System Resilience Research Articles

Lessons for Strengthening a Resilient Health System from the View of Health Facilities During the COVID-19 Pandemic: A Qualitative Study.

Coronavirus disease 2019 caused significant negative damage to the health status of populations and health systems globally. In Uganda, our previous study revealed that the strict Public Health and Social measures (PHSMs) and the closure of Entebbe Regional Referral Hospital (ERRH) led to missed healthcare access among the citizens in the Entebbe municipality. Limited studies, however, exist on the impact of the response measures on the local health systems. This study aims to explore the impacts of these measures on the local health service provision from the views of health facilities in Entebbe. We used a thematic framework method, grounded by the health systems resilience framework consisting of five components: (1) health service delivery; (2) medical products and technology; (3) health workforce; (4) public health functions; and (5) governance and financing. Key informant interviews with eight representatives from four private hospitals and four health centers were conducted from September to October 2022. Fifteen themes and 25 subthemes were identified. With the closure of the ERRH and the strict PHSMs, the citizens faced various difficulties in accessing the needed health services. The facilities received an overwhelming number of patients and faced various challenges, such as a lack of medicine, healthcare workers, facility capacity, and no means to transfer patients. Nevertheless, the facilities made efforts to maintain the required services. Moreover, mobilizing vertical and horizontal actors through a flexible network, from the district health office to community health volunteers, helped to coordinate the medicines, transportation for both patients and healthcare workers, conduct patient tracking, etc. Our study suggested the importance of an integrated system of public health and health service delivery systems, the formalization of a vertical cooperative mechanism, and the introduction of public health insurance for strengthening resilient health systems. These insights may benefit other sub-Saharan cities.

Energy scheduling strategy for energy hubs using reinforcement learning approach

The rapid integration of Renewable Energy Sources (RES) into energy systems is critical for achieving a fossil fuel-free future. However, it also introduces significant challenges, such as increased system complexity and uncertainties in energy output. To overcome these challenges, this paper presents a model-free Deep Reinforcement Learning (DRL) method to energy hub scheduling using state-of-the-art algorithms, specifically Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO). Breaking away from traditional energy management systems that typically use discrete action spaces, this approach employs a multi-dimensional, continuous action space, providing a more accurate framework for decision-making. Furthermore, the current study adopts a comprehensive perspective by considering not only energy supply and operational costs, but also the often-overlooked impact of emitted emissions in the objective function. The results yield a Pareto space encompassing various criteria, offering a diverse scheduling framework that balances economic feasibility with environmental sustainability. By presenting a range of optimal solutions, this study contributes to the development of more resilient and sustainable energy systems.

Climate change stands as the new challenge for whale watching and North Pacific gray whales (Eschrichtius robustus) in Bahia Magdalena, Mexico, after their recovery from overexploitation

IntroductionSocial-ecological systems (SES) recognize the intricate relationship between human activities and the environment and advocate for comprehensive approaches to address complex environmental issues. This study investigates the factors influencing whale watching, particularly the gray whale (Eschrichtius robustus) in northern Mexico, after significant recovery following overexploitation. Despite reaching a peak in 2015, the eastern North Pacific gray whales experienced unusual mortality events (UME), the most recent from 2019 to 2023, leading to a population decline and historically low calf production in 2022. This decline is evident in the reduced presence of whales and calves in winter breeding lagoons. Concurrently, whale watching has become a significant tourist attraction in these areas.MethodsOur objective was to develop a mental model of the SES of gray whale watching, integrating ecological and socioeconomic data to identify key variables and interactions that support system resilience. From an ecological perspective, we analyzed the long-term temporal trends of eight years of gray whale counts in the southernmost breeding and nursing lagoons within the Bahía Magdalena–Bahía Almejas Complex, Mexico. Additionally, we incorporated the current discussion in the literature about the potential impact of global climate change on gray whale populations. In the socioeconomic subsystem, we used participatory methods, including interviews, surveys, and workshops with government officials, tourism operators, and visitors. We also added on-site assessments of compliance with welfare regulations to understand tourism dynamics. ResultsOur findings identified as main external stressors the changes in ice levels in feeding areas and sea warming in breeding areas, and as internal stressors the compliance with official regulations and the number of vessels observing whales at the same time. The key socioeconomic factor of the system was tourist satisfaction, influenced by factors such as the quality of the information provided by operators. DiscussionUltimately, our mental model provides a framework for further exploration of relevant interactions and trajectories, offering insights for developing effective management strategies.

