Browse Articles

Gradient vortex dynamics in 3D-weak turbulence

Abstract Vortex dynamics play a central role in most turbulent processes, whether of physical or chemical origin. In the realm of so-called weak turbulence, which encompasses physical, chemical, and electrochemical processes, understanding and monitoring the emergence of vortices in three dimensions remains a significant challenge. In this study, we propose a novel approach with minimal computational cost that enables the characterization of vortex ring formation and screw-like patterns in 3D-turbulent flows. Our method involves analyzing gradient vortex dynamics by measuring phase fluctuations in gradient patterns derived from the 3D-distribution of the corresponding amplitudes. The investigation focuses on transient primary structures generated by the Complex Ginzburg-Landau amplitude equation. The simulations integrate gradient pattern analysis, allowing for a groundbreaking association between phase fluctuations (commonly referred to as phase turbulence) and the helical oscillations induced by vorticity. As our main result, the phase-gradient analysis, combined with aspect ratio measurements of the primary patterns of coherent structures, enables us to identify at least four distinct regimes characterizing vortex dynamics. To further enhance this characterization, spectral measurements and recurrence plots of the phase-gradient fluctuations are introduced as innovative tools for describing weak turbulence and spatiotemporal chaos in nonlinear ( $$3D+1$$ ) dynamics. This approach provides new insights into the intricate interplay between phase turbulence and vortex dynamics, offering a new perspective on the systematic study of the formation of coherent structures in three dimensions. It is worth highlighting that this is the first time that 3D screw dynamics have been simulated, visualized and analyzed in detail in a phase turbulence process.

Oxytocin and vasopressin enhance social pain empathy via common and distinct of neural expressions, genetic pathways, and networks

Witnessing social distress of others evokes social pain empathy, a complex process engaging cognitive, affective, and motivational dimensions. Although hypothalamic neuropeptides oxytocin (OXT) and arginine vasopressin (AVP) are known to modulate social function, their respective contributions to the process of social pain empathy have not been systematically characterized. To address this, we employed a multimethod approach, combining naturalistic fMRI, functional decoding, gene expression analysis, and pharmacological modulation using intranasal administration of OXT (24 IU) or AVP (20 IU) in a cohort of 163 participants. Our findings indicated that both OXT and AVP significantly enhanced pain empathy compared to placebo, with overlapping yet distinct neurofunctional and genetic modulation patterns. Specially, both neuropeptides engaged a shared perception-cognition-emotion network, including frontal regions, the insula, superior temporal sulcus, and parahippocampal gyrus. Crucially, they exhibited divergent mechanistic profiles: OXT preferentially influenced resting-state connectivity and perceptional-visual processing areas, while AVP exerted stronger modulation on perceptional-execution circuits. These differential effects aligned with their unique receptor expression patterns and interactions with distinct genetic systems. By delineating how OXT and AVP shape the multidimensional nature of social pain empathy, our findings provide a neurobiological framework for understanding these processes and offer potential pathways for targeted interventions in social cognition disorders.

Investigating the application of one piece flow from lean manufacturing in the construction delivery of mass housing projects

Abstract Mass-housing projects (MHPs) are often delivered using mass construction to save time and cost. However, it actually leads to large work in progress (WIP), delays, and cost overrun. Moreover, to finance mass construction works, housing developers often sell off-plan large number of housing units leading to financial losses especially during economic downturns. Accordingly, this research evaluates the effectiveness of adopting the lean concept “One-Piece Flow (OPF)” for delivering MHPs instead of mass construction. OPF-based construction relies on batch production which helps reduce WIP, production cycle time, and rework. A comparative analysis, using an actual case study, was conducted between mass construction and OPF-based construction in terms of performance and economic worthiness. Moreover, despite completing the project in more time and cost, the results showed that the OPF-based construction delivery achieved, on average, 43% higher profitability and reduced time waste by an average of 47% per building. Thus, this research confirms the potential of adopting OPF-based construction for MHPs.

The therapeutic potential of beta-carotene against neuroinflammation and amyloid beta in SH-SY5Y cells

Psychosexual health’s impact on non-suicidal self-injury of college students

Impacts of land use and land cover changes on carbon stocks (1992–2052) Using geospatial technologies in Gena district, Southwest Ethiopia

Comparative evaluation to composite resin bleaching using ozone-enhanced low-concentration hydrogen peroxide

Dynamical description and analytical study of traveling wave solutions for generalized Benjamin-Ono equation

Weather-driven groundnut price forecasting and profitability assessment of cropping patterns in Tamil Nadu using boosting algorithms

Detecting MUNC18-1 related presynaptic dysfunction and rescue in human iPSC-derived neurons

Cultural topography of publicness: Assessment of the publicness of public spaces in traditional settlements

