bluuebunny/crossref_metadata_2025_split huggingface dataset:
Observation of 2D-magnesium-intercalated gallium nitride superlattices
Authors: Wang Jia , Cai Wentao , Lu Weifang , Lu Shun , Kano Emi , Agulto Verdad C. , Sarkar Biplab , Watanabe Hirotaka , Ikarashi Nobuyuki , Iwamoto Toshiyuki , Nakajima Makoto , Honda Yoshio , Amano Hiroshi 
Published: June 2024

AbstractSince the demonstration of p-type gallium nitride (GaN) through doping with substitutional magnesium (Mg) atoms1,2, rapid and comprehensive developments, such as blue light-emitting diodes, have considerably shaped our modern lives and contributed to a more carbon-neutral society3–5. However, the details of the interplay between GaN and Mg have remained largely unknown6–11. Here we observe that Mg-intercalated GaN superlattices can form spontaneously by annealing a metallic Mg film on GaN at atmospheric pressure. To our knowledge, this marks the first instance of a two-dimensional metal intercalated into a bulk semiconductor, with each Mg monolayer being intricately inserted between several monolayers of hexagonal GaN. Characterized as an interstitial intercalation, this process induces substantial uniaxial compressive strain perpendicular to the interstitial layers. Consequently, the GaN layers in the Mg-intercalated GaN superlattices exhibit an exceptional elastic strain exceeding −10% (equivalent to a stress of more than 20 GPa), among the highest recorded for thin-film materials12. The strain alters the electronic band structure and greatly enhances hole transport along the compression direction. Furthermore, the Mg sheets induce a unique periodic transition in GaN polarity, generating polarization-field-induced net charges. These characteristics offer fresh insights into semiconductor doping and conductivity enhancement, as well as into elastic strain engineering of nanomaterials and metal–semiconductor superlattices13.
Migraine incidence and coffee consumption among child-bearing age women: the Korea Nurses’ Health Study
Authors: Cha Chiyoung , Kim Oksoo , Pang Yanghee , Jeong Hyunseon , Lee Jung Eun , Lee Heayoung , Dan Hyunju 
Published: June 2024

AbstractThis longitudinal study aimed to identify factors that influence migraines in Korean women nurses using data from the Korea Nurses’ Health Study. Among those who participated in Survey 1 (2013–2014) and the follow-up survey (2015–2019), we selected 2605 participants for Cox proportional hazard regression analysis, 521 participants who were newly diagnosed with migraine and 2084 controls using a 1:4 incidence density sampling approach. Consuming coffee (≥ 3 cups: RR = 1.666; 95% CI = 1.175–2.362, < 3 cups: RR = 1.439; 95% CI = 1.053–1.966), being obese (BMI ≥ 25: RR = 1.341, 95% CI = 1.003–1.793), and engaging in vigorous physical activity (RR = 1.010; 95% CI = 1.000–1.019) increased the risk of developing a migraine. Nurses with an annual salary greater than $3500 were less likely to develop migraines (RR = 0.786, 95% CI = 0.631–0.979). The results imply that lifestyle factors, such as the amount of coffee consumption, BMI level, and degree of physical activity could be considered when formulating treatment plans for women who have newly developed migraines.
Facile synthesis of iron nanoparticles from Camellia Sinensis leaves catalysed for biodiesel synthesis from Azolla filiculoides
Authors: Sundararaman Sathish , Karthikeyan M. , Aravind kumar J. , Deivasigamani Prabu , Soosai Michael Rahul , Ramaraja A. , Sahana S. , Thamer Badr M. , El-Newehy Mohamed H. , Rajasimman M. , T R Praveenkumar 
Published: June 2024

