bluuebunny/crossref_metadata_2025_split huggingface dataset:
Weather patterns in Southeast Asia: Relationship with tropical variability and heavy precipitation
Authors: Howard Emma , Thomas Simon , Frame Thomas H.A. , Gonzalez Paula L.M. , Methven John , Martínez‐Alvarado Oscar , Woolnough Steven J. 
Published: December 2021

AbstractTwo sets of weather patterns describing variability in 850 hPa winds in Southeast Asia are presented and compared. Patterns are calculated using EOF/k‐means clustering with and without imposing a separation between planetary‐scale and regional‐scale circulation features. The former are labelled as tiered patterns while the latter are referred to as flat. The ability of the patterns to distinguish between known modes of tropical circulation variability is examined. This includes climate modes such as the seasonal monsoons, the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) as well as sub‐seasonal modes including cold surges, phases of the MJO and Boreal summer intraseasonal oscillation (BSISO), tropical cyclones, Borneo vortices and equatorial waves. All these modes are well captured by the weather patterns except for the equatorial waves and the IOD. The tiered patterns are shown to better describe large‐scale modes of variability, while the flat patterns better describe the synoptic variability. Both sets of weather patterns are then used to study the likelihood of heavy precipitation depending on synoptic circulation by considering the regime‐conditioned probability of high‐percentile precipitation using the satellite‐derived Global Precipitation Measurement (GPM) dataset. It is shown that the pattern centroids explain up to 10% of the seasonally anomalous precipitation over land, and that a perfect weather pattern forecast would outperform a perfect MJO forecast. These weather patterns show promising potential in extending the useful forecast range for the risk of heavy precipitation, dependent on their forecastability.
Diversity of Coordination Architecture of Zinc Complexes with Diphenylarsinate
Authors: Chen Xiao , Hu Huijuan , Wang Shan , Li Biao , Wang Hongmei 
Published: December 2021

AbstractThe zinc complexes of diphenylarsinate [(Ph2AsO2)ZnCl] (1), [(Ph2AsO2)Zn(Ph2As(O)OH)Cl] (2) and [(Ph2AsO2)2Zn] (3) were prepared through the reaction of ZnCl2 with dihydroxodiphenylarsonium chloride [Ph2As(OH)2]Cl. Single‐crystal X‐ray diffraction analyses indicate that all three compounds form 1D polymer chain arrangements. Specifically, the [Ph2AsO2]− ligand coordinates with three Zn centres giving a syn,syn,anti μ3‐η1 : η2 mode in compound 1. A more diverse binding mode arised in compound 2: [Ph2AsO2]− shows a syn,anti μ2‐η1 : η1 bridging mode; while free ligand Ph2As(O)OH is bound to Zn through the oxygen atom of the As=O unit (monodentate). In addition, the hydrogen atom of Ph2As(O)OH forms a moderate hydrogen bond with the oxygen atom of [Ph2AsO2]−. Even though all [Ph2AsO2]− ligands also coordinate in a bridging mode in compound 3, a chain‐like arrangement with alternate single (anti,anti μ2‐η1 : η1) and triple (syn,syn μ2‐η1 : η1) bridging diphenylarsinate groups between tetrahedrally coordinated Zn atoms was identified.
miR‐141‐3p regulates saturated fatty acid‐induced cardiomyocyte apoptosis through Notch1/PTEN/AKT pathway via targeting PSEN1
Authors: Xin Xin , Duan Lian , Yang Huimin , Yu Hang , Bao Yandong , Jia Dalin , Wu Nan , Qiao Ying 
Published: December 2021

