Nanocomposites are appropriate materials to meet emerging demands resulting from advances in science and technology. Components with novel structures and better efficiency compared to conventionally processed components are required for technological advances. In this research, polymer-inorganic colloidal nanocomposites can be created by using pulsed laser ablation for tin oxide nanoparticles in polyvinyl alcohol as a host. The optical characteristics and absorption spectra were used to look into the optical characteristics of the nanocomposite. The optical band gap (Eg) was measured; indirect transition values for pure polymer polyvinyl alcohol were determined to be 5 eV and 3.5 eV for PVA-SnO2 nanocomposite. The samples were measured for their extinction coefficient (k), refractive index (n), and dielectric constant. Nanocomposites' dielectric constant decreased while their absorption coefficient, refractive index, and extinction coefficient all increased. The nanocomposites have a high absorbency in the UV region and may be utilized for covering materials to radiation protecting applications. Write a brief abstract about your paper’s subject of study. Write a brief abstract about your paper’s subject of study. Write a brief abstract about your paper’s subject of study. Write a brief abstract about your paper’s subject of study. Write a brief abstract about your paper’s subject of study. Write a brief abstract about your paper’s subject of study.
{"title":"Enhancement Optical Characterized of Tin Oxide in Polymer Polyvinyl alcohol Colloid Prepared by Laser Ablation Method","authors":"Nadheer Jassim Mohammed, Zahraa S. Rasheed","doi":"10.21123/bsj.2023.8494","DOIUrl":"https://doi.org/10.21123/bsj.2023.8494","url":null,"abstract":"Nanocomposites are appropriate materials to meet emerging demands resulting from advances in science and technology. Components with novel structures and better efficiency compared to conventionally processed components are required for technological advances. In this research, polymer-inorganic colloidal nanocomposites can be created by using pulsed laser ablation for tin oxide nanoparticles in polyvinyl alcohol as a host. The optical characteristics and absorption spectra were used to look into the optical characteristics of the nanocomposite. The optical band gap (Eg) was measured; indirect transition values for pure polymer polyvinyl alcohol were determined to be 5 eV and 3.5 eV for PVA-SnO2 nanocomposite. The samples were measured for their extinction coefficient (k), refractive index (n), and dielectric constant. Nanocomposites' dielectric constant decreased while their absorption coefficient, refractive index, and extinction coefficient all increased. The nanocomposites have a high absorbency in the UV region and may be utilized for covering materials to radiation protecting applications. Write a brief abstract about your paper’s subject of study. Write a brief abstract about your paper’s subject of study. Write a brief abstract about your paper’s subject of study. Write a brief abstract about your paper’s subject of study. Write a brief abstract about your paper’s subject of study. Write a brief abstract about your paper’s subject of study.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"743 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139160631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fuzzy programming is especially useful in cases where the coefficients are ambiguous. Because of this feature, in recent years, numerous techniques have emerged for addressing uncertainty. This paper proposes a novel ranking function technique with variables of type decagonal fuzzy numbers for solving fully fuzzy fractional linear programming (FFFLP) problems. This technique is dependent on introducing a new membership function for a decagonal fuzzy number and using a fully fuzzy simplex method. After converting the FFFLP problem to the fully fuzzy linear programming (FFLP) problem by a complementary method, then solved with the fully fuzzy simplex tables, in which all the values are fuzzy decagonal numbers. With the aid of the arithmetic operations of decagonal numbers, the new iteration of the simplex table is reached. Steps are repeated until the optimal fuzzy solution is reached. To demonstrate the proposed method a numerical example is provided to illustrate the steps of finding an optimal fuzzy solution to the problem.
