Unique Node Research Articles

Co-Designing Digital Health Intervention for Monitoring Medication and Consultation Among Transgender People in Underserved Communities: Collaborative Approach.

In many parts of the world, men who have sex with men and transgender individuals face criminalization and discrimination. As a result, they are less likely to seek medical help, despite experiencing higher rates of HIV/AIDS, mental health issues, and other health problems. Reaching key populations (KPs) with essential testing, care, and treatment services can be challenging, as they often have a higher likelihood of contracting and spreading the virus. They have limited access to antiretroviral (ARV) therapy (ART) services, which means that KPs may continue to serve as reservoirs for new HIV infections if they do not receive effective HIV programming. This ongoing issue complicates efforts to control the epidemic. Therefore, modeling a digital health system to track ARV medication access and use is crucial. This paper advocates for the use of digital interventions to manage the health of KPs in underserved regions, using Nigeria as a case study. This study aims to assess digital health interventions for monitoring medication and consultations among transgender people in underserved communities. It also sought to determine whether a system exists that could support ART adherence in Nigeria. Additionally, the study evaluated design strategies to address privacy and confidentiality concerns, aiming to reduce nonadherence to ARV medications among KPs in Nigeria. A qualitative approach was adopted for this research, involving a thematic analysis of information collected from interviews with clinicians and other health practitioners who work directly with these communities, as well as from an interactive (virtual) workshop. The findings from the thematic analysis indicate a need to increase attendance at ART therapy sessions through the implementation of an intensive care web app. Unlike previous solutions, this study highlights the importance of incorporating a reminder feature that integrates with an in-app telemedicine consultancy platform. This platform would facilitate discussions about client challenges, such as adverse drug effects, counseling sessions with clinical psychologists, and the impact of identity discrimination on mental health. Other data-driven health needs identified in the study are unique drug request nodes, client-led viral load calculators, remote requests, and drug delivery features within the web app. Participants also emphasized the importance of monitoring medication compliance and incorporating user feedback mechanisms, such as ratings and encouragement symbols (eg, stars, checkmarks), to motivate adherence. The study concludes that technology-driven solutions could enhance ART adherence and reduce HIV transmission among transgender people. It also recommends that local governments and international organizations collaborate and invest in health management services that prioritize health needs over identity.

(Invited) The U.S. Department of Energy’s Hydrogen Consortium for Advanced Water Splitting Materials