AbstractIt has been reported that miR‐141‐3p levels are markedly upregulated in the cardiomyocytes of obese rats induced by a high‐fat diet. However, the role of miR‐141‐3p in myocardial lipotoxicity remains elusive. In the present study, the role of miR‐141‐3p in lipotoxic injury of H9c2 cells induced by palmitic acid (PA) and its possible mechanisms were assessed. The results indicated that miR‐141‐3p was significantly upregulated in PA‐induced cardiomyocytes. miR‐141‐3p inhibitor enhanced the cell viability, reduced the release of lactate dehydrogenase (LDH), creatine kinase‐MB (CK‐MB), and troponin I (CTN‐I), decreased cell apoptosis rate, and repressed the activation of mitochondrial apoptosis pathway in PA‐treated H9c2, whereas treatment with miR‐141‐3p mimics resulted in the opposite effects. Mechanistically, it was further revealed that miR‐141‐3p could specifically bind to presenilin 1 (PSEN1) 3′UTR, and upregulating miR‐141‐3p levels reduced the expression of PSEN1, thereby inhibiting the activation of the Notch1/PTEN/AKT pathway. Additionally, inhibition of Notch1/AKT signaling pathway by its inhibitor could abrogate the effect of miR‐141‐3p on mitochondrial‐mediated apoptosis induced by PA. In conclusion, the present study demonstrates that miR‐141‐3p regulates saturated fatty acid‐induced cardiomyocyte apoptosis through Notch1/PTEN/AKT pathway via targeting PSEN1, which gains a new insight into the mechanisms of myocardial lipotoxic injury.
Nasolacrimal duct disease secondary to an unerupted tooth in a dog
Authors: Bach Ana , Parry Andrew , de Lacerda Rodrigo Pinheiro , Gomez Alberto Palella 
Published: December 2021

AbstractA 5‐year‐old male neutered Lurcher was evaluated for left‐sided epiphora of 4 months’ duration and persistence following topical treatment and nasolacrimal duct irrigation. Physical examination revealed a missing left maxillary canine tooth, and CT dacryocystorhinography showed extrinsic compression of the left lacrimal duct and adjacent nasal turbinates caused by an impacted maxillary canine tooth. Further irrigation was successful in clearing debris from the nasolacrimal duct and temporarily relieving the patient of epiphora; however, surgical extraction of the tooth and radiographic monitoring was declined by the owner. Ophthalmic disorders may be secondary to overlooked dental conditions. This case describes the importance of inspecting the oral cavity as part of a general physical examination when presented with ocular signs, as well as assessing impacted teeth for evidence of pathology, and being aware of sequelae to dental impaction when advising clients on case management.
Acoustic Exposure to Turbine Operation Quantifies Risk to Bats at Commercial Wind Energy Facilities
Authors: Peterson Trevor S. , Mcgill Brian , Hein Cris D. , Rusk Adam 
Published: December 2021

ABSTRACTTurbine‐related bat mortality at commercial wind energy facilities may threaten populations of migratory tree‐roosting bat species in North America. Industry stakeholders and regulatory agencies alike are investigating strategies to reduce risk of population‐level consequences as the wind energy industry grows. Bats collide with turbines only when turbine rotors are spinning and curtailing turbine operation at low wind speeds can effectively reduce bat fatality rates. Nonetheless, few quantitative data exist to determine appropriate threshold wind speeds below which turbine operations should be curtailed. Carcass monitoring is labor‐intensive and does not provide information on factors linked to bat fatality rates on any scale finer than nightly. We tested whether acoustic bat data recorded at turbine nacelles could provide a more precise and sensitive measure of fatality risk to bats by analyzing acoustics, weather, turbine operation, and carcass data collected at 2 commercial wind energy facilities in West Virginia over 7 years. Each wind facility implemented several distinct curtailment treatments during our study, allowing us to compare fatality rates and acoustic bat activity across multiple operational strategies. We found that bat passes exposed to turbine operation explained close to 80% of the variation in carcass‐based estimates of bat fatality rates and accounted for significant variation in raw carcass counts per turbine and probability of finding bat carcasses during individual turbine searches. Conversely, bat activity occurring when turbines were not operating had little or no relationship to fatality rates. We also found that patterns in bat activity exposure could be predicted accurately among turbines and years. Our results demonstrate that measuring exposure of acoustic bat activity provides a quantitative basis for designing, evaluating, and adaptively managing curtailment strategies. This is an important advance towards using curtailment to reduce bat fatality rates strategically while allowing for increased generation of renewable energy. © 2021 The Authors. Wildlife Society Bulletin published by Wiley Periodicals LLC on behalf of The Wildlife Society.
Drivers of the Formalization of Banking Supervision
Authors: Hotori Eiji , Wendschlag Mikael , Giddey Thibaud 
Published: December 2021

