Network Events Research Articles

Modelling and monitoring multi-relational networks with ordinal information

Network relationships can be widely seen among entities in various fields such as social networks, supply networks and Internet of Things (IoT). Sometimes abnormal events such as cyber-attacks occur to cause an abrupt increase or decrease in the traffic of networks. Many anomaly detection methods have been developed to identify such abnormal events in networks. In recent years, statistical process control (SPC) has attracted more and more attention in network anomaly detection. However, many of the existing statistical models regard the interaction between two nodes in unweighted directed networks as a binary variable, i.e. presence and absence of contacts, which fails to reflect the intensity level of interactions. This article proposes a new model to describe the dyadic interactions with several ordinal levels and introduces special quantities to incorporate the ordinal information into the model. The model can be expressed in a matrix form to enable easy parameter estimation and derivation of a quadratic monitoring statistic. Numerous simulation studies show that the proposed methods detect anomalies in multi-relational networks more quickly than existing monitoring methods. A case study exhibits the implementation and superiority of the proposed method.

Reconstructing a georeferenced inventory of flooding hazards in Raoping, Guangdong province, China, from 1492 to 1985

ABSTRACT In ancient times, there is a scarcity of instrumental water flow data, which challenges the hydrological science community to understand the evolution of flood risk under the changing climate over the centennial scale. Based on the historical records of flood events, the first spatio-temporal database of flood risk occurrence was established in Raoping county, Guangdong, China from 1492 to 1985 (a 494-year period), with intensive human interpretation and local investigation. Specifically, the spatially explicit flood events of the river network were provided for each flood episode. A detailed analysis of the database shows a high frequency of flood risk in summer and early autumn. Specifically, a significant (p < 0.05) increase has been found in flood risks since 1960. However, flooding decreased significantly in recent decades due to meteorological and hydrological factors, as well as the population density and migration during the 500-year period. A spatial clustering of flood events in the northern and southern parts is also confirmed, which shows an impact of population dynamics on a centennial scale. Such methods can be a reference for establishing China's flooding database for ancient periods, promoting a better understanding of natural hazards and associated human behaviors under the context of long-term climate change.

Cyber Security Controls and Countermeasures

Today's digital economy encompasses a conglomeration of devices, data, applications, and complex networks that can be hosted on-premises, in data centers or in cloud environments to support business functions. As a cybersecurity professional, you will be tasked to manage risks like cyber threats and data breaches in the workplace. The risks may emanate from the data that resides in the systems, like servers, computers or external drives and the physical assets themselves, like routers and switches. The onus is on preventing security events and limiting the damage or consequences of any event that might happen. Managing these risks in an organization can be achieved by implementing cyber security controls and countermeasures. Cyber security controls can be categorized into three distinct major groups: administrative control, physical control, and technological control. The administrative controls focus on security policies, guidelines, rules, procedures and standards designed by management to control access and utilization of confidential information. Physical control refers to a set of IT security controls deployed at the physical premise to prevent unauthorized physical access to data centres. Technical controls involve the use of systems and technical solutions to prevent security events in networks and cloud platforms. Additionally, when implementing security controls, the function of cyber security controls can be broadly divided into seven groups: directive, deterrent, preventive, compensating, corrective, detective, and recovery controls. Furthermore, security frameworks such as NIST, CIS controls COBIT, and ISO/IEC series form the cornerstone of cyber security controls and countermeasures. The aforementioned aspects will be discussed further in the later sections. For now, it is to provide a general picture of the cyber security controls and countermeasures. Keywords – Security Controls, Countermeasures, Frameworks, Information Systems, Risk

Factors Influencing Informal Credit Access and Utilization among Smallholder Farmers: Insights from Mountainous Regions of Pakistan

