bluuebunny/crossref_metadata_2025_split huggingface dataset:
A critical re-evaluation of the slope factor of the operational model of agonism: When to exponentiate operational efficacy
Authors: Randáková Alena , Nelic Dominik , Jakubík Jan 
Published: October 2023

AbstractAgonist efficacy denoting the “strength” of agonist action is a cornerstone in the proper assessment of agonist selectivity and signalling bias. The simulation models are very accurate but complex and hard to fit experimental data. The parsimonious operational model of agonism (OMA) has become successful in the determination of agonist efficacies and ranking them. In 1983, Black and Leff introduced the slope factor to the OMA to make it more flexible and allow for fitting steep as well as flat concentration–response curves. First, we performed a functional analysis to indicate the potential pitfalls of the OMA. Namely, exponentiation of operational efficacy may break relationships among the OMA parameters. The fitting of the Black & Leff equation to the theoretical curves of several models of functional responses and the experimental data confirmed the fickleness of the exponentiation of operational efficacy affecting estimates of operational efficacy as well as other OMA parameters. In contrast, fitting The OMA based on the Hill equation to the same data led to better estimates of model parameters. In conclusion, Hill equation-based OMA should be preferred over the Black & Leff equation when functional-response curves differ in the slope factor. Otherwise, the Black & Leff equation should be used with extreme caution acknowledging potential pitfalls.
Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network
Authors: Krenn Mario , Buffoni Lorenzo , Coutinho Bruno , Eppel Sagi , Foster Jacob Gates , Gritsevskiy Andrew , Lee Harlin , Lu Yichao , Moutinho João P. , Sanjabi Nima , Sonthalia Rishi , Tran Ngoc Mai , Valente Francisco , Xie Yangxinyu , Yu Rose , Kopp Michael 
Published: October 2023

AbstractA tool that could suggest new personalized research directions and ideas by taking insights from the scientific literature could profoundly accelerate the progress of science. A field that might benefit from such an approach is artificial intelligence (AI) research, where the number of scientific publications has been growing exponentially over recent years, making it challenging for human researchers to keep track of the progress. Here we use AI techniques to predict the future research directions of AI itself. We introduce a graph-based benchmark based on real-world data—the Science4Cast benchmark, which aims to predict the future state of an evolving semantic network of AI. For that, we use more than 143,000 research papers and build up a knowledge network with more than 64,000 concept nodes. We then present ten diverse methods to tackle this task, ranging from pure statistical to pure learning methods. Surprisingly, the most powerful methods use a carefully curated set of network features, rather than an end-to-end AI approach. These results indicate a great potential that can be unleashed for purely ML approaches without human knowledge. Ultimately, better predictions of new future research directions will be a crucial component of more advanced research suggestion tools.
Blood Surfaces in the Extra Corporeal Circuit: A Scanning Electron Microscopic Study
Authors: Kurusz Mark , Williams Dennis R. 
Published: October 2023

Despite advances in cardiovascular surgery and the use of new equipment for extracorporeal circulation (ECC), the goal of indefinitely safe ECC is not a reality yet. One of the limiting factors is trauma to the blood as it traverses the extracorporeal circuit. Of the factors that have been implicated as sources of trauma, obstructions or disturbances to flow and surface roughness are the two upon which this study is focused.
The method used was scanning electron microscopy because of the ability to visualize the microtopography of a typical ECC with great depth of field and high magnification. Unused components were examined and included: cannulae, tubing, connectors, bubble oxygenator, and microemboli filter. Only components from the major system (ie., excluding cardiotomy suction) were examined because most of the blood volume is contained within it and repeatedly comes in contact with its surfaces. Samples were examined at 50° tilt from the horizontal at magnifications ranging 100-2500X, with SOOX and 1000X considered most useful for visualization of the surfaces on a cellular level.
The results are a series of photomicrographs that show a variety of blood surfaces: some are smooth, and some are rough with ridges, crevices, and surface irregularities often larger than blood cells.
We conclude that rough surfaces do exist within the typical ECC system, and these surfaces are probably responsible, in part, for the trauma blood undergoes during ECC. Long term ECC will require a major improvement in the surface structure of many of the currently used components.
Gamson–Shapley Laws: a formal approach to parliamentary coalition formation
Authors: Demyanenko Nataliya , La Mura Pierfrancesco 
Published: October 2023

