bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synthesis, characterisation of agricultural biomass derived activated carbon and its applications
Authors: Priya Shweta , S A Salmataj , Anusha B , Bhat Pushpanjali 
Published: December 2024

Abstract
As the world moves toward greener energy generation methods and cleaner environments, activated carbon produced from ligninocellulosic resources has attracted unexpected interest due to its easy availability, and economic, renewable, and biodegradable properties, which makes it a viable alternative to exhaustible coal. This review paper provides a comprehensive overview of a systematic procedure to develop activated carbon from plant biomass, its characterization by simple techniques, and the versatile applications of activated carbon. This includes its role in environmental remediation, from emphasizing its efficacy in removing a wide array of pollutants, to sustainable methods of hydrogen capture and energy storage in supercapacitors. A brief comparison of the key aspects of optimal toxicant adsorption, like batch conditions, the best-fit model, isotherms, and maximum adsorption, are also made. To ascertain the efficiency of the supercapacitors, their strategy in designing it, and their output in terms of specific capacitance, power, and energy density are compared.
Gold nanoparticles decorated multilayer mxene: towards highly selective and sensitive gas sensor at room temperature
Authors: Shi Pu , Wang Zhikun , Shi Fan 
Published: December 2024

Abstract
The catalytic properties of noble metal nanoparticles can enhance the adsorption capacity of sensors for target gases when compounded with MXene. In this study, a nanocomposite of gold nanoparticles (Au NPs) and MXene was prepared using a simple self-reduction process, and its microstructure, electrochemical valence states, specific surface area, and other properties were characterized. Gas sensitivity tests conducted on the Au@MXene-20 sensor revealed excellent selectivity for NH3 at room temperature. The response value to 100 ppm NH3 at room temperature was 15.1%, approximately three times that of pure MXene, with response and recovery times of 118 s and 219 s, respectively. Moreover, the composite sensor exhibited a low detection limit, demonstrating a response of 3.496% to 5 ppm NH3 at room temperature. Additionally, the Au@MXene-20 sensor possessed good repeatability, long-term stability, and moisture resistance. Therefore, the Au@MXene-20 sensor studied and prepared in this paper has been proven to be well-suited for NH3 detection in harsh environments.
HeatGSNs: integrating eigenfilters and low-pass graph heat kernels into graph spectral convolutional networks for brain tumor segmentation and classification
Authors: Bae Jihun , Lee Hunmin , Hu Jinglu 
Published: December 2024

Abstract
Recent studies on graph representation learning in brain tumor learning tasks have garnered significant interest by encoding and learning inherent relationships among the geometric features of tumors. A prominent challenge in brain tumor MRI datasets is the issue of class imbalance. While deep learning architectures such as CNN- and Transformer-based models have shown promise in addressing this challenge through their complex models with large parameters spaces., Graph-based networks face significant difficulties. Specifically, graph networks often struggle with issues like over-smoothing and oscillation convergence, limiting their effectiveness in such tasks. To address these challenges, we propose novel graph spectral convolutional networks called HeatGSNs, which incorporate eigenfilters and learnable low-pass graph heat kernels to capture geometric similarities within tumor classes. HeatGSNs utilize a continuous feature propagation mechanism derived from the forward finite difference of graph heat kernels, which is approximated by the cosine form of the shift-scaled Chebyshev polynomial and modified Bessel functions, ensuring efficient and accurate performance achievement. Experimental results on the BRATS2021 dataset demonstrate the effectiveness of HeatGSNs, achieving an average Dice score of 90%, a Hausdorff Distance (95%) of 5.45 mm, and an average accuracy of 80.11%. Furthermore, HeatGSNs require significantly fewer parameters than existing methods, with an average of 1.79M parameters, highlighting efficiency and effectiveness.
Formation mechanism of platinum nanoparticles prepared by atmospheric pressure plasma jet
Authors: ZHAO 赵 Ying 颖 , TAO 陶 Ali 阿丽 , CHEN 陈 Zijing 紫荆 , RUAN 阮 Yue 月 , WANG 王 Kunpeng 鲲鹏 , ZHU 朱 Ling 玲 , CHEN 陈 Longwei 龙威 
Published: December 2024

