Hierarchical Process Research Articles

Hierarchical Assembly of High-Nuclearity Copper(I) Alkynide Nanoclusters: Highly Effective CO2 Electroreduction Catalyst toward Hydrocarbons.

The pursuit of precision in the engineering of metal nanoparticle assemblies has long fascinated scientists, but achieving atomic-level accuracy continues to pose a significant challenge. This research sheds light on the hierarchical assembly processes of two high-nuclearity Cu(I) nanoclusters (NCs). By employing a multiligand cooperative stabilization strategy, we have isolated a series of thiacalix[4]arene (TC4A)/alkynyl coprotected Cu(I) NCs (Cux, where x = 9, 13, 17, 22). These NCs are intricately coassembled from the fundamental building units of {Cu4(TC4A)} and alkynyl-stabilized Cu5L6 in various ratios. By capturing active anion templates such as O2-, Cl-, or C22- that are generated in situ, we have further explored the secondary structural self-assembly of these clusters. Cu13 serves as a secondary assembly module for constructing Cu38 and Cu43, which exhibit the highest nuclearity reported to date among Cu(I) NCs encased in macrocyclic ligands. Notably, Cu38 demonstrates an impressive Faradaic efficiency of 62.01% for hydrocarbons at -1.57 V vs RHE during CO2 electroreduction, with 34.03% for C2H4 and 27.98% for CH4. This performance establishes it as an exceptionally rare, large, atomically precise metal NC (nuclearity >30) capable of catalyzing the formation of highly electro-reduced hydrocarbon products. Our research has introduced a new approach for constructing high-nuclearity Cu(I) NCs through a hierarchical assembly method and investigating their potential in the electrocatalytic transformation of CO2 into hydrocarbons.

FaSS-MVS: Fast Multi-View Stereo with Surface-Aware Semi-Global Matching from UAV-Borne Monocular Imagery.

With FaSS-MVS, we present a fast, surface-aware semi-global optimization approach for multi-view stereo that allows for rapid depth and normal map estimation from monocular aerial video data captured by unmanned aerial vehicles (UAVs). The data estimated by FaSS-MVS, in turn, facilitate online 3D mapping, meaning that a 3D map of the scene is immediately and incrementally generated as the image data are acquired or being received. FaSS-MVS is composed of a hierarchical processing scheme in which depth and normal data, as well as corresponding confidence scores, are estimated in a coarse-to-fine manner, allowing efficient processing of large scene depths, such as those inherent in oblique images acquired by UAVs flying at low altitudes. The actual depth estimation uses a plane-sweep algorithm for dense multi-image matching to produce depth hypotheses from which the actual depth map is extracted by means of a surface-aware semi-global optimization, reducing the fronto-parallel bias of Semi-Global Matching (SGM). Given the estimated depth map, the pixel-wise surface normal information is then computed by reprojecting the depth map into a point cloud and computing the normal vectors within a confined local neighborhood. In a thorough quantitative and ablative study, we show that the accuracy of the 3D information computed by FaSS-MVS is close to that of state-of-the-art offline multi-view stereo approaches, with the error not even an order of magnitude higher than that of COLMAP. At the same time, however, the average runtime of FaSS-MVS for estimating a single depth and normal map is less than 14% of that of COLMAP, allowing us to perform online and incremental processing of full HD images at 1-2 Hz.

The Composition Diagram of a Complex Process: Enhancing Understanding of Hierarchical Business Processes

The article presents the Composition Diagram of a Complex Process (CDCP), a new diagramming method for modelling business processes with complex vertical structures. This Method addresses the limitations of traditional modelling techniques such as BPMN, Activity Diagrams (AD), and Event-Driven Process Chains (EPC).The experiment was carried out on 277 students from different study programs and grades to determine the effectiveness of the methods. The main objective was to evaluate the usability and effectiveness of CDCP compared to established methods, focusing on two primary tasks: interpretation and diagram creation. The participant's performance was evaluated based on the objective results of the tasks and the subjective feedback of the questionnaire. The results indicate that CDCP was the effective method for the reading and drawing tasks, outperforming BPMN and EPC in terms of understanding and ease of use. Statistical analysis of variance showed that while the year of the study did not significantly affect performance, the study program and Method used had a significant effect. These findings highlight the potential of CDCP as a more accessible and intuitive business process modelling tool, even for users with minimal prior experience.

