Vulnerability Of Systems Research Articles

Systemic Vulnerabilities in Hispanic and Latinx Immigrant Communities Led to the Reliance on an Informal Warning System in the December 10–11, 2021, Tornado Outbreak

Systemic Vulnerabilities in Hispanic and Latinx Immigrant Communities Led to the Reliance on an Informal Warning System in the December 10–11, 2021, Tornado Outbreak

Exposure to nanopollutants (nZnO) enhances the negative effects of hypoxia and delays recovery of the mussels' immune system.

Aquatic environments face escalating challenges from multiple stressors like hypoxia and nanoparticle exposure, with impact on mussel immunity, especially under prolonged hypoxia and its potential modulation by nanopollutants, poorly understood. We investigated the individual and combined effects of short-term and long-term hypoxia and exposure to zinc oxide nanoparticles (nZnO) on hemocytes immune response of the mussels (Mytilus edulis). Hemocyte functional traits (mortality, adhesion capacity, phagocytosis, lysosomal abundance, and oxidative burst), and transcript levels of immune-related genes involved in pathogen recognition (the Toll-like receptors, the complement system components, and the adaptor proteins MyD88) were assessed. Short-term hypoxia minimally affected hemocyte parameters, while prolonged exposure led to immunosuppression, impacting hemocyte abundance, viability, phagocytosis, and defensin gene expression. Under normoxia, nZnO stimulated immune responses of mussel hemocytes. However, combined nZnO and hypoxia induced more pronounced and rapid immunosuppression than hypoxia alone, indicating a synergistic interaction. nZnO exposure hindered immune parameter recovery during post-hypoxic reoxygenation, suggesting persistent impact. Opposing trends were observed in pathogen-sensing and pathogen-elimination mechanisms, with a positive correlation between pathogen-recognition system activation and hemocyte mortality. These findings underscore a complex relationship and potential conflict between pathogen-recognition ability, immune function, and cell survival in mussel hemocytes under hypoxia and nanopollutant stress, and emphasize the importance of considering multiple stressors in assessing the vulnerability and adaptability of mussel immune system under complex environmental conditions of anthropogenically modified coastal ecosystems.

Theoretical perspectives on cybersecurity challenges in critical water infrastructure: Insights from Africa and the United States

This review paper examines the theoretical perspectives on cybersecurity challenges facing critical water infrastructure, with a focus on comparative insights from Africa and the United States. By exploring foundational theories of risk management, resilience, and deterrence, the paper delineates how these concepts are applied and adapted to address the cybersecurity needs of critical water systems within the distinct contexts of the two regions. The analysis identifies common challenges such as malware attacks, system vulnerabilities, human factors, and how regional differences influence technological infrastructure, regulatory environments, and cyber threat landscapes. Through a comparative analysis, the paper highlights lessons learned and best practices from both regions, emphasizing the importance of capacity building, comprehensive risk management, and the role of public-private partnerships. The paper concludes with a call for future research to develop adaptable theoretical models that address different regions' unique cybersecurity challenges, underlining theoretical understanding's critical role in enhancing the global resilience of critical water infrastructure against cyber threats.

A novel hybrid database security management technique

Since the advent of the internet and the digitization of information, more individuals and organizations have gained access to the internet, leading to a significant expansion in the size of our database systems. These databases are relied upon for day-to-day activities by individuals and organizations. Furthermore, the advancements in technology have increased the vulnerability of database management systems and enforcing adequate database security measures is of paramount importance to individuals and organizations. In this study, we deployed a hybrid approach to database security system that integrates two techniques: dynamic time-warping algorithm and voice recognition methods. The implementation follows Object-Oriented Analysis and Design procedures, utilizing SQL Management Studio 2014 for the backend and ASP.NET C# for the frontend. Our hybrid approach demonstrates enhanced database security compared to previously employed authentication measures.

