bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting long-term outcomes of kidney transplantation in the era of artificial intelligence
Authors: Badrouchi Samarra , Bacha Mohamed Mongi , Ahmed Abdulaziz , Ben Abdallah Taieb , Abderrahim Ezzedine 
Published: December 2023

AbstractThe ability to accurately predict long-term kidney transplant survival can assist nephrologists in making therapeutic decisions. However, predicting kidney transplantation (KT) outcomes is challenging due to the complexity of the factors involved. Artificial intelligence (AI) has become an increasingly important tool in the prediction of medical outcomes. Our goal was to utilize both conventional and AI-based methods to predict long-term kidney transplant survival. Our study included 407 KTs divided into two groups (group A: with a graft lifespan greater than 5 years and group B: with poor graft survival). We first performed a traditional statistical analysis and then developed predictive models using machine learning (ML) techniques. Donors in group A were significantly younger. The use of Mycophenolate Mofetil (MMF) was the only immunosuppressive drug that was significantly associated with improved graft survival. The average estimated glomerular filtration rate (eGFR) in the 3rd month post-KT was significantly higher in group A. The number of hospital readmissions during the 1st year post-KT was a predictor of graft survival. In terms of early post-transplant complications, delayed graft function (DGF), acute kidney injury (AKI), and acute rejection (AR) were significantly associated with poor graft survival. Among the 35 AI models developed, the best model had an AUC of 89.7% (Se: 91.9%; Sp: 87.5%). It was based on ten variables selected by an ML algorithm, with the most important being hypertension and a history of red-blood-cell transfusion. The use of AI provided us with a robust model enabling fast and precise prediction of 5-year graft survival using early and easily collectible variables. Our model can be used as a decision-support tool to early detect graft status.
Investigation of effects of interlayer interaction and biaxial strain on the phonon dispersion and dielectric response of hexagonal boron arsenide
Authors: Behzad Somayeh , Chegel Raad 
Published: December 2023

AbstractIn this study, the effects of interlayer interaction and biaxial strain on the electronic structure, phonon dispersion and optical properties of monolayer and bilayer BAs are studied, using first-principles calculations within the framework of density functional theory. The interlayer coupling in bilayer BAs causes the splitting of out-of-plane acoustic (ZA) and optical (ZO) mode. For both structures, positive phonon modes across the Brillouin zone have been observed under biaxial tensile strain from 0 to 8%, which indicate their dynamical stability under tensile strain. Also, the phonon band gap between longitudinal acoustic (LA) and longitudinal optical (LO)/transverse optical (TO) modes for monolayer and bilayer BAs decreases under tensile strain. An appreciable degree of optical anisotropy is noticeable in the materials for parallel and perpendicular polarizations, accompanied by significant absorption in the ultraviolet and visible regions. The absorption edge of bilayer BAs is at a lower energy with respect to the monolayer BAs. The results demonstrate that the phonon dispersion and optoelectronic properties of BAs sheet could as well be tuned with both interlayer interaction and biaxial strain that are promising for optoelectronic and thermoelectric applications.
Investigating the effect of textural properties on CO2 adsorption in porous carbons via deep neural networks using various training algorithms
Authors: Mehrmohammadi Pardis , Ghaemi Ahad 
Published: December 2023

