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Electric power utilities must ensure a consistent and undisturbed supply of power, with the voltage levels adhering to specified ranges. Any deviation from these supply specifications can lead to malfunctions in equipment. Monitoring the quality of supplied power is crucial to minimize the impact of fluctuations in voltage. Variations in voltage or current from their ideal values are referred to as \"power quality (PQ) disturbances,\" highlighting the need for vigilant monitoring and management. Signal processing methods are widely used for power system applications which include understanding of voltage disturbance signals and used for retrieval of signal information from the signals Different signal processing methods are used for extracting information about a signal. The method of Fourier analysis involves application of Fourier transform giving frequency information. The method of Short-Time Fourier analysis involves application of Short-Time Fourier transform (STFT) giving time–frequency information. The method of continuous wavelet analysis involves application of Continuous Wavelet transform (CWT) giving signal information in terms of scale and time where frequency is inversely related to scale. The method of discrete wavelet analysis involves application of Discrete Wavelet transform (DWT) giving signal information in terms of approximations and details where approximations and details are low and high frequency representation of original signal. In this paper, an attempt is made to perceive power quality disturbances in MATLAB using Fourier, Short-Time Fourier, Continuous Wavelet and Discrete Wavelet Transforms. Proper understanding of the signals can be possible by transforming the signals into different domains. An emphasis on application of signal processing techniques can be laid for power quality studies. The paper compares the results of each transform using MATLAB-based visualizations. The discussion covers the advantages and disadvantages of each technique, providing valuable insights into the interpretation of power quality disturbances. As the paper delves into the complexities of each method, it takes the reader on a journey of signal processing complexities, culminating in a nuanced understanding of power quality disturbances and their representations across various domains. The outcomes of this research, elucidated through energy values, 3D plots, and comparative analyses, contribute to a comprehensive understanding of power quality disturbances. The findings not only traverse theoretical domains but also find practical utility in real-world scenarios.

Green nanotechnology is an emerging field of science that focuses on the production of nanoparticles by living cells through biological pathways. This topic plays an extremely imperative responsibility in various fields, including pharmaceuticals, nuclear energy, fuel and energy, electronics, and bioengineering. Biological processes by green synthesis tools are more suitable to develop nanoparticles ranging from 1 to 100 nm compared to other related methods, owing to their safety, eco-friendliness, non-toxicity, and cost-effectiveness. In particular, the metal nanoparticles are synthesized by top-down and bottom-up approaches through various techniques like physical, chemical, and biological methods. Their characterization is very vital and the confirmation of nanoparticle traits is done by various instrumentation analyses such as UV–Vis spectrophotometry (UV–Vis), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), atomic force microscopy (AFM), annular dark-field imaging (HAADF), and intracranial pressure (ICP). In this review, we provide especially information on green synthesized metal nanoparticles, which are helpful to improve biomedical and environmental applications. In particular, the methods and conditions of plant-based synthesis, characterization techniques, and applications of green silver, gold, iron, selenium, and copper nanoparticles are overviewed.

Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental. A tutorial for multivariate classification analysis of vibrational spectroscopy data (Fourier-transform infrared, Raman and near-IR) is presented. Guidelines are provided for data preprocessing, data selection, feature extraction, classification and model validation.

Advances in sample preparation and computation analysis make FTIR of biological materials a rapidly expanding research area. Researchers from a number of universities have collaborated to provide procedures for FTIR analysis of biological samples. IR spectroscopy is an excellent method for biological analyses. It enables the nonperturbative, label-free extraction of biochemical information and images toward diagnosis and the assessment of cell functionality. Although not strictly microscopy in the conventional sense, it allows the construction of images of tissue or cell architecture by the passing of spectral data through a variety of computational algorithms. Because such images are constructed from fingerprint spectra, the notion is that they can be an objective reflection of the underlying health status of the analyzed sample. One of the major difficulties in the field has been determining a consensus on spectral pre-processing and data analysis. This manuscript brings together as coauthors some of the leaders in this field to allow the standardization of methods and procedures for adapting a multistage approach to a methodology that can be applied to a variety of cell biological questions or used within a clinical setting for disease screening or diagnosis. We describe a protocol for collecting IR spectra and images from biological samples (e.g., fixed cytology and tissue sections, live cells or biofluids) that assesses the instrumental options available, appropriate sample preparation, different sampling modes as well as important advances in spectral data acquisition. After acquisition, data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. A typical experiment can be completed and analyzed within hours. Example results are presented on the use of IR spectra combined with multivariate data processing.

This protocol describes how in-solution protein FTIR can be used to obtain information about the relative contributions of α-helices, β-sheets, β-turn, and random coil structures to a protein's secondary structure. Fourier transform IR (FTIR) spectroscopy is a nondestructive technique for structural characterization of proteins and polypeptides. The IR spectral data of polymers are usually interpreted in terms of the vibrations of a structural repeat. The repeat units in proteins give rise to nine characteristic IR absorption bands (amides A, B and I–VII). Amide I bands (1,700–1,600 cm −1 ) are the most prominent and sensitive vibrational bands of the protein backbone, and they relate to protein secondary structural components. In this protocol, we have detailed the principles that underlie the determination of protein secondary structure by FTIR spectroscopy, as well as the basic steps involved in protein sample preparation, instrument operation, FTIR spectra collection and spectra analysis in order to estimate protein secondary-structural components in aqueous (both H 2 O and deuterium oxide (D 2 O)) solution using algorithms, such as second-derivative, deconvolution and curve fitting. Small amounts of high-purity (>95%) proteins at high concentrations (>3 mg ml −1 ) are needed in this protocol; typically, the procedure can be completed in 1–2 d.