Access to credit plays an important role in the adoption of modern agricultural practices, such as better seeds, pesticides, and fertilizers, as well as in the sustainable utilization of land by smallholder farmers. This study explores the dynamics of smallholders’ access to and utilization of informal credit to manage local farming systems. The data were collected from rural households in the Dir Kohistan mountainous region of Pakistan in 2021. A bivariate probit model was employed to analyze the data from 300 randomly selected farming households. Our findings indicate that a significant proportion of respondents (70%) reported having access to informal credit, with 65% actively utilizing credit to bolster the economic viability of their farms. This study reveals various strategies that farmers employ in response to credit constraints. The majority of farmers (69%) prioritize meeting their own farm/household needs. Social networks, particularly friends, emerge as key sources of informal credit (58.1%). The results of the bivariate probit regression analysis identify positive relationships with fellow farmers and neighbors/relatives, along with a lower perceived risk, as the most significant factors influencing access to and utilization of informal credit. Therefore, our study recommends the promotion of relationships and linkages among farmers through facilitated networking events, introducing risk mitigation measures and promoting financial literacy programs to empower smallholder capacities on the credit market. Policymakers are urged to recognize the role of social relationships and consider policies that promote community credit practices for the overall financial well-being of farmers, especially in more isolated mountain areas.

Targeting attack activity-driven networks.

Real-world complex systems demonstrated temporal features, i.e., the network topology varies with time and should be described as temporal networks since the traditional static networks cannot accurately characterize. To describe the deliberate attack events in the temporal networks, we propose an activity-based targeted attack on the activity-driven network to investigate temporal networks' temporal percolation properties and resilience. Based on the node activity and network mapping framework, the giant component and temporal percolation threshold are solved according to percolation theory and generating function. The theoretical results coincide with the simulation results near the thresholds. We find that targeted attacks can affect the temporal network, while random attacks cannot. As the probability of a highly active node being deleted increases, the temporal percolation threshold increases, and the giant component increases, thus enhancing robustness. When the network's activity distribution is extremely heterogeneous, network robustness decreases consequently. These findings help us to analyze and understand real-world temporal networks.

Cyber-attack Detection and Mitigation Process under Big Data Consideration: Improved Recursive Feature Elimination-based Feature Selection

Due to the rapid growth of network technology, huge volume and distinct data sent via networks is expanding constantly. The situation shows how complex and dense cyber attacks and hazards are developing. Due to the rapid advancement in network density, cyber security specialists find it difficult to monitor all network activity. Due to frequent and sophisticated cyber attacks, it is becoming more challenging to detect and identify abnormalities in network events. The use of deep learning provides a variety of tools and strategies for automated cyber-attack detection as well as quick attack-type prediction as well as evaluation. This work introduces a novel cyber-attack detection and mitigation process under the following phases including preprocessing, feature extraction via the Map Reduce framework that handles the big data, feature selection, attack detection and mitigation. The Improved Normalisation process is achieved on the preprocessing phase. The work is examined from a big data perspective; hence Map Reduce framework is utilised for this. As a result, the framework will manage the feature extraction process, where features including statistical features, raw features, improved correlation-based features, and info gain-based features will be extracted. Following feature extraction, the Improved Recursive Feature Elimination procedure is processed that selects the relevant features. The hybrid detection model, which combines Recurrent Neural Networks (RNN) Deep and Belief Networks (DBN) is used to detect the attacks. Once an attack has been detected, the attacker must be mitigated. To accomplish this, an improved BAIT-based mitigation procedure is used. The two datasets used in this work are, namely, Intrusion Detection Systems (IDS) 2018 Intrusion CSVs (CSE-CIC-IDS2018) and UNSW_NB15. Finally, the suggested model and the alternative methods are contrasted using a variety of measures such as accuracy, sensitivity, specificity, precision, FDR, FNR and FPR.

