Resilience Index Research Articles

Recovery resilience framework of replaceable AB-BRB for seismic strengthening during the aftershock stage

Earthquakes cause serious damage to structures, and seismic strengthening is an effective solution to improving structural capacity. With the development of prefabricated technology, the assembled buckling-restrained braces (BRBs) have garnered significant attention in structural engineering due to their potentials to enhance seismic resilience and to guarantee recoverability behavior. At this stage, research on the replaceable performance of assembled BRBs is limited in the current body of literature. The specific focus on the replaceability aspect of assembled BRBs, including the ease of replacing individual components or the entire brace system, has not been extensively explored. Meanwhile, research on earthquake resilience during the aftershock stage is relatively limited at present. Despite the recognition of the significant and prolonged impact of aftershocks on communities and infrastructure, there is a paucity of comprehensive studies specifically focusing on the resilience strategies and measures required during this stage. The authors formerly proposed a novel assembled bolt-connected BRB (AB-BRB), and experiments have been conducted to verify its hysteretic and replaceable behaviors. In this paper, the recovery resilience of the proposed replaceable AB-BRB for seismic strengthening is further assessed, especially during the aftershock stage. The replacement realization of AB-BRB in analysis is first introduced. Then the recovery resilience framework for assessment during the aftershock stage is proposed. Finally an implementary example is given to perform the recovery resilience framework, in which two cases and three scenarios are discussed in detail. In general, after using the AB-BRB for seismic strengthening, the recovery time obviously decreases and the resilience index obviously increases when compared with the results in un-strengthened scenario (scenario 3), which demonstrates that the retrofitted system possesses a better resilience recovery capacity. For EEL in case 1, the recovery days are given to be 298.7446 before strengthening (scenario 3), and the results drop to 165.4133 (scenario 1) and 147.0295 (scenario 2) after strengthening. Correspondingly, the resilience index is calculated as 0.5022 before strengthening (scenario 3), and the results increase to 0.7101 (scenario 1) and 0.7411 (scenario 2) after strengthening. Similar conclusions can be given for case 2 and other intensity levels. Meanwhile, after performing the replacement operation of AB-BRB (scenario 2), the seismic performance of the retrofitted system further enhances during the aftershock stage (i.e., less recovery days and larger resilience index). For case 1 and recovery form 1, the resilience index for 1 month is signified as 0.6272, 0.6869 and 0.2066 from scenario 1 to 3, and the resilience index for 3 months is signified as 0.8182, 0.8561 and 0.4965 from scenario 1 to 3. Compared with scenario 1, the recovery ability in scenario 2 is further ensured and the potential risk is further controlled, which demonstrates the importance of replaceable capacity of AB-BRB for resilience improvement especially during the aftershock stage.

Modeling and application of urban waterlogging resilience assessment based on PSR–NES framework and analysis of driving mechanisms

ABSTRACT Extreme weather events are escalating globally, intensifying urban waterlogging disasters and endangering inhabitants and assets while hindering socio-economic progress. This study explores factors hindering cities' waterlogging disaster mitigation using urban resilience theory. It introduces a pressure–state–response (PSR)–natural–economic–social (NES) assessment model to evaluate urban waterlogging resilience, focusing on Nanjing as a key city in flood control. Flood scenarios are simulated using the MIKE model for different rainstorm recurrence periods. Urban flooding resilience is assessed with a hybrid approach. Geodetector analysis identifies primary resilience drivers. Results validate the PSR–NES composite resilience index, showing complex resilience patterns across the study area. High-resilience areas are mainly in the east and southeast, while low-resilience areas cluster along roads and low-lying communities. Key resilience indicators include waterlogging duration, inundation depth, warning capacity, economic density, and road accessibility. These factors interact with nonlinear enhancements and synergies. The study's insights can identify urban management or flood defense infrastructure deficiencies, guiding tailored flood defense strategies for stakeholders.

