Separate Methodologies Research Articles

A transformer-based unified multimodal framework for Alzheimer's disease assessment

In Alzheimer's disease (AD) assessment, traditional deep learning approaches have often employed separate methodologies to handle the diverse modalities of input data. Recognizing the critical need for a cohesive and interconnected analytical framework, we propose the AD-Transformer, a novel transformer-based unified deep learning model. This innovative framework seamlessly integrates structural magnetic resonance imaging (sMRI), clinical, and genetic data from the extensive Alzheimer's Disease Neuroimaging Initiative (ADNI) database, encompassing 1651 subjects. By employing a Patch-CNN block, the AD-Transformer efficiently transforms image data into image tokens, while a linear projection layer adeptly converts non-image data into corresponding tokens. As the core, a transformer block learns comprehensive representations of the input data, capturing the intricate interplay between modalities. The AD-Transformer sets a new benchmark in AD diagnosis and Mild Cognitive Impairment (MCI) conversion prediction, achieving remarkable average area under curve (AUC) values of 0.993 and 0.845, respectively, surpassing those of traditional image-only models and non-unified multimodal models. Our experimental results confirmed the potential of the AD-Transformer as a potent tool in AD diagnosis and MCI conversion prediction. By providing a unified framework that jointly learns holistic representations of both image and non-image data, the AD-Transformer paves the way for more effective and precise clinical assessments, offering a clinically adaptable strategy for leveraging diverse data modalities in the battle against AD.

Interlinkage of an Emerging Market with Financial Assets: Evidence from Three Well-Known Econometric Models

This research seeks to offer a thorough investigation of the return and volatility spillover dynamics between DSEX and five financial assets from Gold, currency rates, energy, and stock indexes. Previous studies only used one econometric model to estimate the results, but this work used three well-known models for cross validation and also used portfolio weights and an optimal hedge to control risk. The DCC-GARCH technique is used to examine volatility spillovers, and the Quantile VAR and TVP-VAR frameworks are used to examine return spillovers. Surprisingly, three separate methodologies all provided equal results and arrived at the same conclusion. This study's findings corroborate the premise that these six components frequently exhibit considerable co-movement in futures trading. This paper illustrates that investors can greatly benefit by altering their portfolios based on the dynamic weights and hedge ratios, even if there is a trade-off amongst the degree of risk mitigation and portfolio revenue.

Comparing fuzzy logic and backstepping control for a buck boost converter in electric vehicles

Significant advances have been made in the control of DC/DC converters, owing to the effective combination of linear and nonlinear approaches. The above-mentioned approaches have greatly improved the efficiency as well as stability of the converter, even when faced with changing conditions. Nevertheless, the emergence of artificial intelligence has introduced new perspectives in the domain. The objective of the article is to examine two separate methodologies for controlling a boost-buck converter: using a nonlinear approach, especially backstepping, and utilizing artificial intelligence through fuzzy logic. The main aim of this work is to illustrate the inherent stability and robustness of fuzzy logic controllers compared to backstepping control in managing effectively various variations and challenges encountered in converter control.

Comparing Methodologies for Stomatal Analyses in the Context of Elevated Modern CO2.

Leaf stomata facilitate the exchange of water and CO2 during photosynthetic gas exchange. The shape, size, and density of leaf pores have not been constant over geologic time, and each morphological trait has potentially been impacted by changing environmental and climatic conditions, especially by changes in the concentration of atmospheric carbon dioxide. As such, stomatal parameters have been used in simple regressions to reconstruct ancient carbon dioxide, as well as incorporated into more complex gas-exchange models that also leverage plant carbon isotope ecology. Most of these proxy relationships are measured on chemically cleared leaves, although newer techniques such as creating stomatal impressions are being increasingly employed. Additionally, many of the proxy relationships use angiosperms with broad leaves, which have been increasingly abundant in the last 130 million years but are absent from the fossil record before this. We focus on the methodology to define stomatal parameters for paleo-CO2 studies using two separate methodologies (one corrosive, one non-destructive) to prepare leaves on both scale- and broad-leaves collected from herbaria with known global atmospheric CO2 levels. We find that the corrosive and non-corrosive methodologies give similar values for stomatal density, but that measurements of stomatal sizes, particularly guard cell width (GCW), for the two methodologies are not comparable. Using those measurements to reconstruct CO2 via the gas exchange model, we found that reconstructed CO2 based on stomatal impressions (due to inaccurate measurements in GCW) far exceeded measured CO2 for modern plants. This bias was observed in both coniferous (scale-shaped) and angiosperm (broad) leaves. Thus, we advise that applications of gas exchange models use cleared leaves rather than impressions.

