bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on ultra-short-term load forecasting method of oil and gas field integrated energy system based on hybrid neural network
Authors: Zhang Zhao , Dong Dezhi , Lv Lili , Peng Liyuan , Li Bing , Peng Miao , Cheng Tingting 
Published: February 2024

Abstract
Oil and gas fields have a large amount of distributed new energy. In order to improve the utilization rate of new energy and respond to the dispatching needs of China's State Grid, it is necessary to study the use of ultra-short-term load forecasting algorithms to improve the load forecasting accuracy of oil and gas fields and support the coordinated interaction of source, grid and load in the integrated energy system of oil and gas fields. This paper proposes an ultra-short-term load forecasting algorithm based on a hybrid neural network called Convolutional-Bidirectional Long Short Term Memory-Skip (CNN-BiLSTM-Skip). Using the operating load data of an oil and gas field in Northeast China as a data set, we first constructed a cooling, heating and power system architecture model with wind turbines, photovoltaics, power grids and natural gas as “source and grid loads”; Secondly, we used an improved hybrid multi-time scale algorithm and unit A prediction model was constructed based on the operating load data, and the prediction results of the nonlinear part and linear part of the model were output and integrated to obtain the final prediction result; Finally, the prediction error evaluation index of the algorithm proposed in this article was compared with algorithms such as BP, LSTM, and CNN-LSTM. The results show that the algorithm proposed in this article has stronger robustness and higher accuracy. The proposed CNN-BiLSTM-SKIP algorithm improves the prediction accuracy. Compared with the BP neural network algorithm, the MAPE evaluation index has an average accuracy increase of 3.78%, compared with the LSTM prediction algorithm, the accuracy has increased by 1.63% on average, and compared with the CNN-LSTM prediction algorithm, the accuracy has increased by 0.74% on average; and the proposed prediction algorithm is compared with the BP neural network algorithm, LSTM prediction algorithm and CNN-LSTM algorithm, the RMSE and MAE evaluation index values are both the smallest, which can support the collaborative interaction of oil and gas field source, network and load and realize the planning and dispatching needs.
Numerical analysis of ground surface settlement induced by shield tunnelling considering the soil parameter spatial variability in loess strata
Authors: Zhang Yanjie , Lai Weizhong , Wang Xu , Zhu Hanxing , Jiang Daijun 
Published: February 2024

Abstract
Ground surface settlement (GSS) induced by shield tunnelling in loess soils represents a substantial challenge for geotechnical and structural engineers. Despite numerous attempts to predict GSS caused by tunnelling, considering the spatial variability of the soil parameters remains open. Thus, this study analysed the Lanzhou Metro Line 1 in China. In particular, the spatial variability of Young's modulus in the loess layer was simulated using random field theory coupled with finite difference analysis. Random fields were generated using the covariance matrix decomposition method, which is consistent with a specified numerical mesh. Subsequently, Monte Carlo simulations were performed using both deterministic and random field difference methods to explore the effect of horizontal scale fluctuations due to shield tunnelling on the ground motion. The results indicated that Young's modulus of loess exhibited a normal distribution. The vertical scale of fluctuation of Young's modulus in the loess layer spanned from 1.32 m to 1.51 m. These findings demonstrate that random field analysis can comprehensively characterise the trend, magnitude and fluctuation range of the GSS. Moreover, the findings indicate that GSS is underestimated when neglecting spatial variability compared with traditional deterministic analysis.
ConvSRGAN: Super-Resolution Inpainting of Traditional Chinese Paintings
Authors: Hu Qiyao , Xie Penglin , Xue Wenzhe , Peng Xianlin , Zhang Xiang , Li Tengfei , Zhao Wanqing , Peng Jinye , Fan Jianping 
Published: February 2024

