Potential Uncertainties Research Articles

ECCE sensitivity studies for single hadron transverse single spin asymmetry measurements

We performed feasibility studies for various single transverse spin measurements that are related to the Sivers effect, transversity and the tensor charge, and the Collins fragmentation function. The processes studied include semi-inclusive deep inelastic scattering (SIDIS) where single hadrons (pions and kaons) were detected in addition to the scattered DIS lepton. The data were obtained in pythia6 and geant4 simulated e+p collisions at 18 GeV on 275 GeV, 18 on 100, 10 on 100, and 5 on 41 that use the ECCE detector configuration. Typical DIS kinematics were selected, most notably Q2>1GeV2, and cover the x range from 10−4 to 1. The single spin asymmetries were extracted as a function of x and Q2, as well as the semi-inclusive variables z, which corresponds to the momentum fraction the detected hadron carries relative to the struck parton, and PT, which corresponds to the transverse momentum of the detected hadron relative to the virtual photon. They are obtained in azimuthal moments in combinations of the azimuthal angles of the hadron transverse momentum and transverse spin of the nucleon relative to the lepton scattering plane. In order to extract asymmetries, the initially unpolarized MonteCarlo was re-weighted in the true kinematic variables, hadron types and parton flavors based on global fits of fixed target SIDIS experiments and e+e− annihilation data. The expected statistical precision of such measurements is extrapolated to 10 fb−1 and potential systematic uncertainties are approximated given the deviations between true and reconstructed yields. Similar neutron information is obtained by comparing the ECCE e+p pseudo-data with the same from the EIC Yellow Report and scaling the corresponding Yellow Report e+3He pseudo-data uncertainties accordingly. The impact on the knowledge of the Sivers functions, transversity and tensor charges, and the Collins function has then been evaluated in the same phenomenological extractions as in the Yellow Report. The impact is found to be comparable to that obtained with the parametrized Yellow Report detector and shows that the ECCE detector configuration can fulfill the physics goals on these quantities.

Uncertain design optimization of automobile structures: A survey

<abstract> <p>In real life, there are a lot of uncertainties in engineering structure design, and the potential uncertainties will have an important impact on the structural performance responses. Therefore, it is of great significance to consider the uncertainty in the initial stage of structural design to improve product performance. The consensus can be reached that the mechanical structure obtained by the reliability and robustness design optimization method considering uncertainty not only has low failure risk but also has highly stable performance. As a large mechanical system, the uncertainty design optimization of key vehicle structural performances is particularly important. This survey mainly discusses the current situation of the uncertain design optimization framework of automobile structures, and successively summarizes the uncertain design optimization of key automobile structures, uncertainty analysis methods, and multi-objective iterative optimization models. The uncertainty analysis method in the design optimization framework needs to consider the existing limited knowledge and limited test data. The importance of the interval model as a non-probabilistic model in the uncertainty analysis and optimization process is discussed. However, it should be noted that the interval model ignores the actual uncertainty distribution rule, which makes the design scheme still have some limitations. With the further improvement of design requirements, the efficiency, accuracy, and calculation cost of the entire design optimization framework of automobile structures need to be further improved iteratively. This survey will provide useful theoretical guidance for engineers and researchers in the automotive engineering field at the early stage of product development.</p> </abstract>

The Impact of Clinical Features in Radiomics of CT Non-Small Cell Lung Cancer

Purpose: To investigate the impact of clinical features on model performance in CT-based Non-Small Cell Lung Cancer (NSCLC) and the potential uncertainty regarding their application in machine learning. Methods: Clinical and radiomic features were retrospectively retrieved from EMR and CT images of 496 NSCLC patients. Five feature datasets were constructed: radiomic features-only (Rad), clinical features-only (Clin), shape features-only (Shape), radiomic and clinical features (RaClin), shape and clinical features (ShClin). Five feature selection methods and seven predictive models, along with different cohort sizes, number of input features and validation methods were included for the uncertainty analysis, with two-year survival as the study endpoint. AUC values were calculated for comparisons and Kruskal-Wallis testing was performed to determine significant differences. Results: A total of 19740 distinct combinations of feature sets, feature selection methods, predictive models, cohort sizes and validation techniques are examined. Of those, 25 combinations produce an AUC > 0.7. The clinical-only feature dataset generally outperforms both the radiomic-only feature dataset and the hybrid (clinical and radiomic) feature dataset (P<0.01), which is primarily determined by the endpoint. The combination of different feature selection methods and predictive models, along with the variations in cohort size, number of input features and validation methods generate inconsistent results. Conclusion: Clinical features are a source of data that can improve machine learning model performance. However, its impact strongly depends on various factors that may lead to inconsistent results. A clear approach to incorporate clinical features to generate reliable results requires further investigation.

