Large Networks Research Articles

Systemic inflammation in patients with atherosclerotic cardiovascular and chronic kidney disease in Spain: a population-based study using electronic medical records from a primary care database

Abstract Background Systemic inflammation (SI) is recognized as a critical driver of atherosclerotic cardiovascular disease (ASCVD), and it is frequently observed in ASCVD patients with comorbid chronic kidney disease (CKD). Elevated C-reactive protein (CRP) levels, a common biomarker for SI, have been associated with poor prognosis in ASCVD and CKD patients, increased risk of major adverse cardiovascular events and renal disease progression, as well as greater mortality. Purpose To estimate the prevalence of SI in patients with ASCVD in Spain overall and by CKD status, and to describe patients’ clinical characteristics. Methods In this population-based observational study, data from THIN Spain, a database of electronic medical records from a large network of general practitioners, were used to identify adult patients with a diagnosis code of ASCVD (atherosclerotic coronary or cerebrovascular event, peripheral arterial disease, or other atherosclerotic vascular event) and ≥1 eligible CRP measurement between January 1st 2014 to July 31st 2023 (latest data available at time of data extraction).[Figure] SI was defined as CRP ≥ 2mg/L. Point prevalence of SI at the end of the study (among patients alive with ≥1 CRP in the prior 18 months) and the proportion of patients with evidence of SI during the study period (2014-2023) were estimated overall and by CKD status (defined as CKD stage 3+, or eGFR <60 mL/min/1.73m², or urinary albumin-to-creatinine ratio ≥30 mg/g). Patients’ comorbidities (defined using primary care diagnoses), and laboratory values were reported by SI status at first CRP measurement during the study period. Results Among 76,423 patients with ASCVD diagnosis, 15,798 patients (20.7%) were included in the study (mean age: 71.1 years; 43% females), of whom 5,111 (34%) had CKD at first eligible CRP (n=975 with unknown CKD status). Mean CRP in ASCVD patients with CKD was 24% higher than in those without CKD (4.36 vs 3.53 mg/L, p<0.001). The proportion of ASCVD patients with SI at first CRP measurement was 58% overall (65% among CKD patients; 55% among patients without CKD; p<0.001). The point prevalence of SI at the end of the study was 56% among ASCVD patients overall (62% among CKD patients; 52% among patients without CKD; p<0.001), while 64% of ASCVD patients had evidence of SI at any time during the study period (72% among CKD patients; 59% among patients without CKD; p<0.001). Compared to ASCVD patients without SI, higher proportion of SI patients were smokers and with comorbidities. Also, ASCVD patients with SI had lower baseline eGFR levels and higher levels of LDL-C and triglycerides than ASCVD patients without SI.[Table] Conclusions SI prevalence is high among patients with ASCVD in Spain, especially among patients with comorbid CKD. Strategies to target SI may have beneficial effects in prevention of cardiovascular events and mortality in ASCVD patients.

Tobacco Free Advent Calendar: Evaluating innovative strategies in Public Health digital campaigns

