Information Network Research Articles

A Paradox of School Social Organization: Positive School Climate, Friendship Network Density, and Adolescent Violence.

Schools are often encouraged to foster a positive climate to reduce adolescent violence, but evidence on the effectiveness of this approach varies significantly. This study investigates the roots of this variation by testing alternative hypotheses about how positive school-level climate and school-level student friendship network density interact to shape adolescent violence perpetration. Research on informal social control and network closure suggests that the violence-reducing association of positive school climate will be enhanced among schools where students are more densely tied through their friendships. Research on youth conflict and subversion of control suggests the opposite. These hypotheses are tested with data from Waves I-II of the National Longitudinal Study of Adolescent to Adult Health (n = 11,771; 49% Female; Age mean = 15.04, SD = 1.60). Consistent with the conflict/subversion hypothesis, analyses indicate that the inverse association between positive school climate and adolescent violence is only evident among schools with a very low density of friendship ties. Strikingly, however, there is evidence that a more positive school climate is associated with increases in violence among youth attending schools with a high density of friendship ties. These findings suggest that efforts to reduce violence by fostering cohesion among youth in their schools and other social contexts can be undermined by youth network processes.

DDI Prediction with Heterogeneous Information Network - Meta-Path Based Approach.

Drug-drug interaction (DDI) indicates where a particular drug's desired course of action is modified when taken with other drug (s). DDIs may hamper, enhance, or reduce the expected effect of either drug or, in the worst possible scenario, cause an adverse side effect. While it is crucial to identify drug-drug interactions, it is quite impossible to detect all possible DDIs for a new drug during the clinical trial. Therefore, many computational methods are proposed for this task. This paper presents a novel method based on a heterogeneous information network (HIN), which consists of drugs and other biomedical entities like proteins, pathways, and side effects. Afterward, we extract the rich semantic relationships among these entities using different meta-path-based topological features and facilitate DDI prediction. In addition, we present a heterogeneous graph attention network-based end-to-end model for DDI prediction in the heterogeneous graph. Experimental results show that our proposed method accurately predicts DDIs and outperforms the baselines significantly.

Spillover effects in transit networks: A parameterized weight matrix spatial lagged approach

Public Transit (PT) systems aim to provide social, economic, and environmental benefits to modern cities offering reliable transportation service to users and ultimately reducing the problems related to traffic externalities due to car dependency. With the growing trend of urbanization and the associated phenomenon of cities’ sprawl being increasingly evident, significant attention should be given to understanding PT systems’ performance and then improving their efficiency. Considering the spatial characteristics of the PT system's performance promotes the environmentally friendly transport “character” that every city endeavors. This paper aims to incorporate the spatial spillover effects of a realistic PT system by augmenting information about the service network along with socio-economic variables, in a spatial demand-supply econometric framework. In detail, geographically separated demand and supply information on bus stops and lines was analyzed by a spatial econometric model, namely, the Spatial Lagged X (SLX) model which may be formatted so that can soundly handle, spatial and –in particular- network data like those coming from the General Transit Feed Specification (GTFS) protocol widely used in PT systems. The novelty and the importance of the proposed model rely on the ability to introduce transit network structure within the framework of spatial econometric regression, fostering the explanatory statistical analysis over networked information. The developed modeling approach was applied to the urban and rural PT system of Nicosia (Cyprus), incorporating the spatial spillover effects of the system over more than 1,500 bus stops, 40 lines, and 252 different but spatially connected postcode areas. The results of the SLX model were compared with other demand models of this form typically used, namely the Ordinary Least Square model and standard Spatial Autoregressive models, providing solid evidence of the benefits of incorporating the network structure in spatial demand modeling, giving valuable input for further planning purposes.

