This commentary is based on the premise that the goal of scientific publications is to advance knowledge and inspire scientific discussion. First, the Nomura et al. paper is briefly summarized and shortly discussed to illustrate some of the challenges faced in this field, and suggestions for improvement are made. Then, the reader is invited to take a step back and reflect on how to continue investigating the effects of natural disasters on pregnant women and their children in the best possible manner. Additional suggestions are made for much-needed research on other types of severe maternal prenatal stress and on high-quality interventions for natural disasters. The goal is to stimulate reflection and exchange so that future choices made by researchers, funding bodies, and policymakers lead to substantial advances in the field of prenatal stress.

As a result of COVID-19 spread, Bangladesh implemented a range of measures including general holidays, lockdown, no lockdown, and strict lockdown which resulted in the dramatic ups and downs of the price level of the products. This study aimed to examine the influence of COVID-19 on poultry products (meat and eggs) in Bangladesh using Gazipur-an intensive poultry growing area - as a case study. Monthly market price data of poultry meat and eggs, and primary panel data from the same respondents using a random sampling technique through a structured questionnaire-based interview, Focus Group Discussion (FGD), and Key Informant Interview (KII) were collected. The results reveal that after the incidence of COVID-19, the price of poultry products at farmgate, wholesale and retail levels dropped drastically for the first three months (February to April 2020). Following that, the market price of farm (broiler) chicken and eggs increased by 40% and more than 30%, respectively. On the other hand, the price of local (deshi) chicken increased by 15%, which was already high on the market. However, in the early phase of COVID-19, many smallholder poultry farmers and hatchery owners were forced to shut down due to less demand/no demand of the product along with high feed cost and inadequate support from external sources. The government should provide financial support with low or no-interest rate to the smallholder growers in order to enhance their resilience against shocks like COVID-19 pandemic, natural disasters, etc.

Progress in reducing both malaria cases and deaths has stalled with regression seen in many geographies. While significant attention is given to the contributing challenges of drug and insecticide resistance, 'residual' malaria is often diminished to transmission resulting from outdoor-biting or zoophagic/opportunistic mosquito vectors. These specific vector bionomic traits are only part of the problem, as residual transmission may be driven by (a combination of) (1) sub-optimal intervention coverage, quality, acceptance, and/or usage, (2) drug resistance, (3) insecticide resistance, (4) refractory, resistant and adaptive vector and human behaviours that lower intervention effectiveness, (5) lack of, limited access to, and/or willingness to use healthcare systems, (6) diagnostic sensitivity along with the parallel issue of hrp2/3 mutations, (7) (inter)national policy, (8) the research and development pipeline, and (9) external factors such as natural disasters and conflict zones. Towards combating the minimization of this extensive and multipronged issue among the scientific community, funding agencies, and public health officials responsible for guiding or developing malaria programmes, an alternative way of describing this transmission is proposed by focusing in on the causative 'gaps in protection'. Defining and wording it as such zeros in on the drivers that result in the observed remaining (or increasing) transmission, allowing the malaria community to focus on solutions by identifying the actual causes. Outlining, defining and quantifying the gaps in protection for a given system is of utmost importance to understand what needs to be done, differentiating what can be done versus what cannot be tackled at that moment, along with delineating the technical and financial capacity required.

Droughts have severely affected Afghanistan over the last four decades, leading to critical food shortages where two-thirds of the country's population are in a food crisis. Long years of conflict have lowered the country's ability to deal with hazards such as drought which can rapidly escalate into disasters. Understanding the spatial and temporal distribution of droughts is needed to be able to respond effectively to disasters and plan for future occurrences. This study used Standardized Precipitation Evapotranspiration Index (SPEI) at monthly, seasonal and annual temporal scales to map the spatiotemporal change dynamics of drought characteristics (distribution, frequency, duration and severity) in Afghanistan. SPEI indices were mapped for river basins, disaggregated into 189 sub-catchments, using monthly precipitation and potential evapotranspiration derived from temperature station observations from 1980 to 2017. The results show these multi-dimensional drought characteristics vary along different years, change among sub-catchments, and differ across temporal scales. During the 38 years, the driest decade and period are 2000s and 1999-2022, respectively. The 2000-01 water year is the driest with the whole country experiencing 'severe' to 'extreme' drought, more than 53% (87 sub-catchments) suffering the worst drought in history, and about 58% (94 sub-catchments) having 'very frequent' drought (7 to 8 months) or 'extremely frequent' drought (9 to 10 months). The estimated seasonal duration and severity present significant variations across the study area and among the study period. The nation also suffers from recurring droughts with varying length and intensity in 2004, 2006, 2008 and most recently 2011. There is a trend towards increasing drought with longer duration and higher severity extending all over sub-catchments from southeast to north and central regions. These datasets and maps help to fill the knowledge gap on detailed sub-catchment scale meteorological drought characteristics in Afghanistan. The study findings improve our understanding of the influences of climate change on the drought dynamics and can guide catchment planning for reliable adaptation to and mitigation against future droughts.