Artificial Intelligence for Sustainable Agribusiness: Innovations and Challenges

The integration of artificial intelligence (AI) in sustainable agribusiness is examined in this research, with particular attention paid to robots, supply chain management, and precision agriculture. AI analyzes a variety of datasets, maximizes resource utilization, forecasts pest outbreaks and production changes, and allows for data-driven decision-making. AI improves supply chain management by lowering food loss, streamlining logistics, and guaranteeing on-time delivery. Robotics powered by AI revolutionize labor-intensive processes, increasing output while reducing the need for chemicals. Notwithstanding its revolutionary potential, issues like data privacy and skill shortages still need to be resolved. Developing AI solutions that are inclusive and appropriate to a given context requires interdisciplinary collaboration. Improved equity and transparency are anticipated from developments in federated learning and explainable AI. All things considered, artificial intelligence (AI) presents hitherto unseen chances to advance sustainability along the agriculture value chain, building food system resilience and equity. Keywords: artificial intelligence, sustainability, efficiency, agribusiness, resilience, functionality

Collision-Free Formation Control for Heterogeneous Multiagent Systems Under DoS Attacks.

A safe time-varying formation (TVF) control framework is proposed in this article for heterogeneous multiagent systems under the constraints of denial of service (DoS) attacks, noncooperative dynamic obstacles, and input saturation. The framework integrates both the cyber-layer and physical-layer components to address the challenges posed by these adverse conditions. In the cyber-layer, a distributed resilient observer is provided based on a control Lyapunov function (CLF)-quadratic program (QP). This observer estimates a reference exosystem, effectively decoupling heterogeneous dynamics from unsafe networks and optimizing the system resilience against DoS attacks. At the physical-layer, for the first time, a collision-free TVF controller is presented based on the CLF-exponential control barrier function-QP. The controller guarantees high-order heterogeneous agents' operation safety under noncooperative obstacles and input saturation. The effectiveness and advantages of the proposed algorithms are verified through the comparative simulations and experiments conducted on a physical system comprising unmanned aerial vehicles and unmanned ground vehicles.

ENHANCING ADAPTIVE LEARNING AND DECISION-MAKING SYSTEMS USING SWARM INTELLIGENCE AND DEEP LEARNING FOR ADVANCED AI APPLICATIONS

The rapid development of autonomous vehicles (AVs) demands robust and adaptive AI systems capable of handling complex real-world environments. Traditional optimization and learning algorithms often struggle with dynamic and uncertain conditions, leading to suboptimal decision-making. Swarm intelligence, particularly Hawk Fire Optimization (HFO), offers a promising solution by simulating cooperative behaviors seen in nature, like hawks in hunting, to optimize decision-making processes. Coupled with advanced deep learning techniques like Federated Dropout Learning (FDL), this hybrid approach can enhance the adaptability, scalability, and efficiency of AI systems. This paper addresses the challenge of improving decisionmaking and learning in autonomous vehicles by integrating HFO with FDL. HFO optimizes parameters in real-time, allowing AVs to adapt rapidly to changing environments. Federated Dropout Learning, a variant of federated learning, further improves system resilience by sharing learning across distributed nodes while minimizing communication overhead and enhancing privacy. By combining these methods, the proposed system ensures robust performance in unpredictable scenarios. Experimental results show that the hybrid model outperforms traditional methods in terms of decision accuracy, response time, and energy efficiency. Specifically, the system achieved a 12% improvement in decision accuracy, reduced processing time by 18%, and cut energy consumption by 22%, compared to standard algorithms. These findings suggest that the combination of HFO and FDL can significantly improve the performance of autonomous vehicles, providing safer and more efficient AI-driven navigation.

