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Renewable energy sources are sporadic and unpredictable, which makes mass adoption extremely difficult. A viable option that is specifically designed to handle this problem is compressed air energy storage, or CAES. It provides large-scale energy storage capabilities. Air is compressed during off- peak hours and stored in subterranean chambers as part of this two-step process. The air that has been stored is released when there is a demand peak, which turns a turbine and produces energy. Notably, CAES has a number of benefits over alternative storage options. Since it can keep energy for long periods of time, it is an excellent tool for managing the erratic output of renewable sources. Its intrinsic scalability also makes it adaptable to a variety of grid requirements. By offering backup power and enabling the smooth integration of renewables, CAES also helps to maintain grid stability. Moreover, its economic feasibility is highlighted by its comparatively elevated efficiency in contrast to alternative solutions. In order to fully realize the promise of renewable energy sources and promote a more robust and sustainable energy future, CAES offers a dependable and scalable method of storing energy.

An advance safety system has been developed to effectively address the critical risks associated with fire and gas leakages in both industrial and residential environments. This advanced system integrates a range of sensors, including flame sensors, gas sensors, and temperature sensors, all connected to a microcontroller. The microcontroller is responsible for processing data from these sensors to continuously monitor and evaluate environmental conditions for any signs of fire or gas leaks. Upon recognizing an anomaly, the system activates an alert mechanism that includes displaying status updates on an LCD screen, sounding an audible alarm via a buzzer, and sending instant notifications through SMS via a GSM module to prompt immediate response. A key innovation of this system is its utilization of the Message Queuing Telemetry Transport (MQTT) protocol for cloud-based monitoring and data management. This protocol supports real-time data transmission and analysis through the Ubidots platform, facilitating efficient and reliable communication between the system and cloud services. The lightweight and efficient MQTT protocol ensures seamless remote monitoring and control, while the cloud integration allows for continuous data storage and detailed analysis. This combination of real-time monitoring, remote access, and advanced alerting mechanisms provides a comprehensive solution for hazard recognition and management, significantly enhancing safety by enabling prompt responses and safeguarding both lives and property. [ABSTRACT FROM AUTHOR]

Clustering aims to partition data into distinct groups where the data points within each cluster are highly similar, while remaining dissimilar to those in other clusters. The K-means algorithm is widely recognized for its effectiveness in clustering; however, a significant limitation is its inability to automatically determine the optimal number of clusters (ONC). To address this issue, various methods have been developed, including the Gap Statistic (GS), which helps identify the ONC. Despite its utility, the GS method can yield inconsistent results due to variations in the reference data units, leading to skewed comparisons and potential misestimation of the ONC. This paper introduces the Enhanced Gap Statistic (EGS), which standardizes the reference data within the GS framework, improving both the accuracy and efficiency of ONC determination. The experimental results show that the EGS method outperforms traditional techniques such as Fuzzy Clustering, Silhouette, Elbow, and the original Gap Statistic, with accuracy and execution times of 79.58 % and 0.21 seconds (Hitter’s dataset), 88.58 % and 0.01 seconds (Time Series), 91.58 % and 1.24 seconds (Well Log), and 96.58 % and 2.14 seconds (Traffic Crash dataset). The hypothesis testing of EGS shows high significance, with p-values less than 0.05 across different datasets, and the coefficient of determination (R²) is significantly higher compared to the Gap Statistic. These results demonstrate that EGS consistently delivers superior performance, particularly for larger datasets, offering both more accurate ONC determinations and faster execution times compared to existing methods. By eliminating biases associated with varying measurement units, the EGS method ensures more reliable and consistent clustering outcomes compared to the traditional GS approach.

Clustering is essential in data analysis, with K-means clustering being widely used for its simplicity and efficiency. However, several challenges can affect its performance, including the handling of outliers, the transformation of non-spherical data into a spherical form, and the selection of the optimal number of clusters. This paper addressed these challenges by developing and enhancing specific models. The primary objective was to improve the robustness and accuracy of K-means clustering in the presence of these issues. To handle outliers, this research employed the winsorization method, which uses threshold values to minimize the influence of extreme data points. For the transformation of non-spherical data into a spherical form, the KROMD method was introduced, which combines Manhattan distance with a Gaussian kernel. This approach ensured a more accurate representation of the data, facilitating better clustering performance. The third objective focused on enhancing the gap statistic for selecting the optimal number of clusters. This was achieved by standardizing the expected value of reference data using an exponential distribution, providing a more reliable criterion for determining the appropriate number of clusters. Experimental results demonstrated that the winsorization method effectively handles outliers, leading to improved clustering stability. The KROMD method significantly enhanced the accuracy of converting non-spherical data into spherical form, achieving an accuracy level of 0.83 percent and an execution time of 0.14 per second. Furthermore, the enhanced gap statistic method outperformed other techniques in selecting the optimal number of clusters, achieving an accuracy of 93.35 percent and an execution time of 0.1433 per second. These advancements collectively enhance the performance of K-means clustering, making it more robust and effective for complex data analysis tasks.

