Adaptation Planning Research Articles

Turning plans into actions: assessing specificity of actions as a step towards more actionable regional resilience plans

Regional-scale climate and drought planning can increase resilience and adaptation outcomes in regions. Yet the under-specification of plans’ actions represents a barrier to implementation and impact. This paper presents a novel “Specificity of Actions” (SoA) analysis; a flexible evaluative instrument for appraising actions in regional planning documents. Prototyped on 15 Australian drought resilience plans, the SoA analysis represents a useful complement to existing plan quality evaluation methods. It provides: (1) a micro-level appraisal of plans’ actions; (2) a means of identifying wider trends across plans such as the “boiler-plating” of identical actions; and (3) a capacity-building tool and means of co-learning between plan evaluators and plan authors. Finally, we reconcile the focused nature of specificity with the uncertainty and flexibility inherent to resilience planning, pointing to the scalability of the SoA method to different planning typologies such as mitigation, adaptation and resilience planning.

Phytoclimatological study in the high plains of Sétif: application of the bioclimatic classification system (Algeria)

The Global Bioclimatic Classification System (WBCS) by Rivas-Martínez is an effective approach to reflect and study the role of climate in determining the potential distribution of natural vegetation at various temporal and spatial scales. In particular, the diversity of physical characteristics in the Setifian high plains leads to the complexity of large-scale phytogeographic belts. Isobioclimates, unique combinations of bioclimatic indices (continentality, ombrotype, and thermotype), have been created for the study area based on Rivas-Martínez's global bioclimatic classification system for current and future climate scenarios, using the parallel climate model from CMIP5 (Coupled Model Intercomparison Project) under the intermediate greenhouse gas emission scenario (RCP4.5). Precipitation and temperature data from the "WorldClim" dataset were used as the data source. These climatic variables were reduced to a spatial resolution of 1 km. The objective is to understand how the climate is evolving in this region, what the consequences are, and what the future holds for these natural ecosystems. A phytosociological study complemented by satellite image analysis reveals a high diversity of pre-forestry formations. The digitally mapped results were used to 1) assess the relative redistribution of isobioclimates and the magnitude of change, 2) identify and locate the new projected isobioclimates, and 3) explore the compositional changes in vegetation types among analogous isobioclimatic zones. The ordination of the relationships between vegetation and bioclimates shows a strong correlation between Rivas-Martinez indices and the distribution and composition of vegetation. Changes in ombrotype, thermotype, bioclimate, ombrotype, and isobioclimate differed between the current and future series. The resulting map presents 9 isobioclimatic units, which are advantageous for confirming the diagnosis of bioclimatic characteristics and describing the relationships between climatic variables and the corresponding distributions of natural vegetation. We identified eight different isobioclimates for the current period (1979-2013) and seven for the future period (2050), three thermotype horizons for both the current and future periods, and finally, four ombrotype horizons for the period (1979-2013), while two ombrohorizons represent the period (2050). This approach is useful for explaining territorial diversity in environmental applications related to ecological adaptation assessment, nature conservation, landscape regionalization, and planning.

Modeling and scenario building for climate change adaptation planning: The case of large mining in Chile

The mining sector in Chile is a strategic industrial sector for the country. Existing evidence shows that it faces several serious climate change threats; precipitation and flooding, droughts, heatwaves, among others. This scene put pressure on the government and the industry to develop efficient adaptation plans. To date, adaptation plans are designed under the rather linear paradigm of predict-then-act or the impact-lea approach as they are characterized in the IPCC Assessment Report 5 (AR5). Literature and the IPCC reports have identified the limits of those planning paradigms showing the relevance of adaptation barriers or enabling conditions, which should be considered as intrinsic part of the planning problem.In methodological terms, planning for climate change risks implies an enriched adaptation plan problem, previously characterized only by an operative climate risk management, that must be described at the beginning of the decision-making process. In this study, our objective is to contribute to the climate change adaptation planning of large-scale mining in Chile. The study is based on a climate change adaptation planning approach that overcomes the limitations of the current paradigm. In doing so, we start from the understanding that what emerges as an object of analysis from adding to the climate risks their enabling managing conditions is a social system. The social system whose function is that social climate risk management takes place. Therefore, we call it the Social Management System for Adaptation to Climate Change (SMSACC). As such the SMSACC should be the adaptation planning key object.In the first place we modeled that system applying a qualitative system methodology and then we developed it into a mathematical model based on graph theory, in particular the signed digraphs. This allows us to simulate two types of intervention on the enlarged object of analysis of the large mining adaptation plan. On the one hand, we carried out a future scenario analysis based on prospective tools which enables us to understand the system's answer to different future behavior of its environment, including climate change. On the other hand, we simulate different strategic interventions options on the system, which facilitates understanding the system's reaction under different public policy approaches.The modeling and simulation results provided an insightful understanding of the dilemma of social adaptation management of large mining in Chile, and as such they are useful input for the planning process.

