Information Network Research Articles

Epidemiology of patch tested patients with permanent tattoos-A comparative analysis of 9693 IVDK patients (2020-2022).

Permanent tattooing is the invasive introduction of tattoo ink (pigments) into the dermis. The ink and aftercare cosmetics applied on pre-damaged skin may contain skin sensitisers. To identify patient characteristics and the pattern of sensitisation in tattooed patients patch tested within the Information Network of Departments of Dermatology (IVDK). Comparative analysis of patient characteristics and reaction frequencies to baseline series allergens in 1648 consecutive patients with and 8045 consecutive patients without permanent tattoos. Non-overlapping 95%-confidence intervals were considered as significant. Having permanent tattoos was related with female sex, age <40 years, tobacco smoking, atopic dermatitis, (occupational) hand dermatitis and being employed in particular occupational groups (e.g., healthcare workers, mechanics, hairdressers). Sensitisation to nickel was increased in tattooed patients and associated with female sex (OR 4.23 [95%-CI, 3.48-5.18]), age ≥40 years (OR 1.26 [95%-CI, 1.08-1.49]), tobacco smoking (OR 1.19 [95%-CI, 1.01-1.40]) and having permanent tattoos (OR 1.27 [95%-CI, 1.05-1.53]). The association between nickel sensitisation and permanent tattoos is probably confounded by past reactions to pierced costume jewellery. Socio-economic factors most probably contribute to the connection between tattoos, tobacco smoking, occupational or hand dermatitis, and being employed in particular occupational groups.

In vitro protamine addition for coagulation assessment using TEG 6s system during cardiopulmonary bypass: a pilot study.

Systemic heparinization during cardiopulmonary bypass (CPB) can significantly affect thromboelastography (TEG). This study investigated the feasibility of adding protamine in vitroto allow assessment of coagulation status using the TEG 6s system during CPB. In this prospective observational study, 21 patients undergoing elective cardiac valve surgery were evaluated. During CPB, protamine was added in vitro to the heparinized blood of these patients at a concentration of 0.05mg/mL and analyzed with the TEG 6s (Pre). The TEG parameters were compared to those analyzed after CPB withdrawal and systemic protamine administration (Post). The citrated kaolin maximal amplitude (CK-MA) and the citrated functional fibrinogen maximal amplitude (CFF-MA) exhibited strong correlations between Pre and Post measurements (r = 0.790 and 0.974, respectively, P < 0.001 for both), despite significant mean differences (-2.23mm for CK-MA and -0.68mm for CFF-MA). Bland-Altman analysis showed a clinically acceptable agreement between Pre and Post measurement of CK-MA and CFF-MA (the percentage error was 10.6% and 12.2%, respectively). In contrast, the citrated kaolin reaction time (CK-R) showed no significant correlation between Pre and Post measurements (r = 0.328, P = 0.146), with a mean difference of 1.42min (95% CI: -0.45 to 3.29). In vitro protamine addition allows assessment of coagulation status during CPB using the TEG 6s system. CK-MA and CFF-MA measured during CPB using this method revealed a strong correlation and agreement with post-CPB measurements, suggesting that our method potentially facilitates early prediction of post-CPB coagulation status and decision-making on transfusion strategies. The study was registered in the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR, registration number: UMIN000041097, date of registration: July 13, 2020, https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046925 ) before the recruitment of participants.

The effect of information integration on team communication in a simulated submarine control room task

Submarine control rooms are characterised by dedicated individual roles for information types (e.g. Sonar operator processes sound energy), with individuals verbally reporting the information that they receive to other team members to help resolve uncertainty in the operational environment (low information integration). We compared this work design with one that ensured critical information was more readily available to all team members (high information integration). We used the Event Analysis of Systemic Teamwork (EAST) method to analyse task, information, and social networks for novice teams operating within a simulated submarine control room under low versus high information integration. Integration impacted team member centrality (importance relative to other operators) and the nature of information shared. Team members with greater centrality reported higher workload. Higher integration across consoles altered how team members interacted and their relative status, the information shared, and how workload was distributed. However, overall network structures remained intact.

