FruitPAL: An IoT-Enabled Framework for Automatic Monitoring of Fruit Consumption in Smart Healthcare

This research proposes FruitPAL and FruitPAL 2.0. They are full automatic devices that can detect fruit consumption to reduce the risk of disease. Allergies to fruits can seriously impair the immune system. A novel device (FruitPAL) detecting fruit that can cause allergies is proposed in this thesis. The device can detect fifteen types of fruit and alert the caregiver when an allergic reaction may have happened. The YOLOv8 model is employed to enhance accuracy and response time in detecting dangers. The notification will be transmitted to the mobile device through the cloud, as it is a commonly utilized medium. The proposed device can detect the fruit with an overall precision of 86%. FruitPAL 2.0 is envisioned as a device that encourages people to consume fruit. Fruits contain a variety of essential nutrients that contribute to the general health of the human body. FruitPAL 2.0 is capable of analyzing the consumed fruit and then determining its nutritional value. FruitPAL 2.0 has been trained on YOLOv5 V6.0. FruitPAL 2.0 has an overall precision of 90% in detecting the fruit. The purpose of this study is to encourage fruit consumption unless it causes illness. Even though fruit plays an important role in people's health, it might cause dangers. The proposed work can not only alert people to fruit that can cause allergies, but also it encourages people to consume fruit that is beneficial for their health

Utilizing a New Eco-Friendly Drilling Mud Additive Generated from Wastes to Minimize the Use of the Conventional Chemical Additives

The cost of the drilling operation is very high. Drilling fluid presents 15 to 30% of the entire expense of the drilling process. Ordinarily, the major drilling fluids additives are viscosity modifiers, filtration control agents, and partial loss treatments. In this experimental work, full-set measurements under fresh and aged conditions, as well as high-temperature and high-pressure (HTHP) API filtration, were conducted to study the impacts of adding 0.5%, 1.5%, 2.5%, and 3.5% of black sunflower seeds’ shell powder (BSSSP) to spud mud. BSSSP of various grain sizes showed their ability to be invested for viscosity modifying, seepage loss controlling, and partial loss remediation. In addition to BSSSP eminent efficiency to be used as a multifunctional additive, the BSSSP is cheap, locally obtainable in commercial quantities, environmentally friendly additive and easy to grind into various desired grain sizes. Besides its outstanding strength to behave under conditions up to 30 h aged time and under 50 °C (122 °F) temperature, the utilization of powdered waste black sunflower shells in the drilling process and other industrial applications can reduce the effects of food waste on the environment and the personnel safety. To sum it up, experimental findings revealed that BSSSP can be used for multiple applications as a novel fibrous and particulate additive. The results elucidated BSSSP suitability in substituting or at least minimizing some of the traditional chemical materials utilized in the petroleum industry such as salt clay, polymers, and lost circulation materials (LCM)

Full-Set Measurements Dataset for a Water-Based Drilling Fluid Utilizing Biodegradable Environmentally Friendly Drilling Fluid Additives Generated from Waste

The oil and gas industry is moving towards more environmentally friendly practices. The environmental regulations regarding drilling waste management and disposal are motivating the industry to be more efficient with drilling operations. Environmentally friendly drilling fluid additives used in drilling operations reduces not only the negative implications on the environment but also reduces costs. This paper provides an experimental dataset of utilizing biodegradable waste materials as environmentally friendly drilling fluid additives. The data were collected through experimental evaluations of several waste materials including Potato Peels Powder (PPP), Mandarin Peels Powder (MPP), Fibrous Food Waste Material (FFWM), Palm Tree Leaves Powder (PTLP), Grass Powder (GP), and Green Olive Pits\u27 Powder (GOPP). The data presented herein are the raw results of the experiments, which were conducted to examine the ability of the biodegradable waste materials to improve the water-based drilling fluids. The data include the effects of adding these waste materials on different drilling fluid properties such as mud weight, filtration, pH, and the rheology. The mud weight was measured using mud balance, the filtration data were collected using API filter press for both low/high pressure and temperature, the pH was measured using pH meter, and the rheology was characterized using viscometer. The dataset is potentially useful to assist researchers working on developing environmentally friendly drilling fluid additives

