Journal of Engineering and Applied Research
Volume:1 Issue: 2, Summer & Autumn 2024

Membrane Distillation (MD) is constantly acknowledged in the research literature as a promising technology for the future of desalination, with an increasing number of studies reported year after year. MD processes encompass various configurations designed for liquid separation applications. Four primary MD configurations are Direct Contact Membrane Distillation (DCMD), Air Gap Membrane Distillation (AGMD), Vacuum Membrane Distillation (VMD), and Sweeping Gas Membrane Distillation (SGMD). However, real MD applications still lag with only a few pilot-plant tests universal. The absence of technology transfer from academia to industry is caused by important gaps between its fundamental basis and the process design. MD has the potential to recover both chemicals and water. This review paper describes the background and properties of interest for desalination processes and reviews the recent literature for novel compounds used in membrane processes. The advantages and disadvantages of using particular methods, examples of applications, and integrated processes are pronounced. Perspectives, benefits, and limitations are also discussed.

Quantum Machine Learning (QML) is a burgeoning field at the convergence of quantum computing and machine learning, with the potential to revolutionize traditional algorithms through principles of quantum mechanics. This article presents a thorough examination of foundational concepts in QML, elucidating qubits, quantum gates, superposition, and entanglement. It explores various QML algorithms, such as quantum neural networks, quantum support vector machines, and quantum clustering, which leverage quantum properties to tackle intricate computational tasks. Additionally, it explores the diverse applications of QML, including quantum chemistry, optimization, cryptography, and big data analysis. The article also discusses applications and various types of quantum machine learning libraries. Despite its promise, QML encounters challenges like scalability, noise, and error correction. Addressing these hurdles and realizing QML's full potential necessitates sustained research efforts and collaborative initiatives, poised to drive transformative progress across industries. This research, spanning four months and drawing insights from over 20 reputable scholarly articles, offers a comprehensive investigation into QML.

The purpose of present study were to assess the antifungal activity in vitro ajwain essential oil by addition to the fungal growth medium of three pathogens (Botrytis cinerea, Aspergillus niger and Penicillium digitatum) isolated from grapes, tomato, and orange. Also, determine its antifungal properties in vivo a potential food preservative. Essential oil of Trachyspermum ammi was extracted by Clevenger-type apparatus and quantified and identified by gas chromatography–mass spectroscopy and gas chromatography. Analysis of the total essential oil show that thymol (60.50%), terpinene (15.65%) and p-cymene (12.51%) were the major constituents of this oil. The oil inhibited completely the growth of A. niger, B. cinerea and P. digitatum at 400–600 µl/l on the PDA medium. Exposure to ajwain oil at 600 µl/l reduced fruits mold incidences by more than 90%. In conclusion, we showed ajwain oil was highly effective against post-harvest diseases, which supports its use in phytopathology for its antiseptic properties..

Breast cancer is the most common type of cancer among women. Early diagnosis of this disease and its treatment can significantly reduce the death rate from this cancer. The separation of benign and malignant masses in mammography images is one of the important things in the timely detection of breast cancer, which in some cases, due to the density and natural structure of the breast, deep and hidden disorders, make the diagnosis difficult for radiologists. In this study, frequency transformations and Naive Bayes classification have been used with the aim of extracting effective features in mammography images. The aim of the presented method is to increase the accuracy of diagnosis between malignant and benign tumors in mammography images. The results obtained from the implementation of the proposed method on the MIAS database show that the proposed method has been able to improve the accuracy of diagnosing this disease on normal and abnormal images by 91%, Precision by 98%, Recall by 987%, and F-measure by 90%.

Detection, identification, and automatic counting of vehicles using video surveillance cameras plays an important role in the field of intelligent transportation management. Despite the progress that researchers have made in these cases, its operational implementation still faces challenges such as "various environmental conditions", "unbalanced data sets", "accuracy" and "speed". Therefore, research can be useful in solving these issues. The proposed algorithm for detection, classification, and counting will be based on deep learning. In this research, after applying the proposed initial preprocessing algorithm, we use the YOLO algorithm to detect and classify vehicles. The DeepSORT algorithm is also used to track several vehicles at the same time. For the accurate counting of vehicles, a developed method is also proposed to increase the processing accuracy. By applying the proposed pre-processing and counting techniques, the practical results show that the "call" criterion in the video with detection at night challenge has been increased to 99.18%.

