Autonomous Adaptation Research Articles

Assessing the impacts of climate change on high mountain land-based livelihoods: An empirical investigation in District Nagar, Gilgit-Baltistan, Pakistan

Land-based livelihoods in Pakistan's high mountain regions are highly vulnerable to climate change due to the complex interactions between people and their natural environment. This study uses a mixed-method approach to explore how climate change affects land-based livelihoods in the high mountain Nagar District, Pakistan. Data were collected using a structured household survey of 430 randomly selected farmers, supplemented by focus group discussions and key informant interviews. The findings reveal that 87.7 % of farmers have observed negative impacts of climate change, such as increased crop diseases, reduced water for irrigation, and lower crop yields. Bivariate results indicate that factors related to farming practices, such as farming experience and cropping zones, significantly influence farmers' perceptions of impacts. The study emphasizes the urgent need for targeted government intervention and agricultural planning to boost the resilience of farmers in Nagar District. It calls for improved irrigation, crop disease management, and support tailored to high-mountain farming practices. The research highlights the importance of developing adaptation strategies to protect vulnerable farming communities from climate change impacts and supports the need for effective autonomous adaptation measures. This research contributes to a better understanding of climate change impacts on high-mountain agriculture and emphasizes the need to safeguard vulnerable farming communities.

Algorithmic assessment of drag on thermally cut sheet metal edges

Abstract Drag is a key criterion in assessing the quality of thermally cut sheet metal edges, which is critical to the reliability of the final product. The evaluation of drag has been described qualitatively and quantitatively, but the scientific literature lacks a methodical description of algorithmic tracking of the drag lines themselves. This absence of a standardized approach has hindered the objective determination of drag. With recent advances in the field towards automated quality assessment aimed at autonomous adaptation of process parameters, the need for consistent, fast and reliable assessment of drag lines has become apparent. To address this gap, this study introduces an innovative drag line tracking algorithm, inspired by the behavior of fluid flowing towards the lowest points, to compute a generalized drag line for an edge with a homogeneous cutting pattern. The algorithm utilizes the height data of the measured cut edges as a data base for the assessment of the drag lines. The results indicate that the drag lines identified by the algorithm are not only subjectively accurate, but also show a strong correlation with human-annotated drag lines across several metrics. This work lays the foundation for the objective evaluation of drag by not only describing an algorithm for the consistent determination of drag lines, but also by presenting a tool for human annotation and suitable customized metrics. As a result, it contributes significantly to the comprehensive evaluation of edge quality and represents a step forward in the automatic optimization of process parameters and the improvement of cutting edge quality.

The vision of the circular factory for the perpetual innovative product

Abstract The growing scarcity of global resources demands a transition from linear to circular production patterns. This article presents a novel vision for integrating linear and circular production processes within a flexible and autonomous production system to achieve perpetual product use. The approach aims to preserve the added value of products and to integrate the design of product generations and production systems. Within the circular factory, the following core aspects must be examined: predicting functions of products, managing uncertainty in used products and process sequences, learning human action for complex tasks, implementing changeable, autonomous production systems, and enabling knowledge modeling for the circular factory across domains. Aspired results are a design for circular factory, effective strategies for uncertainty management and autonomous systems adaptation as well as the externalization of operational knowledge. This research is part of the Collaborative Research Center (CRC) 1574, which explores these aspects in detail. For an in-depth understanding of specific components, it is referred to other publications by the CRC 1574.

How the Resist-Accept-Direct framework is being used by communities for socio-economic climate adaptation: a case study in Australia’s Murray-Darling Basin

