Empowering women : the effect of women's decision-making power on reproductive health services uptake -- evidence from Pakistan

A large body of research has attempted to explore the links between women's autonomy and their uptake of reproductive health services in the South Asia region, but the evidence so far is inconclusive. This study uses the Pakistan Social and Living Standards Measurement Survey to examine the influence of household decision making on women's uptake of reproductive health services. The analysis finds that women's decision-making power has a significant positive correlation with reproductive health services uptake and that influential males'decision-making power has the opposite effect, after controlling for socio-economic indicators and supply-side conditions. The findings suggest that empowering women and increasing their ability to make decisions may increase their uptake of reproductive health services. They also suggest that policies directed toward improving women's utilization of maternity services must target men as well as women in Pakistan.Health Monitoring&Evaluation,Population Policies,Adolescent Health,Gender and Health,Health Systems Development&Reform

Budget Feasible Mechanism Design: From Prior-Free to Bayesian

Budget feasible mechanism design studies procurement combinatorial auctions where the sellers have private costs to produce items, and the buyer(auctioneer) aims to maximize a social valuation function on subsets of items, under the budget constraint on the total payment. One of the most important questions in the field is "which valuation domains admit truthful budget feasible mechanisms with `small' approximations (compared to the social optimum)?" Singer showed that additive and submodular functions have such constant approximations. Recently, Dobzinski, Papadimitriou, and Singer gave an O(log^2 n)-approximation mechanism for subadditive functions; they also remarked that: "A fundamental question is whether, regardless of computational constraints, a constant-factor budget feasible mechanism exists for subadditive functions." We address this question from two viewpoints: prior-free worst case analysis and Bayesian analysis. For the prior-free framework, we use an LP that describes the fractional cover of the valuation function; it is also connected to the concept of approximate core in cooperative game theory. We provide an O(I)-approximation mechanism for subadditive functions, via the worst case integrality gap I of LP. This implies an O(log n)-approximation for subadditive valuations, O(1)-approximation for XOS valuations, and for valuations with a constant I. XOS valuations are an important class of functions that lie between submodular and subadditive classes. We give another polynomial time O(log n/loglog n) sub-logarithmic approximation mechanism for subadditive valuations. For the Bayesian framework, we provide a constant approximation mechanism for all subadditive functions, using the above prior-free mechanism for XOS valuations as a subroutine. Our mechanism allows correlations in the distribution of private information and is universally truthful.Comment: to appear in STOC 201

Birth Weight as a Risk Factor for Breast Cancer: a Meta-Analysis of 18 Epidemiologic Studies

Background: Birth weight has been identified as a birth-related factor associated with the risk of breast cancer. However, the evidence is inconsistent. Methods: To investigate the association between birth weight and breast cancer, we conducted a meta-analysis of published studies between 1996 and 2008. Eighteen studies encompassing 16,424 breast cancer cases were included in the meta-analysis. Data were combined using a fixed-effect or random-effect model depending on the heterogeneity across studies. Results: Women with their own birth weight \u3e4000 g or 8.5 lb had a higher risk for developing breast cancer than those with birth weight(OR¼1.20, 95% CI 1.08, 1.34). Findings were also consistent with a dose-response pattern effect. The summary effect estimate for breast cancer risk per 1 kg increase in birth weight was statistically significant (random effects OR¼1.07, 95% CI 1.02, 1.12). Conclusions: Although these results provided no evidence indicating whether birth weight is more strongly related to early-onset than to later-onset breast cancer, our findings suggest an association between birth weight and breast cancer. The underlying biological mechanism relating to this phenomenon needs additional study

Estimating global cropland production from 1961 to 2010

Global cropland net primary production (NPP) has tripled over the last 50 years, contributing 17–45 % to the increase in global atmospheric CO2 seasonal amplitude. Although many regional-scale comparisons have been made between statistical data and modeling results, long-term national comparisons across global croplands are scarce due to the lack of detailed spatiotemporal management data. Here, we conducted a simulation study of global cropland NPP from 1961 to 2010 using a process-based model called Vegetation–Global Atmosphere–Soil (VEGAS) and compared the results with Food and Agriculture Organization of the United Nations (FAO) statistical data on both continental and country scales. According to the FAO data, the global cropland NPP was 1.3, 1.8, 2.2, 2.6, 3.0, and 3.6 PgC yr−1 in the 1960s, 1970s, 1980s, 1990s, 2000s, and 2010s, respectively. The VEGAS model captured these major trends on global and continental scales. The NPP increased most notably in the US Midwest, western Europe, and the North China Plain and increased modestly in Africa and Oceania. However, significant biases remained in some regions such as Africa and Oceania, especially in temporal evolution. This finding is not surprising as VEGAS is the first global carbon cycle model with full parameterization representing the Green Revolution. To improve model performance for different major regions, we modified the default values of management intensity associated with the agricultural Green Revolution differences across various regions to better match the FAO statistical data at the continental level and for selected countries. Across all the selected countries, the updated results reduced the RMSE from 19.0 to 10.5 TgC yr−1 (∼  45 % decrease). The results suggest that these regional differences in model parameterization are due to differences in socioeconomic development. To better explain the past changes and predict the future trends, it is important to calibrate key parameters on regional scales and develop data sets for land management history

