bluuebunny/crossref_metadata_2025_split huggingface dataset:
Low-voltage organic field-effect transistors by using solution-processable high-κ inorganic-polymer hybrid dielectrics
Authors: Rong Bin , Zhao Wei , Liao Yi , Zhang Yixiao , Zhu Yangyang , Shi Wei , Wei Bin 
Published: September 2024

Abstract
Organic field-effect transistors (OFETs) incorporating hybrid high-κ inorganic Al2O3 and polymer dielectrics, including polyvinyl alcohol (PVA), polystyrene (PS), or polymethyl methacrylate (PMMA), through solution-processing techniques were fabricated. The analyses revealed that the high surface energy and hydrophilicity property of Al2O3 and PVA, and the relatively hydrophobic property of PS surface, hindered the performance of corresponding OFETs. The Al2O3/PMMA-based OFET achieved the optimized performance, with a threshold voltage of −2.7 V, a hole carrier mobility of 0.056 cm2/Vs, and a current on/off ratio of 1.0 × 104 at a low operating voltage of −5 V. Through analyzing the characteristics of leakage current, capacitance, contact resistance, and trap density of OFETs, we found that the PMMA-engaged films possessed the optimized electrical properties. The introduction of PMMA eliminated the interfacial trapping, thereby lowering the threshold voltage and enhancing the performance of the device. The COMSOL Multiphysics simulation was conducted to confirm the physical mechanism. The strategy of utilizing Al2O3/PMMA hybrid dielectric could simultaneously ensure the low operating voltage and good performance of OFET, while guaranteeing the low leakage current by the thick PMMA.
The effect of oxidation on tribology behavior of nickel-graphite coated stainless steel SS420
Authors: Shirvani Kourosh , Taheri Morteza , Ebrahimi Tahere 
Published: September 2024

Abstract
The main solution to the challenge of maintaining maximum sealing in the compressor section of each gas turbine is the use of abradable coatings. These coatings have a double duty, including (a) maintaining the lagging and (b) protecting the tips of the rotor blades. Choosing the type of abradable coating primarily depends on the service temperature of the coating. Nickel-graphite (Ni-G) coating is a good choice for use up to 480 °C and, or steel/sub-alloy rotor blades. In this research, the Ni-G coating was applied by the flame spraying method of Ni-G powder with a thickness of about 250 μm on an SS420 stainless steel substrate. The effect of the composition of the bonding layer was also investigated using two compositions, Ni-5Al and NiCrAlY. Obtaining the knowledge of applying Ni-G coating by flame spraying, identifying the structural and compositional characteristics of the coating (through optical and electron metallography), and the effect that oxidation can have on the tribological behavior of the coating were among the goals of this project. The best conditions for spraying the Ni-G coating were achieved an oxygen gas pressure of 6 bar, oxygen flow rate of 18 L min−1, acetylene pressure of 1.5 bar, acetylene flow rate of 24 L min−1, and the distance between the gun head and the sample surface was 22 cm. The results showed that placing the coating in oxidizing conditions increases its coefficient of friction. The increase in the coefficient of friction was attributed to the formation of oxide shells on the surface of the coating after 500 h of exposure to oxidation conditions. Corresponding to the higher coefficient of friction, the oxidized coating showed a decrease in wear resistance as a result of oxidation. This result can show the decrease in abradable of this coating with increasing service time.
Extracting the crystal electric field levels of Ce-4f1 states in CeB6 by atomic multiplet simulations
Authors: Wang Yue , Wu Qi-Yuan , Chen Cheng , Miao Lin 
Published: September 2024

Abstract
The crystal electric field (CEF) is vital in defining the low-energy electronic structure of lanthanide compound, and thus very essential in understanding the many-body physics of the strongly correlated 4 f electrons. Many efforts have been made to determine the low-energy electronic structure of the intrinsically correlated material CeB6 and its derived compounds. In this paper, we performed atomic multiplet (AM) simulations on the Ce-4f1 states in CeB6 and directly fitted them with previously reported resonant inelastic x-ray scattering spectroscopy (RIXS). The simulation results suggest that the discrepancy of excitations energies extracted from Raman spectroscopy and RIXS is a result of intrinsic varied crystal electric field strength, which can be reconciled by considering the enhanced CEF by eliminating the insulating scenario.
Parametric optimization and energy loss analysis of a solar thermophotonic energy converter
Authors: Fan Chao , Wei Xueling , Liao Tianjun 
Published: September 2024

