bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multiple Attribute Trigonometric Decision-making and
its Application to the Selection of Engineers
Authors: Palanikumar M. , Arulmozhi K. , Jana Chiranjibe , Pal Madhumangal 
Published: September 2022

Abstract
It is concerned with a new method for solving multiple attribute decision-making (MADM) problems under sine trigonometric Pythagorean neutrosophic normal interval-valued set (shortly, ST-PyNSNIVS). The ST-PyNSNIVS is a generalization of the Pythagorean neutrosophic interval-valued set (shortly, PyNSIVS), the Pythagorean neutrosophic set (shortly, PyNSS), and the interval-valued neutrosophic set (shortly, IVNSS). In this paper, a new concept of sine trigonometric Pythagorean neutrosophic normal interval-valued weighted averaging (shortly, ST-PyNSNIVWA), sine trigonometric Pythagorean neutrosophic normal interval-valued weighted geometric (shortly, ST-PyNSNIVWG), sine trigonometric generalised Pythagorean neutrosophic normal interval-valued weighted averaging (shortly, ST-GPyNSNIVWA) and sine trigonometric generalized Pythagorean neutrosophic normal interval-valued weighted geometric (shortly, ST-GPyNSNIVWG). Under the operators ST-PyNSNIVWA, ST-PyNSNIVWG, ST-GPyNSNIVWA, and ST-GPyNSNIVWG, we also acquired an algorithm and a owchart that interact with the MADM technique. For the suggested models, which are future expansions of real-life instances, we addressed new sorts of two distances, such as euclidean and hamming distances. The PyNSNIV set technique was used to examine the sine trigonometric aggregation operations. They are more practical and straightforward, and the finest option may be reached in a short period of time. As a result, the defined models conclusions are more exact and closely dependent on Σ. We also compare some of the current models to the suggested models, demonstrating the validity and usefulness of the investigated models. Finally, some enthralling and intriguing findings from the study.
The Impact of Landslides, Fatal Landslides and their Triggers in Pakistan (2003-2019)
Authors: Shabbir Waqas , Omer Talha , Pilz Juergen 
Published: September 2022

Abstract
The actual impact of landslides in Pakistan is highly underestimated and has not been addressed to its full extent. This study focuses on the impact which landslides had in the last 17 years, with focus on mortality, gender of deceased, main triggers (landslides and fatal landslides) and regional identification of the hot spots in Pakistan. Our study identified 1089 landslides (including rockfalls, rockslides, mudslides, mudflows, debris flows) out of which 180 landslides were fatal and claimed lives of 1072 people. We found that rain (rainfall and heavy rainfall) related landslides were deadliest over the entire study period. The main trigger of landslides in Pakistan is heavy rainfall which comprises over 50% of the triggers for the landslide and combined with normal rainfall this rate climbs to over 63%. The second main reason for landslide occurrence is spontaneous (due to rock instability, erosion, climate change and other geological elements) with landslides accounting for 22.3% of all the landslides. Landslides caused by rain related events amounted to 41.67% of the fatalities whereas spontaneous landslides caused 29.44% of the deaths and the human induced events accounted for 25.5% of the fatalities. The fatal landslides accounted for 19.53% deaths of the children. Our study also found that more than 48% of the deadly landslides occurred between the months of January to April whereas the least fatal landslides occurred in the month of June which accounted for only 3% of all the fatal landslides in Pakistan.
The feasibility of reducing the thresholds for biopsy in ACR TIRADS and AI TIRADS
Authors: Fu Chao , Cui Yiyang , Li Jing , Wang Yan , Si Caifeng , Cui Kefei 
Published: September 2022

