Technical Analysis Research Articles

The predictability of technical analysis in foreign exchange market using forward return: evidence from developed and emerging currencies

The predictability of technical analysis in foreign exchange market using forward return: evidence from developed and emerging currencies

Prediction model and technical and tactical decision analysis of women's badminton singles based on machine learning.

In the Paris Olympic cycle, South Korean women's athlete An Se-young rose to the top of the 2023 BWF Olympic points with a win rate of 89.5%. With An Se-young as the subject, this paper aims to carry out technical and tactical analysis of women's badminton singles and formulate a prediction model based on machine learning. Firstly, An's technical and tactical statistics are analyzed and presented in a proposed "three-stage" data classification method. Secondly, we improve our "three-stage" machine learning dataset using video analysis of 10 matches (21 point games) where An Se-young faced off against four other players ranked in the top five of the World Badminton Federation (BWF) in week 44 of 2023. Finally, we establish a prediction model for the scoring and losing of points in the women's badminton singles based on the 'Decision tree', 'Random forest', 'XGBoost', 'Support vector' and 'K-proximity' algorithms, and analyze the effectiveness of this model. The results show that the improved data classification is reasonable and can be used to predict the final score of a match. When the support vector machine uses the RBF function kernel, the accuracy reaches its highest at 87.5%, and the consistency of this prediction model is strong. An's playstyle is sustained and unified; she does not seek continuous pressure, but rather exploits and maximizes her aggression following any mistake made by her opponents, immediately utilizing assault methods such as kills or dives, often resulting in the conversion of points during the subsequent 2-3 strikes.

INCREASE OF TECHNOLOGICAL INDICATORS USED IN THE ENERGY INDUSTRY

When evaluating the suitability of using coal in various industries, it is subjected to technical analysis and a number of special technological studies. The obtained results in combination with elemental and group analysis allow choosing the most rational and economically efficient directions of their use, which plays not the least role in determining the economic significance of coal mining deposits. The work is based on theoretical research methods based on the basics of probability theory and mathematical statistics - to improve the existing methods of processing results and assessing the uncertainty of measurements; theories of accuracy - for the development of procedures for assessing the uncertainty of measurements of the quality parameters of solid mineral fuel during its tests; statistical modeling methods - to check the reliability of the developed provisions. A generalized parameter of the quality of solid mineral fuel has been synthesized, which makes it possible to evaluate its quality based on the results of tests conducted in coal chemical laboratories. improvement of methods of testing solid mineral fuel according to quality parameters by developing uncertainty assessment procedures.

Techno-Economic Analysis of BAU-STR Dryer for Rice Drying: An Approach to Accelerate Adoption

Postharvest food loss and waste offset worldwide agricultural productivity and food security. Insufficient drying and storage are the prominent drivers of food loss and waste in underdeveloped countries. Mechanical grain drying systems have distinct benefits over sun drying but are inaccessible to underserved communities due to high capital costs and energy demand. This study evaluated the techno-economic and financial performance of a half-ton-capacity BAU-STR dryer. The moisture extraction rate, drying rate, drying efficiency, and energy consumption were used as technical performance indicators. In contrast, the net present value (NPV), internal rate of return (IRR), benefit–cost ratio (BCR), and payback period were considered economic performance indicators. The technical performance analysis results revealed that the moisture content of rice was reduced from 19.5% to 13.5 ± 0.15% in 4.0 h with an average drying rate of 1.5%/h and a drying efficiency of 75.1%. The financial performance analysis resulted in a drying cost, NPV, IRR, BCR, and PBP of USD 0.96 per 100 kg of grain, USD 3018, 135%, 3.0, and 0.73 yr., respectively, when the annual use was 240 h. If the yearly use of the dryer increased from 240 to 720 h, a higher NPV, IRR, and BCR, as well as a lower payback period and drying cost, could be achieved. Adopting a BAU-STR dryer for drying grain (rice and corn) among underserved communities could play a key role in postharvest food loss and waste.

