The first galaxies in the Hubble Frontier Fields

In this thesis we have exploited the power of gravitational lensing of massive clusters to probe galaxy evolution with the galaxy stellar mass functions (GSMF) and UV luminosity functions (UV LF) at $z=6-9$. Throughout the thesis the data utilized is from the Hubble Frontier Fields (HFF) program. We present new measurements of the evolution of the GSMF and UV LF for galaxies from $z=6-9$ within the HFF cluster MACSJ0416.1-2403 and its parallel field. To obtain these results, we have developed a novel method to subtract the massive foreground galaxies that lie close to the critical line from the MACSJ0416.1-2403 cluster, allowing for a deeper and cleaner detection of the faintest systems at $z\geq6$. We derive the stellar masses of our sample by fitting synthetic stellar population models to their observed spectral energy distribution (SED) with the inclusion of nebular emission lines. This is the deepest and most distant mass function measured to date and probes down to a level of M$_{*} = 10^{6.8}M_{\odot}$. The main result of this study is that the low-mass end of our stellar mass functions to these limits and redshifts are measured to be $\alpha=-1.98_{-0.07}^{+0.07}$ at $z=6$ and $\alpha=-2.38_{-0.88}^{+0.72}$ at $z=9$ and we find no evidence of any turnover in the mass range probed. The faint end slope of the UV LF for these system are also measured to be $\alpha=-2.03_{-0.10}^{+0.12}$ at $z=6$ and $\alpha=-2.20_{-0.47}^{+0.51}$ at $z=9$, without any evidence of a turnover in the luminosity range probed. Our $M_{\mathrm{UV}}-M_{*}$ relation exhibit shallower slopes than previously observed and are in accordance with a constant mass-to-light ratio. Integrating our GSMF, we find that the stellar mass density increases from log$_{10}\rho_{*}=5.61_{-0.90}^{+0.92}$ M$_{\odot}$Mpc$^{-3}$ at $z=9$ to log$_{10}\rho_{*}=6.79_{-0.12}^{+0.13}$ M$_{\odot}$Mpc$^{-3}$ at $z=6$. We also find that there is a surprisingly high amount of stellar mass density for galaxies in the early universe up to $z \sim 9$. We estimate the dust-corrected star formation rates (SFRs) to calculate the specific star formation rates ($\mathrm{sSFR}=\mathrm{SFR/M_{*}}$) of our sample, and find that for a fixed stellar mass of $5\times10^{9}M_{\odot}$, sSFR $\propto(1+z)^{2.01\pm0.16}$. From our new measurements, we also estimate the UV luminosity density ($\rho_{\textrm{UV}}$) and find that our results support a smooth decline of $\rho_{\textrm{UV}}$ towards high redshifts. Finally, we use the same dataset to investigate the evolution of the galaxy rest-frame UV colours (UV spectral slope $\beta$) for our sample of high redshift galaxies at $z=6-9$. We measure the UV spectral slope $\beta$ by fitting the observed spectral energy distribution to a set of synthetic stellar population models and estimate the value of $\beta$ from the best-fit model spectrum. With this method, we find no correlation between $\beta$ and rest-frame UV magnitude $M_{1500}$ at all redshifts probed in this work. However, a possible weak evolution of the median $\beta$ values (from $\beta=-2.24$ at $z\sim6$ to $\beta=-2.52$ at $z\sim9$) for galaxies at all luminosities from $z=6-9$ is observed, likely due to increased dust extinction. Furthermore, we find that at $z=7$, the bluest value of our sample is $\beta=-2.31\pm0.31$, which is redder than previously reported values at this redshift in the literature. Similarly, with the help of our SED fitting method, we determine the UV slopes for the first time at $z\sim9$ and find that our bluest data point has a value of $\beta=-2.63\pm0.21$, indicating no evidence as yet for extreme stellar populations at $z>6$. Examining the $\beta$ to stellar mass relation, we find a strong correlation between $\beta$ with stellar mass, in that lower mass galaxies exhibit bluer UV slopes. We also find that low mass galaxies at $\log M/M_{\odot}9$ appear to exhibit a nearly constant $\beta$ at each redshift. We also investigate, for the first time, the correlation between $\beta$ and SFR and find that there is a strong correlation between $\beta$ and SFR, in that galaxies with low SFRs exhibit bluer slopes, and they also appear to get bluer with increasing redshift

Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques

One of the important factors during drilling times is the rate of penetration (ROP), which is controlled based on different variables. Factors affecting different drillings are of paramount importance. In the current research, an attempt was made to better recognize drilling parameters and optimize them based on an optimization algorithm. For this purpose, 618 data sets, including RPM, flushing media, and compressive strength parameters, were measured and collected. After an initial investigation, the compressive strength feature of samples, which is an important parameter from the rocks, was used as a proper criterion for classification. Then using intelligent systems, three different levels of the rock strength and all data were modeled. The results showed that systems which were classified based on compressive strength showed a better performance for ROP assessment due to the proximity of features. Therefore, these three levels were used for classification. A new artificial bee colony algorithm was used to solve this problem. Optimizations were applied to the selected models under different optimization conditions, and optimal states were determined. As determining drilling machine parameters is important, these parameters were determined based on optimal conditions. The obtained results showed that this intelligent system can well improve drilling conditions and increase the ROP value for three strength levels of the rocks. This modeling system can be used in different drilling operations

