Technical Analysis Research Articles

Comparative study of lithium-ion battery and hydrogen fuel cell powered vehicles: Technical, economic, and environmental analysis

Comparative study of lithium-ion battery and hydrogen fuel cell powered vehicles: Technical, economic, and environmental analysis

Technical analysis of exploiting untapped wind power for sustainable hydrogen energy production

Technical analysis of exploiting untapped wind power for sustainable hydrogen energy production

The Influence of Commitment and Job Satisfaction on Employee Performance in Megamendung Bogor

The aim of this research is to determine the effect of commitment and job satisfaction on employee performance in the Bogor Megamendung detective training. This research uses a quantitative method, namely a research method that is post-positivism, namely researching a certain population or sample, where data collection uses research instruments and statistical data analysis with the aim of testing the hypotheses that have been determined so that direct or indirect relationships between variables can be identified. The number of respondents taken was 55 people. Several data collection methods were used, namely questionnaires, direct observation, literature study, documentation. Technical data analysis uses a Likert scale and regression method through the SPSS program. The research results show data that commitment has a positive effect on employee performance, job satisfaction has a positive effect on employee performance, commitment and job satisfaction have a positive effect on employee performance.

Characterization and molecular docking of sustainable wine lees and gelatin-based emulsions: innovative fat substitution.

The present study aimed to determine how various amounts (0.00, 0.58, 1.52 and 4.50 g 100 g-1) of wine lees (WL), which contains numerous essential components, impact the emulsifying properties of fish gelatin (FG) at a low concentration (0.5 g 100 g-1) in the high-fat phase (65 g 100 g-1). This study conducted rheology, physicochemical technical and characterization analyses on the emulsions to provide sustainable and innovative approaches for spreadable oils. The addition of WL to FG emulsions improved oxidative stability, emulsion stability and bioactive compounds. The zeta potential (-101 ± 5.62 mV) of 0.58 g 100 g-1 WL-containing emulsion (PE1) was found to be high, whereas particle size (347.6 ± 5.25 nm) and polydispersity index (0.50) were statistically low. It was also found that the addition of WL improved the intermolecular interactions, crystallinity and microstructural properties of the emulsions. All these results were supported by simulating the molecular configuration between FG and WL. The compounds gallic acid, caffeic acid, myricetin, quercetin and resveratrol showed a strong affinity to FG, with free binding energies of -5.50, -5.88, -6.53, -6.68 and -6.66 kcal mol-1, respectively. As a result, WL-supported FG has the potential to be used as an alternative to egg proteins to develop sustainable low-cost spreadable emulsions. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Strategy for the analysis of lignocellulosic biomass to select a viable transformation route in the Colombian context.

Lignocellulosic biomass is widely available in the world. However, a consensus has yet to be established to evaluate the biomass valorization alternatives. The chemical composition is the primary technical limitation in selecting a transformation route to obtain value-added products. In this paper, the bagasse from non-centrifuged sugar (NCS) production and Pinus patula (PP) wood chips were analyzed in terms of complete chemical composition to establish their potential for selecting the transformation routes. A strategy to select the best route based on the chemical composition was applied and a feedstock criteria model was proposed. Schemes were obtained and compared using a bioprocess selection strategy proposed in previous works. As a result, the preliminary biorefinery schemes were finally defined. The assessment of schemes derived from the outlined strategy included technical, economic, environmental, and social analyses. The environmental evaluation was complemented with a geolocation assessment, revealing a 0.75-ton CO2-eq/yr contribution to the carbon footprint for local distribution. The sustainability index for the PP biorefinery and the bagasse from NCS production was analyzed, resulting in indices of 44.8 and 60.9, respectively. These values were primarily derived from the economic and environmental analyses of both processes.

