bluuebunny/crossref_metadata_2025_split huggingface dataset:
Numerical computation of 3D Brownian motion of thin film nanofluid flow of convective heat transfer over a stretchable rotating surface
Authors: Zeeshan  , Rasheed Haroon Ur , Khan Waris , Khan Ilyas , Alshammari Nawa , Hamadneh Nawaf 
Published: February 2022

AbstractThis research examines the thin-film nanomaterial movement in three dimensions over a stretchable rotating inclined surface. Similarity variables are used to transform fundamental systems of equations into a set of first-order differential equations. The Runge–Kutta Fourth Order approach is utilized for numerical computations. The impact of embedded parameters (variable thickness, unsteadiness, Prandtl number, Schmidt number, Brownian-motion, and thermophoretic) is examined carefully. Physically and statistically, the indispensable terms namely Nusselt and Sherwood numbers are also investigated. Results indicated that, as the dimensionless parameter S raises, the temperature field decreases. In reality, as the values of S increases, heat transmission rate from the disc to the flowing fluid reduces. Internal collisions of liquid particles are physically hampered at a low rate. The momentum boundary layer is cooled when the parameter S is increased, as a consequence local Nusselt number rises. Sherwood number decreases as the parameter S increases because of inter collision of the microscopic fluid particles. Enhancing in the apparent viscosity and concentrations of the chemical reactions, a higher Schmidt number, Sc, lowers the Sherwood number. With increasing values of Prandtl number the Nusselt number decreases. For validation purpose, the RK4 method is also compared with homotopy analysis method (HAM). The results are further verified by establishing an excellent agreement with published data.
Car engine sounds recognition based on deformable feature map residual network
Authors: Wu Zhuangwen , Wan Zhiping , Ge Dongdong , Pan Ludan 
Published: February 2022

AbstractAiming at the difficulty in extracting the features of time–frequency images for the recognition of car engine sounds, we propose a method to recognize them based on a deformable feature map residual network. A deformable feature map residual block includes offset and convolutional layers. The offset layers shift the pixels of the input feature map. The shifted feature map is superimposed on the feature map extracted by the convolutional layers through shortcut connections to concentrate the network to the sampling in the region of interest, and to transmit the information of the offset feature map to the lower network. Then, a deformable convolution residual network is designed, and the features extracted through this network are fused with the Mel frequency cepstral coefficients of car engine sounds. After recalibration by the squeeze and excitation block, the fused results are fed into the fully connected layer for classification. Experiments on a car engine sound dataset show that the accuracy of the proposed method is 84.28%. Compared with the existing state-of-the-art methods, in terms of the accuracy of recognizing car engine sounds under various operating conditions, the proposed method represents an improvement over the method based on dictionary learning and a convolutional neural network.
Convolutional autoencoder anomaly detection and classification based on distribution PMU measurements
Authors: Ehsani Narges , Aminifar Farrokh , Mohsenian‐Rad Hamed 
Published: February 2022

AbstractThe huge volume of data that is streamed from distribution phasor measurement units (DPMU) toward distribution management system (DMS) can rarely be used in the raw format. Data‐driven analysis to extract information out of massive raw data has revealed promising opportunities to overcome this challenge. This paper utilizes convolutional autoencoders (Conv‐AE) for the sake of anomaly detection based on the DPMU measurements in distribution systems. The Conv‐AE is unsupervised and independent of the event type. It dispenses preprocessing and feature extraction along with obtaining the essential information directly from the measured data. The anomaly detection is followed by a supervised classifier which is developed to identify the type of anomaly. This classifier is a convolutional neural network that is designed and fine‐tuned for the problem at hand. It uses an ensemble learning method to augment the fully‐labelled dataset. Performance of the proposed methodology is evaluated on modified IEEE 34‐node and IEEE 123‐node test feeders in various operating conditions, presence of noise, and different scenarios of DPMU outage. Moreover, a real‐world DPMU dataset is utilized to evaluate performance of the Conv‐AE model in practical conditions. Results confirmed effectiveness of the proposed technique to be used in future DMS platforms.
A modified droop control structure for simultaneous power‐sharing and DC voltage oscillations damping in MT‐HVDC grids
Authors: Azizi Neda , Moradi CheshmehBeigi Hassan , Rouzbehi Kumars 
Published: February 2022

