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Typical public institutions such as government offices, hospitals, and schools play important leading and exemplary roles in the green and low-carbon development of the whole society. This paper analyzes the business and energy consumption characteristics of public institutions, as well as the characteristics of personnel travel, and constructs a carbon accounting model for public institutions. It innovatively proposes a methodology for carbon accounting of personnel travel related to public institutions and validates and analyzes it using a comprehensive hospital as an example. The paper analyzes the carbon emission characteristics of various energy types, systems, and types of travelers in hospitals, provides schemes for the transformation of main energy systems, and corresponding emission reduction effects, thereby providing technical support for the full-chain carbon accounting of public institutions. Additionally, this paper explores the carbon reduction pathways for hospitals to support the peak carbon and carbon neutrality goals of public institutions, and promote the high-quality development of public institutions in China. [ABSTRACT FROM AUTHOR]

A method of taking in account heterogeneous and kinetic effects in the preparation of homogenized group neutronic constants for the diffusion model is described. The results of the application of the proposed technique in the preparation of homogenized group neutronic constants for a fast reactor of type BREST-OD-300 are presented. Consideration of heterogeneous and kinetic effects in the process of homogenization is shown to enhance the accuracy of calculating integral parameters and distributed characteristics that are of importance for safety. [ABSTRACT FROM AUTHOR]

Under the dual constraints of China's carbon peaking and carbon neutrality goals, as well as ecological protection and high-quality development of the Yellow River Basin, clarifying the embodied carbon emissions and responsibility sharing of inter-provincial trade is crucial to the carbon reduction strategy of the Yellow River Basin. This paper uses the MRIO (multi-regional input–output) model to measure the production-side and consumption-side responsibility sharing of nine provinces in the Yellow River Basin in 2012 and 2017, revealing the amount and direction of the embodied carbon transfer between provinces, and finally introduces the share of provincial value added as the responsibility sharing factor to compare and analyze the differences between the three responsibility sharing methods. The results show the following: (1) The embodied carbon emissions on the production side in most provinces of the Yellow River Basin were larger than that on the consumption side, with the most significant differences in Shanxi, Inner Mongolia, and Shandong, among which local demand carbon emissions and intermediate product transfer out of carbon emissions were the main causes of production-side carbon emissions. (2) In general, all provinces except Shaanxi were net carbon transfer-in regions, and the embodied carbon was mainly transferred to Beijing, Jiangsu, Guangdong, Zhejiang, and Hebei. (3) Shared responsibility for carbon emissions was jointly determined by the volume of embodied carbon trade and the ability to obtain value added, which lay between production and consumption side responsibility shares. (4) The Yellow River Basin had a large responsibility-sharing factor and embodied carbon trade, and thus needs to take more responsibility for emission reduction. This study is expected to provide scientific support for the strategy of differentiated emission reduction in the Yellow River Basin and enrich the regional carbon accounting methods. [ABSTRACT FROM AUTHOR]

A new technique for assessing functional diversity is proposed, which is essentially a way to calculate Rao's quadratic entropy. In contrast to the original method of calculating entropy, the new method takes into account intraspecific variability and allows us to evaluate other useful aspects of functional diversity, such as functional richness, functional evenness, the uniqueness of each species, and the contributions to functional diversity of intraspecific variability of each species and each trait separately. However, due to the peculiarities of the calculation, the proposed technique can only be applicable for an absolute count of all organisms within the "island" community or when collecting material based on a fixed sampling effort. The article provides an example of the application of the proposed technique to real rodent trapping data. [ABSTRACT FROM AUTHOR]

This study examines the governing role of gender diversity on the board of directors on conditional accounting conservatism and executive remuneration. Using proprietary data about women representation on the board of directors to investigate the impact on the choice between accounting methods and the structure of executives remuneration. A sample of listed firms on the London Stock Exchange from 2019 to 2022 is used. The results first document that firms exhibit a higher level of conditional accounting conservatism when women represent a larger fraction of the board. The analyses further establish different relationships between performance-based versus equity-based remunerations and conditional accounting conservatism. Last, the results show that women representation is related to pay out policy and its impact on conditional accounting conservatism. The findings also offer novel insights on the governing role and consequences of gender diversity of woman chairperson and/or woman CEO. Overall, contrary to notions of opportunistic behavior of executives, the results find that boards achieve more level of governance with more women representation. [ABSTRACT FROM AUTHOR]

We investigate the current method of derivation of the international poverty line used for monitoring SDG 1 in two key directions: (a) we ask whether the official method used by the World Bank is fit for the purpose of measuring extreme poverty and if their method is defensibly valid both internally and externally; and (b) we ask whether the resulting statistics perform acceptably well in terms of accuracy and precision. Using comparative analysis, error accounting methods, and minimization criteria, on key poverty indicators, our investigation gathers compelling evidence that leans towards a negative answer to both questions. We conclude that the adoption of alternative methods for monitoring global poverty should be considered in an official capacity with urgency. [ABSTRACT FROM AUTHOR]

Objectives: Signal drift has been put forward as one of the fundamental confounding factors in diffusion MRI (dMRI) of the brain. This study characterizes signal drift in dMRI of the brain, evaluates correction methods, and exemplifies its impact on parameter estimation for three intravoxel incoherent motion (IVIM) protocols. Materials and methods: dMRI of the brain was acquired in ten healthy subjects using protocols designed to enable retrospective characterization and correction of signal drift. All scans were acquired twice for repeatability analysis. Three temporal polynomial correction methods were evaluated: (1) global, (2) voxelwise, and (3) spatiotemporal. Effects of acquisition order were simulated using estimated drift fields. Results: Signal drift was around 2% per 5 min in the brain as a whole, but reached above 5% per 5 min in the frontal regions. Only correction methods taking spatially varying signal drift into account could achieve effective corrections. Altered acquisition order introduced both systematic changes and differences in repeatability in the presence of signal drift. Discussion: Signal drift in dMRI of the brain was found to be spatially varying, calling for correction methods taking this into account. Without proper corrections, choice of protocol can affect dMRI parameter estimates and their repeatability. [ABSTRACT FROM AUTHOR]

