Smart Techniques Research Articles

Microvibration Control of Hybrid Platform for High-Tech Equipment in Buildings Using Brain Emotional Learning-Based Intelligent Controller

In the semiconductor industry, the vulnerability of high-tech facilities installed on platforms to ground excitations induced by nearby traffic is significantly pronounced, primarily due to their small-scale dimensions. Consequently, it is imperative to design a smart control technique by effectively utilizing model-free controllers. Recently, adaptive intelligent control algorithms have emerged as a viable alternative to conventional model-based control algorithms. To address this issue, this study meticulously designed a hybrid platform using the adaptive intelligent controller known as the brain emotional learning-based intelligent controller, along with a Sugano fuzzy inference system to effectively optimize the controller’s learning parameters. These learning and intelligent-based algorithms offer notable advantages, including the ability to handle nonlinearity, uncertainty, and training capabilities within the control systems. To assess the effectiveness of the proposed controller in mitigating vibrations induced by traffic on high-tech facilities, a three degree-of-freedom structure is employed along with the hybrid platform. Finally, the performance of the hybrid platform in terms of microvibration control levels is meticulously validated using the Bolt Beranek Newman vibration criteria. Simulation results unequivocally demonstrate that the proposed controller outperforms both an uncontrolled system and a traditional linear quadratic regulator controller in terms of reducing the traffic-induced response of the hybrid platform and second floor, respectively. Through the integration of learning and intelligent-based controllers, the velocity levels of both the hybrid platform and the second floor are reduced to approximately 49.02% and remain well within the acceptable standard criteria curves.

Controlling the effectiveness of STATCOM using ANFIS based on PI controller

This research achieves two goals, the first is the modelling of the static synchronous compensator (STATCOM) and design of the control circuits for current, voltage and d.c capacitor voltage (Vd.c) to improve the power factor (PF) by preparing the reactive power by STATCOM so that its work is similar to the work of a synchronous condenser, and the second goal is to use smart techniques to control the Vd.c loop. For comparison, smart technical methods such as fuzzy logic type 1 (FL-T1), fuzzy logic type 2 (FL-T2), and adaptive neuro fuzzy inference system (ANFIS) were used to regulate the Vd.c on the capacitor instead of the traditional controller proportional integral (PI). Simulation was performed in MATLAB 2021 to determine the efficiency of the suggested method or approach controllers for STATCOM. The use of the proposed method improves the signal value of both the current and the voltage and the phase difference between them, which reaches almost zero. Proved that an ANFIS technique provides best Vd.c response in different values of balance and unbalance load compared with the other methods by obtaining the minimum peak overshoot (P-ov) and minimum settling time (ST).

A Novel High-Priority Traffic Light System Based on a Smart Technique

In the world today, traffic congestion in most cities is a severe problem. Recently, the system used is based on a fixed-time system in traffic, which cannot provide incompetence if one or more lane has higher traffic than others. The higher traffic on one side lane needs a longer operation time for a green signal as compared to the fixed time-based systems. This paper proposed a High Priority Traffic Light system HPTL as a technique to compute the period time of green and red lights based on the density of traffic present at that time. To fix this problem an automatic traffic light control system should proposed. The proposed system will be achieved by using PIR proximity Infrared Sensors. When the density is computed, the luminous period of the green and red signal will be selected to help the microcontroller. On each roadside, the sensors can detect the traffic density and calculate the vehicles in a specific lane then forward the density value to the microcontroller. The microcontroller will select the free lane and the high-traffic lane and then assign the priority system. The proposed system is successful in investing time, reducing vehicle density, and increasing reliability.

