Editor(s)
Prof. Hamdy M Afefy
Pharos University, Alexandria, Egypt.

Short Biosketch

ISBN 978-81-969141-9-6 (Print)
ISBN 978-81-969141-0-3 (eBook)
DOI: 10.9734/bpi/taer/v2

This book covers key areas of engineering research. The contributions by the authors include hydraulic jump, energy loss, land irrigation, stream turbulence, unsaturated shear strength, soil index properties, matric suction, regression analysis, predictive models, matric suction, quantum theory, machine learning, Quantum machine learning, molecular biology, cryptography, geopolymerization, gas absorption, compression strength, hydrocarbons, microbial spoilage, drying kinetics, moisture diffusion, cavitation index, energy dissipation, numerical modelling, stepped spillway, turbulence model, MIMO system, ZF uplink, time division duplexing, cellular networks, energy efficiency, spectral efficiency, atmospheric data, global change, atmospheric mercury, graphical user interface, osteoarthritis, magnetic resonance imaging, support vector machine, knee injuries, machine learning-based knee OA detection system. This book contains various materials suitable for students, researchers and academicians in the field of engineering research.

 

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Chapters


The purpose of this study was to determine the effect of shape variations of weirs and stilling basin type USBR-I on the length of the hydraulic jump and energy loss.The Crest of weir is an important part of dam construction because the function is releasing water from the upstream part of the dam. The effect of a dam can create supercritical flow (supercritical) downstream of the weir and cause a water jump (hydraulic jump) which if left untreated will occur local scour the downstream. weir. In weir planning, it is necessary to take into the dam will be used because it affects the flow conditions downstream of the weir.

This study employed primary data from laboratory testing using a Recirculating Flume instrument of 10.0 m long, 0.30 m wide channel, and 0.60 m flume height This was conducted by making variations in the form of test objects, namely ogee crest weirs, 1 radius round type weirs, and 2 radius round type weirs using the USBR-I stilling basin type which was then tested in the laboratory using five different variations of flow rates, namely 3000 cm3/s, 3500 cm3/s, 4000 cm3/s, 4500 cm3/s, and 5000 cm3/s. To determine the most effective way to shorten the hydraulic jump's length and reduce its energy, the data test was analyzed by using equations/formulas and graphically potrayed to produce research findings and compared with crest variations. The results in the study show the effect of dam crest variations on the length of the hydraulic jump (Lj) based on the maximum discharge, which is round type 1 radius of dam crest with 125 cm longest Lj, then a 2 radius round type with the longest Lj 136 cm, and an ogee type with the longest Lj. 142 cm. For the effect of variations of dam crest on energy loss (hf), the average value of the five running discharge experiments on each specimen was taken, the variation of the dam crest that had the greatest to the smallest energy loss (hf) value was the 2 radius round type dam with hf average 31,429 cm, ogee type dam with an average hf of 31.404 cm, and round type 1 radius dam with an average hf of 30.456 cm. Finally, it was concluded that the variation of the dam crest is the most efficient in reducing energy is the 2 radius round type dam crest.

In this chapter, the integration of quantum computing and machine learning is given a lot of attention, which will make perfect sense when applied to the context of modelling quantum machine learning. The mechanism of quantum computing lends credence to the concept of numerous machine learning-related activities as possible applications in quantum technology. The goal of quantum computing is to develop a new standard of processing that is fundamentally different from that of traditional computers. This is accomplished by incorporating ideas from quantum physics, such as superposition and entanglement, into the computing process. In the first part of this chapter, we took a high-level look at some of the principles of quantum theory. In addition to that, an investigation into quantum machine learning has also been looked at in this article. The qubit is the most fundamental component of quantum technology and plays an important role in the implementation of quantum processes in a wide range of different fields of endeavour. The use of standard computing devices is rendered obsolete by the advent of quantum computing, which permits the resolution of issues that were previously intractable. Complicated computations refer to issues that are famously difficult to solve using typical computing methods. These problems are notoriously difficult to solve. Learning software that is based on traditional models performs incredibly well, but it comes with increasing requirements for computer power since it must handle a complex and extensive quantity of data. When modelling supervised machine learning with quantum computing, some of the work that must be done includes the selection of features, the encoding of parameters, and the building of parameterized circuits. Topics of conversation also include the modelling of quantum parameterized circuits, as well as the design and implementation of quantum feature sets for sample data. The application of quantum processes like as superposition and entanglement is used to illustrate the idea of guided machine learning.

