Air Traffic Management Security in the Digital Age: A Proactive Risk Management Strategy

With the continuous expansion of air traffic flow, the increasingly serious aviation network security threats have a significant and far-reaching impact on the safe operation of the aviation industry. The networked air traffic management (ATM) system is an integrated space–air–ground network integrating communication, network, satellite, and data chain technology, which adopts a network-centric structure to provide network-enabled services and applications for the air transportation system. In view of the serious network threats faced by networked ATM, this paper studies the basic theories and key technologies of ATM information security assurance and designs a credible security architecture to provide comprehensive and systematic security assurance for networked ATM. Starting from the problems and development trend of ATM security assurance, this paper investigates the dynamic and complex data processing process of ATM system, analyzes the complex interaction between its cyber and physical system, and then constructs the networked ATM cyber–physical fusion system model and threat model. Furthermore, the causal relationship between ATM security threat alert information is mapped into a Bayesian network, and the game model of networked ATM is proposed using Bayesian Nash equilibrium strategy. At last, the information security assurance architecture of networked ATM system based on blockchain technology is formed by establishing a distributed co-trust mechanism and multi-chain storage structure. We expect this paper to bring some inspiration to the related research in academia and aviation so as to provide useful reference for the construction of ATM safety and security system and the development of technology.

This comprehensive review aims to elucidate the pivotal role of legal practitioners in mitigating corporate risks within the intricate corporate landscape of Nigeria. The method involved a meticulous examination of existing literature, focusing on the legal frameworks, regulatory compliance, collaborative innovation, sustainability principles, education, and capacity-building initiatives, financial stability, infrastructure development, and proactive risk management strategies. The results revealed that legal practitioners are instrumental in shaping robust and dynamic legal frameworks and fostering a culture of adherence to legal and ethical norms. They play a significant role in fostering collaborative innovation, integrating sustainability into corporate governance practices, advancing education and capacity-building initiatives, and implementing effective risk management strategies. The conclusion underscores the indispensable and multifaceted role of legal practitioners in navigating the complexities of corporate governance and risk mitigation in Nigeria. Recommendations include strengthening legal frameworks, enhancing collaborative innovation, promoting sustainable corporate governance, advancing education and capacity building, fostering financial stability and infrastructure development, and implementing proactive risk management strategies. These recommendations are crucial for fostering a resilient, sustainable, and ethically sound corporate landscape in Nigeria
 Keywords: Legal Practitioners, Corporate Risks, Corporate Governance, Risk Mitigation, Nigeria.

Gully pots and lateral connections represent the anterior part of the sewer infrastructure responsible for the majority of flooding events in public areas. This study provides a statistical procedure to quantify the effectiveness of proactive management strategies to improve their performance. The first part consists of non-parametric methods to determine the evolution of gully pot blockages. The second part presents a Bayesian approach to quantify the development of reported flooding events when lateral connections are subject to proactive strategies. To this end, call data were collected from areas that were maintained proactively instead of reactively. Application of the procedure revealed a significant call decrease in one area. In addition, an increasing blockage likelihood over time indicated cyclic cleaning to be effective to improve sewer serviceability. By linking management strategies to performance, this flexible procedure can support sewer managers to balance the merits of proactive and reactive management strategies.

Teacher success is critical to student success, yet teachers are challenged daily to meet their students’ diverse learning and behavioral needs. Of major concern is the time many teachers spend managing student behaviors, which takes time away from instruction. One way to reduce time spent managing student behavior and increase student engagement is to use proactive classroom management strategies. Teachers can embed proactive research-based strategies within their lesson plans to reduce behavior problems and improve achievement for all students, including students with disabilities. This article highlights four research-based proactive strategy categories including whole-group response systems, movement integration, visual supports, and student choice. This article illustrates how teachers can embed strategies from these categories in their lesson planning and includes a lesson plan template with teacher-created examples.

