Network Activities Research Articles

Multiplicative processing in the modeling of cognitive activities in large neural networks.

Explaining the foundation of cognitive abilities in the processing of information by neural systems has been in the beginnings of biophysics since McCulloch and Pitts pioneered work within the biophysics school of Chicago in the 1940s and the interdisciplinary cybernetists meetings in the 1950s, inseparable from the birth of computing and artificial intelligence. Since then, neural network models have traveled a long path, both in the biophysical and the computational disciplines. The biological, neurocomputational aspect reached its representational maturity with the Distributed Associative Memory models developed in the early 70s. In this framework, the inclusion of signal-signal multiplication within neural network models was presented as a necessity to provide matrix associative memories with adaptive, context-sensitive associations, while greatly enhancing their computational capabilities. In this review, we show that several of the most successful neural network models use a form of multiplication of signals. We present several classical models that included such kind of multiplication and the computational reasons for the inclusion. We then turn to the different proposals about the possible biophysical implementation that underlies these computational capacities. We pinpoint the important ideas put forth by different theoretical models using a tensor product representation and show that these models endow memories with the context-dependent adaptive capabilities necessary to allow for evolutionary adaptation to changing and unpredictable environments. Finally, we show how the powerful abilities of contemporary computationally deep-learning models, inspired in neural networks, also depend on multiplications, and discuss some perspectives in view of the wide panorama unfolded. The computational relevance of multiplications calls for the development of new avenues of research that uncover the mechanisms our nervous system uses to achieve multiplication.

CPID: Insider threat detection using profiling and cyber-persona identification

In digital environments, network administrators can benefit from the advanced network traffic monitoring capabilities with respect to the type of users (persona) and their specific network activities. This can be leveraged to derive corresponding persona profiles that can be subsequently used to detect anomalies and security events. Also, upon the occurrence and detection of a security event, it is important to get full details on the underlying entities and to gain relevant insights to mitigate and prevent such occurrences in the future. In this context, this paper proposes an innovative approach leveraging machine learning techniques along with deep learning for persona-specific traffic profile generation. This capability can be deployed as part of online traffic monitoring solutions for persona identification and anomalous network behaviour detection, where no software needs to be installed on deployed workstations. Performed experiments indicate that the proposed approach is efficient, scalable, and suitable for near real-time deployment scenarios.

Untangling temporal graphs of bounded degree

In this contribution we consider a variant of the vertex cover problem in temporal graphs that has been recently introduced to summarize timeline activities in social networks. The problem is NP-hard, even when the time domain considered consists of two timestamps. We further analyze the complexity of this problem, focusing on temporal graphs of bounded degree. We prove that the problem is NP-hard when (1) each vertex has degree at most one in each timestamp and (2) each vertex is connected with at most three neighbors, has degree at most two in each timestamp and the time domain consists of three timestamps. On the other hand, we prove that the problem is in P when each vertex is connected with at most two neighbors. Then we present a fixed-parameter algorithm for the restriction where we bound the number of interactions in each timestamp and the length of the interval where a vertex has incident temporal edges.

Self-regulating bioinspired supramolecular photonic hydrogels based on chemical reaction networks for monitoring activities of enzymes and biofuels

