Multiple Performance Research Articles

Uncovering the dynamic relationship between intrinsic motivation and basic softball skills: An exploratory analysis

Physical education (PE) is a pivotal domain, offering students valuable opportunities for excelling in multiple sports performances when harmonized with contemporary curricula that emphasize cognitive engagement through intrinsic motivation. This blend has the potential to enhance not only softball skills but also performance across various sports. This study investigated the relationship between intrinsic motivation (interest, enjoyment, and personal challenge) and fundamental softball performance skills among 30 active students (F = 15; M = 15) enrolled in the Indonesian University of Education’s physical education program. It employed an ex post facto research design and quantitative method, utilizing the Sport Motivation Scale (SMS) (0.779 > 0.05) and O’Donnell Softball Test (0.701 > 0.05), both instruments demonstrated good reliability and validity. Results from data analysis in IBM SPSS 25.0, reveal that interest and enjoyment moderately correlate but lack of significant impact softball skills. However, personal challenge shows a strong positive correlation with interest and enjoyment. Although personal challenge does not directly influence softball skills, it plays a crucial role in fostering interest and enjoyment in sports. Understanding intrinsic motivation's impact on skill development can inform effective teaching methods and encourage greater engagement in physical activities for improved overall well-being. This research provides insights for educators and coaches to design learning experiences that incorporate challenges and promote intrinsic motivation, enhancing students’ engagement and skill development in physical education activities. Future research could explore intrinsic motivation's impact on softball skills across diverse skill levels, considering factors like the learning environment, individual differences, and coaching styles for optimized skill development.

Аналіз методології оцінки економічних показників відновлення авіаційної техніки

The article delves into the complexities of evaluating the economic aspects of maintaining and restoring aviation equipment, highlighting the challenges associated with assessing the quality of these processes. It critically examines the use of both singular and multiple performance metrics, emphasizing the need for a comprehensive evaluation system that considers various industry-specific factors. Drawing from methodologies utilized in the chemical and shipbuilding sectors, the study underscores the significance of incorporating diverse parameters, including equipment com-plexity, labor costs, and technical infrastructure, in the evaluation framework. The article investi-gates the methodologies for assessing the quality of technical maintenance and restoration of avia-tion equipment, focusing on the complexities associated with evaluating economic indicators. It dis-cusses the limitations of using singular versus multiple performance metrics in assessing the effec-tiveness of maintenance processes. Furthermore, the article emphasizes the significance of not only quantifying the costs but also analyzing the economic outcomes in relation to the operational efficiency of the aviation fleet. It un-derlines the necessity of establishing a direct correlation between the incurred expenses and the re-sultant operational status of the aviation equipment. In light of the research findings, the article ad-vocates for the implementation of a holistic approach that integrates both quantitative expenditure analysis and qualitative assessments of the fleet's functional state. The study concludes by proposing a sequential research framework for investigating the eco-nomic dimensions of aviation equipment maintenance, emphasizing the interconnected nature of maintenance quality, operational status, and equipment utilization efficiency. It calls for further ex-ploration of these interconnected factors to develop a more nuanced understanding of the economic dynamics within the realm of aviation equipment maintenance.

Evaluating Imputation Methods for rainfall data under high variability in Johor River Basin, Malaysia

