Hierarchical Perspective Research Articles

Environmental assessment of steel warehouse: a case study in Pato Branco, Brazil

This article investigates the application of Life Cycle Assessment (LCA) in steel constructions in Brazil, combining a bibliometric analysis and a case study of industrial warehouses. The bibliometric analysis revealed the scarcity of studies using LCA in this context, with a lack of environmental assessments aimed specifically at steel warehouses in Brazil. The case study compared two warehouse projects, assessing the environmental sustainability of Pratt (PTP) and Howe (HTP) truss structures. The LCA was conducted using the functional unit “total steel mass of the shed”, the SimaPro 9.0 software, and data from the international Ecoinvent v3.3 database, applying the ReCiPe 2016 method from a hierarchical perspective. The results showed that the Howe truss project had the lowest environmental impact in all indicators, including the extraction of raw materials, the manufacture of materials and the construction of the shed. This pioneering study addresses a type of construction that is widely used in Brazil and essential for economic development, offering a methodology that can be replicated in other projects and contexts.

Scale-specific effects of urban landscape pattern on the COVID-19 epidemic in Hangzhou, China

ContextUnderstanding the scale-specific effects of different landscape variables on the COVID-19 epidemics is critical for developing the precise and effective prevention and control strategies within urban areas.ObjectiveBased on the landscape epidemiology framework, we analyzed the scale-specific effects of urban landscape pattern on COVID-19 epidemics in Hangzhou, China.MethodsWe collected COVID-19 cases in Hangzhou from 2020‒2022 and combined the datasets of land use and land cover (LULC) and social gathering point (SGP) to quantify the urban landscape pattern. Optimal general linear model with stepwise regression was applied to explore the dominant landscape factors driving the COVID-19 transmission in the city. Furthermore, multi-scale geographically weighted regression illustrated the spatial heterogeneity and scale specificity of these landscape variables’ effects to COVID-19 epidemic.ResultsEight landscape variables of LULC and SGP patterns were identified which explained 68.5% of the variance in spatial risk of COVID-19. Different optimal bandwidths across these variables in MGWR indicated their scale-specific effects. LSI of green space enhanced the spatial risk across the entire region. The effects of landscape contagion, the number of water bodies, LSI of cropland and built-up areas, and the density of commercial houses were detected to vary between urban and suburban areas. The effects of LSI of water bodies and the density of shopping malls were found to vary among different districts.ConclusionsIn this study, we firstly discriminated the scale-specific effects of different landscape variables on the COVID-19 epidemic in the urban region. These findings can help to optimize the differentiated zoning prevention and control strategies for COVID-19 in cities and guide policy-making and urban planning at a multi-scale hierarchical perspective to improve public health and urban sustainability.

Inferring ancestry with the hierarchical soft clustering approach tangleGen.

Understanding the genetic ancestry of populations is central to numerous scientific and societal fields. It contributes to a better understanding of human evolutionary history, advances personalized medicine, aids in forensic identification, and allows individuals to connect to their genealogical roots. Existing methods, such as ADMIXTURE, have significantly improved our ability to infer ancestries. However, these methods typically work with a fixed number of independent ancestral populations. As a result, they provide insight into genetic admixture, but do not include a hierarchical interpretation. In particular, the intricate ancestral population structures remain difficult to unravel. Alternative methods with a consistent inheritance structure, such as hierarchical clustering, may offer benefits in terms of interpreting the inferred ancestries. Here, we present tangleGen, a soft clustering tool that transfers the hierarchical machine learning framework Tangles, which leverages graph theoretical concepts, to the field of population genetics. The hierarchical perspective of tangleGen on the composition and structure of populations improves the interpretability of the inferred ancestral relationships. Moreover, tangleGen adds a new layer of explainability, as it allows identifying the single-nucleotide polymorphisms that are responsible for the clustering structure. We demonstrate the capabilities and benefits of tangleGen for the inference of ancestral relationships, using both simulated data and data from the 1000 Genomes Project.