In this work, the differential quadrature element method (DQEM) is used to conduct heat transfer and thermoelastic analysis for layered beams based on the two-dimensional Fourier's law and thermoelasticity theory. The method accounts for temperature-dependent (TD) material properties, nonuniform thermal boundary conditions, and various end constraints. To implement the method, the layered beam is decomposed into several sub-domains. At the interfaces of arbitrary two adjacent sub-domains, the continuity conditions are exactly enforced. The differential quadrature technique is applied for the spatial discretization of the governing equations together with the interface and boundary conditions. The convergence of the DQEM solutions is checked. The correctness of the DQEM solutions is validated by comparison with those obtained by the finite element method and those existing in the literature. Finally, the effects of various factors such as TD material properties, nonuniform thermal boundary conditions, and end constraints on the heat transfer and thermoelastic behaviors of the beam are studied. [ABSTRACT FROM AUTHOR]

Accounting changes, whether voluntarily undertaken by firms or mandated by standard setters, are generally implemented using one of three application methods—retrospective, modified retrospective, or prospective. This study sheds light on whether the adoption method applied to accounting changes impacts financial statement usefulness. Because individual prior year results that are presented for comparative purposes are restated under retrospective application but not under modified retrospective application, we predict that retrospective application provides data that are more useful for forecasting future earnings. Our first set of tests take advantage of a change in application method for voluntary accounting changes—from modified retrospective application to retrospective application—that occurred with the adoption of SFAS 154. Our evidence shows that retrospective application increases the extent to which future earnings are reflected in current price, decreases analyst earnings forecast errors and dispersion, and moderates abnormal trading volume and return volatility at future earnings announcements. Next, we examine the recent mandated change in revenue recognition, which allowed firms to choose either retrospective or modified retrospective application. We model self-selection arising from application method options presented in that standard, and present corroborating evidence from that mandatory change, after controlling for self-selection. Our results provide valuable feedback regarding the effects of SFAS 154 related to voluntary accounting changes and the effects of the diversity of application methods arising from the application method options provided in the recent revenue recognition standard. More generally, our results also provide useful information about the costs and benefits of different application methods for accounting changes. [ABSTRACT FROM AUTHOR]

Problems of unilateral discrete contact between an elastic half-space and a rigid punch of finite size with a surface microrelief are considered. A variational formulation of the problems is obtained in the form of a boundary variational inequality using the Poincaré–Steklov operator, which maps normal stresses into normal displacements on a part of the boundary of the elastic half-space. A minimization problem equivalent to the variational inequality is presented, as a result of the approximation of which a quadratic programming problem subject to equality and inequality constraints is obtained. To solve this problem, a new computational algorithm based on the conjugate gradient method is proposed, which includes three equations of punch equilibrium in the calculation. The algorithm belongs to the class of active set methods and takes into account the specifics of the set of constraints. Some patterns of contact interaction of surfaces with a regular microrelief are established. [ABSTRACT FROM AUTHOR]

Weakly supervised semantic segmentation with only image-level labels is an essential application since it reduces the considerable human effort to fully annotate image. Most state-of-the-art methods take into account discriminative regions or prior knowledge (called seeds) by applying the recent Classification Activation Maps (CAM) methods. As seeds used in the CAM methods could be considered as segmentation knowledge, increasing seeds will naturally improve the performance of the CAM methods. We treat such task as a seed exploration with clustering learning, which is different from the current methods in which region mining or seeds learning are performed in an end-to-end learning manner. In this paper, we propose a progressive framework containing autoencoders to explore numerous and accurate seeds to further improve the performance of CAM methods. Our explored seeds can replace the initial seeds, and thus, they can be integrated with current weakly supervised semantic segmentation methods for performance improvement. We show experimentally that our provided seeds lead to better model training. As a result, we obtain performance improvement from corresponding counterparts to reach 68.1 and 69.2% mIoU on PASCAL VOC 2012 validation and test dataset, respectively. [ABSTRACT FROM AUTHOR]

Trial-based economic evaluations are increasingly being conducted to support healthcare decision-making. When analysing trial-based economic evaluation data, different methodological challenges may be encountered, including (i) missing data, (ii) correlated costs and effects, (iii) baseline imbalances and (iv) skewness of costs and/or effects. Despite the broad range of methods available to account for these methodological challenges in effectiveness studies, they may not always be directly applicable in trial-based economic evaluations where costs and effects are analysed jointly, and more than one methodological challenge typically needs to be addressed simultaneously. The use of inappropriate methods can bias results and conclusions regarding the cost-effectiveness of healthcare interventions. Eventually, such low-quality evidence can hamper healthcare decision-making, which may in turn result in a waste of already scarce healthcare resources. Therefore, this tutorial aims to provide step-by-step guidance on how to combine appropriate statistical methods for handling the abovementioned methodological challenges using a ready-to-use R script. The theoretical background of the described methods is provided, and their application is illustrated using a simulated trial-based economic evaluation. [ABSTRACT FROM AUTHOR]

Classical gene-by-environment interaction (GxE) analysis can be used to characterize genetic effect heterogeneity but has a high multiple testing burden in the context of genome-wide association studies (GWAS). We adapt a colocalization method, SharePro, to account for effect heterogeneity in fine-mapping and identify candidates for GxE analysis with reduced multiple testing burden. SharePro demonstrates improved power for both fine-mapping and GxE analysis compared to existing methods as well as well-controlled false type I error in simulations. Using smoking status stratified GWAS summary statistics, we identify genetic effects on lung function modulated by smoking status that are not identified by existing methods. Additionally, using sex stratified GWAS summary statistics, we characterize sex differentiated genetic effects on fat distribution. In summary, we have developed an analytical framework to account for effect heterogeneity in fine-mapping and subsequently improve power for GxE analysis. The SharePro software for GxE analysis is openly available at https://github.com/zhwm/SharePro_gxe. Zhang et al. present an efficient method to simultaneously account for heterogeneity in fine-mapping and improve statistical power of gene-environment interaction analysis, with summary statistics from genome-wide association studies. [ABSTRACT FROM AUTHOR]

