bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-View Multi-Label Personalized Classification via Generalized Exclusive Sparse Tensor Factorization
Authors: Fei Luhuan , Lin Weijia , Wang Jiankun , Sun Lu , Kudo Mineichi , Kimura Keigo 
Published: January 2024

Abstract
Multi-Label Classification (MLC) assigns multiple relevant labels to each sample simultaneously,while multi-view MLC aims to apply MLC to handle heterogeneous data represented by multiple feature subsets.In recent years, a variety of methods have been proposed to handle these problems and have achieved great success in a wide range of applications. MLC saves global label correlation by building a single model shared by all samples but ignores sample-specific local structures, while Personalized Learning (PL) is able to preserve sample-specific information by learning local models but ignores the global structure. Integrating PL with MLC is a straightforward way to overcome the limitations, but it still faces three key challenges. 1) capture both local and global structures in a unified model; 2) efficiently preserve full-order interactions between labels, samples and features or multi-view features in heterogeneous data; 3) learn a concise and interpretable model where only a fraction of interactions are associated with multiple labels. In this paper, we propose a novel Multi-Label Personalized Classification (MLPC) method and its multi-view extension to handle these challenges. For 1), it integrates local and global components to preserve sample-specific information and global structure shared across samples, respectively. For 2), a multilinear model is developed to capture full-order label-feature-sample interactions, and over-parameterization is avoided by tensor factorization. For 3), exclusive sparsity regularization penalizes factorization by promoting intra-group competition, thereby eliminating irrelevant and redundant interactions during Exclusive Sparse Tensor Factorization (ESTF). Moreover, theoretical analysis generalizes the proposed ESTF and reveals the equivalence between MLPC and a family of jointly regularized counterparts. We develop an alternating algorithm to solve the optimization problem, and demonstrate its effectiveness based on comprehensive experiments on both synthetic and real-world benchmark datasets.
Gröbner-shirshov Bases and Linear Bases for Free Differential type Algebras Over Algebras
Authors: Qi Zihao , Qin Yufei , Zhou Guodong 
Published: January 2024

Abstract
We study a question which can be roughly stated as follows: Given a (unital or nonunital) algebra A together with a Gröbner-Shirshov basis G, consider the free operated algebra B over A, such that the operator satisfies some polynomial identities Φ which are Gröbner-Shirshov in the sense of Guo et al., when does the union Φ ∪ G will be an operated Gröbner-Shirshov basis for B? We answer this question in the affirmative under a mild condition in our previous work with Wang. When this condition is satisfied, Φ ∪ G is an operated Gröbner-Shirshov basis for B and as a consequence, we also get a linear basis of B. However, the condition could not be applied directly to differential type algebras introduced by Guo, Sit and Zhang, including usual differential algebras.
This paper solves completely this problem for differential type algebras. Some new monomial orders are introduced which, together with some known ones, permit the application of the previous result to most of differential type algebras, thus providing new operated GS bases and linear bases for these differential type algebras. Versions are presented both for unital and nonunital algebras.However, a class of examples are also presented, for which the natural expectation in the question is wrong and these examples are dealt with by direct inspection.
2000 Mathematics Subject Classification. 13P10 03C05 08B20 12H05 16S10 .
Exploring the active components and potential mechanisms of Qingre Liangxue Decoction in the treatment of psoriasis vulgaris with blood-heat syndrome based on UHPLC-Q-exactive Orbitrap/MS and network pharmacology
Authors: Liu Jiayi , Zhou Xiangnan , Cai Rui , Wu Doudou , Bai Yanping 
Published: January 2024

