bluuebunny/crossref_metadata_2025_split huggingface dataset:
WaveNet based Autoencoder Model: Vibration Analysis on Centrifugal Pump for Degradation Estimation.
Authors: Yen Fanyun , Katrib Ziad 
Published: February 2021

Centrifugal pumps are versatile and have been used in a wide range of applications such as agricultural services, wastewater services, and other industrial services. The mechanism behind the pump is converting rotational kinetic energy to induce flow or raise pressure of liquid. Boiler feedewater pump (BFP) is an important piece of equipment in a thermal power generation plant. Generally, the cost of the pump itself only account less than 20% of its life cost and about 30% - 35% of the life cost spend on pump operation and maintenance. Therefore, it is important to understand the degradation status of the pumping system for optimizing the operational procedures and maintenance schedule to reduce the cost. Traditionally, engineers evaluate the performance and/or find faults by observing the vibrational signal on the pump, specifically, looking at the power spectrum density of the vibrational signal measured on different locations of the pump. However, such vibration analysis requires substantial domain knowledge and experience to accommodate all the variables caused by various conditions like different models, sizes in different plants, units and facilities. Often Vibration Analyst have to bin the vibration signal according to a predetermined frequency bins and potentially removing useful markers about vibration health.
This paper presents a novel way of conducting vibration analysis on pumps to determine the degradation trend, without requiring expert domain knowledge by extracting useful information using a WaveNet based autoencoder on the historical vibration data. WaveNet is known for processing raw audio data and building generative models. Unlike recurrent neural network (RNN), WaveNet is capable of handling much longer sequential data, which is very suitable for high frequency signals like sound and vibration signals. The autoencoder model extract essential information for reconstructing the input data. The embeddings from the autoencoders can represent the characteristics of the input data. Combining the two techniques, we were able to compress the vibration data 12x and extract the embeddings from raw vibration data and use them to estimate the degradation status of pumps. We pre-selected a collection of vibration data from pumps under “normal” condition. The degradation trend is estimated by computing the distance of the embeddings from “normal” data to new inputs. Such model provides additional information on pump condition vis-a-vis vibration data with no prior domain knowledge. This technique can assist decision making and reduce costs from improper operation and maintenance.
Aircraft Troubleshooting Optimization Using Case-based Reasoning and Decision Analysis
Authors: Borba Evangelista Gustavo , Conceição Rocha Guilherme , Olivares Loesch Vianna Wlamir 
Published: February 2021

The Fault Isolation Manual (FIM) can be seen as a specialist system that carries the expectations and expertise of engineers and technical team concerning the aircraft components and systems operation. It is basically a manual that supports the maintainers regarding the actions to perform in determined situations to properly isolate a fault. Although the FIM is the most common tool that assists maintainer on the troubleshooting process today, it does not adequately consider field experience and it does not explore situations where the maintenance operator has limited resources, such as a lack of tools and equipment. These drawbacks are essentially caused by the lack of flexibility or adaptability of this method since it is a static manual. There are several dynamic methods studied in the field of system troubleshooting and aircraft maintenance such as Artificial Neural Networks, Support Vector Machine, K Nearest Neighbor and many other machine learning algorithms. These techniques are considered very powerful and useful; however, the training process of the data-driven strategies requires a large amount of data to provide a reliable result.
In this context, the present work proposes a combination of data-driven with legacy knowledge-based approaches. The following techniques are employed to integrate the concepts mentioned: decision trees that explore the legacy knowledge with its topology based on the FIM, truth tables and decision analysis that explores Bayes’ rule to assist the decision- making process and case-based reasoning, technique that enables the learning from the field experience.
A decision-making framework for safe operations of unmanned aerial vehicles in urban scenarios
Authors: Quiñones Marcos , Darrah Timothy , Biswas Gautam , Kulkarni Chetan 
Published: February 2021

