Feyza Guzelcimen

Plants continuously interact with diverse biotic and abiotic factors in their environment, and understanding these intricate ecological relationships is crucial for advancing sustainable agriculture and biodiversity conservation. This study investigates the interactions between wheat plants and mold contaminants in in vitro cultures using Raman Spectroscopy (RS) and biochemical analyses, offering a novel approach to understanding plant responses at a molecular level. Conventional culturing methods identified Aspergillus versicolor, Penicillium sp., and Nigrospora sp. as the primary mold strains present in the cultures. Biochemical assays revealed that mold contamination led to a marked decrease in chlorophyll and carotenoid contents, along with an increase in thiobarbituric acid reactive substances (TBARS), indicating heightened oxidative stress. Antioxidant enzyme activities, including superoxide dismutase (SOD), peroxidase (POX), and catalase (CAT), were significantly elevated, though proline levels remained unchanged. Raman Spectroscopy further uncovered profound metabolomic shifts, particularly in carotenoid-related peaks, signalling impaired photosynthetic function. Additionally, RS detected increased carbohydrate-associated bands, suggesting that carbohydrate-derived osmolytes may play a more pivotal role than proline in maintaining cellular integrity under mold stress. To validate these findings, advanced multivariate techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were employed, achieving high accuracy in distinguishing mold-infected samples from controls. These results highlight the potential of RS as a rapid, non-invasive diagnostic tool for plant health monitoring, offering significant advantages over traditional methods. This approach not only advances our understanding of plant-microbe interactions but also offers practical applications for enhancing agricultural productivity and sustainability in the face of global food security challenges.

Both BaF₂ and MgF₂ compounds and Ba_xMg_1−xF₂ alloy thin films were deposited on glass and silicon (Si) substrates in nanometric sizes (100 ± 10 nm) in a high vacuum environment by radio frequency (rf) magnetron sputtering.

The experimental wavenumbers of 2118 spectral lines from calibrated Fourier transform (FT) spectra have been used to determine revised energy values of 405 fine structure levels of atomic lanthanum, 264 levels of even and 141 levels of odd parity, respectively. For the determination of the level energies a weighted global fit of the center-of-gravity (cg) wavenumbers of all 2118 spectral lines has been done. All lines have been classified previously by different spectroscopic methods and by different authors. In order to achieve high accuracy, the hyperfine (hf) structure was taken into account when determining the cg wavenumbers from the FT spectra. The total uncertainty for all revised energy levels lies below 0.007 cm⁻¹. A compilation of all revised energy levels along with their hf structure data is given, as well as a list of the spectral lines used in this investigation.

The salinity level of the growing medium has diverse effects on the development of plants, including both physical and biochemical changes. To determine the salt stress level of a plant endures, one can measure these structural and chemical changes. Raman spectroscopy and biochemical analysis are some of the most common techniques in the literature. Here, we present a combination of machine learning and Raman spectroscopy with which we can both find out the biochemical change that occurs while the medium salt concentration changes and predict the level of salt stress a wheat sample experiences accurately using our trained regression models. In addition, by applying different machine learning algorithms, we compare the level of success for different algorithms and determine the best method to use in this application. Production units can take actions based on the quantitative information they get from the trained machine learning models related to salt stress, which can potentially increase efficiency and avoid the loss of crops.

In recent years, noise pollution generated by increasing global urbanization has become an important issue. This study focused on determining the sound absorption coefficients (SACs) of untreated tree trunks as natural fiber-based materials and predicting their noise reduction coefficients (NRCs). It also evaluated the effects of noise reduction using numerical data. One of the most effective acoustic characteristics is the normal incidence SAC; this was obtained for trunks belonging to species of black locust, narrow-leafed ash, stone pine, silver lime, sweet chestnut, sessile oak, maritime pine, cedar, and plane in the frequency range of 50–6000 Hz using an impedance tube based on a two-microphone transfer function method. The absorbance of sound was found to correlate with the tree species, trunk diameter, bark shape, and the bark’s fibrous structure. The results confirmed that when compared with deciduous species, the trunks of coniferous species exhibit higher NRC values which were recorded as 0.302 ± 0.005 for stone pine and 0.233 ± 0.004 for cedar. Furthermore, the surface morphology of the outer bark of different tree species obtained via scanning electron microscopy revealed that significant variations exist in the microstructure of the bark’s fiber cells.

Fourier Transform spectra of Holmium (Ho) in the visible spectral range from 14,280 up to 25,000 cm⁻¹, or 400 to 700 nm have been investigated, with a focus on the ionic lines. More than 500 spectral lines were assigned to singly ionized Ho (Ho ii), of which more than 400 were previously unknown. A further 21 lines were assigned to doubly ionized Ho (Ho iii), 1 of which was not reported previously. From among these lines, 60 Ho ii lines and 20 Ho iii lines were classified. A complete list of more than 4000 Ho lines observed in the recorded spectrum is supplied, even if they remain unclassified.

The Fourier Transform spectra of a Holmium hollow cathode discharge lamp have been investigated in the UV spectral range from 25,000 up to 31,530 cm⁻¹ (317 to 400 nm). Two Ho spectra have been measured with neon and argon as buffer gases. Based on the intensity ratios from these two spectra, a distinction was made between atomic and ionic lines (ionic lines are discussed in an accompanying paper). Using the known Ho i energy levels, 71 lines could be classified as transitions of atomic Ho, 34 of which have not been published previously. Another 32 lines, which could not be classified, are listed in the literature and assigned as atomic Ho. An additional 370 spectral lines have been assigned to atomic Ho based on the signal-to-noise ratio in the two spectra measured under different discharge conditions, namely with buffer gases argon and neon, respectively. These 370 lines have not been previously listed in the literature.