Abstract Introduction Two editions, 2022 and 2023, of the Tobacco Free Advent Calendar (TFAC) built a large network of Organizations, Universities, Institutions, individual experts and medical students across Europe, coordinated by ENSPNext (European Network for Smoking and Tobacco Prevention Youth Group), to advocate for Youth and for Public Health. This study has 2 objectives: 1. Monitor the performance of TFAC 2023 vs TFAC 2022 for 31 days 2. Evaluate improvements in reach and engagement made for TFAC 2023. Methods For 24 days, TFAC 2023 featured thematic messages, embedded fact sheets, quizzes, videos and call-to-action buttons. New content was accessible daily starting on the first day of advent, ending on Christmas. TFAC was available in English and Armenian. AI was used to assist for various tasks and challenges during development and research. Comparative and Time Series Analysis were conducted in Excel and IBM SPSS26. Results TFAC 2023 was accessed 3764 times (8.8% increase), the doors were opened 1506 times (3.9% decrease), Call to action buttons were clicked 261 times (169% increase). For english version a strong variation in reach was observed, but not in engagement which remained relatively constant. In Armenian version both reach and engagement varied in 2023 vs 2022. A seasonal component was identified for both versions, particularly an increase at weekends and a decrease on weekdays. Similar to 2022, the reach and engagement were higher at the start and lower as time passed, although in 2023 the reach and engagement remained higher and more constant over time. Completion rates of interactive materials ranged between 21.4% to 100% in the English version and from 7.1% to 100% in the Armenian version. No interactive material remained unanswered. Conclusions Decrease of translations didn’t lower the reach and engagement of TFAC. Engagement with TFAC 2023 was higher, possibly due to the implication of students organizations and collaboration with influencers. Key messages • Targeted dissemination increased the reach and engagement of TFAC 2023 versus TFAC 2022. • Members of the target group should be recruited for being members in the projects affecting them to increase engagement and impact of the campaign.

Target controllability: a feed-forward greedy algorithm in complex networks, meeting Kalman's rank condition.

The concept of controllability within complex networks is pivotal in determining the minimal set of driver vertices required for the exertion of external signals, thereby enabling control over the entire network's vertices. Target controllability further refines this concept by focusing on a subset of vertices within the network as the specific targets for control, both of which are known to be NP-hard problems. Crucially, the effectiveness of the driver set in achieving control of the network is contingent upon satisfying a specific rank condition, as introduced by Kalman. On the other hand, structural controllability provides a complementary approach to understanding network control, emphasizing the identification of driver vertices based on the network's structural properties. However, in structural controllability approaches, the Kalman condition may not always be satisfied. In this study, we address the challenge of target controllability by proposing a feed-forward greedy algorithm designed to efficiently handle large networks while meeting the Kalman controllability rank condition. We further enhance our method's efficacy by integrating it with Barabasi et al.'s structural controllability approach. This integration allows for a more comprehensive control strategy, leveraging both the dynamical requirements specified by Kalman's rank condition and the structural properties of the network. Empirical evaluation across various network topologies demonstrates the superior performance of our algorithms compared to existing methods, consistently requiring fewer driver vertices for effective control. Additionally, our method's application to protein-protein interaction networks associated with breast cancer reveals potential drug repurposing candidates, underscoring its biomedical relevance. This study highlights the importance of addressing both structural and dynamical aspects of network controllability for advancing control strategies in complex systems. The source code is available for free at:Https://github.com/fatemeKhezry/targetControllability.

Large language model-based optical network log analysis using LLaMA2 with instruction tuning

The optical network encompasses numerous devices and links, generating a significant volume of logs. Analyzing these logs is significant for network optimization, failure diagnosis, and health monitoring. However, the large-scale and diverse formats of optical network logs present several challenges, including the high cost and difficulty of manual processing, insufficient semantic understanding in existing analysis methods, and the strict requirements for data security and privacy. Generative artificial intelligence (GAI) with powerful language understanding and generation capabilities has the potential to address these challenges. Large language models (LLMs) as a concrete realization of GAI are well-suited for analyzing DCI logs, replacing human experts and enhancing accuracy. Additionally, LLMs enable intelligent interactions with network administrators, automating tasks and improving operational efficiency. Moreover, fine-tuning with open-source LLMs protects data privacy and enhances log analysis accuracy. Therefore, we introduce LLMs and propose a log analysis method with instruction tuning using LLaMA2 for log parsing, anomaly detection and classification, anomaly analysis, and report generation. Real log data extracted from the field-deployed network was used to design and construct instruction tuning datasets. We utilized the dataset for instruction tuning and demonstrated and evaluated the effectiveness of the proposed scheme. The results indicate that this scheme improves the performance of log analysis tasks, especially a 14% improvement in exact match rate for log parsing, a 13% improvement in F1-score for anomaly detection and classification, and a 23% improvement in usability for anomaly analysis, compared with the best baselines.