Effects of a video-viewing intervention with positive word stimulation on the depressive symptoms of older patients with cardiac disease and subthreshold depression: a pilot randomized controlled trial protocol

BackgroundIntervention for older patients with cardiac disease and subthreshold depression (StD) may be an effective strategy to prevent the development of major depressive disorder. The subliminal priming with supraliminal reward stimulation (SPSRS) website developed by us is an advanced intervention that can improve depressive symptoms in individuals with StD by presenting positive word stimuli in videos. However, its efficacy for treating depressive symptoms in older patients with cardiac disease and StD has not been investigated. Here, we present a pilot randomized controlled trial protocol to investigate the preliminary efficacy of an intervention for older patients with cardiac disease with StD.MethodsThe study was designed as a single-center, open-label, pilot, randomized, parallel-group trial. The participants will include 30 older patients with cardiac disease and StD who are hospitalized in acute wards. The Experimental group received the SPSRS intervention (video viewing with positive word stimuli; n = 15) and the Control group will receive the YouTube intervention (video viewing without positive word stimuli; n = 15). In both groups, the intervention will be administered for 10 min per day, five times per week for 1 week. The primary outcome will be the change in the scores on the Japanese version of the Beck Depression Inventory-II at 1 week after the baseline assessment. The secondary outcomes will be the changes in the Specific Activity Scale, New York Heart Association functional classification, as well as grip strength at 1 week after the baseline assessment.DiscussionThis pilot randomized controlled trial will be the first to evaluate the SPSRS intervention for depressive symptoms in older patients with cardiac disease and StD who are admitted to acute wards. The results will provide tentative indications regarding the impact of the intervention on depressive symptoms among older patients with cardiac disease and StD who are admitted to acute wards, and will contribute to the planning of a full-scale study.Trial registrationUMIN, UMIN000052155. Registered September 8, 2023, https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000059526. This study was registered with the University Hospital Medical Information Network (UMIN) (UMIN000052155) in Japan.

Information Network Assessment of Farm Women in Sub-Himalayan Terai Region of India and the Role of KVK

Krishi Vigyan Kendra play an important role of information network development among the farmers and farm women through different training and extension programme. This empirical study was conducted to find out the role of Krishi Vigyan Kendra on information networks development of the farm women in sub Himalayan terai region of India and to identify the different factors influencing agricultural information network output of the farm women in terms of knowledge. The study was conducted in the Cooch Behar District of West Bengal with the help of an ex post facto research design. It is found from the study that social participation of farm women was high in case of the members of Self Help Groups, Mobility of the farm women were more in panchayat office. Majority of the farm women received farm information from farmers club/FPO, sources of informal farm information of the female farmers were higher in case of discussion with fellow farmers, mass media exposure of the farm women was higher in case of mobile phone and farm women visit the farm science centre whenever problems occur. It is also found from the study that the variables livestock possession, material possession, participation of FSC programme, communication skill, attitude of the farm women towards FSC scientist, mass media exposure, e resources exposure, decision making ability, type of family and occupation were influence more than 70% of the variability of the dependent variable agricultural information network output of the farm women. So, it is very essential for taking consideration above mentioned variable before implementation of any farm women orientated programme. It is revealed from the study that the residual errors were less in the regression model and there was some of variation that is not explained by the model. The research work would play important guidelines for expanding the information network in the field of agriculture through Krishi Vigyan Kendra.

Minimizing Age of Information in Nonorthogonal Random Access Networks

Minimizing Age of Information in Nonorthogonal Random Access Networks

Mindful Self-Compassion Smartphone Intervention for Worker Mental Health in Japan: Protocol for a Randomized Controlled Trial.