Stock Market Forecasting Using a Neural Network Through Fundamental Indicators, Technical Indicators and Market Sentiment Analysis

AbstractThe objective of this research is to provide evidence that it is possible to obtain a prediction that better aligns with the future performance of a stock if a neural network model is trained with stock market analysis variables and qualitative variables. As a case study, thirty-three companies’ representative of the S&P 500 are selected, and a multilayer perceptron artificial neural network is built and trained with input parameter indicators of fundamental analysis, technical analysis, and market sentiment. By incorporating the latter as an additional variable, the model's accuracy increases by 1.5% for 66% of the companies analyzed. The results confirm the crucial role played by the selection of the neural network model and its variables depending on the type of company to be analyzed. The main contributions of this research are the identification of the best variables combination to train a neural network model depending on the market sector to be analyzed, likewise it is demonstrated that, by using market sentiment, it is possible obtain a high accuracy or increase the accuracy to an existing model.

Investigation of Radioxenon Probability Density Functions at IMS Radionuclide Stations Using a Monte Carlo Method for Background Estimation

Abstract The International Monitoring System, the primary means of verification of the Comprehensive Nuclear-Test-Ban Treaty, monitors the planet for any sign of a nuclear explosion. Regarding the International Monitoring System radionuclide stations, it is known that radioxenon released from nuclear facilities such as medical isotope production facilities and nuclear power plants influences the stations. For the purposes of monitoring nuclear explosions, it is important to better understand the radioxenon background based on these nuclear facilities. The probability density functions of background activity concentration at IMS radionuclide stations are estimated using a Monte Carlo method based on emissions from known nuclear facilities and source receptor sensitivity data. This paper describes two case studies of radioxenon detections at radionuclide stations applying the developed approach. This method could be one of several prospective approaches to predict the activity concentrations of isotopes of radioxenon at radionuclide stations in Comprehensive Nuclear Test-Ban Treaty Organization’s (CTBTO’s) prototype xenon background estimation tool software. It can also be used in characterization of CTBT-relevant nuclear events for expert technical analysis. Plain Language Summary Civil nuclear power stations and medical isotope production facilities release radioisotopes of xenon during their normal operations. These emissions would make it harder to detect xenon produced from any nuclear weapon test that might occur. A method is described that starts with information about civil releases and produces a statistical description of the concentrations of isotopes measured at stations designed to detect nuclear tests. This information makes it possible to enhance nuclear explosion detection performance.

INTERVENÇÃO FARMACÊUTICA NA INCIDÊNCIA DE ERROS DE PRESCRIÇÃO RELACIONADA A MEDICAMENTOS

The pharmacist plays a crucial role in healthcare by helping to reduce prescription and medication errors, as well as issues related to pharmacotherapy that could harm hospitalized patients. The aim of this study was to analyze, based on the literature, the contribution of the clinical pharmacist in the hospital setting, focusing on intervention and reducing the incidence of medication-related prescription errors. By working in collaboration with the multidisciplinary team, the clinical pharmacist provides guidance to professionals on the safe and rational use of medications, which enhances the quality of patient therapy. The impact of the clinical pharmacist is significant, as they are capable of reducing the occurrence of avoidable adverse effects by 66%, which can be controlled not only through the discontinuation of medication combinations but also through dose adjustment. It is important to highlight the central role of the pharmacist in medication dispensing, emphasizing the importance of technical and clinical analysis of prescriptions and medication releases in hospital settings, thereby promoting the rational use of medications.

Demystifying the Black Box: A Technical Analysis of Transparency Requirements in Clinical AI Systems

This analysis examines the adequacy of Ghana's current regulatory framework in addressing AI explainability in clinical decision support systems. It evaluates existing healthcare laws, derives underlying principles, and assesses their application to AI governance. The study finds significant gaps in the current regulatory regime, including a lack of AI-specific provisions, insufficient explainability requirements, and inadequate frameworks for liability and accountability. While existing principles provide a foundation, they are insufficient to fully address the challenges posed by AI in healthcare. The analysis concludes that comprehensive regulatory development is needed, including AI-specific healthcare regulations, technical standards for explainability, and enhanced regulatory capacity. This study is significant as it provides a roadmap for developing effective AI explainability regulations in Ghana's healthcare sector. It highlights the urgent need for regulatory evolution to ensure safe, ethical, and transparent use of AI in clinical decision support, potentially serving as a model for other developing nations facing similar challenges.

