Technical Analysis Research Articles

The COVID-19 pandemic impacted the activities of the Schistosomiasis Control Program in Brazil: is the goal of controlling the disease by 2030 at risk?

Schistosomiasis continues to represent a serious public health problem in Brazil. With the coronavirus disease 2019 (COVID-19) pandemic, several control strategies were suspended, probably compromising the goals of eradicating the disease in the country. We aimed to assess the impact of the COVID-19 pandemic on Schistosomiasis Control Program (PCE) actions in all endemic states of Brazil. We performed an ecological study using spatial analysis techniques. The PCE variables assessed were the population surveyed, the number of Kato-Katz tests, positive cases of schistosomiasis and the percentage of cases treated between 2015 and 2021. The percent change was calculated to verify if there was an increase or decrease in 2020 and 2021, along with time trend analyses provided by the Joinpoint model. Spatial distribution maps were elaborated considering the percent change. The surveyed population decreased in 2020 (-65.38%) and 2021 (-37.94%) across Brazil. There was a proportional reduction in the number of Kato-Katz tests (2020, -67.48%; 2021, -40.52%), a decrease in the percentage of positive cases (2020, -71.16%; 2021, -40.5%) and a reduction in the percentage of treated cases (2020, -72.09%; 2021, -41.67%). Time trend analyses showed a decreasing trend in most PCE variables. The PCE activities were impacted by the COVID-19 pandemic in Brazil and PCE strategies must be urgently reviewed, focusing on investments in all endemic areas.

Integration of photovoltaic systems in Algeria's building electrical network: technical and financial analysis

Integration of photovoltaic systems in Algeria's building electrical network: technical and financial analysis

Cryptocurrency price fluctuation and time series analysis through candlestick pattern of bitcoin and ethereum using machine learning

PurposeCandlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.Design/methodology/approachThe data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.FindingsThe inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.Research limitations/implicationsTo statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).Originality/valueThis study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

MINE SLOPE DESIGN SIMULATIONS USING SLIDE 6.0 SOFTWARE OF POST-MINING SLOPE STABILITY

Post-mining slopes are susceptible to landslides due to factors such as slope geometry, rock structure, physical and mechanical properties of rocks, and groundwater content. Monitoring of these slopes is essential to prevent broader environmental issues. This research aims to determine the stability of former mining slopes using soil samples from the Mataraman District, Banjar Regency. The technical analysis method focuses on the physical and mechanical properties of soil, supplemented with mine slope design simulations using Slide 6,0 software to ascertain the safety factors from various slope angles. Safety factor analysis considers the smallest cohesion and bulk density values to represent the material strength of the slope. Findings indicate that the post-mining slopes would remain stable and safe provided no additional destabilizing factors are introduced. The slopes maintain a safety factor greater than 2, implying stability even with a steepness up to 70°. Keywords: mining slope stability, safety factor

AN ANALYSIS OF BEST PERFORMING STOCKS OF BANKING SECTOR THROUGH TECHNICAL ANALYSIS

This research conducts an in-depth analysis of the best performing stocks within the banking sector using technical analysis methods. Through the examination of historical price data, trading volumes, and various technical indicators, the study aims to identify patterns and trends that contribute to the success of these stocks. Key factors such as moving averages, support and resistance levels, and momentum indicators are analyzed to determine the stocks' performance and potential future movements. The findings provide valuable insights into the effectiveness of technical analysis in predicting stock performance within the banking sector. Keywords: banking sector, technical analysis, stock performance, and historical price data, trading volumes, technical indicators, moving averages, support and resistance levels, momentum indicators.

Technical and Enviro-economic Analysis of a 0.78 kWp PV System

Aim: To determine the technical, economic and environmental performance analysis of an installed PV backup system. Study Design: To attain this goal, analysis were performed using a freely available PVSyst 6.7.0 software tool. Economic evaluation was performed using respective present worth of individual component prices. Environmental performance comparison is made between the PV system and diesel generator when each is used as an independent backup system. Input in-situ measured data were determined, measured and keyed into the tool. Place and Duration of Study: The system is installed in an institutional building in a tropical climatic zone with coordinates 0.42 N and 35.03 E. The system was studied for a period of one year where data was collected and recorded for techno-enviroeconomic analysis. Methodology: The system consists of four PV modules rated at 0.78 KWp, charge controller and an inverter unit and battery bank, and is utilized as a power backup system to supply electricity whenever power failure occurs, which is frequent and real in Kenya. The techno-enviroeconomic performances analysis of the system were evaluated using the PVSyst software, a freely available design and analytical tool, where input data were measured and recorded at the site. Results: The performance analysis of the system showed that PV array efficiency of 13.24%, FF of 0.68, CF of 21.23%, and PR of 73.63% and generated electrical energy of 1092 kWh/year. The LCOE of PV electricity was 0.059 $/kWh, while total saving of CO2 emission in tons of CO2 was 9.0 tCO2 with LCE value of 331 gCO2/ kWh. Conclusion: Techno- enviroeconomic performance results of the studied PV system show that the system can contribute significantly to the mitigation of CO2 emission. This work also could be helpful for consumers and policy makers in the choice of renewable based or fossil based backup generation systems.

