bluuebunny/crossref_metadata_2025_split huggingface dataset:
The integrated methodology for assessing the safety of the road network infrastructure in Romania.
Authors: Dimitriu C I , Bujor C R 
Published: September 2024

Abstract
Romania has the highest road fatality rate in the EU and the lowest perception of road quality. This study presents the implementation of a preliminary procedure and tools provided by the European Commission for the safety assessment of the DN2-E85 road infrastructure, which covers 472.23 km. This road was selected due to the high number of road deaths between 2017 and 2022. The research focuses on the application of both proactive and reactive methodologies as required by Directive (EU) 2019/1936, which requires full road network safety assessments by 2024, with subsequent assessments every five years. The proactive methodology assesses the built-in road safety of the road infrastructure based on design parameters, while the reactive methodology is based on historical data on accidents and traffic flows. The integration of these methodologies allows the classification of road segments into five risk levels. Preliminary results indicate that approximately 59% of the length of the DN2-E85 road is classified as “very high priority”, highlighting significant safety deficiencies, which require actions to immediately improve traffic safety. This study highlights the need for systematic road infrastructure safety assessments and prioritisation of high-risk segments to reduce road fatalities and improve overall road safety. The findings serve as crucial information for road safety auditors and the Romanian Road Authority - ARR, supporting the identification of high-risk areas and the allocation of resources to improve safety. In addition, this research provides a basic basis for future adaptations of road infrastructure safety assessment guidelines, considering the specific characteristics of national and county roads. By aligning with EU directives and national standards, the study aims to increase road safety and reduce the incidence of serious accidents in Romania. The insights gained from this research are essential for developing effective road safety strategies and implementing remedial measures that address the most dangerous segments of the road network.
Enhancing tribological properties of pump shafts through shot peening: a study of surface roughness, hardness and performance comparison with heat treatment
Authors: Iyyadurai Jenish , Singh Ravindra Pratap , Arockiasamy Felix Sahayaraj , Mohanavel V , Kannan Sathish , Giri Jayant , Alarfaj Abdullah A 
Published: September 2024

Abstract
This research employed Shot Peening (SP) to enhance the tribological behavior of the En-31 pump shaft material. The study compared the effects of varying peening pressures (2.5, 3, 3.5, and 5 bar) and shot sizes (S110 and S230) on surface roughness, hardness, and tribological behavior. Surface roughness was quantified using profilometry to assess the impact of shot peening on the topography of AISI 4140 steel, while hardness testing evaluated the enhancement in surface hardness post-treatment, correlating with the induced compressive residual stresses. Additionally, wear analysis conducted through tribological testing determined the effectiveness of shot peening and plasma nitriding in improving wear resistance under operational conditions. The results shown highest hardness of 12.5% was achieved at a pressure of 4 bar with the S230 shot material, while the surface roughness increased by over 75% under the same conditions. The 3.5 bar peening process produced the most significant improvements based on fractographic analysis.
Cleanroom-free fabrication of flexible capacitive pressure sensors using paintable silver electrodes on stationery paper and random microstructured polydimethylsiloxane dielectric layer
Authors: Farman Mohd , Prajesh Rahul , Panwar Deepak Kumar , Kaur Maninder , Thouti Eshwar 
Published: September 2024

Abstract
In this work, we propose a facile, low-cost, and cleanroom-free approach for fabricating flexible capacitive pressure sensors based on paintable Ag electrodes on stationery paper substrates (Ag–paper electrodes) and a random microstructured polydimethylsiloxane (PDMS) dielectric layer transferred from emery paper. COMSOL Multiphysics simulations and experimental investigations suggest that the pressure sensor with random microstructured PDMS dielectric layer performs better than the sensor with ordered micropyramidal dielectric layer. The developed Ag–paper electrode and random microstructured PDMS dielectric layer-based pressure sensors are workable in a wide pressure range (up to 630 kPa) and exhibit a high sensitivity of 0.132 kPa−1 up to 1 kPa, low hysteresis (6.6%) with loading–unloading of ∼500 kPa pressure, high stability during a ∼5250 cyclic test, and the ability to sense a low pressure of ∼27 Pa. The developed sensor also successfully transduces arterial pulse wave forms when it is properly attached to the wrist. Using the proposed process, a flexible capacitive pressure sensor matrix of 4 × 4 array is also successfully developed for single- and multiple-point pressure mapping with minimal cross-talk. The proposed sensor process is simple and inexpensive to implement, and offers spatial pressure mapping for e-skin applications.
A qubit-efficient variational selected configuration-interaction method
Authors: Yoffe Daniel , Entin Noga , Natan Amir , Makmal Adi 
Published: September 2024

