Research Paths Research Articles

Entanglement classification – a comparative study of 𝑺𝑼(𝟐) and 𝑺𝑳(𝟐) developed operator model

Entanglement classification is a core aspect of quantum information theory. It ensures successful quantum information processing. This article presents a comparative study of entanglement classification using developed operator models for the special unitary group and special linear group. This study was built upon prior work in entanglement classification in a pure three-qubit quantum system environment, where the operator models for each mathematical group were independently developed. Through extensive analysis, both synthesized models are functionally effective and yield the desired results. However, the comparative analysis reveals that the operator model exhibits certain limitations, particularly in its early phase of development compared to. This study provides significant enlightenments into the practical abilities of the developed operator models in entanglement classification and underlines the theoretical distinction between and paving the path for future research in quantum information theory, specifically entanglement classification.

A Review of Network Evolution: Cross‐Disciplinary Views, Frameworks, Methodologies, and an Agenda for Future Supply Chain Research

ABSTRACTThis article conducts an in‐depth review of the existing literature on network evolution, with an emphasis on understanding how supply chain networks change and evolve. This review has identified a total of 1148 articles in the area of network evolution, comprising 120 articles from business literature and 1028 articles from non‐business literature. Adopting a structured approach, we provide a roadmap for supply chain network evolution (SCNE) to systematically build a multidimensional understanding of SCNE and bridge the gap between theoretical underpinnings and research methods useful to investigate SCNE. We first broaden the scope to identify insights on dynamic network evolution from a wider array of disciplines. Next, we categorize the drivers of SCNE identified within the business literature into exogenous and endogenous factors. We then synthesize theoretical frameworks that lay the groundwork for future research directions proposed in the context of these frameworks. Finally, we outline contemporary methodologies for studying network evolution and evaluate their relevance and application to the supply chain management (SCM) field. Collectively, this roadmap paves the way for a deeper, interdisciplinary understanding of SCNE and outlines a future research agenda, providing a forward‐looking path for supply chain researchers to explore SCNE.

PATHS TO RESEARCH: A LITERATURE REVIEW ON HIGH SCHOOL REFORM, LAW 13.415/2017

This article presents data and reflections on the reform of secondary education, based on a systematic literature review carried out between September 2021 and January 2022. The objective was to look for possible gaps left by the selected scientific articles, in order to understand which research paths are possible in view of the reform of secondary education. This is a qualitative, documentary investigation, based on journals from the CAPES Qualis System database (four-year period 2013–2016, the latest evaluation available at the time of this investigation), in the area of ​​education assessment. A1 journals whose databases were in Federal and State Universities were analyzed, from 2017 to 2021, using the descriptors “secondary education reform” and “new secondary education”. We have observed a large number of studies related to Law 13,415/2017, which demonstrate a multitude of research paths and themes, covering Brazil from North to South. These studies highlight the researchers' concern with cultural diversity, diverse youth, the scarcity and precariousness of public schools, and the neglect and devaluation of the teaching profession, serving as a theoretical basis for many other ongoing studies.

Artocarpus Chaplasha

This article introduces and illustrates an unusual member of the jackfruit family, Artocarpus chaplasha Roxb., in the context of the more commonly consumed members of the Artocarpus genus. The opportunity to see and taste this uncommon fruit led down interesting research paths.

Differences of regional homogeneity and cognitive function between psychotic depression and drug-naïve schizophrenia

BackgroundPsychotic depression (PD) and schizophrenia (SCZ) share overlapping symptoms yet differ in etiology, progression, and treatment approaches. Differentiating these disorders through symptom-based diagnosis is challenging, emphasizing the need for a clearer understanding of their distinct cognitive and neural mechanisms.AimThis study aims to compare cognitive impairments and brain functional activities in PD and SCZ to pinpoint distinguishing characteristics of each disorder.MethodsWe evaluated cognitive function in 42 PD and 30 SCZ patients using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and resting-state functional magnetic resonance imaging (rs-fMRI). Regional homogeneity (ReHo) values were derived from rs-fMRI data, and group differences in RBANS scores were analyzed. Additionally, Pearson correlation analysis was performed to assess the relationship between cognitive domains and brain functional metrics.Results(1) The SCZ group showed significantly lower RBANS scores than the PD group across all cognitive domains, particularly in visuospatial/constructional ability and delayed memory (p < 0.05); (2) The SCZ group exhibited a significantly higher ReHo value in the left precuneus compared to the PD group (p < 0.05); (3) A negative correlation was observed between visuospatial construction, delayed memory scores, and the ReHo value of the left precuneus.ConclusionCognitive impairment is more pronounced in SCZ than in PD, with marked deficits in visuospatial and memory domains. Enhanced left precuneus activity further differentiates SCZ from PD and correlates with cognitive impairments in both disorders, providing neuroimaging-based evidence to aid differential diagnosis and insights into cognitive dysfunction mechanisms, while also paving a clearer path for psychiatric research.