Maintenance plan adaptation based on health ratings of servitised machines through a fleet-wide machine clustering method

The increased requests for value-added services to integrate product performance push manufacturing companies to extend their service offerings to meet customers’ needs. In this context, maintenance planning can leverage new possibilities offered by digital technologies for data analytics services. The present research then proposes an approach for maintenance plan adaptation based on a data-driven method applied over a fleet of machines installed in different production sites. The method relies on collaborative prognostics to develop a clustering of machines’ behaviour aimed at providing the health ratings of the machines and the subsequent maintenance plan adaptation due to the deviation from the expected behaviour. The method is adopted from the perspective of an Original Equipment Manufacturer, as part of a transformation path towards an advanced provision of digitalization for maintenance service offerings. The method is validated in the context of two lines at selected customer’s premises. This demonstrates the viability and effectiveness of adapting the maintenance plans thanks to the data analytics in light of the current behaviour of the machines within the lines.

Socio-demographic disparities in the familiarity with coastal climate adaptation strategies: implications for coastal management and climate justice

AbstractPublic preferences for coastal climate adaptation strategies have been increasingly documented in the face of climate change and sea-level rise in coastal regions worldwide. However, empirical evidence of whether the public is familiar with these strategies remains unclear. In this study, we conducted an online survey among residents in three coastal counties in Southeast Florida, the United States to elicit households’ familiarity with different coastal climate adaptation strategies and examine the socio-demographic disparities. Using a multinomial logit model, we find that although most respondents are familiar with coastal climate adaptation strategies, socio-demographic disparities in the level of familiarity are evident. Specifically, Black respondents are significantly less likely to be familiar with coastal climate adaptation strategies when compared to their White counterparts. Moreover, having college degrees or more advanced degrees significantly increases the respondents’ probability of being very familiar with these strategies. Respondents’ risk perception and their awareness of current flood risk also significantly influence their familiarity with coastal climate adaptation strategies. Since adaptive planning often overlooks equitable access, and local support is essential for implementing climate adaptation strategies, our results can provide insightful policy implications for coastal management. A lack of familiarity with coastal climate adaptation strategies among marginalized and vulnerable populations can lead to insufficient support for these strategies aimed at mitigating climate risks, exacerbating the issue of climate justice.

Forest assisted migration and adaptation plantings in the Northeastern US: perspectives and applications from early adopters

Threats to the future function of forested ecosystems and stability of ecosystem service provisioning due to global change have motivated climate-adaptive forest management strategies that include various forms of tree planting termed “adaptation plantings”. Despite the emergence of these strategies, less is known as to how foresters and other natural resource managers perceive or are engaged with adaptation plantings like forest assisted migration (FAM). This knowledge gap is most pronounced in regions like New England and the North Central US (hereafter, the Northeastern US) where tree planting is less common but expected to be an important forest management tool for adaptation. To address this, we surveyed 33 natural resource managers in this region actively engaged in climate change adaptation (i.e., early adopters of the practice) to assess how tree planting for adaptation is currently being pursued against the perceived barriers, opportunities, and potential future engagement with the strategy. Survey respondents overwhelmingly (93.5%) forecast increases in the future use of adaptation plantings in their work in the region, attributed to increased awareness, acceptance, and interest in the practice. Respondents expressed notable interest in strategies related to diversification and most types of FAM (e.g., assisted population expansion and assisted range expansion), but hesitancy to engage with more contentious planting types like afforestation or FAM linked to the long-distance translocation of exotic species (e.g., assisted species migration). Although examples of local enrichment plantings (i.e., non-FAM) proliferate, nineteen of the top twenty most common tree genera planted contain at least one example of FAM in the study region. The most notable barriers reported were themed as 1) biotic and abiotic, 2) information and material, and 3) policy, social, and economic factors. While most respondents report difficulty obtaining adequate planting material from nurseries (i.e., seedlings), over 80% placed orders shortly before planting (< 1 year) which likely generates difficulty in sourcing seedlings suited for a specific site and future range of environmental conditions. Although this study is limited by focusing on subset of natural resource managers who are early adopters of climate change adaptation within the region, valuable inferences into the barriers and trends are possible from this population serving on the front lines of forest adaptation. Together, these results from early adopters suggest a potentially growing need for allocating resources that engage forest stewards in adaptation planning and serve to refine policy, financing, and management practices to support this adaptation strategy in this region and beyond.

