Critical Assets Research Articles

Efficient Data Processing Pipeline for Event-Based Vision Datasets: Techniques and Insights

Abstract Event-based vision datasets have emerged as a critical asset in advancing the capabilities of real-time perception systems, particularly in fields such as robotics, autonomous vehicles, and human-computer interaction. These datasets enable low-latency processing by capturing asynchronous, pixel-level changes in the scene, providing a distinct advantage over traditional frame-based systems. However, the diverse formats and characteristics of event-based datasets pose significant challenges for efficient processing and analysis, hindering their broader adoption and integration. In this paper, we present a versatile and comprehensive data processing pipeline designed to address these challenges by supporting multiple event-data formats, including newer formats such as EVT2 and EVT3. This pipeline not only converts data into widely supported formats like AEDAT and NPZ, but also ensures that the unique characteristics of event-based data—such as temporal precision and sparse event representation—are preserved throughout the conversion process. By applying this pipeline to several open-source datasets, we establish a standardized, efficient methodology for dataset manipulation that enhances compatibility and reproducibility in event-based vision research. Additionally, we introduce a novel high-resolution event-based action dataset, converted into various formats using our pipeline, which opens new avenues for exploring event-based techniques in action recognition. This dataset and our pipeline serve as valuable resources for the research community, enabling advancements in real-time vision applications and fostering greater collaboration and standardization across studies.

Empowering Youth through Open Data: Map-Based Storytelling of Croatian vs. European Youth

Abstract. In the digital age, open data has become a critical asset for fostering transparency, accountability, and civic engagement. However, the widespread availability of data has outpaced citizens' ability to effectively utilize it, underscoring the need for data literacy skills, especially among youth. This paper addresses the gap in data literacy by proposing an innovative teaching approach that leverages map-based storytelling to engage young people and enhance their data literacy. By integrating real-world open datasets into interactive visual narratives, this approach aims to captivate young audiences, develop critical thinking skills, and foster a deeper understanding of socio-economic issues. Through a case study of a Datathon project titled “Croatian Youth vs. EU: Who has it better?”, this research demonstrates the efficacy of combining data visualization and storytelling to promote data literacy and civic engagement. The findings highlight the potential of open data initiatives and open-source technologies in empowering youth and enhancing their participation in open government initiatives and policy-making processes.

Robust seismic fragility curves for concrete gravity dams incorporating aleatory, epistemic, and fuzzy uncertainties

Concrete gravity dams are critical infrastructure assets, and their failure in seismically active regions poses significant risks. The primary problem addressed in this research is the need for a reliable assessment of the seismic fragility of these dams to ensure safety in such areas. The research aims to develop a comprehensive methodology for seismic fragility analysis that accounts for various uncertainties affecting dam performance, including uncertainties in material properties and seismic characteristics and the fuzziness of damage state thresholds through the Latin Hypercube Sampling method. The methodology involves a three-stage approach: first, a finite element model is created using Abaqus software, incorporating uncertainties in material properties and earthquake characteristics using the Latin Hypercube Sampling method (LHS). Second, incremental dynamic analysis (IDA) defines damage states based on concrete cracking severity and overall stability, utilizing four performance categories of limit states (minor, moderate, extensive, and heavy). Finally, a mathematical model is introduced to characterize the ambiguity in damage state thresholds, recognizing the gradual nature of damage evolution. The primary findings demonstrate that fuzziness substantially influences the probability of exceedance across all damage states, with varying effects on different damage indices because of their differing frequency distributions. This research enhances the reliability and accuracy of seismic fragility assessments, contributing to more robust design guidelines and effective risk mitigation strategies for concrete gravity dams.

Evaluating Machine Learning Algorithms for Fake Currency Detection

Currency is a critical asset in any economy, yet it is vulnerable to counterfeiting, undermining its value and disrupting economic stability. Counterfeit currency is particularly prevalent during economic transition, such as demonetization, as fake notes are circulated to mimic real currency. Due to the subtle similarities between genuine and fake notes, distinguishing between them can be challenging. Consequently, financial institutions like banks and ATMs require robust automated systems to accurately detect counterfeit currency. In this study, we evaluate the effectiveness of six supervised machine learning algorithms—K-Nearest Neighbor, Decision Trees, Support Vector Machine, Random Forests, Logistic Regression, and Naive Bayes—in detecting the authenticity of banknotes. Additionally, we examine the performance of LightGBM, a gradientboosting algorithm, in comparison to these traditional methods. Our findings contribute to developing reliable, automated systems for counterfeit detection, and enhancing financial security.

