Analysis of 14C, 13C and Aspartic Acid Racemization in Teeth and Bones to Facilitate Identification of Unknown Human Remains: Outcomes of Practical Casework

The identification of unknown human remains represents an important task in forensic casework. If there are no clues as to the identity of the remains, then the age, sex, and origin are the most important factors to limit the search for a matching person. Here, we present the outcome of application of so-called bomb pulse radiocarbon (14C derived from above-ground nuclear bomb tests during 1955–1963) analysis to birthdate human remains. In nine identified cases, 14C analysis of tooth crowns provided an estimate of the true date of birth with an average absolute error of 1.2 ± 0.8 years. Analysis of 14C in tooth roots also showed a good precision with an average absolute error of 2.3 ± 2.5 years. Levels of 14C in bones can determine whether a subject has lived after 1955 or not, but more precise carbon turnover data for bones would be needed to calculate date of birth and date of death. Aspartic acid racemization analysis was performed on samples from four cases; in one of these, the year of birth could be predicted with good precision, whereas the other three cases are still unidentified. The stable isotope 13C was analyzed in tooth crowns to estimate provenance. Levels of 13C indicative of Scandinavian provenance were found in known Scandinavian subjects. Teeth from four Polish subjects all showed higher 13C levels than the average for Scandinavian subjects.

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Aspartic acid racemization variability in ancient human remains: implications in the prediction of ancient DNA recovery

Journal of Archaeological Science ◽

10.1016/j.jas.2008.11.009 ◽

2009 ◽

Vol 36 (4) ◽

pp. 965-972 ◽

Cited By ~ 15

Author(s):

E. Fernández ◽

J.E. Ortiz ◽

A. Pérez-Pérez ◽

E. Prats ◽

D. Turbón ◽

...

Keyword(s):

Aspartic Acid ◽

Ancient Dna ◽

Human Remains ◽

Dna Recovery ◽

Aspartic Acid Racemization

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Age estimation of Antarctic minke whales Balaenoptera bonaerensis based on aspartic acid racemization technique

Fisheries Science ◽

10.1007/s12562-017-1122-0 ◽

2017 ◽

Vol 83 (6) ◽

pp. 947-954

Author(s):

Genta Yasunaga ◽

Luis A. Pastene ◽

Takeharu Bando ◽

Takashi Hakamada ◽

Yoshihiro Fujise

Keyword(s):

Aspartic Acid ◽

Age Estimation ◽

Aspartic Acid Racemization ◽

Minke Whales ◽

Balaenoptera Bonaerensis

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Hidden Semi-Markov Models for Predictive Maintenance

Mathematical Problems in Engineering ◽

10.1155/2015/278120 ◽

2015 ◽

Vol 2015 ◽

pp. 1-23 ◽

Cited By ~ 17

Author(s):

Francesco Cartella ◽

Jan Lemeire ◽

Luca Dimiccoli ◽

Hichem Sahli

Keyword(s):

Markov Models ◽

Real Data ◽

Information Criterion ◽

Absolute Error ◽

Predictive Maintenance ◽

Average Absolute Error ◽

Current State ◽

Automatic Model Selection ◽

State Duration ◽

Useful Lifetime

Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs) with (i) no constraints on the state duration density function and (ii) being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL) of the machine is calculated.

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Age estimation based on aspartic acid racemization in dentine: what about caries-affected teeth?

International Journal of Legal Medicine ◽

10.1007/s00414-017-1667-6 ◽

2017 ◽

Vol 132 (2) ◽

pp. 623-628 ◽

Cited By ~ 4

Author(s):

Nazan Sirin ◽

Christian Matzenauer ◽

Alexandra Reckert ◽

Stefanie Ritz-Timme

Keyword(s):

Aspartic Acid ◽

Age Estimation ◽

Aspartic Acid Racemization

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Study of a Privacy Preserving Logistic Regression Algorithm (PPLRA) For Data Privacy in the Context of Big Data

