An improved electroporator with continuous liquid flow and double-exponential waveform for liquid food pasteurization

Pulsed electric field (PEF) pasteurisation keeps treated liquid food fresh and nutritious compared to traditional thermal pasteurisation. However, PEF adoption is still limited on an industrial scale due to a lack of practical systems. As a result, a great deal of research has gone into overcoming the limitations of the existing systems. Keeping this in mind, the current study contributes to the improvement of the electroporator. The heterogeneous electric field's distribution raises the temperature of the treated food samples. Liquid laminar flow is a reason for heterogeneous electric field's distribution in continuous treatment. Hotspots may also be created by using an inefficient high-voltage waveform in addition to the heterogeneous electric field distribution. This study rectifies the heterogeneous distribution by proposing an improved coaxial treatment chamber and double-exponential waveform to replace the exponential-decaying waveform. A static mixer provides an increased mixing, i.e. disrupting the laminar flow, inside the treatment zone. COMSOL based computational model was developed to study flow behaviour and corresponding temperature distribution in the proposed coaxial treatment chamber with sieves. Based on the model, it has been concluded that coaxial electrodes with sieves provide more homogeneous flow properties inside the treatment chamber. The effectiveness of the double-exponential (DE) waveform was validated using MATLAB. A three-stage Marx generator giving the DE waveform was designed and constructed. The performance of the improved treatment chamber together with the DE waveform, known as the electroporator, was studied using chemical and microbial analysis. Untreated, PEF treated, and thermal treated orange samples were stored at 4°C for 9 days before being examined. The lowest microbial growth was observed in both the PEF treated with sieves and thermally treated food samples than the untreated sample. However, treated juices' visual and chemical colour analysis showed that the PEF-treated sample acquired a brighter appearance than a thermally processed sample. Thus, this study provides significant findings in developing and utilising an electroporator to inactivate microorganisms

Response score-based protein structure analysis for cancer prediction aided by the Internet of Things

Abstract Medical diagnosis through prediction and analysis is par excellence in integrating modern technologies such as the Internet of Things (IoT). With the aid of such technologies, clinical assessments are eased with protracted computing. Specifically, cancer research through structure prediction and analysis is improved through human and machine interventions sustaining precision improvements. This article, therefore, introduces a Protein Structure Prediction Technique based on Three-Dimensional Sequence. This sequence is modeled using amino acids and their folds observed during the pre-initial cancer stages. The observed sequences and the inflammatory response score of the structure are used to predict the impact of cancer. In this process, ensemble learning is used to identify sequence and folding responses to improve inflammations. This score is correlated with the clinical data for structures and their folds independently for determining the structure changes. Such changes through different sequences are handled using repeated ensemble learning for matching and unmatching response scores. The introduced idea integrated with deep ensemble learning and IoT combination, notably employing stacking method for enhanced cancer prediction precision and interdisciplinary collaboration. The proposed technique improves prediction precision, data correlation, and change detection by 11.83%, 8.48%, and 13.23%, respectively. This technique reduces correlation time and complexity by 10.43% and 12.33%, respectively

Improved Whale Optimization Algorithm for Transient Response, Robustness, and Stability Enhancement of an Automatic Voltage Regulator System

The proportional integral derivative (PID) controller is one of the most robust and simplest configuration controllers used for industrial applications. However, its performance purely depends on the tuning of its proportional (KP), integral (KI) and derivative (KD) gains. Therefore, a proper combination of these gains is primarily required to achieve an optimal performance of the PID controllers. The conventional methods of PID tuning such as Cohen-Coon (CC) and Ziegler–Nichols (ZN) generate unwanted overshoots and long-lasting oscillations in the system. Owing to the mentioned problems, this paper attempts to achieve an optimized combination of PID controller gains by exploiting the intelligence of the whale optimization algorithm (WOA) and one of its recently introduced modified versions called improved whale optimization algorithm (IWOA) in an automatic voltage regulator (AVR) system. The stability of the IWOA-AVR system was studied by assessing its root-locus, bode maps, and pole/zero plots. The performance superiority of the presented IWOA-AVR design over eight of the recently explored AI-based approaches was validated through a comprehensive comparative analysis based on the most important transient response and stability metrics. Finally, to assess the robustness of the optimized AVR system, robustness analysis was conducted by analyzing the system response during the variation in the time constants of the generator, exciter, and amplifier from −50% to 50% range. The results of the study prove the superiority of the proposed IWOA-based AVR system in terms of transient response and stability metrics