Realities of comfort and discomfort in the heritage language classroom: Looking to transformative positive psychology for juggling a double‐edged sword

AbstractAs emotions research in the field of second language acquisition continues to evolve, it is equally important to explore the impact of social–emotional variables that are specifically relevant to heritage language (HL) contexts. Anchoring on foundations in critical heritage language education (HLE), this study examines the discomforts of the HL classroom from a diverse heritage speaker (HS) perspective. Additionally, comforts that support the HL classroom as a safe space for emotional security and well‐being for HSs across HLs are explored. Examining the HL classroom from the perspective of HL practices and knowledge systems, this study ultimately aims to: (a) outline the emotional complexity of HL pedagogical spaces, and (b) provide concrete and meaningful recommendations for supporting HS well‐being and HL development from a transformative positive psychology lens. Data for the current qualitative study were provided through two separate methodologies. First, 64 HSs of Spanish responded to a qualitative questionnaire probing the emotional reactions and memories instigated by authentic HL classroom reading material on sensitive topics of racism, bilingualism, and immigration. The themes identified in written narrative data through an inductive thematic approach were then used as a foundation for semistructured interviews with language learners (n = 6) and educators (n = 8) from eight different HL backgrounds. Findings revealed feelings of comfort and discomfort, and even trauma and healing, in HLE spaces rooted in (a) language learning experiences, (b) social memories of (dis)comfort, and (c) intergenerational histories. Together, the data suggest how the HL classroom can act both as a trigger of social injustice, linguistic insecurity, and family conflict and, at the same time, as a space instigating affective reactions associated with social rebellion, linguistic confidence, intergenerational healing, and emotional refuge. Specific pedagogical recommendations are made to equip educators with a concrete toolkit for the HL classroom.

Valuation of Software Agency for Pre IPO Using DCF and Relative Valuation Method (Study Case: PT XYZ)

An Initial Public Offering (IPO) is a pivotal moment in a company’s life cycle. It’s a process where a private company offers its shares to the public in the capital market to raise funds. The move from private to public affecting the company to prepare for many changes, including increased supervision and the responsibility of delivering value to shareholders. However, before stepping on this significant IPO step, the company needs a clear understanding of various factors, which includes macroeconomic conditions, the dynamics of the market it operates in, and the company’s internal conditions. The company, PT XYZ, specializes in creating software solutions, a sector that is currently needed by a lot of company due to digitalization era. Fair share price for PT XYZ must be calculated when it goes public. In addition to understanding the macroeconomic condition, the Porter’s Five Forces framework is described to assess the competitive aspect in the market and potential opportunities. After analysing the external factors, the next step is doing in-depth internal analysis. This involved a SWOT analysis by identifying PT XYZ’s Strengths, Weaknesses, Opportunities, and Threats, and an evaluation of the company’s financial health. The business solution proceeded to the valuation stage, using the Discounted Cash Flow (DCF) method with Free Cash Flow to Equity (FCFE). This process involved making growth projections for the next decade. It anticipates that the company’s growth rate would eventually align with Indonesia’s Gross Domestic Product (GDP), serving as the long-term growth rate. To ensure a holistic evaluation, there must be a complementary valuation method, Relative Valuation. Three similar companies in the software sector were picked and their EV/EBITDA was used as a multiplier ratio. The two separate valuation methodologies led to two distinct results. Thus the average of these two methods can be proposed. Overvalued or undervalued share pricing can significantly affect investor decisions and the amount of funds raised from the IPO.

