In this paper, we propose a resolvent-free and projection-free iterative algorithm to solve monotone inclusion problems. The proposed method employs a double inertial extrapolation strategy, in which two distinct inertial steps are used to construct two extrapolated points, together with a new self-adaptive step-size for selecting the inertial parameter in the proposed algorithm. This combination provides an effective strategy to incorporate information from two extrapolated directions without the metric projections and resolvent of an operator. Under suitable assumptions, we establish the strong convergence of the proposed sequence to a solution of the monotone inclusion problem. The obtained convergence result is further applied to minimax and critical point problems. Moreover, numerical experiments in finite and infinite dimensional spaces are presented to compare the proposed method with some existing resolvent-free and inertial schemes. The numerical results demonstrate that the proposed method achieves faster convergence in terms of the number of iterations and provides improved reconstruction performance, with higher SNR and lower MSE for the considered image restoration problems. Further, applications to image reconstruction and compressive sensing provide the practical effectiveness of the proposed approach.
In this work, we develop, from first principles, a discrete oscillation theory for second-order nonlinear advanced difference equations with several deviating arguments, Δr(n)(Δy(n))α+∑i=1mqi(n)Fy(σi(n))=0,σi(n)≥n, studied under the canonical condition ∑s=n0∞r−1/α(s)=∞. This equation models discrete systems governed by anticipatory rather than delayed dynamics, since the second-order difference term depends on a future value y(σi(n)) rather than a past one. We prove an iterative monotonicity lemma, a classification lemma for eventually positive solutions, three oscillation results, a comparison theorem, and a Riccati-type theorem. Two features that are unique to the discrete setting, with no counterpart in the continuous theory, are highlighted. First, the partial sum η(n)=∑s=n∞r−1/α(s) typically lacks a closed form and must be approximated asymptotically. Second, the discrete Riccati step requires an additional power-rule inequality, which introduces a correction term absent from the continuous case. The theoretical thresholds obtained are illustrated by a fully worked example, verified both numerically and symbolically and supported by figures.
Retailers of non-instantaneously deteriorating items must jointly set inventory, preservation technology, and payment decisions. Preservation technology reduces deterioration, but excessive investment increases operational costs, making the determination of an optimal preservation level essential for maximizing profit. Although preservation technology, hybrid payment schemes, and prepayment discounts have been studied individually, their joint treatment alongside partially backlogged shortages remains largely unexplored. To address this gap, this study develops an inventory model that simultaneously incorporates preservation technology investment, a hybrid payment structure, advance payment combined with trade credit, optionally supplemented by a prepayment discount, and partially backlogged shortages for non-instantaneously deteriorating items. A classical optimization approach is employed, yielding quasi-closed-form solutions for the shortage and replenishment timing across four trade credit scenarios, while the profit-maximizing preservation investment level is identified through sensitivity analysis. Numerical examples and sensitivity analysis show that increasing the number of prepayment installments lowers the discount rate offered by the supplier; because this forgone discount outweighs the benefit of retaining capital longer, the retailer’s profit falls. Profit responds most strongly to purchasing cost, the advance payment period, and lead time. These results give retailers a practical basis for balancing preservation investment, payment structure, and shortage policy to maximize profitability. In the sensitivity analysis, profit varies by more than 45% over the tested range of the purchasing cost and by up to 21% depending on the number of prepayment installments negotiated with the supplier. That gives retailers a concrete ranked basis for prioritizing which contract terms to negotiate first.
Global labour shortages in the agricultural sector, combined with diminishing arable land and a growing population, are driving investment in autonomous agricultural machinery. Autonomous systems that can navigate crop environments and perform planting, treatment, and harvesting alongside humans are required, but the gap between published research and commercially deployed systems remains wide across most operational scenarios. Why are agricultural robots still not widely deployed in real farms despite decades of research in autonomous navigation and path planning, and what is preventing full farm autonomy? This paper reviews the principal enabling technologies for autonomous agricultural integration, with a specific focus on path planning as the differentiator between research-stage and deployed systems. Current research in human–robot integration, open-field navigation, row identification and following, crop sensing, and power efficiency is synthesised and evaluated against a deployability criterion. A Deployability Assessment Framework is introduced, comprising structured tables that assign Technology Readiness Levels to twelve path-planning families and benchmark eleven commercial and research platforms against field-validated accuracy data. The analysis shows that point-to-point GNSS navigation has reached TRL 9 with over one million commercial units deployed, vision-based crop row following is at TRL 5–7 depending on crop and season, and whole-farm autonomy with dynamic re-planning is at TRL 3–5. The primary barriers are the absence of standardised evaluation benchmarks, the failure of perception models to generalise across seasons and crop types, and the decoupling of terrain and slip feedback from global path planners. Our review reveals that open-field GNSS navigation is commercially mature, but true whole-farm agricultural autonomy remains unsolved because current systems are not robust enough across seasons, terrain, sensing conditions, and operational transitions.
