Concept Of Network Research Articles

Posttraumatic stress disorder symptomology as measured by PCL-5 and its relationships to resilience, hostility and stress among paramedics and social professionals.

This survey study aimed to contribute to the extensive debate on the dimensionality of the Posttraumatic Stress Disorder Checklist for the DSM-5 (PCL-5) questionnaire by examining the psychometric properties and construct validity of its Hungarian version and relying on the inspection of a conceptual network of related variables, that is, perceived stress, hostility, and resilience. Confirmatory factor analysis (CFA), exploratory structural equation modelling (ESEM) and path analysis were applied on data collected from 177 paramedics and 66 professionals from the social field (58.4% male; Mage=43.5±9.96years). Despite the acceptable fit indices gained with CFA when testing the original four-factor DSM-5 model of PCL-5, strong associations (r=0.69-0.90) between subscales were found. Thus, ESEM was applied and as a result a new, three-factor version of the DSM-5 model of PCL-5 was proposed due to significant crossloadings that was theoretically also supported. The Reexperiencing and Avoidance subscales were merged and named Difficulty with Assimilation of Experience (DAE). In the path analysis only two of the tested associations were not significant using the new factor structure, in which stress fully mediated the relationship between resilience and DAE, and resilience and Hyperarousal. Overall, the hypothesised pathways between variables fit the collected data well. (weighted least squares mean-and variance-adjusted χ2=503.750 (df=270), comparative fit index=0.948, Tucker-Lewis index=0.939, root mean square error of approximation (90% confidence interval)=0.064 (0.055-0.073), weighted root mean square residual=1.024). Our analysis of the Hungarian version of PCL-5 contributes to the testing of a DSM-5-based questionnaire measuring posttraumatic stress disorder symptomology.

Classification of Image using Convolutional Neural Network (CNN)

Abstract: Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images. We have used Convolutional Neural Networks (CNN) in automatic image classification systems. In most cases, we utilize the features from the top layer of the CNN for classification; however, those features may not contain enough useful information to predict an image correctly. In some cases, features from the lower layer carry more discriminative power than those from the top. Therefore, applying features from a specific layer only to classification seems to be a process that does not utilize learned CNN’s potential discriminant power to its full extent. Because of this property we are in need of fusion of features from multiple layers. We want to create a model with multiple layers that will be able to recognize and classify the images. We want to complete our model by using the concepts of Convolutional Neural Network and CIFAR-10 dataset. Moreover, we will show how MatConvNet can be used to implement our model with CPU training as well as less training time. The objective of our work is to learn and practically apply the concepts of Convolutional Neural Network.

UAV-enabled software defined data collection from an adaptive WSN

AbstractUnmanned aerial vehicle (UAV)-based data gathering from wireless sensor networks is one of the recent research topics that has currently attracted research interest. One of the challenges for the UAV-aided WSN data collection efforts is to design an energy-efficient UAV/drone communication with arbitrarily dispersed ground sensors by improving the ground network structure. This paper aims to develop a technique titled UAV Fuzzy Travel Path' that supports UAV smooth path design and enables ground network topology shifting. A comprehensive UAV-based data collection model is proposed to enable dynamic orchestration/re-orchestration of wireless ground sensors to jointly improve network performance and UAV path fluidity. This provides a more flexible ground network framework that can be restructured based on network demands and UAV optimal paths, effectively allowing for a software-defined network concept. The main contribution of this work is the implementation of the software-defined wireless sensor network on the ground network that adaptably supports the movement of the UAV and enhances the communication network’s energy efficiency with a proposed latency analytical analysis via network orchestration/re-orchestration phases. The main significance of this research is in offering a flexible span for UAV path design than being fixed in one strict route for data gathering purposes. Four various simulation tools are employed for modelling and performance evaluation, namely MATLAB, CupCarbon, Contiki-Cooja and Mission Planner. The proposed software-defined ground network system demonstrates encouraging results in terms of network performance metrics including energy consumption of UAV versus ground sensor nodes energy usage, packet delivery rate, and the communication time of the ground orchestrated or/and re-orchestrated network.