Inspired by the way many living organisms utilize chemical/biological reactions to regulate their skin and respond to stimuli in the external environment, we have developed a self-regulating hydrogel design by incorporating chemical reaction networks (CRNs) into biomimetic photonic crystal hydrogels. In this hydrogel system, we used host–guest supramolecular non-covalent bonds between beta-cyclodextrin (β-CD) and ferrocene (Fc) as partial crosslinkers and designed a CRN involving enzyme-fuel couples of horseradish peroxidase (HRP)/H2O2 and glucose oxidase (GOD)/d-glucose, by which the responsive hydrogel was transformed into a glucose-driven self-regulating hydrogel. Due to the biomimetic structural color in the hydrogel, the progress of the chemical reaction was accompanied by a change in the color of the hydrogel. Based on this principle, the designed supramolecular photonic hydrogel (SPH) can not only achieve naked-eye detection of H2O2 and glucose concentrations with the assistance of a smartphone but also monitor the reactions of HRP and GOD enzymes and determine their activity parameters. The sensitivity and stability of the sensor have been proven. In addition, due to the reversibility of the chemical reaction network, the sensor can be reused, thus having the potential to serve as a low-cost point-of-care sensor. The SPH was ultimately used to detect glucose in human plasma and H2O2 in liver tumor tissue. The results are comparable with commercial assay kits. By redesigning the chemical reaction network in the hydrogel, it is expected to be used for detecting other enzymes or fuels.

Mapping the time-varying functional brain networks in response to naturalistic movie stimuli.

One of human brain's remarkable traits lies in its capacity to dynamically coordinate the activities of multiple brain regions or networks, adapting to an externally changing environment. Studying the dynamic functional brain networks (DFNs) and their role in perception, assessment, and action can significantly advance our comprehension of how the brain responds to patterns of sensory input. Movies provide a valuable tool for studying DFNs, as they offer a naturalistic paradigm that can evoke complex cognitive and emotional experiences through rich multimodal and dynamic stimuli. However, most previous research on DFNs have predominantly concentrated on the resting-state paradigm, investigating the topological structure of temporal dynamic brain networks generated via chosen templates. The dynamic spatial configurations of the functional networks elicited by naturalistic stimuli demand further exploration. In this study, we employed an unsupervised dictionary learning and sparse coding method combing with a sliding window strategy to map and quantify the dynamic spatial patterns of functional brain networks (FBNs) present in naturalistic functional magnetic resonance imaging (NfMRI) data, and further evaluated whether the temporal dynamics of distinct FBNs are aligned to the sensory, cognitive, and affective processes involved in the subjective perception of the movie. The results revealed that movie viewing can evoke complex FBNs, and these FBNs were time-varying with the movie storylines and were correlated with the movie annotations and the subjective ratings of viewing experience. The reliability of DFNs was also validated by assessing the Intra-class coefficient (ICC) among two scanning sessions under the same naturalistic paradigm with a three-month interval. Our findings offer novel insight into comprehending the dynamic properties of FBNs in response to naturalistic stimuli, which could potentially deepen our understanding of the neural mechanisms underlying the brain's dynamic changes during the processing of visual and auditory stimuli.

The state of agroecology in Brazil: An indicator-based approach to identifying municipal “bright spots”

Agroecology is increasingly recognized as a pathway for agricultural transformation that can mitigate environmental harms and improve social equity. Yet, the lack of broad-scale assessments that track agroecological indicators in distinct contexts has been identified as a challenge to scaling agroecology out and up. Here, we identify and assess indicators of agroecology based on the Food and Agriculture Organization’s 10 Elements of Agroecology and Tool for Agroecology Performance Evaluation. We created an agroecological index representing the status of agroecological practices and outcomes on farms in Brazil and mapped the results at the municipal level (the smallest autonomous administrative territorial unit in Brazil) using data from the 2017 agricultural census. We found that the extent of agroecological practice across Brazil’s 26 states exhibited strong spatial variability. Within states with low average levels of agroecological practice, we identified “bright spots” of agroecology, or municipalities that performed better than their state average. Bright spot analyses may provide insights on how other municipalities could improve their agroecological status, as well as illustrate potential factors inhibiting agroecological transitions elsewhere. Based on the analysis of local contexts through a literature review, we found that bright spots corresponded to areas with highly visible activities of grassroots farmer networks and nongovernmental organizations, access to public policies and programs, proximity to urban markets, and maintenance of traditional agricultural practices. This suggests that additional institutional investment and support should be directed toward strengthening these enabling factors for agroecology.