Suitable sites for groundwater recharge structures in the north region of Gujarat state using analytical hierarchical process and weighted overlay analysis: A comparative approach

Suitable sites for groundwater recharge structures in the north region of Gujarat state using analytical hierarchical process and weighted overlay analysis: A comparative approach

주민직선교육감제 도입 이후의 교육청 변화에 대한 교육당사자들의 인식 분석 - 2009년 4월부터 2022년 6월 동안을 중심으로 -

The purpose of this study is to analyze the perceptions of educational parties after the introduction of the elected superintendent of education, which became an inflection point for educational autonomy, to derive implications based on the performance of the Office of Education and the causes, patterns, limitations, demands, and improvement tasks of the change. To this end, 25 experts were selected into five groups and conducted group and in-depth interviews. Semi-structured questionnaires were used for interviews, and the contents were classified through categorization and hierarchical process, and cross-verification among researchers was conducted. As a result of the study, the causes of change were society and learning environment, institutions, philosophy and awareness, and the Office of Education, and the paradigm shift to patterns, and limitations were the lack of direction for educational autonomy, lack of structure and system, and the Office of Education. As demands and improvement tasks, progress and conservatism emerged. Based on the research results, suggestions were made to ensure the sustainability of the achievements of the elected superintendent of education.

Gradation optimization of AC-20 asphalt mixture based on the fuzzy analytic hierarchy process and comprehensive evaluation method

The aggregate gradation of asphalt mixture is one of the most important factors affecting the service life of asphalt pavement. In order to study the gradation of asphalt mixture with the best comprehensive performance, this study puts forward a new method of mineral aggregate gradation optimization based on the fuzzy analytic hierarchy process and comprehensive evaluation method, aiming at the multi-level and multi-index evaluation of the road performance of asphalt mixture. First, the orthogonal test design method is applied to design nine target gradations with coarse aggregate (>4.75 mm) and fine aggregate (<4.75 mm) of AC-20 (asphalt concrete) mixture serving as two factors and the upper, middle, and lower positions of the gradation curve as three levels, and then the road performance test research is carried out. Second, a comprehensive model for evaluation of road performance of mineral aggregate gradation is established. A fuzzy complementary judgment matrix is constructed, and the index weights of each level and the hierarchical total ranking weight are calculated. Then, the membership function is introduced into the comprehensive evaluation model for the road performance of mineral aggregate gradation, and the membership values of each index of asphalt mixture road performance are obtained. Finally, the fuzzy comprehensive evaluation method is used to find out the comprehensive evaluation value of the road performance of the nine graded asphalt mixtures, and the mineral aggregate gradation is optimized. The research results show that the 1# gradation of the asphalt mixture has the highest comprehensive road performance evaluation value, and the combination of the fuzzy hierarchical analysis process and comprehensive evaluation method can more objectively and comprehensively evaluate the comprehensive road performance of the asphalt mixture and provide a useful reference for the optimal selection of asphalt mixture mineral gradation.

GPMelt: A hierarchical Gaussian process framework to explore the dark meltome of thermal proteome profiling experiments.