AbstractIn this chapter, the drivers of the formalization of banking supervision are examined from seven perspectives: (a) charter requirements, (b) banknote issuance, (c) liability rules, (d) ensuring the public’s trust, (e) financial crises, (f) economic control, and (g) financial globalization. Our analysis shows that formalization occurred in response to the shifting needs of the time/era and that the formalization process was basically incremental. Notably, financial crises, which are generally considered to be the primary drivers of major regulatory and supervisory reforms, did not always play a leading role in the formalization of banking supervision. It should also be noted that from a historical perspective, regulation and supervision were not “natural” responses to a dysfunctional banking system. Rather, the formalization of banking supervision was the product of complex political actions negotiated by relevant stakeholders with divergent interests in a specific social, political, and economic environment.
Ethical problems in the use of algorithms in data management and in a free market economy
Authors: Szopa Rafał 
Published: December 2021

AbstractThe problem that I present in this paper concerns the issue of ethical evaluation of algorithms, especially those used in social media and which create profiles of users of these media and new technologies that have recently emerged and are intended to change the functioning of technologies used in data management. Systems such as Overton, SambaNova or Snorkel were created to help engineers create data management models, but they are based on different assumptions than the previous approach in machine learning and deep learning. There is a need to analyze both deep learning algorithms and new technologies in database management in terms of their actions towards a person who leaves their digital footprints, on which these technologies work. Then, the possibilities of applying the existing deep learning technology and new Big Data systems in the economy will be shown. The opportunities offered by the systems mentioned above seem to be promising for many companies and—if implemented on a larger scale—they will affect the functioning of the free market.
Extent, pattern, and prognostic value of MGMT promotor methylation: does it differ between glioblastoma and IDH-wildtype/TERT-mutated astrocytoma?
Authors: Teske Nico , Karschnia Philipp , Weller Jonathan , Siller Sebastian , Dorostkar Mario M. , Herms Jochen , von Baumgarten Louisa , Tonn Joerg Christian , Thon Niklas 
Published: December 2021

Abstract
Introduction
The cIMPACT-NOW update 6 first introduced glioblastoma diagnosis based on the combination of IDH-wildtype (IDHwt) status and TERT promotor mutation (pTERTmut). In glioblastoma as defined by histopathology according to the WHO 2016 classification, MGMT promotor status is associated with outcome. Whether this is also true in glioblastoma defined by molecular markers is yet unclear.

Methods
We searched the institutional database for patients with: (1) glioblastoma defined by histopathology; and (2) IDHwt astrocytoma with pTERTmut. MGMT promotor methylation was analysed using methylation-specific PCR and Sanger sequencing of CpG sites within the MGMT promotor region.

Results
We identified 224 patients with glioblastoma diagnosed based on histopathology, and 54 patients with IDHwt astrocytoma with pTERTmut (19 astrocytomas WHO grade II and 38 astrocytomas WHO grade III). There was no difference in the number of MGMT methylated tumors between the two cohorts as determined per PCR, and also neither the number nor the pattern of methylated CpG sites differed as determined per Sanger sequencing. Progression-free (PFS) and overall survival (OS) was similar between the two cohorts when treated with radio- or chemotherapy. In both cohorts, higher numbers of methylated CpG sites were associated with favourable outcome.