“SafeSteps” A Flutter -Based Application to Provide Security to Women’s

With women’s safety being one of the most serious and pressing issues worldwide, rapid technological advancements have created new opportunities to enhance personal security. This paper presents an improved mobile application that prioritizes women’s safety through a SIM card-based location tracking approach instead of relying on GPS services that require an internet connection. This feature ensures consistent and reliable location tracking even in areas with weak or no internet access. Inspired by existing platforms like bSafe and My Safetipin, the app includes an emergency alert system that allows users to trigger SOS signals to authorities and nearby registered users by pressing the power button, volume button, or using specific gestures. The involvement of nearby users aims to provide immediate assistance before law enforcement arrives, potentially preventing harm or saving lives. The paper features a survey and technical analysis detailing the app’s architecture, usability, and the benefits of SIM based tracking for improved response times during emergencies. Key Words: Women’s safety, SIM card location tracking, emergency alert system, SOS message, offline tracking, mobile safety app, user assistance network.

Technical Feasibility Analysis of Digital Twin Technology in the Field of Urban Disaster Prevention

Abstract. In recent years, disasters have occurred frequently around the world, causing great harm to national finances and people's safety. There is still a lack of research on digital disaster prevention technology, but with the development of technologies such as artificial intelligence and computer vision, it has gradually become possible to use digital twin technology in urban disaster prevention. This paper is mainly based on digital twin related technologies, briefly introduces the concept and importance of digital twins, and the applicable fields, and then discusses the mode of data interaction between real entities and twins and the feasibility of digital disaster prevention, so as to prevent and simulate urban disasters and derive strategies for dealing with disasters. This paper reviews the existing research on digital twin technology through the method of literature retrieval and analysis. By analyzing technologies such as the Internet of Things, sensors, three-dimensional laser technology, urban monitoring technology, satellite remote sensing technology, drone remote sensing technology, mobile device technology, lidar technology, and oblique photography, a systematic review of critical analysis is obtained, and a macro method of data interaction and modeling for digital twin technology for disaster prevention is obtained, thereby deriving the feasibility of disaster prevention based on digital twin systems.

P2Y12 Inhibitor Pretreatment in Non-ST-Segment Elevation AcuteCoronary Syndrome: The NCDR Chest Pain-MI Registry.

Although high rates of P2Y12 inhibitor pretreatment (defined as the administration before coronary angiography) for non-ST-segment elevation acute coronary syndrome (NSTE-ACS) have been reported, contemporary U.S. practice patterns are not well studied. The goal of this study was to investigate the temporal U.S. trends, variability, and clinical outcomes of P2Y12 inhibitor pretreatment in NSTE-ACS. Consecutive patients who underwent early invasive strategy for NSTE-ACS (coronary angiography≤24 hours of arrival) in the National Cardiovascular Data Registry Chest Pain-Myocardial Infarction (MI) Registry were analyzed. A time-trend analysis was conducted on a complete cohort between January 1, 2013, and March 31, 2023. Subsequently, a more recent cohort (January 1, 2019, to March 31, 2023) with a complete set of variables was used to construct hierarchical regression models to quantify the variability in the use of pretreatment among operators and institutions. For this contemporary cohort, instrumental variable analysis, with operator preference as the instrument, was performed to compare the in-hospital outcomes between patients who received pretreatment and those who did not. Use of P2Y12 inhibitor pretreatment decreased from 24.8% in 2013Q1 to 12.4% in 2023Q1. Among the contemporary cohort of 110,148 patients (2019-2023; mean age 63.9 ± 12.5 years; 33.0% female), 17,509 (15.9%) received pretreatment. Significant variability in P2Y12 inhibitor pretreatment was observed (range: 0%-100%): hierarchical regression model demonstrated that 2 similar patients would have a >3-fold difference in the odds of pretreatment from 1 random operator or institution as compared with another (median OR: 3.74 [95%CI: 3.57-3.91] and 3.63 [95%CI: 3.51-3.74], respectively). Instrumental variable analysis demonstrated no significant differences in in-hospital all-cause death (1.5% vs 1.7%; P=0.07), recurrent MI (0.6% vs 0.6%; P=0.98), or major bleeding (2.7% vs 2.8%; P=0.98) with pretreatment. However, in patients who underwent coronary artery bypass surgery, pretreatment was associated with a longer length of stay (11.2 ± 5.1days vs 9.8 ± 5.0days; P< 0.01). In a national U.S. registry, we observed significant variability in the use of P2Y12 inhibitor pretreatment among NSTE-ACS patients. Given the lack of clear advantages and the potential for prolonged hospital stays, our findings highlight the importance of efforts to improve standardization.