Abstract
At present, there is still controversy over whether to perform fine needle aspiration (FNA) on sub-centimeter thyroid nodules with high suspicion of malignancy. Our aim was to estimate the feasibility of reducing the original thresholds for biopsy in American College of Radiology Thyroid Imaging Reporting and Data System (ACR TIRADS) and Artificial Intelligence TIRADS (AI TIRADS). A total of 3201 thyroid nodules with definitive histology obtained were included. Ultrasound categories were assigned according to each TIRADS. We lowered the original FNA thresholds of TR3-TR5 in ACR and AI TIRADS and estimated whether the decreased FNA thresholds could be accepted and used to modified ACR and AI TIRADS. Then, we estimated and compared the diagnostic performance between modified TIRADS and original TRADS to determine if the decreased thresholds could be an effective strategy. 1474 (46.0%) thyroid nodules were diagnosed as malignant after thyroidectomy. Modified ACR TIRADS had higher sensitivity and lower specificity, unnecessary biopsy rate, missed malignancy rate compared with original ACR TIRADS (all P< 0.05). Similar trends were seen in modified AI TIRADS versus original AI TIRADS (all P < 0.05). In conclusion, biopsy of all nodules with TR5 in both ACR TIRADS and AI TIRADS might be an effective strategy, regardless of the nodules dimension. This paper contributes to the contradictory concerning whether perform FNA for the nodules smaller than 10 mm.
Flood Damage Assessment Using Machine-learning Methods
Authors: Anisha A , Malavika K , Erfana K , Dheeraj Kanram , Subahan Abdul , Raj A K Shinsha , Mangalathu Sujith , Davis Robin 
Published: September 2022

Abstract
The flood depth-damage function is commonly used to estimate the damage in structures caused by floods. Although depth-damage functions are convenient to use for flood damage assessment due to its simplicity, they ignore the other flood damage influencing parameters such as the duration of the flood, age of the structure, floor area, type of wall and roof materials, etc. A machine learning-based approach is proposed in this study for the flood damage modelling incorporating all the relevant flood damage parameters. Data from the devastating 2018 flood in Kerala, India’s southernmost state, is gathered, and the collected data is utilised to investigate the applicability of the recently developed Machine learning (ML) classification algorithms, namely Naive Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Ada Boost, XG Boost, Light GBM, Cat Boost, and Support Vector Machine to model the flood damage incorporating all the influencing parameters. The Random Forest model is obtained as the best performing algorithm for flood damage prediction, with an accuracy of 84% for test data set. Further, a SHAP (SHapely Additive exPlanations) analysis is used to estimate the order of significance of the input variables and also to explain the reason for the prediction of flood damage state by the best performing machine learning model.
Bilinear Backlund, Lax Pairs, Lump waves and Soliton interaction of (2+1)-dimensional non-autonomous Kadomtsev-Petviashvili equation
Authors: Roy Subrata , Raut Santanu , Kairi Rishi Raj , Chatterjee Prasanta 
Published: September 2022

Abstract
This article describes the the characteristic of integrability via Painleve analysis of the Kadomtsev-Petviashvili (KP) equation under the influence of an external force along with a damping. Introducing the Hirota's approach multi-soliton solution of the said equation is acquired in excited systems. Utilizing the obtained solutions, the interaction of solitary wave is observed with special care. It has been observed that Interactive autonomous solitons appear to remain in their original shape after collision. However, the non-autonomous soliton change their shape and directions after collision. The background from which the solitons rises, also significantly changes due to the action of external forces. Further, lump type soliton and some complicated mixed soliton are derived from the bilinear form of the said equation with the appropriate choice of polynomial functions. On the basis of the obtained mixed soliton, the interaction of the strip soliton and lump wave are graphically described. During the investigation of the interaction, fusion type situation is appeared. Finally, from the analytical results of the relevant motions, it is also confirmed that the velocity, maximum altitude and interacting natures of the wave quantities are all influenced by the damping and forcing terms. The interacting natures of the wave quantities are entirely investigated also from numerical understanding.
The impact of childhood burns on academic performance: A matched population-based cohort study
Authors: Halim Nicole , Holland Andrew J. A. , McMaugh Anne , Cameron Cate M. , Lystad Reidar P. , Badgery-Parker Tim , Mitchell Rebecca J. 
Published: September 2022

