The working population at the brick kilns is considered as the people living under poverty line. This study aims to investigate the socioeconomic and demographic characteristics and housing and living conditions of the brick kiln workers in Bahawalpur district. Total 20 kilns were visited in 5 tehsils of the district as sample sites. A questionnaire was designed and a field survey was conducted by using simple random sampling techniques to collect the data of 400 workers from 20 brick kilns. The quantitative analysis was performed in Statistical Package for Social Sciences (SPSS) v. 21 by applying descriptive and inferential statistics. Findings showed that majority of the workers and heads of working households were male (85.75% and 97.5% in rural and urban kilns respectively) and Saraiki speaking (70%) belonged to Muhajir, Ranagr and Khokhar castes. The early age (14-18 years) marriages were common among the workers especially among the females. Dependency ratio of the kiln workers was 37.6% including mostly the children and aged. Dominant share of the kiln workers (70-80%) were illiterate and very few were literate from primary to intermediate level. The workers’ income varied between PKR 11,000 to 15,000 which was lower than the average wage (PKR 25,000) as per the government rules. Brick moulders and soil suppliers were the main occupants at kilns. Most of the workers were resided in their own houses (1 or 2 rooms) made with kacha (mud made) material. Although various facilities were available to the workers at brick kilns but they have no proper access to safe drinking water (80%), accident risk facility (0%) and first aid (0%) crucial for their health. The chi-square results also verify the miserable social and economic life of the kiln workers. Thus, these facts demonstrated that kiln workers led a meagre life style. This work will serve as a reminder to authorities, planners and Non-Governmental Organizations (NGOs) to take action to improve the living conditions of kiln workers. Lastly, few suggestions were proposed to uplift the lives of the kiln workers.
{"title":"A Statistical Survey on the Socioeconomic and Demographic Livelihood of Brick Kiln Workers: A Case Study of Bahawalpur District, Punjab, Pakistan","authors":"Fakhra Anwar, Muhammad Mohsin, Sana Arshad","doi":"10.53560/ppasa(60-4)826","DOIUrl":"https://doi.org/10.53560/ppasa(60-4)826","url":null,"abstract":"The working population at the brick kilns is considered as the people living under poverty line. This study aims to investigate the socioeconomic and demographic characteristics and housing and living conditions of the brick kiln workers in Bahawalpur district. Total 20 kilns were visited in 5 tehsils of the district as sample sites. A questionnaire was designed and a field survey was conducted by using simple random sampling techniques to collect the data of 400 workers from 20 brick kilns. The quantitative analysis was performed in Statistical Package for Social Sciences (SPSS) v. 21 by applying descriptive and inferential statistics. Findings showed that majority of the workers and heads of working households were male (85.75% and 97.5% in rural and urban kilns respectively) and Saraiki speaking (70%) belonged to Muhajir, Ranagr and Khokhar castes. The early age (14-18 years) marriages were common among the workers especially among the females. Dependency ratio of the kiln workers was 37.6% including mostly the children and aged. Dominant share of the kiln workers (70-80%) were illiterate and very few were literate from primary to intermediate level. The workers’ income varied between PKR 11,000 to 15,000 which was lower than the average wage (PKR 25,000) as per the government rules. Brick moulders and soil suppliers were the main occupants at kilns. Most of the workers were resided in their own houses (1 or 2 rooms) made with kacha (mud made) material. Although various facilities were available to the workers at brick kilns but they have no proper access to safe drinking water (80%), accident risk facility (0%) and first aid (0%) crucial for their health. The chi-square results also verify the miserable social and economic life of the kiln workers. Thus, these facts demonstrated that kiln workers led a meagre life style. This work will serve as a reminder to authorities, planners and Non-Governmental Organizations (NGOs) to take action to improve the living conditions of kiln workers. Lastly, few suggestions were proposed to uplift the lives of the kiln workers.","PeriodicalId":509771,"journal":{"name":"Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139178528","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}
M. Akbar, Saeed Ahmed, M. I. Shahzad, Muhammad Ahmad Raza
We have investigated the Laplacian equation in fractional dimensional space (FDS) that is widely used in physics to describe many complex phenomena. Using this concept, we have applied it on a cylindrical shell of permeable material to find the analytical solution of electric potential in FDS. The derivation of this problem is performed by applying Gegenbauer polynomials. The general solution has been obtained in a closed form in the FDS and can be applied to the cylindrical shell for different materials inside the cylinder core and outside the shell. By setting the fractional parameter α = 3, the derived solution is retrieved for the integer order.
