Accurate prediction of ultra-high-performance concrete (UHPC) compressive strength is essential for optimizing mixture design and reducing experimental iterations. Existing machine learning approaches suffer from limited algorithm diversity, insufficient statistical validation, and inadequate uncertainty quantification. This study presents a comprehensive framework through systematic evaluation of 20 algorithms across seven categories on 863 experimental observations. Six physically meaningful composite features (such as water-cement ratio, total binder content, and fiber aspect ratio) are engineered to capture intrinsic material relationships, with the Boruta algorithm employed for feature selection. Statistical robustness is ensured through 30 repeated experiments analyzed using both frequentist (p-value, effect size, 95% CI) and Bayesian approaches. CatBoost achieves optimal performance (R² = 0.8979 ± 0.0239, RMSE = 10.58 ± 1.45 MPa), with curing age, sand content, and steel fiber volume identified as dominant predictors through multi-perspective interpretability analysis integrating SHAP, ALE, permutation importance, and LIME. External validation on 810 independent samples yields R² = 0.5923 (RMSE = 25.68 MPa) under significant cross-dataset conditions, with performance reduction attributed to feature availability differences and distribution shift. Comprehensive uncertainty quantification yields prediction uncertainty of 3.48%, substantially below previously reported thresholds. The proposed framework offers practitioners a reliable tool for UHPC mixture screening while maintaining prediction confidence for structural engineering applications.

The global optimization-based color correction approach aims to minimize the color differences of multiple images by optimizing the correction model for each image. The color differences in multisource and multitemporal remote sensing images are difficult to express using a simple correction model with few parameters. When employing a more flexible correction model, the number of correction parameters and optimization equations grows rapidly with the increase in the number and resolution of input images. In addition, the correction parameters of all images are coupled together and need to be solved simultaneously. An excessive number of parameters results in solving slowly or potential failure. To solve this problem, we propose a parallelizable color correction approach that decouples the correlation of correction parameters in the optimization equations and optimizes each image separately. First, we introduce auxiliary variables that replace values related to other images in the cost function. Second, we construct optimization equations for each image and parallelly solve the correction parameters. Finally, we correct the input images through a weighted correction model to better eliminate correction artifacts. Our approach iteratively optimizes auxiliary variables and correction parameters until the correction results converge. The experimental results on several challenging datasets show that our approach significantly improves execution efficiency and obtains the global optimal solution using the flexible correction model.

Compact malaria detectors for microcontrollers are almost always benchmarked on the NIH Malaria dataset with a per-cell random split. This leaks slide identity because the cells come from only about 200 slides and a random split mixes same-slide cells across training and testing. The leakage also distorts architectural conclusions: under a leakage-free slide-disjoint protocol, per-module ablation gains collapse to seed noise and an apparent cross-site robustness variant loses most of its advantage. Headline accuracy falls from 97.1% to 95.6%, a gap that sits within the cross-seed noise, and all eight tested architectures move the same way. The evidence is this unanimous direction, not the size of any single gap. This benchmarking finding is our main contribution. Two results survive. First, MalariaNet, our 21 K-parameter detector, reaches about 95.6% accuracy at 23.5 KB of INT8 weights, with a numerically faithful on-chip forward on an STM32H743 at a 1.2 FPS triage rate. Second, it is among the most interference-robust of the eight networks and the most robust microcontroller-deployable model. Scope is limited to single P. falciparum thin-smear cells. Slide-disjoint evaluation should become standard, and we provide MalariaNet as the first leakage-free, on-device-validated point-of-care malaria reference.

Compact malaria detectors for microcontrollers are almost always benchmarked on the NIH Malaria dataset with a per-cell random split. This leaks slide identity, because the cells come from only about 200 slides and a random split mixes same-slide cells across training and test. Under a leakage-free slide-disjoint protocol, the leakage fabricates architectural conclusions. Per-module ablation gains collapse to seed noise, and an apparent cross-site robustness variant loses most of its advantage. Headline accuracy falls from 97.1% to 95.6%, a gap that sits within the cross-seed noise, and all eight tested architectures move the same way. The evidence is this unanimous direction, not the size of any single gap. This self-critical finding is our main contribution. Two results still survive. First, MalariaNet, our 21K-parameter detector, reaches about 95.6% accuracy at 23.5KB of INT8 weights, with a numerically faithful on-chip forward on an STM32H743 at a 1.2FPS triage rate. Second, it is the second most interference-robust of the eight networks and the most robust microcontroller-deployable model. Scope is limited to P. falciparum thin-smear single cells. Slide-disjoint evaluation should become standard, and we provide MalariaNet as the first leakage-free, on-device-validated point-of-care malaria reference.

A point-of-care pathogen-microscopy readout must signal when a sample falls outside what its detector was trained on. PathoSafe is an 816-byte int8-projection low-rank Mahalanobis out-of-distribution (OOD) head reading a frozen detector’s penultimate feature. It reaches a cross-site area under the receiver operating characteristic curve (AUROC) of 0.980 and multi-source AUROC of 0.964 (parasitic-egg-dominated), within 0.01 of a 16,896-byte full-precision dense baseline, and adds a measured 30.76 µs and 816 bytes of read-only memory (ROM) constants on an STM32H743 development board, so abstention adds only a small measured cost on-chip. The advantage is the trunk feature rather than the integer form. Certification is the harder problem and carries our central result: slide clustering silently breaks the standard independent-sample certificate, since distribution-free risk control assumes exchangeable samples that patch-clustered medical data do not supply. On a degraded-input risk certificate, the naive cell-level version holds on only 20% of leakage-free re-splits, whereas the correct slide-level one is valid at 0.266 with its width set by the slide count rather than by any bound we evaluate. The deployed threshold inherits this less severely. A Hoeffding–Bentkus budget gives an illustrative count of 38 independent slides under the stated assumptions (35 when five seeds are used), which is about $1.3\times$ what this benchmark provides. We release that leakage-free slide-disjoint benchmark: an in-distribution malaria task with cross-site (BBBC041) and multi-source (SIPaKMeD, parasitic egg, white blood cells) regimes.