bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of human activity/action recognition: a review
Authors: sedaghati Nazanin , ardebili Sondos , Ghaffari Ali 
Published: January 2025

AbstractHuman activity recognition is a crucial domain in computer science and artificial intelligence that involves the Detection, Classification, and Prediction of human activities using sensor data such as accelerometers, gyroscopes, etc. This field utilizes time-series signals from sensors present in smartphones and wearable devices to extract human activities. Various types of sensors, including inertial HAR sensors, physiological sensors, location sensors, cameras, and temporal sensors, are employed in diverse environments within this domain. It finds valuable applications in various areas such as smart homes, elderly care, the Internet of Things (IoT), personal care, social sciences, rehabilitation engineering, fitness, and more. With the advancement of computational power, deep learning algorithms have been recognized as effective and efficient methods for detecting and solving well-established HAR issues. In this research, a review of various deep learning algorithms is presented with a focus on distinguishing between two key aspects: activity and action. Action refers to specific, short-term movements and behaviors, while activity refers to a set of related, continuous affairs over time. The reviewed articles are categorized based on the type of algorithms and applications, specifically sensor-based and vision-based. The total number of reviewed articles in this research is 80 sources, categorized into 42 references. By offering a detailed classification of relevant articles, this comprehensive review delves into the analysis and scrutiny of the scientific community in the HAR domain using deep learning algorithms. It serves as a valuable guide for researchers and enthusiasts to gain a better understanding of the advancements and challenges within this field.
Point cloud quality metrics for incremental image-based 3D reconstruction
Authors: Neumann Kai A. , Tausch Reimar , Kutlu Hasan , Kuijper Arjan , Santos Pedro , Fellner Dieter 
Published: January 2025

AbstractImage-based 3D reconstruction is a powerful method for accurately reconstructing an object’s geometry and texture from images. A crucial factor for the accuracy and completeness of the resulting reconstructed model is the choice of poses for capturing images, which is called view planning. One possible view planning strategy uses an iterative feedback loop that switches between planning poses and an incremental reconstruction to autonomously digitize an object without prior knowledge. However, this approach requires identifying which parts of an object are “poorly reconstructed” and thus would benefit from being part of additional images. This work explores the use of point cloud quality metrics to provide this feedback by comprehensively comparing a set of existing and newly introduced metrics in terms of their time-dependent behavior, similarity, and their applicability to view planning. Among the newly proposed metrics this work introduces the Reconstruction Quality Feedback (RQF), which shows a significantly improved performance in simulations when being used for view planning. The effectiveness of RQF is also demonstrated for real objects on an autonomous robotic 3D digitization system.
Perceived job security and politicians’ legislative effort
Authors: Nower Michael 
Published: January 2025

AbstractThe relationship between job security and politicians’ legislative effort is bidirectional, making isolating the impacts, and the mechanisms underlying them, in either direction difficult. Increased legislative effort could increase politicians’ job security, as they might be considered to be more desirable by voters, however, increased job security can change the incentives to engage in legislative activity. We analyse the behaviour of UK Members of Parliament (MPs) from 1997 to 2019 to examine the mechanisms driving any impact of job security on legislative effort, using an instrument for job security that is not impacted by individual effort. We show that the impact of job security on effort is primarily driven by an opportunity cost of time allocation mechanism, with MPs who are less secure in their job allocating less of their time to legislative activities. This opportunity cost increases with the breadth of their legislative effort, and is primarily driven by individuals with lower outside options, who face a greater cost of electoral defeat. We provide reduced-form evidence that this opportunity cost is primarily driven by the maintenance of non-parliamentary human capital by MPs, rather than activities designed to improve their electoral prospects.
Accelerating social science knowledge production with the coordinated open-source model
Authors: Turek Konrad 
Published: January 2025

AbstractWith the growing complexity of knowledge production, social science must accelerate and open up to maintain explanatory power and responsiveness. This goal requires redesigning the front end of the research to build an open and expandable knowledge infrastructure that stimulates broad collaborations, enables breaking down inertia and path dependencies of conventional approaches, and boosts discovery and innovation. This article discusses the coordinated open-source model as a promising organizational scheme that can supplement conventional research infrastructure in certain areas. The model offers flexibility, decentralization, and community-based development and aligns with open science ideas, such as reproducibility and transparency. Similar solutions have been successfully applied in natural science, but social science needs to catch up. I present the model’s design and consider its potential and limitations (e.g., regarding development, sustainability, and coordination). I also discuss open-source applications in various areas, including a case study of an open-source survey harmonization project Comparative Panel File.
Scaffolded Agents, for Better or Worse: Assessing the Formative Aspect of Scaffolding
Authors: Dengsø Mads J. 
Published: January 2025

