

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Authors: Azar Sharafianpor, Hossein Rassi, Fahimeh Nemati Mansur

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Cardiovascular diseases (CVD) are the most important cause of death in industrialized and developing countries such as Iran. The most important risk factors for the CVD, genetic factors and chronic infectious agents, such as Helicobacter pylori, can be mentioned. The TNFα gene is one of the most important anti-inflammatory cytokines that can affect the sensitivity, efficacy, and ability of the immune response to chronic infections. Some TNF-α gene polymorphisms, including the replacement of the G nucleotide G with A at position 308 in the promoter region of TNF-α, increase the transcription of cytokines in the target cells and thus predispose a person to chronic infections. This study examines the TNF-α 308 polymorphism and its association with Helicobacter pylori infection in this disease. This study was a case-control study in which 154 patients were examined as cases or patients with symptoms of myocardial infarction or angina and 160 as controls or healthy subjects. All of the subjects at different ages were given venous blood and age, BMI, cholesterol, LDL, and HDL were determined. DNA was extracted from the specimens, and the cagA gene from H. pylori and the TNF-α-308 polymorphism were determined by PCR in patients and healthy subjects. Statistical analysis was performed with Epi Info software. The results showed that the frequency of H. pylori infection in the patients and healthy group were 53.23% (82 out of 154) and 47.5% (76 out of 160). There was no significant difference in H. pylori outbreak between the two groups. The frequencies of TNF-α-308 genotype for GG, GA, and AA in patients were 0.17, 0.49, and 0.34, respectively, whereas for controls 0.47, 0.35, and 0.18 for GG, GA, and AA, respectively. The frequency of genotype analysis of TNF-α-308 polymorphisms in both patients and healthy groups showed that there was a significant difference in the frequency of genotypes and the AA genotype was higher in the affected individuals. Also, there was a significant relationship between the genotype and the contamination with H. pylori and changes in cholesterol, LDL, and HDL levels were observed. The results of the study indicate that H. pylori detection in individuals with AA genotype in people under 50 years of age can play an important role in early diagnosis and treatment of cardiovascular disease.

Keywords: Helicobacter pylori, TNFα gene, cardiovascular diseases, TNFα-308 polymorphism

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: Zhu Weimin, Allegorico Carmine, Ruggiero Gionata

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During decades of Baker Hughes (BH) iCenter experience, it is demonstrated that in addition to conventional insights on equipment steady operation conditions, insights on transient conditions can add significant and exclusive value for anomaly detection, downtime saving, and predictive maintenance. Our work shows examples from the BH iCenter experience to introduce the advantages and features of using transient condition analytics: (i) Operation under critical engine conditions: e.g., high level or high change rate of temperature, pressure, flow, vibration, etc., that would not be reachable in normal operation, (ii) Management of dedicated sub-systems or components, many of which are often bottlenecks for reliability and maintenance, (iii) Indirect detection of anomalies in the absence of instrumentation, (iv) Repetitive sequences: if data is properly processed, the engineering features of transients provide not only anomaly detection but also problem characterization and prognostic indicators for predictive maintenance, (v) Engine variables accounting for fatigue analysis. iCenter has been developing and deploying a series of analytics based on transient conditions. They are contributing to exclusive value adding in the following areas: (i) Reliability improvement, (ii) Startup reliability improvement, (iii) Predictive maintenance, (iv) Repair/overhaul cost down. Illustrative examples for each of the above areas are presented in our study, focusing on challenges and adopted techniques ranging from purely statistical approaches to the implementation of machine learning algorithms. The obtained results demonstrate how the value is obtained using transient condition analytics in the BH iCenter experience.