Thermal proteome profiling (TPP) is a proteome wide technology that enables unbiased detection of protein drug interactions as well as changes in post-translational state of proteins between different biological conditions. Statistical analysis of temperature range TPP (TPP-TR) datasets relies on comparing protein melting curves, describing the amount of non-denatured proteins as a function of temperature, between different conditions (e.g. presence or absence of a drug). However, state-of-the-art models are restricted to sigmoidal melting behaviours while unconventional melting curves, representing up to 50% of TPP-TR datasets, have recently been shown to carry important biological information. We present a novel statistical framework, based on hierarchical Gaussian process models and named GPMelt, to make TPP-TR datasets analysis unbiased with respect to the melting profiles of proteins. GPMelt scales to multiple conditions, and extension of the model to deeper hierarchies (i.e. with additional sub-levels) allows to deal with complex TPP-TR protocols. Collectively, our statistical framework extends the analysis of TPP-TR datasets for both protein and peptide level melting curves, offering access to thousands of previously excluded melting curves and thus substantially increasing the coverage and the ability of TPP to uncover new biology.

Patient mistreatment, emotional exhaustion and work-family conflict among nurses: a moderated mediation model of social sharing of negative work events and perceived organizational support

BackgroundNursing literature suggested that patient mistreatment has significant impacts on nurses’ emotions and job burnout. Yet, further research is needed to understand the underlying mechanism and the spillover effect on nurses’ families. Leveraging the goal progress theory, this study aimed to examine the association between patient mistreatment, nurses’ emotional exhaustion, and work-family conflict, as well as the mediating role of social sharing of negative work events and the moderating role of perceived organizational support.MethodsDuring the COVID-19 pandemic in China, a cross-sectional study was conducted with a sample of 1627 nurses from the Hematology Specialist Alliance of Chongqing from October to November 2022. Questionnaires were administered to measure patient mistreatment, perceived organizational support, social sharing of negative work events, emotional exhaustion, and work-family conflict. Hierarchical linear regression and conditional processes were used for statistical analyses.ResultsPatient mistreatment was positively associated with emotional exhaustion (β = 0.354, p < 0.001) and work-family conflict (β = 0.314, p < 0.001). Social sharing of negative work events played a partial mediating role in the relationship between patient mistreatment and emotional exhaustion (effect = 0.067, SE = 0.013), and work-family conflict (effect = 0.077, SE = 0.014). Moderated mediation analysis found that the mediation effect was stronger when the perceived organizational support was high.ConclusionOur findings reveal the amplifying effect of social sharing of negative work events on nurses’ emotional exhaustion and work-family conflict. Perceived organizational support strengthens the positive effect of patient mistreatment on the social sharing of negative work events, thus resulting in increased emotional exhaustion and work-family conflict. We also discuss practical implications, limitations, and directions for future research.

An advanced dual-layered framework for sustainable supply chain performance

Purpose This study aims to evaluate the sustainable supply chain performance indicators. At a macro level, the identification of the sustainable supply chain management (SSCM) performance indicators is done through exhaustive literature survey and interviews with experts. Furthermore, these indicators are evaluated through a hybrid approach, i.e. total weighted interpretive structural modelling (TWISM) followed by analytic hierarchical process (AHP). Design/methodology/approach Micro small and medium enterprises (MSMEs) in India are a major contributor to nation’s GDP. However, this sector struggles to comprehend benefits from implementation of SSCM due to a lack of appropriate performance evaluation metrics. The purpose of this paper is to contribute to the body of knowledge in SSCM by proposing and evaluating a set of SSCM performance indicators. Findings The paper highlights the SSCM performance indicators and concludes that business strategies, implementation planning and impact of stakeholders are the top SSCM performance indicators (SPIs). Therefore, the decision-makers must initially focus on strategic requirements which foster the implementation of SSCM, thereby ensuring profitability for all stakeholders. Research limitations/implications Although the proposed framework was validated through a case study on Indian automobile component manufacturing MSMEs, future research would explore the extension of the framework to other industries. Originality/value The originality of this study lies in the application of the novel TWISM-AHP tool. Furthermore, the SPIs identified in the study, consider the integration of the triple bottom line from the MSME perspective. The TWISM-AHP analysis will be beneficial for SC decision-makers to enhance the SSCM performance based on the identified indicators and their criticality.