CRYPTOCURRENCY “THE PERFECT ALTERNATIVE SOLUTION TO HIGH-RISK/HIGH-RETURN INVESTMENTS”

In recent years, there has been a notable increase in the number of financial institutions incorporating cryptocurrencies into their investment portfolios. Cryptocurrencies represent a groundbreaking addition to asset managers' repertoire, being the first purely digital assets to be embraced in this manner. While they share some similarities with traditional assets, cryptocurrencies possess unique characteristics that set them apart, making their behavior as an asset class a subject of ongoing exploration. Given the evolving nature of cryptocurrency trading, it is crucial to consolidate existing research findings on the subject. This paper aims to provide a comprehensive overview of cryptocurrency trading research by synthesizing insights from 146 research papers. Topics covered include cryptocurrency trading systems, market bubbles, volatility and return predictions, portfolio construction, technical trading strategies, and more. Through an analysis of datasets, research trends, and distribution among research topics and technologies, this paper sheds light on the current landscape of cryptocurrency trading research. It also identifies promising opportunities for further exploration in this dynamic and rapidly evolving field.

Transactive control of community batteries for voltage regulation in distribution systems

With the proliferation of renewable energy sources in distribution systems, the grid operators are posed with the challenge of voltage regulation. Community batteries have significant capabilities in regulating network voltage but such potentials are rarely studied. The literature also lacks insights into how voltage regulation markets should co-exist with other grid service markets, such as battery energy arbitrage service and frequency control ancillary service (FCAS) markets. Therefore, this paper addresses these gaps by proposing a transactive platform for community batteries to trade voltage regulation services with the grid operator while providing other services. First, voltage sensitivity analysis is conducted to construct a voltage regulation demand curve based on the regulation needs of the system. Next, different operating conditions of battery inverters are investigated to model the cost of supplying voltage regulation. Various market-clearing rules are then established to determine the quantity and price of the voltage regulation service traded using the demand and supply curves. Technical analyses indicate that the proposed transactive platform is able to (1) price the voltage regulation service depending on the severity of voltage issues, (2) set a positive/negative regulation price when the system undergoes overvoltage/undervoltage conditions, (3) help battery owners gain considerable extra regulation revenue with proper incentivisation. This research provides guidance to incorporate voltage regulation service with the presence of other grid services and to implement voltage regulation markets to engage community batteries.

Assessing the Feasibility of Photovoltaic Systems in Türkiye: Technical and Economic Analysis of On-Grid, Off-Grid, and Utility-Scale PV Installations

Assessing the Feasibility of Photovoltaic Systems in Türkiye: Technical and Economic Analysis of On-Grid, Off-Grid, and Utility-Scale PV Installations

Advance Sequential Models for Asset Trading: Leveraging LSTM & RNN with Stochastic RSI and MACD Indicators

Abstract: The goal of stock market prediction is to forecast a company's financial stocks' future worth. The application of machine learning, which creates predictions based on the values of the current stock market indices by training on their prior values, is a recent trend in stock market prediction technologies. Machine learning utilises many models to facilitate accurate and reliable prediction. The study focuses on stock value prediction using regression and LSTM-based machine learning. Considered factors are volume, low, high, open, and closed. We also employ algorithmic trading, which is a technique for automatically executing orders based on pre-programmed trading instructions that take volume, price, and time into account. This kind of trading aims to take advantage of a computer's speed and computational power in comparison to human traders. Algorithmic trading has become more popular in the twenty-first century, prompting traders to respond. According to a 2019 research, 92% of traders use algorithms rather than people. The moving average convergence & divergence and the relative strength index are our primary tools. One technical tool employed in financial market analysis is the relative strength index, or RSI. Charting a stock's historical and present strength or weakness with retail and institutional traders is the goal. Investment banks, mutual funds, pension funds, and hedge funds all utilise it extensively when they need to perform trades. based on the closing prices of a recent trading period, too fast for a human or the market. It is important to distinguish the indicator from relative strength. The RSI measures the speed and size of price changes and is categorised as a momentum oscillator. The rate at which prices rise or fall is known as momentum. The ratio of higher closes to lower closes, where closures are defined as averages of absolute values of price changes, is the relative strength, or RS. The RSI determines momentum by dividing higher closes by overall closes, or stocks with more or stronger positive moves. The RSI is most commonly employed during a 14-day period and is scaled from 0 to 100, with 70 and 30 representing the high and low readings, respectively. Alternately shorter or longer outlooks are associated with shorter or longer time frames. Although they happen less frequently, high and low levels— 80 and 20, or 90 and 10—indicate increased momentum. J. Welles Wilder created the relative strength index, which was first published in the June 1978 issue of commodities magazine and in his 1978 book New concepts in technical trading systems, and in commodities magazine in the June 1978 issue.