Abstract
An atmospheric pressure plasma jet (APPJ) approach is developed to prepare platinum nanoparticles (PtNPs) under mild reaction conditions of lower temperatures and without adding chemical reagents. Optical Emission Spectroscopy (OES) and X-ray Photoelectron Spectroscopy (XPS) tests revealed that the APPJ contains a large number of high-energy active particles, which can generate solvated electrons in liquid thereby promoting the rapid reduction of Pt(IV) ions into Pt(0) atoms, and these atoms gradually grow into nanoparticles. After 3 min of treatment, PtNPs exhibit excellent dispersibility with a particle size distribution ranging from 1.8 to 2.8 nm. After 5 min, the particle size increases, and aggregation occurs. The zeta potentials for the two situations were −56.0 mV and −12.5 mV respectively. The results indicate that the treatment time has a significant impact on the dispersion, particle size distribution, and sol stability of the nanoparticles. Furthermore, it reveals the formation mechanism of PtNPs prepared by APPJ, which involves the generation and expansion of nanocrystalline nuclei, and the construction of negatively charged colloidal particles. The overall mechanism highlights the importance of the plasma-liquid interaction in the synthesis of PtNPs, offering a new perspective on the controllable production of nanomaterials using plasma technology.
Damage evolution mechanism and low-damage grinding technology of silicon carbide ceramics
Authors: Li Chen , Wang Kechong , Zakharov Oleg , Cui Hailong , Wu Mingtao , Zhao Tianchen , Yan Yongda , Geng Yanquan 
Published: December 2024

Abstract
Silicon carbide (SiC) ceramics are extensively utilized in aerospace, national defense, and petrochemical industries due to their superior physical and chemical properties. The processing of bulk SiC ceramics necessitates precise and efficient grinding techniques to produce components with satisfactory functionality. However, the inherent high hardness and brittleness of SiC ceramics present significant challenges during grinding, leading to severe brittle fracture and tool wear that compromise both surface integrity and production efficiency. Although ductile-regime grinding of SiC ceramics can be achieved by enhancing machine tool accuracy and stiffness while optimizing wheel performance alongside appropriate selection of process parameters, a comprehensive summary of the mechanisms underlying damage evolution during grinding is lacking, and a mature grinding process for SiC ceramics has yet to be developed. To bridge this gap, the sintering technologies, mechanical properties, and microstructures of SiC ceramics were briefly covered. The grinding-induced damage mechanism and low-damage grinding technologies of SiC ceramics were summarized. The fundamental science underlying the ductile deformation and removal mechanisms of brittle solids was emphasized. Additionally, attention was directed towards the critical role of hybrid energy field grinding in minimizing brittle damages and promoting removal efficiency. This review not only elucidates the intrinsic interactions between the work material and abrasives, but also offers valuable insights for optimizing the grinding processes of brittle solids.
Achieving multi-response optimization of control parameters for Wire-EDM on additive manufactured AlSi10Mg alloy using Taguchi-grey relational theory
Authors: R Murali Krishnan , Ranganathan Rajesh , A Saiyathibrahim , Velu Rajkumar , Jatti Vijaykumar S , Mohan Dhanesh G , Vijayakumar Praveenkumar 
Published: December 2024

