Achmad Zein Feroza, Nelly Oktavia Adiwijaya, Bayu Taruna Widjaja Putra
The development of Pakcoy cultivation holds good prospects, as seen from the demand for vegetable commodities in Indonesia. Its cultivation is consistently rising in terms of volume and value of vegetable imports. However, the cultivation process encounters multiple issues caused by pests and diseases. In addition, the volatile climate in Indonesia has resulted in uninterrupted pest development and the potential decline of Pakcoy’s productivity. Therefore, the detection system for pests and diseases in the Pakcoy plant is called upon to accurately and quickly assist farmers in determining the right treatment, thereby reducing economic losses and producing abundant quality crops. A web-based application with several well-known Convolutional Neural Network (CNN) were incorporated, such as MobileNetV2, GoogLeNet, and ResNet101. A total of 1,226 images were used for training, validating, and testing the dataset to address the problem in this study. The dataset consisted of several plant conditions with leaf miners, cabbage butterflies, powdery mildew disease, healthy plants, and multiple data labels for pests and diseases presented in the individual image. The results show that the MobileNetV2 provides a minimum loss compared to GoogLeNet and ResNet-101 with scores of 0.076, 0.239, and 0.209, respectively. Since the MobileNetV2 architecture provides a good model, the model was carried out to be integrated and tested with the web-based application. The testing accuracy rate reached 98% from the total dataset of 70 testing images. In this direction, MobileNetV2 can be a viable method to be integrated with web-based applications for classifying an image as the basis for decision-making.
{"title":"Development of a Web-based Application by Employing a Convolutional Neural Network (CNN) to Identify Pests and Diseases on Pakcoy (Brassica rapa subsp. chinensis)","authors":"Achmad Zein Feroza, Nelly Oktavia Adiwijaya, Bayu Taruna Widjaja Putra","doi":"10.47836/pjst.31.6.13","DOIUrl":"https://doi.org/10.47836/pjst.31.6.13","url":null,"abstract":"The development of Pakcoy cultivation holds good prospects, as seen from the demand for vegetable commodities in Indonesia. Its cultivation is consistently rising in terms of volume and value of vegetable imports. However, the cultivation process encounters multiple issues caused by pests and diseases. In addition, the volatile climate in Indonesia has resulted in uninterrupted pest development and the potential decline of Pakcoy’s productivity. Therefore, the detection system for pests and diseases in the Pakcoy plant is called upon to accurately and quickly assist farmers in determining the right treatment, thereby reducing economic losses and producing abundant quality crops. A web-based application with several well-known Convolutional Neural Network (CNN) were incorporated, such as MobileNetV2, GoogLeNet, and ResNet101. A total of 1,226 images were used for training, validating, and testing the dataset to address the problem in this study. The dataset consisted of several plant conditions with leaf miners, cabbage butterflies, powdery mildew disease, healthy plants, and multiple data labels for pests and diseases presented in the individual image. The results show that the MobileNetV2 provides a minimum loss compared to GoogLeNet and ResNet-101 with scores of 0.076, 0.239, and 0.209, respectively. Since the MobileNetV2 architecture provides a good model, the model was carried out to be integrated and tested with the web-based application. The testing accuracy rate reached 98% from the total dataset of 70 testing images. In this direction, MobileNetV2 can be a viable method to be integrated with web-based applications for classifying an image as the basis for decision-making.","PeriodicalId":46234,"journal":{"name":"Pertanika Journal of Science and Technology","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135689357","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}
This study used two product development methods: Kansei and value engineering. Kansei engineering was used to identify and translate consumer psychological impressions or feelings in the form of Kansei words to the design parameters, while value engineering was used to analyze the functional properties by considering cost, reliability, and performance. The consumers determined the priority attributes of analog rice products, namely a good taste, a fluffy and soft texture, as well as a bright color. Three alternative variations of the product development concept were formulated based on these priorities. The concept with the highest value was then concluded as an analog rice produced from 90% Sago flour and 10% MOCAF (Modified Cassava Flour) with a value of 1,131.