Research hotspots and paths of traditional houses in Huizhou, Anhui province, China: A visualization analysis using CiteSpace

As a representative of China&amp;rsquo;s vernacular architecture with unique forms and rich cultural connotations, Huizhou&amp;rsquo;s traditional dwellings have gained international recognition as a World Cultural Heritage site. These dwellings serve as a pi vital case study in exploring the dynamic relationship between globalization and local identity. This study employs CiteSpace software to visualize and analyze literature on traditional Huizhou houses published since 2000. We investigated publication trends and author and institutional collaboration networks and identified major research hotspots and their evolutionary paths at each stage by applying keyword co-occurrence, clustering, and emergence mapping. The results reveal three main phases in academic research on Huizhou traditional dwellings: from 2000 to 2010, studies primarily dissected architectural features and cultural connotations; from 2011 to 2020, research focused on the application of Huizhou&amp;rsquo;s traditional architectural and cultural symbols within contemporary design; and from 2021 to 2023, preliminary exploration began into the industrialized development and spatial narratives of Huizhou architectural elements. Although research on Huizhou&amp;rsquo;s traditional houses has yielded substantial insights, collaboration among authors and institutions remains limited, and theoretical innovation is insufficient. Future studies should prioritize multidisciplinary collaboration and quantitative analysis to advance the integration and sustainable development of theory and practice in this field.

Entertainment interspersed with propaganda: how non-legacy-news accounts deliver explicitly political content to mass audiences on Russia’s most popular social network VK

ABSTRACT Previous research on political communication on Russia's most popular social network VK has concluded that most users avoid news by not following legacy-news accounts. In this study, we expand the universe of scrutinized accounts with the most-followed non-legacy-news accounts (>100,000 followers) that regularly publish what we theorize to be ‘explicitly political content’ (EPC; N = 355). We delineate a typology of six types of EPC accounts, calculate their aggregate follower counts, and determine how many of them were still (1) accessible from Russia and (2) publishing Kremlin-critical content in October 2022. Our findings indicate that non-critical accounts attracted 26 times more followers than Kremlin-critical accounts. Entertainment-focused EPC accounts had seven times more followers than legacy-news accounts. As a result, they became the primary means through which non-critical EPC reached news-avoidant mass audiences. We identify three dimensions through which autocrats can interweave propaganda and entertainment and highlight promising research paths.

Responsible Leadership - Knowledge Status and Future Research Agenda

The present review article deals with the highly relevant and rich research field of responsible leadership (RL). It summarizes existing literature using a wide scope of 96 peer-reviewed journal articles and subsequently provides recommendations for future research paths. The current state of knowledge is depicted in relation to five knowledge areas: In the first area, the conceptualisation of RL, works addressing fundamental views on RL, the development of frameworks and the application of specific philosophies or theories are introduced. In the second area, the operationalisation of RL, results of studies dealing with the development of measurement instruments or applying quantitative empirical study designs in order to test specific research questions are presented. The third area, RL antecedents, covers the depiction of empirically investigated antecedent variables pertaining to either a personal or a contextual level. In the fourth area, RL consequences, empirically examined impacts of RL at a micro, meso and macro level are summarised and further works dealing with effects of RL on relationships between other variables discussed. Finally, the fifth area of RL promotion presents works that are empirically testing RL development interventions that take place either in the context of higher education or work. Derived potential lines of research relate to these five areas of knowledge and address identified research gaps of content-related, empirical and methodological nature.

