Key Approaches Research Articles

Summarizing a Debriefing Session in Professional Development.

The summary of the debriefing session is critical to acknowledge the work of each participant and to identify the learnings. Key approaches include the importance of including everyone and maintaining a positive, upbeat attitude. [J Contin Educ Nurs. 2024;55(9):423-424.].

ЛИНГВОПРАГМАТИЧЕСКИЙ АСПЕКТ ИЗУЧЕНИЯ МЕДИАТЕКСТА

The study delves into the linguopragmatic aspects of media texts, emphasizing their significance in shaping public opinion and cultural norms. Media texts are dynamic communication forms that convey messages within specific media formats and genres, necessitating precise semantic, syntactic, and pragmatic accuracy. This article explores key approaches to the linguopragmatic analysis of media texts, identifying their unique characteristics and practical applications. The methods employed include linguistic analysis, discourse analysis, cognitive analysis, and psycholinguistic analysis, aimed at uncovering the interaction between language elements and social contexts. The findings highlight the importance of headlines, rhetorical devices, and interactive elements in engaging audiences and enhancing the pragmatic effect of media texts. The study also underscores the emotional and cognitive impact of media texts on audiences, influencing their perceptions and behaviors. Future research may focus on detailed analysis of specific media text types and their interaction with diverse social and cultural contexts.

Exploring diachronic variation in discernment politeness in Ancient Egyptian

Abstract In this paper, we investigate evidence of diachronic variation in patterns of Discernment Politeness in letters written in the ancient Egyptian Old Kingdom (c. 2300–2200 bce) and Late Ramesside period (c. 1099–1069 bce). We present examples of requests, information acts and honorifics used in these letters, predominantly those letters sent by subordinates to their superiors, to explore how this relationship dynamic is linguistically indexed. Our evidence shows that throughout the Old and New Kingdoms, communicative acts were regulated by behavioural norms dependent on power structures rather than individual volition, with Power being the most dominant social variable. Finally, we argue that Discernment is a key approach for exploring politeness in texts from ancient Egypt, supporting current research on the topic, and that the approach used here for the ancient Egyptian material has wider applicability for diachronic linguistic analyses of remote civilisations which are highly hierarchical.

Planning and Analyzing a Low-Biomass Microbiome Study: A Data Analysis Perspective.

As investigations of low-biomass microbial communities have become more common, so too has the recognition of major challenges affecting these analyses. These challenges have been shown to compromise biological conclusions and have contributed to several controversies. Here, we review some of the most common and influential challenges in low-biomass microbiome research. We highlight key approaches to alleviate these potential pitfalls, combining experimental planning strategies and data analysis methods.

Development and implementation of digital pedagogical support systems in the context of educational equity

In response to the covid-19 pandemic, the digital strategy of education has become a key pedagogical approach for higher engineering disciplines. In this complex environment, the outcome-oriented higher engineering discipline faces the challenge of educational equity in terms of curriculum design and learning outcome assessment for its online engineering practical courses. In order to solve this problem, this study develops a digital teaching assistant system to facilitate the reform of educational equity in higher engineering disciplines. The teaching and learning functions of the system aim to support the interaction between teachers and students in the teaching process and the visual display of learning outcomes, and its intelligent evaluation and analysis function is based on the multi-criterion group decision-making model of Quality Function Deployment (QFD) combined with t-test to realize the evaluation of course learning outcomes. The results show that the academic performance of this system is significantly better than that of other similar systems. The statistical results of the teacher-student satisfaction survey of the two systems show that the teacher-student interaction and multi-dimensional learning evaluation analysis report function provided by the system are the most highly recognized, and the system also has better educational equity in resource recommendation, interaction and collaboration, learning achievement display and learning achievement evaluation, etc. In addition, the system is more in line with the teaching characteristics of higher engineering disciplines, which can provide effective assistance for teachers of higher engineering disciplines to improve teaching plan and cultivate students’ disciplinary practicality, consequently creating favorable conditions for the establishment of teaching equity.

