bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel facile synthesis of metal nitride@metal oxide (BN/Gd2O3) nanocomposite and their antibacterial and anticancer activities
Authors: Mohsin Mayyadah H. , Khashan Khawla S. , Sulaiman Ghassan M. , Mohammed Hamdoon A. , Qureshi Kamal A. , Aspatwar Ashok 
Published: December 2023

AbstractIn this study, a novel core/shell nanocomposite structure (h-BN@Gd2O3 NCs) was created for the first time by combining hexagonal boron nitride (h-BN) with doped gadolinium oxide (Gd2O3) using different laser pulse numbers, i.e., 150, 338, and 772 pulses. We employed various analytical techniques, including mapping analysis, FE-SEM, EDS, HRTEM, SAED, XRD, zeta potential analysis, DLS, FTIR, Raman spectroscopy, and PL measurements, to characterize the synthesized h-BN, c-Gd2O3, and h-BN@Gd2O3 NCs (338 pulses). XRD results indicated hexagonal and cubic crystal structures for BN and Gd2O3, respectively, while EDS confirmed their chemical composition and elemental mapping. Chemical bonds between B–N–Gd, B–N–O, and Gd–O bands at 412, 455, 474, and 520 cm−1 were identified by FTIR analysis. The antimicrobial and anticancer activities of these NCs using agar well diffusion and MTT assays. They exhibited potent antibacterial properties against both Gram-positive and Gram-negative pathogens. Furthermore, NCs have reduced the proliferation of cancerous cells, i.e., human colon adenocarcinoma cells (HT-29) and human breast cancer cells (MCF-7), while not affecting the proliferation of the normal breast cell line (MCF-10). The anticancer efficacy of NCs was validated by the AO/EtBr assay, which confirmed apoptotic cell death. Blood compatibility on human erythrocytes was also confirmed by hemolytic and in vitro toxicity assessments. The compiled results of the study proposed these nanoparticles could be used as a promising drug delivery system and potentially in healthcare applications.
Temperature-dependent Fermi surface probed by Shubnikov–de Haas oscillations in topological semimetal candidates DyBi and HoBi
Authors: Nowakowska Paulina , Pavlosiuk Orest , Wiśniewski Piotr , Kaczorowski Dariusz 
Published: December 2023

AbstractRare earth-based monopnictides are among the most intensively studied groups of materials in which extremely large magnetoresistance has been observed. This study explores magnetotransport properties of two representatives of this group, DyBi and HoBi. The extreme magnetoresistance is discovered in DyBi and confirmed in HoBi. At $$T=2$$

T
=
2

K and in $$B=14$$

B
=
14

T for both compounds, magnetoresistance reaches the order of magnitude of $$10^4\%$$

10
4

%

. For both materials, standard Kohler’s rule is obeyed only in the temperature range from 50 to 300 K. At lower temperatures, extended Kohler’s rule has to be invoked because carrier concentrations and mobilities strongly change with temperature and magnetic field. This is further proven by the observation of a quite rare temperature-dependence of oscillation frequencies in Shubnikov–de Haas effect. Rate of this dependence clearly changes at Néel temperature, reminiscent of a novel magnetic band splitting. Multi-frequency character of the observed Shubnikov–de Haas oscillations points to the coexistence of electron- and hole-type Fermi pockets in both studied materials. Overall, our results highlight correlation of temperature dependence of the Fermi surface with the magnetotransport properties of DyBi and HoBi.
Urease inhibitors technologies as strategy to mitigate agricultural ammonia emissions and enhance the use efficiency of urea-based fertilizers
Authors: da Fonseca Adrianne Braga , Santos César , Nunes Ana Paula Pereira , Oliveira Damiany Pádua , de Melo Maria Elisa Araújo , Takayama Thalita , Mansur Bethânia Leite , de Jesus Fernandes Thales , do Carmo Alexandrino Gilson , Dias Marcos Altomani Neves , Guelfi Douglas 
Published: December 2023