Developing a healthcare transformational leadership competency framework for disaster resilience and risk management

Climate change-induced hazards increasingly threaten healthcare systems worldwide, amplifying the frequency and severity of disasters and their adverse health effects. This necessitates fortified healthcare leadership and resilient systems capable of delivering quality care amidst escalating environmental challenges and emphasises the need for standardised competency frameworks to enhance disaster readiness and response. As healthcare organisations face evolving challenges, cultivating transformational leadership competencies becomes essential for building adaptive, resilient, and effective disaster management strategies. Purposeof this study: This conceptual paper addresses the research question: "How can healthcare leaders' competencies be developed to support healthcare disaster resilience?". MethodsThe study, conducted in 2023, used a narrative literature review, comparative analysis, and synthesis. It reviewed frameworks on transformational leadership and disaster resilience, employing comparative analysis to examine relationships and leadership competencies. A synthesized framework was developed to clarify dimensions and relationships. ResultsEight ‘domains’ of competencies were identified, in addition to other ‘transferring skills.’ The domains were analysed regarding their component competencies and were broadly classified into knowledge, cognitive and interpersonal skills, and attitudes. This classification is proposed as a ‘Healthcare Transformational Leadership Competency Framework’ (HTLCF) for disaster resilience and risk management. ConclusionThe HTLCF supports leaders in navigating complexities, anticipating risks, and facilitating collaboration, communication, and accountability, leveraging their transformative agency. This study suggests the use of the Healthcare Transformational Leadership Competency Framework to develop a standardised disaster management curriculum and continuous, ongoing training. This would equip leaders to enhance resilience and ensure coordinated, effective disaster responses for healthcare systems.

Strategic analysis of Iran's climate resilient health system.

Climate change is a long-term systematic climate variability caused by human activities that alters the composition of the global atmosphere. Health systems should be adaptive and resilient to climate change. Hence, this research aimed to strategically analyze the resilience of Iran's health system to climate change. This study utilized a multiple methods approach. First, in-depth semi-structured interviews were conducted with 32 key climate change and health experts to identify the strengths, weaknesses, opportunities and threats of the Iranian health system's resilience to climate change. Purposeful and snowball sampling techniques were used to ensure maximum diversity among the participants. Then, a questionnaire was developed based on the findings of the first stage and was completed by 33 climate change and health experts. Finally, the strategic position of Iran's health system's resilience to climate change was determined using the internal - external factors matrix. A total of 84 internal factors and 101 external factors were identified that affect the resilience of Iran's health system against climate change. The internal factors were categorized into seven dimensions (i.e., governance and leadership; health financing; health workforce; facilities, equipment and medicines; health information system; health services delivery, and key results). The external factors were categorized into six dimensions (i.e., political, economic, social, technological, environmental, and legal factors). The average score of internal and external factors were 2.47 and 2.12, out of 4 respectively. Iran's health system was found to be in the strategic position of V in terms of resilience to climate change. Therefore, precautionary strategies such as strengthening the climate resilience of healthcare facilities, promoting healthcare facilities' adaptation to climate change, public-private partnership, strengthening the health service delivery system, quality management and cost management, should be implemented to strengthen the resilience of Iran's health system to climate change. Iran's health system is facing significant weaknesses and challenges that have hindered its resilience to climate change. Iran's health system can better prepare and respond to the health impacts of climate change, and safeguarding the health and well-being of its population by addressing these challenges and implementing adaptive and resilience strategies.

AI-enhanced fault-tolerant control and security in transportation and logistics systems: addressing physical and cyber threats

Transportation and logistics systems are becoming increasingly complex and critical to modern infrastructure. This paper proposes a novel AI-enhanced fault-tolerant control framework to address the dual challenges of physical malfunctions and cyber threats. By leveraging advanced machine learning algorithms and real-time data analytics, the proposed methodology aims to enhance the reliability, safety, and security of transportation and logistics systems. This research explores the foundations and practical implementations of AI-driven anomaly detection, predictive maintenance, and autonomous response systems. The findings demonstrate significant improvements in system resilience and robustness, making a substantial contribution to the field of intelligent transportation management.

Enhancing machine optimization through AI-driven data analysis and gathering: leveraging integrated systems and hybrid technology for industrial efficiency

Industrial automation has long been a driving force in enhancing manufacturing efficiency and productivity. However, traditional systems often rely heavily on human intervention, which can introduce errors and inefficiencies. This article explores the revolutionary potential of deep learning in transforming industrial automation by minimizing human involvement and optimizing operational performance. We present a comprehensive methodology for integrating deep learning models into automation systems, focusing on improving throughput and managing downtime and failures more effectively. The study employs advanced deep learning algorithms to analyse real-time data from industrial processes, enabling predictive maintenance and automated decision-making. Key findings reveal that incorporating deep learning significantly enhances system performance by reducing downtime, preventing failures, and increasing overall throughput. Additionally, the research highlights how minimizing human intervention can lead to more reliable and efficient automation systems. The implications of these findings suggest a paradigm shift in industrial automation, where intelligent algorithms drive process optimization and operational reliability. This shift promises to enhance manufacturing capabilities, reduce operational costs, and improve overall system resilience.