Smart Monitoring of Solar Photovoltaic Panels by the Approach of Machine Learning

Due to the increasingly evident climate change and the recent energy crisis triggered by regional conflicts, the exploitation of renewable energy has become one of the pressing tasks today. In light of this background, today the photovoltaic (PV) market is booming globally. This makes the management and maintenance of solar PV panels a new area of business, as neglecting it may not only lead to significant financial losses but also the failure to combat climate change and the energy crisis. In fact, in the long service life of solar panels, they do face many risks that may degrade their power generation performance, damage their structures, or even cause the complete loss of their power generation capacity. It is hoped that these problems can be found and resolved as soon as possible. However, in reality, this is a challenging task as a large solar power plant may contain hundreds of thousands of solar PV panels. To address this issue, a smart solar panel condition monitoring technique is studied in this paper with the aid of the U-Net neural network and Support vector machine (SVM). The research result has shown that using the developed technique, the thermal infrared images that are remotely collected by drones from solar power plants can be rapidly and automatically processed, analysed and classified with reasonable accuracy. This is of great significance to further improve the asset management skills in solar power plants.

Fabrication of uniform Pd nanoparticles immobilized on crosslinked ionic chitosan support as a super-active catalyst toward regioselective synthesis of pyrazole-fused heterocycles

Selection of biodegradable chitosan as a raw material is a smart technique due to its easy modifiability and high renewability. Herein, taking advantage of these functional characteristics, an ionic biopolymer support is produced from copolymerization of allylated chitosan (with 48 % degree of substitution) and polymerizable ionic liquid ([MEVIm]Br). Next, palladium nanoparticles are successfully stabilized in this designed support through a facile manner based on interconnected porous network, ionic nature and rich functional groups. Then, the Pd@CS-PIL structure was utilized as a heterogeneous catalyst for regioselective synthesis of pyrazole-fused heterocycles. The as-synthesized Pd@CS-PIL was characterized by various techniques such as XRD, EDX, FESEM, elemental mapping, TEM, BET, ICP, TGA, and FT-IR to better determine the structure, morphology, purity and physical properties. The obtained results revealed that the proposed nanostructure provides favorable porosity with significant specific surface area (139.2 m2.g−1), Pd nanoparticles with high dispersion (mean diameter ∼ 22.8 nm) and crosslinked nature with good thermal stability (50 % weight loss about 600 °C). Therefore, Pd@CS-PIL nanostructure showed the key features of a super-active catalyst, and pharmaceutical pyrazole-fused scaffolds were produced in favorable yields (86–96 %) under ultrasound conditions.

Ventral incisional hernia and double parastomal hernia repair with retromuscular biosynthetic mesh using the SMART technique (Stapled Mesh Stoma Reinforcement Technique) after cancer-related pelvic exenteration

We report on the case of a 64-year-old man who underwent the SMART surgical technique using retromuscular biosynthetic mesh for parastomal hernia. This patient had a terminal colostomy and Bricker ileal conduit following pelvic exenteration for locally advanced low rectal adenocarcinoma. Since then, he had undergone a first median incisional hernia repair using intraperitoneal biosynthetic mesh, followed a year later by the onset of a symptomatic parastomal hernia at the stoma sites and next to the median laparotomy scar. It was thus decided to perform a double parastomal hernia repair using the SMART technique with retromuscular biosynthetic mesh.

Efficient approaches to autogenous and autonomous self-healing concrete: a review

Concrete and cementitious composites have far been the main and most widely used construction materials throughout the world due to their outstanding features, such as high structural properties, accessibility and relatively low production costs. However, these materials are prone to degradation caused either by automatic cracking or external excitations, which leads to structural degradation. The increasing concern for maintaining safety and enhancing durability of cement composite structures has led to the development of several smart self-healing techniques by researchers. Regarding the diversity of these methods, this study aims to provide a comprehensive review of state-of-the-art technologies in self-healing concrete. For this purpose, the methods are divided into two general categories: autogenous and autonomous self-healing, each of which are subdivided into different methods. The basic principles and influencing factors as well as the main advantages and challenges of these techniques are discussed. Finally, general conclusions are drawn and some suggestions for future advances are presented.