AbstractExperiments were conducted to evaluate the stability and degradation of NBPT under storage conditions and to quantify urease activity, ammonia losses by volatilization, and agronomic efficiency of urea treated with different urease inhibitors, measured in the field. Experiments included urea treated with 530 mg NBPT kg−1 (UNBPT) in contact with six P-sources (monoammonium phosphate-MAP; single superphosphate; triple superphosphate; P-Agrocote; P-Phusion; P-Policote), with two P-concentrations (30; 70%); the monitoring four N-technologies (SoILC; Limus; Nitrain; Anvol); and the application of conventional urea (UGRAN) or urea treated with urease inhibitors as topdressing in three maize fields, at three N rates. It is concluded that: the mixture of UNBPT and P-fertilizers is incompatible. When MAP granules were coated to control P-release (P-Agrocote), the degradation of NBPT was moderate (approximately 400 mg kg−1 at the end of the storage test). SoILC and Limus solvent technologies extended the NBPT half-life by up to 3.7 and 4.7 months, respectively. Under field, each inhibition technology reduced urease activity, and lowered the intensity of ammonia emission compared to UGRAN by 50–62%. Our results show that the concentration of NBPT is reduced by up to 53.7% for mixing with phosphates. In addition, even with coatings, the storage of mixtures of urea with NBPT and phosphates should be for a time that does not reduce the efficiency of the inhibitor after application, and this time under laboratory conditions was 168 h. The reduction of NBPT concentration in urea is reduced even in isolated storage, our results showed that the half-life time is variable according to the formulation used, being 4.7, 3.7, 2.8 and 2.7 days for Limus, SoILC, Nitrain and Anvol, respectively. The results of these NBPT formulations in the field showed that the average losses by volatilization in the three areas were: 15%, 16%, 17%, 19% and 39% of the N applied, for SoILC, Anvol, Nitrain, Limus and urea, respectively. The rate of nitrogen application affected all agronomic variables, with varied effects in Ingaí. Even without N, yields were higher than 9200 kg ha−1 of grains. The increase in nitrogen rates resulted in linear increases in production and N removal in Luminárias and Ingaí, but in Lavras, production decreased above 95.6 kg ha−1 of N. The highest production in Lavras (13,772 kg ha−1 of grains) occurred with 100 kg ha−1 of N. The application of Anvol reduced the removal of N in Ingaí.
Combining blood glucose and SpO2/FiO2 ratio facilitates prediction of imminent ventilatory needs in emergency room COVID-19 patients
Authors: Sakai Kazuya , Okoda Kai , Nishii Mototsugu , Saji Ryo , Ogawa Fumihiro , Abe Takeru , Takeuchi Ichiro 
Published: December 2023

AbstractThe increasing requirement of mechanical ventilation (MV) due to the novel coronavirus disease (COVID-19) is still a global threat. The aim of this study is to identify markers that can easily stratify the impending use of MV in the emergency room (ER). A total of 106 patients with COVID-19 requiring oxygen support were enrolled. Fifty-nine patients were provided MV 0.5 h (interquartile range: 0.3 to 1.4) post-admission. Clinical and laboratory data before intubation were collected. Using a multivariate logistic regression model, we identified four markers associated with the impending use of MV, including the ratio of peripheral blood oxygen saturation to fraction of inspired oxygen (SpO2/FiO2 ratio), alanine aminotransferase, blood glucose (BG), and lymphocyte counts. Among these markers, SpO2/FiO2 ratio and BG, which can be measured easily and immediately, showed higher accuracy (AUC: 0.88) than SpO2/FiO2 ratio alone (AUC: 0.84), despite no significant difference (DeLong test: P = 0.591). Moreover, even in patients without severe respiratory failure (SpO2/FiO2 ratio > 300), BG (> 138 mg/dL) was predictive of MV use. Measuring BG and SpO2/FiO2 ratio may be a simple and versatile new strategy to accurately identify ER patients with COVID-19 at high risk for the imminent need of MV.
Mechanisms and factors affecting the removal of minocycline from aqueous solutions using graphene-modified resorcinol formaldehyde aerogels
Authors: Yazdanbakhsh Amirhosein , Behzadi Alireza , Moghaddam Armaghan , Salahshoori Iman , Khonakdar Hossein Ali 
Published: December 2023