A proactive energy management strategy for battery-powered autonomous systems

As the frequency of extreme weather events rises, the resilience of power systems is becoming increasingly important. This paper proposes a proactive microgrid management strategy for enhancing the resilience of microgrids (MGs) based on nested Mixed Integer Linear Programming problems with chance constraints. In the proposed method, MGs operate in a special operating mode referred to as the “preparation mode” to protect the vital load and maximize resource operation efficiency when an external grid outage warning is issued. The preparation mode problem is formulated to reflect both the normal and emergency mode operation conditions. The on-event phase-operation problem under emergency-mode operation conditions is nested within the pre-scheduling problem under normal-mode operation conditions in the preparation mode. Further, according to their importance, loads are divided into critical and non-critical ones in the problems. The former is represented by a chance constraint, and the latter is represented by the expected cost of load shedding in the cost function. The numerical examples demonstrate that the proposed preparation mode enables the MG to guarantee a high chance that the critical load will survive and to lower the cost of the non-critical load shedding with a minor increase in resource operation costs.

In today's globalized business landscape, enterprises face increasing reliance on intricate supply chains to deliver goods and services efficiently. However, this interconnectedness exposes organizations to diverse risks, exacerbated by factors like natural disasters, geopolitical tensions, and technological vulnerabilities. The COVID-19 pandemic further emphasized the vulnerability of supply chains, prompting the urgent need for robust risk management strategies. Interstate conflicts also significantly influence supply chain risks, posing challenges such as trade disruptions and security vulnerabilities. To mitigate these risks, enterprises must adopt proactive risk management strategies, enhance supply chain resilience, and leverage technology-enabled solutions for real-time monitoring and response. The study examines supply chain risk management (SCRM) practices in healthcare establishments in Algeria and Ukraine, comparing countries with differing healthcare systems. The research aims to identify disparities in SCRM approaches and their consequences. Results reveal that while both countries prioritize technology for predictive analysis, Algerian managers emphasize collaboration with other institutions. The study suggests integrating SCRM technology into enterprise management system, prioritizing advanced technological solutions, scenario planning, collaborative partnerships, comprehensive risk management plans, continuous monitoring, and strengthening supplier relationships to enhance supply chain resilience and continuity in healthcare service delivery. These findings underscore the importance of efficient SCRM strategies in mitigating supply chain disruptions and ensuring uninterrupted healthcare services. By implementing these strategic measures, healthcare establishments can enhance their SCRM capabilities, thus promoting resilience, efficiency, and continuity in healthcare service delivery across diverse healthcare systems.

This research provides an in-depth analysis of the challenges faced during the construction of the Oyon-Ambo highway in Peru, particularly during the extreme rainfall events associated with the 2019 El Niño phenomenon. Situated in the high Andean area of the Pasco region, this rigid pavement road project encountered significant disruptions due to the extraordinary weather conditions. The adjacent Chaupihuaranga River experienced increased flow rates, causing extensive damage to the road and necessitating substantial modifications to the engineering plans. Focusing on 14 critical sectors identified in the project's technical file as prone to instability, the study details the accelerated degradation and eventual destruction of these sectors due to the intense rainfall. Emphasizing the importance of adaptive risk management in road construction, especially in regions susceptible to natural phenomena like El Niño, the paper underscores the need for responsive reengineering approaches based on specific damage types. It also highlights the critical role of continuous monitoring and evaluation in adapting to environmental changes. The insights from the Oyon-Ambo highway case offer valuable lessons for future road construction projects, underscoring the importance of proactive and adaptive risk management strategies to minimize the impacts of extraordinary natural events.

