Hybrid optimization algorithm for enhanced performance and security of counter-flow shell and tube heat exchangers

A shell and tube heat exchanger (STHE) for heat recovery applications was studied to discover the intricacies of its optimization. To optimize performance, a hybrid optimization methodology was developed by combining the Neural Fitting Tool (NFTool), Particle Swarm Optimization (PSO), and Grey Relational Analysis (GRE). STHE heat exchangers were analyzed systematically using the Taguchi method to analyze the critical elements related to a particular response. To clarify the complex relationship between the heat exchanger efficiency and operational parameters, grey relational grades (GRGs) are first computed. A forecast of the grey relation coefficients was then conducted using NFTool to provide more insight into the complex dynamics. An optimized parameter with a grey coefficient was created after applying PSO analysis, resulting in a higher grey coefficient and improved performance of the heat exchanger. A major and far-reaching application of this study was based on heat recovery. A detailed comparison was conducted between the estimated values and the experimental results as a result of the hybrid optimization algorithm. In the current study, the results demonstrate that the proposed counter-flow shell and tube strategy is effective for optimizing performance

Advances in real time smart monitoring of environmental parameters using IoT and sensors

People who work in dangerous environments include farmers, sailors, travelers, and mining workers. Due to the fact that they must evaluate the changes taking place in their immediate surroundings, they must gather information and data from the real world. It becomes crucial to regularly monitor meteorological parameters such air quality, rainfall, water level, pH value, wind direction and speed, temperature, atmospheric pressure, humidity, soil moisture, light intensity, and turbidity in order to avoid risks or calamities. Enhancing environmental standards is largely influenced by IoT. It greatly advances sustainable living with its innovative and cutting-edge techniques for monitoring air quality and treating water. With the aid of various sensors, microcontroller (Arduino Uno), GSM, Wi-Fi, and HTTP protocols, the suggested system is a real-time smart monitoring system based on the Internet of Things. Also, the proposed system has HTTP-based webpage enabled by Wi-Fi to transfer the data to remote locations. This technology makes it feasible to track changes in the weather from any location at any distance. The proposed system is a sophisticated, efficient, accurate, cost-effective, and dependable weather station that will be valuable to anyone who wants to monitor environmental changes on a regular basis

<span style="font-size:15.0pt;mso-bidi-font-size: 14.0pt;font-family:"Times New Roman";mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:Mangal;mso-ansi-language:EN-GB;mso-fareast-language:EN-US; mso-bidi-language:HI;mso-bidi-font-weight:bold" lang="EN-GB">Spectroscopic studies of Cu²⁺ spin probe in sodium niobium based tellurite glasses</span>

339-345<span style="font-size:9.0pt;mso-bidi-font-size: 12.0pt;font-family:" times="" new="" roman";mso-fareast-font-family:"times="" roman";="" mso-bidi-font-family:mangal;mso-ansi-language:en-gb;mso-fareast-language:en-us;="" mso-bidi-language:hi"="" lang="EN-GB">Differential scanning calorimetry (DSC), electron spin resonance (ESR), Raman, infrared (IR) and optical absorption studies on 10Na2O-xNb2O5-(89-x)TeO2-1CuO glasses (where x =0, 5, 10, 15 and 20 mol%) containing Cu2+ spin probe have been carried out. Glass transition temperature (<i style="mso-bidi-font-style: normal">Tg) and thermal stability of glass samples have been estimated from the DSC measurements and it has been found that both increase with increasing the Nb2O5<i style="mso-bidi-font-style: normal"> content. From Raman and IR spectra, it is clear that present glass system consists of structural units of TeO3 (tp) and TeO4 (tbp) and NbO6 octahedra. Also, TeO4 units convert into TeO3 units with change in Nb2O5 content. The hyperfine splittings in the parallel features of ESR spectra of Cu2+ are recorded for all compositions. ESR results show that g|| > g indicating that the Cu2+ ion is in tetragonal distorted octahedral site and its ground state is dx2- y2. There is considerable variation in Hamiltonian parameters for the different compositions. The observed optical absorption peak of Cu2+ is found to be maximum at 780 nm for 10 mol% of Nb2O5 content. Bonding parameters and % bonding symmetry are calculated from both optical and ESR data changing with increasing Nb2O5 content. </span