AbstractCognitive scaffolding is typically conceptualized in terms of environmental design which serves to offload, facilitate, or enhance the cognitive capacities of interacting agents. Recent contributions to the literature on scaffolding have noted that environmental design might likewise scaffold cognition in ways that undermine the interests of interactant agents—giving rise to notions of problematic or hostile scaffolding. Given the pervasiveness of social and technological scaffolding in contemporary life, the importance of understanding and assessing its effects can hardly be overstated. At the same time, however, the very pervasiveness of scaffolding raises a challenge to the current criteria for evaluating its effects by comparison with agents’ interests. This is because human agents and their interests alike are themselves the results of various forms of social and technological scaffolding. This paper explores the relationship between scaffolding and the formation of agents along with their interests, as well as the implications of this relationship for evaluating scaffolding as being hostile, neutral, or beneficial. I will argue that the formative aspect of scaffolding imparts a degree of uncertainty to evaluations of scaffolding based on agents’ interests. I will then demonstrate the relevance of the formative aspect of scaffolding in evaluating and assessing the potentially pernicious effects of contemporary social and technological scaffolding focusing on the examples of servitization marketing models and social and vocational digital infrastructures.
Electrocatalytic Oxidation of Toxic Wastewater using Electrodes based on Transition d-metal Oxides
Authors: Yaşar Çırak Semra , Öztürk Dilara , Akyol Abdurrahman 
Published: January 2025

Abstract
The process of electrooxidation of the active substances Paracetamol (PCT), benzoquinone (BQ) and hydroquinone (HQ) was studied using a set of different dimensionally stable anode (DSA) and Boron doped diamond (BDD) electrodes. Comparison of the efficiency of electrocatalytic anodes was assessed using percent total organic carbon (%TOC) removal and PCT amount removal values. The removal of %TOC in synthetically prepared waters for the BDD anode reached 96%, for DSA electrodes Ti/PbO2-IrO2-RuO2 57%, Ti/IrO2-RuO2-TiO2 35%, Ti/IrO2-RuO2-SnO2 31%, Ti/RuO2-SnO2 30% and Pt 24%. BDD effectively degrades PCT and almost completely mineralizes BQ and HQ. A DSA-Ti/PbO2-IrO2-RuO2 electrode and a BDD electrode were used in the electrooxidation process of real industrial wastewater containing PCT. The BDD electrode had a TOC removal efficiency of 58%, while the DSA-Ti/PbO2-IrO2-RuO2 electrode achieved 52%. Despite similar values ​​of PCT removal by both electrodes, the Ti/PbO2-IrO2-RuO2 anode showed low mineralization of organic matter. The originality of this paper lies in the study of the electrooxidation of real PCT wastewater and the use of a Ti/PbO2-IrO2-RuO2 electrode.

Graphical Abstract
Framings in Indigenous futures thinking: barriers, opportunities, and innovations
Authors: Cheok Jessica , van Velden Julia , Fulton Elizabeth A. , Gordon Iain J. , Lyons Ilisapeci , Peterson Garry D. , Wren Liz , Hill Rosemary 
Published: January 2025

AbstractHuman societies face existential challenges on multiple fronts: climate change, biodiversity loss, altered biogeochemical flows, social unrest and injustices. Innovative solutions are needed to shift current trajectories towards a sustainable and just future. Futures thinking enables people to explore and articulate alternative futures and find pathways towards their desired futures. Indigenous people have the potential to make significant contributions to futures thinking because of their distinctive perspectives: the viewpoint of already living in a post-apocalyptic world in the context of colonisation, unique knowledges, worldviews, and perspectives on time, as well as significant contributions to safeguarding biological and cultural diversity. A body of literature is emerging where Indigenous peoples contribute to and lead futures thinking approaches; however, this literature is diffuse and highly diverse in its approaches and terminology. Thus, we take an innovation-seeking and systematic approach to (1) identify patterns and processes in futures thinking with, for, and by Indigenous people; (2) highlight innovative approaches; (3) bring together diverse and sector-specific terminology; and (4) foreground emerging strengths and weaknesses. We identified four framings of Indigenous futures thinking: Adaptation oriented, Participatory, Culturally grounded, and Indigenising. Factors contributing to innovation include strong involvement of Indigenous people in the research team, co-design, and authorship, using Indigenous methodologies, cultural protocols, and explicitly employing decolonisation and trauma-informed approaches. We spotlight the challenges of conducting an exhaustive literature review in an emerging field with inconsistent terminology (e.g., capturing regions where Indigeneity is contested). We also create a living glossary of terms to aid other researchers and communities in using and refining the suite of methods identified here, with the aim of stimulating further innovations in this field.
Ulam-hyres stability analysis and fractional operator implications on the Covid-19 virus dynamics with long-term vaccination effects
Authors: Farman Muhammad , Jamil Saba , Hincal Evren , Baleanu Dumitru , Sambas Aceng , Nisar Kottakkaran Sooppy 
Published: January 2025