{"title":"Analytical Solution for Cylindrical Shell of Permeable Material in Fractional Dimensional Space","authors":"M. Akbar, Saeed Ahmed, M. I. Shahzad, Muhammad Ahmad Raza","doi":"10.53560/ppasa(60-4)669","DOIUrl":"https://doi.org/10.53560/ppasa(60-4)669","url":null,"abstract":"We have investigated the Laplacian equation in fractional dimensional space (FDS) that is widely used in physics to describe many complex phenomena. Using this concept, we have applied it on a cylindrical shell of permeable material to find the analytical solution of electric potential in FDS. The derivation of this problem is performed by applying Gegenbauer polynomials. The general solution has been obtained in a closed form in the FDS and can be applied to the cylindrical shell for different materials inside the cylinder core and outside the shell. By setting the fractional parameter α = 3, the derived solution is retrieved for the integer order.","PeriodicalId":509771,"journal":{"name":"Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139177649","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}
Agha Mushtaque Ahmed, Imran Khatri, Irfan Ahmed Halepoto, Masood Nazir Khoso
Worldwide, stored grain pests are massively infesting most stored crops and their by-products. The main losses are due to infestation of these pests occurring on various carriers just prior to harvest, during storage or shipping. Methyl bromide and phosphine fumigation have been widely used for phytosanitary treatment of stored grains but are recognized as highly effective in depleting ozone, and similarly, residue-free grains are important for thermal disinfection. Solarization is one of the best ways to manage and disinfect crops, the traditional solarization methods are already practised by farmers but are inefficient to kill all stages of pests and require additional land exposed to the sun. In this work, thermal disinfection systems using solar heaters are proposed, designed, and developed to offset the actual lethal heat window. For the experiment work, the solar heater boxes were constructed in an octagon shape with 135o at the base for trapping maximum heat inside the solar the heater box. The characteristics of the proposed system proved simpler, faster, and inexpensive but equally effective to achieve the desired results in terms of heat generation and seed moisture.
{"title":"Enhancing Efficiency of Solar Heater Box with Linear Actuator for Maximizing Solarization","authors":"Agha Mushtaque Ahmed, Imran Khatri, Irfan Ahmed Halepoto, Masood Nazir Khoso","doi":"10.53560/ppasa(60-4)811","DOIUrl":"https://doi.org/10.53560/ppasa(60-4)811","url":null,"abstract":"Worldwide, stored grain pests are massively infesting most stored crops and their by-products. The main losses are due to infestation of these pests occurring on various carriers just prior to harvest, during storage or shipping. Methyl bromide and phosphine fumigation have been widely used for phytosanitary treatment of stored grains but are recognized as highly effective in depleting ozone, and similarly, residue-free grains are important for thermal disinfection. Solarization is one of the best ways to manage and disinfect crops, the traditional solarization methods are already practised by farmers but are inefficient to kill all stages of pests and require additional land exposed to the sun. In this work, thermal disinfection systems using solar heaters are proposed, designed, and developed to offset the actual lethal heat window. For the experiment work, the solar heater boxes were constructed in an octagon shape with 135o at the base for trapping maximum heat inside the solar the heater box. The characteristics of the proposed system proved simpler, faster, and inexpensive but equally effective to achieve the desired results in terms of heat generation and seed moisture.","PeriodicalId":509771,"journal":{"name":"Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139180297","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}
Malik Muhammad Hussain, Farrukh Shehzad, Muhammad Islam, Ashique Ali Chohan, Rashid Ahmed, H. M. Muddasar Jamil Shera
The issue of precise crop prediction gained worldwide attention in the midst of food security concerns. In this study, the efficacies of different machine learning (ML) algorithms, i.e., multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), and support vector regression (SVR) are integrated to predict wheat productivity. The performances of ML algorithms are then measured to get the optimized model. The updated dataset is collected from the Crop Reporting Service for various agronomical constraints. Randomized data partitions, hyper-parametric tuning, complexity analysis, cross-validation measures, learning curves, evaluation metrics and prediction errors are used to get the optimized model. ML model is applied using 75% training dataset and 25% testing datasets. RFR achieved the highest R2 value of 0.90 for the training model, followed by DTR, MLR, and SVR. In the testing model, RFR also achieved an R2 value of 0.74, followed by MLR, DTR, and SVR. The lowest prediction error (P.E) is found for the RFR, followed by DTR, MLR, and SVR. K-Fold cross-validation measures also depict that RFR is an optimized model when compared with DTR, MLR and SVR.