Technical Analysis of the Indonesian Stock Market with Gated Recurrent Unit and Temporal Convolutional Network

Big data is essential in the age of 4.0 industry as it becomes the basis of decision making. Deep learning research in the last few years has been proven effective in understanding complex big data patterns, especially in the finance sector. The rapid growth of the Indonesian stock market in the last 20 years, which was driven by globalization, prompted fluctuation in the Bursa Efek Jakarta (JKSE) which was influenced by stock prices, commodity prices, and exchange rate. This study identifies the main indicators of Indonesian stock market crisis, applies and compares deep learning models, particularly Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN), in predicting stock prices. This study identified 20 JKSE crisis points between the 2002-2023 period with average return value at around -6%. All variables correlated positively with JKSE, with SET.BK as the highest correlated variable in lag 0. The American and European stock market, commodity price, and exchange rate tend to show a pattern opposite to the JKSE crisis. Predictor variables such as STI, HIS, KLSE, KS11, SET.BK, PSEI.PS, RUT, and USDIDR are chosen based on significant cross correlation and average return plot. Hyperparameter tuning and cross validation within a 3 years window concluded that the GRU model is accurate and efficient, with RMSE value at 43.35568 and MAE value at 33.66909 in the validation data.

Assessment of Adequacy of Product Appearance Attributes to the Design Objective

A new method for assessment of product aesthetics („Assessment of adequacy of product appearance attributes to the design objective and to values of market segment”, Adequacy Method, in short) was developed by the author. The method is based on the need to assess product aesthetics against the design objective and the human values / goals of the target market segment. In a pre-test, eight products (from four classes) with the most different design were chosen for testing the method. Then, in an experimental study, the products were assessed using the proposed method by students from a technical university. After statistically analysing the assessment results, it was found that the method criteria and the method itself proved to be reliable, efficient, and clear. The proposed method was also tested by comparison with a complex method (FTESE – Functional, Technical, Ergonomic, Significance and Esthetic Analysis). The same products used in the paper presenting the FTESE method were assessed using the proposed method and lead to the same ordering of products according to their aesthetics. But the comparison also indicated that the proposed method allows for a superior refinement.

Techno-Economic analysis of ceramic matrix composites integration in remaining useful Life Aircraft Engine Hot Section Components

AbstractCeramic Matrix Composites (CMCs), specifically SiC/SiC composites, represent a significant innovation in aerospace material technology, offering superior performance over traditional nickel-based superalloys in high-temperature turbine blade applications. This study presents a novel techno-economic assessment, filling a critical gap in the literature by directly comparing the economic and technical viability of CMCs versus superalloys. Unlike previous studies, which primarily focus on technical performance or cost analysis independently, this work integrates both aspects, providing a holistic comparison across key economic metrics, including acquisition, machining, maintenance, and recycling costs. The results demonstrate that SiC/SiC blades offer a 15–20% higher Net Present Value (NPV) and a 17% greater Internal Rate of Return (IRR) over a 20-year lifecycle than superalloys. Despite higher initial costs, CMCs achieve an estimated 2 to 3 years reduction in payback period, mainly due to their superior thermal and creep resistance, leading to fewer maintenance interventions and longer operational lifetimes. Although machining costs for CMCs are higher, these are more than offset by the long-term savings achieved through improved fuel efficiency and lower maintenance costs. A comprehensive sensitivity analysis, incorporating fluctuations in discount rates and material costs, further validates the economic robustness of CMCs in various operational scenarios. This study is the first to compare CMCs and superalloys, offering new insights into the financial implications of material selection in aerospace manufacturing. The findings present critical engineering recommendations that empower aerospace manufacturers and decision-makers to optimise material selection for improved efficiency and cost-effectiveness in high-performance turbine applications.