Abstract
Background: Burn injuries can have a detrimental impact on a young person’s health and social development, which may affect their academic performance. This study aimed to compare academic performance and high school completion of young people hospitalised for a burn compared to young people not hospitalised for an injury.
Methods: A population-based matched case-comparison cohort study of young people aged ≤18 years hospitalised for a burn during 2005-2018 in New South Wales, Australia using linked health and education records. Generalised linear mixed modelling examined risk of performance below the national minimum standard (NMS) on school assessments and generalised linear regression examined risk of not completing high school for young people hospitalised after a burn compared to matched peers.
Results: Young people hospitalised with a burn were below the NMS for reading (ARR: 1.14; 95%CI 1.02-1.49) but not for numeracy (ARR: 1.15; 95%CI 0.94-1.42) when compared to matched peers. Females hospitalised for a burn had a 72% higher risk of poorer literacy compared to their peers (ARR: 1.72; 95%CI 1.33-2.23). Young people hospitalised with a burn had at least twice the risk of not completing Year 10 (ARR: 3.86; 95% CI 1.68-8.86), Year 11 (ARR: 2.45; 95%CI 1.89-3.18), and Year 12 (ARR: 2.09; 95%CI 1.63-2.67) compared to matched counterparts.
Conclusions: Young people hospitalised with a burn displayed poorer academic performance for literacy compared to matched peers and are likely to leave school earlier. Identifying unmet learning support needs of young burn survivors should be investigated.
Glassy Hybrid Network with Excellent Toughness and Self-Healing Ability at Ambient Temperature
Authors: Li Weihang , Wang Hao , Chen Yiwen , Peng Yan , Liu Hui , Wu Haitao , Hou Yujia , Yuan Zhaoyang , Huang Yue , Ye Bang-Jiao , Zhang Hongjun , Wu Jinrong 
Published: September 2022

Abstract
We developed a tough and healable polymeric glass (THG) through tailoring amine-carboxylate salt bridges between a hyperbranched polymer and a high-molecular-weight linear copolymer. The high density of salt bridges leads to high yield strength (up to 43.9 MPa) and Young's modulus (1.1 GPa) of THG. Meanwhile, the large free volume of the hyperbranched polymer and the molecular entanglements of the linear copolymer enable outstanding toughness (up to 86.3 MJ/m3), outperforming most commercial glassy polymers. More interestingly, THG can readily heal mechanical damage at ambient temperature below its glass transition temperature due to the reversible crosslinking and secondary relaxations of the network. Therefore, this approach enables the development of glassy polymers with combination of high strength, excellent toughness and self-healing ability in glassy state.
Machine Learning-based Colorectal Cancer Prediction using Global Dietary Data
Authors: Rahman Hanif Abdul , Ottom Mohammad Ashraf , Dinov Ivo 
Published: September 2022

Abstract

Background Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Active screening for CRC yields detection in increasingly younger cohorts. However, current machine learning algorithms that are trained using older adults and smaller datasets, may not perform well in practice for large populations. Aim To evaluate machine learning algorithms using large datasets accounting for both younger and older adults from multiple regions and diverse sociodemographic. Methods Dietary-related colorectal cancer data was derived for Canada, India, Italy, South Korea, Mexico, Sweden, and United States from the Center for Disease Control and Prevention, Global Dietary database, and other publicly accessible institutional sites. Nine supervised and unsupervised machine learning algorithms were evaluated. Results 109,342 data points were used, of which 7,326 had positive CRC labels. Both supervised and unsupervised models performed well in predicting CRC and non-CRC labels. An artificial neural network (ANN) was found to be the optimal algorithm with CRC misclassification of 1% and non-CRC misclassification of 3%. Conclusions ANN models trained on large heterogeneous datasets may be applicable for both younger and older adults. Such models represent effective clinical decision support systems assisting healthcare providers in dietary-related, non-invasive screening that can be applied in large populations. Using optimal algorithms coupled with high compliance to cancer screening is expected to significantly improve early diagnoses and boost the success rate of timely and appropriate cancer interventions.
OCRL is a novel prognostic biomarker and its association with immunoregulation in breast cancer
Authors: Chen Li-jie , Zhu Fan-shuang , Xu Wang-jin , Fang Hong-yan , Wang Lin-yi , Ma Zhao-sheng , Cao Feilin 
Published: September 2022

