• Energy Research
• 2016-2025close
• AE close
• Applied Sciences close

Students’ feedback is pertinent in measuring the quality of the educational process. For example, by applying lexicon-based sentiment analysis to students’ open-ended course feedback, we can detect not only their sentiment orientation (positive, negative, or neutral) but also their emotional valences, such as anger, anticipation, disgust, fear, joy, sadness, surprise, or trust. However, most currently used assessment tools cannot effectively measure emotional engagement, such as interest level, enjoyment, support, curiosity, and sense of belonging. Moreover, none of those tools utilize Bloom’s taxonomy for students’ learning-level assessment. In this work, we develop a user-friendly application based on NLP to help the teachers understand the students’ perception of their learning by analyzing their open-ended feedback. This allows us to examine the sentiment and the embedded emotions using a customized dictionary of emotions related to education. The application can also classify the students’ emotions according to Bloom’s taxonomy. We believe our application will help teachers improve their course delivery.

As the construction industry is facing the challenge of meeting the ever-increasing demand for environmentally friendly and durable concrete, the role of viscosity-modifying admixtures (VMAs) has become increasingly essential to improve the rheological properties, stability, and mechanical properties of concrete. Additionally, natural polymers are ever evolving, offering multiple opportunities for innovative applications and sustainable solutions. This comprehensive review delves into the historical context and classifications of VMAs, accentuating their impact in enhancing the rheological properties, stability, and mechanical properties of concrete. Emphasis is placed on the environmental impact of synthetic VMAs, promoting the exploration of sustainable alternatives derived from plant-based biopolymers. Indeed, biopolymers, such as cellulose, starch, alginate, pectin, and carrageenan are considered in this paper, focusing on understanding their efficacy in improving concrete properties while enhancing the environmental sustainability within the concrete.

A key component of the design and operation of power transmission systems is the optimal power flow (OPF) problem. To solve this problem, several optimization algorithms have been developed. The primary objectives of the program are to minimize fuel costs, reduce emissions, improve voltage profiles, and reduce power losses. OPF is considered one of the most challenging optimization problems due to its nonconvexity and significant computational difficulty. Teaching–learning-based optimization (TLBO) is an optimization algorithm that can be used to solve engineering problems. Although the method has certain advantages, it does have one significant disadvantage: after several iterations, it becomes stuck in the local optimum. The purpose of this paper is to present a novel adaptive Gaussian TLBO (AGTLBO) that solves the problem and improves the performance of conventional TLBO. Validating the performance of the proposed algorithm is undertaken using test systems for IEEE standards 30-bus, 57-bus, and 118-bus. Twelve different scenarios have been tested to evaluate the algorithm. The results show that the proposed AGTLBO is evidently more efficient and effective when compared to other optimization algorithms published in the literature.

Demand Side Management (DSM) through optimization of home energy consumption in the smart grid environment is now one of the well-known research areas. Appliance scheduling has been done through many different algorithms to reduce peak load and, consequently, the Peak to Average Ratio (PAR). This paper presents a Comprehensive Home Energy Management Architecture (CHEMA) with integration of multiple appliance scheduling options and enhanced load categorization in a smart grid environment. The CHEMA model consists of six layers and has been modeled in Simulink with an embedded MATLAB code. A single Knapsack optimization technique is used for scheduling and four different cases of cost reduction are modeled at the second layer of CHEMA. Fault identification and electricity theft control have also been added in CHEMA. Furthermore, carbon footprint calculations have been incorporated in order to make the users aware of environmental concerns. Simulation results prove the effectiveness of the proposed model.

Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this purpose. Single-, double-, and PV module models are investigated to test ICO's parameter estimation performance. Statistical analysis uses minimum, mean, maximum, and standard deviation. Furthermore, to improve confidence in test results, Wilcoxon and Freidman rank nonparametric tests are also performed. Compared to other state-of-the-art optimization algorithms, the ICO algorithm is proven to be highly reliable and accurate when identifying PV parameters.

Intermittency of electrical power in developing countries, as well as some European countries such as Turkey, can be eluded by taking advantage of solar energy. Correct prediction of solar radiation constitutes a very important step to take advantage of PV solar panels. We propose an experimental study to predict the amount of solar radiation using a classical artificial neural network (ANN) and deep learning methods. PV panel and solar radiation data were collected at Duzce University in Turkey. Moreover, we included meteorological data collected from the Meteorological Ministry of Turkey in Duzce. Data were collected on a daily basis with a 5-min interval. Data were cleaned and preprocessed to train long-short-term memory (LSTM) and ANN models to predict the solar radiation amount of one day ahead. Models were evaluated using coefficient of determination (R2), mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean biased error (MBE). LSTM outperformed ANN with R2, MSE, RMSE, MAE, and MBE of 0.93, 0.008, 0.089, 0.17, and 0.09, respectively. Moreover, we compared our results with two similar studies in the literature. The proposed study paves the way for utilizing renewable energy by leveraging the usage of PV panels.

Artificial intelligence (AI) systems are becoming wiser, even surpassing human performances in some fields, such as image classification, chess, and Go. However, most high-performance AI systems, such as deep learning models, are black boxes (i.e., only system inputs and outputs are visible, but the internal mechanisms are unknown) and, thus, are notably challenging to understand. Thereby a system with better explainability is needed to help humans understand AI. This paper proposes a dual-track AI approach that uses reinforcement learning to supplement fine-grained deep learning-based sentiment classification. Through lifelong machine learning, the dual-track approach can gradually become wiser and realize high performance (while keeping outstanding explainability). The extensive experimental results show that the proposed dual-track approach can provide reasonable fine-grained sentiment analyses to product reviews and remarkably achieve a 133% promotion of the Macro-F1 score on the Twitter sentiment classification task and a 27.12% promotion of the Macro-F1 score on an Amazon iPhone 11 sentiment classification task, respectively.

This paper analyzes the connection between supply chain management maturity (SCMM) and business performance in light of the balanced scorecard (BSC) framework. The goal is to explore the relationship between SCMM and business performance from the financial and customer, innovation and learning, and internal processes perspectives. Industry characteristics (technological dynamism and the level of state support) are examined to determine their moderating effects. The survey was carried out on a sample of organizations from Bosnia and Herzegovina to test if the BSC approach can be a relevant framework for assessing the effects of SCMM on performance, and whether, as in many countries’ political legacies, the role of the government is significant in this relation. PLS-SEM was used to test the proposed hypotheses. The obtained research results confirm a positive relationship between SCMM and business performance from the BSC perspective. This relation is strengthened when an organization operates in an industry with higher technological dynamism. Interestingly, the results confirm that the level of state support does not influence the contribution of SCMM to business performance. This paper provides a more comprehensive view of the role of SCMM and an additional understanding of its contribution to multiple perspectives of business performance. Furthermore, the relevance of industry characteristics for SCMM and business performance has been illustrated by testing the moderation effect of technological dynamism and the level of state support.