THE ROLE OF EMOTIONAL INTELLIGENCE IN CRISIS LEADERSHIP: EFFECTS ON EMPLOYEE MORALE AND ORGANIZATIONAL RESILIENCE

This research paper examines the role of emotional intelligence (EI) in crisis leadership and its influence on employee morale and organizational resilience. By analyzing theoretical frameworks such as Golemans Emotional Intelligence Model, Transformational Leadership Theory, and Crisis Management Theories, the study establishes the significance of EI as a critical asset for leaders navigating crises. The paper delves into how emotionally intelligent leaders-who demonstrate empathy, self-awareness, and effective communication-enhance employee morale, create a supportive work environment, and build trust during times of uncertainty. Key findings indicate that leaders with high EI can mitigate the negative emotional impacts of crises, thereby preserving morale, fostering a sense of security, and promoting collective resilience. Furthermore, the research reveals that emotionally intelligent leadership not only addresses the immediate emotional needs of employees but also contributes to long-term organizational resilience by fostering a culture of psychological safety and adaptability. Through real-world case studies, the paper contrasts the outcomes of EI-driven leadership with those of low EI leadership, highlighting the far-reaching impact on employee engagement, productivity, and overall organizational stability. The implications suggest that developing emotional intelligence in leaders is essential for maintaining a motivated and resilient workforce capable of navigating complex and unpredictable business environments. Ultimately, the research underscores the strategic importance of integrating emotional intelligence into crisis leadership to ensure sustainable organizational success and employee well-being.

Brain health is essential for smooth economic transitions: towards socio-economic sustainability, productivity and well-being

Abstract Optimal brain health is essential to smoothing major global skill-intensive economic transitions, such as the bioeconomy, green, care economy and digital transitions. Good brain health is vital to socio-economic sustainability, productivity and well-being. The care transition focuses on recognizing and investing in care services and care work as essential for economic growth and social well-being. The green transition involves shifting towards environmentally sustainable and fairer societies to combat climate change and environmental degradation. The digital transition aims to unlock digital growth potential and deploy innovative solutions for businesses and citizens, and to improve the accessibility and efficiency of services. The bioeconomy transition refers to the shift towards an economy based on products, services and processes derived from biological resources, such as plants and microorganisms. Brain capital, which encompasses brain health and brain skills, is a critical economic asset for the success of economies of the future. The brain economy transition from a brain-negative (brain-unhealthy) economy, which depletes brain capital, to a brain-positive (brain-healthy) economy, which arrests and reverses the loss of brain capital, will be foundational to these major transitions. Increased brain capital is vital to educational attainment, upskilling and reskilling. In this paper, we provide a detailed roadmap for the brain economy transition.

Threat Modeling for Enhanced Security in the Healthcare Industry with a Focus on Mobile Health and IoT

The advancement of mobile health and the Internet of Things (IoT) promises to enhance healthcare quality while reducing costs, particularly with the transition from inpatient to home and ambulatory care. This shift, driven by an aging population, financial pressures, and a shortage of skilled healthcare professionals, presents significant opportunities and challenges. While mobile health improves access and encourages self-management, it also raises serious concerns regarding security and interoperability, especially with wearable devices equipped with sensors in a patient's Body Area Network (BAN). This paper critically analyzes the security and interoperability risks associated with these technologies, emphasizing the need for robust measures such as configuration and asset management. Utilizing recommendations from ENISA (2016) and conducting a risk and vulnerability assessment, this study develops a comprehensive security model tailored for healthcare architectures. Additionally, it applies the STRIDE threat modeling approach to identify and mitigate potential threats, providing valuable insights for securing healthcare systems and prioritizing critical assets vital to organizational operations.