The analytical hierarchical process (AHP) approach to study the tourist choice behavior in rural southern India: Promoting responsible tourism practices

The primary objective of the study is to evaluate the prioritization of the services marketing mix and compare the outcomes between first-time and repeat visitors in selecting a rural destination. Six rural tourism destinations were selected from four different southern states of India. Approximately 512 respondents consisting of 292 first-time visitors and 220 multiple-time visitors participated in a questionnaire survey carried out from May 2023 to October 2023. Multicriteria method, Analytical Hierarchy Process (AHP) is applied to examine the prioritisation of services marketing mix components and ranking is done concerning the reason behind choosing a particular rural destination. The present study reveals that many new visitors favor rural tourist locations due to their affordability and variety of experiences. In addition, repeat visitors also choose rural areas based on the native resident’s attitude, customer service, cultural orientation, training, language proficiency, information and communication processes and facilities and services. Efficient marketing is important for promoting tourism in rural destinations and increase the arrival of tourists . The article also proposes the adoption of responsible tourism practices, which are essential for protecting cultural assets and ensuring that local populations get fair and equal benefits from tourism. The research also provides significant insights to destination management firms on the formulation of services marketing mix strategies, policy choices, and initiatives aimed at expanding rural tourism in India.

Meeting the multidimensional self: fostering selfhood at the interface of Creative Arts Therapies and neuroscience.

Intriguing explorations at the intersection of the fields of neuroscience and psychology are driven by the quest to understand the neural underpinnings of "the self" and their psychotherapeutic implications. These translational efforts pertain to the unique Creative Arts Therapies (CATs) and the attributes and value of the self-related processes they offer. The self is considered as a multi-layered complex construct, comprising bodily and mental constituents, subjective-objective perspectives, spatial and temporal dimensions. Neuroscience research, mostly functional brain imaging, has proposed cogent models of the constitution, development and experience of the self, elucidating how the multiple dimensions of the self are supported by integrated hierarchical brain processes. The psychotherapeutic use of the art-forms, generating aesthetic experiences and creative processes, touch upon and connect the various layers of self-experience, nurturing the sense of self. The present conceptual analysis will describe and interweave the neural mechanisms and neural network configuration suggested to lie at the core of the ongoing self-experience, its deviations in psychopathology, and implications regarding the psychotherapeutic use of the arts. The well-established, parsimonious and neurobiologically plausible predictive processing account of brain-function will be discussed with regard to selfhood and consciousness. The epistemic affordance of the experiential CATs will further be portrayed, enabling and facilitating the creation of updated self-models of the body in the world. The neuropsychological impact of the relational therapeutic encounter will be delineated, acknowledging the intersubjective brain synchronization through communicative verbal and non-verbal means and aesthetic experiences. The recognition and assimilation of neuroscientific, phenomenological and clinical perspectives concerning the nested dimensionality of the self, ground the relational therapeutic process and the neuroplastic modulations that CATs have to offer on the premise of fostering, shaping and integrating selfhood.

Hierarchical neural processing in γ oscillations for syntactic and semantic operations accounts for first- and second-language epistemology.

We discuss event-related power differences (ERPDs) in low- and broadband-γ oscillations as the embedded-clause edge is processed in wh-dependencies such as Which decision regarding/about him/her did Paul say that Lydie rejected without hesitation? in first (L1) and second language (L2) French speakers. The experimental conditions manipulated whether pronouns appeared in modifiers (Mods; regarding him/her) or in noun complements (Comps; about him/her) and whether they matched or mismatched a matrix-clause subject in gender. Across L1 and L2 speakers, we found that anaphora-linked ERPDs for Mods vs. Comps in evoked power first arose in low γ and then in broadband γ. Referential elements first seem to be retrieved from working memory by narrowband processes in low γ and then referential identification seems to be computed in broadband-γ output. Interactions between discourse- and syntax-based referential processes for the Mods vs. Comps in these ERPDs furthermore suggest that multidomain γ-band processing enables a range of elementary operations for discourse and semantic interpretation. We argue that a multidomain mechanism enabling operations conditioned by the syntactic and semantic nature of the elements processed interacts with local brain microcircuits representing features and feature sets that have been established in L1 or L2 acquisition, accounting for a single language epistemology across learning contexts.

