Modular Level Research Articles

With the growing demand to produce a wider range of products at lower costs, the modularity of assembly lines is becoming a focus of interest for manufacturers. This interest naturally arises from the assumption that modular production is one way to reduce production costs. However, these costs consist of several types of items and are influenced by various factors. For this reason, it is reasonable to replace this indicator with process efficiency. The goals of this study were to determine: (1) whether the efficiency of assembly line task processing is favourably affected by a higher level of relative modularity and (2) whether the efficiency of assembly line task processing is favourably affected by an optimal level of modularity. The results of the research presented in this article revealed that by optimising modularity, it is possible to achieve higher assembly line efficiency. The experimenters found that there is a large positive correlation between optimal modularity and efficiency, since the calculated average Spearman coefficient was 0.764. In addition, the experimental results demonstrated a fundamental difference between the relative and optimal levels of process modularity.

Modular design increases product reusability and boosts the circulation of resources. This paper investigates how modular design impacts manufacturer and retailer pricing decisions, and their collection efficiencies, across three recovery strategies: manufacturer monopoly recovery, cooperative recovery, and retailer monopoly recovery. Then, Stackelberg game models are proposed to analyze the optimal recovery strategies that balance economic goals (supply chain profits) and environmental objectives (collection quantity). The findings reveal that investing in modularity enhances both profitability and collection efficiency in all three strategies. When modularity is endogenous, manufacturer monopoly recovery achieves a higher optimal modularity level than cooperative recovery, indicating a need for greater investment when manufacturers have more control over recovery. With exogenous modularity, the preferred recovery strategy depends on the level of modularity: manufacturer monopoly recovery is optimal for moderate modularity, delivering both the highest collection quantity and supply chain profits. As modularity increases, manufacturer monopoly recovery achieves higher collection quantity, while retailer monopoly recovery maximizes supply chain profits. Overall, a monopoly recovery approach leverages the benefits of high modularity more effectively than a cooperative strategy, yielding superior economic and environmental outcomes. Our models offer insights into improving recovery business efficiency through modular design and diversified disposal options.

Individuals with schizophrenia experience significant cognitive impairments and alterations in brain function. However, the shared and unique brain functional patterns underlying cognition deficits and symptom severity in schizophrenia remain poorly understood. We design an interpretable graph-based multi-task deep learning framework to enhance the simultaneous prediction of schizophrenia illness severity and cognitive functioning measurements by using functional connectivity, and identify both shared and unique brain patterns associated with these phenotypes on 378 subjects from three datasets. Our framework outperforms both single-task and state-of-the-art multi-task learning methods in predicting four Positive and Negative Syndrome Scale (PANSS) subscales and four cognitive domain scores. The performance is replicable across three datasets, and the shared and unique functional changes are confirmed by meta-analysis at both regional and modular levels. Our study provides insights into the neural correlates of illness severity and cognitive implications, offering potential targets for further evaluations of treatment effects and longitudinal follow-up.

Abstract The complexity of network information is on the rise, posing challenges for traditional community discovery methods when dealing with large-scale, multimodal, and dynamic networks. This research utilizes the attention mechanism (AM) to adaptively learn the association weights among nodes and constructs a community discovery model (CDM) based on the AM. The model incorporates convolutional neural networks and spectral clustering algorithms to improve the practical application of CDMs. Performance testing of the research-proposed model revealed that the accuracy of the model was 92.5 and 95.0% in the training and test sets, respectively. Subsequently, in the empirical analysis of the model, it was found that the standardized mutual information level and modularity level of the research proposed CDM were 0.917 and 0.920, respectively, which were significantly better than the conventional model. The results suggest that the proposed CDM has practical applications and provides new perspectives and methods for processing information in complex networks. This helps to reveal the relationships between nodes and the community structure in the network. The research is significant for predicting information dissemination, discovering key nodes, and evaluating network influence.

In the digital age, social interactions have increasingly shifted online, necessitating a deeper understanding of the structure and dynamics of online social networks and their societal impacts. This study examines the stability of network modularity on relationship-based social media platforms and its predictive power for both whole-network structures and ego-network characteristics, using a nationally representative longitudinal dataset of Generation Z interactions on a popular social media platform in China. Our findings reveal that network modularity is a stable network attribute over time, which suggests that individuals tend to maintain existing contacts on relationship-based platforms and that the community structures of their online social networks are likely to persist. Notably, the initial level of network modularity significantly correlates with both whole-network and ego-network characteristics in subsequent periods, thus highlighting modularity’s power to predict long-term network characteristics. These insights contribute to social network theory by deepening our understanding of how the existence of smaller communities within network structures influences interpersonal interactions in digital communication, with broader implications for how social networks evolve within the landscape of social media.