In this paper, we discuss solvers for solving the discretized nonlinear heat equation with generalized constitutive laws. The model features multi-valued graphs, and the nonlinear functions are at best semi-smooth. Their solutions have low regularity. We identify appropriate global and local nonlinear solvers and propose and evaluate a suite of accelerated iterative algorithms with additional enhancements that improve the convergence of schemes compared to those from the literature. We illustrate these solvers with numerical examples.
Pre-trained language models are typically adapted to downstream tasks via full fine tuning. However, this entails substantial computational and memory overhead. Parameter-efficient alternatives such as Low-Rank Adaptation (LoRA), Quantized Low-Rank Adaptation (QLoRA), and frozen backbone mitigate these costs, but empirical evidence on how their performance–cost trade-offs change under low labeled data in Arabic remains limited. This paper compares four adaptation strategies: full fine tuning, frozen backbone, LoRA, and QLoRA for Arabic binary sentiment classification on the Hotel Arabic Reviews Dataset, using CAMeLBERT-Mix as the pre-trained encoder. The methods are evaluated under a unified experimental setting at three labeled-data levels: the full training set, 100 samples per class, and 25 samples per class. The evaluation metrics are reported as means and standard deviations across five random seeds. At the full-data level, full fine tuning, LoRA, and QLoRA achieve macro-F1 scores between 0.9569 and 0.9579 and are comparable within seed variability, while the frozen backbone exhibits performance that is approximately ten points lower. LoRA and QLoRA use approximately 35.0% less peak GPU memory than full fine tuning but require longer training times. Under reduced-data conditions, full fine tuning outperforms all other adaptation strategies with the differences being statically significant.
In this paper, we introduce and study proximal Z-condensing operators in strictly convex Banach spaces by combining simulation functions with measures of noncompactness. A Darbo-type best proximity point theorem is established, and several consequences corresponding to nonlinear condensing conditions are obtained. As an application, a system of nonlinear ordinary differential equations is embedded into a non-self operator problem on an enlarged product space; in this formulation, best proximity points are shown to be equivalent to classical solutions of the system. We also prove a Krasnoselskii-type best proximity point theorem for the sum of a simulation-function contraction and a compact operator and apply it to a nonlinear matrix-valued integral equation. Finally, a multiplicative best proximity point theorem is obtained in strictly convex Banach algebras and is used to study a nonlinear integral equation. The results provide a unified operator-theoretic framework for additive and multiplicative equations involving non-self mappings.
Small unmanned aerial vehicle (UAV) acoustic signatures are relevant to environmental noise assessment, passive monitoring, and preliminary detectability analysis. This study presents a physics-informed reduced-order framework that combines blade-passing frequency (BPF) harmonic synthesis, rotational-speed acoustic scaling, propagation, and signal-to-noise ratio (SNR) threshold crossing. The RPM–OASPL law was calibrated using ten digitized measurements for a four-rotor DJI Phantom II with Original 9450 propellers and validated, without refitting, against eleven Aftermarket 9443 measurements. The fitted exponent was m = 5.285, and the fitted reference level was Lref = 77.01 dB(A) at 5000 RPM and 1 m. A 100,000-realization Monte Carlo analysis that propagated ±100 RPM and ±0.5 dB digitization bounds, together with residual scatter, produced total 95% intervals of 5.01–5.55 for m and 76.58–77.43 dB(A) for Lref. Calibration yielded RMSE = 0.39 dB and R2 = 0.9979; independent-configuration validation yielded MAE = 2.27 dB, RMSE = 2.38 dB, and R2 = 0.9168. At 5000 RPM, nominal free-field threshold-crossing distances were 70.9, 22.4, and 7.1 m for quiet rural, semi-urban, and urban scenarios. Combined statistical 95% screening intervals were 41.3–121.7, 13.0–38.5, and 4.1–12.2 m, respectively. Sensitivity analyses show that β controls harmonic-specific range but not normalized OASPL, while ground interference, band-limited masking, and detector processing can materially change operational range. The framework is therefore a rapid, interpretable screening tool rather than a universal detector performance model.