The goals of the Hydrogen and Fuel Cell Technologies Office (HFTO) support those of the Biden Administration to build a clean and equitable energy economy and address the climate crisis through development and deployment of hydrogen technologies to the benefit of all Americans. HFTO is dedicated to meeting the goals of DOE’s Hydrogen Shot,1 which targets affordable clean hydrogen production at $1/kg within the decade, and the H2@Scale Initiative,2 which aims to advance affordable hydrogen to enable decarbonization and revenue opportunities across multiple sectors.HFTO’s HydroGEN consortium (https://www.h2awsm.org) for advanced water splitting (AWS) materials, part of the DOE’s Energy Materials Network (EMN), focuses on multiple clean hydrogen production pathways, including several relevant to renewable fuels via artificial photosynthesis. Since the consortium’s establishment in 2016, HydroGEN researchers have made significant progress in the areas of low and high temperature electrolysis, as well as solar thermochemical (STCH), and most relevant here, photoelectrochemical (PEC) hydrogen production pathways. The HydroGEN consortium facilitates collaborations between national laboratories, academia, and industry, bringing together world-class technical expertise with state-of-the-art facilities made available to project partners. HydroGEN offers an extensive collection of materials research capabilities for addressing R&D challenges. Project participants can choose from over 40 unique capability nodes. Each capability node is comprised of a combination of a tool, technique, and expertise that is unique to the national laboratory system.HydroGEN has supported over 30 competitively selected R&D projects with industry and academic partners by providing streamlined access to national lab resources and expertise. The consortium is also involved in fostering cross-cutting activities to advance material performance and durability through community engagement and protocol development and initiating critical laboratory-led efforts to address specific R&D gaps in the AWS portfolio.A new cohort of solar fuels focused projects were recently selected through the Funding Opportunity Announcement (FOA) Number: DE-FOA-0002792 with additional funding in this area expected in the future. A total of 11 new projects were selected, six in PEC and five in STCH, to work with the HydroGEN consortium towards advancing solar fuels production technology. Projects from both technology pathways must define materials metrics and targets for meeting the interim hydrogen cost target of $2/kg-H2 with extrapolation to additional requirements for meeting $1/kg-H2.Selected PEC projects focused on the development of materials or material systems for efficient and durable PEC water-splitting with a focus on innovations in bandgap and bandgap alignment, functional interfaces, stable surface catalysis, and protective coatings. A successful PEC photoelectrode project will need to achieve 15% STH efficiency for the entirety of operation exceeding 1,000 hours, with proposed pathways for achieving efficiencies up to 25% and durability up to 10,000 hours. Further, PEC systems will be required to demonstrate hydrogen production at a rate of 0.1 g/h for diurnal operation over two weeks.Selected STCH projects focused on accelerating discovery, development, and demonstration of alternative redox materials for a two-step STCH process and addressing projected costs and materials challenges associated with the required operating temperatures. Successful STCH projects will need to include a demonstration of the STCH process using sunlight or simulated sunlight. STCH systems must demonstrate hydrogen production at a rate of 1 g/h for diurnal operation over two weeks.For the first time, in line with the Justice40 Initiative,3 applicants were required to submit DEIA (diversity equity, inclusion, and accessibility) plans. Objectives from these plans were incorporated into the project objectives with specific goals identified for each year.Despite the progress, there is still significant ground to be covered through water splitting materials R&D. With the support of HydroGEN, these selected projects will help to address remaining challenges and enable low-cost and large-scale hydrogen production. An overview of consortium and project goals and objectives will be discussed. Hydrogen Shot | Department of EnergyH2@Scale | Department of Energy,The Justice40 initiative, created by Executive Order 14008, establishes a goal that 40% of the overall benefits of certain federal investments flow to disadvantaged communities. Figure 1

A Fluid Dynamic Approach to Model and Optimize Energy Flows in Networked Systems

In this paper, attention is focused on the analysis and optimization of energy flows in networked systems via a fluid-dynamic approach. Considering the real case of an energy hub, the proposed model deals with conservation laws on arcs and linear programming problems at nodes. Optimization of the energy flows is accomplished by considering a cost functional, which estimates a term proportional to the kinetic energy of the overall system in consideration. As the real optimization issue deals with an integral formulation for which precise solutions have to be studied through variational methods, a decentralized approach is considered. First, the functional is optimized for a simple network having a unique node, with an incoming arc and two outgoing ones. The optimization deals with distribution coefficients, and explicit solutions are found. Then, global optimization is obtained via the local optimal parameters at the various nodes of the real system. The obtained results prove the correctness of the proposed approach and show the evident advantages of optimization procedures dealing with variational approaches.

A Study on Scan Trajectory Based Effective Localization Method Using Mobile Anchor in Wireless Sensor Networks

A Wireless Sensor Network (WSN) is a collection of different nodes working together in a networked fashion in an ordered way. For many applications, sensor location in wireless sensor networks (WSN) is crucial. It is expensive to equip each sensor node with a GPS receiver. Numerous methods, including range-based and range-free methods, have been put forth in the past to determine the locations of sensor nodes that are randomly distributed. The majority of them make use of unique nodes known as anchor nodes, which are thought to be aware of their own positions. These anchor nodes supply the information that other sensors use to calculate their locations. Massive numbers of sensor nodes are used in wireless sensor networks (WSNs) to gather data about the immediate environment. However, this data is useless until the precise location of the data collection points is known. Sensor node localization in wireless sensor networks (WSNs) is important for a number of applications. The current research study develops a path planning technique to apply mobile anchors to the localization systems that are currently in use. The two mobile anchors supported by the effective trajectories, or effective SCAN, is the focus of the proposed research project. To localise the sensor node and transmit data, this method involves setting the mobile anchors to move in the opposite direction. The advantages of this dual movable anchor are demonstrated by the research study in terms of decreased localization time, localization error, and other factors.