A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability

Data augmentation (DA) is indispensable in modern machine learning and deep neural networks. The basic idea of DA is to construct new training data to improve the model’s generalization by adding slightly disturbed versions of existing data or synthesizing new data. This survey comprehensively reviews a crucial subset of DA techniques, namely Mix-based Data Augmentation (MixDA), which generates novel samples by combining multiple examples. In contrast to traditional DA approaches that operate on single samples or entire datasets, MixDA stands out due to its effectiveness, simplicity, computational efficiency, theoretical foundation, and broad applicability. We begin by introducing a novel taxonomy that categorizes MixDA into Mixup-based, Cutmix-based, and mixture approaches based on a hierarchical perspective of the data mixing operation. Subsequently, we provide an in-depth review of various MixDA techniques, focusing on their underlying motivations. Owing to its versatility, MixDA has penetrated a wide range of applications, which we also thoroughly investigate in this survey. Moreover, we delve into the underlying mechanisms of MixDA’s effectiveness by examining its impact on model generalization and calibration while providing insights into the model’s behavior by analyzing the inherent properties of MixDA. Finally, we recapitulate the critical findings and fundamental challenges of current MixDA studies while outlining the potential directions for future works. Different from previous related surveys that focus on DA approaches in specific domains (e.g., computer vision and natural language processing) or only review a limited subset of MixDA studies, we are the first to provide a systematical survey of MixDA, covering its taxonomy, methodology, application, and explainability. Furthermore, we provide promising directions for researchers interested in this exciting area.

On the embodied nature of knowledge: From neurons to numbers.

Interdisciplinary investigations of the human mind through the cognitive sciences have identified a key role of the body in representing knowledge. After characterizing knowledge at grounded, embodied, and situated levels, number knowledge is analyzed from this hierarchical perspective. Lateralized cortical processing of coarse versus fine detail is identified as a grounding substrate for the population stereotype few/left, many/right, which then contributes to number-related sensory and motor biases at the embodied and situated levels. Implications of this perspective for education and rehabilitation are discussed.

Study on Multiscale Virtual Environment Construction and Spatial Navigation Based on Hierarchical Structure

Multiscale virtual environments (MSVEs) allow the integration of elements and environments at different scale levels into a unified space, which facilitates researchers’ perception, understanding, and experimental research of complex geospatial spaces. Although there have been several methods for achieving multiscale effects in virtual environments (VEs), they cannot assist users in constructing more complete spatial cognitive maps and presenting multiscale information efficiently. This study proposes a hierarchical-structure-based MSVE construction method, which can effectively integrate multiscale information and ensure that the richness of details of information is gradually enhanced with the progression of the hierarchical structure. In addition, a spatial navigation study is conducted, considering the relationship between users’ perspective changes and spatial cognition, and the effects of users’ perspective changes on their spatial cognition in an MSVE are explored. A multiscale virtual wetland environment covering four levels is constructed to conduct a case study of a virtual environment of a wetland of Poyang Lake. The research results show that the proposed method is feasible. Moreover, the spatial navigation based on the change in the hierarchical perspective is in line with the spatial cognitive habits of users, which can satisfy the cognitive needs of users from the macro-region to specific wetland landscapes.

HSeq2Seq: Hierarchical graph neural network for accurate mobile traffic forecasting

Hierarchical graph neural networks (HGNNs) provide a feasible method for modeling complex spatiotemporal dependencies during mobile traffic forecasting. However, most existing studies have adopted spatial node clustering methods to construct a coarsened graph that overlooks the temporal correlation within the original station graph. Furthermore, existing state-of-the-art methods fail to fully exploit the cross-regional feature impacts on stations, which limits their ability to model nonlocal spatial dependency.To overcome these limitations, we proposed a hierarchical sequence-to-sequence (HSeq2Seq) approach that combines HGNNs with a sequence-to-sequence architecture (Seq2Seq) for mobile traffic forecasting. First, a spatiotemporal node clustering method was designed to construct the hierarchical structure. Second, a convolution encoder was employed to extract the local spatiotemporal features from a hierarchical perspective, and a novel attention-based feature fusion module was built to capture the nonlocal spatiotemporal features by identifying both the intra- and cross-regional feature impacts on the stations. Finally, a recurrent decoder is introduced to reweight the spatiotemporal features and recursively produce the final prediction.Extensive experiments on two real-world datasets demonstrate that our model outperforms state-of-the-art methods, while the ablation study also verifies the effectiveness of the spatiotemporal node clustering and the attention-based feature fusion module.