This paper presents a decision-making scheme at the level of individual unmanned aerial vehicles (UAVs) with the goal of maintaining safe operations for urban mobility. The decision-making approach for a single UAV will consider the risks associated with the current trajectory given the existing environmental conditions and the state of the vehicle. The proposed scheme combines the analysis of system performance, environmental conditions, and mission level parameters for contingency management, i.e., make a determination on: (1) to abort mission and land safely; (2) re-plan current mission in full or abbreviated form; and (3) change mission. A path planning and trajectory optimization algorithm with the goal of minimizing the overall risk of mission failure by considering a number of factors such as the uncertainties in the environment and operating state of the vehicle is proposed. We will consider the mission failure as the loss of control of the vehicle resulting in a collision with other objects or a crash into the ground. An offline part of the framework generates an initial mission plan by considering the state of the vehicle, the environmental, conditions, and the static features of a map of the environment. Once the vehicle takes off, the risk of mission’ failure associated with the remaining trajectory is re-computed in an online framework to assess whether re-planning is required or not. A key challenge that we consider in this paper is to study the effects of multiple interacting subsystems of the UAV on system performance, especially under degraded conditions.
A low quiescent power wireless rotating machinery condition monitoring system
Authors: Chen Yan , Chaudhry Zaffir , Mantese Joseph 
Published: February 2021

Vibration-based monitoring of rotating machinery is rapidly evolving within the aerospace industry with priority on detecting impending failures. The workhorse of such monitoring system remains a piezoelectric-based accelerometers which requires a wired-harnesses, connectors, significant power, and signal conditioning, etc. Raytheon Technologies Research Center (RTRC) along with Collins Aerospace and Sandia National Laboratory have jointly developed an Aluminum-Nitride Resonant Integrated Accelerometer Sensors (ARISE).
This is a low power alternate for a conventional wired vibration-based monitoring system. This self-contained sensor system includes: (1) a low quiescent power sensing element with a wake-up module, (2) a wireless communication module, and (3) a coin-cell battery. Leveraging work performed under Defense Advanced Research Projects Agency (DARPA) N-Zero program. This wireless health monitoring system can operate in a quiescent low power mode (~10nW) for a period of several years without servicing. With an exceedance above a preset vibration level (at designate characteristic frequencies), the sensor wakes up and wirelessly sends a warning of a precursor-to-failure.
The ARISE sensor and wake-up module package has been validated with a replicated vibration environment acquired from a selected rotating machinery subject to progressive damage at the Structural Dynamics Laboratory at RTRC. The failure precursor is successfully detected by the sensor which triggers the wake-up module.
This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA) Micro Technology Office (MTO), under Aluminum-Nitride Resonant Integrated Accelerometer Sensors (ARISE) program.
Digital Fleet Management: A Scalable Cloud Framework based on Data-driven Prediction Models
Authors: Thomas Sherin , Dubey Abhishek , Viassolo Daniel E , Zanette Magson 
Published: February 2021

A scalable Digital Fleet Management system can be leveraged by most organizations with high-volume high-value assets. In such scenarios, predictive analytics for tool health becomes central, as it enables decision-making in terms of planning, maintenance, end-of-life replacement, tool selection, etc. End-to-end solutions, spanning all the way from gathering live tool data to the visual representation of tool health, are certainly of major interest.
Long-term fleet management can be accomplished through a consistent evaluation of the fleet performance profile. Predictive analysis can anticipate maintenance needs and resultant downtimes, and in turn it helps improve scheduling of procurement and distribution of the fleet.
This paper focuses on managing a fleet of thousands of downhole tools based on tool health condition and other variables – a very common use case in Oil & Gas Services. An end-to-end automated scalable cloud framework is described in detail. This framework integrates failure prediction models for each single asset in the fleet of tools. Based on historical tool data, the models generate tool risk indices (one index per asset) which correlate to the probability of tool failure during near-future field jobs. These risk indices can be used for optimal asset-to-job mapping. They also help in de-risking field operations by identifying tools for overhaul or retirement. The proposed method integrates the tasks of: fetching data from 200,000+ tools, performing feature engineering, modeling via Machine Learning (ML) , and visualizing into a cloud pipeline. Framework scalability becomes a key requirement as fleet size increases or decreases over time to match market demands. The framework also allows for the addition of new ML models to the platform by citizen data scientists, who are not cloud experts. Finally, it is shown who this framework provides systematic steps for sustenance of such large cloud platform.
Enhancing Fault Isolation for Health Monitoring of Electric Aircraft Propulsion by Embedding Failure Mode and Effect Analysis into Bayesian Networks
Authors: Kulkarni Chetan S , Corbetta Matteo , Robinson Elinirina 
Published: February 2021

