Specific Method Research Articles

PROSPECTS FOR RUSSIAN-INDIAN COOPERATION TRADE AND ECONOMIC COOPERATION IN THE CONTEXT OF SANCTIONS

Introduction: The article highlights the negative impact of Western sanctions on the Russian economy and emphasizes the importance of cooperation with India to diversify export-import flows. India’s high economic potential and growing role in the global economy make it a priority for Russia’s eastern policy. Methods: The study is based on an analysis of customs statistics, national statistical data, and academic publications. Methods of systematization, generalization, and specification were employed to identify the key areas of bilateral cooperation. Results: The findings underline that the core of bilateral interaction is the supply of oil and gas from Russia to India. However, the trade imbalance necessitates increasing Indian exports and developing joint investment projects. Priority sectors include energy, pharmaceuticals, agriculture, and high technology. Available Data: Trade statistics indicate an increase in turnover from $32.5 billion in 2022 to $65 billion in 2023. India’s main exports include pharmaceutical products, organic chemicals, electronics, and mechanical equipment, while Russian exports primarily consist of oil, fertilizers, and metals. Discussion: The discussion highlights the need to address the trade imbalance through the diversification of Indian exports and strengthening cooperation within multilateral frameworks. Risks related to India’s reorientation toward Western markets are discussed, and measures such as expanding joint ventures are proposed to enhance mutual trust. In conclusion, the article underscores the strategic importance of the Indian direction for Russia and outlines solutions to current challenges in economic relations.

TENSAI - Practical and Responsible Observability for Data Quality-aware Large-scale Analytics

Given a large-scale mobile network with a variety of equipment and radio access network technologies for an approximate 20 million subscribers, there are many types of data that can be used for big data analytics and machine learning (ML) tasks for network operations, monitoring, and optimization. However, a variety of data is measured, collected, and propagated through numerous complex data and software systems. Thus, people, software components, and data-driven operations for big data and ML pipelines face great challenges in dealing with data quality impacts. Data quality related problems occur and are propagated through complex operations involving different types of data, people, software components, and analytics that cannot be solved purely through data quality engineering. This paper discusses our TENSAI framework, as a practical and responsible observability for ensuring data quality in such a mobile network. TENSAI focuses on methods of communication, strategy specifications, and data quality engineering for diverse types of data and analytics among different types of operations. TENSAI presents techniques for capturing and communicating causes/effects of data quality problems clearly to all relevant stakeholders, developing data quality-aware adaptation strategies for actions on data that can be integrated into analytics processes, and engineering data quality awareness in software and data pipelines. Thus, TENSAI supports full visibility of data quality problems and impacts among related systems to empower the utilization and adaptation of data analytics for different types of operations. We will illustrate our TENSAI with several real-world data types, pipelines, and cases based on a real-world mobile network.

Novel isothermal nucleic acid amplification method for detecting malaria parasites

