Projection Model Research Articles

Forecasting Africa’s fertility decline by female education groups

While female education has long been recognized as a key driver of fertility decline during the process of demographic transition and most population projection models consider it implicitly or explicitly in their forecasts of overall fertility, there still is need for a method to forecast education-specific fertility trends directly. Here we propose a method for projecting education-specific fertility declines for cohorts of women in Sub-Saharan Africa based on all available demographic and health surveys data for African countries (including 1.03Mio cases). We study at different levels of aggregation (sample clusters, strata, and national) the associations between ideal family size and completed cohort fertility for education groups, on the one hand, and the average level of education in those units, on the other. The consistently very strong empirical associations suggest a plausible narrative by which a higher prevalence of educated women in a spatial unit influences the fertility levels of women in all specific education categories. Empirical associations between education-specific cohort fertility trends at the national level and newly available quality-adjusted human capital data for these cohorts are then operationalized to produce education-specific population projections as they are needed for-among other uses-the shared socioeconomic pathways scenarios that are widely used in the climate change research community. Sensitivity analyses including out-of-sample projections support the validity of the proposed method which is then applied to 37 African countries.

The Resilient Agility of Brand Voice in Volatile Environments

Abstract In an era marked by rapid change, the role of brand voice within brand communication has become increasingly vital to protecting brand identity, moving beyond traditional marketing approaches. This research introduces the concept of “resilient agility” of brand voice and examines it in a variety of uncertain contexts. Aiming to investigate the potential influence of volatile environments on brand voice within the context of brand communication, the author based her research on the examination of various case studies and as a result introduced two new models of projecting brand identity through brand communication, including one tailored for volatile environments. The author identifies the resilient agility of brand voice in projecting the essence of brand identity, consistency in core messaging, and transparency in addressing issues as key success factors for brand communication. This research enhances the understanding of the projection model of brand identity through brand communication, especially in volatile environments, offering valuable insights for marketers aiming to maintain brand integrity and develop lasting customer relationships amidst uncertainty.

The Synergy Between CO2 and Air Pollution Emissions in Chinese Cities by 2060: An Assessment Based on the Emissions Inventory and Dynamic Projection Model

Synergizing air pollution control and climate change mitigation has been of significant academic and policy concern. The synergy between air pollution and carbon emissions is one of the measures to understand the characteristics and process of the air pollution–carbon synergistic control, which will also provide valuable information for collaboratively achieving Sustainable Development Goals (SDGs) (such as SDGs 11 and 13). This study establishes a systematic framework integrating emissions inventory and projection models, correlation mining and typology analysis methods to predictively evaluate the synergy and comprehensive coordination between air pollution and carbon dioxide (CO2) emissions in Chinese cities by 2030, 2050, and 2060 under different policy scenarios for air pollution and CO2 emissions control. The results reveal the significant effects of synergistically implementing clean air and aggressive carbon-reducing policies on mitigating air pollution and CO2 emissions. Under the On-time Peak-Net Zero-Clean Air and Early Peak-Net Zero-Clean Air scenarios, the total reduction and synergy for air pollution and CO2 emissions will be more significant, particularly by 2050 and 2060. This study is the first to integrate scenario projection and synergy evaluation in air pollution and CO2 research, providing a novel supplement to the air pollution–climate change synergy methodology based on co-benefit estimation. The methods and findings will also contribute to measuring the achievement and analyzing the interaction of the SDGs.

Global variations in elderly cancer mortality pattern in 2020 & prediction to 2040: A population-based study