AbstractRecent years have seen an increase in research on biodiesel, an environmentally benign and renewable fuel alternative for traditional fossil fuels. Biodiesel might become more cost-effective and competitive with diesel if a solid heterogeneous catalyst is used in its production. One way to make biodiesel more affordable and competitive with diesel is to employ a solid heterogeneous catalyst in its manufacturing. Based on X-ray diffraction (XRD) and Fourier Transform infrared spectroscopy (FTIR), the researchers in this study proved their hypothesis that iron oxide core–shell nanoparticles were generated during the green synthesis of iron-based nanoparticles (FeNPs) from Camellia Sinensis leaves. The fabrication of spherical iron nanoparticles was successfully confirmed using scanning electron microscopy (SEM). As a heterogeneous catalyst, the synthesised catalyst has shown potential in facilitating the conversion of algae oil into biodiesel. With the optimal parameters (0.5 weight percent catalytic load, 1:6 oil—methanol ratio, 60 °C reaction temperature, and 1 h and 30 min reaction duration), a 93.33% yield was attained. This may be due to its acid–base property, chemical stability, stronger metal support interaction. Furthermore, the catalyst was employed for transesterification reactions five times after regeneration with n-hexane washing followed by calcination at 650 °C for 3 h.
The enhanced photocatalytic performance of CPAA doping with different concentrations of Titanium oxide nanocomposite against MB dyes under simulated sunlight irradiations
Authors: Sayed Marwa M. , Aboraia Abdelaziz M. , Kasem Yara A. , Elewa Nancy N. , Ismail Yasser A. M. , Aly Kamal I. 
Published: June 2024

AbstractThe pure conjugated polyarylene azomethine (CPAA) and its nanocomposites (CPAA-TiO2) with different concentrations of TiO2 nanoparticles were successfully prepared by in-situ technique and analyzed by different advanced techniques. XRD has confirmed the structural properties and crystallinity of (CPAA) and nanocomposites. The SEM clearly shows that the (CPAA) is uniform and homogeneous, with tightly connected aggregate layers in shape. However, the amount of TiO2 in the nanocomposites greatly affects their morphology, revealing structural differences and indicating a reaction between (CPAA) and TiO2, especially at a higher concentration of 5% TiO2. A new composite of (CPAA) was introduced and the photocatalytic effect for MB was studied. The removal efficiency of (pure-CPAA) over MB dye under simulated sunlight was 62%. However, (CPAA-TiO2 1%) destroyed 90% of MB dyes. It was discovered that the low band gap of (CPAA-TiO2 1% (2.84 eV)) accelerates high electron–hole recombination, increasing photocatalytic activity.
Urinary beta-2 microglobulin increases whereas TIMP-2 and IGFBP7 decline after unilateral nephrectomy in healthy kidney donors
Authors: de Rooij Esther N. M. , Hoogeveen Ellen K. , Romijn Fred P. H. T. M. , van der Kooy Sandra W. , Veighey Kristin V. , Dekker Friedo W. , van Kooten Cees , Cobbaert Christa M. , de Fijter Johan W. 
Published: June 2024

AbstractEarly kidney injury may be detected by urinary markers, such as beta-2 microglobulin (B2M), tissue inhibitor of metalloproteinases-2 (TIMP-2), insulin-like growth factor-binding protein 7 (IGFBP7), kidney injury molecule-1 (KIM-1) and/or neutrophil gelatinase-associated lipocalin (NGAL). Of these biomarkers information on pathophysiology and reference ranges in both healthy and diseased populations are scarce. Differences in urinary levels of B2M, TIMP-2, IGFBP7, KIM-1 and NGAL were compared 24 h before and after nephrectomy in 38 living kidney donors from the REnal Protection Against Ischaemia–Reperfusion in transplantation study. Linear regression was used to assess the relation between baseline biomarker concentration and kidney function 1 year after nephrectomy. Median levels of urinary creatinine and creatinine standardized B2M, TIMP-2, IGFBP7, KIM-1, NGAL, and albumin 24 h before nephrectomy in donors were 9.4 mmol/L, 14 μg/mmol, 16 pmol/mmol, 99 pmol/mmol, 63 ng/mmol, 1390 ng/mmol and 0.7 mg/mmol, with median differences 24 h after nephrectomy of − 0.9, + 1906, − 7.1, − 38.3, − 6.9, + 2378 and + 1.2, respectively. The change of donor eGFR after 12 months per SD increment at baseline of B2M, TIMP-2, IGFBP7, KIM-1 and NGAL was: − 1.1, − 2.3, − 0.7, − 1.6 and − 2.8, respectively. Urinary TIMP-2 and IGFBP7 excretion halved after nephrectomy, similar to urinary creatinine, suggesting these markers predominantly reflect glomerular filtration. B2M and NGAL excretion increased significantly, similar to albumin, indicating decreased proximal tubular reabsorption following nephrectomy. KIM-1 did not change considerably after nephrectomy. Even though none of these biomarkers showed a strong relation with long-term donor eGFR, these results provide valuable insight into the pathophysiology of these urinary biomarkers.
A novel model for relation prediction in knowledge graphs exploiting semantic and structural feature integration
Authors: Yang Jianliang , Lu Guoxuan , He Siyuan , Cao Qiuer , Liu Yuenan 
Published: June 2024