Abstract

Existing image super-resolution methods have made remarkable advancements in enhancing the visual quality of real-world images. However, when it comes to restoring Chinese paintings, these methods encounter unique challenges. This is primarily due to the difficulty in preserving intricate non-realistic details and capturing complex semantic information with high dimensionality. Moreover, the preservation of the original artwork’s distinct style and subtle artistic nuances fur- ther amplifies this complexity. To address these challenges and effectively restore traditional Chinese paintings, we propose a Convolutional Super-Resolution Gen- erative Adversarial Network (ConvSRGAN) framework specifically designed for Chinese landscape painting super-resolution. Our ConvSRGAN employs several Enhanced Adaptive Residual Modules (EARM) to delve deeply into multi- scale feature extraction in images, incorporating an Enhanced High-Frequency Retention Module (EHRM) that leverages an Adaptive Deep Convolution Block (ADCB) to capture fine-grained high-frequency details across multiple levels. By combining the Multi-Scale Structural Similarity (MS-SSIM) loss with con- ventional losses, our ConvSRGAN ensures that the model produces outputs with improved fidelity to the original image’s texture and structure. Experimen- tal validation demonstrates significant qualitative and quantitative results when
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processing traditional paintings and murals datasets, particularly excelling in high-definition reconstruction tasks for landscape paintings. The reconstruction effect showcases enhanced visual fidelity and liveliness, thus affirming the effec- tiveness and applicability of our approach in cultural heritage preservation and restoration.
Surface functionalization of cellulose derived from hemp by tetraethyl orthosilicate (TEOS) and poly vinylidene fluoride-based composite separator membrane for of lithium-ion batteries (LIBs)
Authors: Sirichaibhinyo Thanakrit , Thiangtham Satita , Saito Nagahiro , Ummartyotin Sarute 
Published: February 2024

Abstract

Separators played a crucial role in enhancing the electrochemical performance of lithium-ion batteries (LIBs). However, achieving separators with outstanding electrochemical performance and high stability proved to be a challenge. Herein, composite membranes based on polyvinylidene fluoride (PVDF) with variable contents of microcrystalline cellulose/tetraethyl orthosilicate (MCC/TEOS) incorporated into PVDF matrices were carefully designed. These MCC/TEOS-based PVDF separator membranes were subsequently deployed as separators in LIBs. Notably, the 3 wt% MCC/TEOS-based PVDF separator membrane exhibited a remarkable porosity of 92.3%, representing a substantial enhancement compared to the pristine PVDF membrane with a porosity of 82.5%. This heightened porosity, in conjunction with heightened hydrophilicity, endowed the PVDF membrane with 3 wt% MCC/TEOS with superior electrolyte absorption and reduced resistance, resulting in an impressive ionic conductivity of 0.5144 mS/cm. Moreover, the LIB cell employing the 3 wt% MCC/TEOS-based PVDF separator membrane consistently demonstrated stable charge/discharge profiles at a rate of 0.2C, achieving a specific capacity of 98 mAh/g, while the PVDF membrane in isolation only reached 43 mAh/g. These findings underscored the considerable potential of MCC/TEOS as a biofiller for biomembranes, rendering it an optimal choice for applications in LIBs.
Association between Insulin Resistance and Pan-Immune-Inflammation Value (PIV): results from the National Health and Nutrition Examination Survey (NHANES) 2017–2020
Authors: Ramasamy Jagadish , Murugiah Viveka , Dhanapalan Aarathy , Balasubramaniam Geerthana 
Published: February 2024