Background & objectives Cancer contributes to decreasing life expectancy, especially in low- and middle- income countries (LMIC) and countries in transition where resources for diagnosis and care are limited. As the world population is ageing, and by 2050, two-thirds of the population in LMIC will be elderly, the greater impact of cancer on the expectation of life in years at a given age of an adult will be seen in these countries. Methods Estimated cancer mortality for older adults (60 yr or above) was estimated using statistics available on Globocan 2020 platform (gco.iarc.fr). This platform provided the number of deaths and age‐standardized truncated mortality rates (per 100,000) by sex and continent. We calculated the projected cancer deaths in 2040 by applying the projection model, considering a stable rate. Results Globally, with an estimated 7.5 million deaths, cancer deaths in older population represented a total of 71.2 per cent of all cancer-related deaths. The truncated age-adjusted mortality rate among the older population (both sexes) was estimated as 615.1 deaths per 100,000 and the mortality rate was 62.6 per cent higher in elderly (60 yr or above) males as compared to elderly females. The overall future cancer death among the elderly was estimated to increase from 7.05 to 12.7 million deaths, representing a total 80.2 per cent increase in deaths by 2040. Interpretation & conclusions The projected mortality increase will challenge existing healthcare systems, especially in lower or lower medium-income countries where resources are limited. Elderly patients are at an elevated risk of adverse outcomes due to the high prevalence of co-morbid conditions. Geriatric oncology will play an important role in the coming years to ensure the overall health and well-being of elderly patients, which needs to be supported by good stratified data on elderly cancer.

Research on the Coupling and Coordination of Land Ecological Security and High-Quality Agricultural Development in the Han River Basin

This study aims to investigate the coupling and harmonization between land ecological security (LES) and high-quality agricultural development (HAD) in the Han River Basin (HRB), China, with the objective of promoting harmonious coexistence between agriculture and ecosystems. Using 17 cities in the HRB as the research objects, an evaluation index system of two systems, LES and HAD, was constructed, analyzed, and evaluated via projective tracer modeling for multiple intelligent genetic algorithms (MIGA-PTM). The degree of coupling coordination (DCC) was used to quantitatively evaluate the coupling coordination development status of the two systems, the obstacle model (OM) was used to identify the main influencing factors, and the gray predictive model first-order univariate model (GM (1, 1)) was used to predict the DCC of the LES and HAD from 2025 to 2040. The results show the following: (1) the LES and HAD levels of the 17 cities in the HRB tended to increase during the study period, and there was a large gap between cities; (2) the spatial distributions of the DCCs of the LES and HAD in the HRB were uneven, with high values in the southern and low values in the central and northern parts, and the overall degree of coupling tended to fluctuate. The overall DCC showed a fluctuating upward trend; (3) the degree of obstacles, per capita water resources, greening coverage, and rate of return on financial expenditure are the main influencing factors; and (4) the prediction results of GM (1, 1) indicate that the LES and HAD of the HRB will be close to reaching the intermediate stage of coupling in 2035. This research offers critical insights into sustainable development practices that facilitate the alignment of agricultural growth with ecological preservation.

Geometric Wide-Angle Camera Calibration: A Review and Comparative Study

Wide-angle cameras are widely used in photogrammetry and autonomous systems which rely on the accurate metric measurements derived from images. To find the geometric relationship between incoming rays and image pixels, geometric camera calibration (GCC) has been actively developed. Aiming to provide practical calibration guidelines, this work surveys the existing GCC tools and evaluates the representative ones for wide-angle cameras. The survey covers the camera models, calibration targets, and algorithms used in these tools, highlighting their properties and the trends in GCC development. The evaluation compares six target-based GCC tools, namely BabelCalib, Basalt, Camodocal, Kalibr, the MATLAB calibrator, and the OpenCV-based ROS calibrator, with simulated and real data for wide-angle cameras described by four parametric projection models. These tests reveal the strengths and weaknesses of these camera models, as well as the repeatability of these GCC tools. In view of the survey and evaluation, future research directions of wide-angle GCC are also discussed.

Curves on Brill–Noether special K3 surfaces

AbstractMukai showed that projective models of Brill–Noether general polarized K3 surfaces of genus 6–10 and 12 are obtained as linear sections of projective homogeneous varieties, and that their hyperplane sections are Brill–Noether general curves. In general, the question, raised by Knutsen, and attributed to Mukai, of whether the Brill–Noether generality of any polarized K3 surface is equivalent to the Brill–Noether generality of smooth curves in the linear system , is still open. Using Lazarsfeld–Mukai bundle techniques, we answer this question in the affirmative for polarized K3 surfaces of genus , which provides a new and unified proof even in the genera where Mukai models exist.