Environmental Governance Networks and Geography: A Research Agenda at the Confluence of Critical Concepts for Navigating Rapid Environmental Change

Social networks are foundational to modern environmental governance, and particularly influential in supporting societal navigation of rapid environmental change. Social network science crosses disciplinary boundaries and provides theoretical and methodological tools to predict and assess relationships between network characteristics, structures, and the diffusion of information and innovations critical to navigating environmental governance challenges such as climate change and biodiversity loss. Most network studies examine networks that operate at a particular spatial scale, and research questions often rest on the relationships between simple network structures and characteristics of individual network actors and their associations. Geographers are poised to further develop theory underlying social network science specifically relevant to navigating human–environment governance challenges by disentangling the influences of scale, place, and identity in network studies. In this article, we advance the concept of environmental governance networks as multiscale, multilevel, and multi-identity social networks formed organically in response to environmental governance challenges. We propose that geographers are well positioned to expose the socially constructed nature of environmental governance networks as barriers and opportunities for societal navigation of rapid environmental change by explicitly theorizing critical influences across time, space, place, levels of social organization, and the multiple roles and social identities individual network actors hold. We develop a research agenda that leverages geographic theory to expand network science questions relative to concepts such as network proximity, affiliation, interaction, and intra-individual roles and identities to better understand the position and potential of environmental governance networks as a social structure for navigating rapid environmental change.

Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction

Hepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infection, is a major socio-economic and public health problem. Due to the rapid development of deep learning, it has become a common practice to apply deep learning to the healthcare industry to improve the effectiveness and accuracy of disease identification. In order to improve the effectiveness and accuracy of hepatitis C detection, this study proposes an improved denoising autoencoder (IDAE) and applies it to hepatitis C disease detection. Conventional denoising autoencoder introduces random noise at the input layer of the encoder. However, due to the presence of these features, encoders that directly add random noise may mask certain intrinsic properties of the data, making it challenging to learn deeper features. In this study, the problem of data information loss in traditional denoising autoencoding is addressed by incorporating the concept of residual neural networks into an enhanced denoising autoencoder. In our experimental study, we applied this enhanced denoising autoencoder to the open-source Hepatitis C dataset and the results showed significant results in feature extraction. While existing baseline machine learning methods have less than 90% accuracy and integrated algorithms and traditional autoencoders have only 95% correctness, the improved IDAE achieves 99% accuracy in the downstream hepatitis C classification task, which is a 9% improvement over a single algorithm, and a nearly 4% improvement over integrated algorithms and other autoencoders. The above results demonstrate that IDAE can effectively capture key disease features and improve the accuracy of disease prediction in hepatitis C data. This indicates that IDAE has the potential to be widely used in the detection and management of hepatitis C and similar diseases, especially in the development of early warning systems, progression prediction and personalised treatment strategies.

A novel artificial visual system for motion direction detection in color images

In recent years, convolutional neural networks (CNNs) have dominated the field of computer vision. Compared to traditional methods, these neural network algorithms exhibit strong biomimetic performance advantages in complex visual tasks due to their brain-like structure. However, because some necessary neural characteristics are ignored, these neural network algorithms differ greatly from the computational mechanisms of the brain. This paper starts with extracting basic visual features such as motion direction information from the brain and abstracts, generalizes and models a novel artificial visual system (AVS) for detecting object motion direction in color images based on existing relevant neurophysiological knowledge. We propose a mathematical model and quantification mechanism for each component neuron that generates motion direction selectivity in the visual system using dendritic neuron models, spiking neural network concepts and neurophysiological knowledge of retinal direction-selective ganglion cell pathways. The experiment is based on one million instances of object motion under different environments of noise-free, static and dynamic random noise, dynamic salt-and-pepper noise, dynamic Gaussion noise, and dynamic light changing. In comparison with 4 famous CNNs, LeNet-5, EfficientNetB0, ResNet18, and RegNetX-200MF, we test and verify AVS’s effectiveness, efficiency and strong generalization ability as well as other biomimetic performance advantages including high biological rationality, learning-free capability, interpretability and ease-of-use etc. AVS demonstrates that neuroscience still has important implications for guiding and promoting the development of artificial intelligence technology. Furthermore, AVS firstly provides a successful quantitative reference case study for further understanding motion direction selectivity and other primary cortical encoding characteristics in the brain.