{"title":"Employing Novel Ranking Function for Solving Fully Fuzzy Fractional Linear Programming Problems","authors":"Israa H. Hasan, Iden H. Al Kanani","doi":"10.21123/bsj.2023.8243","DOIUrl":"https://doi.org/10.21123/bsj.2023.8243","url":null,"abstract":"Fuzzy programming is especially useful in cases where the coefficients are ambiguous. Because of this feature, in recent years, numerous techniques have emerged for addressing uncertainty. This paper proposes a novel ranking function technique with variables of type decagonal fuzzy numbers for solving fully fuzzy fractional linear programming (FFFLP) problems. This technique is dependent on introducing a new membership function for a decagonal fuzzy number and using a fully fuzzy simplex method. After converting the FFFLP problem to the fully fuzzy linear programming (FFLP) problem by a complementary method, then solved with the fully fuzzy simplex tables, in which all the values are fuzzy decagonal numbers. With the aid of the arithmetic operations of decagonal numbers, the new iteration of the simplex table is reached. Steps are repeated until the optimal fuzzy solution is reached. To demonstrate the proposed method a numerical example is provided to illustrate the steps of finding an optimal fuzzy solution to the problem.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"3 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139159909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the field of molecular simulations, molecular docking is a method that can determine the optimal and preferred orientation of a certain molecule related to another when they are coupled to create a stable complex. The strength of the association, or binding affinity, between two molecules can be predicted using knowledge of their preferred orientation. In this study, a series of prepared compounds were evaluated for their binding modes, potential interactions, and target binding locations. Some derivatives 1,3-oxazepane, and 1,3-benzoxazepine were prepared from three Schiff bases compounds (1S-3S). The compounds (1S-3S) were reacted with succinic anhydride and phthalic anhydride to obtain derivatives of 1,3- oxazepane and 1,3-benzoxazepine (1B-3C). The characterization of prepared compounds was achieved by methods of elemental analysis, FT-IR, 1H, and 13C-NMR spectral analysis. The antibacterial activity of the compounds (1B-3C) was recorded against some isolated bacteria including gram-negative (Staphylococcus aureus), and gram-positive (E.coli) in parallel with Amoxicillin as a regular drug. Compounds (1B-3C) exhibited good values as antibacterial spreading from middling to perfect against the bacteria strains. Moreover, the antioxidant activity of the synthesized compounds (1B-3C) was evaluated using 2,2-diphenyl-1-picrylhydrazyl. The results showed that compounds have the highest values as radical scavenging.
{"title":"Molecular Docking, Synthesis and Evaluation for Antioxidant and Antibacterial Activity of New Oxazepane and Benzoxazepine Derivatives","authors":"A. G. Sager, J. Abaies, Zeena R. Katoof","doi":"10.21123/bsj.2023.8553","DOIUrl":"https://doi.org/10.21123/bsj.2023.8553","url":null,"abstract":"In the field of molecular simulations, molecular docking is a method that can determine the optimal and preferred orientation of a certain molecule related to another when they are coupled to create a stable complex. The strength of the association, or binding affinity, between two molecules can be predicted using knowledge of their preferred orientation. In this study, a series of prepared compounds were evaluated for their binding modes, potential interactions, and target binding locations. Some derivatives 1,3-oxazepane, and 1,3-benzoxazepine were prepared from three Schiff bases compounds (1S-3S). The compounds (1S-3S) were reacted with succinic anhydride and phthalic anhydride to obtain derivatives of 1,3- oxazepane and 1,3-benzoxazepine (1B-3C). The characterization of prepared compounds was achieved by methods of elemental analysis, FT-IR, 1H, and 13C-NMR spectral analysis. The antibacterial activity of the compounds (1B-3C) was recorded against some isolated bacteria including gram-negative (Staphylococcus aureus), and gram-positive (E.coli) in parallel with Amoxicillin as a regular drug. Compounds (1B-3C) exhibited good values as antibacterial spreading from middling to perfect against the bacteria strains. Moreover, the antioxidant activity of the synthesized compounds (1B-3C) was evaluated using 2,2-diphenyl-1-picrylhydrazyl. The results showed that compounds have the highest values as radical scavenging.