Parametric Approach Research Articles

Impact of different parametric Patlak imaging approaches and comparison with a 2-tissue compartment pharmacokinetic model with a long axial field-of-view (LAFOV) PET/CT in oncological patients

AimThe recently introduced Long-Axial-Field-of-View (LAFOV) PET-CT scanners allow for the first-time whole-body dynamic- and parametric imaging. Primary aim of this study was the comparison of direct and indirect Patlak imaging as well as the comparison of different time frames for Patlak calculation with the LAFOV PET-CT in oncological patients. Secondary aims of the study were lesion detectability and comparison of Patlak analysis with a two-tissue-compartment model (2TCM).Methodology50 oncological patients with 346 tumor lesions were enrolled in the study. All patients underwent [18F]FDG PET/CT (skull to upper thigh). Here, the Image-Derived-Input-Function) (IDIF) from the descending aorta was used as the exclusive input function. Four sets of images have been reviewed visually and evaluated quantitatively using the target-to-background (TBR) and contrast-to-noise ratio (CNR): short-time (30 min)-direct (STD) Patlak Ki, short-time (30 min)-indirect (STI) Patlak Ki, long-time (59.25 min)-indirect (LTI) Patlak Ki, and 50–60 min SUV (sumSUV). VOI-based 2TCM was used for the evaluation of tumor lesions and normal tissues and compared with the results of Patlak model.ResultsNo significant differences were observed between the four approaches regarding the number of tumor lesions. However, we found three discordant results: a true positive liver lesion in all Patlak Ki images, a false positive liver lesion delineated only in LTI Ki which was a hemangioma according to MRI and a true negative example in a patient with an atelectasis next to a lung tumor. STD, STI and LTI Ki images had superior TBR in comparison with sumSUV images (2.9-, 3.3- and 4.3-fold higher respectively). TBR of LTI Ki were significantly higher than STD Ki. VOI-based k3 showed a 21-fold higher TBR than sumSUV. Parameters of different models vary in their differential capability between tumor lesions and normal tissue like Patlak Ki which was better in normal lung and 2TCM k3 which was better in normal liver. 2TCM Ki revealed the highest correlation (r = 0.95) with the LTI Patlak Ki in tumor lesions group and demonstrated the highest correlation with the STD Patlak Ki in all tissues group and normal tissues group (r = 0.93 and r = 0.74 respectively).ConclusionsDynamic [18F]-FDG with the new LAFOV PET/CT scanner produces Patlak Ki images with better lesion contrast than SUV images, but does not increase the lesion detection rate. The time window used for Patlak imaging plays a more important role than the direct or indirect method. A combination of different models, like Patlak and 2TCM may be helpful in parametric imaging to obtain the best TBR in the whole body in future.

Signal and image reconstruction with a double parameter Hager–Zhang‐type conjugate gradient method for system of nonlinear equations

AbstractThe one parameter conjugate gradient method by Hager and Zhang (Pac J Optim, 2(1):35–58, 2006) represents a family of descent iterative methods for solving large‐scale minimization problems. The nonnegative parameter of the scheme determines the weight of conjugacy and descent, and by extension, the numerical performance of the method. The scheme, however, does not converge globally for general nonlinear functions, and when the parameter approaches 0, the scheme reduces to the conjugate gradient method by Hestenes and Stiefel (J Res Nat Bur Stand, 49:409–436, 1952), which in practical sense does not perform well due to the jamming phenomenon. By carrying out eigenvalue analysis of an adaptive two parameter Hager–Zhang type method, a new scheme is presented for system of monotone nonlinear equations with its application in compressed sensing. The proposed scheme was inspired by nice attributes of the Hager–Zhang method and the various schemes designed with double parameters. The scheme is also applicable to nonsmooth nonlinear problems. Using fundamental assumptions, analysis of the global convergence of the scheme is conducted and preliminary report of numerical experiments carried out with the scheme and some recent methods indicate that the scheme is promising.