AbstractWith increasing energy demand, the use of renewable energy resources is increasing. Renewable energy sources require power electronic converters to get integrated into power grids. Multi‐terminal high voltage direct current (MT‐HVDC) systems are a promising solution for their grid integration. Using droop‐based controller strategies in power converter stations of MT‐HVDC grid, in addition, to providing power‐sharing, can support the frequency of the connected AC grids. However, power changes lead to direct voltage deviations, thereby disrupting the stability of the MT‐HVDC system. Here, a new control structure for the droop controller of the voltage source converter (VSC) controller is proposed which is named modified droop controller (M‐Droop). This method, in addition to power‐sharing, uses an appropriate control action to provide the required voltage stability. Here, analytical studies of the modified control strategy are reported. Moreover, through a developed MATLAB Simulink platform, its performance is verified in the events of transients, consisting of fault, variable load, and change in power generation.
Viral Enteric Infections in Acute Severe Ulcerative Colitis
Authors: Ostrowski Szymon , Croft Anthony 
Published: February 2022

Abstract

Background and Aims
Excluding superimposed enteric infection is critical in the management of acute severe ulcerative colitis [ASUC]. Whilst infection with Clostridium difficile and cytomegalovirus have been shown to increase colectomy and mortality rates, no data exist regarding the impact of common viral enteropathogens in ASUC. Our aim was to determine if viral enteric infection in ASUC alters the severity or outcomes of these episodes.

Methods
This was a retrospective review of cases presenting to a large tertiary centre. Data were obtained from an in-house, prospectively maintained inflammatory bowel disease database. We identified all ASUC cases treated between October 2015 and January 2021 and compared those testing positive for adenovirus 40/41, human rotavirus or norovirus GI by faecal multiplex polymerase chain reaction [PCR] to those testing negative.

Results
We identified 147 patients with ASUC and faecal multiplex PCR testing for viral pathogens. In total, 22/147 patients [14.9%] tested positive, two of whom tested positive for two viruses. The infected and non-infected cohorts did not differ significantly with regard to admission C-reactive protein [81.7 vs 76.6, p = 0.77], Mayo endoscopic subscore [2.4 vs 2.3, p = 0.43], length of hospital admission [7.9 vs 7.9 p = 0.99], requirement for rescue therapy [59% vs 56%, p = 0.75] or colectomy rate [4.5% vs 4.1%, p = 0.69].

Conclusions
Infection with viral enteropathogens was common in our ASUC cohort, but did not appear to affect disease severity at presentation, the need for rescue therapy or the success rate of rescue therapy.
The 2021 Genetics Society of America Medal: Douglas Koshland
Authors: Koshland Douglas 
Published: February 2022

Abstract
The Genetics Society of America Medal honors an individual member of the Society for outstanding contributions to the field of genetics in the last 15 years. Genetics Society of America established the Medal in 1981 to recognize members who exemplify the ingenuity of the Genetics Society of America membership through elegant and highly meaningful contributions to modern genetics. The 2021 Genetics Society of America Medal has been awarded to Douglas Koshland of the University of California, Berkeley. His advances in chromosome biology have not only illuminated fundamental features of the structure of chromosomes but also provided tools for many others to use.
Change in health, wellbeing and physical activity levels during the COVID-19 pandemic: a longitudinal cohort of parkrun participants in the United Kingdom
Authors: Quirk Helen , Haake Steve , Goyder Elizabeth , Bullas Alice , Graney Mike , Wellington Chrissie 
Published: February 2022