AbstractIn recent years, concerns about the presence of pharmaceutical compounds in wastewater have increased. Various types of residues of tetracycline family antibiotic compounds, which are widely used, are found in environmental waters in relatively low and persistent concentrations, adversely affecting human health and the environment. In this study, a resorcinol formaldehyde (RF) aerogel was prepared using the sol–gel method at resorcinol/catalyst ratio of 400 and resorcinol/water ratio of 2 and drying at ambient pressure for removing antibiotics like minocycline. Next, RF aerogel was modified with graphene and to increase the specific surface area and porosity of the modified sample and to form the graphene plates without compromising the interconnected porous three-dimensional structure of the aerogel. Also, the pores were designed according to the size of the minocycline particles on the meso- and macro-scale, which bestowed the modified sample the ability to remove a significant amount of the minocycline antibiotic from the aqueous solution. The removal percentage of the antibiotic obtained by UV–vis spectroscopy. Ultimately, the performance of prepared aerogels was investigated under various conditions, including adsorbent doses (4–10 mg), solution pHs (2–12), contact times of the adsorbent with the adsorbate (3–24 h), and initial concentration of antibiotic (40–100 mg/l). The results from the BET test demonstrated that the surface area of the resorcinol formaldehyde aerogel sample, which included 1 wt% graphene (RF-G1), exhibited an augmentation in comparison to the surface area of the pure aerogel. Additionally, it was noted that the removal percentage of minocycline antibiotic for both the unmodified and altered samples was 71.6% and 92.1% at the optimal pH values of 4 and 6, respectively. The adsorption capacity of pure and modified aerogel for the minocycline antibiotic was 358 and 460.5 mg/g, respectively. The adsorption data for the modified aerogel was studied by the pseudo-second-order model and the results obtained from the samples for antibiotic adsorption with this model revealed a favorable fit, which indicated that the chemical adsorption in the rapid adsorption of the antibiotic by the modified aerogel had occurred.
Controlling the Goos-Hänchen shift in a double prism structure using three-level Raman gain medium
Authors: Asiri Saeed , Wang Li-Gang 
Published: December 2023

AbstractWe propose a scheme to control the Goos-Hänchen (GH) shift of TE and TM reflected light beams in a double-prism structure, where a three-level Raman gain medium is filling the gap between the two prisms. We find that it is possible to control the GH shift in this structure by externally adjusting the optical properties of the Raman gain atomic medium while the gap width between the two prisms is fixed. Inspired by recent successful implementation of the double-prism configuration with an air gap to measure the GH shift, we expected that our proposal to control the GH shift can be achieved experimentally and used in different potential applications of the GH shift.
Dissecting the heterogeneity of “in the wild” stress from multimodal sensor data
Authors: Nagaraj Sujay , Goodday Sarah , Hartvigsen Thomas , Boch Adrien , Garg Kopal , Gowda Sindhu , Foschini Luca , Ghassemi Marzyeh , Friend Stephen , Goldenberg Anna 
Published: December 2023

AbstractStress is associated with numerous chronic health conditions, both mental and physical. However, the heterogeneity of these associations at the individual level is poorly understood. While data generated from individuals in their day-to-day lives “in the wild” may best represent the heterogeneity of stress, gathering these data and separating signals from noise is challenging. In this work, we report findings from a major data collection effort using Digital Health Technologies (DHTs) and frontline healthcare workers. We provide insights into stress “in the wild”, by using robust methods for its identification from multimodal data and quantifying its heterogeneity. Here we analyze data from the Stress and Recovery in Frontline COVID-19 Workers study following 365 frontline healthcare workers for 4–6 months using wearable devices and smartphone app-based measures. Causal discovery is used to learn how the causal structure governing an individual’s self-reported symptoms and physiological features from DHTs differs between non-stress and potential stress states. Our methods uncover robust representations of potential stress states across a population of frontline healthcare workers. These representations reveal high levels of inter- and intra-individual heterogeneity in stress. We leverage multiple stress definitions that span different modalities (from subjective to physiological) to obtain a comprehensive view of stress, as these differing definitions rarely align in time. We show that these different stress definitions can be robustly represented as changes in the underlying causal structure on and off stress for individuals. This study is an important step toward better understanding potential underlying processes generating stress in individuals.
Construction and modular implementation of the THETA cycle for synthetic CO2 fixation
Authors: Luo Shanshan , Diehl Christoph , He Hai , Bae YoungJun , Klose Melanie , Claus Peter , Cortina Niña Socorro , Fernandez Celia Alvarez , Schulz-Mirbach Helena , McLean Richard , Ramírez Rojas Adán Andrés , Schindler Daniel , Paczia Nicole , Erb Tobias J. 
Published: December 2023