Methodologic considerations in estimating racial disparity of mortality among very preterm infants.

This review explores methodological considerations in estimating racial disparities in mortality among very preterm infants (VPIs). Significant methodological variations are evident across studies, potentially affecting the estimated mortality rates of VPIs across racial groups and influencing the perceived direction and magnitude of racial disparities. Key methodological approaches include the birth-based approach versus the fetuses-at-risk approach, with each offering distinct insights depending on the specific research questions posed. Cohort selection and the decision for crude versus adjusted comparison are also critical elements that shape the outcomes and interpretations of these studies. This review underscores the importance of careful methodological planning and highlights that no single approach is definitively superior; rather, each has its strengths and limitations depending on the research objectives. The findings suggest that adjusting the methodological approach to align with specific research questions and contexts is essential for accurately assessing and addressing racial disparities in neonatal mortality. IMPACT: Elucidates the impact of methodological choices on perceived racial disparities in neonatal mortality. Offers a comprehensive comparison of birth-based vs. fetuses-at-risk approaches in the context of racial disparity research. Provides guidance on the cohort selection and adjustment criteria critical for interpreting studies on racial disparities in very preterm infant mortality.

A Decade of Computational Mass Spectrometry from Reference Spectra to Deep Learning.

Computational methods are playing an increasingly important role as a complement to conventional data evaluation methods in analytical chemistry, and particularly mass spectrometry. Computational mass spectrometry (CompMS) is the application of computational methods on mass spectrometry data. Herein, advances in CompMS for small molecule chemistry are discussed in the areas of spectral libraries, spectrum prediction, and tentative structure identification (annotation): Automatic spectrum curation is facilitating the expansion of openly available spectral libraries, a crucial resource both for compound annotation directly and as a resource for machine learning algorithms. Spectrum prediction and molecular fingerprint prediction have emerged as two key approaches to compound annotation. For both, multiple methods based on classical machine learning and deep learning have been developed. Driven by advances in deep learning-based generative chemistry, de novo structure generation from fragment spectra is emerging as a new field of research. This review highlights key publications in these fields, including our approaches RMassBank (automatic spectrum curation) and MSNovelist (de novo structure generation).

Inter-object discriminative graph modeling for indoor scene recognition

Variable scene layouts and coexisting objects across scenes make indoor scene recognition still a challenging task. Leveraging object information within scenes to enhance the distinguishability of feature representations has emerged as a key approach in this domain. Currently, most object-assisted methods use a separate branch to process object information, combining object and scene features heuristically. However, few of them pay attention to interpretably handling the hidden discriminative knowledge within object information. In this paper, we propose to leverage discriminative object knowledge to enhance scene feature representations. Initially, we capture the object-scene discriminative relationships from a probabilistic perspective, which are transformed into an Inter-Object Discriminative Prototype (IODP). Given the abundant prior knowledge from IODP, we subsequently construct a Discriminative Graph Network (DGN), in which pixel-level scene features are defined as nodes and the discriminative relationships between node features are encoded as edges. DGN aims to incorporate inter-object discriminative knowledge into the image representation through graph convolution and mapping operations (GCN). With the proposed IODP and DGN, we obtain state-of-the-art results on several widely used scene datasets, demonstrating the effectiveness of the proposed approach.

Assessment of the viral contamination of fecal origin over a wide geographical area using an active approach with Dreissena polymorpha

