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Results from the present study revealed that of the three BPN constructs, relatedness was significantly correlated with more behavioral measures than autonomy or competence.
In this paper, the ANN-1 and ANN-2 are designed just to estimate the British Pendulum Number (BPN).
On the basis of our experimental results, we verified that the CHS-based method has the highest watermark bpn of 0.566 bpn, which is 0.147-0.539 bpn higher than the watermark bpns of other methods, and that this method shows the highest capacity efficiency of approximately 1.865, which is 0.622-1.734 higher than that of other methods.
To test competence, autonomy, and relatedness, the study used the Basic Psychological Needs Scale (BPNS).
An important theoretical issue for SDT is whether others are seen as interchangeable with respect to meeting particular BPNs (the so called "generic," or "between persons" needs satisfaction profile) or whether specific others are sought in order to meet particular BPNs (the "specific" or "within persons" needs satisfaction profile; see La Guardia et al., 2000).
 used nonlinear regression equations and then related the forecasting error to some factory conditions and job attributes with a back propagation network (BPN) to improve the forecasting accuracy.
For both monopiling and jacket installed in the BPNS, cumulative SEL of 196 dB re 1 [micro][Pa.sup.2] s @750 m was found.
According to SDT (Deci & Ryan, 1985, 2002) there are a set of basic psychological needs (BPN) -- for autonomy, competence and relatedness - which have to be met by the social context to promote psychological growth (i.e.
BPNs have hierarchical feed forward network architectures.
Recently, Wu and Chen  proposed the GAV approach to aggregate the estimation results from various BPNs, which is modified by incorporating in the concept of FI in this study.
Within the SDT framework, Deci and Ryan (2000b) developed the Basic Psychological Needs Scales (BPNS) to assess the main psychological needs.
Back propagation networks (BPNs) are hierarchical feed forward network architectures composed of a highly interconnected set of processing units, or neurons, which are arranged in a layered structure.
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