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Article: A Fuzzy Logic Model for the Analysis of Ultrasonic Vibration Assisted Turning and Conventional Turning of Ti-Based Alloy.

Muhammad, Riaz

2021 Volume 14, Issue 21

Abstract: Titanium and its alloys are largely used in various applications due its prominent mechanical properties. However, the machining of titanium alloys is associated with assured challenges, including high-strength, low thermal conductivity, and long chips ... ...

Abstract	Titanium and its alloys are largely used in various applications due its prominent mechanical properties. However, the machining of titanium alloys is associated with assured challenges, including high-strength, low thermal conductivity, and long chips produced in conventional machining processes, which result in its poor machinability. Advanced and new machining techniques have been used to improve the machinability of these alloys. Ultrasonic vibration assisted turning (UVAT) is one of these progressive machining techniques, where vibrations are imposed on the cutting insert, and this process has shown considerable improvement in terms of the machinability of hard-to-cut alloys. Therefore, selecting the right cutting parameters for conventional and assisted machining processes is critical for obtaining the anticipated dimensional accuracy and improved surface roughness of Ti-alloys. Hence, fuzzy-based algorithms were developed for the ultrasonic vibration assisted turning (UVAT) and conventional turning (CT) of the Ti-6Al7Zr3Nb4Mo0.9Nd alloy to predict the maximum process zone temperature, cutting forces, surface roughness, shear angle, and chip compression ratio for the selected range of input parameters (speed and depth-of-cut). The fuzzy-measured values were found to be in good agreement with the experimental values, indicating that the created models can be utilized to accurately predict the studied machining output parameters in CT and UVAT processes. The studied alloy resulted in discontinued chips in both the CT and UVAT processes. The achieved results also demonstrated a significant decline in the cutting forces and improvements in the surface quality in the UVAT process. Furthermore, the chip discontinuity is enhanced by the UVAT process due to the higher process zone temperature and the micro-impact imposed by the cutting tool on the workpiece.
Language	English
Publishing date	2021-11-01
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2487261-1
ISSN	1996-1944
ISSN	1996-1944
DOI	10.3390/ma14216572
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Machine Learning Techniques for Estimating Soil Moisture from Smartphone Captured Images

Muhammad Riaz Hasib Hossain / Muhammad Ashad Kabir

Agriculture, Vol 13, Iss 574, p

2023 Volume 574

Abstract: Precise Soil Moisture (SM) assessment is essential in agriculture. By understanding the level of SM, we can improve yield irrigation scheduling which significantly impacts food production and other needs of the global population. The advancements in ... ...

Abstract	Precise Soil Moisture (SM) assessment is essential in agriculture. By understanding the level of SM, we can improve yield irrigation scheduling which significantly impacts food production and other needs of the global population. The advancements in smartphone technologies and computer vision have demonstrated a non-destructive nature of soil properties, including SM. The study aims to analyze the existing Machine Learning (ML) techniques for estimating SM from soil images and understand the moisture accuracy using different smartphones and various sunlight conditions. Therefore, 629 images of 38 soil samples were taken from seven areas in Sydney, Australia, and split into four datasets based on the image-capturing devices used (iPhone 6s and iPhone 11 Pro) and the lighting circumstances (direct and indirect sunlight). A comparison between Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Convolutional Neural Network (CNN) was presented. MLR was performed with higher accuracy using holdout cross-validation, where the images were captured in indirect sunlight with the Mean Absolute Error (MAE) value of 0.35, Root Mean Square Error (RMSE) value of 0.15, and R 2 value of 0.60. Nevertheless, SVR was better with MAE, RMSE, and R 2 values of 0.05, 0.06, and 0.96 for 10-fold cross-validation and 0.22, 0.06, and 0.95 for leave-one-out cross-validation when images were captured in indirect sunlight. It demonstrates a smartphone camera’s potential for predicting SM by utilizing ML. In the future, software developers can develop mobile applications based on the research findings for accurate, easy, and rapid SM estimation.
Keywords	soil moisture ; image processing ; smartphone ; machine learning ; deep learning ; prediction ; Agriculture (General) ; S1-972
Subject code	333
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: How does equity restriction affect innovation quality? Evidence from listed manufacturing companies in China