Missing values in rainfall records might result in erroneous predictions and inefficient management practices with significant economic, environmental, and social consequences. This is particularly important for rainfall datasets in Peninsular Malaysia (PM) due to the high level of missingness that can affect the inherent pattern in the highly variable time series. In this work, 21 target rainfall stations in the Johor River Basin (JRB) with daily data between 1970 and 2015 were used to examine 19 different multiple imputation methods that were carried out using the Multivariate Imputation by Chained Equations (MICE) package in R. For each station, artificial missing data were added at rates of up to 5%, 10%, 20%, and 30% for different types of missingness, namely, Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR), leaving the original missing data intact. The imputation quality was evaluated based on several statistical performance metrics, namely mean absolute error (MAE), root mean square error (RMSE), normalized root mean square error (NRMSE), Nash-Sutcliffe efficiency (NSE), modified degree of agreement (MD), coefficient of determination (R2), Kling-Gupta efficiency (KGE), and volumetric efficiency (VE), which were later ranked and aggregated by using the compromise programming index (CPI) to select the best method. The results showed that linear regression predicted values (norm.predict) consistently ranked the highest under all types and levels of missingness. For example, under MAR, MNAR, and MCAR, this method showed the lowest MAE values, ranging between 0.78 and 2.25, 0.93–2.57, and 0.87–2.43, respectively. It also consistently shows higher NSE and R2 values of 0.71–0.92, 0.6–0.92, and 0.66–0.91, and 0.77–0.92, 0.71–0.93, and 0.75–0.92 under MAR, MCAR, and MNAR, respectively. The methods of mean, rf, and cart also appear to be efficient. The incorporation of the compromise programming index (CPI) as a decision-support tool has enabled an objective assessment of the output from the multiple performance metrics for the ranking and selection of the top-performing method. During validation, the Probability Density Function (PDF) demonstrated that even with up to 30% missingness, the shape of the distribution was retained after imputation compared to the actual data. The methodology proposed in this study can help in choosing suitable imputation methods for other tropical rainfall datasets, leading to improved accuracy in rainfall estimation and prediction.

Investigating roster brick based on multi-objective optimization between daylighting, wind velocity and structural displacement

Roster brick or commonly known as breeze blocks is one of the most used building elements in tropical countries because of its functionality and versatility. Its function varies from being used as a vent in service areas to an element for architectural façade. This hollow concrete block can be used not only for aesthetic values but also for improving passive design strategies through air and daylight. However, the geometric configuration of the roster is currently still relying on the creativity and intuition of the roster’s craftsmen. Therefore, this study investigates the optimal roster geometry shape which accommodates multiple performances by using the integration of multi-objective optimization (MOO) and material fabrication methods. The use of MOO serves the purpose of finding near-optimum solutions among conflicting building performance objectives. The MOO result is 57 generations with 90 genes identified in the last generation. The first near-optimum design options are capable of obtaining daylighting as much as 87% with UDI metric, flowing wind velocity up to 1.98 m/s, and experiencing structural displacement until 0.36 mm. This study provides a valuable contribution to form-finding a high-performance building element, in this regard roster brick.

A comparison of machine learning based‐composite cognitive test scores to track cognitive decline in early stages of Alzheimer’s disease dementia: ADOPIC study

AbstractBackgroundClinical trials of early Alzheimer’s disease (AD) dementia use cognition as a primary outcome and therefore cognitive measures that accurately reflect disease progression and represent multiple cognitive domains are required. Current cognitive endpoints are computed by averaging standardized change from baseline scores (i.e., preclinical Alzheimer cognitive composite (PACC)). Here we compare the PACC to composite scores for which the combination of multiple performance scores has been optimized using machine learning (ML) algorithms in a large, harmonised data set, namely ADOPIC.MethodsA dataset harmonised across various cognitive scores (Table 1) was used to construct composite scores using ML‐based algorithms: a manifold learning dimension reduction technique (UMAP), principal component analysis (PCA) and Latent variable analysis (LVA). Data from ADNI (n = 1470), AIBL (n = 1105) and OASIS participants (n = 412, Table 2) with ≥3 assessments ≤5 years before clinical progression/last visit were included. Participants were classified clinically as; stable cognitively unimpaired (CU), CU progressing to mild cognitive impairment (MCI) or dementia, stable MCI, MCI progressing to dementia, or AD dementia. For UMAP, all stable participants (including dementia) were used in 4‐fold cross‐validation training sets. However, for testing UMAP and unsupervised models (PCA and LVA) all clinical groups (except for dementia) were used. The validity of ML‐based composites was challenged by modelling mean (SD) change over time in the non‐demented clinical groups, using linear mixed model (LMM) analysis. Signal‐to‐noise ratios (SNRs) were calculated (mean change/SD change) for each composite. The mean change was measured in progressives relative to stable participants of each group.ResultsEach ML‐based cognitive composite showed sensitivity to cognitive decline in the progressor groups (Figure 1 A). For the MCI progressors, PCA and UMAP composites had significantly higher SNRs than PACC (P<0.01; Figure 1B), however, LVA performance was not significantly better than PACC. For the CU progressor group, SNRs for PCA and LVA did not show significant differences with PACC and UMAP performed significantly worse than PACC (P<0.01).ConclusionML‐based cognitive composite score computed using PCA provides a practical solution to track cognitive decline with improved performance in tracking cognitive decline in MCI progressors compared to PACC, while being comparable in CUs.