Keywords: analytics, diagnostics, monitoring, turbomachinery

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Authors: Mengying Wang, Dongjing Liu, Holger Schwender, Ping Wang, Hongping Zhu, Tao Wu, Terri H Beaty

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Background: Maternal smoking is a recognized risk factor for nonsyndromic cleft lip with or without cleft palate (NSCL/P). It has been reported that the effect of maternal smoking on oral clefts is mediated through genes that influence nicotine dependence. The polymorphisms of cholinergic receptor nicotinic alpha (CHRNA) and beta (CHRNB) subunits genes have previously shown strong associations with nicotine dependence. Here, we attempted to investigate whether the above genes are associated with clefting risk through testing for potential gene-gene (G×G) and gene-environment (G×E) interaction. Methods: We selected 120 markers in 14 genes associated with nicotine dependence to conduct transmission disequilibrium tests among 806 Chinese NSCL/P case-parent trios ascertained in an international consortium which conducted a genome-wide association study (GWAS) of oral clefts. We applied Cordell’s method using “TRIO” package in R to explore G×G as well as G×E interaction involving environmental tobacco smoke (ETS) based on conditional logistic regression model. Results: while no SNP showed significant association with NSCL/P after Bonferroni correction, we found signals for G×G interaction between 10 pairs of SNPs in CHRNA3, CHRNA5, and CHRNB4 (p<10-8), among which the most significant interaction was found between RS3743077 (CHRNA3) and RS11636753 (CHRNB4, p<8.2×10-12). Linkage disequilibrium (LD) analysis revealed only low level of LD between these markers. However, there were no significant results for G×ETS interaction. Conclusion: This study fails to detect association between nicotine dependence genes and NSCL/P, but illustrates the importance of taking into account potential G×G interaction for genetic association analysis in NSCL/P. This study also suggests nicotine dependence genes should be considered as important candidate genes for NSCL/P in future studies.

Keywords: Gene-Gene Interaction, Maternal Smoking, Nicotine Dependence, Non-Syndromic Cleft Lip with or without Cleft Palate

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Authors: Anastasios Papathanasiou, George Liontos, Athanasios Katsouras, Vasiliki Liagkou, Euripides Glavas

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Email remains a crucial communication tool due to its efficiency, accessibility and cost-effectiveness, enabling rapid information exchange across global networks. However, the global adoption of email has also made it a prime target for cyber threats, including phishing, malware and Business Email Compromise (BEC) attacks, which exploit its integral role in personal and professional realms in order to perform fraud and data breaches. To combat these threats, this research advocates for a multi-layered defense strategy incorporating advanced technological tools such as anti-spam and anti-malware software, machine learning algorithms and authentication protocols. Moreover, we developed an artificial intelligence model specifically designed to analyze email headers and assess their security status. This AI-driven model examines various components of email headers, such as "From" addresses, ‘Received’ paths and the integrity of SPF, DKIM and DMARC records. Upon analysis, it generates comprehensive reports that indicate whether an email is likely to be malicious or benign. This capability empowers users to identify potentially dangerous emails promptly, enhancing their ability to avoid phishing attacks, malware infections and other cyber threats.

Keywords: email security, artificial intelligence, header analysis, threat detection, phishing, DMARC, DKIM, SPF, ai model

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Authors: Hasan Basaran, Emre Unal

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Formation flying of satellites is a mission that involves a relative position keeping of different satellites in the constellation. In this study, different control algorithms are compared with one another in terms of ΔV, velocity increment, and tracking error. Various control methods, covering continuous and impulsive approaches are implemented and tested for satellites flying in low Earth orbit. Feedback linearization, sliding mode control, and model predictive control are designed and compared with an impulsive feedback law, which is based on mean orbital elements. Feedback linearization and sliding mode control approaches have identical mathematical models that include second order Earth oblateness effects. The model predictive control, on the other hand, does not include any perturbations and assumes circular chief orbit. The comparison is done with 4 different initial errors and achieved with velocity increment, root mean square error, maximum steady state error, and settling time. It was observed that impulsive law consumed the least ΔV, while produced the highest maximum error in the steady state. The continuous control laws, however, consumed higher velocity increments and produced lower amounts of tracking errors. Finally, the inversely proportional relationship between tracking error and velocity increment was established.