Future organizational resilience capability structure: a systematic review, trend and future research directions

PurposeThe purpose of this paper is to systematically review the resilience literature to integrate the fragmented views and provide a more comprehensive understanding. This study aims to fill the gaps in the literature by discussing scientific contributions and highlighting the main issues in previous research findings regarding the definitions, dimensions and concepts that make up organizational resilience. This research highlights organizational resilience from a capabilities perspective.Design/methodology/approachThis paper uses the systematic literature review method by searching all previous studies studying organizational resilience from 2014 to 2023. In total, there are 28 articles reviewed from the Scopus database.FindingsThis study found that resilience is a complex concept and has many definitions and dimensions. This study also conceptualizes organizational capability with a pyramid, which illustrates the basic framework of the six stages of the resilience process and hierarchically forms organizational resilience.Research limitations/implicationsFirst, the keyword search strings on the repository database are currently limited to a few keywords. Need to broaden the range of keywords so as to produce a more comprehensive review. Second, the exclusion of books, book chapters and conference papers limits research findings and results. These sources are likely to enrich resilience development from various perspectives. Even though Scopus is the largest repository database, the research findings are not yet fully generalizable. Future researchers can add data from WoS, Ebsco or other databases. Literature obtained from various databases that can provide broader results.Practical implicationsThe practical implications of this study are to provide a basis for managers in making decisions for organizational sustainability. Managers can consider each stage in the resilience capability pyramid as a reference for making strategic plans and relational orientation toward organizational members.Originality/valueThis research provides a hierarchical perspective on organizational resilience capabilities. For academics and practitioners, this study provides a critical and comprehensive systematization of the limited academic literature on resilience. This study also offers opportunities for further research to overcome the limitations of empirical testing of resilience capability construction using various theories and methodologies.

Levels of selection and macroevolution in organisms, colonies, and species

Abstract The fitness of groups is often considered to be the average fitness among constituent members. This assumption has been useful for developing models of multilevel selection, but its uncritical adoption has held back our understanding of how multilevel selection actually works in nature. If group fitness is only equal to mean member fitness, then it is a simple task to erode the importance of group-level selection in all multilevel scenarios—species selection could then be reduced to organismal selection as easily as group selection can. Because selection from different levels can act on a single trait, body size, for example, there must be a way to translate one level of fitness to another. This allows the calculation of the contributions of selection at each level. If high-level fitness is not a simple function of member fitness, then how do they interlace? Here we reintroduce Leigh Van Valen’s argument for the inclusion of expansion as a component of fitness. We show that expansion is an integral part of fitness even if one does not subscribe to the energetic view of fitness from which Van Valen originally derived it. From a hierarchical perspective, expansion is the projection of demographic fitness from one level to the next level up; differential births and deaths at one level produce differential expansion one level above. Including expansion in our conceptual tool kit helps allay concerns about our ability to identify the level of selection using a number of methods as well as allowing for the various forms of multilevel selection to be seen as manifestations of the same basic process.

State Ownership Heterogeneity and Corporate Innovation: New Evidence from a Hierarchical Perspective

Unlike prior research, this study re-examines the relationship between state ownership and corporate innovation from a hierarchical perspective. Drawing upon institutional theory, our findings reveal the heterogeneous impact of state ownership, elucidating the positive role of central state ownership in fostering corporate innovation, while highlighting the inhibitory effect of local state ownership. This conclusion withstands rigorous scrutiny through a battery of robustness checks. Mechanism analysis indicates that central state-owned enterprises stimulate innovation by increasing innovation investment and enhancing efficiency, whereas local state-owned enterprises create obstacles for both innovation investment and efficiency. Our paper offers a hierarchical interpretation of the mixed evidence regarding the relationship between state ownership and corporate innovation. Whether state ownership serves as a facilitator or a hindrance to innovation depends on whether central or local state-owned enterprises dominate the national innovation process. Overall, this study offers new insights into the complex effects of state ownership heterogeneity on corporate innovation activities in emerging economies like China, advancing our understanding of the subtle relationship between corporate governance and innovation.