As Proficient as Adults: Distribution of Children’s Knowledge of Wild Edible Plants in an Arid Environment in Madagascar

AbstractIn drylands, where resources are scarce, wild edible plant (WEP) knowledge is crucial to overcome food scarcity. Understanding the distribution pattern of local ecological knowledge (LEK) about WEP and identifying knowledge holders are key steps to assessing the resilience and vulnerability of knowledge systems. However, little is known about how WEP knowledge is distributed across life stages and gender of people living in arid regions. Here, we study the distribution of WEP knowledge within a small-scale society from southwestern Madagascar, a region known for its dry climate and related food crises. We worked with Tanalana male and female children and adults using semi-structured interviews and free listings. Tanalana people display a sophisticated LEK adapted to the extreme environment in which they live, with a distinct distribution pattern regarding theoretical and practical knowledge across life stages and gender. While women and men cited similar WEP, children and adults cited different sets of WEP, suggesting they hold differentiated bodies of theoretical knowledge, however our results suggest similarity in practical knowledge across life-stage and gender. We argue that resource limitation and food scarcity might be so pervasive in the area that extensive sharing of knowledge on WEP could be an adaptation to the extremely dry environment.

Influence of Regional Temperature Anomalies on Strawberry Yield: A Study Using Multivariate Copula Analysis

A thorough understanding of the impact of climatic factors on agricultural production is crucial for improving crop models and enhancing predictability of crop prices and yields. Fluctuations in crop yield and price can have significant implications for the market sector and farming community. Given the projected increase in frequency and intensity of extreme events, reliable modelling of cropping patterns becomes essential. Temperature anomalies are expected to play a prominent role in future extreme events, emphasizing the need to comprehend their influence on crop yield. Forecasting extreme yield, which encompasses both the highest and lowest levels of agricultural production within a given time period, along with peak crop prices representing the highest market values, poses greater challenges in forecasting compared to other values. Probability-based predictions, accounting for uncertainty and variability, offer a more accurate approach for extreme value estimation and risk assessment. In this study, we employ a multivariate analysis based on vine copula to explore the interdependencies between temperature anomalies and daily strawberry yield in Santa Maria, California. By considering the maximum and minimum daily yields each month, we observe an increased probability of yield loss with rising temperature anomalies. While we do not explicitly consider the specific impacts of temperature anomalies under individual Representative Concentration Pathway (RCP) scenarios, our analysis is conducted within the broader context of the current global warming scenario. This allows us to capture the overall anticipated effects of regional temperature anomalies on agriculture. The findings of this study have potential impacts and consequences for understanding the vulnerability of agricultural systems and improving crop model predictions. By enhancing our understanding of the relationships between temperature anomalies and crop yield, we can inform decision-making processes related to the impact of climate change on agriculture. This research contributes to the ongoing efforts in improving agricultural sustainability and resilience in the face of changing climatic conditions.

Conjunctive management of groundwater and surface water resources using a hybrid simulation–optimization method

Abstract This study aimed to determine the optimal conjunctive utilization of groundwater and surface water resources in the Halil River basin, one of the most significant study regions in Kerman Province (Iran). Multi-verse optimizer (MVO) and the ANFIS (adaptive neuro-fuzzy inference systems) simulation model, known as the MVO–ANFIS simulation–optimization model, were used for this purpose. Moreover, the optimal exploitation policy for the studied basin was presented. The ANFIS model yielded a coefficient of determination greater than 0.99 in Baft, Rabor, and Jiroft.This model had a high capability to simulate groundwater levels in these three regions. Therefore, the ANFIS model was adopted as the simulation model to predict the aquifer water table in these regions. Regarding the conjunctive utilization of groundwater and surface water resources, the exploitation policy resulting from the MVO–ANFIS simulation–optimization model had a desirable performance by supplying 91.70, 87.75, and 97.58% of the total demands of Baft, Rabor, and Jiroft, respectively. Moreover, results of water system performance indicators, including reliability (82.96, 72.65, 95.07), resiliency (70, 53.47, 80), vulnerability (29.54, 25.64, 17.02), and sustainability (74.24%, 66.10%, 85.78%) in the mentioned regions, respectively, showed the appropriate performance of the proposed model for the simulation–optimization problem.