{"title":"Development of Sago-based Analog Rice Using Kansei and Value Engineering","authors":"Vioretta Putri Rizky Septiani, Mirwan Ushada, Suharno Suharno","doi":"10.47836/pjst.31.6.17","DOIUrl":"https://doi.org/10.47836/pjst.31.6.17","url":null,"abstract":"This study used two product development methods: Kansei and value engineering. Kansei engineering was used to identify and translate consumer psychological impressions or feelings in the form of Kansei words to the design parameters, while value engineering was used to analyze the functional properties by considering cost, reliability, and performance. The consumers determined the priority attributes of analog rice products, namely a good taste, a fluffy and soft texture, as well as a bright color. Three alternative variations of the product development concept were formulated based on these priorities. The concept with the highest value was then concluded as an analog rice produced from 90% Sago flour and 10% MOCAF (Modified Cassava Flour) with a value of 1,131.","PeriodicalId":46234,"journal":{"name":"Pertanika Journal of Science and Technology","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135689354","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}
Ansar Jamil, Teo Sheng Ting, Z. Zainal Abidin, Maisara Othman, Mohd Helmy Abdul Wahab, Mohammad Faiz Liew Abdullah, M. J. Homam, Lukman Hanif Muhammad Audah, Shaharil Mohd Shah
Smart hydroponic systems have been introduced to allow farmers to monitor their hydroponic system conditions anywhere and anytime using Internet of Things (IoT) technology. Several sensors are installed on the system, such as Total Dissolved Solids (TDS), nutrient level, and temperature sensors. These sensors must be calibrated to ensure correct and accurate readings. Currently, calibration of a TDS sensor is only possible at one or a very small range of TDS values due to the very limited measurement range of the sensor. Because of this, we propose a TDS sensor calibration method called Sectioned-Polynomial Regression (Sec-PR). The main aim is to extend the measurement range of the TDS sensor and still provide a good accuracy of the sensor reading. Sec-PR computes the polynomial regression line that fits into the TDS sensor values. Then, it divides the regression line into several sections. Sec-PR calculates the average ratio between the polynomial regressed TDS sensor values and the TDS meter in each section. These average ratio values map the TDS sensor reading to the TDS meter. The performance of Sec-PR was determined using mathematical analysis and verified using experiments. The finding shows that Sec-PR provides a good calibration accuracy of about 91% when compared to the uncalibrated TDS sensor reading of just 78% with Mean Average Error (MAE) and Root Mean Square Error (RMSE) equal to 59.36 and 93.69 respectively. Sec-PR provides a comparable performance with Machine Learning and Multilayer Perception method.
{"title":"Polynomial Regression Calibration Method of Total Dissolved Solids Sensor for Hydroponic Systems","authors":"Ansar Jamil, Teo Sheng Ting, Z. Zainal Abidin, Maisara Othman, Mohd Helmy Abdul Wahab, Mohammad Faiz Liew Abdullah, M. J. Homam, Lukman Hanif Muhammad Audah, Shaharil Mohd Shah","doi":"10.47836/pjst.31.6.08","DOIUrl":"https://doi.org/10.47836/pjst.31.6.08","url":null,"abstract":"Smart hydroponic systems have been introduced to allow farmers to monitor their hydroponic system conditions anywhere and anytime using Internet of Things (IoT) technology. Several sensors are installed on the system, such as Total Dissolved Solids (TDS), nutrient level, and temperature sensors. These sensors must be calibrated to ensure correct and accurate readings. Currently, calibration of a TDS sensor is only possible at one or a very small range of TDS values due to the very limited measurement range of the sensor. Because of this, we propose a TDS sensor calibration method called Sectioned-Polynomial Regression (Sec-PR). The main aim is to extend the measurement range of the TDS sensor and still provide a good accuracy of the sensor reading. Sec-PR computes the polynomial regression line that fits into the TDS sensor values. Then, it divides the regression line into several sections. Sec-PR calculates the average ratio between the polynomial regressed TDS sensor values and the TDS meter in each section. These average ratio values map the TDS sensor reading to the TDS meter. The performance of Sec-PR was determined using mathematical analysis and verified using experiments. The finding shows that Sec-PR provides a good calibration accuracy of about 91% when compared to the uncalibrated TDS sensor reading of just 78% with Mean Average Error (MAE) and Root Mean Square Error (RMSE) equal to 59.36 and 93.69 respectively. Sec-PR provides a comparable performance with Machine Learning and Multilayer Perception method.","PeriodicalId":46234,"journal":{"name":"Pertanika Journal of Science and Technology","volume":"25 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82849351","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}
Ana Masara Ahmad Mokhtar, Brennan Tang Yet Shen, Azam Muzafar Ahmad Mokhtar, N. Salikin, Muaz Mohd ZAINI MAKHTAR, Fatin Nur Izzati Mohd Fadzil, Nur Azzalia Kamaruzaman, Muggunna Balasubramaniam
Indiscriminate manure disposal has been highlighted as a significant cause of environmental contamination due to the presence of various biological and chemical irritants. It includes pathogens, antibiotics, and organic pollutants, all of which have the potential to harm not only the environment but also human health. Several incidents have been reported, most notably among farmers and those living near the farms, as a result of air and water pollution caused by manure losses. Acute and chronic exposure to these hazards may result in a variety of health issues, including infection, inflammation, and even cancer. Despite this, humans are constantly exposed to these risk agents due to a lack of awareness of proper disposal methods and knowledge of the risk agents’ associations with diseases. Thus, the review discusses the potential health risk or diseases linked to poultry manure and recommends future measures to minimise the hazards to farmers’ health and the environment posed by their existing practices.