Optimizing energy storage plant discrete system dynamics analysis with graph convolutional networks

Addressing the challenges of suboptimal model performance and excessive parameters and operations in the optimization of energy storage power plants utilizing Graph Convolutional Network (GCN), this paper introduces a novel approach – the packet-switched graph convolutional network. Initially, a GCN extreme learning machine is established. Drawing inspiration from this solid foundation, we have innovatively crafted a group exchange graph convolution module. This module leverages group graph convolution techniques to amalgamate unique node feature information, tailored to diverse topology graph matrices based on various groupings. This innovative approach ensures that information flows freely and effectively among distinct groupings. Furthermore, we have designed a cutting-edge timing depth separation convolution module, comprising two innovative components. The first component introduces timing depth separation convolution, revolutionizing the original timing convolution module. The second component, the packet-switching graph convolutional network, revolutionizes the time sequence depth separation convolution process. It achieves this by employing 1 × 1 convolutional layers between different feature fusion packets, enabling seamless information exchange between distinct packets. Experimental results demonstrate the efficacy of the proposed model, with root mean square error (RMSE) metrics and root mean square error (MAE) metrics for single-step prediction reaching 46.08 and 26.22 at 60 min, respectively. In multi-step testing, the proposed model exhibits a 14.71 % reduction in RMSE error at the 15-min scale and a 9.29 % reduction at the 60-min scale compared to the benchmark model. This performance improvement enhances the operational efficiency and reliability of the energy storage plant, particularly under dynamic changes in the time series.

The effect of distant connections on node anonymity in complex networks

Ensuring privacy of individuals is of paramount importance to social network analysis research. Previous work assessed anonymity in a network based on the non-uniqueness of a node’s ego network. In this work, we show that this approach does not adequately account for the strong de-anonymizing effect of distant connections. We first propose the use of d-k-anonymity, a novel measure that takes knowledge up to distance d of a considered node into account. Second, we introduce anonymity-cascade, which exploits the so-called infectiousness of uniqueness: mere information about being connected to another unique node can make a given node uniquely identifiable. These two approaches, together with relevant “twin node” processing steps in the underlying graph structure, offer practitioners flexible solutions, tunable in precision and computation time. This enables the assessment of anonymity in large-scale networks with up to millions of nodes and edges. Experiments on graph models and a wide range of real-world networks show drastic decreases in anonymity when connections at distance 2 are considered. Moreover, extending the knowledge beyond the ego network with just one extra link often already decreases overall anonymity by over 50%. These findings have important implications for privacy-aware sharing of sensitive network data.

Transport and localization properties of excitations in one-dimensional lattices with diagonal disordered mosaic modulations

We present a numerical study of the transport and localization properties of excitations in one-dimensional lattices with diagonal disordered mosaic modulations. The model is characterized by the modulation period κ and the disorder strength W. We calculate the disorder averages , , and , where T is the transmittance and P is the participation ratio, as a function of energy E and system size L, for different values of κ and W. For excitations at quasiresonance energies determined by κ, we find power-law scaling behaviors of the form , , and , as L increases to a large value. In the strong disorder limit, the exponents are seen to saturate at the values , , and , regardless of the quasiresonance energy value. This behavior is in contrast to the exponential localization behavior occurring at all other energies. The appearance of sharp peaks in the participation ratio spectrum at quasiresonance energies provides additional evidence for the existence of an anomalous power-law localization phenomenon. The corresponding eigenstates demonstrate multifractal behavior and exhibit unique node structures. In addition, we investigate the time-dependent wave packet dynamics and calculate the mean square displacement , spatial probability distribution, participation number, and return probability. When the wave packet’s initial momentum satisfies the quasiresonance condition, we observe a subdiffusive spreading of the wave packet, characterized by where η is always less than 1. We also note the occurrence of partial localization at quasiresonance energies, as indicated by the saturation of the participation number and a nonzero value for the return probability at long times.

Ozanimod-exposed Patients with Ulcerative Colitis Undergoing Total Colectomy Exhibit Unique Lymph Node Histological Changes.

