Technical Analysis Research Articles

Cryptofinancial imaginaries: how neoliberal theories are materialized in the technical principles of cryptocurrencies

ABSTRACT Cryptocurrencies are sociotechnical objects encoding the visions and interpretative frameworks of the communities of practice that ideated and designed them. This paper explores and presents the notion of cryptofinancial imaginaries, a co-productive process between technocratic and antidemocratic ideologies and cryptocurrency technologies that are presented as natural and liberatory, and whose agenda deepens in neoliberal free-market logics. To do so, we first investigate how neoliberal theories framed the imaginaries of cryptocurrencies’ elite. Second, we explain how these political and economic positions produced a series of technical developments that became the normative basis of contemporary cryptocurrencies. Third, we describe how these technologies reproduce these ideological positions today. To situate the political positions of cryptocurrencies’ original imaginaries we conduct a historical analysis of online discussions and technical documents of Cypherpunks, Crypto-anarchists and Extropians between the 1980s and 2000s. To explain how neoliberal principles are encoded and reproduced by particular technologies, we analyze Bitcoin and Ethereum documentation and we conduct a technical analysis of these two cryptocurrencies’ affordances. Finally, we argue that the popularization cryptofinancial imaginaries would magnify the already existing neoliberal dynamics that increase inequality and advance towards a de-democratizing cultural and material framework of economization of social relations.

Technical analysis and simulation of dance movements based on biomechanical theory

Dance movements are a form of expressive physical activity that communicates emotions, stories, and cultural significance through the rhythmic motions of the body. Viewed through the lens of biomechanical theory, it offers a unique understanding of the body’s physical actions and interactions in space. Biomechanics, the science of movement explains the mechanical principles of human motion, including forces, motion, and body structure. It aims to analyze the biomechanical principles underlying various dance movements, including forefoot (FT) landing, entire foot (ET) landing, single-leg landing, bounce, rock step, and side chassé step. A total of 42 dancers performed these movements in the jive and cha-cha, synchronized with corresponding music. Data were collected using a Vicon motion capture system and pressure sensors, which were uploaded into the OpenSim simulation model to create musculoskeletal models. Statistical Parameter Mapping (SPM) analysis was used to assess biomechanical differences across various dance movements. Depending on the data distribution, ANOVA, multiple regression analysis, and paired t-tests were employed to examine muscle forces involved in the different dance movements. The biomechanical analysis revealed that FT landing increased ankle inversion and instability, while ET landing provided greater stability. Single-leg landing generated higher forces, while the bounce movement was energy-efficient with increased plantarflexion. It may also increase the risk of injury due to higher forces. With careful technique to avoid overloading and injury, these findings may be used in dance training by implementing controlled ET landings for stability and balance, as well as single-leg landings to increase force absorption and build lower limb muscles. The side chasse step and rock step required greater lateral stability, with higher muscle activation in the hip and ankle joints. In conclusion, the biomechanical analysis highlights significant differences in muscle activation, joint angles, and stability across the dance movements.

Optimizing of Self-Compacting Concrete (SCC): Synergistic Impact of Marble and Limestone Powders—A Technical and Statistical Analysis

The disposal and recycling of industrial by-products such as marble and limestone powders pose pressing environmental challenges due to the substantial amounts of waste generated annually by marble processing plants and limestone quarries. The integration of these by-products into concrete production is justified by their widespread availability and the potential to alleviate the environmental burden. This study used a statistical mixture design approach to systematically assess the effects of limestone and marble powders, with varying fineness levels, as partial cement replacements (up to 17%) on the rheological and mechanical properties of self-compacting concrete (SCC). The experimental findings revealed that the density of the SCC mixtures ranged from 2475 to 2487 kg/m3. Mixtures incorporating limestone powder exhibited superior flowability, achieving a slump flow of up to 69 cm, an 8% improvement compared to those containing marble powder. However, marble powder with a specific surface area of 330 m2/kg demonstrated significant improvements in compressive and tensile strengths, with increases of 18%. Statistical analysis using analysis of variance (ANOVA) validated the reliability of the predictive models developed, which demonstrated coefficients of determination (R2) that exceeded 0.94 and p-values below 0.05. These models enable precise predictions of critical performance metrics, including density, slump flow, box flow, compressive strength, and tensile strength, thus reducing the need for extensive experimental procedures.