{"title":"Poultry Manure and its Contribution to Inflammation and Cancer Progression","authors":"Ana Masara Ahmad Mokhtar, Brennan Tang Yet Shen, Azam Muzafar Ahmad Mokhtar, N. Salikin, Muaz Mohd ZAINI MAKHTAR, Fatin Nur Izzati Mohd Fadzil, Nur Azzalia Kamaruzaman, Muggunna Balasubramaniam","doi":"10.47836/pjst.31.6.01","DOIUrl":"https://doi.org/10.47836/pjst.31.6.01","url":null,"abstract":"Indiscriminate manure disposal has been highlighted as a significant cause of environmental contamination due to the presence of various biological and chemical irritants. It includes pathogens, antibiotics, and organic pollutants, all of which have the potential to harm not only the environment but also human health. Several incidents have been reported, most notably among farmers and those living near the farms, as a result of air and water pollution caused by manure losses. Acute and chronic exposure to these hazards may result in a variety of health issues, including infection, inflammation, and even cancer. Despite this, humans are constantly exposed to these risk agents due to a lack of awareness of proper disposal methods and knowledge of the risk agents’ associations with diseases. Thus, the review discusses the potential health risk or diseases linked to poultry manure and recommends future measures to minimise the hazards to farmers’ health and the environment posed by their existing practices.","PeriodicalId":46234,"journal":{"name":"Pertanika Journal of Science and Technology","volume":"31 3 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80357995","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}
D. Rachmawati, T. Elfitasari, I. Samidjan, Putut Har Riyadi, Dewi Nurhayati
The increasing demand for livestock and poultry feeds results in the lack of fish meals (FM). Poultry slaughterhouse by-product (PSB) is one promising strategy due to its high protein content despite the limited content of lysine. Thus, supplementing lysine in dietary fish feed is necessary. The present study aimed to investigate how different lysine doses in feed with PSB and FM as animal protein sources affected protein digestibility, feed utilization, growth, hematology, and body composition of Sangkuriang catfish (Clarias gariepinus var. Sangkuriang). Sangkuriang catfish at the grow-out stage (15.54±0.17 g/fish) were used. The fish were fed six experimental diets with similar protein and energy content but different lysine levels at 1.25%, 1.75%, 2.25%, 2.75%, 3.25%, and 3.75%/kg (treatments 1 to 6). The addition of lysine to feed had a significant (P<0.05) effect on protein digestibility (ADCp), efficiency of feed utilization (EFU), and relative growth rate (RGR) of Sangkuriang catfish at a grow-out stage but had no significant (P>0.05) effect on survival rate, hematology, and nutrient content. The optimal doses of dietary lysine with PSB and FM to improve ADCp, EFU, and RGR of Sangkuriang catfish were 2.59%, 2.63%, and 2.62%/kg diet, respectively. However, the supplementation of PSB in experimental diets had no significant effect on glucose, triglyceride, total protein, urea, calcium, magnesium, albumin, globulin, hemoglobin, hematocrit, phosphorous, and mean corpuscular hemoglobin concentration (MCHC). The lysine addition in feed formulated with PSB and FM could improve the growth performance and increase the feed digestibility of Sangkuriang catfish at the grow-out stage.