Engineering of logistics flows

The article is devoted to the engineering of logistics flows. Logistics flow engineering aims to apply methods of fuzzy modeling of logistics processes according to indicators-criteria of fuzzy relationships, search for the most optimal counterparties that will allow achieving the best level of supply quality, taking into account all possible potential uncertainties. The author considers the algorithm of fuzzy modeling processes, which will improve the quality of development and the level of applied solutions to the problem of choosing a counterparty. It is advisable to use the fuzzy modeling algorithm in logistics flow engineering to obtain operational information, but taking into account the aggregation of input information data on the criteria of the fuzzy relationship system. The considered algorithm makes it possible to represent the relationships of criteria of various attributes, on the basis of which decisions can be made on aggregated indicators. This algorithm allows you to integrate the material and financial properties of logistics flows to increase the level of decisions based on the data system of information flows.

Research on the Risk Evaluation and Prevention Strategy of Cross-Border Merger and Acquisition Projects of Energy Enterprises Under the New Situation

At present, the world is undergoing profound changes unseen in a century, and the international political and economic governance system is undergoing profound changes. Protectionism and antiglobalization are on the rise, and the normalization of COVID-19 prevention and control has intensified international investment and economic and trade frictions. The domestic and foreign environment faced by the overseas investment and merger of Chinese power enterprises is increasingly complex, and the potential risks and uncertainties are increasing. Combing the energy industry key risk points of transnational mergers and acquisitions, this paper analyse the energy industry transnational M&A project political risk and legal risk evaluation method, and put forward the company transnational M&A project political risk and legal risk prevention strategy.

Economic Potential Gain, Income Uncertainty, and Rural Migrants’ Urban Homeownership: Evidence from China

Economic Potential Gain, Income Uncertainty, and Rural Migrants’ Urban Homeownership: Evidence from China

Automatic Routing Reconfiguration for Fault Tolerance in Smart Manufacturing Plants

This paper focuses on the parts routing problem in a reconfigurable manufacturing plant, in presence of potential faults and uncertainty on the job scheduling and duration. The plant is modeled as a directed graph, where the nodes represent either transportation modules or machines, and the edges represent the allowed transitions between adjacent nodes. The parts move across the plant along predefined sequences of nodes, therefore the system state tracks the progress of the parts along such sequences and the control inputs are the transitions to be activated to command the parts movement. Provided the sequences, the proposed method automatically generates feedback control rules for deadlock avoidance, which are employed by a path following strategy to compute the suitable control inputs, complying with given temporal-logic constraints and avoiding deadlock states. Additionally, the approach is extended to deal with faults affecting the transportation modules via the selection of new feasible sequences and the online reconfiguration of the system state. Finally, the proposed approach is tested in high-fidelity simulations, showing high computational efficiency and throughput.

Bitna povreda ugovora prema konvenciji UN o ugovorima o međunarodnoj prodaji robe