Global trends in pharmacovigilance-related events: a 30-year analysis from the 2019 global burden of disease study.

Establishing effective pharmacovigilance systems globally is challenging due to the need for comprehensive epidemiological data on pharmacovigilance-related events, particularly in countries at different stages of development. This study aimed to determine magnitude and drivers of change in the global and regional burden of pharmacovigilance-related events from 1990 to 2019, analyzing variations between age groups and sex, providing data support for policymakers to adjust their pharmacovigilance policies. Pharmacovigilance-related events were defined as Adverse Effects of Medical Treatment (AEMT) and Drug Use Disorders (DUD) in the Global Burden of Diseases, Injuries, and Risk Factors Study 2019. Time trend analysis utilized joinpoint regression, age-period-cohort model, and decomposition method. Disease burden was measured in incidence, deaths, and disability-adjusted life years (DALYs). The global burden of pharmacovigilance-related events remained high, driven predominantly by population growth. Children and older adults were identified as particularly susceptible groups. Across various regions and periods of the socio-demographic index (SDI), the risk of death from AEMT showed a decreasing trend. In contrast, the incidence of AEMT and both the incidence and death rates from DUD showed a stable or worsening trend. Significant regional disparities in the burden of these diseases were noted between different SDI levels. The study underscores the critical need for robust pharmacovigilance systems worldwide. The observed trends in the burden of pharmacovigilance-related events offer a clear direction for countries to refine and strengthen their pharmacovigilance policies and practices.

Forecasting cryptocurrency's buy signal with a bagged tree learning approach to enhance purchase decisions

IntroductionThe cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations.MethodsIn this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly.Results and discussionTo make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results.

Analisis Faktor – Faktor Keuangan yang Mempengaruhi Peringkat Obligasi (Studi Kasus Pada Perusahaan Manufacture yang Terdaftar Di Bursa Efek Indonesia) Periode 2016 – 2020

This research aims to determine the effect of productivity, profitability, growth and leverage on bond ratings (Case Study of Manufacturing Companies Listed on the Indonesia Stock Exchange). The population in this study were all manufacturing companies listed on the Indonesia Stock Exchange with a total sample of 17 companies. The sampling method used is purposive sampling method. The data source used is secondary data and technical data analysis using logistic regression analysis. The results of this study indicate that productivity, profitability, growth and leverage simultaneously affect the bond ratings of manufacturing companies listed on the Indonesia Stock Exchange for the period 2016 – 2020. Partially productivity, profitability, growth and leverage affect the bond ratings of manufacturing companies listed on the Stock Exchange Indonesia period 2016 – 2020. Keyword: Bond Rating, Productivity, Profitability, Growth and Leverage

Development of an empirical model and using community sport organizations as the basis for intervening variables in Islamic sports

Purpose Through the development of an empirical model and using community sport organizations (CSOs) as the basis for intervening variables, this study aims to ascertain the impact of Muslim household consumption, sport development officers (SDOs) and CSOs on Islamic sport development (ISD) during the COVID-19 pandemic. Design/methodology/approach All of the sports branch managers in Banten Province make up the study’s sample. Non-probability sampling with a purposive sampling strategy was the sampling method used. Making use of a questionnaire to gather data, 275 different sets of data are available for analysis. Findings Partial least squares is a tool for technical data analysis. With a t-statistic value of 71.358, the Moslem household consumption construct had a favorable and significant impact on the SDO construct. With a t-statistic value of 1.111, the Moslem household consumption construct had a positive but not statistically significant impact on ISD. Originality/value With a t-statistic value of 3.926, the Moslem household consumption construct had a positive and statistically significant impact on CSOs. With a t-statistic value of 1.111, the SDO construct had a positive and statistically significant impact on ISD. This study makes a new contribution by providing practical recommendations for the relationship between ISD authorities, community sports organizations and the positive and substantial impact on the development of the community and ISD.

