Two-dimensional Spectra Research Articles

Fine Root Traits across Different Root Orders and Their Associations with Leaf Traits in 15 Co-Occurring Plant Species from the Desert-Oasis Transition Zone in the Hexi Corridor, Gansu Province, China.

Fine root traits embody trade-offs between resource acquisition and conservation in plants. Yet, the differentiation of these traits across root orders, the existence of a root economics spectrum (RES) spanning these orders, and their linkage with leaf traits remain underexplored. In this study, we analyzed the first three root orders and leaf traits of 15 co-occurring plant species, including ten herbs and five shrubs, from the desert-oasis transition zone of the Hexi Corridor. We measured twelve morphological and chemical traits to investigate the relationships between root and leaf traits. Our results revealed significant variation in root traits both among species and within species across different root orders. We identified RES that spanned root orders, with higher-order roots exhibiting more conservative traits and lower-order roots displaying traits aligned with resource acquisition. Additionally, leaf and fine root traits showed partially decoupled adaptive strategies, yet evidence also supported the existence of a leaf economics spectrum (LES) and a potentially two-dimensional whole plant economics spectrum (WPES). Our findings suggest synergistic resource allocation strategies between fine roots and the entire plant, emphasizing the importance of root order in understanding fine root structure, function, and their interactions with other plant organs. These insights advance the understanding of fine root traits and their integration within the broader plant economics spectrum. Nevertheless, the differences in fine root traits across root orders, the presence of a root economics spectrum (RES) spanning these orders, and the relationships between fine root and leaf traits remain underexplored. We examined the first three root orders and leaves of 15 co-occurring plant species (ten herbs and five shrubs) from the desert-oasis transition zone in the Hexi Corridor, measured twelve key morphological and chemical traits. We observed substantial variation in root traits among species and root orders within species. The root economics spectrum (RES) spanned across root orders, with higher-order roots positioned at the conservative end and lower-order roots at the acquisitive end of the "investment-return" strategy axis. Leaf and fine root traits of the 15 co-occurring plant species exhibited partially decoupled adaptive strategies. However, there was also evidence for the presence of a leaf economics spectrum (LES) and a whole plant economics spectrum (WPES), with the WPES potentially being two-dimensional. Furthermore, our findings suggest synergistic resource strategies between fine roots and the whole plant. Concurrently, the significant interspecific and intraspecific differences in fine root traits, combined with the presence of a root economics spectrum across root orders, underscore the critical importance of root order in studying fine root structure, function, and their associations with other plant organs. Our findings offer valuable insights for future research on fine root traits, the RES, and their integration with the whole plant economics spectrum.

Rearrangement of C2-Spirooxindoles: Conversion to the 2-Hydroxyhemi-Indigo and Chromenoindole.

This study demonstrates the rearrangement of C2-spirooxindoles to the 2-hydroxyhemi-indigo and chromenoindole. The N-H-spirooxindole exhibits double proton translocation and its conversion to the (Z)-2-hydroxyhemi-indigo photoswitch with trifluoroacetic acid, while the N-methyl-spirooxindole undergoes structural rearrangement to the chromenoindole. The mechanism of the reactions was proposed, and the structure of the products was confirmed by one-dimensional (1D) and two-dimensional (2D) NMR spectra and X-ray structure analysis. The photoswitching performance of (Z)-2-hydroxyhemi-indigo, which allows the stabilization of the E-switched form by intramolecular hydrogen bonding, has been studied in solvents of different polarities. It was found that in the less polar solvents, the E-switched metastable isomer is characterized by high stability.

Interconnection between Polarization-Detected and Population-Detected Signals: Theoretical Results and Ab Initio Simulations.

Most of spectroscopic signals are specified by the nonlinear laser-induced polarization. In recent years, population-detection of signals becomes a trend in femtosecond spectroscopy. Polarization-detected (PD) and population-detected signals are fundamentally different, because they are determined by photoinduced processes acting on disparate time scales. In this work, we consider the fluorescence-detected (FD) N-wave-mixing (NWM) signal as a representative example of population-detected signals, derive a rigorous expression for this signal, and discuss its approximate variants suitable for numerical simulations. This leads us to the definition of the phenomenological FD (PFD) signal, which contains as a special case all definitions of FD signals available in the literature. Then we formulate and prove the population-polarization equivalence (PPE) theorem, which states that PFD NWM signals produced by (possibly strong) laser pulses can be evaluated as conventional PD signals in which the effective polarization is determined by the PFD transition dipole moment operator. We use the PPE theorem for the construction of the ab initio protocol for the simulation of PFD 4WM signals. As an example, we calculate electronic two-dimensional (2D) PFD spectra of the gas-phase pyrazine and compare them with the corresponding PD 2D spectra.