Mental health problems among workers cause enormous losses to companies in Japan. However, workers have been considered to have limited access to psychological support because of time constraints, which makes it difficult for them to engage in face-to-face psychological support interventions. This study aimed to present an intervention protocol that describes a randomized controlled trial to examine whether brief guided mindfulness meditation (MM) or self-compassion meditation (SCM) provided by a smartphone app is effective for mental health and work-related outcomes among workers. This is an open-label, 3-arm randomized controlled trial. The participants will be recruited through an open call on relevant websites with the following inclusion criteria: (1) employees who are working more than 20 hours per week, (2) between the ages of 18 and 54 years, (3) not on a leave of absence, (4) not business owners or students, and (5) not currently diagnosed with a mental disorder and have a Kessler Psychological Distress Scale-6 score below 13 points. We will include 200 participants and randomly assign them to an SCM course (n=67), an MM course (n=67), and a waitlist group (n=66). The intervention groups (SCM and MM) will be instructed to engage in daily guided self-help, self-compassion, and MMs lasting 6-12 minutes per day over 4 weeks. Primary outcomes will include psychological distress and job performance, and secondary outcomes will include somatic symptoms, cognitive flexibility, self-esteem, self-compassion, perceived stress, well-being, emotion regulation, work engagement, anger, psychological safety, and creativity. All procedures were approved by the ethics committee of the University of Tokyo (22-326). All participants will be informed of the study via the websites, and written informed consent will be collected via web-based forms. The recruitment of participants began in December 2022, and the intervention began in January 2023. As of September 2023, a total of 375 participants have been enrolled. The intervention and data collection were completed in late October 2023. This study will contribute to the development of effective self-care intervention content that will improve mental health, work performance, and related outcomes and promote mindful and self-compassionate attitudes when faced with distress. University Hospital Medical Information Network Clinical Trials Registry UMIN000049466; https://tinyurl.com/23x8m8nf. DERR1-10.2196/53541.

A network model of stress contagion: evidence from the vocational classroom

PurposeOur study focuses on stress contagion in vocational school classes, examining how students’ stress experiences affect their spatial classmates. For this purpose, we apply a novel formal network model that allows us to differentiate between endogenous and exogenous peer effects in the stress contagion process. Using the network model, we investigate effects on students’ stress levels, considering the stress and coping experiences of spatial peers as well as didactic-methodological context factors.MethodsWe test our statistical model using secondary data collected in a study involving 53 students from two classes at a public German vocational training school. The students’ experiences of stress (time pressure, pressure to succeed) and coping strategies (understanding of the subject matter, self-confidence) were captured using the experience sampling method, while classroom characteristics (e.g., teacher instruction, cooperative work) were recorded through video-based analysis of lessons. Utilizing the panel data, we employ maximum likelihood estimation to assess the spatial peer effects model for both classrooms.ResultsAmong other findings, all model specifications revealed significant peer effects for both stress measures, indicating that the higher the stress experience of immediate peers in the classroom, the higher the individual stress experienced by the students. Concerning the considered context factors, we found, for example, that increased cooperative work leads to higher levels of stress experience.ConclusionFrom a substantive perspective, our results underscore the role of peer-to-peer contagion in the vocational classroom and thus suggest a nuanced examination of cooperative practices. From a research methodology perspective, our approach illustrates how various methods (such as experience sampling, video-based classroom observation, and spatial network information) complement and enrich each other, highlighting the added value of our network analytical approach

МОДЕЛИРОВАНИЕ НАРУШИТЕЛЯ, ИНФРАСТРУКТУРЫ И АТАК В СИСТЕМАХ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИ

Formal models of subjects, infrastructure and attacks for information security systems are proposed. The models include descriptions of information security operators, administrators, users and violators, taking into account their knowledge, qualifications and initial conditions. A comprehensive intruder model is presented, including initial knowledge and access rights, initial location, qualifications and goals. Infrastructure models, vulnerabilities, and information collection methods are also considered, which makes it possible to more accurately predict the behavior of violators and develop effective protection strategies. The results of the study show that the proposed models significantly improve the accuracy of risk assessment and security planning, which is especially important for mission-critical information systems. The practical significance lies in the possibility of using models to develop and improve information network security systems. The results of the practical implementation of the model on real data are also presented.