A Comprehensive Study of Digital Image Steganographic Techniques

Steganography surpasses other mechanisms of securing data from potential threats. The modern digital arena calls for robust information hiding techniques and, thus, it has always been a flash point for researchers and academicians. Nowadays, transmission is susceptible to numerous hacks while sharing secret information through typical correspondence channel. Accordingly, everybody needs the classification, respectability, and realness of his or her privileged information. Particularly, different techniques are used to take on these security issues like advanced declaration, computerized mark, and cryptography. Nevertheless, these strategies alone cannot be negotiated. Steganography is a revolution where current information compression, data hypothesis, spread range, and cryptography advancements are integrated to meet the requirements for protection of data over the Internet. This study investigates and critically analyses various existing cover steganography techniques and identifies the valuable region that everyone can be benefited. Moreover, we present a comprehensive overview of the fundamental concepts with in the domain of the steganographic methods and steganalysis. These systems have been depicted in numerous areas of the steganography such as spatial space, transform domain, and adaptive space. Moreover, each space has its particular traits. A few regularly involved techniques for improving the steganographic security and upgrading steganalysis capacity are elaborated, summed up; and conceivable examination patterns are talked about. We also systematically separate different methodologies in our review and show their pros and cons, qualities, challenges and significance.

Analysis of the national ecosystem database of Bulgaria: (Mis)matches with the MAES framework

The mapping of ecosystems is a significant element in the European Biodiversity Strategy and the results of its implementation should support the maintenance and restoration of ecosystems and their services. The quality of the spatial data is of crucial importance for the achievement of these goals. A methodological framework for Bulgaria in the form of nine separate methodologies has been developed in recent years. In this paper, we analyze the ecosystem typology for Bulgaria and the GIS database to assess the possibilities to develop a common database for the needs of integrated water management. The data analyses were carried out in two dimensions: 1) the typology and attributive data were analyzed by cross-tabular approach; and 2) the spatial data were analyzed by topology rules.  The results of the study reveal three main problems of the typology: 1) for some types it is developed to the fourth level while for others it is to the third level; 2) in some of the ecosystem types, especially in the freshwater ecosystems, different categories are mixed within a single hierarchical level; 3: there are duplicated numerical designations between grassland and forest ecosystems. This necessitates a revision of the typology and the development of a correct uniform classification to be used for the needs of integrated assessment. The topology analyses of the merged data from the eight ecosystem GIS layers show extremely large numbers of gaps and overlaps. The main reason is the use of different sources for the mapping of different ecosystem types. The main conclusion is that it is practically impossible to generate topologically correct integrated GIS layers from the eight ecosystem type layers. Therefore, it is necessary to develop a new approach for mapping all ecosystem types into a uniform database.

Separation of Different Blogs from Skin Disease Data using Artificial Intelligence.

A combination of environmental conditions may cause skin illness everywhere on the earth, and it is one of the most dangerous diseases that can develop as a result. A major goal in the selection of characteristics is to produce predictions about skin disease instances in connection with influencing variables, which is one of the most important tasks. As a consequence of the widespread usage of sensors, the amount of data collected in the health industry is disproportionately large when compared to data collected in other sectors. In the past, researchers have used a variety of machine learning algorithms to determine the relationship between illnesses and other disorders. Forecasting is a procedure that involves many steps, the most important of which are the preprocessing of any scenario and the selection of forecasting features. A major disadvantage of doing business in the health industry is a lack of data availability, which is particularly problematic when data is provided in an unstructured format. Filling in missing numbers and converting between various types of data take somewhat more than 70% of the total time. When dealing with missing data in machine learning applications, the mean, average, and median, as well as the stand mechanism, may all be employed to solve the problem. Previous research has shown that the characteristics chosen for a model's overall performance may have an influence on the overall performance of the model's overall performance. One of the primary goals of this study is to develop an intelligent algorithm for identifying relevant traits in models while simultaneously eliminating nonsignificant attributes that have an impact on model performance. To present a full view of the data, artificial intelligence techniques such as SVM, decision tree, and logistic regression models were used in conjunction with three separate feature combination methodologies, each of which was developed independently. As a consequence of this, their accuracy, F-measure, and precision are all raised by a factor of ten, respectively. We then have a list of the most important features, together with the weights that have been allocated to each of them.