Research on Co-Combustion of High-Calorific Biomass Obtained Using Gasification and Lignite for Sustainable Utilisation of Resources

As part of the transition to low-carbon energy and for the sustainable utilisation of resources, it is necessary to seek a replacement for solid fossil fuels, but unfortunately, it is impossible to completely abandon them for various reasons at the moment, so only partial replacement with new, high-calorific, biomass-based fuels is possible. The purpose of this work is to determine the typical parameters of the co-combustion of carbonisate, coal and their mixtures, taking into account the synergetic effects influencing the combustion intensity of the mixture. Carbonisate was obtained in the process of the gasification of pinewood through the counter-blowing method at a temperature of 800–900 °C, while air was used as an oxidant. Basically, this method of gasification is used for coal in order to obtain high-calorific coke for the metallurgical industry. Also, in this study, for the first time, carbonisate was obtained from 50% pinewood and 50% lignite. The O/C and H/C ratios were determined for carbonisate. A technical and elemental analysis of the investigated fuels was carried out. A thermal analysis in oxidising medium was applied to determining the typical combustion parameters in the process of slow heating of the fuels under study. According to the results of this thermal analysis, typical heating parameters such as the ignition temperature, burnout temperature, maximum mass loss rate, combustion index, etc., were determined. It was noted that the calorific value of carbonised wood is two times higher than that of coal. The combustion index of carbonisates is 2.5–36% lower compared to that of coal. According to the results of the analysis of the interaction of the components among themselves (in the process of their joint combustion), the presence of synergetic interactions between the components was determined, which affected the change in the combustion intensity and heat release intensity. The results of this study may be useful for retrofitting coal-fired boilers to run on a mixture containing carbonisate and lignite. If carbonisate is produced from biomass, the resulting gas could be used as an energy fuel by burning it in a coal-fired boiler.

Gamification for Multi-Generational CRM Engagement: A Technical Analysis

Gamification for Multi-Generational CRM Engagement: A Technical Analysis

Ocean Alkalinity Enhancement Using Bipolar Membrane Electrodialysis: Technical Analysis and Cost Breakdown of a Full-Scale Plant

Ocean Alkalinity Enhancement Using Bipolar Membrane Electrodialysis: Technical Analysis and Cost Breakdown of a Full-Scale Plant

Technical and Tactical Analysis of Men's Dragon Boat Sprint Racing

This study analyzes the 200-meter race of the university dragon boat championship using methods such as questionnaires, interviews, literature review, and observation. Through statistical analysis of the techniques and tactics of the top three teams in the 200-meter race, the results indicate that the technical structure is characterized by fast starts, quick stroke frequency, and effective water catching. Furthermore, the organic combination of physical fitness and technique, along with the adoption of a "preemptive" tactic, is undoubtedly a crucial guarantee for achieving victory in dragon boat competitions.

Efficient Upcycling of Polyolefin Waste to Light Aromatics via Coupling C-C Scission and Carbonylation.

The upcycling of waste polyolefins into light aromatics has great potential to generate hundreds of millions of tons of valuable aromatic carbon feedstock. However, the conventional high-temperature radical cracking method for aromatizing polyolefins on zeolites faces challenges in aromaticity control and rapid deactivation due to coke. Here, we present a unique strategy that integrates traditional cracking-aromatization processof PEwith CO insertion, a key step of Fischer-Tropsch synthesis, achieving a rise of yield of light aromatics by 4 times, with anabsolute value up to 67% by weight at only 280 °C. The insertion of CO into the Ru-alkyl intermediates formed during polyolefin cracking facilitates the generation of active oxygenate species, guarantees an ideal C-C chain length range, and smoothing the way for subsequent efficient aromatization on Hol-ZSM-5@S1 with a short b-axis. According to the technical economic analysis, this low-carbon-footprint and economic approach can reduce approximately 1/3 of carbon emissions compared to traditional naphtha cracking technologies, and holds promise for reshaping the global aromatic hydrocarbon cycle of petrochemical industry through polyolefin degradation.