APOYOnline: A Sustainable Approach to Heritage Preservation Across the Americas

ABSTRACT For 35 years, APOYOnline – Association for Heritage Preservation of the Americas has been building bridges of communication and promoting professional development in the field of heritage preservation, strengthening, empowering, and transforming our community, increasing inclusion and access, and reducing social inequalities in the Americas and in Portuguese- and Spanish-speaking countries. As a grassroots, volunteer-based, non-profit organization, we listen to the community and work to address needs caused by widely recognized discrepancies in working conditions, adverse climate conditions, social inequalities in training, and other factors that directly impact heritage preservation and to provide access to specialized technical resources. This paper presents the voices of APOYOnline and the impact of the organization’s catalyzing work in improving resource accessibility and connecting people. This work has mobilized regional and global networks, providing equal opportunities through translation of reference materials in multiple languages, professional development grants for students and emerging professionals, and training and regional networking events in underserved territories in the region. It also addresses our special community-based initiative Manos a la Obra, which strengthens long-term community engagement as a collaborative knowledge-sharing process, based on learning through participation and dialogue. The program improves access to technical information and fosters the long-term sustainability of local and regional cultural heritage preservation efforts, and has served as a model for similar activities in other countries. APOYOnline pursues sustainability through volunteer-based networking showing that communication and horizontal interaction with local communities creates awareness and better opportunities for our professionals and positively impacts heritage preservation in the region.

Dynamical Fluctuations of Random Walks in Higher-Order Networks

Although higher-order interactions are known to affect the typical state of dynamical processes giving rise to new collective behavior, how they drive the emergence of rare events and fluctuations is still an open problem. We investigate how fluctuations of a dynamical quantity of a random walk exploring a higher-order network arise over time. In the quenched case, where the hypergraph structure is fixed, through large deviation theory we show that the appearance of rare events is hampered in nodes with many higher-order interactions, and promoted elsewhere. Dynamical fluctuations are further boosted in an annealed scenario, where both the diffusion process and higher-order interactions evolve in time. Here, extreme fluctuations generated by optimal higher-order configurations can be predicted in the limit of a saddle-point approximation. Our study lays the groundwork for a wide and general theory of fluctuations and rare events in higher-order networks. Published by the American Physical Society 2024

Investigation of transient extreme events in a mutually coupled star network of theoretical Brusselator system

In this article, we present evidence of a distinct class of extreme events that occur during the transient chaotic state within network modeling using the Brusselator with a mutually coupled star network. We analyze the phenomenon of transient extreme events in the network by focusing on the lifetimes of chaotic states. These events are identified through the finite-time Lyapunov exponent and quantified using threshold and statistical methods, including the probability distribution function (PDF), generalized extreme value (GEV) distribution, and return period plots. We also evaluate the transitions of these extreme events by examining the average synchronization error and the system’s energy function. Our findings, validated across networks of various sizes, demonstrate consistent patterns and behaviors, contributing to a deeper understanding of transient extreme events in complex networks.

Offline hippocampal reactivation during dentate spikes supports flexible memory.

Stabilizing new memories requires coordinated neuronal spiking activity during sleep. Hippocampal sharp-wave ripples (SWRs) in the cornu ammonis (CA) region and dentate spikes (DSs) in the dentate gyrus (DG) are prime candidate network events for supporting this offline process. SWRs have been studied extensively, but the contribution of DSs remains unclear. By combining triple-ensemble (DG-CA3-CA1) recordings and closed-loop optogenetics in mice, we show that, like SWRs, DSs synchronize spiking across DG and CA principal cells to reactivate population-level patterns of neuronal coactivity expressed during prior waking experience. Notably, the population coactivity structure in DSs is more diverse and higher dimensional than that seen during SWRs. Importantly, suppressing DG granule cell spiking selectively during DSs impairs subsequent flexible memory performance during multi-object recognition tasks and associated hippocampal patterns of neuronal coactivity. We conclude that DSs constitute a second offline network event central to hippocampal population dynamics serving memory-guided behavior.

