Section Data Sets Research Articles

Characterization of uncertainties in electron-argon collision cross sections

Abstract The predictive capability of a plasma discharge model depends on accurate representations of electron-impact collision cross sections, which determine the corresponding reaction rates and electron transport properties. The values of cross sections can be known only approximately either through experiments or simulations and are thus subject to uncertainties. Quantifying the uncertainties in plasma simulations allows us to assess the reliability of simulations and to provide a basis for interpreting discrepancies between simulations and experiments. For such uncertainty quantification of plasma simulations, it is essential to quantify the uncertainties of the underlying cross sections. Although much effort has been committed to calibrate the cross section values, their uncertainties are not well investigated. We characterize uncertainties in electron-argon atom collision cross sections using a Bayesian framework. Six collision processes—elastic momentum transfer, ionization, and four excitations—are characterized with semi-empirical models, which effectively capture the features important to the macroscopic properties of the plasma. A probability model for the uncertain parameters of these semi-empirical models is developed. Specifically, a Gaussian-process likelihood model is proposed to capture discrepancies among data sets, as well as the model-form inadequacies of the semiempirical models. Two other likelihood models are compared with the proposed Gaussian-process model, to illustrate the importance of the choice of the likelihood model. The cross section models are calibrated using the electron-beam experiments and ab-inito quantum simulations. The resulting calibrated uncertainties capture well the scattering among the data sets. The calibrated cross section models are further validated against swarm-parameter experiments and zero-dimensional Boltzmann equation simulations of widely used cross section datasets.

Timing of Adolescent Alcohol Initiation and Unexplored Consequences: A Propensity Score Matching Approach

There is scarcity of research comparing two age groups on the consequences of adolescent alcohol initiation. Particularly, it remains unknown whether alcohol initiation in grade 9 or earlier or grade 10–12 predict different or similar consequences in adolescents’ friend network and educational aspiration. The current study focuses on whether initiating alcohol use in grade 9 or earlier or grade 10–12 is associated with adolescents’ frequency of getting drunk, number of drunk friends they have, and their intention to obtain higher education. We used the data from the Monitoring the Future - Public Use Cross Sectional Datasets (1976–2022). We used R to conduct the propensity score matching, multiple linear regression analyses and two-way ANOVA. After propensity score adjustment, only associations between initiating alcohol use in grade 10–12 and getting drunk and initiating alcohol use in grade 10–12 and drunk friends remained significant.

Understanding travel behaviour patterns and their dynamics: Applying fuzzy clustering and age-period-cohort analysis on longterm data of German travellers

This study examines how travel behaviour patterns change over time. It addresses the limitations of traditional segmentation studies, which often focus on static snapshots of travel behaviour. A comprehensive approach is proposed, integrating multi-dimensional segmentation and temporal analysis. Based on a large, repeated, cross- sectional dataset (1983–2018) from Germany, the study employs a research design that combines fuzzy clustering, to identify distinct tourist types including their heterogeneous behaviour, and additive logistic regression analysis, to analyse temporal changes in travel behaviour patterns. The findings reveal five tourist types based on their travel behaviour. These tourist types differ in sociodemographic characteristics and are related to each other. The chance of belonging to those tourist types changes over a tourists' life cycle (age), over time due to external factors (period) and across generations (cohort), providing insights into evolving travel behaviour. The findings from this study can help tourism stakeholders to adapt their strategies to changing tourist behaviour and improve destination management and marketing efforts.

Extension of ion-neutral reactive collision model DNT+ to polar molecules based on average dipole orientation theory

The ion-neutral reactive collision model DNT+, which generates comprehensive ion-neutral collision cross section (CS) data sets for atoms and nonpolar molecules, has been extended to polar molecules. The extension is based on the average dipole orientation (ADO) theory, which adds the dipole moment to Langevin–Hassé CS. Furthermore, the ADO CS for short-range reactive collisions is covered with a rigid core to incorporate long-range elastic and charge-exchange collisions. The modified version of DNT+, i.e., DNT+DM, is applied to gas-phase H2O+–H2O and low-energy CF3+–CO collisions for its validation. The cross sections (CSs) for those collisions using DNT+DM show good agreement with literature data, proving that DNT+DM is valid to some extent. Help with ion swarm analyses and measurements is needed to make the predicted CSs more accurate.