Exploring the total cost of whole fresh, fresh-cut and pre-cooked vegetables

PurposeThe food industry should evolve towards new business models which take into account the damage cost in decision making, considering the impact that its products generate on the natural and human environment. Hence, the present study aims to calculate the damage cost caused by the production of whole fresh (as average of potatoes, aubergines, and broccoli), and processed vegetables (fresh-cut and pre-cooked).MethodsThe environmental life cycle approach was carried out per kilogram of assessed products (from cradle to the entrance of the market). The foreground Life Cycle Inventory was obtained from engineering procurement and construction projects of the whole fresh and processed vegetables industries. The Ecoinvent 3.8 and Agribalyse 3.0.1 databases were used for the background inventory. The ReCiPe 2016 method was used with a hierarchical perspective, evaluating eighteen midpoint categories as well as the endpoint categories (human health, ecosystems, and resources). The monetisation of these environmental impacts was then calculated using the endpoint monetisation factors developed by Ponsioen et al. (Monetisation of sustainability impacts of food production and consumption. Wageningen Economic Research, Wageningen, 2020) for each product. It should be noted that this study does not include a comparative assessment. This study does not intend to compare the results for the three vegetable groups.Results and discussionThe damage costs were 0.16 €/kg for whole fresh vegetables, 0.37 €/kg for fresh-cut vegetables and 0.41 €/kg for pre-cooked vegetables. The agricultural production stage contributed most to these total damage costs due to the impact produced on land use and global warming in midpoint categories and human health and ecosystems in endpoint categories. In addition, the damage cost due to fossil resource scarcity (midpoint) and resource scarcity (endpoint) was mainly caused by the plastic packaging of fresh-cut and pre-cooked vegetables. The total cost was 1.02 €/kg for whole fresh vegetables, 2.99 €/kg for fresh-cut vegetables, and 3.43 €/kg for pre-cooked vegetables.ConclusionsThese results suggest that some efforts should be made to reduce both environmental impacts and damage costs. For instance, to improve agricultural production, special attention should be paid to fertilisation and water consumption. Additionally, new packaging options should be explored as well as the inclusion of renewable sources in the electricity grid, and finally, on transporting the finished products to the market, by using trucks that run on cleaner fuels.Graphical abstract

The impact of local entrepreneurial initiatives on women entrepreneur empowerment: the case of cooperative social ventures in Morocco

PurposeWe explore how NGO’s local entrepreneurial initiatives to empower women entrepreneurs can compensate for weak state policies for women in a context of male-dominated socio-cultural norms.Design/methodology/approachWe use the case of a local entrepreneurial initiative launched in the Atlas region of Morocco, the Empowering Women in the Atlas Initiative (EWA). We collected data through 51 semi-structured interviews of women entrepreneurs in three cooperatives which exploit the natural resources of their region to establish a social venture. Our data are longitudinal as they were collected at two time periods: before and after the initiative.FindingsThe findings of this study suggest that local entrepreneurial initiatives can have a significant impact on rural women entrepreneurs’ empowerment. The improved perception of empowerment has not only helped them develop capacities to leverage the business opportunities linked to the natural resources of their region, but it has also increased their status and role within their family and community.Practical implicationsWe make recommendations for policymakers to encourage this type of initiative to compensate for the absence of supporting policies geared toward women.Originality/valueOur study is one of the first to look at empowerment as a policy instrument to develop women entrepreneurial activities in rural areas of developing countries. Our paper uses a unique hierarchical perspective and a multidimensional framework for analyzing social cooperative ventures and rural women entrepreneurs’ empowerment. Our paper unravels interesting insights for women entrepreneurs’ narration strategies.

KOMUNITA Magazine Website Traffic Analysis

KOMUNITA Magazine is one of the means to develop higher education through various aspects. In this case, KOMUNITA is a communication and information medium that seeks to reach various communities that intersect with universities from a hierarchical perspective from various aspects. Komunita Magazine through online media in the form of a website has now undergone many changes and developments so that in this case it is necessary to conduct research to determine the effectiveness of the website. Komunita Magazine's website traffic analysis aims to determine the effectiveness of changes and website development, the authors try to conduct a study using google analytics. Google analytics can generate visitor analysis reports from the web, so the authors see effectiveness based on the number of visitors.