Malaria, a parasitic disease caused by Plasmodium spp. and transmitted by Anopheles mosquitoes, remains a major global health issue, with an estimated 249 million cases and 608,000 deaths in 2022. Rapid and accurate diagnosis and treatment are crucial for malaria control and elimination. However, limited access to sensitive molecular tests means that microscopic examination and rapid diagnostic tests (RDT) are the most used methods in endemic areas, despite their lower diagnostic accuracy. Therefore, there is a need for developing sensitive, simple, accurate, and rapid diagnostic tools suitable for field conditions. Herein, we aimed to explore the potential of the enzymatic recombinase amplification assay (ERA® Technology) as a remote laboratory test by evaluating and validating the GENEYE® ERA Plasmodium detection kit in Brazilian endemic areas. A cross-sectional cohort study was conducted between June and August of 2023 in the Brazilian Amazon. The study enrolled 323 participants residing in three malaria-affected regions: Cruzeiro do Sul and Mâncio Lima (Acre State) and Guajará (Amazonas State). The participants were tested for malaria by microscopy, rapid diagnostic tests (RDT), nested PCR (nPCR), quantitative real-time PCR (qPCR), and ERA. The sensitivity, specificity, and predictive values were assessed using nPCR as a gold standard. Plasmodium prevalence was 21.7%, 18.8%, 19.2%, 21.7%, and 21.7% by nPCR, microscopy, RDT, qPCR, and ERA respectively. Using nPCR as the standard, qPCR, and ERA showed a sensitivity of 100%. In comparison, microscopy and RDT showed a sensitivity of 87.1% and 88.6%, a negative predictive value (NPV) of 96.56 and 96.93, and kappa values of 0.91 and 0.92, respectively. For Plasmodium falciparum, the sensitivity of qPCR and ERA was 100% while the sensitivity of microscopy and RDT was 96.9% and 93.7%, and the NPV was 99.66 and 99.32, respectively. For Plasmodium vivax, only ERA showed the same sensitivity of nPCR. The sensitivity, NPV, and kappa values were 78.85%, 97.27, and 0.87 for qPCR and microscopy, and 84.21%, 97.94, and 0.9 for RDT. The data presented here show that the GENEYE® ERA Plasmodium detection kit offers a promising alternative to traditional malaria diagnostic methods. Its high sensitivity, specificity, fast processing time, and operational simplicity position it as a valuable point-of-care diagnostic tool, particularly in resource-limited and remote malaria-endemic areas.Key points• GENEYE® ERA kit detects Plasmodium in under 25 min, no DNA purification needed.• The kit matches or exceeds the compared methods in sensitivity and specificity.• The kit is suitable for accurate testing in low-infrastructure, point-of-care settings.Graphical abstract

TCKAN: a novel integrated network model for predicting mortality risk in sepsis patients.

Sepsis poses a major global health threat, accounting for millions of deaths annually and significant economic costs. Accurately predicting the risk of mortality in sepsis patients enables early identification, promotes the efficient allocation of medical resources, and facilitates timely interventions, thereby improving patient outcomes. Current methods typically utilize only one type of data-either constant, temporal, or ICD codes. This study introduces a novel approach, the Time-Constant Kolmogorov-Arnold Network (TCKAN), which uniquely integrates temporal data, constant data, and ICD codes within a single predictive model. Unlike existing methods that typically rely on one type of data, TCKAN leverages a multi-modal data integration strategy, resulting in superior predictive accuracy and robustness in identifying high-risk sepsis patients. Validated against the MIMIC-III and MIMIC-IV datasets, TCKAN surpasses existing machine learning and deep learning methods in accuracy, sensitivity, and specificity. Notably, TCKAN achieved AUCs of 87.76% and 88.07%, demonstrating superior capability in identifying high-risk patients. Additionally, TCKAN effectively combats the prevalent issue of data imbalance in clinical settings, improving the detection of patients at elevated risk of mortality and facilitating timely interventions. These results confirm the model's effectiveness and its potential to transform patient management and treatment optimization in clinical practice. Although the TCKAN model has already incorporated temporal, constant, and ICD code data, future research could include more diverse medical data types, such as imaging and laboratory test results, to achieve a more comprehensive data integration and further improve predictive accuracy.

FACTORS INFLUENCING THE DETERMINATION OF AUDITORS' FEES IN ALGERIA (FROM THE PERSPECTIVE OF PROFESSIONALS) - AN EMPIRICAL STUDY USING DEMATEL ANALYSIS

Objective: This study aims to identify the factors influencing the determination of auditors' fees in Algeria from the perspective of the practitioners, by understanding the relative importance of these factors. Theoretical Framework: The theoretical aspect addressed the most important factors influencing the determination of auditors' fees in Algeria, which are: the duration of the audit process, the size of the audited institution, the reputation and fame of the audit office, the quality of the audit office, and the risks of the audit process. Method: In order to determine the interrelationship between the influencing factors that contribute to the determination of auditors' fees in Algeria, the "DEMATEL" method was used, which relies on the opinions of experts (audit experts). Mathematical techniques represented in matrices were employed to identify the most influential factor among the following: the duration of the audit process, the size of the audited institution, the reputation and fame of the audit office, the quality of the audit office, and the risks of the audit process. Results and Discussion: Through the cause-and-effect relationship, the study concluded that the "reputation and fame of the auditing office" is the most important perspective for evaluation with the highest impact value (5.942), while the "audit process risk factor" is the least important perspective with an estimated value of (4.392), in order to reach the appropriate input for determining the fees of auditors in Algeria. Research Implications: The study results highlight the importance of the factors influencing the determination of auditors' fees in Algeria. As it is necessary to understand the degree of influence of each criterion in determining these fees, and consequently to identify the necessary solutions to reach a unified model for fees in Algeria based on the influencing factors, instead of subjecting the fees of auditors in Algeria to the method of specifications. Originality/Value: This study contributes to the literature on the factors influencing the determination of auditors' fees in Algeria by providing empirical evidence from Career experts in Algeria and analyzing it using mathematical methods to identify the most significant influencing factors, enabling policymakers to formulate the necessary policies.