Keywords: chief-deputy satellites, feedback linearization, follower-leader satellites, formation flight, fuel consumption, model predictive control, rendezvous, sliding mode

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Authors: Archana. S, Usha Rani. G, Anand Balakrishnan, Sanjana.R, Solomon F, Vijayalakshmi. J

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Background: There is evidence that male factors contribute to the infertility of up to 50% of couples, who are evaluated and treated for infertility using advanced assisted reproductive technologies. Genetic abnormalities, including sperm chromosome aneuploidy as well as structural aberrations, are one of the major causes of male infertility. Recent advances in technology expedite the evaluation of sperm aneuploidy. The purpose of the study was to de-termine the prevalence of sperm aneuploidy in infertile males and the degree of association between DNA fragmentation and sperm aneuploidy. Methods: In this study, 75 infertile men were included, and they were divided into four abnormal groups (Oligospermia, Terato-spermia, Asthenospermia and Oligoasthenoteratospermia (OAT)). Men with children who were normozoospermia served as the control group. The Fluorescence in situ hybridization (FISH) method was used to test for sperm aneuploidy, and the Sperm Chromatin Dispersion Assay (SCDA) was used to measure the fragmentation of sperm DNA. Spearman's correla-tion coefficient was used to evaluate the relationship between sperm aneuploidy and sperm DNA fragmentation along with age. P < 0.05 was regarded as significant. Results: 75 partic-ipants' ages varied from 28 to 48 years old (35.5±5.1). The percentage of spermatozoa bear-ing X and Y was determined to be statistically significant (p-value < 0.05) and was found to be 48.92% and 51.18% of CEP X X 1 – nucish (CEP XX 1) [100] and CEP Y X 1 – nucish (CEP Y X 1) [100]. When compared to the rate of DNA fragmentation, it was discovered that infertile males had a greater frequency of sperm aneuploidy. Asthenospermia and OAT groups in sex chromosomal aneuploidy were significantly correlated (p<0.05). Conclusion: Sperm FISH and SCDA assay results showed increased sperm aneuploidy frequency, and DNA fragmentation index in infertile men compared with fertile men. There is a significant relationship observed between sperm aneuploidy and DNA fragmentation in OAT patients. When evaluating male variables and idiopathic infertility, the sperm FISH screening method can be used as a valuable diagnostic tool.

Keywords: ale infertility, dfi (dna fragmentation assay) (scd-sperm chromatin dispersion).art (artificial reproductive technology), trisomy, aneuploidy, fish (fluorescence in-situ hybridization), oat (oligoasthoteratospermia)

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Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

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Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

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Authors: Mayada Mazher, Ahmed Moustafa, Ahmed Abdellatif

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Background: Autophagy is a eukaryotic, highly conserved catabolic process implicated in many pathophysiologies such as wound healing. Autophagy-associated genes serve as a scaffolding platform for signal transduction of macrophage polarization during the inflammatory phase of wound healing and tissue repair process. In the current study, we report a model for the interplay between autophagy-associated genes and macrophages polarization associated genes. Methods: In silico analysis was performed on 249 autophagy-related genes retrieved from the public autophagy database and gene expression data retrieved from Gene Expression Omnibus (GEO); GSE81922 and GSE69607 microarray data macrophages polarization 199 DEGS. An integrated protein-protein interaction network was constructed for autophagy and macrophage gene sets. The gene sets were then used for GO terms pathway enrichment analysis. Common transcription factors for autophagy and macrophages' polarization were identified. Finally, microRNAs enriched in both autophagy and macrophages were predicated. Results: In silico prediction of common transcription factors in DEGs macrophages and autophagy gene sets revealed a new role for the transcription factors, HOMEZ, GABPA, ELK1 and REL, that commonly regulate macrophages associated genes: IL6,IL1M, IL1B, NOS1, SOC3 and autophagy-related genes: Atg12, Rictor, Rb1cc1, Gaparab1, Atg16l1. Conclusions: Autophagy and macrophages' polarization are interdependent cellular processes, and both autophagy-related proteins and macrophages' polarization related proteins coordinate in tissue remodelling via transcription factors and microRNAs regulatory network. The current work highlights a potential new role for transcription factors HOMEZ, GABPA, ELK1 and REL in wound healing.

