text:
← ECC ANNOUNCES LOGO DESIGN COMPETITION
TWO EDGEMONT MASSAGE PARLORS ANNOUNCE 'GRAND OPENINGS' AS TOWN CALLS SPECIAL MEETING TO INTRODUCE ECC'S LAW BANNING THEM →
TOWN FAILS TO INTRODUCE ECC'S LAW OUTLAWING ILLICIT MASSAGE PARLORS
The Greenburgh Town Board tonight failed to introduce landmark legislation proposed by the Edgemont Community Council that would outlaw illicit massage parlors operating as houses of prostitution in unincorporated Greenburgh.
The ten-page proposal would have required the Town's current and future massage establishments to apply for a license from the Town Clerk and then obtain a special permit from the Town Board. Prior to issuing such licenses, the Town Clerk would have to get a favorable report from the Police Department, the Building Inspector and the Fire Marshall, and if any one of them objected, no license would be issued.
The ECC's legislation was drafted after Police Chief Chris McNerney told the ECC in October 2014 that Edgemont had become the illicit massage parlor capital of Westchester County — and ECC directors objected to a hastily conceived measure in November from Town Supervisor Paul Feiner that would have imposed a 120-day moratorium on the issuance of massage parlors permits when the Town itself does not issue any such permits.
A recent Internet search showed at least six massage parlors in Edgemont listed as offering "adult massages." All were located along a narrow swath of Central Avenue and, because of a lack of regulation by the Town, the number of such establishments appears to have doubled from what it was a few years ago.
Mr. Feiner, who had promised last week to introduce the ECC's measure, backed off yesterday when Town Clerk Judith Beville objected to being given any responsibility for issuing licenses to massage establishments covered by the law. The proposed authorization to issue such licenses was word for word identical to language in a 1977 Greenburgh law giving the town clerk the exact same responsibility for issuing cabaret licenses.
The Town's cabaret law was passed to combat what was perceived in the late 1970s as the proliferation of organized crime operating Greenburgh's cabarets and nightclubs.
In addition to Ms. Beville's objection, Town Attorney Tim Lewis made matters worse by unilaterally modifying ECC's proposal to require the Town Board instead of the Town Clerk to issue the licenses. He then provided that if anyone is denied a license by the Town Board they would have a right to file an appeal to the Town Board.
According to Bob Bernstein, who drafted the ECC's legislation, Mr. Lewis's changes made the proposal illegal and unenforceable. Mr. Bernstein also objected to the Town posting the proposed law, as modified by Mr. Lewis, on the Town website under Mr. Bernstein's name because such action conveyed the false and misleading impression to the public that Mr. Bernstein had approved the changes when in fact he was never told about them.
Mr. Bernstein tried repeatedly to get Mr. Feiner to introduce the measure as originally proposed by the ECC, but Mr. Feiner refused, saying he did not want Ms. Beville to have any "discretion" when it comes to issuing the licenses, even if she already had such "discretion" under the Town's cabaret law.
Mr. Bernstein then went further and offered to modify the original draft to eliminate any "discretion" on the part of the Town Clerk — but only if Mr. Feiner would agree to introduce the measure tonight, so that the public hearing could be scheduled. However, Ms. Beville continued to object and other town board members said they wanted more time to review the proposal before they would allow it to be introduced.
Town board members, including Mr. Feiner, had received the proposal on January 7, but as of tonight's public town board meeting , it appeared none of them had read it.
And Mr. Lewis, the town attorney, objected to introduction of the law tonight because he said he wanted time to explain to town board members in executive session why he thinks it would be illegal in any event for the Town to pass any laws outlawing massage parlors operating as brothels.
Other towns and villages in New York have passed such laws and none has ever been challenged. The ECC's law was modeled on one adopted in 1996 by the Town of Clarkstown in Rockland County and on one adopted in 2013 by the Village of Farmingdale on Long Island.
Consequently, no measure was introduced tonight and a last-minute draft resolution to authorize a public hearing on the proposal for February 11, 2014 was withdrawn.
Town officials said they might take up the measure again at its work session next Tuesday, but there is no assurance that they will do so.
Past efforts by the ECC to get the Town to adopt legislation that the ECC has drafted have gone nowhere, either because no one seconded the measure when it was introduced or if it was seconded, the town board simply chose not to take it up.
The proposed law would exempt medical offices, private membership gyms, personal training facilities and certain other businesses that offer massage therapy. As originally drafted, the intent was to give the Town the legal tools to shut down all massage parlors operating illicitly as houses of prostitution, while preserving the right and reputation of legitimate massage establishments like Tranquility Spa and others to operate without fear of being lumped together with those that operate illicitly.
Posted on January 15, 2015 by robertbrucebernstein Leave a comment
This entry was posted in Legislation, Police, Safety, Town Board, Zoning and Planning and tagged Local, Local Politics, Massage Parlors, Prostitution. Bookmark the permalink.


I have been eyeing this bottle so long that when I finally bought it, I was ecstatic. Then disappointed. I wanted chesnut spread, or maybe puree. Not jam. True, it tastes of chestnuts and all of its goodness. But it was too darned sweet for my liking. And heavy too. I should have expected that knowing what chestnuts tastes like.


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Awards » Equal Rights Award
Equal Rights Award
The Equal Rights Award recognizes the achievements and contributions of individuals and organizations whose selfless work helps create a world that is more inclusive, just, and fully equal for all.
Any member of the McLean County Democrats may submit nominations. Each nomination must include a letter addressed to the Equal Rights Awards Committee detailing the nominee's achievements and contributions that promote equality. Nominees need not be from McLean County but should have helped make an impact on its citizens. Nominations should be submitted electronically to [email protected]
The Equal Rights Award will be awarded to one or more individuals or to an organization for a political, educational, social, legal, artistic, or scholarly contribution that helps eliminate unlawful discrimination, advances equality, or promotes understanding between people from different groups.
Past Equal Rights Award Recipients
2019 Dr. Litessa Wallace
All content copyright © McLean County Democrats 2019
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Office: 422 N. Main St. in Downtown Bloomington
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On the morning of January 28, 2016, the North Haledon Fire Department was dispatched to the intersection of Belmont Avenue and Overlook Avenue for the report of a motor vehicle accident with entrapment. Once on scene, department members successfully extricated two occupants from one of the vehicles involved in the accident. A.L.S. and B.L.S. evaluated and transported the patients to a local hospital for further treatment.


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text:
Q: converter uma lista de string em uma lista de números inteiros x = ['443' , '552' , '342' , '368' , '9867' , '335' , '9412' , '7436' , '1692' , '85' , '990' , '332' , '8816' , '4567' , '279' , '119' , '2290' , '234' , '9863' , '345' , '230' , '5574' , '230' , '5432']

Como posso converter essa lista em uma nova lista , mas de números inteiros?
E após fazer isso , como percorro essa lista e defino qual o valor máximo , minimo , e também encontro média e mediana, sem utilizar as funções max(), min(), mean(), median() e count().

A: Primeiro você precisa converter todos as strings para inteiros;
Primeira Solução (convencional)
x = ['123', '456', '789']
valores = []
for val in x:
    valores.append(int(val))

Segunda Solução (list comprehension)
valores = [int(val) for val in x]

Média é a soma de todos os valores dividido pela quantidade de valores.
media = sum(valores) / len(valores)

Mediana é a metade da soma de todos os valores.
mediana = sum(valores) / 2

Máximo e minimo; utilize a função sort para ordenar os valores, assim o primeiro valor sempre sera o menor e o ultimo o maior.
valores.sort()
min = valores[0]
max = valores[-1]

Completo
x = ['123', '456', '789']
valores = [int(val) for val in x]

media = sum(valores) / len(valores)
print(media)

mediana = sum(valores) / 2
print(mediana)

valores.sort()
min = valores[0] # Primeiro valor da lista
print(min)

max = valores[-1] # Ultimo valor da lista
print(max)

Veja funcionando no Ideone

A: Vamos em partes, primeiro converter todos os valores de string para inteiro, vamos percorrer toda a lista para isso. Convertendo um por um todos os elementos da lista que forem string, exemplo:
x = ['11', '14', '152', '987']
novo_x = []
for i in x:
    novo_x.append(int(i))

Assim você criou uma nova lista com os mesmos valores porém agora todos são inteiros.
Sobre o maior e menor valor... Sem utilizar o max() e min() você precisará percorrer toda a lista salvando o maior e o menor valor e ir procurando para ver se encontra outro valor maior ou menor que o atual, exemplo:
x = [11, 14, 152, 987]
Max = 0
for i in range(len(x)):
    if(x[i] > Max):
        Max = x[i]

output: 987

A média você pode pegar a soma de todos os valores da lista utilizando sum() dividido pelo total de itens da lista utilizando len()... Ficando:
x = [11, 14, 152, 987]
media = sum(x)/len(x)

-- edit --
Sobre a mediana... o colega abaixo, Pedro me corrigiu:
A mediana é o valor que separa a metade maior e a metade menor de uma amostra, se essa amostra for par, o exemplo que dei abaixo se aplica, mas se for impar, segue o exemplo que o colega abaixo deu.
x = [11, 14, 152, 987]
mediana = sum(x)/2

A: Só uma coisa sobre a resposta aceita: a mediana, segundo a Wikipédia, é o valor que separa a metade maior e a metade menor de uma amostra, uma população ou uma distribuição de probabilidade.
Quer dizer, é o elemento do meio da lista se a lista tem um número ímpar de elementos, ou a média dos dois elementos do meio se a lista tem um número par de elementos.
Em código:
x = ['443' , '552' , '342' , '368' , '9867' , '335' , '9412' , '7436' , '1692' , '85' , '990' , '332' , '8816' , '4567' , '279' , '119' , '2290' , '234' , '9863' , '345' , '230' , '5574' , '230' , '5432']
valores = list(map(int, x))

if len(valores_ordenados) % 2 == 0:
    mediana = (valores_ordenados[len(valores_ordenados)//2] + valores_ordenados[len(valores_ordenados)//2 - 1])/2
else:
    mediana = valores_ordenados[len(valores_ordenados)//2]

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text:
Peter Nelson (26 April 1931 – 2 February 1977) was an Australian Olympic road and track cyclist.