In the process of rapid urbanization in China, the public spaces of traditional settlements are undergoing significant transformations and facing numerous challenges. Systematically assessing their publicness and improving spatial quality have become critical issues. This study employs the space syntax method and Analytic Hierarchy Process (AHP) to assess the publicness of the public spaces in Zengchong Dong Village and Langde Miao Village, two traditional settlements in the Qiandongnan region, China. Drawing on field research and questionnaire data, we constructed an evaluation index system for publicness from both subjective and objective perspectives, encompassing five dimensions: accessibility, visibility, functionality, iconicity, and inclusiveness. The results show that: 1) The publicness of public spaces varies regionally, with riverside areas exhibiting higher publicness and more vibrant activities compared to adjacent mountainous areas; 2) Validation tests confirmed system reliability (R2 = 0.832) between calculated publicness scores and expert rating; and 3) Residents’ living habits and the differences in urban-rural perception are the main factors affecting the evaluation of public space publicness. On this basis, our study suggests building unique facilities, involving multiple parties in governance, and boosting cultural exchanges. These steps aid in reviving traditional village spaces, backing rural tourism and spurring economic and cultural growth.

Versatile <i>Xenopus tropicalis</i> model with targeted integration of human <i> BRAF ^V600E </i>

Targeting exogenous gene integrations in animals often exhibits low efficiency, limiting the development of gene knock-in models. Theoretically, by screening founder generation individuals based on the cell phenotypes resulting from gene knock-ins and leveraging the high fecundity of animals, heritable descendants with targeted knock-ins can be efficiently generated. Therefore, we utilized the high fecundity of Xenopus tropicalis and easily observable pigment phenotypes to construct a BRAF V600E -targeted mitf locus knock-in model. Results indicated that this approach enabled efficient generation of BRAF V600E knock-in X. tropicalis and produced a versatile frog model. The BRAF V600E knock-in induced the transdifferentiation of RPE cells into retinal cells, resulting in a symmetric retinal structure in the eyes of these frogs. The transformation of RPE cells ultimately leads to these frogs becoming eyeless frogs, which serve as a tool for retinal regeneration research. Additionally, in eyeless frogs the BRAF V600E knock-in led to the abnormal proliferation of both melanocytes and xanthophores into melanocytic and xanthocytic nevi respectively. Consequently, eyeless frogs provide a model for studying abnormal pigment cell proliferation, offering a platform for investigating pigment cell nevus formation. Furthermore, the cdkn2b -knockout eyeless frogs serve as a valuable xanthophoroma model for tumor biology research. Overall, the BRAF V600E -targeted knock-in X. tropicalis not only represents a strategy for constructing gene knock-in animal models but also serves as a versatile tool for research in retinal regeneration and tumor biology.

Machine learning for stroke prediction using imbalanced data

Distribution and diversity of white Grub beetles across agro-ecological zones in Uttarakhand, India

Enhancing cross-cultural applicability in recovery colleges: A global Delphi study protocol

Background Recovery Colleges (RCs) offer an innovative model of mental health support that blends co-production with adult learning to promote personal recovery and social inclusion. While evidence supports their effectiveness, most RC research and practice have been developed in Western contexts, raising concerns about cross-cultural applicability. The RECOLLECT Change Model (RCM) and RECOLLECT Fidelity Measure (RFM) were developed in England to characterise RC mechanisms and assess fidelity. Our previous studies have identified cultural influences on the RC operational model, however how to address these influences remains unknown. Given the increasing global interest in RCs, the aims of this study are to (a) identify the level of cultural influence on the RCM mechanisms and RFM items, and (b) provide recommendations to inform cross-cultural applicability of RCM and RFM. Methods This global Delphi study follows Belton’s six-step methodology and uses a decentring approach to cross-cultural research that seeks to extend the relevance of tools developed in a single culture to multiple cultural contexts. Experts will be recruited via the RECOLLECT International Research Consortium, covering 31 countries across six continents. We aim to recruit approximately 100 panellists with at least three years’ RC experience. Data collection will occur via Microsoft Forms across iterative Delphi rounds. Panellists will rate the importance and cultural difficulty of RCM and RFM items, provide feedback on culturally aligned response types, and suggest revisions for improved cultural fit. Quantitative data will be analysed using non-parametric statistics and a collapsed three-point Likert scale to address cross-cultural response bias. Qualitative responses will be analysed using descriptive content analysis informed by Hofstede’s cultural dimension theory. Member checking will be conducted after the final round to enhance trustworthiness. Discussion This study will identify which RCM and RFM components are cross-culturally applicable and which require adjustment, contributing to the balance between fidelity and fit in mental health approaches. By developing culturally informed recommendations, this study aims to expand the accessibility and relevance of RC frameworks across diverse settings. Findings will benefit RC practitioners, researchers, and policymakers seeking to improve service delivery and recovery outcomes in culturally meaningful ways.