Implementasi Pembelajaran Pendidikan Agama Islam Berbasis Learning Management System Moodle di SMP Al-Fath Cirendeu

The purpose of this study is to describe learning based learning management system in SMP Al-Fath Cirendeu. This study used a descriptive qualitative approach. Data collection is carried out by; observation, interviews, and documentation. Technical data analysis by reducing data, display data and verification. From this study, it was obtained that LMS learning planning at SMP Al-Fath Cirendeu in its stages has a point of difference with learning planning in general, where there are stages for uploading material, converting material and publishing material before the material is taught in class. In its implementation, SMP Al-Fath Cirendeu utilizes LMS as a medium to deliver material, conduct discussions and collect various tasks in the LMS. Learning model with blended learning, learning approach using discovery learning, the method generally uses discussion and presentation. As for the evaluation stage, teachers use quizzes and assignments at the end of each lesson that will be uploaded in the LMS.

Are simple technical trading rules profitable in bitcoin markets?

This paper examines the profitability of simple technical trading rules in bitcoin markets comprehensively, by taking into account realistic investor behaviour and transaction costs, and data mining problems. Realistic investor behaviour is replicated by first employing 75,360 simple technical trading rules, divided over 6 commonly used trading rule classes and daily and intraday frequencies. Next, we select the best performing rules after transaction costs using a multiple hypothesis procedure. Finally, we form portfolios combining the selected rules and analyse their out-of-sample performance. We find that, especially risk–return wise, simple technical trading rules can outperform a buy-and-hold strategy in the bitcoin market out-of-sample.

The impact of the English national health inequalities strategy on inequalities in mortality at age 65: a time-trend analysis.

During the 1997-2010 Labour government, several policies were implemented to narrow health inequalities as part of a national health inequalities strategy. Many of these policies are likely to have had a disproportionately large impact on people aged 65 and over. We aimed to understand the association between the health inequalities strategy period and inequalities in mortality at age 65-69. We use population at risk and mortality data covering 1991-2019 to calculate mortality rate at age 65-69 at the Local Authority level. We use the 2019 Index of Multiple Deprivation to examine geographical inequalities. We employ segmented linear regression models with marginal spline terms for the strategy period and interact these with an indicator of deprivation to understand how inequalities changed before, during and after the strategy. The reporting of this study adheres to STROBE guidelines. Mortality rates in each deprivation quintile improved continuously throughout the period of study. Prior to the programme (1991-9) there was no significant change in absolute inequalities. However, during the strategy (2000-10) there was a significant decrease in absolute inequalities of -9.66 (-17.48 to -1.84). The period following the strategy (2011-19) was associated with a significant increase in absolute inequalities of 12.84 (6.60 to 19.08). Our results were robust to a range of sensitivity tests. The English health inequalities strategy was associated with a significant reduction in absolute inequality in mortality age 65-69. Future strategies to address inequalities in ageing populations may benefit from adopting a similar approach.

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

The transition to sustainable energy sources in the transportation sector has led to the development and adoption of various alternative propulsion technologies. This document offers an analytical comparison between vehicles powered by lithium-ion batteries (LIBs) and those powered by hydrogen fuel cells (HFCs). It scrutinises the technical, economic, and environmental advantages and disadvantages of each. LIB and HFC vehicles are among the most notable competitors in this sector. On a technical level, LIBs offer higher efficiency and lower weight, while fuel cells provide extended range and rapid refueling capabilities. Economic modelling shows battery electric vehicles currently demonstrate the lowest total cost of ownership, though supportive policies could improve fuel cell competitiveness in heavy-duty applications by 2050. Both technologies provide major greenhouse gas emissions reductions versus conventional vehicles, with battery electric vehicles maintaining an advantage in most scenarios. However, resilient domestic supply chains for critical battery materials and hydrogen infrastructure must be established to enable wide-scale adoption. Safety risks exist but can be mitigated through preventative strategies. Overall, LIBs and HFCs have complementary strengths, positioning them to transform transportation sustainability. However, batteries currently hold advantages in light-duty vehicles, while fuel cells show promise in heavy-duty segments. Continued technology advances, cost reductions, infrastructure build-out, and supportive policies will be instrumental in realising the immense potential.

Differences of Economic Factors on Employment between Industrial Sector in Sidoarjo and Gresik Regencies

The problem of unemployment is increasingly worrying, where unemployment in Sidoarjo and Gresik every year cannot decrease significantly. So the potential level of labor absorption is also getting smaller. Unemployment is a problem that must be overcome by every region. Because high unemployment can lead to decreased productivity, slow economic growth, and increased poverty. This study aims to study, analyze, and compare how the condition of labor absorption in the industrial sector in Sidoarjo and Gresik regencies, as well as to determine the differences in population factors, the number of industrial companies, industrial sector investment, and minimum wages on the absorption of labor in the industrial sector in Sidoarjo and Gresik regencies. The method used in this study is a quantitative approach with secondary data from 2008-2022 with technical analysis of different tests. The result of this study is that the population and number of industrial companies have no difference in the absorption of industrial sector workers between Sidoarjo and Gresik regencies, while there are differences in the investment value of the industrial sector and the minimum wage on the absorption of industrial sector workers between Sidoarjo and Gresik regencies.