The understanding of recent climate extremes and the characterization of climate risk require examining these extremes within a historical context. However, the existing datasets of observed extremes generally exhibit spatial gaps and inaccuracies due to inadequate spatial extrapolation. This problem arises from traditional statistical methods used to account for the lack of measurements, particularly prevalent before the mid-20th century. In this work, we use artificial intelligence to reconstruct observations of European climate extremes (warm and cold days and nights) by leveraging Earth system model data from CMIP6 through transfer learning. Our method surpasses conventional statistical techniques and diffusion models, showcasing its ability to reconstruct past extreme events and reveal spatial trends across an extensive time span (1901-2018) that is not covered by most reanalysis datasets. Providing our dataset to the climate community will improve the characterization of climate extremes, resulting in better risk management and policies. The authors use artificial intelligence to accurately reconstruct past climate extremes from sparse observational data, providing quantitative evidence of hot and cold extremes in the early 20th century and shedding light on their evolution. [ABSTRACT FROM AUTHOR]

In the integrated energy system, the transmission delay of the cooling and heating pipeline network is long, which has an essential impact on the optimal scheduling of the integrated energy system. In this paper, a day-ahead optimal scheduling method of integrated energy systems considering the dynamic delay of the pipeline network is proposed. The method takes into account the impact of pipe network delay on day-ahead optimal scheduling of the system, solves the problem of considerable time lag of pipe network in the process of optimal scheduling of the system, and improves the real-time, safety, and economy of the system scheduling. The method uses the nodal method to establish a dynamic model of the pipeline network system. It analyses the impact of dynamic characteristics on the system equipment's output and system scheduling. A mixed integer linear programming method is used to establish an optimal scheduling model for the integrated energy system, and the system is optimized to maximize the daily revenue in combination with time-of-day tariffs. Finally, the proposed model is validated based on the energy station data in a park in Wuhan. The results show that the optimal scheduling strategy of the integrated energy system that considers the dynamic time-delay characteristics of the pipeline network can effectively improve the system's economy, the system scheduling's real-time performance, and the system operation's safety. [ABSTRACT FROM AUTHOR]

To improve slope reliability calculations and address high-nonlinearity in random fields, an AI algorithm, namely Convolutional Neural Network (CNN) with asymmetric convolution is introduced. The method accounts for the interdependence and auto-correlation of soil material and uses Python-based secondary development in ABAQUS Version 6.14 to improve computational efficiency and user-friendliness in finite element simulations. A Cholesky decomposition-based centroid point method is used for random fields to simplify computation. Additionally, an asymmetric convolution-based CNN surrogate model replaces finite element simulations to address challenges such as parameter correlations and random field discretization for improved analysis efficiency. The methodology uses random field samples and safety factors as inputs and outputs for training, which improves predictability and addressing high-dimensional issues. Its effectiveness is demonstrated through case studies involving single-layer undrained saturated clay slopes and double-layer cohesive soil slopes. The results demonstrate the effectiveness of the CNN approach that utilizes asymmetric convolution, with outcomes closely resembling those obtained through finite element simulation. This method demonstrates a 95.8% improvement in time efficiency compared to software-based calculations and a 93.5% enhancement over batch calculations using ABAQUS. These results confirm the effectiveness of the introduced reliability analysis method and the ability to provide accurate results while significantly boosting computational efficiency. [ABSTRACT FROM AUTHOR]

Objectives : This study investigates the effects of the length of imprisonment on reoffending after release. Methods: To account for endogeneity from omitted variables that influence both sentencing decisions and the likelihood to reoffend, this study uses variation in the proclivity to impose lengthier sentences across judges to which criminal cases are randomly assigned within district courts in the Netherlands. Using administrative data on all adult offenders convicted to short-term imprisonment (≤ 1 year) by a single-sitting judge in the Netherlands in 2012, we estimate multiple instrumental variable models. Results: The results indicate that an increase in imprisonment length significantly reduces the amount of offenses committed after release, whereas we only find small and statistically non-significant estimates for the probability to recidivate. Conclusions: We find evidence for a specific preventative effect of longer prison terms on the post-release reoffending frequency, but little evidence for desistance. [ABSTRACT FROM AUTHOR]

In the context of China's freshwater crisis high-resolution data are critical for sustainable water management and economic growth. Yet there is a dearth of data on water withdrawal and scarcity regardless of whether total or subsector amount, for prefectural cities. In administrative and territorial scope, we accounted for water withdrawal of all 63 economic-socio-environmental sectors for all 343 prefectural cities in China, based on a general framework and 2015 data. Spatial and economic-sector resolution is improved compared with previous studies by partitioning general sectors into industrial and agricultural sub-sectors. Construction of these datasets was based on selection of 16 driving forces. We connected a size indicator with corresponding water-withdrawal efficiency. We further accounted for total blue-water withdrawal and quantitative water scarcity status. Then we compared different scopes and methods of official accounts and statistics from various water datasets. These disaggregated and complete data could be used in input-output models for municipal design and governmental planning to help gain in-depth insights into subsector water-saving priorities from local economic activities. [ABSTRACT FROM AUTHOR]

Body Mass Index (BMI) trajectories are important for understanding how BMI develops over time. Missing data is often stated as a limitation in studies that analyse BMI over time and there is limited research exploring how missing data influences BMI trajectories. This study explores the influence missing data has in estimating BMI trajectories and the impact on subsequent analysis. This study uses data from the English Longitudinal Study of Ageing. Distinct BMI trajectories are estimated for adults aged 50 years and over. Next, multiple methods accounting for missing data are implemented and compared. Estimated trajectories are then used to predict the risk of developing type 2 diabetes mellitus (T2DM). Four distinct trajectories are identified using each of the missing data methods: stable overweight, elevated BMI, increasing BMI, and decreasing BMI. However, the likelihoods of individuals following the different trajectories differ between the different methods. The influence of BMI trajectory on T2DM is reduced after accounting for missing data. More work is needed to understand which methods for missing data are most reliable. When estimating BMI trajectories, missing data should be considered. The extent to which accounting for missing data influences cost-effectiveness analyses should be investigated. [ABSTRACT FROM AUTHOR]

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization. [ABSTRACT FROM AUTHOR]

The detection of influential individuals in social networks is called influence maximization which has many applications in advertising and marketing. Several factors including propagation delay affect the degree to which an individual influences the network. Many different methods, including centrality measures, identify high-influence individuals in social networks. The time-sensitive harmonic method (TSHarmonic), which considers time sensitivity to propagation delay and duration, is a centrality measure. TSHarmonic has two weaknesses: high computational complexity and ignoring the influence of the selected node in selecting other influential nodes. Therefore, in this article, the valuable path-finding process in the TSHarmonic method is modified to provide the Fast Time-Sensitive Harmonic algorithm. The provided method has the same accuracy as the TSHarmonic, while the speed is significantly increased. Then, the Time-Sensitive Propagation Values Discount method is proposed to improve detection speed and accuracy. This method takes into account the influence of the selected node for future selection and hence increases the accuracy. [ABSTRACT FROM AUTHOR]