A Tale of Five Cities: Assessing Emergency Management for Future Disasters in the United States

Many cities across the United States (U.S.) face threats from natural hazards, and as climate change continues to worsen, cities will face increased risks. Emergency management helps cities to mitigate these risks and adapt to shifting threats. Based on the Rockefeller City Resilience Framework and City Resilience Index Driver 3, Indicator 3.4, the scope of this research was to develop an emergency management maturity model and explore emergency management capacity for future disasters based on an assessment of five cities across the U.S. With the resulting data, a city’s score would serve as an evaluation of the emergency response indicator (Indicator 3.4) in order to eventually enter the City Resilience Index. The cities selected were Buffalo, New York; Honolulu, Hawaii; Memphis, Tennessee; New Orleans, Louisiana; and Saint Paul, Minnesota, based on their hazard/risk factors. It should be noted that these scores were intended to compare resilience in a city over time and identify unique areas in which the cities could improve emergency management and were not meant to rank the cities against each other. An interesting find was the overlap between the counties and cities regarding policy data, creating a new avenue for research in local level emergency management.

Evaluating the tipping point of a complex system: The case of disruptive technology

AbstractComplex systems often operate in equilibriums that can be disrupted under specific conditions, driving the system to undergo an irreversible phase transition (aka tipping point) into a new equilibrium. Disruptive technology, as introduced in the book Innovator's Dilemma, is a new emerging technology that can successfully displace incumbent technologies and push the market through phase transition into a new equilibrium. In this paper, the authors model the market disruption caused by a disruptive technology as a complex system, with dynamics that show a phase transition or tipping point, after which the system shifts into a new equilibrium aiming at assessing the success or failure of a disruptive technology. The purpose of this study is to mathematically model a tipping point measure of complex networks and a theoretical framework for disruptive technology dynamics. A predator–prey model is used to emulate the behavior of a disruptive technology versus an incumbent technology, and a resilience index is integrated to measure the tipping point in the market where the disruptive technology will overtake the incumbent technology. This methodology is applied to a historical case study of film versus digital versus cell phone cameras, demonstrating the application of this methodology. The study's contribution can be applied to both systems engineering and disruptive innovation management. The proposed approach can help stakeholders assess the impact of a potential new disruptive technology and use the resulting resilience index as a measurement for adjusting technology requirements and systems management approaches to achieve a desired outcome.

From the latin "re-cordis, passing through the heart": autonomic modulation differentiates migraineurs from controls when recounting a significant life event.

The literature on clinical psychophysiology highlights the possibility of using Heart Rate Variability (HRV) as an index of psychophysical balance and resilience to stress. This study investigates the differences in stress reactivity and subsequent recovery between a group of migraineurs and healthy controls. Socio-demographic (i.e., sex, age, profession, marital status, and level of education) and psychophysiological (HR and HRV) measures of a group of thirty subjects with migraine (26 migraineurs without aura (86.7%), 2 migraineurs with aura (6.7%), and 2 migraineurs with and without aura (6.7%)) and from thirty healthy control subjects were collected. In particular, HRV was analyzed through frequency-domain parameters, including Low-Frequency (LF; 0.04-0.15 Hz) and High-Frequency (HF; 0.15-0.4 Hz) bands as well as LF/HF ratio during a Psychophysiological Stress Profile (PSP) structured in seven phases: (1) Baseline, (2) Objective stressor 1 (Stroop Test), (3) Rest 1, (4) Objective stressor 2 (Mental Arithmetic Task), (5) Rest 2, (6) Subjective stressor (recount a significant life event), and (7) Rest 3. The LF, HF, and LF/HF ratio values were transformed into a logarithmic scale (i.e., log-LF, log-HF, and log LF/HF ratio). Additionally, LF and HF were converted into normalized units (0-100) (i.e., LF% and HF%) which, in turn, were used to obtain reactivity and recovery to stress through delta values (Δ) calculation. Subjects with migraine reported greater ΔLF% levels of reactivity and recovery to subjective stressor, demonstrating a prevalence of sympathetic activity while recounting a personal life event. At the same time, a lowering of the same values was found in the subjects of the group control. Our results underline the importance of conducting a psychophysiological assessment in patients with headaches because reduced stress management skills could influence the clinical manifestations of the disease, considering stress as one of the most common triggers for migraine patients.