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"757 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139160619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reliable and accurate crop maps are required for food security from regional to global scale. The increased availability of satellite imagery leads to a “Big Data” problem while producing crop maps. Now, cloud-based platforms have gained a lot of attention for crop classification over large regions. The main goal of the research is to analyze crop classification using various machine learning (ML) such as Support Vector Machine (SVM), Gradient Tree Boosting (GTB), Random Forest (RF), Decision Tree (DT) as well as Classification and Regression Trees (CART) on Google Earth Engine platform. The aim is to explore the Google Earth Engine’s efficiency (GEE) when classification different crops using multi- spectral datasets of Sentinel 2 MSI and Landsat 8 OLI satellites for crop mapping of Mathura district of Uttar Pradesh, India. The best cloud free image (less than 5%) of Landsat 8 OLI and Sentinel 2 MSI datasets ("2020-12-26","2020-12-30") were used for crop classification with the help of automatic filtering i.e. percentage cloud property on the GEE platforms. Moreover that GEE platform perform, acquiring, clarifying as well as preprocessing of satellite dataset could be organized very powerfully. Points as feature spaces were used like training datasets. Furthermore confusion matrixes are used for accuracy assessment (producer and user accuracy) and kappa coefficient. Additionally compare the outcome of the dataset on the basis of overall accuracy (OA), F1 score as well as kappa coefficient. The highest OA is found using GTB (86.7%) followed by RF (82.5%), CART (81.0%), DT (78.1%) and SVM (66.5%) for Landsat 8 OLI image. For the Sentinel 2 image, GTB achieved the highest OA of 84.2% followed by SVM (84%), RF (82.3%), DT (75.2%), and CART (75. 0%) respectively. On the basis of research, found that GTB performed well among all the classifiers to crop mapping using both multi-spectral datasets.
从区域到全球范围的粮食安全都需要可靠而准确的作物地图。卫星图像可用性的提高导致了制作作物地图时的 "大数据 "问题。现在，基于云的平台在大面积农作物分类方面受到了广泛关注。本研究的主要目标是在谷歌地球引擎平台上使用支持向量机（SVM）、梯度树提升（GTB）、随机森林（RF）、决策树（DT）以及分类和回归树（CART）等各种机器学习（ML）分析作物分类。该研究旨在探索谷歌地球引擎在利用哨兵 2 MSI 和大地遥感卫星 8 OLI 的多光谱数据集对不同作物进行分类时的效率（GEE），以绘制印度北方邦马图拉地区的作物图。借助 GEE 平台上的自动过滤功能（即云属性百分比），Landsat 8 OLI 和 Sentinel 2 MSI 数据集（"2020-12-26"、"2020-12-30"）中的最佳无云图像（小于 5%）被用于作物分类。此外，GEE 平台还能对卫星数据集的获取、澄清和预处理进行有效组织。作为特征空间的点被用作训练数据集。此外，混淆矩阵被用于准确度评估（生产者和用户准确度）和卡帕系数。此外，还根据总体准确度（OA）、F1 分数和卡帕系数对数据集的结果进行了比较。对于 Landsat 8 OLI 图像，使用 GTB 的 OA 最高（86.7%），其次是 RF（82.5%）、CART（81.0%）、DT（78.1%）和 SVM（66.5%）。在哨兵 2 号图像中，GTB 的 OA 值最高，达到 84.2%，其次分别是 SVM（84%）、RF（82.3%）、DT（75.2%）和 CART（75.0%）。研究发现，在使用这两个多光谱数据集绘制作物分布图的所有分类器中，GTB 的表现都很出色。
{"title":"A Comparative Study and Machine Learning Enabled Efficient Classification for Multispectral Data in Agriculture","authors":"Priyanka Gupta, S. Kanga, Varun Narayan Mishra","doi":"10.21123/bsj.2023.8952","DOIUrl":"https://doi.org/10.21123/bsj.2023.8952","url":null,"abstract":"Reliable and accurate crop maps are required for food security from regional to global scale. The increased availability of satellite imagery leads to a “Big Data” problem while producing crop maps. Now, cloud-based platforms have gained a lot of attention for crop classification over large regions. The main goal of the research is to analyze crop classification using various machine learning (ML) such as Support Vector Machine (SVM), Gradient Tree Boosting (GTB), Random Forest (RF), Decision Tree (DT) as well as Classification and Regression Trees (CART) on Google Earth Engine platform. The aim is to explore the Google