Modularity Index Research Articles

Differential regulation of belowground rhizospheric ecosystem by biological and chemical nitrogen supplies: implications for maize yield enhancement mechanisms.

Nitrogen (N) content affects aboveground maize growth and nutrient absorption by altering the belowground rhizospheric ecosystem, impacting both yield and quality. However, the mechanisms through which different N supply methods (chemical and biological N supplies) regulate the belowground rhizospheric ecosystem to enhance maize yield remain unclear. To address this issue, we conducted a field experiment in northeast China, comprising three treatments: maize monocropping without N fertilizer application (MM), maize/alfalfa intercropping without N fertilizer application (BNF), and maize monocropping with N fertilizer application (CNS). The MM treatment represents the control, while the BNF treatment represents the biological N supply form, and CNS treatment represents the chemical N supply form. In the autumn of 2019, samples of maize and rhizospheric soil were collected to assess parameters including yield, rhizospheric soil characteristics, and microbial indicators. Both BNF and MM significantly increased maize yield and different yield components compared with MM, with no statistically significant difference in total yield between BNF and CNS. Furthermore, BNF significantly improved N by 12.61% and available N (AN) by 13.20% compared with MM. Furthermore, BNF treatment also significantly increased the Shannon index by 1.90%, while the CNS treatment significantly increased the Chao1 index by 28.1% and ACE index by 29.49%, with no significant difference between CNS and BNF. However, CNS had a more pronounced impact on structure of the rhizosphere soil bacterial community compared to BNF, inducing more significant fluctuations within the microbial network (modularity index and negative cohesion index). Regarding N transformation pathways predicted by bacterial functions, BNF significantly increased the N fixation pathway, while CNS significantly increased assimilatory nitrate reduction. In CNS, AN, NO3-N, NH4-N, assimilatory nitrate reduction, and community structure contributed significantly to maize yield, whereas in BNF, N fixation, community structure, community stability, NO3-N, and NH4-N played significant roles in enhancing maize yield. While CNS and BNF can achieve comparable maize yields in practical agricultural production, they have significantly different impacts on the belowground rhizosphere ecosystem, leading to different mechanisms of yield enhancement.

Evidence-based practice in traditional persian medicine (TPM): a stakeholder and social network analysis

BackgroundThe utilization of complementary and alternative medicine (CAM) is experiencing a global surge, accompanied by the adoption of national CAM policies in numerous countries. Traditional Persian medicine (TPM) is highly used as CAM in Iran, and the ongoing scientific evaluation of its interventions and the implementation of evidence-based medicine (EBM) encounters various barriers. Therefore, comprehending the characteristics and interactions of stakeholders is pivotal in advancing EBM within TPM policies. In this study, we utilized both classical stakeholder analysis and social network analysis to identify key stakeholders and potential communication patterns, thereby promoting EBM in TPM policy-making.MethodsA cross-sectional nationwide stakeholder analysis was conducted in 2023 using snowball sampling. The interviews were carried out using a customized version of the six building blocks of health. Data were collected through semi-structured interviews. Stakeholders were assessed based on five factors (power, interest, influence, position, and competency). The connections and structure of the network were analyzed using degree, betweenness, closeness centrality, and modularity index to detect clusters of smaller networks.ResultsAmong twenty-three identified stakeholders, the Ministry of Health and Medical Education (MOHME) and the Public were the most powerful and influential. The Iranian Academy of Medical Sciences was the most competent stakeholder. Social network analysis revealed a low density of connections among stakeholders. Pharmaceutical companies were identified as key connectors in the network, while the Public, supreme governmental bodies, and guilds acted as gatekeepers or brokers. The MOHME and Maraji were found to be high-ranking stakeholders based on four different centrality measures.ConclusionThis study identifies powerful stakeholders in the network and emphasizes the need to engage uninterested yet significant stakeholders. Recommendations include improving competence through education, strengthening international relations, and fostering stronger relationships. Engaging key connectors and gatekeepers is essential for bridging gaps in the network.