Self-supervised Representations and Node Embedding Graph Neural Networks for Accurate and Multi-scale Analysis of Materials

Abstract We propose a new strategy by self-supervised learning on the node and edge information of the crystal graph neural network (GNN) model, which may gain physical insights by tuning the depth of multi-scale representations. Compared with the popular manually constructed material descriptors, the self-supervised atomic representation can be applied to various machine learning models and reach better prediction performance on material properties. Applying the self-supervised atomic representation on the magnetic moment datasets, we show how they can extract rules and information from the magnetic materials. We develop the node-embedding graph neural networks (NEGNN) framework to incorporate rich physical information into the GNN model, showing significant improvements in the prediction performance. The self-supervised material representation and the NEGNN framework may investigate in-depth information from materials and can be applied to small datasets with increased prediction accuracy.

Self-supervised Representations and Node Embedding Graph Neural Networks for Accurate and Multi-scale Analysis of Materials

Abstract We propose a new strategy by self-supervised learning on the node and edge information of the crystal graph neural network (GNN) model, which may gain physical insights by tuning the depth of multi-scale representations. Compared with the popular manually constructed material descriptors, the self-supervised atomic representation can be applied to various machine learning models and reach better prediction performance on material properties. Applying the self-supervised atomic representation on the magnetic moment datasets, we show how they can extract rules and information from the magnetic materials. We develop the node-embedding graph neural networks (NEGNN) framework to incorporate rich physical information into the GNN model, showing significant improvements in the prediction performance. The self-supervised material representation and the NEGNN framework may investigate in-depth information from materials and can be applied to small datasets with increased prediction accuracy.

Nilotinib vs dasatinib in achieving MR4.5 for de novo chronic myeloid leukemia: the randomized JALSG CML212 study

AbstractTreatment-free remission (TFR) is a new therapeutic goal for chronic myeloid leukemia in chronic phase (CML-CP). Deep molecular response (DMR) is a prerequite condition for TFR. The JALSG (Japan Adult Leukemia Study Group) conducted a multicenter, prospective randomized phase 3 CML212 study for de novo CML-CP to compare the cumulative achievement of molecular response 4.5 (MR4.5; international scale BCR::ABL1 ≤0.0032%) by 18 months between nilotinib and dasatinib treatment as a primary end point. A total of 454 patients were randomly assigned to the 300 mg nilotinib twice daily arm or to the 100 mg dasatinib daily arm (both n = 227). BCR::ABL1 messenger RNA levels were monitored every 3 months. Study treatment was stopped if the patients were judged as having failed treatment according to the European LekemiaNet 2009 criteria or if they showed intolerance. The cumulative achievement rates of MR4.5 by 18 months were 32.6% (95% confidence interval [CI], 26.5-39.1) in the nilotinib arm and 30.8% (95% CI, 24.9-37.3) in the dasatinib arm with no significant difference (P = .66). In addition, the cumulative achievement rates of early molecular response, complete cytogenetic response, and major molecular response, MR4.0 by 12, 18, 24, and 36 months were almost the same between the 2 arms. At 36 months, 66.5% and 65.0% patients continued nilotinib and dasatinib, respectively (P = .76). There was no significant difference in progression-free survival (PFS) or overall survival (OS) between the 2 arms as evaluated by log-rank tests (PFS, P = .58; OS, P = .64). These results suggest that nilotinib and dasatinib would be equally effective for patients with de novo CML-CP with similar continuity. This trial was registered in the University Hospital Medical Information Network Clinical Trials Registry as #UMIN000007909.

Design of Consumer Confidence Prediction Index Model based on DEGWO Algorithm

Based on the CCI index released by China Economic Information Network, combined with the DEGWO difference algorithm and BP neural network regression; Constructed a DEGWO-BP synthesis algorithm under machine learning mode to predict and fit consumer confidence index; The empirical results show that the cumulative error of the consumer confidence index using the DEGWO-BP algorithm is the lowest, only 37.8273; The average absolute error of the model is the lowest, and the model has the minimum level of deviation; The model with the minimum extreme deviation value has the strongest stability.

Design of Consumer Confidence Prediction Index Model based on DEGWO Algorithm

Based on the CCI index released by China Economic Information Network, combined with the DEGWO difference algorithm and BP neural network regression; Constructed a DEGWO-BP synthesis algorithm under machine learning mode to predict and fit consumer confidence index; The empirical results show that the cumulative error of the consumer confidence index using the DEGWO-BP algorithm is the lowest, only 37.8273; The average absolute error of the model is the lowest, and the model has the minimum level of deviation; The model with the minimum extreme deviation value has the strongest stability.

