Smart Techniques Research Articles

An energy-efficient and reliable data gathering infrastructure using the internet of things and smart grids

Introduction: The Internet of Things (IoT) and Smart Grids (SGs) growth results from the advancement of computer hardware and ubiquitous computation for energy-efficient and reliable data gathering. Background: The limitations of the present cloud computing framework in energy systems remain unresolved, such as fully recognizing the requisites of high data usage with low latency which is discussed for a cloud computing scheme in IoT-based smart power stations. Problem Statement: IoT growth results from the advancement of computer hardware and ubiquitous computation. Among the various IoT solutions, SGs stand out because they combine several embedded smart techniques to increase the security and dependability of electricity grids. Methodology: A Particle Swarm Optimization based energy efficient integration of Smart Grid (PSO-SG) is designed using IoT. Modern technology establishes a novel hardware and software architecture and integrates cloud services into the cloud-based electricity network. Findings: As a result, a sizable amount of data produced by the electricity network will be examined, handled, and saved at the network. IoT-based power systems will enable the interconnection and administration of large endpoint devices, offer real-time evaluation and treatment of vast data, and promote the modernization of the power grid with the help of the cloud computing model.

Automation Techniques for Smart and Sustainable Agriculture and its Challenges

In the contemporary scenario, the world is primarily focused on trends and technology that leads to intelligent and accurate results in every field. Automation of everything has come up as a necessary demand due to this reason. Things are automated wherever possible. Agriculture is also one such field where automation has acquired much popularity. With the advent of the Fourth Industrial Revolution, integration of various tools and technologies like IoT, Deep Learning, cloud, etc., has led to the development of several automated agriculture systems, generally termed as "Smart and Sustainable Agriculture". Motivated by this fact, a comparative study of state-of-the-art approaches for Smart and Sustainable Agriculture systems using automated devices is presented in this paper. To build the basics, the paper discusses the Smart and Sustainable agriculture systems techniques for the automation of the agriculture systems. The next section presents the relative performance of different approaches. At last, the upcoming challenges and future work directions in this field are discussed next.

All About Wastewater Treatment Technologies and Application Options

Nowadays, an enormous amount of wastewater is generated, whether by industrial or human activities. Wastewater contaminated with toxic compounds can endanger the ecosystem and human health if released into the environment untreated or partially treated. For this reason, polluted industrial wastewater must be effectively treated before it is discharged into the environment. Although conventional wastewater treatment can efficiently reduce nitrogen and carbon concentrations, it is ineffective in removing so-called emerging pollutants (e.g., pharmaceuticals, pesticides, and personal care products). These compounds pass through the wastewater treatment plant unchanged and enter the environment in this way, posing a threat to nature and human life. Many of the emerging pollutants cause endocrine disruption or are carcinogenic. Efficient treatment is therefore urgently needed to remove these compounds before they enter the environment or water bodies. This special issue of the journal CLEAN – Soil, Air, Water includes the latest available technologies to treat industrial wastewater. It covers extensively the advancement in biological treatments, advanced oxidation techniques, and membrane technology to remove water pollutants. Having an easy-to-use guider manual for all the enlisted smart techniques, it also describes and discusses the emerging futuristic technologies in industrial pollutant removal from wastewater. Here, the new and emerging innovative trends in bioremediation of hazardous pollutants present in wastewater are discussed in depth. It also includes the fate of pollutants produced after the treatment process both at the laboratory scale and at the industrial scale. The special issue intrusively explores the unique biological aspects of the wastewater treatment process and highlights the advantages they provide for engineering applications in various industries. Each paper covers a different biological-based approach and examines the basic principles, practical applications, recent breakthroughs, and associated limitations. It presents an array of cutting-edge wastewater treatment research and thereafter its applications in treatment, remediation, sensing, and pollution prevention processes. The biological process for application in wastewater research has a significant impact on maintaining the long-term quality, availability, and usability of water. In this special issue, biological wastewater treatment technologies are explained. The biological processes presented in wastewater treatment processes include: 1) bioremediation of wastewater that includes aerobic treatment (oxidation ponds, aeration lagoons, aerobic bioreactors, activated sludge, percolating or trickling filters, biological filters, rotating biological contactors, and biological removal of nutrients) and anaerobic treatment (anaerobic bioreactors and anaerobic lagoons); 2) phytoremediation of wastewater that covers constructed wetlands, rhizofiltration, rhizodegradation, phytodegradation, phytoaccumulation, phytotransformation, and hyperaccumulators; and 3) mycoremediation of wastewater. The special issue describes a broad area of biological processes and water research which are considered key components for advanced water purification. It also includes the desalination technologies that remove, reduce, or neutralize water contaminants that threaten human health and/or ecosystem productivity and integrity. This special issue is mainly related to each of the main factors contributing to the removal of toxic pollutants from wastewater, namely, methods and procedures, materials (especially low-cost materials originating from industrial and agricultural waste), wastewater management, valorization possibilities of waste resulting from wastewater treatment processes, etc. This special issue is especially devoted to industrial wastewater treatment and aims to present the current state-of-the-art and innovative research that addresses these challenges, so that wastewater treatment systems can adapt and be fit for purpose, robust, and resilient for the next 100 years. It is equally beneficial for students and professors for understanding the new research advancements in this field. The main objective of this special issue is to summarize the work of the eminent scientists in this field to provide a clear but concise chapter that can be used as a quick reference for environmental engineers and researchers, and to be effectively implemented in higher education teaching undergraduate and graduate students, as well as extension and outreach.