Unveiling the decision making process in Alzheimer’s disease diagnosis: A case-based counterfactual methodology for explainable deep learning

BackgroundThe field of Alzheimer's disease (AD) diagnosis is undergoing significant transformation due to the application of deep learning (DL) models. While DL surpasses traditional machine learning in disease prediction from structural magnetic resonance imaging (sMRI), the lack of explainability limits clinical adoption. Counterfactual inference offers a way to integrate causal explanations into these models, enhancing their robustness and transparency. New methodThis study develops a novel methodology combining U-Net and generative adversarial network (GAN) models to create comprehensive counterfactual diagnostic maps for AD. The proposed methodology uses case-based counterfactual reasoning for robust decision classification for counterfactual maps to understand how changes in specific features affect the model's predictions. Comparison with existing methodsThe proposed methodology is compared with state-of-the-art visual explanation techniques across the ADNI dataset. The proposed methodology is also benchmarked against other gradient-based and generative models for its ability to generate comprehensive counterfactual maps. ResultsThe results demonstrate that the proposed methodology significantly outperforms existing methods in accuracy, sensitivity, and specificity while providing detailed counterfactual maps that visualize how slight changes in brain morphology could lead to different diagnostic outcomes. The proposed methodology achieves an accuracy of 95 % and an AUC of 0.97, illustrating its superiority in capturing subtle yet crucial anatomical features. ConclusionsBy generating intuitive visual explanations, the proposed methodology improves the interpretability and robustness of AD diagnostic models, making them more reliable and accountable. The use of counterfactual inference enhances clinicians' understanding of disease progression and the impact of different interventions, fostering precision medicine in AD care.

지식재산 일반 교과를 통한 특허명세서 작성 교육의 시사점 및 개선 방안

Objectives This study scrutinizes the teaching of patent specification writing through the general intellectual property course, draws implications, and suggests ways to improve the 2022 curriculum. Methods This study was conducted in the following ways First, we compared and analyzed the documents of the general intellectual property curriculum of the 2015 and 2022 curricula to identify changes in the achievement standards related to patent specification writing education. Second, we systematically reviewed textbooks, teacher guides, and teaching and learning materials to investigate the content and methods of patent specification writing education in actual educational settings. Third, we analyzed the requirements of patent specification writing education and the differences between domestic and foreign countries by reviewing relevant laws and regulations such as the Patent Act and the Patent Act Enforcement Rules, and identifying international trends through overseas cases. Fourth, we verified the feasibility and effectiveness of patent specification writing education through generative AI by utilizing actual patent specification and Chet Giffity. Through this multifaceted analysis, we identified problems in the current patent specification writing education through general intellectual property courses and proposed improvement measures. Results The implications of patent specification writing education through general intellectual property courses are, first, that it does not fulfill both relevant performance standards and legal requirements. Second, there is a lack of consideration on the selection of invention ideas that are the subject of patent specification writing education. Third, the number of class hours required to teach patent specification writing through general intellectual property courses and the number of teachers to implement the program are insufficient. Conclusions To improve, first, all of the components of a patent specification should be available for writing. Second, it should be possible to protect student-generated invention ideas while broadening the scope of the patent specification. Third, generative AI can be utilized as an assistant teacher to solve the problem of lack of educational content, time, and teacher resources in patent specification writing education. If these improvements are applied and expanded, it is expected that the patent specification writing education through the general intellectual property curriculum in the 2022 curriculum will be more applicable in schools and have a significant educational effect on problem-solving and computational thinking skills.