Abstract
Background:
Insulin resistance (IR), a hallmark feature of diabetes and metabolic syndrome, is characterized by chronic low-grade inflammation. Pan-immune-inflammation value (PIV), an emerging immune cell count-based inflammatory index, is the global quantifier of the cellular compartment of systemic inflammation. This study determines the association of PIV with various markers of IR.
Methods:
This retrospective, cross-sectional study was done using the data obtained from the Center for Disease Control-National Health and Nutritional Examination Survey (CDC-NHANES) pre-pandemic data from 2017–2020. Data from 4620 survey participants was included after screening. Homeostasis model assessments of insulin resistance (HOMA-IR) and beta-cell function (HOMA-B), triglyceride glucose (TyG) index, visceral adiposity index (VAI), and lipid accumulation product (LAP) were used as markers of IR. The receiver operating characteristic (ROC) curve and multiple logistic regression are carried out to determine the association between PIV and IR.
Results:
PIV levels were significantly higher in obesity, diabetes, and metabolic syndrome. HOMA-IR, HOMA-B, LAP, VAI, and TyG were significantly different across PIV quartiles. PIV showed a significant positive association with IR (based on the HOMA-IR cut-off). However, the AUC of ROC for PIV is 0.58, suggesting it may not be useful as a marker to predict IR.
Conclusion:
In our study, PIV had a significant positive association with IR. However, the ability of PIV to predict IR is not optimal. Hence, large-scale prospective studies are needed to ascertain the role of PIV as a marker for IR.
The genome of the simultaneously hermaphroditic snail Lymnaea stagnalis reveals an evolutionary expansion of FMRFamide-like receptors
Authors: Koene Joris M. , Jackson Daniel J. , Nakadera Yumi , Cerveau Nicolas , Madoui Mohammed-Amin , Noel Benjamin , Jamilloux Véronique , Poulain Julie , Labadie Karine , Silva Corinne Da , Davison Angus , Feng Zhong-Ping , Adema Coen M. , Klopp Christophe , Aury Jean-Marc , Wincker Patrick , Coutellec Marie-Agnès 
Published: February 2024

Abstract

The great pond snail Lymnaea stagnalis has served as a model organism for over a century in diverse disciplines such as neurophysiology, evolution, ecotoxicology and developmental biology. To support both established uses and newly emerging research interests we have performed whole genome sequencing (~ 176 x depth), assembly and annotation of a single individual derived from an inbred line. These efforts resulted in a final assembly of 943 Mb (L50 = 257; N50 = 957,215) with a total of 22,499 predicted gene models. As a first step towards understanding the hermaphroditic reproductive biology of L. stagnalis, we identified molecular receptors, specifically nuclear receptors (including newly discovered 2xDNA binding domain-NRs), G protein-coupled receptors, and receptor tyrosine kinases, that may be involved in the cellular specification and maintenance of simultaneously active male and female reproductive systems. A phylogenetic analysis of one particular family of GPCRs (Rhodopsin neuropeptide FMRFamide-receptor-like genes) shows a remarkable expansion that coincides with the occurrence of simultaneous hermaphroditism in the Euthyneura gastropods. As some GPCRs and NRs also showed qualitative differences in expression in female (albumen gland) and male (prostate gland) organs, it is possible that separate regulation of male and female reproductive processes may in part have been enabled by an increased abundance of receptors in the transition from a separate-sexed state to a hermaphroditic condition. These findings will support efforts to pair receptors with their activating ligands, and more generally stimulate deeper insight into the mechanisms that underlie the modes of action of compounds involved in neuroendocrine regulation of reproduction, induced toxicity, and development in L. stagnalis, and molluscs in general.
T1 Mapping and Amide Proton Transfer Weighted Imaging for Predicting
Lymph Node Metastasis in Patients with Rectal Cancer
Authors: Wang Yue , Chen Anliang , Hu Wenjun , Liu Yuhui , Wang Jiazheng , Lin Liangjie , Song Qingwei , Liu Ailian 
Published: February 2024

Background:
Accurate preoperative judgment of lymph node (LN) metastasis is a critical step in creating a treatment strategy and evaluating prognosis in rectal
cancer (RC) patients.

Objective:
This study aimed to explore the value of T1 mapping and amide proton transfer weighted (APTw) imaging in predicting LN metastasis in patients
with rectal cancer.

