bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Methodological Approach to Validate Pneumonia Encounters from Radiology Reports Using Natural Language Processing
Authors: Panny AlokSagar , Hegde Harshad , Glurich Ingrid , Scannapieco Frank A. , Vedre Jayanth G. , VanWormer Jeffrey J. , Miecznikowski Jeffrey , Acharya Amit 
Published: April 2022

Abstract
Introduction Pneumonia is caused by microbes that establish an infectious process in the lungs. The gold standard for pneumonia diagnosis is radiologist-documented pneumonia-related features in radiology notes that are captured in electronic health records in an unstructured format.
Objective The study objective was to develop a methodological approach for assessing validity of a pneumonia diagnosis based on identifying presence or absence of key radiographic features in radiology reports with subsequent rendering of diagnostic decisions into a structured format.
Methods A pneumonia-specific natural language processing (NLP) pipeline was strategically developed applying Clinical Text Analysis and Knowledge Extraction System (cTAKES) to validate pneumonia diagnoses following development of a pneumonia feature–specific lexicon. Radiographic reports of study-eligible subjects identified by International Classification of Diseases (ICD) codes were parsed through the NLP pipeline. Classification rules were developed to assign each pneumonia episode into one of three categories: “positive,” “negative,” or “not classified: requires manual review” based on tagged concepts that support or refute diagnostic codes.
Results A total of 91,998 pneumonia episodes diagnosed in 65,904 patients were retrieved retrospectively. Approximately 89% (81,707/91,998) of the total pneumonia episodes were documented by 225,893 chest X-ray reports. NLP classified and validated 33% (26,800/81,707) of pneumonia episodes classified as “Pneumonia-positive,” 19% as (15401/81,707) as “Pneumonia-negative,” and 48% (39,209/81,707) as “episode classification pending further manual review.” NLP pipeline performance metrics included accuracy (76.3%), sensitivity (88%), and specificity (75%).
Conclusion The pneumonia-specific NLP pipeline exhibited good performance comparable to other pneumonia-specific NLP systems developed to date.
Automated Identification of Immunocompromised Status in Critically Ill Children
Authors: Kandaswamy Swaminathan , Orenstein Evan W. , Quincer Elizabeth , Fernandez Alfred J. , Gonzalez Mark D. , Lu Lydia , Kamaleswaran Rishikesan , Banerjee Imon , Jaggi Preeti 
Published: April 2022

Abstract
Background Easy identification of immunocompromised hosts (ICHs) would allow for stratification of culture results based on host type.
Methods We utilized antimicrobial stewardship program (ASP) team notes written during handshake stewardship rounds in the pediatric intensive care unit (PICU) as the gold standard for host status; clinical notes from the primary team, medication orders during the encounter, problem list, and billing diagnoses documented prior to the ASP documentation were extracted to develop models that predict host status. We calculated performance for three models based on diagnoses/medications, with and without natural language processing from clinical notes. The susceptibility of pathogens causing bacteremia to commonly used empiric antibiotic regimens was then stratified by host status.
Results We identified 844 antimicrobial episodes from 666 unique patients; 160 (18.9%) were identified as ICHs. We randomly selected 675 initiations (80%) for model training and 169 initiations (20%) for testing. A rule-based model using diagnoses and medications alone yielded a sensitivity of 0.87 (08.6–0.88), specificity of 0.93 (0.92–0.93), and positive predictive value (PPV) of 0.74 (0.73–0.75). Adding clinical notes into XGBoost model led to improved specificity of 0.98 (0.98–0.98) and PPV of 0.9 (0.88–0.91), but with decreased sensitivity 0.77 (0.76–0.79). There were 77 bacteremia episodes during the study period identified and a host-specific visualization was created.
Conclusions An electronic health record–based phenotype based on notes, diagnoses, and medications identifies ICH in the PICU with high specificity.
The CON-SOT-LOT Test for Nonorganic Hearing Loss
Authors: Martin Jeffrey S. , Martin Frederick N. , Champlin Craig A. 
Published: April 2022

