Unique Input Research Articles

Monte Carlo Workflow Unification for Nuclear Reactor Analysis with Metamodel-Driven Modeling

Monte Carlo (MC) transport codes are a cornerstone of nuclear reactor analysis frameworks, providing reference solutions and multigroup cross sections, and even as core components in multiphysics couplings. These applications can be seen in toolkits from the Nuclear Energy Advanced Modeling and Simulation program and the U.S. Nuclear Regulatory Commission’s BlueCRAB (Comprehensive Reactor Analysis Bundle). Contrary to their ubiquitousness in reactor physics modeling and simulation, popular MC codes, such as MCNP, Serpent, and KENO, are still reliant on antiquated textual input formats. These input languages use a plethora of keywords with terse syntax to specify all facets of a model, including its geometry, materials, physics settings, and tally options. This poses a steep learning curve and a poor user experience. Being tied to unique text-based input formats also significantly complicates programmatic input generation or modification that may be desired and/or required within a multiphysics framework. This work demonstrates how the development of programmatic interfaces for MC codes can support model unification and translation activities. Building on the Python application program interface (API) development of MCNPy, similar capabilities are being implemented for Serpent that are able to support model translation. In future work, the Serpent API capabilities will be made more robust and the work will be further expanded to include translations with KENO.

OneTrack - An End2End approach to enhance MOT with Transformers

This paper introduces OneTrack, an innovative end-to-end transformer-based model for Multiple Object Tracking (MOT), focusing on enhancing efficiency without significantly compromising accuracy. Addressing the challenges inherent in MOT, such as occlusions, varied object sizes, and motion prediction, OneTrack leverages the power of vision transformers and attention layers, optimizing them for real-time applications. Utilizing a unique Object Sequence Patch Input and a Vision Transformer Encoder, the model simplifies the standard transformer approach by employing only the encoder component, significantly reducing computational costs. This approach is validated using the MOT17 dataset, a benchmark in the field, ensuring a comprehensive evaluation against established metrics like MOTA, HOTA, and IDF1. The experimental results demonstrate OneTrack's capability to outperform other transformer-based models in inference speed, with a marginal trade-off in accuracy metrics. The model's inherent design limitations, such as a maximum of 100 objects per window, are adjustable to suit specific applications, offering flexibility in various scenarios. The conclusion highlights the model's potential as a lightweight solution for MOT tasks, suggesting future work directions that include exploring alternative data representations and encoders, and developing a dedicated loss function to further enhance detection and tracking capabilities. OneTrack presents a promising step towards efficient and effective MOT solutions, catering to the demands of real-time applications.

Dimensions of parenting during infancy: Testing a latent bifactor model

AbstractIn the current paper, we examined the dimensional structure of parenting using a bifactor model of observed parenting domains during infancy. We validated our parenting dimensions by examining correlations with indicators of family well‐being and child development. Data come from a longitudinal cohort that recruited a diverse sample of mothers (56.5% White) and their newborn child (50.7% males). Parenting was assessed using multiple indicators at an average of 18 months (N = 397). Bifactor modelling revealed a general latent dimension of sensitivity and two unique dimensions indexing emotional input and cognitive input. We found a pattern of shared and unique correlations of the three parenting dimensions with family and child well‐being. The most robust correlations were with the general sensitivity dimension, which we argue cuts across parentings behaviors and is fundamental to development.

The mediating effect of team trust on shared leadership and team performance

PurposeAs team members temporarily assume the role of leader, a system of shared leadership emerges. This study had three purposes: (a) to test the underlying three dimensions of shared leadership behaviors, (b) to examine the relationship between shared leadership behaviors and team performance, and (c) to examine the mediating effect of trust between team members’ perceptions of shared leadership and performance.Design/methodology/approachWe used the sub-dimensions of shared leadership: relation-oriented shared leadership (ROSL), task-oriented shared leadership (TOSL), and creativity-oriented shared leadership (COSL). We collected survey data from college student teams at two different time points.FindingsThis study’s factor analysis results supported a second-order factor model that explains shared leadership with TOSL, ROSL, and a new COSL construct. Additionally, we discovered that shared leadership behaviors predicted team performance both directly and indirectly through team trust.Originality/valueThis study confirms the role of the new sub-dimension of COSL originally discovered by video analysis of project teams (Leight et al., 2018), thereby adding value to shared leadership research. This quantitative study supports the COSL with TOSL and ROSL in a second-order model where each component contributes unique input into the team dynamics. Our findings underscore the significance of shared leadership in elevating team trust, ultimately resulting in improved team performance. This insight holds particular relevance for educational management and leadership, offering a framework for understanding how shared leadership practices can positively influence team dynamics within academic contexts.