Methods:
In a retrospective study, twenty-three patients with pathologically confirmed rectal adenocarcinoma who underwent MRI and surgery from August
2019 to August 2021 were selected. Then, 3.0T/MR sequences included conventional sequences (T1WI, T2WI, and DWI), APTw imaging, and T1
mapping. Patients were divided into LN metastasis (group A) and non-LN metastasis groups (group B). The intra-group correlation coefficient
(ICC) was used to test the inter-observer consistency. Mann-Whitney U test was used to compare the differences between the two groups.
Spearman correlation analysis was performed to evaluate the correlation between T1 and APT values. Logistic regression and receiver operating
characteristic (ROC) curve analyses were performed to assess the differential performance of each parameter and their combination. The difference
between AUCs was compared using the DeLong test.

Results:
The APT value in patients with LN metastasis was significantly higher than in those without LN metastasis group (P=0.020). Also, similar results
were observed for the T1 values (P=0.001). The area under the ROC curve of the APT value in the prediction of LN metastasis was 0.794; when
the cutoff value was 1.73%, the sensitivity and specificity were 71.4% and 88.9%, respectively. The area under the ROC curve of the T1 value was
0.913; when the cutoff value was 1367.36 ms, the sensitivity and specificity were 78.6% and 100.0%, respectively. The area under the ROC curve
of T1+APT was 0.929, with a sensitivity of 78.6% and specificity of 100.0%.

Conclusion:
APT and T1 values show great diagnostic efficiency in predicting LN metastasis in rectal cancer.
Factors Associated with Mortality in Patients with Coronavirus Disease
(COVID-19) in Babol, North of Iran
Authors: Babaei Mansour , Heidari Behzad , Ghorbani Hossein , Ahmadnia Zahra , Rouhi Samaneh , Haddad Zavareh Mahmoud Sadeghi 
Published: February 2024

Background:
The spectrum of the clinical features of coronavirus disease 2019
(COVID-19) spans from asymptomatic or mild disease to severe respiratory failure, while
different factors are related to its mortality rate. This research aimed to determine the clinical
and laboratory characteristics and the associated factors of mortality in patients with
COVID-19.

Background:
The spectrum of the clinical features of coronavirus disease 2019
(COVID-19) spans from asymptomatic or mild disease to severe respiratory failure, while
different factors are related to its mortality rate. This research aimed to determine the clinical
and laboratory characteristics and the associated factors of mortality in patients with
COVID-19.

Materials and Methods:
Data were collected from hospitalized patients (May to June 2020),
and COVID-19 was confirmed in patients by real-time polymerase chain reaction (PCR).
The patient group was classified into survivor and deceased groups. The chi-square and the
independent t-test with Statistical Package for the Social Sciences (SPSS) software program,
version 18, were used for analysis.

Results:
Mean age, hemoglobin, serum C-reactive protein (CRP), alkaline phosphatase
(ALP), white blood cells, and level of procalcitonin in non-survivors were significantly
higher than in survivors. Serum tumour necrosis factor-alpha (TNF-a) had a significant relationship with D-dimer (p = 0.0006). The risk of mortality in patients with serum CRP > 100
mg/dl was significantly higher than those with CRP < 100 mg/dl (odds ratio (OR) = 4.8 (95
% CI, 1.33-12.44, p = 0.01).

Conclusion:
COVID-19 has different clinical features and can be an asymptomatic, mild, or
severe disease. Thus, findings about the disease and the consequent outcomes differ in patients. Further studies are needed to evaluate the clinical features and consequences of
COVID-19 infection.
An In-Depth Analysis of Collision Avoidance Path Planning Algorithms in
Autonomous Vehicles
Authors: Daniel Keren Lois , Poonia Ramesh Chandra 
Published: February 2024