Abstract
Background
Psoriasis is a common, chronic, inflammatory papulosquamous skin disease which is difficult to be cured. Qingre Liangxue Decoction(QRLXD), originated from the classic formula Xijiao Dihuang Decoction, as a famous classical prescription for the treatment of psoriasis vulgaris (PV) with blood-heat syndrome in the traditional Chinese medicine (TCM), shows good efficacy in clinical practice. Since the mechanism of action is still unclear, this article was aimed to profile the absorbed prototype constituents in rat serum after its oral administration and explore the molecular mechanism of QRLXD using network pharmacology and molecular docking technology.
Methods
First, chemical components of QRLXD absorbed in vivo were comprehensively analyzed by Ultra high performance liquid chromatography-high resolution mass spectrometry (UPLC-Q-Exactive-Orbitrap-MS) method. The Swiss Target Prediction was used to predict corresponding potential target genes of active compounds. Simultaneously, 5 disease databases were used to search and filter psoriasis vulgaris (PV) with blood-heat syndrome targets, and then we constructed a protein-protein interaction (PPI) network. Metascape were utilized for GO and KEGG enrichment analysis. After that, we constructed a network of "QRLXD-compounds-target-pathway-PV" by Cytoscape. Molecular docking simulation was conducted to further refine the drug-target interaction by AutoDock software.
Results
A total of 68 ingredients were identified in vitro and 18 ingredients in vivo, targeting 523 proteins, among which 129 were related to psoriasis. The protein with a higher degree in the PPI network is TNF, ALB, AKT1, STAT3 and EGFR. 1488 GO biological process items and 162 KEGG signal pathways were enriched. GO and KEGG indicated that QRLXD treatment of psoriasis mainly involves protein kinase activity, protein tyrosine kinase activity, phosphotransferase activity, alcohol group as acceptor and other biological processes.The signaling pathways mainly include the pathways in cancer,PI3K-Akt ,MAPK and IL-17 signaling pathways. The main compounds and the target protein had a good binding ability in molecular docking. The molecular docking results revealed that Dibutyl phthalate, Ethyl benzoylformate, Scopoletin, Albiflorin R1, 3-Hydroxycoumarin have higher affinity with TNF, ALB and AKT1.
Conclusion
This study suggests that QRLXD against PV with blood-heat syndrome through multiple components, multiple targets and multiple pathways. This study identified effective compounds of QRLXD in vivo and predicted potential molecular mechanisms of QRLXD in psoriasis treatment, serving as an important basis for the modernization of QRLXD.
Determination of stability and activity of immobilized lipase for transesterification reaction in fluorous solvent and deducing the reaction mechanism by molecular docking study
Authors: Jawale Priyanka , Bhanage Bhalchandra 
Published: January 2024

Abstract

The objective of the present study was to immobilize lipase and use it for transesterification reaction in a fluorous solvent. Polymeric substance tylose (Tyl) was chosen as immobilization support. Tylose immobilized lipase from Candida rugosa (CRL) was subjected to fluorous solvents treatment and was found most stable in perfluorooctane (PFO). The immobilized Tyl-CRL was characterized for protein loading, solvent stability, surface morphology, restoration of secondary structure, and thermal stability. Immobilized CRL was further applied for the synthesis of phenethyl propionate in a fluorous solvent. All reaction parameters were optimized using the response surface method (RSM). Optimized reaction parameters were substrate ratio 1:3, immobilized lipase quantity 30 mg, and temperature 45oC. Prepared biocatalyst was evaluated for recyclability, and it was found that catalytic activity decreased by 77% up to the fifth recycle run. The optimized synthesis protocol scope was checked to synthesize various flavoring esters. The molecular-level interactions of substrate and lipase were studied using docking.
A Physical Organic Strategy to Predict and Interpret Stabilities of Chemical Bonds in Energetic Compounds for the Discovery of Thermal-Resistant Properties
Authors: Liu Haitao , Chen Peng , Huang Xin , Wei Xianfeng 
Published: January 2024