DATA BREACHES EXIT STRATEGY: A COMPARATIVE ANALYSIS OF DATA PRIVACY LAWS

Data has become highly valuable in the era of digitalisation and is the main target of cybercriminals. Cybercriminals steal data by exploiting system vulnerabilities. The rise of catastrophic data breach incidents affects business operations, reputation and legal standing, leading to business disruptions, financial loss and reputation damage. These incidents have raised data security concerns. The frequent incident is partly due to insufficient security measures in place. This article employs doctrinal research focusing on legal principles based on legislation to analyse Malaysia’s legal framework for protecting personal data in Malaysia and a comparison with other jurisdictions, i.e. the European Union General Data Protection Regulation (GDPR), the Singapore Personal Data Protection Act 2012 and the China Personal Information Protection Law (PIPL). The findings show that Malaysia’s data protection laws fall short of the international norm in some areas. This article suggests that Malaysian policymakers may amend the Personal Data Protection Act 2010 to align with international data protection standards to strengthen data security measures in preventive, detective and responsive data breaches. Consequently, this article provides an analysis of data protection laws in Malaysia and compares them with other advanced jurisdictions. It offers valuable insights into the challenges and opportunities involved in safeguarding personal data, the legal framework, and organisational strategies related to data privacy and security.

Port berth allocation and microgrid cluster joint optimization scheduling based on master-slave game

The port microgrid cluster, which integrates berth allocation and energy scheduling for joint optimization, is a highly interconnected system of logistics and energy closely coupled microgrids. It can reduce port load fluctuations and ensure the stable and reliable operation of the port energy system. A port berth allocation and microgrid cluster joint optimization scheduling method based on master-slave game is proposed to address issues such as large fluctuations in current port loads, vulnerability of port power supply system to the impact of vessel loading and unloading operations, and conflicts of interest among various participants in the power market. To ensure power quality while achieving peak shaving and valley filling, a two-stage optimization model for microgrid cluster was constructed, enabling joint optimization of berth allocation and energy scheduling. In the first stage, considering the impact of random events during vessel navigation, berth allocation is conducted with the objectives of maximizing peak shaving and valley filling rates and minimizing overall costs, thereby achieving preliminary load leveling while ensuring power supply system quality. In the second stage, dispatching is performed for power generation equipment and adjustable loads, aiming to further achieve peak shaving and valley filling while maintaining the economic viability of the microgrids. Addressing conflicting interests among stakeholders in the port's electricity market, a master-slave game strategy has been established. This strategy aims to balance the interests between the electricity trading agent and the microgrid aggregator composed of multiple microgrid models. By solving the second-order partial derivatives of each optimization variable with respect to the objective function, we demonstrate the existence and uniqueness of the equilibrium solution in the master-slave game. In response to the challenge of difficulties in solving the multi-objective optimization model due to conflicting objective functions, a Dung Beetle Optimization (DBO) algorithm incorporating non-dominated sorting strategy and crowding distance was introduced. This resulted in a novel multi-objective optimization algorithm termed Non-Dominated Sorting Dung Beetle Optimizer (NSDBO). The NSDBO algorithm was demonstrated to possess favorable characteristics in terms of distribution, convergence, and uniformity. Using a northern Chinese port as an example, berth allocation and energy scheduling for three port areas were jointly optimized, and various scenarios were compared. The results indicated that, compared to Scenario 1, which did not consider random events, the proposed joint optimization method increased the peak shaving and valley filling rate by 8.46 %, reduced network losses by 42 kW, and increased the revenue of the microgrid aggregation merchant by 2.11 %. Compared to Scenario 2, which only considered energy scheduling, the joint optimization increased the peak shaving and valley filling rate by 36.89 %, reduced network losses by 18 kW, and increased the revenue of the microgrid aggregation merchant by 2.29 %. Compared to Scenario 3, which was individually optimized, the joint optimization increased the peak shaving and valley filling rate by 48.42 %, reduced network losses by 24 kW, and increased the revenue of the microgrid aggregation merchant by 2.73 %. This validates the superiority of the proposed joint optimization method in mitigating the impact of random events, reducing network losses, stabilizing port load fluctuations, and enhancing the revenue of the microgrid cluster.

A lightweight framework for cyber risk management in Western Balkan higher education institutions.

Higher education institutions (HEIs) have a significant presence in cyberspace. Data breaches in academic institutions are becoming prevalent. Online platforms in HEIs are a new learning mode, particularly in the post-COVID era. Recent studies on information security indicate a substantial increase in cybersecurity attacks in HEIs, because of their decentralized e-learning structure and diversity of users. In Western Balkans, there is a notable absence of incident response plans in universities, colleges, and academic institutions. Moreover, e-learning management systems have been implemented without considering security. This study proposes a cybersecurity methodology called a lightweight framework with proactive controls to address these challenges. The framework aims to identify cybersecurity vulnerabilities in learning management systems in Western Balkan countries and suggest proactive controls based on a penetration test approach.