AbstractWe consider a set of empirical assumptions formulated by Gamson (1961), namely, Gamson’s Laws, which remain at the heart of government formation forecast in parliamentary systems. While the critical resource postulated in Gamson’s approach is the proportion of votes received by each party, other versions of Gamson’s Laws can be defined by a different choice of critical resource. We model coalition formation as a cooperative game, and provide axiomatic foundations for a version of Gamson’s Laws in which the critical resource is identified with strategic influence, as measured by the Shapley value. We compare the empirical accuracy of the resulting Gamson–Shapley theory against the original Gamson’s Laws in a panel of 33 parliamentary elections, and find that it leads to significantly more accurate predictions of both coalition structure and power distribution. Finally, we propose an extension of the Gamson–Shapley approach which also incorporates information about policy distance among coalition partners. In particular, we discuss the advantages of the extended approach in the context of the German elections in 1987 and 2017.
Boosting the photodetection of bulk aluminum nitride crystals-based MSM device through an additional electrode
Authors: Cao Yuan , Fan Zelong , Qin Zuoyan , Jin Lei , Li Baikui , Sun Zhenhua , Wu Honglei 
Published: October 2023

Aluminum nitride (AlN) exhibits excellent high-temperature resistance, chemical stability, and a wide bandgap, making it a prime candidate material for deep ultraviolet detectors. In this study, a modified metal–semiconductor–metal (MSM) photodetector using titanium (Ti) electrodes and millimeter-scale AlN bulk polycrystals grown through physical vapor transport is developed, demonstrating photoresponse to light from visible to vacuum ultraviolet. An additional tungsten (W) electrode is designed on the backside of the device, transforming it into a W-MSM device. A proper bias to the W electrode (VW) is found valid to boost the performance of the photodetector. Representatively, with a V of 20 V and VW of −12 V applied, the device achieves improvements in responsivity (R), detectivity (D*), and external quantum efficiency of 112.84%, 30.5%, and 112.84%, respectively, to 532 nm light and 123.18%, 36.84%, and 123.18%, respectively, to 193 nm light. Furthermore, it is found that with the total voltage being instant, optimizing the distribution of voltage between the Ti electrode and the W electrode would induce a better photoresponse than applying voltage solely to the Ti electrode (VW = 0 V). The reason is elaborated through modeling the voltage distribution in the device, revealing the particular role of the bulk semiconductor in this feature. This research provides a facile and innovative approach to developing low-power photodetectors for bulk materials.
Adjective phrase fronting in the Malacca Creole Portuguese noun phrase
Authors: Baxter Alan N 
Published: October 2023

Abstract
This paper is concerned with the grammar and origins of a focusing rule in Malacca Creole Portuguese, (MCP)
whereby an adjectival phrase (AdjP) may be extracted from the right branch of a noun phrase and fronted to a position prior to the
determiner. It begins by describing the characteristics of AdjP-fronting in MCP, according to determiner type, syntactic role of
the fronted adjective, syntactic role of the determiner phrase, and the structural complexity of the AdjP. Subsequently, it
considers the presence of AdjP-fronting in 19th and 20th century data of the Creole Portuguese of Tugu/Batavia, Mangalore,
Cannanore, Cochin and Sri Lanka. Building on these comparisons, it then addresses the potential influences of Dravidian
(Malayalam, Tamil) and Indo-Aryan (Bangla) substrates, and Dutch and English adstrates. The paper concludes that AdjP-fronting in
MCP may be added to the list of typological features that demonstrate the connection between the southern Indo-Portuguese creoles
and the Malayo-Portuguese creoles.
Review on high-performance bulk MgB2 superconductors
Authors: Muralidhar M , Shadab Malik , Sai Srikanth A , Jirsa M , Noudem J 
Published: October 2023

