Search Results (7,129)

Climate change and rapid urbanisation are intensifying the urban heat island effect, increasing thermal stress, degrading air quality, and leading to rising energy demand. Monitoring neighbourhood-scale heat requires Land Surface Temperature (LST) observations at fine spatial and temporal resolutions, yet satellite thermal products are limited by revisit frequency, acquisition time, and cloud cover. This study developed a novel approach integrating satellite-derived land cover characteristics with continuous contact-based temperature measurements from low-cost LoRaWAN sensors and geostatistical modelling to generate hourly LST maps at 10 m resolution. The technique provides communities with simpler, affordable methods for measuring heat islands and supporting mitigation strategies. Observations from 52 locations across Curtin University’s Bentley campus in Perth, Western Australia, were combined with land cover indices. Empirical Bayesian Kriging captured spatial and temporal urban heat patterns with a root mean square error of approximately 3 °C, representing a bias of near 1 °C. Predictions were consistent with Landsat-derived LST, revealing persistent heat retention over asphalt and cooler conditions associated with vegetation. Integrating satellite-derived predictors with ground measurements provides continuous fine-scale information to identify local heat hotspots and inform targeted mitigation. Unlike satellite data, these low-cost ground measurements could be collected with the help of urban practitioners, developers, and academic institutions. Full article

Simulating CO₂ mineral trapping in basaltic formations requires geochemical reaction networks, but including all candidate reactions is often computationally prohibitive, and no established criterion exists for selecting a reduced yet representative set. We propose a two-stage reduction framework, motivated by the insight that trapping efficiency and pH buffer capacity are orthogonal criteria: a set that maximizes one is not guaranteed to satisfy the other. Stage A combines K-means clustering, Bayesian optimization, and a genetic algorithm to select reactions maximizing a trapping-efficiency score (J-score, integrating reaction extent, saturation state, and ionic contribution). Stage B independently verifies whether the selected set preserves pH buffer capacity (B-score), using CMG-GEM flow-coupled simulations validated against laboratory experiments. This two-stage check proved necessary: a set selected by J-score alone failed to converge in flow simulation due to excessive mineral precipitation, confirming that trapping efficiency alone is insufficient and identifying Anorthite as the dominant pH-buffering phase. A minimal two-reaction set (Anorthite and Dolomite) achieved a 94% reduction in computational cost under the present single-block column model, while maintaining pH fidelity against benchmark data; extrapolation of this efficiency gain to field-scale, multi-cell simulation remains a direction for future work. This framework reframes reaction-set selection from single-objective optimization to constrained design. The results are demonstrated for a single basaltic rock sample (Basalt Sample A); the approach is potentially transferable to reducing reaction-set uncertainty in other CO₂-basalt mineralization systems, subject to lithology-specific recalibration. Full article

While Artificial Intelligence (AI) has transformed materials discovery, the primary bottleneck to technological impact remains the transition from lab-scale synthesis to robust, industrial-scale manufacturing. Most promising materials perish in this depth, which is referred as the “valley of death”. The current review consolidates and critically evaluates the emerging ecosystem of AI-driven strategies and frameworks designed specifically to bridge this “lab-to-fab” gap. The review shifts our attention from property prediction to the engineering-driven problems of manufacturability. Furthermore, the review discusses the main obstacles to scaling the production of materials, such as reproducibility, process optimization in the context of uncertainty, and techno-economic viability, as well as the AI approaches being developed to overcome them. This includes Natural Language Processing (NLP) for method extraction, graph neural networks for reaction modeling, reinforcement learning for process control, Bayesian optimization for definition of process windows, and integrated AI–Techno-Economic Analysis (TEA) frameworks. Equally importantly, the review examines the principal failure modes that constrain practical deployment, including out-of-distribution generalization, incomplete and non-transferable literature-derived data, simulator-to-plant mismatch, uncertainty miscalibration, and the continued need for expert oversight. The article concludes with a forward-looking roadmap for the future of AI in chemical and materials engineering. The proposed conclusion is defined by a paradigm shift from simply finding new materials to creating viable, economical, and scalable pathways to produce them, thereby enabling a new era of synthesis-aware materials innovation. Full article