Spectroscopic and <i>dc</i> conductivity studies of Cu²⁺ spin probe in 10Li₂O- <i style="">x</i>Nb₂O₅-(89-<i style="">x</i>)TeO₂ glass system

241-247 DSC, ESR, IR, Raman, optical absorption and dc conductivity studies on 10Li2O- xNb2O5-(89-x) TeO2 glasses containing Cu2+ spin probe have been carried out. Glass transition temperature (Tg) and thermal stability of glass samples are estimated from the DSC measurements and it has been found that both increase with increasing the Nb2O5 content. From IR and Raman spectra, it is clear that, the glass system consists of structural units of TeO3 (tp), TeO4 (tbp) and NbO6 octahedra. TeO4 units are found to be converted into TeO3 units with increase in Nb2O5 content. From ESR spectra, it is found that the Cu2+ ion is in tetragonal distorted octahedral site with dx2-y2 as ground state. The observed optical absorption peak of Cu2+ is found to be maximum at 800 nm for 10 mol % of Nb2O5 content. Bonding parameters and per cent bonding symmetry are calculated from both optical and ESR data and are found to change with increase in Nb2O5 content. The observed dc conductivity is found to be increasing with temperature and decreasing with increase in Nb2O5 content. </smarttagtype

ESR, IR and optical absorption studies of Cu²⁺ spin probe in <i style="">x</i>Na₂O-(50-<i style="">x</i>)ZnO-50B₂O₃ ternary glasses

833-837 ESR, IR and optical absorption studies on xNa2O-(50-x)ZnO-50B2O3 ternary glasses containing Cu2+ spin probe have been carried out. The absence of band at 806 cm-1 in the IR spectra indicates the absence of boroxol rings. Thus, the present glass system consists of borate triangles [BO3] and borate tetrahedral [BO4] structural units. It was also observed that structural changes are taking place with variation of Na2O and ZnO contents. The hyperfine splittings in the parallel features of ESR spectra of Cu2+ are recorded for all compositions. ESR results show that the g|| > g⊥ indicating that the Cu2+ ion is in tetragonal distorted octahedral site and its ground state is dx2-y2. There is considerable variation in g|| and A|| values with the increasing concentration of Na2O, whereas no perceptible changes have been observed in g⊥ and A⊥ values. The observed optical absorption peak of Cu2+ has been found to be at 800 nm for x=25 mol% of Na2O content. Bonding parameters are calculated from both optical and ESR data. All these variations clearly indicate the structural changes in the present glass system with varying Na2O content. </smarttagtype

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Not AvailableMicrobial insecticides or entomopathogens are effective and eco-friendly insect pest management options. But slow mode of action and lack of a visual pest control, as expected by a farmer, mostly limits their wide commercial usage. The present day regular and high incidences of insect pests, due to intensive monocultures, warrant inevitable use of high doses of chemical pesticides. However, their judicious application depends on the diverse environmental threats associated. So, deployment of both entomopathogenic microbes and chemical pesticides together is considered to reduce the risk to the environment. Various studies also reported more efficient synergistic interactions in combined use than for independent applications. Synergism has the ability to reduce the pesticide doses. Most importantly, the combined application due to synergism can effectively tackles the pest problem and also helps in establishment of an entomopathogen in a given ecosystem. Once established, the entomopathogens can effectively manage the pest population build up in an eco-friendly manner, and over the years they can evade the use of pesticides or, if not so, reduce their dosage. The present chapter critically discusses possible synergism between entomopathogens and chemical pesticides and the present status of pest management achieved through this approach, in the context of latest research findings.Not Availabl

Correction to: A hybrid model for lung cancer prediction using patch processing and deeplearning on CT images (Multimedia Tools and Applications, (2023), 10.1007/s11042-023-17349-8)

The original publication of this article contains error in the author name. The author name J. Chinnababu should be J. Chinna Babu. The original article has been corrected

An automatic diagnostic model for the detection and classification of cardiovascular diseases based on swarm intelligence technique