Systematic Review on Additive Friction Stir Deposition: Materials, Processes, Monitoring and Modelling

Emerging solid-state additive manufacturing (AM) technologies have recently garnered significant interest because they can prevent the defects that other metal AM processes may have due to sintering or melting. Additive friction stir deposition (AFSD), also known as MELD, is a solid-state AM technology that utilises bar feedstocks as the input material and frictional–deformational heat as the energy source. AFSD offers high deposition rates and is a promising technique for achieving defect-free material properties like wrought aluminium, magnesium, steel, and titanium alloys. While it offers benefits in terms of productivity and material properties, its low technology readiness level prevents widespread adoption. Academics and engineers are conducting research across various subfields to better understand the process parameters, material properties, process monitoring, and modelling of the AFSD technology. Yet, it is also crucial to compile and compare the research findings from past studies on this new technology to gain a comprehensive understanding and pinpoint future research paths. This paper aims to present a comprehensive review of AFSD focusing on process parameters, material properties, monitoring, and modelling. In addition to examining data from existing studies, this paper identifies areas where research is lacking and suggests paths for future research efforts.

Questioning educational contexts, promoting equitable actions: reflections on some paths of practice and research with complex instruction in Italy

ABSTRACT Within the framework of comparative education studies, this article discusses the process of borrowing and implementing Complex Instruction (CI) in Europe, focusing on three different multicultural and heterogeneous educational contexts in Italy: five primary schools, a Master academic teaching course, and a first literacy course for migrant adults. The paper explores the potential of CI for intercultural education and active citizenship. It aims to highlight how this cooperative approach can be an opportunity to question the usual school norms and habits and challenge what is taken for granted in the relationship with diversity, by transforming teaching into a creative and emancipatory experience in dialogue with diverse students.

Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review

ABSTRACTThis study presents a comprehensive multidisciplinary review of autonomous monitoring and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies, namely artificial intelligence (AI), machine learning (ML), deep learning (DL), internet of things (IoT), unmanned aerial vehicle (UAV), and big data analytics (BDA), aiming to automate the entire condition monitoring procedures of PV systems. Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to further enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems. This review article covers current trends, recent research paths and developments, and future perspectives of autonomous monitoring and analysis for PV power plants. Additionally, this study identifies the main barriers and research routes for the autonomous and smart condition monitoring of PV systems, to address the current and future challenges of enabling the PV terawatt (TW) transition. The holistic review of the literature shows that the field of autonomous monitoring and analysis of PV plants is rapidly growing and is capable to significantly improve the efficiency and reliability of PV systems. It can also have significant benefits for PV plant operators and maintenance staff, such as reducing the downtime and the need for human operators in maintenance tasks, as well as increasing the generated energy.

Integrating Vision: From Neuroscience to Artificial Intelligence

Abstract. This review article explores the complex interplay between neuroscience and artificial intelligence, focusing on vision processing and its applications. It starts by outlining the biological basis of vision, delving into how visual information is processed and encoded in the brain. The discussion then transitions to artificial intelligence, particularly machine vision, highlighting the advancements and technologies that mimic biological processes. A critical analysis compares how visual information is utilized in both biological organisms and artificial systems, with an emphasis on cognitive functions and neural encoding. The challenges of integrating vision across various sensory modalities are examined, underscoring the technological and cognitive limitations currently faced. The review culminates by identifying potential research paths aimed at closing the gap between neuroscience and AI. This involves enhancing the understanding and functionality of vision in multisensory contexts, striving to foster a more comprehensive approach to artificial intelligence that mirrors the complexity of human perception.

The sustainable challenge: Where does social psychology stand in achieving the sustainable development goals?

The United Nations Agenda 2030, inclusive of its 17 sustainable development goals (SDGs), serves as the global blueprint for sustainability for both present and future generations. Scientific research is entrusted with the responsibility of contributing by informing the current situation and future challenges in achieving the SDGs. This paper investigates the role of social psychology in contributing to the SDGs and the environmental, economic and social pillars of the UN Agenda. We analysed 4808 papers using Natural Language Processing to identify (i) the relevance of social psychology within the SDG-related literature and (ii) the current and potential contribution of social psychology to the SDGs. Results highlight that social psychology contributes to the SDGs by addressing typical social issues, primarily those related to health and gender, while noting its under-representation in some environmental and economic areas, despite social psychology well-established research on these topics. This paper introduces a novel approach for assessing the SDGs, fostering a critical reflection on the SDG framework and social psychology to guide less explored research paths. This approach could potentially enhance the evaluation and advancement of the 2030 Agenda, facilitating a deeper dialogue between the scientific community and policymakers, driving social change.