The concept of fundamental breach of contract under the United Nations Convention on Contracts for the International Sale of Goods (CISG) of 1980 plays a pivotal role in determining the legal remedies available to the aggrieved party. It allows for contract termination only in instances where a breach is deemed fundamental. This paper delves into a comprehensive analysis of the institution of fundamental breach of contract and its characteristics, providing insight into how the Convention distinguishes between a fundamental breach and a non-fundamental breach. As a result, it assists in resolving potential uncertainties and dilemmas the aggrieved party might face concerning the choice of legal remedies. The analysis begins with an exploration of the background and drafting process of Article 25 of the Convention. The focus then shifts to an in-depth analysis of the institution of fundamental breach of contract. This covers how and why the distinction between a fundamental breach and a non-fundamental breach emerged, leading up to an intricate examination of all the conditions and features of a fundamental breach of contract, all with the aim of accurately defining this term in line with the provisions of the Convention. The study also encompasses a review of pertinent judicial and arbitral practices, aiding in a better understanding of the practical application and interpretation of the institution of fundamental breach of contract. Special attention is devoted to analyzing how the aggrieved party can be confident in its right to terminate the contract and how to sidestep potential hazards and consequences of an unjustified termination. Through a detailed review of the Convention's provisions and both judicial and arbitral practices, this paper offers a succinct insight into the institution of fundamental breach of contract in the context of international sales of goods. It investigates how contracting parties can safeguard themselves and how they can act in accordance with the rights and obligations stipulated by the Convention.

Time Synchronization Uncertainty Estimation Methodology for data-centric Production Scenarios

In modern data-centric production, multiple sensors, machines, equipment, and systems are connected providing a high variety and amount of data to enable insights into ongoing production. By adding more of those data sources to production, the Time Synchronization Problem becomes more intense when aggregating the data streams for analysis. To tackle the Time Synchronization Problem and perform the actual synchronization of the data, an estimation of the potential uncertainties is required. In this paper, we present a methodology to identify and describe the chain of time series uncertainties from data sources to aggregation. This calculation takes into account the uncertainties from sensory and signal processing requirements (like signal processing cycle times) to network communication latencies and jitter in the production network. In the end, each data point gets augmented with information regarding the uncertainty of its time information in terms of the minimum and maximum added processing times in the data pipeline. Using this information, the data aggregation can be adapted accordingly, and the final analysis tailored down to the actual result uncertainties. To show this method and its applicability, a use-case in battery cell manufacturing is presented including a description of how this method can be applied and the data pipeline adapted.

Models of necrotizing enterocolitis

Necrotizing enterocolitis (NEC) is the leading cause of death and disability from gastrointestinal disease in premature infants. The mortality of patients with NEC is approximately 30%, a figure that has not changed in many decades, reflecting the need for a greater understanding of its pathogenesis. Progress towards understanding the cellular and molecular mechanisms underlying NEC requires the study of highly translational animal models. Such animal models must mimic the biology and physiology of premature infants, while still allowing for safe experimental manipulation of environmental and microbial factors thought to be associated with the risk and severity of NEC. Findings from animal models have yielded insights into the interactions between the host, the colonizing microbes, and the innate immune receptor Toll-like Receptor 4 (TLR4) in driving disease development. This review discusses the relative strengths and weaknesses of available in vivo, in vitro, and NEC-in-a-dish models of this disease. We also highlight the unique contributions that each model has made to our understanding of the complex interactions between enterocytes, microbiota, and immune cells in the pathogenesis of NEC. The overall purpose of this review is to provide a menu of options regarding currently available animal models of NEC, while in parallel hopefully reducing the potential uncertainty and confusion regarding NEC models to assist those who wish to enter this field from other disciplines.

Credibility consideration for digital twins in manufacturing

Digital Twin has become an important technology for advanced manufacturing. However, to ensure that digital twins provide valuable decision support, the results generated by the digital twins must be trustworthy for real manufacturing systems. Model credibility assessment including Verification, Validation, and Uncertainty Quantification (VVUQ) techniques need to be applied throughout the life cycle of digital twins. Verification and Validation (V&V) activities are necessary to ensure that a digital twin meets its intended purpose and design goals used to establish its credibility. Uncertainty Quantification (UQ) produces a measure of performance that users can apply as part of a credibility assessment for a given digital twin. Credibility assessment of digital twins also includes factors beyond VVUQ. This paper discusses requirements of the digital twin credibility assessment, identifies potential uncertainty areas of digital twins, introduces a new digital-twin framework standard, proposes potential extension of the standard with credibility consideration, and discusses other ongoing relevant standards.