Application study for the high CO2 tolerant Chlorella strain by flue gas culture: Evaluation of growth performance and adaptive mechanisms

The application of microalgae for CO2 biosequestration of flue gas emissions from coal-fired power plants has attracted widespread attention. In this study, one high CO2 tolerant Chlorella strain (LAMB 31) screened by previous experiments was cultured with flue gas of a power plant, whose multiple growth performance and the adaptive mechanisms under the flue gas were investigated. The results indicated LAMB 31 grew well under the flue gas with a specific growth rate of 0.15/d and a maximum carbon fixation rate of 0.24 g/L/d. Correspondingly, LAMB 31 increased intracellular chlorophyll and fatty acid content (especially unsaturated fatty acids), while relatively low intracellular arsenic and mercury were accumulated, showing advantages of LAMB 31 for further food/feed and biodiesel production. Transcriptional results showed that enhancing carbon turnover and storage processes were the major mechanism for LAMB 31 adapting flue gas culture. Furthermore, inhibited uptake and enhanced detoxification of heavy mental were also found, verifying that LAMB 31 could effectively control the intracellular heavy mental accumulations. This study comprehensively evaluated the growth performance, elucidated possible adaptive strategies of Chlorella sp. responding to the flue gas, and provided further directions for the subsequent applications of LAMB 31 biomass.

Multiple corporate and functional performance feedback and problemistic search

AbstractIn this study, we examine how decision makers assess and respond to more than two performance feedback regarding goals across different hierarchical levels. Building on Cyert and March's seminal work and later research on pattern perception, we argue that pattern simplicity and pattern predictability of multiple (functional) feedback help decision makers to better guide problemistic search and generate more plausible solutions to fix a serious corporate performance shortfall. In a longitudinal sample of 97 global incumbent pharmaceutical firms, we find that a simpler and more predictable set of functional performance signals strengthens the relationship between poor performance on a corporate goal (i.e., ROA) and the number of functional solutions (i.e., number of new product development projects) that a firm initiates. We discuss the implications of our findings to the performance feedback and managerial search literature.

Multi-objective optimizations of solar receiver based on deep learning strategy in different application scenarios

Efficient and secure operation of solar receivers is key to the development of concentrated solar power (CSP). Its precise and quick optimization is essential for receiver to achieve optimum thermal performance and safety across diverse application scenarios. In this study, we propose a hybrid model based on numerical simulation and deep learning to achieve this target. The hybrid model accounting for conduction, convection and radiation shows a small relative error (<2%) in over 96 % cases. We apply the model to two different application scenarios considering multiple performance objectives simultaneously and demonstrate that it can identify optimal structure parameters and heliostat scale for each inlet flow rate of the receiver. In combination with gas turbines, maximum efficiencies of 48.9 %, 53.2 %, and 56.8 % are achieved for flow rates of 0.5, 0.75, and 1 kg/s, respectively. When integrated into heating systems, the receiver achieves a maximum thermal efficiency of 89.8 % with a heliostat field scale of 5.76 MW. Furthermore, for the operation optimization, the proposed aiming strategy results in a 76 ℃ reduction in maximum tube wall temperature compared to the conventional aiming strategy. Our approach not only provides an advanced solution for rapid cavity receiver design and operation optimization but also has the potential for extension to other structures.