Toward Few-Label Vertical Federated Learning

Federated Learning (FL) provides a novel paradigm for privacy-preserving machine learning, enabling multiple clients to collaborate on model training without sharing private data. To handle multi-source heterogeneous data, Vertical Federated Learning (VFL) has been extensively investigated. However, in the context of VFL, the label information tends to be kept in one authoritative client and is very limited. This poses two challenges for model training in the VFL scenario. On the one hand, a small number of labels cannot guarantee to train a well VFL model with informative network parameters, resulting in unclear boundaries for classification decisions. On the other hand, the large amount of unlabeled data is dominant and should not be discounted, and it is worthwhile to focus on how to leverage them to improve representation modeling capabilities. To address the preceding two challenges, we first introduce supervised contrastive loss to enhance the intra-class aggregation and inter-class estrangement, which is to deeply explore label information and improve the effectiveness of downstream classification tasks. Then, for unlabeled data, we introduce a pseudo-label-guided consistency mechanism to induce the classification results coherent across clients, which allows the representations learned by local networks to absorb the knowledge from other clients, and alleviates the disagreement between different clients for classification tasks. We conduct sufficient experiments on four commonly used datasets, and the experimental results demonstrate that our method is superior to the state-of-the-art methods, especially in the low-label rate scenario, and the improvement becomes more significant.

DIPG-71. XPEDITE: A STUDY TO EXPEDITE DIPG AND DMG RESEARCH

Abstract BACKGROUND Diffuse midline glioma (DMG), particularly the subtype known as diffuse intrinsic pontine glioma (DIPG), poses a significant challenge due to its aggressive nature and low survival rates, especially in children. Despite ongoing research and clinical trials, the survival rate remains alarmingly low. The scarcity of cases and siloed nature of current registry efforts have hindered the establishment of a reliable natural disease history for benchmarking studies and led to the need for randomization in clinical trials. METHODS xPedite (NCT06140719) is a fully virtual, patient-driven, nationwide, real-time observational study to collect, annotate, standardize, and report the critical data elements of DMG and DIPG in a regulatory-compliant framework. Patients of any age can join via electronic consent, with recruitment supported by provider referrals and patient advocacy organizations DDRFA and My DIPG Navigator. Medical records are accessed from institutions, sequencing vendors, and health information networks. Notably, data elements are annotated comprehensively and longitudinally in a 21 CFR Part 11-compliant clinical database by xCures remote study staff, eliminating the need for data entry by treating site staff or physicians. Data collected include anti-cancer interventions, non-cancer medications, biomarker results, radiological endpoints, steroid use, vitals, demographics, raw DICOM images, and genomics. In addition to clinical data, patient- and caregiver-reported outcome questionnaires (PROs) and video assessments of mobility and neurological status are collected for clinician review. The objective of the study is to collect these clinical, imaging, biomarker, and assessment data in a manner suitable to provide to health authorities to reduce randomization in future trials. In a commitment to scientific understanding and collaborative research, the data collected through the xPedite study is openly available to academic, government, and non-profit researchers. As of January 2024, 182 patients were enrolled in xPedite. Real-time enrollment and data statistics are available at https://xcures.com/dmgdashboard/.

Effects of Heart Rate Variability Biofeedback Training on Anxiety Reduction and Brain Activity: a Randomized Active-Controlled Study Using EEG.

Heart rate variability biofeedback (HRVBF) is a promising anxiety-reducing intervention that increases vagally-mediated heart rate variability (vmHRV) through slow-paced breathing and feedback of heart rhythm. Several studies have reported the anxiety-reducing effects of HRVBF; however, some studies have reported such training as ineffective. Furthermore, the effects of training and underlying brain activity changes remain unclear. This study examined the anxiety-reducing effects of HRVBF training and related brain activity changes by randomly assigning participants, employing an active control group, and measuring anxiety-related attentional bias using the emotional Stroop task and electroencephalography (EEG). Fifty-five healthy students with anxiety were randomly assigned to the HRVBF or control groups, and 21 in the HRVBF group and 19 in the control group were included in the analysis. Both groups performed 10 training sessions of 20min each within 3 weeks. They were assessed using resting vmHRV, event-related potential (ERP), time-frequency EEG, attentional bias, and the State-Trait Anxiety Inventory-JYZ (STAI-JYZ) before and after training. The results demonstrated increased resting vmHRV in the HRVBF group compared to the control group after training. However, no differences were observed in ERP, time-frequency EEG, attentional bias, and STAI-JYZ. Participants with higher pre-training resting vmHRV achieved higher heart rhythm coherence in HRVBF training and had reduced attentional bias. This study suggests that individuals with higher resting vmHRV are more likely to be proficient in HRVBF training and benefit from its anxiety-reducing effects. The findings contribute to participant selection to benefit from HRVBF training and modification of the training protocols for non-responders.Clinical trial registrationOrganization: University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR), JapanRegistration number: UMIN000047096Registration date: March 6, 2022.