AbstractSynthetic biology offers the opportunity to build solutions for improved capture and conversion of carbon dioxide (CO2) that outcompete those evolved by nature. Here we demonstrate the design and construction of a new-to-nature CO2-fixation pathway, the reductive tricarboxylic acid branch/4-hydroxybutyryl-CoA/ethylmalonyl-CoA/acetyl-CoA (THETA) cycle. The THETA cycle encompasses 17 enzymes from 9 organisms and revolves around two of the most efficient CO2-fixing enzymes described in nature, crotonyl-CoA carboxylase/reductase and phosphoenolpyruvate carboxylase. Here using rational and machine learning-guided optimization approaches, we improved the yield of the cycle by two orders of magnitude and demonstrated the formation of different biochemical building blocks directly from CO2. Furthermore, we separated the THETA cycle into three modules that we successfully implemented in vivo by exploiting the natural plasticity of Escherichia coli metabolism. Growth-based selection and/or 13C-labelling confirmed the activity of three different modules, demonstrating the first step towards realizing highly orthogonal and complex CO2-fixation pathways in the background of living cells.
Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models
Authors: Mellor Jonathon , Christie Rachel , Overton Christopher E. , Paton Robert S. , Leslie Rhianna , Tang Maria , Deeny Sarah , Ward Thomas 
Published: December 2023

Abstract
Background
Seasonal influenza places a substantial burden annually on healthcare services. Policies during the COVID-19 pandemic limited the transmission of seasonal influenza, making the timing and magnitude of a potential resurgence difficult to ascertain and its impact important to forecast.

Methods
We have developed a hierarchical generalised additive model (GAM) for the short-term forecasting of hospital admissions with a positive test for the influenza virus sub-regionally across England. The model incorporates a multi-level structure of spatio-temporal splines, weekly cycles in admissions, and spatial correlation. Using multiple performance metrics including interval score, coverage, bias, and median absolute error, the predictive performance is evaluated for the 2022-2023 seasonal wave. Performance is measured against autoregressive integrated moving average (ARIMA) and Prophet time series models.

Results
Across the epidemic phases the hierarchical GAM shows improved performance, at all geographic scales relative to the ARIMA and Prophet models. Temporally, the hierarchical GAM has overall an improved performance at 7 and 14 day time horizons. The performance of the GAM is most sensitive to the flexibility of the smoothing function that measures the national epidemic trend.

Conclusions
This study introduces an approach to short-term forecasting of hospital admissions for the influenza virus using hierarchical, spatial, and temporal components. The methodology was designed for the real time forecasting of epidemics. This modelling framework was used across the 2022-2023 winter for healthcare operational planning by the UK Health Security Agency and the National Health Service in England.
SiO2 supported Ce–Co mixed oxide catalyzed selective allylic oxidation of cyclohexene
Authors: Sitaramulu Palli , Nazeer Silligandla , Yogendra Kamma , Prasad Aratikumari Suresh , Chandrashekar Pendem , Rao Tumula Venkateshwar 
Published: December 2023

CeO2–Co3O4/SiO2 catalyst exhibited superior catalytic activity compared to pure oxides for cyclohexene oxidation with 94% conversion and 90% of selectivity towards allylic products.
iScene: An interpretable framework with hierarchical edge services for scene risk identification in 6G internet of vehicles
Authors: Zhong Wuchang , Wang Siming , Yu Rong 
Published: December 2023