Supporting implementation science and health equity in cancer prevention and control through research networks

The Cancer Prevention and Control Research Network (CPCRN) is a national network of academic, public health, and community organizational partners across multiple geographic sites who collaborate to reduce the cancer burden in diverse communities. Given key recommendations that suggest the need for cross-disciplinary collaboration in cancer prevention and control, we sought to explore the historical and contemporary evolution of health equity and disparities research as an area of focus within the CPCRN over time. We conducted 22 in-depth interviews with former and current leaders, co-investigators, and other members of the network. Several key themes emerged from data that were analyzed and interpreted using a constructivist, reflexive, thematic analysis approach. Nearly all participants reported a strong focus on studying health disparities since the inception of the CPCRN, which offered the network a distinct advantage in recent years for incorporating an intentional focus on health equity. Recent law enforcement injustices and the inequities observed during the COVID-19 pandemic have further invigorated network activities around health equity, such as development of a health equity-focused workgroup toolkit, among other cross-center activities. Several participants noted that, in terms of deep, meaningful, and impactful health equity-oriented research, there are still great strides for the network to make, while also acknowledging CPCRN as well-aligned with the national dialogue led by federal agency partners around health equity. Finally, several future directions were mentioned by the participants, including a focus on supporting a diverse workforce and engaging organizational partners and community members in equity-focused research. Findings from these interviews provide direction for the network in advancing the science in cancer prevention and control, with a strengthened focus on health equity.

Supply chain delivery performance improvement: a white-box perspective

This paper proposes a white-box perspective that portrays a supply chain delivery process as a network of related activities which remains to be improved. It addresses a critical disadvantage of supply chain delivery performance models, namely considering a delivery process as a whole and ignoring characteristics and relationships between activities in the delivery process. A delivery process is modeled using the Graphical Evaluation and Review Technique based on the characteristic function (CF-GERT). Based on the CF-GERT model, a framework for applying managerial effort to activities to improve overall delivery performance is proposed. Then, particle swarm optimization (PSO) based on the penalty function is used to solve the delivery performance improvement framework. Finally, a numerical case shows how applying efforts to activities can effectively improve delivery performance and demonstrates the influence of several parameters on the related costs.

In whose pockets? How small Danish patient organisations balance legitimacy, representation and dependency in collaboration with public sector medical researchers and the life science industry

It is a commonly accepted idea that complex structural challenges can be solved if private actors, NGOs, and the public authorities collaborate (Brogaard in Politica 47(4):541–560, 2015), a view that has come to frame several publicly funded network activities in Denmark. This article takes its point of departure in one such project aimed at setting up a collaborative network between researchers, patient associations, and life science partners to stimulate growth in research and development. We analyse this project as a social arena with a focus on what is at stake for patient organisations in such a collaboration. Using social worlds and arena theory, we map the negotiation of discourses, images, and positions, thereby answering the questions: What kind of relationships emerge as patient organisations network with researchers and the life science industry in the name of patient centricity and how do patient organisations balance legitimacy, representation, and independence in such collaboration? We argue that patient organisations’ positions are expanding and taking new forms as they perform a balancing act, from which a ‘multiplayer position’ seems to have emerged. Lastly we propose that patient organisations’ complex interdependencies should be acknowledged, and introduce an understanding of ‘cumulative drives’ to capture the nature of these complexities.