Mainstreaming Indigenous Knowledge of Pastoralists into Climate Change Policies in Cameroon

Indigenous pastoralists are hardest hit by the impacts of climate change because of their dependence on nature and limited adaptation. This paper seeks to examine the level of integration of traditional knowledge systems into adaptation policies in Cameroon, using the specific case of Mbororo pastoralists in the Western Highlands. Using a mainly qualitative approach in data collection, in-depth interviews were conducted with 28 purposively selected pastoralists from 4 divisions with intense pastoral activity, comprising 19 Mbororo pastoralists, 2 members of the Mbororo Social and Cultural Association (MBOSCUDA), 2 Mbororo traditional rulers, 5 Mbororo women and one focus group discussion. Discussions were recorded and voice notes were transcribed, coded and treated according to content and thematic with the aid of Atlas. Ti software. Results show that Mbororo pastoralists migrated into the region in the 1950s as nomadic herders and occupied marginal lands due to the nature of their activity. With climatic aberrations being acute, their closeness to nature permitted them to develop a wide array of knowledge and traditional practices to adapt and exploit existing opportunities. Semi-nomadic and transhumance methods of rearing, cross-breeding local species with high-breed species to produce resistant breeds of cattle, cultivating resistant pasture species, rotational grazing and using traditional medicines to treat cattle diseases have reduced resource degradation and curbed the effects of climatic aberrations. Nevertheless, these indigenous knowledge systems are eroding fast due to poor indigenous institutions and non-integration into policy. This work will reinforce capacities of decentralized communities in developing and implementing local adaptation plans on climate change.

Unified Fault-Tolerant Control and Adaptive Velocity Planning for 4WID-4WIS Vehicles under Multi-Fault Scenarios

Four-wheel independent drive and four-wheel independent steering (4WID-4WIS) vehicles provide increased redundancy in fault-tolerant control (FTC) schemes, enhancing heterogeneous fault-tolerant capabilities. This paper addresses the challenge of maintaining vehicle safety and maneuverability in the presence of actuator faults in autonomous vehicles, focusing on 4WID-4WIS systems. A novel unified hierarchical active FTC strategy is proposed to handle various actuator failures. The strategy includes an upper-layer motion controller that determines resultant force requirements based on trajectory tracking errors and a middle-layer allocation system that redistributes tire forces to fault-free actuators using fault information. This study, for the first time, considers multi-fault scenarios involving longitudinal and lateral coupling, calculating FTC boundaries for each fault type. Additionally, a fault tolerance index is introduced for 256 fault scenarios, using singular value decomposition to linearly represent the vehicle attainable force domain. Based on this, an adaptive velocity planning strategy is developed to balance safety and maneuverability under fault conditions. Matlab 2021a/Simulink and Carsim 2019 co-simulation results validate the proposed strategies, demonstrating significant improvements in fault-tolerant performance, particularly in complex and emergency scenarios.

Association of heat and cold waves with cause-specific mortality in Iran: a systematic review and meta-analysis

Despite the frequent occurrence of heat waves in the Middle East, there is a lack of evidence regarding the overall estimates for the effect of heat waves on mortality in this region. This study aimed to review the effect of exposure to heat and cold waves and daily cause-specific mortality. Four electronic databases were searched. The titles, abstracts, and full-texts of the articles were carefully reviewed by two researchers. Once eligible studies were identified, the required data were extracted. Separate meta-analyses were conducted based on gender, age group, and health endpoint combinations. According to the meta-analysis, heat waves had a statistically significant effect on all-cause mortality with an RR of 1.23 (CI 95%: 1.08, 1.39). Cardiovascular mortality significantly increased in heat waves with an RR of 1.08 (CI 95%: 1.05, 1.10). However, the increase in respiratory mortality was not statistically significant. Compared to young people (age < 65 years old) and women, elderly and men were more vulnerable to heat waves with RRs of 1.31 (95% CI: 1.05, 1.57) and 1.33 (95% CI: 1.08, 1.58), respectively. This study can be beneficial in developing response or adaptation plans for heat waves. Future studies should focus on other specific health endpoints like ischemic heart disease, chronic obstructive pulmonary diseases, etc., and other outcomes such as hospitalization and emergency visits.