The Problems of Management of Informal Sector Activities: A Case Study of Ikwo Local Government, Ebonyi State, Nigeria

There have been growing concerns about the persistent rise in the size of the informal sector given the troubles and difficulties it presents the government in keeping an actual record of all economic activities in the state. This study investigated the reason behind this continuous rise and how this continuous rise has affected the management of informal activities in Ikwo Local Government. Adopting the Binary Logistics Regression (BLR) model, the study analysed a cross sectional dataset obtained from 200 informal sector operators. Based on the result of the empirical findings, the study discovered that government policies was a major factor that pushed individuals into the informal sector and that the persistent rise in the size informal sector was a strong factor that have made managing the sector difficult for the government. Therefore, the study established that if the government can reduce its tax rates, cut down a bit on the bureaucratic protocols in the formal sector, many informal operators will be encouraged to operate in the formal sector and thus contribute maximally to the overall economy of the state.

OmniSR-M: A Rock Sheet with a Multi-Branch Structure Image Super-Resolution Lightweight Method

With the rapid development of digital core technology, the acquisition of high-resolution rock thin section images has become crucial. Due to the limitation of optical principles, thin section imaging involves a contradiction between resolution and field of view. In order to solve this problem, this paper proposes a lightweight, fully aggregated network with multi-branch structure for super resolution of rock thin section images. The experimental results on the rock thin section dataset demonstrate that the improved method, called OmniSR-M, achieves significant enhancement compared to the original OmniSR method and also surpasses other state-of-the-art methods. OmniSR-M effectively recovers image details while maintaining its lightweight nature. Specifically, OmniSR-M reduces the number of parameters by 26.56% and the computation by 27.66% compared to OmniSR. Moreover, this paper quantitatively analyzes both the facies porosity rate and grain size features in the application scenario. The results show that the images generated by OmniSR-M successfully recover key information about the rock thin section.

Multi-scale feature fusion for prediction of IDH1 mutations in glioma histopathological images

Background and objectiveMutations in isocitrate dehydrogenase 1 (IDH1) play a crucial role in the prognosis, diagnosis, and treatment of gliomas. However, current methods for determining its mutation status, such as immunohistochemistry and gene sequencing, are difficult to implement widely in routine clinical diagnosis. Recent studies have shown that using deep learning methods based on pathological images of glioma can predict the mutation status of the IDH1 gene. However, our research focuses on utilizing multi-scale information in pathological images to improve the accuracy of predicting IDH1 gene mutations, thereby providing an accurate and cost-effective prediction method for routine clinical diagnosis. MethodsIn this paper, we propose a multi-scale fusion gene identification network (MultiGeneNet). The network first uses two feature extractors to obtain feature maps at different scale images, and then by employing a bilinear pooling layer based on Hadamard product to realize the fusion of multi-scale features. Through fully exploiting the complementarity among features at different scales, we are able to obtain a more comprehensive and rich representation of multi-scale features. ResultsBased on the Hematoxylin and Eosin stained pathological section dataset of 296 patients, our method achieved an accuracy of 83.575 % and an AUC of 0.886, thus significantly outperforming other single-scale methods. ConclusionsOur method can be deployed in medical aid systems at very low cost, serving as a diagnostic or prognostic tool for glioma patients in medically underserved areas.

Water masses stratification and circulation in the southern part of Banggai Upwelling Region in Maluku Sea

Banggai Waters is in the western Maluku Sea, where the secondary pathway of Indonesian Throughflow passes and seasonal coastal upwelling takes place in this region, providing a high marine productivity and potential fishing ground. This study aims to describe water mass stratification and circulation using CTD casts and SADCP section datasets from the BUDEE cruise in September 2022 onboard RV Baruna Jaya VIII. The results show that mixed layer depth varies between 43-112 m with a mean temperature of 25.82(± 0.88) ºC. North Pacific water origin (NPSW and NPIW) is present in this southern section. The depth of chlorophyll maximum is much shallower in the upwelling center. Furthermore, for the first time, we identified a strong northward flow from the Bote Strait, disrupting Banggai upwelling water with cooler and fresher Banda water. Estimates of northward transport volume through this strait is about 0.531 Sv.