ÉVALUATION DE LA VARIABILITÉ DE TENSION DANS LES SYSTÈMES ÉLECTRIQUES

Processes in large energy systems exhibit a high variability due to the stochastic nature of electricity production and use. However, the control of the energy transfer aims to achieve steady state operation, which is mostly described by signals fully identifiable by a finite set of parameters. Part of the control system is the process of information delivered through measurements. In this paper, we analyze the impact of the measurement system deployed for reporting the RMS parameter of the voltage signal on the quality of information in LV distribution networks. The operation of such networks is highly stochastic, and the chosen models based on averaging are not always appropriate; therefore, we propose to apply a statistic metric, i.e., the coefficient of variation of the root mean square deviation CV(RMSD).

Efficient SFC Protection Method against Network Attack Risks in Air Traffic Information Networks

With the continuous development of the civil aviation industry toward digitalization and intelligence, the closed architecture of traditional air traffic information networks struggles to meet the rapidly growing demands for air traffic services. Network function virtualization (NFV) is one of the key technologies that can address the rigidity of traditional air traffic information networks. NFV technology has facilitated the flexible deployment of air traffic services, but it has also expanded the attack surface of the network. In addressing the network attack risks faced by service function chains (SFCs) in NFV environments, a SFC protection method based on honeypots and backup technology (PBHB) is proposed to reduce the resource cost of protecting air traffic information networks while enhancing network security. Initially, PBHB utilizes the TAPD algorithm to deploy the primary VNFs as closely as possible to the shortest path between the source and destination endpoints, thus aiming to reduce SFC latency and save bandwidth resource costs. Subsequently, the RAHDR algorithm is employed to install honeypot VNFs in each physical platform that is at risk of side-channel attacks, thus updating the deployment status of honeypot VNFs in real time based on the VNF lifecycle in order to offer primary protection for SFCs. Lastly, the BDMPE algorithm was used to calculate the backup scheme with the highest protection efficiency to implement secondary protection for the SFCs that still do not meet the security requirements. Through experiments, the maximum backup limit for SFCs in PBHB was determined, confirming its satisfactory performance across various SFC arrival rates. Furthermore, performance comparisons with other SFC protection methods revealed that PBHB achieves optimizations in resources cost while ensuring SFC security and latency.

A heterogeneous information network learning model with neighborhood-level structural representation for predicting lncRNA-miRNA interactions

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are closely related to the treatment of human diseases. Traditional biological experiments often require time-consuming and labor-intensive in their search for mechanisms of disease. Computational methods are regarded as an effective way to predict unknown lncRNA-miRNA interactions (LMIs). However, most of them complete their tasks by mainly focusing on a single lncRNA-miRNA network without considering the complex mechanism between biomolecular in life activities, which are believed to be useful for improving the accuracy of LMI prediction. To address this, a heterogeneous information network (HIN) learning model with neighborhood-level structural representation, called HINLMI, to precisely identify LMIs. In particular, HINLMI first constructs a HIN by integrating nine interactions of five biomolecules. After that, different representation learning strategies are applied to learn the biological and network representations of lncRNAs and miRNAs in the HIN from different perspectives. Finally, HINLMI incorporates the XGBoost classifier to predict unknown LMIs using final embeddings of lncRNAs and miRNAs. Experimental results show that HINLMI yields a best performance on the real dataset when compared with state-of-the-art computational models. Moreover, several analysis experiments indicate that the simultaneous consideration of biological knowledge and network topology of lncRNAs and miRNAs allows HINLMI to accurately predict LMIs from a more comprehensive perspective. The promising performance of HINLMI also reveals that the utilization of rich heterogeneous information can provide an alternative insight for HINLMI to identify novel interactions between lncRNAs and miRNAs.