The Nursing Theory of Complex Adaptive Systems: A New Paradigm for Nursing.

This article explores the theoretical and practical implications of the meaning of thinking, living, and acting within the framework of nursing in Complex Adaptive Systems. The Nursing Theory of Complex Adaptive Systems is grounded in the principles of Complex Adaptive Systems (CASs). It seeks to offer a new paradigm for nursing practice that addresses healthcare's dynamic and evolving nature. The Nursing Theory of Complex Adaptive Systems represents a new nursing paradigm capable of addressing the challenges of a constantly evolving healthcare environment. This theory promotes personalized care plans adaptable to patients' changing needs by emphasizing a holistic and interactive approach to care. It also underscores the importance of interprofessional collaboration and effective communication in improving the quality of care. The Nursing Theory of Complex Adaptive Systems has significant implications for nursing practice, education, and research. It provides a robust framework for developing adaptive and resilient nursing practices that can respond to the complexities of modern healthcare. By integrating the principles of CASs into nursing, the Nursing Theory of Complex Adaptive Systems fosters a more flexible, interdependent, and holistic approach to patient care, ultimately enhancing patient outcomes and improving healthcare systems. This theory has practical applications in various healthcare settings, offering a framework for personalized and adaptable care plans that respond to the dynamic needs of patients while improving overall system efficiency. Future research should focus on the empirical validation of the Nursing Theory of Complex Adaptive Systems and its practical implementation in various healthcare settings.

Dosimetric Comparison and Selection Criteria of Intensity-Modulated Proton Therapy and Intensity-Modulated Radiation Therapy for Adaptive Re-Plan in T3-4 Nasopharynx Cancer Patients.

Proton therapy requires caution when treating patients with targets near neural structures. Intuitive and quantitative guidelines are needed to support decision-making concerning the treatment modality. This study compared dosimetric profiles of intensity-modulated proton therapy (IMPT) and intensity-modulated radiation therapy (IMRT) using helical tomotherapy (HT) for adaptive re-planning in cT3-4 nasopharyngeal cancer (NPCa) patients, aiming to establish criteria for selecting appropriate treatment modalities. HT and IMPT plans were generated for 28 cT3-4 NPCa patients undergoing definitive radiotherapy. Dosimetric comparisons were performed for target coverage and high-priority organs at risk (OARs). The correlation between dosimetric parameters and RT modality selection was analyzed with the target OAR distances. Target coverages were similar, while IMPT achieved better dose spillage. HT was more favorable for brainstem D1, optic chiasm Dmax, optic nerves Dmax, and p-cord D1. IMPT showed advantages for oral cavity Dmean. Actually, 14 IMPT and 14 HT plans were selected as adaptive plans, with IMPT allocated to most cT3 patients (92.9% vs. 42.9%, p = 0.013). The shortest distances from the target to neural structures were negatively correlated with OAR doses. Receiver operating characteristic curve analyses were carried out to discover the optimal cut-off values of the shortest distances between the target and the OARs (temporal lobes and brainstem), which were 0.75 cm (AUC = 0.908, specificity = 1.00) and 0.85 cm (AUC = 0.857, specificity = 0.929), respectively. NPCa patients with cT4 tumor or with the shortest distance between the target and critical neural structures < 0.8 cm were suboptimal candidates for IMPT adaptive re-planning. These criteria may improve resource utilization and clinical outcomes.

A simulation study of MR-guided proton therapy system using iron-yoked superconducting open MRI: a conceptual study.