Exploring the Opportunities of Generative Artificial Intelligence in Concept Ship Design

Designing vessels is a complex process requiring the consideration of numerous aspects to develop a successful design. Ship and submarine design often requires the designer to approximate, make assumptions, consider scenarios and imagine how the vessel may be used in operation. Having the ability to consult for feedback or request additional information may be a welcome aid. The onset of Generative AI (GENAI) presents a new opportunity to integrate this resource into the workflows of the concept ship design process. Augmenting the design process could have a positive impact on the outcome of the design, further improving various qualities such as performance, sustainability, equality, diversity and inclusion. Aspects associated with weight groups, payload catalogues, technical analysis and layout set out will be explored to investigate whether GENAI could add value to the design process. A case study will be used to facilitate this investigation, taking note of GENAI’s content throughout the process.

Dynamic numerical modeling and performance optimization of solar and wind assisted combined heat and power system coupled with battery storage and sophisticated control framework

Incorporating distributed renewable energy sources into heating, power, and cooling systems is facilitating the drive toward intelligent building energy solutions. However, the inherent uncertainties associated with controllable renewable resources pose challenges for the thermo-economic scheduling of smart buildings. Nonetheless, the flexibility inherent in smart buildings can be leveraged through various market mechanisms. Hence, this research introduces a sustainable energy-building system driven by an autonomous solar/dish Stirling engine (SDSE) and wind turbine combined with a sophisticated control strategy and battery storage, which is designed to provide heat and power requirements. The proposed control strategy is also devised to optimize energy generation, ensuring prudent conservation and minimal waste, thereby amplifying overall efficiency and financial viability. The meticulous design of the system is accomplished through advanced MATLAB/Simulink® modeling. A thorough technical sensitivity analysis meticulously hones design parameters, revealing optimal operational thresholds. Simulation outcomes unveil a consistent Stirling engine (SE) efficiency, achieving a pinnacle of 35 %, while the SDSE attains over 28 %, respectively. The horizontal axis wind turbine, encompassing 100 kW and 500 kW modules, demonstrates power coefficients spanning from 0.18 to 0.09, with corresponding land area requirements ranging from 4.22 m2 to 21.10 m2, emphasizing the pivotal roles of module power and land area optimization. This paper also casts a spotlight on the environmental repercussions of the system, illustrating its potential to avert up to 30,000 kg of CO2 emissions per kWh/year. Moreover, the levelized cost of electricity ranges from 0.13 to 0.16 $/kWh, accompanied by an hourly cost that fluctuates between 3 $/h and 40 $/h, respectively. Conclusively, the developed modeling can be regarded as a significant stride in the realm of hybrid renewable energy systems, replacing the conventional photovoltaic/wind models with cutting-edge solar/wind configurations managed by a sophisticated control strategy and battery storage system.

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

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

Optimal energy storage system selection for future cost-effective green hydrogen production: Integrating techno-economic, risk-based decision making, and cost projections

This study conducts technical, economic, and safety analysis of a green hydrogen production system consisting of a 1000 kWp photovoltaic cell, 3 options of energy storage namely lead carbon (PbC), lithium-ion (Li-ion), and repurposed lithium-ion (2nd Life Li-ion) battery, and an electrolyzer. Firstly, the system is optimized to maximum hydrogen production by adjusting equipment capacity and capacity factors, informed by a 1-h resolution solar energy generation profile. This optimization yields insights into the system's yearly operational dynamics. Techno-economic analysis was conducted to estimate the levelized cost of hydrogen (LCOH) from 2020 to 2050, additionally, uncertainty analysis using Monte-Carlo simulation to estimate the range of uncertainty in LCOH projection from 2020 to 2050 was performed. Furthermore, a quantitative risk analysis, utilizing the process route index metric, evaluates the risk of battery explosions. Finally, this study employs multi-criteria decision-making to choose the best energy storage technology to produce green hydrogen from economic and safety factors. The result indicates that in 2020, the PbC battery emerged as the most cost-effective option. However, over time, both lithium batteries became the cheapest option with almost similar trends. In terms of explosion hazard, the analysis shows that the PbC battery is a safer option for stationary energy storage. When both aspects are considered, the preferred battery option is almost certainly PbC over the years, but in extreme cases where the decision-maker places significant emphasis on economic factors both Li-ion options may become preferable.

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.