Ozanimod regulates lymphocyte egress from the spleen and lymph nodes into the systemic circulation. The histological changes which occur in the lymph nodes of patients on ozanimod is unknown. This retrospective study included patients with ulcerative colitis [UC] undergoing total colectomy for treatment-refractory disease who received ozanimod, and a cohort of patients with UC undergoing colectomy who did not have ozanimod exposure. Histology of the lymph nodes from the mesentery of colectomy specimens was reviewed and multiple features were scored by experienced pathologists. Six [13%] ozanimod-treated patients with UC required surgery for treatment-refractory disease. Colectomy specimen data were available for five patients [one patient had surgery at an outside centre]. Lymph node specimens from six control patients with UC who had colectomy were examined. Histological examination of lymph nodes showed that patients treated with ozanimod had significantly greater extent of dilated sinuses [p = 0.03] and greater degrees of sinus histiocytosis [p = 0.03] compared with control patients. In addition, there was a trend towards more Castleman-like angiotrophic hyperplasia, plasma cell infiltration, and subcortical interfollicular expansion in ozanimod-treated patients. This study identifies unique histological changes in the lymph nodes of patients with UC treated with ozanimod. The presence of sinus histiocytosis and dilated sinuses is in keeping with the known mechanism of action of ozanimod, and suggests that blocking lymphocyte egression from lymph nodes was insufficient to ameliorate disease severity in these patients. The possibility of Castleman-like features, identified in several of the cases, needs to be further investigated.

An Efficient and Secure Blockchain Consensus Protocol for Internet of Vehicles

Conventional blockchain consensus protocols tailored for the Internet of Vehicles (IoV) usually face low transaction throughput, high latency, and elevated communication overhead issues. To address these issues, in this paper, we propose ESBCP, an efficient and secure blockchain consensus protocol for the IoV environment. Firstly, considering the significant performance differences among nodes in the IoV, we designed a blockchain consensus model for the IoV. Roadside units execute a trust evaluation mechanism to select high-quality vehicle nodes for the consensus process, thereby reducing the likelihood of malicious nodes in the consensus cluster. Secondly, we designed a node partition strategy to adapt to the dynamic feature of the IoV. Finally, addressing the mobility of nodes in the IoV, we introduced a dynamic unique node list. Vehicle nodes can promptly select nodes with high reliability from the list of communicable nodes to join their unique node list, while also promptly removing nodes with low reliability from their unique node list. Combining these strategies, we propose DK-PBFT, an improved Practical Byzantine Fault Tolerance consensus algorithm. The algorithm meets the efficiency and mobility requirements of vehicular networks. Through theoretical analysis, ESBCP could prevent external and internal security risks while reducing communication overhead. Experimental verification demonstrated that ESBCP effectively reduces consensus latency and improves transaction throughput. Our proposed ESBCP can be used in other application scenarios that require high consensus efficiency.

SWEEPER: Secure Waterfall Energy-Efficient Protocol-Enabled Routing in FANET

The recent advancements in ad-hoc networks have resulted in innovations like FANETs. FANETs (Flying Ad hoc Networks) have become diversified in its applications ranging from agriculture, military, emergency services etc. The FANET environment needs to be dynamic and is populated by UAVs. The highly mobile UAVs are responsible for data transmission between nodes. If unchecked, this results in packet loss. Reliable data transmission in FANETs is possible, if effective routing protocols are in place. In this research, a Secure Waterfall Energy-Efficient Protocol-Enabled Routing (SWEEPER) has been proposed which helps conserve the node energy throughout the transmission process. The framework uses a waterfall model approach which includes group key management as an underlying process governing the security aspect of the protocol. Asymmetric key cryptography is applied to our technique, which involves two unique nodes, labeled the Computed Key (CKey) and the Dissemination Key (DKey). The two nodes will generate, verify, and distribute the secret keys. This will help other nodes concentrate on transmission alone and needn’t waste time in computational activities or key handling. Security breaches and malignant nodes are also handled efficiently. The nodes along the route are selected based on a trust factor, which allows our protocol to select only genuine nodes to forward packets along the discovered path. Our work is analyzed with existing protocols of FANETS like SecRIP and MDRMA. The analysis reveals that our protocol outperforms the existing protocols in terms of minimal delay, maximum energy conservation and PDR, which contributes towards a maximized throughput.