We use recently developed difference-in-differences methodologies and a panel of US states over the period 1960–2015 to examine how bank branching deregulation impacted state-level income inequality. Existing research relying on traditional two-way fixed effects estimates and event studies provide mixed results. However, these results potentially suffer from biases due to treatment effect heterogeneity and the failure to account for multiple related treatments. Using bias-corrected difference-in-differences procedures and properly accounting for the timing of treatment, we find evidence that the combined effect of intrastate and interstate banking deregulation increased the income share of the top 10%, 5%, and 1% of income earners, respectively. Conversely, we find no evidence that intrastate branching deregulation in isolation impacted income inequality. [ABSTRACT FROM AUTHOR]

Conservation Agriculture (CA) is actively promoted as an alternative farming system that combines environmental, economic, and social sustainability. Three pillars define CA: (i) minimum mechanical soil disturbance, (ii) permanent soil organic cover, and (iii) species diversification. The local context, constraints, and needs of the farmers influence the translation of the pillars into practices. Currently, there is no method for categorizing this diversity of CA practices, which hampers impact assessment, understanding of farmer choices and pathways, stakeholder communication, and policymaking. This paper presents a systematic method to identify and categorize the diversity of CA practices at the regional level, anchored in the three pillars and based on practices implemented by CA farmers. The classification method is grounded on the intersection of an archetypal analysis and a hierarchical clustering analysis. This method was used to study CA practices in Wallonia, Belgium, based on a survey of practices in a sample of 48 farmers. Combining the two clustering methods increases the proportion of classified farmers while allowing for the distinction between three CA-types with extreme and salient practices, and two intermediate CA-types comprising farmers whose practices fall between these references. The study reveals that three explanatory factors influence the implementation of CA practices in Wallonia: (i) the proportion of tillage-intensive crops and (ii) temporary grasslands in the crop sequence, and (iii) the organic certification. These factors lead to trade-offs that hinder the three pillars of CA from being fully implemented simultaneously. This new classification method can be replicated in other regions where CA is practiced, by adapting input variables according to context and local knowledge. [ABSTRACT FROM AUTHOR]

Within an uncertain environment and following carbon trade policies, this study uses the Extended Exergy Accounting (EEA) method for coal supply chains (SCs) in eight of the world's most significant coal consuming countries. The purpose is to improve the sustainability of coal SCs in terms of Joules rather than money while considering economic, environmental, and social aspects. This model is a multi-product economic production quantity (EPQ) with a single-vendor multi-buyer with shortage as a backorder. Within the SC, there are some real constraints, such as inventory turnover ratio, waste disposal to the environment, carbon dioxide emissions, and available budgets for customers. For optimization purposes, three recent metaheuristic algorithms, including Ant Lion Optimizer, Lion Optimization Algorithm, and Whale Optimization Algorithm, are suggested to determine a near-optimum solution to an "exergy fuzzy nonlinear integer-programming (EFNIP)." Moreover, an exact method (GAMS) is employed to validate the results of the suggested algorithms. Additionally, sensitivity analyses with different percentages of exergy parameters, such as capital, labor, and environmental remediation, are done to gain a deeper understanding of sustainability improvement in coal SCs. The results showed that sustainable coal SC in the USA has the lowest fuzzy total exergy, while Poland and China have the highest. [ABSTRACT FROM AUTHOR]

Composite time trade-off (cTTO) utilities have been found to be higher when adults value health states for children than for themselves. It is not clear if these differences reflect adults assigning truly higher utilities to the same health state in different perspectives, or if they are caused by other factors, which are not accounted for in the valuation procedure. We test if the difference between children's and adults' cTTO valuations changes if a longer duration than the standard 10 years is used. Personal interviews with a representative sample of 151 adults in the UK were conducted. We employed the cTTO method to estimate utilities of four different health states, where adults considered states both from their own and a 10-year-old child's perspective, for durations of 10 and 20 years. We corrected the cTTO valuations for perspective-specific time preferences in a separate task, again for both perspectives. We replicate the finding that cTTO utilities are higher for the child perspective than for the adult perspective, although the difference is only significant when controlling for other variables in a mixed effects regression. Time preferences are close to 0 on average, and smaller for children than adults. After correcting TTO utilities for time preferences, the effect of perspective is no longer significant. No differences were found for cTTO tasks completed with a 10- or 20-year duration. Our results suggest that the child–adult gap is partially related to differences in time preferences and, hence, that correcting cTTO utilities for these preferences could be useful. [ABSTRACT FROM AUTHOR]

One of the most effective and extensively used recommendation technique is collaborative filtering. Based on related users or items, collaborative filtering creates recommendations for the users. Similarity measures play a crucial role in Collaborative Filtering Recommender System. One useful similarity measure for a cold-start situation is the Proximity-Impact-Popularity (PIP) measure. The PIP measure is, nevertheless, subject to several limitations as it penalises users' multiple times throughout similarity computation in addition to ignoring their global rating behaviour. When a user rates the items, their overall rating behavior—whether they are lenient or strict—is referred to as global rating behaviour. In this study, we introduce an improved similarity metric to calculate similarity more accurately and generate high-quality recommendations. Our method takes into account both the user's rating behaviour and the percentage of co-rated items among users. Additionally, we have considered the computation complexity of the suggested work. In addition, to exhibit the performance of the proposed measure, empirical analysis has been done on real datasets. The results of the experiments performed on the dataset show that the suggested work takes precedence over the current similarity metrics. In comparison to state-of-the-art measurements, the suggested work exhibits improvements in MAE of 1.73%, RMSE of 4.01%, and F measure of 1.47% on an average. [ABSTRACT FROM AUTHOR]