AbstractThe adsorption of carbon dioxide (CO2) on porous carbon materials offers a promising avenue for cost-effective CO2 emissions mitigation. This study investigates the impact of textural properties, particularly micropores, on CO2 adsorption capacity. Multilayer perceptron (MLP) neural networks were employed and trained with various algorithms to simulate CO2 adsorption. Study findings reveal that the Levenberg–Marquardt (LM) algorithm excels with a remarkable mean squared error (MSE) of 2.6293E−5, indicating its superior accuracy. Efficiency analysis demonstrates that the scaled conjugate gradient (SCG) algorithm boasts the shortest runtime, while the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm requires the longest. The LM algorithm also converges with the fewest epochs, highlighting its efficiency. Furthermore, optimization identifies an optimal radial basis function (RBF) network configuration with nine neurons in the hidden layer and an MSE of 9.840E−5. Evaluation with new data points shows that the MLP network using the LM and bayesian regularization (BR) algorithms achieves the highest accuracy. This research underscores the potential of MLP deep neural networks with the LM and BR training algorithms for process simulation and provides insights into the pressure-dependent behavior of CO2 adsorption. These findings contribute to our understanding of CO2 adsorption processes and offer valuable insights for predicting gas adsorption behavior, especially in scenarios where micropores dominate at lower pressures and mesopores at higher pressures.
Inefficiencies of augmented reality for different sexes and grades in Chinese vocational education
Authors: Yang Mingjian , She Dandan , Xu Qiong , Zhang Wei , Qu Chaonan , Hu Xiaofei 
Published: December 2023

AbstractNowadays, augmented reality (AR) is becoming more and more prevalent and used in many fields, including education. Previous studies have reported the positive effect of AR to advocate the use of AR in the classroom. However, it is unclear whether such a positive effect can be reported for any student. In this study, we recruited students from a Chinese vocational college. Due to the peculiar conditions in China, students in vocational colleges may have weaker learning abilities and worse academic performance than students in research universities. Furthermore, the sex and grade of students were considered. We conducted a three-stage experiment for the PPT-based group and the AR-based group separately. We taught the students the orthographic projection, a lesson from the Engineering Drawing course, which is widely learned by students who majored in architecture. The students’ academic performances were rated prior to class, right after class, and 1-week after class, through a direct examination at three stages. We found the inefficiency of AR for students in vocational colleges. The interaction effect between sex and grade of students was also reported. Students undergoing AR-based teaching could not perform better or even worse than students undergoing PPT-based teaching. We recommended that the teachers should consider whether to use AR in the classroom based on the characteristics of students.
Intense laser interaction with micro-bars
Authors: Elkind Michal , Cohen Itamar , Blackman David , Meir Talia , Perelmutter Lior , Catabi Tomer , Levanon Assaf , Glenzer Siegfried H. , Arefiev Alexey V. , Pomerantz Ishay 
Published: December 2023

AbstractIntense laser fields interact very differently with micrometric rough surfaces than with flat objects. The interaction features high laser energy absorption and increased emission of MeV electrons, ions, and of hard x-rays. In this work, we irradiated isolated, translationally-symmetric objects in the form of micrometric Au bars. The interaction resulted in the emission of two forward-directed electron jets having a small opening angle, a narrow energy spread in the MeV range, and a positive angle to energy correlation. Our numerical simulations show that following ionization, those electrons that are pulled into vacuum near the object’s edge, remain in-phase with the laser pulse for long enough so that the Lorentz force they experience drive them around the object’s edge. After these electrons pass the object, they form attosecond duration bunches and interact with the laser field over large distances in vacuum in confined volumes that trap and accelerate them within a narrow range of momentum. The selectivity in energy of the interaction, its directionality, and the preservation of the attosecond duration of the electron bunches over large distances, offer new means for designing future laser-based light sources.
Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare
Authors: Oniani David , Hilsman Jordan , Peng Yifan , Poropatich Ronald K. , Pamplin Jeremy C. , Legault Gary L. , Wang Yanshan 
Published: December 2023

AbstractIn 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has garnered a lot of attention in the medical research community, leading to debates about its application in the healthcare sector, mainly due to concerns about transparency and related issues. Meanwhile, questions around the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied. As a result, there are no clear solutions to address ethical concerns, and decision-makers often neglect to consider the significance of ethical principles before implementing generative AI in clinical practice. In an attempt to address these issues, we explore ethical principles from the military perspective and propose the “GREAT PLEA” ethical principles, namely Governability, Reliability, Equity, Accountability, Traceability, Privacy, Lawfulness, Empathy, and Autonomy for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding these ethical principles in a practical way that has been useful in the military and can be applied to healthcare for generative AI, based on contrasting their ethical concerns and risks. Ultimately, we aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI into healthcare practice.
Climate change strongly affects future fire weather danger in Indian forests
Authors: Barik Anasuya , Baidya Roy Somnath 
Published: December 2023