A Cost-Optimizing Analysis of Energy Storage Technologies and Transmission Lines for Decarbonizing the UK Power System by 2035

The UK net zero strategy aims to fully decarbonize the power system by 2035, anticipating a 40–60% increase in demand due to the growing electrification of the transport and heating sectors over the next thirteen years. This paper provides a detailed technical and economic analysis of the role of energy storage technologies and transmission lines in balancing the power system amidst large shares of intermittent renewable energy generation. The analysis is conducted using the cost-optimizing energy system modelling framework REMix, developed by the German Aerospace Center (DLR). The obtained results of multiple optimization scenarios indicate that achieving the lowest system cost, with a 73% share of electricity generated by renewable energy sources, is feasible only if planning rules in England and Wales are flexible enough to allow the construction of 53 GW of onshore wind capacity. This flexibility would enable the UK to become a net electricity exporter, assuming an electricity trading market with neighbouring countries. Depending on the scenario, 2.4–11.8 TWh of energy storage supplies an average of 11% of the electricity feed-in, with underground hydrogen storage representing more than 80% of that total capacity. In terms of storage converter capacity, the optimal mix ranges from 32 to 34 GW of lithium-ion batteries, 13 to 22 GW of adiabatic compressed air energy storage, 4 to 24 GW of underground hydrogen storage, and 6 GW of pumped hydro. Decarbonizing the UK power system by 2035 is estimated to cost $37–56 billion USD, with energy storage accounting for 38% of the total system cost. Transmission lines supply 10–17% of the total electricity feed-in, demonstrating that, when coupled with energy storage, it is possible to reduce the installed capacity of conventional power plants by increasing the utilization of remote renewable generation assets and avoiding curtailment during peak generation times.

New data on the Zhe-Kalgan I site in the Northern Caspian region

The paper presents a comprehensive analysis of materials of the Late Neolithic site in the Northern Caspian region. The typological method established the heterogeneity of the pottery complex. Technical and technological analysis of the initial plastic raw materials and the formula of molding clay demonstrated major differences, which allow distinguishing three groups. The comparison of the stone inventory of the site by different types of raw ma-terials revealed two variants of the flint and quartzite complex. The first one is similar by tool types to the Kairshak materials (trapezoids, scrapers pointed and with a “muzzle”), and the second one — to the Tentexor ones (mas-siveness of blanks, the nature of retouching). This confirms the continuity of the stone industry from the Middle to Late Neolithic. Traceological analysis has shown that trapezoids with flattening retouching were used as arrow-heads. The quartzite assemblages are closer to the Caspian Culture. From the radiocarbon dating results, the age of the Zhe-Kalgan I site is the youngest of the Tentexor-type monuments. As a result, it has been concluded that the site was inhabited several times during the Late Neolithic.

Time Trend Analysis of Early Term Births in Greece (1980-2023): Persistent High Rates Raise Public Health Concerns.

Neonates born at 37-38 weeks of gestation have been shown to face a relatively higher risk of mortality and various morbidities compared to those born at full term (39-41 weeks). The aim of this study was to conduct a comprehensive analysis of early term birth rate (ETBR) trends in Greece from 1980 to 2023. Data on live births in Greece from 1980 to 2023 were obtained from the Hellenic Statistical Authority (ELSTAT), based on birth certificate records. A total of 4,595,020 live births were categorized by gestational age. The annual ETBR was calculated as the number of live births occurring at 37-38 completed gestational weeks (from 37+0 to 38+6 weeks) per 100 total live births. Additionally, ETBR was calculated separately for 37 and 38 gestational weeks, as well as the overall birth rate < 39 weeks of gestation. Time trends were evaluated using joinpoint regression analysis. The ETBR decreased with an annual percent change (APC) of -1.4 (95% confidence interval (CI): -1.8 to -0.9, p = 0.004) between 1980 and 1994, followed by a sharper decline with an APC of -12.8 (95% CI: -14.4 to -8.5, p = 0.005) during 1994-1997. Subsequently, the ETBR increased with an APC of 3.2 (95% CI: 2.0 to 5.6, p = 0.016) from 1997 to 2004. This trend reversed again from 2004 to 2010, with an APC of -2.6 (95% CI: -5.9 to -1.1, p = 0.016), while in the most recent period (2010-2023), the ETBR stabilized (p = 0.630), fluctuating between 41.9% in 2010 and 44.3% in 2012 and 2013. During the study period (1980-2023), the ETBR at 37 weeks increased by an average of 7.1% per year, whereas the ETBR at 38 weeks decreased by an average of -2.1% annually. The overall birth rate < 39 weeks of gestation has remained consistently above 50% over the past two decades (2004-2023). In 2023, the ETBR was 43.7%, while a total of 56.0% of neonates were born < 39 gestational weeks. This study provides a comprehensive analysis of ETBR in Greece, revealing persistently high levels that exceed those of all developed countries. The elevated rate is likely driven by factors such as maternal demographics and obstetrical practices. The alarmingly high levels of ETBR, combined with the country's extremely high preterm birth rates, result in a substantial burden of neonatal morbidity and an increased risk of potential chronic diseases. These findings underscore an enormous public health challenge.