Effect of cover crop on soil fertility and bacterial diversity in a banana plantation in southwestern China

A cover crop can be an effective strategy to mitigate soil degradation in crops, such as banana. In this study, soil bacterial diversity, chemical properties, enzymatic activities, and bacterial nutrient-related functional genes were examined in the intercropping area of a banana plantation from 2018 to 2020 that had conventional tillage with bare soil (CT), natural weed cover (WC), which was primarily goosegrass (Eleusine indica (L.) Gaerth), or a cover crop of cultivated Siratro (Macroptilium atropurpureum (DC.) Urb.) (SC). By 2020, SC followed by WC and CT treatments were higher in soil organic matter, available nitrogen, total nitrogen and total phosphorus content as well as increased phosphatase, catalase, invertase and urease activities. Compared to CT and WC treatments, the co-occurrence network of SC treatment had higher modularity index indicating a more stable bacterial community. The composition of community was significantly different between the CT and SC treatments in 2019 and 2020, and the WC treatment in 2020. The relative abundance of fatty acids and lipid biosynthesis genes in the soil bacterial microbiome were significantly higher with SC compared to CT and WC in 2019 and 2020, as well as for carbon and nitrogen functional genes in 2020. However, the relative abundance of nucleoside and nucleoside biosynthesis genes were significantly lower with SC compared to CT and WC in 2020. SC treatment increased the relative abundance of Bradyrhizobium and MND1 related to nitrogen cycle and utilization compared to CT in 2019 and 2020. In general, the major benefits of a Siratro cover crop was increased soil organic matter, nitrogen content, and nitrogen fixing bacteria, as well as a more stable soil bacterial community, indicating that it provided multiple potential benefits to the soil of banana.

An empirical assessment of whether urban green ecological networks have the capacity to store higher levels of carbon

Carbon–neutral growth is a crucial long-term climatic aim in the context of global warming. This paper introduces complex network theory and explores its potential application to achieve this goal. Specifically, we investigate the spatial and temporal distribution of nodes and sources in the ecological network, and examine whether a relationship exists between the topological index of network nodes and the landscape pattern index of ecological source areas. We also determine the contribution of nodes to the carbon stock of the entire network by exploring the correlation between the carbon stock of nodes and sources to develop an optimization strategy based on the synergistic effect of node-source carbon enhancement. Finally, we test the effect of network optimization through robustness. Our results show that: (1) The correlation topological feature index analysis reveals that the degree distribution of the node network's topological characteristics becomes dispersed and modular, exhibiting the characteristics of small-world networks according to a large clustering coefficient. The heterogeneity and extent of ecological source landscapes have increased by modularity index but remain distributed and locally fragmented; (2) According to correlation analysis, by enhancing the eccentricity of the node topology, the patch cohesion index (COHESION) of the ecological source site can maximize the contribution of the node to the enhancement of the carbon stock benefits of the source site; (3) According to the tests on the robustness of nodes and edges and the robustness of network links, network stability is improved and carbon sink capacity is enhanced. Simultaneously, the restoration and rejuvenation of ecological space through national ecological construction projects can effectively improve the carbon sink within the organized region, contributing to the carbon neutrality aim. This research gives scientific and quantifiable references for potential ecological construction projects for sustainable cities and the optimization of urban ecological space structure.

Patterns of liana diversity and host interaction networks in selectively logged and unlogged forests of Uppangala, Western Ghats, India

AbstractLianas shape tropical forest species composition, structure, and dynamics. Increasing climate fluctuation and anthropogenic disturbances increase liana abundance. Despite the increasing number of liana studies in India, only a few have examined the distribution and association of hosts with lianas, or liana–host interaction networks to determine their functional significance and conservational value. Therefore, our objective was to fill the knowledge gap about the diversity, abundance, and network structure of liana–host interactions in response to logging disturbance in a wet evergreen forest of Uppangala in central Western Ghats, India. We sampled lianas ≥1 cm in diameter at 1.3 m from the base and their host trees in thirty 20 m × 20 m plots in selectively logged and unlogged forest management regimes. We evaluated liana–host tree interactions in logged and unlogged forests and retrieved community‐level measures (nestedness, connectance, modularity, and network specialization index) and species‐level indicators (species specialization index). Diversity and abundance of liana species were considerably greater in the selectively logged forest site. The logged forest site had compartmentalization, anti‐nestedness, and network specialization, while unlogged forests were not showing any significant network structure. Most species of lianas and hosts were peripherals, but others were structurally important (connectors, module hubs, and network hubs) in the two forest sites. Forest management regimes had distinct structurally significant species.

