Fundamental Analyses Research Articles

Dose mapping of a 252Cf based non-destructive on-line elemental analysis device using Monte Carlo simulation and verification with experimental results

Environmental dose values are critical for human health in non-destructive analysis systems. The International Commission on Radiological Protection (ICRP) has defined specific limitations for permissible dose values. This study assesses the dose distribution of a prototype analysis device utilizing Californium-252 (252Cf) radioactive material through both Monte Carlo simulations and laboratory experiments. Conducted at the Istanbul Technical University (ITU) Energy Institute, the experimental investigation measured gamma-ray and neutron dose values surrounding the device. The objective was to compare dose maps generated by the Monte Carlo-based GEANT4 code with experimental data. Ten dose maps were created, and fundamental statistical analyses were performed. Results indicate that the dose values on the operator’s working surface of the prototype device are within ICRP limits, not exceeding 1 millisievert (mSv) annually, with an effective dose rate of 0.949±0.052 mSv per year. These findings underscore the device’s potential for safe and effective use in radiation safety and environmental dose assessment.

Empowering farmer community with technology modernisation to improve rice farming performance through demonstration farms in East Java, Indonesia

Purpose Rice is a strategic commodity and staple food; thus, rice productivity should grow faster than the population. A public agricultural agency launched technology modernisation to improve rice farm performance. This study aims to assess the impact of technology modernisation on rice farming performance and evaluate farmer acceptance of such technology. Design/methodology/approach This study was conducted in 2023-2024, based on selected demonstration farms (demfarm) carried out during 2021-2022 in East Java, Indonesia, one of the rice bowls. Microeconomic theory of production and the double-differences approach were used as fundamental analyses. Farmers were purposively selected to participate in the demfarm. For comparison, farmers with existing technology adjacent to the demfarm were chosen accordingly. Rice production is considered an economic performance indicator, and factors related to socio-demographic and technical aspects were conceptualised using innovation and diffusion theory. Findings The results of demfarm were apparent. Technology modernisation improved rice farming’s economic performance. Farmer acceptance of such technology was relatively high at the first stage. There was no conflict between technology and local culture and norms. The technological package will likely be disseminated to farmers after adequate socialisation. Research limitations/implications This study engaged farmer innovators and early adopters in the demonstration farm. This needs more actions from farmers who are not categorised as innovators and early adopters, which dominate the farmer population. Practical implications Extension officials need field guidance to ensure continual technology adoption because of technology complexity. Originality/value The originality of the study is based on a field experiment and direct observation throughout a crop cycle, and the analysis is established using a solid theory and analytical framework.

Terahertz Meta-Mirror with Scalable Reflective Passband by Decoupling of Cascaded Metasurfaces

Electromagnetic metasurfaces have been playing exotic roles in the construction of ultracompact and versatile metadevices for wave–matter interactions. So far, multiple metasurfaces cascaded with intercouplings have been intensively investigated for extraordinary wavefront control and broadband spectral regulations. However, most cases face high structural complexity and little attention is paid to cascaded metasurfaces without interlayer couplings. In this paper, we demonstrate one type of terahertz Bragg mirror with ideally high reflectivity and ultra-broad bandwidth by simply resorting to decoupled metasurfaces. Cascaded metasurfaces with decoupled mode control prove practically straightforward for analytical design and easy to fabricate for engineering purpose in our scheme. Essentially, by flexibly tuning the decoupled metasurface mode, the middle Fabry–Perot mode that behaves like a defect mode inside the reflective passband can be eliminated for substantial band expanding. Fundamental analyses and rigorous calculations are performed to confirm the feasibility of our metasurface-based THz Bragg mirror with scalable bandgap. In comparison, our meta-mirror provides superior spectral performance of a larger bandgap and higher in-band reflectivity over that composed by ten layers of alternate dielectrics (Rogers 3003 and 3005). Finally, our analytical methodology and numerical results provide a promising way for the rapid design and fabrication of a Bragg mirror in the optical regime.

Multi-view clustering with semantic fusion and contrastive learning

Multi-view data obtained from different sources provides a comprehensive way to model the real world. As one of the fundamental analyses, clustering for multi-view data has attracted increasing attention from researchers in different fields. Although existing multi-view clustering methods have achieved promising results, how to fully utilize the consensus and differences among different views and how to integrate their features to learn a unified representation is still challenging. To overcome the limitations, we put forward a Semantic fusion and Contrastive learning model for Multi-View Clustering (SCMVC) in this paper. SCMVC employs the view-specific encoders to extract embeddings from each view, and reconstructs the topological and attribute information by using the corresponding decoders. Simultaneously, a cross-view contrastive learning module is introduced in SCMVC to assimilate the consensus information across different views. In addition, to distinguish the differences in semantic information across different views, we incorporate an attention mechanism to determine the relative importance of each view in the semantic information fusion. The proposed model is jointly optimized by training and clustering to mutually reinforce each other, gradually improving its performance and robustness. Experiments on three real-world multi-view datasets demonstrate the effectiveness of our SCMVC framework. Source codes are freely available at https://github.com/HexonBang/SCMVC.

