Adaptation Policies Research Articles

Modeling Cybersecurity Operations to Improve Resilience

AbstractIn this paper, we explore the concept of operational resilience of a network or system of computer systems, focusing on the processes of a cybersecurity team within the multi‐disciplinary network security operations center. The computer system under examination has faced a cyber‐attack that has reduced its capability. The organization's reputation is damaged temporarily but can be restored if the network security operations center can quickly restore the organization's ability to produce desired results. After a cyber‐attack, we examine the processes for restoring the system's capability to its original level. These processes will happen sequentially and require close coordination of the cybersecurity team members. We examine a balanced and adaptive assignment policy within the cybersecurity organization to the various processes, showing how these policies can impact the speed with which the system's capability can be restored. Our findings reveal that the adaptive assignment policy among the team members can increase the system restoration rate even though recovering the complete capability of the system may be the same.

Asset-level assessment of climate physical risk matters for adaptation finance

Climate physical risk assessment is crucial to inform adaptation policies and finance. However, science-based and transparent solutions to assess climate physical risks are limited, compounding the adaptation gap. This is a main limitation to fill the adaptation gap. We provide a methodology that quantifies physical risks on geolocalized productive assets, considering their exposure to chronic and acute impacts (hurricanes) across the scenarios of the Intergovernmental Panel on Climate Change. Then, we translate asset-level shocks into economic and financial losses. We apply the methodology to Mexico, a country highly exposed to physical risks, recipient of adaptation finance and foreign investments. We show that investor losses are underestimated up to 70% when neglecting asset-level information, and up to 82% when neglecting tail acute risks. Therefore, neglecting the asset-level and acute dimensions of physical risks leads to large errors in the identification of adaptation policy responses, investments and finance tools aimed to build resilience to climate change.

Research on Intelligent Cybersecurity Protection System in Cloud Computing Environment

In the digital era, cloud computing has transformed data processing, storage, and analysis with its scalability and flexibility. However, it also introduces new security challenges, such as data breaches and unauthorized access, which are exacerbated by Advanced Persistent Threats (APT) and zero-day vulnerabilities. This study aims to construct an intelligent network security protection system that adapts to the dynamic cloud computing environment, leveraging artificial intelligence and machine learning to enhance security efficiency and accuracy. The research objectives include developing a system that maintains privacy protection and user experience while ensuring secure access to cloud services. The paper presents a multi-layered architecture for the intelligent cybersecurity protection system, incorporating data collection, threat detection, access control, and adaptive security policies. Experimental methods and results validate the system’s effectiveness, demonstrating improved detection rates and reduced false positives compared to existing solutions. The study concludes with a discussion on the model’s advantages, limitations, and future application prospects in the field of cloud computing security.

A Sub-Domain-Awareness Adaptive Probabilistic Event-Triggered Policy for Attack-Compensated Output Control of Markov Jump CPSs With Dynamically Matching Modes

A Sub-Domain-Awareness Adaptive Probabilistic Event-Triggered Policy for Attack-Compensated Output Control of Markov Jump CPSs With Dynamically Matching Modes

Flexible recommendation for optimizing the debt collection process based on customer risk using deep reinforcement learning

Finance sector loss can be minimized by reducing the number of defaulters who often miss payments during debt collection. Most research focused on the credit risk analysis before approving the loan to the customer, and few papers related to predicting the defaulters. Certain works are focused on minimizing the number of calls made by debt collectors during the collection process. We have developed a Flexible Recommender System using Deep Reinforcement Learning (FRS-DRL) to provide Flexible recommendations based on the risk category of the customers. Initially, we categorized customers into defaulters and non-defaulters. The defaulters are again classified into Very Low, Low, Medium, High, and Very High-Risk customers. Based on this category, we recommend suitable actions to the Field Collection Agent (FCA) to increase the collection rate. The suggestions include how frequently the customer should be contacted, statements, and the dates and times of visits. At the same time, the statements define instructions to FCA for collecting money. Managers are traditionally in charge of this type of allocation; they must understand each customer personally. Our proposed recommendation method eliminates the need for manual mapping. We build a Hybrid Actor-Critic algorithm with an Adaptive Trust region Policy Optimization algorithm for classifying the customer category and providing the appropriate recommendations to reduce the defaulter rate, thereby increasing the collection efficiency and reducing the human effort. This FRS-DRL is tested in a real-time environment for three months and provides an improved reward rate with and without transfer learning at 73.22 % and 62.44 %. The collection percentage improved by 20.34 % in five different regions, with a 16.30 % reduction in the number of visits made by the FCA.