AbstractChanges in temperature, precipitation, wind speed, and relative humidity due to climate change are likely to alter future fire regimes. We quantified the impact of such changes on the fire weather of Indian forests using a fire weather index and high-resolution downscaled climate projections. While conventional wisdom contends that future temperature increases will increase fire weather indices, we find this to be true only in dry forests. In humid forests, the fire weather index will decrease despite the warming due to future increases in precipitation and/or relative humidity. Days with severe fire weather danger will increase by up to 60% in dry forests but will reduce by up to 40% in humid forests. The fire season will be longer by 3–61 days across the country and the pre-monsoon fire season will become more intense over 55% of forests. This study suggests for countries like India with fragmented forests and diverse ecoclimates, standards and mitigation strategies must be developed at regional instead of national level.
Misalignment between national resource inventories and policy actions drives unevenness in the energy transition
Authors: Owen John R. , Kemp Deanna , Schuele Waleria , Loginova Julia 
Published: December 2023

AbstractTo make projections about the future supply of minerals for the energy transition and set climate targets, it is important to understand inventories of mineral resources as well as national extraction policies. Here, we combine data on mining properties and policies between 2020 and 2023 for 18 countries with substantial resources of energy transition minerals to understand the alignment between the resource inventories and policy actions to make these mineral resources available to market. We find the distribution and near-term production-readiness of energy transition minerals varies across countries. The results show extraction policies align with demand for energy-transition minerals more strongly for countries in the OECD (Organization for Economic Cooperation and Development) compared with their non-OECD counterparts. We suggest these differences between countries could lead to global-scale delays in mitigating climate change and an uneven energy transition structured around national resource endowment, wealth, and inequality.
Improving anti‐Gram‐positive‐bacterial performance and osteogenesis for zinc alloy via mussel mimetic polydopamine with non‐antibiotic lysozyme and parathyroid hormone
Authors: Zhao Deren , Chen Tianhao , Chen Jinqian , Huang Banglun , Nie Jihua , Liu Hulin , Liu Yangkai , Chen Zilin , Zou Feng , Lu Qiqi , Tan Xiaodong , Xiang Yunjie , Li Jing , Zhang Yongping , Feng Xue , Rao Xi 
Published: December 2023

AbstractIn this work, a novel coating with a non‐antibiotic agent for inhibiting Gram‐positive bacteria and promoting osteogenesis was prepared on zinc‐aluminium alloy (ZA6‐1) via mussel mimetic polydopamine (PDA) containing lysozyme (LYS) and parathyroid hormone (PTH). The results indicate that as‐deposited coatings can efficiently decrease the degradation rate of ZA6‐1 from 0.52 to 0.16 mm/year, and the addition of LYS weakens the coating resistance, while the addition of PTH enhances the coating resistance. In spite that no obvious inhibition of Escherichia coli is observed, the coated zinc alloys show good in vitro antibacterial performance against Staphylococcus aureus. Compared with ZA6‐1 zinc alloys, the increase of antibacterial efficacy reaches 86.9%–90.1%. Furthermore, the lower hydrophilicity (26.4°), higher osteoblast cell viability (>100%), good osteoblast cell morphology and better osteoblast cell differentiation (ALP = 107.7%) for PDA‐LYS/PTH coated samples support that as‐prepared coating is promising for modifying biodegradable zinc implants.
Method of fault‐type recognition based on the dissolved gas analysis using a set of diagnostic criteria
Authors: Shutenko Oleg , Kulyk Oleksii 
Published: December 2023