{"title":"Effect of Lysine and Poultry Slaughterhouse by Product Meal on Growth Performance, Feed Efficiency, and Blood Profile of Sangkuriang Catfish (Clarias gariepinus var. Sangkuriang)","authors":"D. Rachmawati, T. Elfitasari, I. Samidjan, Putut Har Riyadi, Dewi Nurhayati","doi":"10.47836/pjst.31.6.07","DOIUrl":"https://doi.org/10.47836/pjst.31.6.07","url":null,"abstract":"The increasing demand for livestock and poultry feeds results in the lack of fish meals (FM). Poultry slaughterhouse by-product (PSB) is one promising strategy due to its high protein content despite the limited content of lysine. Thus, supplementing lysine in dietary fish feed is necessary. The present study aimed to investigate how different lysine doses in feed with PSB and FM as animal protein sources affected protein digestibility, feed utilization, growth, hematology, and body composition of Sangkuriang catfish (Clarias gariepinus var. Sangkuriang). Sangkuriang catfish at the grow-out stage (15.54±0.17 g/fish) were used. The fish were fed six experimental diets with similar protein and energy content but different lysine levels at 1.25%, 1.75%, 2.25%, 2.75%, 3.25%, and 3.75%/kg (treatments 1 to 6). The addition of lysine to feed had a significant (P<0.05) effect on protein digestibility (ADCp), efficiency of feed utilization (EFU), and relative growth rate (RGR) of Sangkuriang catfish at a grow-out stage but had no significant (P>0.05) effect on survival rate, hematology, and nutrient content. The optimal doses of dietary lysine with PSB and FM to improve ADCp, EFU, and RGR of Sangkuriang catfish were 2.59%, 2.63%, and 2.62%/kg diet, respectively. However, the supplementation of PSB in experimental diets had no significant effect on glucose, triglyceride, total protein, urea, calcium, magnesium, albumin, globulin, hemoglobin, hematocrit, phosphorous, and mean corpuscular hemoglobin concentration (MCHC). The lysine addition in feed formulated with PSB and FM could improve the growth performance and increase the feed digestibility of Sangkuriang catfish at the grow-out stage.","PeriodicalId":46234,"journal":{"name":"Pertanika Journal of Science and Technology","volume":"65 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78171930","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}
F. R. Munas, Yu Kok Hwa, Norwahida Yusoff, Abdul Majeed Muzathik, Mohd Azmi Ismail
Aircraft icing remains a key aviation hazard as the global fleet of aircraft in various sectors continues to expand, posing a serious threat to flight safety. As previously stated, the growth of this type of aircraft has been accompanied by an increase in noise levels, and aircraft is reportedly the second most bothersome noise source after traffic. However, integrating an acoustic liner with anti-icing techniques on the leading edge of a nacelle would not efficiently eliminate forward radiated noise and improve the thermal performance of the anti-icing system. Hence, it is of the utmost importance to research the integration of ice protection and noise abatement systems for aircraft applications. This review discusses the integration of ice accretion and noise abatement systems in aircraft applications. The prominence of this review is to explain significant features such as ice protection systems, Computational Fluid Dynamics in ice protection, noise abatement systems, and the integration of ice protection systems and noise abatement systems wherever they are described.
{"title":"Integrating Ice Protection and Noise Abatement Systems for Aircraft Application: A Review","authors":"F. R. Munas, Yu Kok Hwa, Norwahida Yusoff, Abdul Majeed Muzathik, Mohd Azmi Ismail","doi":"10.47836/pjst.31.6.02","DOIUrl":"https://doi.org/10.47836/pjst.31.6.02","url":null,"abstract":"Aircraft icing remains a key aviation hazard as the global fleet of aircraft in various sectors continues to expand, posing a serious threat to flight safety. As previously stated, the growth of this type of aircraft has been accompanied by an increase in noise levels, and aircraft is reportedly the second most bothersome noise source after traffic. However, integrating an acoustic liner with anti-icing techniques on the leading edge of a nacelle would not efficiently eliminate forward radiated noise and improve the thermal performance of the anti-icing system. Hence, it is of the utmost importance to research the integration of ice protection and noise abatement systems for aircraft applications. This review discusses the integration of ice accretion and noise abatement systems in aircraft applications. The prominence of this review is to explain significant features such as ice protection systems, Computational Fluid Dynamics in ice protection, noise abatement systems, and the integration of ice protection systems and noise abatement systems wherever they are described.","PeriodicalId":46234,"journal":{"name":"Pertanika Journal of Science and Technology","volume":"1 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84669117","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}