Journal of Physics Conference Series ◽

10.1088/1742-6596/2083/3/032059 ◽

2021 ◽

Vol 2083 (3) ◽

pp. 032059

Author(s):

Qiang Chen ◽

Meiling Deng

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Privacy Protection ◽

Data Privacy ◽

Absolute Error ◽

Average Absolute Error ◽

Regression Algorithms ◽

Hadoop Platform ◽

Logistic Regression Algorithm ◽

Computing Speed

Abstract Regression algorithms are commonly used in machine learning. Based on encryption and privacy protection methods, the current key hot technology regression algorithm and the same encryption technology are studied. This paper proposes a PPLAR based algorithm. The correlation between data items is obtained by logistic regression formula. The algorithm is distributed and parallelized on Hadoop platform to improve the computing speed of the cluster while ensuring the average absolute error of the algorithm.

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Short-Term Traffic Flow Forecasting Model Based on GA-TCN

Journal of Advanced Transportation ◽

10.1155/2021/1338607 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Rongji Zhang ◽

Feng Sun ◽

Ziwen Song ◽

Xiaolin Wang ◽

Yingcui Du ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Convolutional Neural Network ◽

Traffic Flow ◽

Absolute Error ◽

Forecasting Model ◽

Average Absolute Error ◽

Short Term ◽

Traffic Flow Forecasting ◽

Fitness Value

Traffic flow forecasting is the key to an intelligent transportation system (ITS). Currently, the short-term traffic flow forecasting methods based on deep learning need to be further improved in terms of accuracy and computational efficiency. Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. Finally, the model was tested using the public dataset PEMS. The results showed that the average absolute error of the proposed GA-TCN decreased by 34.09%, 22.42%, and 26.33% compared with LSTM, GRU, and TCN in working days, while the average absolute error of the GA-TCN decreased by 24.42%, 2.33%, and 3.92% in weekend days, respectively. The results indicate that the model proposed in this paper has a better adaptability and higher prediction accuracy in short-term traffic flow forecasting compared with the existing models. The proposed model can provide important support for the formulation of a dynamic traffic control scheme.

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Design and Simulation of Portable Data Terminal for Agriculture Equipment

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.336-338.383 ◽

2013 ◽

Vol 336-338 ◽

pp. 383-387

Author(s):

Yan Xin Yin ◽

Yu Tan ◽

Shu Mao Wang

Keyword(s):

Communication Protocol ◽

Absolute Error ◽

Wireless Sensor ◽

Error Measurement ◽

Average Absolute Error ◽

Wireless Communication Protocol ◽

Data Acquisition Software ◽

Terminal Design ◽

Data Terminal ◽

Reliability And Availability

A portable data terminal design based on wireless sensor network was came up for agriculture equipment working status monitor, a JN5139 module was used as the hardware core of the terminal and Zigbee as the wireless communication protocol. Effect caused by time-delay and pocket loss was simulated and analyzed with Truetime1.5 under matlab, data acquisition software was developed according to the simulation that effectively reduced the influence. Error measurement test showed the analog average absolute error was 6.33mv and frequency average absolute error was 0.56Hz, that indicated the reliability and availability in agriculture application.

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Non-Concordance of Radiocarbon and Amino Acid Racemization Deduced Age Estimates on Human Bone

Radiocarbon ◽

10.1017/s003382220000597x ◽

1983 ◽

Vol 25 (2) ◽

pp. 647-654 ◽

Cited By ~ 26

Author(s):

R E Taylor

Keyword(s):

Amino Acid ◽

Aspartic Acid ◽

Human Bone ◽

Amino Acid Racemization ◽

Aspartic Acid Racemization ◽

Order Of Magnitude ◽

Direct Counting ◽

Organic Fractions ◽

Skeletal Samples ◽

Age Estimates

Radiocarbon determinations, employing both decay and direct counting, were obtained on various organic fractions of four human skeletal samples previously assigned ages ranging from 28,000 to 70,000 years on the basis of their D/L aspartic acid racemization values. In all four cases, the 14C values require an order of magnitude reduction in age.

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