Rapid urban growth in tropical megacities is putting serious pressure on critical ecosystem services, thereby complicating the implementation of sustainable urban planning frameworks. To better understand and address these challenges, we used artificial intelligence (AI) and satellite data to map and predict land-use changes in Chittagong City Corporation (CCC), Bangladesh, from 2000 to 2048. We combined Random Forest (RF) classification with a Cellular Automata–Artificial Neural Network (CA–ANN) model. The Random Forest method classified four land-cover types: vegetation, built-up areas, barren land, and open water bodies, with over 97
Here, cryogels of cellulose derivatives, carboxymethylcellulose (CMC) with varying degrees of substitution (DS) of 0.7, 0.9, and 1.2 denoted as CMC0.7, CMC0.9, and CMC1.2, and hydroxypropyl methylcellulose (HPMC) were prepared at cryogenic conditions, − 20 °C. The cryo-crosslinking reaction using divinylsulfone (DVS) as crosslinking at 100 mol
Purpose This study examines how medical quality indicators and transportation/logistics conditions jointly affect the probability of deceased-donor kidney discard. While previous research has focused on clinical factors, this research quantifies the added impact of distance, airport accessibility and temporal coordination on discard decisions. Design/methodology/approach We analyzed a retrospective panel dataset of deceased-donor kidneys from UNOS Region 4 (2001–2021) using binary logistic regression and pooled logit models with year fixed effects and time-varying interactions. Medical variables, including kidney donor profile index (KDPI) and cold ischemia time, were combined with logistical variables, including donor-to-transplant center distance, airport category, time of day and day of week. We reported results with log-odds coefficients and average marginal effects for better managerial interpretability. Robustness checks addressed multicollinearity and extreme-value sensitivity. Findings Higher KDPI and longer cold ischemia time significantly increase the probability of organ discard. Key logistics variables include donor-to-transplant distance and airport accessibility; organs from non-hub and small-hub airports have higher discard rates than those from large-hub airports, even after accounting for medical risk. Temporal controls reflect system-level shifts over the years, whereas time-of-day and weekday effects remain weak. Interaction analyses reveal that logistics burdens disproportionately impact marginal-quality organs. Originality/value The study conducts an integrated, multidisciplinary examination of clinical risk and transportation infrastructure for kidney discard within a single empirical framework. By translating coefficients into probability-scale AMEs, the research provides actionable insights for allocation design, transport planning and infrastructure prioritization. Findings extend healthcare supply-chain theory to a high-stakes, perishable-asset context, demonstrating that accessibility and coordination constraints significantly affect medical utilization outcomes.
Students often start a course with high expectations and an ambitious plan of action. Some instructors use goal-inducing, non-binding commitment pledges to nudge students to follow through on their intended course of action. Using a field experiment, we asked treated students to set a goal grade, identify the actions they would take to achieve it, and sign a commitment pledge to work toward this grade. We find that treated students pledged a greater time commitment and targeted a higher grade, but their overall test scores decreased by 0.23 standard deviations, and they were 15 percentage points less likely to pass the course.
Mangrove habitats in Myanmar’s Irrawaddy Delta are increasingly exposed to climatic variability and intensifying land-use pressure, yet long-term vegetation greenness dynamics at the delta scale remain insufficiently characterized. In this study, we have integrated Moderate Resolution Imaging Spectroradiometer–derived Normalized Difference Vegetation Index (NDVI) with Land-Use and Land-Cover (LULC) datasets to quantify three decades (1994–2024) of vegetation change and its environmental drivers. LULC maps were generated using a Random Forest classifier (overall accuracy: 88