{"title":"Measuring the Performance of Supervised Machine Learning Algorithms for Optimizing Wheat Productivity Prediction Models: A Comparative Study","authors":"Malik Muhammad Hussain, Farrukh Shehzad, Muhammad Islam, Ashique Ali Chohan, Rashid Ahmed, H. M. Muddasar Jamil Shera","doi":"10.53560/ppasa(60-4)820","DOIUrl":"https://doi.org/10.53560/ppasa(60-4)820","url":null,"abstract":"The issue of precise crop prediction gained worldwide attention in the midst of food security concerns. In this study, the efficacies of different machine learning (ML) algorithms, i.e., multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), and support vector regression (SVR) are integrated to predict wheat productivity. The performances of ML algorithms are then measured to get the optimized model. The updated dataset is collected from the Crop Reporting Service for various agronomical constraints. Randomized data partitions, hyper-parametric tuning, complexity analysis, cross-validation measures, learning curves, evaluation metrics and prediction errors are used to get the optimized model. ML model is applied using 75% training dataset and 25% testing datasets. RFR achieved the highest R2 value of 0.90 for the training model, followed by DTR, MLR, and SVR. In the testing model, RFR also achieved an R2 value of 0.74, followed by MLR, DTR, and SVR. The lowest prediction error (P.E) is found for the RFR, followed by DTR, MLR, and SVR. K-Fold cross-validation measures also depict that RFR is an optimized model when compared with DTR, MLR and SVR.","PeriodicalId":509771,"journal":{"name":"Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139182032","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}
Misha shafi,, Saba Javaid, Roohi Zafar, Ahmed Ali Rajput, Muhammad Mustaqeem Zahid, Muhammad Daniyal
A term symbol is used to describe atomic microstate states, which give the multiplicity and total angular momentum of the atomic state. Russel Sauder coupling scheme is used to generate terms of equivalent and non-equivalent electronic configurations. For equivalent electrons, the terms are calculated using Pauli’s principle, and the number of terms is limited and is calculated by the combination rule. The total possible electrons and total available electrons are used in the combination formula. In case of non-equivalent electrons, the number of terms are found by the permutation rule. The number of terms for equivalent electrons is less than the terms for non-equivalent electrons. The number of possible microstates for p2 and d5 configurations are 15 and 252 respectively. While the number of final microstates for 1p2p and 3d4d configurations are 36 and 100. In the proposed study, a Python programme was developed that generates the microstate according to filled and half-filled subshell electronic configurations for equivalent, non-equivalent, and combinations of both. Examples of microstates for non-equivalent electrons of configuration 1s2s, sp, sd, ss, 2p3p, pd, pf, 3d4d, df, 4f5f and for equivalent electrons of configuration su, pv, dx, and f y are presented.