Modular brain network in volitional eyes closing: enhanced integration with a marked impact on hubs.

Volitional eyes closing would shift brain's information processing modes from the "exteroceptive" to "interoceptive" state. This transition induced by the eyes closing is underpinned by a large-scale reconfiguration of brain network, which is still not fully comprehended. Here, we investigated the eyes-closing-relevant network reconfiguration by examining the functional integration among intrinsic modules. Our investigation utilized a publicly available dataset with 48 subjects being scanned in both eyes closed and eyes open conditions. It was found that the modular integration was significantly enhanced during the eyes closing, including lower modularity index, higher participation coefficient, less provincial hubs, and more connector hubs. Moreover, the eyes-closing-enhanced integration was particularly noticeable in the hubs of network, mainly located in the default-mode network. Finally, the hub-dominant modular enhancement was positively correlated to the eyes-closing-reduced entropy of BOLD signal, suggesting a close connection to the diminished consciousness of individuals. Collectively, our findings strongly suggested that the enhanced modular integration with substantially reorganized hubs characterized the large-scale cortical underpinning of the volitional eyes closing.

The Salinity Survival Strategy of Chenopodium quinoa: Investigating Microbial Community Shifts and Nitrogen Cycling in Saline Soils.

Quinoa is extensively cultivated for its nutritional value, and its exceptional capacity to endure elevated salt levels presents a promising resolution to the agricultural quandaries posed by salinity stress. However, limited research has been dedicated to elucidating the correlation between alterations in the salinity soil microbial community and nitrogen transformations. To scrutinize the underlying mechanisms behind quinoa's salt tolerance, we assessed the changes in microbial community structure and the abundance of nitrogen transformation genes across three distinct salinity thresholds (1 g·kg-1, 3 g·kg-1, and 6 g·kg-1) at two distinct time points (35 and 70 days). The results showed the positive effect of quinoa on the soil microbial community structure, including changes in key populations and its regulatory role in soil nitrogen cycling under salt stress. Choroflexi, Acidobacteriota, and Myxococcota were inhibited by increased salinity, while the relative abundance of Bacteroidota increased. Proteobacteria and Actinobacteria showed relatively stable abundances across time and salinity levels. Quinoa possesses the ability to synthesize or modify the composition of keystone species or promote the establishment of highly complex microbial networks (modularity index > 0.4) to cope with fluctuations in external salt stress environments. Furthermore, quinoa exhibited nitrogen (N) cycling by downregulating denitrification genes (nirS, nosZ), upregulating nitrification genes (Archaeal amoA (AOA), Bacterial amoA (AOB)), and stabilizing nitrogen fixation genes (nifH) to absorb nitrate-nitrogen (NO3-_N). This study paves the way for future research on regulating quinoa, promoting soil microbial communities, and nitrogen transformation in saline environments.

A fast-partitioning decision method for demand side resources based on grid resilience assessment

With the large-scale renewable energy integrated into the distribution grid, the grid’s regulating ability and disturbance tolerance are weakening. When partitioning demand-side resources, it is necessary to enhance resilience to ensure the reliability of electric power. This paper proposes a fast-partitioning method that considers resilience, structure, and functionality to adapt to the evolving requirements of the distribution system. Specifically, the comprehensive partition index system is constructed with the resilience assessment index reflecting the ability of partitions to withstand and mitigate the effects of faults, the modularity index based on electrical distance, and regional power balance indexes. Meanwhile, a modified genetic algorithm is proposed to calculate the comprehensive partition index. The modified algorithm first uses a sensitivity matrix to perform initial partitions and construct initial populations. Then, it utilizes a triangular network adjacency matrix for chromosome encoding, significantly reducing the algorithm’s search space and enhancing partitioning efficiency. Finally, the applicability and effectiveness of the proposed method are verified through simulation analysis of the IEEE 28-node system.

Intra-individual cortical networks in Anorexia Nervosa: Evidence from a longitudinal dataset.

