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Journal of Agricultural Chemistry and Environment 2021年第1期10卷 91-112页

作者： asmak afriliana Endar Hidayat Yoshiharu Mitoma Taizo Masuda Hiroyuki Harada Faculty of Environmental Science Prefectural University of Hiroshima Shobara Japan Faculty of Agricultural Product Technology University of Jember Jember Indonesia

Spent Coffee Ground (SCG) is characterized by high organic content, in the form of insoluble polysaccharides bound and phenol compounds. Phenol compounds are toxic to nature and are a cause of environmental pollution. Composting method of this study is aerobic static batch composting with temperature control with adding activators of some fungi such as Aspergillus sp, and Penicillium sp. The purpose of the research is to fill the research gap from previous studies of spent coffee grounds compost, which requires a long time in composting, so that if it is used directly on the soil and plants, the positive effect also requires a long time. The result of composting for 28 days with this method is that mature compost has black crumb and normal pH, with characteristics of C/N ratio below 10: C1 (7.06), C2 (6.99). This value is far from the control with a C/N ratio of 8.33. Decompose rate of macromolecule are above 40% for lignin and 70% for cellulose. Implementation of compost in radish plants, resulting Germination Index above 80% which indicates that the compost is ripe: control (92.39%), C1 (183.88%), C2 (191.86%). The results of the analysis with FTIR also showed that the compost was mature and stable, and rich in minerals. So, it can be concluded that this composting method can speed up composting time and optimize the results of compost produced.

关键词： Spent Coffee Ground (SCG) Aspergillus sp Aspergillus sp Aerobic Static Batch Temperature Control

Effects of Co-Existing Ions on the Phosphorus Potassium Ratio of the Precipitate Formed in the Potassium Phosphate Crystallization Process

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Journal of Environmental Protection 2017年第11期8卷 1424-1434页

作者： Hiroyuki Harada Yumi Katayama asmak afriliana Minori Inoue Ryota Teranaka Yoshiharu Mitoma Faculty of Environmental Science Prefectural University of Hiroshima Hiroshima Japan Faculty of Agricultural Technology Univerity of Jember Jember Indonesia

Livestock wastewater is mainly treated with activated sludge, but ions such as phosphorus, potassium, ammonium, nitrate and sulfate remain in the effluent. In this study, the effects of residual ions on phosphorus recovery using the magnesium potassium phosphate crystallization method were investigated when magnesium was added to increase the pH. If co-existing ions affect the products, the phosphorus to potassium molar ratio (K/P ratio) of the precipitate will deviate from being equimolar. Artificial wastewater test solutions containing 5.6 - 20.3 mM ammonium, 25.6 mM potassium, 6.5 mM phosphorus, 0 - 7.35 mM nitrate, and 0 - 3.06 mM sulfate were used. First, the optimum operating pH and amount of magnesium added to give a high phosphorus removal rate and recovery rate were determined. The experimental setup was a 10 L aerated and stirred reactor, and a 5 L settling tank. The K/P ratio in precipitate was approximately 1 using the optimum conditions. Continuous 2 h treatment allowed a white precipitate containing about 30 g of needle-like crystals to be obtained. Next, the effects of varying the ammonium, nitrate, and sulfate ion concentrations in the artificial effluent were investigated. Ammonium and sulfate ion concentrations of 8 mM or more and 3 mM or more, respectively, caused the K/P ratio to decrease to about 0.7 and 0.5, respectively. Varying the nitrate concentration did not affect the K/P ratio, even at a nitrate concentration of 7.35 mM.