Siti Zulaikah, Arif Juliansyah, Muhammad Fathur Rouf Hasan, Bambang Heru Iswanto, M. Mariyanto, Ardyanto Tanjung, S. Bijaksana, Ann Marie Hirt
Sumbawa’s Kuris River is one of the rivers contaminated by the island’s traditional gold mine. In order to detect contaminant levels, we examine the magnetic susceptibility, HCN levels, and the heavy metal contents on the river’s surface sediment. Environmental pollution has been widely assessed using a combination of magnetic properties and geochemical analysis. The goals of this research are to discover how magnetic susceptibility (χ) can be used as a first-order proxy for pollution. The relation between susceptibility and HCN is of particular interest, as this is a major contaminant associated with gold mining. The surface sediment samples were collected at ten different locations along the rivers. The magnetic susceptibility was determined using the Bartington MS2B, and the hydrogen cyanide (HCN) concentration was determined using Argentometric titration. The element content was determined by an Atomic Absorption Spectrometer (AAS). The low-frequency magnetic susceptibility (χlf) ranges from 71 to 115×10-8 m3/kg, with an average of 97×10-8 m3/kg, and the χfd(%) analysis ranges from 2% to 4%. The presence of spherical iron oxides, which are indicative of combustion byproducts, was also confirmed by SEM. The samples have low magnetic susceptibility but high levels of Hg and HCN. AAS results showed high Fe, Zn, and Cu concentrations in river sediments, with more variable concentrations of Hg, Mn, As, Cr, and Au. Because Fe, Cu, As, Hg, and HCN have a significant Pearson’s correlation with χfd(%), this parameter can be a useful indicator for contamination caused by gold mining waste.
{"title":"Magnetic Susceptibility and Hydrogen Cyanide Levels as Proxy Indicator for Gold Mining Pollution in River Sediment","authors":"Siti Zulaikah, Arif Juliansyah, Muhammad Fathur Rouf Hasan, Bambang Heru Iswanto, M. Mariyanto, Ardyanto Tanjung, S. Bijaksana, Ann Marie Hirt","doi":"10.47836/pjst.31.6.03","DOIUrl":"https://doi.org/10.47836/pjst.31.6.03","url":null,"abstract":"Sumbawa’s Kuris River is one of the rivers contaminated by the island’s traditional gold mine. In order to detect contaminant levels, we examine the magnetic susceptibility, HCN levels, and the heavy metal contents on the river’s surface sediment. Environmental pollution has been widely assessed using a combination of magnetic properties and geochemical analysis. The goals of this research are to discover how magnetic susceptibility (χ) can be used as a first-order proxy for pollution. The relation between susceptibility and HCN is of particular interest, as this is a major contaminant associated with gold mining. The surface sediment samples were collected at ten different locations along the rivers. The magnetic susceptibility was determined using the Bartington MS2B, and the hydrogen cyanide (HCN) concentration was determined using Argentometric titration. The element content was determined by an Atomic Absorption Spectrometer (AAS). The low-frequency magnetic susceptibility (χlf) ranges from 71 to 115×10-8 m3/kg, with an average of 97×10-8 m3/kg, and the χfd(%) analysis ranges from 2% to 4%. The presence of spherical iron oxides, which are indicative of combustion byproducts, was also confirmed by SEM. The samples have low magnetic susceptibility but high levels of Hg and HCN. AAS results showed high Fe, Zn, and Cu concentrations in river sediments, with more variable concentrations of Hg, Mn, As, Cr, and Au. Because Fe, Cu, As, Hg, and HCN have a significant Pearson’s correlation with χfd(%), this parameter can be a useful indicator for contamination caused by gold mining waste.","PeriodicalId":46234,"journal":{"name":"Pertanika Journal of Science and Technology","volume":"43 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88607384","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}
Azurin protein potentially plays an important role as an anti-cancer therapeutic agent, particularly in treating breast cancer in experiments and showing without having a negative effect on normal cells. Although the interaction mechanism between protein and lipid membrane is complicated, it can be modeled as protein-lipid interaction. Since the all-atom (AA) model simulation is cost computing, we apply a coarse-grained (CG-MARTINI) model to calculate the protein-lipid interaction. We investigate the binding free energy value dependency by varying the windows separation and electrostatic scale parameters. After scaling the electrostatic interactions by a factor of 0.04, the best result in terms of free energy is -140.831 kcal/mol, while after window-separation optimization, it reaches -71.859 kcal/mol. This scaling was necessary because the structures from the CG MARTINI model have a higher density than the corresponding all-atom structures. We thus postulate that electrostatic interactions should be scaled down in this case of CG-MARTINI simulations.
