Network Measures Research Articles

Pathology of the Role of Civil Society in Crime Prevention in Iran's Criminal Justice System

The present study aims to analyze the pathology of the role of civil society in crime prevention in Iran's criminal justice system. A qualitative research strategy, using thematic analysis, has been employed to address the issue. Data were collected through semi-structured interviews with 12 experts in criminal law and criminology, selected via theoretical sampling. The data were analyzed using thematic analysis, specifically the thematic network analysis method, and a conceptual model was constructed based on network measurements. The findings from the qualitative research indicated that the pathology of civil society’s role in crime prevention within Iran's criminal justice system includes the following overarching themes: 1) Internal organizational challenges, including (legal and juridical issues, governmental executive issues, lack of awareness and weak cultural development concerning civil society, weak social capital, economic issues, and legal and executive redundancy and multifactorial challenges); and 2) External organizational challenges, including (structural and managerial weaknesses in civil society institutions and weak knowledge management in crime prevention). Furthermore, for "validity testing" of the constructed themes and model, two methods were used: communicative validation and audit methods. For "reliability testing," two approaches were employed: repeatability and transferability or generalizability.

Social networks and HIV treatment adherence among people with HIV initiating treatment in rural Uganda and peri-urban South Africa

Timely initiation of and adherence to antiretroviral therapy (ART) is critical for improving HIV outcomes and reducing HIV transmissibility. Social networks, or the social relationships individuals have with each other, have been linked with positive health outcomes, but less is known about the extent to which social network composition and structure are associated with improved ART adherence among people living with HIV (PLWH). We conducted an ego-centric network study among 828 previously ART-naïve PLWH presenting for ART initiation at 11 clinics in Mbarara, Uganda (rural population) and Gugulethu, South Africa (peri-urban population). We collected social network data using name generator and name interpreter questions. ART adherence was monitored over 12 months using wireless monitors (Wisepill). Our primary outcome of interest was ART adherence during the 12-month follow-up period. We used generalized linear models to estimate the associations between network measures and ART adherence. PLWH at the Uganda site (compared with the South Africa site) were less isolated, had larger social networks, and had more social ties providing sufficient social support; they were also more likely to bridge different social groups whereby not all social ties were connected to each other. In Uganda, social isolation was associated with a 5.5 percentage point reduction in ART adherence (95% confidence interval [CI] −9.95 to −1.13; p = 0.014), while having more same gender social ties was associated with higher ART adherence (b = 0.13, 95% CI 0.02–0.25, p = 0.025). In South Africa, there was no association between social isolation and ART adherence, and having more friendship ties (vs. family ties) was associated with lower ART adherence (b = −2.20, 95% CI −3.56 to −0.84; p = 0.002). Identifying and supporting PLWH who are isolated may facilitate optimal adherence, but understanding how networks differentially affect ART adherence by country context is important.

A complex of technical tools and measures designed for conducting operational-search measures in the communication networks of ministeral affairs bodies

The article discusses current issues of a holistic understanding of the system of technical means to support the functions of operational investigative measures (SORM), the possibilities of its use in various areas of operational investigative activities of the internal affairs department, as well as problems of application. The effective use of special hardware and software solutions designed to collect information from technical communication channels will greatly improve the efficiency of operational investigative activities and the detection of crimes committed using information and telecommunication technologies.

Optical Power Measurements in Passive Fiber Optic Networks Using the LoRaWAN Protocol

According to the National Telecommunications Agency (ANATEL), in a report released in 2022, it detailed that around 63% of fixed broadband connections used fiber optic networks for connection. This report demonstrates the constant expansion of fiber optic networks in Brazil. The high demand for optical networks means that internet providers face challenges in delivering a quality service that provides a positive customer experience. As a result, companies in the sector have been looking for solutions to solve problems in connecting optical networks. Currently, in the literature and on the market, there are techniques and tools that enable providers to monitor the network connection, carry out speed tests, promote customer service and carry out preventive maintenance. One of these commercial tools is the Optical Power Meter (OPM), a device capable of taking optical measurements in fiber optic networks. Therefore, this work aims to develop an optical power measurement system for passive fiber optic networks, in order to analyze the quality of the signal provided to users, which uses the LoRaWAN communication protocol, thus integrating with the Internet of Things (IoT).