Upcoming activities of the SensorFINT European Network for 2023

Upcoming activities of the SensorFINT European Network for 2023

Relating attention related activities of daily living with cognitive performance on attentional network task in patients with Dementia of Alzheimer’s Type

AbstractBackgroundAttentional functioning plays a crucial role in task performance in the daily facets of life, and impairments in attention are present in a multitude of psychiatric disorders such as Alzheimer’s, Parkinson’s, stroke, and schizophrenia. The assessment of attention has been achieved by Attention Network Test (ANT) in various research which is a computerized testing measure that was developed to measure these three networks of attention viz. alerting, orienting and executive control.MethodThe attentional network task consists of alerting, orienting and executive control. In the present study the relationship between performance on attention network system and activities of daily living was analyzed on 15 mild dementia of Alzheimer’s type. The activities of daily living has been measured through Everyday Activities Scale in India (EASI). Further, correlation has been calculated in alerting, orienting and executive control and scores on EASI.ResultThe result found that alerting has been significantly positive correlated with items on EASI related to understand events, deliver message. The orienting network has been significantly positive correlated with items on EASI related to toilet location, shirt buttoned, lose way in village. The executive control network is significantly positive correlated with items on EASI related to complete task, dress properly.ConclusionThe result showed a relation between attention network and activities of daily living which showed that attention is the prerequisites for various of the daily functioning.

HTA99 Advancing Towards International HTA Collaboration: A Review of International Network Activities to Support HTA Efforts in Ex-Europe Low-Middle-Income Countries (LMICS)

HTA99 Advancing Towards International HTA Collaboration: A Review of International Network Activities to Support HTA Efforts in Ex-Europe Low-Middle-Income Countries (LMICS)

Detection of IoT Botnet Cyber Attacks using Machine Learning

Since they were first used, systems have faced threats from viruses, worms, and hacking attacks. In 2018, there were more devices online than there were people, and this tendency will continue to grow, with an estimated 80 billion devices online by 2024. It is difficult to secure this equipment and the data that flows between them since IoT botnet attacks (IBA) are becoming more and more common. Potential hackers for data theft and cyberattacks have been enticed by the overwhelming quantity and omnipresent presence. One of the biggest issues with the Internet of Things is security. The main goal of this research project is to develop a workable machine learning algorithm-based model to identify and counteract botnet-based attacks on IoT networks. The suggested model addresses the security concern of the dangers provided by bots. The BoT-IoT dataset was used to train a variety of machine learning techniques, including linear regression, logistic regression, K-Nearest Neighbor (KNN), and SVM models. The performance of the system’s results in an F-measure of: 1) 98.0%, 2) 99.0%, and 3) 99.0%. 4) 99.0%. This demonstrates that the proposed models can automatically separate between network activities is malicious or normal.

Spatial analysis of thunderstorms and lightning casualties in Bangladesh

Considering the adverse outcomes of thunderstorm‐mediated lightning in recent years, this study aimed to identify the most thunderstorm‐and‐casualty prone regions and seasons in Bangladesh, via geospatial mapping. We attempted to forecast the number of yearly thunderstorm (TS) days for each meteorological station and district‐level lightning casualties by using TS days as a proxy variable. Data on TS days and lightning casualties were collected from Bangladesh Meteorological Department and Network for Information, Response And Preparedness Activities on Disaster respectively. This study analysed 629 fatalities and 232 injuries. The Integrated Nested Laplace Approximation with Matern covariance function was employed to assess the spatial pattern of TS days. Polynomial regressions were used to forecast the number of TS days and the single clustered Generalized Estimating Equations method was employed to explore the relationship between the number of TS days and lightning casualties. The results indicated that the mean number of TS days per station increased in recent years, particularly in February, March, April, and September. We found the months of April, May, June, and September to be the most threatening months due to TS events and related casualties. The northeast region was identified as the most TS‐prone region with the highest number of casualties in Bangladesh. Our analysis suggests that it might remain the most hazardous region in coming years.