Globally, water governance struggles to reconcile increased demands on water resources with climate change–induced reductions in supply, making climate adaptation in water governance a pressing concern. The Resist-Accept-Direct (RAD) framework has emerged as a climate adaptation tool designed to help make adaptation decisions. However, there is limited understanding of social and political factors, which are critical in driving RAD decisions. This paper explores how communities are employing RAD to make climate adaptation decisions, using a case study of the Goulburn-Murray Resilience Strategy (the Strategy); a community-led strategy that uses a version of the RAD framework to build regional resilience in the Goulburn-Murray Irrigation District (GMID) in Australia’s Murray-Darling Basin (MDB). The Strategy focuses on building socio-economic, rather than ecological, resilience, making this research a valuable contribution to RAD literature. To apply the RAD framework to socio-economic adaptation, we adapted the framework to include IPCC language around incremental, transformational, planned, and autonomous adaptation. With the GMID considered a leader of resilience thinking in Australia, the Strategy may help decision-makers address water overallocation and contested governance in the MDB, and provide lessons for water governance globally. Data analysed from 20 semi-structured interviews with people involved with the Strategy revealed two main findings: (1) Communities and governments prioritise different actions under the RAD framework. Governments, particularly at the state level, preference incremental planned adaptation to maintain the status quo (resist), over incremental autonomous adaptation to changing conditions (accept), and transformational planned adaptation at various scales (direct). (2) Community and government actors perceive that factors driving governments’ preference for incremental-resist adaptation include electoral short-termism, linear planning, and conservative government culture.

Abstract 47: Deletion of PRR in AgRP Neurons of the Arcuate Nucleus Contributes to the Development of DOCA-Salt Hypertension

Agouti-related protein (AgRP) neurons, a key neuronal population within the arcuate nucleus (ARC), are known for their pivotal roles in regulating energy- and glucose homeostasis. However, their involvement in cardiovascular regulation remains poorly understood. Using RNAscope in situ hybridization, we found that 95.4% of the ARC AgRP neurons express the (pro)renin receptor (PRR), a component of the brain renin-angiotensin system that is crucial for blood pressure (BP) regulation. We therefore hypothesized that PRR signaling in the ARC AgRP neurons plays a regulatory role in the development of hypertension. To test this hypothesis, we selectively deleted PRR in ARC AgRP neurons by crossing AgRP-IRES-Cre mice with PRR fl/fl mice. Using RNAscope, as expected, AgRP Cre+ PRR fl/y mice displayed a significantly lower PRR mRNA in the ARC AgRP neurons than WT mice (76.3 ± 6.1 vs. 127.7 ± 2.9 AFU, p < 0.05), indicating efficient ablation of PRR in ARC AgRP neurons. To examine the impact of PRR deletion in the ARC AgRP neurons on BP, the AgRP Cre+ PRR fl/y and control mice were implanted with telemetry probes to monitor BP continuously. After baseline recording, mice received DOCA-salt treatment (50 mg DOCA + saline as drinking water) for two weeks. BP variability and standard time domain heart rate variability (HRV) parameters, including overall HRV (SDNN) and short-term HRV (rMSSD), were analyzed from beat-to-beat intervals (n = 2-7 mice/group). At baseline, there was no difference between the two groups in BP, BP variability, and HRV. Two weeks of DOCA-salt induced a significantly higher BP in AgRP Cre+ PRR fl/y mice than the controls (156.2 ± 6 vs. 119.4 ± 3 mmHg, p < 0.05), while BP variability did not differ between the two groups. Noteworthy, AgRP Cre+ PRR fl/y mice exhibited lower SDNN than the controls (13.3 ± 4 vs. 19.4 ± 4 ms), indicating increased sympathetic and/or reduced parasympathetic nervous activity. This lower SDNN suggests impaired cardiac autonomic function and adaptability to physiological demands in AgRP Cre+ PRR fl/y mice. Concurrently, AgRP Cre+ PRR fl/y mice showed a lower rMSSD (4.7 ± 0.5 vs. 10.8 ± 3.5 ms), implying reduced parasympathetic activity. In conclusion, deletion of PRR in the ARC AgRP neurons results in compromised cardiac autonomic function associated with the development of hypertension. Our findings underscore a novel protective role of PRR signaling in the ARC AgRP neurons in BP regulation.

Presenting the COGNIFOG Framework: Architecture, Building Blocks and Road toward Cognitive Connectivity.