The combined effect of leucine-enriched essential amino acid supplements and locomotion training on physical functions and quality of life in hemodialysis patients

BackgroundLocomotive syndrome (LS) is a condition of reduced mobility (locomotive organ impairment) that is associated with reduced quality of life (QOL) in patients undergoing hemodialysis (HD), and effective interventions for patients with LS undergoing HD are unclear. We conducted a clinical trial to assess the effects of exercise and oral nutritional supplementation (ONS) on QOL in this cohort.MethodsThis study was an open-label, randomized controlled trial. The participants were patients with LS undergoing HD. The intervention period was 3 months. The primary outcome was a change in the physical component summary (PCS) of QOL, and the secondary outcomes were changes in other QOL scores, physical function, body composition, and nutritional status assessed by using the geriatric nutritional risk index. Participants were divided into the locomotion training (LT) and LT + ONS groups. Both groups were instructed on LT that comprised one-leg standing and squats to be carried out four or more times per week. The ONS consisted of 3 g leucin-rich essential amino acids and 800 IU vitamin D per pack, and the participants in the LT + ONS group received one pack of the supplement per day.ResultsIn total, 40 individuals undergoing HD were included in the study; 3 patients in the LT + ONS group were excluded from the analysis because of hospitalization and incomplete assessment. The median age was 73 years (interquartile range: 62–80 years), 23 participants were men (62%), and the duration of dialysis treatment was 6 years (interquartile range: 3–16 years). The change in PCS of the LT + ONS group did not differ from that in the LT group [LT + ONS: −1.6 (−5.3, 6.8) versus LT: −0.1 (−5.3, 6.2), p = 0.94]. In contrast, the LT + ONS group showed maintenance in mental health (MH) and improvement of the two-step value.ConclusionsThe LT + ONS group did not show an effect on PCS in QOL compared with only LT in patients with LS undergoing HD. However, maintenance of MH and improvement of two-step value were confirmed in the LT + ONS group. Future research is need to confirm whether leucine-rich essential amino acids and vitamin D help mental health and physical function in patients with LS undergoing HD with larger sample sizes and long-term interventions.Clinical trial registration University Hospital Medical Information Network Center (UMIN 000032457).

Cross-Social-Network User Identification Based on Bidirectional GCN and MNF-UI Models

Due to the distinct functionalities of various social network platforms, users often register accounts on different platforms, posing significant challenges for unified user management. However, current multi-social-network user identification algorithms heavily rely on user attributes and cannot perform user identification across multiple social networks. To address these issues, this paper proposes two identity recognition models. The first model is a cross-social-network user identification model based on bidirectional GCN. It calculates user intimacy using the Jaccard similarity coefficient and constructs an adjacency matrix to accurately represent user relationships in the social network. It then extracts cross-social-network user information to accomplish user identification tasks. The second model is the multi-network feature user identification (MNF-UI) model, which introduces the concept of network feature vectors. It effectively maps the structural features of different social networks and performs user identification based on the common features of seed nodes in the cross-network environment. Experimental results demonstrate that the bidirectional GCN model significantly outperforms baseline algorithms in cross-social-network user identification tasks. The MNF-UI (multi-network feature user identification) model can operate in situations with two or more networks with inconsistent structures, resulting in improved identification accuracy. These two user identification algorithms provide technical and theoretical support for in-depth research on social network information integration and network security maintenance.