The Impact of Social Networks on Public Opinion and Economic Behavior During COVID-19: A Comparative Analysis of China and the United States

The Covid-19 pandemic has had a significant impact on the world's economic behavior, including China and the United States. Social networks then played an important role both in the spread of the virus and in information diffusion. However, there are limited studies explaining the impact of social networks along with an analysis of public opinion and economic behavior during the COVID outbreak. Therefore, this study aims to investigate the relationship by comparing the different situations in China and the United States and analyzing different economic recovery strategies used to reduce dramatic economic contraction. The study started with a brief explanation of the concept of social networks and their relation with the transmission of Covid-19. Then, it is split into two sections to focus deeper on how social networks relate to each country in the spread of COVID-19. Lastly, the differences in the policies implemented to stimulate economic activity are analysed.

Political reconfiguration in the social space: data analysis and future perspective.

This paper aims to explore the complex and sometimes controversial relationship between social media and politics. The correlation between these two areas of research has always been less linear than a simplistic narrative might suggest, mainly because of the involvement of different scientific disciplines, such as sociology, political science, communication, social psychology and computer science. The decision to explore this topic is motivated by the persistent relevance of social media platforms in the current era. This growing centrality is also due to the accelerated digitization process that occurred during the pandemic phase in the digital ecosystem. In particular, the pandemic has contributed to a significant evolution of the concept of social networks. The methodology used is based on secondary data, and the work seeks to highlight the expansion of digital space resulting from the shift from a simple place of interaction to a digital space that reconfigured the mobilization and political action of Campania's governor Vincenzo De Luca. In conclusion, the study reveals important findings on the president's use of social media. For example, active citizen engagement strategies through direct interaction, timely information sharing, and mobilization of online resources are evident. Therefore, it becomes necessary to ask and understand whether the potential of new social technologies is being used to cultivate critical and massive citizen participation in democratic processes or whether it is being distorted for the sole purpose of increasing or consolidating consent.

Sexual Networks’ role in high STI Rates among minority populations

The stubbornly high and unequally distributed rates of sexually transmitted infections have confounded sociologists for decades. Why would STIs afflict minority populations at higher rates than majority populations? There was no biological rationale. Only knowledge of causation could hope to mitigate the discrepancy and provide pathways to prevention and cure. Late in the twentieth century, spurred by the AIDS crisis, the scrutiny of sexual behavior met up with the social determinants of health, resulting in the concepts of sexual networks and concurrency. This paper explores how these ideas have helped us understand, explain, and intervene in the unacceptably high and disproportionately distributed STI epidemic.

HYPERNETS: a network of automated hyperspectral radiometers to validate water and land surface reflectance (380–1680 nm) from all satellite missions

Satellites are now routinely used for measuring water and land surface reflectance and hence environmentally relevant parameters such as aquatic chlorophyll a concentration and terrestrial vegetation indices. For each satellite mission, radiometric validation is needed at bottom of atmosphere for all spectral bands and covering all typical conditions where the satellite data will be used. Existing networks such as AERONET-OC for water and RadCalNet for land provide vital information for validation, but (AERONET-OC) do not cover all spectral bands or (RadCalNet) do not cover all surface types and viewing angles. In this Perspective Article we discuss recent advances in instrumentation, measurement methods and uncertainty estimation in the field of optical radiometry and put forward the viewpoint that a new network of automated hyperspectral radiometers is needed for multi-mission radiometric validation of water and land surface reflectance. The HYPERNETS federated network concept is described, providing a context for research papers on specific aspects of the network. This network is unique in its common approach to both land and water surfaces. The common aspects and the differences between land and water measurements are explained. Based on early enthusiasm for HYPERNETS data from validation-oriented workshops, it is our viewpoint that this new network of automated hyperspectral radiometers will be useful for multi-mission radiometric validation of water and multi-angle land surface reflectance. The HYPERNETS network has strong synergy with other measurement networks (AERONET, AERONET-OC, RadCalNet, FLUXNET, ICOS, skycam, etc.) and with optional supplementary measurements, e.g., water turbidity and fluorescence, land surface temperature and soil moisture, etc.

