Traditional Methodologies Research Articles

Multi Omics Applications in Biological Systems.

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.

Visualizing the Landscape and Evolution of Solar Energy-Integrated Desalination Systems via Scientometric Analysis

Rising population levels exert significant pressure on available freshwater resources. Scientists and researchers from various countries are diligently seeking a long-lasting solution using solar-powered desalination. This research paper investigates the current advancements in solar desalination research by utilizing the method of “scientometrics”. Scientometrics employs traditional methodologies, including bibliometrics, which entails quantifying the number of research papers published, and citation analysis, which involves examining the frequency with which other researchers cite these papers. By integrating these two approaches, scientometrics provides invaluable information about the most influential countries, institutions, and individual researchers in the field. Utilizing the software program VOSviewer, a comprehensive analysis was conducted on 1855 research papers published between 2010 and 2024. These papers were selected based on a predetermined set of ten key search terms. The results of the analysis indicate that China is the leading country in this field, as it boasts the highest number of published papers and the most citations received overall. Notably, Egyptian research institutions have been identified as the most influential in this area. Moreover, a single author has notably amassed 3419 citations for their 54 published works on solar desalination. This analysis unveiled past and contemporary advancements in the field and identified current trends through keyword analysis. It also offers recommendations based on bibliometric findings, including suggestions for addressing the challenges faced by solar-derived systems and addressing research area saturation.

Integrating Advanced Technologies in Financial Risk Management: A Comprehensive Analysis

This paper delves into the pivotal role of advanced technologies in enhancing financial risk management across various domains, including credit risk, market risk, operational risk, and liquidity risk. It meticulously explores the application of machine learning (ML) algorithms and artificial intelligence (AI) in developing sophisticated risk assessment models, portfolio diversification strategies, and regulatory compliance mechanisms, which collectively surpass traditional methodologies in accuracy, efficiency, and predictive power. Through a detailed examination of enhanced Value at Risk (VaR) models, dynamic hedging strategies, and the impact of geopolitical events on market risk, alongside innovative approaches to operational risk mitigation and liquidity planning, this study underscores the transformative potential of technological advancements in financial risk management. It highlights how these technologies facilitate real-time analysis, predictive modeling, and strategic planning, significantly contributing to the resilience and stability of financial institutions in the face of evolving risks and regulatory requirements.

Integrating Advanced Technologies in Financial Risk Management: A Comprehensive Analysis

This paper delves into the pivotal role of advanced technologies in enhancing financial risk management across various domains, including credit risk, market risk, operational risk, and liquidity risk. It meticulously explores the application of machine learning (ML) algorithms and artificial intelligence (AI) in developing sophisticated risk assessment models, portfolio diversification strategies, and regulatory compliance mechanisms, which collectively surpass traditional methodologies in accuracy, efficiency, and predictive power. Through a detailed examination of enhanced Value at Risk (VaR) models, dynamic hedging strategies, and the impact of geopolitical events on market risk, alongside innovative approaches to operational risk mitigation and liquidity planning, this study underscores the transformative potential of technological advancements in financial risk management. It highlights how these technologies facilitate real-time analysis, predictive modeling, and strategic planning, significantly contributing to the resilience and stability of financial institutions in the face of evolving risks and regulatory requirements.

Strategies for Minimizing the Effects of Observer Variability on Sagittal Parameter Measurements of the Spine.

Exploratory-descriptive study. Evaluate the influence of observer variability on the measurements of both thoracic kyphosis (TK) and lumbar lordosis (LL) obtained with anatomic and functional spinal segmentation methods. Parametric analysis for spinal surgery planning typically relies on anatomic parameters. However, incorporating functional parameters that consider the vertebrae orientation is important to minimizing surgical calculation errors. The authors developed parametric analysis software that integrates traditional and functional methodologies. The proposed method included functional thoracic kyphosis and functional lumbar lordosis calculated from the lines normal to the inflection points of the spine model. Using a synthetic lateral X-ray, the observer variability was computer-simulated generating 20 landmark sets that replicate the annotations of 20 observers. The analysis also included 10 clinical X-rays, annotated twice by 3 judges with a minimum 1-week interval. The spinal curvature angles were derived using the anatomic and functional methods. Statistical analysis were performed for comparison. For the synthetic X-ray, the proposed method presented significantly less variability: TK (<±2.5 degrees, P=0.00023) and LL (<±5 degrees, P=0.00012). For the clinical X-rays, the interobserver reliability analysis yielded higher intraclass correlation coefficients (ICC) for functional TK (ICC>0.97) and functional LL (ICC>0.87) than for TK (ICC<0.91) and LL (ICC<0.89). Statistically significant differences were observed for both TK (P=0.001) and LL (P=0.030). Under the traditional method, observer variability led to measurement differences surpassing ±19 degrees, whereas differences with the proposed method were within ±10 degrees for both parameters. The vertebral endplate is not the most suitable place to measure spinal sagittal curvatures. Small changes in landmark position significantly alter the measured Cobb angle. The proposed method offers a substantial advantage regarding the influence of observer variability, in addition to the more individualized analysis.