Biotextgraph: graphical summarization of functional similarities from textual information.

Functional interpretation of biological entities such as differentially expressed genes is one of the fundamental analyses in bioinformatics. The task can be addressed by using biological pathway databases with enrichment analysis (EA). However, textual description of biological entities in public databases is less explored and integrated in existing tools and it has a potential to reveal new mechanisms. Here, we present a new R package biotextgraph for graphical summarization of omics' textual description data which enables assessment of functional similarities of the lists of biological entities. We illustrate application examples of annotating gene identifiers in addition to EA. The results suggest that the visualization based on words and inspection of biological entities with text can reveal a set of biologically meaningful terms that could not be obtained by using biological pathway databases alone. The results suggest the usefulness of the package in the routine analysis of omics-related data. The package also offers a web-based application for convenient querying. The package, documentation, and web server are available at: https://github.com/noriakis/biotextgraph.

Stock market prediction employing ensemble methods: the Nifty50 index

<span lang="EN-US">Accurately forecasting stock fluctuations can yield high investment returns while minimizing risk. However, market volatility makes these projections unlikely. As a result, stock market data analysis is significant for research. Analysts and researchers have developed various stock price prediction systems to help investors make informed judgments. Extensive studies show that machine learning can anticipate markets by examining stock data. This article proposed and evaluated different ensemble learning techniques such as max voting, bagging, boosting, and stacking to forecast the Nifty50 index efficiently. In addition, an embedded feature selection is performed to choose an optimal set of fundamental indicators as input to the model, and extensive hyperparameter tuning is applied using grid search to each base regressor to enhance performance. Our findings suggest the bagging and stacking ensemble models with random forest (RF) feature selection offer lower error rates. The bagging and stacking regressor model 2 outperformed all other models with the lowest root mean square error (RMSE) of 0.0084 and 0.0085, respectively, showing a better fit of ensemble regressors. Finally, the findings show that machine learning algorithms can help fundamental analyses make stock investment decisions.</span>

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.

Abstract 7378: Using game theory to investigate the therapeutic potential of disrupting tumor-microenvironment interactions

Abstract Cancer is a complex disease involving the interplay of several types of malignant and nonmalignant cells. The growth of tumors and surrounding stromal cells can be modeled as an ecosystem, where populations of cancerous and healthy cells interact and compete for resources. In this work, we have explored the application of game theoretic models of cancer to the design of novel anticancer therapies. Notably, a game theoretic-model of tumor-stromal interactions can capture the impact of interactions between cell types and can be used to investigate frequency-dependent parameters describing the impact of drug action during the initiation and growth of metastatic cancers. Previous studies using game theoretic models to describe cancer have focused on explaining progression patterns of specific tumors with known tumor-stromal interactions. This study takes a more general approach to cancer growth dynamics, modeling cancer as a simple game between healthy and cancer cells. This simplified model is used to make general predictions about cancer progression and the efficacy of treatment strategies. Our work suggests that disruption of beneficial interactions between healthy and cancer cells may be a viable treatment strategy for small tumors and preventing metastasis by ensuring that the healthy cell strategy is evolutionarily stable. The efficacy of treatment strategies that disrupt cancer cell cooperation or tumor-stromal dynamics changes as the cancer cell frequency changes. Our work suggests that drugs that target tumor-stromal cell interactions would need to be combined with other types of treatments to be effective against large tumors. Although based on a simplistic model of cancer initiation and progression, the framework derived here can be extended to the real-world design of experimental anticancer therapeutics, and fundamental analyses of this nature can be of practical utility in setting strategy for Target Product Profile design. Citation Format: Debra Van Egeren, Andrew Chen, Madison Stoddard, Lin Yuan, Arijit Chakravarty. Using game theory to investigate the therapeutic potential of disrupting tumor-microenvironment interactions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7378.