Blocked Gibbs Sampler for Hierarchical Dirichlet Processes

Posterior computation in hierarchical Dirichlet process (HDP) mixture models is an active area of research in nonparametric Bayes inference of grouped data. Existing literature almost exclusively focuses on the Chinese restaurant franchise (CRF) analogy of the marginal distribution of the parameters, which can mix poorly and has a quadratic complexity with the sample size. A recently developed slice sampler allows for efficient blocked updates of the parameters, but is shown to be statistically unstable in our article. We develop a blocked Gibbs sampler that employs a truncated approximation of the underlying random measures to sample from the posterior distribution of HDP, which produces statistically stable results, is highly scalable with respect to sample size, and is shown to have good mixing. The heart of the construction is to endow the shared concentration parameter with an appropriately chosen gamma prior that allows us to break the dependence of the shared mixing proportions and permits independent updates of certain log-concave random variables in a block. En route, we develop an efficient rejection sampler for these random variables leveraging piece-wise tangent-line approximations. Supplementary materials, which include substantive additional details and code, are available online.

Assessment of landslide susceptibility in the Himalayan state of Tripura, India, using a Multi-Model Approach

Landslides are the down slope mass movement of soil, rocks, and debris due to a natural or human activities resulting in widespread hazard events in India. The most affected areas comprise 15 percent of its landmass which includes Tripura and eleven Himalayan states and parts of the Western and Eastern Ghats in India. In Tripura, landslides cause road blockage and destruction of settlements, bringing economic and life losses in every year. Thus, this research is focused on identifying landslide susceptible zones and the significant causative factors behind landslides. Assessment of Landslide Susceptibility (LS) identifies fifteen major causative factors under five broad groups; topographic, geotechnical, hydrological, environmental, and anthropogenic. With application of Analytical Hierarchical Process (AHP), Frequency Ratio (FR), and Random Forest (RF)-based models were performed to extract landslide susceptible zonation map for Tripura. This study reveals that the successive hill ranges formed by young sedimentary lithologic formations associated with deforestation, heavy rainfall during monsoon, and anthropogenic activities (road constructions and jhumming) are the responsible geo-conditions for triggering landslides. In this study, while the AHP and FR model show only 1.95% and 11.46% confined along the hilltop of Jampui, Sakhan, and Longtarai, the RF model designated Tripura’s 30% land area as high and very high landslide susceptible zones (LSZ), predominantly over hills, foothills, and low laying undulating land (tillas). For the accuracy assessment, the ROC curve is used, which shows that RF model appears to be the maximum accurate (0.810) one, followed by FR (0.806) and AHP (0.744).

In-depth comparative pathogenome, virulome, and resistome analysis of an extensive drug resistant Ralstonia mannitolilytica strain isolated from blood

IntroductionRalstonia mannitolilytica is an global opportunistic pathogen responsible for various diseases. In this study, we reported the genome of a R. mannitolilytica isolate responsible for bacteremia in an acute exacerbation of chronic obstructive pulmonary disease (AECOPD). MethodsBacterial identification was performed with a Vitek2™ Automated System and 16S rRNA sequencing with BLASTn against the Non-Redundant Protein Sequence (Nr) database. Genome sequencing and analysis were performed using PacBio RS II sequencer, Hierarchical Genome Assembly Process assembly, as well as multiple annotation databases to better understand the innate features. Antibiotic resistance genes and virulence factors were specifically identified through Antibiotic Resistance Genes database and Virulence Factors of Pathogenic Bacteria databases. ResultsThe complete genome sequence was assembled into two chromosomes with 3,495,817 bp and 1,342,871 bp in length and GC% of 65.37 % and 66.43 %, respectively. The two chromosomes were fully annotated. In chromosome 1 and 2, 19 and 14 antibiotic resistant genes and 48 and 55 virulence factors were predicted, respectively. Specifically, beta-lactam resistance genes blaOXA-443, blaOXA-444 were acquired. ConclusionsThis study aids in the understanding of the innate features of R. mannitolilytica in AECOPD.