This paper describes a fault isolation approach for electric powertrains of unmanned aerial vehicles.The approach leverages the combination of failure mode and effect analysis (FMEA) and Bayesian networks,
thus introducing dependability structures into a diagnostic framework. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded whitin FMEAs. This framework helps the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. The framework is applied to an electric powertrain system of a small, rotary-wing unmanned aerial vehicle, demonstrating how a Bayesian network enhanced by FMEA helps disambiguate between root causes of incipient failures, which would otherwise be considered as equally probable.
ANALISIS KEPUTUSAN PEMBELIAN MAKANAN KHAS DAERAH DITINJAU DARI PENGARUH WORD OF MOUTH DAN KUALITAS PRODUK SERTA DAMPAKNYA PADA LOYALITAS PELANGGAN
Authors: Thalib Supriadi , Setiarini Setiarini , Ardianto Yuli 
Published: February 2021

Purpose- This study analyzes the effect of word of mouth, product quality on consumer purchasing decisions, and simultaneously analyzes purchasing decisions, word of mouth, and product quality on customer loyalty.
Design/methodology/approach- Data were collected from 150 respondents from 3 (three) areas Jakarta, Depok, and Bogor. Data analysis techniques using Path Analysis.
Findings- word of mouth and product quality have a positive effect on purchasing decisions, as well as simultaneous purchasing decisions, word of mouth affects customer loyalty but not product quality. Product quality does not affect loyalty consumption of local specialties in the city of Bogor-Depok
Implications-Local culinary SMEs are advised to maintain consumer satisfaction by interacting with them to create a positive impression. Consumers who have a positive impression will provide the word of mouth so as to increase purchasing decisions and customer loyaltyKeywords: Word of Mouth, Quality Product, Purchasing Decision, Loyalty
COVID-19 REVIEW ON THE PHARMACOLOGY OF THE DRUGS USED FOR COVID-19 OUTBREAK
Authors: Ul ain Noor 
Published: February 2021

BACKGROUND & OBJECTIVE:
The recent global health threat is the outbreak of a viral infectious disease caused by SARS-COV2 (Severe acute respiratory syndrome-corona virus). The origin of this outbreak is from the animal market in Wuhan, China. So, it is considered as zoonotic in origin as its mode of transmission is from animals to human. This outbreak is similar to previous outbreaks of coronavirus like SARS-COV (Severe Acute Respiratory syndrome) and MERS-COV (Middle East Respiratory Syndrome). Until now different pharmacological drugs have been used to mitigate the symptoms of the COVID-19 throughout the world. There is no specific medication prescribed as the treatment of this disease. However, pharmacological drugs and immune boosting dietary therapies have been proved effective in treatment strategies.
METHODOLOGY:
The latest epidemiological articles, research articles, review articles, WHO guidelines and news proceedings are selected in this review. Articles were searched under the keyword search of COVID-19, drugs for COVID-19, latest COVID-19 research, surveys for covid-19. PubMed, Lancet, Elsevier, BMJ and CDC were used to accumulate the latest findings.
RESULTS:
There is no vaccine or other proper treatment available for this infectious disease but the symptomatic treatment is usually given to the infected person as the use of certain pharmacological as well as traditional drugs are given to reduce the symptoms associated with this disease. Furthermore, COVID-19 serious patients on ventilator are treated with the plasma therapy as with the antibiotics from the plasma of recovered patients is also in use to reduce mortality rate.
CONCLUSION:
To prevent this infectious disease it is important to avoid the person-to-person transmission which is mainly via coughing, sneezing, droplets of the respiration, direct contact with the infected person. So, the social distancing and self-quarantine is preferred to avoid the spread of this infectious disease. The drugs which are commonly used to reduce or eliminate the symptoms of COVID-19 are Paracetamol, Aspirin/Loprin, Dabigatran, Azithromycin, Famotidine, Ipratopium bromide, Dexamethasone,Tocilizumab, and Moxifloxacin. These drugs have been practiced by the practitioners to treat the symptoms of covid-19. However no pertinent drug has been approved as a cure of covid-19.
ASSESSMENT OF MEAN DISTANCE BETWEEN CENTRAL INCISOR AND INCISIVE PAPILLA WITH RESPECT TO STANDARDIZED ARCH FORM IN DENTATE SUBJECTS
Authors: Ussamah Waheed Jatala  , Anam Fayyaz Bashir  , Nazia Yazdanie  
Published: February 2021