Biomonitoring appears to be a key approach to assess chemical or microbiological contaminations. The freshwater mussel, Dreissena polymorpha (D. polymorpha), is a suitable tool already used to monitor chemical and, more recently, microbiological pollution. In the present study, we used this sentinel species to monitor viral contamination of fecal origin over a wide geographical distribution. An active approach was implemented based on caging of calibrated and pathogen-free organisms with the same exposure conditions, allowing spatio-temporal comparisons between different water bodies. In addition, different types of sites were selected to investigate the range of environmental concentrations that D. polymorpha are able to translate. Different viral genome targets were measured: norovirus genogroup I and II (NoV GI and GII) and F-specific RNA bacteriophages belonging to the genogroup –I and –II (FRNAPH-I and -II). Total infectious FRNAPH were also monitored.D. polymorpha was able to translate a wide range of concentrations for all the viral targets studied, meaning that this sentinel species can be used for both low and highly anthropised sites. Moreover, D. polymorpha caging proved effective in achieving gradients of viral contamination of fecal origin pressure and to highlight the contribution of tributaries to the main rivers. D. polymorpha provided spatial and temporal variations of the viral contamination. It allowed to highlight the prevalence of the FRNAPH-I and -II genogroups according to the caging site. FRNAPH-II was found to be dominant in urban areas and FRNAPH-I in rural areas. This strategy uses the caging of the sentinel species D. polymorpha on selected sites with standardised analysis methods has proven to be a promising tool for characterizing viral contamination at both large and very fine scales.

A Sentiment Analysis Approach for Exploring Customer Reviews of Online Food Delivery Services: A Greek Case

The unprecedented production and sharing of data, opinions, and comments among people on social media and the Internet in general has highlighted sentiment analysis (SA) as a key machine learning approach in scientific and market research. Sentiment analysis can extract sentiments and opinions from user-generated text, providing useful evidence for new product decision-making and effective customer relationship management. However, there are concerns about existing standard sentiment analysis tools regarding the generation of inaccurate sentiment classification results. The objective of this paper is to determine the efficiency of off-the-shelf sentiment analysis APIs in recognizing low-resource languages, such as Greek. Specifically, we examined whether sentiment analysis performed on 300 online ordering customer reviews using the Meaning Cloud web-based tool produced meaningful results with high accuracy. According to the results of this study, we found low agreement between the web-based and the actual raters in the food delivery services related data. However, the low accuracy of the results highlights the need for specialized sentiment analysis tools capable of recognizing only one low-resource language. Finally, the results highlight the necessity of developing specialized lexicons tailored not only to a specific language but also to a particular field, such as a specific type of restaurant or shop.

Quantum tangent kernel

The quantum kernel method is one of the key approaches to quantum machine learning, which has the advantage of not requiring optimization and its theoretical simplicity. By virtue of these properties, several experimental demonstrations and discussions of the potential advantages have been developed so far. However, as is the case in classical machine learning, not all quantum machine learning models could be regarded as kernel methods. In this work, we explore a quantum machine learning model with a deep parametrized quantum circuit and aim to go beyond the conventional quantum kernel method. In this case, the expressive power and performance are expected to be enhanced, while the training process might be a bottleneck because of the barren plateaus. Moreover, the high computational cost of gradient-based optimization and the large search space of the gradient-free optimization directly make the training intractable. However, we find that parameters of a deep enough quantum circuit do not move much from their initial values during training, allowing for a first-order expansion with respect to the parameters. This behavior is similar to that of the neural tangent kernel in classical machine learning, and such a quantum machine learning with deep variational quantum circuits can be described by another emergent kernel, the . We show that the proposed quantum tangent kernel has the potential to outperform the conventional quantum kernel method by performing a classification task on an ansatz-generated dataset. This work provides a different direction beyond the conventional quantum kernel method and explores the potential power of quantum machine learning with deep parametrized quantum circuits. Published by the American Physical Society 2024

Rational design of ternary heterostructures 3D Flower- like CoS/Fe3O4@PPy for efficient electromagnetic wave absorption

The rational design of heterostructures with well-defined chemical composition and spatial configuration stands as a key approach in achieving exceptional performance in electromagnetic wave absorption. This innovative work investigated the electromagnetic wave absorption properties of flower-like CoS/Fe3O4@PPy composites synthesized by hydrothermal method and in situ polymerization. A comprehensive investigation was conducted into the phase composition, morphology, and absorption properties. Flower-like CoS/Fe3O4@PPy composite demonstrates remarkable microwave reflection loss of −58.4 dB and effective absorption bandwidth of 5.55 GHz in thickness of 3.1 mm. The superior performances are attributed to the synergistic interplay of interfacial polarization, dielectric loss, magnetic loss, and optimized impedance matching. Furthermore, sufficient decrease in radar cross-section value was observed for the optimal sample with reduction of 41.23 dBm². These results highlight the considerable potential of flower-like CoS/Fe3O4@PPy composites in addressing electromagnetic pollution and stealth applications.