Estimation of disease outbreak vulnerability index (DOVI) and resilience index (DORI) at household-level among states of India, (2019–21)

Resilience at the household level is crucial to reduce the impact of disease outbreaks among the mass population. The intensity and nature of preparedness of households are affected by the socio-economic disparities. This study aimed to measure the level of household resilience, by place of residence across states of India. From India's Demographic Health Survey (DHS) data, 12 indicators were taken, representing the Water, Sanitation, and Hygiene (WASH), physical distancing, energy and communication, and economic security dimensions. Alkire and Foster's Multidimensional Poverty Index (MPI) method was used to compute the Disease Outbreak Vulnerability Index (DOVI) and Disease Outbreak Resilience Index (DORI). Results displayed that 0.166 of households were found vulnerable and 0.834 of households were found resilient towards disease outbreaks. Rural households (0.175) were found more vulnerable than urban households (0.144), notably, in the WASH, and energy-communication dimensions, however, urban households were vulnerable in physical distancing and household economic security. Kerala, Goa, Lakshadweep, Ladakh, and Jammu & Kashmir states revealed higher resilience, whereas, Dadra & Nagar Haveli and Daman & Diu, NCT Delhi, Maharashtra, Chandigarh, and Andhra Pradesh states showed the least resilience towards disease outbreak. This study has suggested that policymakers should focus on the WASH, electricity, and mass-media services among rural households and physical distancing, and economic security among urban households, to increase resilience and preparedness for future outbreaks, and to achieve the Sustainable Development Goals (SDGs) 3. D, by the end of 2030.

Probabilistic resilience assessment framework of structures considering the combined functionality-recovery-duration uncertainty

In the current stage, the risk-guided performance-based earthquake engineering (PBEE) has become the main direction in the earthquake community. As an important part of risk, seismic resilience indicators have been increasingly emphasized and elevated to new levels in recent years. Seismic resilience refers to the capacity of social entities to reduce disaster risks and to facilitate recovery efforts with the least social disruption in possible. Currently, the calculations of resilience indicators are commonly based on the deterministic approach. In fact, resilience indicators are also random variables, and the corresponding recovery time, recovery function or residual function in resilience calculation may vary within a range for different intensity levels and limit states. Therefore, characterizing resilience indicators through probabilistic models or confidence intervals can be a more practically valuable and meaningful strategy for future research. In this paper, a probabilistic resilience assessment framework of structures is proposed, and a sinusoidal-based continuous recovery function characterized by different limit states and recovery modes is given. The combined uncertainties of functionality, recovery and duration are incorporated into the framework, and an application example via the reinforced concrete frame is given to implement the framework. Besides, the sensitive analysis of different variables to the resilience assessment result is performed, and the comparison between the deterministic and probabilistic analysis is further discussed. In general, the framework consists of three parts, i.e., (1) Generating samples, fragility and damage states; (2) Calculating total stochastic recovery functions for resilience analyses; and (3) Performing probabilistic seismic resilience analyses. If deterministic analysis is used, the resilience index is only a value, while if probabilistic analysis is used, the resilience index will be a variable with a corresponding probability and distribution range. Compared with other variables, the amplitude and wavelength have a stronger influence in the result of resilience index. This is because these two parameters are function-dependent and will directly affect the envelope curve in the resilience calculation. Although the changes in coefficient of variation (COV) will affect the curve results, the macro characteristics are consistent when comparing different target resilience index. Moreover, as the intensity rises, there is a statistical decrease in both the average residual functionality and resilience indicators, along with a statistical increase in the average recovery durations. The annual exceeding probability decreases as the target resilience level increases, while the life-cycle exceeding probability increases when the target resilience level decreases for the same year. In a sense, the integrated probabilistic resilience framework realizes the decoupling of resilience indicators for different performance groups and limit states, and provides a reference for the further stochastic resilience assessment from a probabilistic perspective.