GLP-1 Receptor Agonists Confer No Increased Rates of IBD Exacerbation Among Patients With IBD.

In patients with inflammatory bowel disease (IBD), multimorbidity with obesity and type 2 diabetes is common and increasing. Glucagon-like peptide 1 (GLP-1) receptor agonists are increasingly being prescribed for patients with IBD, yet their impact on patients with IBD is largely unknown. We aimed to assess the impact of GLP-1 receptor agonists on the course of IBD. We identified all IBD patients prescribed GLP-1 receptor agonists at a large academic healthcare network between 2009 and 2023. We analyzed demographics and IBD characteristics in the year pre- and post-GLP-1 receptor agonist prescription and matched them to non-IBD controls. Our primary outcome was IBD exacerbation in the year following GLP-1 receptor agonist initiation, measured as a composite of IBD-related hospitalization, corticosteroid prescription, medication escalation or changes, or IBD-related surgery. Secondary outcomes included change in metabolic risk factors. Overall, 224 patients met inclusion criteria. At GLP-1 receptor agonist initiation, the median age was 54 years, 63% were female, 77% were White, and median BMI was 33.2kg/m2. Compared to the 12-month period prior to GLP-1 receptor agonist initiation, in the 12 months post-GLP-1 receptor agonist initiation, there was no change in rates of IBD exacerbation, IBD-related hospitalization, steroids prescription, medication escalation or changes, or IBD-related surgery. There was a significant decrease in BMI in the year following GLP-1 receptor agonist initiation (median BMI 33.5 vs 31.6kg/m2, P < .01), with rates of decrease comparable to non-IBD matched controls. In patients with IBD, GLP-1 receptor agonists are effective for weight loss and associated with few episodes of disease exacerbation.

Detectability of Varied Hybridization Scenarios Using Genome-Scale Hybrid Detection Methods

Hybridization events complicate the accurate reconstruction of phylogenies, as they lead to patterns of genetic heritability that are unexpected under traditional, bifurcating models of species trees. This phenomenon has led to the development of methods to infer these varied hybridization events, both methods that reconstruct networks directly, as well as summary methods that predict individual hybridization events from a subset of taxa. However, a lack of empirical comparisons between methods – especially those pertaining to large networks with varied hybridization scenarios – hinders their practical use. Here, we provide a comprehensive review of popular summary methods: TICR, MSCquartets, HyDe, Patterson’s D-Statistic (ABBA-BABA), D3, and Dp. TICR and MSCquartets are based on quartet concordance factors gathered from gene tree topologies and HyDe, Patterson’s D-Statistic, D3, and Dp use site pattern frequencies to identify hybridization events between sets of three taxa. We then use simulated data to address questions of method accuracy and ideal use scenarios by testing methods against complex networks which depict gene flow events that differ in depth (timing), quantity (single vs. multiple, overlapping hybridizations), and rate of gene flow (γ). We find that deeper or multiple hybridization events may introduce noise and weaken the signal of hybridization, leading to higher relative false negative rates across all methods. Despite some forms of hybridization eluding quartet-based detection methods, MSCquartets displays high precision in most scenarios. While HyDe results in high false negative rates when tested on hybridizations involving extinct or unsampled ghost lineages, HyDe is the only method able to identify the direction of hybridization, distinguishing the source parental lineages from recipient hybrid lineages. Lastly, we test the methods on a dataset of ultraconserved elements from the bee subfamily Nomiinae, finding possible hybridization events between clades which correspond to regions of poor support in the species tree estimated in a previous study.