He was South Australian state champion in 1950 for the 15, 25 and 50-mile events, and again in 1953 for the 50-mile events. He attended the 1952 Summer Olympics in Helsinki as part of the Australian road cycling team in the 190 km event and in the track team for the 4000 m team pursuit.

Biography
Peter Nelson was born on 26 April 1931 in Black Forest, South Australia, the son of wheelwright and automotive manufacturing engineer Frederick Nelson, and his wife Winifred (née Mostyn). He was educated at Christian Brothers College, Adelaide. Nelson won his first race as a 16-year-old in 1947, and thereafter committed himself to become a professional cyclist. Jim Nestor, a two-time Olympian, took interest in him and encouraged him to train as a road cyclist and to join a club. Nelson joined the Sturt Cycling Club, based at the outdoor velodrome at Edwardstown Recreational Ground.

His resolution and commitment paid off. In 1950 he was 15, 25 and 50 mile state champion and was included on the state senior team. He was successful in the 100 mile Mail Run and won the Adelaide-Milang one day classic.

In 1951, he won the road race in the Australian Olympic trials and was included in the Australian team to compete in Helsinki. En route to Helsinki, Nelson met his future wife, Marjorie Jackson. At the 1952 Summer Olympics in Helsinki, Nelson competed in the 190 km event and in the track team for the 4000m team pursuit. Despite extremely high expectations, the Australian team finished only seventeenth. Many blame this on the interruption to training the team experienced while being accommodated in London. It was extremely difficult to train in the congested, populous city.

Nelson returned to Adelaide and in 1953 he reclaimed his title as 50 mile state champion. Later that year on 7 November, he was married to Marjorie Jackson in Lithgow, New South Wales. Both returned to Adelaide the following year and retired from their sports. The couple settled down on an almond orchard outside Mclaren Vale where they raised three children.

Later in life Nelson was involved heavily with the Unley Rotary Club and was instrumental in establishing the Unley Memorial Swimming Pool, where he later taught swimming classes.

Death
Peter Nelson died of leukaemia on 2 February 1977 aged 45 years old. The Peter Nelson Leukaemia Foundation stands in his honour, established by his widow.

References

1931 births
1977 deaths
Australian male cyclists
Cyclists from Adelaide
Cyclists at the 1952 Summer Olympics
Olympic cyclists of Australia
Deaths from leukemia
Deaths from cancer in South Australia
Australian track cyclists
20th-century Australian people