Natural Language Commanding via Program Synthesis

We present Semantic Interpreter, a natural language-friendly AI system for productivity software such as Microsoft Office that leverages large language models (LLMs) to execute user intent across application features. While LLMs are excellent at understanding user intent expressed as natural language, they are not sufficient for fulfilling application-specific user intent that requires more than text-to-text transformations. We therefore introduce the Office Domain Specific Language (ODSL), a concise, high-level language specialized for performing actions in and interacting with entities in Office applications. Semantic Interpreter leverages an Analysis-Retrieval prompt construction method with LLMs for program synthesis, translating natural language user utterances to ODSL programs that can be transpiled to application APIs and then executed. We focus our discussion primarily on a research exploration for Microsoft PowerPoint

Rock mass classification for predicting environmental impact of blasting on tropically weathered rock

Tropical climate and post tectonic impact on the rock mass cause severe and deep weathering in complex rock formations. The uniqueness of tropical influence on the geoengineering properties of rock mass leads to significant effects on blast performance especially in the developmental stage. Different rock types such as limestone and granite exhibit different weathering effects which require special attention for classifying rock mass for blastability purpose. Rock mass classification systems have been implemented for last century for various applications to simplify complexity of rock mass. Several research studies have been carried out on rock mass and material properties for five classes of weathered rock- fresh, slightly, moderately, highly and completely weathered rock. There is wide variation in rock mass properties- heterogeneity and strength of weathered rocks in different weathering zones which cause environmental effects due to blasting. Several researchers have developed different techniques for prediction of air overpressure (AOp), peak particle velocity (PPV) and flyrock primarily for production blast. These techniques may not be suitable for prediction of blast performance in development benches in tropically weathered rock mass. In this research, blast monitoring program were carried out from a limestone quarry and two granite quarries. Due to different nature of properties, tropically weathered rock mass was classified as massive, blocky and fractured rock for simpler evaluation of development blast performance. Weathering Index (WI) is introduced based on porosity, water absorption and Point Load Index (PLI) strength properties of rock. Weathering index, porosity index, water absorption index and point load index ratio showed decreasing trend from massive to fractured tropically weathered rock. On the other hand, Block Weathering Index (BWI) was developed based on hypothetical values of exploration data and computational model. Ten blasting data sets were collected for analysis with blasting data varying from 105 to 166 per data set for AOp, PPV and flyrock. For granite, one data set each was analyzed for AOp and PPV and balance five data sets were analyzed for flyrock in granite by variation in input parameters. For prediction of blasting performance, varied techniques such as empirical equations, multivariable regression analysis (MVRA), hypothetical model, computational techniques (artificial intelligence-AI, machine learning- ML) and graphical charts. Measured values of blast performance was also compared with prediction techniques used by previous researchers. Blastability Index (BI), powder factor, WI are found suitable for prediction of all blast performance. Maximum charge per delay, distance of monitoring point are found to be critical factors for prediction of AOp and PPV. Stiffness ratio is found to be a crucial factor for flyrock especially during developmental blast. Empirical equations developed for prediction of PPV in fractured, blocky, and massive limestone showed R2 (0.82, 0.54, and 0.23) respectively confirming that there is an impact of weathering on blasting performance. Best fit equation was developed with multivariable regression analysis (MVRA) with measured blast performance values and input parameters. Prediction of flyrock for granite with MVRA for massive, blocky and fractured demonstrated R2 (0.8843, 0.86, 0.9782) respectively. WI and BWI were interchangeably used and results showed comparable results. For limestone, AOp analysed with model PSO-ANN showed R2(0.961); PPV evaluated with model FA-ANN produced R2 (0.966). For flyrock in granite with prediction model GWO-ANFIS showed R2 (1) The same data set was analysed by replacing WI with BWI showed equivalent results. Model ANFIS produced R2 (1). It is found the best performing models were PSO-ANN for AOp, FA-ANN for PPV and GWO-ANFIS for flyrock. Prediction charts were developed for AOp, PPV and flyrock for simple in use by site personnel. Blastability index and weathering index showed variation with reclassified weathering zones – massive, blocky and fractured and they are useful input parameters for prediction of blast performance in tropically weathered rock

A combination of expert-based system and advanced decision-tree algorithms to predict air-overpressure resulting from quarry blasting

This study combined a fuzzy Delphi method (FDM) and two advanced decision-tree algorithms to predict air-overpressure (AOp) caused by mine blasting. The FDM was used for input selection. Thus, the panel of experts selected four inputs, including powder factor, max charge per delay, stemming length, and distance from the blast face. Once the input selection was completed, two decision-tree algorithms, namely extreme gradient boosting tree (XGBoost-tree) and random forest (RF), were applied using the inputs selected by the experts. The models are evaluated with the following criteria: correlation coefficient, mean absolute error, gains chart, and Taylor diagram. The applied models were compared with the XGBoost-tree and RF models using the full set of data without input selection results. The results of hybridization showed that the XGBoost-tree model outperformed the RF model. Concerning the gains, the XGBoost-tree again outperformed the RF model. In comparison with the single decision-tree models, the single models had slightly better correlation coefficients; however, the hybridized models were simpler and easier to understand, analyze and implement. In addition, the Taylor diagram showed that the models applied outperformed some other conventional machine learning models, including support vector machine, k-nearest neighbors, and artificial neural network. Overall, the findings of this study suggest that combining expert opinion and advanced decision-tree algorithms can result in accurate and easy to understand predictions of AOp resulting from blasting in quarry sites. © 2020, International Association for Mathematical Geosciences