Modular Level Research Articles

Developing Strategies for Carbon Neutrality Through Restoration of Ecological Spatial Networks in the Thal Desert, Punjab

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.

Abnormal alterations in structure-function coupling at the modular level in patients with postherpetic neuralgia

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.

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

Cancer pain is a common symptom in patients with malignant tumors and associated with poor prognosis and a high risk of death. Structural connectivity (SC) and functional connectivity (FC) couplings have not yet been explored in lung cancer patients with bone metastasis pain. In total, 51 patients with lung cancer without bone metastasis pain (BMP-), 52 patients with lung cancer with bone metastasis pain (BMP+), and 28 healthy controls (HC) were prospectively enrolled in our study. Firstly, SC-FC couplings were measured and analyzed at global, regional, and modular levels. Subsequently, individualized SC-FC coupling networks were constructed based on the Euclidean distance metric. In addition, the convolutional neural network (CNN) model was selected to analyze and classify three groups based on individualized networks. The coupling analysis demonstrated that weaker SC-FC couplings related to lung cancer itself were present at various levels, including global, regional, inter-network, and intra-network couplings. Notably, hyper-couplings related to bone metastasis pain were present in several brain regions, mainly involving the default mode network, frontoparietal network, salience network, and limbic system. Significant positive correlations were observed between regional coupling in the right amygdala and the numeric rating scale scores in BMP+. Moreover, CNN model built on individualized networks exhibited relatively great classification performance. Alterations in SC-FC coupling patterns may play a crucial role in the development and modulation of bone metastasis pain. Understanding these changes could provide valuable insights into the neural mechanisms underlying cancer pain.

Power battery modular innovation investment strategies with government subsidy policies

This paper investigates the closed-loop supply chain decisions of battery manufacturers considering innovation investments and different subsidy methods provided by the government. The analysis encompasses four scenarios: whether battery manufacturers engage in innovation investments and whether the government opts for technology R&D subsidies or production subsidies. The study finds that innovation investments by battery manufacturers can enhance the modularization level of batteries. The reward or penalty measures for recycling quality imposed on automobile manufacturers elevate the quality of battery recycling, and also increase the wholesale price of batteries and the retail price of automobiles. After implementing R&D and production subsidies, both the modularization level of batteries and the profits of supply chain members exceed those without subsidies, though these subsidies do not affect the quality of battery recycling. R&D subsidies more effectively promote innovation capacity and product quality among battery manufacturers, whereas production subsidies more significantly boost the profits of both battery manufacturers and automobile manufacturers. Therefore, subsidy measures should be tailored according to different stages of the power battery industry's development. Regardless of whether battery manufacturers invest in innovation or whether the government provides subsidies, collaboration between battery manufacturers and automobile manufacturers is essential to coordinate the closed-loop supply chain. The research findings offer valuable guidance for formulating subsidy policies in the new energy vehicle industry across different periods, and also provide theoretical insights for decision-making behaviors among related enterprises.

Personalized and adaptive e-learning systems for semantic Web: a systematic review and roadmap

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.

Benchmarking requirement template systems: comparing appropriateness, usability, and expressiveness

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.

Resource reallocation across successive systemic innovations: How Rolls‐Royce shaped the evolution of the turbojet, turboprop, and turbofan

AbstractResearch 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.

Comparative morphology in the context of facial reduction: Modularity in primate, dog, and bat crania.

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.

Heterogeneous Evolution of Breast Cancer Cells-An Endogenous Molecular-Cellular Network Study.

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.

Arbuscular mycorrhizal fungal diversity and potential association networks among African tropical forest trees.