AbstractThe efficacy of a screening procedure for detecting nonorganic hearing loss using standard audiometric instrumentation was examined. Thirty normal-hearing subjects' responses to continuous and standard and lengthened off-time pulsed tones, using an ascending-descending method, were compared. Subjects were asked to respond to test stimuli in normal-hearing and simulated hearing loss conditions. Data show that there are clinical and statistical differences between subjects' response patterns in the normal-hearing condition as compared to the feigned hearing loss condition. The procedure is offered as a simple method of screening for nonorganic hearing loss.
Abbreviations: A-D = ascending-descending, ANOVA = analysis of variance, CON = continuous tone, LOT = lengthened off-time pulsed tone, MML = modified method of limits, NOHL = nonorganic hearing loss, STL = simulated threshold level, SOT = standard off-time pulsed tone
Global adaptation readiness and income mitigate sectoral climate change vulnerabilities
Authors: Sarkodie Samuel Asumadu , Ahmed Maruf Yakubu , Owusu Phebe Asantewaa 
Published: April 2022

AbstractClimate change has become a global burden, requiring strong institutional quality and willingness to mitigate future impacts. Though emissions are transboundary and have the tendency of spreading from high emitting countries to low emitting countries, regional exposure, sensitivity, and adaptation readiness determine the extent of climate effects. The existing literature focuses on immediate drivers and damages of emission effects, failing to account for underlying mechanisms occurring via the nexus between emission levels, economic, social, and governance adaptation readiness. Here, this study broadens the scope of previous attempts and simultaneously examines climate change vulnerability across sectors including ecosystem services, food, health, human habitat, infrastructure, and water. We use the Romano–Wolf technique to test multiple hypotheses and present the spatial–temporal severity of climate vulnerability and readiness to combat climate change and its impacts. Besides, we assess the long-term impact of climate change readiness and income expansion on sectoral-climate vulnerabilities. We find that high-income economies with high social, governance, and economic readiness have low climate vulnerability whereas developing economies with low income have high climate change exposure and sensitivity. Our empirical evidence could be used to prioritize limited resources in addressing and managing adaptive actions of extreme climate change vulnerabilities.
Design of ultrasonic flowmeter for wet gas measurement
Authors: Zheng Dandan , Zhai Mengxu , Wang Mi 
Published: April 2022

Abstract
When an ultrasonic flowmeter (UFM) is applied in the measurement of wet gas, the ultrasonic signal attenuates and fluctuates strongly because of the liquid film and droplets in gas. It is hard to obtain the accurate time of flight (TOF) of ultrasound. Besides, the gas velocity distribution on a pipe cross section changes with different flow patterns, making it difficult to determine the measurement model. In order to solve these problems, a dual-channel UFM is specially designed and a measurement system is built. Then, a dynamic threshold method based on statistical average signal amplitude is proposed to obtain the TOF of ultrasound in wet gas. Combined with the classical void fraction model, the wet gas velocity measurement model for the dual-channel UFM is established based on a neural network. Finally, experiments are carried out on a DN50 wet gas device, where pressure is 0.1 MPa, gas superficial velocity is 5–20 m s−1 and liquid volume fraction is 0.2%–5%. The results show that the average relative error of gas velocity is 0.52% and the maximum relative uncertainty is 0.491%.
Analysis of a TB and HIV co-infection model under Mittag-Leffler fractal-fractional derivative
Authors: Liu Xuan , Ahmad Shabir , Rahman Mati ur , Nadeem Yasir , Akgül Ali 
Published: April 2022

Abstract
In this paper, the nonlocal operator with the Mittag-Leffler kernel is used to analyze a TB-HIV co-infection model with recurrent TB and exogenous reinfection. The non-negative invariant region and basic reproduction number of the proposed model are demonstrated. By using the Krasnoselskii fixed result, we investigate that the TB-HIV co-infection model possesses at least one solution. We look at the existence of a unique solution using Banach’s fixed point theorem. Functional analysis is used to demonstrate Ulam-Hyres stability. The numerical solution of the given model is obtained using the Adams-Bashforth technique. We illustrate the achieved results by studying the co-infection of TB and HIV for different fractional and fractal orders.
ALP search using precessing light
in a magnetized Fabry Perot cavity
Authors: Creswick R.J. , Avignone F.T. 
Published: April 2022