Radiotherapy platforms integrated with magnetic resonance imaging (MRI) have been significantly successful and widely used in X-ray therapy over the previous decade. MRI provides greater soft-tissue contrast than conventional X-ray techniques, which enables more precise radiotherapy with on-couch adaptive treatment planning and direct tracking of moving tumors. The integration of MRI into a proton beam irradiation system (PBS) is still in the research stage. However, this could be beneficial as proton therapy is more sensitive to anatomical changes and organ motion. In this simulation study, we considered the integration of PBS into the 0.3-T superconducting open MRI system. Our proposed design involves proton beams traversing a hole at the center of the iron yoke, which allows for a reduced fringe field in the irradiation nozzle while maintaining a large proton scan field of the current PBS. The shape of the bipolar MRI magnets was derived to achieve a large MRI field-of-view. To monitor the beam position and size accurately while maintaining a small beam size, the beam monitor installation was redesigned from the current system. The feasibility of this system was then demonstrated by the treatment plan quality, which showed that the magnetic field did not deteriorate the plan quality from that without the magnetic field for both a rectangular target and a prostate case. Although numerous challenges remain before the proposed simulation model can be implemented in a clinical setting, the presented conceptual design could assist in the initial design for the realization of the MR-guided proton therapy.

Multisource geoscience data-driven framework for subsidence risk assessment in urban area

Land subsidence, especially in developed cities, poses significant risks to human life, social property, and urban sustainability. Taking Liwan District in southern China as an example, this study proposed an acceptable framework for regional land subsidence risk assessment while complying with current national assessment system. With integrating the multi-source geospatial data from remote sensing and various geology surveys into ArcGIS, the subsidence risk assessment was carried out based on the subsidence susceptibility mapping, hazard and vulnerability surveying by using a series of data-driven methods. The results showed that, (i) although not all surface deformations detected by InSAR technology were caused by subsidence, they were instrumental in updating subsidence records; (ii) with the help of spatial correlation analysis using weight evidence as well as multi-source data fusion in high spatial resolution, the Random Forest-based classification models effectively identified the land use types and accurately mapped the land subsidence susceptibility; (iii) the hazard and vulnerability surveying based on a series of newly developed combined weight methods, improved the reliability of risk assessment; (iv) the extremely high- and high-risk areas from the zoning of the land subsidence, provided target areas for further management and prevention of land subsidence. This comprehensive and quantitative assessment framework highlights the need for continued monitoring in subsidence-prone regions, helping to propose strategies for risk mitigation and adaptive planning in urban areas.

Reconstructing 169 years of historical atmospheric and hydrologic conditions in the American River Watershed through the Watershed Environmental Hydrology Hydro-Climate Model system

The risk of more frequent and intensified extreme events is expected to increase with climate change. In semi-arid regions, particularly, both increased flooding and prolonged droughts pose a threat as the water resources system must be prepared for both types of extremes. To understand how future extremes may be altered, however, past extremes must be understood. For this reason, this study employs three physically based models to simulate the atmospheric, snow, and hydrologic conditions in the American River Watershed: the Weather Research and Forecasting Model and the Watershed Environmental Hydrology Hydro-Climate Model with the addition of a SNOW Module. Each model is individually calibrated and validated against either gauge data or data provided by the Parameter-Elevation Regressions on Independent Slopes Model for two 10-year periods before a full 169-year daily reconstruction is performed for the period from 1852 through 2020. To analyze the model performance under hydrologic extremes, three flood years (1997, 2006, and 2017) and two drought periods (1987–1992 and 2012–2016) are further analyzed. The reconstruction outputs are found to successfully simulate historical conditions at both daily and monthly scales. Results highlight the importance of fully accounting for the role of temperature in snow processes and the subsequent flow conditions. By understanding how the models perform under historical extreme conditions, future extreme events can be compared, and necessary adaptation plans can be conceived to ensure that the watershed is prepared for future wet and dry conditions. Additionally, the created reconstruction dataset is comprehensive, simulating conditions across the hydroclimate, at a finer spatial and temporal scale than is typically available from observation data alone. The dataset can be further analyzed to assess changes in the trends and the potential trajectory of extreme events within the American River Watershed.