Web Application Penetration Testing on Udayana University's OASE E-learning Platform Using Information System Security Assessment Framework (ISSAF) and Open Source Security Testing Methodology Manual (OSSTMM)

Education is a field that utilizes information technology to support academic and operational activities. One of the technologies widely used in the education sector is web-based applications. Web-based technologies are vulnerable to exploitation by attackers, which highlights the importance of ensuring strong security measures in web-based systems. As an educational organization, Udayana University utilizes a web-based application called OASE. OASE, being a web-based system, requires thorough security verification. Penetration testing is conducted to assess the security of OASE. This testing can be performed using the ISSAF and OSSTMM frameworks. The penetration testing based on the ISSAF framework consists of 9 steps, while the OSSTMM framework consists of 7 steps for assessment. The results of the OASE penetration testing revealed several system vulnerabilities. Throughout the ISSAF phases, only 4 vulnerabilities and 3 information-level vulnerabilities were identified in the final testing results of OASE. Recommendations for addressing these vulnerabilities are provided as follows. Implement a Web Application Firewall (WAF) to reduce the risk of common web attacks in the OASE web application. input and output validation to prevent the injection of malicious scripts addressing the stored XSS vulnerability. Update the server software regularly and directory permission checks to eliminate unnecessary information files and prevent unauthorized access. Configure a content security policy on the web server to ensure mitigation and prevent potential exploitation by attackers.

Induced alterations driven compromised structural properties in additively manufactured products

PurposeAdditive manufacturing also known as 3D printing is an evolving advanced manufacturing technology critical for the new era of complex machinery and operating systems. Manufacturing systems are increasingly faced with risk of attacks not only by traditional malicious actors such as hackers and cyber-criminals but also by some competitors and organizations engaged in corporate espionage. This paper aims to elaborate a plausible risk practice of designing and demonstrate a case study for the compromised-based malicious for polymer 3D printing system.Design/methodology/approachThis study assumes conditions when a machine was compromised and evaluates the effect of post compromised attack by studying its effects on tensile dog bone specimens as the printed object. The designed algorithm removed predetermined specific number of layers from the tensile samples. The samples were visually identical in terms of external physical dimensions even after removal of the layers. Samples were examined nondestructively for density. Additionally, destructive uniaxial tensile tests were carried out on the modified samples and compared to the unmodified sample as a control for various mechanical properties. It is worth noting that the current approach was adapted for illustrating the impact of cyber altercations on properties of additively produced parts in a quantitative manner. It concurrently pointed towards the vulnerabilities of advanced manufacturing systems and a need for designing robust mitigation/defense mechanism against the cyber altercations.FindingsDensity, Young’s modulus and maximum strength steadily decreased with an increase in the number of missing layers, whereas a no clear trend was observed in the case of % elongation. Post tensile test observations of the sample cross-sections confirmed the successful removal of the layers from the samples by the designed method. As a result, the current work presented a cyber-attack model and its quantitative implications on the mechanical properties of 3D printed objects.Originality/valueTo the best of the authors’ knowledge, this is the original work from the team. It is currently not under consideration for publication in any other avenue. The paper provides quantitative approach of realizing impact of cyber intrusions on deteriorated performance of additively manufactured products. It also enlists important intrusion mechanisms relevant to additive manufacturing.

An integrated metagenomic, metabolomic and transcriptomic survey of Populus across genotypes and environments

Bridging molecular information to ecosystem-level processes would provide the capacity to understand system vulnerability and, potentially, a means for assessing ecosystem health. Here, we present an integrated dataset containing environmental and metagenomic information from plant-associated microbial communities, plant transcriptomics, plant and soil metabolomics, and soil chemistry and activity characterization measurements derived from the model tree species Populus trichocarpa. Soil, rhizosphere, root endosphere, and leaf samples were collected from 27 different P. trichocarpa genotypes grown in two different environments leading to an integrated dataset of 318 metagenomes, 98 plant transcriptomes, and 314 metabolomic profiles that are supported by diverse soil measurements. This expansive dataset will provide insights into causal linkages that relate genomic features and molecular level events to system-level properties and their environmental influences.