Innovative Model of Creative Tourism as a Factor of Sustainable Development in the Context of Modern Challenges

The aim of this study is to develop an innovative model of creative tourism as a key driver of sustainable development in response to modern challenges. In a world where globalization and technological advancements are reshaping traditional tourism approaches, creative tourism emerges as a novel strategy to attract visitors and foster economic growth. This study employs a data-driven analytical approach (BIG DATA analysis) to examine creative tourism, recognizing the need to manage, systematize, classify, and draw insights from large datasets enabled by digital technologies. Creative tourism systems leverage advanced neural networks with multiple hidden layers to intelligently process and extract maximum value from data, providing essential insights for tourism service consumers. This positions creative tourism as both a critical business asset and a valuable tool for improving the human experience. Research Objectives: 1.To develop a model of creative tourism that supports sustainable development. 2.To examine the daily practices and impact of creative tourism on sustainable development. 3.To identify ways to enhance the effectiveness of creative tourism models and their contribution to sustainable development. The study highlights the essential components of an innovative creative tourism model, integrating local culture, environmental responsibility, digital technologies, and the creation of unique tourism products. Special focus is given to sustainable development principles, which not only protect environmental resources but also promote the social well-being of local communities. The paper also presents successful examples of creative tourism practices, showcasing their potential to open new market opportunities and increase regional competitiveness. The findings underscore that this innovative model of creative tourism holds the potential to revolutionize the tourism industry, advancing economic, social, and environmental progress.

Rapid remote volcanic ashfall impact assessment for the 2022 eruption of Hunga volcano, Tonga: a bespoke approach and lessons identified

When disasters occur, rapid impact assessments are required to prioritise response actions, support in-country efforts and inform the mobilisation of aid. The 15 January 2022 eruption of Hunga volcano, Tonga, and the resulting atmospheric shockwave, ashfall, underwater mass disturbance and tsunami, caused substantial impacts across the Kingdom of Tonga. Volcanic impacts on the scale observed after the eruption are rare, necessitating a reliance on international advice and assistance. The situation was complicated by the loss of Tonga’s international submarine fibreoptic cable (causing a complete loss of communications for approximately 20 days) along with border closures due to the COVID-19 pandemic. A need emerged for a rapid remote volcanic impact assessment and provision of specialist advice to help inform the response of international partners. Here we present a novel methodology for conducting rapid remote volcanic ashfall impact assessments, conducted over a 10-day period following the eruption. We used three different hazard models for ashfall thickness across the main island of Tongatapu and available asset information and vulnerability functions for buildings, agriculture, electricity networks, water supply and roads, to provide initial estimates of losses due to ashfall from the 15 January eruption. For buildings, we estimated losses both as total losses and as percentages of the total replacement cost of buildings on Tongatapu. For agriculture, we made probabilistic estimates of production losses for three different crop classes. For ashfall clean-up, we estimated ranges of ashfall volumes requiring clean-up from road surfaces and roofs. For water supply, electricity networks and roads, our analysis was limited to assessing the exposure of important assets to ashfall, as we had insufficient information on system configurations to take the analysis further. Key constraints on our analysis were the limited nature of critical infrastructure asset inventories and the lack of volcanic vulnerability models for tropical regions including Pacific Island nations. Key steps towards iteratively improving rapid remote impact assessments will include developing vulnerability functions for tropical environments as well as ground-truthing estimated losses from remote approaches against in-person impact assessment campaigns.

Emotional intelligence and knowledge-sharing behavior: exploring the dynamics of emotional intelligence on knowledge exchange among filipino academics

The increasing complexity of academia and the growing recognition of knowledge as a critical organizational asset have put knowledge-sharing behavior at the forefront of research and practice. This study investigates the relationship between emotional intelligence and knowledge-sharing behaviors of 167 academicians from public higher education institutions in the Philippines. The researchers utilized a descriptive-correlational research design, employing Pearson product-moment correlation to establish the relationship between the variables. Results of the study show that most academicians have average emotional intelligence, which may impact sharing their knowledge with their peers and the rest of the academic community. As to their knowledge-sharing behaviors, academicians convey their thoughts and expertise to their departments and colleagues, but their publications and knowledge-building contributions to the community remain insignificant. The study also indicates that academicians' knowledge-sharing behavior is associated with their emotional intelligence. These results have implications for individual academicians and institutional management, as they can improve their professional development, leadership, and teamwork.