Sang Chang / Jie Wu / Muhammad Riaz / Zhizhong Hu

PLoS ONE, Vol 18, Iss

2023 Volume 12

Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Machine Learning Techniques for Estimating Soil Moisture from Smartphone Captured Images

Hossain, Muhammad Riaz Hasib / Kabir, Muhammad Ashad

Agriculture. 2023 Feb. 27, v. 13, no. 3

2023

Abstract	Precise Soil Moisture (SM) assessment is essential in agriculture. By understanding the level of SM, we can improve yield irrigation scheduling which significantly impacts food production and other needs of the global population. The advancements in smartphone technologies and computer vision have demonstrated a non-destructive nature of soil properties, including SM. The study aims to analyze the existing Machine Learning (ML) techniques for estimating SM from soil images and understand the moisture accuracy using different smartphones and various sunlight conditions. Therefore, 629 images of 38 soil samples were taken from seven areas in Sydney, Australia, and split into four datasets based on the image-capturing devices used (iPhone 6s and iPhone 11 Pro) and the lighting circumstances (direct and indirect sunlight). A comparison between Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Convolutional Neural Network (CNN) was presented. MLR was performed with higher accuracy using holdout cross-validation, where the images were captured in indirect sunlight with the Mean Absolute Error (MAE) value of 0.35, Root Mean Square Error (RMSE) value of 0.15, and R² value of 0.60. Nevertheless, SVR was better with MAE, RMSE, and R² values of 0.05, 0.06, and 0.96 for 10-fold cross-validation and 0.22, 0.06, and 0.95 for leave-one-out cross-validation when images were captured in indirect sunlight. It demonstrates a smartphone camera’s potential for predicting SM by utilizing ML. In the future, software developers can develop mobile applications based on the research findings for accurate, easy, and rapid SM estimation.
Keywords	agriculture ; cameras ; computer software ; computer vision ; data collection ; food production ; irrigation ; mobile telephones ; neural networks ; regression analysis ; soil water ; solar radiation ; Australia
Language	English
Dates of publication	2023-0227
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2651678-0
ISSN	2077-0472
ISSN	2077-0472
DOI	10.3390/agriculture13030574
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: HOSPITALIZATION DUE TO COMIOGENIC DISEASES IN TERTIARY CARE HOSPITAL PESHAWAR PAKISTAN

Majid Khan / Rahmat Ullah / Muhammad Riaz / Najm ur Rahman

Rehman Journal of Health Sciences, Vol 4, Iss 2, Pp 77-

2023 Volume 81

Abstract: Introduction: This study conducted in order to evaluate the admissions due to comiogenesis that affect the patient quality of life. The identification of iatrogenic admissions and their possible solutions to achieve the optimal therapeutic effects of the ...

Abstract	Introduction: This study conducted in order to evaluate the admissions due to comiogenesis that affect the patient quality of life. The identification of iatrogenic admissions and their possible solutions to achieve the optimal therapeutic effects of the patients. Material & Methods: The total of 32 days study carried out in Endocrinology department, the total of 202 patient cases were evaluated by standard core indicator recommended by “World Health Organization”. Results: The results of current studies showed that 121 (59.9%) male and 81 (40.1%) were female hospitalized in which 35 (17.3%) patients were due to iatrogenic diseases. The most common comiogenic diseases were due irrational use of insulin; 13 patients were due to hypoglycemia and 11 were due lipodystrophy, 5 patients hospitalized with Cushing syndrome due to steroids, 3 hypothyroidism patients were due to Carbimazole and 3 neutropenic patients were due to Piperacillin + Tazobactam. Conclusion: The improper use of medications leads to iatrogenic/comiogenic illnesses that may be prevented if qualified and competent health care providers are consulted. The extensive intervention and standard guidelines implementation are required in order to improve the health of the patients. The prescription standard indicator was not followed up to the mark to improve overall health status and rational prescribing.
Keywords	prescription errors ; iatrogenic diseases ; adverse effects ; Medicine ; R
Subject code	610
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Rehman Medical Institute
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Heat and mass transfer analysis for magnetized flow of $${\mathrm{ZnO}-SAE50}$$ ZnO - S A E 50 nanolubricant with variable properties

Muhammad Riaz / Nargis Khan / M. S. Hashmi / Jihad Younis

Scientific Reports, Vol 13, Iss 1, Pp 1-

an application of Cattaneo–Christov model

2023 Volume 22

Abstract: Abstract The current study scrutinizes heat and mass transfer features of magnetized flow of $${\mathrm{ZnO}-SAE50}$$ ZnO - S A E 50 nanolubricant over Riga plate in a Darcy Forchheimer medium. The effects of variable viscosity, thermal radiation, ... ...