State-of-the-Art Authentication Measures in Satellite Communication Networks: A Comprehensive Analysis

State-of-the-Art Authentication Measures in Satellite Communication Networks: A Comprehensive Analysis

Centrality Measures and Their Applications in Network Analysis: Unveiling Important Elements and Their Impact

The applications of centrality measures in protein-protein interaction (PPI) network analysis are diverse and encompass fundamental biological insights; cancer disease-related discoveries, and practical implications for drug development. This multidimensional approach in PPI network analysis provides a comprehensive understanding of the pivotal elements and their impact on biological systems. Analyzing centrality measures in PPI networks enables the identification of essential proteins, hub and bottleneck proteins that occupy strategic positions within the PPI network structure. Essential proteins in PPI networks are significant elements that indicate their importance in maintaining PPI network integrity and functionality. Studying centrality measures can reveal hidden patterns and relationships within these PPI networks. This paper identifes PPI networks with a high degree of connectivity (”hubs”) and proteins with high betweenness centrality (bottlenecks), along with closeness centrality and clustering coefficient. This measure’s significance in PPI networks has implications for various felds. The proposed approach successfully identifed and characterized infuential proteins and found the top 20 essential proteins. These proteins likely hold significant functional importance through hubs and bottlenecks and serve as potential targets for further investigation. This approach has the potential to identify essential proteins involved in cancer diseases. Leveraging centrality measures in the analysis of PPI networks ofers a multifaceted approach to understanding cancer biology and its implications for personalized medicine, drug design, and the development of innovative cancer therapies.

Uplink Time Synchronization based on Time Drift Measurements in Non-Terrestrial Networks

Uplink Time Synchronization based on Time Drift Measurements in Non-Terrestrial Networks

Investigation of Mesosphere and Lower Thermosphere Dynamics over Central and Northern Peru Using SIMONe Systems

Abstract One year of Spread spectrum Interferometric Multistatic meteor radar Observing Network (SIMONe) measurements are analyzed and compared for the first time between two low-latitude locations in Peru: Jicamarca (12°S, 77°W) and Piura (5°S, 80°W). Investigation of the mean horizontal winds and tides reveals that mesosphere and lower thermosphere (MLT) planetary-scale dynamics are similar between these two locations, although differences can be seen in some tidal components, e.g., the diurnal tide. On the other hand, 28-day median values of the momentum fluxes obtained with 4-h, 4-km time–altitude bins indicate that the mesoscale dynamics differ significantly between Jicamarca and Piura, places separated by approximately 850 km. From the middle of July until October 2021, a strong acceleration of the background zonal wind by westward-propagating gravity waves (GWs) is observed above ∼90 km at both locations, although with larger amplitudes over Jicamarca. From the middle of January until April 2022, a second strong acceleration of the background zonal wind, again by westward-propagating GWs, is observed, but this time with larger amplitudes over Piura. The latter is further supported by the dominance of negative vertical gradients of the zonal momentum flux above 89 km of altitude. Thus, these results observationally confirm the previous studies based on general circulation model simulations indicating that the directions of the zonal GW drag and the zonal background wind coincide in the low-latitude MLT. The weak correlations between the horizontal wind gradients over Jicamarca and Piura reinforce the fact that the mesoscale dynamics are different at these two locations.

A Robust Causal Brain Network Measure and Its Application on Ictal Electrocorticogram Analysis of Drug-resistant Epilepsy.

Measuring causal brain network is a significant topic for exploring complex brain functions. While various data-driven algorithms have been proposed, they still have some drawbacks such as ignoring time non-separability, cumbersome parameter settings, and poor robustness. To solve these deficiencies, we developed a novel framework: "time-shift permutation cross-mapping, TPCM," integrating steps of (1) delayed improved phase-space reconstruction (DIPSR), (2) rank transformation of embedding vectors' distances, (3) cross-mapping with a fitting estimation, and (4) causality quantification using multi-delays. Based on synthetic models and comparison with baseline methods, numerical validation results demonstrate that TPCM significantly improves the robustness for data length with or without noise interference, and achieves the best quantification accuracy in detecting time delay and coupling strength, with the highest determination coefficient ( R2 = 0. 96 ) of fitting verse coupling parameters. The developed TPCM was finally applied to ictal electrocorticogram (ECoG) analysis of patients with drug-resistant epilepsy (DRE). A total of 17 patients with DRE were included into the retrospective study. For 8 patients undergoing successful surgeries, the causal coupling strength (0.58 ± 0.20) within epileptogenic zone network is significantly higher than those suffering failed surgeries (0.38 ± 0.16) with P < 0. 001 through Mann-Whitney-U-test. Therefore, the epileptic brain network measured by TPCM is a credible biomarker for predicting surgical outcomes. These findings additionally confirm TPCM's superior performance and promising potential to advance precision medicine for neurological disorders.

EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset.

Diagnosing and treating dementia, including mild cognitive impairment (MCI), is challenging due to diverse disease types and overlapping symptoms. Early MCI detection is vital as it can precede dementia, yet distinguishing it from later stage dementia is intricate due to subtle symptoms. The primary objective of this study is to adopt a complex network perspective to unravel the underlying pathophysiological mechanisms of dementia-related disorders. Leveraging the extensive availability of electroencephalogram (EEG) data, our study focuses on the meticulous identification and analysis of EEG-based brain functional network (BFNs) associated with dementia-related disorders. To achieve this, we employ the Phase Lag Index (PLI) as a connectivity measure, offering a comprehensive view of neural interactions. To enhance the analytical rigor, we introduce a data-driven threshold selection technique. This innovative approach allows us to compare the topological structures of the formulated BFNs using complex network measures quantitatively and statistically. Furthermore, we harness the power of these BFNs by utilizing them as pre-defined graph inputs for a Graph Convolution Network (GCN-net) based approach. The results demonstrate that graph theory metrics, such as the rich-club coefficient, transitivity, and assortativity coefficients, effectively distinguish between MCI, Alzheimer's disease (AD) and vascular dementia (VD). Furthermore, GCN-net achieves high accuracy (95.07% delta, 80.62% theta) and F1-scores (0.92 delta, 0.67 theta), highlighting the effectiveness of EEG-based BFNs in the analysis of dementia-related disorders.

Designing an Optimal Model for the Role of Civil Society in Crime Prevention within the Iranian Criminal Justice System

The present study aims to design an optimal model for the role of civil society in crime prevention within the Iranian criminal justice system. To address this issue, a qualitative research strategy utilizing thematic analysis was employed. The research data were collected through semi-standardized interviews with 12 experts in the fields of criminal law and criminology, who were selected using theoretical sampling. The data were analyzed through thematic network analysis, and a conceptual network measurement model was constructed. The findings from the qualitative research indicated that the design of an optimal model for the role of civil society in crime prevention within the Iranian criminal justice system includes the overarching theme of: 1) External strategies, which encompass updating and resolving legal and regulatory issues, adopting preventive criminal policy, addressing implementation problems, strengthening the position of civil society, enhancing social capital within the community, promoting public education and awareness, and resolving socio-economic issues; and 2) Internal strategies, which consist of applying knowledge management in crime prevention and strengthening the structure and management of civil society institutions. Additionally, to validate the constructed themes and model, two methods were used: the communicative validation approach and the auditing method. For reliability assessment, two methods were employed: repeatability and transferability.

HSS: A Memory-Efficient, Accurate, and Fast Network Measurement Framework in Sliding Windows

HSS: A Memory-Efficient, Accurate, and Fast Network Measurement Framework in Sliding Windows

Research on hydrostatic leveling system to provide elevation constraints for control network adjustment

As the precise sensor system for monitoring the relative altitude changes among multiple points, the capacity hydrostatic leveling system (HLS) is widely used in particle accelerators. To expand its application in providing the elevation constraint for the control network adjustment, the research on the issue of the HLS for altitude difference measurement between multiple points is carried out. Based on the working principle of the HLS sensor, a comparison system composed of dual-frequency laser interferometer, high-precision Z stage, HLS sensors and others is designed and manufactured. The system is used to control multiple sensors to observe the same liquid level in the same coordinate system. The zero-position difference among sensors can be obtained by comparison. Then the altitude difference measurement can be realized, and it is verified that the measurement accuracy is better than 5 μm. In addition, a simulation experiment for 3D control network measurement is run, in which the HLS system provides the elevation constraint for the adjustment processing. The results show that for the 100m linear tunnel, the errors accumulation in the elevation direction is significantly improved compared to the classics adjustment.