Fine particulate matter (${\mathrm{PM}}_{2.5}$) pollution in Bogotá follows a strongly asymmetric temporal pattern: at the Puente Aranda industrial-corridor monitoring station, 98.7% of days whose daily mean exceeds the WHO 2021 guideline of 15 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$ contain at least one hourly concentration above 25 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$, with a median peak-to-mean ratio of 1.90. Conventional forecasting models, trained to minimise average error, systematically underpredict these critical episodes. To address this structural bias, we trained a Bidirectional Long Short-Term Memory (BiLSTM) network for 24 h ${\mathrm{PM}}_{2.5}$ forecasting using three complementary mechanisms: an asymmetric Huber loss that penalises underprediction $\alpha$-fold more than overprediction, sample weighting that amplifies gradients from exceedance windows, and Bayesian hyperparameter optimisation (Optuna-TPE, 100 trials). Evaluating four model configurations on hourly records from 2022 to 2025 (n = 31,961), we identify an inherent regression–detection tension: among the $\alpha$ values examined ($\alpha \in \{1.5,2.5,2.80\}$), no single value simultaneously minimised global MAE and peak MAE. Bayesian optimisation (Optuna-TPE, 100 trials) selected $\alpha =2.80$ and $w_{\mathrm{high}}=10$, achieving the nominally highest sequence-level detection quality (AP = 0.724, 95% bootstrap CI [0.582, 0.821]) and the lowest ${\mathrm{MAE}}_{\mathrm{peaks}}$ (10.64 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$), but at the cost of near-zero global regression ($R^2\approx 0$). BiLSTM-A1.5 ($\alpha =1.5$) constitutes the best overall compromise (AP = 0.718, CI [0.575, 0.819]; RMSE = 6.65 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$; $R^2=0.372$; sequence F1 = 0.674); the AP difference ($\Delta =0.006$) is within overlapping bootstrap confidence intervals. All models localise the daily peak within 6 h in at least 88% of exceedance sequences. These results support reframing early-warning system evaluation around exceedance detection rather than regression error. Full article

To accurately assess the time-dependent reliability of bridge piers in marine environments, this paper proposes a time-dependent reliability evaluation method for bridge piers based on a Dynamic Bayesian Network (DBN). By establishing a resistance degradation model under the combined effects of reinforcement corrosion and concrete deterioration and embedding it into the DBN framework, dynamic prediction and updating of pier time-dependent reliability are achieved. A case study of a twin-column pier was conducted for verification, showing that the computational results of the proposed DBN model align well with the first-order reliability method (FORM), validating its accuracy and feasibility in time-dependent reliability prediction. Further, using the most severe service condition, that is, the tidal-spray zone as an example, the DBN prediction results were updated with inspection data to achieve dynamic assessment of the actual pier lifespan. Additionally, a comparative analysis of environmental zones revealed that the tidal-spray zone exhibits the fastest reliability degradation, followed by the atmospheric zone, while the submerged zone shows the slowest. Full article

Multilingual online reviews combine ordinal ratings, asymmetric positive and negative evidence, sparse attribute occurrence, and substantial cross-country imbalance. This study presents an integrated inferential framework. Signed topic scores are converted to within-country rank intensities, and country-specific cumulative-logit models distinguish positive and negative occurrence baselines from their corresponding intensity contrasts. The two intensity contrasts are then synthesized jointly through a bivariate Bayesian normal-normal random-effects model that retains their within-country covariance. The primary analysis uses all 30,042 reviews observed in the common 2019–2024 window; the complete 85,373-review corpus and repeated country-capped samples are sensitivity analyses. Separating the two occurrence baselines improves summed AIC from 32,347.7 to 32,167.8, while a transformation-by-function comparison shows that natural splines improve AIC and quadratic-weighted agreement. The linear-rank model is retained to provide comparable scalar intensity contrasts. Targeted partial proportional-odds fits substantially improve in-sample AIC in China and Japan but leave repeated-validation performance and all nine average effect directions essentially unchanged. A secondary semantic mapping audit agrees with 30 of 31 topic assignments. Simulation results show generally adequate interval coverage but reduced Kano-state accuracy under small K, sparse occurrence, and proportional-odds violations. The findings therefore support tiered, uncertainty-aware prioritization rather than a deterministic global ranking. Full article