Populism and policy capacity: Evidence from an opposition municipality in Istanbul

AbstractDespite achievements in its conceptual rigor, policy capacity still represents a relatively depoliticized concept that fails to sufficiently consider the ways in which politics plays a role in its creation, mobilization, or decay. This article seeks to contribute to this debate by investigating the impact of populism on policy capacity, the type of policy capacity challenges posed and how they are related to populist policy design. Based on a case study of a municipality in Istanbul the article shows how populist politics and national populist policy design erode long‐term municipal policy capacity by enabling the (ab)use of the rule of law, loss of expertise, exhaustion of critical assets such as urban land and financial resources, and erosion of public citizenship due to growing expectations in the form of particularistic benefits. Declining policy capacity in turn leads to a shift in municipal policy priorities.

RE-Trace : Re-identification of Modified GPS Trajectories

GPS trajectories are a critical asset for building spatio-temporal predictive models in urban regions in the context of road safety monitoring, traffic management, and mobility services. Currently, reliable and efficient data misuse detection methods for such personal, spatio-temporal data, particularly in data breach cases, are missing. This article addresses an essential aspect of data misuse detection, namely, the re-identification of leaked and potentially modified GPS trajectories. We present RE-Trace —a contrastive learning-based model that facilitates reliable and efficient re-identification of GPS trajectories and resists specific trajectory transformation attacks aimed to obscure a trajectory’s origin. RE-Trace utilizes contrastive learning with a transformer-based trajectory encoder to create trajectory representations, robust to various trajectory modifications. We present a comprehensive threat model for GPS trajectory modifications and demonstrate the effectiveness and efficiency of the RE-Trace re-identification approach on three real-world datasets. Our evaluation results demonstrate that RE-Trace significantly outperforms state-of-the-art baselines on all datasets and identifies modified GPS trajectories effectively and efficiently.

Multi-Task Learning Network-Based Prediction of Hydraulic Fracturing Effects in Horizontal Wells Within the Ordos Yanchang Formation Tight Reservoir

Accurate prediction of fracture volume and morphology in horizontal wells is essential for optimizing reservoir development. Traditional methods struggle to capture the intricate relationships between fracturing effects, geological variables, and operational factors, leading to reduced prediction accuracy. To address these limitations, this paper introduces a multi-task prediction model designed to forecast fracturing outcomes. The model is based on a comprehensive dataset derived from fracturing simulations within the Long 4 + 5 and Long 6 reservoirs, incorporating both operational and geological factors. Pearson correlation analysis was conducted to assess the relationships between these factors, ranking them according to their influence on fracturing performance. The results reveal that operational variables predominantly affect Stimulated Reservoir Volume (SRV), while geological variables exert a stronger influence on fracture morphology. Key operational parameters impacting fracturing performance include fracturing fluid volume, total fluid volume, pre-fluid volume, construction displacement, fracturing fluid viscosity, and sand ratio. Geological factors affecting fracture morphology include vertical stress, minimum horizontal principal stress, maximum horizontal principal stress, and layer thickness. A multi-task prediction model was developed using random forest (RF) and particle swarm optimization (PSO) methodologies. The model independently predicts SRV and fracture morphology, achieving an R2 value of 0.981 for fracture volume predictions, with an average error reduced to 1.644%. Additionally, the model’s fracture morphology classification accuracy reaches 93.36%, outperforming alternative models and demonstrating strong predictive capabilities. This model offers a valuable tool for improving the precision of fracturing effect predictions, making it a critical asset for reservoir development optimization.

Increasing maritime cybersecurity awareness through game-based learning

Abstract Increased connectivity, digitalization and emerging autonomous operations within the maritime sector are paving the road for new cybersecurity risks. Such risks are not just a technical concern, as effective awareness and training programs play an important role in increasing the knowledge of relevant cyberattacks and defences, thereby improving security practices among stakeholders. Serious games have emerged as an engaging method to train individuals, effectively communicate an understanding of emerging cybersecurity threats and promote cybersecurity culture and hygiene. This paper delves into the development process of an asymmetrical strategy game for the maritime sector. The game uses a red versus blue paradigm to educate players about critical assets of maritime operations and how they can be exploited. The novelty of this work has been to design a gameplay based on actual incidents that for instance shipping companies have been facing up until today and likely future events. We employ a design science research methodology to ensure problem relevance and utility for practitioners. The game has been designed towards a demography of students from maritime education and training institutions and professionals working in the maritime sector but should be appealing to others looking to expand their comprehension of the maritime cybersecurity intricacies as well. The game serves as a pedagogical learning tool for elevating cybersecurity awareness, preparedness and response in the maritime digitalization sphere.