Cumulative prospect theory under different types of input data for public health resilience assessment during natural disasters

Despite the significance of health and the threats natural disasters pose to public health and the well-being of the affected communities, comprehensive research that explores critical factors and indicators that measure health implications brought about by disasters is underdeveloped in the literature. The primary objective of this study is twofold: (1) to develop a list of indicators that assess health-related impacts during disasters through a literature survey, and (2) to create a “public health resilience index” that measures the resilience of affected communities with a case study in Southern Cebu, Philippines. A hybrid model based on the fuzzy stepwise weight assessment ratio analysis and the extended cumulative prospect theory was deployed to advance these objectives. The proposed model considers subjective judgment and cognitive biases while exhibiting high scalability and flexibility and accommodating diverse data input formats. Results revealed that the promptness of response, the allocated budget for humanitarian aid and services, the availability of drinking water per household, the number of personnel assigned for humanitarian aid, and the location of water distribution points are highly prioritized during natural disasters. On the other hand, among the 21 municipalities under consideration, 10 demonstrated strong performance, while the other 10 exhibited poor performance, leaving one municipality in the middle of both extremes. Sensitivity and comparative analyses show that the proposed hybrid model is not sensitive to model parameter changes. The findings of this study would aid decision-makers in developing initiatives aimed at optimizing resources and efforts, mitigating risks, and enhancing the overall health resilience of local communities in the onslaught of natural disasters.

The cascade failure model under ecological network is effective for quantifying the resilience of urban regions

Human activities and natural disasters degrade urban ecosystems and cause ecological fragmentation at regional scale. It is therefore necessary to create or rehabilitate useful linkages between ecological spaces. Although ecosystem resilience is receiving increasing attention, most studies focus on the spatiotemporal changes of alternative indicators of ecosystem resilience, without considering the changes in the structure and function of internal ecological nodes. Therefore, this study presents a novel research framework that uses cascade failure model based on the ecological network (EN) to evaluate the resilience of typical urban areas. The framework combines “importance-vulnerability” scored ecological sources identification with minimum cumulative resistance simulation to quantitatively characterize the urban region as an EN and assesses the resilience of the urban EN using a capacity-load cascade failure model. The framework was tested in the Xi'an metropolitan region in China resulted in the regional EN with 191 nodes and 571 edges. It was found that the EN of the Xi'an metropolitan region was relatively stable. The network will completely break down only when the node failure rate exceeds 85 %. The framework we propose offers a novel and powerful approach for policymakers to plan for the conservation and development of ecological infrastructure and land uses in urban region context.

Relationships of Multidimensional Factors to Diabetes Complications: A Cross-Sectional, Correlational Study.

Diabetes complications are prevalent in people with diabetes, causing considerable individual suffering and increased health costs. However, the relationships of multidimensional, modifiable, and nonmodifiable factors to diabetes complications and the role of diabetes distress have been rarely examined. The aims of this study were to examine the associations of age, sex, knowledge, self-efficacy, self-compassion, resilience, self-esteem, depressive symptoms, diabetes distress, social support, and body mass index with diabetes complications and to investigate the mediating role of diabetes distress. In this cross-sectional, correlational study, data on all study variables were collected from 148 people with diabetes through REDCap in 2023. Multiple regression analysis and the PROCESS macro for SPSS were used to address the aims. Older age and higher levels of diabetes distress were associated with more diabetes complications. Depressive symptoms were associated with diabetes distress; and diabetes distress, but not depressive symptoms, was associated with diabetes complications, controlling for all other variables. Depressive symptoms and diabetes distress were directly or indirectly associated with diabetes complications, and diabetes distress was a mediator in the relationship between depressive symptoms and diabetes complications. Health care providers can target reduction of depressive symptoms and diabetes distress to reduce diabetes complications.