Don't be a Square: The processing mechanisms characterising the elemental dimensions of width and height.

What are the geometric and information processing characteristics of elementary figures composed of simple physical dimensions? There have been a number of investigations of perception of rectangles, including debate about configurality (e.g., integrality and gestalt properties) as well as the prime perceptual dimensions. Yet, because of ambiguity even in the "right" definition of configurality and an absence of penetrating methodologies, there is still little known concerning the information processing of these patterns. To this end, the present study brings together two separate theory-driven methodologies, general recognition theory (GRT) and systems factorial technology (SFT). The first attacks the problem of dimensional interactions while the latter seeks to uncover process characteristics such as architecture, decisional stopping rules, and workload capacity. The same observers and as much as possible, the same stimuli were used in both approaches. Through our GRT analyses, we found strong evidence for dependencies between the percepts of height and width on both within-stimulus and cross-stimulus bases. Height perception was better with narrow widths and width perception was superior with short heights. In addition, a significant positive within-trial correlation of dimensions was evidenced within squares but not with rectangles. Our SFT initiative uncovered consistent signatures of parallelism paired with super capacity, the latter appearing both through the traditional conditioning on being correct and still present when modest speed accuracy trade-off was accounted for. Thus, the SFT and GRT inferences were quite compatible with a plausible cause of the positive correlations being across-channel facilitatory interactions which led to super capacity processing.

Selection of Suppliers for Speech Recognition Products in IT Projects by Combining Techniques with an Integrated Fuzzy MCDM

In today’s environment, as the complexity of actual events develops, products become increasingly complicated. As a result, companies should collaborate to integrate disparate technologies while developing a product or service. Additionally, collaborating with the right supplier helps a company increase the flexibility, competitiveness, and profitability of its goods or services. The goal of this study is to look into the factors that influence supplier selection for speech recognition. Twelve sub-criteria for quality, affordability, maintenance, and adaptability are used to evaluate prospective providers. Two separate hybrid methodologies for finding the best supplier of an information technology product are presented. intuitionistic Fuzzy Due to the uncertainty of the data, VIKOR operates as the decision-making matrix and solves the issue by determining the ideal alternative for group utility using VIKOR. The second technique, Q-ROF TOPSIS, selects suppliers by utilizing q-rung orthopair fuzzy sets, which provides decision makers with greater expression flexibility than the majority of uncertainty-related strategies. To demonstrate the effectiveness of the recommended measures, a case study is conducted. The outcomes of various strategies are compared, as well as the associated advantages.

OncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data.

Cell line drug screening datasets can be utilized for a range of different drug discovery applications from drug biomarker discovery to building translational models of drug response. Previously, we described three separate methodologies to (1) correct for general levels of drug sensitivity to enable drug-specific biomarker discovery, (2) predict clinical drug response in patients and (3) associate these predictions with clinical features to perform in vivo drug biomarker discovery. Here, we unite and update these methodologies into one R package (oncoPredict) to facilitate the development and adoption of these tools. This new OncoPredict R package can be applied to various in vitro and in vivo contexts for drug and biomarker discovery.

Balancing research goals and community expectations


 This article explores the possibilities that arise from combining participant observation with body camera recording, through the analysis of the use of a GoPro to study communication at a Cameroonian wildlife sanctuary. The original goal of this research project was to use both video recording and participant observation as separate methodologies in order to understand how Cameroonian animal keepers and European volunteers at the Limbe Wildlife Centre worked across linguistic and ideological barriers to rehabilitate chimpanzees. However, increased participant observation became necessary due both to research participants’ expectations that the researcher contribute to daily work activities, as well as the logistical difficulties of recording highly-mobile work in a loud, wet, and potentially dangerous environment. To negotiate these expectations and constraints, the researcher wore a body camera while working alongside research participants, allowing her to capture a first-person perspective as she assisted animal keepers and volunteers in cleaning enclosures and caring for animals. Although the use of a body camera posed certain complications in terms of both audio quality and camera placement, participating while recording provided a unique window into participants’ daily work experiences, and helped the researcher build strong, mutually beneficial relationships at the field site. For these reasons, this article argues that body cameras create new possibilities for both capturing first-person perspectives in mobile settings, and for allowing researchers to more fully collaborate with their participants.