Unsupervised learning, particularly K-means clustering, seeks to partition data into clusters with distinct intra-class cohesion and inter-class disparity. However, the arbitrary selection of clusters in K-means introduces challenges, leading to trial and error in determining the Optimal Number of Clusters (ONC). To address this, various methodologies have been devised, among which the Gap Statistic is prominent. Gap Statistic reliance on expected values for reference data selection poses limitations, especially in scenarios involving diverse scale, noise, and overlapping data.To tackle these challenges, this study introduces Enhanced Gap Statistic (EGS), which standardizes reference data using an exponential distribution within the Gap Statistic framework, integrating an adjustment factor for a more dependable estimation of the ONC. Application of EGS to K-means clustering facilitates accurate ONC determination. For comparison purposes, EGS is benchmarked against traditional Gap Statistic and other established methods used for ONC selection in K-means, evaluating accuracy and efficiency across datasets with varying characteristics. The results demonstrate EGS superior accuracy and efficiency, affirming its effectiveness in diverse data environments.

The virtual classroom teaching-learning, online meetings, and discussions are becoming most popular throughout the world due to the COVID-19 pandemic event. There is a need to enhance the engagement and thinking ability of the students during a virtual classroom using suitable tools. This study explores the perspective of engineering students and staff members towards virtual classroom teaching-learning in India and Ghana-West Africa. This study also proposes teaching-learning approaches to motivate students for effective engagement and thinking. As an outcome, problem-solving skills and necessary graduate attributes of the students will be developed and accomplished. The participants involved in the survey include 194 students and staff members from engineering colleges and universities in India and Ghana-West Africa. The questionnaire-based research method is used to collect the perspective of students and staff. The disciplines in engineering such as Electrical and Electronics Engineering, Mechanical Engineering, Civil Engineering, and Computer Engineering are considered for analysis. The survey gives the challenges and benefits of virtual learning faced by the students and staff members. It also gives the expectation of the students and staff doing a virtual meeting. The research findings show that peer interaction teaching-learning methodologies such as brainstorming, role plays, group discussion, case studies, animated videos, games, and activities are best suitable in a virtual classroom to overcome the challenges addressed by the students and staff members. Based on the analysis of the survey, ICTbased tools, and applications for incorporating interactive teaching-learning methodologies have been listed with strategies and approaches to motivate and engage the students effectively in a virtual classroom environment. [ABSTRACT FROM AUTHOR]

The global demand for coconut and coconut-based products has increased rapidly over the past decades. Coconut price continues to fluctuate; thus, it is not easy to make predictions. Good price modelling is important to accurately predict the future coconut price. Several studies have been conducted to predict the price of coconut using various models. One of the most important and widely used models in time series forecasting is the autoregressive integrated moving average (ARIMA). However, price fluctuations is considered a problem with uncertain behaviour. The existing ARIMA time series model is unsuitable for solving this problem, because of the nonlinear series. Artificial neural networks (ANN) have been an effective method in solving nonlinear data pattern problems in the last two decades. The non-linear autoregressive neural network (NARNET) gives good forecast, most especially when series are non-linear. Therefore ARIMA- NARNET is considered a universal approach to forecasting the coconut price. The aim of the study is to establish a linear and nonlinear model in time series to forecast coconut prices. The ability of a hybrid approach that combines ARIMA and NARNET(ANN) models is investigated. Based on the experimental study, the experimental results show that the proposed method ARIMA- NARNET, is better at forecasting the price of coconut, an agriculture commodity, than both the ARIMA model and NARNET models. The expected benefit of the proposed forecasting model is it can help farmers, exporters, and the government to maximize profits in the future.

This article aimed to present a new continuous probability density function for a non-negative random variable that serves as an alternative to some bounded domain distributions. The new distribution, termed the log-Kumaraswamy distribution, could faithfully be employed to compete with bounded and unbounded random processes. Some essential features of this distribution were studied, and the parameters of its estimates were obtained based on the maximum product of spacing, least squares, and weighted least squares procedures. The new distribution was proven to be better than traditional models in terms of flexibility and applicability to real-life data sets.