Comprehensive Analysis Of Background Screening Impact On Employee Experience And Organizational Outcomes

This comprehensive study examines the multifaceted impact of background checks on employee experience and organizational outcomes, with a particular focus on the implementation of a nationwide, standardized employment screening system. The research is guided by six interconnected themes: trust, transparency, fairness, diversity and inclusion, engagement, and retention. In the contemporary business landscape, background checks have transcended their traditional role as a mere formality, evolving into critical assets for enhancing employee satisfaction, organizational trust, and retention rates. This review synthesizes a wide array of studies to analyze the complex interplay between background screening practices and employer-employee relationships. It establishes robust evidence for how fair, transparent, and reasonable background checks can significantly enhance organizational commitment and employee loyalty. The results underscore the critical importance of awareness and transparency in the background check process, highlighting how these factors foster trust and perceptions of fairness, ultimately leading to positive employee attitudes and improved organizational outcomes. The study also delves into the potential adverse effects of excessive or biased background checks on workforce diversity, with a particular emphasis on historically marginalized groups and individuals with criminal records. It proposes that employers who embrace equality in the workplace and ensure that their background check processes are free from unlawful discrimination can cultivate and maintain a diverse, inclusive, and highperforming workforce. The implications for practice and research are far-reaching. To optimize employee experience and key outcomes such as organizational commitment, job satisfaction, and reduced turnover intention, employers must prioritize fairness, transparency, and inclusiveness in their background check processes. The study identifies critical areas for further research, including sector-specific effects of background screening practices and the impact of emerging technologies and data privacy concerns on background check processes. By reconceptualizing background checks as a strategic tool for building trust and enhancing employee commitment, rather than a mere compliance exercise, organizations can achieve superior outcomes across multiple dimensions of performance. This research provides a comprehensive framework for understanding and implementing effective background screening practices that balance organizational risk management with positive employee experiences.

A Comprehensive Comparative Analysis of Deep Learning Architectures for Suspicious Activity Detection in Video Surveillance

Abstract: In the landscape of modern security infrastructure, video surveillance has evolved into a ubiquitous and indispensable tool for ensuring public safety and safeguarding critical assets. This research paper delves into an extensive examination of various deep learning architectures for the purpose of detecting suspicious activities in video surveillance. The investigation encompasses Convolutional Long Short-Term Memory (ConvLSTM), Convolutional Neural Network (CNN) combined with Long Short-Term Memory (LSTM), ConvLSTM, Bidirectional Long Short-Term Memory (BiLSTM), and diverse combinations thereof. Each model is rigorously trained and tested on a carefully curated dataset designed to encapsulate a spectrum of normal and suspicious activities like shooting and fighting. The study aspires to identify the most efficacious model for enhancing the accuracy of suspicious activity detection in complex and dynamic environments. Metrics such as accuracy, precision, recall, and F1 score will be rigorously assessed to ascertain the model’s comparative performances

Empowering Pesantren as an Anti-Global Climate Change Center in Jember

Global climate change has been considered a more threatening problem to face. In particular, establishing a pesantren-based anti-global climate change center within Al-Qodiri Islamic Boarding School is seen as the mitigation system. As this program is meant to be a community service program, the ABCD community empowerment method was used. The results suggested that: First, in the research planning (define, discovery, dream, and design), the most critical assets to be developed were the human resource assets of the santri (lit. student). The objective is to develop the quality of the santri as the human resource asset to realize the pesantren-based anti-global climate change center—the service programs in the form of counseling, training, and mentoring. Second, the holistic transformation-based service was done through applicative and evaluative stages, which has improved the quality of the human resource assets of Al-Qodiri Islamic Boarding School Community holistically both in the aspects of knowledge, creativity, and ascertaining character awareness to establish an anti-global climate change society.