{"title":"New Numerical Approach to Calculate Microstates of Equivalent and Non-Equivalent Electrons","authors":"Misha shafi,, Saba Javaid, Roohi Zafar, Ahmed Ali Rajput, Muhammad Mustaqeem Zahid, Muhammad Daniyal","doi":"10.53560/ppasa(60-4)670","DOIUrl":"https://doi.org/10.53560/ppasa(60-4)670","url":null,"abstract":"A term symbol is used to describe atomic microstate states, which give the multiplicity and total angular momentum of the atomic state. Russel Sauder coupling scheme is used to generate terms of equivalent and non-equivalent electronic configurations. For equivalent electrons, the terms are calculated using Pauli’s principle, and the number of terms is limited and is calculated by the combination rule. The total possible electrons and total available electrons are used in the combination formula. In case of non-equivalent electrons, the number of terms are found by the permutation rule. The number of terms for equivalent electrons is less than the terms for non-equivalent electrons. The number of possible microstates for p2 and d5 configurations are 15 and 252 respectively. While the number of final microstates for 1p2p and 3d4d configurations are 36 and 100. In the proposed study, a Python programme was developed that generates the microstate according to filled and half-filled subshell electronic configurations for equivalent, non-equivalent, and combinations of both. Examples of microstates for non-equivalent electrons of configuration 1s2s, sp, sd, ss, 2p3p, pd, pf, 3d4d, df, 4f5f and for equivalent electrons of configuration su, pv, dx, and f y are presented.","PeriodicalId":509771,"journal":{"name":"Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139184268","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}
Machine Learning (ML) is an advanced technology that empowers systems to acquire knowledge autonomously, eliminating the need for explicit programming. The fundamental objective of the machine learning paradigm is to equip computers with the ability to learn independently without human intervention. In the literature, categorization in data mining has received a lot of traction, with applications ranging from health to astronomy and finance to textual classification. The three learning methodologies in machine learning are supervised, unsupervised, and semi-supervised. Humans must give the appropriate input and output and offer feedback on the prediction accuracy throughout the training phase for supervised algorithms. Unsupervised learning methods differ from supervised learning methods because they do not require any training. However, supervised learning methods are more accessible to implement than unsupervised learning methods. This study looks at supervised learning algorithms commonly employed in data classification. The strategies are evaluated based on their objective, methodology, benefits, and drawbacks. It is anticipated that readers will be able to understand the supervised machine learning techniques for data classification.
{"title":"A Supervised Machine Learning Algorithms: Applications, Challenges, and Recommendations","authors":"Aqib Ali, Wali Khan Mashwani","doi":"10.53560/ppasa(60-4)831","DOIUrl":"https://doi.org/10.53560/ppasa(60-4)831","url":null,"abstract":"Machine Learning (ML) is an advanced technology that empowers systems to acquire knowledge autonomously, eliminating the need for explicit programming. The fundamental objective of the machine learning paradigm is to equip computers with the ability to learn independently without human intervention. In the literature, categorization in data mining has received a lot of traction, with applications ranging from health to astronomy and finance to textual classification. The three learning methodologies in machine learning are supervised, unsupervised, and semi-supervised. Humans must give the appropriate input and output and offer feedback on the prediction accuracy throughout the training phase for supervised algorithms. Unsupervised learning methods differ from supervised learning methods because they do not require any training. However, supervised learning methods are more accessible to implement than unsupervised learning methods. This study looks at supervised learning algorithms commonly employed in data classification. The strategies are evaluated based on their objective, methodology, benefits, and drawbacks. It is anticipated that readers will be able to understand the supervised machine learning techniques for data classification.","PeriodicalId":509771,"journal":{"name":"Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139185065","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}
Saeed Ahmed, Muhammad Akbar, Muhammad Imran Shahzad, Muhammad Ahmad Raza, Sania Shaheen
In this paper, we have studied the magnetic shielding effect of a spherical shell analytically in fractional dimensional space (FDS). The Laplacian equation in fractional space predicts the complex phenomena of physics. This is a boundary value problem that has been solved by the separation variable method mathematically by taking low frequency w = 0. Electric potential is obtained in fractional dimensional space for the three regions, namely outside the spherical shell, between the shell and hollow sphere and inside the sphere. Also, the induced dipole moment has been derived. We obtain a general solution that reduces to the classical results by setting fractional parameter α = 3 which takes its value (2 < α ≤ 3).