Machine Learning-Driven Assessment and Security Enhancement for Electronic Health Record Systems

The digitalized patient-centric system, the Electronic Health Record (EHR), is a platform where comprehensive health information is stored, managed, and accessed electronically. The primary findings of this study aim to secure sensitive patient data and increase overall system resilience by demonstrating that machine learning can evaluate vulnerabilities and improve the security of Electronic Health Record (EHR) systems. This research examines the prospects of incorporating machine learning-driven assessment tools and safety improvements in EHRs to enhance data protection in the healthcare industry. The proposed method utilizes the implementation of machine learning classifiers, specifically the XGBoost and LightGBM models. These classifiers are employed to enhance various aspects of the system, such as data protection and security, within the framework of EHRs. The study emphasizes the efficiency of these machine learning classifiers in ensuring that EHR systems are secure enough to deal with any problem that may occur due to threats posed by external factors or hackers. The findings reveal that the XGBoost model always has outstanding performance, with a near-perfect Receiver Operating Characteristic Curve (ROC) having an AUC equal to 1.00, indicating close to perfect accuracy in distinguishing positive from negative cases. Similarly, LightGBM has a perfect ROC curve as well. Therefore, its performance would be considered flawless. Consequently, future developments could lead to sophisticated machine learning models besides those that have already been developed. Improving data storage through encryption and building safer communication protocols should also be considered to make these systems withstand new security problems. Thus, this study contributes to the existing literature on applying technology to safeguard vulnerable medical records while fostering a safe and efficient healthcare ecosystem.

STRATEGIC MARKERS OF INVESTMENT IN CRITICAL INFRASTRUCTURE OBJECTS IN THE CONTEXT OF THE ECONOMIC SECURITY OF UKRAINE

The paper examines the strategic aspects of investing in Ukraine's critical infrastructure in the state's economic security context. The importance of critical infrastructure for the stable functioning of the economy in the face of modern challenges, including military threats, cyber-attacks and natural disasters, is analyzed. The issue of financial instability, which requires the involvement of private investors and international partners to restore and modernize infrastructure, is also considered separately. The study examines the role of public-private partnership and international cooperation as the introduction of innovative technologies, such as artificial intelligence and IoT, to increase the efficiency and sustainability of infrastructure facilities. Critical strategies for effective risk management and attracting investments to restore energy, transport and communications have been identified. Recommendations are offered for assessing investments' economic and social efficiency, considering the payback and impact on the population's well-being. The paper also outlines the prospects for long-term economic growth through investment in critical infrastructure, an essential element of the country's post-crisis recovery. This study delves into the strategic dimensions of investing in Ukraine's critical infrastructure, emphasizing its significance for economic security. The paper thoroughly examines Ukraine's financial constraints due to the ongoing war and economic instability. It underscores the necessity of engaging private investors and fostering international collaboration. Public-private partnerships (PPPs) and international financial institutions are identified as crucial players in providing the necessary funding and expertise for the recovery and modernization of infrastructure. The article also explores how integrating innovative technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), can significantly enhance the resilience and efficiency of infrastructure systems. In conclusion, the article offers strategic recommendations for investing in Ukraine's critical infrastructure. The purpose of the study is to identify strategic priorities for the allocation of investment resources for the reconstruction of critical infrastructure, considering the aspects of economic security.