Globally, cardiovascular diseases (CVDs) rank among the leading causes of mortality. One out of every three deaths is attributed to cardiovascular disease, according to new World Heart Federation research. Cardiovascular disease can be caused by a number of factors, including stress, alcohol, smoking, a poor diet, inactivity, and other medical disorders like high blood pressure or diabetes. In contrast, for the vast majority of heart disorders, early diagnosis of associated ailments results in permanent recovery. Using newly developed data analysis technology, examining a patient's medical record could aid in the early detection of cardiovascular disease. Recent work has employed machine learning algorithms to predict cardiovascular illness on clinical datasets. However, because of their enormous dimension and class imbalance, clinical datasets present serious issues. An inventive model is offered in this work for addressing these problems.An efficient decision support system, also known as an assistive system, is proposed in this paper for the diagnosis and classification of cardiovascular disorders. It makes use of an optimisation technique and a deep learning classifier. The efficacy of traditional techniques for predicting cardiovascular disease using medical data is anticipated to advance with the combination of the two methodologies. Deep learning systems can reduce mortality rates by predicting cardiovascular illness based on clinical data and the patient's severity level. For an adequate sample size of synthesized samples, the optimisation process chooses the right parameters to yield the best prediction from an enhanced classifier. The 99.58% accuracy was obtained by the proposed method. Also, PSNR, sensitivity, specificity, and other metrics were calculated in this work and compared with systems that are currently in use

Low-Power Very-Large-Scale Integration Implementation of Fault-Tolerant Parallel Real Fast Fourier Transform Architectures Using Error Correction Codes and Algorithm-Based Fault-Tolerant Techniques

As technology advances, electronic circuits are more vulnerable to errors. Soft errors are one among them that causes the degradation of a circuit’s reliability. In many applications, protecting critical modules is of main concern. One such module is Fast Fourier Transform (FFT). Real FFT (RFFT) is a memory-based FFT architecture. RFFT architecture can be optimized by its processing element through employing several types of adder and multipliers and an optimized memory usage. It has been seen that various blocks operate simultaneously in many applications. For the protection of parallel FFTs using conventional Error Correction Codes (ECCs), algorithmic-based fault tolerance (ABFT) techniques like Parseval checks and its combination are seen. In this brief, the protection schemes are applied to the single RAM-based parallel RFFTs and dual RAM-based parallel RFFTs. This work is implemented on platforms such as field programmable gate arrays (FPGAs) using Verilog HDL and on application-specific integrated circuit (ASIC) using a cadence encounter digital IC implementation tool. The synthesis results, including LUTs, slices registers, LUT–Flip-Flop pairs, and the frequency of two types of protected parallel RFFTs, are analyzed, along with the existing FFTs. The two proposed architectures with the combined protection scheme Parity-SOS-ECC present an 88% and 33% reduction in area overhead when compared to the existing parallel RFFTs. The performance metrics like area, power, delay, and power delay product (PDP) in an ASIC of 45 nm and 90 nm technology are evaluated, and the proposed single RAM-based parallel RFFTs architecture presents a 62.93% and 57.56% improvement of PDP in 45 nm technology and a 67.20% and 60.31% improvement of PDP in 90 nm technology compared to the dual RAM-based parallel RFFTs and the existing architecture, respectively

Design Optimization of Counter-Flow Double-Pipe Heat Exchanger Using Hybrid Optimization Algorithm

Double-pipe counter-flow heat exchangers are considered more suitable for heat recovery in the heat transfer industry. Numerous studies have been conducted to develop static tools for optimizing operating parameters of heat exchangers. Using this study, an improved heat exchanger system will be developed. This is frequently used to solve optimization problems and find optimal solutions. The Taguchi method determines the critical factor affecting a specific performance parameter of the heat exchanger by identifying the significant level of the factor affecting that parameter. Gray relational analysis was adopted to determine the gray relational grade to represent the multi-factor optimization model, and the heat exchanger gray relation coefficient target values that were predicted have been achieved using ANN with a back propagation model with the Levenberg–Marquardt drive algorithm. The genetic algorithm improved the accuracy of the gray relational grade by assigning gray relational coefficient values as input to the developed effective parameter. This study also demonstrated significant differences between experimental and estimated values. According to the results, selecting the parameters yielded optimal heat exchanger performance. Using a genetic algorithm to solve a double-pipe heat exchanger with counterflow can produce the most efficient heat exchanger