Theoretical and General Philosophical Foundations Operational and Investigative Support of Criminal Prosecution.

This article is devoted to a comprehensive analysis of theoretical and general philosophical foundations of operational and investigative support of criminal prosecution. Various aspects of this activity are considered, including philosophical concepts, theoretical approaches, ethical aspects, epistemological and axiological foundations. The paper provides an overview of the contribution of Soviet, Russian and Central Asian scientists to the development of the theory of operative-search activity. Special attention is paid to the concept of V.A. Lukashov, who expanded the understanding of operational and investigative support to the entire system of law enforcement agencies. The study relies on a number of methods, including dialectical, hermeneutic, system analysis and comparative approach. As a result, the concept of the philosophical and legal foundations of operational and investigative support is formulated, key theoretical approaches to its understanding are identified, a system of ethical principles is developed and an epistemological model of information acquisition is proposed. Operative and investigative support is considered as a complex phenomenon that integrates elements of various philosophical concepts and legal theories. The importance of the balance between public safety and protection of individual rights is emphasized, as well as the necessity of constant rethinking of ethical principles in the context of society and technology development. The article analyzes informative-cognitive, preventive-prophylactic, procedural-security and other approaches to the understanding of operational and investigative support. The issues of integration of operational and investigative support into the system of social control and crime control are considered. Special attention is paid to the influence of technological progress on the development of methods and means of operative-search activity. The conclusion is made about the need for continuous theoretical understanding and practical improvement of operational and investigative support of criminal prosecution in the context of changing social, technological and legal realities.

Green environmental management system and environmental performance: Results from PLS-SEM and fsQCA

Advanced green technology developments have induced key innovation approaches in the manufacturing industry to boost sustainable development (SD) in the present market. Despite the industrial need for green innovation and its associated benefits to regulate and optimise their operations and environmental performance, respectively, green innovation implementation and adoption remain inadequate. Due to the key significance of green innovation for firms operating in emerging markets, this research endeavors to investigate the green innovation influence on the relationship between Environmental Management System adoption and impact on Environmental Performance. To bridge this gap, The current work aimed structuring and validating a study model via the integration of Resource-Based View (RBV) and Institutional theories with the Technology-Organisation-Environment (TOE) framework to persuade companies towards green innovation implementation. Survey questionnaires were disseminated to 183 employees in a manufacturing company to collect the study data, which were then assessed with partial least squares-structural equation modelling (PLS-SEM) and Fuzzy-set Qualitative Comparative Analysis (fsQCA). Resultantly, the model's integrated constructs of perceived benefits, top management support, coercive pressure, normative pressure, and mimetic pressure predicted green management accounting practices. Green management accounting practices directly and significantly impacted green environmental performance, while green innovation significantly and negatively moderated the ‘green management accounting practice-green environmental performance’ link. Thus, the integrated model provides decision-makers with clear implications of green practice and innovative technology adoption for optimal environmental performance. The outcomes derived from literature reviews on advanced green technologies implied a notable ‘green management accounting practice-environmental performance’ relationship within emerging countries.