In the era of ubiquitous computing, the challenges imposed by the increasing demand for real-time data processing, security, and energy efficiency call for innovative solutions. The emergence of fog computing has provided a promising paradigm to address these challenges by bringing computational resources closer to data sources. Despite its advantages, the fog computing characteristics pose challenges in heterogeneous environments in terms of resource allocation and management, provisioning, security, and connectivity, among others. This paper introduces COGNIFOG, a novel cognitive fog framework currently under development, which was designed to leverage intelligent, decentralized decision-making processes, machine learning algorithms, and distributed computing principles to enable the autonomous operation, adaptability, and scalability across the IoT-edge-cloud continuum. By integrating cognitive capabilities, COGNIFOG is expected to increase the efficiency and reliability of next-generation computing environments, potentially providing a seamless bridge between the physical and digital worlds. Preliminary experimental results with a limited set of connectivity-related COGNIFOG building blocks show promising improvements in network resource utilization in a real-world-based IoT scenario. Overall, this work paves the way for further developments on the framework, which are aimed at making it more intelligent, resilient, and aligned with the ever-evolving demands of next-generation computing environments.

Manipulation-Compliant Artificial Potential Field and Deep Q-Network: Large Ships Path Planning Based on Deep Reinforcement Learning and Artificial Potential Field

Enhancing the path planning capabilities of ships is crucial for ensuring navigation safety, saving time, and reducing energy consumption in complex maritime environments. Traditional methods, reliant on static algorithms and singular models, are frequently limited by the physical constraints of ships, such as turning radius, and struggle to adapt to the maritime environment’s variability and emergencies. The development of reinforcement learning has introduced new methods and perspectives to path planning by addressing complex environments, achieving multi-objective optimization, and enhancing autonomous learning and adaptability, significantly improving the performance and application scope. In this study, we introduce a two-stage path planning approach for large ships named MAPF–DQN, combining Manipulation-Compliant Artificial Potential Field (MAPF) with Deep Q-Network (DQN). In the first stage, we improve the reward function in DQN by integrating the artificial potential field method and use a time-varying greedy algorithm to search for paths. In the second stage, we use the nonlinear Nomoto model for path smoothing to enhance maneuverability. To validate the performance and effectiveness of the algorithm, we conducted extensive experiments using the model of “Yupeng” ship. Case studies and experimental results demonstrate that the MAPF–DQN algorithm can find paths that closely match the actual trajectory under normal environmental conditions and U-shaped obstacles. In summary, the MAPF–DQN algorithm not only enhances the efficiency of path planning for large ships, but also finds relatively safe and maneuverable routes, which are of great significance for maritime activities.

Vasopressin and cardiovascular autonomic adjustment in chronic hypertensive pregnancy

Chronic hypertensive pregnancy (CHP) is a growing health issue with unknown etiology. Vasopressin (VP), a nonapeptide synthesized in paraventricular (PVN) and supraoptic nucleus (SON), is a well-known neuroendocrine and autonomic modulator of the cardiovascular system, related to hypertension development. We quantified gene expression of VP and its receptors, V1aR and V1bR, within the PVN and SON in CHP and normal pregnancy, and assessed levels of secreted plasma VP. Also, we evaluated autonomic cardiovascular adaptations to CHP using spectral indices of blood pressure (BPV) and heart rate (HRV) short-term variability, and spontaneous baroreflex sensitivity (BRS). Experiments were performed in female spontaneously hypertensive rats (SHRs) and in normotensive Wistar rats (WRs). Animals were equipped with a radiotelemetry probe for continuous hemodynamic recordings before and during pregnancy. BPV, HRV and BRS were assessed using spectral analysis and the sequence method, respectively. Plasma VP was determined by ELISA whilst VP, V1aR, and V1bR gene expression was analyzed by real-time-quantitative PCR (RT-qPCR). The results show that non-pregnant SHRs exhibit greater VP, V1aR, and V1bR gene expression in both PVN and SON respectively, compared to Wistar dams. Pregnancy decreased VP gene expression in the SON of SHRs but increased it in the PVN and SON of WRs. Pregnant SHRs exhibited a marked drop in plasma VP concentration associated with BP normalization. This triggered marked tachycardia, heart rate variability increase, and BRS increase in pregnant SHRs. It follows that regardless of BP normalization in late pregnancy, SHRs exhibit cardiovascular vulnerability and compensate by recruiting vagal mechanisms.Pregnant SHR dams have reduced expression of VP in SON associated with increased V1bR expression, lower plasma VP, normal BP during late pregnancy and marked signs of enhanced sympathetic cardiac stimulation (increased HR and LFHR variability) and recruitment of vagal mechanisms (enhancement of BRS and HFHR variability).