Relationship between social vulnerability and community resilience: A geospatial study in the context of natural disasters

Social vulnerability and resilience are critical factors for disaster risk management. Despite the significant progress in research on both concepts, only some studies have explored the empirical relationship between them. The relationship between community resilience and social vulnerability to natural disasters in Chile was studied using empirical and geospatial analysis. We used the Social Vulnerability Index (SoVI) and the Community Resilience Index (BRIC), previously calculated nationally for Chile. Based on these indicators, we constructed a matrix to classify the 3100 districts into high, medium, and low levels of vulnerability and resilience. In addition, we performed a spatial autocorrelation analysis using the Global Moran Index. Our results indicate that: Vulnerability and resilience are related concepts, but are not opposite within a continuum, (II) Rather than being randomly distributed, districts with higher (or lower) capacities to prepare for, respond to, and recover from a disaster tend to cluster geographically; (III) the districts with the highest levels of resilience and lowest levels of vulnerability were located in the main cities of the country. We expect that a better understanding of the relationship between vulnerability and resilience in each territory will help institutions in charge of disaster management to identify communities most susceptible to damage and least capable of recovering from a disaster. Consequently, it will facilitate the design and implementation of policies, programs, and plans best adapted to the needs of each community.

Structural variants linked to Alzheimer's Disease and other common age-related clinical and neuropathologic traits.

Advances have led to a greater understanding of the genetics of Alzheimer's Disease (AD). However, the gap between the predicted and observed genetic heritability estimates when using single nucleotide polymorphisms (SNPs) and small indel data remains. Large genomic rearrangements, known as structural variants (SVs), have the potential to account for this missing genetic heritability. By leveraging data from two ongoing cohort studies of aging and dementia, the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP), we performed genome-wide association analysis testing around 20,000 common SVs from 1,088 participants with whole genome sequencing (WGS) data. A range of Alzheimer's Disease and Related Disorders (AD/ADRD) clinical and pathologic traits were examined. Given the limited sample size, no genome-wide significant association was found, but we mapped SVs across 81 AD risk loci and discovered 22 SVs in linkage disequilibrium (LD) with GWAS lead variants and directly associated with AD/ADRD phenotypes (nominal P < 0.05). The strongest association was a deletion of an Alu element in the 3'UTR of the TMEM106B gene. This SV was in high LD with the respective AD GWAS locus and was associated with multiple AD/ADRD phenotypes, including tangle density, TDP-43, and cognitive resilience. The deletion of this element was also linked to lower TMEM106B protein abundance. We also found a 22 kb deletion associated with depression in ROSMAP and bearing similar association patterns as AD GWAS SNPs at the IQCK locus. In addition, genome-wide scans allowed the identification of 7 SVs, with no LD with SNPs and nominally associated with AD/ADRD traits. This result suggests potentially new ADRD risk loci not discoverable using SNP data. Among these findings, we highlight a 5.6 kb duplication of coding regions of the gene C1orf186 at chromosome 1 associated with indices of cognitive impairment, decline, and resilience. While further replication in independent datasets is needed to validate these findings, our results support the potential roles of common structural variations in the pathogenesis of AD/ADRD.

Impact of Covid-19 on companies’ performance and financial resilience: Evidence from Moroccan listed companies