{"title":"Mathematical Analysis on Spherical Shell of Permeable Material in NID Space","authors":"Saeed Ahmed, Muhammad Akbar, Muhammad Imran Shahzad, Muhammad Ahmad Raza, Sania Shaheen","doi":"10.53560/ppasa(60-3)663","DOIUrl":"https://doi.org/10.53560/ppasa(60-3)663","url":null,"abstract":"In this paper, we have studied the magnetic shielding effect of a spherical shell analytically in fractional dimensional space (FDS). The Laplacian equation in fractional space predicts the complex phenomena of physics. This is a boundary value problem that has been solved by the separation variable method mathematically by taking low frequency w = 0. Electric potential is obtained in fractional dimensional space for the three regions, namely outside the spherical shell, between the shell and hollow sphere and inside the sphere. Also, the induced dipole moment has been derived. We obtain a general solution that reduces to the classical results by setting fractional parameter α = 3 which takes its value (2 < α ≤ 3).","PeriodicalId":509771,"journal":{"name":"Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139338236","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}
Agriculture is crucial to economic growth and development. Crop yield forecasting is critical for food production which includes vegetables, fruits, flowers, and cattle. Artificial Intelligence (AI) is rising in agriculture, providing farmers with real-time or long-term insights about their fields. It allows us to identify the areas that require irrigation, fertilization, or pesticide treatment. Statistical models struggle to track complex relationships in crop yields due to numerous factors. Machine Learning (ML) and Deep Learning (DL) algorithms can solve this problem by training themselves in these relationships, enabling accurate predictions in agricultural yield prediction methods. Predicting product performance in agriculture is challenging due to various factors, but profit forecasting improves decision-making, production, economics, and food safety. The present study focuses on the use of ML and DL algorithms to suggest a novel decision support system for crop yield prediction with the objectives to develop a robust, accurate model, investigate algorithm effectiveness, and create a user-friendly system for informed crop production decisions. According to the results, the developed system is capable of making precise predictions, which can support farmers in making better decisions about how to manage their crops. The simulation results demonstrate that the intelligent decision support system proposed for crop yield prediction using ML and DL algorithms is capable of achieving high accuracy and precision. The system can be used to help farmers make better decisions about crop planting and management, which can lead to increased crop yields and profits. The results of our experiment show that our model is better than the others and it achieves an accuracy of 99.82 %. Additionally, we utilized ML to condense the input space while preserving high accuracy.
农业对经济增长和发展至关重要。作物产量预测对于包括蔬菜、水果、花卉和牲畜在内的粮食生产至关重要。人工智能（AI）正在农业领域兴起，为农民提供有关其田地的实时或长期见解。它可以让我们识别需要灌溉、施肥或杀虫剂处理的区域。由于因素众多，统计模型很难跟踪作物产量的复杂关系。机器学习（ML）和深度学习（DL）算法可以通过在这些关系中进行自我训练来解决这一问题，从而在农业产量预测方法中实现准确预测。由于各种因素，预测农业产品性能具有挑战性，但利润预测可以改善决策、生产、经济和食品安全。本研究的重点是使用 ML 和 DL 算法，提出一种新型的作物产量预测决策支持系统，目的是开发一个稳健、准确的模型，研究算法的有效性，并创建一个用户友好型系统，以做出明智的作物生产决策。结果表明，所开发的系统能够进行精确预测，从而帮助农民就如何管理作物做出更好的决策。仿真结果表明，利用 ML 和 DL 算法进行作物产量预测的智能决策支持系统能够实现高精度和高准确性。该系统可用于帮助农民在作物种植和管理方面做出更好的决策，从而提高作物产量和利润。实验结果表明，我们的模型优于其他模型，准确率达到 99.82%。此外，我们还利用 ML 压缩了输入空间，同时保持了较高的准确率。