Summary
Lockdown restrictions imposed across the UK in response to the coronavirus disease 2019 (COVID-19) pandemic had a profound impact on many people’s health and wellbeing. People were encouraged to be active, but population surveys suggest some groups found this easier than others. We explored the changes in health, wellbeing and physical activity levels among a sample in the UK who experienced the sudden loss of a weekly community-based physical activity opportunity, parkrun. A sample of UK parkrun participants responded to two surveys: pre-COVID-19 in January/February 2019 and during the COVID-19 pandemic in September 2020. Outcomes were happiness, life satisfaction, connections with others, physical health, mental health and physical activity. The sample was stratified by gender, age, deprivation status, physical activity and number of parkruns completed. Demographics were reported using descriptive statistics; distributions between sub-groups were compared using Chi-square tests while differences in outcomes were determined using the Mann–Whitney U test. Open text responses were also analysed. Happiness, life satisfaction, connections with others, physical health and mental health of 450 parkrun participants were negatively impacted for all sub-groups, although the impact was not experienced equally. Physical activity fell by 6% while happiness and life satisfaction fell by 12%. People experienced the worst negative impact on their connections with others. The COVID-19 pandemic negatively impacted the wellbeing of a greater proportion of females, younger adults, inactive people, those from higher deprivation areas, and those who had completed fewer parkruns. There is evidence that the wellbeing of those who were more active, and those more involved in a community-based physical activity initiative pre-pandemic, was less negatively affected during the COVID-19 lockdown.
US Special Forces transformation: post-Fordism and the limits of networked warfare
Authors: Bury Patrick 
Published: February 2022

Abstract
The last two decades have witnessed a rapid rise in the use, size and capability of many western Special Operations Forces (SOF). A response to the global jihadist threat, the growing presence, prominence and technology-enabled lethality of SOF in conflict zones has resulted in increased scholarly attention. Some have argued that their rise is indicative of important and ongoing changes in the character of war itself. One of the most influential of these works is Steven Niva's study of the transformation of US Joint Special Operations Command (JSOC) Task Forces in Iraq. Niva argues that JSOC (more accurately its Task Force 714 Command Headquarters, henceforth TF714) was forced to adopt a networked organizational structure to counter Al-Qa'ida in Iraq (AQI). For Niva, this transformation provided evidence of a shift towards ‘counter-net’ and ‘chaoplexic’ forms of warfare conceptualized by Bousquet in this journal. This article uses new primary and secondary sources to critique these claims. It argues that although a network approach was an important part of TF714's transformation, it was only one part. Instead, JSOC's transformation is more accurately understood through a post-Fordist industrial framework of the centralization of management control and the simultaneous decentralization of decision-making; the integration of core and periphery forces; outsourcing; and a network approach to knowledge. From this analysis, it argues that post-Fordist, rather than network or chaoplexic theory, best describes even the most networked military organizations in the twenty-first century, and that more broadly, fully networked warfare is unlikely given the frictions associated with the coming ‘binary’ battlefield.
European populism and the return of ‘illiberal sovereignty’: a case-study of Hungary
Authors: Paris Roland 
Published: February 2022

Abstract
European populist leaders and movements often extol the virtues of sovereignty, but how exactly are they using this term? Scholars have typically relied on conventional understandings of the concept—Westphalian, popular and national sovereignty—to interpret populist discourses, but some have noted that these categories seem inadequate. This article asks whether much older, long overlooked, versions of sovereignty might help to explain the sovereignty discourse of European populists. These older variants include extralegal sovereignty (a leader's power to act outside the constraints of formal rules) and organic sovereignty (the power of a political community understood as a single ‘organism’). Both are inherently illiberal: extralegalism rejects constitutionalism, while organicism is inimical to political pluralism. An examination of the public speeches and statements of one prominent European populist leader, Prime Minister Victor Orbán of Hungary, reveals that he has, indeed, invoked these illiberal versions of sovereignty. By retrieving these older concepts, populists gain a powerful rhetorical tool to advance illiberal principles of political authority.
Evaluating fitness-for-use of electronic health records in pragmatic clinical trials: reported practices and recommendations
Authors: Raman Sudha R , O’Brien Emily C , Hammill Bradley G , Nelson Adam J , Fish Laura J , Curtis Lesley H , Marsolo Keith 
Published: February 2022

Abstract

Objective
To empirically explore how pragmatic clinical trials (PCTs) that used real-world data (RWD) assessed study-specific fitness-for-use.