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text:
Qiming Niu1 , Qingbin Tong2 , Junci Cao3 , Yihuang Zhang4 , Feng Liu5
1, 5School of Computer and Information Technology, Beijing Jiaotong University, Beijing, P. R. China
1Computing Center, Hebei University, BaoDing, P. R. China
2, 3, 4School of Electrical Engineering, Beijing Jiaotong University, Beijing, P. R. China
5Corresponding author
Journal of Vibroengineering, Vol. 21, Issue 1, 2019, p. 82-96. https://doi.org/10.21595/jve.2018.20120
Received 3 August 2018; received in revised form 18 September 2018; accepted 25 September 2018; published 15 February 2019
Copyright © 2019 Qiming Niu, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Niu Qiming, Tong Qingbin, Cao Junci, Zhang Yihuang, Liu Feng On-line prediction remaining useful life for ball bearings via grey NARX. Journal of Vibroengineering, Vol. 21, Issue 1, 2019, p. 82-96. https://doi.org/10.21595/jve.2018.20120
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The Huge vibration data are generated continuously by many sensors in daily high-speed rotating machinery operations. Accurate online prediction based on big vibration data streaming can reduce the risks related to failures and avoid service disruptions. This paper presents a hybrid nonlinear autoregressive network with exogenous inputs (NARX) model to forecast the remaining useful life of ball bearings through health index based on big vibration data streaming. This approach is validated by a real data from PRONOSTIA experimentation platform and industrial test rig compared with backpropagation neural network (BP), Elman and general regression neural network (GRNN) prediction model. Root mean square error, mean absolute error and correlation coefficient were used as performance indexes to evaluate the prediction accuracy of these models. The mean absolute error, the root mean square error and the correlation coefficient of hybrid NARX model evaluation index are 2.04, 2.85 and 0.98 respectively. It shows that the model presented in this paper has higher prediction accuracy. It can meet the needs of actual ball bearing remaining useful life prediction and also provide reference in other fields.
Keywords: health index, remaining useful life, ball bearings, prediction, data streaming, neural network.
With the development of data acquisition, storage and analysis technology, big data analysis and application have become possible. Streaming data analysis is a kind of big data analysis technology. Streaming data analysis of big data is becoming a hot research topic in recent years [1-6]. The vibration data of ball bearing sensor is a kind of big stream data. For example, in 2012, Italy Ministry of Communications Statistics about 600000 bearings in the country. According to the literature, it is known that the data volume obtained by vibration sensors will be very large [7, 8]. Many scholars have made a lot of useful explorations for predicting the remaining useful life of ball bearings with a large number of vibration data. Guo et al. used recurrent neural network to build health index in order to predict the remaining useful life of ball bearings [9]. Wang et al. built health index via multiple statistical indicators and Mahalanobis distance. Then, an enhanced Kalman filter and EM hybrid algorithm were used to predict the remaining useful life of bearing adaptively [10]. Zhao et al. proposed a hybrid method of time frequency representation and supervised dimensionality reduction in order to predict bearing remaining useful life [11]. Every observation has 25,600 dimensions' feature. Wu et al. used moving average to reduce the impact of noise in the bearings signal and apply BP to estimate the life percentile and failure times of bearings [12]. Gebraeel et al. used BP to predict bearing failure times [13]. Wang et al. used PCA and improved logistic regression model to predict remaining useful life of rolling bearing [14]. Wang et al. used time domain, frequency domain, time-frequency domain feature as the original features from the bearing vibration signals. Although these health indexes got good results for predicting bearing remaining useful life, there are still some drawbacks which are needed to be solved. For example, Zhao, Wang and Guo's methods of building health indices are complex and inefficient in the case of big streaming data. Another example is the moving average method is simple and can effectively eliminate the stochastic fluctuation in the prediction, but it makes the predicted value less sensitive to the actual change of the data and the timeliness is bad. It is a great challenge to design an effective remaining useful life scheme of ball bearing based on vibration big data, considering computing resources, calculation time and prediction accuracy rate. One of the challenges is to build an effective health indicator from the massively abundant vibration sensors stream data. The second challenge is the computational accuracy and time of the prediction model.
In order to address the aforementioned shortcomings, this paper presents a health index and grey NARX model in order to predict ball bearing remaining useful life. Based on the health index, a modeling study on the prediction of residual life of ball bearing is carried out. In terms of model research, BP, Elman, NARX and GRNN prediction model were established respectively, and the experimental results were analyzed.
The rest of this paper is organized as follows. In Section 2, a new health index method is put forward for preprocessing original vibration data in order to improve the data quality. Then, the generalized grey neural network prediction framework is used to predict the remaining useful life of ball bearings. The effectiveness of the proposed model is verified by comparative experiments using PRONOSTIA data and industrial bearing data in section three. Finally, conclusions are drawn in the last section.
2. Health index and proposed neural network architecture
This section introduces health index and describes NARX for predicting the remaining useful life of ball bearings.
Firstly, the health index of ball bearings was got by Eq. (1) using the original streaming vibration data:
H i = m a x - m i n 2 * v a r i a n c e ,
where max represents the maximum value in the data segment, min refers to the minimum value in the data segment, and variance is the degree of dispersion of all data in the data segment.
To illustrate the significance of health index, an example is given. Assume two sets A= {0; 8; 12; 20} and B= {8; 9; 11; 12}, they have the same average, 10. The following data can be obtained by calculation. The variance of A is 69.33. The variance of B is 3.33. Hi of A is 693.33. Hi of B is 6.67. From the result of calculations, it can be learned that the health index reflects the overall size and range of data set. Therefore, the health index can reflect the range and overall size of vibration data of the sample files during the bearing working process.
Fig. 1 shows the challenges in predicting the remaining useful life of ball bearings. The curve in Fig. 1(a) shows the health index of life cycle data of PRONOSTIA ball bearing 1-1. The curve in Fig. 1(b) displays the health index of life cycle data of PRONOSTIA ball bearing 1-2. Two different curves were obtained under the same experimental environment and the same type ball bearings. The life span of bearing 1-1 is 2803 cycles, but it is 870 cycles for bearing1-2. The health index of bearing 1-1 is 1242, while the health index of bearing 1-2 is 518.6. On the one hand, it is difficult to find any regular pattern in life span and a threshold. On the other hand, Fig. 1 demonstrates that health index of ball bearing is a non-monotone oscillation sequence.
Secondly, a hybrid method is introduced to predict the remaining useful life of ball bearings based on health index from vibration signals. Neural network with nonlinear mapping characteristics is introduced to help predict data and reduce prediction error. This paper proposed grey NARX model in order to forecast ball bearings remaining useful life.
The first step of the proposed model is to preprocess the health index according to accumulated generating operation (AGO) method in grey theory, and get the input data which NARX needs. The second step is to get cumulative prediction based on input data and NARX algorithm. The third step is to get the prediction data of health index through inverse accumulated generating operation (IAGO) method.
The original health index was set to x(0)(1),x(0)(2),⋯,x(0)(n). Recorded as x(0)=x(0)(1),x(0)(2),⋯,x(0)(n). A new series x(1) was got by using cumulative sum according to the series x(0):
x ( 1 ) = x 1 1 , x 1 2 , ⋯ , x 1 n ,
where x(1)(k)=∑i=1kx(0)(i) and k=1,2,⋯,n.
Fig. 1. Health index from the same type bearing and the same test conditions: a) bearing 1-1, b) bearing 1-2
Applying dynamic neural networks for modeling and predicting data series is the major application of these networks. NARX is a type of recurrent dynamic neural network. NARX is an effective approach to solving nonlinear sequence problems [15].
NARX neural network model can be written as:
y t = f y t - 1 , y t - 2 , ⋯ , y t - i , x t - 1 , x t - 2 , ⋯ , x t - j ,
where x(⋅) and y(⋅) are the input and target data, respectively. The maximum lags of y and x are i and j, respectively. f(⋅) is a nonlinear mapping function. y(t) is the output of NARX neural network.
There are two different types of NARX neural networks, open-loop NARX and close-loop NARX. They can be described by Eq. (4) and (5), respectively:
y ^ t = f y t - 1 , y t - 2 , ⋯ , y t - i , x t - 1 , x t - 2 , ⋯ , x t - j ,
y ^ t = f y ^ t - 1 , y ^ t - 2 , ⋯ , y ^ t - i , x t - 1 , x t - 2 , ⋯ , x t - j ,
where y^(t) is the output at time t, y(⋅) is the true past values set of the sequence. x(⋅) is the inputs set of the network. The number of output delays is i and j is the number of input lags. y^(⋅) is the past predicting values set of the sequence. The difference between open loop architecture and closed loop architecture is that open loop architecture uses the true past values y(⋅) and closed loop architecture uses predictive past values y^(⋅).
Fig. 2. Architecture of NARX: a) open loop architecture, b) close loop architecture
In general, the training time of close loop is much longer than that of open loop and NARX neural network obtained by close loop training is often worse than NARX neural network obtained from open loop training. Therefore, NARX model is trained in open loop architecture and is used in closed loop architecture for multi-step prediction in this paper.
The predicted value of the original sequence is solved by the cumulative predicted value sequence by Eq. (6). This process is called IAGO:
x ^ 0 k = x 1 k - x 1 k - 1 .
To verify the performance of the proposed model, this paper used grey neural network architecture [16-19]. Fig. 3 describes the generalized grey neural network architecture. {x(0)(j),x(0)(j+1),⋯,x(0)(j+m-1)} is a fragment of the streaming health data. AGO is used to preprocess the original data into approximate monotonic sequence data [19]. The purpose of this transformation is to weaken the random fluctuation in series data. A neural network is represented in the dotted line frame. It can be a static neural network, or a dynamic neural network. For example, BP and GRNN are common static neural networks. For instance, Elman and NARX are common dynamic neural networks. Finally, the predictive value of the original vibration time series can be got by IAGO.
Fig. 3. Generalized grey neural network prediction framework
3. Experimental study
The remaining useful life of ball bearings is closely related to the load, temperature and other factors in working environment. In the running process, the real-time monitoring signal of the ball bearing's vibration and temperature can reflect the current running state. The use of vibration signals to predict remaining useful life of ball bearings is the most widely used and most effective method at present. Considering the non-linear and nonstationary characteristics of the vibration signals of ball bearings, this section uses health index of ball bearings. BP, GRNN and Elman prediction model combined with grey data preprocessing method are applied to evaluating the proposed grey NARX model using the PRONOSTIA accelerated life test data of ball bearings and industrial ball bearing test data.
3.1. Experimental setup
This subsection describes the preparation and related knowledge for vibration stream remaining useful life data prediction of ball bearings.
3.1.1. Datasets description
Two experimental data sets of PRONOSTIA and industrial dataset are used to do experiments. These data are obtained from the test rig sensors in Fig. 4.
Fig. 4. Ball bearing test rig: a) PRONOSTIA, b) high speed train traction motor bearing test rig
Firstly, PRONOSTIA dataset has been used in many remaining useful life prognostic studies [7, 9, 11, 15]. There are 17 run-to-failure datasets in order to study the prediction of bearing remaining useful life. Vibration and temperature signals have been saved in ASCII files respectively. Since vibration data are studied in this paper, only vibration data are described as follows. The vibration signal consists of a horizontal vibration signal and a vertical vibration signal. The sampling frequency is 25.6 kHz. 2560 samples are recorded every 10 seconds. Each record is stored in an ASCII file. For each ASCII file, the data obtained by the vibration sensor includes the horizontal direction of vibration data and the vertical direction of vibration data. In addition, the corresponding vibration time is also stored in the ASCII file.
Secondly, the industrial ball bearing test of high-speed train is conducted on NTN bearing test rig. The bearing type of high-speed train is 6311 deep groove ball bearing. Acceleration sensor was used to collect vibration signals of ball bearings in order to monitor bearing health condition. The minimum recording period of the test bed is 10 seconds. The cumulative operation time of two experimental bearings is approximately 3000 and 700 hours. All samples are stored in two database files.
3.1.2. Data preprocessing
PRONOSTIA vertical vibration data is extracted from each sample ASCII file. For vertical vibration data of each sample file, Eq. (1) is used to calculate the time series health index. And then the health index vector X is then obtained. Fig. 5 describes the whole life cycle health index of the bearing 1-1. As can be seen from Fig. 5, the health index after AGO is smoother and larger than the original data and moving average health index.
Fig. 6 describes the part health index of the bearing1-1 from 1250 to 1350 time unit. From Fig. 5 and 6, it can be seen that the health index processed by MA method is not good at eliminating random fluctuations compared with AGO method. Compared with MA method, AGO method has a large difference value in health index at the same time interval. Since AGO method is more sensitive to data change and has good timeliness, the health index data processed by AGO is used to predict the remaining life of ball bearings.
Fig. 5. Health index of the bearing 1-1: a) health index, b) moving average (q= 10) health index, c) accumulated generating operation health index
Fig. 6. Part health index of the bearing 1-1: a) health index, b) moving average (q= 10) health index, c) accumulated generating operation health index
3.1.3. Time window processing
Before using the model to predict the nonlinear time series, it is necessary to reconstitute the data format. Suppose the time series is x1,x2,⋯,xn. After reconstructing the data with window size m, we get the multi-dimensional feature matrix X and the prediction vector Y. X and Y can be expressed in Eq. (7) and Eq. (8) respectively:
X = X 1 X 2 ⋮ X i = x 1 x 2 ⋯ x m x 2 x 3 ⋯ x m + 1 ⋮ ⋮ ⋮ x i x i + 1 ⋯ x i + m - 1 .
For the j feature vector Xj, the expression form is (xj,xj+1,⋯,xj+m-1):
Y = y 1 y 2 ⋮ y i = x m + 1 x m + 2 ⋮ x m + i .
The expression form of yj is (xm+j).
Fig. 7 describes the reconstruction process of the feature vector and the prediction vector when the window width is m= 4. The training set for model training is {Xi,yi}i=1n-4.
Fig. 7. Reconstruction of health index time series data
3.1.4. Performance metrics
The performance metrics are mean absolute error (MAE), root mean square error (RMSE) and Pearson's correlation coefficient (R).
MAE is a measure of difference between two variables. It is given by the following equation:
M A E = 1 n ∑ t = 1 n y ^ t - y t ,
where n refers to the sample size, y^t is the predictive value indexed with t, yt is the observed value indexed with t. MAE output is non-negative value. The best value is 0.
RMSE is a measure of the differences between model predictive values and the actually observed values. RMSE is given by:
R M S E = ∑ t = 1 n ( y ^ t - y t ) 2 n ,
where n is the sample size, y^(t) is the predictive value indexed with t, y(t) is the observed value indexed with t. A smaller RMSE value implies a smaller error variation between predicted values and observed values. RMSE reflects the degree which the predicted values deviate from the observed values.
R is a measure of the linear correlation between two variables. The equation for R is:
R = ∑ t = 1 n x t - x - ( y t - y - ) ∑ t = 1 n x t - x - 2 ∑ t = 1 n y t - y - 2 ,
where n refers to the sample size, xt and yt are the single sample indexed with t, x- is the mean of data set x1,x2⋯,xn, y- is the mean of data set y1,⋯,yn. The R value range is between –1 and 1, where –1 is total negative linear correlation, 0 is no linear correlation, and 1 is total positive linear correlation.