DECENTRALIZED ELECTRONIC VOTING WITH ETHEREUM BLOCKCHAIN IN DEMOCRATIC AND POLITICAL ELECTIONS

The Ethereum blockchain-based electronic voting (e-voting) systems can emerge as a viable strategy in this era of contemporary democracies to revolutionize political elections and augment the efficacy of the electoral process. There are myriad advantages that the Ethereum blockchain has to offer, from fairness to increased voting rates. Unlike traditional voting protocols, the Ethereum blockchain can assure substantial cost savings and eliminate the necessity for electoral intermediaries. The use of the Ethereum blockchain in political contexts also ensures that elections are held with integrity while preserving the voters’ privacy. Due to its popularity, provision of smart contracts logic, and various promising advantages, this systematic review aims to examine the potential deployment of decentralized e-voting systems integrated with Ethereum blockchain technology for democratic political elections. A systematic literature review (SLR) and the PICO approach, which stands for population, intervention, control, and outcomes, were adopted in this study to systematically analyze the existing literature. Key technological approaches identified in the voting system include the hybrid blockchain and privacy-preserving score voting. Among the noteworthy findings are the following: while adoption and complexity remain challenges across numerous e-voting frameworks, scalability, end-to-end security, enhanced efficiency, and effectiveness are key benefits. An exploration into the prospective future innovations, such as the integration of artificial intelligence and big data analytics into the Ethereum blockchain, was also included to further improve the reliability of the e-voting systems. It is believed that the Ethereum blockchain has a promising transformative impact on electoral politics and democratic processes, presenting a ray of hope for future elections.

A facilitated transport membrane composed of amine-containing ionic liquid confined in a GO/CNT network for highly efficient carbon capture

Carbon capture is a key approach to manage carbon dioxide (CO2) emissions and promote net removal of CO2 from the atmosphere. Post combustion flue gas emissions from power plants contribute a significant fraction of CO2 released into the atmosphere. Membrane-based separation strategies offer environmentally sustainable and economically viable solutions for efficient CO2 capture from power plants. Facilitated transport membranes, although showing strong capability for CO2 capture at low concentrations and under humid conditions, suffer from the loss of facilitators, such as amines, leading to decline in performance over long-term operation. Ionic liquids (ILs) are chemically and thermally stable and have low vapor pressure, which is ideal to prevent aforementioned losses. However, making a thin network for effective IL confinement is still a challenge. In this work, we developed a stable high performing IL based CO2 selective membrane that can operate at temperatures as high as 80 °C under humid conditions. An amino acid-based IL (containing facilitated carriers), 1-ethyl-3-methylimidazolium glycinate, [EMIM][GLY], was used as the separating agent. In order to selectively load and confine the IL in a thin selective layer, we designed and fabricated a network composed of stacked graphene oxide (GO) nanosheets intercalated by carbon nanotubes (CNT). While CNT acts as a nanowedge that helps in increasing the nanochannels to facilitate low-resistance gas transport, GO nanosheets act as anchoring sites due to presence of various functional groups resulting in electrostatic interaction between GO and cation of IL. Due to the aforementioned reason, distribution of GO in the network plays a critical role in forming a thin, uniform selective IL layer. The GO distribution was optimized by adjusting the pH of the coating solution; after optimizing IL coating, this results in a thin, defect free selective layer, providing a membrane with high CO2 permeance of ∼1600 GPU and CO2/N2 selectivity of 400, superior to most of the reported IL membranes. This network composed of GO/CNT network not only facilitates fabrication of a stable, ultrathin, high permeance and high selectivity supported IL membrane but also provides a platform for preparation of other IL-based membranes for desired separations.

What Kind of ‘God’ do Hindu Arguments for the Divine Show? Five Novel Divine Attributes of Brahman

This article describes the ultimate ground of reality, Brahman, as a single power unfolding in concert in all things. It uses counterfactual argumentation to imply that a cosmos must consist of telic causal orders or manifested ‘powers’ as its most granular building block – and that they must be unified into a single whole. It is based on an argument for a single causally-conditioning substrate of all things recorded in India’s classical Sāṃkhya Kārikā and Brahma Sūtras; this was used by scholastic Vedāntic thinkers including the non-dualist Śaṃkara and the ‘transformationist’ Bhedābheda thinker Śrīnivāsa. It takes up arguments for satkārya, the pre-existence of a thing’s various transformations in potentio within its substrate, and employs them to paint a picture of reality as an ordered yet entangled pattern of causal trajectories. These manifest as the changing world we know. While Vedānta’s main motivation for making the arguments was to prove monism, this conception of an ‘immanent cause’ of all was also seen as divine. We see how this accords with moves in contemporary analytic philosophy to shift away from a broadly Humean model of constant conjunctions (reminiscent of key Buddhist approaches) toward a Neo-Aristotelian ‘metaphysics of powers’. But we also indicate how it may go further than most modern powers-metaphysicians in proposing a deeper connection between powers and what it is for there to be a cosmos at all.