The influences of boundary spanning capabilities and long–term orientation on opportunism in inter–firm relationships

ABSTRACT Opportunism is a common issue in inter – firm relationships, which increases transaction costs, impairs firm performance and supply chain efficiency, and deteriorates relationship quality. Previous studies have intensively investigated factors influencing opportunism but overfocusing on the organisational level. Recently, supply chain researchers have called for identifying factors influencing opportunism at the individual level to gain a more comprehensive understanding of opportunism. Based on transaction cost theory, resource dependence theory, and boundary spanning theory, this study seeks to advance the emerging research stream on opportunism at the individual level by investigating how buyer agents’ boundary spanning capabilities and firms’ long – term relationship orientation can help manage supplier agents’ opportunism. Using a cross – sectional data set of 406 buyer agents from manufacturing companies collected from February to April 2021 in Japan, we tested a set of hypotheses through regression and conditional process analyses. Our findings suggest that buyer agents’ strategic communication and firms’ long – term relationship orientation are critical factors that reduce supplier agents’ opportunism.

Theoretical cross sections for electron collisions relevant for ammonia discharges part 1: NH3, NH2, and NH

Besides being the worlds’ most important fertilizer precursor, ammonia could play an important role as hydrogen carrier in a decarbonized future. The efficient production and decomposition (or cracking) of ammonia are essential to this end. An electricity-driven technology of interest for both these processes are non-thermal plasmas. Plasma processes have the advantage of activating—even inert—molecules and initiating chemical reactions through electron collisions, rather than through conventional heating. However, a complete set of low-energy cross section data is not available for the electron collisions with ammonia (NH3) and its radicals, amidogen (NH2) and imidogen (NH). Here, we used the ab initio R-matrix method to determine theoretical cross sections for the low-energy electron collision processes with NH3, NH2, and NH. Additionally, we explored the contribution of the different processes towards dissociation (especially from electronic excited states). Where possible, we compared our theoretical cross section data with experimental data and/or previous recommendations. Lastly, our own recommended cross section data for the electron collisions are presented. Use of this complete set of electron collision data should contribute to a more accurate description of and better insights into the plasma-chemical kinetics behind plasma-assisted ammonia production and decomposition processes.

Evaluated and recommended cross section data for production of radionuclides with emerging interest in nuclear medicine imaging. Part 2: Single photon emission computed tomography (SPECT)

Reaction cross sections for production of 11 radionuclides with emerging interest in SPECT imaging are evaluated. Experimental cross section datasets for 40 possible production routes were compiled, discussed and a selection of sets per reaction was proposed for Padé fitting leading to recommended values with uncertainties. Only routes for which at least two independent experimental studies were found, with enough data in overlapping energy regions, were considered for selection and fitting. The results of the compilations and available fits are discussed for reactions leading to interesting SPECT radioisotopes: 44mSc (3 routes), 87mY (5 routes), 87gY(m+) (5 routes), 88Y (1 route), 97Ru (5 routes), 131I (1 route), 155Tb (4 routes), 159Gd (3 routes),167Tm-167mEr (8 routes), 191Os-191mIr (2 routes) and 203Pb (2 routes). Integral production yields, based on the Padé fits as recommended data, are calculated. A comparison of experimental sets with TALYS-code calculations (results in TENDL-2019 and TENDL-2021 libraries) is made.

GAF Representation of Millimeter Wave Drone RCS and Drone Classification Method Based on Deep Fusion Network Using ResNet

In recent years, with the rapid increase in the number of drones, the abuse of drones for activities such as terrorist attacks is bound to pose a threat to public safety and privacy. Therefore, it is very important to identify and classify them. Millimeter-wave (MMW) radar provides an effective means to detect drones. And with the development of B5G/6G technologies, a large number of MMW base stations will be deployed in cities in the future. These MMW base stations can be used as MMW radar through simple modification, which greatly saves the detection cost. Radar cross section, as a common data of radar, can provide a data source for drone recognition and classification. Therefore, using the publicly available radar cross section (RCS) data set of drones in the MMW band. We first encode the RCS series into a 2D Gramian angular field (GAF) representation and design a 2D ResNet-10 to classify them. Second, we propose a deep fusion network, which can be used as an RTR with RCS as the information source. The Experimental results show that 2D ResNet-10 is also effective in classifying GAF representations and its time consumption is less than 2D ResNet-18. Compared with other RCS-based classification methods, the performance of deep fusion network is the best.