Abstract
Although the fascinating effect of superconductivity was discovered more than 100 years ago, only a few low-temperature superconductors, especially Nb3Sn and NbTi, have reached everyday-life use. After the discovery of superconductivity in MgB2 in 2001, a boom appeared in interest in employing MgB2 in commercial applications, facilitated by simple crystallographic structure, high critical temperature, low-cost raw ingredients, simple and fast production, and other advantages for moderate temperature applications. It only lacks high flux pinning and good mechanical performance. In this review, the implications of manufacturing procedures on the superconducting properties, crystal and electronic structure of MgB2 are discussed. The advantages and drawbacks of various manufacturing processes will be presented, as well as the ways how to improve MgB2 superconducting performance. The impact of oxygen reduction, elements doping, the introduction of defects and additives, and the effect of precursor refinement on superconducting properties are here compiled to give an overview of the progress of flux pinning enhancement in bulk MgB2.
Plasma-induced reversible surface modification and its impact on oxygen heterogeneous recombination
Authors: Afonso José , Vialetto Luca , Guerra Vasco , Viegas Pedro 
Published: October 2023

Abstract
A novel model is developed for atomic oxygen surface kinetics in silica-like walls, introducing a plasma-induced surface modification, which may impact intermediate pressure plasma reactors. The model is the first to reproduce experimental measurements in an oxygen glow discharge operating in the pressure range between 0.27 mbar (0.2 Torr) and 4 mbar (3 Torr), showing a decrease with pressure of the O recombination probability on Pyrex between 0.27 mbar and 1 mbar. The numerical simulations suggest that a modification is induced by the production and destruction of metastable chemisorption sites at the surface. As such, the Langmuir–Hinshelwood (L-H) and Eley-Rideal (E-R) recombination mechanisms take place involving not only physisorption and stable chemisorption sites, but also metastable chemisorption sites, produced by the impact of fast O2 ions and neutrals. The presence of metastable sites can be reversed by increasing the plasma pressure.
Reducing cost with MEMS sensor and improving performance of classifier using probabilistic voting method
Authors: Balaji P Arun , Sugumaran V 
Published: October 2023

Abstract
When developing a solution for fault diagnosis, cost is a critical factor that must be considered to ensure a practical and feasible final product. To reduce expenses, the authors conducted a feasibility study using a low-cost MEMS accelerometer in conjunction with an Arduino Uno. This study aimed to determine the effect of sample length on fault classification by varying it from 50 to 5000 to identify the optimal value for this particular application. After finalizing the parameters, the vibration signal was acquired using the MEMS sensor and Arduino Uno. The resulting classification accuracy using the decision tree was 90.2%, a satisfactory result that can be further improved for industrial applications. To enhance the accuracy of decision tree classifiers, the authors proposed a novel approach known as the probabilistic voting method. By implementing this method and utilizing an Arduino Uno and MEMS sensor (ADXL335), they were able to drastically cut down costs while achieving remarkable classification accuracy. The implementation of the probabilistic voting method further elevated the accuracy to an astounding 98.5%, setting a new benchmark in the field.
A visual SLAM method assisted by IMU and deep learning in indoor dynamic blurred scenes
Authors: Liu Fengyu , Cao Yi , Cheng Xianghong , Liu Luhui 
Published: October 2023

Abstract
Dynamic targets in the environment can seriously affect the accuracy of simultaneous localization and mapping (SLAM) systems. This article proposes a novel dynamic visual SLAM method with inertial measurement unit (IMU) and deep learning for indoor dynamic blurred scenes, which improves the front end of ORB-SLAM2, combining deep learning with geometric constraint to make the dynamic feature points elimination more reasonable and robust. First, a multi-directional superposition blur augmentation algorithm is added to the YOLOv5s network to compensate for errors caused by fast-moving targets, camera shake and camera focus. Then, the fine-tuned YOLOv5s model is used to detect potential dynamic regions. Afterward, IMU measurements are introduced for rotation compensation to calculate the feature point velocity and estimate the motion speed of the camera, in order to estimate the real motion state of potential dynamic targets. Finally, real dynamic points will be removed and potential dynamic points will be reserved for subsequent pose estimation. Experiments are conducted on Technische Universität München dynamic dataset and in the real world. The results demonstrate that the proposed method has significant improvement compared with ORB-SLAM2, and has a more robust performance over some other state-of-the-art dynamic visual SLAM systems.
Extended in-field and out-of-field validation of a compact Monte Carlo model of an IBA PROTEUS®ONE proton beam in TOPAS/GEANT4
Authors: Colson Dries , Blommaert Jeroen , Poels Kenneth , De Saint-Hubert Marijke , Reniers Brigitte , Depuydt Tom 
Published: October 2023

