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Accurate assessments of current and future fertility—including overall trends and changing population age structures across countries and regions—are essential to help plan for the profound social, economic, environmental, and geopolitical challenges that these changes will bring. Estimates and projections of fertility are necessary to inform policies involving resource and health-care needs, labour supply, education, gender equality, and family planning and support. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 produced up-to-date and comprehensive demographic assessments of key fertility indicators at global, regional, and national levels from 1950 to 2021 and forecast fertility metrics to 2100 based on a reference scenario and key policy-dependent alternative scenarios. To estimate fertility indicators from 1950 to 2021, mixed-effects regression models and spatiotemporal Gaussian process regression were used to synthesise data from 8709 country-years of vital and sample registrations, 1455 surveys and censuses, and 150 other sources, and to generate age-specific fertility rates (ASFRs) for 5-year age groups from age 10 years to 54 years. ASFRs were summed across age groups to produce estimates of total fertility rate (TFR). Livebirths were calculated by multiplying ASFR and age-specific female population, then summing across ages 10–54 years. To forecast future fertility up to 2100, our Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR. CCF50 was modelled using an ensemble approach in which three sub-models (with two, three, and four covariates variously consisting of female educational attainment, contraceptive met need, population density in habitable areas, and under-5 mortality) were given equal weights, and analyses were conducted utilising the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. To capture time-series trends in CCF50 not explained by these covariates, we used a first-order autoregressive model on the residual term. CCF50 as a proportion of each 5-year ASFR was predicted using a linear mixed-effects model with fixed-effects covariates (female educational attainment and contraceptive met need) and random intercepts for geographical regions. Projected TFRs were then computed for each calendar year as the sum of single-year ASFRs across age groups. The reference forecast is our estimate of the most likely fertility future given the model, past fertility, forecasts of covariates, and historical relationships between covariates and fertility. We additionally produced forecasts for multiple alternative scenarios in each location: the UN Sustainable Development Goal (SDG) for education is achieved by 2030; the contraceptive met need SDG is achieved by 2030; pro-natal policies are enacted to create supportive environments for those who give birth; and the previous three scenarios combined. Uncertainty from past data inputs and model estimation was propagated throughout analyses by taking 1000 draws for past and present fertility estimates and 500 draws for future forecasts from the estimated distribution for each metric, with 95% uncertainty intervals (UIs) given as the 2·5 and 97·5 percentiles of the draws. To evaluate the forecasting performance of our model and others, we computed skill values—a metric assessing gain in forecasting accuracy—by comparing predicted versus observed ASFRs from the past 15 years (2007–21). A positive skill metric indicates that the model being evaluated performs better than the baseline model (here, a simplified model holding 2007 values constant in the future), and a negative metric indicates that the evaluated model performs worse than baseline. During the period from 1950 to 2021, global TFR more than halved, from 4·84 (95% UI 4·63–5·06) to 2·23 (2·09–2·38). Global annual livebirths peaked in 2016 at 142 million (95% UI 137–147), declining to 129 million (121–138) in 2021. Fertility rates declined in all countries and territories since 1950, with TFR remaining above 2·1—canonically considered replacement-level fertility—in 94 (46·1%) countries and territories in 2021. This included 44 of 46 countries in sub-Saharan Africa, which was the super-region with the largest share of livebirths in 2021 (29·2% [28·7–29·6]). 47 countries and territories in which lowest estimated fertility between 1950 and 2021 was below replacement experienced one or more subsequent years with higher fertility; only three of these locations rebounded above replacement levels. Future fertility rates were projected to continue to decline worldwide, reaching a global TFR of 1·83 (1·59–2·08) in 2050 and 1·59 (1·25–1·96) in 2100 under the reference scenario. The number of countries and territories with fertility rates remaining above replacement was forecast to be 49 (24·0%) in 2050 and only six (2·9%) in 2100, with three of these six countries included in the 2021 World Bank-defined low-income group, all located in the GBD super-region of sub-Saharan Africa. The proportion of livebirths occurring in sub-Saharan Africa was forecast to increase to more than half of the world's livebirths in 2100, to 41·3% (39·6–43·1) in 2050 and 54·3% (47·1–59·5) in 2100. The share of livebirths was projected to decline between 2021 and 2100 in most of the six other super-regions—decreasing, for example, in south Asia from 24·8% (23·7–25·8) in 2021 to 16·7% (14·3–19·1) in 2050 and 7·1% (4·4–10·1) in 2100—but was forecast to increase modestly in the north Africa and Middle East and high-income super-regions. Forecast estimates for the alternative combined scenario suggest that meeting SDG targets for education and contraceptive met need, as well as implementing pro-natal policies, would result in global TFRs of 1·65 (1·40–1·92) in 2050 and 1·62 (1·35–1·95) in 2100. The forecasting skill metric values for the IHME model were positive across all age groups, indicating that the model is better than the constant prediction. Fertility is declining globally, with rates in more than half of all countries and territories in 2021 below replacement level. Trends since 2000 show considerable heterogeneity in the steepness of declines, and only a small number of countries experienced even a slight fertility rebound after their lowest observed rate, with none reaching replacement level. Additionally, the distribution of livebirths across the globe is shifting, with a greater proportion occurring in the lowest-income countries. Future fertility rates will continue to decline worldwide and will remain low even under successful implementation of pro-natal policies. These changes will have far-reaching economic and societal consequences due to ageing populations and declining workforces in higher-income countries, combined with an increasing share of livebirths among the already poorest regions of the world. Bill & Melinda Gates Foundation.