{"title":"Effect of Scaling the Electrostatic Interactions on the Free Energy of Transfer of Azurin from Water to Lipid Membrane Determined by Coarse-Grained Simulations","authors":"D. Fitrasari, A. Purqon, S. Suprijadi","doi":"10.47836/pjst.31.6.06","DOIUrl":"https://doi.org/10.47836/pjst.31.6.06","url":null,"abstract":"Azurin protein potentially plays an important role as an anti-cancer therapeutic agent, particularly in treating breast cancer in experiments and showing without having a negative effect on normal cells. Although the interaction mechanism between protein and lipid membrane is complicated, it can be modeled as protein-lipid interaction. Since the all-atom (AA) model simulation is cost computing, we apply a coarse-grained (CG-MARTINI) model to calculate the protein-lipid interaction. We investigate the binding free energy value dependency by varying the windows separation and electrostatic scale parameters. After scaling the electrostatic interactions by a factor of 0.04, the best result in terms of free energy is -140.831 kcal/mol, while after window-separation optimization, it reaches -71.859 kcal/mol. This scaling was necessary because the structures from the CG MARTINI model have a higher density than the corresponding all-atom structures. We thus postulate that electrostatic interactions should be scaled down in this case of CG-MARTINI simulations.","PeriodicalId":46234,"journal":{"name":"Pertanika Journal of Science and Technology","volume":"76 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82204810","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}
This paper proposes a method to recognize fruits whose quality, including their ripeness, grades, brix values, and flesh characteristics, cannot be determined visually from their skin but from striking and flicking sounds. Four fruit types consisting of durians, watermelons, guavas, and pineapples were studied in this research. In recognition of fruit types, preprocessing removes the non-striking/non-flicking parts from the striking and flicking sounds. Then the sequences of frequency domain acoustic features containing 13 Mel Frequency Cepstral Coefficients (MFCCs) and their 13 first- and 13 second-order derivatives were extracted from striking and flicking sounds. The sequences were used to create the Hidden Markov Models (HMMs). The HMM acoustic models, dictionary, and grammar were incorporated to recognize striking and flicking sounds. When testing the striking and flicking sounds obtained from the fruits used to create the training set but were collected at different times, the recognition accuracy using 1 through 5 strikes/flicks was 98.48%, 98.91%, 99.13%, 98.91%, and 99.57%, respectively. For an unknown test set, of which the sounds obtained from the fruits that were not used to create the training set, the recognition accuracy using 1 through 5 strikes/flicks were 95.23%, 96.82%, 96.82%, 97.05%, and 96.59%, respectively. The results also revealed that the proposed method could accurately distinguish the striking sounds of durians from the flicking sounds of watermelons, guavas, and pineapples.
{"title":"Recognition of Fruit Types from Striking and Flicking Sounds","authors":"Rong Phoophuangpairoj","doi":"10.47836/pjst.31.6.04","DOIUrl":"https://doi.org/10.47836/pjst.31.6.04","url":null,"abstract":"This paper proposes a method to recognize fruits whose quality, including their ripeness, grades, brix values, and flesh characteristics, cannot be determined visually from their skin but from striking and flicking sounds. Four fruit types consisting of durians, watermelons, guavas, and pineapples were studied in this research. In recognition of fruit types, preprocessing removes the non-striking/non-flicking parts from the striking and flicking sounds. Then the sequences of frequency domain acoustic features containing 13 Mel Frequency Cepstral Coefficients (MFCCs) and their 13 first- and 13 second-order derivatives were extracted from striking and flicking sounds. The sequences were used to create the Hidden Markov Models (HMMs). The HMM acoustic models, dictionary, and grammar were incorporated to recognize striking and flicking sounds. When testing the striking and flicking sounds obtained from the fruits used to create the training set but were collected at different times, the recognition accuracy using 1 through 5 strikes/flicks was 98.48%, 98.91%, 99.13%, 98.91%, and 99.57%, respectively. For an unknown test set, of which the sounds obtained from the fruits that were not used to create the training set, the recognition accuracy using 1 through 5 strikes/flicks were 95.23%, 96.82%, 96.82%, 97.05%, and 96.59%, respectively. The results also revealed that the proposed method could accurately distinguish the striking sounds of durians from the flicking sounds of watermelons, guavas, and pineapples.","PeriodicalId":46234,"journal":{"name":"Pertanika Journal of Science and Technology","volume":"34 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76608324","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}