Improved Whale Optimization Algorithm for Transient Response, Robustness, and Stability Enhancement of an Automatic Voltage Regulator System

The proportional integral derivative (PID) controller is one of the most robust and simplest configuration controllers used for industrial applications. However, its performance purely depends on the tuning of its proportional (KP), integral (KI) and derivative (KD) gains. Therefore, a proper combination of these gains is primarily required to achieve an optimal performance of the PID controllers. The conventional methods of PID tuning such as Cohen-Coon (CC) and Ziegler–Nichols (ZN) generate unwanted overshoots and long-lasting oscillations in the system. Owing to the mentioned problems, this paper attempts to achieve an optimized combination of PID controller gains by exploiting the intelligence of the whale optimization algorithm (WOA) and one of its recently introduced modified versions called improved whale optimization algorithm (IWOA) in an automatic voltage regulator (AVR) system. The stability of the IWOA-AVR system was studied by assessing its root-locus, bode maps, and pole/zero plots. The performance superiority of the presented IWOA-AVR design over eight of the recently explored AI-based approaches was validated through a comprehensive comparative analysis based on the most important transient response and stability metrics. Finally, to assess the robustness of the optimized AVR system, robustness analysis was conducted by analyzing the system response during the variation in the time constants of the generator, exciter, and amplifier from −50% to 50% range. The results of the study prove the superiority of the proposed IWOA-based AVR system in terms of transient response and stability metrics

A novel feature engineered-CatBoost-based supervised machine learning framework for electricity theft detection

This paper presents a novel supervised machine learning-based electric theft detection approach using the feature engineered-CatBoost algorithm in conjunction with the SMOTETomek algorithm. Contrary to the previous literature, where the missing observations in data are either ignored or imputed with average values, this work utilizes k-Nearest neighbor technique for missing data imputation; thus, an accurate and realistic estimation of the missing data is achieved. To mitigate the biasness to the majority data class, the proposed model utilizes the SMOTETomek algorithm, which neutralizes the mentioned effect by managing a proper balance between over-sampling and under-sampling techniques

Detection of non-technical losses in power utilities—a comprehensive systematic review

Electricity theft and fraud in energy consumption are two of the major issues for power distribution companies (PDCs) for many years. PDCs around the world are trying different methodologies for detecting electricity theft. The traditional methods for non-technical losses (NTLs) detection such as onsite inspection and reward and penalty policy have lost their place in the modern era because of their ineffective and time-consuming mechanism. With the advancement in the field of Artificial Intelligence (AI), newer and efficient NTL detection methods have been proposed by different researchers working in the field of data mining and AI. The AI-based NTL detection methods are superior to the conventional methods in terms of accuracy, efficiency, time-consumption, precision, and labor required. The importance of such AI-based NTL detection methods can be judged by looking at the growing trend toward the increasing number of research articles on this important development. However, the authors felt the lack of a comprehensive study that can provide a one-stop source of information on these AI-based NTL methods and hence became the motivation for carrying out this comprehensive review on this significant field of science. This article systematically reviews and classifies the methods explored for NTL detection in recent literature, along with their benefits and limitations. For accomplishing the mentioned objective, the opted research articles for the review are classified based on algorithms used, features extracted, and metrics used for evaluation. Furthermore, a summary of different types of algorithms used for NTL detection is provided along with their applications in the studied field of research. Lastly, a comparison among the major NTL categories, i.e., data-based, network-based, and hybrid methods, is provided on the basis of their performance, expenses, and response time. It is expected that this comprehensive study will provide a one-stop source of information for all the new researchers and the experts working in the mentioned area of research