Conclusions
Extent and pattern of methylated CpG sites are similar in glioblastoma and IDHwt astrocytoma with pTERTmut. In both tumor entities, higher numbers of methylated CpG sites appear associated with more favourable outcome. Evaluation in larger prospective cohorts is warranted.
Prediction of critical liquid loading time for water-producing gas wells: Effect of liquid drop rotation
Authors: Chongyang Xiao , Heng Fu , Leli Cheng , Wenyu Pei 
Published: December 2021

AbstractAfter more than 20 years of continuous development, part of the wells in the Moxilei-1 gas reservoir located at the Sichuan Basin have entered the middle–later production stage. With the continuous decline in formation pressure and production rates, some of the gas wells have entered the potential period of liquid loading, while some have already suffered water plugging. Currently, the field engineers usually carry out some corresponding drainage measures after the occurrence of liquid loading in the gas well, which will first affect the production progress of the gas field, then increase the difficulty in drainage and reduce the drainage effect afterward. On the basis of Pan’s model for evaluating critical liquid-carrying flow rate, the influence of liquid drop rotation was considered in the new model. Further, combined with the Arps production decline equation, a prediction model of liquid loading timing was deduced. Taking a typical well in the Moxilei-1 gas reservoir as an example, based on the early-stage production data of the gas well, the model was used to predict the liquid loading timing accurately. The model can predict the possibility and timing of liquid loading in gas wells at different production stages. It can check the gas wells with potential liquid loading, so as to reduce the workload for field workers. Furthermore, it can predict the potential liquid accumulation and its timing in advance, so as to guide the field workers to prepare for drainage in advance.
Decomposition approaches for parallel machine scheduling of step-deteriorating jobs to minimize total tardiness and energy consumption
Authors: Wu Xiao , Guo Peng , Wang Yi , Wang Yakun 
Published: December 2021

AbstractIn this paper, an identical parallel machine scheduling problem with step-deteriorating jobs is considered to minimize the weighted sum of tardiness cost and extra energy consumption cost. In particular, the actual processing time of a job is assumed to be a step function of its starting time and its deteriorating threshold. When the starting time of a job is later than its deteriorating threshold, the job faces two choices: (1) maintaining its status in holding equipment and being processed with a base processing time and (2) consuming an extra penalty time to finish its processing. The two work patterns need different amounts of energy consumption. To implement energy-efficient scheduling, the selection of the pre-processing patterns must be carefully considered. In this paper, a mixed integer linear programming (MILP) model is proposed to minimize the total tardiness cost and the extra energy cost. Decomposition approaches based on logic-based Benders decomposition (LBBD) are developed by reformulating the studied problem into a master problem and some independent sub-problems. The master problem is relaxed by only making assignment decisions. The sub-problems are to find optimal schedules in the job-to-machine assignments given by the master problem. Moreover, MILP and heuristic based on Tabu search are used to solve the sub-problems. To evaluate the performance of our methods, three groups of test instances were generated inspired by both real-world applications and benchmarks from the literature. The computational results demonstrate that the proposed decomposition approaches can compute competitive schedules for medium- and large-size problems in terms of solution quality. In particular, the LBBD with Tabu search performs the best among the suggested four methods.
A Novel Binary Slime Mould Algorithm with AU Strategy for Cognitive Radio Spectrum Allocation
Authors: Li Ling , Pan Tien-Szu , Sun Xiao-Xue , Chu Shu-Chuan , Pan Jeng-Shyang 
Published: December 2021

AbstractSpectrum has now become a scarce resource due to the continuous development of wireless communication technology. Cognitive radio technology is considered to be a new method to solve the shortage of spectrum resources. The spectrum allocation model of cognitive radio can effectively avoid the waste of spectrum resources. A novel binary version of slime mould algorithm is proposed for the spectrum allocation model to solve the spectrum allocation scheme. In addition, adding unselected factors strategy can make the approach find a better solution. Compared with other algorithms, the novel binary slime mould algorithm and the strategy of adding unselected factors proposed in this paper have a good performance in spectrum allocation. The resulting spectrum allocation scheme can achieve efficient use of network resources.
Research on the Minimum Size of Received Signal Strength Difference Localization Network
Authors: Ma Fangli , Xu Yang , Xu Peng 
Published: December 2021