Joint Credibility Estimation of News, User, and Publisher via Role-relational Graph Convolutional Networks

The presence of fake news on online social media is overwhelming and is responsible for having impacted several aspects of people’s lives, from health to politics, the economy, and response to natural disasters. Although significant effort has been made to mitigate fake news spread, current research focuses on single aspects of the problem, such as detecting fake news spreaders and classifying stories as either factual or fake. In this article, we propose a new method to exploit inter-relationships between stories, sources, and final users and integrate prior knowledge of these three entities to jointly estimate the credibility degree of each entity involved in the news ecosystem. Specifically, we develop a new graph convolutional network, namely, Role-Relational Graph Convolutional Networks (Role-RGCN), to learn, for each node type (or role), a unique node representation space and jointly connect the different representation spaces with edge relations. To test our proposed approach, we conducted an experimental evaluation on the state-of-the-art FakeNewsNet-Politifact dataset and a new dataset with ground truth on news credibility degrees we collected. Experimental results show a superior performance of our Role-RGCN proposed method at predicting the credibility degree of stories, sources, and users compared to state-of-the-art approaches and other baselines.

Identifying the integrated visual characteristics of greenway landscape: A focus on human perception

Understanding the integrated landscape characteristics and spatial sequences of greenways and the surrounding visual environments is crucial for connecting fragmented natural spaces within cities and enhancing residents’ natural experiences. However, previous studies have mostly predominantly focused on individual perceptions of greenway landscapes, rather than considering the integrated visual characteristics of both the inner and the visible surroundings. This disparity often deviates from people's actual perception of these spaces. To address this gap, we have adopted a new approach to identify the integrated greenway visual characteristics in inner and surrounding landscapes from a human perception perspective, in the city of Wuhan, China. Our methodology involved the utilization of deep learning systems, text semantic analysis, and other techniques to integrate multi-source data, including street view images, remote sensing images, and perceptual comments, to quantitatively assess the landscape sequences and naturalness of landscape changes along the greenway paths. We identify the positions and characteristics of unique landscape nodes within the greenway. The findings of our study revealed ten main landscape types of greenways in Wuhan, with over 72 % of the greenways comprising more than five types. As the length of the greenways increases, the number of landscape types initially experiences rapid growth before reaching a stable state. Furthermore, the frequency of landscape change exhibits a linear and positive correlation with greenway length. Notably, the naturalness of the majority of greenway inner landscapes closely aligns with that of the surrounding visible environment. The openness of all greenways was positively correlated with the proportion of farmland and grassland in their surrounding areas. Furthermore, people exhibit higher positive sentiment responses to greenways that maintain a high level of natural consistency between the inside and outside environments. Additionally, over 47 % of the unique nodes primarily coincide with a specific landscape type and play a vital role in shaping the rhythm of greenway landscape change. These findings provide valuable insights to support the construction and management of greenway landscapes, as well as guidance for urban planners and designers aiming to harness natural resources effectively in the development of urban greenways.

(Invited) The U.S. Department of Energy’s HydroGEN Consortium for Advanced Water Splitting Materials