Inference for Parameters of Exponential Distribution under Combined Type II Progressive Hybrid Censoring Scheme

In recent years, various forms of progressive hybrid censoring schemes (PHCS) have gained significant traction in survival and reliability analysis studies due to their versatility. However, these PHCS variants are often characterized by complexity stemming from the multitude of parameters involved in their specification. Consequently, the primary objective of this paper is to propose a unified approach termed combined type II progressive hybrid censoring scheme (ComT2PHCS) capable of encompassing several existing PHCS variations. Our analysis focuses specifically on the exponential distribution (ExDist). Bayesian inference techniques are employed to estimate the parameters of the ExDist under the ComT2PHCS. Additionally, we conduct fundamental distributional analyses and likelihood inference procedures. We derive the conditional moment-generating function (CondMGF) of maximum likelihood estimator (MLE) for parameters of the ExDist under ComT2PHCS. Further, we use CondMGF for the distribution of MLE for parameters of ExDist under ComT2PHCS. Finally, we provide an illustrative example to elucidate the inference methods derived in this paper.

The Dynamics of Individual Investment Decisions: Evidence From The Dhaka and Chittagong Stock Exchange, Bangladesh

Research aim: Behavioural finance plays an analogous role to fundamental and technical analyses for investment decisions in the stock market. It deals with various factors that manipulate investment decisions. This study explores the factors influencing individual investment decisions in the Dhaka and Chittagong Stock Exchange (DSE and CSE) of Bangladesh. Design/Methodology/Approach: Based on existing finance theories and literature, several factors were included in the self-developed questionnaire, with data collected using random sampling. We used SPSS (Version 26) to analyse the primary data collected. In the questionnaire, there were 24 variables, and statistical analysis (multiple regression) was carried out. Research finding: There were 24 variables, five of which (i.e., investor experience, impact of P/E ratio, impact of fundamental analysis, impact of securities and brokerage firm, and impact of investor psychology) had significant impact on individual investment decisions in the CSE and DSE. Theoretical contribution/ Originality: Theories pertaining to individual investment decisions are based on individual judgment, sentiment, risk perception, and risk attitude. These mechanisms are associated with behavioural finance. Various behavioural finance studies have been conducted in developed countries, but not in developing countries like Bangladesh. As such, the dynamics of the investment decision are the main focus of the present study. Practitioner/ Policy implication: This article can serve as input to investment decisions that have an impact on economic value at the firm, industry, and macroeconomic level. Moreover, it highlights the role of related stakeholders and extent to which they influence investment decisions. Research limitation: This study is country-specific, and the number of respondents is limited to 125 only. We could not consider the impact of technological advancement during this period.

The Confronts of Edutourism in the Post-COVID-19 Pandemic Era: A Malaysian Perspective

COVID-19 triggered a crisis and sliced through every facet of humankind. People were advised to practise physical distancing, and most educational institutions have integrated different digital technologies and virtual learning environments (VLE) to limit the likelihood of pandemic pedagogy. This study sought to investigate the challenges of edutourism in Malaysia for sustaining viable education during this delicate time. Therefore, it examines the determinants of sustainable education, perceived value, risk assessment, quality service, and knowledge management in choosing institutions to resume edutourism. Based on a non-probability survey sample of 308 international students in Malaysia, the data were analysed with SPSS for fundamental analyses and SEM-AMOS for hypothesis testing. The results demonstrated that most predictors highly influenced the choice of the institution; thus, the government must acquire appropriate pedagogical knowledge to assure learning coherence. This study has presented a collection of influencing factors that impact how students adjust their objectives when choosing academic institutions in Malaysia.

Inspecting the efficiency of cryptocurrency markets: New evidence

The purpose of this study is to examine the market efficiency of cryptocurrencies, specifically at a weak level. The study focuses on six prominent cryptocurrencies selected based on their significant market capitalization: Bitcoin (BTC), Tether (USDT), Ethereum (ETH), Binance Coin (BNB-USD), Ripple (XRP-USD), and Cardano USD (ADA-USD). The analysis utilizes unit root, Ljung–Box, variance ratio, runs, and the Brock–Dechert–Scheinkman (BDS) tests to assess different aspects of market efficiency. The data spans from September 2017 to April 2023, encompassing a wide time frame to capture potential shifts in market behavior. The results of all the tests, except the BDS test, indicate that the tested cryptocurrencies' markets are inefficient. However, the BDS test yielded different results, suggesting that BTC and ETH exhibit market efficiency compared to the other cryptocurrencies. This discrepancy indicates that the BDS test may be capturing different aspects of the time series behavior. The practical implication is that investors and market participants should exercise caution and consider the varying levels of efficiency when making decisions regarding these cryptocurrencies. Also, investors should consider a range of factors, including technical and fundamental analyses, when making investment decisions in a dynamic and evolving market.