Application of Artificial Intelligence Technology in Vulnerability Analysis of Intelligent Ship Network

The improvement in transportation efficiency, security, safety, and environmental effects may be possible due to the impending advent of autonomous ships. Automatic situational awareness, risk detection, and intelligent decision-making are the key features of the intelligent ship network, differentiating it from conventional ships. There is an immediate need to implement a system for marine information management and network security due to the growing importance of this field, which poses a risk to national and societal stability due to factors, such as the diversity and complexity of marine information types, the challenges associated with data collection, and other similar factors. By recognizing different vulnerabilities and through research cases of the ship systems and Artificial Intelligence (AI) technologies, this paper presents Adaptive Fuzzy Logic-assisted Vulnerability Analysis of Intelligent Ship Networks (AFL-VA-ISN) in various cyberattack scenarios for autonomous ship intrusion detection and information management. Fuzzy logic has been combined with AI, providing a framework for handling uncertainty and imprecision in intelligent ship networks and effective decision-making. This work presents a method for detecting anomalies in risk data based on the collaborative control structure of the Ship Information System. Maintaining the network security of intelligent ships is the primary focus of this research, which mainly employed multi-sensor nodes to evaluate data containing information about malicious attacks and placed self-execution protection organize generating nodes into place to intercept and protect against attacks. The experimental outcomes demonstrate that the suggested AFL-VA-ISN model increases the data transmission rate by 99.2%, attack detection rate by 98.5%, risk assessment rate by 97.5%, and access control rate of 96.3%, and reduces the network latency rate of 11.4% compared to other existing models.

Effect of time pressure on informal advice relations across organizational units: Evidence from a study of collaboration within a Formula One racing team

Informal advice relations across units in an organization are beneficial for knowledge sharing and problem-solving. Prior research suggests that despite their benefits, there are costs to informal advice relations across units. However, the mechanisms by which these costs are mitigated remain unclear. We theorize that this lack of clarity is because work factors have not been sufficiently considered. We examine one such work factor, time pressure, and develop a cost-based explanation for how time pressure influences cross-unit advice relationships. We investigate two time-pressure levels. In the first, work is conducted under lower time pressure, and there is less likelihood of a negative outcome. In the second, work is conducted under higher time-pressure conditions, and there is a greater likelihood of a negative outcome. We theorize that under lower time-pressure conditions, the costs of advice relations across units are mitigated by reciprocal advice relationships. However, under higher time pressure, the cost of informal advice relations across units is higher owing to the need for quick coordination of advice, and these costs are mitigated by reciprocal advice relationships in conjunction with cross-unit formal workflow relationships. To test our hypotheses, we examine the informal advice network and formal workflow network in lower and higher time-pressure conditions among 118 members of the Information Technology and Systems division of a Formula One racing team. Our results indicate that under lower time-pressure conditions, reciprocal advice ties are sufficient to overcome costs. However, under higher time-pressure conditions, cross-unit advice ties are facilitated by reciprocal advice ties embedded in the workflow ties between units. Thus, our findings have implications for how knowledge is managed and how problems are solved in organizations.

Understanding Twitter Usage through Linked Data: An Analysis of Motivations and Online Behavior