Prediction of heatwave related mortality magnitude, duration and frequency with climate variability and climate change information

Given the link between climatic factors on one hand, such as climate change and low frequency climate oscillation indices, and the occurrence and magnitude of heat waves on the other hand, and given the impact of heat waves on mortality, these climatic factors could provide some predictive skill for mortality. We propose a new model, the Mortality-Duration-Frequency (MDF) relationship, to relate the intensity of an extreme summer mortality event to its duration and frequency. The MDF model takes into account the non-stationarities observed in the mortality data through covariates by integrating information concerning climate change through the time trend and climate variability through climate oscillation indices. The proposed approach was applied to all-cause mortality data from 1983 to 2018 in the metropolitan regions of Quebec and Montreal in eastern Canada. In all cases, models introducing covariates lead to a substantial improvement in the goodness-of-fit in comparison to stationary models without covariates. Climate change signal is more important than climate variability signal in explaining maximum summer mortality. However, climate indices successfully explain a part of the interannual variability in the maximum summer mortality. Overall, the best models are obtained with the time trend and the North Atlantic Oscillation (NAO) used as covariates. No country has yet integrated teleconnection information in their heat-health watch and warning systems or adaptation plans. MDF modeling has the potential to be useful to public health managers for the planning and management of health services. It allows predicting future MDF curves for adaptive management using the values of the covariates.

Impact of Manual Contour Editing on Plan Quality for Online Adaptive Radiation Therapy for Head and Neck Cancer

PurposeOnline adaptive radiotherapy (oART) has high resource costs especially for head-and-neck (H&N) cancer, which requires recontouring complex targets and numerous organs-at-risk (OARs). ART systems provide auto-contours to help, we aim to explore the optimal level of editing automatic contours to maintain plan quality in a cone-beam-computed-tomography (CBCT)-based oART system for H&N. In this system influencer OAR contours are generated and reviewed first, which then drives the auto-contouring of the remaining OARs and targets. Methods and MaterialsThree-hundred-and-forty-nine adapted fractions of forty-four H&N patients were retrospectively analyzed, with physician-edited OARs and targets. These contours and associated online adapted plans served as the gold standard for comparison. We simulated three contour editing workflows: (1) no editing of contours, (2) only editing the influencers, (3) editing the influencers and targets. The geometric difference was quantified with Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). The dosimetric differences in target coverage and OAR doses were calculated between the gold standard and these three simulated workflows. ResultsWorkflow 1 resulted in significantly inferior contour quality for all OARs (mean DSC 0.85±0.17 and HD95 3.10±5.80mm), dosimetric data was hence not calculated for workflow 1. In workflow (2), the frequency of physician editing targets and remaining OARs were 80.8%-95.7% and 2.3% (brachial plexus)-67.7% (oral cavity) respectively, where the OAR differences were geometrically minor (mean DSC>0.95 with std≤0.09). However, due to the unedited target contours of workflow 2 (mean DSC 0.86-0.92 and mean HD95 2.56-3.30mm versus the ground-truth targets), plans were inadequate with insufficient coverage. In workflow (3) when both targets and influencers were edited (non-influencer OARs were unedited), over 95.5% of the adapted plans achieved the patient-specific dosimetry goals. ConclusionThe CBCT-based H&N oART workflow can be meaningfully accelerated by only editing the influencers and targets while omitting the remaining OARs without compromising the quality of the adaptive plans.

Adaptation of virtual surgical planning to midface reconstruction with intraoperative navigation: “navigation mediated midface reconstruction”

ObjectiveThe production of three dimensional models and materials according to preoperative virtual surgical planning is a time consuming process and causes high costs. We aimed to demonstrate the navigation mediated reconstruction of the patients who underwent the removal of tumoral mass in midfacial region according to their preoperatively prepared surgical plannings. Study DesignPatients who underwent the removal of tumoral mass and reconstruction in their midfacial region were included in the study. Virtual surgical planning was performed by the mirror imaging of unaffected side of maxillofacial bones. New created models were converted to DICOM (Digital Imaging and Communications in Medicine) data to use in the navigation system. Histogram analysis was performed to reveal the compatibility of navigation mediated reconstruction with preoperative virtual surgical planning. ResultsReconstruction of orbital floor was achieved in all of the cases. Histogram comparison of the localizations of orbital floor and maxillary walls was calculated at a confidence level of 95 % (p > 0.95) and mean difference was found 0.85 mm; while standard deviation was 3.55 mm. ConclusionWe found that virtually prepared reconstruction of midfacial area can be succesfully adapted to the surgery with the assistance of intraoperative navigation system.