Abstract
The COVID-19 pandemic has necessitated the development of highly efficient mathematical models to manage its spread, particularly concerning vaccination strategies. Traditional models, however, often fail to account for memory effects observed in real-world scenarios, which can be effectively captured using fractional derivatives. This paper introduces a novel COVID-19 model incorporating fractional-order derivatives to reflect better the dependence of the pandemic’s growth on historical events. By approximating the non-locality of fractional derivatives through a generalized Mittag-Leffler kernel, the model effectively captures long-term vaccination effects. To enhance the accuracy of numerical results, the model also integrates the concept of a two-step Lagrange polynomial. The local asymptotic stability of the disease-free equilibrium point is examined through sensitivity and qualitative analyses, particularly using the basic reproduction number,

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. The proposed scheme is rigorously validated through fixed point theory, ensuring the model is evidence-based. A case study conducted in Saudi Arabia demonstrates the effectiveness of the proposed model, yielding successful verification through real-world numerical analysis. The results indicate that incorporating fractional derivatives leads to a more robust model, particularly in assessing the long-term impact of vaccination on the COVID-19 epidemic. The findings underscore the significant role of vaccination in reducing

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and achieving a disease-free state. This work highlights the potential of fractional operators in epidemiological modeling, providing crucial insights into strategies for preventing the transmission of COVID-19 and similar diseases.
From latest buzzword to conceptual framework: Unraveling the complexities of democratic resilience
Authors: Walz Kevin , Merten Lion , Rosenthal Matthias , Niedringhaus Jana , Helgest Johannes 
Published: January 2025

AbstractResilience makes its debut in an increasing number of research areas, most recently also in democracy research. Even though progress has been made in conceptualizing democratic resilience, the concept still appears to be rather diffuse and underdeveloped. We address these shortcomings by (re-)conceptualizing democratic resilience through the lens of Gerring and Christensons’s criteria of conceptual goodness with a special focus on resonance, internal coherence, external differentiation, and theoretical utility. We argue that democratic resilience is best understood as a capacity that stems from various resources at different system levels. Drawing on these resources, the resilience capacity provides the democratic system with certain abilities that enable democratic actors to apply concrete strategies to deal with stressors. In doing so, the resilience capacity ultimately operates as a moderator in the stressors-processing that make certain outcomes more or less likely. We contribute to the existing literature by systematically strengthening the concept of democratic resilience and distinguishing it from neighbouring concepts like consolidation. We conclude with a brief outlook on how the developed conceptualization of democratic resilience could contribute to systemizing and advancing empirical research on the decline and survival of democracies in the future.
Stathmin 1 expression in neuroendocrine and proliferating prostate cancer
Authors: Shi Yingli , Yeh Yunshin A. , Cheng Siyuan , Gu Xin , Yang Shu , Li Lin , Khater Nazih P. , Kasper Susan , Yu Xiuping 
Published: January 2025

AbstractProstate cancer (PCa) is the second leading cause of cancer-related mortality among men in the United States. While PCa initially responds to androgen deprivation therapy, a significant portion progresses to castration-resistant PCa. Approximately 20–25% of these cases acquire aggressive neuroendocrine (NE) features, ultimately leading to neuroendocrine prostate cancer (NEPC). In this study, we investigated the expression of stathmin 1 (STMN1) across PCa subtypes using bioinformatics, western blotting, and immunohistochemical staining analyses in human and murine models. We found that elevated STMN1 expression correlated with high Gleason Scores, increased cell proliferation, and poor clinical outcomes in PCa patients. Notably, STMN1 expression was significantly higher in NEPC compared to prostate adenocarcinoma, suggesting its role in NEPC progression. Findings from TRAMP tumors, a murine NEPC model, further supported these results. In conclusion, STMN1 expression is elevated in advanced PCa, particularly in NEPC, suggesting its involvement in the progression of aggressive forms of PCa. While STMN1 shows potential as a diagnostic and prognostic marker for aggressive PCa, further studies are necessary to establish its clinical utility.
Targeting protein synthesis pathways in MYC-amplified medulloblastoma
Authors: Kumar Devendra , Kanchan Ranjana , Chaturvedi Nagendra K. 
Published: January 2025