Investigating the effects of load carriage on military soldiers using optical motion capture is challenging. However, inertial measurement units (IMUs) provide a promising alternative. Our purpose was to compare optical motion capture with an Xsens IMU system in terms of movement reconstruction using principal component analysis (PCA) using correlation coefficients and joint kinematics using root mean squared error (RMSE). Eighteen civilians performed military-type movements while their motion was recorded using both optical and IMU-based systems. Tasks included walking, running, and transitioning between running, kneeling, and prone positions. PCA was applied to both the optical and virtual IMU markers, and the correlations between the principal component (PC) scores were assessed. Full-body joint angles were calculated and compared using RMSE between optical markers, IMU data, and virtual markers generated from IMU data with and without coordinate system alignment. There was good agreement in movement reconstruction using PCA; the average correlation coefficient was 0.81 ± 0.14. RMSE values between the optical markers and IMU data for flexion-extension were less than 9°, and 15° for the lower and upper limbs, respectively, across all tasks. The underlying biomechanical model and associated coordinate systems appear to influence RMSE values the most. The IMU system appears appropriate for capturing and reconstructing full-body motion variability for military-based movements.

Effective machine learning regression models are useful toolsets for managing and planning energy in PV grid-connected systems. Machine learning regression models, however, have been crucial in the analysis, forecasting, and prediction of numerous parameters that support the efficient management of the production and distribution of green energy. This article proposes multiple regression models for power prediction using the Sharda University PV dataset (2022 Edition). The proposed regression model is inspired by a unique data pre-processing technique for forecasting PV power generation. Performance metrics, namely mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R2-score, and predicted vs. actual value plots, have been used to compare the performance of the different regression. Simulation results show that the multilayer perceptron regressor outperforms the other algorithms, with an RMSE of 17.870 and an R2 score of 0.9377. Feature importance analysis has been performed to determine the most significant features that influence PV power generation.

Electrified transportation systems are emerging quickly worldwide, helping to diminish carbon gas emissions and paving the way for the reduction of global warming possessions. Battery remaining useful life (RUL) prediction is gaining attention in real world applications to tone down maintenance expenses and improve system reliability and efficiency. RUL forms the prominent component of fault analysis forecast and health management when the equipment operation life cycle is considered. The uprightness of RUL prediction is vital in providing the effectiveness of electric batteries and reducing the chance of battery illness. In assessing battery performance, the existing prediction approaches are unsatisfactory even though the battery operational parameters are well tabulated. In addition, battery management has an important contribution to several sustainable development goals, such as Clean and Affordable Energy (SDG 7), and Climate Action (SDG 13). The current work attempts to increase the prediction accuracy and robustness with selected machine learning algorithms. A Real battery life cycle data set from the Hawaii National Energy Institute (HNEI) is used to evaluate accuracy estimation using selected machine learning algorithms and is validated in Google Co-laboratory using Python. Evaluated error metrics such as Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-Squared, and execution time are computed for different L methods and relevant inferences are presented which highlight the potential of battery RUL prediction close to the most accurate values.

Computational spectrometers hold significant potential for mobile applications, such as on-site detection and self-diagnosis, due to their compact size, fast operation time, high resolution, wide working range, and low-cost production. Although extensively studied, prior demonstrations have been confined to a few examples of straightforward spectra. This study demonstrates a deep learning (DL)-based single-shot computational spectrometer capable of recovering narrow and broad spectra using a multilayer thin-film filter array. Our device can measure spectral intensities of incident light by combining a filter array, fabricated using wafer-level stencil lithography, with a complementary metal-oxide-semiconductor (CMOS) image sensor through a simple attachment. All the intensities were extracted from a monochrome image captured with a single exposure. Our DL architecture, comprising a dense layer and a U-Net backbone with residual connections, was employed for spectrum reconstruction. The measured intensities were input into the DL architecture to reconstruct the spectra. We collected 3,223 spectra, encompassing both broad and narrow spectra, using color filters and a monochromator to train and evaluate the proposed model. We reconstructed 323 test spectra, achieving an average root mean squared error of 0.0288 over a wavelength range from 500 to 850 nm with a 1 nm spacing. Additionally, the proposed multilayer thin-film filters were validated through scanning electron microscope (SEM) analysis, which confirmed uniform layer deposition and a high fabrication yield. Our computational spectrometer boasts a compact design, a rapid measurement time, a high reconstruction accuracy, a broad spectral range, and CMOS compatibility, making it well-suited for commercialization.