Abstract
Background: OCRL (Oculocerebrorenal syndrome of Lowe Protein) was originally well-known as the Lowe’s protein. However, the expression, significance and regulation mechanisms of OCRL in any cancers were not clear.
Methods: The bioinformation of breast cancer (BC) patients was required from TCGA (The Cancer Genome Atlas), and the expression was validated via IHC (immuno-histochemical analysis), qPCR and western blot. The UALCAN database was used to explore the promoter methylation level of OCRL and its role in gene silencing and survival. The prognostic roles of various clinicopathologic characteristics, including the OCRL, were evaluated by univariate and multivariate regression analysis. OCRL-related differentially expressed genes (DEGs) and their functions were explored by LinkedOmics database. The protein-protein interaction (PPI) and immunomodulatory roles were further analyzed with STRING and TISIDB database.
Results: Compared with normal and paracancerous samples, the up-regulated expression of OCRL was identified in BC samples. The OCRL was co-expressed with a variety of unfavorable survival-related genes, which also was identified as an independent prognostic factor. Thus, the prognosis of BC patients with overexpressed OCRL was notably more unfavorable from TCGA database. Also, the hypomethylation of OCRL at certain sites was associated with gene silencing and poor survival. Moreover, the overexpression level of OCRL was negatively associated with the infiltration of the most immune cells and the expression of various immune biomarkers in BC. Finally, a OCRL expression-based nomogram integrating independent prognostic factors was constructed to predict at one-, five-, and ten-year the overall survival (OS).
Conclusion: OCRL was a promising prognostic predictor and potential immune inhibition modulator.
Effects of water gradient change on nutrient resorption in the desert steppe of Northern China
Authors: Bai Liu , Wang Zhongwu , Wang Jing , Li Zhiguo , Ren Haiyan , Sun Hailian , Zhang Guogang , Han Guodong 
Published: September 2022

Abstract
Background: Plant growth is often water limited in drylands, and it has been proposed that alleviation of this resource stress by future increases in reactive precipitation may affect internal plant nutrient cycling, thus influencing grassland ecosystem productivity.
Methods: A 4-year field experiment was performed to examine the effects of water reduction and addition on nutrient concentrations in soil and plants. We studied five dominant plants in Stipa breviflora desert steppe in Inner Mongolia, and through a simulated precipitation experiment, measured the changes in plant leaf nitrogen (N) concentration, nitrogen resorption efficiency (NRE) and phosphorus resorption efficiency (PRE) natural precipitation, 50% water reduction, 50%water addition and 100% water addition. The effects of different water treatments on nutrient resorption efficiency and its influencing factors were analyzed.
Results: Compared with natural precipitation, 100% water addition significantly increased soil moisture and significantly decreased soil available nitrogen. The aboveground biomass of the grassland ecosystem was significantly increased, and the nitrogen and phosphorus concentrations in green and senescent leaves were reduced, thus reducing NRE and PRE. NRE was significantly negatively correlated with N concentration in senescent leaves, and PRE was significantly negatively correlated with phosphorus concentration in senescent leaves.
Conclusions: Water addition dilutes the nutrient concentration of plant leaves by increasing aboveground biomass, resulting in the reduction of plant nutrient resorption efficiency. Dominant plants are resistant to nutrient poor habitats in the desert steppe of northern China.
CCL20 and CD8A as diagnostic biomarkers for HBV infection-induced hepatic fibrosis development, including immune infiltration in HBV-LF
Authors: Song Jingru , Liu Lu , Wang Zheng , Xie Dong , Azami Nisma Lena Bahaji , Lu Lu , Huang Yanping , Zhang Qin , Sun Mingyu 
Published: September 2022

Abstract
Background

Histologically, hepatic fibrosis still occures progressively in chronic hepatitis B patients, even if HBV-DNA is negative or undetectable. The diagnosis of hepatic fibrosis is beneficial to control the development of it or promote the reversal. Liver biopsy is a traumatic diagnosis, which is the gold standard of diagnosis at present. The diagnosis of hepatic fibrosis badly needs diagnostic biomarkers. We emphasize that a good clinical biomarker for patients should be simple to test and minimally invasive, highly disease, specific,and highly sensitive. Early detection of HBV-LF development is crucial in the prevention, treatment, and prognosis prediction of HBV-LF. In this bioinformatic study, we analyzed the relationship between HBV-LF and immune infiltration and identified key genes to uncover new therapeutic targets.

Objectives

To find potential biomarkers for hepatic fibrosis in the development of chronic hepatic B patients.

Materials and Methods

Two sets of data from the Gene Expression Omnibus (GEO) database were obtained including CHB/ healthy control and CHB/HBV-LF, which were selected for differential expression analysis. Protein-protein interaction (PPI) network was also generated, and key genes and important gene modules involved in the occurrence and development of HBV-LF were identified. These key genes were then analyzed by functional enrichment analysis, module analysis, and survival analysis. Furthermore, the relationship between these two diseases and immune infiltration was explored.