The aim of this research is to compare nickel-alumina nanocomposite coatings. The content of alumina nanoparticles has the most important effect in these coatings. In this research, the electrolyte composition was modified by adding Sodium dodecyl sulfate (SDS). The concentration of alumina nanoparticles in the plating bath was 3 grams per liter. In this research, nickel-alumina nanocomposite coating was created by pulsed electric current and under ultrasonic turbulence in electroplating baths. Two nickel plating baths with the combination of watt with the addition of SDS and without SDS were used to create coatings, and before plating, the zeta potential of alumina nanoparticles was measured in two different baths. After the plating process, the cross section of the coatings and the amount of alumina nanoparticles incorporated in the coating and the morphology of each coating were analyzed by a scanning electron microscope (SEM) equipped with an Energy Dispersive x-ray Spectroscopy (EDS). The results showed that by increasing the SDS to the Watt’s solution, the zeta potential of nanoparticles increases from negative 2 mV to positive 42.5 mV and subsequently, the content of alumina reinforcing nanoparticles in the coatings increases from 2.6% by volume percentage to 3.5% Volume percentage. It seems that the SDS may be able to act as a proper surfactant and affect the hydrated layer on the nanoparticles and improve the co-deposition of alumina nanoparticles with nickel in the composite coatings.

In this paper, a text-independent speaker recognition system in Persian is implemented by deep neural networks. The x-vector technique based on Time Delay Neural Network (TDNN) is used to extract the embeddings from speech signals. This method attracts researcher’s attention due to noise robustness and high performance. Data augmentation and noise addition are used to improve system performance. The PLDA classifier is used to recognize the speaker. Previous research in the field of “speaker recognition in Persian” is limited. In this work, the network is trained on the Persian part of the CommonVoice dataset. According to the error analysis, non-speech parts of an utterance decrease the accuracy of speaker recognition. So, the non-speech parts are removed by a Convolutional Recurrent Deep Neural Networks (CRDNN). The accuracy of speaker recognition and verification in CommonVoice is 95.24% and 95.56%, respectively. The Equal Error Rate (EER) evaluation metric of the speaker verification system is 4.72%. The attendance monitoring system was developed as one of the applications of the speaker recognition system. System accuracy for 12 and 15 seconds of collected data(includes 16 women and 12 men) is 98.92% and 100%, respectivly.

In the rapidly evolving landscape of modern organizations, maintaining robust situation awareness is crucial for agility, informed decision-making, and sustained competitive advantage. Traditional approaches often rely on documented processes, which, while useful, may fail to capture the dynamic and complex nature of actual workflows. Enter process mining—a powerful analytical tool that delves into real-time data, uncovering the true flow of tasks, identifying bottlenecks, and predicting future process behaviors. By transforming raw data into actionable insights, process mining offers an unparalleled level of transparency, enabling organizations to anticipate disruptions, optimize resource allocation, and enhance operational efficiency. This review explores the intersection of situation awareness and process mining, providing a comprehensive analysis of how these methodologies converge to offer a clearer understanding of organizational processes. We begin by examining the theoretical foundations of situation awareness and process mining. The paper then reviews existing research on the application of process mining in enhancing situation awareness, highlighting key advancements, use cases, and the transformative impact on decision-making processes. Despite its numerous benefits, the integration of process mining into situation awareness is not without challenges. This review identifies several open issues, including data quality concerns, the complexity of real-world processes, and the need for more sophisticated analytical techniques. To address these gaps, we propose future research directions, particularly in the context of cyber situation awareness. By advancing the state of the art in process mining, we aim to pave the way for more resilient, adaptable, and aware organizations in the digital age.

Diagnosing and controlling the level of stress in order to reduce the risks is so important. In this study, a system for detecting five levels of stress, i.e. physical stress, semi-emotional stress, emotional stress, cognitive stress, and resting state in people based on physiological signals, is presented. In the proposed method, the Non-EEG Dataset for Assessment of Neurological Status database, which is available on the Physionet website, is used. This database contains physiological signals from twenty people. These data were collected using non-invasive wrist biosensors. A set of statistical and frequency and wavelet features are calculated for electrodermal (EDA), temperature, acceleration, heart rate (HR) and arterial oxygen level (SpO2) signals. The determined features are applied as input to the classification units to detect the stress levels. Support vector machine (SVM), k nearest neighbor (kNN), decision tree (DT), ensemble learning and neural networks are evaluated as classification methods. Experimental results show that neural networks can separate different neural states of 5 classes with 97% accuracy.