This study applies the Gaussian Analytical Hierarchy Process (Gaussian AHP) to landslide susceptibility mapping and demonstrates its superior methodological rigor and predictive performance relative to the traditional AHP method. Susceptibility maps produced by Gaussian AHP allocated 26.31% of the study area to the very high susceptibility class, outperforming the traditional AHP’s estimated share (23.52%), and achieved a more balanced distribution across all five classes. Validation against a high resolution inventory of 97,742 landslide samples collected during the February 2023 São Sebastião event—divided into 70% training and 30% validation subsets—yielded improved metrics: ROC area under the curve of 0.6360 versus 0.6220; overall accuracy of 0.6364 versus 0.6229, balanced accuracy of 0.6356 versus 0.6221; and sensitivity of 0.3585 versus 0.3116, for the Gaussian and traditional AHP methods respectively. An uncertainty analysis quantified a 56.16% disagreement between the two methods, revealing that Gaussian AHP reduced classification ambiguity in critical classes. A complementary density-based assessment, comparing observed landslide crown points and scar polygons against susceptibility class areas, showed that Gaussian AHP produced a gradual, coherent increase in normalized landslide density from very low to very high susceptibility, whereas traditional AHP displayed sharp breaks in intermediate classes. These findings confirm that Gaussian AHP enhances objectivity, spatial coherence, and operational reliability, better aligning high density landslide clusters with the highest susceptibility zones. By leveraging statistical weighting, Gaussian AHP streamlines data preprocessing and reduces the need for expert calibration, making it well suited for assessments in data rich environments. The statistical weighting procedure facilitates the integration of diverse geospatial datasets and supports robust, reproducible multicriteria decision analysis. Its integration with accurate machine learning-derived land use/land cover data and refined climate data is recommended to further improve predictive accuracy and support proactive landslide risk management strategies. The proposed approach can additionally meet operational purposes, provided that near real-time climate data, updated geospatial databases, and massive computing resources are available.

Cybersecurity governance is increasingly recognized as a cornerstone of national security, especially in protecting critical infrastructure sectors such as energy, healthcare, finance, telecommunications, and transportation. This study investigates how governance frameworks, IT auditing, and risk management practices collectively reduce cyber threats and enhance the resilience of essential services. Using data from CISA, GAO audit reports, Verizon DBIR, and the World Economic Forum, the study assesses governance effectiveness through a combination of statistical techniques, including regression analysis, survival modeling, and data reduction methods. Findings reveal that organizations adopting both the NIST Cybersecurity Framework and ISO 27001 report a 75.8% reduction in cyber exploits, while IT audits lead to a 38–45% decrease in identified vulnerabilities. Additionally, proactive risk management strategies significantly delay the occurrence of cyber incidents, extending the average time to breach by over 260 days. These results underscore the critical importance of structured cybersecurity governance in minimizing threats and ensuring the continuity of national infrastructure. However, the study also highlights several implementation challenges, including regulatory inconsistencies, budget constraints, and a shortage of skilled cybersecurity professionals. These obstacles vary by region and sector, with under-resourced public institutions and developing economies facing the most significant barriers to effective governance. Recommendations include regulatory harmonization, mandating regular cybersecurity audits, and increasing investments in cybersecurity training and threat intelligence particularly in regions with fragmented oversight. The study offers valuable guidance for policymakers, regulators, and industry leaders seeking to strengthen national resilience in an evolving cyber threat landscape.

This chapter examines mental health professionals’ experiences with clients who stalk, threaten, or harass them. The authors review the growing literature on the prevalence of such experiences and link it to the broader literature on typologies of stalking perpetrators. Two conceptual risk management models are described aimed at helping clinicians navigate these challenging scenarios, and the authors review the recent empirical data on risk management responses that were found to be more and less effective. The risk management responses available to many private practitioners are limited relative to clinicians practicing in other settings, increasing the importance of establishing a proactive risk management strategy. Recommendations for the private practitioner emphasize a team approach to provide logistical and emotional support to the targeted clinician for such stalking, threats, and harassment.

An economic risk early warning model identifies and assesses potential financial threats by analysing key economic indicators, market trends, and geopolitical events. Using machine learning algorithms, this model continuously monitors data to detect warning signs, such as shifts in inflation, unemployment, or trade imbalances. Early risk alerts enable policymakers and businesses to proactively address vulnerabilities, mitigate financial impacts, and make informed decisions that strengthen economic resilience against potential downturns. This paper presents a comprehensive analysis of risk assessment and financial prediction within the Chinese finance sector. The study investigates the intricate relationships between various financial indicators and their implications for risk assessment with associative rule mining, financial analysis, and predictive modelling techniques. By examining associative rules, the paper illuminates key patterns and associations, offering valuable insights into the drivers of financial risk. Subsequently, a detailed financial analysis is conducted, highlighting the varying risk profiles among prominent players in the sector based on metrics such as debt-to-equity ratio, profit margin, and market capitalization. Furthermore, predictive modelling results provide insights into the effectiveness of predictive models in forecasting the probability of default for financial companies, aiding stakeholders in making informed risk management decisions. The findings underscore the importance of robust financial health and proactive risk management strategies in navigating the dynamic landscape of the Chinese finance sector. For instance, entities like the Industrial and Commercial Bank of China (ICBC) exhibit a low debt-to-equity ratio of 8.2 and a healthy profit margin of 18%, resulting in a low financial risk level. Conversely, companies like Ping An Bank display higher risk profiles with elevated debt levels, such as a debt-to-equity ratio of 10.2, coupled with lower profit margins, resulting in a high financial risk level. Furthermore, predictive modelling results provide insights into the effectiveness of predictive models in forecasting the probability of default for financial companies. For example, Bank of China (BOC) and the Agricultural Bank of China (ABC) exhibit predicted probabilities of default of 12.1% and 15.5%, respectively, aligning closely with their actual default status.

