Technical Indicators Research Articles

INFLUENCE OF TECHNOLOGICAL FACTORS ON CONCRETE EFFICIENCY INDICATORS

The article presents the results of studies of the influence of technological factors (clinker factor of cements, grain composition of aggregates, cement consumption in concrete, modifier additives) on technical and environmental indicators of concrete efficiency. It has been shown that in combination, the parameters of clinker and CO2 intensities characterize the clinker efficiency of concrete, which can be improved by replacing part of the clinker in mixed cements with active mineral additives. Optimization of the granulometric composition of fine and coarse aggregates and the use of superplasticizers of the polycarboxylate type ensure the formation of a dense microstructure of the cementing matrix, which allows to increase the strength of concrete by 1-2 classes with an unchanged cement consumption and helps to reduce the CO2 emission rate. With the correct combination of various technological factors affecting concrete mixtures, a real opportunity is created to produce modern low-carbon concrete that meets the requirements of sustainability.

SYNTHESIS OF RATIONAL CONSTRUCTIVE SOLUTION OF STEEL ROOF TRUSSES

The article examines the issue of the occurrence and influence of bending moments on the bearing capacity of a combined steel truss made of rectangular bent-welded profiles. A comparative analysis of technical and economic indicators in terms of material intensity, labor intensity, and cost of traditional-typical and lightweight combined steel trusses was carried out. The paper examines steel trusses of long-span buildings with a span of 30 m. The force graphs (bending moments and axial forces) for various calculation schemes of the combined steel truss are shown. An analysis of the forces in the combined truss for different ways of connecting the lattice to the chords was carried out. The plot of normal stresses along the middle line of the stiffening girder of the combined truss for various calculation schemes is presented. The load-bearing capacity of compressed rods with different connection methods was calculated. According to the research results, an engineering method of taking into account the influence of bending moments is proposed.

Comparison of Long-Term Clinical Outcomes and Costs Between Transesophageal Echocardiography-Guided and X-ray-Guided Percutaneous Atrial Septal Defect Closure in Children.

This study aimed to compare the long-term clinical outcomes and costs between using either transesophageal echocardiography (TEE) or X-ray fluoroscopy for Percutaneous atrial septal defect (ASD) closure in children. An analysis was conducted on clinical data from children undergoing TEE-guided (n = 168) and X-ray-guided (n = 139) percutaneous ASD closure. Demographic characteristics, technical indices, acute complications, follow-up outcomes, and costs were compared between the groups. The results are that TEE-guided closure demonstrated shorter surgical times (20.3 ± 7.6min vs. 32.8 ± 7.9min, P < 0.001) and lower procedural costs ($3093.3 ± 451.5 vs. $3589.1 ± 219.4, P < 0.001) compared to X-ray guidance. Initial successful closure rates were similar between the groups (TEE: 98.2%, XR: 97.1%, P = 0.691). TEE guidance also resulted in fewer acute complications and reduced radiation exposure. TEE-guided percutaneous ASD closure offers advantages in terms of shorter surgical times, lower procedural costs, and reduced radiation exposure compared to X-ray guidance. These findings support the preference for TEE guidance in pediatric ASD closure procedures, with potential implications for improving patient outcomes and reducing healthcare costs.

A Novel Techno-Economical Control of UPFC against Cyber-Physical Attacks Considering Power System Interarea Oscillations

In the field of electrical engineering, there is an increasing concern among managers and operators about the secure and cost-efficient operation of smart power systems in response to disturbances caused by physical cyber attacks and natural disasters. This paper introduces an innovative framework for the hybrid, coordinated control of Unified Power Flow Controllers (UPFCs) and Power System Stabilizers (PSSs) within a power system. The primary objective of this framework is to enhance the system’s security metrics, including stability and resilience, while also considering the operational costs associated with defending against cyber-physical attacks. The main novelty of this paper lies in the introduction of a real-time online framework that optimally coordinates a power system stabilizer, power oscillation damper, and unified power flow controller to enhance the power system’s resilience against transient disturbances caused by cyber-physical attacks. The proposed approach considers technical performance indicators of power systems, such as voltage fluctuations and losses, in addition to economic objectives, when determining the optimal dynamic coordination of UPFCs and PSSs—aspects that have been neglected in previous modern research. To address the optimization problem, a novel multi-objective search algorithm inspired by Harris hawks, known as the Multi-Objective Harris Hawks (MOHH) algorithm, was developed. This algorithm is crucial in identifying the optimal controller coefficient settings. The proposed methodology was tested using standard IEEE9-bus and IEEE39-bus test systems. Simulation results demonstrate the effectiveness and efficiency of this approach in achieving optimal system recovery, both technically and economically, in the face of cyber-physical attacks.