Supplemental Material for Psychopathy and Interpersonal Functioning in a University Sample: A Hierarchical Perspective Using the Comprehensive Assessment of Psychopathic Personality: A Registered Report

Supplemental Material for Psychopathy and Interpersonal Functioning in a University Sample: A Hierarchical Perspective Using the Comprehensive Assessment of Psychopathic Personality: A Registered Report

A Hierarchical Mechanotransduction System: From Macro to Micro.

Mechanotransduction is a strictly regulated process whereby mechanical stimuli, including mechanical forces and properties, are sensed and translated into biochemical signals. Increasing data demonstrate that mechanotransduction is crucial for regulating macroscopic and microscopic dynamics and functionalities. However, the actions and mechanisms of mechanotransduction across multiple hierarchies, from molecules, subcellular structures, cells, tissues/organs, to the whole-body level, have not been yet comprehensively documented. Herein, the biological roles and operational mechanisms of mechanotransduction from macro to micro are revisited, with a focus on the orchestrations across diverse hierarchies. The implications, applications, and challenges of mechanotransduction in human diseases are also summarized and discussed. Together, this knowledge from a hierarchical perspective has the potential to refresh insights into mechanotransduction regulation and disease pathogenesis and therapy, and ultimately revolutionize the prevention, diagnosis, and treatment of human diseases.

State hierarchies versus criminal hierarchies

Medellín in Colombia, Rio de Janeiro in Brazil and Ciudad Juárez in Mexico show important similarities if the reference point is the chronically high levels of homicides, classified here as lethal violence. Robbery, extortion and kidnapping - characterized in this text as predatory violence - are also conceived as common practices that make coexistence difficult for the inhabitants of these three cities. But a finer reading of both types of violence and the multiple indicators that shape them requires a more precise identification of its relational components. Agents from various state institutions -mainly those nominally in charge of providing security- and members of criminal organizations (especially dedicated to drug trafficking) appear as first-level protagonists in the works that analyze the topic. But it cannot be ignored that neither the agents of government institutions for the enforcement of the law, nor the members of criminal organizations, act in a homogeneous manner. This text evaluates in a comparative way some of the ways in which the State hierarchies and criminal hierarchies have been configured and deployed on the ground in the three aforementioned cities. Approaching the topic from a hierarchical perspective contains the hypothesis that both types of hierarchy represent differentiated forms of social ordering. Understanding these alterities makes it necessary to observe the type of criminalized activities, the degree of organizational complexity that arises from them, the participating social agents -individuals or groups-, their interaction formats, as well as the different types of temporalities and territorialities. The approach of this article, comparative by definition, seeks to briefly explain overlapping spatialities, articulated evolutionary processes, interspersed forms of division of labor and different mechanisms of legitimation and insertion into local frameworks.

Other-Oriented Empathetic Concern as the Underlying Factor of Extrinsic Emotion Regulation Motives and Its Positive Application for Social Well-Being.

In recent years, research on interpersonal emotion regulation has garnered increasing attention. Extrinsic emotion regulation (EER) or the regulation of others' emotions is associated with individual personal relationships and mental health outcomes. However, investigations into why people engage in extrinsic emotion regulation are relatively rare. The current study aimed to identify the underlying factor structure of the motives of extrinsic emotion regulation, based on the Extrinsic Emotion Regulation Motives Scale (EERMS), to examine how these factors related to people's moral identity and social relationships during the COVID-19 pandemic. The participants of this study were 464 adults (73.1% females) from China who completed an online survey based on EERMS and other related measures. An exploratory factor analysis (EFA) was utilized to examine the underlying structure of EER motives followed by a bifactor EFA (bi-EFA). Analyses revealed that the bi-EFA model, with an overarching factor (other-oriented empathetic concern) and three specific factors (self-orientation, reciprocation, and obligation), was best applied to the data. These factors were differently associated with social well-being indicators (moral self-identity, peer relationship quality, and perceived social support). This preliminary study supports the use of a hierarchical perspective to understand the different dimensions of motives for extrinsic emotion regulation.