Persistent late-time variation can remain in physics-informed neural network (PINN) solutions after the governing transient has effectively decayed, while conventional error norms do not reveal whether this variation has a systematic temporal–frequency structure. This study develops an offline temporal–spectral diagnostic and postprocessing workflow for a one-dimensional advection–diffusion benchmark. A high-accuracy analytical reference and three fixed-resolution finite-difference baselines are used to assess a PINN whose architecture is selected by a fully supervised neural architecture search and whose parameters are trained with progressive temporal windowing. Candidate late-time intervals are selected without using the reference solution by applying the Bayesian information criterion (BIC) to a breakpoint model for the inter-reconstruction sensitivity; the selected field is subsequently reconstructed by retaining a prescribed fraction of its temporal spectral energy and is evaluated independently through reference-error and physics-consistency measures. For $[t_{\mathrm{cut}},t_{max}]=[1.8,5]$, the zero-frequency component contains $0.9999996$ of the raw-field energy, so the $q=0.95$ reconstruction retains only the temporal mean. This projection reduces the final-time spatial error norm from $2.70\times {10}^{-3}$ to $1.06\times {10}^{-3}$, a factor of approximately $2.5$, while changing the discrete governing-equation residual by less than $0.3\%$ over the filtered window. Mean-removed tests for $q=0.90,0.95,0.99$ show that the discarded fluctuation is dominated by low-frequency approximation error rather than high-frequency noise. The result supports the proposed selection–validation workflow for this controlled benchmark but does not establish a universally transferable filter. Full article

Raw mix-design variables used to predict ultra-high-performance concrete (UHPC) properties cannot fully represent internal proportions and structural compatibility. This study develops mechanism-informed proxy-core features based on particle packing, water film thickness, and rheology, and evaluates four feature systems using literature-derived datasets for compressive strength (924 samples), flexural strength (406 samples), and slump flow (192 samples). Eight regression models were compared using five-fold cross-validation, Bayesian optimization, SHAP, and ablation analysis. Proxy-W achieved an R² of 0.918 for compressive strength, only 0.005 higher than baseline-W on the original split. For flexural strength, baseline-WB achieved the highest R² (0.906), whereas proxy-W yielded the lowest MAE (2.140 MPa). The largest single-split difference occurred for slump flow (R²: 0.740 to 0.838 for W-based systems), but 30 repeated splits yielded mean R² values of 0.667 ± 0.169 and 0.738 ± 0.101 for baseline-W and proxy-W, respectively, while proxy-WB showed no average improvement over baseline-WB. Repeated and source-group validation further indicated that the predictive effects of proxy-core features were property-, representation-, partition-, and source-dependent. Overall, proxy-core features are best interpreted as physically informed relational representations rather than universally accuracy-enhancing features. Full article

Frequent light-to-moderate rainfall may influence metro demand differently across rainfall stages and station ridership rhythms. Using automatic fare collection data from 80 Hangzhou Metro stations and station-matched hourly meteorological observations, this study examines weekday entry-ridership associations with rainfall onset, continuation, and the first post-rain hour. Three station ridership-rhythm types were identified from non-rain weekday entry–exit profiles: destination-oriented, balanced bidirectional, and origin-oriented. A Bayesian hierarchical negative binomial model estimated stage-specific associations while accounting for station-hour baseline ridership, hourly temperature, peak periods, and station- and date-level heterogeneity. During off-peak periods, rainfall onset and continuation were associated with 4.95% and 6.82% reductions in expected entry ridership at balanced bidirectional stations. Continuation associations were more negative in the primary model, but this difference was substantially attenuated after accounting for cumulative precipitation and elapsed episode duration. Origin-oriented stations generally showed more negative onset and continuation associations, while negative first-post-rain associations were observed across all three station types. These findings demonstrate that routine rainfall responses vary across both event stages and station ridership rhythms, supporting more stage-specific and station-sensitive passenger-flow monitoring. Full article