Forecasting school enrollments in the Australian Capital Territory

Abstract School enrollment forecasts are vital for effective planning. This study introduces a probabilistic multiregional population projection model, which accounts for different components, including preschool entries, migration, grade progression, and graduations. Using different distributions with a cohort component projection and Monte Carlo simulation, this paper forecasts student enrollments for each school and academic year level in the Australian Capital Territory based on the annual record-level administrative data. In the in-sample validation tests, the model’s overall performance is robust, and the probabilistic design offers reliable prediction intervals reflecting the variation in observed values. The paper ends with a discussion on the importance of prediction intervals for informing school planning.

Modelling and Performance Evaluation of Machine Learning Techniques in Forecasting Cereal Yield for Shri Ganganagar Region, Rajasthan, India

One of agriculture's most difficult issues is predicting crop yield. Crop yield forecasting enables necessary decisions to be made to guarantee food security. The current study looks into the use of statistical and machine learning approaches for wheat and rice yield prediction using long-term weather and yield data of ShriGanganagar, Rajasthan, India. Weather-based models may give accurate crop production estimates, but choosing the right model for agricultural output projections can be difficult. As a result, different models were compared in this study to determine the best model for rice and wheat yield prediction including Multiple linear regression (SMLR), Artificial neural network (ANN), Least absolute shrinkage and selection operator (LASSO), Elastic net (ELNET), Ridge regression, and K-nearest neighbor (K-NN). K-NN outperformed ANN in rice crops and fared better in wheat crops based on the lowest nRMSE value. Ridge regression based on nRMSE was the next best model for Rice yield prediction in the examined region, and KNN was the second best model for Wheat crop.

A blueprint for coupling a hydrological model with fine- and coarse-scale atmospheric regional climate change models for probabilistic streamflow projections

In cold regions, climate change, including warming and changes in snowmelt dynamics, have profound impacts on streamflow patterns, often leading to flooding events. Understanding the projected changes in hydrometeorological factors contributing to streamflow, such as snowmelt runoff and rain-on-snowmelt, is crucial for effective adaptation and mitigation strategies. It is also essential to consider uncertainty in streamflow projections and incorporate this uncertainty into flood predictions. This study presents a blueprint for calculating probabilistic future streamflow and flow duration curves in a mountainous cold region. The methodology integrated forcings from an atmospheric model (WRF) with a hydrological model (MESH) at fine spatial (4 km) and temporal (3-hourly) resolutions. To account for uncertainty, an ensemble of 15 CanRCM4 regional climate simulations with varying boundary conditions for the RCP 8.5 scenario was utilized. A novel method was developed to perturb the CanRCM4 simulations’ precipitation using a WRF Pseudo Global Warming run to incorporate uncertainty. This comprehensive approach revealed significant uncertainty in streamflow driven by internal variability. WRF-driven simulations without uncertainty showed a decrease in future high streamflows, while the perturbed simulations demonstrated the potential for substantially higher streamflows under climate change. Moreover, the study derived probabilistic flow duration curves from the streamflow projections, aiding in estimating flood frequency for floodplain mapping when driving local hydraulic models. The methodology developed in this work can be extended to other river basins in Canada and elsewhere where gridded downscaled climate model outputs are available.

Risk assessment of PM2.5 from fossil energy consumption on the respiratory health of the elderly