Introduction & BackgroundUses and gratification (U&G) theory posits individuals’ engagement with social media is a deliberate effort to fulfill various needs, like information seeking, entertainment, and networking. However, prior studies predominantly addressed whether individuals use social media to satisfy their needs, leaving a gap in understanding how individuals behave online to satisfy needs. This study fills this gap by merging survey responses with actual Twitter activity, to investigate how individuals behave online to satisfy distinctive motivations, including (a) self-expression, (b) seeking entertainment, (c) business and working, (d) staying informed with news, and (e) networking. We also investigated how these online behaviors vary among individuals with different demographic features, including socio-economic classes, gender, and age. Objectives & ApproachOur research addressed questions by linking survey responses with actual Twitter activities within the U.K. Participants were asked to provide survey responses surrounding age, gender, socio-economic class, and motivations for using social media. They were also queried about the existence of Twitter account, willingness to disclose Twitter username, and, if agreeable, the username itself. The survey continued until a total of 2,195 individuals shared Twitter handles. Following the removal of accounts that were either suspended or nonexistent, the study proceeded with a final count of 1,915. We collected each user’s Twitter metadata with Twitter API, including tweet count, follower count, following count, and bio information, and linked each user’s metadata with survey responses. To ensure respondents’ anonymity, survey, Twitter and linked data are stored separately, and can only be accessed by designated researcher. Relevance to Digital FootprintsThe study's approach of linking survey responses with actual Twitter activity offers a detailed insight into the digital footprints left by users as they engage with social media to satisfy their diverse needs. By analyzing the behaviors associated with motivations, this research illuminates the specific ways individuals curate their digital presence. ResultsRegression analysis indicated that individuals motivated by self-expression tend to tweet (b = .28, SE = .06, p < .001), follow account (b = .38, SE = .06, p < .001), gain followers (b = .13, SE = .06, p = .035), and post bio details (b = .89, SE = .13, p < .001). Work and business motivation leads to post bio information (b = .38, SE = .15, p = .012), while networking leads to follow more accounts (b = .28, SE = .06, p < .001). Social-economic class moderated associations between networking motivation and tweet count (b = -.25, SE = .09, p = .004), and between self-expression and tweet count (b = .20, SE = .08, p = .009). For individuals with higher socio-economic, self-expression has a higher effect on tweet count, whereas networking motivation has a less effect on tweet count. Additionally, we found gender moderated the association between self-expression and tweet count (b = .25, SE = .12, p = .04) and between keeping updated with news and tweet count (b = .11, SE = .05, p = .03). Conclusions & ImplicationsThese findings offer a nuanced understanding of social media usage, highlighting how different motivations influence specific online behaviors. The novel approach of linking surveys with actual social media activity provides a more accurate representation of user behavior, contributing insights for academic and practical social media strategy and design.

Interest-driven community detection on attributed heterogeneous information networks

Community structures within attributed heterogeneous information networks (AHINs) serve as valuable tools for comprehending the functional properties inherent in the real-world systems they mirror. The diverse semantics embedded in AHINs play a pivotal role in shaping distinct community formations. Many existing methods detect communities in AHINs based on the same-type nodes without specifying semantic meanings, resulting in unclear and potentially ambiguous outcomes. Additionally, by adopting a simple strategy that focuses solely on either graph structures or node attributes, they fail to sufficiently integrate heterogeneous information and maintain semantic consistency throughout the entire detection process. In this paper, we propose IDCD, a user-interest-driven community detection framework on AHINs, which groups heterogeneous nodes following specified user guidance for semantics-clear results. Firstly, we map heterogeneous nodes into one unified representation space by neatly integrating user-interest-related heterogeneous information. Then we assign pseudo community labels to nodes and refine them in a self-training way. These two processes are iteratively optimized and enhanced mutually in a unified framework. Extensive experiments demonstrate the superiority of IDCD.

Application of Recommendation Algorithm Based on Matrix Dimensionality Reduction Model in Network Information Analysis Model

Application of Recommendation Algorithm Based on Matrix Dimensionality Reduction Model in Network Information Analysis Model

A Spectral Clustering Algorithm for Non-Linear Graph Embedding in Information Networks

With the development of network technology, information networks have become one of the most important means for people to understand society. As the scale of information networks expands, the construction of network graphs and high-dimensional feature representation will become major factors affecting the performance of spectral clustering algorithms. To address this issue, in this paper, we propose a spectral clustering algorithm based on similarity graphs and non-linear deep embedding, named SEG_SC. This algorithm introduces a new spectral clustering model that explores the underlying structure of graphs through sparse similarity graphs and deep graph representation learning, thereby enhancing graph clustering performance. Experimental analysis with multiple types of real datasets shows that the performance of this model surpasses several advanced benchmark algorithms and performs well in clustering on medium- to large-scale information networks.

