Hierarchical Construction Research Articles

Numerical investigation of hierarchical composite construction for enhancing toughness and seismic protection of offshore structures

Numerical investigation of hierarchical composite construction for enhancing toughness and seismic protection of offshore structures

Development and Validation of a Laparoscopic Sacrocolpopexy Training Model.

Proper training is necessary to develop the highly specialized skills required to safely perform laparoscopic sacrocolpopexy. Currently, there is no validated training model for laparoscopic sacrocolpopexy that includes dissection of the presacral space, both vaginal and presacral mesh attachments, and peritoneal closure. This study aimed to create a procedure specific hierarchical task analysis for laparoscopic sacrocolpopexy and then develop and validate a corresponding laparoscopic sacrocolpopexy pelvic training model for the simulation environment. This was an observational simulation study that was divided into 5 phases: (1) development of hierarchical task analysis, (2) model construction, (3) participant recruitment and simulation testing, (4) reliability and validity testing, and (5) creation of a standard passing performance measure. Construct, face, and content validity were established for this model. According to the participating experts, the model was able to replicate the steps of presacral dissection, anterior vaginal and sacral mesh attachment, and peritoneal closure. Thirteen trainees and 5 experts completed the simulation, and all "agreed" or "strongly agreed" that the model seemed useful for improving suturing technique and learning the procedure. Additionally, a passing performance measure was determined through contrasting groups methodology. We developed a novel, reusable, and validated training model that can be utilized as a training resource for the many critical skills necessary to safely and efficiently perform laparoscopic sacrocolpopexy.

A dynamic capability perspective on the impact of big data analytics and enterprise architecture on innovation: an empirical study

PurposeThe prevailing conceptualization of information system (IS) capabilities, rooted in the resource-based view (RBV) framework, tends to focus on unique firm resources. In the digital age, as emphasized by dynamic capabilities (DC), resource reconfiguration is critical in maintaining strategic advantage. This paper focuses on big data analytics capabilities (BDAC) from a DC perspective to present a novel conceptualization of BDAC–DC. We examine its effects on product, business model and business process innovation, including the effects of enterprise architecture (EA) on the BDAC business model innovation relationship.Design/methodology/approachThis research presents a novel DC-based BDAC conceptualization, operationalized as a hierarchical construct. A survey-based approach is used for data collection and data analysis is done using partial least squares structural equation modeling (PLS-SEM).FindingsThe novel conceptualization and the effects of BDAC DC on BDA sensing-seizing and reconfiguration capacities support BDAC’s functional and evolutionary roleplay. Empirical results confirm the positive effects of BDAC–DC on first-order value targets (innovation) and the moderating effects of EA.Research limitations/implicationsThe novel BDAC–DC conceptualization has several implications for BDAC, DC, EA and business value research. Practicing managers must adopt a multifaceted approach to BDAC development by considering non-technical and organizational factors, collaborate with their business counterparts to explore unique big data ideas, initiate proof-of-concept projects to secure support and allocate resources synchronously, considering a multidimensional view of the process, product and business model innovation.Practical implicationsPracticing managers must adopt a multifaceted approach to BDAC development by considering non-technical and organizational factors, collaborate with their business counterparts to explore unique big data ideas, initiate proof-of-concept projects to secure support and allocate resources synchronously, considering a multidimensional view of the process, product and business model innovation for synergistic outcomes.Originality/valueTo the best of our knowledge, this research is the first attempt toward DC-based BDAC conceptualization, empirical validation of first-order effects on various forms of innovation and the often-overlooked role of critical EA capability.