3.1.5. Compared approaches
Four methods are used in this paper, which are BP neural network, GRNN, Elman and NARX. BP and GRNN are static neural network. Elman and NARX neural network are dynamic neural networks. Static neural networks are characterized by no feedback, no memory, and the output depends only on the current input. The output of dynamic neural networks with feedback depends not only on current and previous inputs, but also on previous outputs.
BP was put forward by the scientific research group headed by Rumelhart and McCelland in 1986 [20]. BP neural network is a multilayer feed-forward network trained by the error back-propagation algorithm. It is one of the most widely used neural network models. The basic idea of BP is that the learning process consists of two processes, namely, the signals of feedforward and the back-propagation of errors. When forward propagation, the input samples are sent from the input layer, and processed by the hidden layers, and then transmitted to the output layer. If the actual output of the output layer is not consistent with the expected output, it will go back to the error back-propagation stage. In back-propagation, the output is retransmitted to the input layer by the hidden layer in some form, and the error is apportioned to all the units of each layer, thus the error signal of each layer is obtained. The error signal is the basis of correcting the weight of each unit. BP neural network is composed of input layer, hidden layer and output layer. Considering the regression prediction problem here, the transfer function used in BP network is tansig function.
GRNN was proposed by Specht in 1991 [21]. GRNN is a one-pass learning algorithm. Its network structure is highly parallel. GRNN can be used for nonlinear prediction. GRNN is a solution for online dynamical systems. It is a kind of radial basis neural network [22]. The network structure of GRNN is composed of four layers, namely, input layer, pattern layer, summation layer and output layer. Its network input is X=[x1,x2,⋯,xn]T, and the output is Y=[y1,y2,⋯,yk]T. GRNN model can be expressed as Eq. (12):
Y x = ∑ k = 1 N y k K x , x k ∑ k = 1 N K x , x k ,
where Y(x) is the prediction value of input x. yk is the activation weight for the pattern layer neuron at k. K(x,xk) is the Gaussian kernel as formulated K(x,xk)=e-(x-xk)T(x-xk)/2σ2.
In 1990, Elman proposed the Elman network, which is a kind of recurrent neural network [23]. Elman neural network is used in many fields, such as cognitive science, economics and others. Elman network structure consist of four layers: input layer, hidden layer, context layer and output layer. The context layer can provide a short memory. Elman networks are used for predicting time series. Elman neural network model can be expressed as Eq. (13):
y k = g ω 3 f ω 1 x k - 1 + ω 2 u k - 1 ,
where y(k) is output vector. g(⋅) is the transfer function of output neurons. f(⋅) is the transfer function of middle layer neurons. x(k)=f(⋅) is hidden layer vector. x(k-1) is a feedback state vector. u is input vector. ω3 is the connection weight of the middle layer to the output layer. ω2 is the connection weight of the input layer to the middle layer. ω1 is the connection weight of the context layer to the middle layer.
NARX is a dynamic neural network. NARX can learn to predict time series given past values of the same time series, the feedback input, and the exogenous time series. It can be applied for predicting nonlinear sequence data. It can be used as a recurrent dynamic neural network to predict the next value of the input data.
3.2. Experiment and analysis
This subsection describes experimental process and analyses experimental results.
3.2.1. Experimental process
Our goal is to provide multi-step ahead prediction of ball bearings health index. One step ahead is to predict the next data using historical data in a fixed window. For multi-step ahead predicting, the first step is predicted by applying one step ahead predicting. Subsequently, the predicted value is included as the latest component of input series to predict the next step using the one step ahead training method. This procedure is repeated for the continuous predicting. The following content briefly describes the basic steps in experimental research. Firstly, raw data set is from the vertical vibration streaming data of PRONOSTIA ball bearings and industrial ball bearing test data. Secondly, health index feature is extracted by Eq. (1). Thirdly, the data format required by the grey neural network framework is reconstructed by using the time window processing technique. Fourthly, the learning set 1 includes AGO health indexes from Bearing 1-1, Bearing 1-2, Bearing 1-3, Bearing 1-4, Bearing 2-1, Bearing 2-2, Bearing 2-3 and Bearing 2-4. The learning set 2 includes AGO health indexes from bearing 6311-1. The test set 1 consists of 8 AGO health indexes: Bearing 1-5, Bearing 1-6, Bearing 1-7, Bearing 2-5, Bearing 2-6 and Bearing 2-7. The test set 2 is AGO health index of bearing 6311-2. The learning set is divided into training set, validation set and test set, and the corresponding allocation ratio is 75 %, 15 %, 15 %. Fifthly, BP, GRNN, Elman and NARX learn on the learning set respectively in order to predict the health index of ball bearings remaining useful life. Finally, the training model is used to predict the test set. MAE, RMSE, R and test running time are used to evaluate the performance of the four neural networks on the test set.
3.2.2. Performance comparison
The experimental results are divided into two parts. The first part is the life prediction result of ball bearing life cycle. The second part is the prediction result of the later period life of ball bearings. A comparison of the predicted and real values of the four algorithms in the two bearing test sets is shown in Fig. 8 and Fig. 9. The experimental performance results of two test sets are summarized in Table 1, Table 2, Table 3 and Table 4.
From Fig. 8, it can be seen that BP and GRNN methods are getting smaller and smaller with the increasing number of data obtained. This is not good for predicting the remaining useful life of ball bearings with relatively short life. With the increasing number of data acquired by sensors, Elman and NARX method prediction error is relatively stable. This is beneficial for predicting the residual service life of the ball bearings. From the results in Fig. 9, it can be seen that the prediction error of four methods on test set 2 is BP, GRNN, Elman and NARX in descending order.
Table 1 shows the performance of four different models. Results are expressed as mean ± SD. After calculation, the value of MAE, RMSE and R indexes of BP are 49.71±15.84, 53.77±12.87 and 1.00±0.01 respectively. The test running time required for BP on the test set 1 is 0.01±0.00 seconds. The value of MAE, RMSE and R indexes of GRNN is 40.51±10.55,44.89±8.43 and 0.99±0.01 respectively. The test running time required for GRNN on the test set is 0.27±0.17 seconds. The value of MAE, RMSE and R indexes of Elman is 6.84±1.38, 7.54±1.46 and 1.00±0.00 respectively. The average time required for Elman on the test set is 0.01±0.00 seconds. And the value of MAE, RMSE and R indexes of NARX is 1.60±1.41 2.94±3.04 and 0.99±0.02 respectively. The test time required for NARX on the test set is 0.03±0.01 seconds. By comparison, the performance index of NARX were significantly higher than that of BP, Elman and GRNN. GRNN test runs for the longest time in four models. The other three models have a similar test run time. As it can be observed, compared with other methods, NARX method is more suitable for continuous prediction than the other methods using PRONOSTIA data.
Fig. 8. Comparison: a) bearing 1-5, b) bearing1-6, c) bearing 1-7, d) bearing 2-5, e) bearing 2-6, f) bearing 2-7
Fig. 9. Comparison of 4 algorithms on bearing 6311 life cycles
Considering the late prediction of the remaining useful life of ball bearings is more important, the last one hundred health indexes are selected for comparison. It can be calculated from Table 2 that the values of MAE, RMSE and R of BP are 59.14±36.36,59.29±36.28 and 1.00±0.01 respectively. The test time required for BP on the test set 1 is 0.01±0.00seconds. The values of MAE, RMSE and R of GRNN are 22.92±15.58, 23.43±16.27 and 1.00±0.01 respectively. The test time required for GRNN on the test set 1 is 0.03±0.02 seconds. The values of MAE, RMSE and R of Elman are 9.78±3.27, 9.99±3.18 and 1.00±0.01 respectively. The test time required for Elman on the test set 1 is 0.01±0.00 seconds. And the values of MAE, RMSE and R of NARX are 2.04±2.16,2.85±3.53 and 0.98±0.03 respectively. The test time required for NARX on the test set 1 is 0.01±0.00 seconds. After analysis, BP is still the worst of the four hybrid models. However, the average performance of the last part of the GRNN model is lower than that of the total data. The average value of MAE and RMSE index of Elman method is lower than that of GRNN, but higher than that of NARX. The average values of MAE, RMSE and R of NARX are the lowest among the four methods. The average performance of the last part of the NARX method is quite similar to the average performance of the whole life cycle data. According to the calculation results of Tables 1 and 2, grey NARX predict model has a higher prediction accuracy for predicting the remaining useful life of PRONOSTIA ball bearings.
From Table 3, it can be seen that BP has the largest MAE and RMSE on the test set 2, while the MAE and RMSE of NARX are the smallest. The four algorithms are almost completely positively correlated. The test run time of GRNN is the most, while the test run time of the other models is approximately equal. As can be seen from Table 4, the results of prediction performance of bearing 6311 last 100 health indexes and prediction performance of bearing 6311 all health indexes were similar. Because of the test rig conditions, time and financial constraints, only two bearings were used for the experiment. If the number of bearings increases, the prediction error of industrial bearings will be further reduced in real scenarios. As can be seen from table 1-4, grey NARX is the most suitable model for continuous prediction the remaining useful life of ball bearings than other three models.
Table 1. Prediction performance of PRONOSTIA all health indexes
Bearing No
RMSE
Test time (s)
Bearing 1_5
49.71±15.84
1.00±0.01
GRNN
44.89±8.43
NARX
After calculating the health index of the bearing during the entire service process, the relationship between the health index of the bearing vibration data and the running mileage in the bearing life cycle can be obtained, as shown in Fig. 10(a). After getting the relationship between the mileage and the health index, the remainder life of rolling bearings can be obtained by using the design life subtracted from the mileage corresponding to the health index, as shown in Fig. 10(b).
Table 2. Prediction performance of PRONOSTIA last 100 health indexes
Table 3. Prediction performance of bearing 6311-2 all health indexes
Test run time (s)
Table 4. Prediction performance of bearing 6311-2 last 100 health indexes
Fig. 10. Relationship between mileage, health index and remaining useful life (RUL)
Remaining useful life prediction accuracy highly relies on the performance of health index and predict model. In this paper, BP, GRNN, Elman and NARX neural network model were used to forecast the remaining useful life from the vibration acceleration data of the ball bearing. The results show that the precision of grey NARX neural network model is higher than BP, GRNN and Elman. The predict value of grey NARX neural network model is closer to the actual data, especially when the noise is relatively large. NARX neural network model has shorter prediction time compared with GRNN method. On the other hand, the research on the feature of ball bearing vibration acceleration, not only provides a feasible and effective method for establishing bearing remaining useful life index, also can establish remaining useful life prediction model of ball bearings provide some references. At the same time, the selection of prediction feature is not limited to the vibration acceleration. It can also be used for other features such as temperature. Grey NARX model and health index can be used to realize the remaining useful life prediction of the related components in future.
This work was partially supported by Comprehensive standardization and new mode application project of Intelligent Manufacturing in Ministry of industry and information technology of People's Republic of China No. 2017ZNZZ01-06, the Science and Technology Research and Development Major/Key Program of China Railway Corporation No. 2016J007-B, National Natural Science Foundation of China No.51577007, and Beijing Natural Science Foundation (3162023). Finally, the authors are grateful to the anonymous reviewers for their helpful comments and constructive suggestions.
Nair L. R., Shetty S. D., Shetty S. D. Applying spark based machine learning model on streaming big data for health status prediction. Computers and Electrical Engineering, Vol. 65, 2017, p. 393-399. [Publisher]
Han D., Li S., Wei F., et al. Two birds with one stone: classifying positive and unlabeled examples on uncertain data streams. Neurocomputing, Vol. 277, 2017, p. 149-160. [Publisher]
Fernández Rodríguez J.-Y., Álvarez García J.-A., Fisteus J. A., et al. Benchmarking real-time vehicle data streaming models for a smart city. Information Systems, Vol. 72, 2017, p. 62-76. [Publisher]
Morales G. D. F., Bifet A. SAMOA: scalable advanced massive online analysis. Journal of Machine Learning Research, Vol. 16, 2015, p. 149-153. [Search CrossRef]
Parker B. S., Khan L., Bifet A. Incremental ensemble classifier addressing non-stationary fast data streams. IEEE International Conference on Data Mining Workshops, 2014, p. 716-723. [Search CrossRef]
Zeng X. Q., Li G. Z. Incremental partial least squares analysis of big streaming data. Pattern Recognition, Vol. 47, Issue 11, 2014, p. 3726-3735. [Publisher]
Fumeo E., Oneto L., Anguita D. Condition based maintenance in railway transportation systems based on big data streaming analysis. Procedia Computer Science, Vol. 53, Issue 1, 2015, p. 437-446. [Publisher]
Schoen R. R., Habetler T. G., Kamran F., et al. Motor bearing damage detection using stator current monitoring. IEEE Transactions on Industry Applications, Vol. 31, Issue 6, 1994, p. 1274-1279. [Publisher]
Guo L., Li N., Jia F., et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing, Vol. 240, 2017, p. 98-109. [Publisher]
Wang Y., Peng Y., Zi Y., et al. A two-stage data-driven-based prognostic approach for bearing degradation problem. IEEE Transactions on Industrial Informatics, Vol. 12, Issue 3, 2016, p. 924-932. [Publisher]
Zhao M., Tang B., Tan Q. Bearing remaining useful life estimation based on time-frequency representation and supervised dimensionality reduction. Measurement, Vol. 86, 2016, p. 41-55. [Publisher]
Wu S., Gebraeel N., Lawley M. A., et al. System for condition-based optimal predictive maintenance policy. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 37, 2007, p. 226-236. [Publisher]
Gebraeel N., Lawley M., Liu R., et al. Residual life predictions from vibration-based degradation signals: a neural network approach. IEEE Transactions on Industrial Electronics, Vol. 51, Issue 3, 2004, p. 694-700. [Publisher]
Wang F., Wang B., Dun B., et al. Remaining life prediction of rolling bearing based on PCA and improved logistic regression model. Journal of Vibroengineering, Vol. 18, Issue 8, 2016, p. 5192-5203. [Publisher]
Rai A., Upadhyay S. H. The use of MD-CUMSUM and NARX neural network for anticipating the remaining useful life of bearings. Measurement, Vol. 111, 2017, p. 397-410. [Publisher]
Zeng X. Y., Shu L., Huang G. M., et al. Triangular fuzzy series forecasting based on grey model and neural network. Applied Mathematical Modelling, Vol. 40, Issue 3, 2016, p. 1717-1727. [Publisher]
Lei Y., Guo M., Hu D. D., et al. Short-term prediction of UT1-UTC by combination of the grey model and neural networks. Advances in Space Research, Vol. 59, Issue 2, 2017, p. 524-531. [Publisher]
Liu X., Moreno B., Garcia A. S. A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors. Energy, Vol. 115, 2016, p. 1042-1054. [Search CrossRef]
Abdulshahed A. M., Longstaff A. P., Fletcher S., et al. Thermal error modelling of a gantry-type 5-axis machine tool using a grey neural network model. Journal of Manufacturing Systems, Vol. 41, 2016, p. 130-142. [Publisher]
Rumelhart D. E., Hinton G. E., Williams R. J. Learning representations by back-propagating errors. Nature, Vol. 323, Issue 6088, 1986, p. 533-536. [Publisher]
Specht D. F. A general regression neural network. IEEE Transactions on Neural Networks, Vol. 2, Issue 6, 1991, p. 568-576. [Publisher]
Tse P. W., Atherton D. P. Prediction of machine deterioration using vibration based fault trends and recurrent neural networks. Journal of Vibration and Acoustics, Vol. 121, Issue 3, 1999, p. 355-362. [Publisher]
Elman J. L. Finding structure in time. Cognitive Science, Vol. 14, Issue 2, 1990, p. 179-211. [Publisher]
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Guido Masanetz (17. května 1914 Frýdek – 5. listopadu 2015 Berlín) byl německý dirigent, skladatel a kapelník, jedna z nejvýznamnějších a nejúspěšnějších hudebních osobností bývalé NDR.