BACKGROUND & OBJECTIVE: Edentulous patients require accurate positioning of artificial maxillary anterior teeth in complete dentures to recover the esthetic and phonetic characteristics. This positioning is guided by measuring distance between central incisor (CI) and incisive papilla (IP) in dentate subjects which varies according to the ovoid, square and tapered maxillary arch forms.
Objectives: 1- To determine the frequency of three different arch forms in dentate patients;
2- To assess the mean distance between central incisor and incisive papilla in each of the three maxillary arch forms.
METHODOLOGY: 130 cases were selected by non-probability consecutive sampling, which included both genders and age range of 20-40 years. Impressions of maxillary arches were recorded and their casts photocopied. Arch form template was used to standardize the assessment of arch forms by best-fit method on the photocopies. The distance between mesio-incisal edge of CI and posterior border of IP was measured with digital verniercalipers (SparkFun,Colorado).
RESULTS: Out of 130 subjects, 54 males and 76 females were present. The ovoid arch form was the most frequent form recorded at 67% (n=87) while the tapered arch was the least at 14% (n=18). The overall mean distance between central incisor (CI) and incisive papilla (IP) was 11.34mm (7.58mm - 16.45mm). The mean distance was the highest for ovoid arch form (11.58mm) and lowest for square (10.49) with a statistically significant difference (p=0.016) between the two arch forms.
CONCLUSION: The ovoid arch was the most frequent arch form found in dentate subjects. The mean distance recorded was highest for ovoid arch followed by tapering arch and least for square arches.
CLINICAL AND HISTOPATHOLOGICAL CORRELATION IN TUBERCULOSIS OF SPINE BIOPSIES
Authors: Amin Muhammad Shahbaz , Seema Butt  , Abdullah Shahbaz  , Majid Zaheer  , Ashfaq Ahmed  , Shazia Nilofer Ibnerasa  
Published: February 2021

ABSTRACT:
BACKGROUND & OBJECTIVE: The tuberculosis (TB) of the spine is causing permanent deformities since long time. Earliest most possible accurate diagnosis is required to treat either TB or to exclude other lesions to avoid permanent disability.
METHODOLOGY:
This is a descriptive study conducted at pathology department of Lahore Medical & Dental College (LM&DC), Lahore from January 2017 to December 2019. All the spine biopsies received in Pathology department were included in the study irrespective of age and sex. Each biopsy was grossly and microscopically examined and reported by a histopathologist.
RESULTS:
Out of 386 specimens received, 189 (48.9%) were clinically diagnosed as tuberculosis. Out of these 189 cases, 127 were confirmed on histopathological examination showing clinicopathological correlations in 67.19 % of cases. The rest 62 (32.80%) of the cases were reported as chronic non specific inflammation (38), degenerative changes (11), metastatic tumor (04), and atypical cell infiltrate (02), plasma cell neoplasm (01), Non Hodgkin Lymphoma (01) and other lesions (05) which all were benign. Out of the total 386 spine biopsies, 154 (39.89 %) were histopathologically proven for tuberculosis. From these 154 histologically proven cases, 127 were clinically suspected cases of Tuberculosis. No clinical diagnosis was provided in 02 patients. The rest of 25 patients were clinically labeled as pathological lesion without specific diagnosis.
CONCLUSION:
Histopathology should be considered as the most reliable tool for spinal tuberculosis to minimize permanent disability associated with wrong management based on potentially erroneous clinical diagnosis.
Understanding the Impacts of Stellar Companions on Planet Formation and Evolution: A Survey of Stellar and Planetary Companions within 25 pc
Authors: Hirsch Lea A. , Rosenthal Lee , Fulton Benjamin J. , Howard Andrew W. , Ciardi David R. , Marcy Geoffrey W. , Nielsen Eric , Petigura Erik A. , de Rosa Robert J. , Isaacson Howard , Weiss Lauren M. , Sinukoff Evan , Macintosh Bruce 
Published: February 2021