Depression Detection and Analysis using ML

Abstract: Physiological interference in a person’s life can bring out many difficulties which affect the basic abilities of a person to do simple tasks. Depression, one such issue, can be detected through only medically trained psychiatrists and the early detection of depression is crucial in providing effective treatment. Social Media platforms prove to be a valuable source of information for this motive. Social Media provides a safe platform for individuals to express their emotions and feelings while at the same time, it can also contribute to the development of depression symptoms particularly in vulnerable populations i.e. Younger generations, due to factors such as social comparison, cyberbullying, social isolation and constant need for validation. This paper further explores evidence of the link between social media use and depression in aiding early detection. The objective of the current study is to apply various ML techniques to assist psychiatrists in recognizing patient symptoms. The plat-form used for our research is Twitter. The model detects the symptoms/behavior of early depression in the users through their current and past tweets. For this purpose, we have deployed various approaches to train and test an ML model or classifier using the right features depending on the information gathered from a questionnaire and through the features extracted from a user’s tweets and their social network activities.

FMRI reveals overlapping and non-overlapping neural bases of domain-general and emotional conflict control.

The present study uses functional magnetic resonance image (fMRI) to examine the overlapping and specific neural correlates of contextualized emotional conflict control and domain-general conflict control. During a performance on emotional and domain-general conflict tasks, conjunction analyses showed that neural areas distributed in the frontoparietal network were engaged in both processes, supporting the notion that similar neural mechanisms are implemented in these two types of control. Importantly, disjunction analyses revealed a broader neural recruitment of emotional conflict control compared to domain-general conflict control as shown by the possible lateralization of the lateral prefrontal cortex (lPFC), such that emotional conflict control significantly involved the left lPFC while domain-general conflict control seemly involved the right lPFC. Results of generalized psychophysiological interaction (gPPI) analyses further demonstrated that emotional conflict control, compared to domain-general conflict control, elicited broader synergistic activities in individuals' brain networks. Together, these findings offer novel and compelling neural evidence that furthers our understanding of the complex relationship between domain-general and emotional conflict control.

<i>A Focus in Which the Interests and Requests of the Peasantry Intersect</i>: Village Reading Rooms of the Crimean ASSR in the 1920s

Village reading rooms, being an element of the library system of the 1920s, served as the main political educational institution in the village. The article deals with the functioning of institutions of this type on the territory of the Crimean ASSR. Based on archival sources and publications in the local periodical press, a comprehensive analysis of the activities of the reading rooms network in the region during the designated period (the dynamics of their number, financial situation, material and personnel support, forms of work with the population, etc.) was undertaken. The results presented in the study complement the ideas about the history of librarianship, cultural and national policy in the Crimean ASSR of the 1920s and contribute to better understanding of the history of librarianship in Russia.

The Nexus Among Transformational Leadership and Firm Performance: Testing the Mediating Role of Knowledge Sharing

The purpose of this paper is to clarify the mediating roles of active and passive knowledge sharing (KS) in the relationship between transformational leadership (TL) and specific aspects of firm performance namely operational and financial performance. The paper used Analysis of Moment Structures (AMOS) and Structural Equation Modelling (SEM) to investigate the influence of TL and knowledge sharing (KS) on aspects of firm performance using data from a survey of 235 manufacturers and suppliers in Vietnam. The empirical findings show the significant and positive influence of TL, and KS activities in supply chain networks on firm performance. It highlighted the key role of active and passive KS in linking the effect of TL on firm’s operational and financial performance. Especially, this study reveals that TL has a greater effect on financial performance compared to its effects on operational performance. Especially, active KS has a greater effect on both firm’s operational and financial performance compared to the influence of passive KS. The paper has extended the theory of leadership, knowledge management and organizational performance by clarifying the critical roles of TL practice and processes of KS in the supply chain in improving firm’s operational and financial performance.