The factors obstructing the blockchain adoption in supply chain finance: a hybrid fuzzy DELPHI-AHP-DEMATEL approach

PurposeSupply chain finance (SCF) is an advanced financing solution for medium and small organizations. However, due to its nascent stage, SCF is still struggling with its application in the supply chain. The blockchain application in SCF can bring lots of opportunities and smart techniques to deal with financing to organizations. However, some factors restrict the proper adoption of blockchain in SCF. Therefore, this study aims to identify and analyze the factors obstructing blockchain adoption in SCF.Design/methodology/approachThe present study applied a hybrid fuzzy DELPHI-AHP-DEMATEL to validate and analyze the factors. The fuzzy Delphi techniques were adopted to validate the identified factors. Thereafter, the analytic hierarchy process (AHP) has been used to prioritize the validated factors. Further, the decision-making trial and evaluation laboratory (DEMATEL) method has opted to classify the factors into cause-and-effect groups.FindingsThe result of the study revealed that lack of technological competence and confidentiality leaking are the most prioritized factors and act as the prime factors for the obstacles in the blockchain application in the SCF. The application of the DEMATEL method revealed that the highest causing factor is authenticity, and the highest effecting factor is high cost.Practical implicationsThe current study enhances the knowledge and information of managers and policymakers about the challenges associated with the blockchain application in SCF as well as provides better insight to deal with various factors obstructing blockchain adoption in SCF.Originality/valueThe current study is the first of its kind, where factors obstructing blockchain adoption in SCF are identified and validated with the help of experts and practitioners by incorporating the fuzzy Delphi method. Thereafter, the validated factors are prioritized by using fuzzy AHP and fuzzy DEMATEL techniques helps to identify the cause-and-effect group, which opens the door for future research work.

Economic Analysis of the Coffee Market in Rwanda

Coffee contributes significantly to the economy of Rwanda as a major export product and the livelihood of thousands of Rwandans. As such, the government of Rwanda has taken great strides to develop the coffee production sector particularly in developing infrastructure impacted by the genocide and implementing policies to ensure sustainable growth. This study aimed to give an economic analysis of the coffee market in Rwanda. It examined the factors affecting the coffee market and identified the major in the market globally and regionally and used them as a benchmark for comparison. The study showed various measures being taken to increase the quantity of coffee produced including increasing the area of harvested coffee following a drop in area from a high of 55 000 hectares in 2014 to 21 700 hectares in 2019. Rwanda’s National Agricultural Export Board implemented measures to improve yields and production quality through providing fertilizers, improving management of wash stations and engaging private stakeholders and NGOs to train farmers in smart agriculture techniques. Consequentially the quantity of coffee produced and exported improved between 2014 and 2019.

Renew or die amidst COVID-19: investigating the effect of organizational learning culture on strategic renewal through strategic reconfiguration and digital transformation

PurposeBased on the dynamic capabilities view, the current study aims to empirically investigate the effects of organizational learning culture (OLC), strategic reconfiguration (SREC) and digital transformation (DT), altogether, on Egyptian private hospitals' strategic renewal in the face of the COVID-19 pandemic.Design/methodology/approachThis study adopted a cross-sectional design to collect the data used to carry out mediation analysis. A self-administered questionnaire was used to collect data from a sample consisted of 264 Egyptian private hospitals. The smart partial least square structural equation modeling technique (PLS-SEM) was adopted to test the hypotheses.FindingsThe results demonstrate that OLC directly and positively affects SR. Besides, SREC and DT partially and serially mediate the OLC-SR relationship.Research limitations/implicationsThe sample size was small, covering only Egyptian private hospitals. The results may be different in the manufacturing sector and in other countries. The study was cross-sectional which is limited to trace long-term effects of OLC, SREC and DT on SR. Accordingly, a longitudinal study may be undertaken.Practical implicationsPrivate hospitals' managers must actively explore and dig out valuable resources in order to discover potential information and trends endeavor to redesign internal structures, and reconfigure their current resources, structures and strategies to achieve strategic renewal. The findings also provide new insights to mangers of private sectors' institutions and direct their attention toward adopting the strategic renewal option to survive amidst crises instead of retrenchment, persevering, or quitting business.Social implicationsThe study's results imply that health care providers have sought to improve the capacities of their health care systems to address the patient-level social needs through continuous learning, internal reconfigurations and the transformation toward digitalization to renew their services.Originality/value This study therefore contributes to SR literature by being the first empirical study to introduce an integrative model for the antecedents of SR amidst the pandemic.