Groundwater resilience, security, and safety in the four largest cities in Denmark

Denmark's complete reliance on groundwater for water supply presents a unique case study in management of natural resources, urban planning, and water resilience in the face of climate change. This paper examines the groundwater management strategies in Denmark in general, focusing on Denmark's four largest cities—Copenhagen, Aarhus, Odense, and Aalborg— each facing distinct challenges due to their demographic, geographical, hydrogeological, and economic characteristics. Through analysis of these cities' approaches to groundwater management, this research contributes to the global discourse on sustainable urban water supply systems. As coastal groundwater cities (CGC), these urban areas must navigate the complexities of sustaining growing populations, mitigating climate change impacts, and coastal processes while ensuring the long-term viability of their groundwater resources. Copenhagen and Aalborg, built atop semi-confined fractured and locally karstic carbonate rocks, highlights the specific challenges associated with karstic groundwater systems, while, Aarhus, and Odense built on glaciofluvial aquifers faces different issues. The different groundwater challenges in these cities underscores the importance of integrating urban development with water resource management and environmental sustainability, offering valuable insights and lessons learned for other regions facing similar challenges. This study, thus not only sheds light on Denmark's groundwater management practices, but also emphasizes the need for innovative solutions to ensure the resilience of urban water supply systems in a changing climate and increasing pressures of emerging organic contaminants and elevated concentrations of geogenic elements induced by water abstraction and fluctuating water tables. Advanced Danish monitoring and modelling tools applied to support decision-making and innovation within the water sector are continuously developed and improved to support resilient and sustainable management of the available water resources.

Coastal Chronicles: Journey into the World of Coastal Systems

Coastal systems are complex and dynamic settings where the atmosphere, sea, and land combine to create a variety of landscapes, including coral reefs, beaches, and estuaries. These areas contribute to global sustainability initiatives delineated in the United Nations Sustainable Development Goals (SDGs), sustain biodiversity, and play crucial ecological and economic roles. For sustainable management and protection of coastal environments, it is imperative to possess an understanding of the physical attributes, biological constituents, and mechanisms that dictate their dynamics. The natural processes of tides, waves, and currents impact the geomorphology of coastal systems, resulting in the formation of features including dunes, beaches, estuaries, and deltas. Coastal vegetation that stabilizes sediments and supports a variety of habitats, like seagrasses, mangroves, and salt marshes, further enriches these landscapes. Fish, crabs, mollusks, and birds are among the coastal fauna that flourish in these habitats and add to the diversity of coastal life. However, human activities that accelerate natural processes like erosion, accretion, and sea-level riseuch as industrialization, pollution, and climate change pose an increasing threat to coastal systems. Thus, it is essential to use sustainable management techniques that strike a balance between development objectives and conservation initiatives. Coastal ecosystems must be protected and enhanced through the implementation of policies like Integrated Coastal Zone Management (ICZM), habitat restoration, and Marine Protected Areas (MPAs). A combination of mitigation and adaptation strategies, such as building coastal fortifications and promoting nature-based solutions, are also necessary for climate change adaption. We can ensure the resilience and vitality of coastal systems for present and future generations, as well as their sustained ecological and economic significance in the global environment, by tackling these intricate concerns cooperatively and comprehensively.

COVID-19 and Service Provisions at Darou Khoudous Health Center of Touba

Introduction: The objective is to study the consequences of COVID-19 on service provision at the Serigne Mbacke Madina health center in Darou Khoudous in the Touba district. Materials and methods: To achieve this objective, a retrospective, descriptive study was conducted from July 2021 to December 2021. Results: Over the period 2016-2020, the age group 50-59 years and 60 years and over rarely attended the health structure. This low attendance at the health structure was further accentuated in 2020. According to consultation by service: We note that General Medicine, TRI and On-call are quite busy over the period with a significant drop in these consultations within these services in 2020. Indeed, General Medicine went from 37,378 patients in 2017 to 22,332 patients in 2020, i.e. 40%, TRI went from 35,461 patients in 2017 to 26,080 patients in 2020, i.e. 26.45%, and On-call went from 21,880 patients to 12,008 patients in 2020, i.e. 45.11%. Between 2019 and 2020 in Medicine we noted a decrease of 27.84%, in TRI a decrease of 27.8%. For patients followed for HIV at the Darou Khoudous health center, we noticed an almost complete absence of the 0-14 age group. We also noticed a low rate of new cases before 2018 and a sharp drop in the rate of new cases in 2020, a decrease of 87.84% compared to 2019. For tuberculosis patients followed in the department, we noted a strong impact on the age group >15 years, more accentuated between 15 and 35 years over the period 2016-2020. For this same age group, we also noticed a decrease in cases between 2019 and 2020, a decrease of 10%. In 2018 we also noticed a peak in cases compared to the previous 2 years. According to the EPI: variations are almost non-existent during the pandemic, on the contrary, attendances have increased since 2016, this would surely be due to awareness of the beneficial effects of EPI vaccines and that the disease initially only affected the elderly, rare are the cases of children reported. Conclusion: The data collected as part of this study show that the COVID-19 pandemic has disrupted health services and put intolerable pressure on health systems and their staff. We urgently need to reverse this trend and strengthen the resilience of health systems.