Stock Market Prediction and Visualisation

Abstract: Stock price forecasting is a widely discussed and significant topic in both financial and academic circles. The stock market is inherently unpredictable, lacking clear rules for estimating or predicting share prices. Various methods, including technical analysis, fundamental analysis, time series analysis, and statistical analysis, have been employed to forecast stock prices. However, none of these methods consistently serve as reliable prediction tools. In this paper, we explore the implementation, prediction, and analysis of stock market prices. Artificial Neural Networks and Machine Learning prove effective for forecasting stock prices, returns, and modeling stock behavior. By conducting statistical analyses, we establish relationships between selected factors and share prices, contributing to more accurate predictions. While the stock market remains inherently uncertain, this paper aims to apply data analysis and prediction concepts to forecast stock prices. In the era of global digitization, stock market prediction has undergone significant technological advancements, transforming traditional trading models. As market capitalization continues to rise, stock trading becomes a focal point for financial investors. Analysts and researchers have developed tools and techniques to predict stock price movements, aiding decision-making. Advanced models leverage non-traditional textual data from social platforms for market prediction. Machine learning approaches, including text data analytics and ensemble methods, have significantly improved prediction accuracy. However, analyzing and predicting stock markets remains challenging due to dynamic, erratic, and chaotic data. This study explores machine learning-based approaches for stock market prediction, emphasizing a generic framework. By critically analyzing findings from the last decade (2011–2021) from digital libraries like ACM and Scopus, we provide insights for emerging researchers to delve into this promising area.

Systematic technical analysis: Enhancing AI deployment in procurement for optimal transparency and accountability

In today's rapidly evolving business landscape, the integration of Artificial Intelligence (AI) in procurement processes holds significant promise for improving transparency and accountability. This systematic technical analysis explores the implications of AI deployment in procurement and its potential to enhance transparency and accountability within organizations. The study adopts a mixed-methods approach, combining quantitative surveys and qualitative interviews to gather comprehensive data on the use of AI in procurement, legal and ethical challenges faced by organizations, and the effectiveness of current legal and ethical frameworks. Quantitative surveys are distributed to a diverse sample of organizations to assess the extent of AI use in procurement, perceived impacts on legal and ethical frameworks, and overall satisfaction with AI deployment. Qualitative interviews with key stakeholders, including procurement managers and legal advisors, provide deeper insights into the nuances of AI deployment and its implications for transparency and accountability. The analysis of quantitative data reveals the prevalence of AI use in procurement processes and its perceived impact on legal and ethical frameworks. Organizations report improvements in contract management, risk reduction, and compliance with regulations due to AI deployment. However, concerns remain regarding the legal implications of AI decision-making and the need for robust ethical frameworks to ensure accountability. Qualitative interviews shed light on the practical challenges and opportunities associated with AI deployment in procurement. Stakeholders highlight the importance of transparency, fairness, and human oversight in AI-driven processes. They emphasize the need for clear ethical guidelines and oversight mechanisms to address potential biases and ensure accountability. Overall, this systematic technical analysis provides valuable insights into the implications of AI deployment in procurement for transparency and accountability. The findings underscore the importance of integrating AI-related training and education into procurement processes and developing robust legal and ethical frameworks to mitigate risks and ensure responsible AI deployment.

Comparative technical analysis of legal and ethical frameworks in AI-enhanced procurement processes

This study presents a Comparative Technical Analysis of Legal and Ethical Frameworks in AI-Enhanced Procurement Processes. The research aims to evaluate the existing legal and ethical frameworks governing the use of artificial intelligence (AI) in procurement and to identify best practices for ensuring transparency, accountability, and ethical decision-making in AI-driven procurement processes. The study adopts a mixed-methods approach, combining quantitative surveys and qualitative interviews with procurement professionals and legal experts. The research design allows for a comprehensive analysis of the legal and ethical challenges associated with AI deployment in procurement and provides insights into practical strategies for addressing these challenges. Findings from the study indicate a growing recognition of the need for clear guidelines and regulations to govern the use of AI in procurement. While respondents acknowledge the potential benefits of AI, such as improved efficiency and cost savings, they also express concerns about algorithmic bias, data privacy, and the lack of transparency in AI-driven decision-making processes. Based on these findings, the study recommends several strategies for enhancing the legal and ethical frameworks in AI-enhanced procurement processes. These include developing clear guidelines for AI deployment, providing training and support for procurement professionals, and establishing mechanisms for monitoring and evaluating AI systems. Overall, the study highlights the importance of integrating legal and ethical considerations into AI deployment in procurement to ensure transparency, accountability, and ethical decision-making. The findings contribute to the growing body of literature on AI governance and provide practical insights for policymakers, procurement professionals, and other stakeholders involved in AI-driven procurement processes.