With the rise of artificial intelligence (AI), an increasing number of AI-based diagnostic tools are being developed. Before clinical implementation, these tools must be validated against existing gold standards. This requires trials that quantify the agreement between AI predictions and reference measurements. However, designing such agreement studies poses methodological challenges that differ substantially from classical superiority trials. This paper aims to provide statistical methods for assessing diagnostic agreement. Methods were categorized according to the measurement scale of the data (nominal, ordinal, continuous)—with a separate group for methods that apply across several scales—and according to the number of raters or measurements involved. A decision tree is provided as a simplified educational framework for method selection rather than as a general method-selection algorithm: design features such as repeated measurements, clustering, spectrum effects, dependence between raters, and an imperfect reference method are not encoded in it and are discussed separately, together with the circularity and confounding issues specific to the validation of AI-based tools. For each method, we summarized assumptions, appropriate use cases, interpretation of results, and available open-source software for sample size calculation and analysis, and we illustrate the sample size calculations in three fully worked examples covering binary, ordinal, and continuous outcomes. We further distinguish conditional inference about one fixed, frozen model version from the broader generalization to a class of algorithms or to future model versions, which require additional sources of algorithmic and dataset variability to be represented in the design and analysis. We conclude by outlining open methodological questions—including Bayesian approaches to agreement estimation, methods for complex AI outputs, agreement models for clustered and repeated-measures designs, and the limited software support for Gwet’s AC1/AC2 sample size planning—that warrant further work as diagnostic technologies and statistical methodology continue to evolve. Full article

Non-inferiority (NI) clinical trials have gained immense popularity within the last decades, especially in cancer and cardiovascular studies. These trials are designed to establish the non-inferiority of a new experimental treatment as compared to the existing active control. In other words, NI trials are required to demonstrate that the efficacy of an experimental treatment is not unacceptably worse than that of an active control by more than a pre-specified small margin. We consider three-arm NI trials that have been widely acknowledged as the Gold Standard. Three-arm NI trials aim to simultaneously establish both NI and the assay sensitivity (AS). Hence, the analysis of three-arm NI trials involves multiple hypothesis testing. The existing literature on the Bayesian modeling of three-arm NI trials suggests implementing a test procedure based on the joint posterior probability of the NI and AS hypotheses. This joint testing of NI with AS resembles the framework of intersection-union (IU) testing, which may result in a very conservative test. In this article we propose a novel Bayesian testing based on an isotonic transformation in conjunction with Bayes factors. Bayes factors for assessing NI with AS are computed based on Gibbs Sampling. The performance of the proposed testing is evaluated through simulated data sets under varying scenarios. Empirical results show that the proposed Bayesian method gives better control in terms of Type-I error rates, and more powers than existing Bayesian tests. The usefulness of our test is illustrated by synthetic data from the Mildly Asthmatic Study. Full article

Precision tests of the Standard Model at low energy are increasingly limited by nuclear-structure theory rather than by experiments. We review two such cases: the two-photon-exchange correction to the Lamb shift in muonic atoms, and the γW box radiative correction to superallowed β decays. Although they probe different physics, both are governed by the same generalized hadronic tensor, so that the chiral effective field theory Hamiltonians and currents, Lanczos-based response methods, and Bayesian uncertainty quantification developed for one carry over directly to the other. We summarize recent ab initio progress in light nuclei and its impact on nuclear charge radii, on the helium isotope-shift puzzle, and on the extraction of V_ud for the top-row CKM unitarity test, and state a future outlook. Full article