Abstract
Finding the ground-state energy of molecules is an important and challenging computational problem for which quantum computing can potentially find efficient solutions. The variational quantum eigensolver (VQE) is a quantum algorithm that tackles the molecular groundstate problem and is regarded as one of the flagships of quantum computing. Yet, to date, only very small molecules were computed via VQE, due to high noise levels in current quantum devices. Here we present an alternative variational quantum scheme that requires significantly less qubits than VQE. The reduction in the qubit number allows for shallower circuits to be sufficient, rendering the method more resistant to noise. The proposed algorithm, termed variational quantum selected-configuration-interaction (VQ-SCI), is based on: (a) representing the target groundstate as a superposition of Slater determinant configurations, encoded directly upon the quantum computational basis states; and (b) selecting a-priory only the most dominant configurations. This is demonstrated through a set of groundstate calculations of the H2, LiH, BeH2, H2O, NH3 and C2H4 molecules in the sto-3g basis set, performed on IBM quantum devices. We show that the VQ-SCI reaches the full configuration interaction energy within chemical accuracy using the lowest number of qubits reported to date. Moreover, when the SCI matrix is generated ‘on the fly’, the VQ-SCI requires exponentially less memory than classical SCI methods. This offers a potential remedy to a severe memory bottleneck problem in classical SCI calculations. Finally, the proposed scheme is general and can be straightforwardly applied for finding the groundstate of any Hermitian matrix, outside the chemical context.
Controlling and monitoring laser-mediated localized synthesis of silver nanoparticles within gold nanoapertures
Authors: Pillanagrovi Jayakumar , Dutta-Gupta Shourya 
Published: September 2024

Abstract
Photoreduction of metallic nanoparticles can be used for localized and deterministic synthesis of nanostructures required for various applications. Specifically, in-situ photoreduction of silver nanoparticles (AgNPs) is a facile fabrication route that enables the fabrication of nanoplasmonic substrates. In the present work, we demonstrate an approach to fabricate hybrid plasmonic substrates using in-situ photoreduction of AgNPs within gold nanoapertures (NAs). The approach involves the top-down approach to fabricate NAs followed by a site-specific synthesis of AgNPs within the NAs. Furthermore, we use dark-field imaging to monitor and understand the photoreduction within the gold NAs in real time. We demonstrate that a careful selection of experiment parameters aid to synthesize the AgNPs within NAs and the scattered light intensity can be used to map the characteristics of the formed AgNPs. The illumination time, laser power and AgNO3 concentration can be used to tune the AgNPs characteristics. The demonstrated strategy for realizing hybrid plasmonic substrates can be used for the fabrication of substrates for SERS based sensors, enhancing fluorescence of molecules, and photocatalytic applications.
Anderson impurity mechanism for a multi-level model in δ-Pu
Authors: Muñoz Alexander R , Jones Travis E 
Published: September 2024

Abstract
Electronic correlations and spin–orbit interactions in plutonium create variations in the bonding behavior of each of its allotropes. In δ-Pu, the 5f electrons lie at the tipping point between itinerant and localized behavior which has made the use of mixed-level models successful in describing its mechanical properties. The mechanism for the emergence of a mixed-level model has not yet been understood. We use a series of density functional theory approximations to understand the interactions that create a mixed-level description of δ-Pu which leads to accurate physical properties. With the intersite interactions present in the hybrid functional, we show that a single 5f electron engages in orbital-selective bonding that can be understood with an Anderson impurity picture. The Anderson model gives us a mechanism to understand how the bonding in δ-Pu evolves as a function of the interactions in the material such that we obtain both the accuracy and physics of the multi-level models from ab initio theory.
Experimental investigation on sustainable machining of monel using vegetable oils as cutting fluids and machine learning-based surface roughness prediction
Authors: M Ganesh , Arunkumar N , Siva M , Leo G M Lionus 
Published: September 2024