A dynamic correction method for the optimal value settings of the solution purification process at multiple time scales

The solution purification process includes multiple continuous reactors. Setting the key technical indicators of each reactor through global optimization is the prerequisite for realizing the optimal operation of the entire process. Affected by fluctuations in inlet conditions, adjustments of operating parameters, and random disturbances, the operating status of the solution purification process will change accordingly, causing the optimal value settings based on global optimization to become no longer applicable. To ensure the applicability of the optimal value settings as the process changes and considering that the production data collected at different time scales contain different process information, this study proposes a dynamic correction method for the optimal value settings of the solution purification process at multiple time scales. First, considering the low-frequency testing data that can reflect the operation effect, the low-frequency correction is realized by combining mechanism knowledge and expert experience. Second, based on the characteristic that the high-frequency detection data can reflect the changing operating status in time, a supervised self-organizing map method is proposed to classify the changing trends in the operating status. Finally, an integrated, spatiotemporal, just-in-time learning method (with multiple changing trends in the operating status) is proposed to realize high-frequency correction. The experimental results show that the proposed method can dynamically correct the optimal value settings and reduce resource consumption while ensuring product quality.

Unlocking sustainable livestock production potential in the Colombian Amazon through paddock division and gender inclusivity

Low-emissions livestock production can be achieved through scaling production systems integrating trees, forages, and livestock within the same area. Such systems are known as silvopastoral production systems (SPS). However, despite SPS reported benefits, adoption rates globally remain low. This paper, therefore, aims to inform land use policy oriented towards increasing SPS adoption. This study intends to capture this by assessing SPS adoption benefits, identifying determinants of SPS adoption, and thus to contribute to policy for scaling low-emissions livestock production. Data was collected on socioeconomic status, livestock farming technical and economic indicators, and farm and paddock practices through farm-level surveys in four municipalities in the Amazon Piedmont of Colombia. Unlike previous studies that assume homogeneous farm management, when in fact, it is heterogeneous, this study assessed SPS adoption determinants using the paddock (n = 2819) as the unit of analysis. This methodological approach is consistent with paddock-level land use decisions taken by farmers based on socioeconomic and biophysical factors such as soil financial resources, type, and topography. The methodological approach allows us to provide new insights into the determinant of adopting SPS and an understanding of intra-farm level land use decisions. The results show that the adoption of SPS at both paddocks and farm levels in Caquetá is low. The main factors associated with higher SPS adoption levels at the paddock level are framed in gender, resources, and knowledge. We observe that women are more motivated to conserve the environment. Cattle paddocks managed by women, smaller in size, and those with more SPS-related projects show a tendency for medium or high SPS adoption. Furthermore, the positive relationship between access to credit and SPS adoption emphasizes the importance of financial resources tailored to SPS projects. Enhancing gender roles, improving access to finance in land use, and providing training programs on SPS systems can contribute to low-emission livestock production in Colombia. This research paper highlights the significance of implementing diverse management strategies and reaching out to farmers not involved in SPS projects. It emphasizes investments in low-emission livestock production, especially for female heads of households. This approach recognizes the broader benefits of SPS, beyond production and financial gains, promoting the division of paddocks and the adoption of SPS.