Tropical forests represent one of the most diverse and productive ecosystems on Earth. High productivity is sustained by efficient and rapid cycling of nutrients, which is in large part made possible by symbiotic associations between plants and mycorrhizal fungi. In these associations, an individual plant typically associates simultaneously with multiple fungi and the fungi associate with multiple plants, creating complex networks among fungi and plants. However, there are few studies that have investigated mycorrhizal fungal composition and diversity in tropical forest trees, particularly in Africa, or that assessed the structure of the network of associations among fungi and trees. In this study, we collected root and soil samples from Ise Forest Reserve (Southwest Nigeria) and used a metabarcoding approach to identify the dominant arbuscular mycorrhizal (AM) fungal taxa in the soil and associating with ten co-occurring tree species to assess variation in AM communities. Network analysis was used to elucidate the architecture of the network of associations between fungi and tree species. A total of 194 Operational Taxonomic Units (OTUs) belonging to six AM fungal families were identified, with 68% of all OTUs belonging to Glomeraceae. While AM fungal diversity did not differ among tree species, AM fungal community composition did. Network analyses showed that the network of associations was not significantly nested and showed a relatively low level of specialization (H2 = 0.43) and modularity (M = 0.44). We conclude that, although there were some differences in AM fungal community composition, the studied tree species associate with a large number of AM fungi. Similarly, most AM fungi had great host breadth and were detected in most tree species, thereby potentially working as interaction network hubs.

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

The existence of potential pathogens may lead to severe water pollution, disease transmission, and the risk of infectious diseases, posing threats to the stability of aquatic ecosystems and human health. In-depth research on the dynamic of potential pathogenic communities is of significant importance, it can provide crucial support for assessing the health status of aquatic ecosystems, maintaining ecological balance, promoting sustainable economic development, and safeguarding human health. Nevertheless, the current understanding of the distribution and geographic patterns of potential pathogens in coastal ecosystems remains rather limited. Here, we investigated the diversity, assembly, and co-occurrence network of potential pathogenic communities in two anthropogenic coastal regions, i.e., the eight mouths (EPR) and nearshore region (NSE), of the Pearl River Estuary (PRE) and a total of 11 potential pathogenic types were detected. The composition and diversity of potential pathogenic communities exhibited noteworthy distinctions between the EPR and NSE, with 6 shared potential pathogenic families. Additionally, in the NSE, a significant pattern of geographic decay was observed, whereas in the EPR, the pattern of geographic decay was not significant. Based on the Stegen null model, it was noted that undominant processes (53.36%/69.24%) and heterogeneous selection (27.35%/25.19%) dominated the assembly of potential pathogenic communities in EPR and NSE. Co-occurrence network analysis showed higher number of nodes, a lower average path length and graph diameter, as well as higher level of negative co-occurrences and modularity in EPR than those in NSE, indicating more complex and stable correlations between potential pathogens in EPR. These findings lay the groundwork for the effective management of potential pathogens, offering essential information for ecosystem conservation and public health considerations in the anthropogenic coastal regions.

Global effects of land-use intensity and exotic plants on the structure and phylogenetic signal of plant-herbivore networks

Interactions between plants and herbivorous insects are often phylogenetically structured, with closely related insect species using similar sets of species or lineages of plants, while phylogenetically closer plants tend to share high proportions of their herbivore insect species. Notably, these phylogenetic constraints in plant-herbivore interactions tend to be more pronounced among internal plant-feeding herbivores (i.e., endophages) than among external feeders (i.e., exophages). In the context of growing human-induced habitat conversion and the global proliferation of exotic species, it is crucial to understand how ecological networks respond to land-use intensification and the increasing presence of exotic plants. In this study, we analyzed plant-herbivore network data from various locations of the World to ascertain the degree to which land-use intensity and the prevalence of exotic plants induce predictable changes in their network topology - measured by levels of nestedness and modularity - and phylogenetic structures. Additionally, we investigated whether the intimacy of plant-herbivore interactions, contrasting endophagous with exophagous networks, modulate changes in network structure. Our findings reveal that most plant-herbivore networks are characterized by significant phylogenetic and topological structures. However, neither these structures did not show consistent changes in response to increased levels of land-use intensify. On the other hand, for the networks composed of endophagous herbivores, the level of nestedness was higher in the presence of a high proportion of exotic plants. Additionally, for networks of exophagous herbivores, we observed an increase in the phylogenetic structure of interactions due to exotic host dominance. These results underscore the differential impacts of exotic species and land-use intensity on the phylogenetic and topological structures of plant-herbivore networks.