The linguistic leap: Understanding, evaluating, and integrating AI in language education

The landscape of language education is undergoing a pivotal transformation, spurred by the integration of Generative Artificial Intelligence (Gen-AI) into every facet of traditional and new methodologies and practices. Given the rapid societal adoption of AI, we believe that all language instructors – from the most technologically savvy to the most tech-averse – must engage critically and ethically with AI. To ensure that AI tools are brought into language education in pedagogically appropriate and ethical ways, we have developed two large projects at our university: 1) an AI Working Group in our Modern Languages Department and 2) a chatbot that all instructors can incorporate into their classroom practices. In this article, we describe the rationale for these projects and the steps we took to implement them. We hope that this work can help other departments come together to address the challenges and achieve a balance between technological advancement and the intrinsically human facets of language education.

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

In maritime logistics optimization, considerable research efforts are focused on the extraction of deep behavioral characteristics from comprehensive shipping data to discern patterns in maritime vessel behavior. The effective linkage of these characteristics with maritime infrastructure, such as berths, is critical for the enhancement of ship navigation systems. This endeavor is paramount not only as a research focus within maritime information science but also for the progression of intelligent maritime systems. Traditional methodologies have primarily emphasized the analysis of navigational paths of vessels without an extensive consideration of the geographical dynamics between ships and port infrastructure. However, the introduction of knowledge graphs has enabled the integration of disparate data sources, facilitating new insights that propel the development of intelligent maritime systems. This manuscript presents a novel framework using knowledge graph technology for profound analysis of maritime data. Utilizing automatic identification system (AIS) data alongside spatial information from port facilities, the framework forms semantic triplet connections among ships, anchorages, berths, and waterways. This enables the semantic modeling of maritime behaviors, offering precise identification of ships through their diverse semantic information. Moreover, by exploiting the semantic relations between ships and berths, a reverse semantic knowledge graph for berths is constructed, which is specifically tailored to ship type, size, and category. The manuscript critically evaluates a range of graph embedding techniques, dimensionality reduction methods, and classification strategies through experimental frameworks to determine the most efficacious methodologies. The findings reveal that the maritime knowledge graph significantly enhances the semantic understanding of unmanned maritime equipment, thereby improving decision-making capabilities. Additionally, it establishes a semantic foundation for the development of expansive maritime models, illustrating the potential of knowledge graph technology in advancing intelligent maritime systems.

Unveiling reflectance spectral characteristics of floating plastics across varying coverages: insights and retrieval model

Marine floating debris, particularly chemically stable plastics, poses a significant global environmental concern. These materials, due to their prevalence and durability, linger on the ocean surface for extended durations, inflicting considerable harm on marine ecosystems, life, and the food chain. The traditional methodology for investigating marine floating debris mainly uses field observations, which are time-consuming, laborious, and constrained in observational scope. Consequently, there is an urgent need for more effective methodologies, such as remote sensing, to monitor marine floating debris, which will be of great significance for enhancing the management of their pollution. In this study, we employ controlled experiments and theoretical model simulations to investigate the spectral characteristics of remote sensing reflectance (Rrs(λ)) of two common types of floating plastic debris, specifically polyvinyl chloride (PVC) buoys and polypropylene (PP) bottles. Our analysis reveals distinct Rrs(λ) spectral characteristics for each type of plastic debris, differing significantly from that of the background water. Furthermore, both PVC buoys and PP bottles exhibit a similar absorption valley in the short-wave infrared region, with its depth increasing alongside the plastic coverage. Based on these findings, we develop a novel floating plastic index (FPI) and a corresponding retrieval model for estimating the coverage of floating plastic debris. Validation with simulated data and measurements from control experiments shows good performance of the retrieval model with high inversion accuracy, demonstrated by the values of the coefficient of determination, mean percentage error, mean absolute percentage error, and root mean square error of 0.97, -0.3%, 17.5%, and 3.98%, respectively, for the experimentally measured dataset. Our research provides a theoretical and methodological foundation for remote sensing retrieval of the coverages of floating PVC and PP plastics, as well as offers valuable insights for the analysis of other floating debris types in future studies.