Principled pragmatism: A Latin American contribution for foreign policy analysis?

The key purpose of this paper is to identify key Latin American theories proposed to study foreign policy. The essay points out that Latin America has made a significant contribution to foreign policy analysis. The most important conceptual contribution in the field is the concept of principled pragmatism. A crucial premise is that Latin American nations resort to this type of policy to come to agreement with the United States, address domestic issues, advance national interests, and cope with economic problems. The paper is divided into four sections. The first describes key analytical models and theoretical approaches created to explain foreign policy. The second identifies the Latin American thought on this subject. The third section explains the concept of principled pragmatism. This approach stems from the models and theories explained in the second section and from the Latin American thinking described in the third section. The last segment explains the case study of Cuban foreign policy in the early 1960s using the notion of principled pragmatism.

The Reform and Evolution of ASEAN Human Rights Regime: A Communicative Action Perspective

ASEAN has experienced significant progress since the initiation of the ASEAN Community Vision and the incorporation of human rights into its foundations. This development has ignited continuous scholarly discussions over the degree to which the ASEAN human rights framework shape regional identity and impacts member states’ behavior towards human rights. These debates often revolve around two key theoretical approaches: the logic of consequences and the logic of appropriateness. These logics pose challenges in fully understanding the complexity of state behavior, as they are often observed operating simultaneously and interchangeably within state actions. The article investigates the ASEAN human rights regime through the communicative action perspective and its logic of arguing. It employs a qualitative instrumental case study approach, focusing on the ASEAN Intergovernmental Commission on Human Rights (AICHR) and the ASEAN Humanitarian Assistance (AHA) Centre to illuminate the region's evolving human rights strategies. It is characterized by both deliberate reforms and informal adaptations, reflecting a complex interplay of traditional principles, and emerging human rights norms. The findings indicate that ASEAN states are open to being convinced and persuaded by ideas related to human rights instead of dismissing them outright. ASEAN's approach to human rights, while progressing gradually, suggests a shift towards more substantive engagement with global human rights norms, facilitated by the discursive processes embedded within the AICHR and AHA Centre.

Enhanced measurement and optimization of railway profile parameters for large tamping machine operations

Abstract In the realm of modern railway maintenance, the employment of large tamping machine for the enhancement of track geometry and elasticity, and the minimization of disturbance to the roadbed, has become a key operational approach. However, factors such as repeated load applications, changes in topography and geology, and ongoing maintenance activities have been known to cause significant deviations of the actual track location from its original design. These deviations present challenges, such as increased construction difficulty and workload, during tamping operations predicated on the original design. Moreover, methods based on manual intervention have rendered large machine maintenance operations inefficient. In this paper, an optimisation method for railway profile parameters suitable for large tamping machine operations is proposed. The method integrates the total least squares method and the direct search method, enabling the accurate fitting of slope segments preceding and following the slope change points and the alignment of circular curve segments to determine the optimal curve radius. Consequently, optimised profile parameters for the continuous track section are obtained. Focusing on the Beijing–Guangzhou line, the operational efficacy of the proposed optimisation method is compared with that of the artificial slope method. The results showed that the proposed optimisation method is not only more accurate and efficient but also adheres to the principle of ‘prefer lifting rather than descending’ of the railways. The method further provides a theoretical basis and practical guidance for the optimisation of railway profile, underscoring the potential for improved maintenance efficiency and enhanced safety in train operations.