Evolution of CFD numerical methods and physical models towards a full discrete approach

The physical models and numerical methodologies of Computational Fluid Dynamics (CFD) are historically linked to the concept of continuous medium and to analysis where continuity, derivation and integration are defined as limits at a point. The first consequence is the need to extend these notions in a multidimensional space by establishing a global inertial frame of reference in order to project the variables there. In recent decades, the emergence of methodologies based on differential geometry or exterior calculus has changed the point of view by starting with the creation of entangled polygonal and polyhedral structures where the variables are located. Mimetic methods and Discrete Exterior Calculus, notably, have intrinsic conservation properties which make them very efficient for solving fluid dynamics equations. The natural extension of this discrete vision relates to the derivation of the equations of mechanics by abandoning the notion of continuous medium. The Galilean frame of reference is replaced by a local frame of reference composed of an oriented segment where the acceleration of the material medium or of a particle is defined. The extension to a higher dimensional space is carried from cause to effect, from one local structure to another. The conservation of acceleration over a segment and the Helmholtz–Hodge decomposition are two essential principles adopted for the derivation of a discrete law of motion. As the fields covered by CFD are increasingly broad, it is natural to return to the deeper meaning of physical phenomena to try a new research or new path which would preserve the properties of current formulations.

At the Knot of Presence: Weaving with the embodied knowledge of my artistic palette in Liza Lim's One and the Other (Speculative Polskas for Karin)

This artistic research exposition unfolds the shared work between Australian composer Liza Lim and Swedish violinist Karin Hellqvist, from the viewpoint of Hellqvist as performer and co-creator. Together, the artists have created the violin solo work One and the Other (Speculative Polskas for Karin) (2021–22). The Swedish folk music tradition that Hellqvist has carried with her since her childhood, and especially the polska dance, serves as their point of departure. This tradition resides in Hellqvist’s body and performance practice as embodied knowledge – a term introduced by Maurice Merleau-Ponty (1962), and it becomes their main path of research. A central concept in the reflections is the ‘artistic palette’ – a concept created by Hellqvist to conceptualise the skills and abilities used in creative work. Hellqvist’s embodied knowledge connected to the tradition is woven into the work through explorations of elements as the specific pulse of the polska and its ornamentation. Furthermore, those embodied skills are explored as decoupled in the third movement, capturing indeterminate aspects. The main question addressed is how the embodied knowledge of Hellqvist’s artistic palette serves as resource and inspiration in the shared process and how it affects the ontology of One and the Other (Speculative Polskas for Karin). Topics of distributed creativity, shared work as mycelial structure, instrument-building, ownership, and temporal ecology are being unpacked in the light of the artistic palette. The artistic research exposition unfolds a compositional process whereby the performer is participating actively, thus problematising the view of where creativity may be located in compositional work. It comprises written reflections, audio examples, pictures, and video material from the creative process as well as a video of the whole work. The research context comprises historical and aesthetic perspectives, as well as recent research on performer creativity and embodiment. &lt;style&gt; /* rules to make button only show up in META */ .download-accessible { display:none; } .meta-right-col .download-accessible { display: inline-block; padding: 9px; margin-bottom:25px; border: 1px solid black; background-color:white; } &lt;/style&gt; &lt;a class="download-accessible" href="/profile/download-media?work=2936353&amp;file=2959705" title="This accessible PDF is a derivative of the original which it is meant to support and not replace."&gt;Download Accessible PDF&lt;/a&gt;

A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style Patterns

Classroom EEG recordings classification has the capacity to significantly enhance comprehension and learning by revealing complex neural patterns linked to various cognitive processes. Electroencephalography (EEG) in academic settings allows researchers to study brain activity while students are in class, revealing learning preferences. The purpose of this study was to develop a machine learning framework to automatically classify different learning-style EEG patterns in real classroom environments. Method: In this study, a set of EEG features was investigated, including statistical features, fractal dimension, higher-order spectra, entropy, and a combination of all sets. Three different machine learning classifiers, random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), were used to evaluate the performance. The proposed framework was evaluated on the real classroom EEG dataset, involving EEG recordings featuring different teaching blocks: reading, discussion, lecture, and video. Results: The findings revealed that statistical features are the most sensitive feature metric in distinguishing learning patterns from EEG. The statistical features and RF classifier method tested in this study achieved an overall best average accuracy of 78.45% when estimated by fivefold cross-validation. Conclusions: Our results suggest that EEG time domain statistics have a substantial role and are more reliable for internal state classification. This study might be used to highlight the importance of using EEG signals in the education context, opening the path for educational automation research and development.