Beyond Static Planning: Computational Predictive Modeling to Avoid Coronary Artery Occlusion in TAVR

BackgroundCoronary artery occlusion (CO) during transcatheter aortic valve replacement (TAVR) is a devastating complication. The objective of this study was to assess the clinical impact of a computational predictive modeling algorithm for CO during TAVR planning. MethodsFrom January 2020 to December 2022, 116 patients (7.6%) who underwent TAVR evaluation were deemed to be at increased risk of CO on the basis of traditional criteria. Patients underwent prospective computational modeling (DASI Simulations) to assess their risk of CO during TAVR; procedural modifications and clinical results were reviewed retrospectively. ResultsOf the 116 patients at risk for CO by traditional methodology, 53 had native aortic valve stenosis (45.7%), 47 had undergone previous surgical AVR (40.5%), and 16 had undergone previous TAVR (13.8%). Transcatheter valve choice, size, or implantation depth was modeled for all patients. Computational modeling predicted an increased risk of CO in 39 of 116 (31.9%) patients. Within this subcohort, 29 patients proceeded with TAVR. Procedural modifications to augment the risk of CO included bioprosthetic or native aortic scallop intentional laceration to prevent iatrogenic coronary artery obstruction during TAVR (n = 10), chimney coronary stents (n = 8), and coronary access without stents (n = 3). There were no episodes of coronary artery compromise among patients after TAVR, either for those predicted to be at high risk of CO (with procedural modifications) or those predicted to be at low risk (standard TAVR). ConclusionsThe use of preoperative simulations for TAVR in patient-specific geometry through computational predictive modeling of CO is an effective enhancement to procedure planning.

Rethinking Digital Customer Complaints’ Problem Solving: The Synchronous Model

Abstract In the current business environment, problem-solving remains a critical and highly relevant skill for achieving customer satisfaction especially in the context of digitalization and industry 4.0 and a strong requirement from IATF 16949. In the present paper, a comprehensive analysis of existing problem-solving models is conducted, examining their strengths, limitations, and applicability in the manufacturing companies. By drawing insights from traditional methodologies, as already addressed in the scientific literature, such as Six-Step Model A3 methodology, 8 Disciplines, Six Sigma, the Cynefin Framework, Kepner-Tregoe Methodology and 5P (Principles) for Problem Solving, a new and integrative model designed to address the shortcomings identified in current approaches is created. This innovative framework, named “Synchronous Model” (Greek word for modern) synthesizes key elements from diverse models, offering a more adaptable solution to complex and modern problem-solving challenges. Emphasizing its potential for business digital transformation, we discuss how the proposed model fosters efficiency, agility, and strategic decision-making within organizational settings. This research not only contributes to the theoretical understanding of problem-solving methodologies but also provides valuable insights for practitioners seeking innovative approaches to address challenges in their respective industries, for which the current methods are either not sufficient or too complex. The integration of “Synchronous Model” is meant to elevate problem-solving practices, offering a transformative impact on business operations and strategic decision-making, all in the benefit of the customer.

Numerical Examination of Loading Types Impact on Mat Foundations: A Case Study of a 10-storey Building in Gaziantep, Türkiye

This study leverages advanced numerical analysis techniques to model the intricate interactions among soil, foundation, and structures alongside the nonlinear load-deformation characteristics of soils in three-dimensional environments. This research underscores mat foundation calculations' enhanced realism and cost-efficiency, offering improvements over traditional methodologies. The objective is to explore the effects of different superstructure loading types on mat foundation behaviour using three-dimensional finite element models. Thirty-six distinct models were developed to examine the influence of superstructure loads, which were varied across uniformly distributed loads, column loads, and comprehensive building models. Each mat foundation was analysed using separate models incorporating 3D volume elements, utilizing the Mohr-Coulomb material model to represent soil conditions as either 'normally loaded' or 'over-consolidated.’ The study presents detailed findings on total and differential settlements and internal forces, illustrated through figures and graphs, based on data from an existing ten-story building. Regarding bending moments, the most notable difference among loading types lies in the varied locations of maximum moments, independent of shear wall placement, particularly evident when simulating uniformly distributed loads. It is evident that the type of superstructure loading influences settlement patterns and internal forces, underscoring the necessity of considering this factor in analyses. Our findings reveal a significant disparity in construction costs between buildings erected on different soil classes, particularly between ZA and ZB. Structures situated on ZB-classified soil incur expenses approximately 1.5% higher than those on ZA-classified soil.