Wearable Sensors and the Evaluation of Physiological Performance in Elite Field Hockey Players

Sports performance tracking has gained a lot of interest and widespread use in recent years, especially in elite and sub-elite sports. This makes it possible to improve the effectiveness of training, to calibrate and balance workloads according to real energy expenditure, and to reduce the likelihood of injuries due to excessive physical stress. In this context, the aim of this review was to map the scientific literature on wearable devices used in field hockey, evaluating their characteristics and the available evidence on their validity in measuring physiological and movement parameters. A systematic investigation was carried out by employing five electronic databases and search terms that incorporated field hockey, wearables, and performance analysis. Two independent reviewers conducted assessments of the 3401 titles and abstracts for inclusion, and at the end of the screening process, 102 full texts were analyzed. Lastly, a total of 23 research articles that specifically concentrated on field hockey were incorporated. The selected papers dealt with performance monitoring (6 papers), technical analysis and strategy game (6), injury prevention (1), and physiological measurements (10). To appraise the quality of the evaluations, the Oxford quality scoring system scale was employed. The extraction of information was carried out through the utilization of the participants, intervention, comparison, and outcomes (PICOS) format. The analysis encompassed research studies that implemented wearable devices during training and competitive events. Among elite field hockey competitions, GPS units were identified as the predominant wearable, followed by heart rate monitors. The intraclass correlation coefficient (ICC) related to wearable devices showed reasonably high between-trial ICCs ranging from 0.77 to 0.99. The utilization of wearable devices in field hockey primarily centers around the measurement of player activity profiles and physiological demands. The presence of discrepancies in sampling rates and performance bands makes it arduous to draw comparisons between studies. Nevertheless, this analysis attested to the fact that wearable devices are being employed for diverse applications in the realm of field hockey.

Navigating Market Sentiments: A Novel Approach to Iron Ore Price Forecasting with Weighted Fuzzy Time Series

The global iron ore price is influenced by numerous factors, thus showcasing a complex interplay among them. The collective expectations of market participants over time shape the variations and trends within the iron ore price time series. Consequently, devising a robust forecasting model for the volatility of iron ore prices, as well as for other assets connected to this commodity, is critical for guiding future investments and decision-making processes in mining companies. Within this framework, the integration of artificial intelligence techniques, encompassing both technical and fundamental analyses, is aimed at developing a comprehensive, autonomous hybrid system for decision support, which is specialized in iron ore asset management. This approach not only enhances the accuracy of predictions but also supports strategic planning in the mining sector.

A Multi-Stage Approach to Assessing the Echo-Tech Feasibility of a Hybrid SAM-CREST Model for Solar PV Power Plants in Maryland, USA

Maryland is actively working towards doubling its Renewable Portfolio Standard (RPS) target, aiming to increase the share of renewable energy from 25% by 2020 to 50% by 2030. Furthermore, Maryland stands out as a state that strongly supports solar initiatives, offering incentives and specialized programs to assist residents in adopting solar energy solutions. The paper presents a multi-stage approach: Stage 1—Location Selection Process, Stage 2—Technical Feasibility Study, and Stage 3—Economical Feasibility Study. In Stage 1, the study focuses on three potential solar farm locations in Maryland: Westover, Princess Anne, and Eden. Stages 2 and 3 involve a feasibility assessment with detailed technical analysis using the NREL System Advisor Model (SAM) and PVWatts to determine monthly power to the grid and Energy Yield. Subsequently, economic feasibility is assessed using the NREL Clean Renewable Energy Estimation Simulation Tool (CREST), focusing on competitive levelized costs of energy (LCOE), payback time, and cumulative cash flows. Results indicate that all three locations exhibit promising solar irradiance levels, system outputs, and potential energy yields. Due to high solar irradiation, the Westover area has the highest energy yield at 1583.13 kWh/kW, while Princess Anne boasts the highest system output at 333.59 GWh. The economic evaluation suggests that all three locations become profitable within a two-year payback time, with competitive levelized costs of energy (LCOE). Westover emerges as the most cost-effective option at 5.99 cents/kWh, attributed to its higher solar irradiation values and energy yield compared to Princess Anne and Eden. Cumulative cash flows provide insights into long-term profitability, with Princess Anne, MD, having the highest Cumulative Cash Flow over 25 years at $183,383,304. By evaluating technical and economic aspects, this feasibility study offers quantitative insights to guide decision-making for the installation of Solar PV, considering both technological and economic feasibility.