Economic and technical analysis of hydrogen production and transport: a case study of Egypt

This study investigates the economic, technical, and logistical aspects of hydrogen production, with a particular focus on Egypt’s potential to emerge as a global hydrogen leader. The research is motivated by Egypt’s abundant renewable resources, strategic location, and increasing interest in hydrogen as a cornerstone of the energy transition. Using the Hydra simulation model developed in MATLAB/Simulink, the study evaluates the Levelized Cost of Hydrogen (LCOH) and Levelized Supply Costs of Hydrogen (LSCOH) across various scenarios, spanning from 2024 to 2050. These scenarios incorporate factors such as economic growth, technological advancements, energy policies, and infrastructure developments. Projections indicate that the hydrogen demand in Egypt is expected to reach 6.0 million tons by 2050, including both domestic consumption and export potential. Egypt’s low hydrogen production costs (4.5/kg) and strategic location position it to meet growing domestic demand while supporting exports. Comparative analyses with countries including France, Italy, Saudi Arabia, the UAE, Libya, and Jordan highlight Egypt’s competitive advantage, driven by its abundant solar and wind resources. The novel integration of LCOH and LSCOH methodologies, contextualized by Egypt’s renewable energy potential, fills a critical gap in the literature and offers a comprehensive framework for evaluating hydrogen production and trade competitiveness. The findings emphasize the importance of robust policy frameworks, including substantial incentives for renewable energy projects, de-risking mechanisms, and measures to reduce transport costs, in realizing Egypt’s hydrogen potential. Furthermore, the study explores the environmental and geopolitical implications of hydrogen production, underscoring Egypt’s strategic role in the global energy market. The results provide actionable insights for policymakers and industry stakeholders to position Egypt as a leader in hydrogen production, contributing significantly to the global energy transition.

The effect of service quality, tourist satisfaction, place attachment on environmentally responsible behavior in Praiijing traditional village

This study aims to find out and analyze the influence of Service Quality, Tourist Satisfaction, Place Attachment, and Environmentally Responsible Behavior on tourist attractions in Praiijing Traditional Village. Data processing is carried out using quantitative approaches and this type of research is causal. Variables used in this study are service quality, tourist satisfaction, place attachment, and environmentally responsible behavior. Technical analysis of the data used is Structural Equation Modeling (SEM) by using the SPSS 16.0 for Windows and AMOS version 22. This study used samples in the form of responses from respondents who had visited Praiijing Traditional Village with the number of samples used in the study 150 respondents. The results of this study showed the mutual influence between service quality, tourist satisfaction, place attachment, and environmentally responsible behavior.

Leveraging Cloud Computing for Real-Time Marketing Analytics: A Technical Perspective

This article examines how cloud computing has revolutionized marketing analytics by enabling real-time data processing and decision-making capabilities previously unattainable with traditional on-premise systems. It presents a comprehensive technical analysis of cloud-native architectures for marketing analytics, detailing the multi-layered framework that spans from data ingestion through to action delivery. The article explores how organizations have overcome the historical limitations of traditional analytics environments—including data silos, batch processing constraints, and limited computational resources—through the implementation of cloud-based platforms. The technical architecture is dissected across its primary components: the data ingestion layer that captures customer interactions as they occur; the processing layer that transforms raw data into actionable insights within milliseconds; specialized storage technologies optimized for analytical workloads; the analytics layer with its visualization and machine learning capabilities; and the action layer that enables immediate customer engagement. The article further addresses critical implementation considerations related to performance optimization, scalability, data governance, and cost management. Through examination of real-world applications like dynamic audience segmentation, predictive customer lifetime value modeling, and personalized content orchestration, it demonstrates how cloud technologies deliver substantial competitive advantages. The article concludes by exploring emerging trends at the intersection of artificial intelligence and cloud computing that will shape the next generation of marketing analytics capabilities.