Machine learning based reservoir characterization and numerical modeling from integrated well log and core data

In recent years, artificial intelligence applications have proved useful for analyzing petrophysical data and characterizing subsurface formations. Conventional reservoir characterization employs core data measurements and local correlations between porosity and permeability as input data for reservoir property modeling. The vertically and laterally sparse nature of core and well-log data respectively present challenges in developing three-dimensional models of subsurface properties. In addition, a strong correlation between porosity and permeability as well as reliable core measurements are not always available. The complex interrelationship within the dataset is further evidenced through the application of parametric and nonparametric approaches, which are commonly utilized in conventional regression methods. The parametric method yielded an RMSE value of 0.335 and the nonparametric, 0.418, necessitating the implementation of an algorithm capable of modeling the complex relationship within the dataset. The proposed approach uses an improved machine learning (ML) workflow to generate and evaluate the performance of different porosity and permeability models from integrated well-log and core data, comparing the performance to traditional methods. The workflow combines a suite of unsupervised and supervised ML methods in establishing the relationship between porosity and permeability for different zones. Unsupervised ML algorithms such as K-means, K-median, and hierarchical clustering are applied to the data to generate groups with similar trends. Supervised ML regression methods such as Gaussian process regression (GPR), neural network regression, support vector machine (SVM) regression, and ensemble regression are implemented to predict permeability based on gamma ray, bulk density, and deep resistivity logs. This method demonstrated enhanced dataset modeling, as indicated by the decrease in RMSE values when using the GPR following the hierarchical clustering technique. This recorded RMSE values as low as 0.125, which is roughly 34% lower than those observed with traditional methods. Two of the predicted models were assessed using a history-matching process, which showed an almost perfect fit for nearly all the wells, thereby confirming the accuracy of the regression.

Enhanced Tobacco Yield Prediction Using Spatial Information and Exogenous Variable-driven Machine Learning Models

Remote sensing technology has been essential in studying the relationship between tobacco canopy spectral characteristics and biomass yield. This study has been conducted in Garnepudi, Andhra Pradesh, employed satellite imagery obtained between 2015 and 2023 to extract vegetation indices (VI’s). Accurately predicting yield is crucial for India's economy. This study investigates the efficacy of various predictive models for tobacco yield forecasting using multiple vegetation indices: Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI), Leaf Area Index (LAI) and Leaf Surface Water Index (LSWI). The models assessed include traditional parametric approaches (ARIMAX, MLR), machine learning techniques (ANN, SVR, RFR), and advanced ensemble methods like XGBoost. The results highlight XGBoost as the most accurate model, consistently delivering the lowest error metrics, including RMSE and MAE, across all vegetation indices. Specifically, XGBoost achieved the best performance with LAI showing RMSE of 86.657, MAE of 58.324, sMAPE of 14.354, MASE of 1.001, and QL of 29.162 respectively. They exhibited lower error metrics, as compare to the statistical and ML models underscoring their effectiveness and potential in tobacco yield prediction. This study highlights the significant role of remote sensing technology in capturing crop development patterns and accurately forecasting tobacco yield, thereby offering valuable insights for agricultural planning and decision-making. The study also addresses challenges such as data quality and model generalization, providing a comprehensive view of the research impact and future directions.

Thresholdless coherence in a superradiant laser

Lasing threshold in the conventional lasers is the minimum input power required to initiate laser oscillation. It has been widely accepted that the conventional laser threshold occurring around a unity intracavity photon number can be eliminated in the input-output curve by making the so-called β parameter approach unity. The recent experiments, however, have revealed that even in this case the photon statistics still undergo a transition from coherent to thermal statistics when the intracavity mean photon number is decreased below unity. Since the coherent output is only available above the diminished threshold, the long-sought promise of thresholdless lasers to produce always coherent light has become questionable. Here, we present an always-coherent thresholdless laser based on superradiance by two-level atoms in a quantum superposition state with the same phase traversing a high-Q cavity. Superradiant lasing was observed without the conventional lasing threshold around the unity photon number and the photon statistics remained near coherent even below it. The coherence was improved by reducing the coupling constant as well as the excited-state amplitude in the superposition state. Our results pave a way toward always-coherent thresholdless lasers with more practical media such as quantum dots, nitrogen-vacancy centers and doped ions in crystals.