AbstractMYC is one of the most deregulated oncogenic transcription factors in human cancers. MYC amplification/or overexpression is most common in Group 3 medulloblastoma and is positively associated with poor prognosis. MYC is known to regulate the transcription of major components of protein synthesis (translation) machinery, leading to promoted rates of protein synthesis and tumorigenesis. MTOR signaling-driven deregulated protein synthesis is widespread in various cancers, including medulloblastoma, which can promote the stabilization of MYC. Indeed, our previous studies demonstrate that the key components of protein synthesis machinery, including mTOR signaling and MYC targets, are overexpressed and activated in MYC-amplified medulloblastoma, confirming MYC-dependent addiction of enhanced protein synthesis in medulloblastoma. Further, targeting this enhanced protein synthesis pathway with combined inhibition of MYC transcription and mTOR translation by small-molecule inhibitors, demonstrates preclinical synergistic anti-tumor potential against MYC-driven medulloblastoma in vitro and in vivo. Thus, inhibiting enhanced protein synthesis by targeting the MYC indirectly and mTOR pathways together may present a highly appropriate strategy for treating MYC-driven medulloblastoma and other MYC-addicted cancers. Evidence strongly proposes that MYC/mTOR-driven tumorigenic signaling can predominantly control the translational machinery to elicit cooperative effects on increased cell proliferation, cell cycle progression, and genome dysregulation as a mechanism of cancer initiation. Several small molecule inhibitors of targeting MYC indirectly and mTOR signaling have been developed and used clinically with immunosuppressants and chemotherapy in multiple cancers. Only a few of them have been investigated as treatments for medulloblastoma and other pediatric tumors. This review explores concurrent targeting of MYC and mTOR signaling against MYC-driven medulloblastoma. Based on existing evidence, targeting of MYC and mTOR pathways together produces functional synergy that could be the basis for effective therapies against medulloblastoma.
When conventional approach in toxicity assays falls short for nanomedicines: a case study with nanoemulsions
Authors: Nikolić Ines , Đoković Jelena , Mehn Dora , Guerrini Giuditta , Savić Snežana , Jordan Olivier , Borchard Gerrit 
Published: January 2025

AbstractThe aim of this study was to assess the critical quality attributes of parenteral nanoemulsion formulations by measuring several physicochemical parameters and linking them to their in vitro performance, illustrating how simplistic and routinely used approaches are insufficient for understanding a potential nanomedicine. Physicochemical characterization should encompass size and size distribution through at least two orthogonal techniques, such as dynamic light scattering (DLS) and electron microscopy, with added value from analytical ultracentrifugation. In vitro toxicity assessment was performed using three different assays to determine mitochondrial activity (WST-1), membrane integrity (lactate dehydrogenase release (LDH) assay), and cell viability (propidium iodide (PI) staining). Special focus was placed on estimating appropriate incubation times for relevant results in biological investigations. All formulations had an average diameter of around 100 nm. Conclusions regarding in vitro safety were assay-dependent: LDH and PI-based assays showed good correlation, while the WST-1 assay indicated that the non-PEGylated formulation altered mitochondrial activity more significantly compared to the PEGylated ones. The study underlined that the selection of appropriate cytotoxicity assays should be based on the possible mechanism of cellular perturbation. Alternatively, different aspects of cellular toxicity should be tested. Additionally, there is a need for well-designed controls to overcome nanoparticle scattering effects and avoid potentially false high toxicity results, which was demonstrated. Combining orthogonal, well-designed physicochemical and biological assays in a standardized manner as an initial step in the reliable preclinical characterization of nanomedicines is suggested. This represents a key aspect of new methodologies in nanomedicine characterization.
Graphical abstract
Regional Brain Analysis and Machine Learning Techniques for Classifying Familiar and Unfamiliar Faces Using EEG
Authors: Bardak F. Kebire , Temurtaş Feyzullah 
Published: January 2025

AbstractAmong the processes critical for human social interactions are perception, memorization, and bonding, and the ability to recognize familiar and unfamiliar faces is one of the most essential aspects of the human brain. This is a valuable communication skill, as well as remembering people and interpersonal interactions to recognize the faces of the people. Given the importance of these aspects of cognitive functioning, the present research seeks to establish the neural basis for recognizing familiar and unfamiliar faces from EEG data through a regional brain perspective and simple neural networks. The EEG data used in this research were gathered from typically developed subjects, and the features were derived using discrete wavelet transform (DWT). These features were then employed for the classification of the network using three different algorithms, which include k-nearest neighbors (KNN), support vector machines (SVM), and probabilistic neural networks (PNN). KNN was seen to have the highest classification accuracy than the other classifiers; the accuracy was considered for different brain regions and all the channels. The temporal and occipital lobes were found to be involved in face recognition, and the patterns of activation differed between familiar and unfamiliar faces. This work contributes to the literature by describing how face recognition is implemented in the brain, which areas of the brain are most important, and by comparing machine learning techniques for classifying the EEG signal. These findings are helpful for the current literature. They can help to inform future research into the neural structure of face recognition and what this might mean for prosopagnosia and similar conditions.