The soil water retention curve (SWRC) provides information for a wide range of geoenvironmental problems, such as analyses of transient two-phase flow, the bearing capacity and shear strength of unsaturated soils. Many past studies have shown experimentally the effects of stress on the SWRC. Unfortunately, direct stress-dependent water retention measurements are relatively time-consuming and generally require special equipment and a certain level of expertise. This study primarily aimed to develop a novel predictive framework within the context of soft computing to capture the dependency of the SWRC on several variables, with an emphasis on stress and soil type. To achieve this, the three shape parameters of van Genuchten’s water retention model were estimated using a comprehensive database of 102 SWRC tests retrieved from the literature. In this study, 60% of the datasets were employed for model training, with an additional 20% being designated for validation, while the remaining 20% were set aside for testing the model's performance. The data were analyzed using two machine learning techniques: the group method of data handling and multi-layer perceptron approaches. Results showed excellent performance of the two methods. A sensitivity analysis was conducted to explore the relative significance of the different variables. Interestingly, net stress was found to be almost as significant as soil type. The introduced artificial intelligence based predictive framework provided a very effective method of integrating theory and practice.

The arrival of molecules in molecular communication via diffusion obeys, by its nature, the binomial distribution, considering the hitting probability as the success probability. It is, however, hard to work with the binomial cumulative distribution function (CDF) when considering consecutively sent symbols as it is necessary to add the distribution. Therefore, in the literature, two approximations of the binomial distribution are used. In the present reported work, the Gaussian and Poisson approximations of the binomial model of the molecule arrival process have been analysed. Considering the distance and the number of emitted molecules, the regions in which the Poisson or Gaussian model is better in terms of root mean squared error of CDFs are investigated and the regions using numerical simulations are confirmed.

The proposed model is a Deep Learning (DL) based method employing Long Short-Term Memory (LSTM) networks for forecasting stocks. The aim of this approach is forecasting stock prices of Apple Inc. using statistics on previous stock prices obtained from Tiingo. The proposed model consists of several stages of processing and modelling, including data cleaning, feature selection, feature scaling, model building, model evaluation, model improvement, and prediction. Cleaning, organising, and transforming raw data into a format appropriate for analysis are all parts of data pre-processing. Feature engineering involves the data extraction and selection of relevant features for accuracy improvement of the model. The scaling of features involves normalising the data to prevent bias in the model. The LSTM models are built and evaluated using multiple metrics such as Mean Squared Error (MAE) and Root Mean Squared Error (RMSE). The model is iteratively improved using a combination of hyperparameter tuning and feature engineering. Finally, the model is then used to forecast stock prices for the following 30 days, and the accuracy of the forecasts is determined. The proposed methodology is designed to outperform traditional LSTM models for predicting the future price of stock by incorporating novel techniques, for feature engineering and model refinement. The suggested design is a comprehensive approach for forecasting future stock prices using DL based techniques. The model is designed to be flexible and adaptable, allowing for customization for different datasets and prediction horizons. It represents a significant improvement over existing LSTM models for stock price prediction to be valuable in a variety of financial industry applications. This paper collects data from Tiingo API and uses stacked LSTM to train the model. The experimental results give only 0.0813 RMSE, which proves that the model is more accurate and precise.

This study aims to explore the research methodology of applying the Transformer model algorithm to Chinese word sense disambiguation, seeking to resolve word sense ambiguity in the Chinese language. The study introduces deep learning and designs a Chinese word sense disambiguation model based on the fusion of the Transformer with the Bi-directional Long Short-Term Memory (BiLSTM) algorithm. By utilizing the self-attention mechanism of Transformer and the sequence modeling capability of BiLSTM, this model efficiently captures semantic information and context relationships in Chinese sentences, leading to accurate word sense disambiguation. The model’s evaluation is conducted using the PKU Paraphrase Bank, a Chinese text paraphrase dataset. The results demonstrate that the model achieves a precision rate of 83.71% in Chinese word sense disambiguation, significantly outperforming the Long Short-Term Memory algorithm. Additionally, the root mean squared error of this algorithm is less than 17, with a loss function value remaining around 0.14. Thus, this study validates that the constructed Transformer-fused BiLSTM-based Chinese word sense disambiguation model algorithm exhibits both high accuracy and robustness in identifying word senses in the Chinese language. The findings of this study provide valuable insights for advancing the intelligent development of word senses in Chinese language applications.

The literature offers various methods for measuring sound localization. In this study, we aimed to compare these methods to determine their effectiveness in addressing different research questions by examining the effect sizes obtained from each measure. Data from 150 participants who identified the location of a sound source were analyzed to explore the effects of speaker angle, stimuli, HPD type, and condition (with/without HPD) on sound localization, using six methods for analysis: mean absolute deviation (MAD), root-mean-squared error (RMSE), very large errors (VLE), percentage of errors larger than the average error observed in a group of participants (pMean), percentage of errors larger than half the distance between two consecutive loudspeakers (pHalf), and mirror image reversal errors (MIRE). Results indicated that the MIRE measure was the most sensitive to the effects of speaker angle and HPD type, while the VLE measure was most sensitive to the effect of stimuli type. The condition variable provided the largest effect sizes, with no difference observed between measures. The data suggest that when effect sizes are substantial, all methods are adequate. However, for cases where the effect size is expected to be small, methods that yield larger effect sizes should be considered, considering their alignment with the research question.