Abstract
The current research aims to optimize the multi-response during Wire Electrical Discharge Machining (Wire-EDM) of SLMed AlSi10Mg by using Taguchi integrated Grey Relational Analysis (GRA). Selective Laser Melting (SLM) is one of the most well-known and practical Additive Manufacturing (AM) methods, with the potential to replace many traditional production processes. The complex metallic support structures created during SLM require special attention as they are difficult to remove by hand. Therefore, this study performs post-processing using the Wire-EDM precision machining technique to evaluate the machinability of the SLMed AlSi10Mg as-built part. The multi-response optimization in this study aims to achieve the maximum material removal rate and the lowest surface roughness while considering four important influencing factors (pulse On time, pulse Off time, servo voltage, and wire feed rate) at four distinct levels. The Taguchi integrated Grey Relational Analysis (GRA) revealed that a pulse On time of 118 μs (Level 3), a pulse Off time of 44 μs (Level 1), a servo voltage of 60 V (Level 4), and a wire feed rate of 7 m s−1 (Level 4) are recommended to achieve optimal machining of SLMed AlSi10Mg. Furthermore, the derived optimization results were carefully verified through a confirmatory experiment, which showed a 38.57% improvement in multi-response characteristics compared to the initial Wire-EDM parameter settings. The methodology proposed in this work offers a standardized approach that has the potential to be implemented for the rapid and precise prediction and optimization of surface roughness, while achieving better material removal during Wire-EDM of SLMed AlSi10Mg.
Abnormal temperature rise and discharge characteristics of decay-like composite insulators
Authors: Jia Boyan , Yi Xiaoyu , Li Zhaohui , Ding Likun , Yang Yang , Liu Hongliang , Liu Jie , Zhang Jiaxin , Zhang Peng , Pan Jin 
Published: December 2024

Abstract
In recent years, there have been multiple incidents of fracture faults caused by the deterioration of composite insulators in China, seriously threatening the safe operation of the power system. This article takes a 500 kV decay-like composite insulator collected from the operation site as the research object, conducts visual inspection and analysis of its shed and core rod degradation status, and tests its infrared temperature rise and ultraviolet discharge distribution under power frequency operating voltage and related electrical parameters. Finally, an electric thermal coupling finite element model is established to analyze its discharge and tem-perature rise mechanism. The research results found that the infrared temperature rise characteristics and ultraviolet discharge characteristics of decay-like composite insulators can reflect their degradation status. As the testing distance increases, the measured infrared temperature rise gradually decreases. The distortion of dielectric parameters and the decrease in insulation resistance have caused partial discharge and abnormal temperature rise of decay-like insulators. In addition, compared to ultraviolet imaging detection, infrared temperature measurement can better reflect the internal defects of decay-like composite insulators and is less susceptible to interference from unrelated discharges around them.
Improved quality micro-hole fabrication using backside water assisted laser micro-drilling in titanium
Authors: Mendiratta Mehul , Prakash Shashi 
Published: December 2024

Abstract
Laser micro-drilling is an effective technique for producing micro-scale holes in different materials. However, titanium poses challenges due to its high melting point, low thermal conductivity, and oxide layer formation at elevated temperatures. This study investigates the influence of a thin water layer on the backside of titanium workpiece during laser micro-drilling to mitigate these issues. A 0.5 mm thick water layer was applied at the backside of the workpiece to assess its impact on hole characteristics such as heat-affected zone (HAZ), circularity, and taper angle. Experiments were performed with constant laser power, pulse frequency, and pulse width, while varying scanning speed, number of passes, and input diameters (50 μm, 100 μm, and 150 μm) using the trepanning method. The results revealed that backside water-assisted drilling, particularly with a thin water layer, significantly reduced thermal defects and produced nearly taperless microholes. The formation of cavitation bubbles and related phenomena at the backside enhanced material removal and minimize hole taper compared to open-air drilling. This study offers valuable insights into the mechanism of backside water assisted laser drilling compared to open air drilling and highlights the benefits of backside water layer in reducing thermal damage and enhancing the precision of micro-drilling in titanium.
Assessment of gear failure severity in wind turbines based on few-shot learning and graph neural networks
Authors: Yang Shuai , Deng Chunyan , Chuan Li 
Published: December 2024

Abstract
Distinguishing vibration signals associated with different levels of gear damage, combined with the challenging operating environment of wind turbines, complicates the collection of sufficient data for effective fault diagnosis. This paper proposes a few-shot learning (FSL) based graph neural network (GNN) for evaluating gear tooth fracture levels within small datasets.The short-time fourier transform (STFT) is used to convert the original signal into two-dimensional data for preprocessing. Fault severity features, extracted by a convolutional neural network (CNN), are then input into the GNN for severity classification. A natural wind turbine experiment platform was developed to simulate various operating conditions. To further validate the proposed method, comparative experiments were conducted using Siamese Networks, Matching Networks, and Relation Networks. The results demonstrate that the proposed method outperforms these alternatives in evaluating gear damage severity.
Scope+: an open source generalizable architecture for single-cell RNA-seq atlases at sample and cell levels
Authors: Yin Danqing , Cao Yue , Chen Junyi , Mak Candice L Y , Yu Ken H O , Zhang Jiaxuan , Li Jia , Lin Yingxin , Ho Joshua W K , Yang Jean Y H 
Published: December 2024