The goals of the Hydrogen and Fuel Cell Technologies Office (HFTO) support those of the Biden Administration to build a clean and equitable energy economy and address the climate crisis through development and deployment of hydrogen technologies to the benefit of all Americans. HFTO is dedicated to meet the goals of DOE’s Hydrogen Shot,[1] which targets affordable clean hydrogen production at $1/kg within the decade, and the H2@Scale Initiative,[2] which aims to advance affordable hydrogen to enable decarbonization and revenue opportunities across multiple sectors.HFTO’s HydroGEN consortium (https://www.h2awsm.org/) for advanced water splitting materials, part of the DOE’s Energy Materials Network (EMN), includes a focus on multiple clean hydrogen production pathways, including several relevant to solar hydrogen production. Since the consortium’s establishment in 2016, HydroGEN researchers have made significant progress in the areas of low and high temperature electrolysis, as well as photoelectrochemical (PEC) and solar thermochemical (STCH) hydrogen production pathways. The HydroGEN consortium facilitates collaborations between national laboratories, academia, and industry bringing together world-class technical expertise with state-of-the-art facilities made available to project partners. HydroGEN offers an extensive collection of materials research capabilities for addressing R&D challenges. Project participants can choose from over 40 unique capability nodes. Each capability comprises—a combination of a tool, technique, and expertise—that is unique to the national laboratory system.HydroGEN has supported over 30 competitively selected R&D projects with industry and academic partners by providing streamlined access to national lab resources and expertise. The consortium is also credited with contributing over 100 publications, fostering cross-cutting activities to advance material performance and durability through community engagement and protocol development, and initiating critical laboratory-led efforts to address specific R&D gaps in the AWS portfolio. Despite the progress, there is still significant ground to be covered in the water splitting materials R&D space to enable low-cost and large-scale hydrogen production, especially in the direct solar hydrogen production pathway of artificial photosynthesis.Current consortium efforts include photoelectrochemical (PEC) hydrogen production among other advanced water splitting pathways. The consortium seeks to discover and design new low-cost materials and increase the efficiency and durability of current systems for carbon-free hydrogen production. The consortium is also engaged in connecting the research community by developing a data platform for secure data transfers and public data sharing. Additionally, HydroGEN supports benchmarking performance and protocols of electrolyzer systems to ensure apples-to-apples comparisons across academia and industry. A new cohort of solar fuels focused projects were recently selected from the Funding Opportunity Announcement (FOA) Number: DE-FOA-0002792 with additional funding in this area expected in the future.[1] Hydrogen Shot | Department of Energy, https://www.energy.gov/fuelcells/hydroge-shot[2] H2@Scale | Department of Energy, https://www.energy.gov/eere/fuelcells/h2scale Figure 1

Daam2 phosphorylation by CK2α negatively regulates Wnt activity during white matter development and injury

Wnt signaling plays an essential role in developmental and regenerative myelination in the central nervous system. The Wnt signaling pathway is composed of multiple regulatory layers; thus, how these processes are coordinated to orchestrate oligodendrocyte (OL) development remains unclear. Here, we show CK2α, a Wnt/β-catenin signaling Ser/Thr kinase, phosphorylates Daam2, inhibiting its function and Wnt activity during OL development. Intriguingly, we found Daam2 phosphorylation differentially impacts distinct stages of OL development, accelerating early differentiation followed by decelerating maturation and myelination. Application toward white matter injury revealed CK2α-mediated Daam2 phosphorylation plays a protective role for developmental and behavioral recovery after neonatal hypoxia, while promoting myelin repair following adult demyelination. Together, our findings identify a unique regulatory node in the Wnt pathway that regulates OL development via protein phosphorylation-induced signaling complex instability and highlights a new biological mechanism for myelin restoration.

6-Oxofurostane and (iso)Spirostane Types of Saponins in Smilax sieboldii: UHPLC-QToF-MS/MS and GNPS-Molecular Networking Approach for the Rapid Dereplication and Biodistribution of Specialized Metabolites.

Identifying novel phytochemical secondary metabolites following classical pharmacognostic investigations is tedious and often involves repetitive chromatographic efforts. During the past decade, Ultra-High Performance Liquid Chromatography-Quadrupole Time of Flight-Tandem Mass Spectrometry (UHPLC-QToF-MS/MS), in combination with molecular networking, has been successfully demonstrated for the rapid dereplication of novel natural products in complex mixtures. As a logical application of such innovative tools in botanical research, more than 40 unique 3-oxy-, 3, 6-dioxy-, and 3, 6, 27-trioxy-steroidal saponins were identified in aerial parts and rhizomes of botanically verified Smilax sieboldii. Tandem mass diagnostic fragmentation patterns of aglycones, diosgenin, sarsasapogenin/tigogenin, or laxogenin were critical to establishing the unique nodes belonging to six groups of nineteen unknown steroidal saponins identified in S. sieboldii. Mass fragmentation analysis resulted in the identification of 6-hydroxy sapogenins, believed to be key precursors in the biogenesis of characteristic smilaxins and sieboldins, along with other saponins identified within S. sieboldii. These analytes' relative biodistribution and characteristic molecular networking profiles were established by analyzing the leaf, stem, and root/rhizome of S. sieboldii. Deducing such profiles is anticipated to aid the overall product integrity of botanical dietary supplements while avoiding tedious pharmacognostic investigations and helping identify exogenous components within the finished products.