Purpose This study aims to examine how new knowledge search intensity affects firm innovation performance, focusing on the moderating roles of degree assortativity and modularity in intra-firm collaboration networks. Design/methodology/approach This research analyzes a sample of semiconductor patents from the United States Patent and Trademark Office database between 1975 and 2007, using negative binomial regression models with fixed effects for the panel data. Findings The results show that an increase in new knowledge search intensity can promote firms’ innovation performance, and this effect is influenced by the structural characteristics of collaboration networks (degree assortativity and network modularity). Firms with higher levels of degree assortativity in their intra-firm collaboration networks strengthen the positive relationship between new knowledge search intensity and innovation performance. Similarly, higher levels of network modularity also enhance the positive impact of new knowledge search intensity on innovation performance. Practical implications Firms that achieve higher innovation performance should not only enhance their strategies for new knowledge search but also consider the mechanism design of collaboration. Facilitating collaboration among inventors with similar levels of network centrality and fostering modular, community-based collaboration structures can help firms realize the innovative potential of new knowledge searches. Originality/value This research advances the understanding of how knowledge search influences innovation outcomes and highlights the moderating role of collaboration network structures. By integrating perspectives from knowledge search and collaboration network literature, this study expands current understanding of how firm-level search behavior can enhance innovation capabilities. Additionally, it enriches the literature on collaboration networks, particularly the effects of degree assortativity and modularity on innovation.

PurposeThe purpose of this study is to bring conceptual clarity to the business model and ecosystem literature.Design/methodology/approachThe paper uses a systems thinking approach to develop a conceptualization of business model and ecosystem constructs that seeks to integrate their divergent literature streams.FindingsThis paper posits that ecosystems and business models are concepts that emerged simultaneously for the same reasons. They also share several common attributes, while being firm-centric and industry-centric concepts respectively. This study expounds the relationship between the two, classifying them as distinct levels within a single dimension.Research limitations/implicationsThe contribution of this study is in bringing conceptual clarity to these widely used terms and providing direction for the integration of their divergent literature streams.Practical implicationsThis paper also offers heuristics to help practitioners understand how the viability of a system rests, to a great extent, on the alignment of its modular configuration.Originality/valueBuilding on this understanding, the study suggests that business models and ecosystems together exhibit a clear system–module relationship, where business models, rather than firms per se, represent units within their respective ecosystems. Furthermore, it explains why sustaining system-level equilibrium is contingent on facilitating value appropriation at the modular level.

Neurovascular coupling alterations related to cognitive impairment in cerebral small vessel disease: A multiscale brain network perspective.

Converging evidence from neuroscience and psychological sciences demonstrates that continuous sensory stimulation within the intensive internet use affects brain functioning on a broad scale. This includes essential memory, and metacognitive functions extend to the healthiness and disorders which are critical for the adaptive behavior and learning processes. This study aims to show the relation between intensive internet use and the responds of the brain in neural and behavioral levels through some working memory attributes and associated metacognitive functions and long-term memory processing. Accordingly, functional magnetic resonance imaging (fMRI) studies suggest that being online intensively has an impact on activation levels in Anterior Cingulate Cortex, Dorsolateral Prefrontal Cortex, Orbitofrontal Cortex, Medial and Medial frontal Gyrus, ventral striatum, and the dynamic connectivity pathways including frontal, medial and parietal networks such as Default Mode Network and Task Network which are important for memory and metacognitive functions. Moreover, intensive internet use habits affect some cognitive skills such as the selection of information cues, manipulation and retention of the information and attentional control; evaluation of one’s present knowledge, and regulation of the learning processes for; encoding, consolidation and retrieval of information for long-term memory processes are the effected features. As a conclusion, intensive internet use has a critical impact on neural processes in modular and whole brain level and could play a strong role for the alteration of memory and metacognitive processes.

Redox flow batteries (RFBs) are known for their exceptional attributes, including remarkable energy efficiency of up to 80%, an extended lifespan, safe operation, low environmental contamination concerns, sustainable recyclability, and easy scalability. One of their standout characteristics is the separation of electrolytes into two distinct tanks, isolating them from the electrochemical stack. This unique design allows for the separate design of energy capacity and power, offering a significantly higher level of adaptability and modularity compared to traditional technologies like lithium batteries. RFBs are also an improved technology for storing renewable energy in small or remote communities, benefiting from larger storage capacity, lower maintenance requirements, longer life, and more flexibility in scaling the battery system. However, flow batteries also have disadvantages compared to other energy storage technologies, including a lower energy density and the potential use of expensive or scarce materials. Despite these limitations, the potential benefits of flow batteries in terms of scalability, long cycle life, and cost effectiveness make them a key strategic technology for progressing to net zero. Specifically, in Australia, RFBs are good candidates for storing the increasingly large amount of energy generated from green sources such as photovoltaic panels and wind turbines. Additionally, the geographical distribution of the population around Australia makes large central energy storage economically and logistically difficult, but RFBs can offer a more locally tailored approach to overcome this. This review examines the status of RFBs and the viability of this technology for use in Australia.