Cardiac autonomic function in REM-related obstructive sleep apnoea: insights from nocturnal heart rate variability profiles

PurposeIn light of the reported association between REM-related obstructive sleep apnoea (OSA) and heightened cardiovascular risk, this study aims to compare cardiac autonomic function in patients with REM-OSA and OSA independent of sleep stage. We hypothesized that REM-OSA patients would exhibit higher sympathetic cardiac modulation based on heart rate variability (HRV) profiles.MethodsHRV was compared between the OSA group (AHI ≥ 5 events/h, n = 252) and the REM-OSA group (AHI ≥ 5 events/h, AHIREM:AHINREM ≥ 2, n = 137). Time- and frequency-domain measures of HRV were analysed during N2 and REM sleep.ResultsClinical characteristics between the two test groups differed significantly, 45% of REM-OSA patients were female, with mild OSA (median, interquartile range (IQR)) AHI of 10 (7) events/h. Only 26% of the OSA cohort were female with moderate OSA (AHI = 17 (20) events/h, p < 0.001). Compared with the OSA group, the low frequency to high frequency ratio (LF:HF) and LF power were lower and HF power was higher in the REM-OSA group during N2 (LF:HF, p = 0.012; LF; p = 0.013; HF, p = 0.007) and in REM sleep (LF:HF, p = 0.002; LF, p = 0.004; HF, p < 0.001). Patient sex and OSA severity had a significant combined effect on average N to N interval, LF power, and LF:HF ratio during N2 and REM sleep (all p < 0.001).ConclusionContrary to our hypothesis, REM-OSA patients demonstrated consistently higher cardiac vagal modulation, reflecting better cardiac autonomic adaptation. These results were attributed to differences in OSA severity and sex in these two groups, both independently affecting HRV. This study emphasises the need for future research into the underlying pathophysiology of REM-OSA and the potential implications of sex and OSA severity on cardiovascular risk.

Food security governance: taming the endemic starvation for peace and sustainable development in Africa

This study investigated food security governance in relation to taming the endemic starvation for peace and sustainable development in Africa. Food and nutrition security remain a multi-dimensional concept to ensure sustainability of the food system by 2030. It is a prerequisite to meaningful development as starving people are not capable of learning, innovating, nor prioritizing sustainable development. The COVID-19 preventive measures such as lockdowns and curfews, and persistent conflict in Africa, plunged deteriorating food insecurity into steep descent that necessitates collaborative efforts and partnerships to change the trajectory. The partnerships will restore livelihoods, and eliminate starvation, disease, abject poverty, wars and eventually restore peace. Data for the study were collected through systematic review of scholarly reviewed publications obtained from Access to Global Online Research in Agriculture (AGORA) database, University of Nairobi and Google search engines. The objectives of this paper, therefore, are to analyse underlying causes of persistent starvation in Africa, examine the factors that contribute to food security issues degenerating into conflict, and highlight the role of governance in finding lasting solutions to persistent starvation in the continent. However, it was revealed that, there is congruence that conflict and disease (COVID-19) may require more attention, with unanimous calls for the adoption of multilateralism in food governance as strategy to handle the nexus issues on food, water, energy, climate, economy, conflict and disease. Attention is also required to be focused on innovations supporting small-scale farmers, especially women farmers who are the most vulnerable, to benefit from autonomous measures (such as climate-smart agriculture practices) and adaptation to climate extremes. Conclusions are drawn on the best possible strategies available to mitigate endemic starvation in Africa, the urgent need for joint efforts to eliminate conflict –induced food insecurity, and emphasis on a systems approach to tackle nexus issues (Food-Water-Energy-Climate-Economy-Conflict and Disease) to ensure sustainable development. Key words: Food security, Starvation, Conflict, Peace, Sustainable development, Africa

YS-SLAM: YOLACT++ based semantic visual SLAM for autonomous adaptation to dynamic environments of mobile robots