Sustainable Operation of Active Distribution Networks

The present and future conditions in the energy market impose extremely high standards to the operation of building energy systems. Moreover, distribution networks face new operational and technical challenges as a result of the rapid penetration of renewable energy sources (RES) and other forms of distributed generation. Consequently, active distribution networks (ADNs) will play a crucial role in the exploitation of smart building prosumers, smart grids, and RES. In this paper, an optimization method for the sustainable operation of active distribution networks hosting smart residential building prosumers, plug-in electric vehicle (PEV) aggregators, and RES was developed. The thermal and electrical loads of the residential buildings were modeled in detail and an aggregation method was implemented to the hosted PEVs. Moreover, smart power dispatch techniques were applied at each building prosumer and PEV aggregator hosted by the active distribution network. Simultaneously, all the operational limitations of the active distribution network, building energy systems, and the hosted PEVs were satisfied. The constrained optimal power flow (OPF) algorithm was exploited to keep the voltages of the hosting distribution network between the permissible bounds. A significant operation cost reduction of 17% was achieved. The developed models were verified through detailed simulation results.

Sustainable Agriculture: Rare-Actinomycetes to the Rescue

The failure of sustainable and agricultural intensifications in saving the ecosystem/public health has caused a paradigm shift to microbiome resource engineering through sustainable approaches. As agricultural intensification systems prioritize synthetic input applications over environmental health, sustainable intensification fails to define the end point of intensification, giving room for the application of “intensification” over “sustainability” to suit farmers’ needs. However, sustainable agricultural practices through microbiome resource services have been well harnessed and appreciated for their significant role in plant health and disease management due to their ability to secret agroactive metabolites with notable functionalities in a cooperative manner or as bioinoculants. The complexity of a cooperative microbiome and the uncontrollable nature of its numerous influencing parameters as well as the non-specificity associated with bioinoculant application, results in the direct utilization of agroactive compounds to obtain greater preventive efficiency. In this regard, the known bacterial trove has been seriously ransacked, yet there exists an inexhaustible bank of unknown compounds, which are conserved in Actinomycetes. However, the rare Actinomycetes group has received less attention than other plant growth-promoting bacteria; thus, the possibility exists that the Actinomycetes may encode novel useful metabolites. To unravel the possible uses of these metabolites for phytoprotection, smart culture-based techniques and genometabolomics technology have been applied. Hence the aim of this review is to express the sustainable nature of agro-antibiotics or biopesticide from these bacterial resources for the resolution of phytopathogenic havoc that reduces crop productivity.