Design optimization of hybrid isolation bearing for base-isolated buildings catering to multi-level performance fortification criteria

The adaptive seismic isolation system has attracted significant attention from researchers due to its multi-stage behavior and which can be used for multi-staged performance-based design. This paper introduces the configuration and mechanical behavior of a novel hybrid isolation bearing (HIB) combined with sliding bearings and rubber springs. The proposed isolator has three levels of stiffness and can achieve decoupling of horizontal and vertical performance. From the static test conducted on HIB500_R200, it is evident that the HIB displays a three-stage stiffness characteristic. The experimental stiffness values match well with the theoretical values, with a maximal error of 7.18%. Subsequently, an optimal design function for HIB-based isolation structures aiming to satisfy multiple performance objectives is established based on isolation effectiveness and displacement, utilizing the mode-superposition response spectrum and equivalent linear method. The optimization function can effectively and swiftly provide the optimal friction coefficient and the diameter of the rubber spring. A Performing elastoplastic analysis of HIB-based structures adopted for optimization function and traditional design method is performed using Seismostruct software. The numerical analysis reveals that the maximum error between the seismic isolation coefficient and the ratio of shear displacement amplitude values, as well as their theoretical predictions, is only 3.4%. And it also has higher safety redundancy than the traditionally designed isolation structures under mega earthquakes.

Design of an Integral Fuzzy Logic Controller for a Variable-Speed Wind Turbine Model

The demand for electricity is continuously growing around the world and thus the need for renewable and long-lasting sources of energy has become an essential challenge. Wind turbines are considered one of the major sources of renewable electricity generation. Therefore, there is a crucial demand for wind turbine model and control systems that are capable of precisely simulating the actual wind power systems. In this paper, an advanced fuzzy logic controller is proposed to control the speed of a wind turbine system. Initially, aero dynamical, mechanical and electrical models of two mass wind turbines models are derived. Analytical calculation of the power coefficient is adopted through a nonlinear function of six coefficients that mainly depends on pitch angle and tip speed ratio. The ultimate power output from the turbine can reach up to 50 % which is achieved at zero pitch angle with an approximately tip speed ratio of eight. This is then followed by designing a hybrid fuzzy-plus I pitch controller to regulate the speed of the wind turbine shaft. In general, fuzzy logic control strategy have the advantages over traditional control techniques especially when the system is highly non-linear and has to deal with strong disturbances such as wind turbulence. To evaluate the reliability and robustness of the controller, the response of the wind turbine system is tested under several types of disturbances including wind fluctuation, sudden disturbances on high and low speed shafts. Simulation findings reveals that the performance of fuzzy-integral control technique outweighs that of conventional fuzzy approach in terms of multiple performance evaluation indexes such as zero overshoot and steady state error, rise time and a settling time of (32.9 s) (44.7 s) respectively. The reliability and robustness of the controller is tested by applying speed and torque disturbances of 25% of their maximum ranges. Results have revealed that the controller was able to reject all disturbances efficiently with a change in pitch angle up to a maximum of 10 degrees in order to retain a constant rotor speed at 1000 rpm.