Understanding Co-Offending Patterns and Criminal Mentorship Using Police Registry Data

ABSTRACT The current study examines registration data from the Dutch police pertaining to 2,757 individuals and 10 different crime categories, at the offense, individual, and network levels of analysis. Moreover, a novel estimate is introduced which identifies individuals’ tendency to operate as mentors or mentees in criminal networks. We find co-offending to be conditional on the type of crime and to typically involve groups of two to three offenders. Moreover, we find that men and those aged 18–25 are most likely to co-offend and that co-offenders typically do not show a preference for particular co-offenders. Individuals with central positions in the co-offending network were found to be older and to offend more frequently with a higher average of co-offenders per offense, than those less central. Lastly, we find the novel estimate to be a promising tool for identifying mentor and mentee roles in large quantitative network data. The results of the current study emphasize the importance of studying co-offending on different analytical levels, and to differentiate co-offending by different crime types, network positions, and individual roles, to better inform theory and policy.

Phenotype of patients with late diagnosis of 22q11 deletion: a review and retrospective study.

Chromosome 22q11.2 deletion syndrome (22q11DS) is the most common microdeletion syndrome, typically presenting in neonates with congenital cardiac anomalies, hypocalcaemia and thymic hypoplasia. Some patients are diagnosed later in adolescence and adulthood, with less known about the clinical phenotype of these patients. To summarise key clinical features in cases of 22q11DS diagnosed during adolescence and adulthood. This is a retrospective cohort study of 22q11DS patients diagnosed after 13 years of age over 2010-2021, with a literature review of published cases highlighting other late diagnoses. The study was performed in a large multicentre tertiary health network in Melbourne, Australia. Patients diagnosed with 22q11DS after the age of 13 years were included in the study. Main outcome measures were key clinical features in cases of late diagnosis of 22q11DS. A literature search yielded 53 published case reports and one cohort study for review (62 subjects). Additionally, 10 cases of late diagnosis of 22q11DS were identified through a retrospective electronic medical chart review. Findings suggest that intellectual disability and learning difficulties, hypocalcaemia with hypoparathyroidism and facial dysmorphism remain key features in patients with a late diagnosis of 22q11DS, with hypocalcaemia being the most common presentation leading to diagnosis. Patients diagnosed in adulthood may lack classical clinical features of congenital cardiac anomalies and thymic hypoplasia. Immunological consequences of 22q11DS are also an important late-onset consideration. Atypical features may include basal ganglia calcification. Chromosome 22q11DS has diverse clinical features and a highly variable phenotype, likely contributing to underdiagnosis and later diagnoses.

An Information Gradient Approach to Optimizing Traffic Sensor Placement in Statewide Networks

Traffic sensors are vital to the development and operation of Intelligent Transportation Systems, providing essential data for traffic monitoring, management, and transportation infrastructure planning. However, optimizing the placement of these sensors, particularly across large and complex statewide highway networks, remains a challenging task. In this research, we presented a novel search algorithm designed to address this challenge by leveraging information gradients from K-nearest neighbors within an embedding space. Our method enabled more informed and strategic sensor placement under budget and resource constraints, enhancing overall network coverage and data quality. Additionally, we incorporated spatial kriging analysis, harnessing spatial correlations of existing sensors to refine and reduce the search space. Our proposed approach was tested against the widely used Genetic Algorithm, demonstrating superior efficiency in terms of convergence time and producing more effective solutions with reduced information loss.

Hospital-Level Variation in COVID-19 Treatment Among Hospitalized Adults in the United States: A Retrospective Cohort Study.