Život
Guido Masanetz pocházel z hudebně založené rodiny a od svých osmi let byl vyučován ve hře na klavír a hudební teorii. Brzy si ujasnil, že se chce stát koncertním pianistou. Navštěvoval obecnou školu ve Frýdku, poté studoval na gymnáziu v Moravské Ostravě a dále na Městské hudební škole v Šumperku. Vojenskou službu absolvoval u 35. pěšího pluku československé armády v Plzni; během ní pokračoval ve studiu hudební teorie a skladby u Josefa Bartovského.

Těžištěm jeho působení se ale brzy stalo kapelnictví a skladba. V letech 1938–39 působil v Brně jako korepetitor baletu tehdejšího německého Městského divadla (v budově dnešního Mahenova divadla). Zde měla také v roce 1941 premiéru jeho opereta Barbara (opereta). Od roku 1940 pracoval v Protektorátu Čechy a Morava jako koncertní pianista brněnského studia Sender Brünn a jako cenzor. V roce 1941 byl povolán ke 172. pěšímu pluku Wehrmachtu do Budyšína, kde působil převážně jako vojenský hudebník.

Po druhé světové válce a propuštění z válečného zajetí působil v letech 1945–49 jako kapelník Městského divadla v Žitavě a současně vyučoval hudební teorii na Johanneu. Od roku 1951 do roku 1955 byl v Berlíně vrchním hudebním vedoucím Státního souboru lidového umění NDR. V letech 1962–66 byl hudebním dramaturgem nakladatelství , poté se stal volně působícím klavíristou, skladatelem a dirigentem. Po tři desetiletí byl dirigentem berlínského divadla Metropol-Theater, kde také provedl premiéry svých vlastních děl.

Masanetz složil množství písní, šlágrů, operet a muzikálů; psal také hudbu k filmům, zejména k pohádkám. Je autorem muzikálu , který sklízel úspěch řadu let. Z jeho pera pochází mimo jiné známé písně Bunte Lampions (1946 pro Rudi Schurickeho) a Seemann hast du mich vergessen? z r. 1962.

Roku 2005 jej Manfred Stolpe na Polabském festivalu ve Wittenbergu vyznamenal čestným titulem "Musikdirektor".

Jeho uměleckým krédem vždy bylo "… posílit a vytěžit různorodost harmonické melodie".

Zemřel 5. listopadu 2015 ve věku 101 let v Berlíně.

Dílo

Opery
 Der Wundervogel, 1955
 Sprengstoff für Santa Ines, 1973

Operety před r. 1949
 Barbara, libreto Erich Elstner, premiéra 7. června 1941, Městské divadlo Brno (napsáno 1939/1941)
 Die Reise nach Budapest, premiéra 28. listopadu 1942, Budyšín
 Die Mandelblüte, premiéra 1948, Budyšín

Tvorba pro Heiteres Musiktheater der DDR
 Eine unmögliche Frau, opereta, libreto Peter Bejach, premiéra 25. září 1954 Rostock
 Wer braucht Geld?, opereta, libreto Otto Schneidereit (první verze In Frisco ist der Teufel los, připravováno pod názvem Hotel Nevada) premiéra 17. listopadu 1956, Metropol-Theater, Berlín
 In Frisco ist der Teufel los, opereta, libreto Otto Schneidereit, premiéra 23. března 1962, Metropol-Theater, Berlín
 Eva und ihr Moralist, opereta, libreto Helmut Baierl, premiéra 9. ledna 1958, Nordhausen
 Der Instrukteur soll heiraten, opereta, libreto Jan Hall, premiéra 7. října 1959, Musikalische Komödie, Lipsko
 Mein schöner Benjamino, muzikál, libreto Jo Schulz, premiéra 11. května 1963, Metropol-Theater, Berlín
 Vasantasena, Nach einem alten indischen Motiv, muzikál, libreto Peter Ensikat, premiéra 8. září 1978, Metropol-Theater Berlín

Vyznamenání
 1969: čestné občanství obce Prerow
 1974: bronzový Vaterländischer Verdienstorden
 1977: umělecká cena "Berliner Zeitung"
 1979: umělecká cena NDR
 1979: národní cena NDR III. třídy za umění a literaturu, za zhudebnění indického díla "Vasantasena"
 1984: Cena Theodora Körnera
 1988: čestné členství v GEMA
 1989: stříbrný Vaterländischer Verdienstorden
 2005: čestný titul Musikdirektor (MD) na VI. Polabském festivalu Wittenberge
 2007: Sudetoněmecká kulturní cena za hudbu
 2008: řádný čestný člen Europäische Kulturwerkstatt e. V. (EKW) Berlin-Wien

Reference

Externí odkazy

Literatura
 Roland Dippel: Heiteres Musiktheater - Operette in der Deutschen Demokratischen Republik (1949-1989) in: Die Welt der Operette | Frivol, erotisch und modern (Herausgeber: Marie-Theres Arnbom, Kevin Clarke, Thomas Trabitsch); Wien 2011 (Christian Brandstätter Verlag) - S. 213 až 239
 Roland H. Dippel: Das Ende einer musikalischen Epoche. Komponist Guido Masanetz 101-jährig gestorben in: Leipziger Volkszeitung 10. listopad 2015, č. 261 - S. 10
 Roland H. Dippel: Zwischen Experiment und Erfolg. Herbert Kawan, Guido Masanetz und Conny Odd (Serie "Operette und Musical der DDR", Folge 7) in: Leipziger Volkszeitung, 10. srpen 2016, č. 186, S. 10
 Otto Schneidereit: Operette A - Z. Ein Streifzug durch die Welt der Operette und des Musicals; Berlin 1986 (Henschelverlag Kunst und Gesellschaft)

Čeští klavíristé
Němečtí klavíristé
Čeští emigranti a exulanti
Sudetští Němci
Němečtí hudební skladatelé
Skladatelé filmové hudby
Němečtí dirigenti
Narození v roce 1914
Narození ve Frýdku
Úmrtí v roce 2015
Narození 17. května
Úmrtí 5. listopadu
Úmrtí v Berlíně
Čeští století lidé
Muži