PNNUAD: Perception Neural Networks Uncertainty Aware Decision-Making for Autonomous Vehicle

Most environment perception methods in autonomous vehicles rely on deep neural networks because of their impressive performance. However, neural networks have black-box characteristics in nature, which may lead to perception uncertainty and untrustworthy autonomous vehicles. Thus, this work proposes a decision-making method to adapt the potential perception uncertainty due to the sensor noises, fuzzy features, and unfamiliar inputs. The whole method is named as Perception Neural Networks Uncertainty Aware Decision-Making (PNNUAD) method. PNNUAD first uses the Monte Carlo dropout method to estimate the perception neural network uncertainty into a distribution around the original output. Then, the perception uncertainty will be considered in a designed reinforcement learning-based planner using a distributed value function. Finally, a backup policy will maintain the vehicle’s performance to avoid disastrous perception uncertainty. The evaluation section uses an augmented reality urban driving scenario; namely, the scenario builds in the CARLA simulator while the perception uncertainty comes from the real dataset. This case study focuses on the object class uncertainty of a widely used neural network, i.e., YOLO-V3. The results indicate that the proposed method can maintain AV safety even with poor perception performance. Meanwhile, the AV has not become too conservative by defending the perception uncertainty. This work is necessary for applying the statistics neural networks to safety-critical autonomous vehicles, and the source code will be open-source in this work.

Stochastic modelling of wellbore leakage in British Columbia

Wellbore leakage is complicated to understand due to the range of potential leakage pathways and uncertainties regarding their capacities. In this study we present a novel approach to modelling realistic leakage along microannulus pathways of varying thickness. We use stochastic methods to calibrate leakage pathway dimensions to the surface casing vent flow (SCVF) leakage rates reported in British Columbia, Canada. Results shows that representing dry microannulus thicknesses with a lognormal distribution provides a good fit for the intermediate ranges of SVCF flow rates, but that a dry microannulus alone cannot account for all instances of wellbore leakage. We then approach small and high flow rates independently, offering explanations for these. This includes a wet microannulus/mud channel model to account for instances of poor mud removal, which is better able to account for the less frequent higher leakage rates. We conclude that flow rates above 10 m3/day are progressively likely to be caused by significant failures in mud displacement during primary cementing, or other extreme events such as casing failure due to geological or operational factors.

From structural to transition effects: Institutional dynamism as a deterrent to long-term investments by MNEs

The effect of institutional change on foreign direct investment is often conceptualized through the lens of an improving or deteriorating level of institutional quality that alters transaction costs. However, in the context of comprehensive government intervention in the past decades, this perspective ignores the potential uncertainties and costs associated with the process of institutional change. We propose that institutional change causes structural changes in transaction costs as well as accompanying transition effects due to uncertainty and learning costs. The extent of such transition effects is linked to the process characteristics of institutional change, e.g., institutional dynamism. In this paper, we examine the effects of institutional dynamism on foreign direct investment in long-term capital commitments and hypothesize a negative relationship between institutional dynamism and FDI, and a moderating effect of institutional dynamism on the relationship between institutional quality and FDI. Using investment data by US MNEs aggregated on the host country level, we find support for our hypotheses with some qualifications. We derive implications for the middle-income trap discussion as well as the ongoing fast-paced transition towards a sustainable global economy that is bound to shift attention from differences in the level of institutional quality towards differences in transition processes.

Contrastive and Selective Hidden Embeddings for Medical Image Segmentation.

Medical image segmentation is fundamental and essential for the analysis of medical images. Although prevalent success has been achieved by convolutional neural networks (CNN), challenges are encountered in the domain of medical image analysis by two aspects: 1) lack of discriminative features to handle similar textures of distinct structures and 2) lack of selective features for potential blurred boundaries in medical images. In this paper, we extend the concept of contrastive learning (CL) to the segmentation task to learn more discriminative representation. Specifically, we propose a novel patch-dragsaw contrastive regularization (PDCR) to perform patch-level tugging and repulsing. In addition, a new structure, namely uncertainty-aware feature re- weighting block (UAFR), is designed to address the potential high uncertainty regions in the feature maps and serves as a better feature re- weighting. Our proposed method achieves state-of-the-art results across 8 public datasets from 6 domains. Besides, the method also demonstrates robustness in the limited-data scenario. The code is publicly available at https://github.com/lzh19961031/PDCR_UAFR-MIShttps://github.com/lzh19961031/PDCR_UAFR-MIS.