Lack of consistency in measurement methods and semantics used for network measures in adolescent health behaviour studies using social network analysis: a systematic review

BackgroundSocial network analysis (SNA) is often used to examine how social relationships influence adolescent health behaviours, but no study has documented the range of network measures used to do so....

Mesenchymal stromal cell chondrogenesis under ALK1/2/3-specific BMP inhibition: a revision of the prohypertrophic signalling network concept

BackgroundIn vitro chondrogenesis of mesenchymal stromal cells (MSCs) driven by the essential chondro-inducer transforming growth factor (TGF)-β is instable and yields undesired hypertrophic cartilage predisposed to bone formation in vivo. TGF-β can non-canonically activate bone morphogenetic protein-associated ALK1/2/3 receptors. These have been accused of driving hypertrophic MSC misdifferentiation, but data remained conflicting. We here tested the antihypertrophic capacity of two highly specific ALK1/2/3 inhibitors – compound A (CompA) and LDN-212854 (LDN21) – in order to reveal potential prohypertrophic contributions of these BMP/non-canonical TGF-β receptors during MSC in vitro chondrogenesis.MethodsStandard chondrogenic pellet cultures of human bone marrow-derived MSCs were treated with TGF-β and CompA (500 nM) or LDN21 (500 nM). Daily 6-hour pulses of parathyroid hormone-related peptide (PTHrP[1–34], 2.5 nM, from day 7) served as potent antihypertrophic control treatment. Day 28 samples were subcutaneously implanted into immunodeficient mice.ResultsAll groups underwent strong chondrogenesis, but GAG/DNA deposition and ACAN expression were slightly but significantly reduced by ALK inhibition compared to solvent controls along with a mild decrease of the hypertrophy markers IHH-, SPP1-mRNA, and Alkaline phosphatase (ALP) activity. When corrected for the degree of chondrogenesis (COL2A1 expression), only pulsed PTHrP but not ALK1/2/3 inhibition qualified as antihypertrophic treatment. In vivo, all subcutaneous cartilaginous implants mineralized within 8 weeks, but PTHrP pretreated samples formed less bone and attracted significantly less haematopoietic marrow than ALK1/2/3 inhibitor groups.ConclusionsOverall, our data show that BMP-ALK1/2/3 inhibition cannot program mesenchymal stromal cells toward stable chondrogenesis. BMP-ALK1/2/3 signalling is no driver of hypertrophic MSC misdifferentiation and BMP receptor induction is not an adverse prohypertrophic side effect of TGF-β that leads to endochondral MSC misdifferentiation. Instead, the prohypertrophic network comprises misregulated PTHrP/hedgehog signalling and WNT activity, and a potential contribution of TGF-β-ALK4/5-mediated SMAD1/5/9 signalling should be further investigated to decide about its postulated prohypertrophic activity. This will help to successfully engineer cartilage replacement tissues from MSCs in vitro and translate these into clinical cartilage regenerative therapies.

Examining Sexual Health Organizational Networks in Urban African American Communities Using Social Network Theory.