Abstract
In this paper we outline an experiment to detect the conversion of photons to axion-like particles (ALPs) in a strong magnetic field. We show that by modulating the polarization of the light passing through a Fabry-Perot cavity so that it effectively precesses at the modulation frequency, a signal is produced that is proportional to the square, as opposed to the fourth power, of the ALP-photon coupling constant. Assuming shot noise to be the dominant source of noise, we estimate that this approach is sensitive to ALP masses less than 10-4 eV and couplings on the order to gaγ
> 1.6 × 10-11 GeV-1 with a 10 m, 10 T magnet, and gaγ
> 1.6 × 10-12 GeV-1 with a 100 m magnet as envisaged by ALPs-IIc. ALPs with these properties have been invoked to explain the apparent transparency of the extragalactic background light (EBL) to ultra high-energy gamma rays emitted by BLAZARs.
Axion homeopathy: screening dilaton interactions
Authors: Burgess C.P. , Quevedo F. 
Published: April 2022

Abstract
Cosmologically active Brans-Dicke (or dilaton) scalar fields are generically ruled out by solar system tests of gravity unless their couplings to ordinary matter are much suppressed relative to gravitational strength, and this is a major hindrance when building realistic models of light dilatons coupled to matter. We propose a new mechanism for evading such bounds if matter also couples to a light axion, that exploits nonlinear target-space curvature interactions to qualitatively change how the fields respond to a gravitating source. We find that dilaton-matter couplings that would be excluded in the absence of an axion can become acceptable given an additional small axion-matter coupling, and this is possible because the axion-dilaton interactions end up converting the would-be dilaton profile into an axion profile. The trajectories of matter test bodies are then controlled by the much weaker axion-matter couplings and can easily be small enough to escape detection. We call this mechanism Axion Homeopathy because the evasion of the dilaton-coupling bounds persists for extremely small axion couplings provided only that they are nonzero. We explore the mechanism using axio-dilaton equations that are SL(2,ℝ) invariant (as often appear in string compactifications), since for these the general solutions exterior to a spherically symmetric source can be found analytically. We use this solution to compute the relevant PPN parameters, γ
PPN and β
PPN, and verify that their deviation from unity can be much smaller than it would have been in the absence of axion-matter couplings and can therefore evade the experimental bounds.
Echo chambers in the Ising model and implications on the mean magnetization
Authors: Baravi Talia , Feinerman Ofer , Raz Oren 
Published: April 2022

AbstractThe echo-chamber effect is a common term in opinion dynamic modeling to describe how a person’s opinion might be artificially enhanced as it is reflected back at her through social interactions. Here, we study the existence of this effect in statistical mechanics models, which are commonly used to study opinion dynamics. We show that the Ising model does not exhibit echo-chambers, but this result is a consequence of a special symmetry. We then distinguish between three types of models: (i) those with a strong echo-chamber symmetry, that have no echo-chambers at all; (ii) those with a weak echo-chamber symmetry that can exhibit echo-chambers but only if there are external fields in the system, and (iii) models without echo-chamber symmetry that generically have echo-chambers. We use these results to construct an efficient algorithm to efficiently and precisely calculate magnetization in arbitrary tree networks. Finally, we apply this algorithm to study two systems: phase transitions in the random field Ising model on a Bethe lattice and the influence optimization problem in social networks.
Speech Emotion Based Sentiment Recognition using Deep Neural Networks
Authors: Choudhary Ravi Raj , Meena Gaurav , Mohbey Krishna Kumar 
Published: April 2022

