bluuebunny/crossref_metadata_2025_split huggingface dataset:
An IoT and machine learning‐based routing protocol for reconfigurable engineering application
Authors: Natarajan Yuvaraj , Srihari Kannan , Dhiman Gaurav , Chandragandhi Selvaraj , Gheisari Mehdi , Liu Yang , Lee Cheng‐Chi , Singh Krishna Kant , Yadav Kusum , Alharbi Hadeel Fahad 
Published: August 2021

AbstractWith new telecommunications engineering applications, the cognitive radio (CR) network‐based internet of things (IoT) resolves the bandwidth problem and spectrum problem. However, the CR‐IoT routing method sometimes presents issues in terms of road finding, spectrum resource diversity and mobility. This study presents an upgradable cross‐layer routing protocol based on CR‐IoT to improve routing efficiency and optimize data transmission in a reconfigurable network. In this context, the system is developing a distributed controller which is designed with multiple activities, including load balancing, neighbourhood sensing and machine‐learning path construction. The proposed approach is based on network traffic and load and various other network metrics including energy efficiency, network capacity and interference, on an average of 2 bps/Hz/W. The trials are carried out with conventional models, demonstrating the residual energy and resource scalability and robustness of the reconfigurable CR‐IoT.
Two‐dimensional transmit arrays for polar over‐the‐horizon radar
Authors: Henault Simon , Riddolls Ryan J. 
Published: August 2021

AbstractTwo‐dimensional (2‐D) transmit arrays offer many advantages over traditional over‐the‐horizon radar (OTHR) arrays, including the capability to steer and mitigate auroral clutter, a clutter type prevalent in polar operation, in all azimuth and elevation directions. The approaches of previous studies on OTHR 2‐D transmit arrays are considered suboptimal, but it can be shown that the 2‐D array is an effective OTHR transmitter when operated in an appropriate manner. A reference system derived from publicly available information on an operational OTHR array is used as a benchmark to compare the performance of various 2‐D OTHR transmit systems with respect to effective isotropic radiated power on target, sidelobe levels, and elevation selectivity.
Therapie der diastolischen Herzinsuffizienz (HFpEF/HFmrEF): Lichtstreifen am Horizont?
Authors: von Scheidt Wolfgang 
Published: August 2021

ZusammenfassungEine evidenzbasierte medikamentöse Therapie der diastolischen Herzinsuffizienz (HFpEF/HFmrEF) ist bislang, im Gegensatz zur systolischen Herzinsuffizienz (HFrEF), nicht etabliert. Die ESC-Leitlinien 2016 empfehlen als einzige Substanzklasse Diuretika zur Symptomverbesserung. Betablocker, ACE-Hemmer, Angiotensin-Rezeptor-Blocker zeigen keinen belegten Nutzen im Vergleich zu Placebo. Mit Mineralokortikoid-Rezeptor-Antagonisten (MRA), Angiotensin-Rezeptor-Neprilysin-Inhibitor (ARNI)und SGLT2-Inhibitoren stehen 3 unterschiedliche Wirkmechanismen zur Verfügung, die bei HFpEF/HFmrEF bezüglich Letalität und Morbidität in randomisierten kontrollierten Studien geprüft wurden oder werden. Die bisherigen Ergebnisse sind ambivalent, in einigen Aspekten aber ermutigend. Spironolacton wird im Focused Update 2017 der ACC/AHA-Leitlinie zur Symptomverbesserung bei HFpEF zurückhaltend empfohlen (IIb B). Die FDA hat im Januar 2021 Sacubitril/Valsartan zur Therapie der HFpEF/HFmrEF (EF
unterhalb des Normalbereichs) zugelassen. Für die Therapie der ATTR-Amyloidose als seltene Ursache einer schweren Compliancestörung sind extrem kostenintensive Oligonukleotid-Substanzen verfügbar.
The Effects of Kinesio Taping with LASER Therapy to Improve on Pain Threshold, Blood Flow, and Balance Ability in Patient with Knee Osteoarthritis
Authors: Kim Kyung-Hun , Kim Dong Hoon 
Published: August 2021