Utilization of artificial intelligence in Outcome-Based Curriculum Evaluation and Development

The integration of artificial intelligence (AI) in education has revolutionized traditional teaching and learning methodologies, offering significant improvements in curriculum evaluation and development. This study explores the utilization of AI in the evaluation and development of an Outcome-Based Curriculum (OBC). AI technologies, including machine learning algorithms and natural language processing, are employed to analyze vast amounts of educational data, providing insights into student performance and curriculum effectiveness. The research highlights how AI can identify learning gaps, predict student outcomes, and recommend personalized learning paths, thereby enhancing the overall educational experience. By leveraging AI, educators can design more adaptive and responsive curricula that meet the dynamic needs of students and industry standards. The findings suggest that AI-driven tools not only streamline the evaluation process but also facilitate continuous curriculum improvement, ensuring that educational programs remain relevant and outcome-focused. This paper underscores the potential of AI to transform curriculum development practices, promoting a more efficient, data-driven approach to education that aligns with contemporary educational goals and outcomes.

Performing Suction Blister Skin Biopsies.

Traditional skin sampling methods include punch or shave biopsies to produce a solid tissue sample for analysis. These biopsy procedures are painful, require anesthesia, and leave permanent scars. This unit describes a suction blister skin biopsy method that can be used in place of traditional biopsy methodologies as a minimally invasive, non-scarring skin sampling technique. The induction of suction blisters uses an instrument with a chamber that applies negative pressure and gentle heat to the skin. Blister formation occurs within 1 hr, producing up to five blisters, each 10 mm in diameter per biopsy site. Blister fluid can be extracted and centrifuged to retrieve cells from the epidermis and upper dermis for flow cytometry, single-cell RNA sequencing, cell culture, and more without the need for digestion protocols. In addition, the blister fluid can be used to measure soluble proteins and metabolites. This unit describes the preparation of supplies and subjects, the suction blister biopsy procedure and blister formation, fluid extraction, and post-blistering care. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Preparation of supplies and subject Basic Protocol 2: Suction blister biopsy procedure and formation Basic Protocol 3: Blister fluid extraction Basic Protocol 4: Post-blister care and clean up.

3D-printed topological-structured electrodes with exceptional mechanical properties for high-performance flexible Li-ion batteries

A vital aspect in advancing flexible batteries is the development of flexible electrodes capable of enduring repeated stretching while upholding satisfactory electrochemical performance. Thus, adopting a systematic and efficient approach to structural design and fabrication becomes imperative. In this study, we introduce an optimal structural design achieved through topology optimization and fabricate flexible electrodes via 3D printing, representing a departure from traditional design and manufacture methodologies in the development of flexible electrodes for batteries. Our research underscores the impressive mechanical strength of these topologically-structured electrodes (TSEs), validated through rigorous finite element analysis (FEA) and tensile strength testing. The results of both the stretch deformation and twist deformation analysis on the TSEs and the conventional mesh-structured electrodes (MSEs) show that the peak strain and stress of TSEs are much lower than those of MSEs. Notably, even under 50 % stretching, the TSEs maintain structural integrity, contrasting sharply with conventional mesh-structured electrodes (MSEs) and flat film electrodes which often crack under similar conditions. Moreover, after enduring 50 cycles of stretching, the TSEs retain an outstanding 98 % of their original capacity, surpassing MSEs which retain only 80 % of their capacity. These findings highlight the significant potential of topologically designed flexible electrodes, offering promising avenues for the development of stretchable and flexible energy storage devices such as wearable tech and bio-integrated electronics.

Trends in research approaches and gender in plant ecology dissertations over four decades.

Dissertations are a foundational scientific product; they are the formative product that early-career scientists create and share original knowledge. The methodological approaches used in dissertations vary with the research field. In plant ecology, these approaches include observations, experiments (field or controlled environment), literature reviews, theoretical approaches, or analyses of existing data (including "big data"). Recently, concerns have been raised about the rise of "big data" studies and the loss of observational and field-based studies in ecology, but such trends have not been formally quantified. Therefore, we examined how the emphasis on each of these categories has changed over time and whether male and female authors differ in the methods employed. We found remarkable temporal consistency, with observational studies being dominant over the entire time span examined. There was an increase in the number of approaches employed per dissertation, with increases in analyses of databases and theoretical studies adding to rather than replacing traditional methodologies (like observations and field experiments). The representation of women increased over time. There were some differences in the approaches taken by men and women, which requires further investigation.

Smartphone-Based Methodology Applied to Electromagnetic Field Exposure Assessment.