The Role of Integration Associations in the Fight against Pandemics on the Example of the European Union and Eurasian Economic Union

INTRODUCTION. The academic value of this article lies in augmenting the scientific understanding of the role of integration unions in healthcare and the methods and means of combating pandemics and epidemics at an integration level. This is achieved by addressing the research objective: to identify the legal and institutional structures and mechanisms existing at the level of integration unions for combating pandemics and their consequences, and to assess their effectiveness. The research objectives are defined as follows: to examine the actions of integration unions in combating the pandemic from legal and material- economic perspectives; to determine the actual role of integration unions in combating pandemics and similar threats to public health for the further improvement of international cooperation and Eurasian integration; to evaluate the compliance of the actions of integration union institutions with their founding documents; and to develop ways to improve integrations in the field of health protection based on the experience gained.MATERIALS AND METHODS. The methodological basis of the research is grounded in the general philosophical method of dialectics. Special and specific scientific methods applied include: the comparative legal method (comparing the legal systems and regulations of different integration unions, and the various measures taken in response to the pandemic), the sociological method (examining the impact of pandemic conditions on society in different integration environments), and the axiological method (studying values as the foundation of integration unions, their reflection, and influence on making internationally significant decisions).RESEARCH RESULTS. In determining the effectiveness of the institutions, structures, and mechanisms of integration unions in ensuring epidemiological wellbeing, a significant role of integrations in counteracting pandemic and epidemiological threats to health has been identified. This is evidenced by the actions and measures taken by the EU and the EAEU during the recent COVID-19 pandemic. Aspects of cooperation between member countries (transport communication, coordination of sanitary measures, support of the economy and social sphere, information and experience exchange) and the institutions of these integration unions (European Commission, European Parliament, Eurasian Economic Commission) were dictated by their founding acts (TEU, TFEU, Treaty on the EAEU) and other documents.DISCUSSION AND CONCLUSIONS. During the crisis, not only challenges requiring solutions in ensuring well-being (balance of freedoms and restrictions, rational allocation of resources, balance of national and integration interests) emerged, but also prospects for the future development of integrations (NextGenerationEU Plan, the emergence of a Global Digital Health Certification Network). Judicial practice was formed (lawsuits by the European Commission, work of the EAEU Court). Conclusions highlight the need to increase the resilience and centralization of EU healthcare systems, prepare the budget for potential future threats, which serves as a useful experience for Eurasian integration, and the importance of deepening integration, reforming existing institutions, and establishing interstate dialogue for the EAEU. The crisis became a catalyst for the development of integrations, the cause of the emergence of new conflicts and contradictions, the solutions of which determined the direction of evolution in the future.

A Review on Deep Learning Approaches and Optimization Techniques For Political Security Threat Prediction

In an era of complex geopolitical dynamics and evolving security threats, the accurate prediction and proactive management of political security risks are imperative. This article provides a comprehensive review of the application of deep learning methodologies and optimization techniques to enhance political security threat prediction. Beginning with analyzing the dynamic landscape of political security threats, the paper emphasizes the necessity for adaptive, data-driven predictive tools. It then delves into the fundamentals of deep learning, elucidating core principles, notable architectural frameworks, and their diverse applications across domains. Expanding upon this foundation, the study evaluates the suitability of deep learning models for addressing the multifaceted challenges associated with political security threat prediction. To maximize the utility of these models, the article explores optimization techniques encompassing hyperparameter tuning, transfer learning, and ensemble strategies, assessing their effectiveness in fine-tuning predictions and bolstering the resilience of threat prediction systems. This review involved the utilization of four journal databases: IEEE, Science Direct, Association for Computing Machinery (ACM), and SpringerLink. We analyzed and examined 39 articles, paying close attention to the different patterns and techniques found within the chosen research framework. Through a critical synthesis of existing research, this review offers insights into the strengths, limitations, and future directions of deep learning-based political security threat prediction, contributing to the ongoing discourse on leveraging artificial intelligence for safeguarding global stability and security.