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Podcasting in Ethiopia
Let's talk about podcasts. What is a podcast, first of all? The term "podcast" is a portmanteau. What is a portmanteau?
Let's talk about podcasts. What is a podcast, first of all? From Wikipedia, a podcast is an episodic series of spoken word digital audio files that a user can download or stream to a personal device for easy listening. The term "podcast" is a portmanteau, a combination of the word iPod and broadcast. The term "podcasting" was first suggested by The Guardian columnist and BBC journalist Ben Hammersley, who invented it in early February 2004, while writing an article for The Guardian newspaper.
Podcasts are similar to radio programs in form, but what makes them stand out is that they exist as an audio files that can be played at a listener's convenience, anytime or anywhere. In an interview with The H3 Podcasts, Jordan Peterson, a Canadian professor of psychology and author stated, "What's cool about podcasts and what's game-changing about them is, you can't read while you're driving, while you're doing the dishes or exercising, and so all of a sudden, with the podcast world people have all this time to gain knowledge that was never possible with television or even with radio for that matter."
Radio stations in Ethiopia stream different types of programs on their live radio. But most of the content will be unnoticed after their live streams. In other countries, most of the famous radio and tv channels have readily available podcasts. I have seen some Ethiopian stations providing their content through their website or YouTube, and it's good. But if the contents from the several stations were in the same place, it would be much better.
Several podcasting services offer access to a wide range of podcasts, including free apps and few subscription platforms. These include Apple Podcasts, Google Podcasts, Spotify, and Audible, to name a few.
In Ethiopia, there is a podcasting app or service to be launched very soon. The name of the app is Teraki. From their social media account, they have posted that they are launching this week.
There are some Ethiopian podcasts available in different podcasting services. You can discover Amharic podcasts on the Tech scene, such as Zemach FM. Zemach FM is a podcast that goes through technological achievements in the world and Ethiopia. There is also a podcast called Cyber Ethiopia that focuses on technology.
Another podcast that I find fascinating is called Tarik. Tarik is an Ethiopian history podcast that strives to educate Ethiopians and non-Ethiopians alike in the rich, diverse, and ancient history of Ethiopia. You can find their five episodes that came out on Google Podcast and other podcasting services.
Podcasts like Cyber Ethiopia, Zemach FM, and Tarik are great for the people providing reasons like not having time as an excuse not to read. With apps like Teraki coming up, people will also get a chance to share their knowledge in different fields by creating a podcast. There will also be opportunities to monetize their podcasts through advertisements and sponsors. I hope apps like Teraki provide an organized and centralized place for programs and shows that aired on Ethiopian radios or other channels.
I like to finish this article with the last words of Jordan Peterson in the interview that I pointed out earlier; "Podcasts and services like YouTube are like the invention of the printing press, it's that big. For the first time in human history, the spoken word has as large a reach and as long a duration as a book. That just blew me away. I thought, oh, this is a Gutenberg Revolution. This is a big deal!"
FETCH_RELATED
Loline is an Ethiopian Digital Media that aims to empower the youth through entrepreneurship and technology.
Loline Mag


A dashing blush pink pocket square, from the menswear designers at Puccini. This piece is made from a wrinkle and stain resistant microfiber handwoven on a jacquard loom for texture that looks and feels like real silk. Its soft shade of pink is accented by a pattern of white pin dots. Complete the look by pairing this piece with Puccini's matching men's necktie.


text:
DeBethune's fresh take on meteorite-dial watches
Watches sporting unique meteorite dials are proliferating, but De Bethune has opted for a completely different appearance.
By Justin Mastine-frost 16/03/2017
Louis Vuitton Is About to Drop A Colourful Smartwatch
8 Watch Industry Predictions for 2022
Blancpain's Commanding Presence
From Jaeger-LeCoultre's Master Calendar to Parmigiani Fleurier's Tonda 1950 Meteorite, watches sporting unique meteorite dials are proliferating throughout the industry. That said, most current watches with these otherworldly dials are finished with a Widmanstätten pattern — created by etching a cross-section of meteorite with weak acid.
De Bethune has opted for a completely different appearance for its latest unveiling, though this is not the first time we have seen the company work with the material in this way. Just over a year ago, the brand unveiled an updated, one-off variant of the Dream Watch 5, $US446,000 (about $A587,500), whose case was made entirely from a bluish-purple meteorite.
This time around, the brand is slightly less ambitious in its use of the rare type of ferrous meteorite — taken from a space rock originally unearthed in Santiago del Estero, Argentina — for the new DB28 Kind of Blue Tourbillon Meteorite.
Due to the unique colouration of the meteorite in question, pairing a dial of this precious material with the original DB28 Kind of Blue case is a match made in heaven. The DB28 has seen a wide assortment of variations in recent years, but by all accounts the Kind of Blue Tourbillon Meteorite is now the champion of the line.
It is powered by De Bethune's DB2019v3 30-second tourbillon calibre, which when fully wound is good for a power reserve of five days. Offered as a unique piece with a retail price of $US278,000 (about $A366,200), we have no doubt that the new one-off piece will find a new owner in no time. (debethune.com)
H. Moser & Cie And The Armoury Team Up For Minimalist Watch
The limited watch features a vantablack dial.
By Paige Reddinger
Available January 15, the watch features a sophisticated custom operating system and a brand new design.
By Demetrius Simms
Tag Heuer Drops 3 Limited-Edition Watches
Celebrating 60 years of the Autavia line.
Greubel Forsey's New CEO Outlines His Grand Plans
Hint: more watches, fewer collections and (relatively) lower price points.
By Carol Besler
Omega Unveils A New White Gold Speedmaster
The timepiece celebrates the manufacture's 65th anniversary of the Speedmaster.
The 15 Best New Watches Of 2021
In a year that saw soaring demand for high-end watches, these models stood out.
By Victoria Gomelsky
Robb Read: When Rare Is Not Enough
A bespoke timepiece is the ultimate for watch aficionados. One collector describes the long road from commission to big reveal.
By Mark Cho; Christian Klings Photography Nicolas Blandin; Mark Cho And Watch Photography Tsz Fung Chan


This service provides smps measurements of relative humidity observed at Prague-Suchdol, Czech Rep. (20160104 - 20160527). The observations contain level 2 data. The time resolution is 1h. The observations are stored in the EBAS database (http://ebas.nilu.no/).


It's a good job we had 4Matrix on results day. Due to various problems (not with 4Matrix) we had all our results in a spreadsheet and needed a straightforward way of producing consolidated reports for our students. I must also say a big thank you to your support staff for sorting out a download problem. Apart from the excellent product I would suggest that your totally dependable support system is a crucial positive factor with 4Matrix.


Mike Ryan | Radio Texas, Live!
Tops in Texas: Mike Ryan, Cody Johnson, and Jesse Raub Jr.
This week Mike Ryan, Jesse Raub Jr., and Cody Johnson are all eyeballing the top spot on Texas radio, but only one can be No.1. Who's got what it takes?
Tops in Texas: Who Lands at No. 1 on Texas Radio This Week?
This week Mike Ryan, Wade Bowen and Josh Abbott Band are both eyeballing the top spot on Texas radio, but only one can be No.1. Who's got what it takes?
Every year it's one of West Texas' most anticipated music festivals, but this year's lineup may be the best one yet.
KOKE FM is Radio Texas, LIVE!'s radio home in Austin. This weekend our pals will play host to their annual KOKE FEST -- and it's gonna be HUGE.
It's one of West Texas' most anticipated annual music festivals.
KOKE FM is Radio Texas, LIVE!'s radio home in Austin. On August 10th and 11th our pals are set to host their annual KOKE FEST, and we've got your chance to win our VIP tickets.
For the second straight year a stellar Texas Music lineup is waiting on us -- but this time it's two nights strong. Oh yeah, and we're hooking up one Radio Texas, LIVE! listener with a grand prize VIP access to both nights.


Wow, we've looked at a lot of neat stuff at the SDS. There was the 10-foot plasma-guided rocket, aerodynamic farings for two bicycles, and even a life-size remote control and motion base. But surely not everything can be so exciting?
Today I'll tell you about some of the most mundane things I saw at the SDS. Despite their pedestrian nature, each one is still a real-life problem solved by engineering students. With SCIENCE!
We've already covered the 10-foot plasma-guided rocket and aerodynamic farings for two bicycles. What else did we see at the SDS?
Well, how about this life-size remote control and model car?
I've wanted to do this sort of thing with a model airplane forever. Shouldn't be too hard, I figured: just add a wireless camera, and build a cockpit-shaped box with big joysticks for the controls. Voila! Real flying action!
These guys went a step farther. They put some accelerometers on their car, then hooked the whole thing to a custom-made motion base!


Tina Boysha got her first taste of life in parks and recreation working as an unpaid college intern. She quickly discovered how incredibly rewarding the job could be.
With sixteen years in the parks and recreation field, the past eight as the Superintendent of Athletics for the city of Clarksville Parks and Recreation in Tennessee, Boysha, and her staff, have been putting smiles on thousands of children's faces through their creative and tireless efforts.
Their programs—Blastball!, T-ball, baseball, softball, and basketball—are fun-filled and engaging, and promote learning and skill development.
Tina: My favorite sport was volleyball. I loved being a part of the intricate process of pre-planned teamwork, with everyone having a position, and a score came with every play. I didn't have to run with the ball like I did in basketball, and it didn't put a premium on any kind of speed. Since quickness was a must, I was more adept at this sport than some of the others.
Tina: The pressure that is put on our youth today to win, win, win. That's not to say winning isn't important, but learning the game and enjoying the sport are also important. I understand that parents want their children to be all they can be, but sometimes parents need to take a step back and allow their kids to enjoy being children.
Tina: I would say my favorite professional athlete is Venus Williams. I believe she exemplifies what love and dedication are to a sport. She plays the game of tennis in an admirable way, with sportsmanship and dignity, all while standing strong in coping with the challenges of an energy-sapping autoimmune disease. She also understands the importance of losing, knowing that she can never get complacent. Her biggest ambition is to enjoy life, and do exactly what she wants, and she does.
Fred: If you were talking to a room full of first-year recreation professionals, what would be three tips to share in order to help them be successful in their position?
Tina: Don't take any criticism too personally. Communication is a must! Make it fun by loving what you do.
Fred: Who is the person you most admire?
Tina: I would have to say my mother. She basically raised me single-handedly, taught me to be independent, and to do the best I could in anything that I chose. She used to tell me that if I were going to do anything, give it my all and not half. She never let adversity get her down, and always tried to see the good in everything.
Tina: The coaches and children who return year after year because the league is well run, is fun for the kids, and is an enjoyable experience for the coaches.