Structural and functional connectivity coupling as an imaging marker for bone metastasis pain in lung cancer patients.

Carbon neutrality is an important goal for addressing global warming. It can be achieved by increasing carbon storage and reducing carbon emissions. Vegetation plays a key role in storing carbon, but it is often lost or damaged, especially in areas affected by desertification. Therefore, restoring vegetation in these areas is crucial. Using advanced techniques to improve ecosystem structure can support ecological processes, and enhance soil and environmental conditions, encourage vegetation growth, and boost carbon storage effectively. This study focuses on optimizing Ecological Spatial Networks (ESNs) for revitalization and regional development, employing advanced techniques such as the MCR model for corridor construction, spatial analysis, and Gephi for mapping topological attributes. Various ecological and topological metrics were used to evaluate network performance, while the EFCT model was applied to optimize the ESN and maximize carbon sinks. In the Thal Desert, ecological source patches (ESPs) were divided into four modularity levels (15.6% to 49.54%) and five communities. The northeastern and southwestern regions showed higher ecological functionality but lower connectivity, while the central region exhibited the reverse. To enhance the ESN structure, 27 patches and 51 corridors were added to 76 existing patches, including 56 forest and 20 water/wetland patches, using the EFCT model. The optimized ESN resulted in a 14.97% improvement in carbon sink capacity compared to the unoptimized structure, primarily due to better functioning of forest and wetland areas. Enhanced connectivity between components contributed to a more resilient and stable ESN, supporting both ecological sustainability and carbon sequestration.

To investigate the presence of modular loss of coupling and abnormal alterations in functional and structural networks in the brain networks of patients with postherpetic neuralgia (PHN). We collected resting-state functional magnetic resonance imaging data and diffusion tensor imaging data from 82 healthy controls (HCs) and 71 PHN patients, generated structural connectivity (SC) and functional connectivity (FC) networks, and assessed the corresponding clinical information assessment. Based on AAL(90) mapping, the brain network was divided into 9 modules, and the structural–functional connectivity (SC–FC) coupling was compared at the whole-brain level and within the modules, as well as alterations in the topological properties of the brain network in the patient group. Finally, correlation analyses were performed using the following clinical scales: Visual Analogue Scale (VAS), Hamilton Anxiety Scale (HAMA), and Hamilton Depression Scale (HAMD). Compared with HCs, patients with PHN had reduced global efficiency (Eg) and local efficiency (Eloc) of structural and functional networks. The FC in the PHN group presented abnormal node clustering coefficients (NCp), local node efficiencies (NLe), and node efficiencies (Ne), and the SC presented abnormal node degrees (Dc), NCp, NLe, characteristic path lengths (NLp), and Ne. In addition, SC–FC coupling was reduced in the patient default network (DMN), salient network (SN), and visual network (VIS). Moreover, the degree of impairment of graph theory indicators was significantly positively correlated with scales such as VAS scores, and the coupling of modules was significantly negatively correlated with the early course of the patient’s disease. Large-scale impaired topological properties of the FC and SC networks were observed in patients with PHN, and SC–FC decoupling was detected in these modules of the DMN, SN, and VIS. These aberrant alterations may have led to over-transmission of pain information or central sensitization of pain.

Power battery modular innovation investment strategies with government subsidy policies

Purpose The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems. Design/methodology/approach Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners. Findings The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes. Research limitations/implications The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process. Social implications E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status. Originality/value A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.

Various semi-formal syntax templates for natural language requirements foster to reduce ambiguity while preserving human readability. Existing studies on their effectiveness focus on individual notations only and do not allow to systematically investigate quality benefits. We strive for a comparative benchmark and evaluation of template systems to assist practitioners in selecting appropriate ones and enable researchers to work on pinpoint improvements and domain-specific adaptions. We conduct comparative experiments with five popular template systems—EARS, Adv-EARS, Boilerplates, MASTeR, and SPIDER. First, we compare a control group of free-text requirements and treatment groups of their variants following the different templates. Second, we compare MASTeR and EARS in user experiments for reading and writing. Third, we analyse all five meta-models’ formality and ontological expressiveness based on the Bunge-Wand-Weber reference ontology. The comparison of the requirement phrasings across seven relevant quality characteristics and a dataset of 1764 requirements indicates that, except SPIDER, all template systems have positive effects on all characteristics. In a user experiment with 43 participants, mostly students, we learned that templates are a method that requires substantial prior training and that profound domain knowledge and experience is necessary to understand and write requirements in general. The evaluation of templates systems’ meta-models suggests different levels of formality, modularity, and expressiveness. MASTeR and Boilerplates provide high numbers of variants to express requirements and achieve the best results with respect to completeness. Templates can generally improve various quality factors compared to free text. Although MASTeR leads the field, there is no conclusive favourite choice, as most effect sizes are relatively similar.