Results

Among the identified genes, 150 were individually associated with CHB and healthy control in the differential gene expression (DGE) analysis. while 14 with CHB and HBV-LF. It was also analyzed in the Robust rank aggregation (RRA) data sets, 34 differential genes were further identified by Cytohubba. Among 34 differential genes, two core genes were determined : CCL20 and CD8A.

Conclusion

CCL20 and CD8A were found to be potential biomarkers and therapeutic targets for HBV-LF. It is instructive for research on the progression of LF in HBV patients, suppression of chronic inflammation, and development of molecularly targeted-therapy for HBV-LF.
Losing the maternal effect gene Nlrp2 alters the ovulated mouse oocytes transcriptome and impacts histone demethylase KDM1B expression
Authors: Anvar Zahra , Chakchouk Imen , Sharif Momal , Mahadevan Sangeetha , Nasiotis Eleni Theodora , Su Li , Liu Zhandong , Wan Ying-Wooi , Veyver Ignatia B. 
Published: September 2022

Abstract
The subcortical maternal complex (SCMC) is a multiprotein complex in oocytes and preimplantation embryos that is encoded by maternal effect genes. The SCMC is essential for zygote-to-embryo transition, early embryogenesis, and critical zygotic cellular processes like spindle positioning and symmetric division. Maternal deletion of Nlrp2, which encodes an SCMC protein, results in increased early embryonic loss and abnormal DNA methylation in embryos. We performed RNA sequencing on pools of oocytes that we isolated and collected from cumulus-oocyte complexes (COCs) after inducing ovarian stimulation in wild-type and Nlrp2-null female mice. Using a mouse reference genome-based analysis, we found 231 differentially expressed genes (DEGs) in Nlrp2-null compared to WT oocytes (123 up- and 108 downregulated; adjusted p < 0.05). The DEGs we identified were enriched for processes involved in neurogenesis, gland morphogenesis, and protein metabolism and for post-translationally methylated proteins. When we compared our RNA sequencing results to an oocyte-specific reference transcriptome that contains many previously unannotated transcripts, we found 228 DEGs, including genes not identified with the first analysis. Intriguingly, 68% and 56% of DEGs from the first and second analyses, respectively, overlap with oocyte-specific hyper- and hypomethylated domains. Thus, the differentially expressed transcripts in the oocytes of mice lacking NLRP2 are enriched for genes that overlap with oocyte-specific methylated domains. This is consistent with the known functional link between transcription and methylation in oocytes.
Wireless Power Transfer for Electric Vehicle Using Parallel-Series Compensation Circuit
Authors: Modi Vikram , Kumar Praveen , Choudhary Amit Kumar 
Published: September 2022

Abstract
Resonant wireless power transfer is a method of transferring power without having a wire connecting between the transmitting coil and the receiving coil. Both sides have a power electronics converter. A single phase full bridge inverter is cascaded to a resonant wireless power transfer circuit on the transmitting side, which is fed through the grid and connected to a power factor correction rectifier to increase power factor or compensate reactive power in order to reduce the amount of input VA needed. A resonant wireless power transfer works on the resonance where maximum power is transferred through wireless to receiving side. Resonant WPT circuit has four different topologies based on the connection of capacitance either connected in series or in parallel among them parallel series connection is used here. A resonant power transfer circuit is used where high power charging is required, and it can transfer power for long-distance as compared to inductive power transfer. The efficiency of Resonant WPT depends on the quality factor of the transmitting and receiving side and also depends on the effective coupling factor of the coil.
Parameterized Complexities of Dominating and Independent Set Reconfiguration
Authors: Bodlaender Hans Leo , Groenland Carla , Swennenhuis Céline M. F. 
Published: September 2022

Abstract
We settle the parameterized complexities of several variants of independent set reconfiguration and dominating set reconfiguration, parameterized by the number of tokens. We show that both problems are XL-complete when there is no limit on the number of moves, XNL-complete when a maximum length l for the sequence is given in binary in the input, and XNLP-complete when l is given in unary. The problems were known to be W[1]- and W[2]-hard respectively when l is also a parameter. We complete the picture by showing membership in those classes. Moreover, we show that for all the variants that we consider, token sliding and token jumping are equivalent under pl-reductions. We introduce partitioned variants of token jumping and token sliding, and give pl-reductions between the four variants that have precise control over the number of tokens and the length of the reconfiguration sequence.