Properties of vertebrate predator-prey networks in the high Arctic.

Predation is an important ecological process that can significantly impact the maintenance of ecosystem services. In arctic environments, the relative ecological importance of predation is thought to be increasing due to climate change, partly because of increased productivity with rising temperatures. Therefore, understanding predator-prey interactions in arctic ecosystems is vital for the sustainable management of these northern regions. Network theory provides a framework for quantifying the structures of ecological interactions. In this study, we use dietary observations on mammalian and avian predators in a high arctic region, including isolated peninsulas on Ellesmere Island and north Greenland, to construct bipartite trophic networks. We quantify the complexity, specialization, and nested as well as modular structures of these networks and also determine if these properties varied among the peninsulas. Mammal prey remains were the dominant diet item for all predators, but there was spatial variation in diet composition among peninsulas. The predator-prey networks were less complex, had more specialized interactions, and were more nested and more modular than random expectations. However, the networks displayed only moderate levels of modularity. Predator species had less specialized interactions with prey than prey had with predators. All network properties differed among the peninsulas, which highlights that ecosystems often show complex responses to environmental characteristics. We suggest that gaining knowledge about spatial variation in the characteristics of predator-prey interactions can enhance our ability to manage ecosystems exposed to environmental perturbations, particularly in high arctic environments subject to rapid environmental change.

Role of Bacillus subtilis exopolymeric genes in modulating rhizosphere microbiome assembly

BackgroundBacillus subtilis is well known for promoting plant growth and reducing abiotic and biotic stresses. Mutant gene-defective models can be created to understand important traits associated with rhizosphere fitness. This study aimed to analyze the role of exopolymeric genes in modulating tomato rhizosphere microbiome assembly under a gradient of soil microbiome diversities using the B. subtilis wild-type strain UD1022 and its corresponding mutant strain UD1022eps−TasA, which is defective in exopolysaccharide (EPS) and TasA protein production.ResultsqPCR revealed that the B. subtilis UD1022eps−TasA− strain has a diminished capacity to colonize tomato roots in soils with diluted microbial diversity. The analysis of bacterial β-diversity revealed significant differences in bacterial and fungal community structures following inoculation with either the wild-type or mutant B. subtilis strains. The Verrucomicrobiota, Patescibacteria, and Nitrospirota phyla were more enriched with the wild-type strain inoculation than with the mutant inoculation. Co-occurrence analysis revealed that when the mutant was inoculated in tomato, the rhizosphere microbial community exhibited a lower level of modularity, fewer nodes, and fewer communities compared to communities inoculated with wild-type B. subtilis.ConclusionThis study advances our understanding of the EPS and TasA genes, which are not only important for root colonization but also play a significant role in shaping rhizosphere microbiome assembly. Future research should concentrate on specific microbiome genetic traits and their implications for rhizosphere colonization, coupled with rhizosphere microbiome modulation. These efforts will be crucial for optimizing PGPR-based approaches in agriculture.

Structure of a metacommunity of urban bees: Species diversity and spatio-temporal modularity