Real Time-Cutting Algorithmic Trading

a. Brief overview of algorithmic trading. Algorithmic trading, also known as algo trading or automated trading, refers to the use of computer algorithms and mathematical models to execute trading orders in financial markets. The primary goal of algorithmic trading is to achieve efficient and optimized execution of trading strategies, leveraging the speed and precision of computer systems. Here is a brief overview of algorithmic trading: 1. **Automation:** Algorithmic trading involves automating the process of buying or selling financial instruments, such as stocks, bonds, currencies, or commodities. The trading decisions are based on predefined rules and algorithms, eliminating the need for manual intervention. 2. **Speed:** One of the key advantages of algorithmic trading is its ability to execute trades at extremely high speeds. Algorithms can analyze market conditions, make decisions, and execute trades in a fraction of a second, responding to market changes much faster than human traders. 3. **Quantitative Analysis:** Algorithmic trading relies heavily on quantitative analysis and statistical models. Traders develop algorithms that are based on mathematical formulas, historical price data, technical indicators, and other relevant factors to identify trading opportunities. 4. **Market Making:** Some algorithmic trading strategies are designed for market making, where traders provide liquidity to the market by quoting bid and ask prices. Market-making algorithms aim to profit from the spread between the buying and selling prices. 5. **Arbitrage:** Algorithmic trading is often employed for arbitrage strategies, which involve exploiting price discrepancies between different markets or instruments. These discrepancies may arise due to inefficiencies, delays in information dissemination, or other market imperfections. 6. **Risk Management:** Algo trading systems typically incorporate risk management measures to control exposure and mitigate potential losses. These measures may include setting stop-loss orders, position sizing algorithms, and other risk controls. 7. **Backtesting:** Before deploying algorithms in live markets, traders often perform extensive backtesting. This involves running historical market data through the algorithm to simulate how it would have performed in the past. This helps refine and optimize the algorithm for real-world conditions. 8. **Machine Learning:** Some advanced algorithmic trading strategies incorporate machine learning techniques to adapt and learn from changing market conditions. Machine learning algorithms can analyze vast amounts of data to identify patterns and make predictions. 9. **Regulatory Considerations:** Algorithmic trading is subject to various regulations to ensure fair and orderly markets. Regulatory bodies may impose rules regarding algorithmic trading practices, transparency, and risk management to prevent market manipulation and protect investors. Algorithmic trading has become increasingly prevalent in financial markets, with institutional investors, hedge funds, and proprietary trading firms utilizing sophisticated algorithms to execute trades efficiently and capitalize on market opportunities.

Machine learning Ethereum cryptocurrency prediction and knowledge-based investment strategies

This work proposes a novel methodology to help in decision making in the cryptocurrency market. Two investment strategies have been designed for Ethereum (ETH), based on predictions of the price and trend of this cryptocurrency using real data. The two Ethereum cryptocurrency prediction systems rely solely on past values of other contextual stock indices, market indicators and online trends, and ignore any technical indicators of price evolution. Real data from cryptocurrency market has been collected and processed with different feature selection methods. Applying a regression approach with Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks, prediction models for the ETH price for 1, 7 and 15 days are obtained and compared. Also, support vector machine (SVM) is applied to predict the ETH price trend by applying a classification approach. In both approaches, sentiment analysis has been included to check its effect on the prediction results. The reliability of these prediction models in the current market has been evaluated by designing two original knowledge-based investment strategies. They are tested over two different time periods with real cryptocurrency market data. The results show that it is possible to generate up to 5.16 profit factor with few operations using these models. Furthermore, adding sentiment analysis has shown to have little influence. In this way, we contribute to the advancement of our knowledge of this volatile and still young cryptocurrency market, and specifically of the evolution of Ethereum and the factors that can influence its behavior.

Semi-Varying Coefficient Panel Data Model with Technical Indicators Predicts Stock Returns in Financial Market

Semi-Varying Coefficient Panel Data Model with Technical Indicators Predicts Stock Returns in Financial Market

Integrating Technical Indicators and Ensemble Learning for Predicting the Opening Stock Price

Accurately predicting stock prices poses a significant challenge due to the dynamic and complex nature of financial markets. This paper introduces a novel method that combines technical indicators with ensemble learning techniques to effectively forecast opening stock prices. Technical indicators offer valuable insights into market trends and patterns, while ensemble learning methods merge multiple models to enhance predictive precision. The study utilizes various technical indicators such as moving averages, Relative Strength Index (RSI), and Bollinger Bands to capture diverse aspects of market behaviour. Ensemble learning techniques like Random Forest, Gradient Boosting, Support Vector Regressor, and ARIMA model are then employed to consolidate the forecasts from these indicators. The proposed framework is assessed using historical stock market data, and extensive experiments showcase its superior performance compared to individual indicators and traditional forecasting approaches. The findings reveal that integrating technical indicators with ensemble learning leads to a significant improvement in accuracy, with a success rate of 91.45% in predicting opening stock prices, thus providing valuable insights for investors and financial analysts.