AbstractThe received signal strength difference (RSSD) localization is a kind of method to locate emission sources by measuring the differences of received signal strength level between the monitoring stations and is essentially the truth value ratios of measured signal strength. In the existing literatures, only the rule of RSSD localization circle of two monitoring stations and the geometric relation of RSSD localization circle of five monitoring stations were analyzed, but the number and the station layout of the minimum RSSD localization network have not been investigated. In the present work, first, based on the existing RSSD localization equation, the constants of the commonly used wave propagation models are provided. Then, the minimum RSSD localization network is proved through algebraic analysis, which is that four monitoring stations not distributed on a straight line can locate the signal source at one point. The relationship between the localization accuracy and the signal strength error of the RSSD location network with different scales is studied further and formulated as a nonlinear programming optimization problem. It is found that the localization stability of the network with four stations is poor, and there is a serious localization deviation outlier phenomenon. Therefore, the network with four stations is not available for radio monitoring networks with a signal strength error of ± 5 to  ± 10 dB. The RSSD network with five stations is basically the minimum available size, and the RSSD network with nine stations can approach the localization accuracy of the angle of arrival (AOA) network with three stations.
Evolutionary Social Poisson Factorizationfor Temporal Recommendation
Authors: Yin ChunYan , Chen YongHeng , Zuo Wanli 
Published: December 2021

AbstractPreference-based recommendation systems analyze user-item interactions to reveal latent factors that explain our latent preferences for items and form personalized recommendations based on the behavior of others with similar tastes. Most of the works in the recommendation systems literature have been developed under the assumption that user preference is a static pattern, although user preferences and item attributes may be changed through time. To achieve this goal, we develop an Evolutionary Social Poisson Factorization (EPF$$\_$$
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Social) model, a new Bayesian factorization model that can effectively model the smoothly drifting latent factors using Conjugate Gamma–Markov chains. Otherwise, EPF$$\_$$
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Social can obtain the impact of friends on social network for user’ latent preferences. We studied our models with two large real-world datasets, and demonstrated that our model gives better predictive performance than state-of-the-art static factorization models.
A New X-bar Control Chart for Multiple Dependent State Sampling Using Neutrosophic Exponentially Weighted Moving Average Statistics with Application to Monitoring Road Accidents and Road Injuries
Authors: Khan Nasrullah , Ahmad Liaquat , Rao G. Srinivasa , Aslam Muhammad , AL-Marshadi Ali Hussein 
Published: December 2021

AbstractIn this article, an efficient mean chart for symmetric data have been presented for multiple dependent state (MDS) sampling using neutrosophic exponentially weighted moving average (NEWMA) statistics. The existing neutrosophic exponentially weighted moving average charts are not capable of seizure the unusual changes threatened to the manufacturing processes. The control chart coefficients have been estimated using the symmetry property of the Gaussian distribution for the uncertain environment. The neutrosophic Monte Carlo simulation methodology has been developed to check the efficiency and performance of the proposed chart by calculating the neutrosophic average run lengths and neutrosophic standard deviations. The proposed chart has been compared with the counterpart charts for confirmation of the proposed technique and found to be a robust chart.
SCMFTS: Scalable and Distributed Complexity Measures and Features for Univariate and Multivariate Time Series in Big Data Environments
Authors: Baldán Francisco J. , Peralta Daniel , Saeys Yvan , Benítez José M. 
Published: December 2021

AbstractTime series data are becoming increasingly important due to the interconnectedness of the world. Classical problems, which are getting bigger and bigger, require more and more resources for their processing, and Big Data technologies offer many solutions. Although the principal algorithms for traditional vector-based problems are available in Big Data environments, the lack of tools for time series processing in these environments needs to be addressed. In this work, we propose a scalable and distributed time series transformation for Big Data environments based on well-known time series features (SCMFTS), which allows practitioners to apply traditional vector-based algorithms to time series problems. The proposed transformation, along with the algorithms available in Spark, improved the best results in the state-of-the-art on the Wearable Stress and Affect Detection dataset, which is the biggest publicly available multivariate time series dataset in the University of California Irvine (UCI) Machine Learning Repository. In addition, SCMFTS showed a linear relationship between its runtime and the number of processed time series, demonstrating a linear scalable behavior, which is mandatory in Big Data environments. SCMFTS has been implemented in the Scala programming language for the Apache Spark framework, and the code is publicly available.