As the globe gets more urbanised, the question about how natural biodiversity is structured in cities becomes increasingly pertinent. To contribute to an answer, we studied species richness and spatio-temporal structure of bees in a North European metropolitan area. A gradient of 13 sites in the city of Aarhus, Denmark, was censused from April to September 2016. Forty species, i.e. 29 solitary species (40 % of all individuals), ten Bombus species (28 %), and Apis mellifera (32 %), were sampled monthly in pan traps. (i) Information about species traits was extracted from the literature, and trait values were correlated and used to characterize the fauna. Most were soil-nesters, pollen generalists, and common. (ii) Habitat diversity within five concentric circles with trap at the centre and radii from 50 m to 1000 m was related to bee α diversity. The relationship was significant only within 1,000 m for all bees and for bumblebees. Solitary bee diversity was uncorrelated with habitat diversity at all spatial levels. (iii) Spatio-temporal structure was analysed as two networks, one for bees linked to sites, and one for bees linked to months. Link patterns were analysed for levels of nestedness, modularity, and spatio-temporal β diversity. The two networks were weakly and non-significantly nested, but strongly modular, being composed of five and four modules of co-occurring bees, respectively. (iv) Finally, we studied total β diversity, βTOTAL, being the sum of species turnover, βTURN, and species loss/gain or nestedness, βNEST. For both site and season, βTURN was higher than βNEST, and site βTOTAL was higher than season βTOTAL. One reason for this metacommunity structure may be a high spatio-temporal habitat patchiness, sustaining a rich biodiversity. Thus, a few large areas may not compensate for the loss of several small patches. Consequently, establishment of many green, even small habitats is recommended.

Learning ambidexterity and technology innovation: The moderating effect of knowledge network modularity

The aim of this paper is to investigate the impact of learning ambidexterity on firms’ technological innovation, especially in the context of varying degrees of modularity in firms’ knowledge networks. By combining temporal and cognitive aspects, this study focuses on the micro-level utilization of knowledge and redefines exploratory and exploitative learning as learning unfamiliar and familiar knowledge, respectively. Building upon previous research, we classify learning ambidexterity into combined and balanced forms, further investigate their influence on innovation performance. Utilizing a panel data set spanning 10 years of 63 semiconductor firms listed in the US market, our findings reveal that combined learning ambidexterity effectively harnesses the complementary effects of both learning patterns, resulting in a positive influence on technological innovation. Conversely, balanced learning ambidexterity hampers knowledge transferability between these two patterns leading to a negative impact on technological innovation. Additionally, we employ patent data to measure the degree of modularity within firms’ knowledge networks. Our results indicate that higher levels of modularity enhance the positive effect of combined learning ambidexterity on innovation while mitigating its negative impact for balanced learning ambidexterity. These findings suggest the importance for firms to strategically manage and dynamically orchestrate learning ambidexterity alongside double-loop learning practices, while continuously structuring and facilitating reuse within organizational knowledge networks to create favorable circumstances for the effective implementation of learning ambidexterity.

Host Identity Determines the Bacterial and Fungal Community and Network Structures in the Phyllosphere of Plant Species in a Temperate Steppe

Elucidating the plant−microbiome network is of importance in understanding species coexistence in natural ecosystems. The phyllosphere, which is the aerial parts of terrestrial plants, is inhabited by diverse microbes. However, few studies have focused on the phyllosphere microbiome and plant-microbiome network in temperate grasslands. In this study, we explored the diversity, community structure, and network architecture of phyllosphere bacteria and fungi in 19 plant species native to the temperate grassland in Inner Mongolia, China. We obtained 3,313 and 758 phyllosphere bacterial and fungal operational taxonomic units, respectively, and found that the bacterial community was dominated by Proteobacteria, Actinobacteriota, and Firmicutes, whereas the fungal community was dominated by Ascomycota and Basidiomycota. Plant identity exerted significant impacts on α-diversities of both bacterial and fungal communities. The composition of bacterial and fungal communities differed among plant species. Plant identity had a greater effect on fungal than bacterial communities. Both bacterial and fungal network structures were characterized by specialized, modular, lowly connected, and no nested properties. The plant−fungal network had a high level of specification, modularity, antinestedness, and connectance compared with the plant−bacterial network. Our results suggest more intimate relationships between plants and phyllosphere fungi than between plants and phyllosphere bacteria and also that the phyllosphere fungal community in the temperate grassland ecosystem is more resistant to environmental disturbance than the phyllosphere bacterial community. These findings may contribute to our understanding of the mechanisms by which species coexist and communities stabilize in grassland ecosystems.