This study focuses on the experimental investigation and theoretical comparison of drying kinetics for Nagpur orange fruit in a hot-air electrical dryer. Given the high perishability of orange fruit, immediate consumption or processing is crucial post-harvest. Dehydration emerges as a practical preservation method, prompting an exploration of thin layer drying characteristics under varying conditions such as drying air temperatures, relative humidity, and air velocities across different moisture contents. The research employs thin layer models, specifically Wang and Singh, Page, and Henderson, to compare theoretical predictions with experimental findings. Understanding drying kinetics proves essential for pinpointing precise drying times and optimal air flow velocities corresponding to different moisture levels. The drying operation is executed at velocities of 1 m/s and 1.25 m/s for temperatures of 55°C, 65°C, and 75°C. Results indicate that drying temperature significantly influences moisture removal, whereas velocity exerts the least impact. The drying rate increases with increasing temperature and diminishes over time. The statistical correlation of experimental data through drying characteristic curve plotting reveals that Wang and Singh's model outperforms others in elucidating the drying behavior of Nagpur orange fruit (R²=0.9711). This comprehensive analysis enhances our understanding of the intricate interplay between drying parameters, contributing valuable insights for optimal drying conditions and efficient preservation.

Improved Spectral Efficiency in Uplink Multicell Massive MIMO Cellular Communication Systems Using MMSE Channel Estimation and ZF Uplink Combining

Rajeev Kumar Shakya, Yibeltal Abebaw , Demissie Jobir Gelmecha, Eshetu Tessema Ware

Theory and Applications of Engineering Research Vol. 2, 3 January 2024, Page 42-66
https://doi.org/10.9734/bpi/taer/v2/8384A

In this chapter, we consider the multi-user multiple input multiple outputs (MU-MIMO) and SE performance analysis is presented against pilot contamination mitigation with different parameters.

The maximum area throughput limit of a multi-cell Massive MIMO system can be reached by increasing bandwidth, cell density, and spectral efficiency. The Spectral efficiency (SE), which makes use of the linear Zero Forcing uplink combining method, can be modeled under the rician fading channel in order to assess the area throughput for such scenarios. In the case of uplinks, the BS is in charge of channel estimation. Different from existing work, the proposed model incorporates various estimators such as Minimum Mean Square Error (MMSE), Element-Wise Minimum Mean Square Error estimators under rician fading. The multi-cell scenarios with uplink (UL) massive MIMO has been analyzed using the proposed model under different cases such as pilot reuse factor, coherence block length, different number of antennas and different estimators. Based on these parameters, the analysis and outcomes of the simulation are presented. It is discovered that employing an efficient pilot reuse factor, building multiple BS antennas, servicing multiple numbers of UEs per cell, and optimizing MMSE channel estimation utilizing ZF UL combiner can all improve the average summation of SE per cell. The MMSE and ZF uplink combining are found to more suitable in improving SE as compared to MMSE-MR. For examples, the uplink SE of MMSE channel estimator for pilot reuse factors,1,3,4 is calculated as 22.5 bit/s/Hz/cell, 22.3 bit/s/Hz/cell and 21 bit/s/Hz/cell respectively. The uplink SE for EW-MMSE channel estimator with pilot reuse factors, 1, 3, 4 is calculated as 22.5bit/s/Hz/cell, 22 bit/s/Hz/cell and 22 bit/s/Hz/cell respectively. For the uplink SE of LS channel estimators, it can be 17.9bit/s/Hz/cell, 20.2 bit/s/Hz/cell, 20bit/s/Hz/cell with pilot reuse factors as f = 1, 3, 4 respectively. So, for f=3, the maximum calculated uplink SE for MMSE, EW-MMSE and LS is 17.6 bit/s/Hz/cell, 17.8bit/s/Hz/cell and 13bit/s/Hz/cell respectively. There is not much effect on coherence block as when it increases then the SE increases as well. It is also observed based on results that the ZF uplink combining technique can suppress the coherence interference and hence the average sum SE is enhanced with MMSE channel estimator and a pilot reuse factor of f=3 with ZF uplink combining. There is also trade-off between the pilot contamination mitigation and the larger SE. However, there is not much effect on coherence block as when it increases then the SE increases as well.