Aiming at the problem of poor autonomous adaptability of mobile robots to dynamic environments, this paper propose a YOLACT++ based semantic visual SLAM for autonomous adaptation to dynamic environments of mobile robots. First, a light-weight YOLACT++ is utilized to detect and segment potential dynamic objects, and Mahalanobis distance is combined to remove feature points on active dynamic objects, also, epipolar constraint and clustering are employed to eliminate feature points on passive dynamic objects. Then, in terms of the semantic labels of dynamic and static components, the global semantic map is divided into three parts for construction. The semantic overlap and uniform motion model are chose to track moving objects and the dynamic components are added to the background map. Finally, a 3D semantic octree map is constructed that is consistent with the real environment and updated in real time. A series of simulations and experiments demonstrated the feasibility and effectiveness of the proposed approach.

Power-law productivity of highly biodiverse agroecosystems supports land recovery and climate resilience

Transformative change in primary food production is urgently needed in the face of climate change and biodiversity loss. Although there are a growing number of studies aimed at global policymaking, actual implementations require on-site analyses of social feasibility anchored by ecological rationale. This article reports the in-depth characterizations of low-input mixed polyculture of highly diverse crops managed on the self-organization of ecosystems, which performed better compared to conventional monoculture methods in Japan and Burkina Faso. Analyses on crop productivity and diversity showed that the primary production of ecosystems followed a power law, and through the underlying mechanisms excelled in (1) promoting diversity and total quantity of products along with the rapid increase of in-field biodiversity, especially useful for the recovery of local regime shift in a semi-arid environment; (2) a fundamental reduction of inputs and environmental load; and (3) ecosystem-based autonomous adaptation of the crop portfolio to climatic variability. The overall benefits imply substantial possibilities of a new typology of sustainable farming for smallholders sensitive to climate change, which could overcome the historical trade-off between productivity and biodiversity based on the human-guided augmentation of ecosystems.

An assessment of the distributional impacts of autonomous adaptation to climate change from European agriculture

Farmers facing a durable change in climate conditions may autonomously adapt through the intensive margin, the extensive margin, or through the adoption of new practices. Based on a coupling between a microeconomic model of European agriculture (AROPAj) and a crop model (STICS), this article investigates the potential distributional impacts of farm-level autonomous adaptation to climate change within the European Union (EU-27). Considering the representative concentration pathway (RCP) 4.5 of the second report on emission scenario of the fifth assessment report (SRES AR5), we implement two levels of autonomous adaptation for farmers, and three time horizons. The results indicate that ceteris paribus, climate change may lead in terms of social welfare to a slightly worse situation in the middle term and a slightly better situation in the long term with respect to the present. However, the ranking of agents in the distribution is importantly impacted. Our Shapley inequality decomposition shows that income inequality is largely explained by the region and type of farming. Climate change barely affects the marginal contribution of these two characteristics to overall income inequality.

Application Study on the Reinforcement Learning Strategies in the Network Awareness Risk Perception and Prevention

The intricacy of wireless network ecosystems and Internet of Things (IoT) connected devices have increased rapidly as technology advances and cyber threats increase. The existing methods cannot make sequential decisions in complex network environments, particularly in scenarios with partial observability and non-stationarity. Network awareness monitors and comprehends the network's assets, vulnerabilities, and ongoing activities in real-time. Advanced analytics, machine learning algorithms, and artificial intelligence are used to improve risk perception by analyzing massive amounts of information, identifying trends, and anticipating future security breaches. Hence, this study suggests the Deep Reinforcement Learning-assisted Network Awareness Risk Perception and Prevention Model (DRL-NARPP) for detecting malicious activity in cybersecurity. The proposed system begins with the concept of network awareness, which uses DRL algorithms to constantly monitor and evaluate the condition of the network in terms of factors like asset configurations, traffic patterns, and vulnerabilities. DRL provides autonomous learning and adaptation to changing network settings, revealing the ever-changing nature of network awareness risks in real time. Incorporating DRL into risk perception increases the system's capacity to recognize advanced attack methods while simultaneously decreasing the number of false positives and enhancing the reliability of risk assessments. DRL algorithms drive dynamic and context-aware response mechanisms, making up the adaptive network prevention component of the development. Predicting new threats and proactively deploying preventive measures, such as changing firewall rules, isolating compromised devices, or dynamically reallocating resources to reduce developing risks, is made possible by the system's ability to learn from historical data and prevailing network activity. The suggested DRL-NARPP model increases the anomaly detection rate by 98.3%, the attack prediction accuracy rate by 97.4%, and the network risk assessment ratio by 96.4%, reducing the false positive ratio by 11.2% compared to other popular methodologies.

