scholarly journals Advanced Sensor Dynamic Measurement and Heuristic Data Analysis Model for Bridge Health Monitoring System

2019 ◽  
Vol 8 (3) ◽  
pp. 1907-1912
Keyword(s):  
Health And Safety ◽  
Vibration Signal ◽  
Health Condition ◽  
Data Sets ◽  
Analysis Model ◽  
Independent Analysis ◽  
Effective Manner ◽  
Significant Area ◽  
Using Data ◽  
Data Analytic

Presently the health and safety monitoring of a bridge is considered as a significant area of research where the attention has been paid by many researchers. In this article the bridge structural damages due to environmental fluctuations and other parameters has been analyzed using cutting-edge technologies. In this research the technology of advanced Intelligent Internet of Things (IIoT) sensors with signal processing systems is designed and developed to monitor the health condition of the bridge using data analytic techniques. In the recent past these sensor systems has been used collect the vibration signal sets caused by the vehicles movement on the bridge. Further, these collected data sets are analyzed with the help data analytic approach using traditional independent analysis models which fails to produce optimum results in terms of reliability, efficiency, stability, corrosion and crack of the bridge. In this article to overcome this issue an improved heuristic nonlinear model has been developed to analyze the data sets using non-linear and linear separation analogy. This optimized data analytics technique with advanced sensing mechanisms is validated experimentally and the outcomes shows promising solutions to monitor bridge health in effective manner than traditional strategies

Geological structures beneath the River Thames in London: findings from the Thames Tideway Tunnel investigations

2021 ◽  
pp. qjegh2020-157
Author(s):  
T.G. Newman ◽  
N.W Hadlow
Keyword(s):  
Fault Zone ◽  
Health And Safety ◽  
Data Sets ◽  
Ground Model ◽  
Geological Structures ◽  
Content Type ◽  
Ground Conditions ◽  
Slip Displacement ◽  
River Thames ◽  
Using Data

The Thames Tideway Tunnel is 25 km long and extends west – east through central London, beneath the River Thames for most of its route. A detailed preconstruction ground model has been assembled, using data from borehole and river-borne seismic reflection survey investigations. The two data sets have together delineated several significant geological structures along the route.The investigations have led to an improved understanding of the morphology of some structures, such as the Greenwich Fault, London Bridge Fault Zone, Millwall Anticline and Greenwich-Plaistow Syncline, which were only generally indicated during preliminary desk studies. Other structures, such as the Putney-Hammersmith Fault Zone, Chelsea Embankment Fault Zone and Lambeth Anticline, are entirely new discoveries.Most of the structures described here have characteristics compatible with strike-slip displacement and, although this has been previously widely suspected, this paper presents new evidence towards this. When intersected by the tunnel during its construction phase, they have imposed significant changes in geological strata, leading to changes in the performance of tunnelling plant or creating adverse ground conditions. Their early identification by the ground model has assisted engineering design and planning, for the benefit of construction cost efficiency and, importantly, Health and Safety of underground personnel.Thematic collection: This article is part of the Geology of London and its implications for ground engineering collection available at: https://www.lyellcollection.org/cc/london-basin

Methodological Issues in Educational Performance and School Effectiveness Research: A Discussion with Worked Examples

1995 ◽  
Vol 39 (3) ◽  
pp. 217-248 ◽  
Author(s):  
Kenneth J. Rowe ◽  
Peter W. Hill ◽  
Philip Holmes-Smith
Keyword(s):  
School Effectiveness ◽  
Multilevel Models ◽  
Worked Examples ◽  
Data Sets ◽  
Methodological Issues ◽  
School Effectiveness Research ◽  
Sampling Structure ◽  
Using Data ◽  
Analytic Models ◽  
Data Analytic

There has been a growing awareness among educational researchers of the consequences of using data-analytic models that fail to account for the inherent clustered or hierarchical sampling structure of the data typically obtained. Such clustering poses special analytic problems related to levels of analysis, aggregation bias, heterogeneity of regression and parameter mis-estimation, with important implications for the correct interpretation of effects. This paper compares the results obtained from fitting single-level and multilevel models to two hierarchically structured data sets designed to explain variation in student achievement. Emphasis is given to the crucial importance of fitting models commensurate with the sampling structure of the data to which they are applied.