Artificial Intelligence in Finance: Valuations and Opportunities

This study examines the financial opportunities arising from the new Artificial Intelligence (AI) innovation. Firstly, we present the current and projected AI revenue for the upcoming decade. Secondly, we introduce a valuation model for AI stocks and ETFs, incorporating both AI fundamental and sentiment analyses. We offer two primary models for stock valuation adoption. Our analyses can serve as a benchmark framework for stock valuations and their influence on AI technology. This study holds particular significance as we witness an enthusiastic embrace of AI technology, potentially signaling the financial market's entry into an AI bubble.

A wide ranging experimental and kinetic modeling study of TMEDA pyrolysis and oxidation

Traditional liquid propellants like unsymmetrical dimethylhydrazine include several issues, such as toxicity, short storage time and preservation difficulty at low temperatures. For this reason, exploring green and stable propellant fuels is imperative in the development of propellants. Tetramethyl ethylenediamine (TMEDA) is considered to be a promising green propellant fuel with high specific impulse, non-toxicity, and long-term storage stability. Thus, investigating the combustion characteristics of TMEDA and constructing a detailed chemical kinetic mechanism are of fundamental importance in describing its combustion in computational fluid simulation. For this work, ignition delay times of TMEDA were measured in a shock tube at equivalent ratios of 0.5, 1.0 and 2.0, at pressures of 2, 10, and 20 bar, and in the temperature range of 870–1500 K with fixed fuel concentration at 2 %. After the reflected shock at 990–1500 K, the pyrolysis products of 2000 and 5000 ppm TMEDA in Argon were also measured at 5 and 10 bar conditions in a single pulse shock tube. In addition, laminar flame speeds of TMEDA were measured at initial pressures of 1 bar, initial temperatures of 333 and 353 K, and equivalence ratios ranging from 0.8 to 1.5 in a constant volume reactor. The experimental results were simulated using a detailed TMEDA kinetic mechanism. Reaction path analysis and sensitivity analysis were provided to clarify the chemical kinetics of TMEDA oxidation and pyrolysis. The current work provides new experimental data and fundamental analyses for understanding the oxidation and pyrolysis characteristics of TMEDA, and sheds light on optimal directions for future models.

Mastering The Stock Market: Strategies For Success

The stock market is a dynamic and complex arena where investors and traders aim to grow their wealth. This paper, "Mastering the Stock Market: Strategies for Success," provides a comprehensive exploration of the stock market, offering valuable insights, strategies, and techniques for achieving success in the world of investments.
 The paper begins by delving into the historical perspective of stock markets, tracing their evolution and development over time. It emphasizes the various participants in the stock market, from individual investors to institutional players, shedding light on their roles, motivations, and impact.
 A fundamental understanding of investment principles is crucial, and this paper takes the time to examine the differences between stocks and other asset classes. It dissects the intricacies of risk and return, offering a detailed explanation of how diversification can mitigate risk and enhance returns.
 The heart of this paper lies in its exploration of stock market strategies. It covers both fundamental analyses, emphasizing the importance of financial metrics and value investing, and technical analysis, where charts and indicators are used to make informed decisions. Market timing, including long-term investing and short-term trading, is discussed in detail, along with key strategies for managing risk and protecting capital.

Prediction of stock price leveraging Arima model

In today`s world, more and more machine learning methods and prediction methods have been proposed. There are also lots of investors who choose the more difficult and complex models to use as technical analysis tools to predict stock prices. However, Rome wasn't built in a day. So, it`s necessary to learn the traditional models penetratingly. To have a more thorough understanding of classic models in the past, this paper provides a great way to analyze the ARIMA model. It explains the methods of fitting the ARIMA model step by step and improves the ARIMA model by adding seasonal parameters to fit the SARIMA model, enabling readers to better understand the advantages and disadvantages of this model. Although the results of the ARIMA model are unfortunately similar, and difficult to demonstrate its predictive power in images, the SARIMA model presents a trend prediction that conforms to people's imagine. As of today, the change in the price of Netflix's stock has been visible, which is different from the predicted price. Therefore, while making technical predictions, investors also need to combine fundamental analyses like the supply and demand balance, changes in interest rates, government regulations, macroeconomic indicators, and unique features of the industry in question, among others. Rather than relying solely on a model.

Assessing the Level of Competence in Automated Trading Among Malaysian Traders

In today's technology world, financial trading instrument such as stock, currencies, futures, and acts is now done electronically via the internet due to technological improvements. Financial market trading traders apply technical and fundamental analyses to forecast the best price when buying or selling the finance instrument, and they develop their trading tactics and strategies with technical analysis tools. The objective of this research is to figure out the level of competence that Malaysian retail traders have in Automated or Algorithmic Trading (AT) focusing on currency trading. The searches were conducted on a sample of Malaysian retail traders. The viewpoints of retail traders were investigated using a questionnaire designed specifically for this purpose. The data was analyzed using statistical software (SPSS). The findings of this study reveal that Malaysian traders have an excellent comprehension of AT. This study is useful for traders and researchers who want to design their own AT systems in the future.