Purpose: Market-based GHG accounting allows companies to report their emissions based on the purchase of emission attributes. This practice is widespread for reporting 'scope 2' electricity emissions and has recently been proposed for both 'scope 1' (direct) and 'scope 3' (other value chain) emission sources. However, the market-based method has been criticised for undermining the accuracy of GHG disclosures, and it is therefore highly important to explore the requirements for accurate GHG inventories and the solutions to market-based accounting. Methods: This paper uses two methods: firstly, thought experiments are used to identify principles for accurate corporate GHG inventories and, secondly, formal prescriptions are developed for possible solutions to market-based accounting. Results and discussion: The findings identify six principles for accurate corporate GHG inventories, which are then used to inform the development of two possible solutions. The first solution is to report changes in emissions caused by company actions separately from the GHG inventory, including any changes caused by the purchase of emission attribute certificates. The second solution proposes a causality requirement for the use of emission attributes in GHG inventories. Although the analysis focuses on corporate or organisational GHG inventories, the principles and solutions apply equally to attributional product carbon footprinting and life cycle assessment more broadly. Conclusions: We emphasise that inventories are only one form of accounting method, and their accuracy should not be undermined by attempting to fulfil functions that are best served by other methods. [ABSTRACT FROM AUTHOR]

The fundamentals of the quark–gluon string (QGS) model for describing inelastic interactions of light nuclei at high energies are outlined. Basic ideas of the QGS model, which include the formation of quark–gluon strings, the choice of their limited number, and a simulation of breaking of a string with hadron formation, are presented. The results of theoretical calculations based on the QGS model are compared with experimental data obtained for inelastic C interactions. A brief account of the procedure for obtaining these experimental data is given. An analysis and a comparison of model results and experimental information shows that the QGS model reproduces faithfully the interactions of colliding light nuclei at momenta around 4.2 GeV/ and is applicable up to nuclear-interaction energies corresponding to 10 GeV per nucleon. [ABSTRACT FROM AUTHOR]

Purpose: American Indian/Alaska Native (AI/AN) populations experience significantly higher incidence and mortality rates of cervical cancer. The objective of this systematic scoping review is to characterize the volume and nature of research being conducted specific to the AI/AN population regarding cervical cancer and related clinical themes. Methods: This scoping review was conducted in collaboration with the Pacific Northwest Evidence-based Practice Center. Search strategies identified eligible publications from 1990 through 4 February 2022. Two reviewers independently abstracted study data, including clinical area, number of participants and percent inclusion of AI/AN, intervention or risk factor, outcomes reported, Indian Health Service (IHS) Region, and funding source. We used published algorithms to assess study design. Results: Database searches identified 300 unique citations. After full-text evaluation of 129 articles, 78 studies and 9 secondary publications were included (total of 87). Approximately 74% of studies were observational in design, with cross-sectional methodology accounting for 42.7% of all included studies. The most common clinical theme was cervical cancer screening. The most common intervention/exposure was risk factor, typically race (AI/AN compared with other groups) (69%). For studies with documented funding sources, 67% were funded by the US Government. Conclusion: Of the small number of publications identified, the majority are funded through government agencies, are descriptive and/or cross-sectional studies that are hypothesis generating in nature, and fail to represent the diversity of the AI/AN populations in the US. This systematic scoping review highlights the paucity of rigorous research being conducted in a population suffering from a greater burden of disease. [ABSTRACT FROM AUTHOR]

Geospatial analytics increasingly rely on data fusion methods to extract patterns from data; however robust results are difficult to achieve because of the need for spatial and temporal regularization and latent structures within data. Tensor decomposition is a promising approach because it can accommodate multidimensional structure of data (e.g., trajectory information about users, locations, and time periods). To address these challenges, we introduce Multi-Type Clustering using Regularized tensor Decomposition (MCRD), an innovative method for data analysis that provides insight not just about groupings within data types (e.g., clusters of users), but also about the interactions between data types (e.g., clusters of users and locations) in the latent features of complex multi-type datasets. This is done by combining two innovations. First, a tensor representing spatiotemporal data is decomposed using a novel regularization method to account for structure within the data. Next, within- and cross-type groups are found through the application of novel hypergraph community detection methods to the decomposed results. Experimentation on both synthetic and real trajectory data demonstrates MCRD's capacity to reveal the within- and cross-type grouping in data, and MCRD outperforms related methods including tensor decomposition without regularization, unfolding of tensors, Laplacian regularization, and tensor block models. The robust and versatile analysis provided by combining new regularization and clustering techniques outlined in this paper likely have utility in geospatial analytics beyond the movement applications explicitly studied. [ABSTRACT FROM AUTHOR]

The discrepancy of the scores on responses to negatively and positively worded items has led to hypotheses of inattention, confusion, difficulty, and differential processing of negatively worded items. The present study, utilizing eye-tracking methodology, aimed to fill an explanatory gap regarding response behavior, providing observations on the item-level response process. It experimentally examined characteristics of the items (wording type, self-relevance) and characteristics of the respondents (neuroticism, verbal abilities, and mood) for their impact on response outcomes. A sample of 87 university students completed a computerized version of a questionnaire with items presented in four alternative wording types: positive, negative, negated positive, and negated negative; half of the items referred to attitudes toward the self and the other half to attitudes toward others. Participants' eye movements during item completion were recorded with the Gazepoint-GP3-HD desk-mounted eye tracker. In linear mixed effects models, wording type and self-relevance were found to relate to response time, time of viewing and revisits to the body of the items and the response options, indicating that there were effects at the stages of comprehension and selection of response. Neuroticism was associated with differential item responses, suggesting a role in later levels of the response process, the retrieval, judgment, and response selection stages. Eye-tracking measures can enhance the examination of response tendencies with regards to item content, item wording, and person characteristics. [ABSTRACT FROM AUTHOR]