Design, global energy integration, and sustainability analyses of a process coupling renewable energy water electrolysis for hydrogen production with ammonia synthesis

The conventional ammonia synthesis process typically depends on fossil energy and faces challenges such as low utilization of elements and high CO2 emissions, leading to unsatisfactory economic performance. In order to achieve green synthesis and sustainable development of ammonia, this study constructed a process for renewable energy water electrolysis hydrogen production coupled with ammonia synthesis. The process includes: PEM electrolysis water hydrogen production coupling synthesis ammonia (PEM-GHtA) and SOEC electrolysis water hydrogen production coupling synthesis ammonia (SOEC-GHtA). Compared to traditional ammonia synthesis, the new process eliminates the use of carbon conversion for hydrogen production. Instead, it utilizes green electrolysis to split water into hydrogen and oxygen, leading to enhanced energy efficiency and decreased carbon emissions. By employing modeling, simulation, global energy integration, and conducting technical and economic analysis of the new process, this study elucidates the technical advantages and economic competitiveness of the new process over the traditional ammonia synthesis process. According to the analysis results, the utilization rates of hydrogen element in the PEM-GHtA and SOEC-GHtA processes are 92.64% and 92.59%, and the utilization rate of nitrogen element is 90.56% and 90.50%, respectively, much higher than the traditional ammonia synthesis process. In addition, the SOEC-GHtA demonstrated improved energy efficiency, achieving a range of 72.19–75.08%. The CO2 emissions of the two green ammonia synthesis processes are 0.17–0.81 t CO2/t NH3, much lower than the 1.6–3.6 t CO2/t NH3 of the traditional ammonia synthesis process. Based on the current market price, the production cost of green ammonia is relatively high, which has no economic advantage over the traditional ammonia synthesis process. Considering the future implementation of carbon taxes and the decreasing prices of renewable energy electricity to 0.014 $/kWh, the production costs of PEM-GHtA and SOEC-GHtA will be reduced to 359.7 $/t NH3 and 343.5 $/t NH3 respectively. In comparison to the traditional process (403.1–514.7 $/t NH3), the new process offers substantial economic benefits and shows promising prospects for further development.

The Current Situation, Characteristics and Prospects of the Research on Tennis Theme Literature in China from the Perspective of Artificial Intelligence

With the rapid development of artificial intelligence technology, the function of artificial intelligence to help the high-quality development of sports is increasingly visible, and the continuous, comprehensive and deep integration of artificial intelligence and sports will be an inevitable trend. This paper summarized and analyzed the characteristics of the tennis research theme literature published in the core journals of China from 2012 to 2022 by using the methods of literature and logical induction. The results show that, in the past 10 years, there are problems such as insufficient use of artificial intelligence in technical and tactical statistical analysis, insufficient use in tennis professionalization research, lack of artificial intelligence perspectives in tennis tournament research, and shortage of artificial intelligence use in tennis psychology research. In the future, scientific research on tennis in China should rely on scientific and technological means such as 5G technology, big data technology, computer vision and intelligent wearable devices. etc, cross integrate multidisciplinary knowledge, accurately locate the physical fitness weaknesses of Chinese tennis players, focus on building an intelligent measurement system for tennis players, and scientifically develop a big data platform for tennis skills and tactics; And guided by practical problems, we should focus on exploring intelligent wearable devices suitable for tennis sports programs, and integrating computer vision technology with intelligent wearable devices to achieve complementary advantages and enhance the accuracy and credibility of data collection. And use natural science methods to conduct quantitative analysis to provide scientific solutions for the development of tennis.