关键词： Magnesium Potassium Phosphate Ammonium Sulfate Nitrate K/P Ratio pH hComponent Formatting Style Styling

Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoT Medical Environment

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Computers, Materials & Continua 2024年第4期79卷 1157-1185页

作者： Sapiah Sakri Shakila Basheer Zuhaira Muhammad Zain Nurul Halimatul asmak Ismail Dua’Abdellatef Nassar Manal Abdullah Alohali Mais Ayman Alharaki Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Kingdom of Saudi Arabia Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityRiyadh11671Kingdom of Saudi Arabia

Background:Sepsis,a potentially fatal inflammatory disease triggered by infection,carries significant healthimplications *** detection is crucial as sepsis can rapidly escalate if left *** in deep learning(DL)offer powerful tools to address this ***:Thus,this study proposeda hybrid CNNBDLSTM,a combination of a convolutional neural network(CNN)with a bi-directional long shorttermmemory(BDLSTM)model to predict sepsis *** the proposed model provides a robustframework that capitalizes on the complementary strengths of both architectures,resulting in more accurate andtimelier ***:The sepsis prediction method proposed here utilizes temporal feature extraction todelineate six distinct time frames before the onset of *** time frames adhere to the sepsis-3 standardrequirement,which incorporates 12-h observation windows preceding sepsis *** models were trained usingthe Medical Information Mart for Intensive Care III(MIMIC-III)dataset,which sourced 61,522 patients with 40clinical variables obtained from the IoT medical *** confusion matrix,the area under the receiveroperating characteristic curve(AUCROC)curve,the accuracy,the precision,the F1-score,and the recall weredeployed to evaluate ***:The CNNBDLSTMmodel demonstrated superior performance comparedto the benchmark and other models,achieving an AUCROC of 99.74%and an accuracy of 99.15%one hour beforesepsis *** results indicate that the CNNBDLSTM model is highly effective in predicting sepsis onset,particularly within a close proximity of one ***:The results could assist practitioners in increasingthe potential survival of the patient one hour before sepsis onset.

关键词： Temporal derivatives hybrid deep learning predicting sepsis onset MIMIC III machine learning(ML) deep learning

Software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

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Computers, Materials & Continua 2022年第4期71卷 1521-1546页

作者： Zuhaira Muhammad Zain Sapiah Sakri Nurul Halimatul asmak Ismail Reza M.Parizi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Information Technology College of CommunityPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia College of Computing and Software Engineering Kennesaw State UniversityMariettaGA30060USA

Developing successful software with no defects is one of the main goals of software *** order to provide a software project with the anticipated software quality,the prediction of software defects plays a vital *** learning,and particularly deep learning,have been advocated for predicting software defects,however both suffer from inadequate accuracy,overfitting,and complicated *** this paper,we aim to address such issues in predicting software *** propose a novel structure of 1-Dimensional Convolutional Neural Network(1D-CNN),a deep learning architecture to extract useful knowledge,identifying and modelling the knowledge in the data sequence,reduce overfitting,and finally,predict whether the units of code are defects *** design large-scale empirical studies to reveal the proposed model’s effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA *** experimental results demonstrate that in terms of f-measure,an optimal and modest 1DCNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79%and 23.88%,respectively,in ways that minimize overfitting and improving prediction performance for software *** to the results,1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software ***,in turn,could lead to saving software development resources and producing more reliable software.

关键词： Defects software defect prediction deep learning convolutional neural network machine learning

Phosphorus Adsorption and Nitric Acid Reduction by Ferrous Sulfate-Treated Foamed Waste Glass

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Journal of Materials Science and Chemical Engineering 2018年第3期6卷 21-30页

作者： asmak Afiliana Harada Hiroyuki Katayama Yumi Nishikawa Hanami Mitoma Yoshiharu Tomoyuki Miyamoto Faculty of Environmental Science Prefectural University of Hiroshima Hiroshima Japan Recycle Center COCCO Co. Kure City Hiroshima

Wastewaters containing phosphorus and nitric acid are produced during biological treatment processes. In this study, a material for treating such wastewaters was developed. Foamed glass was produced from waste glass and then heated with iron sulfate to prepare an adsorbent for phosphorus and carrier for reducing nitric acid. The adsorbent performance was evaluated in batch and continuous experiments. The saturated adsorption amount of phosphate was 6.23 mg/g for the product obtained from glass of size 3 to 12 mm;the amount adsorbed was relatively high, in spite of the large glass size. The denitrification by reduction of nitrate was around 25%.

关键词： Foamed Waste Glass Iron Loaded Adsorbent Phosphorus Recovery Continuous Column Experiment