Air pollution mainly comes from fossil energy consumption (FEC), and it brings great threat to public health. The respiratory system of the elderly is highly susceptible to the effects of air pollution due to the decline in body functions. PM2.5 is a major component of air pollution, so the study of the impact of PM2.5 generated by FEC on the respiratory health of the elderly is of great significance. The existing studies have focused more on the effect of PM2.5 on mortality, and this paper is a useful addition to the existing studies by examining the effect of PM2.5 from FEC on the health of the elderly from the perspective of prevalence. In this paper, the binary Logistic regression model was used to calculate the exposure-response relationship coefficient for respiratory health in older adults using the data in 2018 from the Chinese Longitudinal Healthy Longevity Survey. And referring to the Dynamic Projection model for Emissions in China, the changes in the number of older persons suffering from respiratory diseases due to PM2.5 from FEC in the baseline scenario, the clean air scenario, and the on-time peak-clean air scenario were predicted. The results indicated that: (1) PM2.5 from FEC mainly came from coal; (2) PM2.5 from FEC was detrimental to the respiratory health of the elderly, and older seniors were more affected as they age; (3) In the on-time peak-clean air scenario, the number of elderly people suffering from respiratory diseases due to PM2.5 from FEC was growing at the slowest rate. Based on the above results, this paper raised recommendations for reducing the effect of PM2.5 from FEC on the health of the elderly.

Impacts of Climate Change on Rainfall ‘Seasonality Index’ and Its Potential Implications on Water Savings and Reliability through Household Rainwater Tanks

This study depicts potential climate change impacts on annual rainwater savings from household rainwater harvesting using two different climate projection models; ACCESS 1.0 and CSIRO-Mk3.6. This paper also investigates potential changes in the relationships of ‘water saving efficiency’ and reliability with rainfall ‘seasonality index’ under the mentioned climate change scenarios. The annual water savings were calculated for three weather conditions: dry, average, and wet. Historical daily rainfall amounts provided by the Australian Bureau of Meteorology were used for three locations within the city of Brisbane (Australia). For the same locations, projected future daily rainfall amounts were collected from an online data portal facilitated by the Australian government. Potential annual water savings, water saving efficiency, and reliability values for the selected locations were calculated through a widely used tool, eTank, developed on water balance methodology at a daily scale. It was found that for the coastal location, Manly, the future water savings are not likely to change significantly. However, for the inland location, Sunnybank, the future water savings are expected to decrease under all the weather conditions through both the considered climate projections. For the far inner location, Oxley, the water savings are likely to decrease in the dry year, whereas in wet year, they are likely to increase. Also, it was found that the overall average relationship of SI–water saving efficiency is steeper for ACCESS 1.0 projected data compared to that produced through CSIRO-Mk3.6 data, and that significant differences exist among individual relationships for each location. The overall reliabilities calculated through the model projected data show lower values compared to the reliabilities calculated using historical data.

Multi-year La Niña frequency tied to southward tropical Pacific wind shift

AbstractMulti-year La Niña events cause prolonged climate disruptions worldwide, but a systematic understanding of the underlying mechanisms is not yet established. Here we show using observations and models from the sixth phase of Coupled Model Intercomparison Project that a greater frequency of consecutive La Niña events is tied to the upper equatorial Pacific Ocean when it favors more rapid heat discharge. The propensity for heat discharge is underscored by negative skewness in upper-ocean heat content, underpinned by southward tropical Pacific wind shift during austral summer. Models with stronger westerly anomalies south of the equator simulate steeper east-to-west upward tilt of the thermocline that is favorable for a greater discharge rate. This highlights the crucial role of the southward wind shift in the nonlinear system of the El Niño-Southern Oscillation. The large inter-model spread in multi-year La Niña processes underscores the need in constraining models for reliable climate prediction and projection.

RanBALL: An Ensemble Random Projection Model for Identifying Subtypes of B-cell Acute Lymphoblastic Leukemia.

As the most common pediatric malignancy, B-cell acute lymphoblastic leukemia (B-ALL) has multiple distinct subtypes characterized by recurrent and sporadic somatic and germline genetic alterations. Identification of B-ALL subtypes can facilitate risk stratification and enable tailored therapeutic approaches. Existing methods for B-ALL subtyping primarily depend on immunophenotypic, cytogenetic and genomic analyses, which would be costly, complicated, and laborious in clinical practice applications. To overcome these challenges, we present RanBALL (an Ensemble Ran dom Projection-Based Model for Identifying B -Cell A cute L ymphoblastic L eukemia Subtypes), an accurate and cost-effective model for B-ALL subtype identification based on transcriptomic profiling only. RanBALL leverages random projection (RP) to construct an ensemble of dimension-reduced multi-class support vector machine (SVM) classifiers for B-ALL subtyping. Results based on 100 times 5-fold cross validation tests for >1700 B-ALL patients demonstrated that the proposed model achieved an accuracy of 93.35%, indicating promising prediction capabilities of RanBALL for B-ALL subtyping. The high accuracies of RanBALL suggested that our model could effectively capture underlying patterns of transcriptomic profiling for accurate B-ALL subtype identification. We believe RanBALL will facilitate the discovery of B-ALL subtype-specific marker genes and therapeutic targets, and eventually have consequential positive impacts on downstream risk stratification and tailored treatment design.