Abstract

Objective: This study evaluates a compact Monte Carlo (MC) model of a pencil beam scanning clinical proton beam using TOPAS to estimate the dose out-of-field (OOF). Compact modelling means that the model starts from a pristine proton beam at the nozzle exit, customised based on acceptance and commissioning data, instead of modelling the full treatment head and room. Approach: First, in-field validation tests were performed. Then, the OOF dose was validated in an RW3 phantom with bubble detectors for personal neutron dosimetry (measuring the neutron dose equivalent) and thermoluminiescent detectors (measuring the absorbed dose by protons and gammas). Measurements were performed at 15 and 35 cm from the distal edge of the field for five different irradiation plans, covering different beam orientations, proton energies and a 40 mm range shifter. TOPAS simulations were performed with QGSP Binary Cascade HP (BIC) and QGSP Bertini HP (Bertini) hadron physics lists. Main results: In-field validation shows that MC simulations agree with point dose measurements within −2.5 % and +1.5 % at locations on- and off-axis and before, in and after the Bragg peak or plateau. The gamma passing rate 2%/3mm of four simulated treatment plans compared to the dose distribution calculated by the TPS exceeds 97 % agreement score. OOF dose simulations showed an average overestimation of 27 % of the neutron dose equivalent for the BIC hadron physics list and an average underestimation of 20 % for the Bertini hadron physics list. The simulated absorbed dose of protons and gammas showed a systematic underestimation which was on average 21 % and 51 % for BIC and Bertini respectively. Significance: Our study demonstrates that a compact MC model can reliably produce in-field data, while out-of-field dose data are within the uncertainties of the detector systems and MC simulations nuclear models, and do so with shorter modelling and faster calculation time.
Optimal batch determination for improved harmonization and prognostication of multi-center PET/CT radiomics feature in head and neck cancer
Authors: Wu Huiqin , Liu Xiaohui , Peng Lihong , Yang Yuling , Zhou Zidong , Du Dongyang , Xu Hui , Lv Wenbing , Lu Lijun 
Published: October 2023

Abstract

Objective. To determine the optimal approach for identifying and mitigating batch effects in PET/CT radiomics features, and further improve the prognosis of patients with head and neck cancer (HNC), this study investigated the performance of three batch harmonization methods. Approach. Unsupervised harmonization identified the batch labels by K-means clustering. Supervised harmonization regarding the image acquisition factors (center, manufacturer, scanner, filter kernel) as known/given batch labels, and Combat harmonization was then implemented separately and sequentially based on the batch labels, i.e. harmonizing features among batches determined by each factor individually or harmonizing features among batches determined by multiple factors successively. Extensive experiments were conducted to predict overall survival (OS) on public PET/CT datasets that contain 800 patients from 9 centers. Main results. In the external validation cohort, results show that compared to original models without harmonization, Combat harmonization would be beneficial in OS prediction with C-index of 0.687–0.740 versus 0.684–0.767. Supervised harmonization slightly outperformed unsupervised harmonization in all models (C-index: 0.692–0.767 versus 0.684–0.750). Separate harmonization outperformed sequential harmonization in CT_m+clinic and CT_cm+clinic models with C-index of 0.752 and 0.722, respectively, while sequential harmonization involved clinical features in PET_rs+clinic model further improving the performance and achieving the highest C-index of 0.767. Significance. Optimal batch determination especially sequential harmonization for Combat holds the potential to improve the prognostic power of radiomics model in multi-center HNC dataset with PET/CT imaging.
Optimization of high-temperature superconducting multilayer films using artificial intelligence
Authors: Rivasto Elmeri , Todorović Milica , Huhtinen Hannu , Paturi Petriina 
Published: October 2023