Keywords: autophagy related proteins, integrated network analysis, macrophages polarization M1 and M2, tissue remodelling

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

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Authors: Abdelkader Elhanaoui, Mhamed Hadji, Rachid Skouri, Said Agounad

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The purpose of this paper is to study the phenomenon of acoustic scattering by using a new method. The signal processing (Fast Fourier Transform FFT Inverse Fast Fourier Transform iFFT and BESSEL functions) is widely applied to obtain information with high precision accuracy. Signal processing has a wider implementation in general-purpose pro-cessors. Our interest was focused on the use of FPGAs (Field-Programmable Gate Ar-rays) in order to minimize the computational complexity in single processor architecture, then be accelerated on FPGA and meet real-time and energy efficiency requirements. Gen-eral-purpose processors are not efficient for signal processing. We implemented the acous-tic backscattered signal processing model on the Altera DE-SOC board and compared it to Odroid xu4. By comparison, the computing latency of Odroid xu4 and FPGA is 60 sec-onds and 3 seconds, respectively. The detailed SoC FPGA-based system has shown that acoustic spectra are performed up to 20 times faster than the Odroid xu4 implementation. FPGA-based system of processing algorithms is realized with an absolute error of about 10⁻³. This study underlines the increasing importance of embedded systems in underwater acoustics, especially in non-destructive testing. It is possible to obtain information related to the detection and characterization of submerged cells. So we have achieved good exper-imental results in real-time and energy efficiency.

Keywords: DE1 FPGA, acoustic scattering, form function, signal processing, non-destructive testing

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Authors: Nazira B. Bekenova, Lydia A. Mukovozova, Andrej M. Grjibovski, Alma Z. Tokayeva, Yerbol M. Smail, Nurlan E. Aukenov

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Erysipelas is an infectious disease with socio-economic significance and prone to prolonged recurrent course (30%). Contribution of genetic factors, in particular the gene polymorphism of cytokines, can be essential in disease etiology and pathogenesis. Interleukin – 17 A are produced by T helpers of 17 type and plays a key role in development of local inflammation process. Local inflammatory process is a dominant in the clinic of erysipelas. Established that the skin and mucosas are primary areas of migration (homing) Th17-cell and their cytokines are stimulate the barrier function of the epithelium. We studied associations between gene polymorphism of IL-17A (C737T) rs 8193036 and IL-17A level in patients with erysipelas Kazakh population. Altogether, 90 cases with erysipelas and 90 healthy controls from an ethnic Kazakh population comprised the sample. Cases were identified at Clinical Infectious Diseases Hospital of Semey (Kazakhstan). The IL-17A (rs8193036) polymorphism was analyzed by a real time polymerase chain reaction. Plasma levels of IL-17 A were assessed by immuneenzyme analysis method using ‘Vector-Best’ test-system (Russia). Differences in levels of IL-17 A between CC, TT, CT groups were studied using Kruskal — Wallis test. Pairwise comparisons were performed using Mann-Whitney tests with Bonferroni correction (New significance level was set to 0.025). We found, that in patients with erysipelas with CC genotype the level of IL-17 A was higher (p= 0, 010) compared to the carriers of CT genotype. When compared the level of IL – 17 A between the patients with TT genotype and patients with CC genotype, also between the patients with CT genotype and patients with TT genotype statistically significant differences are not revealed (p = 0.374 and p = 0.043, respectively). Comparisons of IL-17 A plasma levels between the CC and CT genotypes, between the CC and TT genotypes, and between the TT and CT in healthy patients did not reveal significant differences (p = 0, 291). Therefore, we are determined the associations of gene polymorphism of IL-17 A (C737T) with its level in patients erysipelas carriers CC genotype.

Keywords: erysipelas, interleukin – 17 A, Kazakh, polymorphism

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Authors: Wafa Al Jabri

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Sickle cell disease (SCD) is a genetic blood disorder that is characterized by a severe painful crisis. SCD among children requires long term dependencies and high caregiving demands that increase the overall family burdens. It is, therefore, essential to examine, support, and promote the well-being of families of children with SCD. Although there has been considerable progress in the international research on family quality of life (FQOL) in recent years; however, research in this field is relatively recent and diverse. Oman is a country in which family quality of life has definitely been under-researched. Therefore, the purpose of the study is to describe the FQOL in families of children with SCD in Oman. The study will also examine the relationships between child, mother, and family-related factors that may influence the overall FQOL. Theoretical Framework: The study is guided by the unified theory of family quality of life to help in understanding the concept of FQOL and the factors that shape it. Method:A convenience sample of 98 mothers of children with SCD will be recruited from the pediatric hematology clinic at Sultan Qaboos University Hospital in Oman to participate in this descriptive, cross sectional, correlational study. Data will be obtained using a self-administered questionnaire that includes child and mother socio-demographic data, questions about the number of visits and admissions to health care facilities for vaso- occlusive crises (VOCs), the Perceived Stress Scale-10, and the Beachcenter-FQOL scale. Anticipated Results: It is expected to find an association among frequency of VOCs, mother’s perceived stress level, and FQOL in families of children with SCD in Oman. Family type, socio-economic status, and number of SCD children in the family are also expected to influence the overall FQOL. Conclusion: The findings of the study might be pivotal in designing and implementing tailored family-based interventions to improve families’ wellbeing.