Automatic Number Plate Recognition - SMART Security check

With the advancements in Technology in recent Trends the Artificial Intelligence has shown its own recognition in the society. We are now converting all the Human interaction with AI in different places of our daily life. AI has been replacing all the human efforts to make the smart work. In such different areas we have selected an area where there is a much scope and necessity for developing and using smart techniques to simplify our daily routine. The vehicles that are entering into the campus by a Security Guard. For this it requires the image of the vehicle at the time of entry. The step by step process of recognizing the Number Plate is implemented by taking the image and identifying the part of number of the vehicle and read the vehicle number on the plate and store it in our database. Vehicle number plate region is taken and the image is preprocessed with CNN. In this paper we have implemented the process using Haarcascade_russian_plate_number library to recognize the number plate and OCR is used to extract the characters from the image.

Optimal frequency support method for urban microgrids of building prosumers

High penetration of renewable energy sources (RES) is raising a number of technical challenges for the sustainable and reliable operation of electric power systems. To this end, the need for ancillary services provided by the building sector to the electric power system is becoming particularly significant; with frequency support being one among them. Urban microgrids and especially those of them hosting building prosumers will have a key role in frequency support as they comprise flexible components that can be exploited to this end. In this paper, detailed models for the accurate simulation of frequency support mode of operation are developed. Frequency support in microgrids of buildings is enhanced by properly utilizing the flexibility of building prosumers. Frequency is also controlled by optimally regulating building local generation units and their adjustable electrical loads in a flexibility-based way and taking into consideration gas emission limitation. Emphasis is placed on the enhancement of the computational efficiency of the proposed support method by suitably exploiting smart power dispatch techniques. The developed method is validated through the simulation of a realistic complex urban microgrid.

A versatile nature of effective photocatalytic activity for degradation of the organic pollutant Rhodamine-B dye by PVA/ZnO/CuO nanocomposite

The photocatalytic degradation of dyes using metal oxides and their nanocomposites has been shown to be a smart technique for the detoxification of pollutants from the wastewater system. The desired PVA/ZnO/CuO nanocomposite was synthesized by the chemical precipitation method and characterized with XRD, FTIR, FESEM and UV–Visible spectroscopy. The X-ray diffraction depicts that the composite has poly-crystalline properties with multiphase heterojunctions. FTIR spectroscopy depicts the presence of functional groups of the polyvinyl alcohol polymer and metal oxides for the nanocomposite. FESEM depicts the surface morphology of the synthesized nanocomposite and the UV–Visible spectrum depicts photo-harvesting in the visible light region with a band gap of 1.68 eV for the catalyst. The photocatalyst degradation activity was examined against the organic pollutant Rhodamine B, which concluded a maximum efficiency of 71% degradation; the dynamic profile reached 0.28 (a.u.) in 90 min and the kinetic profile depicted a pseudo first order kinetic reaction rate R2=0.996. The results revealed that the synthesized PVA/ZnO/CuO nanocomposite showed enhanced effective photocatalytic activity under visible light.

Food safety among and beyond: the power of market actors, institutions and researchers in the new era of food safety from farm-to-table