{"title":"An Intelligent Decision Support System for Crop Yield Prediction Using Machine Learning and Deep Learning Algorithms","authors":"Maryum Bibi, S. Rehman, Khalid Mahmood","doi":"10.53560/ppasa(60-3)825","DOIUrl":"https://doi.org/10.53560/ppasa(60-3)825","url":null,"abstract":"Agriculture is crucial to economic growth and development. Crop yield forecasting is critical for food production which includes vegetables, fruits, flowers, and cattle. Artificial Intelligence (AI) is rising in agriculture, providing farmers with real-time or long-term insights about their fields. It allows us to identify the areas that require irrigation, fertilization, or pesticide treatment. Statistical models struggle to track complex relationships in crop yields due to numerous factors. Machine Learning (ML) and Deep Learning (DL) algorithms can solve this problem by training themselves in these relationships, enabling accurate predictions in agricultural yield prediction methods. Predicting product performance in agriculture is challenging due to various factors, but profit forecasting improves decision-making, production, economics, and food safety. The present study focuses on the use of ML and DL algorithms to suggest a novel decision support system for crop yield prediction with the objectives to develop a robust, accurate model, investigate algorithm effectiveness, and create a user-friendly system for informed crop production decisions. According to the results, the developed system is capable of making precise predictions, which can support farmers in making better decisions about how to manage their crops. The simulation results demonstrate that the intelligent decision support system proposed for crop yield prediction using ML and DL algorithms is capable of achieving high accuracy and precision. The system can be used to help farmers make better decisions about crop planting and management, which can lead to increased crop yields and profits. The results of our experiment show that our model is better than the others and it achieves an accuracy of 99.82 %. Additionally, we utilized ML to condense the input space while preserving high accuracy.","PeriodicalId":509771,"journal":{"name":"Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139339060","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}
Performance evaluation of textile materials is necessary to determine the use of an end-product. Woven and knitted materials are the most preferred manufacturing techniques due to their certain characteristics suitable for apparel and upholstery. The present study aims at determining the bending length, rigidity and modulus of fabrics through standardized test procedures to measure the draping behaviour of an end product. The results identified the phenomenon that specimens manufactured with woven fabrics were better in drapability compared with knitted fabrics. Moreover, it was identified that type of fiber also plays an important role in determining the stiffness of fabrics such as silk fiber showed excellent results. A comprehensive comparison was made between various types of fabrics. The study can be helpful for textile producers to make amendments to their construction parameters to present acceptable stiffness and draping qualities of fabrics to the end consumers.
{"title":"Evaluation of Bending Length, Rigidity and Modulus of Woven and Knitted Fabrics","authors":"Mehreen Ijaz, Namood-e-Sahar, Zohra Tariq","doi":"10.53560/ppasa(60-3)822","DOIUrl":"https://doi.org/10.53560/ppasa(60-3)822","url":null,"abstract":"Performance evaluation of textile materials is necessary to determine the use of an end-product. Woven and knitted materials are the most preferred manufacturing techniques due to their certain characteristics suitable for apparel and upholstery. The present study aims at determining the bending length, rigidity and modulus of fabrics through standardized test procedures to measure the draping behaviour of an end product. The results identified the phenomenon that specimens manufactured with woven fabrics were better in drapability compared with knitted fabrics. Moreover, it was identified that type of fiber also plays an important role in determining the stiffness of fabrics such as silk fiber showed excellent results. A comprehensive comparison was made between various types of fabrics. The study can be helpful for textile producers to make amendments to their construction parameters to present acceptable stiffness and draping qualities of fabrics to the end consumers.","PeriodicalId":509771,"journal":{"name":"Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139339544","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}