Retrospective cohort study. To characterize variation in dexamethasone and remdesivir use over time among hospitals. Little is known about hospital-level variation in COVID-19 drug treatments in a large and diverse network in the United States. We selected individuals hospitalized with COVID-19 across 163 hospitals between February 23, 2020 and October 31, 2021 from using the HCA CHARGE, an electronic health record repository from a network of community health care facilities in the United States. We quantified receipt of dexamethasone, remdesivir, and combined use of dexamethasone and remdesivir during the hospital stay. We used 2-level logistic regression models to determine the intraclass correlation coefficient (ICC) at the hospital level, adjusting for patient and hospital characteristics. The ICC shows the proportion of total variation in drug use accounted for by hospitals. Among 161,667 individuals hospitalized with COVID-19, 73.0% were treated with dexamethasone, 49.1% with remdesivir, and 45.0% with both dexamethasone and remdesivir. The proportion of variation in dexamethasone use was 12.7% (adjusted ICC: 0.127), 8.5% for remdesivir, and 11.3% for combined drug use, indicating low interhospital variation. In the fully adjusted models, between-facility variation in dexamethasone use declined from 34.1% in February-March 2020 to 11.3% in January-March 2021 and then increased to 17.3% in July-October 2021. The variation in remdesivir use remained relatively stable during the study period. During the first 2 years of the pandemic, there was relatively consistent use of dexamethasone and remdesivir across the hospitals examined. Consistent adoption and implementation of treatment guidelines across the hospitals examined may have led to a decrease in variation in drug usage over time.

Feasibility and Acceptability of Providing Cognitive Remediation in a Large USA System of Coordinated Specialty Care for Early Psychosis.

Multinational treatment guidelines support providing cognitive remediation to people recently diagnosed with schizophrenia, but the feasibility of implementing the treatment on a large scale is less well understood. This study took place between 2019 and 2023 at 14 clinics within a large network of programs providing early intervention services to people aged 16-30 experiencing nonaffective psychosis. Clinics were randomly assigned to deliver cognitive remediation as twice-weekly clinician-led groups (N = 5), cognitive remediation as once-weekly clinician-led groups with homework (N = 6), or treatment as usual (N = 3). All clinics screened for cognitive health need to guide treatment planning. Clinical teams (N = 11) received training to provide cognitive remediation. Program evaluation data were analysed for feasibility and acceptability. Screening for cognitive health needs was completed on 77% of the 1193 participants enrolled at the 11 clinics offering cognitive remediation. Clinicians identified cognitive difficulties in 53.9% (n = 496) of screened participants and referred 27% (n = 134) of these participants to cognitive remediation. Of referred participants, 77.6% (n = 104) initiated treatment, and n = 41 completed the treatment. The rate of referral was nearly double, and treatment initiation was significantly higher at programs delivering once-weekly (84.3%) than twice-weekly (64.4%) treatment but the difference in the rate of treatment completion was statistically nonsignificant. Satisfaction among treatment completers was high. Referrals to cognitive remediation required systemic support of a feasible cognitive health screening process. About a quarter of people with clinician-identified cognitive health needs were referred to cognitive remediation. Feasibility data suggest a flexible model of treatment delivery may facilitate implementation in this service setting.

The clinical and genetic spectrum of paediatric speech and language disorders.

Speech and language disorders are known to have a substantial genetic contribution. Although frequently examined as components of other conditions, research on the genetic basis of linguistic differences as separate phenotypic subgroups has been limited so far. Here, we performed an in-depth characterization of speech and language disorders in 52 143 individuals, reconstructing clinical histories using a large-scale data-mining approach of the electronic medical records from an entire large paediatric healthcare network. The reported frequency of these disorders was the highest between 2 and 5 years old and spanned a spectrum of 26 broad speech and language diagnoses. We used natural language processing to assess the degree to which clinical diagnoses in full-text notes were reflected in ICD-10 diagnosis codes. We found that aphasia and speech apraxia could be retrieved easily through ICD-10 diagnosis codes, whereas stuttering as a speech phenotype was coded in only 12% of individuals through appropriate ICD-10 codes. We found significant comorbidity of speech and language disorders in neurodevelopmental conditions (30.31%) and, to a lesser degree, with epilepsies (6.07%) and movement disorders (2.05%). The most common genetic disorders retrievable in our analysis of electronic medical records were STXBP1 (n = 21), PTEN (n = 20) and CACNA1A (n = 18). When assessing associations of genetic diagnoses with specific linguistic phenotypes, we observed associations of STXBP1 and aphasia (P = 8.57 × 10-7, 95% confidence interval = 18.62-130.39) and MYO7A with speech and language development delay attributable to hearing loss (P = 1.24 × 10-5, 95% confidence interval = 17.46-infinity). Finally, in a sub-cohort of 726 individuals with whole-exome sequencing data, we identified an enrichment of rare variants in neuronal receptor pathways, in addition to associations of UQCRC1 and KIF17 with expressive aphasia, MROH8 and BCHE with poor speech, and USP37, SLC22A9 and UMODL1 with aphasia. In summary, our study outlines the landscape of paediatric speech and language disorders, confirming the phenotypic complexity of linguistic traits and novel genotype-phenotype associations. Subgroups of paediatric speech and language disorders differ significantly with respect to the composition of monogenic aetiologies.