MAFormer: A cross-channel spatio-temporal feature aggregation method for human action recognition

Human action recognition has been widely used in fields such as human–computer interaction and virtual reality. Despite significant progress, existing approaches still struggle with effectively integrating hierarchical information and processing data beyond a certain frame count. To address these challenges, we introduce the Multi-AxisFormer (MAFormer) model, which is organized in terms of spatial, temporal, and channel dimensions of the action sequence, thereby enhancing the model’s understanding of correlations and intricate structures among and within features. Drawing on the Transformer architecture, we propose the Cross-channel Spatio-temporal Aggregation (CSA) structure for more refined feature extraction and the Multi-Axis Attention (MAA) module for more comprehensive feature aggregation. Moreover, the integration of Rotary Position Embedding (RoPE) boosts the model’s extrapolation and generalization abilities. MAFormer surpasses the known state-of-the-art on multiple skeleton-based action recognition benchmarks with the accuracy of 93.2% on NTU RGB+D 60 cross-subject split, 89.9% on NTU RGB+D 120 cross-subject split, and 97.2% on N-UCLA, offering a novel paradigm for hierarchical modeling in human action recognition.

L-FNNG: Accelerating Large-Scale KNN Graph Construction on CPU-FPGA Heterogeneous Platform

Due to the high complexity of constructing exact k -nearest neighbor graphs, approximate construction has become a popular research topic. The NN-Descent algorithm is one of the representative in-memory algorithms. To effectively handle large datasets, existing state-of-the-art solutions combine the divide-and-conquer approach and the NN-Descent algorithm, where large datasets are divided into multiple partitions, and a subgraph is constructed for each partition before all the subgraphs are merged, reducing the memory pressure significantly. However, such solutions fail to address inefficiencies in large-scale k -nearest neighbor graph construction. In this paper, we propose L-FNNG, a novel solution for accelerating large-scale k -nearest neighbor graph construction on CPU-FPGA heterogeneous platform. The CPU is responsible for dividing data and determining the order of partition processing, while the FPGA executes all construction tasks to utilize the acceleration capability fully. To accelerate the execution of construction tasks, we design an efficient FPGA accelerator, which includes the Block-based Scheduling (BS) and Useless Computation Aborting (UCA) techniques to address the problems of memory access and computation in the NN-Descent algorithm. We also propose an efficient scheduling strategy that includes a KD-tree-based data partitioning method and a hierarchical processing method to address scheduling inefficiency. We evaluate L-FNNG on a Xilinx Alveo U280 board hosted by a 64-core Xeon server. On multiple large-scale datasets, L-FNNG achieves, on average, 2.3× construction speedup over the state-of-the-art GPU-based solution.

Site suitability analysis of urban green parks in Ranchi city using GIS–AHP based multi-criteria decision analysis