Decoding the networking strategies of Asian and Black workers in the London Insurance Market

PurposeThis study utilizes Bourdieu’s concepts of field, capital and habitus to investigate the networking strategies of Asian and Black knowledge workers in the London Insurance Market. It also examines the factors contributing to the success or failure of these strategies. The trading activities of the London Insurance Market are underpinned by interdependent relations among its participants. It provides an appropriate context for examining the networking strategies adopted by Asian and Black workers to accelerate their careers.Design/methodology/approachThis research employed a qualitative methodology, gathering data from 24 participants through semi-structured interviews. Participants were selected using purposive, convenience, and snowball sampling methods. Thematic analysis was used to analyze data and develop aggregated concepts from the identified themes and subthemes.FindingsThe London Insurance Market accords great importance to networking. Interpersonal connections significantly influenced career progression, often overshadowing educational attainments. Asian and Black workers faced systemic nepotism and limited access to influential networks in this field. Participants strategically used their interactions to overcome these challenges and advance their careers. Many believed that their careers had a better chance of progressing through informal networks than through formal channels such as Human Resources. Some participants declined to engage in the commonly accepted networking practices, choosing alternative ways to further their careers.Practical implicationsFindings underscore the need for implementing specific organizational policies to address systemic biases and nepotism, particularly in front-office recruitment. Such policies could include prioritizing merit-based hiring practices and developing targeted initiatives to reduce the underrepresentation of minority ethnic workers in front-office positions. By adopting these measures, organizations can create more equitable career advancement opportunities and leverage the full potential of their diverse workforce.Originality/valueThis study contributes to the existing literature on minority ethnic workers' careers, networking theory and workplace diversity. It provides insights into the networking strategies of Asian and Black workers within the London Insurance Market, revealing that these strategies are dependent on contextual factors. The study also highlights the pervasive practice of nepotism deeply ingrained in the habitus of the London Insurance Market and which acts as a barrier for gaining access to influential networks.

Robustness of space information networks based on coverage centrality

This study aims to explore cost-effective strategies for efficiently dismantling global complex networks into mutually isolated connected components, particularly in the context of a space information network (SIN). We introduce a novel metric called the coverage centrality, which comprehensively considers the centrality of nodes and links within a satellite coverage area, which integrates the topological and geographical structural information about the network. Through numerical experiments on real-world networks, we demonstrate that the proposed indicator significantly outperforms traditional methods in terms of both effectiveness and efficiency, which tends to select nodes close to the average betweenness of the network for removal rather than solely focusing on nodes with higher centrality, providing a new perspective for us to deeply understand the disintegration behavior of complex networks. The current work not only offers potential application in areas such as anti-terrorist measures and epidemic prevention or control, but also provides new tools to analyze the characteristics of SIN models.

Epidemiological case-control study on contact sensitisation in female foot care specialists with occupational contact dermatitis: Patch test data of the Information Network of Departments of Dermatology (IVDK), 2008 to 2022.

Mainly women work as foot care specialists (FCS). They are at risk to develop occupational dermatitis (OD). The objective of this study is to describe the contact sensitisation pattern of female FCS with OD. In a retrospective study, patch test and clinical data collected by the Network of Departments of Dermatology (IVDK) from 2008 to 2022 were analysed. Data of 116 female FCS with OD were compared with data of 13 930 female patients with OD working in other professions and 78 612 female patients without OD. Hand dermatitis (93.1%) was significantly more common and face dermatitis (0.9%) significantly less common in female FCS with OD compared to other female patients with or without OD. Frequent suspected allergen sources were disinfectants, gloves, leave-on and nail cosmetics. Occlusion and wetness were important co-factors. The most common diagnoses were irritant contact dermatitis (26.7%) and allergic contact dermatitis (21.6%). No sensitisation to any of the baseline series allergens was significantly more frequent in female FCS with OD than in the two control groups. However, sensitisations to allergens which FCS are abundantly exposed to, including fragrances, preservatives, rubber ingredients and disinfectants, were most common. FCS should be aware of the OD risk and prevention should be promoted.