Drain bias dependence of Y 22 and Y 21 signals at low frequency for on-state conditions in AlGaN/GaN high electron mobility transistors

The drain bias dependence of Y 22 and Y 21 signals has been investigated by two-port network measurement in the on-state condition of AlGaN/GaN high electron mobility transistors. Y 22 has signals for Fe-related traps in GaN layers and the self-heating effect. The Y 21 signal is unique among signals with the same drain bias dependence as Y 22 signals. This unique signal is considered to originate from AlGaN traps, using device simulation with a GaN trap, AlGaN trap and the self-heating effect. The peak frequency of GaN and AlGaN traps increases with increasing drain voltage, while the peak frequency of self-heating does not depend on the drain voltage.

Multiple sclerosis is associated with differences in semantic memory structure.

Although language is often considered to be largely intact in multiple sclerosis (MS), word-finding difficulties are a common complaint. Recent work suggests that declines in language are not solely the result of motoric and cognitive slowing that is most strongly associated with MS. Network science approaches have been effectively used to examine network structure as it relates to clinical conditions, aging, and language. The present study utilizes a network science approach to investigate whether individuals with MS exhibit less interconnected and resilient semantic networks compared to age-matched neurotypical peers. We used semantic fluency data from 89 participants with MS and 88 neurotypical participants to estimate and analyze the semantic network structure for each participant group. Additionally, we conducted a percolation analysis to examine the resilience of each network. Network measures showed that individuals with MS had lower local and global clustering coefficients, longer average shortest path lengths, and higher modularity values compared to neurotypical peers. Small-worldness, network portrait divergence measures, and community detection analyses were consistent with these results and indicated that macroscopic properties of the two networks differed and that the semantic network for individuals with MS was more fractured than the neurotypical peer network. Moreover, a spreading activation simulation and percolation analysis suggested that the semantic networks of individuals with MS are less flexible and activation degrades faster than those of age-matched neurotypical participants. These differing semantic network structures suggest that language retrieval difficulties in MS partially result from decline in language-specific factors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Ground Truthing: Perceived Versus Objectively Measured Built Environment Attributes in South African Communities

Introduction :No ground truthing study of ‘neighbourhood walkability’ has been conducted in Africa that contrasts the lived experience of the neighbourhood-built environment against objectively measured attributes using GIS and subscales of the NEWS-Africa instrument. Methods: This is a cross-sectional survey and observation study. A convenient sample of residents (n=52) aged 18-65 years from four urban suburbs in low- and high-income settings, self-reported transport, leisure-time physical activity (PA) and device-measured moderate-to-vigorous PA (MVPA) were assessed. Built environment constructs was derived from individual-level street network measures (1000m buffer, using ArcGIS 10.51). We also assessed PA between the four groups based on income and GIS walkability. Results: There was no significant relationship between self-reported MVPA and GIS-measured walkability in the study sample. Only intersection density had a significant inversely association with moderate and MVPA (rho=-0.31, P&lt;0.05) in the overall study sample. Self-report transport PA differed between groups (P&lt;0.013). Device-measured vigorous activity differed between groups and was lower in both Low-SES groups compared to the High-SES/Low Walkable group (P&lt;0.04). Total PA was not associated with GIS measured walkability, but inversely associated with positive perceived neighbourhood attributes. Conclusions: There is a modest inverse association between device-measured PA and GIS-measured intersection density. These results suggest that constructs of walkability, whether measured or perceived, may predict volitional and utilitarian PA differently in our study population.

A Regional Aerosol Model for the Middle Urals Based on CALIPSO Measurements

The present work aims to develop a regional Middle Urals Aerosol model (MUrA model) based on the joint analysis of long-term ground-based photometric measurements of the Aerosol Robotic NETwork (AERONET) and the results of lidar measurements of the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite relying on information on the air trajectories at different altitudes calculated using the HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory model) software package. The MUrA model contains parameters of normalized volume size distributions (NVSDs) characterizing the tropospheric aerosol subtypes detected by the CALIPSO satellite. When comparing the MUrA model with the global CALIPSO Aerosol Model (CAMel), we found significant differences in NVSDs for elevated smoke and clean continental aerosol types. NVSDs for dust and polluted continental/smoke aerosol types in the global and regional models differ much less. The total volumes of aerosol particles along the atmospheric column reconstructed from satellite measurements of the attenuation coefficient at a wavelength of 532 nm based on the regional MUrA model and global CAMel are compared with the AERONET inversion data. The mean bias error for the regional model is 0.016 μm3/μm2, and 0.043 μm3/μm2 for the global model.

Network-based statistics distinguish anomic and Broca's aphasia.