Autonomous and policy-induced behavior change during the COVID-19 pandemic: Towards understanding and modeling the interplay of behavioral adaptation.

Changes in human behaviors, such as reductions of physical contacts and the adoption of preventive measures, impact the transmission of infectious diseases considerably. Behavioral adaptations may be the result of individuals aiming to protect themselves or mere responses to public containment measures, or a combination of both. What drives autonomous and policy-induced adaptation, how they are related and change over time is insufficiently understood. Here, we develop a framework for more precise analysis of behavioral adaptation, focusing on confluence, interactions and time variance of autonomous and policy-induced adaptation. We carry out an empirical analysis of Germany during the fall of 2020 and beyond. Subsequently, we discuss how behavioral adaptation processes can be better represented in behavioral-epidemiological models. We find that our framework is useful to understand the interplay of autonomous and policy-induced adaptation as a "moving target". Our empirical analysis suggests that mobility patterns in Germany changed significantly due to both autonomous and policy-induced adaption, with potentially weaker effects over time due to decreasing risk signals, diminishing risk perceptions and an erosion of trust in the government. We find that while a number of simulation and prediction models have made great efforts to represent behavioral adaptation, the interplay of autonomous and policy-induced adaption needs to be better understood to construct convincing counterfactual scenarios for policy analysis. The insights presented here are of interest to modelers and policy makers aiming to understand and account for behaviors during a pandemic response more accurately.

Flexible Peripheral Nerve Interfacing Electrode for Joint Position Control in Closed-Loop Neuromuscular Stimulation.

Addressing peripheral nerve disorders with electronic medicine poses significant challenges, especially in replicating the dynamic mechanical properties of nerves and understanding their functionality. In the field of electronic medicine, it is crucial to design a system that thoroughly understands the functions of the nervous system and ensures a stable interface with nervous tissue, facilitating autonomous neural adaptation. Herein, we present a novel neural interface platform that modulates the peripheral nervous system using flexible nerve electrodes and advanced neuromodulation techniques. Specifically, we have developed a surface-based inverse recruitment model for effective joint position control via direct electrical nerve stimulation. Utilizing barycentric coordinates, this model constructs a three-dimensional framework that accurately interpolates inverse isometric recruitment values across various joint positions, thereby enhancing control stability during stimulation. Experimental results from rabbit ankle joint control trials demonstrate our model's effectiveness. In combination with a proportional-integral-derivative (PID) controller, it shows superior performance by achieving reduced settling time (less than 1.63 s), faster rising time (less than 0.39 s), and smaller steady-state error (less than 3 degrees) compared to the legacy model. Moreover, the model's compatibility with recent advances in flexible interfacing technologies and its integration into a closed-loop controlled functional neuromuscular stimulation (FNS) system highlight its potential for precise neuroprosthetic applications in joint position control. This approach marks a significant advancement in the management of neurological disorders with advanced neuroprosthetic solutions.

Autonomous Agent-Based Adaptation of Energy-Optimized Production Schedules Using Extensive-Form Games

Modular cyber-physical production systems are an important paradigm of Industry 4.0 to react flexibly to changes. The flexibility of those systems is further increased with skill-based engineering and can be used to adapt to customer requirements or to adapt manufacturing to disturbances in supply chains. Further potential for application of these systems can be found in the topic of electrical energy supply, which is also characterized by fluctuations. The relevance of energy-optimized production schedules for manufacturing systems in general becomes more important with the increased use of renewable energies. Nevertheless, it is often difficult to adapt when short-term energy price updates or unforeseen events occur. To address these challenges with an autonomous approach, this contribution focuses on extensive-form games to adapt energy-optimized production schedules in an agent-based manner. The paper presents agent-based modeling to transform and monitor energy-optimized production schedules into game trees to respond to changing energy prices and disturbances in production. The game is setup with a scheduler agent and energy agents who are considered players. The implementation of the mechanism is presented in two use cases, realizing decision making for an energy price update in a simulation example and for unforeseen events in a real-world demonstrator.