Inverted distributions, also known as inverse distributions, are essential statistical models for analyzing real-life data in biomedical sciences, engineering, and other fields. In this paper, we use the odd beta prime-G family and the inverted Kumaraswamy distribution to introduce a new inverted distribution called the odd beta prime inverted Kumaraswamy. The new distribution exhibits right-skewed, J-shaped densities and features increasing-constant, concave-convex, and bathtub hazard functions. Some of its statistical properties are explored. The parameters are estimated via the maximum likelihood method. The empirical importance of the new model is proved through its application to COVID-19 mortality data from Italy. Numerical results demonstrate that the proposed model outperforms its competitors. We hope that this proposed distribution can be considered as a viable alternative to some well-established distributions for modeling real-life data across various application areas.

The Burr III distribution is extended in this work as a substitute for the numerous Burr III distributions. A new distribution is developed by applying the log transformation technique to define the transformed log-Burr III distribution. Moments and quantile function are the structural features established in this study. The model parameters are derived using the maximum likelihood technique. The applicability of the new distribution was assessed using real-world data on the transformed total milk production in the first birth of 107 cows of the SINDI race. The results showed that the proposed distribution might be used as the optimal distribution for this dataset.

The advent of smart materials and modern control theory has seen the rapid development of ultrasonic motors over the last decades. They have become promising precision driving components due to their unique piezoelectric transduction and friction drive mechanisms. This review summarizes the synthesis of the key technical aspects, research efforts, conclusions, and challenges that need to be highlighted concerning ultrasonic motor structural design and tribological performance. The analysis shows that the development of ultrasonic motor with new principles is in the ascendant. Simplifying the body structure, developing advanced friction contact models and life prediction models, developing new and environmentally friendly friction materials, and developing customized tribological testing devices are goals and tasks that should be considered in the future design of efficient ultrasonic motors.

Bioremediation is an important technology to remediate the chromium (Cr) contaminated soil and water. In this study, Shewanella putrefaciens (MTTC8410) was used to investigate the influence of carbon concentration, pH, and temperature on reduction of hexavalent chromium [Cr(VI)] into trivalent chromium [Cr(III)]. The increased bacterial growth rate was significantly reduced the Cr(VI) concentration. In batch mode experiments, 1% starch recorded the highest reduction of Cr(VI) (90%) followed by 1% glucose (88% reduction) and a reduction of 77% was by 1% cellulose. By using various pH conditions the maximum Cr(VI) reduction was achieved at pH 7.0. In this experiment the maximum Cr(VI) reduction (75%) was observed at 35°C, followed by 30°C with 62% of Cr(VI) reduction. Bioreactor analysis revealed the highest reduction of Cr(VI) (88%) in unsterile tannery effluent. The significant levels of physico- chemical parameters were reduced in unsterile tannery effluent, as compared to the sterile tannery effluent. The experimental results revealed that the S. putrefaciens (MTTC8410) could be used as a potential bacterial strain for reduction of Cr(VI) from contaminated groundwater.

Biohydrogen production from microalgae is a potential alternative energy source that is now intensively being researched. The complex natures of the biological processes involved have afflicted the accuracy of traditional modelling and optimization, besides being costly. Accordingly, machine learning algorithms have been employed to overcome setbacks, as these approaches have the capability to predict nonlinear interactions and handle multivariate data from microalgal biohydrogen studies. Thus, the review focuses on revealing the recent applications of machine learning techniques in microalgal biohydrogen production. The working principles of random forests, artificial neural networks, support vector machines, and regression algorithms are covered. The applications of these techniques are analyzed and compared for their effectiveness, advantages and disadvantages in the relationship studies, classification of results, and prediction of microalgal hydrogen production. These techniques have shown great performance despite limited data sets that are complex and nonlinear. However, the current techniques are still susceptible to overfitting, which could potentially reduce prediction performance. These could be potentially resolved or mitigated by comparing the methods, should the input data be limited.

The formation of stable self-assembled monolayers of organothiol molecules on nickel (Ni) surfaces is hindered by the presence of oxides layers. In this work, 2-hydroxyethylammonium formate ionic liquid (HEAF) is shown to be an efficient medium for the electrochemical reduction of the Ni oxide layer present on the surface of polycrystalline Ni substrates. The so formed oxide free Ni surface is found to be very suitable for the preparation of self-assembled monolayers of n-dodecanethiol (n-C12SH). X-ray photoelectron spectroscopy, polarization modulation infrared reflection absorption spectroscopy, water contact angle measurements and electrochemical techniques are used for the characterization of the monolayers. The results show the formation of self-assembled monolayer with good molecular orientation and surface coverage. Electrochemical impedance measurements (EIS) indicate a surface coverage up to 99% after 18 h of self-assembly in n-C12SH containing ethanol solutions. [ABSTRACT FROM AUTHOR]