Likert-type responses are often analyzed as continuous scores, although their categories are ordered rather than truly metric. This study uses Monte Carlo simulation to examine how ordinal discretization, response perturbation, and non-Gaussian dependence affect four association estimators: Pearson correlation, Spearman correlation, Kendall’s τb, and latent-normal polychoric correlation. In the main latent-normal design, bivariate normal variables with known correlations were discretized into 3-, 5-, and 7-category scales using equal-probability thresholds, with n=300 and 200 replications per scenario. Responses were either left unchanged or modified by adjacent-category error and central-tendency shifts. A complementary Gumbel-copula extension used n=1000, 1500 replications, k=5,…,10 categories, and upper-tail dependence at Kendall’s τC=0.50,0.70,0.90. In the unperturbed latent-normal setting, Pearson and Spearman underestimated the latent association, especially with three categories, whereas the polychoric estimator closely recovered the latent correlation. Under response perturbation, this advantage weakened; at k=5, ρ=0.70, and 20% adjacent error, polychoric bias increased to −0.072. In the Gumbel design, the preferred estimator changed because the target changed: Kendall’s τb showed the smallest bias and mean squared error for copula-scale dependence, while polychoric correlation returned larger Pearson-type latent associations and overestimated τC. Overall, Pearson may be reasonable for many-category, symmetric observed-score questions; polychoric correlation is preferable when latent-normal assumptions are plausible; and Kendall’s τ is the natural choice for ordinal concordance or copula-scale dependence.
Multi-view clustering (MvC) aims to discover cluster structures by exploiting complementary information across views. Most existing MvC methods assume that same-index observations across views describe the same semantic instance. In practice, however, index-aligned observations can be semantically unrelated. This inconsistency between observed index-level correspondence and underlying semantic correspondence is known as noisy correspondence (NC). Learning from such mismatched pairs imposes erroneous cross-view constraints and distorts clustering. Many existing methods only suppress unreliable pairs. This discriminative strategy avoids incorrect alignment but also excludes suspicious pairs from cross-view learning. To reuse these pairs without enforcing incorrect correspondence, we propose Reliability-Aware Gaussian Residual Counterpart Generation. Using reliability estimates derived from cross-view losses, the framework retains observed counterparts for reliable pairs and routes unreliable pairs to counterpart generation. For each unreliable pair, prototype-level semantic transport locates a matched target-view prototype. A Gaussian residual model estimated from reliable target-view samples captures variations around this prototype. The framework samples a residual from this model and adds it to the prototype center, yielding a semantically matched yet diverse counterpart. Random walk-based intra-view contrastive learning further preserves neighborhood structures. Experiments on Scene15, LandUse21, Reuters, and CCV20 achieve the best average ACC, NMI, and ARI across the evaluated NC ratios. Ablation and transfer studies further support the effectiveness of the proposed design.
Brain arteriovenous malformations (AVMs) are complex cerebrovascular lesions characterized by abnormal direct connections between arteries and veins, resulting in altered hemodynamics and an increased risk of rupture. Following PRISMA, a comprehensive review on CFD-based modelling in brain AVMs was conducted across major scientific databases, including Pub-Med/MEDLINE, Scopus, Web of Science, Google Scholar, EBSCO Academic Search and IEEE Xplore, evaluating its role in hemodynamic analysis and pre-procedural planning. Twenty-three studies met the inclusion criteria and were analyzed through both qualitative synthesis and bibliometric approaches. Bibliometric analysis revealed a growing research interest in image-based modelling, 4D flow imaging and virtual embolization after 2021. Despite recent advances in study of hemodynamics simulations, the application of computational fluid dynamics (CFD) to brain AVMs remains challenging due to their complex vascular architecture and highly heterogeneous flow patterns. Nevertheless, CFD remains an important imaging modality for characterizing the lesion and guiding pre-interventional decision making.