Aphasia is a speech-language impairment commonly caused by damage to the left hemisphere. The neural mechanisms that underpin different types of aphasia and their symptoms are still not fully understood. This study aims to identify differences in resting-state functional connectivity between anomic and Broca's aphasia measured through resting-state functional magnetic resonance imaging (rs-fMRI). We used the network-based statistic (NBS) method, as well as voxel- and connectome-based lesion symptom mapping (V-, CLSM), to identify distinct neural correlates of the anomic and Broca's groups. To control for lesion effect, we included lesion volume as a covariate in both the NBS method and LSM. NBS identified a subnetwork located in the dorsal language stream bilaterally, including supramarginal gyrus, primary sensory, motor, and auditory cortices, and insula. The connections in the subnetwork were weaker in the Broca's group than the anomic group. The properties of the subnetwork were examined through complex network measures, which indicated that regions in right inferior frontal sulcus, right paracentral lobule, and bilateral superior temporal gyrus exhibit intensive interaction. Left superior temporal gyrus, right postcentral gyrus, and left supramarginal gyrus play an important role in information flow and overall communication efficiency. Disruption of this network underlies the constellation of symptoms associated with Broca's aphasia. Whole-brain CLSM did not detect any significant connections, suggesting an advantage of NBS when thousands of connections are considered. However, CLSM identified connections that differentiated Broca's from anomic aphasia when analysis was restricted to a hypothesized network of interest. We identified novel signatures of resting-state brain network differences between groups of individuals with anomic and Broca's aphasia. We identified a subnetwork of connections that statistically differentiated the resting-state brain networks of the two groups, in comparison with standard CLSM results that yielded isolated connections. Network-level analyses are useful tools for the investigation of the neural correlates of language deficits post-stroke.

Flagellar dynamics reveal fluctuations and kinetic limit in the Escherichia coli chemotaxis network

The Escherichia coli chemotaxis network, by which bacteria modulate their random run/tumble swimming pattern to navigate their environment, must cope with unavoidable number fluctuations (“noise”) in its molecular constituents like other signaling networks. The probability of clockwise (CW) flagellar rotation, or CW bias, is a measure of the chemotaxis network’s output, and its temporal fluctuations provide a proxy for network noise. Here we quantify fluctuations in the chemotaxis signaling network from the switching statistics of flagella, observed using time-resolved fluorescence microscopy of individual optically trapped E. coli cells. This approach allows noise to be quantified across the dynamic range of the network. Large CW bias fluctuations are revealed at steady state, which may play a critical role in driving flagellar switching and cell tumbling. When the network is stimulated chemically to higher activity, fluctuations dramatically decrease. A stochastic theoretical model, inspired by work on gene expression noise, points to CheY activation occurring in bursts, driving CW bias fluctuations. This model also shows that an intrinsic kinetic ceiling on network activity places an upper limit on activated CheY and CW bias, which when encountered suppresses network fluctuations. This limit may also prevent cells from tumbling unproductively in steep gradients.

Edge-based graph neural network for ranking critical road segments in a network.

Transportation networks play a crucial role in society by enabling the smooth movement of people and goods during regular times and acting as arteries for evacuations during catastrophes and natural disasters. Identifying the critical road segments in a large and complex network is essential for planners and emergency managers to enhance the network's efficiency, robustness, and resilience to such stressors. We propose a novel approach to rapidly identify critical and vital network components (road segments in a transportation network) for resilience improvement or post-disaster recovery. We pose the transportation network as a graph with roads as edges and intersections as nodes and deploy a Graph Neural Network (GNN) trained on a broad range of network parameter changes and disruption events to rank the importance of road segments. The trained GNN model can rapidly estimate the criticality rank of individual road segments in the modified network resulting from an interruption. We address two main limitations in the existing literature that can arise in capital planning or during emergencies: ranking a complete network after changes to components and addressing situations in post-disaster recovery sequencing where some critical segments cannot be recovered. Importantly, our approach overcomes the computational overhead associated with the repeated calculation of network performance metrics, which can limit its use in large networks. To highlight scenarios where our method can prove beneficial, we present examples of synthetic graphs and two real-world transportation networks. Through these examples, we show how our method can support planners and emergency managers in undertaking rapid decisions for planning infrastructure hardening measures in large networks or during emergencies, which otherwise would require repeated ranking calculations for the entire network.