Frequent Failures of Motor Shaft in Seawater Desalination Plant: Some Case Studies

The failure of a shaft from a motor in a pump or a compressor has been a phenomenon of common occurrence in seawater desalination plants. The origin of the problem in majority of cases is either the inability of the material to withstand the level of dynamic stresses to which shaft is subjected during operation and/or inadequacy of the design. The shortcoming in the design may be responsible for initiating localized corrosion which ultimately leads to failure of the component. The mode of failure of the shaft could be stress-related failure such as stress corrosion cracking, mechanical fatigue or corrosion fatigue, and/or localized corrosion such as crevice corrosion. This paper describes some recent case studies related to shaft failures in seawater desalination plants. The case studies include shearing of a shaft in brine recycle pump in which a combination of environment, design, and stresses played important role in failure. In another case, ingress of chloride inside the key slot was the main cause of the problem. The failure in a high pressure seawater pump in a SWRO plant occurred due to cracking in the middle of the shaft.     

Artificial Intelligence Based Sentiment Analysis for Health Crisis Management in Smart Cities

Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living and sustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install IT platforms to collect and examine massive quantities of data. At the same time, it is essential to design effective artificial intelligence (AI) based tools to handle healthcare crisis situations in smart cities. To offer proficient services to people during healthcare crisis time, the authorities need to look closer towards them. Sentiment analysis (SA) in social networking can provide valuable information regarding public opinion towards government actions. With this motivation, this paper presents a new AI based SA tool for healthcare crisis management (AISA-HCM) in smart cities. The AISA-HCM technique aims to determine the emotions of the people during the healthcare crisis time, such as COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides, brain storm optimization (BSO) with deep belief network (DBN), called BSO-DBN model is employed for feature extraction. Moreover, beetle antenna search with extreme learning machine (BAS-ELM) method was utilized for classifying the sentiments as to various classes. The use of BSO and BAS algorithms helps to effectively modify the parameters involved in the DBN and ELM models respectively. The performance validation of the AISA-HCM technique takes place using Twitter data and the outcomes are examined with respect to various measures. The experimental outcomes highlighted the enhanced performance of the AISA-HCM technique over the recent state of art SA approaches with the maximum precision of 0.89, recall of 0.88, F-measure of 0.89, and accuracy of 0.94.     

MODELING AND SIMULATION OF SPARK IGNITION ENGINES

ABSTRACT

Modeling is important because it saves time, effort and cost needed for engine development and prediction of performance. In this work, losses due to imperfect construction of the real engine, including progressive combustion, valve timing and heat transfer have been modeled besides engine friction. Hence, it becomes possible to convert the output of the fuel-air cycle into net brake performance. Simulation of engine performance was carried out by varying engine speed, compression ratio and spark advance over wide range. Hence, it was possible to compare the results with those from experiments on a single cylinder engine. The model predictions were found to compare favourably with experiment within 4·6% in power and 2·9% in SFC. The losses considered in this work amount to about 14% of the fuel energy input.     

An experimental study of gas void fraction in dilute alcohol solutions in annular gap bubble columns using a four-point conductivity probe

The influence of alcohol concentration on the gas void fraction in open tube and annular gap bubble columns has been investigated using a vertical column with an internal diameter of 0.102 m, containing a range of concentric inner tubes, which formed an annular gap; the inner tubes had diameter ratios from 0.25 to 0.69. Gas (air) superficial velocities in the range 0.014–0.200 m/s were investigated. Tap water and aqueous solutions of ethanol and isopropanol, with concentrations in the range 8–300 ppm by mass, were used as the working liquids. Radial profiles of the local void fraction were obtained using a four-point conductivity probe and were cross-sectionally averaged to give mean values that were within 12% of the volume-averaged gas void fractions obtained from changes in the aerated level. The presence of alcohol inhibited the coalescence between the bubbles and consequently increased the mean gas void fraction at a given gas superficial velocity in both the open tube and the annular gap bubble columns. This effect also extended the range of homogeneous bubbly flow and delayed the transition to heterogeneous flow. Moreover, isopropanol results gave slightly higher mean void fractions compared to those for ethanol at the same mass fraction, due to their increased carbon chain length. It was shown that the void fraction profiles in the annular gap bubble column were far from uniform, leading to lower mean void fractions than were obtained in an open tube for the same gas superficial velocity and liquid composition.     

A wirelessly powered low-power digital temperature sensor

A wirelessly powered temperature sensor is presented in complementary metal-oxide-semiconductor (CMOS) 180-nm process. The wireless power transfer (WPT) is performed using resonant magnetic coupling, and a diode-less AC to DC conversion is achieved through a quadrature-oscillator with native-MOS. The quadrature-signals are subsequently used to control the diode-less rectifier switches. The on-chip temperature sensor exploits the subthreshold region temperature, and the sensed temperature is converted to frequency using a ring-oscillator, which is implemented using differential cross coupled oscillator-based delay cells. The temperature sensor architecture also employs a temperature-insensitive replica circuit to minimize process dependence and enhance power-supply rejection ratio (PSRR) of the sensing process. The application-specific integrated circuit has been designed and fabricated in 180-nm CMOS process and has dimensions of 2 mm × 2 mm. The measurement results demonstrate that the WPT circuit generates a DC voltage of 1V with a power transfer efficiency of 85% for distances 2 to 8 mm with settling time of microseconds to milliseconds. The temperature sensor demonstrates a resolution of < ±0.6C with a sensitivity of 0.52 mV/C and 126.9 Hz/C along with PSRR of −63dB and Integral Non-Linraity (INL) of 5% measured across six different dies. The back-scattering communication demonstrates a −53-dB signal at a distance of 4 mm without affecting the WPT efficiency. The total power consumption of the temperature sensor along with the integrated biases is 120 nW.     