Utility of Light-Adapted Full-Field Electroretinogram ON and OFF Responses for Detecting Glaucomatous Functional Damage.

To compare parameters of electroretinogram (ERG) responses for their ability to detect functional loss in early stages of nonhuman primate (NHP) experimental glaucoma (EG), including photopic negative responses (PhNR) to a standard brief red flash on a blue background (R/B) and 200-ms-long R/B and white-on-white (W/W) flashes, to W/W flicker stimuli (5-50Hz), and to a dark-adapted intensity series. Light-adapted ERGs were recorded in 12 anesthetized monkeys with unilateral EG. Amplitudes and implicit times of the a-wave, b-wave, and d-wave were measured, as well as amplitudes of PhNRs and oscillatory potentials for flash onset and offset. Flicker ERGs were measured using peak-trough and fundamental frequency analyses. Dark-adapted ERG parameters were modeled by Naka-Rushton relationships. Only PhNR amplitudes were significantly reduced in EG eyes compared to fellow control (FC) eyes. The d-wave implicit time was delayed in EG versus FC eyes only for the W/W long flash, but in all eyes it was 10 to 20 ms slower for R/B versus the W/W condition. Flicker ERGs were <0.5 ms delayed in EG versus FC overall, but amplitudes were affected only at 5Hz. The brief R/B PhNR amplitude had the highest sensitivity to detect EG and strongest correlation to parameters of structural damage. The PhNR to the standard brief R/B stimulus was best for detecting and following early-stage functional loss in NHP EG. These results suggest that there would be no benefit in using longer duration flashes to separate onset and offset responses for clinical management of glaucoma.

Cultural intelligence and interactional adjustment of Chinese expatriates in Ghana

Orientation: Expatriates must adjust to geographical and cultural gap, a change that demands cultural intelligence (CQ). Chinese self-initiated expatriates (SIEs), who have travelled out of their own volition with no institutional support, are in Ghana for investment opportunities due to Ghana’s political stability and the recently discovered oil in commercial quantities.Research purpose: The study purpose was to examine which construct of CQ (metacognitive CQ, cognitive CQ, behavioural CQ and motivational CQ) best predicts interactional adjustment (IA) of Chinese SIEs in Ghana.Motivation for the study: These business opportunities in Ghana for the Chinese SIEs come together with a new set of challenges – inability of most Chinese SIEs to interactionally adjust to the Ghanaian cultural context. The SIEs could be particularly susceptible to cultural failure because they have no institutional support.Research design, approach and method: This study had a sample size of 397 Chinese SIEs and used quantitative design. Data for this study were gathered using an online survey and analysed using partial least square (PLS)-structural equation model. A number of fundamental analyses were performed, including a description of data entry, missing value processing, outlier handling, assessing normalcy and linearity, and analysing measurement reliability and validity issues. The bivariate correlations between principal variables were investigated.Main findings: A positive relationship between all constructs of CQ and IA of Chinese SIEs in Ghana was found by the study.Practical/managerial implications: The study suggests that cross-cultural managers and expatriate coaches should focus their training on the four constructs of CQ for effective IA of SIEs.Contribution/value added: This study contributes to CQ research on SIEs’ cross-cultural assimilation, and it will serve as a reference for further studies.

Fundamental analysis vs technical analysis: Are investors' long and short positions related to their sentiment?

Subject. This article examines the relationship between long and short positions of investors and their sentiment. Objectives. The article aims to determine the relationship between the emotional component and the sector of investment. Methods. For the study, we used the methods of induction and deduction, statistical methods for processing the results of observations, methods of physiognomy in determining the emotions of investment decision makers. Results. The article reveals the content of methods for taking into account the emotions of investors when choosing an investment sector and describes the results of research on practical situations in the field of investment decision-making based on fundamental and technical analyses. Conclusions and Relevance. When taking into account new indicators, the analyst has the means to bring the forecast result closer to real conditions, when in addition to traditional indicators, emerging combinations of traditional and psychological indicators in the markets give a new interpretation of the events taking place there. This improves the quality of analytical work and its results. The use of new indicators complements and expands the classic analytical tools, improves the quality of forecasts in the markets. The results of the research described in the article can be applied in the work of a nowadays market analyst.