AbstractRelation prediction is a critical task in knowledge graph completion and associated downstream tasks that rely on knowledge representation. Previous studies indicate that both structural features and semantic information are meaningful for predicting missing relations in knowledge graphs. This has led to the development of two types of methods: structure-based methods and semantics-based methods. Since these two approaches represent two distinct learning paradigms, it is difficult to fully utilize both sets of features within a single learning model, especially deep features. As a result, existing studies usually focus on only one type of feature. This leads to an insufficient representation of knowledge in current methods and makes them prone to overlooking certain patterns when predicting missing relations. In this study, we introduce a novel model, RP-ISS, which combines deep semantic and structural features for relation prediction. The RP-ISS model utilizes a two-part architecture, with the first component being a RoBERTa module that is responsible for extracting semantic features from entity nodes. The second part of the system employs an edge-based relational message-passing network designed to capture and interpret structural information within the data. To alleviate the computational burden of the message-passing network on the RoBERTa module during the sampling process, RP-ISS introduces a node embedding memory bank, which updates asynchronously to circumvent excessive computation. The model was assessed on three publicly accessible datasets (WN18RR, WN18, and FB15k-237), and the results revealed that RP-ISS surpasses all baseline methods across all evaluation metrics. Moreover, RP-ISS showcases robust performance in graph inductive learning.
Ecological restoration at pilot-scale employing site-specific rationales for small-patch degraded mangroves in Indian Sundarbans
Authors: Ray Krishna , Basak Sandip Kumar , Giri Chayan Kumar , Kotal Hemendra Nath , Mandal Anup , Chatterjee Kiranmoy , Saha Subhajit , Biswas Biswajit , Mondal Sumana , Das Ipsita , Ghosh Anwesha , Bhadury Punyasloke , Joshi Rahul 
Published: June 2024

AbstractTo date, degraded mangrove ecosystem restoration accomplished worldwide primarily aligns towards rehabilitation with monotypic plantations, while ecological restoration principles are rarely followed in these interventions. However, researchers admit that most of these initiatives' success rate is not appreciable often. An integrative framework of ecological restoration for degraded mangroves where site-specific observations could be scientifically rationalized, with co-located reference pristine mangroves as the target ecosystem to achieve is currently distinctively lacking. Through this experimental scale study, we studied the suitability of site-specific strategies to ecologically restore degraded mangrove patches vis-à-vis the conventional mono-species plantations in a highly vulnerable mangrove ecosystem in Indian Sundarbans. This comprehensive restoration framework was trialed in small discrete degraded mangrove patches spanning ~ 65 ha. Site-specific key restoration components applied are statistically validated through RDA analyses and Bayesian t-tests. 25 quantifiable metrics evaluate the restoration success of a ~ 3 ha degraded mangrove patch with Ridgeline distribution, Kolmogorov–Smirnov (K-S) tests, and Mahalanobis Distance (D2) measure to prove the site’s near-equivalence to pristine reference in multiple ecosystem attributes. This restoration intervention irrevocably establishes the greater potential of this framework in the recovery of ecosystem functions and self-sustenance compared to that of predominant monoculture practices for vulnerable mangroves.
Model test study on the mechanical response of the deep buried tunnel lining
Authors: Duan Longyan , Feng Jimeng , Song Jiadai , Yao Shiyu 
Published: June 2024