This study presents the measurements of exposure to electromagnetic fields, carried out comparatively following standard methods from fixed sites using a broadband meter and using a smartphone on which an App designed for this purpose has been installed. The results of two measurement campaigns carried out on the campus of the University of Alcalá over an area of 1.9 km2 are presented. To characterize the exposure, 20 fixed points were measured in the first case and 860 points along the route made with a bicycle in the last case. The results obtained indicate that there is proportionality between the two methods, making it possible to use the smartphone for comparative measurements. The presented methodology makes it possible to characterize the exposure in the area under study in four times less time than that required with the traditional methodology.

Optimizing agile project management methodologies in high-tech software development

This review paper examines the challenges and limitations of traditional Agile methodologies in high-tech software development. It proposes enhancements to optimise efficiency and outcomes. Traditional Agile methodologies, such as Scrum and Kanban, have revolutionised software development practices but face scalability issues and struggle to adapt to rapid technological changes. To address these challenges, this paper proposes integrating DevOps practices, incorporating Lean principles, adopting hybrid Agile methodologies, and emphasising continuous feedback and iterative learning. These enhancements aim to streamline development processes, eliminate waste, tailor Agile practices to specific needs, and foster a culture of continuous improvement. The potential impact of these enhancements on improving efficiency and outcomes in high-tech software development projects is significant. However, further research and experimentation are needed to validate their effectiveness in real-world settings. Continuous improvement and adaptation are essential for organisations to stay competitive in the ever-evolving landscape of high-tech industries.

Adapting to new challenges in medical education: a three-step digitization approach for blended learning

This research work focuses on evaluating the development of our three-step digitization approach, designed to transform traditional lectures into engaging digital learning experiences. Aimed at offering an easy-to-use solution for educators, this approach addresses the pressing challenges of modern educational environments by enhancing accessibility, engagement, and effectiveness of learning through digital means.Quantitative analysis demonstrated a notable increase in knowledge gain for students engaged with the digital format (t(230) = -2.795, p = 0.006) over traditional methods. Additionally, it was observed that the online setting significantly improved concentration levels (t(230) = -5.801, p < .001) and reduced distractions (t(230) = 2.848, p = 0.005). Emotional assessments, based on the Achievement Emotions Questionnaire (AEQ), indicated an elevation in enjoyment (t(230) = -4.717, p < .001) and a reduction in anxiety (t(230) = 9.446, p < .001) within the digital learning environment. A substantial preference for the digital course format was expressed by 61.0% of participants, with 71.4% acknowledging its superior efficiency compared to 14.3% for traditional lectures.Qualitative feedback underscored the digital format's flexibility, comprehensive learning experience, and immediate feedback as key benefits. Nevertheless, nuances such as a preferred understanding in face-to-face interactions and the absence of a social component in digital settings were noted.To conclude, the findings from this study illuminate the significant advantages of the three-step digitization approach in meeting contemporary educational challenges. By facilitating an enhanced knowledge acquisition and fostering a supportive emotional climate, this approach signifies a promising direction for the future of medical education and beyond, fusing the convenience of digital solutions with the depth and engagement of traditional learning methodologies.

Taxonomical and molecular authentication of the Ceropegia candelabrum L. var. candelabrum, (Apocynaceae): A new record from Eastern India

Ceropegia candelabrum L. is a type species of the genus Ceropegia that is native to the Western Ghats of India. This species is reporting for the first time from West Bengal, which is the eastern bottleneck of India. This species has very attractive, eye-catching flower characteristics viz., corolla-tubular; corolla tube long abruptly dilated at the base, claw is fused forming tube-like structure, corolla limb connate at the tip forming a globular cage. We apply both traditional and advanced methodologies to properly authenticate the newly collected species. However, the taxonomy, as well as the molecular (rbcL, ITS, and rbcL+ITS) data, confirm the identity of that species. Detailed morphology, a new distributional geographic area, and molecular identification are presented here.

Variational autoencoder-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives

The generation and optimization of simulation data for electrical machines remain challenging, largely due to the complexities of magneto-static finite element analysis. Traditional methodologies are not only resource-intensive, but also time-consuming. Deep learning models can be used to shortcut these calculations. However, challenges arise when considering the unique parameter sets specific to each machine topology. Building on two recent studies (Parekh et al. in IEEE Trans. Magn. 58(9):1–4, 2022; Parekh et al., Deep learning based meta-modeling for multi-objective technology optimization of electrical machines, 2023, arXiv:2306.09087), that utilized a variational autoencoder to cohesively map diverse topologies into a singular latent space for subsequent optimization, this paper proposes a refined architecture and optimization workflow. Our modifications aim to streamline and enhance the robustness of both the training and optimization processes, and compare the results with the variational autoencoder architecture proposed recently.