The study aims to evaluate the impact of Covid-19 on company performance and explore the moderating effect of companies’ financial resilience. Data were retrieved from 312 firm-year observations corresponding to 76 companies listed on the Moroccan Stock Exchange Market throughout 2018–2021. Five regression models are used to examine the overall impact of the Covid-19 pandemic on corporate performance, as well as the specific effect on corporate performance of each of the financial indicators of corporate resilience, namely sales, leverage, liquidity, and financial autonomy. The results show that although there is a decrease in the mean of both ratios of financial and operational performance of Moroccan listed companies, statistical tests confirm only a significant negative effect of Covid-19 on operational performance. The results also show that companies with high sales, low debt, high liquidity, and financial autonomy are more resilient to the negative impact of the Covid-19 pandemic. Furthermore, given the significant size and the sectorial concentration of Moroccan listed companies, there is no statistical evidence that the negative impact of Covid-19 pandemic varies according to business sector or size. Based on the results, a number of recommendations are made for both governments and companies. Governments should maintain, despite the crisis, both public and private investment in order to sustain the growth of companies’ business sales. It is also important to implement solutions for rescheduling social and tax debts to safeguard the liquidity of companies and limit their recourse to costly debt.

Optimization of isolation valve placement to improve reliability and resilience of water distribution systems

Optimization of isolation valve layout is critical to improve post-failure response restoration of water distribution systems (WDSs) when pipes are damaged. Many studies have been done to optimize isolation valves to improve reliability of WDSs, but less attention has been given to improving the seismic resilience. This paper proposes a multi-objective optimization model for isolation valves placement considering reliability and seismic resilience simultaneously. The newly developed model employs topology-based reliability and resilience indicators to improve efficiency of optimization algorithm by identification of accidental segments and unintended isolation segments using graph theory without hydraulic simulation. The optimization model was applied to two networks with different loop structures. Subsequently, searching trajectory guided by constraints is investigated and performance of isolation valve placements under different valve densities are assessed including topological structure of isolation valves, criticality of segments and isolation valves, and seismic resilience. The results showed that optimal layout of isolation valves obtained through optimization can improve the reliability and resilience of the system. Our novel approach can benefit water utilities that frequently encounter optimal placement of isolation valves to improve the reliability and resilience of WDSs.

Watering the trees for the forest: Drought alleviation in oaks and pines by ancestral ditches

Traditional ditches (“acequias” in Spanish) derive meltwater and infiltrate groundwater providing ecological services downstream in the semi-arid Sierra Nevada range (SE Spain). Therefore, they may act as a nature-based solution by alleviating drought stress in trees growing near ditches by enhancing growth and reducing their intrinsic water-use efficiency (iWUE). Such a mitigation role of acequias is critical given that some oak (Quercus pyrenaica) and pine (Pinus sylvestris) stands reach their xeric distribution limits in Europe. We compared tree-ring width data and wood δ13C, a proxy of iWUE, in oak and pine stands located near or far (control) from ditches with different infiltration capacity in two watersheds. We assessed how trees responded to climate data, drought stress, and vegetation greenness through correlations and resilience indices. Oak trees located near ditches grew more and responded less to precipitation, soil moisture, a drought index, and greenness than control trees. In pines, we did not find this pattern, and ditch trees grew more than control trees only during an extremely dry year (1995). Climate-growth correlations suggested a longer growing season in ditch pines. Growth of ditch oaks from the “Acequia Nueva” (AN), with high infiltration capacity, responded more to autumn soil moisture and showed the lowest δ13C. Growth was enhanced by cool-wet spring conditions in pines and also by warm-wet conditions in the prior winter in the case of oaks. Control trees showed lower resistance to drought. Control trees presented higher wood δ13C values except for old oaks from the “Acequia Grande” (AG) site which may show long-term acclimation. Traditional ditches alleviate drought stress in oak and pine stands subjected to regional xeric climate conditions.