Longitudinal correlates of bullying victimization among Chinese early adolescents: A cross-lagged panel network analysis

BackgroundBullying victimization is a major public health issue often faced by adolescents. This highlights the need to identify the relevant risk factors to inform intervention. Based on the ecological systems theory and applied cross-lagged panel network analysis, this study explored the longitudinal correlates of bullying victimization among Chinese early adolescents. MethodsA total of 1686 early adolescents (60.4 % were boys) from the Chinese Early Adolescent Cohort study were included in this study. Bullying victimization and its associated factors were assessed using the self-report questionnaires, which was administered from 2019 (T1), 2021 (T2), and 2022 (T3). The longitudinal relationships between bullying victimization and its correlates were examined using a cross-lagged panel network analysis. Results27.0 %, 14.9 %, and 13.2 % of the participants reported being bullied by peers at T1, T2, and T3, respectively. The temporal network suggested that individual-level (sex, depression, and anxiety), family-level (child abuse), school-level (satisfaction with classmates), and social-level (satisfaction with society) factors were associated with bullying victimization. The node with the greatest centrality strength was anxiety. Notably, relationship with teachers and classmates were the unique nodes in the T2 → T3 replication network. LimitationsThe sample is unrepresentative, as it is from only one middle school. ConclusionsThe findings provide important insights into bullying victimization prevention and intervention among Chinese early adolescents: 1) highlighting the importance of joint interventions across multiple departments; 2) focusing on the most central factors of bullying victimization; and 3) considering the effect of time when exploring the correlates of bullying victimization.

Driving Forces and Influences of Flood Diversion on Discharge Fraction and Peak Water Levels at an H-Shaped Compound River Node in the Pearl River Delta, South China

The SiXianJiao (SXJ) is the first-order exchange node of the West River and the North River and redistributes water (mass) to the downstream river network in the Pearl River Delta (PRD), South China. The lateral SXJ waterway plays a critical role in flow (mass) diversion between the West River and the North River, forming a unique H-shaped compound river node. Previous studies mainly focused on Y-shaped bifurcation and confluence nodes, and there is a lack of research on deltaic H-shaped river nodes. This study established the Delft3D model to investigate the driving forces and influences of flood diversion at the SXJ node. The results showed that the H-shaped SXJ river node was usually in hydraulic equilibrium but was often disturbed by large water level differences between the two rivers, due to unbalanced and asynchronous upstream flood waves. The large water level differences drove mutual flood diversion through the lateral SXJ waterway, which synchronized the downstream discharge and reduced the peak water levels (flood hazards), resulting in similar water levels or hydraulic equilibrium in the two rivers. There exists a critical flow fraction—about 75.9% (West River)—at which the incoming flow from both rivers presents similar water levels at the SXJ node, resulting in little flood diversion. Above the threshold, the flood water will divert from the West River to the North River with a maximum rate of −11,900 m3/s, accounting for 20% of the West River, reducing the peak water level up to 1.48 m at Makou. Below the threshold, the flood water will divert from the North River to the West River with a maximum rate of 11,990 m3/s, accounting for 55% of the North River, reducing the peak water level up to 6.63 m at Sanshui. Meanwhile, the discharge fraction at downstream Makou (Sanshui) maintained a near-constant value during individual floods and fluctuated around 76.6% (23.4%). This critical discharge fraction and the analytical approach are of significance in flood-risk management and hydraulic engineering design in the PRD. The concept model of the H-shaped compound river node clearly elucidates the flood diversion mechanism via the lateral SXJ waterway and may work for other similar river nodes as well.