Assessment of Regional Explosion Discriminants Using Data Sets of Unparalleled Spatial Sampling

2012 ◽  
Author(s):  
Kate C. Miller ◽  
Lindsay L. Worthington ◽  
Steven Harder ◽  
Scott Phillips ◽  
Hans Hartse ◽  
...  
Keyword(s):  
Spatial Sampling ◽  
Data Sets ◽  
Using Data

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

POSTPARTUM AMENORRHOEA IN RURAL EASTERN UTTAR PRADESH, INDIA

1998 ◽  
Vol 30 (2) ◽  
pp. 227-243
Author(s):  
K. N. S. YADAVA ◽  
S. K. JAIN
Keyword(s):  
Higher Education ◽  
Uttar Pradesh ◽  
Current Status Data ◽  
North India ◽  
Current Status ◽  
Data Sets ◽  
Survival Status ◽  
The Mean ◽  
The Difference ◽  
Using Data

This paper calculates the mean duration of the postpartum amenorrhoea (PPA) and examines its demographic, and socioeconomic correlates in rural north India, using data collected through 'retrospective' (last but one child) as well as 'current status' (last child) reporting of the duration of PPA.The mean duration of PPA was higher in the current status than in the retrospective data;n the difference being statistically significant. However, for the same mothers who gave PPA information in both the data sets, the difference in mean duration of PPA was not statistically significant. The correlates were identical in both the data sets. The current status data were more complete in terms of the coverage, and perhaps less distorted by reporting errors caused by recall lapse.A positive relationship of the mean duration of PPA was found with longer breast-feeding, higher parity and age of mother at the birth of the child, and the survival status of the child. An inverse relationship was found with higher education of a woman, higher education of her husband and higher socioeconomic status of her household, these variables possibly acting as proxies for women's better nutritional status.

scholarly journals Modeling Mine Workforce Fatigue: Finding Leading Indicators of Fatigue in Operational Data Sets

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 621
Author(s):  
Elaheh Talebi ◽  
W. Pratt Rogers ◽  
Tyler Morgan ◽  
Frank A. Drews
Keyword(s):  
Health And Safety ◽  
Time Of Day ◽  
Weather Data ◽  
Data Sets ◽  
Leading Indicators ◽  
Safety Control ◽  
Model Complex ◽  
Fatigue Monitoring ◽  
The Impact ◽  
Operational Data

Mine workers operate heavy equipment while experiencing varying psychological and physiological impacts caused by fatigue. These impacts vary in scope and severity across operators and unique mine operations. Previous studies show the impact of fatigue on individuals, raising substantial concerns about the safety of operation. Unfortunately, while data exist to illustrate the risks, the mechanisms and complex pattern of contributors to fatigue are not understood sufficiently, illustrating the need for new methods to model and manage the severity of fatigue’s impact on performance and safety. Modern technology and computational intelligence can provide tools to improve practitioners’ understanding of workforce fatigue. Many mines have invested in fatigue monitoring technology (PERCLOS, EEG caps, etc.) as a part of their health and safety control system. Unfortunately, these systems provide “lagging indicators” of fatigue and, in many instances, only provide fatigue alerts too late in the worker fatigue cycle. Thus, the following question arises: can other operational technology systems provide leading indicators that managers and front-line supervisors can use to help their operators to cope with fatigue levels? This paper explores common data sets available at most modern mines and how these operational data sets can be used to model fatigue. The available data sets include operational, health and safety, equipment health, fatigue monitoring and weather data. A machine learning (ML) algorithm is presented as a tool to process and model complex issues such as fatigue. Thus, ML is used in this study to identify potential leading indicators that can help management to make better decisions. Initial findings confirm existing knowledge tying fatigue to time of day and hours worked. These are the first generation of models and future models will be forthcoming.

scholarly journals Using Machine Learning Algorithms on data residing in SAP ERP Application to predict equipment failures

2018 ◽  
Vol 7 (2.28) ◽  
pp. 312
Author(s):  
Manu Kohli
Keyword(s):  
Data Extraction ◽  
Indirect Costs ◽  
True Positive Rate ◽  
Spare Parts ◽  
Machine Learning Algorithms ◽  
Machine Language ◽  
Support Vector ◽  
Data Sets ◽  
Equipment Failure ◽  
Using Data