In recent years, the increasing number of natural disasters has raised concerns about the sustainability of our planet's future. As young people comprise the generation that will suffer from the negative effects of climate change, they have become involved in a new climate activism that is also gaining interest in the public debate thanks to the Fridays for Future (FFF) movement. This paper analyses the results of a survey of 1,138 young people in a southern Italian region to explore their perceptions of the extent of environmental problems and their participation in protests of green movements such as the FFF. The statistical analyses perform an ordinal classification tree using an original impurity measure considering both the ordinal nature of the response variable and the heterogeneity of its ordered categories. The results show that respondents are concerned about the threat of climate change and participate in the FFF to claim their right to a healthier planet and encourage people to adopt environmentally friendly practices in their lifestyles. Young people feel they are global citizens, connected through the Internet and social media, and show greater sensitivity to the planet's environmental problems, so they are willing to take effective action to demand sustainable policies from decision-makers. When planning public policies that will affect future generations, it is important for policymakers to know the demands and opinions of key stakeholders, especially young people, in order to plan the most appropriate measures, such as climate change mitigation.

COVID-19 and global crises/events are driving governments to rethink their national manufacturing strategies. The drastic change of societal conditions has exposed our reliance on a constrained set of production practices. Furthermore, the future manufacturing landscape indicates - supply chain crises, trade agreements and natural disasters - a high level of volatility which requires a response that is far from being achieved. While these emergent challenges have called the efficacy of established practices into question, new manufacturing technologies, such as Additive Manufacturing (AM), present the capability to provide a solution. One proposal is agent-based brokering of AM which could be a method for tackling local, regional, national, and international production needs. However, to achieve the reality of brokered AM, it is imperative that the diversity of AM capability is considered. Diversity that existing homogeneous modelling of AM and manufacturing systems rarely consider or capture. This paper conceptualizes the reality of AM systems and elucidates parameters that are necessary for successful modelling and subsequent co-ordination. Having presented the required parameters the paper continues to discuss requisite levels of abstraction, suitable performance metrics and the role of humans in agent-based manufacturing systems.

In the context of the COVID-19 epidemic, a "double-hazard scenario" consisting of a natural disaster and a public health event occurring simultaneously is likely to arise. Focusing on this double-hazard scenario, this study developed a new opinion dynamics model that verifies the effect of opinion dynamic in practical applications and extends the realistic meaning of the logic matrix. The new model can be used to quickly identify changing trends in public opinion about two co-occurring public safety events in China, helping the government to better anticipate and respond to these real double-hazard scenarios. The new model was tested with three real double-hazard scenarios involving natural disasters and public health events in China and the simulation results were analyzed. Using visualization and Pearson correlation coefficients to analyze more than a million items of network-wide public opinion data, the new model was found to show a good fit with reality. The study finally found that in China, public attention to both natural hazards and public health events was greater when these public safety events co-occurred (double-hazard scenario) than when they occurred separately (single-hazard scenarios). These results verify the coupling phenomenon of different disasters in a multi-hazard scenario at the information level for the first time, which is greatly meaningful for multi-hazard research.

Soybean is highly sensitive to flooding and extreme rainfall. The phenotypic variation of flooding tolerance is a complex quantitative trait controlled by many genes and their interaction with environmental factors. We previously constructed a gene-pool relevant to soybean flooding-tolerant responses from integrated multiple omics and non-omics databases, and selected 144 prioritized flooding tolerance genes (FTgenes). In this study, we proposed a comprehensive framework at the systems level, using competitive (hypergeometric test) and self-contained (sum-statistic, sum-square-statistic) pathway-based approaches to identify biologically enriched pathways through evaluating the joint effects of the FTgenes within annotated pathways. These FTgenes were significantly enriched in 36 pathways in the Gene Ontology database. These pathways were related to plant hormones, defense-related, primary metabolic process, and system development pathways, which plays key roles in soybean flooding-induced responses. We further identified nine key FTgenes from important subnetworks extracted from several gene networks of enriched pathways. The nine key FTgenes were significantly expressed in soybean root under flooding stress in a qRT-PCR analysis. We demonstrated that this systems biology framework is promising to uncover important key genes underlying the molecular mechanisms of flooding-tolerant responses in soybean. This result supplied a good foundation for gene function analysis in further work.