Research priorities in children and adults with congenital heart disease: a James Lind Alliance Priority Setting Partnership

ObjectiveTo bring together patients, parents, charities and clinicians in a Priority Setting Partnership to establish national clinical priorities for research in children and adults with congenital heart disease.MethodsThe established James...

Adaptive on Demand: Leveraging Cone Beam Computed Tomography-Based Adaptive Online Radiotherapy System as Simulation-Free Replan Platform

<h3>Purpose/Objective(s)</h3> Artificial intelligence powered cone beam computed tomography (CBCT) based online adaptive radiotherapy (ART) system offers a streamlined and efficient process for daily ART as the default. However, patients with gradual anatomy change from tumor response or weight loss may not significantly benefit from the daily ART. Instead, it will be more practical to adapt when changes are observed. In this work, we present a workflow and our early experience to utilize CBCT based ART system as a quick replan platform and the adapted plan is used for image guided radiotherapy (IGRT) until next adaptation. <h3>Materials/Methods</h3> Adaptive on demand is designed for patients treated with conventional fractionated dose and planned with conventional margins. The preplan is designed with 12-18 static IMRT fields with consideration of potential geometry/density change in beam arrangement and optimization to ensure the robustness of ART plan quality. Robustness evaluation is performed as needed. A plan revision process is required in the ART treatment planning system (TPS) to switch between adaptive and non-adaptive fractions in a treatment course. The plan is converted to the adaptive treatment upon physician's request. After each ART, the adapted plan is imported to TPS and physicist checks the quality of the synthetic CT (SCT), which is a deformed planning CT to CBCT geometry, and determines SCT/CBCT to be used as reference for the upcoming IGRT fractions. <h3>Results</h3> A total of 23 HN patients and 13 lung patients were treated with ART on demand from 09/2021-01/2022. 110 on-demand ART fractions analyzed. On average, each HN/lung patient received 1.9 (range 1-6)/5.2 (range 4-6) ART and 90.6%/85.3% frequency that the adapted plan was selected for treatment. Overall, the adapted plans show statistically significant improvements in tumor coverage and dose conformity comparing to original plan and meet the OAR objectives. Detailed dosimetric benefits are presented in other abstracts. Given the SCT is generated via deformable registration, the distortion of the SCT was observed in some cases. While dosimetric impact are negligible for the majority of cases, the geometric distortions of bony anatomy can lead to setup difficulty for the IGRT fractions and two patients were rescanned due to this reason. Cutting out the mask is recommended when it becomes loose to avoid SCT picking up mask as body and results in unsatisfactory planning target volume cropping from skin. The conversion to adaptive fraction requires 15 minutes while setting up adapted plan for IGRT fraction takes 25 minutes. Physician's time at console time is 18/14 minutes for HN and lung. <h3>Conclusion</h3> Leveraging CBCT based ART system as replan platform is feasible and satisfactory in plan quality. Physicist check on the SCT quality is crucial to prevent potential target generation uncertainties and setup error. This process significantly improved the turnaround time for replan and saves a trip for rescan.

Feasibility of 4D VMAT-CT

Objective. Feasibility of three-dimensional (3D) tracking of volumetric modulated arc therapy (VMAT) based on VMAT–computed tomography (VMAT-CT) has been shown previously by our group. However, 3D VMAT-CT is not suitable for treatments that involve significant target movement due to patient breathing. The goal of this study was to reconstruct four-dimensional (4D) VMAT-CT and evaluate the feasibility of tracking based on 4D VMAT-CT. Approach. Synchronized portal images of phantoms and linac log were both sorted into four phases, and VMAT-CT+ was generated in each phase by fusing reconstructed VMAT-CT and planning CT using rigid or deformable registration. Dose was calculated in each phase and was registered to the mean position planning CT for 4D dose reconstruction. Trackings based on 4D VMAT-CT+ and 4D cone beam CT (CBCT) were compared. Potential uncertainties were also evaluated. Main results. Tracking based on 4D VMAT-CT+ was accurate, could detect phantom deformation and/or change of breathing pattern, and was superior to that based on 4D CBCT. The impact of uncertainties on tracking was minimal. Significance. Our study shows it is feasible to accurately track position and dose based on 4D VMAT-CT for patients whose VMAT treatments are subject to respiratory motion. It will significantly increase the confidence of VMAT and is a clinically viable solution to daily patient positioning, in vivo dosimetry and treatment monitoring.