Chaos quasi-opposition crayfish based modified new controller designed for hybrid deregulated power environment considering cyber-attack

Frequency security is critical to the stability and dependability of the electrical systems. The integration of renewable energy resources presents new challenges for power networks and frequency security. In order to provide a more reliable control mechanism for improved frequency regulation in multi area deregulated environments with penetrations of renewable energy sources like Solar photovoltaic and wind system has been included considering along with its intermitance nature. Additionally, to get more realistic approach and its analysis, nonlinearities such as generation rate constant and governor dead band has been taken into consideration for the anticipated Load Frequency Control (LFC). A novel modified cascaded Dual-Loop Linear Active Disturbance Rejection based Tilted controller has been proposed for the suggested LFC paradigm. A modified version of the Chaotic Quasi-Opposition Crayfish Optimisation algorithm (CQOCOA) has been offered as a novel optimization approach for optimal parameters of the proposed controller. ITSE has been taken as objective function for optimization purpose. Moreover, a comprehensive detailed examination for the proposed LFC system has been investigated and successfully implemented. The performance has been examined under a variety of operating scenarios, including multiple-step, random load disturbances and Distributed Generation (DG) analysis along with its technical core advancements. This study has been investigated and validated over large multi area power system i.e., IEEE-118 bus system. This work also put forward the deep analysis and implementation of learning-based attack detection and mitigation method for the grid's frequency regulation with cyber-physical model. The effectiveness of the proposed controller has been compared and verified over previous published literature work of international repute. The compressive results analysis provide strong evidence in favour of effectiveness and efficacy of the proposed control methodology. Furthermore, this work also suggests its potential to implement in hybrid multi area power system for enhancing the performance and stability in deregulated power structure.

Daylighting and energy performance optimization of anidolic ceiling systems for tropical office buildings

A simple window is often unable to ensure standard illumination to the deep parts of large office spaces, and advanced daylighting systems are necessary to ensure the level of interior illuminance. Anidolic ceiling systems are one of the less popular daylighting strategies designed to redirect and redistribute daylight deeper, which can reduce the demand for artificial lighting and energy use for lighting purposes. This study shows how a parametric approach can be applied to optimize the daylighting and energy performance of an anidolic ceiling system in a tropical climate. Multi-objective optimization (MOO) techniques were applied to combine different design variables to establish multiple design alternatives and find the optimal design by comparing their performances. A simulation study of an anidolic ceiling system was conducted with test models primarily developed by Rhinoceros and ClimateStudio, and optimization of performance metrics was conducted using Grasshopper and Octopus. Data exportation was done using the TT toolbox, while data analysis was done through mathematical and statistical analyses and cross-checked by Design Explorer. By incorporating an optimum anidolic ceiling system into the case office space, spatial daylight autonomy (sDA) increased 20.51 %∼68.18 %, annual sunlight exposure (ASE) decreased 15.83 %∼8.52 %, mean illuminance increased 221 lux–3027 lux and energy use intensity (EUI) reduced 189.3 kWh/(m2y)∼174 kWh/(m2y) for different configurations of test models. The results demonstrated a MOO methodology that successfully finds the optimum solutions for the anidolic ceiling systems in a deep plan office space to enhance daylighting and energy performance.

Optimization of BIPV Utilization with Parametric Approach: Case Study on Nearly Zero Carbon Building Studio Design in Medan, Indonesia

Buildings significantly consume fossil energy, leading to substantial carbon emissions contributing to global warming and rising sea levels. These environmental impacts pose serious challenges, necessitating a shift towards sustainable building designs. By integrating clean energy solutions into building architecture, we can reduce the environmental burden of emissions. This approach mitigates adverse effects on living organisms and ecosystems and promotes long-term sustainability and resilience in urban development. Building-Integrated Photovoltaic (BIPV) systems represent one such approach for substituting fossil energy. However, implementing BIPV effectively necessitates comprehensive analysis considering numerous parameters and variables. This paper introduces building design strategies employing the BIPV approach. The decision-making process in designing alternatives utilizes parametric methods, recognized for their ability to yield optimization results for various variables through the development of design algorithms. The study focuses on the design of a studio building at the University of North Sumatra. Comparative analysis of the results obtained through parametric design aims to identify the most favorable design outcomes. The research includes developing a parameterized design algorithm, workflow, and assessment results for selecting optimal design alternatives. The results indicate that the placement of PV panels on the side area (PVOS) has more potential than placement on the top area (PVOT). Despite the PVOT area being observed to be 0.57% smaller than PVOS, PVOS shows a significant increase in total Incident Radiation (IR), average IR, and total Energy Generated by 137.14%, 128.40%, and 132.97%, respectively.