Abstract	Abstract The current study scrutinizes heat and mass transfer features of magnetized flow of $${\mathrm{ZnO}-SAE50}$$ ZnO - S A E 50 nanolubricant over Riga plate in a Darcy Forchheimer medium. The effects of variable viscosity, thermal radiation, variable thermal conductivity, viscous dissipation and uniform heat source/sink are examined in this study. The diffusion model presented by Cattaneo–Christov is incorporated in this study to enclose heat and mass transport phenomenon. Additionally, the mass transfer rate is inspected subjected to the effects of variable solutal diffusivity and higher order chemical reaction. Heat and mass transfer phenomena have significant applications in the disciplines of science and technology that can be seen everywhere in nature. This simultaneous transportation phenomenon indicates a variety of applications in manufacturing processes, aerodynamics, cooling systems, environmental sciences, oceanography, food industries, biological disciplines, and energy transport systems etc. The modeled system of PDEs is metamorphosed to nonlinear ODEs with the introduction of appropriate transformations. An eminent bvp4c method in MATLAB has been incorporated to execute the resulting system of ODEs numerically. The outcomes of velocity, temperature and concentration profiles corresponding to various emerging parameters have been exposed graphically. The motion of $${\mathrm{ZnO}-SAE50}$$ ZnO - S A E 50 nanolubricant tends to enhance significantly with larger modified Hartmann number, whereas converse behavior is reported by increasing porosity parameter and variable viscosity parameter. The greater heat transfer rate is observed for variable thermal conductivity parameter. The rates of heat and mass transfer slow down for thermal and solutal time relaxation parameters respectively. The concentration profile gets enriched by growing the order of the chemical reaction and variable mass diffusivity parameter. It is concluded that by increasing solid volume fraction up to $$1.5\%$$ 1.5 % , the ...
Keywords	Medicine ; R ; Science ; Q
Subject code	532
Language	English
Publishing date	2023-05-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Editorial

Muhammad Zia-Ul-Haq / Romina Alina Marc / Muhammad Riaz

Frontiers in Pharmacology, Vol

Natural products, medicinal foods and complementary and alternative medicine as cancer-preventive agents

2023 Volume 14

Keywords	natural products ; medicinal foods ; complementary ; alternative medicine ; cancer-preventive agents ; Therapeutics. Pharmacology ; RM1-950
Language	English
Publishing date	2023-07-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Sparse Representations Optimization with Coupled Bayesian Dictionary and Dictionary Classifier for Efficient Classification

Muhammad Riaz-ud-din / Salman Abdul Ghafoor / Faisal Shafait

Applied Sciences, Vol 14, Iss 1, p

2023 Volume 306

Abstract: Among the numerous techniques followed to learn a linear classifier through the discriminative dictionary and sparse representations learning of signals, the techniques to learn a nonparametric Bayesian classifier jointly and discriminately with the ... ...

Abstract	Among the numerous techniques followed to learn a linear classifier through the discriminative dictionary and sparse representations learning of signals, the techniques to learn a nonparametric Bayesian classifier jointly and discriminately with the dictionary and the corresponding sparse representations have drawn considerable attention from researchers. These techniques jointly learn two sets of sparse representations, one for the training samples over the dictionary and the other for the corresponding labels over the dictionary classifier. At the prediction stage, the representations of the test samples computed over the learned dictionary do not truly represent the corresponding labels, exposing weakness in the joint learning claim of these techniques. We mitigate this problem and strengthen the joint by learning a set of weights over the dictionary to represent the training data and further optimizing the same weights over the dictionary classifier to represent the labels of the corresponding classes of the training data. Now, at the prediction stage, the representation weights of the test samples computed over the learned dictionary also represent the labels of the corresponding classes of the test samples, resulting in the accurate reconstruction of the labels of the classes by the learned dictionary classifier. Overall, a reduction in the size of the Bayesian model’s parameters also improves training time. We analytically and nonparametrically derived the posterior conditional probabilities of the model from the overall joint probability of the model using Bayes’ theorem. We used the Gibbs sampler to solve the joint probability of the model using the derived conditional probabilities, which also supports our claim of efficient optimization of the coupled/joint dictionaries and the sparse representation parameters. We demonstrated the effectiveness of our approach through experiments on the standard datasets, i.e., the Extended YaleB and AR face databases for face recognition, Caltech-101 and Fifteen ...
Keywords	linear classifier ; dictionary learning ; nonparametric Bayesian ; discriminative ; sparse representation ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Phytotoxicity response of sugar beet (Beta vulgaris L.) seedlings to herbicide fomesafen in soil