The Development of a Table Tennis Outstanding Program of International Competition for Mixed Doubles

Background and Aims: Table tennis mixed doubles is an emerging event in the Olympic Games. my country's academic community has only begun to study table tennis mixed doubles techniques and tactics in recent years. Research on statistical methods for mixed doubles techniques and tactics is still in the preliminary exploration stage. Based on this, this article aims to explore and use more scientific and effective statistical methods to analyze the techniques and tactics of my country's main opponents in the table tennis mixed doubles event of the Paris Olympic Games, to find technical and tactical statistical methods suitable for the mixed doubles event, and to explore the mixed doubles The characteristics and winning rules of the game provide certain theoretical reference for preparing for the table tennis mixed doubles competition of the 2024 Paris Olympic Games. The objective of this research is to develop a table tennis program for international competitions for mixed doubles. Methodology: This article mainly uses the four-stage and four-round evaluation method of table tennis mixed doubles technical and tactical statistical methods as the research object, and analyzes the international mixed doubles competition held between November 2021 and August 2023 by four athletes from different countries ranked top two in the world. Analysis and in-depth study of major mixed doubles matches. First, we review previous literature on table tennis doubles technical and tactical statistical methods. Secondly, we searched the literature from authoritative academic platforms at home and abroad in the past 10 years and selected and formulated technical and tactical research methods related to this article. Then, refer to Simi Scout technical and tactical video-related analysis software to study the technical and tactical use, points gained and lost, and competitive ability of the world's outstanding mixed doubles athletes in the game, at the same time. Combining the gender characteristics of table tennis doubles and mixed doubles events, a four-round model of serving and receiving for men and women was established, and a four-stage and four-round evaluation method of technical and tactical statistical methods for table tennis mixed doubles events was established. Finally, the study is summarized and recommendations are made. Results: The statistical method of "Four Stages and Four Rounds Evaluation Method" used in this article is a new technical and tactical statistical method for table tennis mixed doubles projects based on previous research on table tennis doubles. It is based on both theory and practice. Perspective, it improves the existing technical and tactical statistical indicator system, provides a theoretical reference for winning the gold medal in the table tennis mixed doubles event of the 2024 Paris Olympic Games and the future training and competition of my country's table tennis mixed doubles event, and enhances the overall strength of China's table tennis mixed doubles event., contributing to the continuous development of the world table tennis mixed doubles project Conclusion: The main conclusions of this article are as follows: 1. The four-stage and four-round evaluation method of mixed doubles technical and tactical statistical methods is feasible and effective. 2. The eight-round three-stage index evaluation method can specifically analyze the performance of athletes' techniques and tactics in a certain round, but it cannot reflect the use of techniques and tactics by mixed doubles athletes when facing different opponents. 3. The dual-system four-round evaluation method can analyze the advantages and disadvantages of rounds based on the gender differences between men and women, but it does not fundamentally solve the problem of inconsistency in the statistical data of each section. 4. The four-stage and four-round evaluation method can not only analyze the advantages and disadvantages of mixed doubles players according to gender characteristics but also summarize the techniques and tactics used by mixed doubles players when facing different opponents.

Accurate Surge Arrester Modeling for Optimal Risk-Aware Lightning Protection Utilizing a Hybrid Monte Carlo–Particle Swarm Optimization Algorithm

The application of arresters is critical for the safe operation of electric grids against lightning. Arresters limit the consequences of lightning-induced over-voltages. However, surge arrester protection in electric grids is challenging due to the intrinsic complexities of distribution grids, including overhead lines and power components such as transformers. In this paper, an optimal arrester placement technique is developed by proposing a multi-objective function that includes technical, safety and risk, and economic indices. However, an effective placement model demands a comprehensive and accurate modeling of an electric grid’s components. In this light, appropriate models of a grid’s components including an arrester, the earth, an oil-immersed transformer, overhead lines, and lightning-induced voltage are developed. To achieve accurate models, high-frequency transient mathematical models are developed for the grid’s components. Notably, to have an accurate model of the arrester, which critically impacts the performance of the arrester placement technique, a new arrester model is developed and evaluated based on real technical data from manufacturers such as Pars, Tridelta, and Siemens. Then, the proposed model is compared with the IEEE, Fernandez, and Pinceti models. The arrester model is incorporated in an optimization problem considering the performance of the over-voltage protection and the risk, technical, and economic indices, and it is solved using the particle swarm optimization (PSO) and Monte Carlo (MC) techniques. To validate the proposed arrester model and the placement technique, real data from the Chopoghloo feeder in Bahar, Hamedan, Iran, are simulated. The feeder is expanded over three different geographical areas, including rural, agricultural plain, and mountainous areas.