Establishing a Comprehensive Hierarchical construct of Eustress (CHE)

AbstractEustress as a positive response to challenging situations has received increasing attention across diverse literatures, reflecting its potential to improve wellbeing, work performance, and personal growth. In the process, eustress has been defined, measured, and manipulated in myriad ways, leading to fragmentation and vagueness. Because a unified and well-specified construct would significantly support eustress research, we developed one here. Rather than basing it on our subjective views, we developed it empirically, extracting 57 unique features of eustress from 80 theoretical, interventional, empirical, and psychometric articles. Organizing and interpreting these 57 features produced a Comprehensive Hierarchical construct of Eustress (CHE). According to CHE, eustress emerges from three sources: (1) successful goal-directed action, (2) experiencing the moment in an enjoyable, fulfilling, or meaningful manner, and (3) positive stable qualities of the individual. Within each source, CHE establishes specific facets of eustress hierarchically, which in turn organize the 57 eustress features extracted initially. Bibliometric analyses identified CHE’s hierarchical elements addressed most often in the eustress literatures. Overall, these results suggest that eustress cannot be specified with a simple definition but should instead be viewed as a family resemblance structure having statistical properties. Rather than taking a single form, eustress manifests itself as diverse states during successful goal-directed action and fulfilling momentary experience. Regularly producing eustress in these manners likely establishes CHE’s trait-like qualities for generating eustress effectively on future occasions. Interestingly, these qualities overlap highly with well-established elements of wellbeing, suggesting that wellbeing contributes to eustress in challenging situations.

3D-printed redox-active polymer electrode with high-mass loading for ultra-low temperature proton pseudocapacitor

The stable operation of supercapacitors at extremely low temperatures is crucial for applications in harsh environments. Unfortunately, conventional inorganic electrodes suffer from sluggish diffusion kinetics and poor cycling stability for proton pseudocapacitors. Here, a redox-active polymer poly(1,5-diaminonaphthalene) is developed and synthesized as an ultrafast, high-mass loading, and durable pseudocapacitive anode. The charge storage of poly(1,5-diaminonaphthalene) depends on the reversible coordination reaction of the C=N group with H+, which enables fast kinetics associated with surface-controlled reactions. The 3D-printed organic electrode delivers a remarkable areal capacitance (8.43 F cm–2 at 30.78 mg cm−2) and thickness-independent rate performance. Furthermore, the 3D-printed proton pseudocapacitor exhibits great low-temperature tolerance and delivers a high energy density of 0.44 mWh cm−2 at −60 °C, as well as operates well even at −80 °C. This work signifies that combining organic material design with 3D hierarchical network electrode construction can provide a promising solution for low-temperature-resistant supercapacitors.

Why do people share (mis)information? Power motives in social media

We investigated whether individuals driven by power motives are more inclined to disseminate (mis)information within their online networks. Four studies (N = 1882) assessed or manipulated chronic and context-specific power motives, alongside other social hierarchical constructs such as actual power. Our findings revealed that both chronic and context-specific power motives were significantly associated with increased dissemination of posts and news in daily interactions and in a simulated sharing task. Power-motivated individuals were found to disproportionately spread more misinformation and demonstrated greater awareness of having disseminated misinformation in the past. Moreover, sharing (mis)information appeared to reinforce the sense of power among these individuals. Effect size magnitudes were moderate in an internal meta-analysis. Interestingly, actual power per se did not influence the spread of (mis)information. This study contributes valuable insights to the ongoing discourse on the motivations behind the spread of (mis)information on social media, highlighting the role of power motives in driving such behaviors.

Hierarchical Interfacial Construction by Grafting Cellulose Nanocrystals onto Carbon Fiber for Improving the Mechanical Performance of Epoxy Composites.

Carbon fiber-reinforced composites have been widely used in the aerospace industry because of their superior comprehensive performance, including high strength, low density, fatigue resistance, long service life, etc. The interface between the fiber reinforcement and the matrix is one of the key factors that determines the performance of the composites. The construction of covalent bonding connections between the components has proven to be an effective strategy for improving the interfacial bonding strength but always reduces the toughness. In this work, dual silane coupling agents are applied to covalently connect cellulose nanocrystals (CNCs) onto carbon fibers, constructing hierarchical interfacial connections between the fibers and the epoxy matrix and significantly improving the interfacial bonding strength. As a result, the tensile strength of the epoxy composites increased from 519 MPa to nearly 900 MPa, which provides a potential approach for significantly improving the mechanical performance of composites.