Unsaturated Shear Strength Assessment Based on Soil Index Properties

Armand Augustin Fondjo, Elizabeth Theron , Richard P. Ray

Theory and Applications of Engineering Research Vol. 2, 3 January 2024, Page 67-94
https://doi.org/10.9734/bpi/taer/v2/8340A

The shear strength is a fundamental property of soil material. The measurement of the shear strength of unsaturated soils is challenging. Several tests are necessary to establish the strength variation with matric suction, and a long time is required to achieve the matric suction equilibrium in specimens before testing. Predictive models can evaluate the unsaturated shear strength of heaving soil. The research aims to develop models to determine the shear strength parameters of partly saturated soils. These include the angle of increase in shear strength with variation in matric suction (\(\phi\)b), angle of internal friction related to net normal stress (\(\phi\)'), and effective cohesion (c'). Soil properties were evaluated through particle size distribution, specific gravity, consistency limits, swelling test, modified Proctor compaction test, suction test, and advanced triaxial testing. Regression analysis was performed using MINITAB 20 Software to develop predictive models. The validation process includes the p-value, determination coefficient, comparing predicted with experimental values, and comparing other models in literature with models developed in this study. The engineered models can estimate the shear strength characteristics of compacted, unsaturated soils with acceptable precision.

Cavitation damage to concrete structures is common in dam spillways. Cavitation can lead to cracks on the concrete surface which further increases the risk of damage to concrete by means of sulfate attack, freeze-thaw, alkali-silica reaction, and others. Cavitation damage occurs on concrete surfaces when discontinuities or irregularities are encountered in the path of high speed water flow. Stepped spillways are suitable, economic options for high-volume storage dams that require significant energy dissipation, for structural adaptations to roller-compacted concrete dams, and to promote spontaneous flow aeration. In this study, numerical analysis of the hydraulic characteristics for the skimming flow regime of over the stepped spillway of the Zirdan Dam is carried out. A comparison between the flow characteristics for stepped and smooth spillways is provided. By preparing numerical models using the k-\(\varepsilon\) RNG turbulence model and the multiphase mixture method, a hydraulic analysis of the flow was completed. To verify the performance of the numerical model, field data was collected and used for validation. The results show that the presence of steps along the spillway cause a significant reduction in the length of the boundary layer and faster aeration occurs. In a stepped spillway, the cavitation index is higher than the critical limit along the entire length of the spillway. Thus, the risk of cavitation and destruction is negligible. On the other hand, with a smooth spillway, the possibility of cavitation may occur. This negative effect (negative pressures) occurs 56 m from the crest of the spillway on the downstream side. For the design discharge, the difference in energy dissipation for the stepped and smooth spillway is 47%.

Integrated Process Design: Production of Packing Material and Packed-Column Design for the Efficient Ammonia Gas Absorption

Lola Domnina Pestaño , Clariezle Joy E. Soliven , Jewel Christine F. Tirado

Theory and Applications of Engineering Research Vol. 2, 3 January 2024, Page 129-150
https://doi.org/10.9734/bpi/taer/v2/19995D