Автоматизований метод перевірки та налагодження тестових сценаріїв на основі формальної моделі

Introduction. Model-based test cases generation is a popular strategy for test automation. It helps to reduce time spent on the development of a test suite and can improve level of coverage. However, many reports show shortage of such test cases in poor quality and doubtable efficiency. Purpose. The main goal of the proposed method is cost-effective validation, assessment, debugging and concretization of generated test cases. The method helps improve quality and efficiency of the test cases, make their scenario meaningful and goal-oriented. The method also develops debugging facilities and simplifies data dependency analysis and test scenario editing. Methods. We propose an automated post-processing method which allows to evaluate path that is examined by the test case, and to make safe changes to the path which will eliminate the shortcomings while leaving the coverage targets of the test case unharmed. The method is based on visualization of the path along the control flow graph of the model with additional information about factual evaluation history of all variables and possible alternative variants of behavior. For consistent substitution of certain values in the signal parameters, which would determine the artifacts of the test environment (such as, for example, files, databases, etc.) and check boundary cases (in predicates of conditions, indexing of arrays, etc.), a method of interactive specification of symbolic traces has been developed. Results. The role of the user in deciding whether to add a test case to the project test suite and make changes to it remains crucial, but to reduce labor intensity, the following processes are automated: evaluation of test scenarios according to certain objective characteristics (level of coverage, ability to detect defects, data cohesion, etc.); highlighting of possible alternatives for making corrections; consistent updating of computations for the corresponding corrections. A prototype was developed based on the proposed methods. The empirical results demonstrated a positive impact on the overall efficiency (ability to detect defects and reduce resource consumption) and quality (meaningfulness, readability, maintenance, usefulness for debugging, etc) of the generated test suites. The method allows to make automatically generated test cases trustable and usable. Conclusion. The proposed toolkit significantly reduces the time spent on researching the results of test generation and validation of the obtained tests and their editing. Unlike existing simulation methods, the proposed method not only informs about the values of variables, but also explores the history of their computations and additionally provides information about admissible alternatives. Further we plan to improve the process of localizing the causes of test failure at execution phase to speed up the search for defects.

A comprehensive survey of UPPAAL‐assisted formal modeling and verification

AbstractUPPAAL is a formal modeling and verification tool based on timed automata, capable of effectively analyzing real‐time software and hardware systems. In this article, we investigate research on UPPAAL‐assisted formal modeling and verification. First, we propose four research questions considering tool characteristics, modeling methods, verification means and application domains. Then, the state‐of‐the‐art methods for model specification and verification in UPPAAL are discussed, involving model transformation, model repair, property specification, as well as verification and testing methods. Next, typical application cases of formal modeling and verification assisted by UPPAAL are analyzed, spanning across domains such as network protocol, multi‐agent system, cyber‐physical system, rail traffic and aerospace systems, cloud and edge computing systems, as well as biological and medical systems. Finally, we address the four proposed questions based on our survey and outline future research directions. By responding to these questions, we aim to provide summaries and insights into potential avenues for further exploration in this field.

A Novel Simplified PID Controller Tuning Method for Exact Maximum Sensitivity Specification

Many PID (Proportional plus Integral plus Derivative) controller tuning methods are proposed in the control literature based on different closed-loop specifications. Also, tuning techniques, based on the maximum sensitivity specification, have been proposed in the literature. In this paper, a novel PID controller design method, based on maximum sensitivity, has been proposed. In this work, the PID controller is viewed as a gain and dynamic part and tuned separately. The PID controller dynamic part is tuned using one of the existing popular tuning methods. The tuned dynamic part is cascaded with the process. After that, the PID controller gain is tuned for the maximum sensitivity specification. The maximum sensitivity versus controller gain graph can be generated. The user can choose the controller gain for the desired maximum sensitivity specification. The popular Ziegler & Nichols and IMC (Internal Model Control) methods tune the dynamic part of the system in this work. These proposed techniques can achieve the desired maximum sensitivity by tuning the controller gain in the subsequent stage. Many illustrative instances are considered to display the applicability and the proposed technique’s simplicity.