DeepExtract: Semantic-driven extractive text summarization framework using LLMs and hierarchical positional encoding

In the age of information overload, the ability to distill essential content from extensive texts is invaluable. DeepExtract introduces an advanced framework for extractive summarization, utilizing the groundbreaking capabilities of GPT-4 along with innovative hierarchical positional encoding to redefine information extraction. This manuscript details the development of DeepExtract, which integrates semantic-driven techniques to analyze and summarize complex documents effectively. The framework is structured around a novel hierarchical tree construction that categorizes sentences and sections not just by their physical placement within a text, but by their contextual and thematic significance, leveraging dynamic embeddings generated by GPT-4. We introduce a multi-faceted scoring system that evaluates sentences based on coherence, relevance, and novelty, ensuring that summaries are not only concise but rich with essential content. Further, DeepExtract employs optimized semantic clustering to group thematic elements, which enhances the representativeness of the summaries. This paper demonstrates through comprehensive evaluations that DeepExtract significantly outperforms existing extractive summarization models in terms of accuracy and efficiency, making it a potent tool for academic, professional, and general use. We conclude with a discussion on the practical applications of DeepExtract in various domains, highlighting its adaptability and potential in navigating the vast expanses of digital text.

Is L2 grit a hierarchical construct? A meta-analysis targeting language learners

ABSTRACT L2 grit has attracted much attention from language acquisition researchers since it is an important contributing factor in students’ achieving language learning goals. The current meta-analysis reviewed and synthesised L2 grit literature among language learners to investigate whether L2 grit is a hierarchical construct. Results based on 293 effect sizes from 57 studies representing data from 60 unique samples and 25,570 participants showed two major findings: first, the correlation between perseverance of effort (PE) and consistency of interest (CI) (i.e. the two components of L2 grit) was at a medium-to-large effect size of ρ = .57, 95% CI [.51, .62]; second, the overall L2 grit exhibited no less predictive power than each facet alone. Results suggest that L2 grit could be regarded as a hierarchical construct above PE and CI in the language learning context. Implications of the present study and future study directions were discussed.

Hierarchical visual semantic guidance for enhanced relationship recognition in domain knowledge graphs

Multi-modal entity relationship recognition is a crucial foundation for constructing accurate and comprehensive domain knowledge graphs. However, due to the sparse and diverse nature of textual semantic information and the semantic differences exhibited by multi-modal data, existing methods often suffer from issues such as information loss and noise features. In this paper, the Hierarchical Visual Semantic Guidance (HVSG) Network is proposed, which utilizes images to furnish visual semantic information for text, facilitating the identification of implicit relationships between entities. Specifically, salient local instance objects are initially extracted from the global image, followed by the use of a hierarchical visual semantic construction module to establish multi-level visual semantics. The integration of multi-level visual and textual features is then achieved through a visual semantic guidance module. This work constructed a Chinese multi-modal entity relation classification dataset in the domain of unmanned vessels and conducted experiments on two datasets. Compared to state-of-the-art (SOTA) models, HVSG achieved the best performance, with F1 scores improved by 1.05% and 0.58% respectively, indicating that HVSG can offer better performance in exploring implicit relationships between textual entities.

Hierarchical multi-task deep learning-assisted construction of human gut microbiota reactive oxygen species-scavenging enzymes database.