Background: As interest in the voluntary soil carbon market surges, carbon registries have been developing new soil carbon measurement, reporting, and verification (MRV) protocols. These protocols are inconsistent in their approaches to measuring soil organic carbon (SOC). Two areas of concern include the type of SOC stock accounting method (fixed-depth (FD) vs. equivalent soil mass (ESM)) and sampling depth requirement. Despite evidence that fixed-depth measurements can result in error because of changes in soil bulk density and that sampling to 30 cm neglects a significant portion of the soil profile's SOC stock, most MRV protocols do not specify which sampling method to use and only require sampling to 30 cm. Using data from UC Davis's Century Experiment ("Century") and UW Madison's Wisconsin Integrated Cropping Systems Trial (WICST), we quantify differences in SOC stock changes estimated by FD and ESM over 20 years, investigate how sampling at-depth (> 30 cm) affects SOC stock change estimates, and estimate how crediting outcomes taking an empirical sampling-only crediting approach differ when stocks are calculated using ESM or FD at different depths. Results: We find that FD and ESM estimates of stock change can differ by over 100 percent and that, as expected, much of this difference is associated with changes in bulk density in surface soils (e.g., r = 0.90 for Century maize treatments). This led to substantial differences in crediting outcomes between ESM and FD-based stocks, although many treatments did not receive credits due to declines in SOC stocks over time. While increased variability of soils at depth makes it challenging to accurately quantify stocks across the profile, sampling to 60 cm can capture changes in bulk density, potential SOC redistribution, and a larger proportion of the overall SOC stock. Conclusions: ESM accounting and sampling to 60 cm (using multiple depth increments) should be considered best practice when quantifying change in SOC stocks in annual, row crop agroecosystems. For carbon markets, the cost of achieving an accurate estimate of SOC stocks that reflect management impacts on soils at-depth should be reflected in the price of carbon credits. [ABSTRACT FROM AUTHOR]

Based on the thermodynamics of irreversible processes, the relationship between the reaction rate in a deformable solid and the stresses is demonstrated without introducing any additional hypotheses. Well known procedures of accounting for the dependence of the reaction rate on the structure are described. A possible generalization of the classical models by extending the thermodynamics is presented. An example of a transition from the continuum modeling to a two-level modeling is given, which makes it possible to establish a relationship between the parameters affecting the reaction rate at different scale levels. A similar approach can be applied to any scalar process. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) have been widely concerned as an important precursor of haze. The petrochemical industry not only has many VOC emission sources, but also produces a large amount of VOCs. Although some scholars have analyzed the emission sources of VOCs in the oilfield industry, there still exists the gap in quantitative accounting the emission of organic volatiles from oilfield sewage pools. Moreover, such studies lack certain standardization, accuracy and validity in China. At present, most of the sewage pools in oilfield stations are open-ended. A large amount of VOCs are released into the surrounding environment during the storage of oily wastewater, which has caused serious environmental pollution. In this work, considering six significant influencing factors, a fast VOC emission accounting method from sewage pool in oilfield has been established. To the best of our knowledge, this is the first time to conduct VOC emission quantitative accounting study based on multi-field coupling numerical simulation technology, method of experimental design, and statistical analysis of the response surface methodology. Through comparing the results of numerical simulation with COMSOL, laboratory experiment, and the published literature, the results show that the error is in the range of reasonable, which can satisfy the rapid accounting of VOC emissions from sewage pool in oilfield. [ABSTRACT FROM AUTHOR]

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This paper presents a new MATLAB code facilitating the localization of lineaments in the earth's crust. The proposed approach is based on a combination of several gravity data processing methods (polynomial separation, upward continuation, horizontal gradient calculation, localization of gradient maxima). To write this code, an algorithm based on a mathematical analysis which takes into account these methods was established. This code separates Bouguer anomalies into regional and residual anomalies, upward-continues residual anomalies at several depths, calculates their horizontal gradients for each depth, and determines the maxima of these gradients. To demonstrate its reliability and efficiency, the code was first applied to synthetic data produced by a prism before being applied to gravity data from the transition zone between the Benue basin and Lake Chad basin. Analysis of the results obtained shows that several lineaments already detected by previous geological and geophysical studies have been identified and localized and new lineaments have been highlighted. The MATLAB code presented in this study allows to simultaneously performing several filters such as polynomial separation, upward continuation, horizontal gradient, and also the localization of the maxima used in geosciences to localize the lineaments. This code is simple, runs quickly, and can be easily modified and adapted to the needs of the researcher. [ABSTRACT FROM AUTHOR]

The rupture process of the recent moderate-to-large earthquakes in the Zagros area along the Iran plateau is investigated by analysing the strong motion data provided by the Iranian Building and Housing Research Centre (BHRC). The selected dataset includes the largest and deadliest 2017 Mw 7.3, Iran-Iraq (Ezgeleh) earthquake. The earthquake source parameters (moment magnitude, rupture duration and length, average slip, and static stress drop) are determined using a time-domain, parametric modelling technique based on the time evolution of the P-wave displacement signals. The earthquake source parameters are calculated from simulated triangular moment-rate functions assuming the circular source models for a constant rupture velocity. The anelastic attenuation effect is modelled through the independent frequency-Q parameter ranging from 50 to 200 and accounted for by a post-processing procedure that retrieves the attenuation-corrected, moment-rate triangular shape. Results show that the average static stress-drop with different Q P , varies between <Δσ> = 0.9 (0.7–1.2) MPa and <Δσ> = 1.6 (1.2–2.0) MPa. Overall, in this research, the rupture radius/length empirically scales with the seismic moment with a self-similar, near-constant stress drop of about 1 MPa. Assuming a circular rupture model for the Ezgeleh earthquake, we estimate a moment magnitude of 6.9, rupture duration of 7 s, source radius of 16 km, average slip of about 2 m and static stress drop of 3.4 MPa. [ABSTRACT FROM AUTHOR]

Floor diaphragms are structural elements mainly responsible for transmitting lateral loads to the adjoining vertical members of the Seismic-Force-Resisting System (SFRS). Under significant seismic events, the actual in-plane stiffness of floor diaphragms affects patterns of load distribution in-between horizontal and vertical elements and, as such, contributes to the local nonlinear response and the global dynamic behavior. However, in SFRS with mass timber and hybrid timber-based floor diaphragms, neither design provisions nor procedures exist to account for its in-plane flexibility. To highlight the influence of the actual in-plane stiffness of diaphragms on a timber-steel hybrid building system, this paper analyzes its dynamic response via nonlinear dynamic analysis. The OpenSees framework is adopted to develop three finite-element building models that feature diaphragms with (i) rigid behavior, (ii) actual members and connections diaphragm subassembly behavior, including panel-to-panel slab connections, or (iii) neglecting the contribution of the slab connections, respectively. The SFRS of the building entails a Special Concentrically Braced Frame whose nonlinear behaviors are explicitly simulated, including global buckling, tensile yielding, and post-buckling behaviors. The collapse fragility analysis reveals that the conditional probability of exceeding all three considered limit states given any level of ground motion intensity for the SFRS equipped with the CLT-steel floor diaphragm is close to that of the SFRS with rigid diaphragm behavior. Removal of CLT panel-to-panel connections attracts disproportionate and excessive lateral deformation demands towards the unbraced frames due to the increased in-plane diaphragm flexibility. [ABSTRACT FROM AUTHOR]