Abstract
In the present work, the model of a solar thermophotonic energy converter (STEC) is established, of which the energy balance constraint equations at both the hot and cold sides are numerically solved to uncover the dependencies of the operating temperatures of the light emitting diode (LED) and photovoltaic (PV) cell on thermal, electrical, and structural parameters. For a given band-gap 0.36 eV of the semiconductor and a concentrated factor 20 of the concentrator, the LED’s bias voltage and the PV cell’s output voltage are optimized to achieve a local maximum efficiency 13% of the STEC. Furthermore, the conditions are optimized to attain an overall maximum efficiency of 15.94%. The distributions of energy losses are presented to reveal the underlying loss mechanisms. The results obtained in this work can provide theoretical guidance for the efficient utilization of solar energy.
Investigation of compressive strength of primitive geometries printed through the fused deposition modeling technique with different path patterns
Authors: Yousefi Navid , Hashemi Ramin , Sedighi Mohammad 
Published: September 2024

Abstract
Fused Deposition Modeling (FDM) is a material-extrusion-based technique used primarily for rapid prototyping and sometimes for an actual servicing part. In the FDM technique, input parent materials are commercial polymers. FDM also has some manufacturing parameters, and the raster pattern significantly affects the mechanical performance of the FDM products. Due to its intrinsic nature, Acrylonitrile Butadiene Styrene (ABS) is widely used in many industries, such as automobiles, medicine, etc. Producing the primitive geometry and selecting the proper infill pattern is challenging. Therefore, the current research paper investigates the effects of various infill patterns on the compressive performance of the three geometries (sphere, 3-side, and 4-side pyramids) printed through the FDM technique out of ABS material. The compressive experiments were conducted on the printed samples and load-displacement curves were evaluated. The results reveal that the concentrate path pattern in the sphere samples has the highest compressive failure load (40127 N). Also, the compressive failure loads in the 3-side and 4-side pyramids fabricated with a 45°/−45° raster pattern are 30444 N and 44396 N, respectively. Finally, comprehensive discussions about the obtained results are stated.
Photon blockade in a double-transmon system with ultrastrong coupling
Authors: Tian 田 Tian 甜 , Zhu 朱 Xianhe 先和 , Tian 田 Xi 茜 
Published: September 2024

Abstract
The circuit quantum electrodynamics (QED) system has brought us into an ultrastrong and deep coupling regime in the light–matter interaction community, in which the quantum effect has attracted significant interest. In this study, we theoretically investigated the photon blockade phenomenon in a double-transmon system operating in an ultrastrong coupling regime. We considered the effect of the counter-rotating wave terms in the interaction Hamiltonian and derived the master equation in the eigenpresentation. We found that photon blockade occurred in only one of the eigenmodes, and the counter-rotating wave terms enhanced the blockade by reducing the minimum value of the second-order correlation function. This study will be beneficial for the design of single-photon devices in circuit QED systems, especially in the ultrastrong coupling regime.
SM-GMVAE: an intelligent model for defect quantification evaluation based on few ultrasonic signals
Authors: Li Zhifeng , Liu Xiaojian , Li Runchen , Song Shaoheng , Liu Weihua , Song Yaqin 
Published: September 2024

Abstract
The conventional defect quantification evaluation approaches based on machine learning requires massive amounts of labelled defect signals, which is expensive and time-consuming works. This paper proposed a novel Similarity Metric Gaussian Mixture Variational Auto-Encoder (SM-GMVAE) model, which enables quantify defect with few labelled defect signals. The SM-GMVAE model is designed based on few-shot learning, which includes two modules: feature extraction (FE) module and similarity metric (SM) module. The FE module is designed to extract the feature of defect signal via the Variational Auto-Encoder (VAE). The SM module is used to measure the similarity of two defect signals based on the Gaussian Mixture Model (GMM). Moreover, sparse filtering techniques are used to enhance the sparsity of the features in the SM module. To validate proposed model, some specimens with four various depth defects are designed and fabricated for ultrasonic non-destructive testing experiments. A dataset with defects of different depths is established to compare proposed model with other methods. Our method obtains state-of-the-art experimental results with few labelled defect signals. Different from many published papers, our model is trained with few labelled data, which is more close to engineering practical application than other evaluation model trained using large numbers of labelled data. In other words, the developed approach can realize more complex defect evaluation tasks (such as: size, location, shapes, etc) at very low data labelling cost.
A novel dual-band defected ground structure wearable microstrip patch antenna for breast tumor detection in biomedical applications
Authors: Gour Sonam , Rathi Amit , Rawal Pallav 
Published: September 2024