Regularized Ridge Regression Models to Estimate Static Elastic Moduli from Wireline Measurements: Case Study from Southern Iraq

Elastic moduli such as Young\u27s modulus (E), Poisson\u27s ratio (v), and bulk modulus (K) are vital to creating geomechanical models for wellbore stability, hydraulic fracturing, sand production, etc. Due to the difficulty of obtaining core samples and performing rock testing, alternatively, wireline measurements can be used to estimate dynamic moduli. However, dynamic moduli are significantly different from elastic moduli due to many factors. In this paper, correlations for three zones (Nahr Umr shale, Zubair shale, and Zubair sandstone) located in southern Iraq were created to estimate static E, K, and ν from dynamic data. Core plugs from the aforementioned three zones alongside wireline measurements for the same sections were acquired. Single-stage triaxial (SST) tests with CT scans were executed for the core plugs. The data were separated into two parts; training (70%), and testing (30%) to ensure the models can be generalized to new data. Regularized ridge regression models were created to estimate static E, K, and ν from dynamic data (wireline measurements). The shrinkage parameter (α) was selected for each model based on an iterative process, where the goal is to ensure having the smallest error. The results showed that all models had testing R2 ranging between 0.92 and 0.997 and consistent with the training results. All models of E, K, and ν were linear besides ν for the Zubair sandstone and shale which were second-degree polynomial. Furthermore, root means squared error (RMSE) and mean absolute error (MAE) were utilized to assess the error of the models. Both RMSE and MAE were consistently low in training and testing without a large discrepancy. Thus, with the regularization of ridge regression and consistent low error during the training and testing, it can be concluded that the proposed models can be generalized to new data and no overfitting can be observed. The proposed models for Nahr Umr shale, Zubair shale, and Zubair sandstone can be utilized to estimate E, K, and ν based on readily available dynamic data which can contribute to creating robust geomechanical models for hydraulic fracturing, sand production, wellbore stability, etc

Efficient Multimodal Deep-Learning-Based COVID-19 Diagnostic System for Noisy and Corrupted Images

Introduction: In humanity\u27s ongoing fight against its common enemy of COVID-19, researchers have been relentless in finding efficient technologies to support mitigation, diagnosis, management, contact tracing, and ultimately vaccination. Objectives: Engineers and computer scientists have deployed the potent properties of deep learning models (DLMs) in COVID-19 detection and diagnosis. However, publicly available datasets are often adulterated during collation, transmission, or storage. Meanwhile, inadequate, and corrupted data are known to impact the learnability and efficiency of DLMs. Methods: This study focuses on enhancing previous efforts via two multimodal diagnostic systems to extract required features for COVID-19 detection using adulterated chest X-ray images. Our proposed DLM consists of a hierarchy of convolutional and pooling layers that are combined to support efficient COVID-19 detection using chest X-ray images. Additionally, a batch normalization layer is used to curtail overfitting that usually arises from the convolution and pooling (CP) layers. Results: In addition to matching the performance of standard techniques reported in the literature, our proposed diagnostic systems attain an average accuracy of 98% in the detection of normal, COVID-19, and viral pneumonia cases using corrupted and noisy images. Conclusions: Such robustness is crucial for real-world applications where data is usually unavailable, corrupted, or adulterated

Experimental Investigation of Environmentally Friendly Drilling Fluid Additives (Mandarin Peels Powder) to Substitute the Conventional Chemicals Used in Water-Based Drilling Fluid

The non-biodegradable additives used in controlling drilling fluid properties cause harm to the environment and personal safety. Thus, there is a need for alternative drilling fluid additives to reduce the amount of non-biodegradable waste disposed to the environment. This work investigates the potential of using mandarin peels powder (MPP), a food waste product, as a new environmentally friendly drilling fluid additive. A complete set of tests were conducted to recognize the impact of MPP on the drilling fluid properties. The results of MPP were compared to low viscosity polyanionic cellulose (PAC-LV), commonly used chemical additive for the drilling fluid. The results showed that MPP reduced the alkalinity by 20-32% and modified the rheological properties (plastic viscosity, yield point, and gel strength) of the drilling fluid. The fluid loss decreased by 44-68% at concentrations of MPP as less as 1-4%, and filter cake was enhanced as well when comparing to the reference mud. In addition, MPP had a negligible to minor impact on mud weight, and this effect was resulted due to foaming issues. Other properties such as salinity, calcium content, and resistivity were negligibly affected by MPP. This makes MPP an effective material to be used as pH reducer, a viscosity modifier, and an excellent fluid loss agent. This work also provides a practical guide for minimizing the cost of the drilling fluid through economic, environmental, and safety considerations, by comparing MPP with PAC-LV