Abstract
Objective We investigated the effects of Kinesio Taping with Laser Therapy (KTLT) on the Pain threshold, Blood flow, and Balance ability of patients with Knee Osteoarthritis.
Methods A total of 32 patients were randomly divided into KTLT and Control (CON) group. KTLT group was performed 30 min three times a week for four weeks in low-level laser therapy (LLLT) treatment process with Taping. CON group performed 30 min three times a week for four weeks in sham LLLT treatment process with sham taping. To measure the oppressive pain threshold, Commander Algometer and for the measurement of the blood flow rate of the skin surface, a noninvasive approach, Laser Doppler imaging equipment was used and the standing balance ability test was performed by Center of Pressure (COP), Limited of Stability (LOS).
Results There was a significant difference (P<0.05) between before and after training in both groups, and KTLT group showed significant improvement in both groups.
Conclusions In this study, we confirmed KTLT on the Pain threshold, Blood flow, and Balance ability of Patient with Knee Osteoarthritis.
Serum Cotinine and Adverse Cardiovascular Outcomes: A Cross-sectional Secondary Analysis of the nuMoM2b Heart Health Study
Authors: Theilen Lauren H. , McNeil Rebecca B. , Hunter Shannon , Grobman William A. , Parker Corette B. , Catov Janet M. , Pemberton Victoria L. , Ehrenthal Deborah B. , Haas David M. , Hoffman Matthew K. , Chung Judith H. , Mukhtar Farhana , Arzumanyan Zorayr , Mercer Brian , Parry Samuel , Saade George R. , Simhan Hyagriv N. , Wapner Ronald J. , Silver Robert M. ,   for the NHLBI nuMoM2b Heart Health Study Network
Published: August 2021

Objective We aimed to (1) compare serum cotinine with self-report for ascertaining smoking status among reproductive-aged women; (2) estimate the relative odds of adverse cardiovascular (CV) outcomes among women by smoking status; (3) assess whether the association between adverse pregnancy outcomes (APOs) and CV outcomes varies by smoking status.
Study Design We conducted a cross-sectional study of the nuMoM2b Heart Health Study. Women attended a study visit 2 to 7 years after their first pregnancy. The exposure was smoking status, determined by self-report and by serum cotinine. Outcomes included incident chronic hypertension (HTN), metabolic syndrome (MetS), and dyslipidemia. Multivariable logistic regression estimated odds ratios (ORs) for each outcome by smoking status.
Results Of 4,392 women with serum cotinine measured, 3,610 were categorized as nonsmokers, 62 as secondhand smoke exposure, and 720 as smokers. Of 3,144 women who denied tobacco smoke exposure, serum cotinine was consistent with secondhand smoke exposure in 48 (1.5%) and current smoking in 131 (4.2%) After adjustment for APOs, smoking defined by serum cotinine was associated with MetS (adjusted OR [aOR] = 1.52, 95% confidence interval [CI]: 1.21, 1.91) and dyslipidemia (aOR = 1.28, 95% CI: 1.01, 1.62). When stratified by nicotine exposure, nonsmokers with an APO in their index pregnancy had higher odds of stage 1 (aOR = 1.64, 95% CI: 1.32, 2.03) and stage 2 HTN (aOR = 2.92, 95% CI: 2.17, 3.93), MetS (aOR = 1.76, 95% CI: 1.42, 2.18), and dyslipidemia (aOR = 1.55, 95% CI: 1.25, 1.91) relative to women with no APO. Results were similar when smoking exposure was defined by self-report.
Conclusion Whether determined by serum cotinine or self-report, smoking is associated with subsequent CV outcomes in reproductive-aged women. APOs are also independently associated with CV outcomes in women.
Key Points
Inertially enhanced mass transport using 3D-printed porous flow-through electrodes with periodic lattice structures
Authors: Beck Victor A. , Ivanovskaya Anna N. , Chandrasekaran Swetha , Forien Jean-Baptiste , Baker Sarah E. , Duoss Eric B. , Worsley Marcus A. 
Published: August 2021