Median incisional hernia and double parastomal hernia repair with retromuscular prosthetic biomaterial mesh using SMART technique (Stapled Mesh Stoma Reinforcement Technique) after pelvic exenteration due to cancer

Patient aged 64 with parastomal hernia on a terminal colostomy and a Bricker ileal conduit following pelvic exenteration in the context of a locally advanced low rectal adenocarcinoma.

Smart Edge Detection Technique in X-ray Images for Improving PSNR using Robert Edge Detection Algorithm with Gaussian Filter in Comparison with Laplacian Algorithm

Aim: This study aims to propose smart edge detection techniques in x-ray images for improving PSNR using the Robert edge detection algorithm and compared it with the laplacian algorithm. Materials and Methods: For the design of edge detection technique to improve PSNR Robert edge detection algorithm is used along with the gaussian filter and it is compared with the laplacian algorithm. Robert edge detection algorithm and laplacian algorithm are the two groups considered in this study. For each group, the sample size is 20 and the total sample size is 40. Sample size calculation was done using clinicalc.com by keeping g-power at 80%, confidence interval at 95%, and the threshold at 0.05%. Result: When comparing the two algorithms, it is clear that the Robert edge detection algorithm has a higher mean PSNR value of 43.83 db than the laplacian algorithm 43.33 db. It is observed that the Robert edge detection algorithm has statistically insignificant difference from the laplacian algorithm by performing an independent sample t-test with p value greater than 0.05. Conclusion: Robert edge detection has significantly greater PSNR when compared to the Laplacian algorithm

Smart Edge Detection Technique in X-ray Images for Improving PSNR using Sobel Edge Detection Algorithm with Gaussian Filter in Comparison with Laplacian Algorithm

Aim: The aim of this study is to propose smart edge detection techniques in x-ray images for improving PSNR using the sobel edge detection algorithm and comparing it with the laplacian algorithm. Materials and methods: For the design of edge detection technique to improve PSNR Sobel edge detection algorithm is used along with the gaussian filter and it is compared with the laplacian algorithm. Sobel edge detection algorithm and laplacian algorithm are the two groups considered in this study. For each group, the sample size is 20 and the total sample size is 40. Sample size calculation was done using clinicalc. com by keeping g-power at 80%, confidence interval at 95%, and the threshold at 0.05%. Result: When comparing the two algorithms, it is clear that the Sobel edge detection algorithm has a higher mean PSNR value of 39.15db than the laplacian algorithm 36.79db. It is observed that the Sobel edge detection algorithm performed better than the laplacian algorithm by performing an independent sample t-test. The p value is 0.09 which is greater than the normal value(p>0.05). Conclusion: Sobel edge detection has insignificantly greater PSNR when compared to the Laplacian algorithm.

Smart Edge Detection Technique in X-ray Images for Improving PSNR using Prewitt Edge Detection Algorithm with Gaussian Filter in Comparison with Laplacian Algorithm

Aim: This study aims to propose smart edge detection techniques in x-ray images for improving PSNR using the Prewitt edge detection algorithm and comparing it with the laplacian algorithm. Materials and methods: For the design of edge detection technique to improve PSNR Prewitt edge detection algorithm is used along with a gaussian filter and it is compared with the laplacian algorithm. Prewitt edge detection algorithm and laplacian algorithm are the two groups considered in this study. For each group, the sample size is 20 and the total sample size is 40. Sample size calculation was done using clinicalc. com by keeping g-power at 80%, confidence interval at 95%, and the threshold at 0.05%. Result: When comparing the two algorithms, it is clear that the Prewitt edge detection algorithm has a higher mean PSNR value of 39.19 db than the laplacian algorithm of 37.56 db. It is observed that the Prewitt edge detection algorithm is statistically significant p<0.05 than the laplacian algorithm by performing an independent sample t-test. Conclusion: Prewitt edge detection has insignificantly greater PSNR when compared to the Laplacian algorithm.