Artificial Intelligence-Assisted MRI Diagnosis in Lumbar Degenerative Disc Disease: A Systematic Review.

Systematic review. Lumbar degenerative disc disease (DDD) poses a significant global health care challenge, with accurate diagnosis being difficult using conventional methods. Artificial intelligence (AI), particularly machine learning and deep learning, offers promising tools for improving diagnostic accuracy and workflow in lumbar DDD. This study aims to review AI-assisted magnetic resonance imaging (MRI) diagnosis in lumbar DDD and discuss current research for clinical use. A systematic search of electronic databases identified studies on AI applications in MRI-based lumbar DDD diagnosis, following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Search terms included combinations of "Artificial Intelligence," "Machine Learning," "Deep Learning," "Low Back Pain," "Lumbar," "Disc," "Degeneration," and "MRI," targeting studies in English from January 1, 2010, to January 1, 2024. Inclusion criteria encompassed experimental and observational studies in peer-reviewed journals. Data extraction focused on study characteristics, AI techniques, performance metrics, and diagnostic outcomes, with quality assessed using predefined criteria. Twenty studies met the inclusion criteria, employing various AI methodologies, including machine learning and deep learning, to diagnose lumbar DDD manifestations such as disc degeneration, herniation, and bulging. AI models consistently outperformed conventional methods in accuracy, sensitivity, and specificity, with performance metrics ranging from 71.5% to 99% across different diagnostic objectives. The algorithm model provides a structured framework for integrating AI into routine clinical practice, enhancing diagnostic precision and patient outcomes in lumbar DDD management. Further research and validation are needed to refine AI algorithms for real-world application in lumbar DDD diagnosis.

Research on the vertical compressive bearing characteristics of offshore wind power steel pipe piles

In the absence of a comparison of static load test results for large-diameter steel pipe piles at home and abroad, optimize the design parameters based on the test pile results to guide later construction. This paper is based on the pile foundation design calculation method of API specification, as well as the relationship between the t-z, Q-z, p-y curve modes suggested by this specification, vertical ultimate bearing capacity, and stratum parameters. The conclusions and results obtained are limited to pile foundation calculations using API specifications. Analyze and back-analyze the results of static load tests on large-diameter steel pipe piles, and verify the pile foundation parameters. Combined with the data obtained from the test pile tests, analyze the stratum parameters to guide the optimization design of pile foundations in the construction drawing design stage of the offshore wind farm expansion project.

Detection of Helicobacter pylori in saliva based on surface plasmon resonance by binding of Lewis b (Leb) blood group antigen to specific adhesin BabA

Helicobacter pylori affects about half of the population around the world. Therefore, timely and reliable diagnosis of the disease is required. Non-invasive detection of H. pylori is the technique of choice for infection control. Sample collection from saliva is non-invasive, but the bacteria load in human saliva is lower than in samples collected from the stomach, blood or stool. Hence, a detection method of high specificity and sensitivity is required. The purpose of this study was to evaluate the applicability of the Surface Plasmon Resonance (SPR) biosensor, based on the binding of Lewis b (Leb) blood group antigen to the specific adhesin BabA, for H.pylori detection in saliva. Our studies on saliva artificially contaminated with H. pylori, demonstrated that the BabA - Leb binding reaction had high specificity. The results obtained indicated that there are two mechanisms that inhibit the viability of H. pylori. The first involves the direct interaction between H. pylori and the oral antagonistic bacteria, and the second involves the bactericidal effect of certain proteins produced by the oral background bacterial flora. Although the sensitivity of detection of the proposed method needs to be increased, the results obtained proved its applicability and feasibility for H. pylori detection in saliva and the good prospects it offers for both clinical application and as a rapid point-of-care test.