SignificanceThe efficient utilization of electrical energy is an increasingly important challenge, especially as renewable energy sources become cheaper and demand increases. Electrochemical reactors utilizing flow-through electrodes (FTEs) provide an attractive path toward the efficient utilization of electrical energy. Their commercial viability and ultimate adoption hinge on attaining high current densities to drive cost competitiveness. There are limited opportunities for engineering FTE materials, as these are often random, disordered media. Alternatively, three-dimensional (3D)–printed FTEs provide the opportunity to quickly explore the impact of engineered electrode architectures on device performance. We demonstrate that 3D-printed FTEs have the potential to exceed the performance of conventional materials by using the expanded design freedom to engineer the internal flow.
A self-exciting point process to study multicellular spatial signaling patterns
Authors: Verma Archit , Jena Siddhartha G. , Isakov Danielle R. , Aoki Kazuhiro , Toettcher Jared E. , Engelhardt Barbara E. 
Published: August 2021

Significance
Cells are under constant pressure to integrate information from both their environment and internal cellular processes. However, these effects often use the same signaling pathways, making autonomous and coupled signaling difficult to decouple from one another. Here, we present a statistical modeling framework, the cellular point process (CPP), that decouples these two modes of signaling using videos of living, actively signaling cells as input. Our model reveals modulation of autonomous and coupled signaling parameters in a number of contexts ranging from pharmacological treatment to wound healing that were previously unavailable. The CPP enhances our understanding of cellular information processing and can be extended to a wide range of systems.
Aphis gossypii
. [Distribution map].
Authors: UK, CAB International  
Published: August 2021

Abstract

A new distribution map is provided for
Aphis gossypii
Glover (
Doralis frangulae
[Aphis frangulae]
Kalt.,
Cerosipha gossypii
[Aphis gossypii]
(Glov.)) (Hemipt. Aphididae) (Cotton Aphis, Melon Aphis). Host Plants: polyphagous, especially cotton and Cucurbits. Vector of crinkle, mosaic, rosette and other virus diseases. Information is given on the geographical distribution in EUROPE (excl. USSR), Austria, Azores, Belgium, Britain, Bulgaria, Czechoslovakia, Denmark, Finland, France, Germany, Greece, Hungary, Italy, Malta, Netherlands, Norway, Poland, Portugal, Romania, Sardinia, Sicily, Spain, Sweden, Switzerland, Yugoslavia, ASIA (excl. USSR), Afghanistan, Bonin Islands, Burma, Ceylon, China, Cyprus, Gaza, Strip, Hong Kong, India, Indonesia, Iran, Iraq, Israel, Japan, Jordan, Korea, Lebanon, Malaysia, Nepal, Pakistan, Philippine Islands, Saudi Arabia, Singapore, South Arabian Protectorates, Syria, Taiwan, Thailand, Turkey, Vietnam, Yemen Arab Republic, USSR, AFRICA, Algeria, Angola, Burundi, Cameroun, Central African Republic, Chad, Congo, Dahomey, Egypt, Ethiopia, Ghana, Guinea, Ivory Coast, Kenya, Libya, Madagascar, Madeira, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Niger, Nigeria, Réunion, Rwanda, Rhodesia, St. Helena, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Sudan, Swaziland, Tanzania, Togo, Uganda, Upper Volta, Zambia, AUSTRALASIA and PACIFIC ISLANDS, Australia, Caroline Islands, Fiji, Gilbert Islands, Hawaii, Johnston Island, Kermadec Island, Mariana Islands, Marshall Islands, New Caledonia, New Hebrides, New Zealand, Norfolk Island, Papua & New Guinea, Phoenix Islands, Pitcairn Island, Samoa, Society Islands, Tokelau Islands, Tonga, Wallis Island, Irian Jaya, NORTH AMERICA, Canada, Mexico, U.S.A., CENTRAL AMERICA and WEST INDIES, Costa Rica, Salvador, Vicente, Guatemala, Honduras, Nicaragua Panama, West Indies, SOUTH AMERICA, Argentina, Bolivia, Brazil, Chile, Colombia, Guyana, Paraguay, Peru, Surinam, Uruguay, Venezuela.
Myiopardalis pardalina
. [Distribution map].
Authors: UK, CAB International  
Published: August 2021