Many scientific and engineering domains rely on simple models whose usefulness is limited by uncertain parameters and incompatible formulations. We present a computational framework that converts deterministic, semi-empirical, and heuristic equations into stochastic model families by using bounded parameter representations and Monte Carlo sampling. An explicit semantic layer preserves differences in variable meaning, enabling comparison without forcing structural equivalence. The framework also supports supermodels—weighted mixtures of heterogeneous model families evaluated in a shared diagnostic space. The method is implemented as a reproducible pipeline for five structurally distinct Drake–Fermi–Brin models, with joint and marginal distribution analysis, exploratory clustering, parameter-importance diagnostics, and layered uncertainty decomposition. Results show that model structure and epistemic parameterization materially shape the induced distributions. Comparisons therefore remain conditional on the declared parameter bounds, sampling families, semantic bridges, and model-family weights. Nevertheless, ensemble integration can reveal behavior not visible within individual models. The space of possible supermodel configurations exceeds 1027, illustrating both the scale of the problem and the value of a structured probabilistic workflow. The framework provides an extensible basis for uncertainty propagation and comparison across incompatible models.
Background/Objectives: This study applies the previously introduced structure-preserving hybrid framework for compartmental pharmacokinetic models and extends its clinical evaluation using published clinical datasets. The framework combines a mechanistic pharmacokinetic backbone with a bounded data-driven correction. The aim was to assess predictive performance in held-out subjects while keeping the main pharmacokinetic structure and avoiding a fully black-box model. Methods: This applied extension of the framework was tested in several numerical studies using published clinical pharmacokinetic datasets for polymyxin B, linezolid, and tacrolimus. For polymyxin B and linezolid, repeated subject-wise cross-validation with nested tuning was used to compare the mechanistic baseline with unconstrained and constrained hybrid corrections and a boosted-tree residual benchmark. The studies were designed to assess its behavior in a main application setting, across different drugs, under difficult fitting conditions, and under changes in correction strength and mechanistic parameters. Computational time was also assessed. Results: The results showed that the constrained correction remained close to the mechanistic baseline in the held-out analyses of polymyxin B and linezolid, but it did not significantly improve subject-level prediction. The unconstrained correction showed greater deterioration, while the boosted-tree benchmark gave mixed results and no significant subject-level improvement. The additional analyses showed that tighter correction bounds were generally selected and that the constrained hybrid still responds to changes in the mechanistic parameters in a sensible manner. Conclusions: Overall, the results suggest that the bounded data-driven correction can control the poorer performance seen with an unrestricted correction while keeping prediction close to the mechanistic baseline. It therefore provides a cautious way to combine mechanistic pharmacokinetic modeling with data-driven correction while preserving interpretability. Further external validation is still needed.
The influence of pleat angle on the pressure drop of H14 HEPA filters was investigated through a mathematical model that represents the filter as a system of converging–diverging channels coupled with porous filtration media. The analysis was conducted for pleat angles ranging from 1° to 20° under a constant laminar airflow rate of 0.167 m3/s and 0.45 m/s velocity. The model combines Darcy–Forchheimer flow through the filtration media with laminar channel flow theory, enabling the total pressure drop to be expressed as a function of pleat geometry and subsequently optimised through analytical differentiation. The results show that the pressure drop contribution of the filtration media increases with the pleat angle, from 10.57 Pa at 1° to 213.49 Pa at 20°, whereas channel losses decrease sharply from 1121.71 Pa to 2.75 Pa over the same interval. The competing behaviour of these two mechanisms generates a minimum total pressure drop of 94.56 Pa at a pleat angle of approximately 6°, compared with 120 Pa for the current industrial configuration operating at 3.73°. This represents a pressure drop reduction of approximately 21.2%, implying a corresponding decrease in fan energy consumption without compromising filtration performance. The analysis further demonstrates that very small pleat angles (1–2°) are highly unfavourable, producing total pressure drops between 301 and 1132 Pa due to severe channel constriction, while for angles above 13–14°, the channel contribution becomes negligible, and the overall pressure drop is governed almost entirely by the filtration media. These findings provide quantitative design criteria for optimising HEPA, EPA, and ULPA filter geometries, highlighting pleat angle as a critical parameter for improving aerodynamic performance, flow uniformity, and energy efficiency in high-purity environments. The proposed model was further assessed using a commercially available H14 HEPA filter with 188 pleats, an effective filtration area of 10.618 m2, and a nominal airflow rate of 600 m3/h, demonstrating its applicability to real industrial filter configurations.