Keywords: family quality of life, sickle cell disaes, children, family well-being

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Authors: Soumya Sajjan

Abstract:

Liver cancer is the most common cancer disease worldwide in men and women, and is one of the few cancers still on the rise. Liver disease is the 4th leading cause of death. According to new NHS (National Health Service) figures, deaths from liver diseases have reached record levels, rising by 25% in less than a decade; heavy drinking, obesity, and hepatitis are believed to be behind the rise. In this study, we focus on Development of Diagnostic Classifier for Ultrasound liver lesion. Ultrasound (US) Sonography is an easy-to-use and widely popular imaging modality because of its ability to visualize many human soft tissues/organs without any harmful effect. This paper will provide an overview of underlying concepts, along with algorithms for processing of liver ultrasound images Naturaly, Ultrasound liver lesion images are having more spackle noise. Developing classifier for ultrasound liver lesion image is a challenging task. We approach fully automatic machine learning system for developing this classifier. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. For classification, Support Vector Machine is used based on the risk bounds of statistical learning theory. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver lesion image is given to the SVM classifier system, the features are calculated, classified, as normal and diseased liver lesion. We hope the result will be helpful to the physician to identify the liver cancer in non-invasive method.

Keywords: segmentation, Support Vector Machine, ultrasound liver lesion, co-occurance Matrix

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Authors: Hichem Sassi

Abstract:

Pneumonia, being a respiratory infection, has garnered global attention due to its rapid transmission and relatively high mortality rates. Timely detection and treatment play a crucial role in significantly reducing mortality associated with pneumonia. Presently, X-ray diagnosis stands out as a reasonably effective method. However, the manual scrutiny of a patient's X-ray chest radiograph by a proficient practitioner usually requires 5 to 15 minutes. In situations where cases are concentrated, this places immense pressure on clinicians for timely diagnosis. Relying solely on the visual acumen of imaging doctors proves to be inefficient, particularly given the low speed of manual analysis. Therefore, the integration of artificial intelligence into the clinical image diagnosis of pneumonia becomes imperative. Additionally, AI recognition is notably rapid, with convolutional neural networks (CNNs) demonstrating superior performance compared to human counterparts in image identification tasks. To conduct our study, we utilized a dataset comprising chest X-ray images obtained from Kaggle, encompassing a total of 5216 training images and 624 test images, categorized into two classes: normal and pneumonia. Employing five mainstream network algorithms, we undertook a comprehensive analysis to classify these diseases within the dataset, subsequently comparing the results. The integration of artificial intelligence, particularly through improved network architectures, stands as a transformative step towards more efficient and accurate clinical diagnoses across various medical domains.

Keywords: deep learning, computer vision, pneumonia, models, comparative study

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Authors: Shadi Fathi, Moura Mehravar, Mujib Rahman

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The railwaytransportation network in the UK is over 100 years old and is known as one of the oldest mass transit systems in the world. This aged track network requires frequent closure for maintenance. One of the main reasons for closure is inadequate drainage due to the leakage in the buried drainage pipes. The leaking water can cause localised subgrade weakness, which subsequently can lead to major ground/substructure failure.Different condition assessment methods are available to assess the railway substructure. However, the existing condition assessment methods are not able to detect any local ground weakness/damageand provide details of the damage (e.g. size and location). To tackle this issue, a hybrid back-analysis technique based on artificial neural network (ANN) and genetic algorithm (GA) has been developed to predict the substructurelayers’ moduli and identify any soil weaknesses. At first, afinite element (FE) model of a railway track section under Falling Weight Deflection (FWD) testing was developed and validated against field trial. Then a drainage pipe and various scenarios of the local defect/ soil weakness around the buried pipe with various geometriesand physical properties were modelled. The impact of the soil local weaknesson the track surface deflection wasalso studied. The FE simulations results were used to generate a database for ANN training, and then a GA wasemployed as an optimisation tool to optimise and back-calculate layers’ moduli and soil weakness moduli (ANN’s input). The hybrid ANN-GA back-analysis technique is a computationally efficient method with no dependency on seed modulus values. The modelcan estimate substructures’ layer moduli and the presence of any localised foundation weakness.