Asset intensive Organizations have searched long for a framework model that would timely predict equipment failure. Timely prediction of equipment failure substantially reduces direct and indirect costs, unexpected equipment shut-downs, accidents, and unwarranted emission risk. In this paper, the author proposes a model that can predict equipment failure by using data from SAP Plant Maintenance module. To achieve that author has applied data extraction algorithm and numerous data manipulations to prepare a classification data model consisting of maintenance records parameters such as spare parts usage, time elapsed since last completed maintenance and the period to the next scheduled maintained and so on. By using unsupervised learning technique of clustering, the author observed a class to cluster evaluation of 80% accuracy. After that classifier model was trained using various machine language (ML) algorithms and subsequently tested on mutually exclusive data sets with an objective to predict equipment breakdown. The classifier model using ML algorithms such as Support Vector Machine (SVM) and Decision Tree (DT) returned an accuracy and true positive rate (TPR) of greater than 95% to predict equipment failure. The proposed model acts as an Advanced Intelligent Control system contributing to the Cyber-Physical Systems for asset intensive organizations. 

Relationship Between Environmental Temperature and Yields of Subarctic and Temperate Zone Fish Species

1983 ◽  
Vol 40 (10) ◽  
pp. 1829-1837 ◽  
Author(s):  
David A. Schlesinger ◽  
Henry A. Regier
Keyword(s):  
Optimal Growth ◽  
Temperate Zone ◽  
Data Sets ◽  
Coregonus Clupeaformis ◽  
Influence Of Temperature On ◽  
Northern United States ◽  
Using Data ◽  
Curvilinear Relationships ◽  
Fish Yields

Fishes inhabiting subarctic and temperate zone lakes exhibit distinct optimal growth temperatures and temperature preferenda. However, within regional data sets, attempts to correlate fish yields with temperature variables have generally been unsuccessful. In our study, curvilinear relationships between "long-term mean annual air temperature" (TEMP) and sustained yields of three species were fitted using data from 23 intensively fished lakes in Canada and the northern United States. Optimum TEMP values for sustained yield were approximately −1.0, 1.5, and 2 °C, respectively, for lake whitefish (Coregonus clupeaformis), northern pike (Esox lucius), and walleye (Stizostedion vitreum vitreum). These differences suggest that the influence of temperature on sustained fish yields from subarctic and temperate zone lakes may, in the past, have been underestimated.

Three-centre C—H...O hydrogen bonds in the DNA minor groove: analysis of oligonucleotide crystal structures

1999 ◽  
Vol 55 (12) ◽  
pp. 2005-2012 ◽  
Author(s):  
Anirban Ghosh ◽  
Manju Bansal
Keyword(s):  
Hydrogen Bonds ◽  
Crystal Structures ◽  
Base Pair ◽  
Minor Groove ◽  
Local Geometry ◽  
Data Sets ◽  
Base Pairs ◽  
Dna Minor Groove ◽  
Close Proximity ◽  
Using Data

AA·TT and GA·TC dinucleotide steps in B-DNA-type oligomeric crystal structures and in protein-bound DNA fragments (solved using data with resolution <2.6 Å) show very small variations in their local dinucleotide geometries. A detailed analysis of these crystal structures reveals that in AA·TT and GA·TC steps the electropositive C2—H2 group of adenine is in very close proximity to the keto O atoms of both the pyrimidine bases in the antiparallel strand of the duplex structure, suggesting the possibility of intra-base pair as well as cross-strand inter-base pair C—H...O hydrogen bonds in the DNA minor groove. The C2—H2...O2 hydrogen bonds in the A·T base pairs could be a natural consequence of Watson–Crick pairing. However, the cross-strand interactions between the bases at the 3′-end of the AA·TT and GA·TC steps obviously arise owing to specific local geometry of these steps, since a majority of the H2...O2 distances in both data sets are considerably shorter than their values in the uniform fibre model (3.3 Å) and many are even smaller than the sum of the van der Waals radii. The analysis suggests that in addition to already documented features such as the large propeller twist of A·T base pairs and the hydration of the minor groove, these C2—H2...O2 cross-strand interactions may also play a role in the narrowing of the minor groove in A-tract regions of DNA and help explain the high structural rigidity and stability observed for poly(dA)·poly(dT).

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