Algorithm selection using edge ML and case-based reasoning

In practical data mining, a wide range of classification algorithms is employed for prediction tasks. However, selecting the best algorithm poses a challenging task for machine learning practitioners and experts, primarily due to the inherent variability in the characteristics of classification problems, referred to as datasets, and the unpredictable performance of these algorithms. Dataset characteristics are quantified in terms of meta-features, while classifier performance is evaluated using various performance metrics. The assessment of classifiers through empirical methods across multiple classification datasets, while considering multiple performance metrics, presents a computationally expensive and time-consuming obstacle in the pursuit of selecting the optimal algorithm. Furthermore, the scarcity of sufficient training data, denoted by dimensions representing the number of datasets and the feature space described by meta-feature perspectives, adds further complexity to the process of algorithm selection using classical machine learning methods. This research paper presents an integrated framework called eML-CBR that combines edge edge-ML and case-based reasoning methodologies to accurately address the algorithm selection problem. It adapts a multi-level, multi-view case-based reasoning methodology, considering data from diverse feature dimensions and the algorithms from multiple performance aspects, that distributes computations to both cloud edges and centralized nodes. On the edge, the first-level reasoning employs machine learning methods to recommend a family of classification algorithms, while at the second level, it recommends a list of the top-k algorithms within that family. This list is further refined by an algorithm conflict resolver module. The eML-CBR framework offers a suite of contributions, including integrated algorithm selection, multi-view meta-feature extraction, innovative performance criteria, improved algorithm recommendation, data scarcity mitigation through incremental learning, and an open-source CBR module, reshaping research paradigms. The CBR module, trained on 100 datasets and tested with 52 datasets using 9 decision tree algorithms, achieved an accuracy of 94% for correct classifier recommendations within the top k=3 algorithms, making it highly suitable for practical classification applications.

Noninvasive Deep Brain Stimulation via Temporal Interference Electric Fields Enhanced Motor Performance of Mice and Its Neuroplasticity Mechanisms.

A noninvasive deep brain stimulation via temporal interference (TI) electric fields is a novel neuromodulation technology, but few advances about TI stimulation effectiveness and mechanisms have been reported. One hundred twenty-six mice were selected for the experiment by power analysis. In the present study, TI stimulation was proved to stimulate noninvasively primary motor cortex (M1) of mice, and 7-day TI stimulation with an envelope frequency of 20 Hz (∆f =20 Hz), instead of an envelope frequency of 10 Hz (∆f =10 Hz), could obviously improve mice motor performance. The mechanism of action may be related to enhancing the strength of synaptic connections, improving synaptic transmission efficiency, increasing dendritic spine density, promoting neurotransmitter release, and increasing the expression and activity of synapse-related proteins, such as brain-derived neurotrophic factor (BDNF), postsynaptic density protein-95 (PSD-95), and glutamate receptor protein. Furthermore, the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway and its upstream BDNF play an important role in the enhancement of locomotor performance in mice by TI stimulation. To our knowledge, it is the first report about TI stimulation promoting multiple motor performances and describing its mechanisms. TI stimulation might serve as a novel promising approach to enhance motor performance and treat dysfunction in deep brain regions.

A GRA-based approach for optimal selection of SALBP-1 assembly line balancing heuristics

Assembly line balancing is a compulsory approach which enables mass production of similar parts with proper efficacy. Multiple heuristics are proposed in literature to solve single model assembly line balancing problems (SALBP-1). Previous work has focused on developing and evaluating assembly line balancing heuristics and apply them to different fields. Other work investigates the factors affecting the performance of particular assembly line balancing heuristic. However, there is still a need for a guideline for selecting the most suitable heuristic for a particular application with insights into multiple performance measures. This research presents a grey relational analysis (GRA)-based approach to aid process designers in selecting the heuristic generating that yields the best line balancing. Specifically, combinations of factors of cycle time, order strength, number of tasks, and time variability are considered as operational setups of the line balancing process. The solution of each setup is solved by each heuristic, and the performance of the solution is evaluated. Then, GRA is applied to rank the heuristics on basis of the resulting grey rational grades. The proposed approach provides useful insights into the combinations suitable for a particular heuristic. In this study, time variability and cycle time are shown to have the greatest impact on the overall performance of the considered heuristics.