Keywords: finite element (FE) model, drainage defect, falling weight deflectometer (FWD), hybrid ANN-GA

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Authors: Siwei Chang, Heng Li, Haitao Wu, Xin Fang

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Construction robots are receiving more and more attention as a promising solution to the manpower shortage issue in the construction industry. The development of intelligent control techniques that assist in controlling the robots to avoid transparency and reflected building obstacles is crucial for guaranteeing the adaptability and flexibility of mobile construction robots in complex construction environments. With the boom of computer vision techniques, a number of studies have proposed vision-based methods for transparent obstacle avoidance to improve operation accuracy. However, vision-based methods are also associated with disadvantages such as high computational costs. To provide better perception and value evaluation, this study aims to analyze the performance of vision-based techniques for avoiding transparent building obstacles. To achieve this, commonly used sensors, including a lidar, an ultrasonic sensor, and a USB camera, are equipped on the robotic platform to detect obstacles. A Raspberry Pi 3 computer board is employed to compute data collecting and control algorithms. The turtlebot3 burger is employed to test the programs. On-site experiments are carried out to observe the performance in terms of success rate and detection distance. Control variables include obstacle shapes and environmental conditions. The findings contribute to demonstrating how effectively vision-based obstacle avoidance strategies for transparent building obstacle avoidance and provide insights and informed knowledge when introducing computer vision techniques in the aforementioned domain.

Keywords: construction robot, obstacle avoidance, computer vision, transparent obstacle

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Authors: Weaam Aldeeban, Majd Aljamali, Lama A. Youssef

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Background & Objective: Warfarin is considered a problematic drug due to its narrow therapeutic window and wide inter-individual response variations, which are attributed to demographic, environmental, and genetic factors, particularly single nucleotide polymorphism (SNPs) in the genes encoding VKORC1 and CYP2C9 involved in warfarin's mechanism of action and metabolism, respectively. CYP2C9*2rs1799853 and CYP2C9*3rs1057910 alleles are linked to reduced enzyme activity, as carriers of either or both alleles are classified as moderate or slow metabolizers, and therefore exhibit higher sensitivity of warfarin compared with wild type (CYP2C9*1*1). Our study aimed to assess the frequency of *1, *2, and *3 alleles in the CYP2C9 gene in a cohort of Syrian patients receiving a maintenance dose of warfarin for different indications, the impact of genotypes on warfarin dosing, and the frequency of adverse effects (i.e., bleedings). Subjects & Methods: This retrospective cohort study encompassed 94 patients treated with warfarin. Patients’ genotypes were identified by sequencing the polymerase chain reaction (PCR) specific products of the gene encoding CYP2C9, and the effects on warfarin therapeutic outcomes were investigated. Results: Sequencing revealed that 43.6% of the study population has the *2 and/or *3 SNPs. The mean weekly maintenance dose of warfarin was 37.42 ± 15.5 mg for patients with the wild-type allele (CYP2C9*1*1), whereas patients with one or both variants (*2 and/or *3) demanded a significantly lower dose (28.59 ±11.58 mg) of warfarin, (P= 0.015). A higher percentage (40.7%) of patients with allele *2 and/or *3 experienced hemorrhagic accidents compared with only 17.9% of patients with the wild type *1*1, (P = 0.04). Conclusions: Our study proves an association between *2 and *3 genotypes and higher sensitivity to warfarin and a tendency to bleed, which necessitates lowering the dose. These findings emphasize the significance of CYP2C9 genotyping prior to commencing warfarin therapy in order to achieve optimal and faster dose control and to ensure effectiveness and safety.