Data Mining Approach for Evil Twin Attack Identification in Wi-Fi Networks

Recent cyber security solutions for wireless networks during internet open access have become critically important for personal data security. The newest WPA3 network security protocol has been used to maximize this protection; however, attackers can use an Evil Twin attack to replace a legitimate access point. The article is devoted to solving the problem of intrusion detection at the OSI model’s physical layers. To solve this, a hardware–software complex has been developed to collect information about the signal strength from Wi-Fi access points using wireless sensor networks. The collected data were supplemented with a generative algorithm considering all possible combinations of signal strength. The k-nearest neighbor model was trained on the obtained data to distinguish the signal strength of legitimate from illegitimate access points. To verify the authenticity of the data, an Evil Twin attack was physically simulated, and a machine learning model analyzed the data from the sensors. As a result, the Evil Twin attack was successfully identified based on the signal strength in the radio spectrum. The proposed model can be used in open access points as well as in large corporate and home Wi-Fi networks to detect intrusions aimed at substituting devices in the radio spectrum where IEEE 802.11 networking equipment operates.

Art Market as a Factor in Shaping the Artistic Environment of Ukraine: Institutional Foundations and Development Prospects

The purpose of the article. The article is devoted to the influence of the leading persons and institutions of the Ukrainian art market on the formation of the circle of the most famous figures in the artistic environment, the dominant trends in the artistic life of the country, the tastes of the public, as well as the growing influence of representatives of the art business on the activities of state institutions in the field of culture. The methodology of the work is based on the application of methods of observation, analysis and generalisation, as well as the structural-functional method, considering the art market and the artistic environment as closely related social phenomena. A sociological approach to art as a collective activity is applied, in which not only artists are involved, but a large network of other participants in cultural life and the art market. The scientific novelty of the work consists in the study of the role of local art business in shaping the structure of the contemporary artistic environment of Ukraine. For the first time, the question of how decisive is the influence of the participants of the Ukrainian art market (primarily – art dealers, collectors and institutions related to them) on the formation of a certain hierarchical ranking of artists in the Ukrainian art environment on representatives of the mainstream and the rest of other, less well-known figures was highlighted, a significant part of which is, so to speak, on the periphery of cultural life; the impact of the art market on public tastes and the activities of state bodies in the field of culture is also outlined. Until now, the topic of close interdependence of the art world and the art market has been avoided in the Ukrainian art history discourse. Conclusions. The art market selects artists who become the most famous figures in Ukrainian artistic life and get the opportunity to represent the country at the international level. The art market shapes public tastes; the most successful participants of the art market (primarily art dealers and collectors) seek to participate in the formation of state cultural policy by integrating into state institutions. In accordance with world trends, the private sector will gain more and more influence in the modern cultural life of the country, which will have both a number of advantages for the development of the Ukrainian art market and artistic life, as well as certain, potentially possible, disadvantages.