Cytotoxicity of Newly Synthesized Quinazoline–Sulfonamide Derivatives in Human Leukemia Cell Lines and Their Effect on Hematopoiesis in Zebrafish Embryos

Many quinazoline derivatives with pharmacological properties, such as anticancer activity, have been synthesized. Fourteen quinazoline derivatives bearing a substituted sulfonamide moiety (4a–n) were previously synthesized and fully characterized. These compounds exerted antiproliferative activity against cell lines derived from solid tumors. Herein, the antileukemic activities of these compounds (4a–n) against two different leukemia cell lines (Jurkat acute T cell and THP-1 acute monocytic) were investigated. Our investigation included examining their activity in vivo in a zebrafish embryo model. Remarkably, compounds 4a and 4d were the most potent in suppressing cell proliferation, with an IC₅₀ value range of 4–6.5 µM. Flow cytometry analysis indicated that both compounds halted cell progression at the G2/M phase and induced apoptosis in a dose-dependent manner. RT-PCR and Western blot analyses also showed that both compounds effectively induced apoptosis by upregulating the expression of proapoptotic factors while downregulating that of antiapoptotic factors. In vivo animal toxicity assays performed in zebrafish embryos indicated that compound 4d was more toxic than compound 4a, with compound 4d inducing multiple levels of teratogenic phenotypes in zebrafish embryos at a sublethal concentration. Moreover, both compounds perturbed the hematopoiesis process in developing zebrafish embryos. Collectively, our data suggest that compounds 4a and 4d have the potential to be used as antileukemic agents.     

A Joint Algorithm for Resource Allocation in D2D 5G Wireless Networks

With the rapid development of Internet technology, users have an increasing demand for data. The continuous popularization of traffic-intensive applications such as high-definition video, 3D visualization, and cloud computing has promoted the rapid evolution of the communications industry. In order to cope with the huge traffic demand of today’s users, 5G networks must be fast, flexible, reliable and sustainable. Based on these research backgrounds, the academic community has proposed D2D communication. The main feature of D2D communication is that it enables direct communication between devices, thereby effectively improve resource utilization and reduce the dependence on base stations, so it can effectively improve the throughput of multimedia data. One of the most considerable factor which affects the performance of D2D communication is the co-channel interference which results due to the multiplexing of multiple D2D user using the same channel resource of the cellular user. To solve this problem, this paper proposes a joint algorithm time scheduling and power control. The main idea is to effectively maximize the number of allocated resources in each scheduling period with satisfied quality of service requirements. The constraint problem is decomposed into time scheduling and power control subproblems. The power control subproblem has the characteristics of mixed-integer linear programming of NP-hard. Therefore, we proposed a gradual power control method. The time scheduling subproblem belongs to the NP-hard problem having convex-cordinality, therefore, we proposed a heuristic scheme to optimize resource allocation. Simulation results show that the proposed algorithm effectively improved the resource allocation and overcome the co-channel interference as compared with existing algorithms.     

Particle Swarm Optimization for Solving Sine-Gordan Equation

The term ‘optimization’ refers to the process of maximizing the beneficial attributes of a mathematical function or system while minimizing the unfavorable ones. The majority of real-world situations can be modelled as an optimization problem. The complex nature of models restricts traditional optimization techniques to obtain a global optimal solution and paves the path for global optimization methods. Particle Swarm Optimization is a potential global optimization technique that has been widely used to address problems in a variety of fields. The idea of this research is to use exponential basis functions and the particle swarm optimization technique to find a numerical solution for the Sine-Gordan equation, whose numerical solutions show the soliton form and has diverse applications. The implemented optimization technique is employed to determine the involved parameter in the basis functions, which was previously approximated as a random number in the work reported till now in the literature. The obtained results are comparable with the results obtained in the literature. The work is presented in the form of figures and tables and is found encouraging.     

IoT-Driven Optimal Lightweight RetinaNet-Based Object Detection for Visually Impaired People

Visual impairment is one of the major problems among people of all age groups across the globe. Visually Impaired Persons (VIPs) require help from others to carry out their day-to-day tasks. Since they experience several problems in their daily lives, technical intervention can help them resolve the challenges. In this background, an automatic object detection tool is the need of the hour to empower VIPs with safe navigation. The recent advances in the Internet of Things (IoT) and Deep Learning (DL) techniques make it possible. The current study proposes IoT-assisted Transient Search Optimization with a Lightweight RetinaNet-based object detection (TSOLWR-ODVIP) model to help VIPs. The primary aim of the presented TSOLWR-ODVIP technique is to identify different objects surrounding VIPs and to convey the information via audio message to them. For data acquisition, IoT devices are used in this study. Then, the Lightweight RetinaNet (LWR) model is applied to detect objects accurately. Next, the TSO algorithm is employed for fine-tuning the hyperparameters involved in the LWR model. Finally, the Long Short-Term Memory (LSTM) model is exploited for classifying objects. The performance of the proposed TSOLWR-ODVIP technique was evaluated using a set of objects, and the results were examined under distinct aspects. The comparison study outcomes confirmed that the TSOLWR-ODVIP model could effectually detect and classify the objects, enhancing the quality of life of VIPs.