The analysis of process signals is a key method for gaining experimental insight into the underlying layer formation mechanisms in plasma electrolytic oxidation (PEO). This is made possible by the simultaneous measurement of electrical and optical process signals with high temporal resolution. However, according to the current state of the art, the interaction between these signals is primarily discussed in qualitative terms. Therefore, this article presents a robust methodology for analysing the current signal, which makes it possible to categorise the charge electro-chemical and plasma-chemical dominated subprocesses and to quantify their respective contributions. The evaluation is performed by taking additional process signals into account. The experimental setup for measuring process voltage, current, and photovoltage, as well as the measurement routine, are briefly described. This is followed by a detailed description of the numerical procedure. This includes the application of fundamental mathematical methods to the time-discrete measurement data, the automated selection of the pulse segment to be examined and the identification of discharge initiation to determine the interval boundaries of the electro- and plasma-chemically dominated pulse subsegments. The description of the routine is primarily intended for experimental scientists and is meant to provide them with a tool for extracting additional information from their process data. These can then be used to better understand electro-chemical side reactions and parasitic subprocesses in PEO.
Pornography use during adolescence is a relevant issue from social, educational, and public health perspectives. As with other behaviours, its correlates may involve complex and nonlinear relationships. This study uses data from the 2023 Spanish Survey on Drug Use in Secondary Education (ESTUDES), a major source for analysing potentially addictive behaviours among adolescents in Spain because of its large sample size (original sample: N = 42,208; complete-case analytical sample: N = 33,543). Pornography use was modelled as a binary outcome using logistic regression, XGBoost, LightGBM, and CatBoost. The models included sociodemographic characteristics, family-related factors, parental control, substance use, selected sexual behaviours, indicators of mental well-being, and addictive Internet use. The four methods showed similar predictive performance. CatBoost achieved the highest AUC and the lowest Brier score and log loss, whereas LightGBM obtained the highest accuracy, specificity, and precision. Logistic regression yielded the highest sensitivity, negative predictive value, balanced accuracy, and F1-score. SHAP analysis identified sex as the most influential predictor, followed by addictive Internet use, cannabis and alcohol use, family conflict, and selected risky sexual behaviours. The findings suggest that pornography use among adolescents forms part of a broader behavioural and psychosocial profile. Whereas sex operated mainly as a strong direct predictor, problematic Internet use showed substantial interactions with age and cannabis and alcohol use. This study demonstrates how explainable machine learning can complement conventional regression by identifying the main correlates, nonlinear patterns, and interactions associated with adolescent pornography use.
In this paper, we propose an image encryption and digital signature scheme based on a modification of ElGamal cryptosystem over a large modulus represented by 2pn, where p is a large prime number and n>2 is a dynamic exponent derived from a shared secret established by the communicating participants through a modification of the Diffie–Hellman Key Exchange Protocol. The proposed scheme combines a modified ElGamal framework with image encryption and digital signature mechanisms to support confidentiality, image authentication and integrity verification while preserving the standard security assumptions of discrete-logarithm-based cryptography. Empirical performance and image-statistical evaluations, including histogram analysis, entropy, correlation coefficients, NPCR and UACI, are conducted on applying the procedures of the proposed scheme on four standard images. The experimental results demonstrate the correctness and favorable empirical image-statistical behavior of the proposed scheme for grayscale image encryption and authentication under the classical DLP assumptions. Furthermore, comparisons with representative symmetric and asymmetric cryptographic schemes are provided to evaluate the performance of the proposed scheme.
Current evaluation of bypass graft performance relies predominantly on wall shear stress metrics, even though thrombosis and atherogenesis are fundamentally governed by particle transport and residence within disturbed flow regions. This disconnect limits the ability of conventional hemodynamic indicators to capture mechanisms directly linked to graft failure. In this study, we investigate how helical bypass geometry reorganises the flow and, consequently, modifies transport behaviour within the distal anastomosis by combining experimentally validated flow visualisation with computational fluid dynamics under pulsatile conditions. Particle transport was quantified using a controlled injection of 151 tracers, enabling direct assessment of retention and washout across the graft-anastomosis system. The straight configuration exhibited persistent recirculation structures that promoted localised particle retention and delayed clearance. In contrast, the helical geometry disrupted these structures, enhancing flow mixing and accelerating downstream transport. At late stages of the cardiac cycle, the helical configuration reduced residual particle retention by approximately 43% compared to the straight bypass. These findings demonstrate a transition from recirculation-driven retention to washout-dominated transport, providing a mechanistic basis for interpreting bypass performance beyond shear-based metrics. This transport-centred perspective provides a mechanistic link between flow organisation and particle residence, supporting the functional relevance of helical graft design while remaining distinct from direct modelling of biological thrombosis or atherogenesis.
