Diablo Valley College

Financial markets are very dynamic and thus difficult to predict the price of stocks. There are financial theories like the efficient market hypothesis, which indicate that the future prices of stocks are hard to predict. Nonetheless, empirical research also suggests that there can be some patterns and inefficiencies in financial markets. In this paper, the author reviews and analyzes the typical methods that researchers apply to stock price predictions, such as traditional statistical methods, machine learning methods, and deep learning methods. The paper is aimed at comparing the various methods used to analyze financial time series data, including ARIMA and GARCH models, and machine learning algorithms, including the support vector machine, random forest, and neural network. Besides, the latest trends regarding the implementation of deep learning and the utilization of alternative data are explained to give a more comprehensive outlook on the contemporary prediction frameworks. According to the existing literature, machine learning and deep learning models are likely to be more successful in attaining the nonlinear relationship and the temporal dependence in stock prices than traditional statistical models. Nevertheless, their performance is very dependent on the quality of data, selecting features, and the market conditions.

The current document suggests a possibility to generate hydro-electric power by combining a low head hydro-turbine with an existing coal-based thermal power plant. A comparative analysis is conducted by comparing scenarios with and without adjustable moving blades on the hydro-turbine. The findings demonstrate that incorporating a surface hydro-kinetic turbine system into the cooling water system of a 500 MW power plant can produce approximately 170 kWh of clean energy with a cooling water flow rate of around 16.7 m3/s and a gross head of about 1.5 m between the cooling tower (CT) and the cooling water (CW) basin. The efficiency of the hydro-power plant with fixed-moving blades is lower than that of the adjustable blades due to the reduced turbine efficiency at low flow velocity. This proposed system is expected to reduce the CO2 emissions of the primary plant by approximately 3.34t/day. The power generation cost and the simple payback period for the proposed mini hydro project at full capacity are approximately INR 2.1/kWh and about 6 years, respectively.

Abstract Electrospun polyacrylonitrile (PAN) nonwoven fabrics can be converted into stabilized, flame-retardant materials through air pre-oxidation, but excessive treatment may damage the fibrous morphology without providing proportional thermal benefits. Here, we investigated the effect of dwell time within a fixed four-stage air pre-oxidation program at 240, 255, 270, and 280°C, using dwell times of 14, 21, and 28 min at each stage. Representative scanning electron microscopy images showed that the electrospun fibrous network was largely retained after moderate treatment, whereas OPAN-28 exhibited greater local fiber deformation and adhesion. X-ray diffraction and Fourier-transform infrared spectroscopy indicated disruption of the ordered PAN chain packing and formation of a stabilized conjugated structure. Thermogravimetric analysis showed residual masses of 68.17%, 70.12%, and 69.65% at approximately 590°C for OPAN-14, OPAN-21, and OPAN-28, respectively. OPAN-21 was selected for further evaluation because it provided the most favorable balance between structural stabilization, residual mass, and morphology preservation among the tested conditions. After 24 h of immersion in acidic and alkaline solutions, the OPAN-21 electrospun nonwoven fabric showed mass losses of 1.7% and 3.8%, respectively, while retaining its macroscopic fabric form. Under the laboratory-scale vertical flame-exposure conditions, OPAN-21 exhibited an average afterflame time of 0.46 ± 0.11 s, an average afterglow time of 7.90 ± 0.28 s, and no visible burn-through. These results demonstrate that OPAN-21 is an electrospun PAN-based nonwoven fabric with flame-retardant behavior under the tested conditions and has potential for further development as a flame-retardant textile material.

Context. Protostars contain icy ingredients necessary for the formation of potential habitable worlds, therefore, it is crucial to understand their chemical and physical environments. This work is focused on the ice features towards the binary protostellar system Ced 110 IRS4A and IRS4B, separated by 250 au and observed with James Webb Space Telescope (JWST) as part of the Early Release Science (ERS) Ice Age collaboration. Aims. This study is aimed at exploring the JWST observations of the binary protostellar system Ced 110 IRS4A and IRS4B primarily to unveil and quantify the ice inventories towards these sources. Finally, we compare the ice abundances with those found for the same molecular cloud. Methods. We used data from multiple JWST instruments (NIRSpec, NIRCam, and MIRI) to identify and quantify ice species in the Ced 110 IRS4 system. The analysis was performed by fitting or comparing the laboratory infrared spectra of ices to the observations. Spectral fits are carried out with the ENIIGMA fitting tool that searches for the best fit out of a large number of solutions. The degeneracies of the fits are also addressed and the ice column densities are calculated. In cases where the full nature of the absorption features is not yet known, we explore different laboratory ice spectra to compare them with the observations. Results. We provide a list of securely and tentatively detected ice species towards the primary and the companion sources. For Ced 110 IRS4B, we detected the major ice species H2O, CO, CO2, and NH3. All species are found in a mixture except for CO and CO2, which have both mixed and pure ice components. In the case of Ced 110 IRS4A, we detected the same major species as in Ced 110 IRS4B, as well as the following minor species: CH4, SO2, CH3OH, OCN-, NH4+, and HCOOH. A tentative detection of N2O ice (7.75 mu m), forsterite dust (11.2 mu m), and CH3+ gas emission (7.18 mu m) in the primary source was also made. Compared with the two lines of sight towards background stars in the Chameleon I molecular cloud, the protostar exhibits similar ice abundances, except in the case of the ions that are higher in IRS4A. The most clear differences are the absence of the 7.2 and 7.4 mu m absorption features due to HCOO- and icy complex organic molecules in IRS4A. There is also evidence of thermal processing in both IRS4A and IRS4B, as probed by the CO2 ice features. Conclusions. We conclude that the binary protostellar system Ced 110 IRS4A and IRS4B has a large inventory of icy species. The similar ice abundances in comparison to the starless regions in the same molecular cloud suggests that the chemical conditions of the protostar were set at earlier stages in the molecular cloud. It is also possible that the source inclination and complex geometry cause a low column density along the line of sight, which hides the bands at 7.2 and 7.4 mu m. Finally, we highlight that a comprehensive analysis using radiative transfer modelling is needed to disentangle the spectral energy distributions of these sources.

The dusty interstellar medium (ISM) of the Milky Way is distributed in a complex, cloudy structure. It is fundamental to the radiation balance within the Milky Way, provides a reaction surface to form complex molecules, and is the feedstock for future generations of stars and planets. The life cycle of interstellar dust is not completely understood, and neither are its structure nor composition. The abundance, composition, and structure of dust in the diffuse ISM can be determined by combining infrared, optical, and ultraviolet spectroscopy. JWST enables measurement of the faint absorption of ISM dust grains against bright stars at kiloparsec distances across the infrared spectrum. Here we present an overview of the project “Webb Investigation of Silicates, Carbons, and Ices” (WISCI) along with interpretation of two targets, GSC 08152-02121 and CPD-59 5831. Observations of 12 WISCI target stars were taken by JWST, the Hubble Space Telescope, Himalayan Chandra Telescope, and the Very Large Telescope. We use these to characterize the targets’ spectral types and calculate their line-of-sight extinction parameters, A V and R V . We find absorption in the JWST spectra of GSC 08152-02121 and CPD-59 5831 associated with carbonaceous dust around 3.4 and 6.2 μ m and amorphous silicates at 9.7 μ m. In GSC 08152-02121, we also find indications of absorption by trapped water around 3 μ m. This first look from WISCI demonstrates the line-of-sight variability within the sample, and the program’s potential to identify and correlate features across ultraviolet to mid-infrared wavelengths.