The identification and diagnosis of plant diseases have long been considered. This research presents a system for diagnosing the volume and type of apple diseases and the spoilage percentage of rotten apples. To estimate the volume of apples, the method of immersion in water to change the volume of the container was used, ensuring more accurate volume estimation. For disease detection and spoilage analysis, a chamber with constant lighting conditions and a halogen lamp was used. Four images were taken with a camera for better analysis. The volume of apples was calculated through two approximations of the cylinder and incomplete cone. The average error rate in this system was 5%. Also, in the present research, a novel method for feature selection was identified using a combination of the weight feature and the calculated volume of hollow apples. To calculate the percentage of failure of each apple, first, the type of failure was identified. Then, the ratio of loss of each apple relative to the whole apple was calculated and compared with the number obtained from the desired region method, which was accurate. In this study, three major diseases of apples were studied, and an algorithm was written to distinguish these three types of infections from healthy apples. The results showed that the proposed method had the necessary efficiency to calculate the volume and percentage of failure and diagnose the type of apple diseases. In addition, the system's accuracy compared to previous studies increased by up to 95%.

With the increase in population, emerging chemical pollutants such as pharmaceutical compounds have entered water sources that conventional water purification methods cannot completely remove. Therefore, it is necessary to remove them considering the dangers of these pollutants and their toxicity to the environment and human society. In this research, iron oxide-magnetized bentonite nanocomposite was synthesized in situ using the co-precipitation method to remove acetaminophen from water sources. The structure of the synthesized nanocomposite was investigated using FTIR, BET, XRD, EDX and FSEM analyses and its performance in acetaminophen removal was studied. The results showed that the synthesized iron oxide-magnetized bentonite nanocomposite has an appropriate performance (100% in 30 min) in the process of acetaminophen removal. The test conditions (nanocomposite amount, time, pH and acetaminophen concentration) were optimized. The highest amount of acetaminophen removal was obtained by using iron oxide-magnetized bentonite nanocomposite with a reaction time of less than 30 min, pH= 4, initial acetaminophen concentration of 25 mg/L and adsorbent concentration of 1 g/dL.

In the present paper, a 16-channel implantable wireless neural recording system is presented. The proposed system employs frequency-division multiplexing (FDM) to transfer multiple neural channels to an external setup wirelessly. The main advantage of the proposed system is that, while increasing the number of channels, it efficiently utilizes the limited bandwidth allocated for wireless data transmission such as the 402-405 MHz, 174-216 MHz, or 88-108 MHz bands allocated for medical implant communication services. In this system, neural activities from multiple channels are detected using two-dimensional or three-dimensional microelectrodes. After preconditioning, the multiple parallel channels are multiplexed in the frequency domain within the FDM module. As a result of FDM, preconditioned neural signals are placed in the frequency domain with 100kHz spacing, occupying a 1600kHz bandwidth starting from 10MHz. Finally, the resulting FDM band is shifted in the frequency domain by the frequency modulation (FM) block with a carrier frequency of 100 MHz. A 16-channel prototype system is designed and simulated using 0.18 µm CMOS technology, with a chip area of 0.55 × 0.58 mm²and a power consumption of 3.35 mW at a supply voltage of 1.8V.

The present article examines a new method of heating from the floor. After introducing this method of heating, its variations and how it corresponds to the process and mechanism of energy release from the human body and the comparison of these two cases were considered. In another part of this preliminary research, the general structure and components of the underfloor heating system and its implementation including the insulating layer and the pipes used were investigated. Furthermore, with the help of simulation software, a room with floor heating was simulated and the thermal comfort parameter was checked.

The pin profile in Friction Stir Welding (FSW) has a significant influence on the temperature and strain distribution during the welding process. The shape and geometry of the pin can affect the material flow and heat generation, which in turn impacts the temperature within the weld zone. In this research, the effect of different square, triangular, and cylindrical pin profiles on strain and temperature during FSW was investigated numerically. The coupled Eulerian-Lagrangian (CEL) method was utilized to model the FSW process and further analyze strain and temperature. In this approach, the workpiece is represented using an Eulerian formulation, while the tool is described through a Lagrangian formulation. Results showed that the cylindrical tool generates higher temperatures due to its larger surface area, while the triangular tool keeps the sample cooler with a smaller pin surface area. Despite the triangular tool's longer revolving arm causing greater strain, it results in less pulsation, limiting the strain's impact to a smaller area. The maximum strain generated by the circular tool was approximately 16, whereas the triangular tool produced a maximum strain of 45.