Structural differences in the gut microbiome of bats using terrestrial vs. aquatic feeding resources

Bat gut microbiomes are adapted to the specific diets of their hosts. Despite diet variation has been associated with differences in bat microbiome diversity, the influence of diet on microbial community assembly have not been fully elucidated. In the present study, we used available data on bat gut microbiome to characterize the microbial community assembly of five selected bat species (i.e., Miniopterus schreibersii, Myotis capaccinii, Myotis myotis, Myotis pilosus, and Myotis vivesi), using network analysis. These bat species with contrasting habitat and food preferences (i.e., My. capaccinii and My. pilosus can be piscivorous and/or insectivorous; Mi. schreibersii and My. myotis are exclusively insectivorous; while My. vivesi is a marine predator) offer an invaluable opportunity to test the impact of diet on bat gut microbiome assembly. The results showed that My. myotis showed the most complex network, with the highest number of nodes, while My. vivesi has the least complex structured microbiome, with lowest number of nodes in its network. No common nodes were observed in the networks of the five bat species, with My. myotis possessing the highest number of unique nodes. Only three bat species, My. myotis, My. pilosus and My. vivesi, presented a core microbiome and the distribution of local centrality measures of nodes was different in the five networks. Taxa removal followed by measurement of network connectivity revealed that My. myotis had the most robust network, while the network of My. vivesi presented the lowest tolerance to taxa removal. Prediction of metabolic pathways using PICRUSt2 revealed that Mi. schreibersii had significantly higher functional pathway’s richness compared to the other bat species. Most of predicted pathways (82%, total 435) were shared between all bat species, while My. capaccinii, My. myotis and My. vivesi, but no Mi. schreibersii or My. pilosus, showed specific pathways. We concluded that despite similar feeding habits, microbial community assembly can differ between bat species. Other factors beyond diet may play a major role in bat microbial community assembly, with host ecology, sociality and overlap in roosts likely providing additional predictors governing gut microbiome of insectivorous bats.

A variational nodal formulation for multi-dimensional unstructured neutron diffusion problems

A variational nodal method (VNM) with unstructured-mesh is presented for solving steady-state and dynamic neutron diffusion equations. Orthogonal polynomials are employed for spatial discretization, and the stiffness confinement method (SCM) is implemented for temporal discretization. Coordinate transformation relations are derived to map unstructured triangular nodes to a standard node. Methods for constructing triangular prism space trial functions and identifying unique nodes are elaborated. Additionally, the partitioned matrix (PM) and generalized partitioned matrix (GPM) methods are proposed to accelerate the within-group and power iterations.Neutron diffusion problems with different fuel assembly geometries validate the method. With less than 5 pcm eigenvalue (keff) error and 1% relative power error, the accuracy is comparable to reference methods. In addition, a test case based on the kilowatt heat pipe reactor, KRUSTY, is created, simulated, and evaluated to illustrate the method's precision and geometrical flexibility. The Dodds problem with a step transient perturbation proves that the SCM allows for sufficiently accurate power predictions even with a large time-step of approximately 0.1 s. In addition, combining the PM and GPM results in a speedup ratio of 2–3.

CUNA: A privacy preserving medical records storage in cloud environment using deep encryption

Securing the medical images in cloud environment becomes mandatory because of massive image accumulation and information storage in cloud premises. Large accommodation of information in the cloud brings another risk of data security. Privacy preserving data mining techniques investigate privacy of data to be maintained before or either after the data mining technique. Lightweight technique to store the patient data securely in the cloud is focused. The proposed study is focused on designing a robust model in which medical records privacy is preserved using Customizable Unique node access (CUNA) architecture. The system design undergoes data preparation, encryption, cloud transfer, decryption and analysis modules. The input to the proposed system is patient health care data such as CT images, Medical records, Clinical treatment procedures etc. The CT images are encrypted with respect to their medical records, further transferred into the cloud by hiding the text based confidential data into the images. Inspite of normal cloud storage process, the proposed approach utilized deep cryptography technique to make authenticated storage mechanism adaptive in the cloud. CUNA is the customized commonly allocated key assigned at every node. The unique key word is encrypted, and utilized to transfer the privacy data of the patients. The data can be retrieved from the cloud, after the decryption of CUNA code. The correlation of the key is systematically achieved using Gaussian regression process algorithm (GRA). The proposed approach is compared with various state-of-art approaches and obtained the accuracy of 92% with image MSE of 0.0012%.