The leaf is always greener on the other side of the lab: Optical in-situ indicators for leaf chlorophyll content need improvement for semi-natural grassland areas

• Leaf chlorophyll content (LCC) is often assessed in-situ with the SPAD instrument. • We test this approach for herbs and grasses in temperate grasslands across seasons. • Potential uncertainties in the LCC-SPAD relationship were assessed. • Several species with distinct traits were not suitable for SPAD-LCC measures. • Related uncertainties in LCC assessments have to be considered in applications. Leaf chlorophyll content (LCC) is an important indicator of plant health. Earth observation facilitates LCC monitoring on large spatial scales and within short time intervals by retrieving canopy LCC from spectral signals. However, in-situ measurements are still necessary to validate the predicted values and the accurate quantification of LCC is labour-intensive and costly. Handheld leaf chlorophyll metres, such as the SPAD-502 (Konica Minolta) promise quick and non-destructive in-situ LCC estimates. SPAD values can be easily converted to LCC by established calibration curves that are based on measurements of single plant species. Due to these practical advantages, the application of SPAD-LCC calibration curves, originally derived in an agricultural context (Markwell et al., 1995), has become common practice in field studies. Although associated with a variety of uncertainties, the transfer of general SPAD-to-LCC calibration equations to natural ecosystems has not yet been comprehensively investigated. In this study, we test the reliability of SPAD-to-LCC conversions in three structurally different semi-natural grassland areas with mixed species compositions. We measured SPAD values in the field and determined the corresponding LCC of the very leaf samples via chemical extraction and spectrophotometry. Our results show that SPAD-to-LCC conversion equations lack accuracy in semi-natural grassland with mixed species compositions (n = 157, R 2 = 0.046, p < 0.05, for all sites). This study highlights the uncertainties in optical approaches for in-situ LCC assessments. It further underlines the need to find feasible and reliable solutions for a quick and efficient chlorophyll determination in natural habitats with mixed species compositions towards a reliable monitoring of ecosystem properties.

Global Distributions of Tropospheric and Stratospheric Gravity Wave Momentum Fluxes Resolved by the 9-km ECMWF Experiments

Abstract Based on 20-day control forecasts by the 9-km Integrated Forecasting System (IFS) at the European Centre for Medium-Range Weather Forecasts (ECMWF) for selected periods of summer and winter events, this study investigates global distributions of gravity wave momentum fluxes resolved by the highest-resolution-ever global operational numerical weather prediction model. Two supplementary datasets, including 18-km ECMWF IFS experiments and the 30-km ERA5, are included for comparison. In the stratosphere, there is a clear dominance of westward momentum fluxes over the winter extratropics with strong baroclinic instability, while eastward momentum fluxes are found in the summer tropics. However, meridional momentum fluxes, locally as important as the above zonal counterpart, show different behaviors of global distribution characteristics, with northward and southward momentum fluxes alternating with each other especially at lower altitudes. Both events illustrate conclusive evidence that stronger stratospheric fluxes are found in the ECMWF forecast with finer resolution, and that ERA5 datasets have the weakest signals in general, regardless of whether regridding is applied. In the troposphere, probability distributions of vertical motion perturbations are highly asymmetric with more strong positive signals especially over latitudes covering heavy rainfall, likely caused by convective forcing. With the aid of precipitation accumulation, a simple filtering method is proposed in an attempt to eliminate those tropospheric asymmetries by convective forcing, before calculating tropospheric wave-induced fluxes. Furthermore, this research demonstrates promising findings that the proposed filtering method could help in reducing the potential uncertainties with respect to estimating tropospheric wave-induced fluxes. Finally, absolute momentum flux distributions with proposed approaches are presented, for further assessment in the future.