Earth Engine’s efficiency (GEE) when classification different crops using multi- spectral datasets of Sentinel 2 MSI and Landsat 8 OLI satellites for crop mapping of Mathura district of Uttar Pradesh, India. The best cloud free image (less than 5%) of Landsat 8 OLI and Sentinel 2 MSI datasets (\"2020-12-26\",\"2020-12-30\") were used for crop classification with the help of automatic filtering i.e. percentage cloud property on the GEE platforms. Moreover that GEE platform perform, acquiring, clarifying as well as preprocessing of satellite dataset could be organized very powerfully. Points as feature spaces were used like training datasets. Furthermore confusion matrixes are used for accuracy assessment (producer and user accuracy) and kappa coefficient. Additionally compare the outcome of the dataset on the basis of overall accuracy (OA), F1 score as well as kappa coefficient. The highest OA is found using GTB (86.7%) followed by RF (82.5%), CART (81.0%), DT (78.1%) and SVM (66.5%) for Landsat 8 OLI image. For the Sentinel 2 image, GTB achieved the highest OA of 84.2% followed by SVM (84%), RF (82.3%), DT (75.2%), and CART (75. 0%) respectively. On the basis of research, found that GTB performed well among all the classifiers to crop mapping using both multi-spectral datasets.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"599 3","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139160638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Darwich, Ebrahim Ismaiel, Ayman Al-kayal, Mujtaba Ali, Mohamed Masri, H. Nazha
Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforward neural network (FNN) model. Data acquisition involved 60 subjects diagnosed with the studied cases. The implementation of FNN achieved an accuracy of 96.6% using 50% of the dataset as training data and 92.8% using only 30% training data. The comparison with related work shows good impact of using the differential values of pressure points as input for neural networks compared with raw data.
{"title":"Recognizing Different Foot Deformities Using FSR Sensors by Static Classification of Neural Networks","authors":"A. Darwich, Ebrahim Ismaiel, Ayman Al-kayal, Mujtaba Ali, Mohamed Masri, H. Nazha","doi":"10.21123/bsj.2023.8968","DOIUrl":"https://doi.org/10.21123/bsj.2023.8968","url":null,"abstract":"Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforward neural network (FNN) model. Data acquisition involved 60 subjects diagnosed with the studied cases. The implementation of FNN achieved an accuracy of 96.6% using 50% of the dataset as training data and 92.8% using only 30% training data. The comparison with related work shows good impact of using the differential values of pressure points as input for neural networks compared with raw data.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"105 7","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138600016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the leaves of Olea europaea L. Olive trees an endophytic fungus was discovered. Cladosporium sp. was identified to be the fungus based on its morphological characteristics and nuclear ribosomal DNA ITS sequence analysis and was registered in NCBI as the Cladosporium genus has been registered under the number (0P939922.1) The species was not specified, and it was considered of unknown species after comparing it to global isolates. In comparison to olive leaf extract, Cladosporium sp. including total flavonoid, total phenolic, total terpenoid, and total saponins, Which were 121.9%, 198.1%, 89.13%, and 29.87 % respectively compared to its content in olive leaf extract, which was 61.54 %, 67.88 % , 17.1%, and 20.19% respectively. The Cladosporium sp. extract inhibited the growth of 27 isolates belonging to different species of candida which were Candida albicans , C. lypolitica , C. tropicalis , C. sphaerica , C. krusei , C. guilliermondii , C. parapsilosis , C. norvegicus , C. glabrata , and C. kefyr , the inhibition effects increased with increasing concentration to reach the highest level to suppress fungal growth when concentrated 30 mg/ml. This proves the antifungal potential of endophytic fungi in the future.