Analysis of the genetic variance of fibre diameter measured along the wool staple for use as a potential indicator of resilience in sheep

BackgroundThe effects of environmental disturbances on livestock are often observed indirectly through the variability patterns of repeated performance records over time. Sheep are frequently exposed to diverse extensive environments but currently lack appropriate measures of resilience (or sensitivity) towards environmental disturbance. In this study, random regression models were used to analyse repeated records of the fibre diameter of wool taken along the wool staple (bundle of wool fibres) to investigate how the genetic and environmental variance of fibre diameter changes with different growing environments.ResultsA model containing a fifth, fourth and second-order Legendre polynomial applied to the fixed, additive and permanent environmental effects, respectively, was optimal for modelling fibre diameter along the wool staple. The additive genetic and permanent environmental variance both showed variability across the staple length trajectory. The ranking of sire estimated breeding values (EBV) for fibre diameter was shown to change along the staple and the genetic correlations decreased as the distance between measurements along the staple increased. This result suggests that some genotypes were potentially more resilient towards the changes in the growing environment compared to others. In addition, the eigenfunctions of the random regression model implied the ability to change the fibre diameter trajectory to reduce its variability along the wool staple.ConclusionsThese results show that genetic variation in fibre diameter measured along the wool staple exists and this could be used to provide greater insight into the ability to select for resilience in extensively raised sheep populations.

Tree Resilience Indices of Norway Spruce Provenances Tested in Long-Term Common Garden Experiments in the Romanian Carpathians.

Provenance trials provide a valuable opportunity to evaluate the impact of extreme events on growth and wood properties. In this study, we have evaluated 81 Norway spruce provenances, tested in three provenance trials established in the Romanian Carpathians in 1972. The response to drought of the Norway spruce provenances has been examined using the following tree resilience indices: resistance, recovery, resilience, and relative resilience. The relationship between climate and growth, the correlations between wood traits, and the coordinates of the origin and tree resilience indices were also analysed. In each provenance trial, there were significant differences between provenances and years regarding wood widths and latewood percentage (LWP). Regarding drought extreme events, the years when they occurred in all three provenance trials were 2000 and 2003. Significant differences between provenances for at least one tree resilience index have been found in all provenance trials, for the year 2000. By using subperiods of 25 years, changes in the relationship between climate and growth have been observed. Several provenances with high radial growth and good resistance and/or recovery have been identified. Provenances that performed better in common garden experiments could be used in assisted migration, even in the proximity of the current natural range.

Study on Rural Classification and Resilience Evaluation Based on PSR Model: A Case Study of Lvshunkou District, Dalian City, China

When implementing a rural revitalization strategy, it is of great significance to understand how to identify potential risks in different types of villages and how to achieve resilience governance. In this paper, a rural classification evaluation method is proposed from the perspective of resilience. The stress–state–response model and the TOPSIS method were used to analyze the resilience risks in different types of villages in Lvshunkou District. The results showed that the comprehensive resilience of rural areas in Lvshunkou District has the spatial distribution characteristics of “high center and low wings”, and the spatial differences of each resilience subsystem are different: the pressure resilience subsystem shows a trend of “increasing step by step” from the coast to the inland, the state resilience subsystem shows a trend of “decreasing step by step from the center to the two wings”, and the response resilience subsystem shows a trend of “blocky aggregation and balanced distribution”. This paper further proposes systematic reconstruction strategies for rural resilience related to the four aspects of rural space, industry, ecology, and governance, providing development ideas for measuring rural resilience indicators in Lvshunkou District and realizing long-term governance in different types of villages.

Learning From Countries on Measuring and Defining Community-Based Resilience in Health Systems: Voices From Nepal, Sierra Leone, Liberia, and Ethiopia