Keywords: warfarin, CYP2C9, polymorphisms, Syrian, hemorrhage

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Authors: Yazan Al-Kofahi, Jamal Alqawasmi.

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In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model.

Keywords: machine learning, deep learning, artificial intelligence, sustainable building

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Authors: Junbeom Lee, Jaehyuck Cho, Wookyeong Jeong, Jonghan Won, Jungmin Hwang, Youngseok Song, Taikyeong Jeong

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This paper investigates and analyzes meta-learning technologies that use multiple-cameras to monitor and check abnormal behavior in people in real-time in the area of healthcare fields. Advances in artificial intelligence and computer vision technologies have confirmed that cameras can be useful for individual health monitoring and abnormal behavior detection. Through this, it is possible to establish a system that can respond early by automatically detecting abnormal behavior of the elderly, such as patients and the elderly. In this paper, we use a technique called meta-learning to analyze image data collected from cameras and develop a commercial product to determine abnormal behavior. Meta-learning applies machine learning algorithms to help systems learn and adapt quickly to new real data. Through this, the accuracy and reliability of the abnormal behavior discrimination system can be improved. In addition, this study proposes a meta-learning-based abnormal behavior detection system that includes steps such as data collection and preprocessing, feature extraction and selection, and classification model development. Various healthcare scenarios and experiments analyze the performance of the proposed system and demonstrate excellence compared to other existing methods. Through this study, we present the possibility that camera-based meta-learning technology can be useful for monitoring and testing abnormal behavior in the healthcare area.

Keywords: artificial intelligence, abnormal behavior, early detection, health monitoring

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Authors: Hamed Yousefi, Farnaz Mohammadi, Niloufar Mirian, Navid Amini

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Investigating possible capacities of visual functions where adapted mechanisms can enhance the capability of sports trainees is a promising area of research, not only from the cognitive viewpoint but also in terms of unlimited applications in sports training. In this paper, the visual evoked potential (VEP) and event-related potential (ERP) signals of amateur and trained volleyball players in a pilot study were processed. Two groups of amateur and trained subjects are asked to imagine themselves in the state of receiving a ball while they are shown a simulated volleyball field. The proposed method is based on a set of time-frequency features using algorithms such as Gabor filter, continuous wavelet transform, and a multi-stage wavelet decomposition that are extracted from VEP signals that can be indicative of being amateur or trained. The linear discriminant classifier achieves the accuracy, sensitivity, and specificity of 100% when the average of the repetitions of the signal corresponding to the task is used. The main purpose of this study is to investigate the feasibility of a fast, robust, and reliable feature/model determination as a neurofeedback parameter to be utilized for improving the volleyball players’ performance. The proposed measure has potential applications in brain-computer interface technology where a real-time biomarker is needed.

Keywords: visual evoked potential, time-frequency feature extraction, short-time Fourier transform, event-related spectrum potential classification, linear discriminant analysis

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Authors: Elham Mansouri, Asghar Mesbahi

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Purpose: In the current study, we aimed to investigate the macroscopic and microscopic dose enhancement effect of metallic nanoparticles in interstitial brachytherapy of uterus cancer by Iodin-125 source using a nano-lattice model in MCNPX (5) and MCNP6.1 codes. Materials and methods: Based on a nano-lattice simulation model containing a radiation source and a tumor tissue with cellular compartments loaded with 7mg/g spherical nanoparticles (bismuth, gold, and gadolinium), the energy deposited by the secondary electrons in microscopic and macroscopic level was estimated. Results: The results show that the values of macroscopic DEF is higher than microscopic DEF values and the macroscopic DEF values decreases as a function of distance from the brachytherapy source surface. Also, the results revealed a remarkable discrepancy between the DEF and secondary electron spectra calculated by MCNPX (5) and MCNP6.1 codes, which could be justified by the difference in energy cut-off and electron transport algorithms of two codes. Conclusion: According to the both MCNPX (5) and MCNP6.1 outputs, it could be concluded that the presence of metallic nanoparticles in the tumor tissue of uteruscancer increases the physical effectiveness of brachytherapy by I-125 source. The results presented herein give a physical view of radiosensitization potential of different metallic nanoparticles and could be considered in design of analytical and experimental radiosensitization studies in tumor regions using various radiotherapy modalities in the presence of heavy nanomaterials.