Трехмерное моделирование городов: исторические реконструкции и альтернативная реальность

The paper considers the technology of creating 3D models of cities, photogrammetric and other ways of their construction. The author shows projects created on the basis of this technology - 3D models of Yaroslavl, Moscow and St. Petersburg. The paper also describes the essence and results of the research performed on the basis of the constructed three-dimensional urban planning models. The paper presents historical recreation projects of cities for different time periods: Yaroslavl at the beginning of the XX century and Moscow at the XIX-beginning of the XX century. The author presents a list of monuments lost in reality, but recreated in a virtual three-dimensional environment and inscribed in the 3D model of the city. The paper presents a project involving the modelling of unrealised projects of avant-garde architects, as well as projects of the 1930s-1970s on the basis of a Moscow 3D model. The author concludes about the growing popularity of such digital models and the penetration of augmented and virtual reality technologies into our daily life.

Severe fire regimes decrease resilience of ectothermic populations.

Understanding populations' responses to environmental change is crucial for mitigating human-induced disturbances. Here, we test hypotheses regarding how three essential components of demographic resilience (resistance, compensation and recovery) co-vary along the distinct life histories of three lizard species exposed to variable, prescribed fire regimes. Using a Bayesian hierarchical framework, we estimate vital rates (survival, growth and reproduction) with 14 years of monthly individual-level data and mark-recapture models to parameterize stochastic integral projection models from five sites in Brazilian savannas, each historically subjected to different fire regimes. With these models, we investigate how weather, microclimate and ecophysiological traits of each species influence their vital rates, emergent life history traits and demographic resilience components in varying fire regimes. Overall, weather and microclimate are better predictors of the species' vital rates, rather than their ecophysiological traits. Our findings reveal that severe fire regimes increase populations' resistance but decrease compensation or recovery abilities. Instead, populations have higher compensatory and recovery abilities at intermediate degrees of fire severity. Additionally, we identify generation time and reproductive output as predictors of resilience trends across fire regimes and climate. Our analyses demonstrate that the probability and quantity of monthly reproduction are the proximal drivers of demographic resilience across the three species. Our findings suggest that populations surpass a tipping point in severe fire regimes and achieve an alternative stable state to persist. Thus, higher heterogeneity in fire regimes can increase the reproductive aspects and resilience of different populations and avoid high-severity regimes that homogenize the environment. Despite being more resistant, species with long generation times and low reproductive output take longer to recover and cannot compensate as much as species with faster paces of life. We emphasize how reproductive constraints, such as viviparity and fixed clutch sizes, impact the ability of ectothermic populations to benefit and recover from disturbances, underscoring their relevance in conservation assessments.

Cooling from aerosol–radiation interaction of anthropogenic coarse particles in China

Climate assessments have largely overlooked the radiative effect of anthropogenic coarse particulate matter (PMcoarse, with an aerodynamic diameter between 2.5 and 10 µm) in China. Despite its similar mass concentration to fine particulate matter (PM2.5), anthropogenic sources of PMcoarse in China have been much less studied and typically underrepresented in models. Here, we present a new model simulation for PMcoarse in China that incorporates various anthropogenic sources. The model successfully captures the magnitude and distribution of observed PMcoarse and recently available aerosol optical depth measurements at near-infrared wavelengths, which are substantially underestimated if anthropogenic PMcoarse is not included. We find that anthropogenic PMcoarse exerts a cooling effect of -0.11 Wm−2 (-0.03 to -0.42 Wm−2) in China by aerosol–radiation interaction, capable of completely offsetting the warming effect from black carbon by 2060 under Dynamic Projection model for Emissions in China (DPEC) 1.1 scenario. We conclude that the radiative effect due to anthropogenic PMcoarse will likely dampen the warming penalty caused by the emission reduction of other aerosols in China and should be incorporated into climate models.