Robustness and Security in Deep Learning: Adversarial Attacks and Countermeasures

Deep learning models have demonstrated remarkable performance across various domains, yet their susceptibility to adversarial attacks remains a significant concern. In this study, we investigate the effectiveness of three defense mechanisms—Baseline (No Defense), Adversarial Training, and Input Preprocessing—in enhancing the robustness of deep learning models against adversarial attacks. The baseline model serves as a reference point, highlighting the vulnerability of deep learning systems to adversarial perturbations. Adversarial Training, involving the augmentation of training data with adversarial examples, significantly improves model resilience, demonstrating higher accuracy under both Fast Gradient Sign Method (FGSM) and Iterative Gradient Sign Method (IGSM) attacks. Similarly, Input Preprocessing techniques mitigate the impact of adversarial perturbations on model predictions by modifying input data before inference. However, each defense mechanism presents trade-offs in terms of computational complexity and performance. Adversarial Training requires additional computational resources and longer training times, while Input Preprocessing techniques may introduce distortions affecting model generalization. Future research directions may focus on developing more sophisticated defense mechanisms, including ensemble methods, gradient masking, and certified defense strategies, to provide robust and reliable deep learning systems in real-world scenarios. This study contributes to a deeper understanding of defense mechanisms against adversarial attacks in deep learning, highlighting the importance of implementing robust strategies to enhance model resilience.

Hardware-in-Loop (HIL) Testbed Design of Thermal Power Plant for Threat Modeling and Attack Vector Analysis

Industrial control systems (ICSs) are extensively utilized worldwide to control and regulate various processes in energy utilities. It consists of various field devices, control and monitoring devices and communication devices. This paper focuses on the testing and analysis of various attack vectors that could potentially occur in a hardware-in-loop (HIL) Industrial Control System (ICS) testbed designed for a 500 MW thermal power. In the testbed, four typical process scenarios have been identified that can be manipulated through cyber-attacks, leading to severe issues such as plant shutdown or even explosions. The four significant plant scenarios recognized include minimal coal mill levels and increased temperatures in the classifier, heightened primary airflow to the coal mill, the tripping of an ID Fan, and adjustment of the Super-heater temperature to its lowest setting. Also, we utilize the STRIDE threat modeling methodology to accurately represents the elements of Cyber-Physical Systems (CPS), their inter-dependencies, and the potential attack entry points and system vulnerabilities.

Resting state changes in aging and Parkinson’s disease are shaped by underlying neurotransmission – a normative modeling study

BackgroundHuman healthy and pathological aging is linked to a steady decline in brain resting state activity and connectivity measures. The neurophysiological mechanisms underlying these changes remain poorly understood. MethodsMaking use of recent developments in normative modeling and availability of in vivo maps for various neurochemical systems, we test in the UK Biobank cohort (N=25917) if and how age- and Parkinson’s disease related resting state changes in commonly applied local and global activity and connectivity measures co-localize with underlying neurotransmitter systems. ResultsWe find the distributions of several major neurotransmitter systems including serotonergic, dopaminergic, noradrenergic, and glutamatergic neurotransmission to correlate with age-related changes as observed across functional activity and connectivity measures. Co-localization patterns in Parkinson’s disease deviate from normative aging trajectories for these, as well as for cholinergic and GABAergic, neurotransmission. The deviation from normal co-localization of brain function and GABAa correlates with disease duration. ConclusionsThese findings provide new insights into molecular mechanisms underlying age- and Parkinson’s related brain functional changes by extending the existing evidence elucidating the vulnerability of specific neurochemical attributes to normal aging and Parkinson’s disease. The results particularly indicate that alongside dopamine and serotonin, increased vulnerability of glutamatergic, cholinergic, and GABAergic systems may also contribute to Parkinson’s disease-related functional alterations. Combining normative modeling and neurotransmitter mapping may aid future research and drug development through deeper understanding of neurophysiological mechanisms underlying specific clinical conditions.

Utilization of Antenatal Care Services Within the Context of COVID-19, Security Challenges, and an Unstable Healthcare System at Primary Health Care Centers.