Keywords: MCNPX, MCNP6, nanoparticle, brachytherapy

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Authors: Yahia Massinissa, Yahia Mouloud

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Cystic fibrosis (CF), the most common lethal genetic disease in the Europe population, is caused by mutations in the transmembrane conductance regulator gene (CFTR). It affects most organs including an epithelial tissue, base of hydroelectrolytic transepithelial transport, notably that aerial ways, the pancreas, the biliary ways, the intestine, sweat glands and the genital tractus. The gene whose anomalies are responsible of the cystic fibrosis codes for a protein Cl channel named CFTR (cystic fibrosis transmembrane conductance regulator) that exercises multiple functions in the cell, one of the most important in control of sodium and chlorine through epithelia. The deficient function translates itself notably by an abnormal production of viscous secretion that obstructs the execrator channels of this target organ: one observes then a dilatation, an inflammation and an atrophy of these organs. It also translates itself by an increase of the concentration in sodium and in chloride in sweat, to the basis of the sweat test. In order to do a phenotypical and genotypical diagnosis at a part of the Algerian population, our survey has been carried on 16 patients with evocative symptoms of the cystic fibrosis at that the clinical context has been confirmed by a sweat test. However, anomalies of the CFTR gene have been determined by electrophoresis in gel of polyacrylamide of the PCR products (polymerase chain reaction), after enzymatic digestion, then visualized to the ultraviolet (UV) after action of the ethidium bromide. All mutations detected at the time of our survey have already been identified at patients attained by this pathology in other populations of the world. However, the important number of found mutation with regard to the one of the studied patients testifies that the origin of this big clinical variability that characterizes the illness in the consequences of an enormous diversity of molecular defects of the CFTR gene.

Keywords: cystic fibrosis, CFTR gene, polymorphism, algerian population, sweat test, genotypical diagnosis

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Authors: Sharique Ahmed, Khushtar Anwar Salman

Abstract:

Alpha-1-antitrypsin (AAT) is a major plasma serine protease inhibitor (SERPIN). Hereditary AAT deficiency is one of the common diseases in some part of the world. AAT is mainly produced in the liver and functions to protect the lung against proteolytic damage (e.g., from neutrophil elastase) acting as the major inhibitor for neutrophil elastase. α (1)-Antitrypsin (AAT) deficiency is an under recognized genetic condition that affects approximately 1 in 2,000 to 1 in 5,000 individuals and predisposes to liver disease and early-onset emphysema. Not only does α-1-antitrypsin deficiency lead to disabling syndrome of pulmonary emphysema, there are other disorders too which include ANCA (antineutrophilic cytoplasmic antibody) positive Wegener's granulomatosis, diffuse bronchiectasis, necrotizing panniculitis in α-1-antitrypsin phenotype (S), idiopathic pulmonary fibrosis and steroid dependent asthma. Augmentation therapy with alpha-1 antitrypsin (AAT) from human plasma has been available for specific treatment of emphysema due to AAT deficiency. Apart from this several observations have also suggested a role for endogenous suppressors of HIV-1, alpha-1 antitrypsin (AAT) has been identified to be one of those. In view of its varied important role in humans, serum from a mammalian source was chosen for the isolation and purification. Studies were performed on the homogeneous fraction. This study suggests that the buffalo serum α-1-antritrypsin has characteristics close to ovine, dog, horse and more importantly to human α-1-antritrypsin in terms of its hydrodynamic properties such as molecular weight, carbohydrate content, etc. The similarities in the hydrodynamic properties of buffalo serum α-1-antitrypsin with other sources of mammalian α-1-antitrypsin mean that it can be further studied and be a potential source for "augmentation therapy", as well as a source of AAT replacement therapy to raise serum levels above the protective threshold. Other parameters like the amino acid sequence, the effect of denaturants, and the thermolability or thermostability of the inhibitor will be the interesting basis of future studies on buffalo serum alpha-1 antitrypsin (AAT).

Keywords: α-1-antitrypsin, augmentation therapy , hydrodynamic properties, serine protease inhibitor

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