This work investigates cortical thickness (CT) and gyrification patterns in Anorexia Nervosa (AN) before and after short-term weight restoration using graph theory tools. 38 female adolescents with AN underwent structural magnetic resonance imaging scans at baseline and after - on average-3.5months following short-term weight restoration while 53 age-matched healthy controls (HCs) were scanned once. Graph measures were compared between groups and longitudinally within the AN group. Associations with clinical measures such as age of onset, duration of illness, BMI standard deviation score (BMI-SDS), and longitudinal weight changes were tested via stepwise regression. Cortical thickness graphs of patients with acute AN displayed lower modularity and small-world index (SWI) than HCs. Modularity recovered after weight gain. Reduced global efficiency and SWI were observed in patients at baseline compared to HCs based on gyrification networks. Significant associations between local clustering of CT at admission and BMI-SDS, and clustering/global efficiency of gyrification and duration of illness emerged. Our results indicate a shift towards less organised CT networks in patients with acute AN. After weight recovery, the disarrangement seems to be partially reduced. However, longer-term follow-ups are needed to determine whether cortical organizational patterns fully return to normal.

The leguminous Hedysarum shrubs effectively drive the diversity and structural composition of soil bacterial community through rhizocompartments in the process of desertification reversal

AbstractIn this study, we aimed to assess the impact of vegetation restoration measures on the soil microbial community in degraded land via assessing the structural changes and diversity of soil bacterial communities and their response to environmental variables in the rhizocompartments (i.e., intershrub bulk soil, rhizosphere soil, and roots) of two legumes (Hedysarum scoparium and Hedysarum mongolicum) in Mu Us Desert, China. The bacterial diversity and soil physicochemical characteristics were studied using high‐throughput 16S rRNA genome sequencing and traditional soil physicochemical indices, respectively. The rhizocompartment types and plant species jointly affected the alpha and beta diversities of bacterial communities in the rhizosphere and non‐rhizosphere soil; however, rhizocompartments played a dominant role (P < 0.05). Compared with the network of root endophytes, the bacterial network in the rhizosphere soil exhibited increased total nodes, total links, harmonic geodesic distance, and modularity indices, and the structure was more complex. The root endophytic community was primarily impacted by soil nutrients (particularly nitrogen and soil organic carbon), whereas bacterial communities of bacteria in the nonrhizosphere and rhizosphere soil were significantly impacted by ammonium nitrogen and soil pH because of the response of the core bacterial taxa to soil physicochemical properties. These findings revealed that plant restoration techniques drive desertification reversal via altering the composition and diversity of soil bacterial communities. This study provided new insights regarding the ecological restoration of degraded lands in deserts.

Decentralized Control Strategy for Participation of Electric Vehicles in Distribution Voltage Control

With the accelerating transportation electrification, increasing numbers of electric vehicles (EVs) are connected to distribution networks via electric vehicle charging stations (EVCSs). Due to the uncertain charging behaviors of EV users, concentrated high-power EV charging during peak hours can result in significant undervoltage issues, affecting the regular operation of distribution networks and having detrimental effects on regular EV charging. Conventional EV charging scheduling methods are conducted in a centralized way, requiring extensive investment in communication infrastructure and intensive computing power. Accordingly, this paper proposes a novel decentralized voltage control scheme with the participation of EVs and EVCSs. The proposed control scheme first divides the distribution network into clusters based on a modified modularity index. Then, it applies the rolling optimization-based control of the EVs and EVCSs within each cluster to solve the regional voltage problems. The optimization window length is self-adapted based on the monitored states of the grid-connected EVs and EVCSs. Case studies show that the proposed decentralized voltage control strategy could effectively address the undervoltage violation issues and alleviate the pressure on peak shaving during peak hours, while satisfying the charging requirements of EV users.

Complete and partial synchronization in empirical brain networks

The chimera state, characterized by the coexistence of synchronization and asynchronization in a network, associates with special brain functions and disorders. This paper studies the chimera state in empirical networks reconstructed from the human, macaque, marmoset, and mouse brain data and also a constructed random network. The structural properties, such as clustering coefficient, efficiency, and modularity index of the networks, are computed and compared. The degree distributions of networks are also represented, yielding a more different degree distributon for the mouse network with the others. To determine the collective behavior of the network, the global and local order parameters and the master stability function are applied. Under increasing coupling strength, all the networks generically display the sequence of transitions from asynchronous state, over chimeras to the synchronous regime. There are also multistable regions during the transitions. The results represent that based on the coupling strength required to reach a fully synchronized state, human, marmoset, and macaque networks have relatively similar dynamical regions of synchronization. Mouse and random networks, however, require considerably stronger and weaker couplings, respectively. Changing the dynamics of the individual neurons shows that the asynchronous and chimera regions extend as the dynamics of the neurons become more complex.