A Systematic Review and Meta-Analysis of Factors Influencing Water Use Behaviour and the Efficiency of Agricultural Production in South Africa

Water use behaviour and efficiency are essential topics regarding water scarcity. Water is a life-sustaining resource used for various activities within the three primary sectors: agricultural, industrial, and domestic. Increasing competition among these sectors could affect the availability and sustainability of water use. The higher demand for agricultural-related commodities emphasizes the efficient and productive use of water. Still, to achieve this, the behaviour of consumers regarding water use needs to be changed. This systematic review paper aims to highlight the factors affecting water use behaviour and efficiency for agricultural production in South Africa. It further aims to determine how agricultural producers change their behaviour to improve their water use efficiency. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria were used as a reporting framework and guidelines to identify the articles included in the review. The review only included articles focussing on agricultural water use behaviour and efficiency and articles written in English and excluded articles from web pages, blogs, magazines, etc. The databases used for the review were Google Scholar and Web of Science. The articles were reviewed by the five authors to avoid the risk of bias, along with the inclusion and exclusion criteria. The final review included 30 peer-reviewed articles. A word frequency table was developed using the NVivo 14 software to conduct a thematic analysis for the review. The main factors which played a role in the water use behaviour and efficiency of farmers were (i) climate and adaptation strategies, (ii) policy and water pricing, and (iii) agricultural production and management. Each category elaborated on how water use could be improved and the different measures adopted to incorporate sustainable farm water use. This could be a guideline for farmers, stakeholders, and policymakers to improve and enhance water use behaviour and efficiency in Sub-Saharan Africa, particularly South Africa. This could ultimately assist in efficiently using the water while enhancing sustainability within the agricultural sector and attaining Sustainable Development Goal (SDG) six, which is to increase water use efficiency. The limitation of this study was that it was only narrowed down to the geographical context of South Africa. This review was funded by the Water Research Commission (WRC) of South Africa (Project Number: C20222023-00798). This review was not registered.

Future land use and land cover simulations with cellular automata-based artificial neural network: A case study over Delhi megacity (India)

According to United Nations projections, future global urban growth will mostly occur in Asian megacities. In this study, a Cellular Automata based Artificial Neural Network (CA-ANN) model is used to simulate the future land use and land cover (LULC) over Delhi megacity (India). Delhi, projected to become the world's most populated city by 2030, is an example of a data poor city in Asia, having millions of climate vulnerable people. The CA-ANN model of Modules for Land Change Simulation (MOLUSCE), an open-source plugin, is first tested to simulate the LULC for 2009. Based on good validation results-structural similarity (SSIM; 0.8288), overall accuracy (79.78 %), kappa index of agreement (KIA; 77.25 %), and minimum validation overall error (0.0379), the same model set-up is used to carry out LULC simulation for 2030. This model is found to be simple, efficient, and computationally less expensive tool, and can be used to model future LULCs with a minimal set of inputs, a constraint often found in data poor cities. Results show continued increase in built-up area from 38.3 % (2014) to 53.8 % (2030), at the expense of cultivable areas, forests, and wastelands. The study incorporates past and future LULC change trajectories to highlight the changing LULC dynamics of the megacity from 1977 to 2030. Rate of urban sprawl, calculated using compound annual growth rate (CAGR) is projected to be 2.51 % for 2014–2030, substantially higher than the estimates for 2006–2014 (0.62 %). Further, the past and future urban growth patterns for Delhi are found to mimic other big Asian cities. The database generated from the present study has wide applicability for scientific research community, governmental bodies, profit and non-profit organizations for topics concerning-future urban climate research, climate risk and adaption policy frameworks, climate finance budgeting, future town planning, etc.

Child health prioritisation in national adaptation policies on climate change: a policy document analysis across 160 countries.

Integration of child-specific adaptation measures into health policies is imperative given children's heightened susceptibility to the health impacts of climate change. Using a document analysis method, we examined 160 national adaptation policies for inclusion of child-relevant measures and identified 19 child health-related adaptation domains. 44 (28%) of 160 countries' policies that were analysed failed to include any domains, 49 (31%) included at least one child-related domain, 62 (39%) included between two and six domains, and five (3%) included at least seven domains. Predominant domains among child-specific adaptation measures included education and awareness raising, followed by community engagement and nutrition. No country addressed children's direct needs in the domain of mental health. National adaptation policies tend towards overly simple conceptualisations of children across four major lenses: age, social role, gender, and agency. Limited inclusion of child-specific measures in national adaptation policies suggests insufficient recognition of and action on children's susceptibility to climate change effects.