Abstract
The capacity to comprehend and communicate with others via language is one of the most valuable human abilities. We are well-trained in our experience reading awareness of different emotions since they play a vital part in communication. Contrary to popular belief, emotion recognition is a challenging task for computers or robots due to the subjective nature of human mood. This research proposes a framework for acknowledging the passionate sections of conversation, independent of the semantic content, via the recognition of discourse feelings. To categorize the emotional content of audio files, this article employs deep learning techniques such as convolutional neural networks (CNNs) and long short-term memories (LSTMs). In order to make sound information as helpful as possible for future use, models using Mel-frequency cepstral coefficients (MFCCs) were created. It was tested using RAVDESS and TESS datasets and found that the CNN had a 97.1% accuracy rate.
Deep learning for facial emotion recognition using custom CNN architecture
Authors: Appasaheb Borgalli Mr. Rohan , Surve Dr. Sunil 
Published: April 2022

Abstract
Human facial expressions are an indication of true emotions. To recognize facial expressions accurately is useful in the field of Artificial Intelligence, Computing, Medical, e-Education, and many more. The facial expression recognition (FER) system detects emotion through facial expression. But, it is challenging to detect facial emotions accurately. However, recent advancements in technology, research, and availability of facial expression datasets have led to the development of many FER systems which can accurately detect facial emotions. Past research in the field of FER indicates With Convolutional Neural Networks (CNNs), deep learning techniques are the most advanced presently. Custom CNN Architecture is used to implement basic facial emotion recognition in static images in this paper. A K-fold cross-validation method was used to train them using FER13, CK+, and the JAFFE data set. On the seven classes of fundamental emotions, including anger, disgust, fear, happiness, neutrality, sorrow, and surprise, the FER13, CK+, and JAFFE datasets had an accuracy rate of 91.58 percent. Given the difficulty of developing unique CNN architecture, this study’s accurate findings contrast well with those of previous studies.
A sustainable embedded shade management system for tea cultivation
Authors: Ningombam Hemarjit , Roy O.P 
Published: April 2022

Abstract
Tea farming stands on a vast area. The quality and quantity of products enormously depend on the management and control of the environmental conditions of the tea farms. Feasible and continuous monitoring of the plantations’ parameters is needed to achieve the desired farm output. 50%-60% of the light intensity needs to be filtered on each tea plant throughout the year. Traditionally, shading is achieved by planting leguminous trees inside the farms. So it is extensively required to monitor the shading capacity continuously to overcome the degradation of the farm’s product. In this paper, a simple, sustainable shade management system is designed. The system is embedded with Atmega 2560 controller, and a wireless network has been achieved using the ZigBee module. A systematic array of LDRs acquires light Intensity. The measured parameters are wirelessly transmitted continuously to the central decision support system with associated information like locations, sensor values, etc. The proposed method is easily extensible and user-friendly.
Internet of Things for sustainable urbanism
Authors: Sharma Paribhasha , Jangirala Srinivas 
Published: April 2022

Abstract
Cities around the world are facing enormous strain to sustain and improve the quality of life (QoL) owing to rapid urbanization and rising populations. Management of urban resources in a responsible manner is key to sustainable development in rapidly urbanizing regions. Cities are increasingly making use of modern technologies with a focus on cost reduction, optimal resource utilization and creation of more liveable urban environment. Such cities, called smart cities, have gained traction with policy makers, politicians and urban managers having the attributes of sustainable urbanism, QoL, and smartness. Smart cities provide digital intelligence to existing cities by creating a ubiquitous, integrated and smart environment where IoT applications impart seamless interconnection, interaction, control and insights about the isolated systems within the cities. This paper discusses and reviews the role of IoT for sustainable smart cities by highlighting IoT applications for smart cities. The challenges and opportunities associated with IoT enabled smart cities are also highlighted.
Formant Measure of Indian English Vowels for Speaker Identity
Authors: Kumar Vinod , Roy Om Prakash 
Published: April 2022