Federated transfer learning for rare attack class detection in network intrusion detection systems

Abstract Federated learning (FL) offers a promising approach for training machine learning models with minimal data sharing, enhancing privacy and performance. However, building effective FL-based network intrusion detection systems (NIDS) remains challenging due to the need for large, diverse training datasets. Identifying rare attack types with limited instances is a persistent obstacle, and their detection is critical in cybersecurity. This research introduces a novel FL framework to address these challenges. By incorporating adaptive, personalized layers at the client level, the model reduces false alarm rates for zero-day attack types and improves the detection of rare classes. The model also leverages Transfer Learning (TL) to identify zero-day attacks, where client-specific gradients are collected and used to update a global model on the server side after multiple rounds of exposure to new data. The proposed sustainable framework aims to disseminate knowledge about rare attack types across clients through a server-based global model within the FL ecosystem. This study achieves two main objectives: (i) improving the detection of rare attack classes and (ii) identifying zero-day attacks in a NIDS context. Evaluations on the CSE-CICIDS-2018, Edge IIoT, and UNSW-NB 15 datasets, which encompass diverse class distributions, demonstrate that the proposed approach outperforms existing models in detecting and handling rare and novel attack types. The proposed model achieves 98.90% accuracy on CICIDS 2018, 98.70% on UNSW-NB 15, and 97.92% on Edge-IIoT, surpassing the FL-TL-CNN model by 2.78%, 1.51%, and 2.03%, respectively. These results highlight the effectiveness, robustness, and adaptability of the proposed approach in enhancing intrusion detection across heterogeneous network environments.

DFT based structural modeling of chemotherapy drugs via topological indices and curvilinear regression

Correction: Effectiveness of blocking primers and a peptide nucleic acid (PNA) clamp for 18S metabarcoding dietary analysis of herbivorous fish

Designing of guava quality classification model based on ANOVA and machine learning

Abstract The precise maturity quality classification of guava is crucial at farm level, retail, storage, and supply chain. The manual classification causes substantial postharvest losses, which increases the demand for material and resources. Therefore, the present study proposed an automated, precise, and accurate model for quality classification according to the maturity stages (Green, Mature Green, Ripe) of three varieties (Local Sindhi, Riyali, Thadhrami) of guava. The study aimed to develop a precise, accurate and automated model for the classification of guava according to their maturity stages. The guava images were used to extract color, shape, and texture features. Analysis of Variance (ANOVA) was used for the selection of important features. The six different machine learning (ML) classifiers; Artificial Neural Network (ANN), k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Cubic SVM, Quadratic SVM, and Random Forest (RF) were used to find out the best classifier for maturity classification. Among the proposed classifiers, the RF classifier was found to be the best classifier for all three varieties of guava. The Quadratic SVM classifier showed the lowest classification accuracy. The study concluded that RF classifier was found to be a robust model for the maturity classification of guava.

A personalized federated hypernetworks based aggregation approach for intrusion detection systems

Abstract Traditional network intrusion detection systems (NIDS) face significant scalability challenges due to the vast amount of data generated by Internet of Things (IoT) devices, compounded by growing privacy concerns. Federated Learning (FL) has emerged as a promising solution, offering a distributed, privacy-preserving paradigm that enables Deep Learning (DL) models to be trained locally, thereby mitigating privacy risks associated with centralized data processing. However, conventional FL strategies come with inherent limitations. First, they require all clients to use the same model architecture, making personalized learning difficult particularly in Non-Independent and Identically Distributed (Non-IID) heterogeneous data settings. Second, the weight aggregation process in FL introduces significant communication overhead, potentially slowing down training. While encryption techniques such as homomorphic encryption and differential privacy enhance security, they also increase computational costs and can still reveal data distribution patterns if compromised. These challenges are further exacerbated in dynamic IoT environments, where evolving attack types continuously alter data distributions. To address these issues, we propose a Personalized Federated Hypernetworks-based aggregation strategy for Intrusion Detection Systems (PerFedHypID). Unlike conventional FL approaches that rely on weight-based aggregation, PerFedHypID utilizes embedding vectors, which are computationally lighter and enable enhanced personalization. Our method leverages personalized layers and hypernetwork-based aggregation to achieve both efficiency and adaptability. We extensively evaluate PerFedHypID on the CSE-CICIDS-2018 and UNSW-NB-15 datasets under various non-IID heterogeneous settings. The results demonstrate that our approach outperforms state-of-the-art personalized federated learning algorithms, offering robust performance and improved adaptability in dynamic IoT environments.