Abstract

Summary
With the recent advancement in single-cell RNA-sequencing technologies and the increased availability of integrative tools, challenges arise in easy and fast access to large collections of cell atlas. Existing cell atlas portals rarely are open sourced and adaptable, and do not support meta-analysis at cell level. Here, we present an open source, highly optimized and scalable architecture, named Scope+, to allow quick access, meta-analysis and cell-level selection of the atlas data. We applied this architecture to our well-curated 5 million COVID-19 blood and immune cells, as a portal called Covidscope. We achieved efficient access to atlas-scale data via three strategies, such as cell-as-unit data modelling, novel database optimization techniques and innovative software architectural design. Scope+ serves as an open source architecture for researchers to build on with their own atlas.

Availability and implementation
The COVID-19 web portal, data and meta-analysis are available on Covidscope (https://covidsc.d24h.hk/). User tutorials on how to implement Scope+ architecture with their atlases can be found at https://hiyin.github.io/scopeplus-user-tutorial/. Scope+ source code can be found at https://doi.org/10.5281/zenodo.14174632 and https://github.com/hiyin/scopeplus.
ImmuNet: a segmentation-free machine learning pipeline for immune landscape phenotyping in tumors by multiplex imaging
Authors: Sultan Shabaz , Gorris Mark A J , Martynova Evgenia , van der Woude Lieke L , Buytenhuijs Franka , van Wilpe Sandra , Verrijp Kiek , Figdor Carl G , de Vries I Jolanda M , Textor Johannes 
Published: December 2024

Abstract
Tissue specimens taken from primary tumors or metastases contain important information for diagnosis and treatment of cancer patients. Multiplex imaging allows in situ visualization of heterogeneous cell populations, such as immune cells, in tissue samples. Most image processing pipelines first segment cell boundaries and then measure marker expression to assign cell phenotypes. In dense tissue environments, this segmentation-first approach can be inaccurate due to segmentation errors or overlapping cells. Here, we introduce the machine-learning pipeline “ImmuNet”, which identifies positions and phenotypes of cells without segmenting them. ImmuNet is easy to train: human annotators only need to click on an immune cell and score its expression of each marker—drawing a full cell outline is not required. We trained and evaluated ImmuNet on multiplex images from human tonsil, lung cancer, prostate cancer, melanoma, and bladder cancer tissue samples and found it to consistently achieve error rates below 5%–10% across tissue types, cell types, and tissue densities, outperforming a segmentation-based baseline method. Furthermore, we externally validate ImmuNet results by comparing them to flow cytometric cell count measurements from the same tissue. In summary, ImmuNet is an effective, simpler alternative to segmentation-based approaches when only cell positions and phenotypes, but not their shapes, are required for downstream analyses. Thus, ImmuNet helps researchers to analyze cell positions in multiplex tissue images more easily and accurately.
Brain age in genetic and idiopathic Parkinson's disease
Authors: Teipel Stefan J , Hoffmann Hauke , Storch Alexander , Hermann Andreas , Dyrba Martin , Schumacher Julia 
Published: December 2024