WHEN MACHINES LEARN TECHNICAL ANALYSIS: AN APPLICATION ON TECHNICAL ANALYSIS WITH MACHINE LEARNING IN BORSA ISTANBUL

There are two approaches to analyzing the value of a stock in financial markets: fundamental analysis and technical analysis. While fundamental analysis focuses on finding the intrinsic value of a stock based on a company's financial condition and current market conditions, technical analysis focuses on identifying trading signals in patterns by examining historical price behavior and statistics. Although technical analysis, which is based on the assumption that past price movements can be an indicator for future price movements, has a predefined set of rules, the interpretation of the results is closely related to the experience of the analyst. Therefore, the interpretive part of technical analysis has a subjective dimension. This subjective dimension and predefined set of rules indicate that machine learning methods with experience-based learning logic can be an important tool in identifying trading signals or predicting price movements. The aim of this study is to investigate the potential use of machine learning algorithms that use technical analysis indicators of stocks traded in Borsa Istanbul as input to predict trading signals and price movements. In the study, technical analysis indicators are analyzed with models based on machine learning methods and the results are compared. The findings show that the addition of machine learning methods to technical analysis strategies increases the predictive power of trading signals and price movements.

Cultural power dynamics and narrative transformation: a comparative analysis of Hollywood film remakes in contemporary Asian cinema

This study examines the complex dynamics of cultural adaptation in Asian remakes of Hollywood films, focusing on two pairs of films produced between 2015 and 2023. Through systematic intertextual and comparative analysis, the research investigates how Asian film remakes negotiate and transform global narratives within local cultural contexts. The study analyzes “Ghost” (1990) and its Japanese remake “Ghost: In Your Arms Again” (2018), and “Miss Congeniality” (2000) and its Chinese remake “Beauty Queen” (2021). The analysis reveals sophisticated patterns of cultural transformation across multiple dimensions, including narrative structure, character relationships, visual aesthetics, and thematic emphasis. The findings demonstrate that Asian remakes employ complex strategies of cultural recalibration, moving beyond simple translation to create culturally resonant narratives that balance global appeal with local cultural values. The research identifies systematic patterns in negotiating power dynamics, particularly in treating authority, gender relations, and social hierarchies. Technical analysis reveals consistent shifts in cinematographic approaches, with Asian remakes favoring more contemplative visual styles that integrate traditional aesthetic principles. The study contributes to our understanding of contemporary global media flows and cultural power dynamics, suggesting that successful film adaptations engage in sophisticated processes of cultural hybridization that actively reshape rather than merely reproduce global narratives. These findings significantly impact understanding of cultural adaptation in an increasingly interconnected global media landscape.

Predicting Stock Price Movements with Technical, Fundamental, and Sentiment Analysis Using the LSTM Model

The challenge of minimizing risk and maximizing profit is what traders in the stock market have been endeavoring to solve for years. Stock prices typically exhibit the characteristic of volatility, influenced by various factors and necessitate a substantial amount of data to identify patterns in price movements. Considering the significant data requirements and the rapid advancement of big data and artificial intelligence, the LSTM (Long-Short Term Memory) model stands as a suitable approach for utilization in Deep Learning. The independent variables employed encompass technical indicator variables, currency exchange rates, interest rates, the Jakarta Composite Index (IHSG), and sentiment data extracted from Twitter tweets. The results indicate that sentiment analysis using the IndoBERT model achieved an accuracy of 0.69, while LSTM analysis produced the model with the smallest error for the fourth (4th) combination of variables, comprising closing price, technical indicators, IHSG, exchange rate, and Twitter sentiment, as well as the twelfth (12th) combination of variables, encompassing closing price, technical indicators, and IHSG. These combinations yielded average RMSE errors of 1.765E-04 and 1.978E-04, respectively. Following hyperparameter optimization, the best-identified model was the fourth (4th) combination of variables, yielding a minimal error of 7.580E-05 and an RMSE of 332.66 in the evaluation of test data.

