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1. Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Author

Iftikhar Ahmad, Adil Sana, Manabu Kano, Izzat Iqbal Cheema, Brenno C. Menezes, Junaid Shahzad, Zahid Ullah, Muzammil Khan, and Asad Habib

Subjects
bio-energy, artificial intelligence, industry 4.0, biodiesel, biogas, renewable energy, Technology
Abstract

Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models, and the frequently used input and output variables for each stage. The findings of this article would be beneficial for academia and industry-related professionals involved in model development in different stages of biofuel’s life cycle.

Published
2021
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3. Role of Computer Science (Artificial Intelligence) In Poultry Management

Author

Hrishitva Patel and Adil Sana

Abstract

The precise control of animals is the focus of a new strategy to enhance animal welfare in the poultry industry. We notice that good welfare circumstances significantly impact the health of the birds and the quality of the poultry products, which affects economic effectiveness in the production of poultry. An innovation that can aid farmers in more successfully controlling the environment and birds' health is using technology solutions in various animal production systems. Additionally, as public concern over chicken breeding and welfare increases, resolutions are being developed to improve control and monitoring in this area of animal agriculture. PLF (precision livestock farming) uses various techniques to gather real-time data about birds. By spotting diseases and stressful conditions in the early stages and enabling action to be taken swiftly enough to avoid the negative impacts, PLF can assist prevent reducing animal wellbeing. To enhance precision livestock farming, this review links the potential uses of the most recent technology to monitor laying hens and broilers

Published
2022

4. Maternal Care Disparities: Impact Of Pregnancy Profiles, Socioeconomic Factors, and Age on Maternal Outcomes

Author

Gul, Hemasa, iftikhar, Hina, Shah, Amara, Adil, Sana, Anum, Aneesa, Gul, Hemasa, iftikhar, Hina, Shah, Amara, Adil, Sana, and Anum, Aneesa

Abstract

OBJECTIVES This study aimed to evaluate the impact of pregnancy profiles, socioeconomic factors, and age on maternal at teaching hospitals in Mardan, Khyber Pakhtunkhwa. METHODOLOGY This descriptive-exploratory/qualitative study was conducted at the genealogical outside-patient department of the Mardan Medical Complex, with the approval of the ethics committee. A total of 1187 patients attended the complex between January 2021 and January 2022. Informed consent was obtained, and a comprehensive history and physical examination were conducted using a pre-designed proforma. Patients meeting specific criteria, such as gestational age >36 weeks, singleton pregnancies, and absence of maternal co-morbidities, were included. Exclusion criteria encompassed conditions such as antepartum haemorrhage and fetal anomalies. Data analysis was performed using SPSS-24 version, ensuring the confidentiality and anonymity of participants. RESULTSOut % of 1187 patients, 48% had previous pregnancies (multiparous), while 52% were experiencing their first pregnancy (primiparous). Gestational age distribution showed that 45% of cases fell within the 36-38 week range, and 55% were between 39-40 weeks. Regarding booking status, 29% of patients were booked, while 71% were unbooked. Finally, the socioeconomic status analysis indicates that 61% of cases were above the poverty line, while 39% were below. These findings highlight important factors such as previous pregnancies, gestational age, booking status, and socioeconomic status that can influence maternal care and outcomes. Among the different age groups, the <20 age group had the highest percentage of spontaneous deliveries (31%), while the >30 age group had the highest percentage of cesarean sections (25%). Induced labour rates were relatively consistent across age groups. Not being in labour was more prevalent in the 25-30 and >30 age groups. CONCLUSION The findings highlight that important factors such as previous pregnancies

Published
2023

6. Presentation And Diagnosis: Pregnancy Induce Diabetes And Hypertension In Medical Doctors At Hospital And Clinics

Author

Adil, Sana, Hidayat, Isra, Ali, Ammad, Ali , Saad, Ayaz, Hina, Ayub, Muhammad, Ayub, Faiza, Adil, Sana, Hidayat, Isra, Ali, Ammad, Ali , Saad, Ayaz, Hina, Ayub, Muhammad, and Ayub, Faiza

Abstract

OBJECTIVES The aim of this study was to assess pregnancy induce diabetes and hypertension in medical doctors. METHODOLOGY This case-control study was conducted after approval by the ethics committee. A total of 72 physicians who were pregnant and diagnosed with pregnancies causing hypertension or diabetes participated in the study. Daily details of hourly blood glucose levels were recorded at the beginning and end of the day. Use SPSS version 24.0 to analyze the results. RESULTS Out of the 33 patients, 23 remained hypertensive at the end of the day, while 10 had normal hypertension at the beginning of the day. Hypotension appeared from the 39th day of which 24 of her ended high, 15 were normal and 10 were diet controlled. The maximum number of pregnancies was 40 in high-care controls and 32 in low-care controls. Out of 30(16) had high FBS at the beginning of the day and 17 of the 42 who had the highest at the end of the day had low FBS. The highest score of 17(42) was presented at the beginning of the day with low FBS and 17 at end of the day with a high ratio and high count of 42. CONCLUSION Health care is important for all patients, whether they are health workers or not. Daily Record helps patients and providers understand patient disease management and take appropriate action.

Published
2022

7. Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Author

Iftikhar Ahmad, Asad Habib, Muzammil Khan, Izzat Iqbal Cheema, Brenno C. Menezes, Zahid Ullah, Manabu Kano, Adil Sana, and Junaid Shahzad

Subjects
Technology, Control and Optimization, Energy Engineering and Power Technology, biodiesel, Raw material, Machine learning, computer.software_genre, Biogas, Bioenergy, Suitability analysis, biogas, Production (economics), Electrical and Electronic Engineering, industry 4.0, energy_fuel_technology, Engineering (miscellaneous), Consumption (economics), Biodiesel, Waste management, Renewable Energy, Sustainability and the Environment, business.industry, other, bio-energy, artificial intelligence, renewable energy, Renewable energy, Biofuel, Fuel efficiency, Environmental science, Artificial intelligence, business, computer, Energy (miscellaneous)
Abstract

Machine learning (ML) is penetrating in all walks of life and is one of the major driving forces behind the fourth industrial revolution, typically known as Industry 4.0. The purpose of the present study is to review the state-of-the-art ML applications in the biofuels' life cycle stages, i.e., soil, feedstock, production, consumption, and emissions. A keyword search is performed to retrieve relevant articles from the databases of the Web of Science and Google Scholar. ML applications in the soil stage were mostly based on the use of satellite images of land for estimation of biofuels yield or suitability analysis of agricultural land. In the second stage of the life cycle, assessment of rheological properties of the feedstocks and their effect on the quality of biofuels were dominant studies reported in the literature. The production stage included estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage included analysis of engine performance and estimation of emissions temperature and composition, such as NOx CO, and CO2. This study identified the following trends: dominant ML method, the stage of life cycle getting more usage of ML, the type of data used for the development of the ML-based models, and the stage-wise frequently used input and output variables. The findings of this article are beneficial for academia and industry-related people involved in model development in different stages of biofuel’s life cycle.

Published
2020

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