Investigation of distribution system modification using operation voltage unification in order to decrease interruptions in dust climate

Many events in power networks which caused failure and outage of subsystems have root in design and initial selection of equipment and their specification. This concern will be important while the any kind of expanding and acclimation plan need to be executed to strengthen the existing infrastructures in order to provide better condition with regard to satisfied criteria. In Iran one of the most adverse environmental occurrences known Fine Dust, may force several distribution utilities to be changed due to new technical requirements. Medium voltage level regrading in way to decrease or unification of distribution system have been proposed in this paper to reach reduced faults and fast replacement of equipment in damaged situations. Voltage level changing and selection have been investigated in order to increase the network strengthen and reselection of equipment and redesigning critical sections of system against natural events that called fine dust with aim to attain an upgraded distribution utility. Operating of existing 33 kV and 11 kV network in 20 kV level and investigation of changing process have been done for Ahvaz medium voltage distribution system as a case study in these paper's sections. Calculations and results will be contained maximum current capacities in different line conductors, insulation and air clearances assessment, poles acceptable mechanical values, short circuit current and protection coordination for changing voltage level in distribution system.

Hard‐state Protocol Independent Multicast—Source‐Specific Multicast (HPIM‐SSM)

AbstractSource‐specific multicast is a key technology for multicast services such as IPTV broadcasting, which relies on IGMPv3/MLDv2 for source‐group membership signalling and multicast routing protocols such as PIM‐SSM for building and maintaining receiver‐initiated source‐based distribution trees across the network. The authors propose the Hard‐state Protocol Independent Multicast—Source‐Specific Multicast (HPIM‐SSM), a novel multicast routing protocol that keeps the design principles of PIM‐SSM but overcomes its limitations, such as slow convergence and the possibility of creating suboptimal trees. The state machines of HPIM‐SSM were designed to react promptly to all network events susceptible to reconfiguring the multicast trees, avoiding the need for soft‐state maintenance through the periodic transmission of control messages. Moreover, the authors eliminated the need for designated routers, which led to suboptimal trees, and introduced a control‐driven assert protocol that operates per source, allowing for considerable memory savings. Finally, the protocol enables the coexistence of multiple unicast routing protocols. HPIM‐SSM was implemented in Python, and its correctness was extensively validated through model‐checking techniques. Furthermore, a comparison between HPIM‐SSM and PIM‐SSM was conducted, encompassing both theoretical analysis and experimental evaluation of convergence times. The results demonstrate clearly that HPIM‐SSM outperforms PIM‐SSM, exhibiting significantly faster convergence times and completely avoiding suboptimal trees.

Machine learning based classifiers for dynamic and transient disturbance classification in smart microgrid system

The smart microgrid system should have the ability to rapidly detect and classify every type of disturbance that happens in the network to operate the protection scheme and maintain the power quality. Both dynamic and transient types of disturbances are considered in this study, and the classification of each type of disturbance has been done using different machine learning-based techniques. A novel ensemble classifier has been proposed after comparing the performance of the decision tree (DT), k nearest neighbor (KNN), support vector machine (SVM), and ensemble classifier. The performances of all the classifiers have been evaluated in terms of accuracy, sensitivity, confusion matrices, and receiver operating characteristics (ROC). A very satisfactory result has been obtained in the ensemble tree classification technique with 99.3% accuracy in 2.1 s of training time. Therefore, it can be suggested to identify and classify every transient as well as steady-state events of the microgrid network.

Multi-Stage Burst Localization Based on Spatio-Temporal Information Analysis for District Metered Areas in Water Distribution Networks

Burst events in Water Distribution Networks (WDNs) pose a significant threat to the safety of water supply, leading people to focus on efficient methods for burst localization and prompt repair. This paper proposes a multi-stage burst localization method, which includes preliminary region determination and precise localization analysis. Based on the hydraulic model and spatio-temporal information, the effective sensor sequences and monitoring areas of the nodes are determined. In the first stage, the preliminary burst region is determined based on the monitoring region of sensors and the alarm sensors. In the second stage, localization metrics are used to analyze the dissimilarity degree between burst data from the hydraulic model and the monitoring data from the effective sensors at each node. This analysis helps identify candidate burst nodes and determine their localization priorities. The localization model is tested on the C-Town network to obtain comparative results. The method effectively reduces the burst region, minimizes the search region, and significantly improves the efficiency of burst localization. For precise localization, it accurately localizes the burst event by prioritizing the possibilities of the burst location.

