In this paper, we consider Chance constrained programming (CCP) technique when at least two of the LHS input coefficients are random and distributed as two-parameter exponential distribution- Since two-parameter exponential distribution is more applicable in most real life applications than the single-parameter exponential distribution.

Two approaches are introduced to transform CCP into deterministic: (i) The first approach proposed under the assumption of independence between exponential variables and (ii) the second approach assumes that random input coefficients are dependent with correlation coefficient p.

The first approach of independence is an extension of Biswal's approach to deal with two-parameter exponential variables instead of single-parameter exponential variables. That is through two lemmas and a theorem for m independent input coefficients. The second approach of dependence uses Downton bivariate exponential distribution for reflecting the joint distribution of correlated input coefficients under two cases; the first introduced case assumes that dependent input coefficients have single-parameter exponential marginals and the second introduced case is an extension of Downton bivariate exponential distribution for case of two-parameter exponential random input coefficients. The deterministic equivalent of chance constraints are obtained through a theorem and a corollary for each case.

 Finally; It was shown that the equivalent deterministic transformation for the extension of Downton exponential distribution for two-parameter exponential marginals is a generalization of the first approach for m=2 when the correlation coefficient is zero and the second approach for single-parameter exponential marginal when the second parameter is zero.

The subsurface of Anambra basin was assessed for geothermal energy source which is a renewable energy using airborne magnetic data. Gridded data of digitized twelve (12) airborne magnetic maps of high resolution were acquired from the Nigerian Geological Survey Agency, Abuja for this research. The data were combined to form a composite map -  total magnetic intensity (TMI) anomaly map. Regional-residual separation of the total magnetic intensity data was performed using Polynomial Fitting method. The filtered residual data was Fourier transformed after dividing the whole area into Thirty-five (35) overlapping cells for spectral analysis. The subsurface geology of the study area was delineated into shallow and deeper sources. Spectral analysis for crustal magnetization was used for the determination of the depth to magnetic sources giving rise to magnetic anomalies for the determination of; curie point depth and geothermal heat flow supply. Conclusively, the results show that the study area has good sedimentary thickness with the highest value of 3.86 km, Curie point highest value was 38.62 km and geothermal heat flow highest value was 99.02 mWm^-2 around Aimeke, Enugu, Mbashere, Ankpa and Ogobia. Due to the moderate to low Curie point depths, there was no anomalous geothermal heat flow in the study area, thus, the basin has a likelihood of geothermal heat flow prospect.

By definition, a placebo has no biological effect. Therefore, besides classical non-specific effects, the outcome associated to a placebo rests on its “meaning”. Meaning is always for someone and understanding the effects attributed to placebo requires to describe the expectations and interpretations of the agents involved in the experiment.

We present a probabilistic modeling of the “placebo effect” that has its roots in the act of measuring and – in contrast with other hypotheses such as patient’s expectation or conditioning – is centered on experimenters and not only on patients. Therefore, this modeling potentially applies to any biology experiment aimed to demonstrate a causal relationship. Its originality is the description of the experimental situation from the point of view of an uninvolved participant who does not interact with the experimenters and the biological system.

When probability fluctuations inherent to any measurement are taken into account, a counterintuitive result emerges: two placebos with different “meanings” can be associated with different “effects” after measurement of a biological system. In clinical trials, this “meaning effect” due to the experimenters could add to the drug effect and contribute to the “placebo effect”.

This simple modeling suggests that the act of measuring is not always neutral and some correlations between apparent causes and observed outcomes may emerge, thus contributing to conclude for obvious – but false – causal relationship. These results could have consequences in the design and interpretation of experiments in life sciences, medicine and psychology.