Methods
We conducted interviews and surveys with PCT teams who used electronic health record (EHR) data to ascertain endpoints. The survey cataloged key concerns about RWD, activities used to assess data fitness-for-use, and related barriers encountered by study teams. Patterns and commonalities across trials were used to develop recommendations for study-specific fitness-for-use assessments.

Results
Of 15 invited trial teams, 7 interviews were conducted. Of 31 invited trials, 15 responded to the survey. Most respondents had prior experience using RWD (93%). Major concerns about EHR data were data reliability, missingness or incompleteness of EHR elements, variation in data quality across study sites, and presence of implausible or incorrect values. Although many PCTs conducted fitness-for-use activities (eg, data quality assessments, 11/14, 79%), less than a quarter did so before choosing a data source. Fitness-for-use activities, findings, and resulting study design changes were not often publically documented. Overall costs and personnel costs were barriers to fitness-for-use assessments.

Discussion
These results support three recommendations for PCTs that use EHR data for endpoint ascertainment. Trials should detail the rationale and plan for study-specific fitness-for-use activities, conduct study-specific fitness-for-use assessments early in the prestudy phase to inform study design changes before the trial begins, and share results of fitness-for-use assessments and description of relevant challenges and facilitators.

Conclusion
These recommendations can help researchers and end-users of real-world evidence improve characterization of RWD reliability and relevance in the PCT-specific context.
Detection and nudge-intervention on sensitive information in social networks
Authors: Alemany J , Botti-Cebriá V , Val E del , García-Fornes A 
Published: February 2022

Abstract
Detecting sensitive information considering privacy is a relevant issue on Online Social Networks (OSNs). It is often difficult for users to manage the privacy associated with their posts on social networks taking into account all the possible consequences. The aim of this work is to provide information about the sensitivity of the content of a publication when a user is going to share it in OSN. For this purpose, we developed a privacy-assistant agent that detects sensitive information. Based on this information, the agent provides a message through a nudge mechanism warning about the possible risks of sharing the message. To avoid being annoying, the agent also considers the user’s previous behaviour (e.g. if he previously ignored certain nudges) and adapts the messages it sends to give more relevance to those categories that are more important to the user from the point of view of the privacy risk. This agent was integrated into the social network Pesedia. We analysed the performance of different models to detect a set of sensitive categories (i.e. location, medical, drug/alcohol, emotion, personal attacks, stereotyping, family and association details, personal details and personally identifiable information) in a dataset of tweets in Spanish. The model that obtained the best results (i.e. F1 and accuracy) and that was finally integrated into the privacy-assistant agent was transformer-based.
Neural architecture search for the estimation of relative positioning of the autonomous mobile robot
Authors: Teso-Fz-Betoño Daniel , Zulueta Ekaitz , Sanchez-Chica Ander , Fernandez-Gamiz Unai , Teso-Fz-Betoño Adrian , Lopez-Guede Jose Manuel 
Published: February 2022

Abstract
In the present work, an artificial neural network (ANN) will be developed to estimate the relative rotation and translation of the autonomous mobile robot (AMR). The ANN will work as an iterative closed point, which is commonly used with the singular value decomposition algorithm. This development will provide better resolution for a relative positioning technique that is essential for the AMR localization. The ANN requires a specific architecture, although in the current work a neural architecture search will be adapted to select the best ANN for estimating the relative motion. At the end, these ANNs will be compared with conventional algorithms to check the good performance of adopting an intelligent method for relative positioning estimation.
The local hole: a galaxy underdensity covering 90 per cent of sky to ≈200 Mpc
Authors: Wong Jonathan H W , Shanks T , Metcalfe N , Whitbourn J R 
Published: February 2022