At Deaf & Hear Alberta, we consider openness and transparency towards our clients, partners, funders, and stakeholders of utmost importance. As such, our financial statements can be viewed below. If you have any questions or comments, please do not hesitate to contact us.


Can SW simulate volume filling of a cylinder?
Is there a way of using the flow simulation tools within SolidWorks to fill an empty cylinder with a specified flow rate and as an output, get the time for the cylinder to be filled fully? This would all be done on a static cylinder in its in-stroke, all the is required is the time to find how long it takes for the cylinder in that position to fill.
I understand that I can use hand calculations to solve this (simply calculating the volume and comparing against flow rate) but I am trying to show this as a simulation also.
Re: Can SW simulate volume filling of a cylinder?
No, because CFD is using the mass conservation principle, which means the mass that enters the domain should exit and be the same mass. Actually should be the same mass flow rate. In your case you fill up a cavity with mass but nothing exits. I calculated the flow through a valve that lets the fluid entering/exiting a piston chamber but the calculation domain was just the top of the cylinder, the mass flow calculation was done previously on a spreadsheet. The time was set by the RPM of the crankshaft. For your problem you need multi-physiscs which Solidworks is not capable to simulate, maybe Simulia-Abaqus can.
Simulia-Abaqus cannot do morphing mesh but ANSYS and Star-CCM+ can.
You just need an Euler code to do this and the right sort of physics implemented. Easy peazy in say Abaqus explicit or LS-Dyna. You could not do it in a lagrangian a code- like your typical FEA code. Flow Sim is an Euler code but you would need to be a bit creative as I mentioned earlier and understand the approximation you are formulating.
It's technically a free-surface problem which is not solvable at this time using SWx Flow Sim. However, there is a tank evacuation calculator in Flow Sim that may be wrangled in to service for this type of problem. Search this forum for tank evacuation.
2. Tank filling or evacuation with time dependent simulation.
This is a very involved simulation.
You can simulate this phenomenon using a Time Dependent simulation, with the High Mach number option ON.
This option is required to calculate propagation of compression waves (see note below). But this option forces the calculation time step to be very small. Typical values are of the order of 1E-7s. Consequently, consequently this calculation will require a very large number of iterations to be run, and consequently a lot of time. Perhaps 5 ms is the real time which can be calculated, unless you are willing to accept days of calculation time.. Suppose for instance that the model is quite simple and that an iteration takes 2s to solve. If the time step is 2E-7s, solving 5ms of physical time will take 14 hours (25000 iterations). It is worth noting that you cannot manually modify the time step when you use the High Mach number option. If you are trying to simulate a phenomenon which takes sensibly more than a few ms, the calculation time is likely to be days. Also, in the case of tank evacuation, it may be required to simulate an external flow to correctly simulate the jet of fluid just outside the opening (as opposed to simply defining a lid with an environment pressure boundary condition on it).
the mass conservation is a on a cell by cell basis not the whole problem. And further mass accumulation is a part of mass conservation. Just model a simple control volume (finite volume). mass in = mass gain/loss in volume + mass leaving volume.
You could approximate this by having 2 species - fill the cylinder with one and make it light with very low conductivity so the mixing is low and the other heavy and again with low conductivity and then add a goal for say some high volume or mass fraction for the cylinder volume. That should get it close.
In regards to the comments on mass conservation and this not being suitable for this code I disagree. All flow codes will solve for the mass conservation if at all possible. This is a function of the fact that two phase and free surfaces are not supported. You could also approximate this with an empty cylinder (very low pressure) and see how long it take to normalize to whatever ambient is......you just need to pose a question the code can answer.
Most of you guys are discussing the wrong kind of cylinder.
As Andrei noted in his answer to Suraj, Flow can't simulate filling a hydraulic/pneumatic cylinder (actuator) and moving the piston/rod from compressed to full-stroke.
You are commenting on filling something more like a high pressure gas cylinder (tank) which can be done in Flow because it does not require phase change in the mesh.
The OP did start with this....."Is there a way of using the flow simulation tools within SolidWorks to fill an empty cylinder with a specified flow rate and as an output, get the time for the cylinder to be filled fully? This would all be done on a static cylinder in its in-stroke, all the is required is the time to find how long it takes for the cylinder in that position to fill." Can you blame us for being a bit confused? anyway the CEL (Coupled Eulerian Lagrangian) in Abaqus explicit could do the problem with good fidelity but ........the point is probably moot.


"On the "A" w/Souleo: Visual Artists Celebrate For Colored Girls," The Huffington Post, 2016.
"Women artists share diverse inspirations," Newsday, March 12, 2012.
"Northern Manhattan Arts Alliance Highlights 'Celebrations' in 4th Annual 'Women in the Heights' Exhibition," NY Daily News, 2012.


text:
GERMANY DONATES FIRE TRUCK TO SAMOA
Submitted by admin on Tue, 07/13/2010 - 00:00
German team trains Samoa fire department
By Ali Rae APIA, Samoa (Samoa Observer, July 11, 2010) - German efforts to help support Samoa's Fire and Rescue service were officially recognized with the official handing over of the new Blue Angel Rescue Tender.
In the presence of the Prime Minister, the keys to the vehicle were passed to the Commissioner of Fire and Rescue Services on behalf of the German Technical Relief. Honorary Console of the Federal Republic of Germany for Samoa, Arne Schreiber told those present that the initial idea was sparked by German Lothar Fuerst while on holiday in 2008.
"[Mr. Fuerst] mentioned to me [in 2008] that there may be a possibility to get for Samoa a very useful vehicle from Germany," Mr. Schreiber said. "After passing quite a lot of hurdles, the donation papers were signed in December 2009, and Samoa became the owner of the vehicle."
However, many challenges – such as shipping costs - were soon faced in order to get the Blue Angel to Samoa in June this year.
"With a lot of effort we finally got the German Technical Cooperation (GTZ) as a sponsor of the freight cost," Schreiber said.
Furthermore, additional support was given to the Samoa in the form of technical on-site training in Apia.
"A team of four Germans came to Samoa at their own expense to train the colleges of the Fire and Emergency Authority on the equipment that the Rescue Tender is fully loaded with," Schreiber said.
To conclude the official presentation ceremony, the audience witnessed an exercise drill performed by the Fire and Rescue team.
INDONESIAN GOVERNMENT DECLARES MILITARY ALERT ON PNG BORDER
TALKS CONTINUE IN SOLOMON ISLANDS ON PROPOSED PEACE SUMMIT
UN REPRESENTATIVE PRESENTS CREDENTIALS TO SOLOMONS
Work At Papua's Freeport Mine Halted As Company Struggles To Divest Controlling Ownership
SOLOMONS DECLARE AUSSIE POLICE CHIEF 'UNDESIREABLE'
AMERICAN SAMOA GASOLINE PRICES ON THE RISE


Composalik Studios is the music producing branch of Eternity Inc. It was created for the purpose of the making of music, and sounds, for different games, that the group made. Member of the Eternity Council, Luminoria, created it, as a way to express her love for music, as she plays a couple of music in her everyday life. Everyone in the Eternity Council, but Starscape, participates in this.


Published: Jan 28 at 9:39 a.m.
Updated: Jan 28 at 6:45 p.m.
This month, thousands of people have taken to the streets throughout Guatemala to demand an end to corruption and impunity, opposing President Jimmy Morales' move to shutdown a UN mandated anti-corruption commission. Many fear that actions such as his attacks against the commission and continued violation of Constitutional Court rulings signal a technical coup, which could lead the country down a path of dictatorship.
In fact, the country is still recovering from a 36-year internal armed conflict marked by a series of dictatorships and resulting in the deaths of upwards of 200,000 people.
Since 2006, the International Commission Against Impunity in Guatemala (CICIG) has been a key actor in the struggle against corruption and impunity in Guatemala, working alongside the Public Prosecutor's office. Their most high-profile case is that of former president Otto Perez Molina and former vice-president Roxana Baldetti, who were arrested on corruption charges in 2015.
The Public Prosecutor's office and CICIG have also sought to have President Morales tried on corruption charges for illicit campaign financing. Morales' brother and son, as well as members of his political party are currently under investigation for corruption. Shutting down CICIG serves to shield these powerful interests from prosecution.
Canadian mining companies, active in Guatemala since the 1970s, have long since been criticized for human rights abuses and environmental degradation. Local communities also point to the illegal ways mining licenses and permits have been obtained by Canadian companies from the Guatemalan state. The CICIG was only beginning to touch on such cases when attacks against the Commission began.
In August 2018, Morales caused a national uproar when it was announced that he would not renew the Commission's mandate beyond 2019. Soon after, he barred CICIG Commissioner and Colombian jurist Ivan Velasquez from re-entering the country. At least eleven CICIG officials have also had their visas denied or revoked. All the while, the Constitutional Court has consistently reiterated the body's legitimacy.
The situation flared up again this past Jan. 5 with the 25-hour illegal detainment of CICIG investigator and Colombian national Yilen Osorio Zuluaga by Guatemalan officials at the country's international airport. Two days later, Guatemala's Foreign Minister Sandra Jovel announced a unilateral shut down of CICIG, effective within 24 hours. She denied the legitimacy of Constitutional Court rulings in statements which were later affirmed by the Guatemalan president. Within days, the Constitutional Court voted to suspend that decision.
Now, an attack against three Constitutional Court judges who've made a number of important decisions to check the power of Morales and the government's executive branch is moving forward. The Supreme Court of Justice, allied with the Morales government, has accepted a motion by a right-wing interest group which threatens judicial independence. The motion seeks to remove the immunity of the three judges so they can be investigated for alleged violations such as abuse of power. Currently, the matter is before an investigative committee made up of five members of Congress.
Actions by the Morales government signify a move to consolidate power and constitute an attack against the rule of law, as well as democratic institutions. Many fear that this technical coup could once again thrust Guatemala into a violent dictatorship.
Canada, one of the top four donors to the Commission, recently issued a statement highlighting its "disappointment" at the Guatemalan government's decision to unilaterally end CICIG's work. While it's a step in the right direction, Canadian civil society organizations (CSOs) are calling for a stronger response. On January 22nd, BTS and 29 other Canadian CSOs sent a letter to our government stressing our concerns regarding the technical coup in Guatemala, as well as urging the Embassy to use all channels available to advocate its ongoing support for the Commission and for respect of the country's Constitutional Court.
Lisa Rankin and Stacey Gomez are coordinators of the Maritimes-Guatemala Breaking the Silence Network, an organization that has been working in solidarity with Guatemalans since the 1980s.