DATA SECURITY IN IOT DEVICES AND SENSOR NETWORKS FOR ROBUST THREAT DETECTION AND PRIVACY PROTECTION

The rapid proliferation of Internet of Things (IoT) devices and sensor networks has revolutionized various industries by enhancing automation, connectivity, and operational efficiency. However, these advancements have also introduced significant security challenges due to the resource constraints and decentralized nature of IoT environments. This paper provides a systematic review of IoT security solutions, focusing on encryption techniques, authentication protocols, and machine learning-based anomaly detection methods. A total of 55 peer-reviewed articles were analyzed following the PRISMA guidelines. The findings reveal that while lightweight cryptographic algorithms, such as elliptic curve cryptography (ECC), offer robust security with low energy consumption, scalability across large IoT networks remains a challenge. Blockchain-based authentication has emerged as a promising decentralized solution, but issues related to energy consumption and latency hinder its widespread adoption. Machine learning techniques have shown high accuracy in detecting threats in real-time, but their resource-intensive nature limits their application in low-power IoT devices. This review underscores the need for multi-layered, integrated security frameworks and highlights gaps in research on quantum-resistant cryptography and interoperable security standards. Future research must focus on developing scalable, energy-efficient security solutions to ensure data integrity and privacy in expanding IoT ecosystems.

Control valve simulation method integrable into the global gradient algorithm

ABSTRACT This paper presents a refinement of a method that simulates flow- and pressure-regulating valves by replacing them with pipes and adjusting the resistances (diameters) of those pipes to meet the valve settings. The method, referred to as the replacing pipe method (RPM), is readily integrable into the global gradient algorithm, which is the heart of the commonly used EPANET 2 water distribution system modeling software. The RPM proved to be more robust, yet slower than the EPANET 2 valve handling heuristics. In this work, the RPM was modified to increase its computational speed. The modification involved the RPM check valve handling algorithm and condition for updating the diameters of the valve-replacing pipes. The modified RPM was tested as part of a hybrid simulator, which switches from the EPANET 2 valve handling heuristics to the RPM when the former fails. The test networks included a large network with thousands of nodes, pipes, and valves, and a case of an ill-posed problem. As the testing showed, the modified RPM offers a significant reduction in the number of iterations compared to its previous version.

Cluster-guided denoising graph auto-encoder for enhanced traffic data imputation and fault detection

In an era of increasing digitalization, the integration of diverse sensors has become commonplace. Within the transportation sector, state Departments of Transportation have extensively deployed traffic sensors to support various engineering endeavors. However, these sensors are prone to faults such as data loss, random noise, bias, and drift due to sensor aging, defects, or environmental influences. Therefore, detecting these faults is imperative to maintain data integrity. This paper presents a novel approach for detecting faults in traffic data based on a cluster-guided data reconstruction process. First, a traffic sensor clustering module is designed using a dual-encoding attention graph auto-encoder (DA-GAE) to identify clusters of traffic sensors. This module leverages a joint embedding of node and edge features in a low-dimensional vector space. Subsequently, utilizing the identified clusters, a cluster-guided denoising graph auto-encoder (CG-DGAE) is devised and trained for data reconstruction. The CG-DGAE employs a diffusion graph convolutional network (DGCN) and is trained with a cluster-wise sampling strategy. Extensive experiments were conducted using traffic data obtained from a real-world large sensor network, demonstrating superior performance of the CG-DGAE model in data reconstruction compared to various baseline methods. For fault detection, a score function is devised to discern potential faults by contrasting the sensor data sequence and the reconstructed data sequence. The fault detection results show that our proposed CG-DGAE model achieved an impressive overall accuracy of 99.09%, a precision of 99.13%, a recall of 99.53%, and a F1 score of 99.53%. The superior capability of CG-DGAE in data reconstruction and fault detection is attributable to the cluster-wise spatial–temporal context leveraged by the model.

Assessing the use of unstructured electronic health record data to identify exposure to firearm violence.