THE MORAL AND ETHICAL POTENTIAL OF A FUTURE TEACHER’S PERSONALITY – THE FOUNDATION FOR THE PRODUCTIVITY OF THEIR PROFESSIONAL TRAINING

The article examines the resource capacity of future teachers’ moral and ethical potential as a guarantee of the productivity of their training for creative professional activity. The article aims to substantiate the moral and ethical potential of an individual’s personality as a personal resource to ensure the productivity of future teachers’ professional training for pedagogical activities and the ways of its formation in the conditions of university education. The study operated methods of analysis, generalisation, and specification of theoretical positions and the results of empirical research on forming the moral and ethical potential of future teachers in university education conditions. The essence, content, structure, and main characteristics of this complex phenomenon have been clarified and specified. The moral and ethical potential of a personality is defined as an integrated personal creation that organically combines professional moral and ethical views, the professional orientation of mental processes towards interaction and communication, pedagogical optimism and democracy, moral efforts, and actions aimed at improving the moral and ethical sphere of the future teacher and ensuring positive results in the professional branch. It has been established that these indicators of the moral and ethical potential of a personality positively affect the productivity of future teachers’ professional training if they achieve their goals in the process of developing moral and ethical qualities and professional improvement. This potential has a complex structure, encompassing interconnected components, including motivational one – a positive attitude towards people and moral norms regulating their behaviour in life and activity; moral-orientational one – knowledge of moral norms and ethics of behaviour concerning the rules accepted in society, requirements for the personality of a professional, and their actions and deeds; ethical-operational one – mastering moral and ethical experience in the professional and pedagogical sphere of activity; volitional one – self- control, management of one’s behaviour concerning adherence to moral and ethical norms in the course of pedagogical activity; control-evaluative component – self-assessment of one’s actions in transmitting moral and ethical norms, behaviour, and during the resolution of pedagogical problems. The practical course “Secrets of Pedagogical Ethics” has been developed and tested, aimed at forming these components of the future teachers’ moral and ethical potential. The effectiveness of active forms and methods of forming the professional-moral orientation of future teachers has been confirmed, including discussions, ethical conversations, debates, and presentations, as well as technologies of personality- oriented learning, cooperation technologies, and creativity development (SCAMPER). Based on the research results, it is concluded that moral and ethical potential serves as an effective means of performing professional functions, demonstrating moral and ethical positions, humanity, ethical actions and deeds, pedagogical tact, and professional pedagogical ethics of a teacher. Its formation should be considered as a core task of professional pedagogical training. Prospects for further research are seen directly in the development of the following aspects of the problem: updating the techniques for using the moral and ethical potential of students in ensuring the success of their professional training; theoretical substantiation of various types of technologies in the development of the moral and ethical potential of future teachers, forms, and systems for assessing and controlling the formation of this complex personal creation as an important resource for the positive dynamics of their professionalism level.

Study on quality control methods and pharmacodynamic material basis of different specifications of Corydalis Rhizoma produced in Zhejiang Province

Ethnopharmacological relevanceThe quality control methods of different specifications of Corydalis Rhizoma in Zhejiang China (ZJ CR) are the same, so the quality of each specification couldnot be guaranteed. To clarify the quality control methods and pharmacodynamic material basis of ZJ CR with different specifications could provide reference for the quality control of ZJ CR. Aim of the studyThe purpose of this study was to establish a quality control method for ZJ CR with different specifications and to screen out the pharmacodynamic material basis of ZJ CR with different specifications. Materials and methodsFirstly, according to the existing grading standards, the medicinal materials were divided into specifications, and the character indexes of ZJ CR with different specifications were established. The quality indexes were established by HPLC, network pharmacology and literature retrieval. The correlation between the trait indexes and quality indexes of ZJ CR with different specifications was analyzed, and the best quality control method was established. Further combined with the pharmacodynamic indexes of ZJ CR with different specifications, the pharmacodynamic material basis of ZJ CR with different specifications was screened out by spectrum-effect analysis. The correlation between trait indexes and pharmacodynamic indexes was analyzed to verify the rationality of grade standard. ResultsThe three specifications of ZJ CR were CR (Diameter ≥1.1 cm), CR (Diameter <1.1 cm), and CR (No size distinction). Diameter, width, thickness, grain weight, volume and 50 g grain number could be used as the trait indexes of ZJ CR. Protopine (CR1), palmatine hydrochloride (CR2), berberine hydrochloride (CR3), dehydrocorydaline (CR4), tetrahydropalmatine (CR5), tetrahydroberberine (CR6), corydaline (CR7), stylopine (CR8) and isoimperatorin (CR9) were identified. Total components, core components (CR5, CR6, CR7 and CR8), alcohol-soluble extracts (ASE) could be used as quality indexes. The best quality control methods of the three specifications respectively were: the larger the diameter and grain weight, the smaller the number of 50 g grains; The larger the diameter, the smaller the volume, thickness, width and number of 50 g particles; The larger the grain weight and volume, the smaller the number of 50 g grains. The main analgesic components of the three specifications respectively were: CR1, CR2 and core components; CR2, CR4; CR8, CR9. The larger the diameter and the less the number of 50 g grains, the better the analgesic effect of ZJ CR, and the grade standard was reasonable. ConclusionsThis study showed that the quality control methods and pharmacodynamic material basis of ZJ CR with different specifications were different, which may be caused by the differences in traits and the contribution of main active ingredients. This study constructed an evaluation model combining external traits, internal quality and overall efficacy, and provided theoretical support for the rationality of ZJ CR grade standard.