Utilizing antenatal care (ANC) is vital for maternal and neonatal well-being, especially in low-resource settings with healthcare challenges. Identifying factors impacting antenatal care this context, t of current situation in Sudan is crucial for developing strategies to improve maternal care. This study explores ANC utilization amidst COVID-19, security issues, and healthcare instability in primary health care centers in a resource-constrained environment, aiming to enhance maternal care access and quality. This is a mixed-methods study. Quantitative data were collected through a retrospective analysis of ANC attendance records before and during the pandemic. Demographic variables were analyzed for their association with ANC utilization. In-depth interviews were conducted to collect qualitative data from pregnant women and healthcare providers. These interviews focused on capturing the experiences, perceptions, and obstacles associated with antenatal care services during the pandemic and within the healthcare system challenges. Preliminary quantitative analysis revealed a significant decline in ANC utilization during the pandemic. Fear of COVID-19 infection, disrupted transportation, and resource constraints have emerged as significant barriers to ANC attendance. The qualitative findings highlighted the impact of security challenges on healthcare access and the adaptation strategies employed, including mobile ANC clinics and telehealth consultations. This study illuminates the intricate landscape of ANC utilization in the face of a pandemic, security instability, and healthcare system vulnerability. Policymakers and stakeholders should collaborate to strengthen healthcare systems and ensure the continued provision of essential maternal health services during challenging times.

On the dynamic vulnerability of an urban rail transit system and the impact of human mobility

Urban rail transit (URT) plays a pivotal role in facilitating human mobility within urban environments. It is significant to understand its vulnerability, i.e., the variation in capacity and demand when confronted with unexpected events, particularly operational disruptions. Although the network topology is generally fixed, the hourly-changing travel demand greatly impacts the actual vulnerability of a URT system. Unfortunately, few existing studies consider the combined influence of dynamic travel demand and network topology. To fill the gap, this paper proposes a network vulnerability assessment method with the joint consideration of static network topology and dynamic travel demand. This method includes a defined reasonable path, an accessibility-based identification of station importance with time-varying passenger demand, and a new dynamic vulnerability delay index considering affected travel demand. An empirical analysis was carried out by taking the URT system of Beijing, China as an example, and the impact of the more realistic multiple consecutive station failures in a URT system is also examined. Results show that the distribution of high-importance stations indeed varies with the time of day, affected by both static topology and hourly-changing passenger flow. When the disturbance of operation delay occurs, the impact of high-importance stations on the network vulnerability changes nonlinearly with the increase of delayed travel demand. Some stations that serve as bridges and are visited by large passenger flows have the greatest impact on network vulnerability. Network performance degradation is obviously segmented and stratified in the case of interval continuous failure. The disruption between different lines is the main cause of network performance degradation, and some high-importance stations within the lines act as catalysts to accelerate the performance degradation. The proposed method not only offers a valuable reference for quantifying network vulnerability arising from fluctuations in passenger mobility but also introduces a novel vulnerability evaluation index to the URT system.

METHODOLOGY FOR REDUCING THE RISKS OF CYBERSECURITY BREACHES AND PROTECTING INFORMATION FROM CYBER ATTACKS

The scientific article examines the problems associated with the risks of cybersecurity violations and information protection from cyber attacks. Given the dramatically increased amounts of information transmitted over networks, the number of cyber attacks is simultaneously increasing, with the aim of stealing various kinds of secret and confidential information. At the same time, the role of specialists involved in the development and implementation of information system security technologies has increased.
 The article analyzes vulnerabilities in corporate systems and suggests methods for improving encryption protocols for users' personal data. Special attention is paid to the technologies for implementing protection mechanisms at the software development stage.

Power electronics anomaly detection and diagnosis with machine learning and deep learning methods: A survey

Power electronics pertains to the conception, regulation, and utilization of electronic power circuits to proficiently administer and transform electrical energy. Power electronics play a crucial role in maintaining the reliability, efficiency, and security of complex production systems. Also, increasingly important in various applications such as renewable energy systems, electric vehicles, and industrial automation. However, modern power electronics systems are vulnerable to both cyber and physical anomalies due to the integration of information and communication technologies. So far, different methods have been used to detect abnormalities. This survey provides an overview of the state-of-the-art in anomaly detection in power electronics using machine learning and deep learning methods. It highlights the potential of these techniques in addressing the growing complexity and vulnerability of power electronics systems.