Abstract

A new distribution map is provided for
Myiopardalis pardalina
(Big.) (Dipt., Trypetidae) (Baluchistan Melon Fly) Hosts: Cucurbits. Information is given on the geographical distribution in ASIA (excl.USSR), Afghanistan, Cyprus, India, Iraq, Israel, Lebanon, Pakistan, Persia, Syria, Turkey, USSR, AFRICA, Senegal.
Scrobipalpa heliopa
. [Distribution map].
Authors: UK, CAB International  
Published: August 2021

Abstract

A new distribution map is provided for
Scrobipalpa heliopa
(Lower) (=
Gnorimoschema heliopa
(Lower)) (Lep., Gelechiidae) (Tobacco Stem-borer, Eggplant Budworm). Host Plants: Cultivated and wild tobacco, eggplant. Information is given on the geographical distribution in EUROPE, Greece, ASIA, Burma, Ceylon, China, Cyprus, India, Indonesia, Israel, Malaysia, Pakistan, Philippines, Taiwan, Thailand, Turkey, Vietnam, AFRICA, Comoro Islands, Congo (Kinshasa), Ivory Coast, Kenya, Madagascar, Malawi, Mozambique, Nigeria, Rhodesia, Somalia, South Africa, Sudan, Tanzania, Uganda, Zambia, AUSTRALASIA AND PACIFIC ISLANDS, Australia, Fiji, New Caledonia, Papua & New Guinea, Western Samoa, Solomon Islands, Tonga, Wallis Islands.
Diarthronomyia chrysanthemi
. [Distribution map].
Authors: UK, CAB International  
Published: August 2021

Abstract

A new distribution map is provided for
Diarthronomyia chrysanthemi
Ahlberg (
D. hypogaea
F. Lw.) (Dipt., Cecidomyiidae) (Chrysanthemum Midge). Host Plants: Chrysanthemum,
Achillea, Senecio
. Information is given on the geographical distribution in EUROPE, Britain, Channel Islands, Denmark, Finland, France, Germany, Ireland, Northern, Sweden, Switzerland, AUSTRALASIA, New Zealand, NORTH AMERICA, Canada, U.S.A.
Leptocorisa acuta
. [Distribution map].
Authors: UK, CAB International  
Published: August 2021

Abstract

A new distribution map is provided for
Leptocorisa acuta
(Thunb.) (L.
varicornis
F.) (Hemipt., Coreidae). Host Plants: Rice, wild grasses. Information is given on the geographical distribution in ASIA, Bhutan, Burma, Ceylon, China, Fukien, Hong Kong, India, Indonesia, Malaya, Pakistan, Philippine Islands, Sabah, Sarawak, Taiwan, Thailand, Vietnam, AUSTRALASIA and PACIFIC ISLANDS, Australia, Fiji, New Caledonia, New Hebrides, Papua and New Guinea, Samoa, Solomon Islands, Tonga, Irian Jaya.
Agrilus planipennis
. [Distribution map].
Authors: CABI  , EPPO  
Published: August 2021