Mixed reality navigation training system for liver surgery based on a high-definition human cross-sectional anatomy data set.

This study aims to use the three-dimensional (3D) mixed-reality model of liver, entailing complex intrahepatic systems and to deeply study the anatomical structures and to promote the training, diagnosis and treatment of liver diseases. Vascular perfusion human specimens were used for thin-layer frozen milling to obtain liver cross-sections. The 104-megapixel-high-definition cross sectional data set was established and registered to achieve structure identification and manual segmentation. The digital model was reconstructed and data was used to print a 3D hepatic model. The model was combined with HoloLens mixed reality technology to reflect the complex relationships of intrahepatic systems. We simulated 3D patient specific anatomy for identification and preoperative planning, conducted a questionnaire survey, and evaluated the results. The 3D digital model and 1:1 transparent and colored model of liver established truly reflected intrahepatic vessels and their complex relationships. The reconstructed model imported into HoloLens could be accurately matched with the 3D model. Only 7.7% participants could identify accessory hepatic veins. The depth and spatial-relationship of intrahepatic structures were better understandable for 92%. The 100%, 84.6%, 69% and 84% believed the 3D models were useful in planning, safer surgical paths, reducing intraoperative complications and training of young surgeons respectively. A detailed 3D model can be reconstructed using the higher quality cross-sectional anatomical data set. When combined with 3D printing and HoloLens technology, a novel hybrid-reality navigation-training system for liver surgery is created. Mixed Reality training is a worthy alternative to provide 3D information to clinicians and its possible application in surgery. This conclusion was obtained based on a questionnaire and evaluation. Surgeons with extensive experience in surgical operations perceived in the questionnaire that this technology might be useful in liver surgery, would help in precise preoperative planning, accurate intraoperative identification, and reduction of hepatic injury.

Evaluated electron scattering cross section dataset for gaseous benzene in the energy range 0.1-1000 eV.

In this study, a complete and self-consistent cross section dataset for electron transport simulations through gaseous benzene in the energy range 0.1-1000 eV has been critically compiled. Its reliability has been evaluated through a joint experimental and computational procedure. To accomplish this, the compiled dataset has been used as input for event-by-event Monte Carlo simulations of the magnetically confined electron transport through gaseous benzene, and the simulated transmitted intensity has been compared with the experimental one for different incident energies and benzene gas pressures.

Student Camera Use in Synchronous Classrooms: A Two-Study Exploration of CTML’s Embodiment Principle

Two studies were conducted to ascertain whether or not the embodiment principle of the Cognitive Theory of Multimedia Learning would apply to student’s use of cameras in synchronous online instruction. Results from a cross sectional dataset indicate that students who utilize their cameras report more positive outcomes than students who do not utilize their cameras. Results from a quasi-experimental design indicate that students do not report any significant differences between experiencing classes where their peers keep their cameras-on or when their peers keep their cameras-off.

Joint optimization of train scheduling and routing in a coupled multi-resolution space–time railway network

Train scheduling and routing are two critical and fundamental problems in the field of railway operations, especially under the large demand scenarios or complicated operation schemes. These problems are often solved separately and sequentially from the perspectives of different decision stages. To further make these two decision-making processes consistent, this paper develops a joint optimization model in a multi-resolution space–time network, by coupling the conflict-free routing constraints into the train scheduling process. To solve this model, a feedback iterative framework consisting of the Lagrangian relaxation and the branch-and-bound algorithm is developed, in which the Lagrangian relaxation aims to generate the train schedule and the branch-and-bound strategy with the constraint updating procedure resolves the potential micro conflicts. Compared with some existing approaches in the literature, the proposed model and solution algorithm have the merit of no requirement of optimization solvers. To test the performance of the proposed approach, we apply the algorithm to different small-scale operation scenarios and a real-life case with the INFORMS RAS 2016 (Railway Applications Section) dataset.