Background: The best approach for defining and measuring community health care resilience in times of crisis remains elusive. We aimed to synthesise definitions and indicators of resilience from countries who had recently undergone shocks (i.e., outbreaks and natural disasters). Methods: We purposively selected four countries that had recently or were currently experiencing a shock: Nepal, Ethiopia, Sierra Leone, and Liberia. Focus group discussions and key informant interviews were conducted with participants at the community, facility, district, sub-national, national, and international levels. Interviews and discussions were translated and transcribed verbatim. Data were open coded in ATLAS.ti using a grounded theory approach and were thematically collated to a pre-specified framework. Results: A total of 486 people participated in the study (n=378 community members, n=108 non-community members). Emergent themes defining community-based health system resilience included: the importance of communities, health system characteristics, learning from shocks, preventing and preparing for shocks, and considerations for sustainability and intersectoral engagement. Participants identified 193 potential indicators for measuring resilience, which fell into the domains of: 1) preparedness, 2) response and recovery, 3) communities, 4) health systems, and 5) intersectoral engagement. Conclusion: Despite varying definitions and understanding of the concept of resilience, community-centred responses to shocks were key in building resilience. Further insight is needed into how the definitions and indicators identified in this study compare to other shocks and contexts and can be used to further our understanding of health system resilience. Metrics and definitions could assist policy makers, researchers, and practitioners in evaluating the readiness of systems to respond to shocks and to allow comparability across health systems. We must build health systems that can continue to function and ensure quality, equity, community-focused care, and engagement, regardless of the pressures put upon them and ensure they are linked to strong primary health care.

Trait development and genetic parameters of resilience indicators based on variability in milk consumption recorded by automated milk feeders in North American Holstein calves

The implementation of automated milk feeders (AMF) on precision dairy farms has enabled efficient management of large numbers of group-housed replacement calves with reduced labor requirements and improved calf welfare. In this study, we investigated the feasibility of deriving calf resilience indicators based on variability in milk consumption using data from 10,076 North American Holstein calves collected between 2015 and 2021. We modeled and evaluated deviations in observed and predicted daily milk consumption trajectories as indicators of resilience to environmental perturbations. We also analyzed average milk intake and the number of treatments for bovine respiratory disease (BRD) and their genetic correlations with the derived resilience parameters. Milk consumption was recorded using the Förster-Technik AMF. Deviations in cumulative milk intake were modeled using various methods, including quantile regression and the Gompertz function. Ten resilience indicators were derived to quantify the degree and duration of perturbations, including amplitude, perturbation time, recovery time, and deviation velocities. After data editing, genomic data from 9,273 calves and pedigree information from 10,076 calves with 321,388 phenotypic records were used to estimate genetic parameters for 12 traits, including 10 calf resilience indicators as well as average milk intake and treatments for bovine respiratory disease. Substantial phenotypic variability was observed for all calf resilience indicators derived and genetic parameters related to these novel resilience indicators were estimated. The heritability estimates for the resilience traits are as follows: amplitude of the deviation (in L) 0.047 (0.032, 0.064) (HPD interval), perturbation time of deviation (in d) 0.011 (0.0056, 0.016), recovery time of the deviation (in d) 0.025 (0.016, 0.035), maximum velocity of perturbation (L/d) 0.039 (0.024, 0.053), average velocity of perturbation (L/d) 0.038 (0.022, 0.050), area between the curves (L x d) 0.039 (0.027, 0.054), recovery ratio 0.053 (0.036, 0.072), deviation variance 0.049 (0.32, 0.068), log-deviation variance 0.027 (0.016, 0.044), deviation auto-correlation 0.010 (0.0042, 0.017) and number of deviation occurrences 0.023 (0.0094, 0.036). Some of the highlighted genetic correlations observed with average milk consumption include amplitude: 0.569 (0.474, 0.666), perturbation time: -0.534 (-0.73, -0.342), and average velocity: 0.554 (0.432, 0.672). Similarly, the genetic correlations between the number of times treated for BRD with perturbation time was 0.494 (0.251, 0.723), -0.294 (-0.52, -0.095) with number of deviations, and 0.348 (0.131, 0.578) with deviation autocorrelation. This study highlights the genetic influence on various resilience traits in calves, including amplitude, perturbation time, recovery time, and velocity measures of the perturbation. Our findings suggest the need for prioritizing genetic selection based on traits like recovery time, which exhibits higher heritability and a moderate genetic correlation with the number of times a calf is treated for BRD. The combination of AMF data, mathematical modeling, and genomic evaluation provides a comprehensive framework for assessing and breeding more resilient dairy calves in the face of environmental and health challenges.