Background: Multiple myeloma (MM) remains an incurable plasma cell malignancy characterized by marked clinical heterogeneity. Existing prognostic frameworks, including the International Staging System (ISS) and FISH-defined cytogenetic risk, are anchored at diagnosis and do not capture the evolutionary dynamics of disease or treatment response, leaving an unmet need for risk models that retain prognostic validity longitudinally. Methods: Using transcriptomic data from 762 CD138-selected MM plasma cells from newly diagnosed patient samples in the MMRF CoMMpass study (NCT01454297), we computed single-sample pathway activity scores for 469 curated cancer-relevant pathways (MSigDB Hallmark; Reactome) and learned a Bayesian causal network linking pathway activity to survival. The model was validated in five independent diagnostic cohorts (n = 1255) and in two independent treatment and relapsed/refractory cohorts (n = 319). Longitudinal risk tracking was additionally assessed in a 46-patient subset of the discovery cohort with serial pre- and post-treatment sampling. Results: The network identified five pathways associated with survival: unfolded protein response (UPR), FLT3 signaling through SRC family kinases, G2M DNA replication checkpoint, metabolism of selenium compound (SeMet), and nicotinate metabolism. The composite survival score stratified patients into high-risk (n = 76; 10%) and standard-risk groups with markedly divergent survival (median 1170 days vs. not reached; p < 0.0001). The score remained an independent prognostic factor after adjustment for age, sex, ISS stage, and KRAS, TP53, and UBR5 mutational status (HR 4.93; 95% CI 2.96–8.19; p < 0.001), and replicated across all five external diagnostic cohorts. Critically, the model retained prognostic discrimination in previously treated (GSE57317; p < 0.0001) and relapsed/refractory (GSE9782; p < 0.0001) settings, and patients transitioning from standard- to high-risk between serial samples exhibited significantly inferior survival compared to standard-risk patients. Conclusions: This pathway-based Bayesian network provides a reproducible, dynamically applicable risk model for MM that captures information complementary to ISS and FISH-defined cytogenetics. The framework supports longitudinal patient monitoring and may inform trial enrichment strategies and closer surveillance for high-risk subpopulations. Full article

Fertilization recommendations are frequently proposed by a limited number of calibration experiments conducted in a few regions, with few cultivars. Thus, recommendations concerning nutrients’ critical levels (CLs) and sufficiency ranges (SRs) are not always the most suitable ones. In addition, it is not known whether CL and SR differ when it comes to peach yield and quality variables, such as pulp firmness or total soluble solids (TSS) concentration. CL and SR can be estimated by using Bayesian segmented quantile regression (BSQR) in combination with databases. The aim is to propose nutrients’ CL and SR in soil and in leaves of peach trees grown under a subtropical climate, by combining databases and BSQR models. The dataset comprised 208 observations of Prunus persica cultivars ‘Maciel’ and ‘Chimarrita’, grown in southern Brazil. Models were developed through plateau regression based on BSQR models to measure the association between dependent variables (fruit yield and quality) and nutrient concentrations in the soil and leaves. CL and SR of N and K in leaves recorded values for variables related to fruit quality, pulp firmness, and TSS lower than values recorded for variables related to yield. We show for the first time that CL and SR in leaves for N and K related to yield are different between cultivars. A similar outcome was recorded for CL and SR of N in leaves between growing regions/sites presenting different soil types and climatic variables. CL and SR of N in leaves related to yield were higher in peach trees grown in sandy-soil sites with low organic matter content. This study presents critical advances for the Southern Brazilian peach market, but with implications for the approach presented in the present study (combination of database and BSQR modeling) relevant for application in orchards around the world. Full article

The mining industry in Ghana, despite its economic significance, continues to grapple with occupational health and safety (OHS) issues, which include but are not limited to high accident rates and a largely reactive approach to safety. In this study, current practices in OHS management are assessed and a model is developed that reflects the interdependencies and associations between workplace hazards, accidents, health effects and preventative actions in a probabilistic fashion. A quantitative analytical design was employed and a sample of 298 workers, safety officers and supervisors from mines were surveyed. The data were analysed using Principal Component Analysis (PCA) to derive latent OHS factors, K-modes clustering and Hierarchical Clustering to classify worker safety profiles, and a Bayesian Network model was employed to investigate probabilistic dependencies. Three major OHS dimensions emerged from PCA: perceived adequacy of safety measures, formal training exposure, and safety resources. Three distinct worker profiles were identified, suggesting that the provision of physical safety equipment does not necessarily reflect perceived operational safety. Moreover, the Bayesian Network model indicated a conditional dependency between workers’ reported health issues and formal accident reporting, and that high hazard environments more than double the probability of an accident occurring (from 0.216 to 0.453). The results indicate that the industry is now operating in an incident-based manner. In order to mitigate the likelihood of accidents, management needs to move towards anticipatory safety management systems, which involve proactive health monitoring, equipment maintenance, and implementation of safety policies in practice. Full article