AbstractScene risk identification is essential for the traffic safety of Internet of Vehicles. However, the performance of existing risk identification approaches is heavily limited by the imbalanced historical data and the poor model interpretability. Meanwhile, the large processing delay and the potential privacy leakage threat also restrict their application. In this paper, a novel risk identification model is proposed that leverages the synthetic minority over‐sampling technique nearest neighbor (SMOTEENN) method to balance between high‐risk and low‐risk data. The risk identification model has fine interpretability by using recursive feature elimination cross validation (RFECV) with the Shapley additive explanation (SHAP) to analyze the importance of different features, and further elaborately design the Focal Loss function to tackle the disparity between the difficult and easy sample learning. The proposed interpretability scene risk identification framework, named iScene, is built on the infrastructure of 6G space‐air‐ground integrated networks (SAGINs) with blockchain assistance. The model updata efficiency and privacy preservation are effectively enhanced. An elastic computing offloading algorithm is applied to minimize the system overhead under the hierarchical edge service architecture. The experimental evaluation is carried out to verify the effectiveness of the proposed risk identification framework. The results indicate that the G‐Mean value is increased by 23.4%, while the task average response delay is reduced by 21.2%, compared to that in the traditional risk identification approaches with local computing services.
STLAR: A localization and authenticity recognition method for specific targets based on land‐based hyperspectral images
Authors: Zhao Jiale , Zhou Bing , Wang Guanglong , Ying Jiaju , Liu Jie , Chen Qi 
Published: December 2023

AbstractIn traditional two‐dimensional space object detection, it is believed that ‘objects with similar shapes belong to the same type’, making it difficult to distinguish true and false targets. In order to improve the efficiency of real object localization and detection, a specific target localization and authenticity recognition method (STLAR) based on joint spatial and spectral information is proposed. Firstly, the ground imaging spectrometer is utilized to obtain hyperspectral image data of the area to be measured. Then use a spatial object detection model to frame specific targets. Finally, compare the similarity between specific targets detected in space and prior spectral information. The experimental results successfully completed the task of target localization and recognition of authenticity. The proposed method can simultaneously solve the problems of spectral dimension detection or spatial dimension detection, providing new ideas for future rapid localization and recognition of specific targets.
Utilizing grid‐supportive load response to shape resilient frequency control of the power grid
Authors: Albeladi Faisal , Barati Masoud 
Published: December 2023

AbstractThe increasing penetration of renewable energy sources and the retirement of conventional generation units have decreased system inertia, making power systems more vulnerable to resilience and stability issues. To address this problem, this paper proposes a novel approach using grid‐supportive loads (GSLs) to provide a fast and concise primary frequency response and a deep deterministic policy gradient agent‐based secondary controller to restore the system frequency to the nominal value. The proposed method is evaluated on the single‐area and multi‐area test systems. The simulation results demonstrate that using GSLs enhances the power system's stability and resilience. Compared to conventional controllers, the frequency nadir is improved with GSLs. Additionally, the proposed method effectively enhances resilience even with high penetration. These findings indicate that the proposed approach can improve the resilience and stability of power systems and provide a promising solution for future power systems. The results of this study underscore the importance of utilizing innovative approaches to enhance the stability and resilience of power systems in the context of high penetration of renewable energy sources and the retirement of conventional generation.
A DTI registration algorithm based on spectral representation
Authors: Xu Ning , Zhou Zimo , Cai Hao 
Published: December 2023

AbstractDiffusion Tensor Imaging (DTI) is a new technique to characterize the brain structure. It is a special modality for Magnetic Resonance (MRI) which provides the information of fiber orientation. By quantitatively comparing the difference of the fiber information of different image pairs, one can effectively track the status of the nerve diseases. For this purpose, it is necessary to deform the DTI image of the patient (floating image) to the same shape of the template image (target image). This is so called DTI registration, which is usually formulated by a 3D variational problem. However, the numerical implementation for 3D variational problem costs too much CPU consumption. To reduce CPU consumption, it is meaningful to give some research on the structure of the solution for DTI registration. In this paper, the structure of the solution for DTI registration is mainly focused on. For this purpose, an explicit representation of the solution for a DTI registration model is presented. Based on this representation, a new DTI registration algorithm is induced. Several numerical experiments are also performed to show the efficiency of the proposed algorithm in DTI registration.