A hippocampal circuit mechanism to balance memory reactivation during sleep.

Memory consolidation involves the synchronous reactivation of hippocampal cells active during recent experience in sleep sharp-wave ripples (SWRs). How this increase in firing rates and synchrony after learning is counterbalanced to preserve network stability is not understood. We discovered a network event generated by an intrahippocampal circuit formed by a subset of CA2 pyramidal cells to cholecystokinin-expressing (CCK+) basket cells, which fire a barrage of action potentials ("BARR") during non-rapid eye movement sleep. CA1 neurons and assemblies that increased their activity during learning were reactivated during SWRs but inhibited during BARRs. The initial increase in reactivation during SWRs returned to baseline through sleep. This trend was abolished by silencing CCK+ basket cells during BARRs, resulting in higher synchrony of CA1 assemblies and impaired memory consolidation.

Efficient generation of barrier crossing trajectories using approximate Brownian bridges.

We examine continuous random walks that are conditioned to reach one region before another. These conditioned processes are used to generate stochastic trajectories for barrier crossing events, which are generally rare and difficult to sample. The processes are generated using a Brownian bridge technique, resulting in near perfect sampling efficiency without accruing error in the conditional statistics of the process. The construction requires the hitting probability or committer function, which is a solution to the backward Fokker-Planck equation, a partial-differential equationthat can be difficult to solve through general means. Therefore, we derive a one-dimensional approximation through asymptotic methods for barrier crossing trajectories. We show that this approximation has a simple analytical representation and approaches the true solution as the barrier height increases. Brownian bridge trajectories generated with this approximate solution are then shown to result in accurate conditional statistics when used in conjunction with importance sampling, even in the case when potential energy barriers are not large. We show this idea's effectiveness by simulating rare events in a stochastic chemical reaction network (Schögl reaction) with multiple steady states. This methodology shows great promise for future implementation to simulate rare barrier crossing events for a wide variety of physical processes.

Networking Frictions and Entrepreneurial Learning in Developing Economies

Relationships with peers help entrepreneurs learn and improve firm performance. Recent scholarship confirms that events—social gatherings such as mixers, conferences, or training programs—can help entrepreneurs build valuable social connections. Yet, for entrepreneurs in developing economies, networking frictions may make connecting with peers challenging and undermine the benefits of events. We argue that when networking frictions are high, the value of events will lie more in connecting neighbors rather than bringing together distant peers. In the presence of networking frictions, neighbors are both less likely to be someone the entrepreneur has already learned from and easier to sustain a relationship with. To test this argument, we use data from a series of networking events in Togo during which entrepreneurs were randomly assigned to meet with peers from across the city of Lomé. We find that entrepreneurs who were assigned to neighboring peers were much more likely to sustain a relationship, learn from their peer’s management knowledge, and in turn benefit more: Profits increase by 10% when entrepreneurs get to know peers who are located on average 1 km closer to them. Our results highlight the central role that networking frictions play in shaping who entrepreneurs in developing economies can successfully learn from. This paper was accepted by Lamar Pierce, organizations. Funding: Ewing Marion Kauffman Foundation [Dissertation Fellowship] and the Strategic Management Society [SRF Dissertation Fellowship]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00281 .