Stock Trading Indicator using Reinforcement Learning

Traditional trading strategies often struggle to consistently achieve profitable results in highly volatile and complex financial markets. This research explores the potential of Reinforcement Learning (RL) to develop a trading indicator capable of learning and adapting to market changes. By leveraging RL, the trading indicator aims to improve decision-making and enhance trading performance through continuous learning from experiences. The outcomes of this research include a flexible and adaptive RL-based trading indicator, trading strategies that generate higher returns with better risk management, and a thorough comparison with traditional methods. This study demonstrates how advanced machine learning can be effectively applied to enhance trading in financial markets. In contrast to traditional technical analysis, which focuses on statistical trends from trading activity, RL offers an experience-driven approach that can adapt to evolving market conditions. The RL-based trading indicator utilizes Q-learning, a model-free reinforcement learning algorithm, to learn optimal action-selection policies. Through iterative updates of Q-values, the agent can derive the optimal policy by selecting the action with the highest Q-value in any given state. The research evaluates the performance of the RL-based trading indicator against traditional indicators like MACD and RSI across multiple stocks. The results consistently demonstrate the superior performance of the RL-based indicator in terms of profitability and adaptability. This highlights the potential of RL as a promising tool for enhancing financial trading strategies.

Comparative and Systematic Review of Technical Analysis-Based Forecasting Techniques: Global V/S Indian Perspectives

This paper conducts a systematic review of Technical Analysis (TA) based forecasting research from 2013 to 2022, encompassing global and Indian authors. It addresses the evolving landscape of financial forecasting, including tool choices, variable selection, error measurement, and accuracy. Employing the PRISMA framework, we analyze 176 papers (88 global, 88 Indian) to examine trends in TA research. Key inquiries involve the publication trends, popular forecasting approaches, consideration of Transaction Costs and Data Snooping Bias, research time cycles, outcomes, and performance metrics. The study reveals that Indian researchers favor TA-based machine learning (ML) techniques, while global counterparts rely more on manual forecasting methods. Notably, many studies neglect to incorporate ‘transaction costs’ and ‘data snooping bias’ in their models. Most research focuses on forecasting within a 5–10-year timeframe, with TA-based techniques generally viewed as profitable. However, Indian authors tend to prioritize risk mitigation in their models, in contrast to global authors who often measure performance through return and profitability metrics. Additionally, this paper critiques the prevailing forecasting techniques, highlighting gaps in both global and Indian research. Its aim is to aid future researchers in identifying gaps, formulating research questions, and understanding methodological considerations within financial forecasting.

Evaluation of elite table tennis players’ comprehensive technical and tactical performance

BackgroundGame performance analysis has been playing a significant role in sports events which has reached an international consensus. In the field of technical and tactical analysis of table tennis, many studies conducted the segmented evaluation of players based on the phased-theory. The present study proposed the concepts of “competitive technical and tactical performance” of elite table tennis players. The purpose of this study was to develop an entropy-based weighting system integrated with three comprehensive evaluation methods, aiming to examine its feasibility for evaluation in two practical applications: inter-athlete comparison and performance fluctuations across time periods. Another purpose was to explore the applicability of different comprehensive evaluation methods through comparative application.MethodsA total of 40 matches of six elite male table tennis players in 2019 were selected to evaluate their competitive technical and tactical performance. The technical effectiveness of four phases in a match as the relevant indexes, three comprehensive evaluation methods, including the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, the Rank-sum ratio method and the Osculating value method were applied based on the Entropy weight method. Kendall's concordance coefficient and Spearman's correlation coefficient were employed to examine the consistency and correlation.Results(1) The technical strength ranking of the six elite table tennis players was as follows: Player A, Player B, Player C, Player F, Player E, and Player D. This ranking exhibited a high degree of consistency with the international rankings as of December 2019. Notably, Player C's performance showed significant fluctuations, whereas Player E demonstrated a steady and upward performance trend. (2) All Kendall's concordance coefficients exceeded 0.948, and all Spearman's correlation coefficients were above 0.883, with statistically highly significant results (p < 0.01).ConclusionsAll three comprehensive methods were shown to have strong applicability and high consistency in evaluating the comprehensive performance of athletes. The TOPSIS method performed more well. This approach facilitated a more in-depth and comprehensive representation of table tennis match analysis, providing practical applications for athletes and coaches.