Combating Global Warming and Modern Wars is much about Sedimentology

The Paris Agreement on climate change came into force on 4 November 2016 which was adopted by 196 Parties at the UN Climate Change Conference (COP21) in Paris on 12 December 2015. Under this agreement, 175 countries are legally bound to commit themselves to limit the increase of global warming temperatures to under 2 °C above pre-industrial levels. To achieve this goal, mitigation and adaptation policies have been adopted at the national and international levels. Mitigation measures include decarbonisation policy, for example the large-scale development of low-carbon energies to replace fossil fuel energy in a sustainable way. At present, efforts are focussed on the energy transition from fossil fuel driven energy to low-carbon energy which is mineral intensive. Rare earth elements (REEs) are core components of clean energy technologies such as solar, wind turbines and electric vehicles in the process of energy transition. In the quest for slow down the global temperature rise, development of low-carbon energy to achieve carbon emission reduction goals, rare earth elements are the key components to the energy transition. The criticality of supply and demand of raw materials is the key issue in planning the sustainable transition from fossil fuel driven energy to green energy. Over the past few years many countries established critical raw materials lists, in which rare earth elements have been given most attention.

Critical Challenges and Opportunity Factors in Stakeholder Collaboration for Indonesian Defense Technology Innovation

Exploring the landscape of defense technology innovation collaboration in Indonesia requires an in-depth study to investigate the important factors that shape the direction of collaboration efforts. Unveiling challenges like the silo effect, funding limitations, and bureaucratic disparities, the research navigated through the intricate landscape of collaboration. Opportunities emerged, encompassing the development of collaboration scopes and enhancing overall effectiveness. The findings underscored the need for adaptive policies, alternative funding models, and cultural transformations to overcome identified hurdles. Recommendations leaned toward fostering a collaborative organizational ethos and implementing structural changes to facilitate seamless integration. Positioned as a roadmap, these insights offer stakeholders and policymakers actionable strategies, paving the way for Indonesia's advancements in the defense technology sector.

Contrastive learning based on linguistic knowledge and adaptive augmentation for text classification

Pre-trained language models based on contrastive learning have shown to be effective in text classification. Despite its great success, contrastive learning still has some limitations. First, external linguistic knowledge has been shown to improve the performance of pre-trained language models, but how to use it in contrastive learning is still unclear. Second, general contrastive learning generates training samples with fixed data augmentation during the whole training period, while different augmentation methods are suitable for different downstream tasks. Fixed data augmentation can lead to suboptimal settings. In this paper, we propose contrastive learning based on linguistic knowledge and adaptive augmentation, which can obtain high-quality sentence representations to improve the performance of text classification. Specifically, we construct word-level positive and negative sample pairs by WordNet and propose a novel word-level contrastive learning function to inject linguistic knowledge. Then we dynamically select the augmentation policy by alignment and uniformity. This adaptive augmentation policy can acquire more generalized sentence representations with little computational overhead. Experiments on multiple public datasets demonstrate that our method outperforms state-of-the-art methods.

Analysis of Climate Change Adaptation Policies in Asia

Analysis of Climate Change Adaptation Policies in Asia

Tailoring evidence-based local adaptation policies for climate change - a Košice urban case study

Tailoring evidence-based local adaptation policies for climate change - a Košice urban case study

Dynamic adaptive decision support for strategic decision-making in healthcare organizations

PurposeThe main objective of this study was to design a dynamic adaptive decision support model for healthcare organizations facing deep uncertainties by considering promising dynamic adaptive approaches. The main argument for this is that healthcare organizations have to make strategic decisions under deep uncertainty, but lack an approach to deal with this.Design/methodology/approachA Dynamic Adaptive Decision Support model (DADS) is designed using the Design Science Research methodology. The evaluation of an initial model leads, through two case studies on ongoing and strategic decision-making, to the final design of this needed model for healthcare organizations.FindingsThe research reveals the relevance of the designed dynamic and adaptive tool to support strategic decision-making for healthcare organizations. The final design of DADS innovates Decision Making under Deep Uncertainty (DMDU) approaches in an organizational context for ongoing and strategic decision-making.Originality/valueThe designed model applies the Dynamic Adaptive Policy Pathways approach in an organizational context and more specifically in health care organizations. It further integrates Corporate Real Estate Management knowledge and experience to develop a most needed tool for decision-makers in healthcare. This is the first DADS designed for an organization facing deep uncertainties in a rapidly changing healthcare environment and dealing with ongoing and strategic decision-making.