IMRF is one of 13 public pension systems covered under the Illinois Retirement Systems Reciprocal Act. This Act allows a member to combine service credit from reciprocal systems at retirement.
Service credit earned with different Illinois Reciprocal Retirement systems can be combined to receive a larger pension.
Each system uses the member's highest earnings when it calculates a pension.
In most cases, a member will receive a higher combined benefit by retiring under the Reciprocal Act.
To find out if retiring reciprocally is the better choice, a member should contact the system he or she worked for most recently for a reciprocal estimate.
Retiring under the reciprocal act is optional.
A member must apply with all reciprocal systems at the same time.
A member must complete a separate pension application for each system, and make sure to tell each system that he or she is applying for a reciprocal pension.


If you are a graduate student at UIUC, please submit the Unit Student Application Form. As a Unit Affiliate you will be placed on our email list and will have access to various programs and fellowships sponsored by the Unit.
If you are faculty, staff, an undergraduate student, or are off-campus, please fill out our subscription form.


text:
Beloit Bulletin
Momentum Stocks
Northern Dynasty Minerals Ltd. (NAK): Chaikin Oscillator Reading Over Zero on These Shares
by Beloit Contributor —	December 3, 2019
Receive News & Ratings Via Email - Enter your email address below to receive a concise daily summary of the latest news and analysts' ratings with MarketBeat.com's FREE daily email newsletter.
Tracking the numbers for Northern Dynasty Minerals Ltd. (NAK), we can now see that the Chaikin Oscillator reading is above zero. Tracking this signal, traders may be looking for signs of a possible bullish trend developing.
When it comes to investing, overconfidence can be detrimental to securing profits in the stock market. When investors have some early short-term wins, this may lead them to believe that it is their skill and superior knowledge that produced the winners. All though this may occasionally be the case, investors may quickly realize that it is very hard to consistently produce winning results. Sometimes a few wins can lead the investor to believe that they can make any trade work. This may create a situation where the individual gets in much deeper than they should have. Conducting the proper stock research before any trade can help the investor make sure that they are getting into a position for the right reasons.
A commonly used tool among technical stock analysts is the moving average. Moving averages are considered to be lagging indicators that simply take the average price of a stock over a certain period of time. Moving averages can be very helpful for identifying peaks and troughs. They may also be used to assist the trader figure out proper support and resistance levels for the stock. Currently, the 200-day MA is sitting at 0.60.
Currently, the 14-day ADX for Northern Dynasty Minerals Ltd. (NAK) is sitting at 21.38. Generally speaking, an ADX value from 0-25 would indicate an absent or weak trend. A value of 25-50 would support a strong trend. A value of 50-75 would identify a very strong trend, and a value of 75-100 would lead to an extremely strong trend. ADX is used to gauge trend strength but not trend direction. Traders often add the Plus Directional Indicator (+DI) and Minus Directional Indicator (-DI) to identify the direction of a trend.
Investors and Traders using technical analysis to review stocks may be focusing on the ATR or Average True Range. Currently, Northern Dynasty Minerals Ltd. (NAK) has a 14-day ATR of 0.03. The Average True Range is an investor tool used to measure stock volatility. The ATR is not used to figure out price direction, just to measure volatility. The ATR is an indicator developed by J. Welles Wilder. Wilder has developed multiple indicators that are still quite popular in today's investing landscape. The general interpretation of the ATR is the higher the ATR value, the higher the volatility.
Taking a closer look from a technical standpoint, Northern Dynasty Minerals Ltd. (NAK) presently has a 14-day Commodity Channel Index (CCI) of -62.46. Typically, the CCI oscillates above and below a zero line. Normal oscillations tend to stay in the range of -100 to +100. A CCI reading of +100 may represent overbought conditions, while readings near -100 may indicate oversold territory. Although the CCI indicator was developed for commodities, it has become a popular tool for equity evaluation as well.
Some investors may find the Williams Percent Range or Williams %R as a helpful technical indicator. Presently, Northern Dynasty Minerals Ltd. (NAK)'s Williams Percent Range or 14 day Williams %R is resting at -83.22. Values can range from 0 to -100. A reading between -80 to -100 may be typically viewed as strong oversold territory. A value between 0 to -20 would represent a strong overbought condition. As a momentum indicator, the Williams R% may be used with other technicals to help define a specific trend.
Investors have a few distinct options when approaching the stock market. One option is to follow the crowd and trade with the consensus. Another way is to go against the herd and adopt a contrarian strategy. When it comes down to it, the investor will typically have to make this decision with their best interests in mind. In general, no investor wants to miss out on a winning stock. Far too often, investors will be overcome with the fear of missing out and get into a stock way too late. Just because a stock has been over performing and seeing large gains does not mean that those gains are going to continue into the future. Investors may be too quick to get into a hot stock without putting in the proper time and energy to research whether or not it is still a good stock at current trading levels. Investors who take the time to do their homework for every trade may find themselves a step ahead of the crowd in the long run.
Author: Beloit Contributor
Asian Stock Markets Slip as Hong Kong Protest Worries Persist; Alibaba Advances in Hong Kong Debut
Mackay Shields LLC Has $11.72 Million Stock Position in AXA Equitable Holdings Inc (NYSE:EQH)
IFM Investors Pty Ltd Has $2.68 Million Stock Position in Waste Connections Inc (NYSE:WCN)
Copyright © 2020 Beloit Bulletin. All Rights Reserved.


Manage My Website has just redesigned a Squarespace website for CF-LART, using the Wells template. Based in London, they engage with a European base of collectors, curators and museums to promote Chilean artists who for years have been underrepresented locally and abroad. With a focus on the 1970s to the present, the works include photography, mixed media and paper. These seminal authors came to define the history of Chilean photography in the 70s/90s and influenced a whole new generation of photographers.
This was a fantastic project to work on and we'd love to you take a look.


text:
export class Logger {
	constructor(name: string);
	trace(...args: any[]): void;
	debug(...args: any[]): void;
	info(...args: any[]): void;
	warn(...args: any[]): void;
	error(...args: any[]): void;
	replicants(...args: any[]): void;
	static globalReconfigure(
		opts: LoggerOptions & {file: {path: string}}
	): void;
}

/**
 * Options used for Logger constructor
 */
export interface LoggerOptions {
	replicants?: boolean;
	console?: {
		enabled: boolean;
		timestamps: boolean;
		level: LoggerLevel;
	};
	file?: {
		enabled: boolean;
		timestamps: boolean;
		level: LoggerLevel;
	};
}

/**
 * NodeCG logger level enum
 */
export type LoggerLevel = 'trace' | 'debug' | 'info' | 'warn' | 'error';

redpajama_set_name: RedPajamaGithub


The much awaited WordPress version 4.6 has been in the pipeline for quite some time and with every passing day, we're heading towards its release. The traditional weekly round-ups and meetings are also underway which unveil the features of the upcoming version.
The core team at WordPress.org announced the partial merger of the Shiny Updates plugin to v4.6. The announcement came just after an extra meeting (as the core team called it) which was held yesterday. The provision about its inclusion in the main WordPress core had been in debate and finally it's here; which means a great user experience and interface when it comes to updating WordPress releases, themes, and plugins.
The plugin was developed to add a bit of a crisp to the updating process in WordPress. We've all encountered some major to minor updates for the plugins, themes, etc. which may be automatic or inline. Shiny Updates handles all those actions which are iterated on your screen each time the WordPress is installing and updating a theme or plugin. Currently, the plugin enjoys 1000+ active installs, but it will undoubtedly increase primarily after this announcement.
Easy to install plugins, themes, etc.
Talking about the technical insights, then only a few segments of the plugin's functionality is added to the core. Konstantin Obenland, the man behind Shiny Updates, merged the features like install, update, and delete for themes and plugins in single and multisite. This will affect the files like plugin.php, plugin-install.php, import.php, themes.php and themes-install.php.
After the things got finalized, the team will have to get through the core patch committed by June 15, 2016.
The news of this partial merger came as a relief and users can now enjoy "a smoother experience for managing plugins and themes" as the plugin tagline says so. Shiny Updates together with all the previous features which have been revealed so far, makes one thing clear i.e. WordPress version 4.6 is undoubtedly packed with some fantastic surprises and I'm looking forward exploring them.
So, what's your viewpoint on this news? I'd love to hear your feedback. Share it with us. You can also reach me out through Twitter.


There is only one chance to get it right, so let Cloud 9 make the memories of your event last a lifetime! From weddings, birthday parties or business events, we know how to throw a party. Contact us to discuss your upcoming special event.
Browse through our website and check out our services, packages and photos of our past events!