Smart Edge Detection Technique in X ray Images for Improving PSNR using Canny Edge Detection Algorithm with Gaussian Filter in Comparison with Laplacian Algorithm

Aim: The aim of this study is to propose smart edge detection techniques in x-ray images for improving PSNR using canny edge detection algorithm and compared with laplacian algorithm. Materials and Methods: Using the design of edge detection technique and to improve PSNR, canny edge detection algorithm is used along with gaussian filter and it is compared with laplacian algorithm. Canny edge detection algorithm and laplacian algorithm are the two groups considered in this study. For each group the sample size is 20 and the total sample size is 40. Sample size calculation was done using clinicalc. com by keeping g-power at 80%, confidence interval at 95% and threshold at 0.05%. Result: When comparing the two algorithms, it is clear that the canny edge detection algorithm has a higher mean PSNR value of 28.98db than the laplacian algorithm 27.08 db. It is observed that the canny edge detection algorithm performed better than the laplacian algorithm (p>0.05) by performing an independent sample t-test. Conclusion: Canny edge detection has insignificantly greater PSNR when compared to laplacian algorithm.

Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production

Technological advancements have led to an increased use of the internet of things (IoT) to enhance the resource use efficiency, productivity, and cost-effectiveness of agricultural production systems, particularly under the current scenario of climate change. Increasing world population, climate variations, and propelling demand for the food are the hot discussions these days. Keeping in view the importance of the abovementioned issues, this manuscript summarizes the modern approaches of IoT and smart techniques to aid sustainable crop production. The study also demonstrates the benefits of using modern IoT approaches and smart techniques in the establishment of smart- and resource-use-efficient farming systems. Modern technology not only aids in sustaining productivity under limited resources, but also can help in observing climatic variations, monitoring soil nutrients, water dynamics, supporting data management in farming systems, and assisting in insect, pest, and disease management. Various type of sensors and computer tools can be utilized in data recording and management of cropping systems, which ensure an opportunity for timely decisions. Digital tools and camera-assisted cropping systems can aid producers to monitor their crops remotely. IoT and smart farming techniques can help to simulate and predict the yield production under forecasted climatic conditions, and thus assist in decision making for various crop management practices, including irrigation, fertilizer, insecticide, and weedicide applications. We found that various neural networks and simulation models could aid in yield prediction for better decision support with an average simulation accuracy of up to 92%. Different numerical models and smart irrigation tools help to save energy use by reducing it up to 8%, whereas advanced irrigation helped in reducing the cost by 25.34% as compared to soil-moisture-based irrigation system. Several leaf diseases on various crops can be managed by using image processing techniques using a genetic algorithm with 90% precision accuracy. Establishment of indoor vertical farming systems worldwide, especially in the countries either lacking the supply of sufficient water for the crops or suffering an intense urbanization, is ultimately helping to increase yield as well as enhancing the metabolite profile of the plants. Hence, employing the advanced tools, a modern and smart agricultural farming system could be used to stabilize and enhance crop productivity by improving resource use efficiency of applied resources i.e., irrigation water and fertilizers.

Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network

In this work, we provide a smart home occupancy prediction technique based on environmental variables such as CO2, noise, and relative temperature via our machine learning method and forecasting strategy. The proposed algorithms enhance the energy management system through the optimal use of the electric heating system. The Long Short-Term Memory (LSTM) neural network is a special deep learning strategy for processing time series prediction that has shown promising prediction results in recent years. To improve the performance of the LSTM algorithm, particularly for autocorrelation prediction, we will focus on optimizing weight updates using various approaches such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performances of the proposed methods are evaluated using real available datasets. Test results reveal that the GA and the PSO can forecast the parameters with higher prediction fidelity compared to the LSTM networks. Indeed, all experimental predictions reached a range in their correlation coefficients between 99.16% and 99.97%, which proves the efficiency of the proposed approaches.