Abstract
The brain-age gap, i.e. the difference between the brain age estimated from structural MRI data and the chronological age of an individual, has been proposed as a summary measure of brain integrity in neurodegenerative diseases. Here, we aimed to determine the brain-age gap in genetic and idiopathic Parkinson's disease and its association with surrogate markers of Alzheimer's disease and Parkinson's disease pathology and with rates of cognitive and motor function decline. We studied 1200 cases from the Parkinson's Progression Markers Initiative cohort, including idiopathic Parkinson's disease, asymptomatic and clinical mutation carriers in the leucine-rich repeat kinase 2 gene (LRRK2) and the glucocerebrosidase gene (GBA), and normal controls using a cohort study design. For comparison, we studied 187 Alzheimer's disease dementia cases and 254 controls from the Alzheimer's Disease Neuroimaging Initiative cohort. We used Bayesian ANOVA to determine associations of the brain-age gap with diagnosis, and baseline measures of motor and cognitive function, dopamine transporter activity and CSF markers of Alzheimer's disease type amyloid-β42 and phosphotau pathology. Associations of brain-age gap with rates of cognitive and motor function decline were determined using Bayesian generalized mixed effect models. The brain-age gap in idiopathic Parkinson's disease patients was 0.7 years compared to controls, but 5.9 years in Alzheimer's disease dementia cases. In contrast, asymptomatic LRRK2 individuals had a 1.1. year younger brain age than controls. Across all cases, the brain-age gap was associated with motor impairment and (in the clinically manifest PD cases) reduced dopamine transporter activity, but less with CSF amyloid-β42 and phosphotau. In idiopathic Parkinson's disease cases, however, the brain-age gap was associated with lower CSF amyloid-β42 levels. In sporadic and genetic Parkinson's disease cases, a higher brain-age gap was associated with faster decline in episodic memory, and executive and motor function, whereas in asymptomatic LRRK2 cases, a smaller brain-age gap was associated with faster cognitive decline. In conclusion, brain age was sensitive to Alzheimer's disease like rather than Parkinson's disease like brain atrophy. Once an individual had idiopathic Parkinson's disease, their brain age was associated with markers of Alzheimer's disease rather than Parkinson's disease. Asymptomatic LRRK2 cases had seemingly younger brains than controls, and in these cases, younger brain age was associated with poorer cognitive outcome. This suggests that the term brain age is misleading when applied to disease stages where reactive brain changes with apparent volume increases rather than atrophy may drive the calculation of the brain age.
Effects of spironolactone on intradialytic hypertension: a multicentre, double-blind, randomized, crossover study
Authors: Vongchaiudomchoke Thanawat , Khumngeon Phimpraphan , Wachiraphansakul Nuttaya , Songwattana Wipada , Luechoowong Jomchai , Nochaiwong Surapon , Ruengorn Chidchanok , Noppakun Kajohnsak 
Published: December 2024

ABSTRACT

Background
Intradialytic hypertension (IDH) is associated with an increase in hospitalization and mortality. Several studies have shown that spironolactone reduces pre-dialysis blood pressure. However, none evaluated its effect on IDH. This study aims to evaluate the effects of spironolactone on IDH.

Methods
A 24-week, two-treatment, four-period, multicentre, double-blind, randomized, crossover study was conducted in stable maintenance haemodialysis (HD) patients who experienced IDH in &gt;30% of their sessions during the past 3 months. Each participant was randomly assigned to one of four treatment sequences. In each intervention period, patients received a single dose of 50 mg spironolactone or a placebo 30 minutes before undergoing HD for 4 weeks according to their preassigned sequence, separated by a 2-week washout period. The primary outcome was an incidence of IDH.

Results
A total of 49 eligible patients were recruited with a total of 1211 dialysis sessions. The mean age was 54 ± 14 years and the mean systolic and diastolic blood pressures (SBP and DBP) were 145 ± 15 and 75 ± 10 mmHg, respectively. All patients had hypertension. The average number of antihypertensive drugs was 3.5 ± 1.4. Spironolactone reduced the incidence of IDH compared with placebo (57% versus 69%, P for treatment effect &lt; .001). Patients receiving spironolactone had lower peak intradialytic SBP (161 ± 14 versus 165 ± 13 mmHg, P = .003), mean intradialytic SBP (149 ± 13 versus 152 ± 12 mmHg, P = .01) and post-dialysis SBP (152 ± 15 versus 157 ± 14 mmHg, P &lt; .001) than placebo.

Conclusion
In maintenance HD patients who had frequent IDH, a pre-dialysis 50-mg spironolactone administration significantly decreased the incidence of IDH.

Trial registration
Thai Clinical Trial Registry, reference number TCTR20200604013.