Abstract
With the growth of communication technology, reliability and security of communication devices became a challenging job. Voice biometrics are becoming increasingly popular as a promising alternative to traditional passwords to authenticate the user on devices for secure communication. Speech processing technology in voice identification may ensures and secure the user access over a range of systems, devices, and services. The formant frequency is the resonant frequency of the vocal cord. Frame-to-frame formants achieved using linear predictive coding (LPC) analysis technique by obtaining the tops of the envelopes. The source voice for vowel is a periodic signal in combination of fundamental frequency and a random noise generator generates unvoiced consonants. In this study, we used voice test samples of different male speakers from age 15 to 20 years. From each speaker utterance of different Indian English words, including vowels and consonants recorded using a digital audio editor software GoldWave v6.57. Formant frequency extracted from the spectrogram of recorded words using MATLAB 2016a signal processing toolbox. Finally, presented an approach for extracting vowels in the words spoken based on three formant frequencies such F1, F2 and F3. The results has shown the significance of vowels in Indian English words when formant frequency of vocal tract is considered.
A Compact Wide Stopband Band Pass SIW Filter For K-Band Applications
Authors: Edison Singh M 
Published: April 2022

Abstract
Direct coupled filters have narrow stopband due to higher order modes passband. A compact dual layer band pass filter is proposed with wide stop band. The proposed filter provides the return loss of 16.87db and insertion loss of 2.5dB at the passband frequency 20.9 GHz. The filter size is half of the conventional direct coupled filters. The stop band covers up to 40 GHz with insertion loss below 35dB. The filter has no slot which makes it immune to the EMI/EMC problems.
An Accelerated Post-processing Calculation Method of Curved Surface Profile Extraction Based on the Total Focusing Method of Ultrasonic Phased Array
Authors: Li Fan , Yao Aizhou , Zhu LiMin , Yang Chenlong , Zhang Yang 
Published: April 2022

Abstract
Surface profile extraction is a significant process for determining the ultrasonic beam propagation path based on the Fermat’s principle while applying the total focusing method (TFM) of ultrasonic phased array on a curved component immersed in water. However, TFM calculation is time consuming due to the large amounts of data post-processing. Hence, a trade-off between computation cost and precision of result image is must to be made. In this paper, an accelerated total focusing method (ATFM) was proposed to extract the surface profile of an arc-shaped component. The experimental results show that compared with TFM, ATFM could shorten the imaging time by more than 70% under various conditions of grid spacing. Meanwhile, the maximal error of extracted surface profile compared to the practical profile curve is about 0.7mm and the surface extraction errors near the central area are less than 0.1mm. It shows that the proposed ATFM can significantly improve the computation efficiency while ensuring the accuracy of surface extraction.
Multiple Motion Modes Analysis of Assembly Based on Monopole Motor Principle
Authors: Guo Junxi , Li Wenbo , Wu Dongqiong , Pang Bowen , Fan Jifeng , Wang Tianyuan 
Published: April 2022

Abstract
Dc monopole motor with low voltage and high current is widely used in the market. It has the advantages of high efficiency, no commutator, no iron loss and so on. In this paper, an assembly (a cylindrical battery with button-type magnets attached at both ends) is placed on aluminum foil. The phenomenon that the assembly will move based on the principle of a single motor is explained. By adjusting the specifications of the button magnets and other parameters, a variety of its movement modes are theoretically analyzed and experimentally verified. The conclusion is that when the magnets at both ends are placed at the same level relative to each other and the diameter is the same, the composite body moves in a straight line with first acceleration and then uniform speed. When the diameter is different, the circular motion is accelerated first and then uniform. Changing the magnetic property size, battery length and other parameters will affect the change of motion parameters. This provides a new idea for further innovation of monopole motor in the future.
Research on Resolution Enhancement Technology of Orthogonal Multimode Fiber Imaging
Authors: Wang Xinyi , Yin Longfei , Wu Guohua , Luo Bin , Yin Pengqi 
Published: April 2022

Abstract
This paper presents a new scheme to improve the imaging resolution of optical fiber endoscope. Multimode fiber imaging is combined with correlation imaging experimental architecture, and Schmidt orthogonalization algorithm is used to reduce the correlation of light field. The simulation and experimental results show that this scheme can greatly improve the imaging quality and resolution, especially in the case of under sampling. In addition, the scheme can also resist the low resolution problem caused by the light field divergence of multimode fiber correlation imaging, and when the light field divergence reaches a certain extent, the resolution of the orthogonalized multimode fiber light field will no longer be affected. The feasibility of orthogonal multimode fiber imaging scheme in large depth of field is verified.