A method for assessing the resilience of urban interdependent systems integrating physical damage and social loss

As systems in urban areas become increasingly interdependent, the effects of disasters extend beyond facilities damage, precipitating casualties, and public service disruptions. Establishing a holistic resilience assessment method is crucial for urban sustainability planning. Existing resilience assessment methods often focus on the physical damage; however, few studies examine the social loss. This study proposes a resilience assessment method that integrates physical damage and social loss. A multilayer network model of physical-social systems is first developed based on their interdependency. Then, the evolution of disaster damage within and between systems process, and the multi-task parallel restoration process are quantified. Resilience is assessed by measuring dynamic changes in service supply and demand throughout these processes. The method considers social loss as a combination of population harm and service interruption caused by physical damage. The proposed method was applied to an urban area in Southern China. The results reveal the impact of seismic events on infrastructure, buildings, and populations. Furthermore, they demonstrate the coupling impacts of supply and transportation on emergency and healthcare systems’ resilience. This comprehensive method could facilitate the development of efficient strategies to improve urban resilience to disasters.

Enhancing manufacturing efficiency and quality through automation and deep learning: addressing redundancy, defects, vibration analysis, and material strength optimization

This article explores the integration of automation and deep learning in modern manufacturing to address critical challenges such as redundancy, defects, vibration analysis, and material strength. As manufacturing processes evolve, the need for more sophisticated methods to optimize production efficiency and product quality becomes paramount. Automation, coupled with deep learning techniques, offers powerful tools for enhancing manufacturing processes. These technologies enable predictive maintenance, reducing downtime by identifying potential equipment failures before they occur. Furthermore, deep learning algorithms can analyse complex data sets to detect defects in products with greater accuracy and speed than traditional methods. Vibration analysis, a key aspect of predictive maintenance, benefits from automated systems that monitor and diagnose issues in real-time, preventing costly disruptions. Additionally, deep learning models can assess material strength and predict potential failures, ensuring that products meet rigorous safety and quality standards. The synergy between automation and deep learning not only streamlines manufacturing processes but also enhances the ability to adapt to changing conditions, thereby minimizing operational inefficiencies. This article highlights the transformative impact of these technologies on the manufacturing industry, illustrating their potential through case studies and practical examples. By addressing key challenges such as redundancy and defects, automation and deep learning contribute to the creation of more reliable, efficient, and resilient manufacturing systems. The insights provided in this study underscore the importance of continued innovation in integrating these technologies to maintain a competitive edge in the rapidly evolving manufacturing landscape.

Women's Knowledge of Local Plants and Their Gastronomic Heritage in Chitral, NW Pakistan.

Women are the "guardians of the kitchen" and central to household food security, yet their role has never been studied across the Hindukush region or Pamiri Knot. This study explores the women's knowledge (specifically from the Khowar (Kho) and Wakhi linguistic groups) of local food systems and determines their role in ensuring household food security and sustainability in the mountain regions of northern Pakistan. Based on in-depth semi-structured interviews with female informants in the Rech and Broghil valleys of upper Chitral, 91 different types of food products were recorded, including wild and cultivated species. Eremurus stenophyllus and Allium barsczewskii were the commonly utilized plant species, though distinct preferences between the Kho and Wakhi groups were noted. Prominent differences were perceived in using certain cultivated plants among the two ethnic groups. For instance, Kho preferred plants like Beta vulgaris, Zea mays, and Brassica napus as indicated by the use reports, while Wakhi concentrated on Thymus serpyllum, Zygophyllum obliquum and Papaver involucratum. Both groups had shared dairy practices, but cottage cheese and curd were highly cited among Kho, while double-fermented curd and Qurut for Wakhi. The study recorded some new food uses for specific plants, such as Atriplex hortensis, Carthamus tinctorius, Hylotelephium spp., and Saxifraga sibirica. Cross-culture analyses revealed a mosaic pattern of homogenous and heterogenous trends based on reported food species of plants/animals and their use reports. Our findings emphasize the significant role of women in sustaining local food diversity, food sustainability, and the preservation and security of the local food systems, cultural legacy, and household food management. Therefore, inclusive research addressing their social, economic, and environmental issues must be conducted. Furthermore, policies must incorporate women's traditional knowledge to build resilient food systems.