A Comprehensive Literature Review of Traditional and Spatial Methods for Black Spot Identification in Road Crash Analysis

The desire to greatly reduce traffic accidents, enhance road safety, and remove black spots from junctions has led to an increased interest in researching the various black spot (BS) identification systems. Conventional and spatial Black spot detection systems are created using a variety of concepts and methodologies; however, as each approach has a unique input and output based on zones, area, size, and other factors, it might not be suitable for every situation. The next step in this research is to conduct a comprehensive literature review of various Black spot detection approaches and the technology tools that support them. The goal is to identify, evaluate, and assess the acceptability, feasibility, accuracy, and appropriateness of various scenarios and parameters. Through accounting for several types of inadvertent correlation between the location of the accident and traffic information, the research discovered multiple methods for Black spot identification, each based on unique ideas and a range of methods and instruments. According to the study, each BS identification method has particular advantages and disadvantages. The principal objectives of this review are to assess the principal methods for evaluating traffic accidents, identify black spots, and explore possible distinctions between traditional and spatial traffic data analysis. To execute the statistical outcomes of occurrences that are examined, both the conventional method and GIS are used. This review paper provides an overview of three basic GIS approaches, explains a traditional method for identifying traffic incidents, and provides some often-used accident analysis tools for traffic safety.

Towards sustainable manufacturing: A comprehensive analysis of circular economy key performance indicators in the manufacturing industry

In the pursuit of sustainable development, the concept of Circular Economy (CE) has emerged as a transformative alternative to the traditional linear economic model characterized by take, make, use, and dispose practices. However, there is a growing recognition of the limitations of the linear economy in addressing contemporary global challenges, leading to a pressing need for a more regenerative approach. This study aims to explore and analyze Key Performance Indicators (KPIs) associated with the circular economy, particularly focusing on the manufacturing industry, to provide a valuable guide for enterprises looking to integrate CE principles effectively and reinforce sustainability. The novelty of this study lies in its comprehensive examination of KPIs that embrace sustainability's triple bottom lines - environmental, economic, and social dimensions. The systematic selection of relevant KPIs and the utilization of Social Network Analysis (SNA) to identify the most influential CE KPIs within the manufacturing industry add depth to the research. By identifying and upgrading significant KPIs representing various facets of CE development, this study contributes to a more meticulous understanding of how enterprises can measure their progress towards circularity. In each of the triple bottom lines of sustainability, five significant KPIs were recognized and upgraded to the second assessment process, where these 15 KPIs are analyzed using SNA to obtain 5 aggregated KPIs. The comprehensive analysis of the obtained fifteen KPIs culminates in aggregated CE indicators, primarily strategies and initiatives (SI), material efficiency (ME), productivity of remanufacturing (OR), technology investment (ROR), and eco-innovation (EI). The aggregated Key Performance Indicators (KPIs) displayed an extreme value of the weighted average index attributed to strategies and initiatives KPI with a value of 0.067. These aggregated KPIs provide a practical framework for manufacturers to assess and optimize their circular economy practices. It's essential to note that all binary values incorporated in the proposed approach are derived from an extensive literature review. These values may vary depending on the specific objectives of individual manufacturing firms or focus groups. As a result, the proposed approach is adaptable to diverse manufacturing industries, allowing customization to suit the unique input criteria of each firm or group. Significantly, the current work emphasizes the influential role of manufacturers in shaping a sustainable future in alignment with development goals.

Enhancing Procedural Writing Through Personalized Example Retrieval: A Case Study on Cooking Recipes

AbstractWriting high-quality procedural texts is a challenging task for many learners. While example-based learning has shown promise as a feedback approach, a limitation arises when all learners receive the same content without considering their individual input or prior knowledge. Consequently, some learners struggle to grasp or relate to the feedback, finding it redundant and unhelpful. To address this issue, we present , an adaptive learning system designed to enhance procedural writing through personalized example-based learning. The core of our system is a multi-step example retrieval pipeline that selects a higher quality and contextually relevant example for each learner based on their unique input. We instantiate our system in the domain of cooking recipes. Specifically, we leverage a fine-tuned Large Language Model to predict the quality score of the learner’s cooking recipe. Using this score, we retrieve recipes with higher quality from a vast database of over 180,000 recipes. Next, we apply to select the semantically most similar recipe in real-time. Finally, we use domain knowledge and regular expressions to enrich the selected example recipe with personalized instructional explanations. We evaluate in a 2x2 controlled study (personalized vs. non-personalized examples, reflective prompts vs. none) with 200 participants. Our results show that providing tailored examples contributes to better writing performance and user experience.