Abstract

A new distribution map is provided for
Agrilus planipennis
Fairmaire. Coleoptera: Buprestidae. Main hosts:
Fraxinus
spp. Information is given on the geographical distribution in Europe (Russia, Central Russia, Russian Far East and Southern Russia), Asia (China, Hebei, Heilongjiang, Jilin, Liaoning, Nei Mongol, Shandong, Sichuan, Xinjiang, Japan, Hokkaido, Honshu, Kyushu, Shikoku, Korea Democratic People's Republic, Korea Republic, Mongolia and Taiwan) and North America (Canada, Ontario, Quebec, USA, Alabama, Arkansas, Colorado, Connecticut, Delaware, District of Columbia, Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Massachusetts, Michigan, Minnesota, Missouri, Nebraska, New Hampshire, New Jersey, New York, North Carolina, Ohio, Oklahoma, Pennsylvania, Tennessee, Texas, Virginia, West Virginia and Wisconsin).
Effect of sliding velocity on the wear behavior of HVOF sprayed Al2O3coating
Authors: Tyagi Ankit , Raj Jitendra , Chourasia Shubhangi , Meena SL , Pandey SM , Murtaza Qasim , Walia R.S. , Kumar Ajay 
Published: August 2021

AbstractIn present study, Al2O3coating has been deposited on steel substrate using HVOF process in controlled process parameters. For surface analysis SEM characterization was used to characterize the developed coating. Residual stresses, hardness, wear and COF are studied at variable sliding velocity of 0.1, 0.2, 0.3 and 0.4 m/s. It is observed that with increased sliding velocity its residual stresses, hardness, wear and COF was enhanced i.e. at 0.4 m/s, wear and coefficient of friction decreased to a value of 90 microns and 0.3, whereas its hardness was 490 HV and residual stresses was -15 MPa, increased at sliding velocity. This showed the sliding velocity dependency of the coating, with increased in sliding velocity, the coating showed better results.
Development of a thermal-visible video surveillance system based on fractional order tv-model
Authors: Kumar Pushpendra 
Published: August 2021

Abstract
Detection and tracking of moving objects in video are an active and challenging research area of computer vision devoted to the applications of video surveillance. The several environmental conditions such as dark, foggy, snowing and rainy, and open-ended goals of the problem motivate to develop a robust surveillance system that based on the thermal-visible video spectrum fusion. However, visible-visible and thermal-thermal spectrum based surveillance model show insufficiency under such conditions. The detection of moving objects is performed by means of optical flow. This paper presents a novel fractional order total variation (TV) model in the estimation of optical flow. In particular, the presented fractional order TV-model is designed by generalizing an integer order total variation model formed by using a combination of total variation and quadratic regularization terms. However, it is challenging to solve such complex minimization problem due to the non-differentiability nature of fractional order TV-term. The fractional order derivative is discretized using the Grünwald-Letnikov derivative. The primal-dual algorithm is used as an iterative scheme to solve the resulting formulation. Finally, a number of experimental results on a fusion of visible-visible, thermal-thermal and visible-thermal video spectrum demonstrate the effectiveness of the model.
Fault Identification and Classification in Motorcycle Engine Using Acoustic Emission Signal and Machine Learning Techniques
Authors: Sahoo Sudarsan , Laskar Ruhul Amin , Chakraborty Sudipta 
Published: August 2021

Abstract
When the engine of a motor-cycle goes into its defective stage, it affects the performance of the motorcycle and hence decreases its efficiency. Therefore, for the smooth running of the motorcycle, the health monitoring of its engine is very essential which increases its life and efficiency. Acoustic emission signature from the motorcycle engine is a measuring parameter to identify the faults in the engine. During its healthy stage, the motor vehicle engines generate a specific acoustic emission signature. But when the engine goes into the defective stage then a significant change in these acoustic emission signature occurs. This change can be used to diagnose the defects in motor engines. The objective of this work is to diagnose the faults in the motorcycle engine using statistical and time-frequency analysis of the acoustic emission signal and then classify the faults using machine learning classifiers. In this experimental work, an instrumentation system is used to acquire the acoustic emission signal from the engines under different conditions. Then the statistical parameters from the acoustic signatures are computed and compared to identify the faults. The wavelet analysis of the acquired acoustic emission signal is also done to diagnose the faults. The classifiers are used to classify the faults by using the statistical parameters as its input. At first stage, the experimental set-up is developed to acquire acoustic emission signatures from the healthy and defective engines. In second stage, signal analysis using statistical and wavelet signal processing technique is done to identify the faults in the engine and in the third stage, the faults in the engine are classified using the machine learning techniques. The result of the proposed work shows that the classification accuracy of the random forest classifier is better than the decision tree classifier. The novelty of the proposed work is that the wavelet analysis along with machine learning technique is used to diagnose the faults in the engine
Perspective of wireless sensor networks towards Environment: Gaps and Challenges
Authors: Rajshree  , Passi Abhiruchi , Soni M.K. 
Published: August 2021