APTrans: Transformer-Based Multilayer Semantic and Locational Feature Integration for Efficient Text Classification

Text classification is not only a prerequisite for natural language processing work, such as sentiment analysis and natural language reasoning, but is also of great significance for screening massive amounts of information in daily life. However, the performance of classification algorithms is always affected due to the diversity of language expressions, inaccurate semantic information, colloquial information, and many other problems. We identify three clues in this study, namely, core relevance information, semantic location associations, and the mining characteristics of deep and shallow networks for different information, to cope with these challenges. Two key insights about the text are revealed based on these three clues: key information relationship and word group inline relationship. We propose a novel attention feature fusion network, Attention Pyramid Transformer (APTrans), which is capable of learning the core semantic and location information from sentences using the above-mentioned two key insights. Specially, a hierarchical feature fusion module, Feature Fusion Connection (FFCon), is proposed to merge the semantic features of higher layers with positional features of lower layers. Thereafter, a Transformer-based XLNet network is used as the backbone to initially extract the long dependencies from statements. Comprehensive experiments show that APTrans can achieve leading results on the THUCNews Chinese dataset, AG News, and TREC-QA English dataset, outperforming most excellent pre-trained models. Furthermore, extended experiments are carried out on a self-built Chinese dataset theme analysis of teachers’ classroom corpus. We also provide visualization work, further proving that APTrans has good potential in text classification work.

Application of accuracy improvement algorithms for extraction of topographic information and drainage network from DEM using GIS

The extraction of drainage network and watershed information is prerequisite for the study of watershed characteristics like morphometric analysis, which provides a basis for hydrological planning and modeling. The advanced tools of algorithms, Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) data and Geographical Information System (GIS) software are used to extract drainage networks and their watershed boundaries. These tools are complicated to use or produce more errors in the extraction of elevation and drainage networks when applied to flat areas. For removal of errors and to improve the accuracy in preparation of DEM and extraction of drainage network, Burada Kalava River Basin, Andhra Pradesh, India has been taken for application of accuracy improvement algorithms. An automatic generation of drainage network and watershed using digital elevation model results in positional errors due to variations in slope and topography. This study aimed to generate a catchment area and stream network that closely represent the natural stream network and the streams’ real positions. The step-by-step methodology using GRASS-interfaced Quantum GIS algorithms are given for pre-processing of DEM data to improve the positional accuracy before automatic extraction of the stream network and catchment area to resemble the real situation of the watershed. Secondly, efforts are made to analyze the DEM during automatic generation of the stream network and catchment area by assigning various area threshold values, including the application of pour point coordinates in improving the stream network and watershed characteristics. The results are verified and validated with the field information in order to improve the accuracy levels of DEM quality in generation of drainage network and catchment area.

Treegraph: tree architecture from terrestrial laser scanning point clouds

AbstractAccurate quantification of tree architecture is critical to interpreting the growth, health and functioning of trees and forests. Terrestrial laser scanning (TLS) offers millimetre‐level point cloud data, but current approaches to 3D tree reconstruction from TLS point clouds primarily focus on retrieving total volume at tree scale for aboveground biomass (AGB) estimation. Few methods have been designed specifically to provide tree architectural properties, including branch‐level morphology and topology, rather than AGB; derived topological traits have tended to be a compromise, and of secondary importance to volume. We present Treegraph, a new approach explicitly designed to retrieve the architectural traits of trees at multiple scales, from the whole tree scale down to individual branches and internodes, using TLS data with limited assumptions about tree form. It provides morphological traits such as branch length and diameter, alongside topological traits including parent–daughter connections of branches and internodes, furcation (branching) number and branch order. We compare Treegraph‐derived morphological and topological traits with manual measurements of branches from eight destructively harvested trees, yielding RMSE values of 0.60 m (5.96%) for branch length, 2.99 cm (33.45%) for branch diameter, 0.46 (19.38%) for furcation number and 0.08 m (33.16%) for internode length, respectively. In a broader application to 603 trees from tropical, temperate and urban forests, we demonstrate that the derived morphological and topological traits support testing of structure‐related metabolic scaling theories. Testing branches over 10 cm in diameter across 18 657 branching nodes shows that Treegraph‐derived branch‐level scaling exponents deviate from WBE predictions, exhibiting area‐preserving behaviour while displaying asymmetry in length and diameter of daughter branches. Available as open‐source Python software, Treegraph provides fine‐level branching network information, promoting improved insights into tree structure and function. This data‐driven approach reduces the need for empirical heuristic parameters, which has the potential for advancing large‐scale ecological studies on tree architecture.