A Comprehensive Study on Trend Analysis of Selected Stocks by Using Selected Technical Indicators

In this research, we delve into an in-depth exploration of the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and pivot points indicators to gauge their efficacy in forecasting stock price trends. The study draws upon a rich dataset sourced from reputable financial platforms, covering the period from January 2023 to April 2024. To ensure a representative sample, we adopt a stratified sampling approach, meticulously selecting five major companies based on their market capitalization. These companies—Reliance Industries, Tata Consultancy Services, HDFC Bank, ICICI Bank, and Infosys—serve as focal points for our analysis, offering insights into various sectors of the economy. By offering a nuanced understanding of the interplay between technical indicators and market dynamics, our study endeavors to contribute meaningfully to the discourse on stock price forecasting and investment strategies. Key Words: Stock price prediction, Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Data analysis, Statistical methods, Market capitalization,

Does an increase in portfolio volatility create more returns? Evidence from India

The classical view of experts associates greater risks with greater rewards. The present study explores whether increased volatility in portfolios can create more returns for investors by using technical indicators or the buy-and-hold (BH) strategy. The study used closing prices of National Stock Exchange (NSE) 500 index firms for a period of 16 years (2007–2022). Five portfolios ranging from low to high volatility were created using standard deviation as a key measure. Findings indicate that as the volatility of the portfolios increases, the moving average (MA) returns seem to be higher. Across the various MA time frames, the 20-day MA seems to have generated the highest return annually (36.53% before transaction costs and 31.05% after transaction costs) due to reasonable trading opportunities with adjustable transaction costs. The CAPM also generated positive alpha (after bearing transaction costs) in the case of 20, 50, and 100 days MA, with the values being 16.66%, 13.29%, and 12.09%, respectively, in the case of highly volatile portfolios. On the other hand, while the BH strategy created substantial returns in all scenarios, the risk factor was extremely high due to the high standard deviation. Hence, it is suggested that investors/traders consider the BH strategy more cautiously while choosing between technical analysis returns and BH returns. Investors with high-risk preferences may have BH as their choice, while day traders with managed risk appetites may prefer technical tools over BH returns. AcknowledgmentThe infrastructural support provided by the FORE School of Management, New Delhi in completing this paper is gratefully acknowledged.

FACTORS INFLUENCING FOLLOW-ON PUBLIC OFFERING OF SHIPPNG COMPANIES FROM INVESTOR PERSPECTIVE – A HYBRID MULTIPLE-CRITERIA DECISION-MAKING APPROACH

The shipping industry transports nearly 80% of the goods worldwide and requires large funding. The shipping industry shifted from debt to equity as the source of funding in the last decade. Because most shipping companies already had their initial public offering before 2013, these companies tend to engage in follow-on equity offerings (FPO). However, the challenge faced by the shipping companies is the lack of knowledge on successful FPO. The purpose of this study is to identify the most influential factors affecting shipping companies’ FPO from the investor perspective. This research applies a hybrid multiple-criteria decision-making model integrating the fuzzy-Delphi method and Decision-Making Trial and Evaluation Laboratory, processing survey responses covering four dimensions and 16 criteria from 33 investment experts. The results show that financial indicator is the primary cause affecting offering condition, technical indicators. An increase in earnings per share would help the financial performance of the shipping companies to appear most attractive to investors.

An Android Sensors-Based Portable Road Health Monitoring System Utilizing Measurement Uncertainty Analysis

Abstract Road health monitoring systems aim to obtain the technical indexes of roads, and then analyze the usage and the degree of damage of the roads, which can provide an important basis for road construction, maintenance, and management. Road roughness is one of the main technical indexes for road quality evaluation and road health monitoring. This study built a system and implemented it as an application to measure and analyze road longitudinal profiles simply and conveniently using the sensors in a mobile phone. The application uses the accelerometer sensor and the gravity sensor to obtain vertical acceleration by a projection method, then denoises through empirical mode decompositions and a Butterworth filter, which has a repeated measurement error of 11%. Different filters were compared and the repeatability, accuracy, robustness, and effectiveness of the system were analyzed. An index to evaluated road longitudinal profiles is given, so that the results can be analyzed and viewed interactively in the application, and a series of cases are given in this paper.