{"title":"Antifungal Potential of Cladosporium sp. (Endophytic fungi) Associated with Olea europaea L. Leaves","authors":"M. Mezher, R. Abed","doi":"10.21123/bsj.2023.9004","DOIUrl":"https://doi.org/10.21123/bsj.2023.9004","url":null,"abstract":"In the leaves of Olea europaea L. Olive trees an endophytic fungus was discovered. Cladosporium sp. was identified to be the fungus based on its morphological characteristics and nuclear ribosomal DNA ITS sequence analysis and was registered in NCBI as the Cladosporium genus has been registered under the number (0P939922.1) The species was not specified, and it was considered of unknown species after comparing it to global isolates. In comparison to olive leaf extract, Cladosporium sp. including total flavonoid, total phenolic, total terpenoid, and total saponins, Which were 121.9%, 198.1%, 89.13%, and 29.87 % respectively compared to its content in olive leaf extract, which was 61.54 %, 67.88 % , 17.1%, and 20.19% respectively. The Cladosporium sp. extract inhibited the growth of 27 isolates belonging to different species of candida which were Candida albicans , C. lypolitica , C. tropicalis , C. sphaerica , C. krusei , C. guilliermondii , C. parapsilosis , C. norvegicus , C. glabrata , and C. kefyr , the inhibition effects increased with increasing concentration to reach the highest level to suppress fungal growth when concentrated 30 mg/ml. This proves the antifungal potential of endophytic fungi in the future.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"5 12","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
These days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that. The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm have been combined to create an important working mechanism that aids in this field. The proposed system can be used for essential tasks in life, such as analysis, automation, control, recognition, and other tasks. Crossover and mutation are the two primary mechanisms used by the genetic algorithm in the proposed system to replace the back propagation process in ANN. While the feedforward artificial neural network technique is focused on input processing, this should be based on the process of breaking the feedforward artificial neural network algorithm. Additionally, the result is computed from each ANN during the breaking up process, which is based on the breaking up of the artificial neural network algorithm into multiple ANNs based on the number of ANN layers, and therefore, each layer in the original artificial neural network algorithm is assessed. The best layers are chosen for the crossover phase after the breakage process, while the other layers go through the mutation process. The output of this generation is then determined by combining the artificial neural networks into a single ANN; the outcome is then checked to see if the process needs to create a new generation. The system performed well and produced accurate findings when it was used with data taken from the Vicon Robot system, which was primarily designed to record human behaviors based on three coordinates and classify them as either normal or aggressive.
{"title":"An improved neurogenetic model for recognition of 3D kinetic data of human extracted from the Vicon Robot system","authors":"I. Stepanyan, Safa A. Hameed","doi":"10.21123/bsj.2023.9087","DOIUrl":"https://doi.org/10.21123/bsj.2023.9087","url":null,"abstract":"These days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that. The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm have been combined to create an important working mechanism that aids in this field. The proposed system can be used for essential tasks in life, such as analysis, automation, control, recognition, and other tasks. Crossover and mutation are the two primary mechanisms used by the genetic algorithm in the proposed system to replace the back propagation process in ANN. While the feedforward artificial neural network technique is focused on input processing, this should be based on the process of breaking the feedforward artificial neural network algorithm. Additionally, the result is computed from each ANN during the breaking up process, which is based on the breaking up of the artificial neural network algorithm into multiple ANNs based on the number of ANN layers, and therefore, each layer in the original artificial neural network algorithm is assessed. The best layers are chosen for the crossover phase after the breakage process, while the other layers go through the mutation process. The output of this generation is then determined by combining the artificial neural networks into a single ANN; the outcome is then checked to see if the process needs to create a new generation. The system performed well and produced accurate findings when it was used with data taken from the Vicon Robot system, which was primarily designed to record human behaviors based on three coordinates and classify them as either normal or aggressive.