Abstract:
Path planning is a way to define the motion of an autonomous surface vehicle
(ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the
path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, which
are to be modified using the deterministic path that leads the ASV to reach a goal or a desired
location while finding an optimal solution has become a challenge in the field of optimization
along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment.
This review paper explores the different techniques available in path planning and collision
avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacle’s
movement of different vehicles. Different path planning technical approaches are compared
with their performance and collision avoidance for unmanned vehicles in marine environments
by early researchers. This paper gives us a clear idea for developing an effective path planning
technique to overcome marine accidents in the dynamic ocean environment while choosing the
shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance
the safety of unmanned vehicle movement in a harsh ocean environment.
EZYDeep: A Deep Learning Tool for Enzyme Function Prediction based on Sequence Information
Authors: Boulahrouf Khaled , Aliouane Salah Eddine , Chehili Hamza , Skander Daas Mohamed , Belbekri Adel , Hamidechi Mohamed Abdelhafid 
Published: February 2024

Introduction:
Enzymes play a crucial role in numerous chemical processes that are essential for life. Accurate prediction and classification of enzymes are crucial for bioindustrial and biomedical applications.

Methods:
In this study, we present EZYDeep, a deep learning tool based on convolutional neural networks, for classifying enzymes based on their sequence information. The tool was evaluated against two existing methods, HECNet and DEEPre, on the HECNet July 2019 dataset, and showed exceptional performance with accuracy rates over 95% at all four levels of prediction.

Results:
Additionally, our tool was compared to state-of-the-art enzyme function prediction tools and demonstrated superior performance at all levels of prediction. We also developed a user-friendly web application for the tool, making it easily accessible to researchers and practitioners.

Conclusion:
Our work demonstrates the potential of using machine learning techniques for accurate and efficient enzyme classification, highlighting the significance of sequence information in predicting enzyme function.
Privacy, Security, and Legal Issues in the Health Cloud: Structured Review for Taxonomy Development
Authors: Zandesh Zahra 
Published: February 2024

Background
Privacy in our digital world is a very complicated topic, especially when meeting cloud computing technological achievements with its multidimensional context. Here, privacy is an extended concept that is sometimes referred to as legal, philosophical, or even technical. Consequently, there is a need to harmonize it with other aspects in health care in order to provide a new ecosystem. This new ecosystem can lead to a paradigm shift involving the reconstruction and redesign of some of the most important and essential requirements like privacy concepts, legal issues, and security services. Cloud computing in the health domain has markedly contributed to other technologies, such as mobile health, health Internet of Things, and wireless body area networks, with their increasing numbers of embedded applications. Other dependent applications, which are usually used in health businesses like social networks, or some newly introduced applications have issues regarding privacy transparency boundaries and privacy-preserving principles, which have made policy making difficult in the field.

Objective
One way to overcome this challenge is to develop a taxonomy to identify all relevant factors. A taxonomy serves to bring conceptual clarity to the set of alternatives in in-person health care delivery. This study aimed to construct a comprehensive taxonomy for privacy in the health cloud, which also provides a prospective landscape for privacy in related technologies.

Methods
A search was performed for relevant published English papers in databases, including Web of Science, IEEE Digital Library, Google Scholar, Scopus, and PubMed. A total of 2042 papers were related to the health cloud privacy concept according to predefined keywords and search strings. Taxonomy designing was performed using the deductive methodology.

Results
This taxonomy has 3 layers. The first layer has 4 main dimensions, including cloud, data, device, and legal. The second layer has 15 components, and the final layer has related subcategories (n=57). This taxonomy covers some related concepts, such as privacy, security, confidentiality, and legal issues, which are categorized here and defined by their expansion and distinctive boundaries. The main merits of this taxonomy are its ability to clarify privacy terms for different scenarios and signalize the privacy multidisciplinary objectification in eHealth.

Conclusions
This taxonomy can cover health industry requirements with its specifications like health data and scenarios, which are considered as the most complicated among businesses and industries. Therefore, the use of this taxonomy could be generalized and customized to other domains and businesses that have less complications. Moreover, this taxonomy has different stockholders, including people, organizations, and systems. If the antecedent effort in the taxonomy is proven, subject matter experts could enhance the extent of privacy in the health cloud by verifying, evaluating, and revising this taxonomy.