Online Predictive Visual Servo Control for Constrained Target Tracking of Fixed-Wing Unmanned Aerial Vehicles

This paper proposes an online predictive control method for fixed-wing unmanned aerial vehicles (UAVs) with a pan-tilt camera in target tracking. It aims to achieve long-term tracking while concurrently maintaining the target near the image center. Particularly, this work takes the UAV and pan-tilt camera as an overall system and deals with the target tracking problem via joint optimization, so that the tracking ability of the UAV can be improved. The image captured by the pan-tilt camera is the unique input associated with the target, and model predictive control (MPC) is used to solve the optimization problem with constraints that cannot be performed by the classic image-based visual servoing (IBVS). In addition to the dynamic constraint of the UAV, the perception constraint of the camera is also taken into consideration, which is described by the maximum distance between the target and the camera. The accurate detection of the target depends on the amount of its feature information contained in the image, which is highly related to the relative distance between the target and the camera. Moreover, considering the real-time requirements of practical applications, an MPC strategy based on soft constraints and a warm start is presented. Furthermore, a switching-based approach is proposed to return the target back to the perception range quickly once it exceeds the range, and the exponential asymptotic stability of the switched controller is proven as well. Both numerical and hardware-in-the-loop (HITL) simulations are conducted to verify the effectiveness and superiority of the proposed method compared with the existing method.

Measurement of Ξc+ production in pPb collisions at sNN=8.16 TeV at LHCb

A study of prompt Ξc+ production in proton-lead collisions is performed with the LHCb experiment at a center-of-mass energy per nucleon pair of 8.16 TeV in 2016 in pPb and Pbp collisions with an estimated integrated luminosity of approximately 12.5 and 17.4 nb−1, respectively. The Ξc+ production cross section, as well as the Ξc+ to Λc+ production cross-section ratio, are measured as a function of the transverse momentum and rapidity and compared to the latest theory predictions. The forward-backward asymmetry is also measured as a function of the Ξc+ transverse momentum. The results provide strong constraints on theoretical calculation and are a unique input for hadronization studies in different collision systems. ©2024 CERN, for the LHCb Collaboration 2024 CERN

Counter-Offset mode: A new paradigm in resisting differential cryptanalysis

This study introduces the Counter-Offset mode, a novel advancement in block cipher encryption techniques designed to enhance the traditional Counter mode’s resistance to differential cryptanalysis. By integrating a unique input transformation mechanism, the Counter-Offset mode significantly improves upon the security features of the conventional Counter mode without compromising its efficiency and ability to process data blocks in parallel. Through a rigorous security analysis, we demonstrate that this innovative mode not only maintains the essential advantages of its predecessor—including parallelizability and low overhead—but also offers increased protection against cryptanalytic attacks. Our findings suggest that the Counter-Offset mode presents a compelling solution for applications requiring high security without the expense of reduced performance. This work lays the groundwork for future investigations into its application across various cryptographic systems, highlighting its potential to address emerging security challenges in the digital age.

Measurement of the Centrality Dependence of the Dijet Yield in p+Pb Collisions at sqrt[s_{NN}]=8.16 TeV with the ATLAS Detector.

ATLAS measured the centrality dependence of the dijet yield using 165 nb^{-1} of p+Pb data collected at sqrt[s_{NN}]=8.16 TeV in 2016. The event centrality, which reflects the p+Pb impact parameter, is characterized by the total transverse energy registered in the Pb-going side of the forward calorimeter. The central-to-peripheral ratio of the scaled dijet yields, R_{CP}, is evaluated, and the results are presented as a function of variables that reflect the kinematics of the initial hard parton scattering process. The R_{CP} shows a scaling with the Bjorken x of the parton originating from the proton, x_{p}, while no such trend is observed as a function of x_{Pb}. This analysis provides unique input to understanding the role of small proton spatial configurations in p+Pb collisions by covering parton momentum fractions from the valence region down to x_{p}∼10^{-3} and x_{Pb}∼4×10^{-4}.

Primena matematičke optimizacije u odlučivanju značajna za otpornost nacionalne bezbednosti - umreženo društvo kao osnov međuzavisnosti kritičnih resursa

Introduction/purpose: Destabilization of critical resources (CRs) or critical infrastructure (CI) important for the stability of the state can be dangerous for society, economy, and especially national security. Disruption of one CI object or one of its parts often affects and causes disruption of other dependent CI, because the modern society has become a "networked society". The paper proposes a model for quantifying and defining the interdependence between different CIs and their priorities, based on statements of experts. Methods 9 : The proposed methods that combine the Laboratory for Testing and Evaluation of Decision Making (DEMATEL) and the Analytical Network Process (ANP) have been successfully modified by fuzzy logic theory in this work. Results: Integrating multiple methods into a unique input data analysis model significantly affects the change in ranking. Conclusion: The work contributes to military science in making strategic decisions related to national security management through increasing the resilience of CRs and the societies that rely on them.