Is Innovation in Pakistan Driven by Specialisation or Diversity?

Innovation is among the main drivers of industrial development leading to economic growth. However, the question triggers that what drives innovation? Is innovation driven by specialisation or diversification? The literature has supported both, the specialisation and diversification as driver of innovation. Therefore, the purpose of this paper is to explore the determinants of innovation in Pakistan with a special emphasis on specialisation and diversity. The analysis is based on the cross sectional data set of 784 firms across 13 different cities of Pakistan, i.e. Investment Climate Survey (ICS) 2007, compiled by the World Bank Enterprise Group. Our findings have showed the positive relation between innovation and diversity i.e. diversity is conducive to innovation. On the other hand, specialisation has a negative effect i.e. it hinders innovation in cities of Pakistan. JEL Classification: C21, C25, O31, O32 Keywords: Innovation, Specialisation, Diversity, Logistic Regression

Utilizing high fidelity data into engineering model calculations for accurate wind turbine performance and load assessments under design load cases

AbstractWind turbines often have lower performance and experience higher loading in real operation compared to the original design performance. The reasons stem from the influences of complex atmospheric turbulence, blade contamination, surface imperfection and airfoil‐shape changes. Engineering models used for designing wind turbines are limited to information derived from blade sectional datasets, while details on the three‐dimensional blade characteristics are not captured. In these studies, a dedicated strategy to improve the prediction accuracy of engineering model calculations will be presented. The main aim is to present an elaborated effort to obtain a better estimate of the turbine loads in realistic operating conditions. The present studies are carried out by carefully utilizing data from high fidelity Computational Fluid Dynamics (CFD) computations into Blade Element Momentum (BEM) and Vortexline methods. The results are in a good agreement with detailed field measurement data of a 2.3 MW turbine. The studies are further extended to a large turbine having a rated power of 10 MW to provide an overview of its suitability for large turbines. Finally, calculations of the wind turbine under a realistic IEC design load case are demonstrated. The studies highlight important considerations for engineering modeling using BEM and Vortexline methods.

Comprehensive ion-molecule reactive collision model for processing plasmas

The ion-molecule collision model for endothermic reactions created by Denpoh and Nanbu, which is the so-called “Denpoh–Nanbu theory (DNT),” has been extended to exothermic reactions. In addition to short-range charge exchanges between ions and molecules, a long-range charge exchange has been incorporated into the extended theory named “DNT+” in this work. Although, even today, there still is a lack of ion-molecule collision data required as fundamental input to plasma simulations for processing plasmas, DNT+ can provide a comprehensive cross section data set of ion-molecule collisions, including elastic, both endothermic and exothermic reactions, and short- and long-range charge exchanges, as well as other inelastic collisions. The cross sections for Ar+-CF4 and H2+-H2 collisions obtained using DNT+ agree well with data from the literature. Therefore, DNT+ could be widely used, not only for plasma simulation but also as a tool to easily generate preliminary data prior to experiments, especially when ion-molecule cross sections are not available.

A model for Geant4-DNA to simulate ionization and excitation of liquid water by protons travelling above 100 MeV

Biological damage induced by ionizing radiation plays a major role in many application fields as radiotherapy and microdosimetry. Geant4-DNA Monte Carlo track-structure code has the capability to simulate the passage of radiation through liquid water, containing physical, physicochemical and chemical processes that lead the early DNA damage. For proton projectile, current models reach up to an incident energy of 100 MeV. In order to cover the entire energy regime involved in proton radiotherapy, this work presents a new model that extends proton ionization and excitation of liquid water up to 300 MeV. Calculation of cross section dataset is made for ionization of five ionization shells and five excitation levels of liquid water using the Relativistic Plane Wave Born Approximation (RPWBA). Implementation is validated through the spower and range examples of the official release, obtaining an agreement within 1% with respect to reference data published in ICRU90 report.