Yoga and Stress Management: The Impact on Health

This study aims to explain the significance and importance of Yoga in our daily lives. People in the global village or science and technology age work tirelessly to sustain and improve their lives. They are individuals of all socioeconomic backgrounds who strive for social and peaceful living. Anxiety, pressure, tension, and despair are examples of stressors. Stress is a bad energy or difficult challenge that all beings must manage. Stress management is considered a significant factor for happiness and success in today's society. It creates a variety of physical. Stress management is considered a significant factor for happiness and success in today's society. Individuals experience a range of physical and mental symptoms, which vary depending on their situation. Stress management offers several strategies to reduce anxiety and promote general well-being. Yoga is the most effective strategy to manage stress through smart techniques.

Smart UV Derivative Spectrophotometric Methods for Simultaneous Determination of Metformin and Remogliflozin: Development, Validation and Application to the Formulation

Abstract: Background: The result of pharmaceutical industry research for the new class and the new combination of drugs for the treatments of diabetes is the newly approved combination of metformin (MET) and remogliflozin (REM). For the quality control of this formulation, three smart, reproducible and non-sophisticated spectroscopic techniques were developed by modification of UV spectra. Materials and Methods: The first two methods were based on the measurements of the peak height of the third derivative and second derivative ratio spectra of MET and REM and the third method was the constant center spectrum subtraction method. Results: The proposed methods exhibited Beer’s law in the range of 2.5 to 30 μg/ml and 1 to 24 μg/ml for MET and REM correspondingly by all three methods. The mean percentage recovery was found to be in the range of 99.08% to 100.15% for MET and 98.73% to 100.27% for REM. Further, both analytes were quantified from the formulation using proposed spectroscopic methods with high accuracy. Comparison of all three methods with the reported HPLC method showed no variation in the assay outcomes in relation to accuracy and precision. Conclusion: The suggested techniques are simple, accurate and reproducible, hence could be used for regular quality control of formulation consisting of MET and REM. Key words: Metformin, Remogliflozin, UV, Derivative spectroscopy, Validation, Formulation.

Leveraging the Role of Dynamic Reconfigurable Antennas in Viewpoint of Industry 4.0 and Beyond

Industry 4.0 is a digital paradigm that refers to the integration of cutting-edge computing and digital technologies into global industries because of which the state of manufacturing, communication, and control of smart industries has changed altogether. Industry 4.0 has been profoundly influenced by some major disruptive technologies such as the Internet of Things (IoT), smart sensors, machine learning and artificial intelligence, cloud computing, big data analytics, advanced robotics, augmented reality, 3D printing, and smart adaptive communication. In this review paper, we discuss physical layer-based solutions with a focus on high reliability and seamless connectivity for Industry 4.0 and beyond applications. First, we present a harmonized review of the industrial revolution journey, industrial communication infrastructure, key performance requirements, and potential sub-6-GHz frequency bands. Then, based on that, we present a comprehensive review of intelligent tunable dynamic antenna systems at sub-6 GHz as key enablers for next-generation smart industrial applications. State-of-the-art smart antenna techniques such as agile pattern reconfigurability using electrical components, machine learning- and artificial intelligence-based agile beam-scanning antennas, and beam-steerable dynamic metasurface antennas are thoroughly reviewed and emphasized. We unfolded the exciting prospects of reconfigurable dynamic antennas for intelligent and reliable connectivity in application scenarios of Industry 4.0 and beyond such as Industrial IoT and smart manufacturing.

A Time-Domain Method for Ultrasound Concrete Health Monitoring Using In Situ Piezoelectric Transducers

In this article, a smart time-domain technique is presented for detecting flaws in concrete structures using embedded piezoelectric transducers. The method relies on the time-of-flight of ultrasound (US) waves generated and sensed by piezoelectric transducers which are embedded in a concrete structure before pouring the concrete. A damage index is introduced to quantify the likelihood of defects in the region between the two probes. The index is comprised of a normalization function that is independent of the strength of the captured signal and not sensitive with respect to the amount of coupling between the embedded sensors and the structure. A sliding mode extremum-seeking (SMES) method is utilized for real time calculation of the damage index. The proposed health monitoring system was verified by experimental results. Comparisons are performed when using a conventional approach by a grid search method to demonstrate performance improvement of the proposed system.