Nature-based approaches to enhancing the absorbency of urban environments are being used to address the linked issues of climate change, flooding, sanitation and health. Fid Thompson reports.

Throughout history, many buildings of significant cultural value have been lost due to natural disasters (e.g., earthquakes, volcanic eruptions, subsidence, etc.) as well as other human causes (e.g., fire, war destruction, etc.). The Tajo Opera theatre was hit by an earthquake that led to the collapse of the entire structure eight months after it was built. This paper deals with the revival of the acoustic characteristics of one of the masterpieces of the architect Giovanni Carlo Sicinio Galli Bibiena. The realization of a three-dimensional (3D) model that faithfully reproduces the architectural features of the Royal Tajo Opera theatre of Lisbon allows the authors to perform acoustic simulations that reveal the sound field representing the environment perceived by the audience during artistic performances in Lisbon in 1755. In addition, the simulated results have been compared with the values of the Teatro Comunale of Bologna, which has a similar bell-shaped plan layout and has already been studied by the authors. For the comparison of the two opera theatres, both the stalls and the balconies have been considered.

The spatial heterogeneity of landslide influencing factors is the main reason for the poor generalizability of the susceptibility evaluation model. This study aimed to construct a comprehensive explanatory framework for landslide susceptibility evaluation models based on the SHAP (SHapley Additive explanation)-XGBoost (eXtreme Gradient Boosting) algorithm, analyze the regional characteristics and spatial heterogeneity of landslide influencing factors, and discuss the heterogeneity of the generalizability of the models under different landscapes. Firstly, we selected different regions in typical mountainous hilly region and constructed a geospatial database containing 12 landslide influencing factors such as elevation, annual average rainfall, slope, lithology, and NDVI through field surveys, satellite images, and a literature review. Subsequently, the landslide susceptibility evaluation model was constructed based on the XGBoost algorithm and spatial database, and the prediction results of the landslide susceptibility evaluation model were explained based on regional topography, geology, and hydrology using the SHAP algorithm. Finally, the model was generalized and applied to regions with both similar and very different topography, geology, meteorology, and vegetation, to explore the spatial heterogeneity of the generalizability of the model. The following conclusions were drawn: the spatial distribution of landslides is heterogeneous and complex, and the contribution of each influencing factor on the occurrence of landslides has obvious regional characteristics and spatial heterogeneity. The generalizability of the landslide susceptibility evaluation model is spatially heterogeneous and has better generalizability to regions with similar regional characteristics. Further explanation of the XGBoost landslide susceptibility evaluation model using the SHAP method allows quantitative analysis of the differences in how much various factors contribute to disasters due to spatial heterogeneity, from the perspective of global and local evaluation units. In summary, the integrated explanatory framework based on the SHAP-XGBoost model can quantify the contribution of influencing factors on landslide occurrence at both global and local levels, which is conducive to the construction and improvement of the influencing factor system of landslide susceptibility in different regions. It can also provide a reference for predicting potential landslide hazard-prone areas and for Explainable Artificial Intelligence (XAI) research.

This paper aims to empirically test the dynamics of budget outcomes of Italian municipalities in the aftermath of floods by accounting for heterogeneous levels of resilience and vulnerability to natural disasters. Our findings are based on a dynamic difference-in-differences model after propensity score matching. They point to substantial impacts in terms of increased capital expenditure and revenues from transfers, which depend on the degree of resilience and vulnerability. Through our analysis, we account for multiple aspects of risk to support policy decisions related to both ex-ante and ex-post disaster occurrence management.

In recent decades, droughts have critically limited crop production, inducing food system shocks regionally and globally. It was estimated that crop yield variability in around one-third to three-fourths of global harvested areas is explained significantly by drought, revealing the notable vulnerability of crop systems to such climate-related stressors. However, understanding the key factors determining the global pattern of crop yield sensitivity to drought is limited. Here, we investigate a wide range of physical and socioeconomic factors that may determine crop-drought vulnerability in terms of yield sensitivity to drought based on the Standardized Precipitation Index at 0.5° resolution from 1981 to 2016 using machine learning approaches. The results indicate that the spatial variations of the crop-drought sensitivity were mainly explained by environmental factors (i.e., annual precipitation, soil water-holding capacity, soil acidity, annual potential evapotranspiration) and crop management factors (i.e., fertilizer rate, growing season). Several factors might have a positive effect in mitigating crop-drought vulnerability, such as annual precipitation, soil water holding capacity, and fertilizer rate. This study quantitatively assesses the possible effect of various determinants which might control crop vulnerability to drought. This understanding may provide insights for further studies addressing better crop vulnerability measures under future drought stress.