Calcium-Antimony Alloys as Electrodes for Liquid Metal Batteries

The performance of a calcium-antimony (Ca-Sb) alloy serving as the positive electrode in a Ca∥Sb liquid metal battery was investigated in an electrochemical cell, Ca(in Bi) | LiCl-NaCl-CaCl[subscript 2] | Ca(in Sb). The equilibrium potential of the Ca-Sb electrode was found to lie on the interval, 1.2–0.95 V versus Ca, in good agreement with electromotive force (emf) measurements in the literature. During both alloying and dealloying of Ca at the Sb electrode, the charge transfer and mass transport at the interface are facile enough that the electrode potential varies linearly from 0.95 to 0.75 V vs Ca(s) as current density varies from 50 to 500 mA cm[superscript −2]. The discharge capacity of the Ca∥Sb cells increases as the operating temperature increases due to the higher solubility and diffusivity of Ca in Sb. The cell was successfully cycled with high coulombic efficiency (∼100%) and small fade rate (<0.01% cycle[superscript −1]). These data combined with the favorable costs of these metals and salts make the Ca∥Sb liquid metal battery attractive for grid-scale energy storage.United States. Advanced Research Projects Agency-Energy (Award DE-AR0000047)TOTAL (Firm)Marubun Research Promotion FoundationMurata Overseas Scholarship Foundatio

AI-Driven Patient Monitoring with Multi-Agent Deep Reinforcement Learning

Effective patient monitoring is vital for timely interventions and improved healthcare outcomes. Traditional monitoring systems often struggle to handle complex, dynamic environments with fluctuating vital signs, leading to delays in identifying critical conditions. To address this challenge, we propose a novel AI-driven patient monitoring framework using multi-agent deep reinforcement learning (DRL). Our approach deploys multiple learning agents, each dedicated to monitoring a specific physiological feature, such as heart rate, respiration, and temperature. These agents interact with a generic healthcare monitoring environment, learn the patients' behavior patterns, and make informed decisions to alert the corresponding Medical Emergency Teams (METs) based on the level of emergency estimated. In this study, we evaluate the performance of the proposed multi-agent DRL framework using real-world physiological and motion data from two datasets: PPG-DaLiA and WESAD. We compare the results with several baseline models, including Q-Learning, PPO, Actor-Critic, Double DQN, and DDPG, as well as monitoring frameworks like WISEML and CA-MAQL. Our experiments demonstrate that the proposed DRL approach outperforms all other baseline models, achieving more accurate monitoring of patient's vital signs. Furthermore, we conduct hyperparameter optimization to fine-tune the learning process of each agent. By optimizing hyperparameters, we enhance the learning rate and discount factor, thereby improving the agents' overall performance in monitoring patient health status. Our AI-driven patient monitoring system offers several advantages over traditional methods, including the ability to handle complex and uncertain environments, adapt to varying patient conditions, and make real-time decisions without external supervision.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. arXiv admin note: text overlap with arXiv:2309.1057

Prioritization of Cognitive Assessments in Alzheimer's Disease via Learning to Rank using Brain Morphometric Data

We propose an innovative machine learning paradigm enabling precision medicine for prioritizing cognitive assessments according to their relevance to Alzheimer’s disease at the individual patient level. The paradigm tailors the cognitive biomarker discovery and cognitive assessment selection process to the brain morphometric characteristics of each individual patient. We implement this paradigm using a newly developed learning-to-rank method PLTR. Our empirical study on the ADNI data yields promising results to identify and prioritize individual-specific cognitive biomarkers as well as cognitive assessment tasks based on the individual’s structural MRI data. The resulting top ranked cognitive biomarkers and assessment tasks have the potential to aid personalized diagnosis and disease subtyping