Study of stable sodium ion storage in porous carbon derived from puffball biomass

The pursuit of affordable anode materials for sodium-ion batteries (SIBs) has become imperative in light of the rising energy demand in modern society. Hard carbon (HC), a highly promising candidate for SIBs, has garnered interest among researchers. In this study, we present a novel approach involving the thermal treatment of low-cost puffball (PB) at various temperatures to produce cost-effective porous carbon, which holds great potential as an anode material for SIBs. The carbon derived from the treated puffball biomass showcases distinctive morphological features, including carbon tubes and plentiful balloon-like porous structures. This morphological structure can prevent the agglomeration and stacking of carbon materials, which can effectively increase the active sites and energy storage channels. The resulting puffball biomass-derived carbon exhibits a reversible capacity of 205.05 mAh g−1 at 100 mA g−1. Even after 600 cycles at a current density of 2 A g−1, the discharge capacity remains as high as 146.98 mAh g−1. These findings emphasize the remarkable performance and cycling stability of the carbon material. Significantly, the puffball biomass-derived carbon exhibited an impressive initial Coulomb efficiency of 57.6%, which is a noteworthy achievement. Moreover, we employed in situ XRD to investigate its energy storage mechanism in sodium-ion batteries. When integrated into a complete cell with Na3V2(PO4)3 (NVP) and bulking materials, the carbon anode exhibited impressive multiplicative performance and maintained good cycling stability. These findings not only provide new insights into the design of carbon anodes for sodium-ion batteries but also emphasize their exceptional attributes such as excellent initial coulombic efficiency, impressive rate capability, and cost-effectiveness for sodium storage.

Effect of Item Order on the Reliability of Mathematics Test among Secondary School Students in Rivers State

The study investigated the effect of item order on the reliability of mathematics test among secondary school students in Rivers State. One research question and one null hypothesis guided the study. Quasi – experimental research design was used and the study was carried out in nine public senior secondary schools in three local government areas in River State which was drawn using Simple Random Sampling Technique. A sample size of 507 students were drawn from the population of 904 through Stratified Random Sampling Technique and the application of Taro Yemen formula to determine the minimum sample size. The instrument used was Multiple Choice Format Mathematics Performance Test (MCFMPT) arranged in ascending, descending and inconsistent order of difficulty and students’ scores were used for data collection. The reliability of the instrument was 0.75 using split-half method of establishing reliability and Spearman Brown prophecy formula for internal consistency of the instrument. Mean and Standard deviation were used to answer research question while t-test and Analysis of variance (ANOVA) were used to test the null hypotheses at 0.05 alpha level. The results revealed that item order based of ascending, descending and inconsistent order do not have significant effect on the reliability of mathematics test among secondary school students. Based on the findings, it was recommended that all pattern of item order (ascending, descending and inconsistent order of difficulty) should be encouraged among secondary school students as it has no significant effect on reliability coefficient. Students should be motivated to improve their real abilities in mathematics related courses and not judging their underperformance in mathematics based on the arrangement of items.

Design and Multiple Performance Evaluation of Green Sustainable Process for Azeotropes Separation via Extractive Distillation

Design and Multiple Performance Evaluation of Green Sustainable Process for Azeotropes Separation via Extractive Distillation

Choice for smart investment in mutual funds: Single- or multi-period performance ranks

This study presents strong evidence of fund performance persistence using a newly proposed multi-period performance rank (MR). The MR is the average of the performance ranks, calculated using multiple performance evaluation periods. With regard to 60-month returns to the performance rank, the performance persists over the next three years. This study explains performance persistence as a combination of fund investors’ behavior and fund managers’ abilities. The MR-based portfolios have low outflows and high turnover rates. MR-based investments are a smart choice for obtaining higher future returns.