Economic efficiency of small-scale wheat production in Jigawa state, Nigeria

Africa’s most populous country has failed to grow more food for its fast-rising population. With 420,000 metric tons of wheat produced in Nigeria in 2020, the country is still far from bridging its 4.6 million metric tons annual wheat gap. Increasing wheat production is a challenge for the nation to fulfill the food requirements of its growing population. So far literature has shown a rise in research on wheat in different parts of Nigeria, with a paucity of information on the economic efficiency of wheat production in the study area, and the country in general. To bridge these knowledge and empirical gaps, this research investigates the economic efficiency of wheat production in Jigawa State of Nigeria using information gathered from a survey elicited by a well-structured questionnaire coupled with an interview schedule from 341 active wheat farmers selected through a multi-stage sampling technique. Both descriptive and inferential statistics were used to analyze the data collected. Empirically, despite the wheat enterprise being profitable in the study area, farmers didn’t achieve the targeted goals of output maximization, cost minimization, and profit maximization as evident respectively by the technical, cost, and economic efficiency indexes. However, these goals were challenged by induced human risks viz. gender discrimination that affects women’s access to productive resources, poor labor productivity due to diminishing marginal returns associated with old age, capital consumption triggered by increased income, poor prioritization of wheat enterprise as a business, and vulnerable household size. Furthermore, the identified constraints mediating the links that affected the economic efficiency of wheat farmers were price/marketing, technological, managerial, and infrastructural risks. Therefore, for long-run sustainable wheat production, the responsibility lies on policymakers to concentrate more on marketing and technological risks challenging wheat production in the study area as empirically established by this research.

Performance Analysis in Chinese Female Club Soccer Based on Offensive and Defensive Technical-Tactical Indicators

Abstract Introduction. This study aimed to analyze how technical and tactical performance indicators differ by different match outcomes in women’s soccer and apply a comprehensive approach to match analysis. Material and Methods. The investigation analyzed many technical and tactical indicators of female club players from 28 matches in the Chinese Women’s Super League during the 2020 and 2021 seasons. These included both offensive and defensive indicators, such as goal kicks, clearances, interceptions, and more. Standard scores and T-scores were considered in illustrating the typical performance status of the team. In evaluating the technical and tactical performance indicators across various match outcomes, we applied non-parametric Kruskal-Wallis tests. This approach was taken because the data were found appropriate for the non-normal distribution, allowing the comprehensive detection of the differences across game states. Results. Kruskal-Wallis test-based statistical analysis showed that there is a substantial number of differences in team performance across slumped and high-performance phases within the games. The passable offensive indicators, such as shot frequency and forward passes, dropped significantly during the slump (p &lt; 0.05). Amongst the defensive actions, there were significant increases. By contrast, in high-performance phases, offensive actions-shots and forward passes were significantly more frequent (p &lt; 0.01), indicating dynamics in the change of the team approach according to the state of play. Conclusions. Our analysis showed that more offensive actions relate to a win in a match, while defensive action usually leads to a lost match. This result will offer valuable information to coaches for training and game strategies.

Long-term performance analysis of operational efficiency of a grid-connected solar power plant under Mauritania climate

This work examines a solar power plant connected to the Nouakchott electricity grid in Mauritania. Operating since 2013, the 15 MWp plant's reliability and energy yield have been evaluated using a performance index. The assessment involves three phases. First, the plant's meteorological environment and technical indicators are presented. In the second phase, mathematical performance models specified by the International Energy Agency (IEA) are applied to calculate performance indices using data from the data acquisition system (SCADA). The third phase compares actual production data for 2015, 2017, and 2020 with results simulated for PVsyst for the same years. The obtained results are thoroughly analyzed to highlight relevant physical phenomena. The analysis focuses on the plant's 7-year operating period and its impact on performance indicators for electricity production fed into the grid. This study provides insights into the solar power plant's reliability and energy yield, aiding future operational enhancements. It underscores the importance of performance monitoring and assessment in optimizing solar power generation systems.

Predicting the Brazilian Stock Market with Sentiment Analysis, Technical Indicators and Stock Prices: A Deep Learning Approach

Predicting the Brazilian Stock Market with Sentiment Analysis, Technical Indicators and Stock Prices: A Deep Learning Approach