Abstract
The in-depth understanding about the stability of chemical bonds in energetic compounds plays a central role for molecular design and safety-related evaluations. Most energetic compounds contain nitro as explosophores, and nitro cleavage is fundamental for thermal and mechanical stability. However, the quantum chemistry approach to accurately predict energy and temperature properties related to bond stability is challenging, due to the tradeoff between computational costs and deviations. Herein, the bond orders are proposed as accurate and computational-cost efficient descriptors for predicting the chemical bond stability and thermal-resistant properties. The intrinsic bond strength index (IBSI) demonstrates the best prediction for experimental homolytic bond dissociation energies (R2 > 0.996), which is on par with the results from high-precision quantum chemistry methods. The effects from bond connectivity and steric hindrance hierarchy were analyzed to reveal underlying mechanisms. Additionally, the IBSI descriptors are successfully applied to predict the thermal decomposition temperatures of 24 heat-resistant energetic compounds (R2 = 0.995), thus validating the effectiveness for the prediction and interpretation of chemical bond stability in energetic compounds via a physical organic approach.
WITHDRAWN: Object Extraction and Detection UsingU2-Net and YOLOv7
Authors: Bhatt Pooja , Makwana Dhruv , Joshi Akshit , Raol Krishnaraj 
Published: January 2024

Abstract
Object extraction, detection and mattingof background are constitute foundational pursuits incomputer vision across diverse application domains.For serving this purpose we delves into the usageof U2-Net, a deep learning model, and YOLOv7, anadvanced object detection framework. This attempt ofinvestigation of YOLOv7, an advanced object recogni-tion framework, and U2-Net, a deep learning networkwith a focus on noticeable object extraction. In ourstudy, U2-Net and YOLOv7 are trained on mentioneddata-sets, with a focus on their unique contributionsto object extraction and detection and matting. Theprimary focus of this study is the practical exami-nation of their application, with particular attentionto their real-world implementation along with associ-ated challenges. Notably, we emphasize our intentionto reinforce this effort by providing these modelswith domain-specific information through training onpertinent data-sets. We carefully examine how U2-Net and YOLOv7 could improve the object detectionand recognition performance of our study. The mainobjective of this research is to boost imagination inapplications involving computer vision by providingreal-world examples of how these models might beused.
Label Distribution Learning by Utilizing Common and Label-Specific Feature Fusion Space
Authors: Zhang Ziyun , Wang Jing , Geng Xin 
Published: January 2024

Abstract

Label Distribution Learning (LDL) is a novel machine learning paradigm that focuses on the description degrees of labels to a particular instance. Existing LDL algorithms generally learn with the original input space, that is, all features are simply employed in the discrimination processes of all class labels. However, this common-used data representation strategy ignores that each label is supposed to possess some specific characteristics of its own and therefore might have sub-optimal performance. We propose in this paper Label Distribution Learning by utilizing Common and Label-Specific Feature fusion Space. It first partitions all instances by label-value ranking. Second, it constructs label-specific features of each label by conducting clustering analysis on different instance categories. Third, it performs training and testing by querying the clustering results. Comprehensive experiments on several real-world label distribution data sets validate the superiority of our method against other LDL algorithms as well as the effectiveness of label-specific features.
Effects of Alternative Therapies and Physical Exercise on Neuroplasticity and Skill Acquisition in healthy volunteers: A Metaplasticity Study
Authors: Lin Jiahui , Li Jiemei , Huang Jianpeng , Li Sheng , Sun Jian , Liu Jianhua 
Published: January 2024

Abstract
Background To explore the effects of combining electroacupuncture (EA) with motor training (MT) on corticospinal excitability and motor learning, focusing on the concepts of homeostatic and non-homeostatic metaplasticity.
Methods The study enrolled twenty healthy volunteers who underwent four distinct interventions: EA alone, MT alone, EA-priming prior to MT, and MT-priming prior to EA. Motor evoked potentials (MEPs) and the Grooved Pegboard Test (GPT) were employed to assess changes in M1 excitability and motor performance, respectively.
Results Both EA and MT enhanced M1 excitability and motor performance. However, additional EA or MT priming did not further modulate MEPs amplitudes. EA-priming improved GPT completion times, suggesting a role in enhancing motor learning
Conclusion The study concludes that while EA and MT individually augment motor cortex excitability and performance, their combined application does not further modulate corticospinal excitability, indicating the involvement of non-homeostatic metaplasticity mechanisms. Nevertheless, EA may play a critical role in preventing M1 overstimulation, continually promoting motor learning. This underscores the need for further research into the application of EA, either independently or in conjunction with MT, in clinical settings.
Trial Registration Number This trail was registered in Chinese Clinical Trial Register (NO. ChiCTR2000039910) on November 11, 2020.
East Meets West: Evidence for Validity for the China Medical Professionalism Inventory
Authors: Li Honghe , Hirsh David , Jin Jing , Krupat Edward , Jiang Nan , Ding Ning , Ho Ming-Jung , Manning Dianne , Wen Deliang 
Published: January 2024