Purpose: This study explores the impact of financial market instability on economic growth and long-term investment. It investigates how market volatility, liquidity crises, and weak institutional frameworks influence resource allocation, investor confidence, and sustainable investment sectors, particularly in developing economies. Research Design and Methodology: The research employs a qualitative systematic literature review (SLR) approach, synthesizing theoretical and empirical studies from credible sources such as Emerald, Springer, Elsevier, and Wiley. The SLR method allows for a comprehensive literature analysis to identify trends, gaps, and critical insights regarding financial instability and its broader implications. Findings and Discussion: The findings reveal that financial market instability disrupts capital allocation, weakens investor confidence, and significantly impacts sectors requiring stability, such as green equity markets and infrastructure. Institutional weaknesses exacerbate these effects, particularly in developing countries. Robust regulatory frameworks like Basel III are critical in mitigating instability, enhancing market resilience, and promoting long-term investments. However, the study identifies challenges in regulatory implementation and institutional capacity, emphasizing the need for targeted policy reforms and proactive risk management strategies. Implications: This research underscores the importance of strengthening institutional and regulatory frameworks to stabilize financial markets and foster sustainable economic growth. For practitioners, adopting risk management tools such as portfolio diversification and hedging is essential to protect investments from market volatility. Policymakers are encouraged to develop transparent and inclusive policies that enhance investor confidence and support sustainable investments in critical sectors.

The prediction of low visibility is essential for proactive traffic safety management on freeways under fog conditions. However, few studies have developed prediction models for visibility on freeways at a short‐term time interval. This study proposes an ensemble learning approach to develop a short‐term prediction model of low visibility on freeways using meteorological data. Spearman's rank correlation coefficient is used to select meteorological variables related to low visibility. Random forests (RF) and extreme gradient boosting (XGB) are employed to develop visibility prediction models, and back propagation neural network (BPNN) and logistic regression (LR) are used for comparison. The models are evaluated over five prediction time intervals (5, 10, 15, 30, and 60 min). The results indicate that the RF models outperform the other models with precision of 73.9%, recall of 59.8% and F1 score of 0.65. Moreover, the prediction model with a 15‐min time interval shows better performance. With the proposed short‐term prediction of low visibility, it is expected that more crashes could be prevented with more appropriate proactive traffic safety management strategies.

The article examines the risks associated with the quality assurance process in higher education, focusing on various aspects that can affect the effectiveness, integrity and evolution of this process. Through the detailed analysis of the listed risks, from chaotic changes in legislation to the lack of specialized human resources and the decrease in the quality of external evaluation reports, the article explores how these aspects can influence the quality of university education and the strategic decisions of higher education institutions. Methods by which universities can manage and minimize these risks are also presented, emphasizing the need to implement proactive risk management strategies and strengthen institutional capacity to respond to the changing challenges of the educational environment. The article highlights the importance of addressing risks and opportunities in the quality assurance process, with a particular focus on the digitization of quality assurance processes as an opportunity for continuous improvement. In addition, it identifies ten risks relevant to the quality assurance process in a university, classifying them according to their consequences and evaluating them by the risk index method, for better evaluation using the risk matrix. The article emphasizes the fact that, by systematically approaching risks and opportunities and promoting the exchange of good practices between universities, it is possible to contribute to strengthening the quality of university education throughout the country, effectively responding to the ever-changing requirements of education and contributing to the development of society.