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"25 12","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138600977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The primary toxin class discovered in freshwater pufferfish is a category of neurotoxins called PSTs (Paralytic shellfish toxins) and pufferfish toxin has been observed to have biological, biochemical, and cytotoxic effects on cancer cell lines. Therefore, it is crucial to determine the cytotoxic activity, toxins present in the ovary of T. leiurus, and interaction between ligand (toxin compound) and receptors test. This study used the MTT method in the T47D cell lines, liquid chromatograph-tandem mass spectrometry (LC-MS/MS), and analysis of the molecular interaction using molecular docking. The ovary of T. leiurus had cytotoxicity on the T47D cell, having an IC50 value of 229.535 μg/ml, and generated a chromatogram with a retention duration of 1.25 min that was similar to the Decarbamoylneosaxitoxin (dcNEO) standard solution. In molecular interactions between the dcNEO ligand to receptors, the lowest ΔG value was -9.29 kcal/mol at the Nav 1.7 receptor, and the lowest KI value was 1.23 µM at the Mcl-1 receptor. These findings indicate that the ovary of T. leiurus is cytotoxic to the T47D cell line and contains dcNEO toxin. It is more stable for the dcNEO ligand to engage with the Nav 1.7 receptor than with other receptors, and it inhibits the Mcl-1 receptor more potently than with other receptors. These findings indicate that the ovary of T. leiurus may be chemotherapy for the prevention of cancer strategy.
{"title":"Cytotoxic Activities, Determining Toxin, and Molecular Docking of Ovary Pufferfish (Tetraodon leiurus) in Singkarak Lake as Cancer Chemoprevention Candidate","authors":"D. Roesma, D. Tjong, Monica Mulnia Hanif","doi":"10.21123/bsj.2023.8785","DOIUrl":"https://doi.org/10.21123/bsj.2023.8785","url":null,"abstract":"The primary toxin class discovered in freshwater pufferfish is a category of neurotoxins called PSTs (Paralytic shellfish toxins) and pufferfish toxin has been observed to have biological, biochemical, and cytotoxic effects on cancer cell lines. Therefore, it is crucial to determine the cytotoxic activity, toxins present in the ovary of T. leiurus, and interaction between ligand (toxin compound) and receptors test. This study used the MTT method in the T47D cell lines, liquid chromatograph-tandem mass spectrometry (LC-MS/MS), and analysis of the molecular interaction using molecular docking. The ovary of T. leiurus had cytotoxicity on the T47D cell, having an IC50 value of 229.535 μg/ml, and generated a chromatogram with a retention duration of 1.25 min that was similar to the Decarbamoylneosaxitoxin (dcNEO) standard solution. In molecular interactions between the dcNEO ligand to receptors, the lowest ΔG value was -9.29 kcal/mol at the Nav 1.7 receptor, and the lowest KI value was 1.23 µM at the Mcl-1 receptor. These findings indicate that the ovary of T. leiurus is cytotoxic to the T47D cell line and contains dcNEO toxin. It is more stable for the dcNEO ligand to engage with the Nav 1.7 receptor than with other receptors, and it inhibits the Mcl-1 receptor more potently than with other receptors. These findings indicate that the ovary of T. leiurus may be chemotherapy for the prevention of cancer strategy.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"44 11","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138600618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Information security in data storage and transmission is increasingly important. On the other hand, images are used in many procedures. Therefore, preventing unauthorized access to image data is crucial by encrypting images to protect sensitive data or privacy. The methods and algorithms for masking or encoding images vary from simple spatial-domain methods to frequency-domain methods, which are the most complex and reliable. In this paper, a new cryptographic system based on the random key generator hybridization methodology by taking advantage of the properties of Discrete Cosine Transform (DCT) to generate an indefinite set of random keys and taking advantage of the low-frequency region coefficients after the DCT stage to pass them to a subsystem consisting of an Reversible Logic Gate (RLG) group to obtain the secret keys that are passed to Rivest Shamir Adleman (RSA) to finish encrypting the image. The results indicate that the proposed method has the ability to generate a very large set of highly complex and secure secret keys that can be used later in the encryption stage. Moreover, the number and complexity of those keys will change each time the image is changed, and this represents the contribution of the proposed method. They experienced no time loss throughout the encryption and decryption processes when using RLG, which indicates that the proposed system did a good job in making different keys from the same image. And it differs in the strength of the key from one image to another, depending on the nature of the color imge.