Fuzzy system reliability analysis based on new aging indexes

The concept of aging plays an important role in reliability analysis. The aim of this paper is providing the novel method to definition aging indexes based on fuzzy random variables and using the concept of α-pessimistic in both parametric and non-parametric approaches. In parametric approach, the concepts of survival function, hazard rate function and mean residual function were extended for fuzzy random variables. After that, we obtain and analyse these indexes for fuzzy exponential and fuzzy two-parameter Weibull distributions as two frequently used lifetime distribution in reliability theory. Moreover, with the assumption that the distribution of fuzzy observations is unknown (non-parametric approach), first an estimate for the fuzzy distribution function is presented, and then an estimate for the aging indices of the system is investigated. To do this, the concept of fuzzy empirical distribution function is investigated as an estimator of cumulative distribution function. Moreover, some properties of investigated estimator have been established.

A parametric modeling method for 3D woven composites considering realistic meso-structural characteristics

The precision of mesoscale simulation for three-dimensional woven composites (3DWCs) is intricately linked to the fidelity of the geometric representation. This paper aims to present a novel parametric modeling approach for generating representative volume element (RVE) of the 3DWCs while considering its realistic meso-structural characteristics. The real architecture of 3DWCs is defined to consider the squeezed surface warp tow. Tow geometry is specified, accounting for torsion of tow cross-section and crimp of weft tow path. The RVEs of the composites are geometrically assembled via a specific translational symmetry. The tensile response of the composites with the novel geometry is scrutinized utilizing a progressive damage model, compared with that of the ideal geometry. Additionally, the impact of weft tow size on the tensile response of the composites is explored.

Analyzing the heterogenous effects of factors on high-range speeding likelihood of taxi speeders: Does explainable deep learning provides more insights than random parameter approach?

The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to the challenge of transferring its predefined errors. Recently, the emergence of explainable AI techniques has illuminated a new path for analyzing factors associated with risky driving behaviors. Despite this, there remains a gap that comparing results from machine and deep learning (ML/DL) approaches with those from random parameters GLM. This study aims to apply the random parameter GLM and explainable deep learning to evaluate the heterogenous effects of factors on the taxis’ high-range speeding likelihood. Initially, a Beta GLM with random parameters (BGLM-RP) is developed to model the high-range speeding likelihood among taxi drivers. Additionally, XGBoost, a simple convolutional neural network (Simple-CNN), a deeper CNN (DCNN), and a deeper CNN with self-attention (DCNN-SA) are developed. The quantified explanations and illustrations of the factors’ heterogenous effects from ML/DL models are derived from pseudo coefficients by decomposing factors’ SHapley Additive exPlanations (SHAP) values. All the developed statistical, ML, and DL models are compared in terms of mean absolute errors and mean square errors on testing and full data. Results show that DCNN-SA excels in prediction on testing data, indicating its superior generalization capabilities, while BGLM-RP outperforms other models on full data. The DCNN-SA can reveal the heterogenous effects of factors for both in-sample and out-of-sample data, which is not possible for the random parameter GLM. However, BGLM-RP can reveal larger magnitudes of the factors’ heterogenous effects for in-sample data. The signs and significances are identical between the varying coefficients from BGLM-RP and the pseudo coefficients from the ML/DL models, demonstrating the validity and rationale of using the proposed explanation framework to quantify the factors’ effects in ML/DL models. The study also discusses the contributions of various factors to the high-range speeding likelihood of taxi drivers.