Wax: A benign hydrogen-storage material that rapidly releases H2-rich gases through microwave-assisted catalytic decomposition

Hydrogen is often described as the fuel of the future, especially for application in hydrogen powered fuel-cell vehicles (HFCV’s). However, its widespread implementation in this role has been thwarted by the lack of a lightweight, safe, on-board hydrogen storage material. Here we show that benign, readily-available hydrocarbon wax is capable of rapidly releasing large amounts of hydrogen through microwave-assisted catalytic decomposition. This discovery offers a new material and system for safe and efficient hydrogen storage and could facilitate its application in a HFCV. Importantly, hydrogen storage materials made of wax can be manufactured through completely sustainable processes utilizing biomass or other renewable feedstocks

Circular Intensely Orthogonal Double Cover Design of Balanced Complete Multipartite Graphs

In this paper, we generalize the orthogonal double covers (ODC) of Kn,n as follows. The circular intensely orthogonal double cover design (CIODCD) of X=Kn,n,…,n︸m is defined as a collection T={G00,G10,…,G(n−1)0}∪{G01,G11,…,G(n−1)1} of isomorphic spanning subgraphs of X such that every edge of X appears twice in the collection T,E(Gi0)∩E(Gj0)=E(Gi1)∩E(Gj1)=0,i≠jand E(Gi0)∩E(Gj1)=λ=m2,i,j∈ℤn. We define the half starters and the symmetric starters matrices as constructing methods for the CIODCD of X. Then, we introduce some results as a direct application to the construction of CIODCD of X by the symmetric starters matrices

Prediction of Lost Circulation Prior to Drilling for Induced Fractures Formations using Artificial Neural Networks

Lost circulation is a complicated problem to be predicted with conventional statistical tools. As the drilling environment is getting more complicated nowadays, more advanced techniques such as artificial neural networks (ANNs) are required to help to estimate mud losses prior to drilling. The aim of this work is to estimate mud losses for induced fractures formations prior to drilling to assist the drilling personnel in preparing remedies for this problem prior to entering the losses zone. Once the severity of losses is known, the key drilling parameters can be adjusted to avoid or at least mitigate losses as a proactive approach. Lost circulation data were extracted from over 1500 wells drilled worldwide. The data were divided into three sets; training, validation, and testing datasets. 60% of the data are used for training, 20% for validation, and 20% for testing. Any ANN consists of the following layers, the input layer, hidden layer(s), and the output layer. A determination of the optimum number of hidden layers and the number of neurons in each hidden layer is required to have the best estimation, this is done using the mean square of error (MSE). A supervised ANNs was created for induced fractures formations. A decision was made to have one hidden layer in the network with ten neurons in the hidden layer. Since there are many training algorithms to choose from, it was necessary to choose the best algorithm for this specific data set. Ten different training algorithms were tested, the Levenberg-Marquardt (LM) algorithm was chosen since it gave the lowest MSE and it had the highest R-squared. The final results showed that the supervised ANN has the ability to predict lost circulation with an overall R-squared of 0.925 for induced fractures formations. This is a very good estimation that will help the drilling personnel prepare remedies before entering the losses zone as well as adjusting the key drilling parameters to avoid or at least mitigate losses as a proactive approach. This ANN can be used globally for any induced fractures formations that are suffering from the lost circulation problem to estimate mud losses. As the demand for energy increases, the drilling process is becoming more challenging. Thus, more advanced tools such as ANNs are required to better tackle these problems. The ANN built in this paper can be adapted to commercial software that predicts lost circulation for any induced fractures formations globally