{"title":"Robust Color Image Encryption Scheme Based on RSA via DCT by Using an Advanced Logic Design Approach","authors":"K. K. Jabbar, F. Ghozzi, Ahmed Fakhfakh","doi":"10.21123/bsj.2023.8715","DOIUrl":"https://doi.org/10.21123/bsj.2023.8715","url":null,"abstract":"Information security in data storage and transmission is increasingly important. On the other hand, images are used in many procedures. Therefore, preventing unauthorized access to image data is crucial by encrypting images to protect sensitive data or privacy. The methods and algorithms for masking or encoding images vary from simple spatial-domain methods to frequency-domain methods, which are the most complex and reliable. In this paper, a new cryptographic system based on the random key generator hybridization methodology by taking advantage of the properties of Discrete Cosine Transform (DCT) to generate an indefinite set of random keys and taking advantage of the low-frequency region coefficients after the DCT stage to pass them to a subsystem consisting of an Reversible Logic Gate (RLG) group to obtain the secret keys that are passed to Rivest Shamir Adleman (RSA) to finish encrypting the image. The results indicate that the proposed method has the ability to generate a very large set of highly complex and secure secret keys that can be used later in the encryption stage. Moreover, the number and complexity of those keys will change each time the image is changed, and this represents the contribution of the proposed method. They experienced no time loss throughout the encryption and decryption processes when using RLG, which indicates that the proposed system did a good job in making different keys from the same image. And it differs in the strength of the key from one image to another, depending on the nature of the color imge.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"48 5","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has 350 images. Three fully connected (FC) layers were utilized for feature extraction, namely fc6, fc7, and fc8. The classifiers employed were support vector machine (SVM), k-nearest neighbors (KNN), and Naive Bayes. The study demonstrated that the most effective feature extraction layer was fc6, achieving an accuracy of 90.7% with SVM. SVM outperformed KNN and Naive Bayes, exhibiting an accuracy of 90.7%, sensitivity of 83.5%, specificity of 93.7%, and F1-score of 83.5%. This research successfully addressed the challenges in classifying cassava species by leveraging deep learning and machine learning methods, specifically with SVM and the fc6 layer of AlexNet. The proposed approach holds promise for enhancing plant classification techniques, benefiting researchers, farmers, and environmentalists in plant species identification, ecosystem monitoring, and agricultural management.
{"title":"AlexNet-Based Feature Extraction for Cassava Classification: A Machine Learning Approach","authors":"M. Sholihin, M. Fudzee, Mohd. Norasri Ismail","doi":"10.21123/bsj.2023.9120","DOIUrl":"https://doi.org/10.21123/bsj.2023.9120","url":null,"abstract":"Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has 350 images. Three fully connected (FC) layers were utilized for feature extraction, namely fc6, fc7, and fc8. The classifiers employed were support vector machine (SVM), k-nearest neighbors (KNN), and Naive Bayes. The study demonstrated that the most effective feature extraction layer was fc6, achieving an accuracy of 90.7% with SVM. SVM outperformed KNN and Naive Bayes, exhibiting an accuracy of 90.7%, sensitivity of 83.5%, specificity of 93.7%, and F1-score of 83.5%. This research successfully addressed the challenges in classifying cassava species by leveraging deep learning and machine learning methods, specifically with SVM and the fc6 layer of AlexNet. The proposed approach holds promise for enhancing plant classification techniques, benefiting researchers, farmers, and environmentalists in plant species identification, ecosystem monitoring, and agricultural management.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"133 33","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138599041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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