AbstractDeep-buried tunnels with weak surrounding rock are frequently encountered issues in traffic engineering. It plays an important role in the excavation process and the project operation. This paper applies the theoretical analysis and laboratory test related to four different conditions in terms of their thickness to determine the mechanical response of deep-buried tunnel lining. Then, the energy dissipative structure theory is employed to explain the experimental results. This paper has made the following achievements: firstly, it is found that the toughness of the secondary lining was found to be often the most important indicator of tunnel safety, with better-toughness linings having higher tensile strength and crack resistance. Secondly, it suggests that the inclusion of steel reinforcement in the concrete lining can effectively improve the secondary lining toughness. Finally, it proves that the more ductile liner had more energy, higher load-carrying capacity, and was better able to maintain the overall stability of the structure.
Suspended sediment load prediction using sparrow search algorithm-based support vector machine model
Authors: Samantaray Sandeep , Sahoo Abinash , Satapathy Deba Prakash , Oudah Atheer Y. , Yaseen Zaher Mundher 
Published: June 2024

AbstractPrediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash–Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.
RhizoNet segments plant roots to assess biomass and growth for enabling self-driving labs
Authors: Sordo Zineb , Andeer Peter , Sethian James , Northen Trent , Ushizima Daniela 
Published: June 2024

AbstractFlatbed scanners are commonly used for root analysis, but typical manual segmentation methods are time-consuming and prone to errors, especially in large-scale, multi-plant studies. Furthermore, the complex nature of root structures combined with noisy backgrounds in images complicates automated analysis. Addressing these challenges, this article introduces RhizoNet, a deep learning-based workflow to semantically segment plant root scans. Utilizing a sophisticated Residual U-Net architecture, RhizoNet enhances prediction accuracy and employs a convex hull operation for delineation of the primary root component. Its main objective is to accurately segment root biomass and monitor its growth over time. RhizoNet processes color scans of plants grown in a hydroponic system known as EcoFAB, subjected to specific nutritional treatments. The root detection model using RhizoNet demonstrates strong generalization in the validation tests of all experiments despite variable treatments. The main contributions are the standardization of root segmentation and phenotyping, systematic and accelerated analysis of thousands of images, significantly aiding in the precise assessment of root growth dynamics under varying plant conditions, and offering a path toward self-driving labs.
Influence of air intake from existing shafts on the safety of operating trains
Authors: Huang Changfu , Li Shaohua , Zhang Zhenbo , Yao Tiejun , Shi Xianming , Tian Jingwei , Hu Zhinan , Wang Yuhai 
Published: June 2024

AbstractGiven the influence of air intake from inclined shafts in existing tunnel ventilation systems on train comfort and aerodynamic safety, a numerical analysis method is used to study the comfort and aerodynamic safety of operating trains under three conditions—inclined shaft closed and inclined shaft open without and with air intake—and to explore the variation law of transient pressure and aerodynamic force (lift coefficient, transverse force coefficient, and overturning moment coefficient). Combined with practical engineering and requirements, the influence of inclined shaft air intake on train operation comfort and aerodynamic safety is analyzed. Through this research, the influence of using air intake from the inclined shaft of an existing tunnel, a ventilation scheme of the new Wushaoling Tunnel, on the comfort and aerodynamic force of trains is revealed, and the comfort and aerodynamic safety of trains in an actual project are evaluated, verifying the rationality of the ventilation scheme of the Wushaoling Tunnel.
Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for electric drive applications
Authors: Shahid Muhammad Bilal , Jin Weidong , Abbasi Muhammad Abbas , Li Lian , Rasool Akhtar , Bhatti Abdul Rauf , Hussen Abdulkerim Sherefa 
Published: June 2024