Nano-scale modeling of energy dissipation in a single lamella reveals the significant contribution of collagen-mineral sliding to intrinsic bone toughness

Although collagen-mineral sliding is recognized as one of the intrinsic mechanisms that contribute to the bone toughness, its precise role in energy release at the nano-scale, as well as its relationship to other intrinsic toughness mechanisms, remains unclear. This lack of understanding stems from the challenges associated with quantifying the specific nature of contact at the mineral-collagen interface, and the energy dissipation capacity resulting from plastic deformation at this scale. To address this issue, we developed a three-dimensional model of mineralized collagen fibril using ABAQUS, incorporating hyperelastic isotropic collagen, elastic isotropic mineral, and a cohesive contact formulation to represent the collagen-mineral interface. Knowing the properties of collagen and mineral and using a parametric modeling approach, we estimated the properties of contact based on experimental measurements of the stress-strain behavior in a single bone lamella, considering the angle of the collagen fibril in relation to the loading axis. By comparing the experimental elastic modulus with the model predictions, we determined that the cohesive contact exhibited a maximum strength of 9 MPa and a fracture energy of 50 mJ/m2 within the bone. Further analysis indicated that fracture energy at the collagen-mineral interface is potentially closer to 5 mJ/m2. Using these findings, we concluded that the interfaces were responsible for 52% to 98% of the energy release in mineralized collagen fibrils, depending on collagen angle, both directly and indirectly by influencing stress and strain in adjacent collagen fibril. The results of this study demonstrated that plastic deformation at the collagen-mineral interface is the primary mechanism underlying bone toughness at the nano-scale. Importantly, we found that these conclusions held true regardless of the angle of the collagen fibril relative to the loading axis. The modeling framework presented in this study provides a valuable tool for investigating the elastic and post-yield behavior of bone across different mineral volume fractions at the nano-scale, and at the lamellar level.

Constructing a temperature transferable coarse-grained model of cis-1,4-polyisoprene with the structural and thermodynamic consistency aided by machine learning

Polyisoprene (PI) is a widely used polymer and constructing a systematic coarse-grained (CG) PI model with the structural and thermodynamic consistency with the underlying atomic model over a wide range of thermodynamic conditions is very important for the predictive capability of CG model on overall properties of PI polymer materials and the establishment of their structure-property relationship. However, as the number of tunable CG potential parameters and target properties grows, traditional parameter tuning methods become impractical. In this work, we present a novel approach for determining the optimal CGPI non-bonded potential parameters by employing Particle Swarm Optimization as the calibrator with machine learning-based models trained using molecular dynamics data. The resulting CG model is further augmented with temperature factors through a multistate parameterization approach. This enhancement ensures the model's temperature transferability of structure and thermodynamics in a wide temperature of 150K∼750K.

Rational Polynomial Coefficient Estimation via Adaptive Sparse PCA-Based Method

The Rational Function Model (RFM) is composed of numerous highly correlated Rational Polynomial Coefficients (RPCs), establishing a mathematical relationship between two-dimensional images and three-dimensional spatial coordinates. Due to the existence of ill-posedness and overparameterization, the estimated RPCs are sensitive to any slight perturbations in the observation data, particularly when handling a limited number of Ground Control Points (GCPs). Recently, Principal Component Analysis (PCA) has demonstrated significant performance improvements in the RFM optimization problem. In the PCA-based RFM, each Principal Component (PC) is a linear combination of all variables in the design matrix. However, some original variables are noise related and have very small or almost zero contributions to the construction of PCs, which leads to the overparameterization problem and makes the RPC estimation process ill posed. To address this problem, in this paper, we propose an Adaptive Sparse Principal Component Analysis-based RFM method (ASPCA-RFM) for RPC estimation. In this method, the Elastic Net sparsity constraint is introduced to ensure that each PC contains only a small number of original variables, which automatically eliminates unnecessary variables during PC computation. Since the optimal regularization parameters of the Elastic Net vary significantly in different scenarios, an adaptive regularization parameter approach is proposed to dynamically adjust the regularization parameters according to the explained variance of PCs and degrees of freedom. By adopting the proposed method, the noise and error in the design matrix can be reduced, and the ill-posedness and overparameterization of the RPC estimation can be significantly mitigated. Additionally, we conduct extensive experiments to validate the effectiveness of our method. Compared to existing state-of-the-art methods, the proposed method yields markedly improved or competitive performance.