Abstract Research SummaryDespite the importance of resource reallocation in shaping a variety of strategic outcomes, strategy scholars have paid only limited attention to the processes by which firms reallocate their resources across successive systemic innovations. To explore these processes, we conducted an in‐depth historical case study on Rolls‐Royce's role in three distinct systemic innovations that marked the transition from piston engines to jet engines in the civil aviation industry: the turbojet, the turboprop, and the turbofan. The analysis helps explain how and why Rolls‐Royce's central role stemmed from its ability to reallocate existing non‐scale free organizational and technical resources. A key finding of this study is the identification of the horizontal transfer of functional modules as a critical process, especially during the incipient phase of a systemic innovation. The analysis also highlights the role that specific organizational arrangements, particularly a firm's integrative capabilities, have in shaping the effectiveness with which resources are reallocated.Managerial SummaryFocusing on resource reallocation is important to understand why some firms effectively reallocate their resources through successive systemic innovations while others cannot, even if they have similar resources and face the same environmental conditions. By delving into the technological aspects of aeroengine development and exploring why Rolls‐Royce had the capabilities to successfully integrate key functional modules across various modular levels, we clarify the relationship between technology and organization that underlies resource reallocation—a topic that has received only scant attention in the strategy literature.

Biological variation in the mammalian skull is the product of a series of factors including changes in gene expression, developmental timing, and environmental pressures. When considering the diversity of extant mammalian crania, it is important to understand these mechanisms that contribute to cranial growth and in turn, how differences in cranial morphology have been attained. Various researchers, including Dr. Sue Herring, have proposed a variety of mechanisms to explain the process of cranial growth. This work has set the foundation on which modern analysis of craniofacial morphology happens today. This study focused on the analysis of modularity in three mammalian taxa, all of which exhibit facial reduction. Specifically, we examined facial reduction as a morphological phenomenon through the use of two-module and six-module modularity hypotheses. We recorded three-dimensional coordinate data for 55 cranial landmarks that allowed us to analyze differences in cranial shape in these three taxa (primates n = 88, bats n = 64, dogs n = 81). When assessing modularity within the two-module modularity hypothesis specifically, dogs exhibited the lowest levels of modularity, while bats and primates both showed a slightly more modular covariance structure. We further assessed modularity in the same sample using the Goswami six-module model, where again dogs exhibited a low degree of modularity, with bats and primates being more moderate. We then broke the sample into subsets by analyzing each morphotype separately. We hypothesized that the modularity would be more pronounced in the brachycephalic morphotype. Surprisingly, we found that in brachycephalic dogs, normocephalic dogs, brachycephalic primates, and normocephalic primates, there was a moderate degree of modularity. Brachycephalic bats had a low degree of modularity, while normocephalic bats were the most modular group observed in this study. Based on these results, it is evident that facial reduction is a complex and multifaceted phenomenon with unique morphological changes observed in each of the three taxa studied.

Breast cancer heterogeneity presents a significant challenge in clinical therapy, such as over-treatment and drug resistance. These challenges are largely due to its obscure normal epithelial origins, evolutionary stability, and transitions on the cancer subtypes. This study aims to elucidate the cellular emergence and maintenance of heterogeneous breast cancer via quantitative bio-process modeling, with potential benefit to therapeutic strategies for the disease. An endogenous molecular-cellular hypothesis posits that both pathological and physiological states are phenotypes evolved from and shaped by interactions among a number of conserved modules and cellular factors within a biological network. We hereby developed a model of core endogenous network for breast cancer in accordance with the theory, quantifying its intrinsic dynamic properties with dynamic modeling. The model spontaneously generates cell states that align with molecular classifications at both the molecular and modular level, replicating four widely recognized molecular subtypes of the cancer and validating against data extracted from the TCGA database. Further analysis shows that topologically, a singular progression gateway from normal breast cells to cancerous states is identified as the Luminal A-type breast cancer. Activated positive feedback loops are found to stabilize cellular states, while negative feedback loops facilitate state transitions. Overall, more routes are revealed on the cellular transition between stable states, and a traceable count explains the origin of breast cancer heterogeneity. Ultimately, the research intended to strength the search for therapeutic targets.

Deciphering assembly processes, network complexity and stability of potential pathogenic communities in two anthropogenic coastal regions of a highly urbanized estuary