This study re-examines the relationship between women on corporate boards (WOCB) and corporate social performance (CSP) for a sample of companies from the Fortune 1000 ranking over the period 2004 to 2018 (ranked from 501 to 1000). To take into account the complex and non-linear relationship as well as endogeneity issues, we use a two-stage generalised additive model. This contribution is significant, as many authors have demonstrated the non-linearity of factors influencing performance, whether of a financial or social nature. Consistent with token and critical mass theories, our results shows that the effects of WOCB on CSP vary significantly depending on their numerical representation and that there are departures from linearity. Our findings provide explanations for the existing mixed empirical results, which all rely on parametric methods. As such, we suggest the use of semiparametric methods takin into account endogeneity issues to assess WOCB–CSP relationship. This study sheds some new light on that relationship, which remains a controversial issue. [ABSTRACT FROM AUTHOR]

The article presents a comparative analysis of the methods for manufacturing polypropylene Raschig rings (taking account of consumption coefficients in manufacture of thermoplastic products) on the example of mass-exchange packings for the drying tower of a sulfuric acid plant. Graphical relationships and regression equations for Raschig ring (diameter 75 mm) mass-exchange packings made of polypropylene (taking account of the manufacturing method) are presented. [ABSTRACT FROM AUTHOR]

The uniqueness of the nuclear fuel cycle facilities and their equipment, which must be adjusted to the specific conditions of manufacturing and (or) reprocessing the fuel of a particular reactor type in each case, complicates the application of traditional methods for assessing reliability and safety, thus relying on the availability of a significant operating experience. As a result, at the design stage, the reliability level of such equipment is determined by the available data on the reliability of analog elements. Following completion of commissioning of a nuclear fuel cycle facility, the regulatory requirements demand the updating of reliability and safety calculations to bring them in line with the actual state of the facility based on the results of the construction, commissioning, as well as pilot- and pilot-industrial operation. The article presents a universal method for accounting for the statistics of equipment operation at a high level of reliability in the absence of representative samples during the first years of its operation, which is compensated by the use of prior information within the framework of the Bayesian approach. The simulation of the time between failures is demonstrated using the synthesis of the prior information and the results of prototype bench tests for 5 years. [ABSTRACT FROM AUTHOR]

Using an extended full-cycle carbon model from the Faustmann framework, which allows for management strategies of several uses concurrently implemented in the same area of forest, this paper investigates the selection of management objectives that are beneficial and optimal for forest plantations in Vietnam. Three scenarios are considered: Scenario 1 investigates the management objective of maximizing the land economic value (LEV) of timber as a single production. Scenario 2 investigates the joint production of timber and bioenergy sources. Scenario 3 analyzes the joint production of timber, bioenergy production, and carbon sequestration. The findings reveal that if growers pursue a timber management objective (Scenario 1), the farming business only provides considerable benefits under the government-subsidized credit scheme of 8.4%. For a higher non-subsidized interest rate of 15.24% of commercial banks, such gains reduce substantially and become negative value under the mean interest rate of 19.08% of private credit sources. Altering the management objective to a joint production of timber and raw bioenergy production (Scenario 2) will boost the LEV by a moderate level, but at the expense of timber production and thus carbon stock. However, the reduction tendency of timber and carbon balance is not substantial due mainly to the relatively small proportion of bioenergy production compared to timber production; therefore, decision-making frameworks targeting carbon uptake may be capable of incorporating such levels of confliction. Further internalizing the carbon value of the forest into management objectives generally leads to a longer rotation length, thus improving both carbon sequestration and income gains. Shifting the current timber-dominant management objective to a joint production of timber and bioenergy sources, or, alternatively, a joint production of timber, bioenergy production, and carbon sequestration when the carbon market is emerging, is a good alternative strategy for forest management in Vietnam. [ABSTRACT FROM AUTHOR]

The quality of the reflector image reconstructed by the digital antenna focusing (DFA) method deteriorates due to nonoptimal focusing if there is a spread of "stray" delays between antenna array elements of more than one fourth of the carrier frequency period. The paper proposes a method for taking into account the influence of the "stray" delays between antenna array elements during DFA image reconstruction. The application of this method allows increasing the frontal resolving ability of the DFA-image of reflectors approximately twice when using a low-quality antenna array with a range of "stray" delays of one period. DFA-images reconstructed taking into account the coherent factor are presented. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the impact of ontology-based knowledge management on improving tax accounting procedures and reducing tax risks, specifically focusing on the income tax system in a developing country. To achieve this objective, a mixed methodology approach was adopted, combining quantitative and qualitative data collection and analysis methods. The data were gathered through document analysis, interviews, and questionnaires administered to stakeholders in the Egyptian Income Tax Authority. The findings of this study hold significance for both academic researchers and accounting practitioners. The results demonstrate numerous advantages associated with the utilization of ontology in tax management. It has the potential to reduce costs in tax advisory services, minimize errors in tax calculations, and enhance tax revenue generation by promoting transparency and accountability. The empirical analysis further confirms a positive correlation between ontology-based knowledge management and improved accountability procedures, as well as a positive correlation with the reduction of tax risks. These statistically significant relationships (r = 0.85, p < 0.01 and r = 0.79, p < 0.01, respectively) provide empirical evidence of the actual effects of implementing ontology-based knowledge management in improving tax accounting procedures and mitigating tax risks. [ABSTRACT FROM AUTHOR]