TVET SI: Evaluating Boundary Crossing Social Learning in Vocational Education and Training: A value creation approach

This article focuses on the development and application of an evaluation model and approach for evaluating boundary crossing social learning in a Vocational Education and Training (VET) learning network in South Africa, with an emphasis on a Training of Trainers (ToT) course that helped to catalyse and strengthen this learning network via two iterations of the course over an eight-year period. The article shares how we adapted the value creation framework (VCF) of Wenger, Traynor and De Laat (2011; Wenger & Wenger-Traynor, 2020) in the evaluation of a VET Training of Trainers (ToT) programme and learning network that focussed on the uptake and circulation of rainwater harvesting and conservation (RWH&C) knowledge in a particular formal and informal VET context in the Eastern Cape, South Africa, where smallholder farmers were struggling to find water for producing food. The evaluated ToT course was catalytic in establishing a boundary crossing social learning network approach in a VET context that linked formal and informal VET (Lotz-Sisitka et al., 2016; Lotz-Sisitka et al., 2022; Pesanayi, 2019); hence we found it important to develop adequate tools for its evaluation. The focus of this article is to share how we developed an evaluation approach to this work. We share insights on the indicators developed for diff erent types of value created, and also insights gained into the use of this evaluation approach in a boundary crossing VET social learning project that took a ToT course as focus. In short, evaluation findings show that the boundary crossing ToT course off ers strong immediate, potential and applied value that can lead to realised and reframed value, especially if supported by ongoing learning network activities that follow the initial engagement in the boundary crossing ToT course. Th is leads, over time, to transformative value which is important in achieving the overall objective of such social learning, namely making knowledge more co-engaging, accessible and useful in the context where improved food security via better use of rainwater harvesting and conservation amongst smallholder farmers and household food producers is a necessary form of sustainable development. Orientation value, and enabling value were found to be vital for the emergence of other kinds of value. The evaluation model also allows for the lifting out of strategic value which points to wider uptake potential. All this creates the possibility for indicator development that can help inform iterative development of boundary crossing VET courses used to stimulate the co-construction of learning networks and ongoing social learning for sustainable development.
 Keywords: Vocational Education and Training, evaluation, social learning, value creation framework

TVET SI: Evaluating Boundary Crossing Social Learning in Vocational Education and Training: A value creation approach

This article focuses on the development and application of an evaluation model and approach for evaluating boundary crossing social learning in a Vocational Education and Training (VET) learning network in South Africa, with an emphasis on a Training of Trainers (ToT) course that helped to catalyse and strengthen this learning network via two iterations of the course over an eight-year period. The article shares how we adapted the value creation framework (VCF) of Wenger, Traynor and De Laat (2011; Wenger & Wenger-Traynor, 2020) in the evaluation of a VET Training of Trainers (ToT) programme and learning network that focussed on the uptake and circulation of rainwater harvesting and conservation (RWH&C) knowledge in a particular formal and informal VET context in the Eastern Cape, South Africa, where smallholder farmers were struggling to find water for producing food. The evaluated ToT course was catalytic in establishing a boundary crossing social learning network approach in a VET context that linked formal and informal VET (Lotz-Sisitka et al., 2016; Lotz-Sisitka et al., 2022; Pesanayi, 2019); hence we found it important to develop adequate tools for its evaluation. The focus of this article is to share how we developed an evaluation approach to this work. We share insights on the indicators developed for diff erent types of value created, and also insights gained into the use of this evaluation approach in a boundary crossing VET social learning project that took a ToT course as focus. In short, evaluation findings show that the boundary crossing ToT course off ers strong immediate, potential and applied value that can lead to realised and reframed value, especially if supported by ongoing learning network activities that follow the initial engagement in the boundary crossing ToT course. Th is leads, over time, to transformative value which is important in achieving the overall objective of such social learning, namely making knowledge more co-engaging, accessible and useful in the context where improved food security via better use of rainwater harvesting and conservation amongst smallholder farmers and household food producers is a necessary form of sustainable development. Orientation value, and enabling value were found to be vital for the emergence of other kinds of value. The evaluation model also allows for the lifting out of strategic value which points to wider uptake potential. All this creates the possibility for indicator development that can help inform iterative development of boundary crossing VET courses used to stimulate the co-construction of learning networks and ongoing social learning for sustainable development.
 Keywords: Vocational Education and Training, evaluation, social learning, value creation framework