An appearance quality classification method for Auricularia auricula based on deep learning

The intelligent appearance quality classification method for Auricularia auricula is of great significance to promote this industry. This paper proposes an appearance quality classification method for Auricularia auricula based on the improved Faster Region-based Convolutional Neural Networks (improved Faster RCNN) framework. The original Faster RCNN is improved by establishing a multiscale feature fusion detection model to improve the accuracy and real-time performance of the model. The multiscale feature fusion detection model makes full use of shallow feature information to complete target detection. It fuses shallow features with rich detailed information with deep features rich in strong semantic information. Since the fusion algorithm directly uses the existing information of the feature extraction network, there is no additional calculation. The fused features contain more original detailed feature information. Therefore, the improved Faster RCNN can improve the final detection rate without sacrificing speed. By comparing with the original Faster RCNN model, the mean average precision (mAP) of the improved Faster RCNN is increased by 2.13%. The average precision (AP) of the first-level Auricularia auricula is almost unchanged at a high level. The AP of the second-level Auricularia auricula is increased by nearly 5%. And the third-level Auricularia auricula AP is increased by 1%. The improved Faster RCNN improves the frames per second from 6.81 of the original Faster RCNN to 13.5. Meanwhile, the influence of complex environment and image resolution on the Auricularia auricula detection is explored.

DyHANE: dynamic heterogeneous attributed network embedding through experience node replay

With real-world network systems typically comprising a large number of interactive components and inherently dynamic, Graph Continual Learning (GCL) has gained increasing popularity in recent years. Furthermore, most applications involve multiple entities and relationships with associated attributes, which has led to widely adopting Heterogeneous Information Networks (HINs) for capturing such rich structural and semantic meaning. In this context, we deal with the problem of learning multi-type node representations in a time evolving graph setting, harnessing the expressive power of Graph Neural Networks (GNNs). To this purpose, we propose a novel framework, named DyHANE—Dynamic Heterogeneous Attributed Network Embedding, which dynamically identifies a representative sample of multi-typed nodes as training set and updates the parameters of a GNN module, enabling the generation of up-to-date representations for all nodes in the network. We show the advantage of employing HINs on a data-incremental classification task. We compare the results obtained by DyHANE on a multi-step, incremental heterogeneous GAT model trained on a sample of changed and unchanged nodes, with the results obtained by either the same model trained from scratch or the same model trained solely on changed nodes. We demonstrate the effectiveness of the proposed approach in facing two major related challenges: (i) to avoid model re-train from scratch if only a subset of the network has been changed and (ii) to mitigate the risk of losing established patterns if the new nodes exhibit unseen properties. To the best of our knowledge, this is the first work that deals with the task of (deep) graph continual learning on HINs.

Combating temporal composition inference by high-order camouflaged network topology obfuscation

Topology inference driven by non-collaborative or incomplete prior knowledge is widely used in pivotal target network sieving and completion. However, perceivable topology also allows attackers to identify the fragile bottlenecks and perform efficacious attacks that are difficult to defend against by injecting indistinguishable low-volume attacks. Most existing countermeasures are proposed to obfuscate network data or set up honeypots with adversarial examples. However, there are two challenges when adding perturbations to live network links or nodes. Firstly, the perturbations imposed on the network cannot be conveniently projected to the original network with poor scalability. Secondly, applying significant changes to network information is laborious and impractical. In short, making a good trade-off between concealment and complexity is challenging. To address the above issues, we propose a fraudulent proactive defending tactic, namely HBB-TSP, to protect live network privacy by combating attacks of temporal network inference. Specifically, to penetrate the critical network structures, HBB-TSP first brings in the Statistical Validation of Hypergraph (SVH) method to identify the pivotal connection information of the network and extract the deep backbone structure. Then, the Temporal Simple Decomposition Weighting (TSDW) strategy is introduced, which can predict the backbone network with evolution rules and add highly obfuscated features at a minimized overhead. Finally, a discriminator with multiple centrality models is used to evaluate the deceptiveness and, in turn, affect the TSDW prediction. The entire process ensures the consistency and robustness of network changes while ensuring effective adversarial resistance. Experimental results on two scale real-world datasets demonstrate the effectiveness and generalization of adversarial perturbations. In particular, it is encouraging that our proposed defending scheme outperforms the advanced countermeasures. It ensures the realization of a deceptive obfuscated network at minimum overhead and is suitable for widespread deployment in scenarios of different scales.