Left ventricular (LV) myocardial mass is important in the evaluation of cardiac remodeling and requires accurate assessment when performed on linear measurements in two-dimensional echocardiography (Echo). We aimed to compare the accuracy of the Devereaux formula (DEV) and the Teichholz formula (TEICH) in calculating LV myocardial mass in Echo using cardiac magnetic resonance (CMR) as the reference method. Based on preceding mathematical calculations, we identified primarily LV size rather than wall thickness as the main source of bias between DEV and TEICH in a retrospective derivation cohort (n = 1276). Although LV mass from DEV and TEICH were correlated with CMR, TEICH did not show a proportional bias as did DEV (− 2 g/m2 vs. + 22 g/m2). This could be validated in an independent prospective cohort (n = 226) with symptomatic non-ischemic heart failure. DEV systematically overestimated LV mass in all tiers of LV remodeling as compared to TEICH. In conclusion, the TEICH method accounts for the changes in LV geometry with increasing LV mass and thus better reflects the different pattern of LV remodeling than the DEV method. This has important clinical implications, as TEICH may be more appropriate for use in clinical practice, rather than DEV, currently recommended. [ABSTRACT FROM AUTHOR]

One-step reinforcement learning explanation methods account for individual actions but fail to consider the agent's future behavior, which can make their interpretation ambiguous. We propose to address this limitation by providing hierarchical goals as context for one-step explanations. By considering the current hierarchical goal as a context, one-step explanations can be interpreted with higher certainty, as the agent's future behavior is more predictable. We combine reward decomposition with hierarchical reinforcement learning into a novel explainable reinforcement learning framework, which yields more interpretable, goal-contextualized one-step explanations. With a qualitative analysis of one-step reward decomposition explanations, we first show that their interpretability is indeed limited in scenarios with multiple, different optimal policies—a characteristic shared by other one-step explanation methods. Then, we show that our framework retains high interpretability in such cases, as the hierarchical goal can be considered as context for the explanation. To the best of our knowledge, our work is the first to investigate hierarchical goals not as an explanation directly but as additional context for one-step reinforcement learning explanations. [ABSTRACT FROM AUTHOR]

The determination of seismic risk in urban settlements has received increasing attention in the scientific community during the last decades since it allows to identify the most vulnerable portions of urban areas and therefore to plan appropriate strategies for seismic risk reduction. In order to accurately evaluate the seismic risk of urban settlements it should be necessary to estimate in detail the seismic vulnerability of all the existing buildings in the considered area. This task could be very cumbersome due to both the great number of information needed to accurately characterize each building and the huge related computational effort. Several simplified methods for the assessment of the seismic vulnerability of existing buildings have been therefore presented in the literature. In order to estimate the occurrence of damage in buildings due to possible seismic phenomena, the published studies usually refer to response spectra evaluated according to seismic events expected in the territory with assumed probabilities. In the present paper seismic events are instead simulated using a modified Olami–Feder–Christensen (OFC) model, within the framework of self-organized criticality. The proposed methodology takes into account some geological parameters in the evaluation of the seismic intensities perceived by each single building, extending the approach presented in a previous study of some of the authors. Here, a large territory in the Sicilian oriental coast, the metropolitan area of Catania, which includes several urbanized zones with different features, has been considered as a new case study. Applications of the procedure are presented first with reference to seismic sequences of variable intensity, whose occurrence is rather frequent in seismic territories, showing that the damage can be progressively accumulated in the buildings and may lead to their collapse even when the intensities of each single event are moderate. Moreover, statistically significant simulations of single major seismic events, equivalent to a given sequence in terms of produced damages on buildings, are also performed. The latter match well with a novel a-priori risk index, introduced with the aim of characterizing the seismic risk of each single municipality in the considered metropolitan area. The proposed procedure can be applied to any large urbanized territory and, allowing to identify the most vulnerable areas, can represent a useful tool to prioritize the allocation of funds. This could be a novelty for risk policies in many countries in which public subsidies are currently assigned on a case-by-case basis, taking into account only hazard and vulnerability. The use of an a-priori risk index in the allocation process will allow to take into due account the relevant role of exposure. [ABSTRACT FROM AUTHOR]

The direct simulation Monte Carlo method is widely used for solving rarefied gas dynamics problems. The focus in this paper is on the study of the error introduced by spatial regularization of the interaction between two particles. Two approaches to spatial regularization and three direct simulation Monte Carlo algorithms implementing these approaches are considered. An upper bound on the error of these algorithms in the metric of the space of continuous functions is constructed, and conditionally optimal parameters that guarantee a prescribed error level in probability are obtained. Using the classical Fourier problem as an example, the error introduced by regularization is numerically investigated, and the constructed conditionally optimal parameters are tested. [ABSTRACT FROM AUTHOR]

It is shown that the Schrödinger equation for the sum of the potential of a square well and the Coulomb potential of a uniformly charged sphere admits an analytical solution for arbitrary values of the orbital angular momentum. An explicit form of this solution has been found. Using the obtained solution, the influence of the Coulomb interaction for both point and distributed nuclear charges on the values of asymptotic normalization coefficients for various nuclear systems is investigated. It is shown that taking into account the non-point distribution of the nuclear charge has little effect on the calculated values of the asymptotic normalization coefficients, provided that the binding energy of the system is assumed to be fixed. [ABSTRACT FROM AUTHOR]

Rationality, the premise of economics, is an ideal behavioral norm. In the real world, however, intertemporal decision-making is based on adaptive behavioral principles from companies to individual households. It bases on managerial accounting procedures, whereby action plans are formulated and implemented, differences from actual results are recognized, and revisions accumulate over time. We take the intertemporal decision-making problem of households' consumption/saving (investment) planning in this paper. And we compare the validity of rationality and adaptability as decision-making principles. First, rational decision-making in the optimal growth model leads to a unique path. However, optimal growth planning is practically unstable on the saddle-point path and can only realize if it assumes rationality leading to perfect foresight. On the other hand, the growth paths guided by budget-controlled adaptive decision-making are diverse and distributed in the myriad around the optimal growth path. This redundancy creates stability in the management and operation of the plan. Because through the trial-and-error process of planning and actual comparison, we can implement a more advantageous plan while allowing for multiple next-best goals, including the optimal growth path. Moreover, the numerical results show that the sequentially adaptive consumption/investment planning is comparable to the optimal growth plan on a social welfare basis calculated by accumulating consumption utility and is practically manageable. For example, paths that exceed 0.9 as a ratio to the optimal growth plan are reachable from the initial planning stage at a ratio of 0.58. Based on the above results, we can now analyze intertemporal economic problems with this realistic, practical, and simple method, replacing dynamic optimization ones. [ABSTRACT FROM AUTHOR]