Purpose The presence of green spaces plays a vital role in promoting urban sustainability. Urban green parks (UGPs) help create sustainable cities while providing fundamental ecological functions. However, rapid urbanization has destroyed crucial green areas in Ranchi City, endangering inhabitants’ health. This study aims to locate current UGPs and predict future UGP sites in Ranchi City, Jharkhand. Design/methodology/approach It uses geographic information system (GIS) and analytical hierarchical process (AHP) to evaluate potential UGP sites. It involves the active participation of urban communities to ensure that the UGPs are designed to meet dweller’s needs. The site suitability assessment is based on several parameters, including the normalized difference vegetation index (NDVI), land use and land cover (LULC), population distribution, PM 2.5 levels and the Urban Heat Island (UHI) effect. The integration of these factors enables an evaluation of potential UGP’s sites. Findings The findings of this research reveal that 54.39% of the evaluated areas are unsuitable, 15.55% are less suitable, 12.76% are moderately suitable, 11.52% are highly suitable and 5.78% are very highly suitable for UGPs site selection. These results emphasize that the middle and outer regions of Ranchi City are the most favorable locations for establishing UGPs. The NDVI is the most important element in UGP site appropriateness, followed by LULC, population distribution, PM 2.5 levels and the UHI effect. Originality/value This study improves the process of integrating AHP and GIS, and UGPs site selection maps help urban planners and decision-makers make better choices for Ranchi City’s sustainability and greenness.

Geospatial analysis of elephant habitat suitability and movement for mapping the elephant corridor in Dalma Wildlife Sanctuary (India).

Wildlife and natural resources constitute an integral part of the ecosystem, whereas human interventions dismantled the living conditions of the wildlife. This is testified in the Dalma Wildlife Sanctuary (DWS) where the habitats of Asian elephants have changed due to human intervention and deforestation over the decades. The present study aimed to assess the elephant habitat suitability in the DWS of Jharkhand state (India) using the geospatial parameters such as forest density, degree of slope, proximity to water bodies, land use land cover, proximity to agricultural land, built-up density, and road density. The analytical hierarchical process technique was utilized to determine habitat preference and selection of relevant factors to categorize criteria. The study revealed that about 6.7% (26.74 km2) of the area is very highly suitable for elephant habitat, while 52.26% (208.49 km2) of the forest area was found highly suitable. The most suitable habitat was identified in the core parts of the forest, while the least suitable areas were found in the southern part, where the presence of roads, built-up, and agricultural land was prominent. It was also observed that most human-elephant conflicts were exhibited in the low and very low suitable areas, while 90% of the elephant movement was witnessed in the high and very high suitable areas. Among the four identified corridors, three are inactive, and their location corresponds with low to very low suitable habitats. The study identified the migratory corridor routes inside the sanctuary where effective management is required for the conservation of elephant habitats and minimizing conflicts.

Unveiling Hierarchical Self-Assembly of Triazolylferrocenyl Dendrimers: Producing Non-Traditional Intrinsically Green Fluorescent Vesosomes for Nanotheranostics.

Dendrimers and supramolecular chemistry continue to fascinate researchers due to the endless unrevealed potential of their combination. This study investigates the self-assembly process of a series of hydrophobic triazolylferrocenyl dendrimers in aqueous medium. Deep investigation through NMR spectroscopy, absorption UV-vis spectroscopy along with theoretical simulations demonstrates that the ferrocenyl moieties interact intramolecularly and intermolecularly driving the self-assembly process. Data obtained by DLS, NTA, SEM, TEM, and EF-TEM demonstrate that these dendrimers, in water, spontaneously self-assemble through a hierarchical process. The dendrimers first self-assemble into uniform nanovesicles, which in turn self-assemble into larger vesosomes. The resulting vesosomes emit green non-traditional intrinsic fluorescence, which is a property that emerged from the self-assembled architectures. The vesosomes are efficiently uptaken by cancer cells and induce significant cytotoxic activity against the cancer cell line MCF-7, up to the submicromolar concentration. Positive dendritic effects are identified in the fluorescence intensity and in the cytotoxic activity of the vesosomes, which follow the trend G0-9Fc < G1-27Fc < G2-81Fc. This work showcases the remarkable potential of combining the two dynamic fields of dendrimers and supramolecular chemistry, which resulted in green fluorescent vesosomes capable of performing the dual role of cell imaging and killing, with potential applications in nanotheranostics.