Collaboration among organizations offering sexual health and youth development services has the potential to provide youth with effective sexual health support. However, formally structured efforts (eg, coalition formation) may be impractical or unsuitable for low-income communities where resources are often already limited. Social network theories provide an alternative approach for building collaborative organizational networks. Research aims to evaluate the barriers and facilitators to collaboration in sexual health organizational networks. Organizations in low income, urban, communities in Chicago and San Francisco that serve African American adolescents. Providers (n = 22) from organizations that offer sexual health services and youth development services. Focus groups (n = 4) were conducted and analyzed utilizing a combination of coding strategies. Barriers to collaboration included resource limitations and competition, differences in organizational roles and deliverables, and prejudice and stigma. Identifying common ground among organizations was found to be a facilitator to collaboration. Social network concepts in conjunction with study findings lead to the development of a practice model that hypothesizes a pathway for organizations to improve collaboration without formally structured efforts. Our findings offer ways to encourage collaboration among organizations that support youth sexual health in low-income, urban, African American communities without relying on formal structures. Such collaborations may be critical for improving the provision of comprehensive sexual health support.

Emotional analysis of joint sports quality expansion tasks based on multi-modal feature fusion

A multi-modal feature based motion emotion analysis model based on a fusion deep learning model is proposed for the problem of analyzing the motion emotions of participants in the joint exercise quality expansion task. This model involves three major modalities: EEG signals, peripheral physiological signals, and facial expression signals, and processes and fuses the information of these three main modalities to achieve the effect of processing multi-dimensional motor emotional information. At the same time, this study introduces the design concept of residual networks, using self attention modules and multi head mutual attention modules to process different modal features. The results showed that the combination of peripheral physiological modality and facial expression modality had the highest accuracy among the three modality combinations, with an accuracy rate of 88.8 %. The feature fusion method based on the cascaded residual attention mechanism module has better accuracy and F1 Score performance than other methods. In addition, different emotional states can be effectively identified and distinguished in these three modalities, indicating that the model has a wide range of possibilities in practical applications.

Young networked athletes and performance-enhancing substances: who are the actors in their network, and how do the actors shape athletes’ meaning-making?

Ambitious youth athletes continually strive to enhance their performance, and as they age, more people are involved in this process. Athletes are immersed in a network of actors who wield significant influence over their performance-enhancing endeavours, encompassing training, nutrition, and at times, the use of various performance-enhancing substances. Our study predominantly revolves around various permitted substances, primarily in the form of nutritional supplements of various kinds. Also relevant in this regard is anti-doping, as knowledge of and a critical attitude towards supplements are a central part in this preventative educational work. More specifically, in this study, we explore the actors that contribute to young athletes meaning-making of nutritional supplements and anti-doping and examine how the young athletes describe these actors’ contribution. Twenty-four athletes from private elite upper secondary sport schools (PEUSS) in Norway were interviewed, with a semi-structured approach to facilitate the various tales. Our analysis revealed seven actors/actor groups in athletes’ network. The actors/actor groups were diverse and contributed to athletes meaning-making in different ways. We argue that the diversity of actors illustrates the relevance of the networked athlete concept and that the concept should be further developed by interactionist perspectives.

Retraction Note: Converging concepts of sustainability and supply chain networks: a systematic literature review approach.

Retraction Note: Converging concepts of sustainability and supply chain networks: a systematic literature review approach.

Value exchanges within stakeholder networks throughout a megaproject's lifecycle

Megaprojects are complex projects with huge investments and numerous stakeholders. Megaproject value is created by networks of multiple stakeholders conducting value exchange, which refers to the processes involved in satisfying stakeholders’ various needs reciprocally. To understand this intricate dance, by integrating the new stakeholder theory (NST) into the stakeholder value network (SVN) concept, this study proposes a theoretical framework of megaproject stakeholder value network (MSVN) to unravel the underlying processes driving reciprocal value exchange and explore how they change throughout the megaproject lifecycle. Moreover, this study adopts an exploratory sequential mixed-methods design to develop a novel network approach to qualitatively and quantitatively analyze MSVN and identify the critical value exchanges for value creation. An empirical investigation was undertaken on Chinese transportation megaprojects. The results indicate that owners and market stakeholders (especially designers) create the most value in the implementation phase, while the collaboration between owners and nonmarket stakeholders (especially governmental bodies) delivers the most value during the front-end and operation stages. Of the six categorized value flow types identified, communication/evaluation is the most significant for the front-end and operation phases. At the same time, support/opportunity comes to the fore in the implementation phase. These findings shed new light on the value exchange process of megaprojects and guide stakeholders engaged in value creation.