High-Frequency Trading (HFT) dashboards require rapid reception, aggregation, and visualization of order book and trade update streams that may arrive at multi-million message rates. Conventional CPU-based and CPU-GPU hybrid visualization pipelines can suffer from significant delays during periods of burst due to CPU-mediated rendering, synchronization, kernel launch overhead, and copies on the host. This paper presents a visualization pipeline that is entirely resident on the graphics processor with zero-copy access to NIC accessible pinned buffers, persistent CUDA processing, fused stage execution of the parse-aggregate pipeline, and persistent CUDA OpenGL buffer interoperation. The goal is not to reach production status but rather to see whether host-to-host data movement can be decreased and whether the stages of GPU processing can be consolidated to improve latency, throughput and frame cadence in controlled HFT-style workloads. The evaluated workstation achieved a mean ingest-to-pixel latency of 6.3 ms using the proposed design compared to 29.4 ms for the current design, with sustained throughput of 10.2 million messages per second, which is 20 times greater than the current design, and a steady-state range of 185 to 192 frames per second with a burst floor of 178 frames per second for the proposed design. The improvement observed can be attributed to both the zero-copy ingestion and fused persistent kernel execution. Based on the obtained results, the proposed method of use of this technique in the implementation of real-time financial visualization under the proposed conditions is possible. More general testing is still required on other NICs, other generations of GPUs and PCIe configurations, workload traces, and actual exchange feeds.
Ceramic additive manufacturing offers strong potential for fabricating geometrically complex and application-specific components, yet achieving reliable dimensional fidelity remains challenging because dimensional deviation is governed by highly coupled material, process, thermal, and environmental factors. To address this problem, this study proposes an uncertainty-aware computational framework for dimensional error prediction in ceramic 3D printing under variable material and process conditions. The contribution is positioned as a system-level integration of established learning, uncertainty estimation, calibration, and reliability-interpretation components within a ceramic additive manufacturing dimensional-error prediction workflow, rather than as a fundamental methodological breakthrough. The validation is conducted using the publicly available Ceramic 3D Printing Process Control Dataset, a 1000-sample tabular dataset, and the resulting findings are therefore interpreted as dataset-specific computational evidence rather than direct proof of industrial deployment readiness. The methodology begins with a structured data-driven preprocessing pipeline that transforms the Ceramic 3D Printing Process Control Dataset into a multi-condition feature space through data cleaning, one-hot material encoding, min–max normalization, and engineered descriptors capturing extrusion–speed balance, thermal gradients, cooling intensity, deposition density, and material-conditioned interactions. A multi-branch deep computational architecture is then developed to encode material, process, thermal-environmental, and engineered-feature streams separately, followed by adaptive cross-condition fusion to learn nonlinear dependencies across ceramic printing regimes. To improve reliability beyond deterministic regression, the framework jointly models aleatoric and epistemic uncertainty and incorporates calibration refinement to align predictive confidence with observed error behavior, thereby enabling preliminary reliability-oriented interpretation of stable and high-risk operating conditions. Experimental results demonstrate that the full model achieves the best overall within-dataset performance, with a test MAE of 0.0118, RMSE of 0.0172, R2=0.999, MAPE of 1.74%, calibration error of 0.003, PICP of 0.996, reliability score of 0.992, and a stable prediction rate of 98.7%. Although these values indicate strong predictive behavior under the current structured dataset, the exceptionally high R2 should be interpreted cautiously because external experimental validation, larger measured datasets, and cross-machine ceramic printing trials are still required. These findings show that the proposed framework provides an effective system-level computational strategy for dataset-specific reliability-aware dimensional quality prediction in ceramic additive manufacturing and offers a preliminary data-driven foundation for uncertainty-aware intelligent process optimization.