Statistical downscaling model for future projection of daily IDF relationship by Markov chain and kernel density estimator

ABSTRACT Changes in climate might have a significant impact on rainfall characteristics, including extreme rainfall. This study aims to project the future daily rainfall, preserving most of the rainfall characteristics, including extreme rainfall incorporating climate changes. This paper presents two hybrid semi-parametric statistical downscaling models for future projection of IDF curves. The precipitation flux from seven scenarios of ten GCMs and observed daily rainfall data are considered as predictors and predictand variables, respectively. At site, daily rainfall occurrence is modeled using a two-state first-order Markov chain. Rainfall amounts on each wet day are modelled using a univariate nonparametric kernel density estimator. Two types of amount generation models are presented in this study. The bounded model (KDE-SP) is developed, considering the support for the kernel distribution as positive. In the unbounded model (KDE-Ext), the wet days are reclassified as extreme and non-extreme rainy days. A significant increasing trend can be observed in the future projected intensity–duration–frequency relationships. The maximum increment using empirical distribution is observed as 93.21 and 80.93% on a 5-year return period in the far future for the SSP5-8.5 scenario, using KDE-Ext and KDE-SP models, respectively. Although both methods show similar results, the KDE-Ext model performs better in simulating extreme rainfall.

Wildfire Burnt Area and Associated Greenhouse Gas Emissions under Future Climate Change Scenarios in the Mediterranean: Developing a Robust Estimation Approach

Wildfires burn annually over 400,000 ha in Mediterranean countries. By the end of the 21st century, wildfire Burnt Area (BA) and associated Green House Gas (GHG) emissions may double to triple due to climate change. Regional projections of future BA are urgently required to update wildfire policies. We present a robust methodology for estimating regional wildfire BA and GHG emissions under future climate change scenarios in the Mediterranean. The Fire Weather Index, selected drought indices, and meteorological variables were correlated against BA/GHG emissions data to create area-specific statistical projection models. State-of-the-art regional climate models (horizontal resolution: 12 km), developed within the EURO-CORDEX initiative, simulated data under three climate change scenarios (RCP2.6, RCP4.5, and RCP8.5) up to 2070. These data drove the statistical models to estimate future wildfire BA and GHG emissions in three pilot areas in Greece, Montenegro, and France. Wildfire BA is projected to increase by 20% to 130% up to 2070, depending on the study area and climate scenario. The future expansion of fire-prone areas into the north Mediterranean and mountain environments is particularly alarming, given the large biomass present here. Fire-smart landscape management may, however, greatly reduce the projected future wildfire BA and GHG increases.

Change of global land extreme temperature in the future

Understanding future temperature extremes is pivotal to preparing for and mitigating the impacts of climate change. This study proposed machine learning techniques to develop a multi-model ensemble model for high-resolution projection of global land temperature extremes under different emission scenarios, hence providing enhanced precision over previous climate model projections. By utilizing the NEX-GDDP-CMIP6 dataset with bias adjustment and the Gradient Booster algorithm, we reduced the biases that existed in Global Climate Models. The model significantly reduces the root mean square errors (RMSEs) for both the daily maximum and daily minimum temperature extremes. A future scenario analysis revealed that global temperature extremes would substantially increase under high-emission scenarios, highlighting the urgency for stringent emission reduction commitments. This study also identified regions like Greenland, the Tibetan Plateau, and the regional Arctic Archipelago as potential hotspots of temperature extremes under these scenarios. The multi-model ensemble approach, tuned with machine learning and driven by high-resolution data, contributes to climate science by providing refined insights into future temperature extremes, thereby offering direction to climate change mitigation and adaptation strategies.