Housing infrastructure and women in care: COVID-19 experiences from urban informal settlements of Khulna, Bangladesh

Urban informal settlements in the Global South, home to over a billion people, face significant challenges due to limited space and inadequate Water, Sanitation, and Hygiene (WASH) and Solid Waste Management (SWM) infrastructure. These deficiencies significantly impact women's ‘triple care role’—reproductive, productive, and community caregiving responsibilities—within their families, society, and the economy, exacerbated by the COVID-19 pandemic. This study aimed to explore how inadequate infrastructure in three informal settlements in Khulna, Bangladesh, influenced women's caregiving roles during the pandemic and their responses. Through semi-structured interviews with 24 women community leaders, who served as front-line workers during COVID-19, and supporting fieldwork, we discovered that women adapted by modifying their habits and the spatial layouts of their homes for isolation. Although some improvements were noted in WASH and SWM conditions, significant challenges remained, particularly in temporary housing, household adaptability, and reproductive responsibilities Despite facing informal employment, financial constraints, and food insecurity, women showed resilience through leadership, socio-political networking, and community initiatives. This study underscores the urgent need for adaptive infrastructure planning to improve living conditions in informal settlements, with a focus on proactive management of both spatial and non-spatial resources to enhance adaptation in resource-scarce contexts.

Temporal variability of spatial patterns of correlations between summer rainfall and the Oceanic Niño Index in the Pampean region

The Argentinean Pampean region is essential for global food security, known for its extensive production of soybeans, corn, and wheat. The November to January (NDJ) trimester is critical for rainfed summer crops, as precipitation during this period directly affects soil moisture and crop yields. The El Niño-Southern Oscillation (ENSO), particularly its Oceanic Niño Index (ONI), plays a crucial role in influencing precipitation patterns in this region. This study investigates the spatial correlation between the ONI and NDJ precipitation from 1990 to 2021 sing a 20-year sliding window approach. We conducted Pearson correlation and cluster analyses to identify regions with consistent ONI-precipitation relationships. Our findings reveal notable temporal variability, with a general decrease in correlation strength since the window 1995–2014. This decline is likely driven by changes in ENSO dynamics and the influence of other climate variability modes, highlighting the need to consider factors modulating the ONI-precipitation relationship. Notably, the addition of the strong 2015 El Niño, which exhibited inconsistent precipitation behaviour compared to previous strong El Niño events such as 1997, contributed to this weakening. While the 1997 El Niño brought widespread positive rainfall anomalies, the 2015 event was marked by weaker and even negative precipitation anomalies in part of the region. This difference mirrors similar patterns reported in other parts of South America, such as Ecuador and Coastal Peru, and may be linked to a poleward shift in the jet streams during the 2015/2016 El Niño. The results underscore the complexity of ENSO's impact on regional climate and highlight the need for adaptive agricultural planning. By enhancing the understanding of ONI-precipitation dynamics, this study aims to improve long-term climate predictions and support sustainable agricultural practices in the Pampean region.

Deep learning of 3D point clouds for detecting geometric defects in gears

Geometric integrity directly impacts the functionality, reliability, and safety of final manufactured products, making the qualification of parts based on measurements of their geometry a fundamental quality control activity in modern manufacturing. Recent advancements in three-dimensional (3D) metrology technologies have enabled fine-scale inspection of geometric integrity characterized by dimensional accuracy, surface quality, and shape conformity. However, the widespread adoption of high-resolution 3D metrology in manufacturing faces some significant challenges posed by the inherent data structures of 3D point clouds such as high dimensionality, unstructured nature, and sparsity in defective regions. To address these challenges, this paper first creates a “MFGNet-gear” dataset, which is a scalable and comprehensive benchmark dataset comprising 12 part designs with four quality classes for each design. Subsequently, we develop a deep learning model adapted from the PointNet++ architecture to enable automated, end-to-end analysis of 3D point clouds. The model can be configured for different decision-making tasks including part design classification and multi-class geometric defect detection. Implementations of the proposed model on the “MFGNet-gear” dataset achieve accuracies up to 100% in classifying gear designs and up to 85% in four-class quality inspection. Additionally, we systematically investigate the impacts of measurement resolution and precision on the classification performance through a series of case studies. The obtained results highlight the potential of using deep learning methods for automated analysis of 3D point clouds for a variety of quality control tasks beyond gear manufacturing. This study also proposes future research directions, including the development of new deep learning architectures specifically designed for manufacturing 3D point clouds and strategies for adaptive measurement planning.