Personalised parameter estimation of the cardiovascular system: Leveraging data assimilation and sensitivity analysis

Detailed models of dynamical systems used in the life sciences may include hundreds of state variables and many input parameters, often with physical meanings. Therefore, efficient and unique input parameter identification, from experimental data, is an essential but challenging task for this class of models. This study presents a comprehensive analysis of a nine-dimensional single ventricle lumped-parameter model, representing the systemic circulation. This model is formulated in terms of differential algebraic equations, often found in other areas of the life sciences. We introduce a novel computational algorithm designed to incorporate patient-specific beat-to-beat variability into model investigations, utilising the Unscented Kalman Filter (UKF) for efficient parameter estimation. Our findings demonstrate the exceptional adaptability of the UKF to severe parameter perturbations, representing significant physiological changes. Furthermore, we provide novel insights into the continuous sensitivity of model input parameters, illustrating the robustness and efficacy of UKF. The monitoring of a patient’s physiological state, with minimal delay, becomes feasible, by incorporating patient-specific measurements and leveraging the UKF. The workflow presented in this paper enables prompt identification of pathophysiological conditions and will improve patient care.

Temporal Structures in Positron Spectra and Charge-Sign Effects in Galactic Cosmic Rays.

We present the precision measurements of 11years of daily cosmic positron fluxes in the rigidity range from 1.00 to 41.9GV based on 3.4×10^{6} positrons collected with the Alpha Magnetic Spectrometer (AMS) aboard the International Space Station. The positron fluxes show distinctly different time variations from the electron fluxes at short and long timescales. A hysteresis between the electron fluxes and the positron fluxes is observed with a significance greater than 5σ at rigidities below 8.5GV. On the contrary, the positron fluxes and the proton fluxes show similar time variation. Remarkably, we found that positron fluxes are modulated more than proton fluxes with a significance greater than 5σ for rigidities below 7GV. These continuous daily positron fluxes, together with AMS daily electron, proton, and helium fluxes over an 11-year solar cycle, provide unique input to the understanding of both the charge-sign and mass dependencies of cosmic rays in the heliosphere.

TransDose: Transformer-based radiotherapy dose prediction from CT images guided by super-pixel-level GCN classification.

Radiotherapy is a mainstay treatment for cancer in clinic. An excellent radiotherapy treatment plan is always based on a high-quality dose distribution map which is produced by repeated manual trial-and-errors of experienced experts. To accelerate the radiotherapy planning process, many automatic dose distribution prediction methods have been proposed recently and achieved considerable fruits. Nevertheless, these methods require certain auxiliary inputs besides CT images, such as segmentation masks of the tumor and organs at risk (OARs), which limits their prediction efficiency and application potential. To address this issue, we design a novel approach named as TransDose for dose distribution prediction that treats CT images as the unique input in this paper. Specifically, instead of inputting the segmentation masks to provide the prior anatomical information, we utilize a super-pixel-based graph convolutional network (GCN) to extract category-specific features, thereby compensating the network for the necessary anatomical knowledge. Besides, considering the strong continuous dependency between adjacent CT slices as well as adjacent dose maps, we embed the Transformer into the backbone, and make use of its superior ability of long-range sequence modeling to endow input features with inter-slice continuity message. To our knowledge, this is the first network that specially designed for the task of dose prediction from only CT images without ignoring necessary anatomical structure. Finally, we evaluate our model on two real datasets, and extensive experiments demonstrate the generalizability and advantages of our method.

Management Accounting Research Opportunities in Climate Change Reporting

ABSTRACT Climate risk is a critically important challenge for society. Current accounting-related efforts include initiatives by regulators to promulgate climate risk disclosure standards, focusing on disclosure of nonfinancial and forward-looking information. These proposals are certain to evolve over time. With management accounting research’s focus on decision-making and performance evaluation using nonfinancial performance measures and effective management control systems, management accounting researchers are in a strong position to provide unique input into the ongoing development of these reporting standards. In this commentary, we discuss topics where management accounting researchers can contribute to the standard-setting efforts. Contributing to these efforts requires a change in mindset for management accounting researchers from primarily focusing on internal and potentially idiosyncratic decision-makers to a broader audience that includes external providers of capital. However, the reward to doing so is participation in interesting and important research avenues that can have a real impact on society. JEL Classifications: M49; M48; M14.