ABSTRACT
We investigate the ‘Local Hole’, an anomalous underdensity in the local galaxy environment, by extending our previous galaxy K-band number-redshift and number-magnitude counts to ≈90 per cent of the sky. Our redshift samples are taken from the 2MASS Redshift Survey (2MRS) and the 2M++ catalogues, limited to K &lt; 11.5. We find that both surveys are in good agreement, showing an $\approx 21\!-\!22{{\ \rm per\ cent}}$ underdensity at z &lt; 0.075 when compared to our homogeneous counts model that assumes the same luminosity function (LF) and other parameters as in our earlier papers. Using the Two Micron All Sky Survey (2MASS) for n(K) galaxy counts, we measure an underdensity relative to this model of $20 \pm 2 {{\ \rm per\ cent}}$ at K &lt; 11.5, which is consistent in both form and scale with the observed n(z) underdensity. To examine further the accuracy of the counts model, we compare its prediction for the fainter n(K) counts of the Galaxy and Mass Assembly (GAMA) survey. We further compare these data with a model assuming the parameters of a previous study where little evidence for the Local Hole was found. At 13 &lt; K &lt; 16, we find a significantly better fit for our galaxy counts model, arguing for our higher LF normalization. Although our implied underdensity of $\approx 20{{\ \rm per\ cent}}$ means local measurements of the Hubble Constant have been overestimated by ≈3 per cent, such a scale of underdensity is in tension with a global ΛCDM cosmology at an ≈3σ level.
Synthetic Cooling Agents in US-marketed E-cigarette Refill Liquids and Popular Disposable E-cigarettes: Chemical Analysis and Risk Assessment
Authors: Jabba Sairam V , Erythropel Hanno C , Torres Deyri Garcia , Delgado Lauren A , Woodrow Jackson G , Anastas Paul T , Zimmerman Julie B , Jordt Sven-Eric 
Published: February 2022

Abstract

Introduction
Menthol, through its cooling and pleasant sensory effects, facilitates smoking and tobacco product initiation, resulting in the high popularity of mint/menthol-flavored E-cigarettes. More recently, E-cigarette vendors started marketing synthetic cooling agents as additives that impart a cooling effect but lack a characteristic minty odor. Knowledge about content of synthetic coolants in US-marketed E-cigarette products and associated health risks is limited.

Aims and Methods
E-liquid vendor sites were searched with the terms “koolada”, “kool/cool”, “ice”, or WS-3/WS-23, denoting individual cooling agents, and relevant refill E-liquids were purchased. “Ice” flavor varieties of Puffbar, the most popular disposable E-cigarette brand, were compared with non-“Ice” varieties. E-liquids were characterized, and synthetic coolants quantified using GC/MS. Margin of exposure (MOE), a risk assessment parameter, was calculated to assess the risk associated with synthetic coolant exposure from E-cigarette use.

Results
WS-3 was detected in 24/25 refill E-liquids analyzed. All Puffbar flavor varieties contained either WS-23 (13/14) or WS-3 (5/14), in both “Ice”- and non-“Ice” flavors. Modeling consumption of WS-3 from vaped E-liquids, resulted in MOEs below the safe margin of 100 for most daily use scenarios. MOEs for WS-23 were &lt;100 for 10/13 Puffbar flavors in all use scenarios. Puffbar power specifications are identical to Juul devices.

Conclusions
Synthetic cooling agents (WS-3/WS-23) were present in US-marketed E-cigarettes, at levels that may result in consumer exposures exceeding safety thresholds set by regulatory agencies. Synthetic coolants are not only found in mint- or menthol-flavored products but also in fruit- and candy-flavored products, including popular disposable E-cigarette products such as Puffbar.

Implications
Synthetic cooling agents are widely used in “kool/cool”- and “ice”-flavored E-liquids and in E-liquids without these labels, both as a potential replacement for menthol or to add cooling “notes” to nonmenthol flavors. These agents may be used to bypass current and future regulatory limits on menthol content in tobacco products, and not just E-cigarettes. Because synthetic cooling agents are odorless, they may not fall under the category of “characterizing flavor”, potentially circumventing regulatory measures based on this concept. Regulators need to consider the additional health risks associated with exposure to synthetic cooling agents.