Abstract
We have studied the possibility of utilizing artificial intelligence (AI) models to optimize high-temperature superconducting (HTS) multilayer structures for applications working in a specific field and temperature range. For this, we propose a new vortex dynamics simulation method that enables unprecedented efficiency in the sampling of training data required by the AI models. The performance of several different types of AI models has been studied, including kernel ridge regression (KRR), gradient-boosted decision tree (GBDT) and neural network. From these, the GBDT based model was observed to be clearly the best fitted for the associated problem. We have demonstrated the use of GBDT for finding optimal multilayer structure at 10 K temperature under 1 T field. The GBDT model predicts that simple doped-undoped bilayer structures, where the vast majority of the film is undoped superconductor, provide the best performance under the given environment. The obtained results coincide well with our previous studies providing further validation for the use of AI in the associated problem. We generally consider the AI models as highly efficient tools for the broad-scale optimization of HTS multilayer structures and suggest them to be used as the foremost method to further push the limits of HTS films for specific applications.
Nonlinear dynamics in eyring-powell fluid flow with darcy-forchheimer effects. An asymptotic analysis
Authors: Rahman S , Luis Díaz Palencia José , Reyes Enrique G 
Published: October 2023

Abstract
We provide a mathematical treatment, analytical and numerical, for a fluid constructed as an hybrid of the Eyring-Powell and Darcy-Forchheimer fluid models. The Eyring-Powell model departs from the kinetic theory of liquids and it allows for a description of shear stresses and viscous terms. The Darcy-Forchheimer model permits to describe the fluid effects given in a porous media, and it provides non-linear reaction terms when considered as part of the momentum equations. Hence, it is natural to investigate mathematical characteristics of solutions for a fluid flow formulated as a combination of these two fluid models. First of all, we prove boundedness and uniqueness of solutions arising from rough (i.e. in L
1(R) ∩ L
∞(R)) initial data. This is physically relevant, since it means that we are considering general descriptions of the velocity distribution of the fluid, in a media with particular porosity distributions. Afterwards, stationary profiles are obtained by using a Hamiltonian description, and our construction is supported by numerical validating evidences. Furthermore, asymptotic solutions are explored based on an exponential scaling and a non-linear transport Jacobi equation. Finally, a region of validity for this asymptotic approach is provided, and a numerical validation of our asymptotic analysis is presented. Our main conclusion is that a fluid model combining Eyring-Powell and Darcy-Forchheimer characteristics is indeed possible to introduce, and that solutions of potential physical interest, can be obtained.
An attention-based multi-modal MRI fusion model for major depressive disorder diagnosis
Authors: Zheng Guowei , Zheng Weihao , Zhang Yu , Wang Junyu , Chen Miao , Wang Yin , Cai Tianhong , Yao Zhijun , Hu Bin 
Published: October 2023

Abstract

Objective. Major depressive disorder (MDD) is one of the biggest threats to human mental health. MDD is characterized by aberrant changes in both structure and function of the brain. Although recent studies have developed some deep learning models based on multi-modal magnetic resonance imaging (MRI) for MDD diagnosis, the latent associations between deep features derived from different modalities were largely unexplored by previous studies, which we hypothesized may have potential benefits in improving the diagnostic accuracy of MDD. Approach. In this study, we proposed a novel deep learning model that fused both structural MRI (sMRI) and resting-state MRI (rs-fMRI) data to enhance the diagnosis of MDD by capturing the interactions between deep features extracted from different modalities. Specifically, we first employed a brain function encoder (BFE) and a brain structure encoder (BSE) to extract the deep features from fMRI and sMRI, respectively. Then, we designed a function and structure co-attention fusion (FSCF) module that captured inter-modal interactions and adaptively fused multi-modal deep features for MDD diagnosis. Main results. This model was evaluated on a large cohort and achieved a high classification accuracy of 75.2% for MDD diagnosis. Moreover, the attention distribution of the FSCF module assigned higher attention weights to structural features than functional features for diagnosing MDD. Significance. The high classification accuracy highlights the effectiveness and potential clinical of the proposed model.