In the process of oxygen reduction, reactive oxygen species (ROS) are generated as intermediates, including superoxide anion (O2-), hydrogen peroxide (H2O2), and hydroxyl radicals (OH-). ROS can be destructive, and an imbalance between oxidants and antioxidants in the body can lead to pathological inflammation. Inappropriate ROS production can cause oxidative damage, disrupting the balance in the body and potentially leading to DNA damage in intestinal epithelial cells and beneficial bacteria. Microorganisms have evolved various enzymes to mitigate the harmful effects of ROS. Accurately predicting the types of ROS-scavenging enzymes (ROSes) is crucial for understanding the oxidative stress mechanisms and formulating strategies to combat diseases related to the "gut-organ axis." Currently, there are no available ROSes databases (DBs). In this study, we propose a systematic workflow comprising three modules and employ a hierarchical multi-task deep learning approach to collect, expand, and explore ROSes-related entries. Based on this, we have developed the human gut microbiota ROSes DB (http://39.101.72.186/), which includes 7,689 entries. This DB provides user-friendly browsing and search features to support various applications. With the assistance of ROSes DB, various communication-based microbial interactions can be explored, further enabling the construction and analysis of the evolutionary and complex networks of ROSes DB in human gut microbiota species.IMPORTANCEReactive oxygen species (ROS) is generated during the process of oxygen reduction, including superoxide anion, hydrogen peroxide, and hydroxyl radicals. ROS can potentially cause damage to cells and DNA, leading to pathological inflammation within the body. Microorganisms have evolved various enzymes to mitigate the harmful effects of ROS, thereby maintaining a balance of microorganisms within the host. The study highlights the current absence of a ROSes DB, emphasizing the crucial importance of accurately predicting the types of ROSes for understanding oxidative stress mechanisms and developing strategies for diseases related to the "gut-organ axis." This research proposes a systematic workflow and employs a multi-task deep learning approach to establish the human gut microbiota ROSes DB. This DB comprises 7,689 entries and serves as a valuable tool for researchers to delve into the role of ROSes in the human gut microbiota.

Fatigue study of twisted polyamide sub-rope for floating wind turbines: Fast evaluation with heat build-up protocol and tomography study of mechanisms

Polyamide 6 fibre ropes are of interest for floating offshore wind turbine mooring lines but their fatigue durability represents a key aspect to characterize and validate on the new constructions and coatings developed for this long term application. This paper presents a fatigue study on laboratory scale polyamide 6 sub-ropes which were wetted before testing. A T-N curve describing ranges of [2%, 70%] of MBL and [102,105] cycles is obtained. An investigation of the damage mechanisms, using X-ray tomography analysis and SEM images, is performed and highlights the complexity of the mechanical response of twisted ropes, due to their hierarchical multi-scale construction. Cyclic loading changes significantly the sub-rope aspect and architecture. Finally, this study describes the use of a heat build-up measurement protocol, for a rapid evaluation of the fatigue properties of ropes. The cyclic dissipated energy was determined from temperature measurements by an infrared camera. A first prediction using this method is proposed and shows that it could provide a powerful solution, to overcome the very long testing times required for fatigue studies on synthetic sub-ropes.

Adaptive hierarchical origami-based metastructures

Shape-morphing capabilities are crucial for enabling multifunctionality in both biological and artificial systems. Various strategies for shape morphing have been proposed for applications in metamaterials and robotics. However, few of these approaches have achieved the ability to seamlessly transform into a multitude of volumetric shapes post-fabrication using a relatively simple actuation and control mechanism. Taking inspiration from thick origami and hierarchies in nature, we present a hierarchical construction method based on polyhedrons to create an extensive library of compact origami metastructures. We show that a single hierarchical origami structure can autonomously adapt to over 103 versatile architectural configurations, achieved with the utilization of fewer than 3 actuation degrees of freedom and employing simple transition kinematics. We uncover the fundamental principles governing theses shape transformation through theoretical models. Furthermore, we also demonstrate the wide-ranging potential applications of these transformable hierarchical structures. These include their uses as untethered and autonomous robotic transformers capable of various gait-shifting and multidirectional locomotion, as well as rapidly self-deployable and self-reconfigurable architecture, exemplifying its scalability up to the meter scale. Lastly, we introduce the concept of multitask reconfigurable and deployable space robots and habitats, showcasing the adaptability and versatility of these metastructures.

Optimization techniques for green supply chain practice challenges: a systematic hybrid approach

Optimization techniques necessitate a diverse focus to overcome green supply chain practice (GSCP) challenges, to address a variety of conflicting problems and uncertain challenges. This study aims to review the literature on optimization techniques for GSCP challenges, revealing the intricate interrelationship among attributes under uncertainty for hierarchical structure construction, and presenting insights and priorities for future research. Data were collected from the Scopus database, resulting in a total of 465 publications. An integrated method of systematic review was used to review and validate the GSCP optimization techniques. Hence, assessments of multi-objective optimization and uncertainty are pivotal for enhancing GSCP. The challenges posed by GSCP underscore the need to use heuristic algorithms, mixed integer programming, goal programming, stochastic programming and the non-dominated sorting genetic algorithm II in practical applications. This study offers a contribution to the literature on GSCP and provides valuable information to assist researchers and practitioners using optimization techniques.