Floral and pollinator functional diversity mediate network structure along an elevational gradient

Elevational gradients in alpine ecosystems are well suited to study how plant and pollinator communities respond to climate change. In the Austrian Alps, we tested how the taxonomic and functional diversity of plants and their pollinators change with increasing elevation and how this affects plant–pollinator network structure. We measured the phenotypes of flowering plants and their pollinators and observed their interactions in 24 communities along an elevational gradient. To assess variation in floral and pollinator traits, we then quantified trait spaces (n-dimensional hypervolumes) occupied by flowers and pollinators in each community. To assess plant–pollinator network structure, we quantified the levels of complementary specialization (H2’), modularity and nestedness (weighted NODF) for each community. As elevation increased, most measures of diversity and network specialization either declined linearly or in an oscillating manner. Communities that exhibited higher pollinator functional diversity exhibited larger degrees of complementary specialization and modularity; and communities with greater floral and pollinator functional diversity and higher phylogenetic diversity were less nested. Altogether, the degree to which elevation, species diversity, functional diversity and network structure changed in conjunction suggests environmental effects on the functional and phylogenetic diversity of plants and pollinators and consequently network structure. Our results suggest that the effects of climate change on plant and pollinator community composition will impact plant–pollinator network structure and potentially pollination services at the community scale.

Enhancing sustainability in mass customisation of consumer electronics with salvage value through supply chain financing

ABSTRACT The consumer electronics industry is crucial in the global shift toward sustainability. To meet customers’ personalised needs efficiently and reduce production costs, electronic product suppliers increasingly focus on Mass Customization (MC) and modular production. Implementing a buyback policy for unsold module products with salvage value is a sustainable practice that reduces waste. This study explores two buyback support financing tools to enhance sustainability in the industry, enabling successful MC production. We also investigate how the salvage value based on modularity level and the buyback policy impact operational and financing decisions in the Mass Customization Supply Chain (MCSC). The research reveals that a higher modularity level positively impacts supplier’s profits and optimal capacity levels under both financing strategies. Consequently, this study offers valuable insights into elevating sustainability practices and optimising operational and financing decisions in the context of MC within the consumer electronics industry.

Preoperative structural-functional coupling at the default mode network predicts surgical outcomes of temporal lobe epilepsy.

Structural-functional coupling (SFC) has shown great promise in predicting postsurgical seizure recurrence in patients with temporal lobe epilepsy (TLE). In this study, we aimed to clarify the global alterations in SFC in TLE patients and predict their surgical outcomes using SFC features. This study analyzed presurgical diffusion and functional magnetic resonance imaging data from 71 TLE patients and 48 healthy controls (HCs). TLE patients were categorized into seizure-free (SF) and non-seizure-free (nSF) groups based on postsurgical recurrence. Individual functional connectivity (FC), structural connectivity (SC), and SFC were quantified at the regional and modular levels. The data were compared between the TLE and HC groups as well as among the TLE, SF, and nSF groups. The features of SFC, SC, and FC were categorized into three datasets: the modular SFC dataset, regional SFC dataset, and SC/FC dataset. Each dataset was independently integrated into a cross-validated machine learning model to classify surgical outcomes. Compared with HCs, the visual and subcortical modules exhibited decoupling in TLE patients (p < .05). Multiple default mode network (DMN)-related SFCs were significantly higher in the nSF group than in the SF group (p < .05). Models trained using the modular SFC dataset demonstrated the highest predictive performance. The final prediction model achieved an area under the receiver operating characteristic curve of .893 with an overall accuracy of .887. Presurgical hyper-SFC in the DMN was strongly associated with postoperative seizure recurrence. Furthermore, our results introduce a novel SFC-based machine learning model to precisely classify the surgical outcomes of TLE.