Abstract
Wireless sensor networks have a huge number of applications in the environment for sensing and broadcasting the information at very low cost. Wireless sensor networks are composed of a large number of nodes which are deployed in close proximity to the phenomenon to be monitored. The wireless sensor network is comprised of computing, communication and sensing elements which gives the opportunity to the administrator to an object, sense and an instrument in the events in a specified environment. They are popular due to unique and advance property of wireless sensor but they have fundamental problems like limited battery life, overhearing, coverage area, node deployment, blindness and much more This paper will address various issues related to wireless sensor networks in environmental field The various challenges comprehended in wireless sensor network while constructing the deployment algorithms and communication protocols are coverage area, efficient data gathering, sensor lifetime and overhearing.
An Artificial Intelligence Approach for Forecasting Ebola Disease
Authors: Soni Umang , Gupta Nishu , Sakshi  
Published: August 2021

Abstract
The abrupt explosion of the Ebola virus in 2014 in Western Africa was one of the world’s most widespread and deadliest epidemics with the highest number of casualties being reported in the regions of West and Central Africa. Ebola, a fatal hemorrhagic fever syndrome, is caused by the Ebola virus (EBOV). The World Health Organization proclaimed the disease as a world healthcare crisis. In most of the cases, the patients are known to have died before the antibodies could respond. This indicates the need to improve upon the diagnosis and prediction techniques available for this disease. This paper aims to analyze and improve upon the accuracy of the prediction systems for the Ebola disease using several inputs. The input relies on the symptoms shown by the patient during the early stages of the disease. The data mining techniques employed to carry out this research include Decision Trees; Bagging classifier, KNN, Support Vector Machine, Stochastic Gradient Descent classifier, Logistic Regression, Random Forest, Gradient Boosting classifier, Ridge Classifier, and Hybrid Neural Networks. The hybrid models recommended in this study include the use of classifiers, namely, Stochastic Gradient Descent, Random Forest and KNN classifier. The experimental results show the accuracy obtained by each classification technique and the hybrid models that were applied to the dataset.
Mental Stress Detection Using Artificial Intelligence Models
Authors: Dham Vishal , Rai Kirtiman , Soni Umang 
Published: August 2021

Abstract
Stress is a natural and common occurrence in humans. It leads to the release of hormones which help deal with the situation, but chronic stress affects our health and could lead to deleterious effects like depression, insomnia or headaches and therefore, early detection of stress becomes imperative to prevent such harmful consequences. This manuscript aims to automate the process of mental stress detection and help classify a stressed individual from a normal one through the use of physiological data collected from a wearable device. A publicly available dataset was used to evaluate our solution. Different Artificial Intelligence models like Artificial Neural Network (ANN), Hybrid of Artificial Neural Network and Support Vector Machine (ANN-SVM), Stacking Classifier and Radial Basis Function (RBF) Network were used, and their performance was compared using the accuracy of predicting correct stress state. During the study, Stacking Classifier gave the highest accuracy value of 99.92% while the RBF gave the least accuracy of 84.46% for three class classification of stress. The obtained results indicate the effectiveness of the proposed models in continuous monitoring of mental stress. The experimental results serve to demonstrate that the physiological signals can have a significant appositeness in mental stress detection.