Sustainable response strategy for COVID-19: Pandemic zoning with urban multimodal transport data.

In the post-COVID-19 era, the pandemic response is increasingly difficult and entails a high cost to society. Existing pandemic control methods, such as lockdowns, greatly affect residents' normal lives. This paper proposes a pandemic control method, consisting of the scientific delineation of urban areas based on multimodal transportation data. An improved Leiden method based on the gravity model is used to construct a preliminary zoning scheme, which is then modified by spatial constraints. The modularity index demonstrates the suitability of this method for community detection. This method can minimize cut-off traffic flows between pandemic control areas. The results show that only 24.8% of travel links are disrupted using our method, which could reduce both the impact of pandemic control on the daily life of residents and its cost. These findings can help develop sustainable strategies and proposals for effective pandemic response.

Community detection in brain connectomes with hybrid quantum computing

Recent advancements in network neuroscience are pointing in the direction of considering the brain as a small-world system with an efficient integration-segregation balance that facilitates different cognitive tasks and functions. In this context, community detection is a pivotal issue in computational neuroscience. In this paper we explored community detection within brain connectomes using the power of quantum annealers, and in particular the Leap’s Hybrid Solver in D-Wave. By reframing the modularity optimization problem into a Discrete Quadratic Model, we show that quantum annealers achieved higher modularity indices compared to the Louvain Community Detection Algorithm without the need to overcomplicate the mathematical formulation. We also found that the number of communities detected in brain connectomes slightly differed while still being biologically interpretable. These promising preliminary results, together with recent findings, strengthen the claim that quantum optimization methods might be a suitable alternative against classical approaches when dealing with community assignment in networks.

Global brain c-Fos profiling reveals major functional brain networks rearrangements after alcohol reexposure

Many fundamental questions on alcohol use disorder (AUD) are frequently difficult to address by examining a single brain structure, but should be viewed from the whole brain perspective. c-Fos is a marker of neuronal activation. Global brain c-Fos profiling in rodents represents a promising platform to study brain functional networks rearrangements in AUD.We used a mouse model of alcohol drinking in IntelliCage. We trained mice to voluntarily drink alcohol, next subjected them to withdrawal and alcohol reexposure. We have developed a dedicated image computational workflow to identify c-Fos-positive cells in three-dimensional images obtained after whole-brain optical clearing and imaging in the light-sheet microscope.We provide a complete list of 169 brain structures with annotated c-Fos expression. We analyzed functional networks, brain modularity and engram index. Brain c-Fos levels in animals reexposed to alcohol were different from both control and binge drinking animals. Structures involved in reward processing, decision making and characteristic for addictive behaviors, such as precommissural nucleus, nucleus Raphe, parts of colliculus and tecta stood out particularly. Alcohol reexposure leads to a massive change of brain modularity including a formation of numerous smaller functional modules grouping structures involved in addiction development.Binge drinking can lead to substantial functional remodeling in the brain. We provide a list of structures that can be used as a target in pharmacotherapy but also point to the networks and modules that can hold therapeutic potential demonstrated by a clinical trial in patients.

An improved hybrid community detection algorithm for partitioning of water distribution networks

District metered areas (DMAs) are widely used by water utilities to manage water distribution networks (WDNs). This study presents a novel methodology that couples the improved hybrid community detection algorithm and combinatorial optimization process for partitioning WDNs into DMAs. In the node clustering phase, the hybrid algorithm based on the improved modularity index enables the fast formation of sufficient partition solutions with a more balanced water demand distribution and reduced diameters of boundary pipes. Then, in the partition dividing phase, a three-step optimization method, comprising preliminary hydraulic analysis, search for a suboptimal solution and multi-objective optimization, is presented to find a fast and optimal solution for the location of flow meters and isolation valves in WDNs. The overall methodology is applied to a large-scale WDN, proving its applicability and superiority in generating engineering partition configurations.