Abstract
The characteristics of medical professionalism (MP) vary across cultural contexts. Theories of professionalism and MP tools currently rely on Western cultural perspectives. The authors developed the China Medical Professionalism Inventory (CMPI) using standard processes through four steps: Phase I) systematic review and synthesis of the literature to develop an item pool; Phase II) survey of Chinese clinical and educational experts to develop evidence based on test content; Phase III) two rounds of pilot studies of practicing physicians to measure internal structure and internal consistency; and Phase IV) refinement of the instrument using an expert survey and pilot study. Systematic review of the English- and Chinese-language literature identified 1537 professionalism-specific items from 63 sources as foundation of the item pool. The authors conducted two rounds of expert review, including surveying nationally prominent Chinese healthcare leaders (n = 34, response rate 85%, and n = 76, response rate 63%). The authors conducted three pilot studies of practicing Chinese physicians (n = 360, response rate 92%; n = 3653, response rate 89%; and n = 955, response rate 95%). The results generated a 40-item CMPI and a shortened 20-item CMPI, with four domains “Respect, Compassion, and Communication; Integrity; Excellence; and Responsibility.” Applying the standards of Messick, the American Psychological Association, and Kane, the authors present the CMPI's validity evidence related to content, response process, internal structure, relationship to other variables, and consequences. This study aims to extend the conceptualization and reach of MP measurement.
ARIR: An Intent Recognition-Based Approach for API Recommendation
Authors: Cao Buqing , Wang Siyuan , Xie Xiang , Peng Qian , Yi Yating , Peng Zhenlian 
Published: January 2024

Abstract
With the prevalence of service composition, how to recommend API services that meet the Mashup requirements for developers has become a challenging in the field of service computing. Existing works on Web service recommendation typically conduct service recommendation based on the semantic requirements and interaction relationships of Web services, while ignoring the construction intent of Mashup creators. Moreover, they often only focus on the impact of single intent on recommendation, neglecting the diverse requirements of Mashup creators at different intent-levels. As a result, the recommendation models fail to comprehensively understand the construction intent of Mashup creators, which affects the quality and effectiveness of Web service recommendation. To address this problem, this paper proposes an intent recognition-based API recommendation method, denoted as ARIR. This method utilizes the annotation information of Mashup and API to analyse the creation intent of Mashup and the functional intent of API, thereby providing more accurate API with high-quality to Mashup creators. Firstly, it decouples the representations of Mashup and API at different intent-levels and independently initializes the node representations at each intent-level. Secondly, it trains the vector representations at each intent-level using a decoupled graph convolutional neural network module and obtains the representation vectors of Mashup/API at different intent-levels with attention weights. Then, it aggregates the intents of Mashup and API using the Mashup-API interaction relationships, resulting in the final node representations of Mashup/API. Furthermore, it constructs a similarity heterogeneous network by calculating the Mashup-to-Mashup similarity and API-to-API similarity, updating the node representations by using the Mashup and API feature matrices and adjacency matrices, and obtaining the final recommendation prediction results by using a fully connected layer. Finally, the experimental results conducted on real-world Web service datasets demonstrate that the ARIR outperforms the best-performing baseline method with Recall@20 of 3% and NDCG@20 of 5%.
SGCNet: Multi-task Self-Gated Convolution based UNet with Multi Scale Convolution and Global Attention Module
Authors: Zhang Xiaohu , Huang Haifeng 
Published: January 2024