Asad Gulzar, Bisma Sher, Shabbir Hussain, Abdul Ahad Rasheed, Muhammad Salman, Shazma Massey, Abdur Rauf
The present study was conducted to investigate the nutritional study of cow's milk of various breeds from Bhatta Chowk Lahore. Different cow breeds were found to possess variable amounts of nutritional contents, i.e., highest moisture and ash in the Cholistani cow, highest fat in the Sahiwal cow, highest calcium and specific gravity in the Holstein cow and highest contents of protein, solid-not-fat and total solid in Red Sindhi. Red Sindhi cow’s milk was found to be more nutritious in terms of its richness in proteins, solid-not-fat and total solids whereas Holstein cow was rich in calcium. Calcium was found to be in a range of 550 to 630 ppm with the decrease of concentration in the following order: Sahiwal>Holstein Frisian= Red Sindhi>Cholistani. The Cholistani cow milk showed the presence of 3.126 % protein, 3.5 % fat, 88.2 % moisture, 8.3 % solids-not fat, 11.8 % total solids, 0.791 % ash, 30.5o lactometer reading and 1.0305 kg/m3 specific gravity. Sahiwal cow milk showed 3.318 % of protein, 4.1 % fat, 87.5 % moisture, 30.1o lactometer reading, 1.03 kg/m3 specific gravity, 8.4 % solids-not-fat, 12.5 % total solids and 0.79 % ash. Holstein Frisian’s milk demonstrated the presence of 3.33 % protein, 3.8 % fat, 87.35% moisture, 31o lactometer reading, 1.03 kg/m3 specific gravity, 8.85 % solids-not-fat, 12.65 % total solids and 0.77 % ash. Red Sindhi’s milk revealed the presence of 3.38 % protein, 3.95 % fat, 85.65 % moisture, 28o Lactometer reading, 1.028 kg/m3 specific gravity, 10.4% solids-not-fat, 14.35% total solids and 0.705% ash.
{"title":"Nutritional Study of Various Cow Breeds from Bhatta Chowk Lahore (Punjab), Pakistan","authors":"Asad Gulzar, Bisma Sher, Shabbir Hussain, Abdul Ahad Rasheed, Muhammad Salman, Shazma Massey, Abdur Rauf","doi":"10.53560/ppasa(60-3)652","DOIUrl":"https://doi.org/10.53560/ppasa(60-3)652","url":null,"abstract":"The present study was conducted to investigate the nutritional study of cow's milk of various breeds from Bhatta Chowk Lahore. Different cow breeds were found to possess variable amounts of nutritional contents, i.e., highest moisture and ash in the Cholistani cow, highest fat in the Sahiwal cow, highest calcium and specific gravity in the Holstein cow and highest contents of protein, solid-not-fat and total solid in Red Sindhi. Red Sindhi cow’s milk was found to be more nutritious in terms of its richness in proteins, solid-not-fat and total solids whereas Holstein cow was rich in calcium. Calcium was found to be in a range of 550 to 630 ppm with the decrease of concentration in the following order: Sahiwal>Holstein Frisian= Red Sindhi>Cholistani. The Cholistani cow milk showed the presence of 3.126 % protein, 3.5 % fat, 88.2 % moisture, 8.3 % solids-not fat, 11.8 % total solids, 0.791 % ash, 30.5o lactometer reading and 1.0305 kg/m3 specific gravity. Sahiwal cow milk showed 3.318 % of protein, 4.1 % fat, 87.5 % moisture, 30.1o lactometer reading, 1.03 kg/m3 specific gravity, 8.4 % solids-not-fat, 12.5 % total solids and 0.79 % ash. Holstein Frisian’s milk demonstrated the presence of 3.33 % protein, 3.8 % fat, 87.35% moisture, 31o lactometer reading, 1.03 kg/m3 specific gravity, 8.85 % solids-not-fat, 12.65 % total solids and 0.77 % ash. Red Sindhi’s milk revealed the presence of 3.38 % protein, 3.95 % fat, 85.65 % moisture, 28o Lactometer reading, 1.028 kg/m3 specific gravity, 10.4% solids-not-fat, 14.35% total solids and 0.705% ash.","PeriodicalId":509771,"journal":{"name":"Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139340497","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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