Technical and Economic Analyses for the Implementation of a Biohydrogen Production System Using Bioelectricity from Vinasse Biogas of the Sugarcane and Alcohol Industry

This paper studies the implementation of a system consisting of an electrolyzer connected to a bioelectricity generator set that operates using vinasse biogas. Data on total ethanol production across all regions of Brazil were collected, and two ethanol plants in São Paulo state were selected for analysis (São Jose da Estiva and São Manoel). The feasibility of implementing this system at these facilities was evaluated using energy analysis for biohydrogen production. The process’s energy efficiency was assessed, allowing for the construction of a Sankey Diagram for both plants. Additionally, the costs of producing bioelectricity and biohydrogen via electrolysis using vinasse biogas from Brazil’s sugar/alcohol industry were determined, factoring in the payback period, equivalent utilization period, and annual interest rate. The study demonstrates the efficiency and cost-effectiveness of producing biohydrogen using vinasse biogas. The cost of producing bioelectricity at the two plants decreases significantly over the 25-year system implementation period, starting at around 0.159–0.197 USD/kWh in the first year and dropping to a minimum of 0.027–0.039 USD/kWh by the end of the period. The initial cost of producing biohydrogen ranges from 1.154 to 1.236 USD/kWh at São Jose da Estiva and from 1.491 to 1.597 USD/kWh at São Manoel in the first year. However, prices drop by nearly half in the second year, reaching 0.630 to 0.693 USD/kWh at São Jose da Estiva and 0.808 to 0.891 USD/kWh at São Manoel. The payback period for the biohydrogen and bioelectricity production system increases, with São Jose da Estiva generating income by year 5 at a 4% interest rate and by year 8 at a 12% rate. For São Manoel, income is expected by year 8 at a 4% interest rate and by year 16 at a 12% rate. Both plants recover their investment within the first 10 years of operation. By the final year, São Jose da Estiva’s income is projected to be between 363,262.20 and 678,129.46 USD/kWh, while São Manoel’s income is expected to range from 85,765.59 to 433,347.70 USD/kWh, demonstrating that the system is viable for sugar and ethanol plants.

The Rehabilitation of the Old Medina of Bejaad, Morocco: a Technological Approach (BIM) to Enhance the Built Heritage

Objective: This study proposes a process of revitalization and regeneration of the old medina of Bejaad, as well as a methodology for the technical analysis of its urban fabric and traditional structures, while respecting the inherent values of its constructions. Through data related to the location of each building and a more in-depth investigation, a global relationship with its environment and history is established. Theoretical Framework: The old medina of Bejaad, through its ancient fabric and its potential social and economic wealth, should be leveraged for the development of this spiritual city. This built heritage is characterized by buildings that, for the most part, have preserved an uncommon restraint, maintaining the art of past construction and the secrets of local housing. Its housing is composed of two types of built fabric: traditional and more recent structures. These two types of buildings are connected by a network of public spaces that bring the inhabitants together. Method: Creation of specific Building Information Modelling (BIM) object libraries, derived from three-dimensional models created by 3D scanning using Lidar technology. Results and Discussion: The results of this investigation highlight the need to identify new methodologies for studying the built environment, which no longer focus solely on individual building types, but encompass the broader scale of the fabric, within which it is possible to assess the constructive relationships between the elements composing the block. This is achieved through a process of adopting measures for the protection, rehabilitation, and appropriation of their living environment. Originality/Value: The study proposes a practical evaluation approach by providing precise data to improve the preservation of the built heritage of the old medina of Bejaad, with the aim of establishing an architectural, urban, and landscape charter for the city and its components.

Time-Trend Analysis and Risk Factors for Niraparib-Induced Nausea and Vomiting in Ovarian Cancer: A Prospective Study.