AbstractIn the conventional finite control set model predictive torque control, the cost function consists of different control objectives with varying units of measurements. Due to presence of diverse variables in cost function, weighting factors are used to set the relative importance of these objectives. However, selection of these weighting factors in predictive control of electric drives and power converters still remains an open research challenge. Improper selection of weighting factors can lead to deterioration of the controller performance. This work proposes a novel weighting factor tuning method based on the Multi-Criteria-Decision-Making (MCDM) technique called the Entropy method. This technique has several advantages for multi-objective problem optimization. It provides a quantitive approach and incorporates uncertainties and adaptability to assess the relative importance of different criteria or objectives. This technique performs the online tuning of the weighting factor by forming a data set of the control objectives, i.e., electromagnetic torque and stator flux magnitude. After obtaining the error set of control variables, the objective matrix is normalized, and the entropy technique is applied to design the corresponding weights. An experimental setup based on the dSpace dS1104 controller is used to validate the effectiveness of the proposed method for a two-level, three-phase voltage source inverter (2L-3P) fed induction motor drive. The dynamic response of the proposed technique is compared with the previously proposed MCDM-based weighting factor tuning technique and conventional MPTC. The results reveal that the proposed method provides an improved dynamic response of the drive under changing operating conditions with a reduction of 28% in computational burden and 38% in total harmonic distortion, respectively.
Blocking Oncostatin M receptor abrogates STAT3 mediated integrin signaling and overcomes chemoresistance in ovarian cancer
Authors: Geethadevi Anjali , Ku Zhiqiang , Tsaih Shirng-Wern , Parashar Deepak , Kadamberi Ishaque P. , Xiong Wei , Deng Hui , George Jasmine , Kumar Sudhir , Mittal Sonam , Zhang Ningyan , Pradeep Sunila , An Zhiqiang , Chaluvally-Raghavan Pradeep 
Published: June 2024

AbstractChemotherapy such as cisplatin is widely used to treat ovarian cancer either before or after surgical debulking. However, cancer relapse due to chemotherapy resistance is a major challenge in the treatment of ovarian cancer. The underlying mechanisms related to chemotherapy resistance remain largely unclear. Therefore, identification of effective therapeutic strategies is urgently needed to overcome therapy resistance. Transcriptome-based analysis, in vitro studies and functional assays identified that cisplatin-resistant ovarian cancer cells express high levels of OSMR compared to cisplatin sensitive cells. Furthermore, OSMR expression associated with a module of integrin family genes and predominantly linked with integrin αV (ITGAV) and integrin β3 (ITGB3) for cisplatin resistance. Using ectopic expression and knockdown approaches, we proved that OSMR directly regulates ITGAV and ITGB3 gene expression through STAT3 activation. Notably, targeting OSMR using anti-OSMR human antibody inhibited the growth and metastasis of ovarian cancer cells and sensitized cisplatin treatment. Taken together, our results underscore the pivotal role of OSMR as a requirement for cisplatin resistance in ovarian cancer. Notably, OSMR fostered the expression of a distinct set of integrin genes, which in turn resulted into a crosstalk between OSMR and integrins for signaling activation that is critical for cisplatin resistance. Therefore, targeting OSMR emerges as a promising and viable strategy to reverse cisplatin-resistance in ovarian cancer.
Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children
Authors: Nygaard Ulrikka , Nielsen Annelaura Bach , Dungu Kia Hee Schultz , Drici Lylia , Holm Mette , Ottenheijm Maud Eline , Nielsen Allan Bybeck , Glenthøj Jonathan Peter , Schmidt Lisbeth Samsø , Cortes Dina , Jørgensen Inger Merete , Mogensen Trine Hyrup , Schmiegelow Kjeld , Mann Matthias , Vissing Nadja Hawwa , Wewer Albrechtsen Nicolai J. 
Published: June 2024

AbstractMultisystem inflammatory syndrome in children (MIS-C) is a severe disease that emerged during the COVID-19 pandemic. Although recognized as an immune-mediated condition, the pathogenesis remains unresolved. Furthermore, the absence of a diagnostic test can lead to delayed immunotherapy. Using state-of-the-art mass-spectrometry proteomics, assisted by artificial intelligence (AI), we aimed to identify a diagnostic signature for MIS-C and to gain insights into disease mechanisms. We identified a highly specific 4-protein diagnostic signature in children with MIS-C. Furthermore, we identified seven clusters that differed between MIS-C and controls, indicating an interplay between apolipoproteins, immune response proteins, coagulation factors, platelet function, and the complement cascade. These intricate protein patterns indicated MIS-C as an immunometabolic condition with global hypercoagulability. Our findings emphasize the potential of AI-assisted proteomics as a powerful and unbiased tool for assessing disease pathogenesis and suggesting avenues for future interventions and impact on pediatric disease trajectories through early diagnosis.