Theory of Transport in Highly Concentrated Electrolytes

Ionic liquids are promising candidates for novel electrolytes as they possess large electrochemical and thermodynamic stability and offer a high degree of tunability. As purely-ionic electrolyte without neutral solvent they exhibit characteristic structures near electrified interfaces and in the bulk, both being described theoretically via separate frameworks and methodologies. We present a holistic continuum theory applying to both regions. This transport theory for pure ionic liquids and ionic liquids-mixtures allows the systematic description of the electrolyte evolution. In particular, dynamic bulk-transport effects and interfacial structures can be studied. The theory is thermodynamically consistent and describes multi-component solutions (ionic liquids, highly concentrated electrolytes, water-in-salt electrolytes). Here, we give a detailed derivation of the theory and focus on bulk transport processes of ionic liquids as appearing in electrochemical cells. In addition, we validate our framework for a zinc-ion battery based on a mixture of ionic-liquid and water as electrolyte.

Searching for new long-term urinary metabolites of metenolone and drostanolone using gas chromatography-mass spectrometry with a focus on non-hydrolysed sulfates.

Sulfated metabolites have been shown to have potential as long-term markers of anabolic-androgenic steroid (AAS) abuse. In 2019, the compatibility of gas chromatography-mass spectrometry (GC-MS) with non-hydrolysed sulfated steroids was demonstrated, and this approach allowed the incorporation of these compounds in a broad GC-MS initial testing procedure at a later stage. However, research is needed to identify which are beneficial. In this study, a search for new long-term metabolites of two popular AAS, metenolone and drostanolone, was undertaken through two excretion studies each. The excretion samples were analysed using GC-chemical ionization-triple quadrupole MS (GC-CI-MS/MS) after the application of three separate sample preparation methodologies (i.e. hydrolysis with Escherichia coli-derived β-glucuronidase, Helix pomatia-derived β-glucuronidase/arylsulfatase and non-hydrolysed sulfated steroids). For metenolone, a non-hydrolysed sulfated metabolite, 1β-methyl-5α-androstan-17-one-3ζ-sulfate, was documented for the first time to provide the longest detection time of up to 17 days. This metabolite increased the detection time by nearly a factor of 2 in comparison with the currently monitored markers for metenolone in a routine doping control initial testing procedure. In the second excretion study, it prolonged the detection window by 25%. In the case of drostanolone, the non-hydrolysed sulfated metabolite with the longest detection time was the sulfated analogue of the main drostanolone metabolite (3α-hydroxy-2α-methyl-5α-androstan-17-one) with a detection time of up to 24 days. However, the currently monitored main drostanolone metabolite in routine doping control, after hydrolysis of the glucuronide with E.coli, remained superior in detection time (i.e. up to 29 days).

Product development in Islamic finance and banking in secular economies

PurposeAs the global paradigm in economics shifts, Islamic economics is attracting more attention as an alternative sector. The most common and most active institutional structure of Islamic economics is in the form of Islamic finance and banking. Islamic finance and banking have been the centre of innovation in many economies in recent years. In this regard, product development is a vital element in driving the success of Islamic financial institutions (IFIs). The product development of IFIs is one of the key elements of their overall economic performance. This study aims to fill the gap in the literature concerning the product development process of IFIs in secular economies.Design/methodology/approachVerily, product development is a complex process; it is likely that introducing specific models will be useful for expanding the activities of IFIs. In this study, contemporary source materials are used to develop this conceptual research.FindingsIt suggests two separate methodologies for the product development process of IFIs in secular economies to overhaul two criticised product-based problems. To the best of the author’s knowledge, it is the first attempt to model the product development process for IFIs in a secular economic setup.Originality/valueRecently, this study is the first attempt for modelling product development in IFIs under secular economies. Advances in the field of Shari’ah-compliant product development is important for researchers and professional.