Efisiensi dan Potensi Industri Manufaktur Propinsi Jawa Timur

This study aims to determine which manufacturing sub-sectors in East Java are efficient and which manufacturing sub-sectors are the basis or superior in East Java by using the data in 2011. The analytical method used to measure efficiency level with non parametric approach by using Data Envelopment Analaysis (DEA). While to identify the sub-sector of base or superior used two tools that is Location Quetion (LQ) and Specialization Index (IS). The results showed that the industrial sub-sector in East Java has achieved the most efficiency and about the base sector almost 50% of the East Java sub-sector is the base sector that there are 11 sub- sectors, this result is also reinforced by the Specialization Index which is also in the same direction that there are 11 positive sub-sectors of the 24 existing sub- sectors.

Experimental and numerical investigation of water freezing and thawing in fully saturated sand

Abstract This paper presents an experimental and numerical study of the freezing-thawing behavior of water in fully saturated sand. A relatively inexpensive and easily replicable experimental procedure was developed to simulate the freezing-thawing cycles in a medium-sized sand sample placed in a modified commercial freezer. By insulating the sides and bottom of the sample well, while allowing good thermal conductivity at the top of the sample, a nearly vertical advance of the freezing and thawing front was achieved. A series of freeze-thaw cycles were performed with higher and lower temperature gradients. A numerical multiphysics model, assuming an axially symmetric geometry based on the transient heat transfer during the phase transition, used a parametric approach to estimate the effective thermal properties of the sand-water-ice system. A good agreement between experimental and modelling results was shown, but slightly different parameter sets were obtained for each temperature gradient. The presented method could be a simple way to characterize the freeze-thaw process in natural and artificial porous materials.

Assessment of groundwater potential recharge areas using advanced decision models and spatial data: Applied to Sbeitla aquifer system, central Tunisia

Sbeitla is located in a semi-arid region, central western Tunisia, where groundwater is considered an essential resource for economic development and social well-being. The main objective of this study is the assessment of potential groundwater recharge and delineation of aquifer recharge. Structural and hydrostructural database coupled with remote sensing techniques (RST), was employed. The lateral variation of recharging zones was determined through ArcGIS software, utilizing TOPSIS approaches. Results reveal that the study area is classified in five classes, very low recharge (45%), low recharge (15%), moderate recharge (20%) and 15% of the plain have good to very good potential recharge. Predominantly located in the southern part of the Sbeitla region, these areas distribution is controlled by some natural features such as geology and geomorphology. The findings show that there are five places in the Sbeitla plain where water can soak into the ground. About 40% of the area doesn't have much water soaking in, while 15% has a little bit. Also, around 25% of the plain has medium amounts soaking in, and 20% has a lot. This tells us where water is going into the ground in Sbeitla, which helps us use it better. The potential recharge for the Sbeitla aquifer system, using TOPSIS and RST, is estimated to 13.5 Mm³/year. Although this recharging rate constitutes only 7% of the total rainfall, there is potential for improvement. TOPSIS and RST approach proves valuable for potential recharge areas mapping. This integrated approach underscores the significance of informed resource management in addressing water challenges in front to climate change. Thus, the combination of parametric methods and MCDM approaches such as TOPSIS, has shown its efficiency in decision for classifying aquifer recharge zones and will be an effective tool to assist researchers in this field.