Natural disasters affect the health and well-being of mothers with young children. During natural disasters, this population is at risk of discontinuation of their breastfeeding practices. Pakistan is a middle-income country that is susceptible to natural disasters. This study intended to examine sociocultural factors that shape the breastfeeding experiences and practices of internally displaced mothers in Pakistan. This critical ethnographic study was undertaken in disaster-affected villages of Chitral, Pakistan. Data were collected utilizing multiple methods, including in-depth interviews with 18 internally displaced mothers and field observations. Multiple sociocultural factors were identified as either barriers or facilitators to these mothers' capacities to breastfeed their children. Informal support, formal support, breastfeeding culture, and spiritual practices facilitated displaced mothers to sustain their breastfeeding practices. On the other hand, lack of privacy, cultural beliefs, practices and expectations, covert oppression, and lack of healthcare support served as barriers to the breastfeeding practices of displaced mothers.

Background: The probability of sexual violence in areas affected by natural disasters may increase. An increase in the rate of unwanted pregnancies and subsequent intentional abortions, physical and mental injuries, and death can be the consequences of sexual violence. One of the characteristics of natural disasters is their unpredictability and ambiguity. Since decisions must be made quickly during the response, there must be adequate planning in advance, and preparedness in the community will reduce surprises in the responding organizations. This study aimed to identify the factors affecting the preparedness against sexual violence in natural disasters. Methods: In this study, an electronic search was performed in the MEDLINE (PubMed), Scopus, Web of Science, and ProQuest databases. Other search resources, such as Science.gov, Scienceopen.cond Meta-search engines, organizational websites, including UNICEF, UNFPA, and IFRC, as well as key journals and the International Disaster and Risk Conference were hand-searched from 1/1/1990 to 8/29/2021. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist was used to select the studies, and the findings were finally analyzed by thematic analysis method using MAXQDA10 software. Results: A total of 40 papers out of a total of 2978 studies were considered in this analysis. Macropolicies, empowerment, contextualization, management and organization, command community-based, and responsive planning were the five primary categories that were found. Conclusion: Preparedness measures are highly important to respond promptly and effectively to sexual violence and should be integrated into disaster preparedness programs. The findings of this study can be used by disaster response managers and policymakers in developing and improving preparedness programs.

Decline in mesophyll conductance (gm) plays a key role in limiting photosynthesis in plants exposed to elevated ozone (O3). Leaf anatomical traits are known to influence gm , but the potential effects of O3 -induced changes in leaf anatomy on gm has not yet been clarified. Here, two poplar clones were exposed to elevated O3 . The effects of O3 on the photosynthetic capacity and anatomical characteristics were assessed to investigate the leaf anatomical properties that potentially affect gm . We also conducted global meta-analysis to explore the general response patterns of gm and leaf anatomy to O3 exposure. We found that the O3 -induced reduction in gm was critical in limiting leaf photosynthesis. Changes in liquid-phase conductance rather than gas-phase conductance drive the decline in gm under elevated O3, and this effect was associated with thicker cell walls and smaller chloroplast sizes. The effects of O3 on palisade and spongy mesophyll cell traits and their contributions to gm were highly genotype-dependent. Our results suggest that, while anatomical adjustments under elevated O3 may contribute to defense against O3 stress, they also cause declines in gm and photosynthesis. These results provide the first evidence of anatomical constraints on gm under elevated O3 .

During public health crises, people living with HIV (PLWH) may become disengaged from care. The goal of this study was to understand the impact of the COVID-19 pandemic and recent flooding disasters on HIV care delivery in western Kenya. We conducted ten individual in-depth interviews with HIV providers across four health facilities. We used an iterative and integrated inductive and deductive data analysis approach to generate four themes. First, increased structural interruptions created exacerbating strain on health facilities. Second, there was increased physical and psychosocial burnout among providers. Third, patient uptake of services along the HIV continuum decreased, particularly among vulnerable patients. Finally, existing community-based programs and teleconsultations could be adapted to provide differentiated HIV care. Community-centric care programs, with an emphasis on overcoming the social, economic, and structural barriers will be crucial to ensure optimal care and limit the impact of public health disruptions on HIV care globally.

Coral reefs offer natural coastal protection by attenuating incoming waves. Here we combine unique coral disturbance-recovery observations with hydrodynamic models to quantify how structural complexity dissipates incoming wave energy. We find that if the structural complexity of healthy coral reefs conditions is halved, extreme wave run-up heights that occur once in a 100-years will become 50 times more frequent, threatening reef-backed coastal communities with increased waves, erosion, and flooding.