Xingfan Li / Muhammad Riaz / Baiquan Song / Huajun Liu

Ecotoxicology and Environmental Safety, Vol 239, Iss , Pp 113628- (2022)

2022

Abstract: Fomesafen is the most widely used herbicide in the soybean field. However, there are urgent practical challenges with the long-term persistence of fomesafen in soil and its effects on the subsequent crops in agricultural production. Therefore, pot ... ...

Abstract	Fomesafen is the most widely used herbicide in the soybean field. However, there are urgent practical challenges with the long-term persistence of fomesafen in soil and its effects on the subsequent crops in agricultural production. Therefore, pot experiments were conducted to study the effects of fomesafen residues (0–0.05 mg kg–1) on growth, photosynthetic characteristics, and the antioxidant defense system of sugar beet seedlings. The results showed that with the increase of fomesafen residues, the phytotoxicity index increased, while the plant height, leaf area, root length, root volume, and dry weight of sugar beet decreased. Photosynthetic pigment content, net photosynthetic rate (Pn), maximum photosynthetic efficiency (Fv/Fm), and actual photosynthetic efficiency (Y(II)) declined with a dose-dependent manner of fomesafen, but the intercellular CO2 concentration (Ci) and non-photochemical quenching coefficient (NPQ) increased under fomesafen. On the other hand, the residues of fomesafen increased the content of malondialdehyde (MDA) and membrane permeability by aggravating oxidative stress and triggering the activities of superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and polyphenol oxidase (PPO). In addition, sugar beet seedlings were significantly sensitive to fomesafen as the concentration of fomesafen in the soil was up to 0.025 mg kg–1. In conclusion, the present study showed that fomesafen residues in the soil could affect the morphophysiology and photosynthetic performance of sugar beet. This study is beneficial for understanding the effects of the herbicide fomesafen residues on non-target crops.
Keywords	Herbicide residue ; Toxicity ; Photosynthetic characteristics ; Chlorophyll fluorescence ; Oxidative stress ; Environmental pollution ; TD172-193.5 ; Environmental sciences ; GE1-350
Subject code	580
Language	English
Publishing date	2022-07-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Two-Stage Deep-Learning Model for Link Prediction Based on Network Structure and Node Attributes

Peng Liu / Liang Gui / Huirong Wang / Muhammad Riaz

Sustainability, Vol 14, Iss 16299, p

2022 Volume 16299

Abstract: Link prediction, which is used to identify the potential relationship between nodes, is an important issue in network science. In existing studies, the traditional methods based on the structural similarity of nodes make it challenging to complete the ... ...

Abstract	Link prediction, which is used to identify the potential relationship between nodes, is an important issue in network science. In existing studies, the traditional methods based on the structural similarity of nodes make it challenging to complete the task of link prediction in large-scale or sparse networks. Although emerging methods based on deep learning can solve this problem, most of the work mainly completes the link prediction through the similarity of the representation vector of network structure information. Many empirical studies show that link formation is affected by node attributes, and similarity is not the only criterion for the formation of links in reality. Accordingly, this paper proposed a two-stage deep-learning model for link prediction (i.e, TDLP), where the node representation vector of the network structure and attributes was obtained in the first stage, while link prediction was realized through supervised learning in the second stage. The empirical results on real networks showed that our model significantly outperforms the traditional methods (e.g., CN and RA), as well as newly proposed deep-learning methods (e.g., GCN and VGAE). This study not only proposed a deep-learning framework for link prediction from the perspective of structure and attribute fusion and link distribution capture, but also lays a methodological foundation for practical applications based on link prediction.
Keywords	link prediction ; deep learning ; network structure ; node attribute ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
Subject code	006
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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