Online commodity recommendation model for interaction between user ratings and intensity-weighted hierarchical sentiment: A case study of LYCOM

The online commodity recommendation (OCR) model mines users’ historical behavior characteristics and recommends products that may be of interest according to user preferences. Online reviews are among the most important information sources for OCR. However, the explicit and implicit emotional words in online review texts have different structures in the expression of multi-attribute emotions. To fully utilize review information and improve the recommendation accuracy, we propose an OCR model that considers the interaction of multiple attributes and hierarchical emotions and calculates a score weighted by emotion intensity. First, to balance the efficiency and accuracy of information extraction while considering the coexistence of explicit and implicit expressions in online review text, a multi-attribute hierarchical emotion lexicon construction method is proposed. Second, based on the advantage of intuitionistic fuzzy sets in terms of information expression superiority, multi-attribute review text information expression of the affective polarity and intensity of online review text is realized. Then, combined with the weighted singular value decomposition and factorization machine method, we propose an OCR model for interactions between multi-attribute emotions and scores through fusion and recombination of the eigenvectors of users and products. Finally, tourism products on the LYCOM website are used as an example to verify the effectiveness of the proposed method.

Scroll, Stop, Shop: Decoding impulsive buying in social commerce

Technological advancements within social commerce platforms, such as “action buttons” on Facebook or Instagram (e.g., the “Shop” button), have simplified how customers interact with brands, leading to greater impulse buying opportunities. Using a mixed methodology in two studies involving Instagram users in the United States (US), this research conceptualizes (Study 1) and operationalizes (Study 2) a higher-order model of consumer motivation for social commerce engagement as a hierarchical construct that drives the urge to impulse buy. Further, consumer–brand identification and perceived social risk of commenting are included in the proposed framework. Results show consumer motives of brand intimacy, shopping planning, entertainment, channel advantage, remuneration, and escapism influence impulse buying. Findings further identify consumer–brand identification as a mediating mechanism between consumer motivation and the urge for impulse buying, which also neutralizes the negative moderating effect of the perceived social risk of commenting on social platforms. This study contributes to motivation and impulse buying theory as well as social commerce practice. The findings inform content marketing activities seeking to heighten customers’ urge for impulse buying and mitigate contingency variables. This study also acknowledges related ethical considerations, including the need for brands to understand the urge to impulse buying behaviors to ensure responsible marketing practices that align with principles of consumer well-being.

Outlier classification for microbiological open set recognition

Precise detection and effective intervention of pathogenic microorganisms are pivotal to ensure crop yield. Traditional open set recognition methods distinguish unknown classes, i.e., those not involved in training, as outliers or separate classes, which is insufficient for real-world applications like recognizing airborne microorganisms and particles. In this paper, a new setting for the classification of outliers for open set recognition is proposed. The proposed method classifies unknown classes while achieving the classification of known classes, treating each category in the open set equally. The two cores of the method are hierarchical feature representation and feature gallery construction. To enhance the feature representation, this paper proposes a novel architectural module of spatial attention on region hierarchy, namely SPARC. This module extracts features from hierarchical levels and models spatial interaction through the attention mechanism. With the induction of local-to-global description, minute differences with ambiguous appearances between classes can be detected, and the negative effects of image quality degradation can be ameliorated. Furthermore, a feature gallery containing the feature representation from both known and unknown classes is developed for feature retrieval. To demonstrate the effectiveness of the proposed model, the first dataset composed of agricultural microorganisms, named SMAD, is constructed, and experiments are conducted on existing datasets and the newly proposed SMAD dataset. The experimental results outperform the prior art on these datasets under both the outlier rejection and outlier classification settings.

A novel method of swin transformer with time-frequency characteristics for ECG-based arrhythmia detection.