In this paper we present a comprehensive treatment of function spaces with logarithmic smoothness (Besov, Sobolev, Triebel-Lizorkin). We establish the following results: The key tools behind our results are limiting interpolation techniques and new characterizations of Besov and Sobolev norms in terms of the behavior of the Fourier transforms for functions such that their Fourier transforms are of monotone type or lacunary series.

Fourier transform infrared (FTIR) spectroscopy was introduced in 1991 as a technique to identify and classify microbes. Since then, it has gained growing interest and has undergone a remarkable evolution. Highly sophisticated spectrometers have been developed, enabling a high sample throughput. Today, the generation of high-quality data in a short time and the application of the technique for rapid and reliable identification of microbes to the species level are well documented. What makes FTIR spectroscopy even more attractive is the fact that spectral information can also be exploited for strain typing purposes, which is particularly important for epidemiological analyses and some technological applications. Accordingly, in recent years, FTIR spectroscopy has been increasingly used for typing and classifying microorganisms below the species level. The intention of this review is to give an overview over current knowledge and strategies of using FTIR spectroscopy for species identification and to describe different approaches for strain typing.

New and creative methodologies for the fabrication of silver nanoparticles (Ag-NPs), which are exploited in a wide range of consumer items, are of significant interest. Hence, this research emphasizes the biological approach of Ag-NPs through Egyptian henna leaves ( Lawsonia inermis Linn.) extracts and analysis of the prepared Ag-NPs. Plant extract components were identified by gas chromatography mass spectrometry (GC-mass). The analyses of prepared Ag-NPs were carried out through UV–visible (UV–Vis), X-ray diffraction (XRD), transmission electron microscope (TEM), scanning electron microscope (SEM), and Fourier transform infrared (FTIR) analysis. UV–Vis reveals that Ag-NPs have a maximum peak at 460 nm in visible light. Structural characterization recorded peaks that corresponded to Bragg’s diffractions for silver nano-crystal, with average crystallite sizes varying from 28 to 60 nm. Antibacterial activities of Ag-NPs were examined, and it is observed that all microorganisms are very sensitive to biologically synthesized Ag-NPs.

Microbial rhodopsins are photoreceptive membrane proteins that transport various ions using light energy. While they are widely used in optogenetics to optically control neuronal activity, rhodopsins that function with longer-wavelength light are highly demanded because of their low phototoxicity and high tissue penetration. Here, we achieve a 40-nm red-shift in the absorption wavelength of a sodium-pump rhodopsin (KR2) by altering dipole moment of residues around the retinal chromophore (KR2 P219T/S254A) without impairing its ion-transport activity. Structural differences in the chromophore of the red-shifted protein from that of the wildtype are observed by Fourier transform infrared spectroscopy. QM/MM models generated with an automated protocol show that the changes in the electrostatic interaction between protein and chromophore induced by the amino-acid replacements, lowered the energy gap between the ground and the first electronically excited state. Based on these insights, a natural sodium pump with red-shifted absorption is identified from Jannaschia seosinensis . Microbial rhodopsins are photoreceptive and widely used in optogenetics for which they should preferable function with longer-wavelength light. Here, authors achieve a 40-nm red-shift in the absorption wavelength of a sodium-pump rhodopsin (KR2) by altering the distribution of the retinal chromophore.

•ATR FT-IR spectroscopy can detect blood, saliva, semen and vaginal secretions.•ATR FT-IR can discriminate between the spectra of blood, saliva, semen and vaginal secretions.•ATR FT-IR has demonstrated potential for confirmatory body fluid screening. Blood, saliva, semen and vaginal secretions are the main human body fluids encountered at crime scenes. Currently presumptive tests are routinely utilised to indicate the presence of body fluids, although these are often subject to false positives and limited to particular body fluids. Over the last decade more sensitive and specific body fluid identification methods have been explored, such as mRNA analysis and proteomics, although these are not yet appropriate for routine application. This research investigated the application of ATR FT-IR spectroscopy for the detection and discrimination of human blood, saliva, semen and vaginal secretions. The results demonstrated that ATR FT-IR spectroscopy can detect and distinguish between these body fluids based on the unique spectral pattern, combination of peaks and peak frequencies corresponding to the macromolecule groups common within biological material. Comparisons with known abundant proteins relevant to each body fluid were also analysed to enable specific peaks to be attributed to the relevant protein components, which further reinforced the discrimination and identification of each body fluid. Overall, this preliminary research has demonstrated the potential for ATR FT-IR spectroscopy to be utilised in the routine confirmatory screening of biological evidence due to its quick and robust application within forensic science.