Abstract
We explore the impact of outer stellar companions on the occurrence rate of giant planets detected with radial velocities. We searched for stellar and planetary companions to a volume-limited sample of solar-type stars within 25 pc. Using adaptive optics imaging observations from the Lick 3 m and Palomar 200″ Telescopes, we characterized the multiplicity of our sample stars, down to the bottom of the main sequence. With these data, we confirm field star multiplicity statistics from previous surveys. We additionally combined three decades of radial velocity (RV) data from the California Planet Search with newly collected RV data from Keck/HIRES and the Automated Planet Finder/Levy Spectrometer to search for planetary companions in these same systems. Using an updated catalog of both stellar and planetary companions, as well as detailed injection/recovery tests to determine our sensitivity and completeness, we measured the occurrence rate of planets among the single- and multiple-star systems. We found that planets with masses in the range of 0.1–10 M
J and with semimajor axes of 0.1–10 au have an occurrence rate of

planets per star when they orbit single stars and an occurrence rate of 0.12 ± 0.04 planets per star when they orbit a star in a binary system. Breaking the sample down by the binary separation, we found that only one planet-hosting binary system had a binary separation <100 au, and none had a separation <50 au. These numbers yielded planet occurrence rates of

planets per star for binaries with separation a

B
> 100 au and

planets per star for binaries with separation a

B
< 100 au. The similarity in the planet occurrence rate around single stars and wide primaries implies that wide binary systems should actually host more planets than single-star systems, since they have more potential host stars. We estimated a system-wide planet occurrence rate of 0.3 planets per wide binary system for binaries with separations a

B
> 100 au. Finally, we found evidence that giant planets in binary systems have a different semimajor-axis distribution than their counterparts in single-star systems. The planets in the single-star sample had a significantly higher occurrence rate outside of 1 au than inside 1 au by nearly 4σ, in line with expectations that giant planets are most common near the snow line. However, the planets in the wide binary systems did not follow this distribution, but rather had equivalent occurrence rates interior and exterior to 1 au. This may point to binary-mediated planet migration acting on our sample, even in binaries wider than 100 au.
Searching for Active Low-mass Stars in the CMa Star-forming Region: Multi-band Photometry with T80S
Authors: Gregorio-Hetem J. , Navarete F. , Hetem A. , Santos-Silva T. , Galli P. A. B. , Fernandes B. , Montmerle T. , Jatenco-Pereira V. , Fernandes M. Borges , Perottoni H. D. , Schoenell W. , Ribeiro T. , Kanaan A. 
Published: February 2021

Abstract
An exotic environment surrounds the young stellar groups associated with the Canis Major (CMa) OB1/R1 region, which probably was formed under feedback from at least three supernova events having occurred a few million years ago. We use astrometric data from the Gaia-DR2 to confirm the membership of the stars in CMa R1, based on proper motion and parallax, which revealed 514 new members and candidates. The mean age of 5 Myr estimated from the color–magnitude diagram characterizes the sources as likely pre-main-sequence candidates. In total, a sample of 694 stars detected with the T80-South telescope was analyzed according to different color–color diagrams, which were compared with theoretical colors from evolutionary models, aiming to reveal the objects that exhibit color excess due to accretion processes. Accretion and magnetic activity were also explored on the basis of empirical flux–flux relation, such as F
660 and F
861 that are related to Hα and Ca ii triplet emission, respectively. A low fraction (∼3 percent) of the sample have Hα excess and other colors expected for stars exhibiting chromospheric activity. The number of Class I and Class II objects, identified by the infrared (Wide-field Infrared Survey Explorer) colors, indicates a disk fraction of ∼6 percent, which is lower than that expected for stellar clusters with a similar age. Such a large sample of objects associated with CMa R1 without evidences of circumstellar accretion can be interpreted as a lack of disk-bearing stars, unusual for young star-forming regions. However, this may be explained as the result of supernova events.