Simultaneous determination of six catechins and caffeine in tea and wine using salting-out assisted liquid-liquid extraction and high-performance liquid chromatography with ultraviolet detection.

Catechins, renowned for their antioxidant properties and health benefits, are commonly present in beverages, particularly tea and wine. An efficient and cost-effective salting-out assisted liquid-liquid extraction (SALLE) method has been developed and validated for the simultaneous determination of six catechins and caffeine in tea and wine samples using high-performance liquid chromatography-ultraviolet (HPLC-UV). This method demonstrates outstanding performance: linearity (1-120µg/mL, r2>0.999), accuracy (96.5%-103.4% recovery), and precision (≤14.7% relative standard deviation), meeting validation requirements set by the US Food and Drug Administration. The reduced sample size (0.1g) minimizes matrix interferences and costs without compromising sensitivity. All analytes were detected in Camellia sinensis teas, with green tea displaying the highest total catechin content (47.5-100.1mg/mL), followed by white and black teas. Analysis of wine samples reveals the presence of catechin in all red and white wines, and epigallocatechin gallate in all red wine samples, highlighting the impact of winemaking processes on catechin content. The SALLE-HPLC-UV approach represents a green alternative by eliminating organic waste, surpassing conventional dilution methods in specificity and sensitivity for catechin determination. AGREEprep assessment emphasizes the strengths of the SALLE procedure, including material reusability, throughput efficiency, minimal sample requirements, low energy consumption, and the absence of organic waste generation.

DEVELOPMENT OF DIGITAL TECHNOLOGIES IN PUBLIC ADMINISTRATION

Goal. Generalization of the features of the use and development of digital technologies in public administration. Method. In the research process, general scientific methods of research, abstraction and specification were used; system analysis. The results. The development of information and communication technologies in the state sector is one of the main tasks of the socio-economic development of the country. Innovative and rational application of digital technologies will become the basis for the creation of a single economic and informational space. The foundations of the development of the digital economy in public administration are considered. Scientific novelty. Systematized tools of digital development in public administration; the complex of tasks regarding the use of digital technologies in public administration is substantiated; approaches to the formation of a balanced portfolio of projects in the field of digital development have been developed under the conditions of identification and determination of risk gradation levels. Practical significance. The implementation of recommendations on the comprehensive application of digital technology development tools in the public sector makes it possible to improve the quality of public administration.

What is the place of interpretation in text analysis? An example using ALCESTE® software

ABSTRACT Statistical text analyses appear at the borderline of quantitative and qualitative research, raising epistemological questions in the field. This paper focuses on a specific method of text analysis: the Reinert method. It aims to address the role of interpretation and subjectivity in the construction of the results using this kind of analysis. To this end, this manuscript presents a dual interpretation of a same corpus and software outputs highlighting the convergences and divergences in both researchers’ interpretations. It discusses how the use of statistics on qualitative data may provide a false sense of objectivity and sweep the questions of interpretation and epistemology away. Finally, it offers clear guidelines regarding individual and dual interpretation to those who wish to start using the Reinert method. We argue that this type of analysis is adaptable and can be applied across a variety of epistemological stances and research questions.