Abstract
The material processing industry is anticipated to mitigate environmental degradation. The protocols established by the International Organisation for Standardisation were adhered to. As a result, it would be prudent to investigate the feasibility of minimizing the use of synthetic cutting fluids from the machining process. This study discusses an environmentally-friendly machining technique for turning nickel-based alloy Monel-500, which evaluates four different cooling conditions: dry machining, flood machining, Co-MQL (coconut oil), and Rb-MQL (Rice Bran Oil). These conditions were tested by experimenting with various machining parameters to investigate four aspects of the turning process: surface finish,cutting temperature, tool wear and chip morphology. Rice bran oil is considered eco-friendly compared to synthetic cutting fluids, and employing it in minimum quantity is economical and helps improve the machined workpiece’s surface finish. The investigation has been further extended by applying machine learning algorithms to predict surface roughness, utilising two logical regressions implemented in Python. Among the two machine learning approaches, the random forest regression technique has demonstrated superior results, achieving a prediction accuracy of 99.8%. Consequently, a decision tree has been developed using this regression model to predict the surface roughness. The structured analysis of the decision tree provides more accurate conclusions, offering flexibility in adjusting parameters and expanding options for operation. As a result, the decision tree approach enables the efficient utilisation of production resources and enhances production capacity by making informed choices about cooling methods during the turning process. Rb-MQL has performed better in all aspects than the other three cooling conditions. When comparing machining under dry conditions, flood cooling, Co-MQL, and Rb-MQL (rice bran oil) reduce the tooltip temperature by 39.5%,25.45 and 24.11%, respectively. Rb-MQL reduced surface roughness by 28.23%,43.59 and 60.49% in contrast with machining under dry, flood, and Co-MQL.
Enhanced fault classification in inverter-fed transmission lines using deep learning
Authors: Etukuri Suresh , Siva M , Varma B R K 
Published: September 2024

Abstract
The conventional power transmission system with synchronous generators is protected with phase angle-based approach that decides which type of faut is occurred on the transmission line system. However, the phase angle-based fault classification method is no longer applicable for systems that have integrated Inverter Based Resources (IBR). The control technology employed for grid integration of renewable sources controls the fault current magnitude depending upon Grid code requirements which results in mal operation of relay. A new fault classification scheme is proposed in this paper which aims at precise faulty phase selection using deep learning techniques. Deep learning techniques have gained significance in the field of protection because of the big data availability from Phasor Measurement Units (PMU’s). This paper describes a new approach of fault classification for systems connected to renewables with the application of Long Short-Term Memory (LSTM) Deep learning network.
Mapping causal pathways with structural modes fingerprint for perovskite oxides
Authors: Ghosh Ayana , Ghosh Saurabh 
Published: September 2024

Abstract
Causality is innate to the determination of the fundamental mechanism controlling any physical phenomena. However, combining causality within the standard practices of computational modelling to understand structure-functionality connections is extremely rare. This work proposes a fingerprint based on key structural modes for ABO3-type perovskite oxides and its derivatives, combined with causal models, for predicting Kohn–Sham energies. Our study of causal models captures the inherent coupling between structural modes such as rotation, tilt and antiferroelectric displacements, responsible for phase transition, polarization, magnetization and metal–insulator transition, exhibited by these materials. Although developed for modelling specific functionality, this method is universally applicable to derive other functionalities and even different material classes while tracking hidden causal mechanisms via structural distortions.
Development and deployment of data-driven turbulence model for three-dimensional complex configurations
Authors: Sun Xuxiang , Liu Yilang , Zhang Weiwei , Wang Yongzhong , Zou Jingyuan , Han Zhengrong , Su Yun 
Published: September 2024

Abstract
In recent years, the synergy between artificial intelligence and turbulence big data has given rise to a new data-driven paradigm in turbulence research. Data-driven turbulence modeling has emerged as one of the forefront directions in fluid mechanics. Most existing studies focus on feature construction, selection, and the development of modeling frameworks, often overlooking the practical deployment and application of trained models. This paper examines the entire process from model construction to real-world deployment, using data-driven turbulence modeling for high Reynolds number flows over complex three-dimensional configurations as a case study. Key stages include data generation, input-output feature construction, model training, model compilation and optimization, deployment, and validation. We successfully implemented the entire workflow in a heterogeneous supercomputing environment and, through mixed programming techniques, integrated the resulting turbulence model into the Platform for Hybrid Engineering Simulation of Flows (PHengLEI) open-source software framework. This allowed for mixed-precision simulations, with the main equations solved in double precision and the turbulence model in half precision. The new computational framework was validated through large-scale parallel numerical simulations on grids with tens of millions of elements for three-dimensional complex configurations. The results highlight the efficiency of our model deployment, with overall computational efficiency improving by 13.35% and the turbulence model’s solution speed increasing by approximately 3.9 times. The accuracy of the computations was also confirmed, with the average relative error in the lift and drag coefficients calculated by the data-driven turbulence model within 3%. Across various computing nodes, the relative error in the computed aerodynamic coefficients remained within 1%, demonstrating the framework’s scalability. Notably, our contributions have been incorporated as a case study in the latest PHengLEI open-source project5

5

https://forge.osredm.com/PHengLEI/PHengLEI-TestCases/tree/master/Y02_ThreeD_M6_Unstruct_Branch_Ascend.
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Clinical bioinformatics desiderata for molecular tumor boards
Authors: Pallocca Matteo , Betti Martina , Baldinelli Sara , Palombo Ramona , Bucci Gabriele , Mazzarella Luca , Tonon Giovanni , Ciliberto Gennaro 
Published: September 2024