A Novel Artificial Intelligence-Based Parameterization Approach of the Stromal Landscape in Merkel Cell Carcinoma: A Multi-Institutional Study

Tumor–stroma ratio (TSR) has been recognized as a valuable prognostic indicator in various solid tumors. This study aimed to examine the clinicopathologic relevance of TSR in Merkel cell carcinoma (MCC) using artificial intelligence (AI)-based parameterization of the stromal landscape and validate TSR scores generated by our AI model against those assessed by humans. One hundred twelve MCC cases with whole-slide images were collected from 4 different institutions. Whole-slide images were first partitioned into 128 × 128-pixel “mini-patches,” then classified using a novel framework, termed pre-tumor and stroma (Pre-TOAST) and TOAST, whose output equaled the probability of the minipatch representing tumor cells rather than stroma. Hierarchical random samplings of 50 minipatches per region were performed throughout 50 regions per slide. TSR and tumor–stroma landscape (TSL) parameters were estimated using the maximum-likelihood algorithm. Receiver operating characteristic curves showed that the area under the curve value of Pre-TOAST in discriminating classes of interest including tumor cells, collagenous stroma, and lymphocytes from nonclasses of interest including hemorrhage, space, and necrosis was 1.00. The area under the curve value of TOAST in differentiating tumor cells from related stroma was 0.93. MCC stroma was categorized into TSR high (TSR ≥ 50%) and TSR low (TSR < 50%) using both AI- and human pathology–based methods. The AI-based TSR-high subgroup exhibited notably shorter metastasis-free survival (MFS) with a statistical significance of P = .029. Interestingly, pathologist-determined TSR subgroups lacked statistical significance in recurrence-free survival, MFS, and overall survival (P > .05). Density-based spatial clustering of applications with noise analysis identified the following 2 distinct TSL clusters: TSL1 and TSL2. TSL2 showed significantly shorter recurrence-free survival (P = .045) and markedly reduced MFS (P < .001) compared with TSL1. TSL classification appears to offer better prognostic discrimination than traditional TSR evaluation in MCC. TSL can be reliably calculated using an AI-based classification framework and predict various prognostic features of MCC.

A unified model of tensile and creep deformation for use in niobium alloy materials selection and design for high-temperature applications

There is renewed interest in refractory alloys that possess higher service temperatures than incumbent Ni-based superalloys (e.g., ⪆1100 °C). This study provides a review of the high-temperature constitutive responses of Nb-alloys measured over a wide range of temperatures (≈860 °C < T < ≈1760 °C) and strain rates (≈10–9 s-1< ε˙ < ≈10–1 s-1). Nevertheless, the extant data is sparse and informed materials selection decisions require constitutive expressions to interpolate and reliably extrapolate. The Larson-Miller parameter approach to describe creep-life provides a conservative estimate of material response at the highest temperatures and lowest strain rates, whereas the Sellars-Tegart model describes both steady-state creep and high-temperature tensile test data with a single, universal equation. A minimum flow stress based on the combination of these two models is proposed for design considerations to address the overprediction of strength that can arise from applying one or the other independently. This effort highlights the fact that refractory alloys exhibit strain rate sensitive flow strengths in the temperature range of interest for applications. The roles of alloying, thermomechanical processing, and impurity levels are discussed, and highlight the fact that these advanced Nb-alloys evidence Class 1 (Class A) solute drag controlled creep behavior, except the carbide precipitation strengthened alloy, D-43. In addition, the high-temperature strengths are confirmed to be strongly correlated with alloy melting point.

Probabilistic short-term load forecasting models based on a parametric approach

The forecasting of electric load plays an essential role in the effective management of electric power systems. Specifically, short-term models, which predict hourly load over the 24 hours of the subsequent day, hold significant value for applications within the realm of electricity markets. In this context, research efforts have predominantly concentrated on the development of point load forecasting models. These models provide solely the estimated value of hourly load, omitting any information regarding its associated uncertainty. Probabilistic forecasting models aim to address this limitation by offering comprehensive information on forecasted values, including their associated uncertainty, thereby enabling their more effective utilization in risky decision-making environments. This paper presents a parametric probabilistic model designed for hourly load forecasting. The model is refined through a multi-objective genetic algorithm optimization process that identifies explanatory variables from a specified set. The selected variables are combined linearly to predict the parameters of a probability distribution function for the hourly load. The process also selects the type of distribution from among those characterized by two parameters. The model is applied to data from a real distribution substation, yielding superior forecasting evaluation indexes compared to those achieved by two benchmark probabilistic load forecasting models.