From Theory to Practice: Applying the Markowitz Model in Stock Portfolio Management under ESG

The study consisted of a month-long simulated stock market operation focusing on the comprehensive analysis of equity portfolio creation and management. Against the backdrop of growing concern for environmental protection, the S&P 500 Net Zero 2050 Climate Transition ESG Index was deemed the appropriate benchmark for the portfolios due to the average level of risk tolerance of customers. The investigation began with an in-depth assessment of macroeconomic and sector conditions, followed by careful selection of securities using both fundamental and technical analysis techniques. The portfolio was then optimized using the Markowitz model and subsequently managed using a variety of strategies, including trading, monitoring, and rebalancing. The subsequent phase of the research involves a rigorous evaluation of performance utilizing various metrics including single-period returns and the Sharpe ratio. The study culminates in a reflective analysis of the overall investment project. Despite the portfolio's underperformance against the benchmark, the project provides invaluable insights into the intricacies of stock market investment and portfolio management, accentuating the impact of market volatility and the significance of strategic asset allocation.

Does Self-Control and Over-Confidence Influence Investment Decision: The Mediating Role of Fundamental and Technical Analysis

This study investigates the influence of self-control and over-confidence on investment decision-making, with a focus on the mediating role of fundamental and technical analysis. The respondents of the study are institutional investors operating in the Pakistan Stock Exchange Market (PSX), which includes the Lahore Stock Exchange, Karachi Stock Exchange, and Islamabad Stock Exchange. The behavior of these investors is assessed to determine the impact of stock market fluctuations on their investing decisions. The unit of analysis in this research is the institutional investors themselves. The study aims to examine the behavioral consequences of integrating self-control and overconfidence variables in investment decisions, particularly in relation to market instability and variations in fundamental and technical analysis. To select the sample, both stratified sampling and cluster sampling techniques were employed. Cluster sampling was used to save costs, while stratified sampling ensured that the sample accurately reflects the population. The states were divided into clusters to facilitate the use of cluster sampling. In terms of sample size, the study solely focuses on institutional investors in the Pakistan stock exchange PSX. Therefore, the sample consists of 375 institutional investors. Data collection for this study utilized a 5-point Likert scale, which is a commonly used measurement instrument for determining the relationship between variables. The scale allows respondents to provide their opinions on a continuum from strongly disagree to strongly agree. This approach enhances the originality and reliability of the data while minimizing respondent annoyance. Overall, this study aims to contribute to the understanding of how self-control and over-confidence influence investment decisions, with fundamental and technical analysis serving as mediators in the context of the Pakistan Stock Exchange Market. The findings from this research can provide valuable insights for institutional investors in their decision-making processes. Integrating self-control and over-confidence in investment decision-making aligns with SDG Goal 8: Decent Work and Economic Growth, by promoting responsible and sustainable investment practices.

Rising incidence of severe maxillofacial space infections in Germany

ObjectivesSevere maxillofacial space infection (MSI) as an end stage of dentoalveolar diseases or complication of sialadenitis is a potentially life-threatening disease accompanied by complications including airway obstruction, jugular vein thrombosis, descending mediastinitis, sepsis and acute respiratory distress syndrome. The aim of this study was to analyze the incidence and time trends of severe MSI and potentially influencing factors in the German healthcare system over time.Materials and methodsNationwide data regarding the national diagnosis-related-group (DRG) inpatient billing system was received from the German Federal Statistical Office. A retrospective analysis of incidence and time trends of MSI-associated procedures classified with the Operation and Procedure Classification System (OPS), were statistically evaluated using Poisson regression analysis between 2005 and 2022 and were associated with different epidemiological factors.ResultsThe total standardized incidence rate of MSI-associated procedures in the observational period 2005—2022 was 9.8 (♀8.2; ♂11.4) per 100,000 person years. For all age groups a significant increase of 46.1% in severe MSI – related surgical interventions was registered within the observational period. The largest increase (120.5%) was found in elderly patients over 80 years. There were significant differences of the incidences of MSI-associated surgeries between the different federal states in Germany.ConclusionsSevere MSI are a growing challenge in German health care especially among elderly patients over 80 years.Clinical relevanceSevere MSI is a promising target for prevention. There should be more focus in primary dental and medical care especially in groups depending on social support.