\section{Conclusions}
In this work, multi-wavelength observations enable us to analyse the derivation of physical properties in the interstellar medium. By doing this, we provide a reliable estimate of the energy and momentum in the cloud and the shell-like structure of an embedded B-star wind. We find that, although this one shell is not powerful enough to sustain the turbulence in the entire cloud, it does inject a substantial amount of energy into the cloud. Here we list our conclusions:

\begin{itemize}
\item Three of the most common ways to trace the column density in molecular clouds include the near-infrared extinction, mid/far-infrared thermal emission and molecular line emission. Comparing the results using data from 2MASS, IRIS, Herschel and FCRAO, we find that the 2MASS/NICER-based extinction measurement serves as the most reliable tracer of column densities in the cloud. However, each of these different tracers, including the 2MASS/NICER-based extinction, suffers from its own limits. This is because none of these tracers directly trace the most dominant component of mass in molecular clouds: the molecular hydrogen. The variation of dust-to-gas ratio and the dust properties affect the effectiveness of both dust tracers, while the excitation criterion (for example, the critical density and temperature) makes it difficult to assess the conversion from the line emission to the gas column density. In dense regions, the molecular line emission can be optically thick or depleted, making it unsuitable for tracing the column density. Following \citet{Goodman_2009}, we propose that when choosing tracers of the column density, one should always consider the physical environment of the target region. 
\item Two of the complexities of using the mid/far-infrared emission as a density tracer are the variation of physical properties along the line of sight and the noise in the data. The commonly used modified blackbody equation assumes a single temperature, column density and the emissivity spectral index $\beta$ throughout the entire line of sight. This makes it difficult to describe the observed SED with the modified blackbody equation, especially when we free both the temperature and $\beta$ in the fitting. By building a toy line of sight, we find that the temperature and $\beta$ derived by fitting the modified blackbody equation are not representative of a line of sight with a varying temperature and $\beta$. On the other hand, we experiment with the hierarchical Bayesian-fitting method in the hope of decreasing the bias caused by noise in the data. We find that while it decreases the bias we see in previous works, including the anti-correlation between the temperature and $\beta$, the original code used in \citet{Kelly_2012} is not readily applicable to extended sources. To build a hierarchical Bayesian-fitting method that treats the distributions in the parameter space and of the observed fluxes at the same time, we need a more sophisticated model for these distributions than the multivariate Student-T distribution used in \citet{Kelly_2012}.
\item With a better understanding of various density tracers, we calculate the total turbulent energy in the Ophiuchus cloud and the energy entrained in the embedded B-star wind. By comparing the two, we find that the energy in the shell strucuture caused by the B-star wind amounts to $\sim$ 12 \% of the total turbulent energy in the cloud. The wind's energy injection rate is also comparable to the dissipation rate of the turbulent energy in the entire cloud. Comparing this result to shells in the Perseus cloud (Arce et al. 2011), we find that the shell in the Ophiuchus has energy comparable to the largest few shells in the Perseus and its energy injection rate is comparable to the \emph{sum} of all shells in the Perseus. We conclude that the embedded stellar winds can contribute to the turbulent energy in molecular clouds. In future simulations, embedded winds driven by B or even later types of stars should be included as important sources of turbulence in molecular clouds.
\end{itemize}