Cold-induced FOXO1 nuclear transport aids cold survival and tissue storage

Cold-induced injuries severely limit opportunities and outcomes of hypothermic therapies and organ preservation, calling for better understanding of cold adaptation. Here, by surveying cold-altered chromatin accessibility and integrated CUT&Tag/RNA-seq analyses in human stem cells, we reveal forkhead box O1 (FOXO1) as a key transcription factor for autonomous cold adaptation. Accordingly, we find a nonconventional, temperature-sensitive FOXO1 transport mechanism involving the nuclear pore complex protein RANBP2, SUMO-modification of transporter proteins Importin-7 and Exportin-1, and a SUMO-interacting motif on FOXO1. Our conclusions are supported by cold survival experiments with human cell models and zebrafish larvae. Promoting FOXO1 nuclear entry by the Exportin-1 inhibitor KPT-330 enhances cold tolerance in pre-diabetic obese mice, and greatly prolongs the shelf-life of human and mouse pancreatic tissues and islets. Transplantation of mouse islets cold-stored for 14 days reestablishes normoglycemia in diabetic mice. Our findings uncover a regulatory network and potential therapeutic targets to boost spontaneous cold adaptation.

Climate change policy instruments in agriculture sector: From policy to adaptation practice at local level in Nepal

The current study explores synergies between climate change policy instruments and agriculture sector, reviews identified gaps and understands the translation of policy instruments at local level in five sample municipalities of three ecological regions. Understanding the adaptation actions and knowledge on implementation practices at household level is as the objective of this study. It adopted qualitative research approach using both primary (KII, FGD and household survey) and secondary data (review of existing policy documents) based on qualitative document analysis, (QDA). The study found that, policy and programs formulation at national and sub-national level in different period, are designed within the same setting from national to local level but there is divergence in programs formulation and implementation methods. Capacity and understanding of policy instruments has resulted different policy output which implicate farmers’ response to implementation practices. It is found that municipal authorities are implementing climate friendly agriculture programs without explicitly declaring climate action. The farmers are practicing autonomous adaption in agriculture with limited understanding and technical knowledge of long term climate change effect on farming. This has resulted maladaptive practices, which has contested planned climate change adaptation plan and programs. It devaluates the effort and resources spent by national and international institutions on CC policy instrument formulation and implementation. The need of field based demonstrative understanding and awareness campaigns for smallholder farmers is anticipated. It concludes that, flexibility of policy instruments through regular improvements and modification aids context-specific localization provide custom solutions. The custom solution match the traditional and existing adaptation practices assuring ownership at ground level.

Impacts of Global Warming on Regional Energy and Economy: Uneven consequences arising from global warming-induced heating and cooling demand of households

The impacts of global warming vary across regions. This paper studies the distributional implications of global warming impacts on household energy use for heating and cooling and the induced macroeconomic responses under different scenarios. Our research updates the direct impact of global warming on household energy demand in 140 regions worldwide by utilizing existing estimations of damage functions related to temperature changes. Subsequently, the updated direct impact is used in a global static computable general equilibrium (CGE) model to evaluate the macroeconomic responses. We find that at the global level, the market effects cause a reduction in the direct impact on the demand for oil and gas, while that for electricity displays a positive but moderate growth. Whereas the regional effects vary across countries and lead to changes in both directions, in which the autonomous adaptation embodied in the global market plays a vital role. Furthermore, we find strong inequality in the socioeconomic responses to global warming across regions. Notably, low-income countries are most strongly affected by increased primary energy use and decreased gross domestic product (GDP). Disparities in the impacts on carbon-based energy sources yield a near-perfect inequality as per the adjusted Gini index for CO2 emission changes, which potentially intensify the distributional consequences of global climate change.