Abstract
Concrete cracks pose significant safety hazards to concrete buildings, and semantic segmentation models based on deep learning has achieved the state-of-arts results in concrete crack detection. However, there are some limitations in these models. Firstly, stains in crack images would inter-fere the extraction of crack features. Secondly, current crack detection models usually use convo-lutional kernels of uniform size, which could not adapt cracks with varying scales. Thirdly, due to the diversity and irregularity of cracks' edges, existing semantic segmentation models could not effectively segment these edges. Finally, current mainstream model only use serial stacked convo-lution operation, which is limited to extract global features. To solve these problems above, a Self-Gated Convolution (SGC) based U-Net model with Multi Scale Convolution Module (MSCM) and Global Attention Module (GAM) named as SGCNet is proposed by us. In this model, SGC is used to remove interference features and MSCM is used for extract features of different scales with dilated convolutions. Additionally, edge detection of cracks is used as the second task to en-hance the edge feature extraction ability of the model. Finally, GAM is used to extract global con-textual information. Experimental results show that the SGCNet proposed by us could achieve the accuracy of 0.942, 0.836 and 0.991 on the Cracktree200, CRACK500 and CFD datasets respectively, which is higher than that of the traditional models.
Electrochemical self-signal identification of circulating tumor DNA based on poly-xanthurenic acid assembled on black phosphorus nanosheets
Authors: Yan Xinyu , Zhang Wei , Yang Jimin 
Published: January 2024

Abstract
A self-signal electrochemical identification interface was prepared for the determination of circulating tumor DNA (ctDNA) in peripheral blood based on poly-xanthurenic acid (PXTA) assembled on black phosphorus nanosheets (BPNSs) acquired through simple ultrasonication method. The electropolymerization efficiency was promoted due to the physical adsorption between the xanthurenic acid (XTA) monomers and BPNSs, which was beneficial to the enlargement of the signal response of PXTA. The assembled PXTA/BPNSs nanocomposite with attractive electrochemical activity was adopted as a platform for the recognition of DNA immobilization and hybridization. The probe ssDNA was covalently fixed onto the PXTA/BPNSs nanocomposite with plentiful carboxyl groups through the terminate free amines of DNA probes by use of the 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide and N-hydrosulfosuccinimide cross-linking reaction, accompanied with the decline of the self-signal response. When the hybridization between the probe ssDNA and the target DNA was accomplished, the self-signal response of the nanocomposite interface reproduced by virtue of the shaping of helix construction. The determination limit of the assembled DNA identification interface was 2.1×10− 19 mol/L, and the complementary target DNA concentrations varied from 1.0×10− 18 mol/L to 1.0×10− 12 mol/L. The DNA identification platform displayed magnificent sensitivity, specificity and stability, and was efficaciously implemented to the mensuration of ctDNA derived from colorectal cancer.
Climate Change Trend Analysis and Future Projection in Guguf Watershed, Northern Ethiopia
Authors: Mohammed Mekin , Bezabeh Seyoum 
Published: January 2024

Abstract
According to Intergovernmental panel on Climate Change (IPCC) Climate change is the weather characteristic condition such as precipitation, air temperature, humidity, wind, sunshine, cloud cover and atmospheric pressure at a specific location determined over a long period of at least 30 years. The main objective of this study was to analyse the climate trend and future projection in Guguf watershed of Southern Tigray, Ethiopia. 32 years (1987–2018) Meteorological data were collected from Ethiopia National Meteorological Agency (NMA). Download canESM2 (Canadian Second Generation Earth System Model) which was freely available at the Canadian climate change scenario group website. The Mann-Kendal trend test was used to test for the presence of trends using XLSTAT. SDSM 4.2.9 decision support tool was used to downscale large scale predictors and project future climate change. The period from 1987–2018 were considered as a base period whereas the period from 2019–2100 were considered as future periods. Historically, slight decrease in rainfall, and an overall increase in the mean annual minimum and maximum temperatures in the study area for the last 32 years. The highest increment of maximum temperature recorded in October month up to + 2.7°C in RCP8.5 scenarios. The precipitation increases up to a maximum of 49% (2073–2100) for RCP4.5 scenario and 66% (2073–2100) for RCP4.5 scenario in the Belg. Precipitation decreases in Kiremt (Jun–September) season by 8% (2019–2045) and 23% (2073–2100) for RCP4.5 scenarios. Future work needs to consider studying the effects of different climate change adaptation strategie.