Research on firearm violence is largely limited to people who experienced acute bodily trauma and death which is readily gathered from Inpatient and Emergency Department settings and mortality data. Exposures to firearm violence, such as witnessing firearm violence or losing a loved one to firearm violence, are not routinely collected in health care. As a result, the true public health burden of firearm violence is underestimated. Clinical notes from electronic health records (EHRs) are a promising source of data that may expand our understanding of the impact of firearm violence on health. Pilot work was conducted on a sample of clinical notes to assess how firearm terms present in unstructured clinical notes as part of a larger initiative to develop a natural language processing (NLP) model to identify firearm exposure and injury in ambulatory care data. We used EHR data from 2012 to 2022 from a large multistate network of primary care and behavioral health clinics. A text string search of broad, gun-only, and shooting terms was applied to 9,598 patients with either/both an ICD-10 or an OCHIN-developed structured data field indicating exposure to firearm violence. A sample of clinical notes from 90 patients was reviewed to ascertain the meaning of terms. Among the 90 clinical patient notes, 13 (14%) notes reflect documentation of exposure to firearm violence or injury from firearms. Results from this study identified refinements that should be considered for NLP text classification. Unstructured clinical notes from primary and behavioral health clinics have potential to expand understanding of firearm violence.

Ethnoeconomy of Europe: Turkish Segment

The article examines the ethnic factor of the European economy. The migration of the Turks to Europe in the 1960s, formed a special segment of entrepreneurship in the region. A significant part of it was occupied by the representatives of the Turkish diaspora. Several factors including the social one contribute to the active development of the Turkish ethno-business in the European space. In addition, the formation and prosperity of the Turkish ethnic entrepreneurship in Europe is influenced by the high development of family and clan relations as well as the presence of various kinds of auxiliary mechanisms in the form of trade unions and business associations. In the context of small and medium-sized businesses, the key attention is paid to the development of the Turkish fast food industry related to the organization of food outlets, the hotel business, the provision of general services as well as tourism and logistics services. Large network business and transnational corporations of Turkey in most cases are engaged in such areas as: restaurant business; hotel business, including the construction of hotels; distribution of retail chains and Turkish brands. Islamic banking has also become a relatively new rapidly developing area of the Turkish ethnobusiness. The author concludes that the Turkish migration has significantly changed the nature of the European business. However, despite the wide representation of Turkish entrepreneurs in Europe, not all of them achieve the desired success and often face various kinds of discriminatory policies of the European authorities that restrict their activities.

Monte Carlo safeguarding of key links through multiple random walks in large network

Safeguarding important links is a useful way to promote network vulnerability, especially in sparse networks where random link failures can disconnect the network. Sustaining network connectivity is the main goal of safeguarding and can be handled by selectively safeguarding links belonging to certain cuts of a network. However, due to the inherent high complexity of cut enumeration, existing algorithms can only handle small‐scale networks with limited nodes. It is important to find practical algorithms that are feasible for large‐scale graphs, as a significant portion of real‐world graphs under investigation are not as small as dozens of nodes. To address the problem, we propose to use a high‐precision approximate approach to accelerate the first step of the two‐step safeguard process and thus accelerate the whole process. A Monte Carlo algorithm is proposed to exploit efficient random paths for small cuts enumeration, which can help locate important edges with high probability in large‐scale sparse networks. These edges will then be utilized to find the approximated minimum cost edge set whose safeguarding can sustain the expected network connectivity. The algorithm is validated using various sizes of graphs of random/Barbasi–Albert scale‐free/Clustered scale‐free/unit disk/Watts–Strogatz small‐world and real‐world graphs. The experimental results show that the algorithm can perfectly sustain the network connectivity, and the acceleration ratio of more than 105 can be achieved with a little additional overhead. Specifically, in large graphs of one million nodes, approximated safeguard solutions survive at least 99.9% random link failures, and the solution time can be further reduced to dozens of seconds when the algorithm steps are easily implemented in full parallel.