Shaping Novel Paths for Future Management Research and Practice in Enhancing Intuition

Shaping Novel Paths for Future Management Research and Practice in Enhancing Intuition

Physics Models and Machine Learning in Molecular Dynamics

Abstract. Tackling the complexity of particle interactions, this investigation introduces a unified computational methodology employing Moldy, Gnuplot, and Visual Molecular Dynamics (VMD). Inspired by the n-body problem's persistent intrigue, specifically the three-body dynamics within molecular systems, we leverage Molecular Dynamics (MD) simulations to forecast and scrutinize the nuanced behavior of atomic and molecular entities. Moldy, a flexible MD software, facilitated the simulation of particle trajectories and interactions across diverse scenarios, with meticulous documentation of the resulting data. For visual analytics and insight extraction, Gnuplot crafted detailed plots depicting kinetic and potential energies alongside other thermodynamic metrics over temporal scales. The integration of VMD culminated in our analysis by vividly portraying the molecular motions, enhancing comprehension of emergent patterns. This study not only endorses Moldy's precision in MD simulations but also highlights the potent alliance among simulation, analysis, and visualization in elucidating intricate particle interactions. In summary, our work underscores the efficacy of the Moldy-Gnuplot-VMD triad as a formidable resource for scientists engaged in molecular motion prediction and analysis, paving fresh paths in computational physics and chemistry research.

Post-Bruner Reflections on the Human Mind

AbstractAfter a short presentation of some key aspects of Jerome Bruner’s theories of the mind, this paper aims to assess his thinking on the mind in light of a broader context. This is done from three different angles: (1) By focusing on Roman Jakobson’s theories about language, which Bruner refers to. (2) By bringing in some post-structuralist perspectives that problematize narratives and logocentric entrenchments in scientific thinking. (3) By analyzing the eighteenth-century German discussion among Christian Wolff, Alexander Gottlieb Baumgarten and Immanuel Kant about the role of metaphysics in their understanding of psychology. The findings indicate that Roman Jakobson, with his poetic understanding of language, may point to a valid path for future research on the mind. Moreover, Jakobson’s understanding is also compatible with Baumgarten’s use of poetic aspects. Both manage to subvert the logocentric aspect of humans’ use of language and by this demonstrate how metacognition has to be understood through esthetic functions that go beyond a logocentric use of language. The Wolffian emphasis on mathematics and logic in Wolff’s understanding of psychology is comparable with many of the premises for the cognitive revolution in the 1950s. The Kantian focus on concepts and rule-governed understanding also points in this direction. Bruner seems to ignore the historical background for discussions of psychology in the twentieth century, and because of this has problems moving beyond the logocentric premises that govern most of the cognitive sciences.

A Systematic Literature Review on Knowledge Management in Healthcare: Best Practices and Future Directions

The role of knowledge management (KM) in improving the standard and effectiveness of healthcare is gaining more attention. However, because of the complexity and the sensitive nature of medical information, the use of knowledge management approaches in this industry faces unique challenges. In order to identify best practices, summarize current research on knowledge management (KM) in healthcare, and suggest future research paths, a comprehensive literature review was conducted. A detailed search was carried out across many important databases, including PubMed, Scopus, IEEE Xplore and Web of Science, in order to gather the relevant studies for the review. The review focused on studies published between 2010 and 2024 that specifically addressed KM practices within healthcare settings and adhered to the PRISMA guidelines for systematic reviews. The review found 50 papers that satisfied the requirements for inclusion. Significant findings illustrate how important organizational culture, leadership backing, and technology infrastructure are, to the effective use of knowledge management. Some of the most significant techniques in Knowledge Management that are emphasized are creating communities of practice, fusing KM systems with medical guidelines, and employing data analytics for knowledge creation. On the other side, challenges include hesitation to change and interoperability issues