HYDRODYNAMIC AND HYDRODISPERSIVE CHARACTERIZATION OF A FLUVIC CAMBISOL IN THE NORTHEAST REGION OF BRAZIL1

ABSTRACT The determination of the hydraulic and hydrodispersive properties of soil is necessary for the understanding of water dynamics and soil solute transport, and they are used as input data in several models. Determinations of soil hydraulic and hydrodispersive parameters are usually obtained by separate methodologies. Axisymmetric infiltration tests using a single ring infiltrometer with a conservative tracer (KBr) in the field (Beerkan-Solute) lead to the determination of the most realistic hydraulic and hydrodispersive properties of the study area. The objective of this work was the hydrodynamic and hydrodispersive characterization of an irrigated soil, classified as Fluvic Cambisol, from the lowland areas of the northeastern semi-arid region, located in the backwoods of the state of Pernambuco, in the municipality of Serra Talhada-PE. The hydrodispersive parameters were determined with the CXTFIT 2.0 program, using the CDE and MIM models. The studied soil presented hydrodynamic characteristics with high values of sorptivity (S) that were associated with high values of saturated hydraulic conductivity (Ks), indicating that the soil has good permeability and resistance to surface crumbling caused by rain drops or soil management. It was observed the predominance of the convective process, being the CDE model the one that presented the best performance in the transport of the KBr tracer under field conditions, indicating the absence of two water regions mobile and immobile in the studied soil.

Thinking about decisions: An integrative approach of person and task factors

AbstractDecisions vary. They may vary in both content and complexity. People also vary. An important way that people vary is how much they think. Some prior research investigating thinking and decision making largely conflicts with most traditional decision theories. For example, if considering an array of products to choose from, thinking more about the alternative's attributes should lead to a better decision. However, some research indicates that thinking more may also lead to focusing on irrelevant aspects of the decision and a less optimal outcome. We propose that this conflict in the literature exists because of a failure to consider the interaction between the individual and the decision task. To test this, we used separate methodologies that enhance or attenuate a person's thinking. In Study 1, we selected people who were especially high or low in need for cognition and had them complete a robust decision‐making inventory, which included both complex and simple tasks. In Study 2, we manipulated participant's level of glucose, which acts as the brain's fuel to enhance or attenuate thinking ability. Both studies provide insight for understanding our central tenant that more thought leads to better decisions in complex tasks but does not influence simple decisions. These findings show how the individual's thinking can interact with the constructive elements of the task to shape decision choice.

ПРОБЛЕМИ ТИПІЗАЦІЇ ОКРЕМИХ КРИМІНАЛІСТИЧНИХ МЕТОДИК

Статтю присвячено проблемам формування та типізації окремих криміналістичних ме­тодик, визначенню їх змісту і структурних складників. Акцентовано увагу на необхідності застосування уніфікованих підходів до окремих криміналістичних методик та створення їх реєстру. Розроблення окремих криміналістичних методик має бути спрямовано на різних учасників та стадії кримінального провадження у змагальному процесі. Відстоюється по­зиція про доцільність віднесення до структури окремої криміналістичної методики типових тактичних операцій, які певним чином співвідносяться із постановленням та вирішенням стратегічних (перспективних) завдань. Оптимальність застосування окремих криміналіс­тичних методик пов'язано із технологізацією процесу розслідування та судового розгляду.

Evaluation of Pattern Based Customized Approach for Stock Market Trend Prediction With Big Data and Machine Learning Techniques

The stock market is very volatile and non-stationary and generates huge volumes of data in every second. In this article, the existing machine learning algorithms are analyzed for stock market forecasting and also a new pattern-finding algorithm for forecasting stock trend is developed. Three approaches can be used to solve the problem: fundamental analysis, technical analysis, and the machine learning. Experimental analysis done in this article shows that the machine learning could be useful for investors to make profitable decisions. In order to conduct these processes, a real-time dataset has been obtained from the Indian stock market. This article learns the model from Indian National Stock Exchange (NSE) data obtained from Yahoo API to forecast stock prices and targets to make a profit over time. In this article, two separate algorithms and methodologies are analyzed to forecast stock market trends and iteratively improve the model to achieve higher accuracy. Results are showing that the proposed pattern-based customized algorithm is more accurate (10 to 15%) as compared to other two machine learning techniques, which are also increased as the time window increases.