Arrhythmia is an important indication of underlying cardiovascular diseases (CVD) and is prevalent worldwide. Accurate diagnosis of arrhythmia is crucial for timely and effective treatment. Electrocardiogram (ECG) plays a key role in the diagnosis of arrhythmia. With the continuous development of deep learning and machine learning processes in the clinical field, ECG processing algorithms have significantly advanced the field with timely and accurate diagnosis of arrhythmia. In this study, we combined the wavelet time-frequency maps with the novel Swin Transformer deep learning model for the automatic detection of cardiac arrhythmias. In specific practice, we used the MIT-BIH arrhythmia dataset, and to improve the signal quality, we removed the high-frequency noise, artifacts, electromyographic noise and respiratory motion effects in the ECG signals by the wavelet thresholding method; we used the complex Morlet wavelet for the feature extraction, and plotted wavelet time-frequency maps to visualise the time-frequency information of the ECG; we introduced the Swin Transformer model for classification and achieve high classification accuracy of ECG signals through hierarchical construction and self attention mechanism, and combines windowed multi-head self-attention (W-MSA) and shifted window-based multi-head self-attention (SW-MSA) to comprehensively utilise the local and global information. To enhance the confidence of the experimental results, we evaluated the performance using intra-patient and inter-patient paradigm analyses, and the model classification accuracies reached 99.34% and 98.37%, respectively, which are better than the currently available detection methods. The results reveal that our proposed method is superior to currently available methods for detecting arrhythmia ECG. This provides a new idea for ECG based arrhythmia diagnosis.

To copy verbatim, paraphrase or summarize – listeners’ methods of discourse representation while recalling academic lectures

Abstract It is unanimously agreed that comprehension of academic lectures is cognitively demanding; however, few studies have focused on a listener’s real-time discourse representation of a lecture. Based on the qualitative analysis of the verbal protocols, the present study investigated sixteen Chinese university students’ verbal recall of an academic mini-lecture to explore how they made sense of the lecture and represented its discourse when they recalled it episode by episode, and to what extent they differed in discourse representation. The results show that listeners’ discourse representation involved a range of cognitive processes such as paraphrasing, summarizing, and verbatim copying. Paraphrasing and summarizing were the main methods of discourse representation used by the participants when they verbally recalled the lecture. Those who correctly paraphrased more idea units recalled more content of the lecture. They were able to select and retain more idea units in their short-term memory, build more associations between the selected idea units, integrate them with the existing discourse structures and ensure contextual coherence in the construction of the local discourse structures. The findings of the study contribute to a better understanding of how listeners comprehend academic lectures and confirm that improving students’ paraphrasing skills and hierarchical discourse construction in recall are conducive to better comprehension of academic lectures.

Variational inference: uncertainty quantification in additive models

Markov chain Monte Carlo (MCMC)-based simulation approaches are by far the most common method in Bayesian inference to access the posterior distribution. Recently, motivated by successes in machine learning, variational inference (VI) has gained in interest in statistics since it promises a computationally efficient alternative to MCMC enabling approximate access to the posterior. Classical approaches such as mean-field VI (MFVI), however, are based on the strong mean-field assumption for the approximate posterior where parameters or parameter blocks are assumed to be mutually independent. As a consequence, parameter uncertainties are often underestimated and alternatives such as semi-implicit VI (SIVI) have been suggested to avoid the mean-field assumption and to improve uncertainty estimates. SIVI uses a hierarchical construction of the variational parameters to restore parameter dependencies and relies on a highly flexible implicit mixing distribution whose probability density function is not analytic but samples can be taken via a stochastic procedure. With this paper, we investigate how different forms of VI perform in semiparametric additive regression models as one of the most important fields of application of Bayesian inference in statistics. A particular focus is on the ability of the rivalling approaches to quantify uncertainty, especially with correlated covariates that are likely to aggravate the difficulties of simplifying VI assumptions. Moreover, we propose a method, where we combine both advantages of MFVI and SIVI and compare its performance. The different VI approaches are studied in comparison with MCMC in simulations and an application to tree height models of douglas fir based on a large-scale forestry data set.