Olayemi Joshua Ibidoja, Fam Pei Shan, Mukhtar Eri Suheri, Jumat Sulaiman, Majid Khan Majahar Ali
The parameters that determine the removal of moisture content have become necessary in seaweed research as they can reduce cost and improve the quality and quantity of the seaweed. During the seaweed’s drying process, many drying parameters are involved, so it is hard to find a model that can determine the drying parameters. This study compares seaweed big data performance using machine learning algorithms. To achieve the objectives, four machine learning algorithms, such as bagging, boosting, support vector machine, and random forest, were used to determine the significant parameters from the data obtained from v-GHSD (v-Groove Hybrid Solar Drier). The mean absolute percentage error (MAPE) and coefficient of determination (R2) were used to assess the model. The importance of variable selection cannot be overstated in big data due to the large number of variables and parameters that exceed the number of observations. It will reduce the complexity of the model, avoid the curse of dimensionality, reduce cost, remove irrelevant variables, and increase precision. A total of 435 drying parameters determined the moisture content removal, and each algorithm was used to select 15, 25, 35 and 45 significant parameters. The MAPE and R-Square for the 45 highest variable importance for random forest are 2.13 and 0.9732, respectively. It performed best, with the lowest error and the highest R-square. These results show that random forest is the best algorithm to decide the vital drying parameters for removing moisture content.
决定海藻含水率去除的参数在海藻研究中是必要的，因为它们可以降低成本，提高海藻的质量和数量。在海藻的干燥过程中，涉及到许多干燥参数，因此很难找到一个可以确定干燥参数的模型。本研究使用机器学习算法比较海藻的大数据性能。为了实现这一目标，使用了四种机器学习算法，如bagging、boosting、support vector machine和random forest，从v-GHSD (v-Groove Hybrid Solar dry)获得的数据中确定重要参数。采用平均绝对百分比误差(MAPE)和决定系数(R2)对模型进行评价。在大数据中，由于大量的变量和参数超过了观测值的数量，因此变量选择的重要性再怎么强调也不为过。它可以降低模型的复杂性，避免维数的困扰，降低成本，去除不相关的变量，提高精度。共有435个干燥参数决定了含水率的去除，每个算法分别选择15、25、35和45个显著参数。随机森林45个最高变量重要性的MAPE和R-Square分别为2.13和0.9732。它表现最好，误差最小，r平方最高。这些结果表明，随机森林是确定去除水分的关键干燥参数的最佳算法。
{"title":"Intelligence System via Machine Learning Algorithms in Detecting the Moisture Content Removal Parameters of Seaweed Big Data","authors":"Olayemi Joshua Ibidoja, Fam Pei Shan, Mukhtar Eri Suheri, Jumat Sulaiman, Majid Khan Majahar Ali","doi":"10.47836/pjst.31.6.09","DOIUrl":"https://doi.org/10.47836/pjst.31.6.09","url":null,"abstract":"The parameters that determine the removal of moisture content have become necessary in seaweed research as they can reduce cost and improve the quality and quantity of the seaweed. During the seaweed’s drying process, many drying parameters are involved, so it is hard to find a model that can determine the drying parameters. This study compares seaweed big data performance using machine learning algorithms. To achieve the objectives, four machine learning algorithms, such as bagging, boosting, support vector machine, and random forest, were used to determine the significant parameters from the data obtained from v-GHSD (v-Groove Hybrid Solar Drier). The mean absolute percentage error (MAPE) and coefficient of determination (R2) were used to assess the model. The importance of variable selection cannot be overstated in big data due to the large number of variables and parameters that exceed the number of observations. It will reduce the complexity of the model, avoid the curse of dimensionality, reduce cost, remove irrelevant variables, and increase precision. A total of 435 drying parameters determined the moisture content removal, and each algorithm was used to select 15, 25, 35 and 45 significant parameters. The MAPE and R-Square for the 45 highest variable importance for random forest are 2.13 and 0.9732, respectively. It performed best, with the lowest error and the highest R-square. These results show that random forest is the best algorithm to decide the vital drying parameters for removing moisture content.","PeriodicalId":46234,"journal":{"name":"Pertanika Journal of Science and Technology","volume":"14 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84414129","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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