The climate change adaptation readiness of co‐operative housing in Nova Scotia, Canada

AbstractClimate adaptation policy in Canada is emerging in the context of another major challenge: the diminishing availability of affordable housing. Housing is a well‐known driver of social vulnerability to environmental risks, so as governments respond to these challenges, it will be essential to understand how housing is being situated within adaptation, particularly with respect to differences in housing tenure and how decisions around equity and social vulnerability are factored into planning and policy processes. This research examines how adaptation plans and policies in Nova Scotia are addressing the needs of the non‐profit co‐operative housing sector and assesses the adaptation readiness of housing co‐operatives in the province. Two methods are employed: a systematic content analysis of municipal and provincial climate policy documents, and interviews with key informants across the co‐operative housing sector and government agencies. Using a modified adaptation readiness framework, we consider the potential for co‐operative adaptation and complimentary public policy to address vulnerability at the intersection of housing and climate change. Findings indicate that non‐market forms of tenure have been largely neglected by adaptation planners and state policymakers. Several barriers which contribute to a low level of adaptation readiness for co‐ops are highlighted, notably a lack of usable science and funding to facilitate adaptation. Characteristics such as affordability and a propensity for collective action position housing co‐ops to be agents of equitable and systemic adaptation, but this potential will only be realized in Canada if key barriers are overcome through targeted governmental rt for non‐profit housing organizations.

Situated usage of generative AI in policy education: implications for teaching, learning, and research

ABSTRACT This study explores the contrasting sentiments towards the use of generative AI technologies among research postgraduate students in public policy. 14 interviews about the usage of generative AI technologies in the students’ research, teaching, and learning practices were conducted and used as the empirical data source for this project. Through qualitative and sentiment analysis, the research identified domains where students applied generative AI and discovered both positive and negative sentiments within the same application domains. The divergence in sentiments was interpreted using the ‘plans and situated actions’ framework, suggesting that technological expectations constrained by contextual environments lead to varied experiences of ‘enchantment’ and ‘disenchantment’. The findings emphasize the imperative for adaptable academic policies delineating acceptable AI usage in research, the implementation of discipline-specific AI training in universities, and the development of discipline-specific AI systems to cater to unique academic field needs.

Nexus between Indigenous Knowledge Systems and Adaptation to Climate Change Strategies by Farmers in Kajiado County, Kenya

Indigenous knowledge (IK) has played an important role in adaptation to climate change in traditional agricultural communities. These approaches have been shown to be more effective when integrated with scientifically developed strategies. In this study, a perception survey questionnaire was used to find out whether there is a nexus between IK systems and the uptake of modern Climate Change Adaptation Strategies (CCAS) among farmers in Kajiado County, Kenya. A mixed research approach that involved the use of household questionnaire surveys, key informant interviews, focus group discussions (FGD), and observations was used to obtain data. The two theories, namely the Situated Learning Theory (SLT) and the Theory of Planned Behavior (TPB), formed the basis for illustrating the relationship between variables, while the Model of Private Proactive Adaptation to Climate Change (MPPACC) was the key theoretical model employed. A total of 382 small-scale rural household representatives randomly selected from 3 Kajiado sub-counties were the principal respondents. The key informants included officers in the departments of agriculture, environment, meteorology, trade, and industry, as well as local administration and village elders. Statistical tools such as chi-square, correlation, and binary logistic regression analysis were used to determine associations. Thematic analysis of data collected from key informants and focus group discussions (FGD) was conducted to triangulate the survey results. Farmers' awareness of climate change, risk perceptions of impacts, existing locally developed approaches to climate change adaptation, and perceptions of CCAS were the main areas of investigation. The majority of farmers (85%) indicated that prolonged drought was the main extreme event that affected their farming activities. Of the indicators observed, drought (X2 = 13.861, p =.000) was perceived as the greatest risk associated with climate change to their agricultural productivity. Their IK adaptation strategies resulted from their interactions with the effects of drought on their livelihoods over the years. Farmers who were nomadic (IK practitioners) were not willing to apply for CCAS. For example, they were 72% less likely to increase their irrigated cropped area and 79% less likely to increase the number of better livestock breeds. Among the CCAS approaches, water resource management was the only preferred strategy. This particular finding shows that climate change policies can be easily adopted by indigenous farming communities if they are developed around an issue that matches their socio-ecological priorities. Without taking into account the prevailing social, cultural, and geo-environmental context of specific communities, the adoption of CCAS by farmers would be significantly affected. The study recommends that integrating indigenous agricultural knowledge into climate change adaptation policies enhances community resilience. Aligning adaptation strategies with local responses enriches farmer knowledge and boosts resilience against climate change impacts. Effective climate change adaptation plans should incorporate local weather prediction, environmental conservation, and proven community-based strategies, especially in water resource management for arid and semi-arid pastoralist communities.