Purpose. The study objective is to assess the impact of a number of factors, such as buying and psychological behaviours, experience, knowledge and information about food quality and safety, trust in actors and institutions, risk perceptions, safety knowledge and willingness to pay, increased risks in last years and change in consumption, on the food safety of conventional agricultural products (vegetables) in the markets of the city of Tirana, Albania. Methodology / approach. The questionnaire used for achieving the study objective was initially discussed at the level of a focus group and after improvements was used in food markets of agri-products in the city of Tirana, Albania. Interviewing process (220 persons) was carried out according to the procedure of the random choice and an appropriate statistical model was used. Results. The study presents an assessment of the impact of some important factors to food safety and the specifics of recent years, illustrated by a research on the markets of conventional agri-products in Tirana, Albania. The results suggest special attention to the sustainability of food safety in the new era through coordinated actions of the key actors in the agri-food chain, governmental institutions, and especially researchers, considering the material and social concerns of the functioning of markets, building new consumption models and risk managements strategies. Originality / scientific novelty. In addition to a simplistic approach, the problem of food safety in Albania is related to the multi-plane development, regarding the functioning of institutions (eg formal, informal) and trust in them, actors in markets (eg producers, retailers, wholesalers, etc.) or socio-economic phenomena (eg migration, etc.), as factors that may affect differently to food safety. Sustainability of food safety is important for agricultural production, sustainable consumption and trade, which represent sectors with scope and importance for the country’s economy. Based on the multidisciplinary and multidimensional aspects of a wide group of factors (eg external, internal, etc.) acting in a predictable or unpredictable way and influencing food safety, the identification of possible links of some more specific ones in the case of Albania is of particular research interest and in its essence it represents a new approach applied in the country’s studies in that field. Practical value / implications. In the new era of science and innovations, of smart techniques and policies and consumers’ behaviours, characterized by the interaction of a multitude of factors, the research focus on the food safety is reasonable and makes a particular sense. The study may be useful in several aspects, and especially for (1) consumer protection agencies; (2) agricultural producer units; and (3) future research given the challenges of food safety in the new era.

Early experience with bridging stent graft deployment without sheath support in branched and fenestrated endovascular aortic repair

ObjectiveTo assess the early experience with modified version of simplified bare-wire target vessel (SMART) technique, implying delivery of bridging stent grafts without historically established sheath support, and to compare its outcome to standard endovascular aortic repair procedures with fenestrated/branched devices. MethodsA retrospective analysis of 102 consecutive patients treated with fenestrated/branched devices from January 2020 to December 2022 was undertaken. The study population was divided into three groups—a sheath group (SG), SMART group, and nonsheath group (NSG). Primary end points were radiation exposure (dose-area product), fluoroscopy time, dose of contrast agent, operation time, and incidence of intraoperative target vessel (TV) complications and additional procedures. Freedom from secondary TV related reinterventions at the three follow-up phases were defined as secondary end points. ResultsA total of 183 TVs (38.8% visceral arteries [VA]; 56.3% renal arteries [RA]) in the SG, 36 TVs (44.4% VA, 55.6% RA) in the SMART group, and 168 TVs (47.6% VA; 50% RA) in the NSG were accessed. The mean number of fenestrations and bridging stent grafts was equally distributed in all three groups. The SMART group only included cases treated with fenestrated devices. The dose-area product was significantly lower in the SMART (median, 203 Gy × cm2; interquartile range [IQR], 179-365 Gy × cm2) and NSG (median, 340 Gy × cm2; IQR, 220-651 Gy × cm2) groups vs the SG (median, 464 Gy × cm2; IQR, 267-871 Gy × cm2; P = .007). Operation time was also significantly lower in the NSG (median, 265 minutes; IQR, 221-337 minutes) and SMART (median, 292 minutes; IQR, 234-351 minutes) groups vs the SG (median, 326 minutes; IQR, 277-375 minutes; P = .004), respectively. Intraoperative TV-related complications were most frequently observed in the SG (9/183 TVs; P = .008). ConclusionsThis study reports the outcomes of three currently available TV stenting approaches. Previously reported SMART technique, and its modified version (NSG) proved to be a safe alternative to historically established TV stenting technique with sheath support (SG).

A secure emotion aware intelligent system for Internet of healthcare

Emotional aware intelligent healthcare mechanism is seen as one of the emergent techniques that can be used to overcome the traditional communication issues like storage overhead and delay in transmission process in healthcare systems. It is a smart technique primarily used to ensure faster connectivity and transmission as well as healthcare data processing through various heterogeneous networks by understanding human emotions. Researchers have focused on various processing and transmitting techniques of emotional aware intelligent systems. However, very few of them have focused on the need for security while transmitting or reading the patients’ record. The aim of this paper is to propose a secure and transparent communication mechanism in intelligent emotional aware systems using Analytical Hierarchical Process (AHP), blockchain system and a mathematical model. AHP and the mathematical model are used to ensure accuracy during the data transmission process by analysing the legitimacy of each device. In addition, the identity based trust and blockchain mechanism is used to ensure the continuous analysis and transparency among network entities while transferring the information. The proposed framework is further analysed against existing models over various security matrices such as data alteration, report generation, accuracy etc. The out performance of proposed mechanism is approximate 89% as compare to existing mechanism.

Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review

The importance of and need for cyber security have increased in the last decade. The critical infrastructure of the country, modeled with cyber-physical systems (CPS), is becoming vulnerable because of a lack of efficient safety measures. Attackers are becoming more innovative, and attacks are becoming undetectable, thereby causing huge risks to these systems. In this scenario, intelligent and evolving detection methods should be introduced to replace basic and outworn methods. The ability of artificial intelligence (AI) to analyze data and predict outcomes has created an opportunity for researchers to explore the power of AI in cyber security. This article discusses new-age intelligence and smart techniques such as pattern recognition models, deep neural networks, generative adversarial networks, and reinforcement learning for cyber security in CPS. The differences between the traditional security methods used in information technology and the security methods used in CPS are analyzed, and the need for a transition into intelligent methods is discussed in detail. A deep neural network-based controller that detects and mitigates cyber attacks is designed for microgrid systems. As a case study, a stealthy local covert attack that overcomes the existing microgrid protection is modeled. The ability of the DNN controller to detect and mitigate the SLCA is observed. The experiment is performed in a simulation and also in real-time to analyze the effectiveness of AI in cyber security.

Fuzzy logic control based maximum power point tracking technique in standalone photovoltaic system

This study describes the development of a smart technique for tracking the highest power point on a standalone photovoltaic (SAPV) system when temperature and irradiance conditions are changing using fuzzy logic control (FLC). The PV systems comprises of a PV array, a boost converter, a controller for tracking the maximum power point, and an inverter to power the AC loads. The FLC-based maximum power point tracking (MPPT) is proposed because the technique is not complex and does not need a deep comprehension of the particular model of the system. Furthermore, the technique is efficient and fast response in tracking maximum power (MP) from the PV array. The technique overcame the limitation of the conventional MPPT technique that resulted in slow tracking of the MP and was not accurate on the optimal position of the PV array output power. The SAPV system is integrated with the sealed lead acid (SLA) battery bank as an alternative power source and makes up for power shortages by supplying energy to the load without interruption during power shortages. A system for managing the battery has been included in the system to preserve the battery's longevity by controlling the battery's charging process. The PV system is able to supply single-phase output AC voltage of 230 Vrms and has low total harmonic distortion (THD) that is suitable for home appliances. The achieved simulation results demonstrate the effectiveness of the suggested fuzzy logic controller in tracking the MPP.

Crop Disease Detection and Pesticide Recommendation Using CNN

Abstract: Agriculture plays a crucial role in India’s economy, supporting the livelihoods of 58 percent of the population and contributing 17-18 percent of the GDP. However, plant pests anddiseases pose significant challenges, leading to biotic stress that hampers yield potential and diminishes the quality and quantity of food. Safeguarding crops against diseases is imperative to meet the increasing food demand. Globally, the losses caused by pathogens, pests, and weeds account for 20-40 percent of agricultural productivity. The detection of diseases in cultivated plants is a vital and complex task in agricultural practices. Conventional methods of disease detection and classification are time-consuming and labor-intensive, making it difficult to find optimized solutions. This issue is particularly problematic as farmers and professionals in developing countries require efficient methods to monitor and identify diseases affecting their crops. The implementation of program-based identification for plant diseases offers advantages such as improved detection, reduced human effort, and time savings. In this article, a smart and efficient technique is proposed to detect and classify plant diseases with higher accuracy than existing methods. The pro- posed technique employs Convolutional Neural Networks (CNNs)and focuses on leaf diseases as the main area of interest.

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.