Abstract
Clinical Bioinformatics is a knowledge framework required to interpret data of medical interest via computational methods. This area became of dramatic importance in precision oncology, fueled by cancer genomic profiling: most definitions of Molecular Tumor Boards require the presence of bioinformaticians. However, all available literature remained rather vague on what are the specific needs in terms of digital tools and expertise to tackle and interpret genomics data to assign novel targeted or biomarker-driven targeted therapies to cancer patients. To fill this gap, in this article, we present a catalog of software families and human skills required for the tumor board bioinformatician, with specific examples of real-world applications associated with each element presented.
An efficient protocol for the extraction of pigment-free active polyphenol oxidase and soluble proteins from plant cells
Authors: Yuzuak Seyit , Xie De-Yu 
Published: September 2024

Abstract
The elimination of brownish pigments from plant protein extracts has been a challenge in plant biochemistry studies. Although numerous approaches have been developed to reduce pigments for enzyme assays, none has been able to completely remove pigments from plant protein extracts for biochemical studies. A simple and effective protocol was developed to completely remove pigments from plant protein extracts. Proteins were extracted from red anthocyanin-rich transgenic and greenish wild-type tobacco cells cultured on agar-solidified Murashige and Skoog medium. Protein extracts from these cells were brownish or dark due to the pigments. Four approaches were comparatively tested to show that the diethylaminoethyl (DEAE)-Sephadex anion exchange gel column was effective in completely removing pigments to obtain transparent pigment-free protein extracts. A Millipore Amicon® Ultra 10K cut-off filter unit was used to effectively desalt proteins. Moreover, the removal of pigments significantly improved the measurement accuracy of total soluble proteins. Furthermore, enzymatic assays using catechol as a substrate coupled with high-performance liquid chromatography analysis demonstrated that the pigment-free proteins not only showed polyphenol oxidase (PPO) activity but also enhanced the catalytic activity of PPO. Taken together, this protocol is effective for extracting pigment-free plant proteins for plant biochemistry studies. A simple and effective protocol was successfully developed to not only completely and effectively remove anthocyanin and polyphenolics-derived quinone pigments from plant protein extracts but also to decrease the effects of pigments on the measurement accuracy of total soluble proteins. This robust protocol will enhance plant biochemical studies using pigment-free native proteins, which in turn increase their reliability and sensitivity.
The poster boys of aspirational labor: parables of success and failure in The Viral Fever’s web shows
Authors: Guha Suryansu 
Published: September 2024

Abstract
Aspirational labor has been understood by scholars based in the Global North to encapsulate a form of labor undertaken by social media content creators that is mostly uncompensated for, but is, nonetheless, integral to the functioning of its economy. While this dominant theorization of aspirational labor is sometimes helpful for explaining the precarity of digital media labor in countries of the Global South, its uncritical application could also be severely limiting. I undertake a close textual analysis of select web shows created by the entertainment brand and YouTube content creators The Viral Fever (TVF) to demonstrate how it would be imprecise to see them as just victims of an exploitative economy. My article shows how TVF web shows consistently imagine elaborate cinematic narratives that valorize and embody the neoliberal values of aspiration and individual growth that in turn is complicit in the machinations of platform economy’s extractive affordances.
GRNet: a graph reasoning network for enhanced multi-modal learning in scene text recognition
Authors: Jia Zeguang , Wang Jianming , Jin Rize 
Published: September 2024

Abstract
Recent advancements in scene text recognition have predominantly focused on leveraging textual semantics. However, an over-reliance on linguistic priors can impede a model’s ability to handle irregular text scenes, including non-standard word usage, occlusions, severe distortions, or stretching. The key challenges lie in effectively localizing occlusions, perceiving multi-scale text, and inferring text based on scene context. To address these challenges and enhance visual capabilities, we introduce the Graph Reasoning Model (GRM). The GRM employs a novel feature fusion method to align spatial context information across different scales, beginning with a feature aggregation stage that extracts rich spatial contextual information from various feature maps. Visual reasoning representations are then obtained through graph convolution. We integrate the GRM module with a language model to form a two-stream architecture called GRNet. This architecture combines pure visual predictions with joint visual-linguistic predictions to produce the final recognition results. Additionally, we propose a dynamic iteration refinement for the language model to prevent over-correction of prediction results, ensuring a balanced contribution from both visual and linguistic cues. Extensive experiments demonstrate that GRNet achieves state-of-the-art average recognition accuracy across six mainstream benchmarks. These results highlight the efficacy of our multi-modal approach in scene text recognition, particularly in challenging scenarios where visual reasoning plays a crucial role.