"In the Studio" with William Kentridge

Abstract&amp;nbsp;Abstract&amp;nbsp;This interview attempts to explore both the individual and collective creative processes of the prolific and world-renowned multiple media visual and performance artist, William Kentridge. It takes place in Paris where the artist has under 30 minutes to engage in a dialogue before the very first performance of his Chamber opera, Sybil, in a theatre new to the troupe. The conversation takes place backstage in the green room, but the dialogue is engaged in a complicit and playful manner, drawing on common backgrounds and histories to cut corners and spare formalities.The result is a new angle on what “Studioness” has become for the artist, although he had never really considered it that way. Exploring the inward-looking and outward-reaching movements within studio practice, the author seeks to enlarge the notion of studio to include remote and virtual practices in the reflection. The impact of, and response to, the COVID-19 lockdown period is central to the discussion.

Introduction of ultra-hypofractionation in breast cancer: Implications for costs and resource use

PurposeA shift towards (ultra-)hypofractionated breast irradiation can have important implications for the practice of contemporary radiation oncology. This paper presents a systematic analysis of the impact of different fractionation schedules on multiple key performance indicators, namely resource use, costs, work times, throughput and waiting times. Materials and MethodsTime-driven activity-based costing (TD-ABC) is applied to calculate the costs and resources consumed where the perspective of the radiotherapy department in adopted. Three fractionation regimens are considered: ultra-hypofractionation (5 x 5.2 Gy, UHF), moderate hypofractionation (15 x 2.67 Gy, HF) and conventional fractionation (25 x 2 Gy, CF). Subsequently, a discrete event simulation (DES) model of the radiotherapy care pathway is developed and scenarios are compared in which the following factors are varied: distribution of fractionation regimens, patient volume and operating hours. ResultsThe application of (U)HF can permit radiotherapy departments to reduce the use of scarce resources, realise work time and cost savings, increase throughput and reduce waiting times. The financial advantages of (U)HF are, however, reduced in cases of excess capacity and cost savings may therefore be limited in the short-term. Moreover, although an extension of operating hours has favourable effects on throughput and waiting times, it may also reduce cost differences between fractionation schedules by increasing the capacity of resources. ConclusionBy providing an in-depth analysis of the consequences associated with a shift towards (U)HF in breast cancer, the present study demonstrates how a DES model based on TD-ABC costing can assist radiotherapy professionals in making data-driven decisions.

Reshaping concrete

Less Economically Developed Countries (LEDCs) struggle to meet the demand for affordable housing in their growing cities. There are several reasons for this, but a major constraint is the high cost of construction materials. In LEDCs, material costs can constitute 60 to 80 percent of the total cost of residential construction. Nonetheless, their construction mimics the materially inefficient practices of the More Economically Developed Countries (MEDCs), which were developed to reduce labor over material costs. As a result, prismatic beams and flat slabs are often used despite their structural inefficiency. The mounting use of steel-reinforced concrete structures in LEDC cities also raises concern for the environmental costs of construction; construction accounts for 20-30 percent of LEDC carbon emissions. This research addresses these challenges with a flexible and accessible methodology for the design and analysis of materially efficient concrete elements that may reduce the economic and environmental costs of urban construction. Designed for the constraints of LEDCs, structural elements are optimized to reduce the embodied carbon associated with the concrete and reinforcing steel while resisting the required loads of a standard building structure. The optimization method includes a novel approach to 3D-shape parameterization, as well as a decoupled analytical engineering analysis method that accounts for the key failure modes and constraints of reinforced concrete design. This method is then built into an open-source toolkit, combined with machine learning for real-time analysis and visualization, and tested using lab- and full-scale prototypes. The goal of this research is to present several generalizable methods that are applicable and accessible to LEDC building designers. These methods can enable the design of concrete elements for multiple performance criteria such as structural behavior, acoustic transmission, and thermal mass. They can also enable an accessible design practice through machine learning, real-time iterative workflows, and visualization tools that include the end-user in the architectural design process. This paper provides a high-level overview of ongoing research that explores how materially efficient design methods might enable sustainable development through low-cost, low-carbon concrete structural systems for affordable housing in LEDCs.