Nausea and vomiting are major non-hematological adverse events associated with niraparib maintenance therapy. This study aimed to investigate the time-trend patterns of niraparib-induced nausea and vomiting (NINV) and the associated risk factors in patients with ovarian cancer. In this prospective study, we enrolled patients with stage III-IV epithelial ovarian cancer who received niraparib as frontline maintenance therapy. The clinicopathological characteristics and time-trend patterns of patients with NINV were collected through in-person surveys and electronic medical records from the National Cancer Center. Of 53 patients, 50 (94.3%) were diagnosed with high-grade serous ovarian carcinoma. BRCA mutations and homologous recombination deficiency (HRD) were identified in 23 (43.4%) and 32 (60.4%) patients, respectively. Thirty-one patients (58.5%) had NINV. Time-trend analyses revealed that the first peak intensity of NINV was reached at 3 h post-dose, and the second peak intensity was reached at 11 h post-dose. NINV significantly decreased from week 1 to weeks 8 and 12. In multivariate analyses of risk factors for NINV, HRD-positive tumors (p<0.001) and prior experience of chemotherapy-induced nausea and vomiting (p=0.004) were associated with the occurrence of NINV. Pre-emptive treatment with antiemetics are required to manage early-phase NINV during niraparib maintenance therapy in patients with risk factors. Additional larger studies are needed to confirm these findings and to develop optimal preventive strategies for NINV.

Lyme borreliosis in Brazil: a critical review on the Baggio-Yoshinari syndrome (Brazilian Lyme-like disease).

SUMMARYLyme borreliosis or Lyme disease is the most frequently reported tick-borne disease in the Northern Hemisphere. In countries of the Southern Hemisphere, such as Brazil, since the early 1990s, some researchers have argued for the existence of an autochthonous Lyme-like borreliosis, known locally as the Baggio-Yoshinari syndrome (BYS), an alleged "Brazilian borreliosis" supposedly caused by a different strain of Borrelia burgdorferi and transmitted by hard ticks. Currently, the existence of BYS in Brazil is still accepted by a large part of the human health care workers, scientists, medical societies, and patients. In fact, this alleged "Brazilian borreliosis" has been the tick-borne zoonotic disease with the greatest number of reported cases and published studies in Brazil during this century, second only to Brazilian spotted fever. In this manuscript, we reviewed all manuscripts directly related to BYS that have been published in Brazil during the last 35 years. This analysis included 199 individual human cases that have been reported in Brazil since 1989, plus multiple studies on ticks, domestic, and wild animals. Our revision aimed to provide a critical opinion on whether the current published works allow healthcare workers, public health agencies, and patients to accept the existence of Lyme disease, BYS, or other Lyme borreliosis-related disease in Brazil. For this purpose, we evaluated the strengths and weaknesses of each published study, considering the diagnostic methods used, such as serological, microbiological, and molecular analyses. Based on these evaluations, we conclude that there is not enough evidence to support the occurrence of Lyme borreliosis in Brazil or that BYS (Brazilian Lyme-like disease) is caused by a bacterium of the genus Borrelia. This assumption is based on the inaccuracy, unreliability, and misinterpretation of the different diagnostic methods that have been used in Brazil. Recognizing the lack of technical evidence for the occurrence of Lyme borreliosis in Brazil has highly relevant implications. For example, it becomes imperative to raise awareness among the country's medical profession, as they have adopted unnecessary and extreme therapies recommended for patients with a supposed borrelial infection, including BYS, in Brazil. Finally, the technical analyses carried out in this study could be applied to other countries in the Southern Hemisphere (e.g., Argentina, South Africa, Australia), where cases classified and alleged as Lyme disease have been reported.

Physiological and Technical Analysis of Male Athletes from the Bumi Siliwangi Pencak Silat Club in Indonesia

Physiological and Technical Analysis of Male Athletes from the Bumi Siliwangi Pencak Silat Club in Indonesia

ESTADO DINÁMICO DE UNA MICRORRED TRIFÁSICA INCLUYENDO GENERACIÓN FOTOVOLTAICA BAJO TRANSITORIOS EN LA FRECUENCIA FUNDAMENTAL DE OPERACIÓN

This technical note analyses the transient response of a microgrid with photovoltaic (PV) generation controlled by DC/AC inverters and a dq0 current controller to regulate the power flow, voltage and current at the point of common connection (PCC).