A Comprehensive Review of Harmonic Issues and Estimation Techniques in Power System Networks Based on Traditional and Artificial Intelligence/Machine Learning

Modern industrial and commercial devices that are fed by power electronics circuits and behave non-linearly tend to produce power quality issues in power systems including harmonics and interharmonics, swell, flicker, spikes, notches, and transient instabilities. Among them, harmonic emission is the most significant challenge to be overcome by the distribution networks. Unwanted current, overheating motors and transformers, equipment failure, and circuit breaker misoperation are some of the harmonic consequences. While it is important to employ the best methods to mitigate or suppress the harmonic distortions in power systems, it is even more essential to estimate these harmonics at the outset by developing smart, efficient, and accurate techniques. Due to their capability for learning, predicting, and identifying, researchers have turned to Artificial Intelligence technologies for harmonic estimation in distribution networks. Although the power system parameters (impedance/admittance model) and many harmonic monitors are prerequisites for traditional harmonic estimation methods, by utilizing Artificial Intelligence, these requirements are minimized. In this paper, a comprehensive review of traditional and modern (smart) harmonic estimation techniques are discussed.

Project management and supply chain 4.0 improvement: the case of infant formulas in the face of the challenge of COVID-19

The changes in the environment make it necessary for organizations to prepare to face in the most effective and flexible way without negatively affecting the quality of the products or services offered, making this area of opportunity become growth and a competitive advantage against to the other companies.While Industry 4.0 has been a critical factor in overcoming the challenges of pandemic restrictions and improving productivity by providing continuous operations during the crisis, the smart supply chain has enabled a constant stock of products to reach their destination. Despite the efforts and success of the Industry 4.0 and the Intelligent Supply Chain during the pandemic to meet market needs, stockouts were still significant, making it a critical point to review project and risk management tools and techniques in its operations. Smart digital technologies and techniques have been useful in developing strategies to mitigate the effects of emergencies; however, these strategies are still highly vulnerable to crises of this magnitude. That is why the aim of this paper is to show the importance of adequate project and risk management within Industry 4.0 to achieve a flexible, intelligent, and resilient supply chain.

A evolução da investigação sobre a Economia da Prata: Investigações atuais, tendências e implicações para direções futuras

Abstract Society is undergoing unprecedented transformations caused by big economic, political, technological, social and mostly demographic changes. In this line, the role and weight of older adults turns more important due to the modifications such transformation will bring about in their characteristics, needs, expectations and goals as regards their personal, economic, political, technological and social aspects. This situation has given rise to the concept of Silver Economy, which broadly speaking represents that part of the economy related to the demographic change caused by aging population, including all those economic activities, products and services intended to meet the needs of older adults. Its being a growing field of knowledge makes it complex to identify the driving forces and agents that boost its development and therefore to promote and consolidate this new concept. In accordance with this opportunity, this piece of research maps the main research themes available in literature, making use of SciMAT-based smart bibliometric tools and techniques. In addition to other actions, this research makes it possible to identify the main themes, who the relevant agents are, the pioneering countries in this field, and the more productive organizations and sources. To this end, the Scopus database has been used as main source, bearing in mind that it concentrates 281 publications from 1979 to 2022 related to older adults and their role in economic, political, technological and social development, which has resulted a detailed frame of reference on Silver Economy.

A review on smart water management in various domestic areas: an approach for water consumption and leakage perspectives

One of the major issues that is affecting the water utilities in domestic areas is leakages through the distribution networks, non-revenue water estimation, and failure in the prediction of water consumption at individual spaces. This review paper basically focuses on these three important directions to understand the feasible domestic water management systems with integrated models of internet of things and artificial intelligence approaches. Different smart water management techniques have been reported in the literature to improve efficiency and avoid non-revenue water issues, and these are consolidated in this paper. Prediction failure of water utility in the water distribution systems is also presented in this paper. This article summarises a measure of water consumption statistics, a detailed summary of non-revenue water, and leak detection technologies at extensive areas to provide a wide sense of understanding for the prediction of the water consumption and leakage detection.