AbstractThis paper proposes a method for recognising the fault type using a set of diagnostic criteria. Existing approaches to the interpretation of dissolved gas analysis (DGA) results usually rely on single diagnostic criteria. The proposed method solves the problems of incomplete use of diagnostic information and discrepancies in the norms and criteria for recognising the same fault inherent in the existing methods. The feature of the method is the analysis of the compliance of the DGA results of the diagnosed equipment with the ones of the equipment with a confirmed diagnosis from the fault database. This approach outperforms the existing methods in the number of faults recognised, allowing a wider range of faults to be detected, including combined faults. In addition, the method provides an opportunity to identify possible causes and localise the fault. This feature is unique and provides a significant advantage when interpreting DGA results. The proposed method demonstrates significantly higher accuracy (100%) in recognising various fault types, including combined faults, in comparison to existing methods (about 50%). The proposed method has a 60% success rate in determining the cause and location of a fault, but this figure depends on the database with faults of various types used.
Data‐driven train delay prediction incorporating dispatching commands: An XGBoost‐metaheuristic framework
Authors: Gao Tianze , Chen Junhua , Xu Huizhang 
Published: December 2023

AbstractTrain delays can significantly impact the punctuality and service quality of high‐speed trains, which also play a crucial role in affecting dispatchers with their decision‐making. In this study, a data‐driven train delay prediction framework was proposed and strengthened by considering the impact of dispatching commands and the mechanisms of train delay propagation using XGBoost. Four metaheuristic algorithms were utilized to fine‐tune its hyperparameters. A vast dataset comprising 1.9 million records spanning 38 months of train operation data was utilized for feature extraction and model training. The model's accuracy was evaluated using three statistical metrics, and a comparison of the four tuning frameworks was performed. To emphasize the model's interpretability and its practical guidance for train rescheduling, the relationship of dispatching commands, delay propagation and delay prediction was validated by combining the theory and practical results, and a SHAP (SHapley Additive exPlanations) analysis was used for a clearer model explanation. The results revealed that distinct XGBoost‐Metaheuristic models exhibit unique effects in different criteria, yet they all demonstrated high accuracy and low prediction errors, thereby revealing the potential of using machine learning for train delay prediction, which is valuable for decision‐making and rescheduling.
Identifying Alfvén wave modes in the solar corona
Authors: Enerhaug E. , Howson T. A. , De Moortel I. 
Published: December 2023

Context. Oscillations are observed to be pervasive throughout the solar corona, but it remains challenging to positively identify different wave modes. Improving this identification would provide a powerful tool for investigating coronal wave heating and improving seismological inversions.
Aims. We aim to establish whether theoretical methods used to identify magnetohydrodynamical wave modes in numerical simulations can be employed on observational datasets.
Methods. We applied wave identifiers based on fundamental wave characteristics such as compressibility and direction of propagation to a fully 3D numerical simulation of a transversely oscillating coronal loop. The same wave identifiers were applied to the line-of-sight integrated synthetic emission derived from the numerical simulation data to investigate whether this method could feasibly be useful for observational studies.
Results. We established that for particular line(s) of sight and assumptions about the magnetic field, we can correctly identify the properties of the Alfvén mode in synthetic observations of a transversely oscillating loop. Under suitable conditions, there is a strong agreement between the simulation and synthetic emission results.
Conclusions. For the first time, we have provided a proof of concept that this theoretically derived classification of magnetohydrodynamic wave modes can be applied to observational data.
Enlightening the Universe behind the Milky Way bulge
Authors: Duplancic Fernanda , Alonso Sol , Coldwell Georgina , Galdeano Daniela , Minniti Dante , Fernandez Julia , Mesa Valeria , Perez Noelia R. , Pereyra Luis , Pavesich Franco 
Published: December 2023