Abstract
This paper presents a dual-band, low-profile wearable antenna for detecting breast tumors in biomedical applications. The antenna is designed on the FR4 substrate with an optimized dimension of 32 × 27.5 × 0.61 mm3. The compact rectangular patch is miniaturized by cutting the rectangular slots on the front side. The partial ground structure is used for moving the resonance frequency in the ISM band region and for giving the tripping result of the return loss. A breast phantom is created between the transmitter and receiver for detecting the tumor in the phantom model. The different sizes and positions of the tumor in the breast phantom provide different values of the return loss. The final optimized design of the antenna is operated at 2.43 GHz and 3.32 GHz with −35 and −24 return losses respectively. It has been observed from the simulated and measured results that the antenna has a negligible difference between the reflected coefficient, radiation pattern and gain. Afterward, the antenna is also checked for biocompatibility, which represents its good performance. The on-body analysis of the antenna represents a minor variation in the results. The Specific Absorption Ratio (SAR) value of the antenna is in the acceptable range as defined for the wearable device. It is a promising compact wearable antenna that can be used in biomedical applications.
Plug-in charging or electric roads? Powering U.S. long-haul heavy-duty trucks in 2050
Authors: Yeow Lih Wei , Posen I Daniel , MacLean Heather L 
Published: September 2024

Abstract
Pervasive plug-in fast chargers and/or electrified roadways (eRoads) might address the limited range, long recharging times, and reliance on greenhouse gas (GHG)-intensive, costly, and heavy batteries associated with electrifying long-haul heavy-duty trucks (HDTs). While these large-scale interventions shift environmental and cost burdens onto infrastructure, there is a lack of studies investigating how eRoads affect system-level GHG emissions, costs, material use, and peak electric grid power demands. We compare these aspects for the case of electrifying U.S long-haul HDTs (Class 8) in 2050 powered by combinations of plug-in fast chargers and eRoads. Our model accounts for battery downsizing, energy consumption, and truck operation patterns in quantifying life cycle impacts of batteries, plug-in chargers, eRoads, and hourly truck electricity demand. We find that plug-in fast chargers and eRoads reduce annualized 2050 HDT life cycle GHG emissions by 8% to 14% compared to using long-range batteries, which in turn have at least 50% lower emissions than diesel trucks. Conductive rails, overhead lines, and wireless eRoads (amortized across light- and heavy-duty vehicles) have lower system-wide costs than long-range batteries, plug-in fast chargers, or diesel trucks. Cost savings from smaller batteries, lower energy use and avoided recharging time offset high eRoads capital costs. While eRoads can reduce both system-level GHG and costs compared to diesel trucks, these reductions are sensitive to eRoads capital costs and losses from wireless power transfer and air resistance. eRoads require less lithium (87%) and copper (67%) than long-range batteries but increase regional peak power demands by up to 32%. Efficient wireless power transfer and aerodynamic pantographs enhance eRoads’ GHG and cost advantages, which may diminish if future batteries are more energy-dense, cheaper, or less GHG intensive. If successfully deployed, eRoads present opportunities for tighter integration between the transportation and electricity infrastructure systems, enabling optimized charging strategies to lower GHG emissions and costs.
Fraud: The Evolving Threat
Authors: Betts Michael J , Eshelby Laura , Whitehouse-Hayes David 
Published: August 2024

Abstract
This chapter provides an overview of the emerging face of fraud, explained through the use of ‘fraudster personas’. The chapter draws on insights from tackling fraud during the global Covid-19 pandemic. It explores the range of methods used to commit fraud including the ever-increasing use of technology and digital enablers. It examines a range of fraud typologies focusing on the key enablers and vulnerabilities. It provides insight that counter fraud professionals can adopt to respond to fraud threats. It explores the factors around why fraud is more visible and recognized as a national security threat. It reviews the theories of fraud, and challenges their relevance in the twenty-first century. It suggests new models that could be adopted by the counter fraud professional. The chapter explains why investigation is just one of the many tools that may be deployed to counter fraud.
Methodological Approaches to Structural Change: Epidemiology and the Case for Reparations
Authors: Lawrence Jourdyn A , Shi Joy , Jahn Jaquelyn L , Himmelstein Kathryn E W , Feldman Justin M , Bassett Mary T 
Published: September 2024

Abstract
Scholars, activists, and policymakers have long called for reparations – a process of repair and restitution for harm and injustices done – to descendants of enslaved Africans in the U.S. as a structural intervention to address historic and ongoing injustices. However, there has been very limited epidemiologic work examining reparations. We explore some of the epidemiologic benefits and challenges of using causal inference frameworks to model reparations as an example of a large-scale, structural intervention that pushes the limits of what is considered “well-defined” and may violate key identification assumptions. Finally, we weigh these methodological limitations with the utility of assessing public health implications of reparations policies and conclude by discussing implications for future epidemiologic research.
Enteropathway: the metabolic pathway database for the human gut microbiota
Authors: Shiroma Hirotsugu , Darzi Youssef , Terajima Etsuko , Nakagawa Zenichi , Tsuchikura Hirotaka , Tsukuda Naoki , Moriya Yuki , Okuda Shujiro , Goto Susumu , Yamada Takuji 
Published: September 2024