The Catalytic Effect of Low Molecular Weight Acids on the Physicochemical and Dielectric Properties of Oil-Paper Insulation Systems.

In most industrialized countries, power transformers built several decades ago are approaching the end of their operational lifespan. The ongoing energy transition, focused on developing 100% renewable energy sources and accelerating global transportation electrification, further exacerbates these assets. Combined with rising electricity demand, there is an increasing risk of critical transformers' degradation acceleration. In this context, understanding the aging mechanisms of the insulation system inside these essential assets, which form the core of every energy network, becomes paramount for today's managers and engineers responsible for their operations. The acids generated through oil oxidation can be classified into two categories: low molecular weight acids (LMAs), which are inherently more hydrophilic and consequently have a greater impact on the degradation rate of solid insulation through hydrolysis, and high molecular weight acids (HMAs), which do not significantly contribute to the degradation of paper insulation. This study specifically addresses the impact of acids generated through oil oxidation-focusing on LMAs. New oil samples were infused with different ratios of LMAs before impregnation. The impregnated paper samples underwent thermal aging at 115 °C. Different physicochemical and dielectric properties were investigated. The investigations revealed that oils blended with formic acid exhibited more adverse effects on the insulation system compared to other LMAs. This information is essential for industry professionals seeking to mitigate the risks associated with transformer degradation and extend the lifespan of these critical assets during the energy transition.

Operational status effect on the seismic risk assessment of oil refineries

The operational status of an oil refinery (type and scale of operations that take place at any time instance) largely determines the amount of fuel produced, circulated within the facility, and stored in tanks. This status is affected by seasonality, periods of peak or low demand, as well as periods of routine maintenance. However, it is an aspect that is typically neglected even though it stands out among the factors that determine the seismic performance of several critical industrial assets, such as the storage tanks, as well as the consequences of any potential failure. An open-source refinery testbed is employed herein to demonstrate the effect of the refinery's operational status on the seismic risk estimates. Alternative realistic operational scenarios are developed following typical industry practices and are arranged over a time period between two refinery major maintenance shutdown events. The most probable damage state is selected for each asset to identify the most vulnerable ones. Based on the type and importance of the impacted assets, the potential consequences are determined at the facility level. Resulting estimates are very different if an earthquake strikes during a regular/high/low-demand period, or a maintenance period. The framework can be utilized to identify the locations within the refinery that may trigger cascading failures and secondary damages, should their assets be damaged by a seismic event. The outcomes can be exploited by stakeholders, risk engineers, and emergency action planners for developing customized and businesslike procedures to enhance the seismic resilience of the facility.

Security in the era of wireless innovations: analysis of potential threats and protective measures

In today's interconnected world, wireless data transmission technologies have seamlessly integrated into the fabric of modern business operations. As reliance on these technologies grows, so does the imperative to ensure robust cyber security measures. Particularly in the age of wireless innovations, exemplified by the proliferation of the Internet of Things (IoT), the discourse surrounding the security of wireless technologies underscores the necessity of comprehending both established threats and the continuous emergence of new vulnerabilities. This underscores the urgent need for timely detection and mitigation strategies. While the convenience afforded by wireless data transmission technologies grants society unprecedented access to information and facilitates the management of diverse devices, processes, and systems, it also exposes users and modern information and communication systems (ICS) to significant cyber threats and vulnerabilities. Consequently, there arises a pressing need to address these challenges comprehensively. This research dissects contemporary methodologies aimed at restricting access to wireless networks, identifying potential vulnerabilities, and crafting effective responses to cyberattacks. It delves into various facets of cyber security, including data encryption, user authentication mechanisms, traffic monitoring protocols, and anomaly detection algorithms. Furthermore, it delves into the crucial aspect of educating personnel on wireless security practices, equipping them with threat awareness and incident response capabilities. Given the dynamic landscape of cybersecurity technologies and threats, this work seeks to establish a foundational understanding of the security landscape within wireless networks. By doing so, it aims to outline pragmatic strategies for effectively managing security risks, thereby fortifying the resilience of modern organizations and safeguarding critical information assets.