Context. The location of the Solar System complicates the detection extragalactic sources beyond the Milky Way plane. The optical observations are hampered in the so-called zone of avoidance (ZOA), where stellar crowding and Galactic absorption are severe. Observations at longer wavelengths are needed in order to discover new background galaxies and complete the census in the ZOA.
Aims. The goal of this work is to identify galaxies behind the Milky Way bulge using near-infrared (NIR) data from the VISTA Variables in Vía Láctea (VVV) survey.
Methods. To this end, we made use of different VISTA Science Archive (VSA) tools in order to extract relevant information from more than 32 billion catalogued sources in the VVV bulge region. We find that initial photometric restriction on sources from the VSA vvvSource table combined with restrictions on star–galaxy separation parameters obtained from Source Extractor is a successful strategy for achieving acceptable levels of contamination (60%) and high completeness (75%) in the construction of a galaxy target sample. To remove contaminating Galactic sources from the initial target sample, our methodology also incorporates a visual inspection of false-colour RGB images, a crucial quality control carried out following a specifically defined procedure.
Results. Under this methodology, we find 14 480 galaxy candidates in the VVV bulge region, making this sample the largest catalogue to date in the ZOA. Moreover, these new sources provide a fresh picture of the Universe hidden behind the curtain of stars, dust, and gas in the unexplored Milky Way bulge region.
Conclusions. The results from this work further demonstrate the potential of the VVV/VVVX survey to find and study a large number of galaxies and extragalactic structures obscured by the Milky Way, expanding our knowledge of the Universe in this challenging and impressive region of the sky.
Lichen sclerosus im Kindes- und Jugendalter
Authors: Horn Katharina , Dornhöfer Nadja 
Published: December 2023

ZUSAMMENFASSUNGLichen sclerosus ist eine chronisch-entzündliche Erkrankung der Haut und Schleimhäute, die insbesondere im Anogenitalbereich, aber auch anderen Körperregionen auftreten kann. Die Ätiopathogenese ist bis heute nur unzureichend bekannt und vermutlich multifaktoriell. Im Vordergrund stehen immunologische und genetische Faktoren. Die ersten Symptome sind meistens unspezifisch: Juckreiz, Brennen, Schmerzen, Missempfindungen und Entzündung. Es kommt typischerweise im Verlauf zu Hautveränderungen der Anogenitalregion, die neben weißlicher Verfärbung und abnehmender Elastizität zu Narbenbildungen und einer veränderten Anatomie der Vulva führen kann. Langjähriger, unbehandelter vulvärer Lichen sclerosus ist außerdem ein Risikofaktor für die spätere Entstehung eines Vulvakarzinoms. Häufig kommt es aufgrund der unspezifischen Symptome im frühen Krankheitsstadium zu einer prolongierten Diagnosestellung. Die frühzeitige Einleitung einer spezifischen Therapie ist von wesentlicher Bedeutung, um zum einen die teils quälenden Symptome zu lindern und zum anderen den Langzeitfolgen entgegenzuwirken. Hier steht an erster Stelle die Therapie mit ultrapotenten Glukokortikoiden.
HPV-Impfung: Impfungen können vor Krebs schützen
Authors: Stark Sylvia , Hösemann Cornelia 
Published: December 2023

ZUSAMMENFASSUNGDie Infektion mit humanen Papillomaviren (HPV) zählt zu den weltweit häufigsten sexuell übertragbaren Erkrankungen. Die oft symptomlose Infektion heilt in ca. 90 % der Fälle folgenlos aus. Bei Persistenz der Infektion mit HPV kann dies zu präkanzerösen Läsionen und Karzinomen führen. Die häufigste Inzidenz verzeichnet hier das Zervixkarzinom (Gebärmutterhalskrebs), hier sind in über 99 % HP-Viren nachweisbar. Kondome bieten keinen sicheren Schutz. Die seit 2006 verfügbaren Impfstoffe gegen HPV verzeichnen einen signifikanten Rückgang von Präkanzerosen und Karzinomen nach erfolgter Impfung. Die Impfung wird Mädchen und Jungen vor Aufnahme der sexuellen Aktivität im Alter von 9–14 Jahren mit einem 2-Dosen-Schema empfohlen. Trotz guter Datenlage ist die Impfquote in Deutschland im Vergleich zu anderen Ländern sehr niedrig, erreicht nur knapp 50 % und ist nicht zufriedenstellend. Schulbasierte Impfprogramme können die Impfbereitschaft und Impfquote erhöhen, wie ein Pilotprojekt aus Sachsen zeigt.