Analysis on the Low-carbon Transformation Path of China's Logistics Industry under the Background of Carbon Neutralization

Under the background of “carbon neutrality”, green and low-carbon have gradually become a broad consensus of the whole society. As an important part of social and economic development, the logistics industry consumes a large amount of energy and urgently needs low-carbon transformation.This paper analyzes the problems encountered in the low-carbon transformation development of our country's logistics industry and puts forward the necessary solutions, which will not only promote the development of the low-carbontransformation of our country's logistics industry, but also promote the realization of the goal of "carbon neutrality" as soon as possible. Based on the economic and social development of our country under the "double carbon" policy, facing the dilemma of low-carbon transformation of logistics industry in China, this paper puts forward the general and concrete ideas of low-carbon transformation of logistics industry in our country, and puts forward the specific path and method under the guidance of the thought of low-carbontransformation, hoping to provide some references for the low-carbon transformation of logistics industry in China.

Abstract 325: Validation of Aventa FusionPlus: An LDT NGS assay for comprehensive cancer genomic profiling

Abstract Next-generation sequencing (NGS) assays play a pivotal role in illuminating the genomic landscape of cancer by providing insights into gene regulation and biomarker detection for the purpose of guiding therapeutic decisions to advance personalized medicine. We validated the Aventa FusionPlus Test, a Laboratory-Developed Test (LDT) that integrates HiC technology to enhance detection of actionable gene fusions and rearrangements in solid tumors, including those that are missed by current standard of care tests. This is a DNA-based test that is robust to DNA degradation in FFPE specimens, in contrast with RNA-seq and long-read technologies. The test utilizes the Element Biosciences AVITI sequencing platform to achieve high quality sequencing data at low cost per sample. The Aventa FusionPlus test enables gene-level detection of structural variants near or within 361 medically actionable or diagnostically relevant cancer genes. The 3D structure and long-range linkages captured by the test amplify the signal counts for each rearrangement. The test integrates the Arima-HiC+ FFPE sample prep and the Arima Library Prep Kit workflow. After dewaxing and rehydrating the FFPE tissue, the chromatinized DNA is fragmented and the fragmented ends are filled in with a biotinylated nucleotide. Next, spatially proximal fragmented ends of DNA are ligated, capturing the 3D structure of the genome. The proximally-ligated DNA is then purified and further fragmented, and the biotinylated ligation fragments are enriched. DNA libraries are prepared from these enriched fragments and sequenced in “paired-end” mode. Genes involved in genomic rearrangements were detected using the Arima-SV pipeline along with manual curation. The test achieved robust detection of structural variants, with accuracy of 98.8% relative to reference methods. FFPE tumor specimens from 10 different tissue types and a wide range of tumor content (20%-100%) were included. No correlation between tumor cellularity and sensitivity was observed, suggesting that samples with as low as 20% tumor cellularity can be tested. When comparing to RNA-based and other targeted reference methods, we found additional fusions nearby oncogenes missed by these reference methods. As an example, in a breast cancer case, a fusion of genomic regions neighboring BRAF and MYC oncogenes on chromosomes 7 and 8 respectively, was missed by the reference method but was successfully detected by the Aventa FusionPlus test. The Aventa FusionPlus test is a highly sensitive, specific, and accurate method for comprehensive cancer genomic profiling. The enhanced understanding of gene fusions and rearrangements positions the test as a valuable tool for clinical decision-making in oncology. Citation Format: Shadi Melnyk, Jon Belton, Sofia Nomikou, Katelyn Powell, Daniel Heiney, Roy Khalife, Damien Jones, Anthony Schmitt, Catherine Wilson, Melissa Won, Anthony Magliocco, Chris Roberts. Validation of Aventa FusionPlus: An LDT NGS assay for comprehensive cancer genomic profiling [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 325.