Research On the Impact of Green Finance on Enterprise Innovation

With the rapid development of China's economy, the unique high input and high consumption of the extensive growth model has brought great negative impacts on the environment. China has begun to explore sustainable and high-quality development models, and how to effectively promote enterprise innovation and improve enterprise innovation capabilities has become an urgent problem that needs to be solved. This article selects manufacturing companies in the Chinese A-share market from 2012 to 2021 as research samples and uses a fixed effects model to study the relationship between green finance and enterprise innovation. The results show that green finance can effectively alleviate the problem of enterprise funding shortage, support enterprise innovation, and improve innovation output. Finally, this article proposes corresponding policy recommendations to encourage the country to continuously play the important role of green finance in deepening China's economic reform.

Development of deep convolutional neural network for prediction of cycle maximum pin power peaking factor in pressurized water reactor

A deep convolutional neural network (DCNN) model based on the SE-ResNet architecture and AlphaGo’s policy architecture is developed to predict the cycle maximum pin power peaking factor (PPPF) for a pressurized water reactor core. The DCNN model is structuralized to predict an output, the PPPF, corresponding to a unique input set by removing the pooling layer in between the blocks and introducing a padding for a quarter core input model. The developed DCNN model is trained on 17,000 randomly generated loading patterns (LPs) of an OPR1000 core and tested on 2,000 LPs. The accuracy of the newly developed model for the cycle maximum PPPF prediction is on average 6.5 times higher against a fully connected neural network model having the equivalent number of trainable weights.

Automatic modeling of PWR-core in the two-step reactor-core physics analysis code NECP-Bamboo

In the field of PWR-core physics analysis, the dominant methodology remains two-step, which requires analysts to provide core structure and operating parameters, as well as a comprehensive understanding of the methodology for performing lattice and core calculations in multiple and sequential steps. This approach is inefficient and error-prone, resulting in a time-consuming modeling process. To address these challenges, we introduce Bamboo-Frame, an automatic modeling program within the NECP-Bamboo reactor-core physics analysis code that streamlines the modeling process by automating the identification of unique lattices, generating lattice and core input files, and performing lattice and core calculations using Bamboo-Lattice and Bamboo-Core/Transient, respectively. This approach lowers the analysis threshold, reduces workload, ensures parameter consistency, and enables users to switch modeling options with minimal adjustments. Verification and analysis based on the BEAVRS benchmark demonstrate the effectiveness of Bamboo-Frame, including a quantitative assessment of the radial nodal number in each assembly and spacer treatment in the axial direction. Results show that while 1 × 1 nodal modeling exhibits higher relative errors in radial and axial detector responses compared to 2 × 2 nodal modeling, they still align well with measurements. The analysis of spacer treatment indicates that modeling spacers solely in their respective layers have a more pronounced effect on axial profiles, including power and detector response, while modeling spacers along the entire axial direction also offers good overall agreement.

Forest density and snowpack stability regulate root zone water stress and percolation differently at two sites with contrasting ephemeral vs. stable seasonal snowpacks

Forested, snow-dominated watersheds provide a range of ecosystem services for adjacent and downstream communities including water supply, wildlife habitat, and recreation. While forest-snow-water relations have been well studied in areas with stable seasonal snowpacks, less is known about these interactions in the growing number of areas with ephemeral snowpacks. Here, we evaluate how forest cover and climate variability regulate the seasonality of water inputs, soil moisture, root zone water stress, and percolation at two contrasting seasonal vs. ephemeral snowpack sites, in Arizona, USA. We use a soil moisture model (Hydrus-1D) and three years of continuous soil moisture measurements at two depths (0–10 cm and 0–30 cm) for 6 locations/site distributed across gradients of forest cover. Each soil moisture measurement location has a unique daily moisture input time series previously developed from local snow/rain observations and modeling. Compared to the seasonal snowpack site, the ephemeral site has wetter soils during winters due to frequent mid-winter melt events, leading to greater annual percolation. However, earlier snowmelt at the ephemeral snowpack site also prolongs the duration of root zone water stress. At both sites, dense forest cover is associated with less percolation and longer duration of soil drought, due to increased transpiration. These effects are amplified during an abnormally dry winter. This study examines two sites over three years and develops key steps towards using spatially distributed snowpack and soil moisture measurements to assess how changing climate and forest cover jointly regulate water availability for future ecosystems and downstream use.