Teeth-based age and sex estimation is an important task in mass disasters, criminal scenes, and archeology. Although various methods have been proposed, most of them are subjective and influenced by observers' experiences. In this study, we aimed to develop a deep learning model for automatic dental age and sex estimation from orthopantomograms (OPGs) and compare to manual methods. A large dataset of 15,195 OPGs (age range, 16 ~ 50 years; mean age, 29.65 years ± 9.36 [SD]; 10,218 females) was used to train and test a hybrid deep learning model which is a combination of convolutional neural network and transformer model. The final performance of this model was evaluated on additional independent 100 OPGs and compared to the manual method for external validation. In the test of 1413 OPGs, the mean absolute error (MAE) of age estimation was 2.61 years by this model. The accuracy and the area under the receiver operating characteristic curve (AUC) of sex estimation were 95.54% and 0.984. The heatmap indicated that the crown and pulp chamber of premolars and molars contain the most age-related information. In the additional independent 100 OPGs, this model achieved an MAE of 3.28 years for males and 3.79 years for females. The accuracy of this model was much higher than that of the manual models. Therefore, this model has the potential to assist radiologists in automated age and sex estimation.

With the effect of global warming, the frequency of floods, one of the most important natural disasters, increases, and this increases the damage it causes to people and the environment. Flood routing models play an important role in predicting floods so that all necessary precautions are taken before floods reach the region, loss of life and property in the region is prevented, and agricultural lands are protected. This research aims to compare the performance of hybrid machine learning models such as least-squares support vector machine technique hybridized with particle swarm optimization, empirical mode decomposition, variational mode decomposition, and discrete wavelet transform processes for flood routing estimation models in Ordu, Eastern Black Sea Basin, Türkiye. In addition, it is aimed to examine the effect of data division in flood forecasting. Accordingly, 70%, 80%, and 90% of the data were used for training, respectively. For this purpose, the flood data of 2009 and 2013 in Ordu were used. The performance of the established models was evaluated with the help of statistical indicators such as mean bias error, mean absolute percentage error, determination coefficient, Nash-Sutcliffe efficiency, Taylor Diagrams, and boxplot. As a result of the study, the particle swarm optimization least-squares support vector machine technique was chosen as the most successful model in predicting flood routing results. In addition, the optimum data partition ratio was found to be Train:70:Test:30 in the flood routing calculation. The findings are essential regarding flood management and taking necessary precautions before the flood occurs.

Field trips are steadily declining due to limited funding, time constraints, safety concerns, and other logistical issues. Many schools are resorting to a virtual field trip (VFT), especially when education is disrupted due to public health concerns, natural disasters, or other unforeseen significant events. Virtual reality as a common form of VFT is likely not an option for many schools due to cost and other barriers. The purpose of our study was to explore the potential of going in a VFT using 360-degree (360°) videos as an alternative to a physical field trip in primary education. We recruited third-grade pupils (aged 8-9) from two private elementary schools to experience VFTs using 360° videos (360V) and regular videos (REGV). Using a switching-replications experimental design, we compared their content recall (assessment tests) and VFT experience (attitude, perceived usefulness, involvement, inquiry, video engagement, and virtual guide) across four-time points. Our results show that the increase in content recall scores of 360V groups after VFTs was consistently higher compared to REGV groups at all time points, although it was only significant in one quarter. We also found pupils' video engagement, involvement, and attitude as significant factors in their VFT experience. These results call attention to a possible implementation of VFTs and continue the long-standing tradition that has been acknowledged as a student-centered, interactive instructional method.

Flooding represents an environmental stress that has widespread effects on plants in natural ecosystems as well as causing major crop losses. As climate change leads to more severe weather extremes, severe flooding events are likely to become even more frequent. Thus, there is intense interest in understanding how flooding affects plants and in identifying cellular and molecular targets for engineering flood resilience into crop species. Such research requires well controlled, highly reproducible flooding protocols for use in the laboratory. However, there are many ways that a plant can be flooded. For example, waterlogging of the soil, where water levels reach the soil surface, generally generates a hypoxic environment around the root system. In contrast, full submergence of the plant adds effects on the aerial organs such as impaired photosynthesis from the combination of lowered CO2 availability and the reduced light penetration into often turbid flood waters. In this chapter, approaches to imposing controlled flooding conditions to the model plant Arabidopsis thaliana are discussed. A series of straight-forward assays are then described to document the effects of stress-related changes in growth patterns, pigment accumulation and levels of oxidative stress. These assays are complemented by monitoring the expression of a series of molecular markers of flood response by qPCR.