Abstract
The human gut microbiota produces diverse, extensive metabolites that have the potential to affect host physiology. Despite significant efforts to identify metabolic pathways for producing these microbial metabolites, a comprehensive metabolic pathway database for the human gut microbiota is still lacking. Here, we present Enteropathway, a metabolic pathway database that integrates 3269 compounds, 3677 reactions, and 876 modules that were obtained from 1012 manually curated scientific literature. Notably, 698 modules of these modules are new entries and cannot be found in any other databases. The database is accessible from a web application (https://enteropathway.org) that offers a metabolic diagram for graphical visualization of metabolic pathways, a customization interface, and an enrichment analysis feature for highlighting enriched modules on the metabolic diagram. Overall, Enteropathway is a comprehensive reference database that can complement widely used databases, and a tool for visual and statistical analysis in human gut microbiota studies and was designed to help researchers pinpoint new insights into the complex interplay between microbiota and host metabolism.
CAIM: coverage-based analysis for identification of microbiome
Authors: Acheampong Daniel A , Jenjaroenpun Piroon , Wongsurawat Thidathip , Kurilung Alongkorn , Pomyen Yotsawat , Kandel Sangam , Kunadirek Pattapon , Chuaypen Natthaya , Kusonmano Kanthida , Nookaew Intawat 
Published: September 2024

Abstract
Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic approach. In this study, we developed a new bioinformatics tool, coverage-based analysis for identification of microbiome (CAIM), for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count–based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consistently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similarity of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and 44 primary liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers.
Graph neural networks are promising for phenotypic virtual screening on cancer cell lines
Authors: Vishwakarma Sachin , Hernandez-Hernandez Saiveth , Ballester Pedro J 
Published: September 2024

Abstract
Artificial intelligence is increasingly driving early drug design, offering novel approaches to virtual screening. Phenotypic virtual screening (PVS) aims to predict how cancer cell lines respond to different compounds by focusing on observable characteristics rather than specific molecular targets. Some studies have suggested that deep learning may not be the best approach for PVS. However, these studies are limited by the small number of tested molecules as well as not employing suitable performance metrics and dissimilar-molecules splits better mimicking the challenging chemical diversity of real-world screening libraries. Here we prepared 60 datasets, each containing approximately 30 000–50 000 molecules tested for their growth inhibitory activities on one of the NCI-60 cancer cell lines. We conducted multiple performance evaluations of each of the five machine learning algorithms for PVS on these 60 problem instances. To provide even a more comprehensive evaluation, we used two model validation types: the random split and the dissimilar-molecules split. Overall, about 14 440 training runs aczross datasets were carried out per algorithm. The models were primarily evaluated using hit rate, a more suitable metric in VS contexts. The results show that all models are more challenged by test molecules that are substantially different from those in the training data. In both validation types, the D-MPNN algorithm, a graph-based deep neural network, was found to be the most suitable for building predictive models for this PVS problem.
Temporal dynamics of post-feminist narratives: boundaries of empowerment in female-focused car ads (1967–2021)
Authors: Humeres Mónica , Farías Catalina 
Published: September 2024

Abstract
This study critically examines post-feminism through a 50-year analysis of car advertising targeted at women in Chile. It explores feminism’s evolving significance in media culture, aiming to establish a comparative framework to identify changes and consistencies in feminist-themed media discourses over different periods. Using a qualitative approach, we analyzed 74 ads from a women’s magazine from 1967 to 2021. The results reveal changing gender role discourses, showing a nuanced interplay between progressive components and neo-conservative principles across historical periods. The evolution of female-focused advertising within post-feminist narratives depicts portrayals of women isolated or alongside family, highlighting individuality but implying limited representations of collective empowerment. The findings underscore the need to analyze media messages aimed at women in shifting temporal contexts. We argue that analytical frameworks must critically assess the progression, or lack thereof, in women’s (misleading) emancipation amidst the transitions from commodity feminism to post-feminism and femvertising.
“Instagram is like a karela”: transnational digital queer politics and online censorship and surveillance in India
Authors: Kanchan Tanvi 
Published: September 2024

Abstract
This article explores transnational queer political flows and negotiations in Indian queer/trans communities on Instagram, situating this in its limits and restrictions as a public sphere that is corporate- and state-governed and subject to conditions of profitability, censorship, regulation, and algorithmic disciplining. Using insights from 23 in-depth interviews with queer/trans women and non-binary Instagram users and community organizers across India, I argue that binaries of Western/Indigenous, global/local, authentic/inauthentic are insufficient to understand Indian queer digital politics. I instead explore the political utility of agentic reclamations and negotiations of queer/trans identity by marginal queer/trans users. At the same time, drawing on participant experiences of content moderation, censorship, and corporate and state surveillance, I examine how the potentials of Instagram as a site to mediate articulations of a radical politics of queer liberation are restricted, thwarted, and reconfigured by platform design and policing.