Clustering Product Development Project Organization From the Perspective of Social Network Analysis

In product development (PD) organizations, coordinating technical dependencies among teams with different expertise in overlapping processes is a fundamental challenge. This article takes a more sophisticated approach than prior methodologies to improve coordination via organizational clustering, by accounting for both team structural and attribute similarity from the perspective of social network analysis. We built models to quantify the impact of the overlapping processes on the interaction strength among PD teams, which we then used to construct structural similarity by combining tie strength and social cohesion among teams via the design structure matrix. To evaluate the organization network, we propose social embeddedness-related centrality indices within (intracluster) and across (intercluster) team groupings. To facilitate knowledge sharing, we base team attribute similarity on product- and process-related expertise among teams. We integrate the modularity index and an improved silhouette index to find an optimal number of clusters, which we then incorporate with team similarity measures as inputs to a spectral clustering algorithm. An industrial example illustrates the proposed model. The clustering results reinforce several managerial practices but also yield new insights, such as how to measure similarity among teams based on organizational network characteristics and how structural and attribute similarities impact the optimal organizational structure.

A novel complex network community clustering method for fault diagnosis

The complex network, as a method for the analysis of nonlinear and non-stationary signals, overcomes the shortcomings of traditional time-frequency analysis methods and proves its effectiveness in mechanical fault diagnosis. Community clustering, a type of complex network, has made great progress in recent years. However, the existing community clustering algorithms have disadvantages in that they lack significant global extreme value and huge search spaces. Therefore, a Fast Newman algorithm based on reliability judgment is proposed. Starting from the community structure characteristics of the complex network, with the fault sample as a network node, the relationship between the samples as a connected edge and a complex network model of fault data is established. Clusters in troubleshooting are transformed into community structure discovery in the network. Firstly, the initial division of the community is obtained by measuring the distance between the samples. Then, the modularity index of the network is used as a standard function of the community division, and the bottom-up community merger is performed. The local edge density index is used for reliability determination before each combination to achieve global optimization, and the network block structure is the most obvious. Finally, with all the data merged into one community, the optimal division of the community structure is obtained, while accurate community clustering and fault diagnosis is realized. The benchmark graphs for testing community detection (Lancichinetti–Fortunato–Radicchi benchmark standard test network, LFR) and different fault data of rolling bearings under multiple operating conditions are applied to verify the effectiveness of this method; the results prove that the modified Fast Newman algorithm has better clustering effects and a higher accuracy rate than the original algorithm. Compared with K-means clustering and fuzzy clustering, the modified Fast Newman algorithm achieves higher performance in fault diagnosis of rolling bearings under multiple operating conditions.

On maximization of the modularity index in network psychometrics.

The modularity index (Q) is an important criterion for many community detection heuristics used in network psychometrics and its subareas (e.g., exploratory graph analysis). Some heuristics seek to directly maximize Q, whereas others, such as the walktrap algorithm, only use the modularity index post hoc to determine the number of communities. Researchers in network psychometrics have typically not employed methods that are guaranteed to find a partition that maximizes Q, perhaps because of the complexity of the underlying mathematical programming problem. In this paper, for networks of the size commonly encountered in network psychometrics, we explore the utility of finding the partition that maximizes Q via formulation and solution of a clique partitioning problem (CPP). A key benefit of the CPP is that the number of communities is naturally determined by its solution and, therefore, need not be prespecified in advance. The results of two simulation studies comparing maximization of Q to two other methods that seek to maximize modularity (fast greedy and Louvain), as well as one popular method that does not (walktrap algorithm), provide interesting insights as to the relative performances of the methods with respect to identification of the correct number of communities and the recovery of underlying community structure.

DEVELOPING A NEW TECHNIQUE FOR DETERMINING SERVICE MODULARITY LEVEL

This paper addresses the existing methodological gap in assessing service modularity level. The study uses a qualitative approach and develops a technique to quickly and cost-effectively determine service modularity level. The theoretical framework builds on the principles of measurement theory, evaluation theory, and multicriteria analysis (MCA). To test the developed technique, an exploratory case study was conducted focusing on three service companies in Lithuania. Data were collected by interviewing key informants using the proposed instrument. The collected data were analysed and linked to the developed multidimensional scale for measuring the service modularity level. The obtained results support the view that qualitative evaluation can be an alternative for calculating modularity indices in the field of service modularity. The paper provides a practical tool for service modularity assessment for service industry practitioners to accelerate decision-making. The study contributes to the field of service modularity by delivering insights into assessing service modularity level. These insights are particularly important because the literature on measuring modularity level in services has been quite scarce.