Policy analysis for informing climate adaptation, environmental resilience, and irrigation demands in the Rio Grande Basin

Water policymakers internationally face the challenge of adapting to climate, supporting environmental resource needs, and meeting irrigation demands for food security in the world's arid and semi-arid regions. Much recent work has assessed the economic performance of environmental river flows to support endangered species habitat protection. However, little published work to date has systematically formulated and applied methods to identify the economic performance of various policy measures that adapt to climate, support endangered species, and meet irrigation demands for water. This work's original contribution with international relevance is to address those gaps by identifying the economic performance of various water shortage sharing methods for handling climate water stress while supporting protection of critical habitat to protect the endangered Southwestern willow flycatcher (Empidonax traillii extimus) along with Willow tree (Salix bonplandiana) habitat in the middle part of the Rio Grande Basin in North America, a region facing a number of conflicts between commercial uses of water and protection of key ecological assets. It develops a hydroeconomic optimization model containing information on crop water use and endangered species requirements in that region to identify the economic performance of three climate adaptation policy scenarios for handling water shortages while respecting endangered species habitat protection requirements. Results show how water shortages as well as policy responses for handing those shortages affect the economic value of water in agriculture for food security both with and without critical habitat to support the endangered flycatcher. This work's international relevance comes from its capacity to inform policy debates on the costs of protecting endangered species habitat under various climate scenarios and climate policy adaptation measures. Findings provide a general framework to address existing gaps in understanding and measuring the economic performance of measures to promote environmental resilience.

Global inflation: modern determinism and challenges for policy

The article studies global inflation determinants, causing it in recent decades and at the present stage under the influence of cascading shocks in world economy and financial system. The relevance of the problems summarized and analyzed in the article is explained by the significant risks of the inflation duration at the present stage, which is affected by an increasing number of shocks from demand, supply, and global financial system. In these conditions, even adaptive monetary policy may cease to be effective. The purpose of the study is to assess the nature and extent of developed and developing countries’ economies vulnerability due to inflationary shocks and to identify the inflation fundamental determinants in crisis conditions on the basis of retrospective analysis. Achievement of the goal implied the solution of such priority tasks as: global inflation dynamics factors identification considering the risks of stagflationary and recessionary trends in world economy; inflation new determinants identification in the context of changes in the scale and structure of the global financial market; assessment of monetary policy adjustment and adaptation mechanisms in the current inflationary environment; consideration of the current nature of supply and demand shocks and their impact on global inflation. The current inflationary shock is unprecedented, and the study assesses it in its historical context and for its similarities and differences with significant inflation episodes observed in the post-war decades. The key factors of inflation at the present stage have been analyzed in their relationship with the monetary policy key trends.

Beneficial effect of residential greenness on sperm quality and the role of air pollution: A multicenter population-based study

BackgroundPoor sperm quality is a major cause of male infertility. However, evidence remains scarce on how greenness affects male sperm quality. ObjectivesTo assess the associations of residential greenness with male sperm quality and the modification effect of air pollution exposure on the relationship. MethodsA total of 78,742 samples from 33,184 sperm donors from 6 regions across China during 2014–2020 were included and analyzed. Individual residential greenness exposures of study subjects were estimated using the Normalized Difference Vegetation Index (NDVI) during the entire (0–90 lag days) and two key stages (0–37, and 34–77 lag days) of sperm development. Contemporaneous personal exposure levels to air pollutants were estimated using a spatio-temporal deep learning method. Linear mixed models were employed to assess the impact of greenspace in relation to sperm quality. The modification effect of air pollution on the greenspace-sperm quality relationship was also estimated. ResultsPer IQR increment in NDVI exposure throughout spermatogenesis were statistically associated with increasing sperm count by 0.0122 (95 % CI: 0.0007, 0.0237), progressive motility by 0.0162 (95 % CI: 0.0045, 0.0280), and total motility by 0.0147 (95 % CI: 0.0014, 0.0281), respectively. Similar results were observed when the model added air pollutants (PM1, PM2.5 or O3) for adjustment. Additionally, specific air pollutants, including PM1, PM2.5, and O3, were found to modify this association. Notably, the protective effects of greenness exposure were more pronounced at higher concentrations of PM1 and PM2.5 and lower concentrations of O3 (all Pinteraction < 0.05). Statistically significant positive effects of NDVI were observed on sperm motility in early spermatogenesis and sperm count in late spermatogenesis. ConclusionsExposure to residential greenness may have beneficial effects on sperm quality and air pollution modifies their relationship. These findings highlight the importance of adopting adaptable urban greenspace planning and policies to safeguard male fertility against environmental factors.