Arginine vasopressin activates serotonergic neurons in the dorsal raphe nucleus during neonatal development in vitro and in vivo

Birth stress is a risk factor for psychiatric disorders and associated with exaggerated release of the stress hormone arginine vasopressin (AVP) into circulation and in the brain. In perinatal hippocampus, AVP activates GABAergic interneurons which leads to suppression of spontaneous network events and suggests a protective function of AVP on cortical networks during birth. However, the role of AVP in developing subcortical networks is not known. Here we tested the effect of AVP on the dorsal raphe nucleus (DRN) 5-hydroxytryptamine (5-HT, serotonin) system in male and female neonatal rats, since early 5-HT homeostasis is critical for the development of cortical brain regions and emotional behaviors. We show that AVP is strongly excitatory in neonatal DRN: it increases excitatory synaptic inputs of 5-HT neurons via V1A receptors in vitro and promotes their action potential firing through a combination of its effect on glutamatergic synaptic transmission and a direct effect on the excitability of these neurons. Furthermore, we identified two major firing patterns of neonatal 5-HT neurons in vivo, tonic regular firing and low frequency oscillations of regular spike trains and confirmed that these neurons are also activated by AVP in vivo. Finally, we show that the sparse vasopressinergic innervation in neonatal DRN originates exclusively from cell groups in medial amygdala and bed nucleus of stria terminalis. Hyperactivation of the neonatal 5-HT system by AVP during birth stress may impact its own functional development and affect the maturation of cortical target regions, which may increase the risk for psychiatric conditions later on.

Welcome to the first issue of the College’s Asia Pacific Journal of Health Management for 2024.

The College is going from strength to strength with membership increasing notably over the past few months and great attendance at College free webcast events as well as growing participation in other ACHSM professional development opportunities. In March a very successful one day Conference was held in Auckland, New Zealand with many from Australia and nearby countries amongst the more than 250 who attended this event. Through attending that conference, we now have 58 new College members in New Zealand who can take advantage of the benefits of College membership like this Journal, free webcasts, local networking events and access to bespoke professional development for health leaders. Our Fellowship Program for 2024 has commenced with 46 Associate Fellows applying themselves to our most prestigious capstone program. Further we have over 30 current enrolments to undertake Certification with those College members committing to maintaining their profession through the expectations of this credential. We also launched a Managing Digital Health micro credential that aligns fully with the digital health competencies in the ACHSM Master Health Service Management Competency Framework. We are in the process of distributing digital badges to all membership levels and Certified Health Managers and Executives for use on media such as LinkedIn, CVs and email signatures. These badges provide and outline of what it means to be a Fellow, Certified Health Manager or Executive of ACHSM. In this issue, as is the purpose of the Journal, there are learned articles on the importance of continuing to self-reflect on our leadership behaviours and workplace relationships. I take this opportunity of thanking Prof Mark Avery our Editor for his great work in maintaining a high quality of publication and ensuring relevancy to leaders in all sectors. I commend these to you and continue to wish you success in your ongoing leadership journey.

PMUs data based detection of oscillatory events and identification of their associated variable: Estimation of information measures approach

Information theory can be a useful tool for quantifying the perturbations in the associated state variables at the time of disturbance occurrence. The study introduces a framework for the spectral decomposition of multivariate information measures to detect initiation of low frequency oscillations (LFOs), caused due to physical events in the power grid. A frequency-specific quantification of the information is shared between a target variable and two source variables from their time series data. Initially, the approach is applied on different synthetic test signals having different oscillatory frequency modes and decay time constant. Then, approach is extended on PMUs signals. The combination of cross-spectral and information-theoretic approaches is applied for the multi-variable analysis of PMUs signals from the same bus. The interdependence among the frequency, voltage angle and voltage magnitude, corresponding to specific oscillations, manifested due to cause-effect relationships obtained in terms of statistics is estimated. The dynamics in terms of unique (interaction), redundant and synergetic information is determined with the contribution from two of these three signals as source variables to target variable (frequency/voltage angle). This provides a direct coupling to identify driver-response relationships between source variables and target variable to indicate the onset of LFOs, following physical events in power network. The extension of approach among the variables from different buses aids to identify the responsible area of event occurrence.