Redefining Intelligence: The Deep Learning Revolution in AI

In the ever-evolving realm of artificial intelligence (AI), deep learning stands as a beacon of innovation, transforming the essence and trajectory of AI systems. This paper ventures into the heart of deep learning’s transformative power, charting its journey from the early neural network concepts to the advanced architectures that propel today’s technological breakthroughs. We delve into the harmonious interplay between deep learning and the surge in computational prowess, amplified by the vast seas of big data, which have jointly propelled AI to unprecedented levels of functionality and societal integration. Through a tapestry of case studies, we illuminate the tangible applications of deep learning across diverse sectors such as healthcare, finance, and autonomous navigation, showcasing how these intelligent algorithms have not only sharpened efficiency and accuracy but also brought forth pressing ethical dilemmas. The conversation on AI’s ethical landscape, with a spotlight on transparency, accountability, and privacy, emerges as an indispensable facet of conscientious AI evolution. Peering into the horizon, we ponder the onward march of deep learning, recognizing the promise it holds for awe-inspiring innovation as well as the hurdles that await. The paper underscores the imperative for ongoing inquiry into algorithmic refinement, data morality, and the ecological footprint of AI technologies. In our closing reflections, this paper celebrates the profound sway of deep learning over AI, with a nod to the indomitable human spirit that fuels technological advancement. It is this fusion of human ingenuity, moral vision, and technical mastery that will chart the course for AI’s future, steering it towards our shared ideals and dreams for a brighter tomorrow

Justifying political choice: from probability and institutions to communication

The article is devoted to the study of the dynamics of development of concepts that justify political choice. There is a general turn from the moral justification of political choice to ontological concepts based on the ideas of pragmatic and impartial behavior in the search for criteria for the “correct” decision, which includes the truth and the institutions that ensure it. The epistemic concept of democracy, based on Condorcet's jury theorem, defines the possibility of achieving the correct choice through majority rule voting, implies a competent, independent and sincere voter, which ensures an increase in the probability of the correct choice. The expansion of the composition of participants in political choice increased the role of diversity of knowledge and opinions in the justification, which led to the concept of the “smart plurality”, which ensures the correct choice is made by reducing its overall error through random mass participation. The development of social networks and their argumentative function in disputes have contributed to the transition to the concepts of argumentative networks that ensure the correct option is selected through communication. The identified dynamics allow us to conclude that the concept of politics is reviving its importance as a reasoning.

Relationship Between Environmental Parameters and Manta Ray Occurrence in Raja Ampat Archipelago, Indonesia

Understanding the influence and impact of environmental factors on manta ray sightings is critical to understanding the spatial and temporal ecology of a highly mobile species. Therefore, this study aims to determine the influence and impact of environmental factors as indicated by the parameters of wind speed, chlorophyll-a, SST, salinity, pH, dissolved oxygen, and the number of phytoplankton and zooplankton species. The mapped chlorophyll-a was re-analyzed based on the seasonal period throughout 2021 downloaded from marine copernicus and analyzed by kriging method. The influence and effects of environmental parameters on the short-term appearance of eye rays were studied using an adaptive model (GAM). The analysis showed a significant influence of environmental factors on manta ray sightings in Raja Ampat, namely Calanoid spp, Oithona nana, Acartia clausi, Calanus helgoladicus, and Oithona brevicornis. Based on this model, zooplankton is an important parameter that can describe the influence of environmental parameters on manta ray sightings at observation points in Raja Ampat MPA. The results of the reanalysis of chlorophyll-a concentrations were highest in the eastern to transitional seasons, which were scattered on the west side of Raja Ampat waters. Meanwhile, chlorophyll-a concentrations were low in the west to transitional season on the east side. This mechanism may drive the foraging strategy of manta rays, which visit shallow waters where zooplankton density and biomass are abundant. Adopting the BHS MPA network concept, as it has been implemented, would be in line with broader conservation expectations for the sustainability of manta rays in Raja Ampat.