Process design is the manner of designing and developing a production process for a particular chemical product. This research aims to design a process for the production of geopolymerized perlite as packing material that will be utilized in the design of a packed-column for ammonia gas absorption. The process involves the following steps: (1) collection of raw perlite from Lamba, Albay, Bicol; (2) geopolymerization of perlite; (3) quality testing; and (4) fabrication of geopolymerized packing material. A conceptual design for the production of geopolymerized perlite packings was developed. The result of its thermal stability, chemical resistance, and compression strength shall be the basis of an ideal packing material. Recent research indicates that 45% by weight Na2SiO3 exhibited the highest retained mass and the highest compressive strength making this geopolymerized perlite a potential packing material with a packing factor of 0.00175/ft. Other studies presented the least Na2SiO3 weight percent results in better geopolymer properties. Based on these results, a packed-column was designed for the absorption of ammonia gas with a length-to-diameter, L/D ratio of 3.8. This process design of the fabrication of geopolymerized packing material will provide a solution to the dilemmas of existing packing materials used in the industry.

Meteo Read: Client Software for Inserting Observed Atmospheric Data into MySQL\(_{TM}\) Database

Beáta Szabó-Takács , Tamás Takács , Ales Rocek

Theory and Applications of Engineering Research Vol. 2, 3 January 2024, Page 151-165
https://doi.org/10.9734/bpi/taer/v2/6972E

This chapter investigates the tendency of climate change and its effects on ecology, economy and sociology is essential for long-term policy making. High-quality data for these investigations is provided by the long-term, state-of-the-art instrumentation measurements of the atmospheric properties, both chemical and physical. MeteoRead is a client database software that imports the observed atmospheric data, e.g. wind direction, wind speed, aerosol particle concentration, etc. and makes them available in different file formats most commonly used in climate research. MeteoRead was developed in JavaTM language. One of the most significant benefits of JavaTM language is its platform independence, as the development and execution are available on almost all devices. JavaTM is an object-oriented language where the created objects remain available throughout the runtime thanks to its memory-handling technique. This JavaTM-based program applies the Structured Query Language (SQL) functions such as table creation on a database server, data or figures insertion into the table and data selection via Graphical User Interface. The selected data can be stored in NetCDF, HDF5, DataBase or TXT file formats, and the figures can be available in PNG, JPG, JPNG, PDF or GIF file formats. The program was tested on Linux and Windows platforms with different JavaTM Development Kit. The structure of the software was demonstrated thoroughly in this chapter. Finally, it was concluded that MeteoRead, a client database software, importing observed atmospheric data and figures, and for also filtering and exporting the data in the most commonly applied file formats such as NetCDF, HDF5, DataBase, and TXT and the figures in PNG, JPG, JPNG, PDF and GIF formats. Monthly, yearly as well as hourly average data value can be used to visualized through the MeteoRead. It can be used  in the functionality of the SQL database to calculate various mathematical and statistical correlations.

In this chapter, a machine learning-based knee Osteoarthritis (OA) detection system from magnetic resonance (MR) images is proposed. This system is capable of detecting the presence of OA considering two classification categories: ‘non-OA’ and ‘OA’. OA is one of the most prevalent condition resulting to disability particularly in elderly population. OA is the most common articular disease of the developed world and a leading cause of chronic disability, mainly as a consequence of the knee OA and/or hip OA. Nowadays, medical images such as MR images are widely used for the OA diagnosis. For this, a medical specialist analyzes medical images by measuring the changes and in particular for knee OA, the changes in the compartment of the tibio-femoral cartilage. The proposed method consists mainly of both a data processing module and binary classification module, which process the 3-D data from MR images. In this study, we present a novel knee OA diagnostic approach that can identify the condition using magnetic resonance MR images using the Support Vector Machine (SVM) algorithm. Our suggested method is predicated on using 3-D data from MR scans of an actual cohort and the Independent Component Analysis (ICA) technique. The experimental results showed that our ICA-SVM machine learning model achieved 86% of testing accuracy with both 72% of specificity and 100% of sensitivity, once trained with a small MR image dataset. Furthermore, a benchmark evaluation was performed. The results suggest that using a larger and more diverse dataset could ensure the robustness of the proposed method. In future works, we will study the complementary use of ICA components from MR images and a convolutional neural network (CNN) to try to achieve better predictive rates in supervised learning using a larger dataset.