Whole-body metabolic connectivity framework with functional PET

The nervous and circulatory system interconnects the various organs of the human body, building hierarchically organized subsystems, enabling fine-tuned, metabolically expensive brain-body and inter-organ crosstalk to appropriately adapt to internal and external demands. A deviation or failure in the function of a single organ or subsystem could trigger unforeseen biases or dysfunctions of the entire network, leading to maladaptive physiological or psychological responses. Therefore, quantifying these networks in healthy individuals and patients may help further our understanding of complex disorders involving body-brain crosstalk.Here we present a generalized framework to automatically estimate metabolic inter-organ connectivity utilizing whole-body functional positron emission tomography (fPET). The developed framework was applied to 16 healthy subjects (mean age ± SD, 25 ± 6 years; 13 female) that underwent one dynamic 18F-FDG PET/CT scan. Multiple procedures of organ segmentation (manual, automatic, circular volumes) and connectivity estimation (polynomial fitting, spatiotemporal filtering, covariance matrices) were compared to provide an optimized thorough overview of the workflow.The proposed approach was able to estimate the metabolic connectivity patterns within brain regions and organs as well as their interactions. Automated organ delineation, but not simplified circular volumes, showed high agreement with manual delineation. Polynomial fitting yielded similar connectivity as spatiotemporal filtering at the individual subject level. Furthermore, connectivity measures and group-level covariance matrices did not match. The strongest brain-body connectivity was observed for the liver and kidneys.The proposed framework offers novel opportunities towards analyzing metabolic function from a systemic, hierarchical perspective in a multitude of physiological pathological states.

Research on the characteristics and influencing factors of the Beijing-Tianjin-Hebei urban network structure from the perspective of listed manufacturing enterprises.

The production connection is a crucial component of the Inter City Association. An urban network based on the enterprise perspective better reflects the structural characteristics of regional cities. Based on data gathered from the headquarters and branches of the listed manufacturing enterprises in 2020, this paper analyzes the county-level administrative units in the Beijing-Tianjin-Hebei region of China. Using the subordinate connection model and the social network analysis method, this paper examines the spatial structural characteristics and factors of urban networks in the Beijing-Tianjin-Hebei region. The results suggest that resource allocation in the Beijing-Tianjin-Hebei region is unbalanced, with a significant difference in urban radiation and agglomeration capacity. As the administrative centers, Beijing, Tianjin, and Shijiazhuang show a strong ability to allocate resources within the network. The overall network density in the region was shown to be relatively low, with the main links being of low or medium level. The urban network is defined by the network connection led by Beijing and Tianjin with Beijing-Tianjin-Tangshan and Beijing-Baoding-Shijiazhuang as the main axis. These cities exhibit a "dense southeast and sparse northwest" pattern. From a hierarchical perspective, high-level network connections are based mainly on spatial proximity. Analyzing the agglomerative subgroups, the study found that the inner and outer connections of the core subgroups were relatively high. Semi-marginal subgroups generally accepted the radiation of core subgroups, while marginal subgroups had little connection with other subgroups in the region. The results of the QAP analysis show that the administrative relationship, spatial distance, city size difference, economic development level difference, industrial structure difference, and labor cost difference have a significant influence on the urban network of the Beijing-Tianjin-Hebei region.

Impact of Public Environmental Concerns on the Digital Transformation of Heavily Polluting Enterprises.

China is currently facing the arduous tasks of energy conservation, emission reduction and structural transformation, making it of great significance to study the digital transformation of heavily polluting enterprises. As an important informal regulatory system, public environmental concerns affect corporate environmental behavior by increasing external environmental pressure. This study uses the data of listed companies in China's heavily polluting industries from 2012 to 2020 and Baidu Index data to analyze how public environmental concerns affect the digital transformation of heavily polluting enterprises. This study finds that public environmental concerns can significantly promote the digital transformation of heavily polluting enterprises. For non-state-owned, green image and high-tech enterprises, the impact is even more obvious. Furthermore, based on the structural and hierarchical perspective of enterprise digital transformation, we find that public environmental concerns significantly promote digital technology application. This study puts forward some suggestions for government departments to formulate environmental protection regulations, enterprises to fulfill their green responsibilities and the public to participate in environmental governance.