Proximity or Directional Model of Voting for the Turkish Voter?

Voting behavior is a very complex type of political behavior. Therefore, understanding why voters vote for a particular political party or a candidate requires developing complex models. In 1957, Anthony Downs, who built his model on Hottelings’ and Smithies’ models, argued that political parties’ and candidates’ ideological and issue positions can be expressed on a one-dimensional space. On one hand, it was highly reductionist to argue that political ideas on a particular issue can be expressed this way, on the other, it was highly practical from analytical point of view. Locating parties, candidates and voters on a one-dimensional space according to their ideological or issue positions was then a revolutionaly idea and helped comparing party, candidate and voter ideological and issue positions within and across countries. These models, which were called spatial models of party competition were further developed over time and helped understanding voting behavior. Currently, spatial models of party competition have two major competing models linking voter ideological positions with party ideological positions. Simply, while the proximity model proposes that voters vote for the parties or candidates that hold ideological positions in the political space that are closest to their own, the directional model suggests that the voters vote for the parties or candidates that are on their side of the two-dimensional political spectrum and more extreme than their own while being within the acceptability region. This research aims to test the applicability of these two voting models for the Turkish voter. Türkiye constitutes an interesting case study with its long-term PR electoral system as it was suggested in the extant literature that proximity model is a more appropriate tool to explain voting behavior in Proportional (PR) systems. Thus, we hypothesize that in Türkiye, where a PR electoral system is in effect for parliamentary elections, voter electoral preferences are better explained by the proximity model than the directional model. Our research analyzes Comparative Study of Electoral Systems (CSES) data for voters of the four major political parties in Türkiye, the Justice and Development Party (JDP), the Republican People’s Party (RPP), the National Action Party (NAP), and the People’s Democratic Party (PDP). A series of Multiple Linear Regression Analyses were conducted to reveal associations between the dependent and the independent variables. Voter embracement, as expressed as like-dislike of each political party for each voter, is seperately used as the dependent variable for each analysis. Issue distance and issue scalar product were used as key independent variables representing the formulas for the proximity and the directional models, respectively. Additionally, education, age, gender and income were recruited as classical control variables. Comparing explanatory powers of the statistical models showed that, contrary to the findings of MacDonald and his colleagues, the proximity model of voting is a more appropriate tool than the directional model to explain voting behavior in Türkiye. From a macro-political perspective, this finding supports Westholm’s (1997) argument that the PR provides a more appropriate tool to explain voting behavior in PR systems. Yet, it should be noted that further multi-country comperative analyses required for certain results.

Exploring multiple phases and first-order phase transitions in Kármán Vortex Street

Kármán Vortex Street, a fascinating phenomenon of fluid dynamics, has intrigued the scientific community for a long time. Many researchers have dedicated their efforts to unraveling the essence of this intriguing flow pattern. Here, we apply the lattice Boltzmann method with curved boundary conditions to simulate flows around a circular cylinder and study the emergence of Kármán Vortex Street using the eigen microstate approach, which can identify phase transition and its order-parameter. At low Reynolds number, there is only one dominant eigen microstate W1 of laminar flow. At Rec1 = 53.6, there is a phase transition with the emergence of an eigen microstate pair W2,3 of pressure and velocity fields. Further at Rec2. = 56, there is another phase transition with the emergence of two eigen microstate pairs W4,5 and W6,7. Using the renormalization group theory of eigen microstate, both phase transitions are determined to be first-order. The two-dimensional energy spectrum of eigen microstate for W1, W2,3 after Rec1, W4–7 after Rec2 exhibit −5/3 power-law behavior of Kolnogorov’s K41 theory. These results reveal the complexity and provide an analysis of the Kármán Vortex Street from the perspective of phase transitions.

Analysis of chi angle distributions in free amino acids via multiplet fitting of proton scalar couplings

Abstract. Scalar couplings are a fundamental aspect of nuclear magnetic resonance (NMR) experiments and provide rich information about electron-mediated interactions between nuclei. 3J couplings are particularly useful for determining molecular structure through the Karplus relationship, a mathematical formula used for calculating 3J coupling constants from dihedral angles. In small molecules, scalar couplings are often determined through analysis of one-dimensional proton spectra. Larger proteins have typically required specialized multidimensional pulse programs designed to overcome spectral crowding and multiplet complexity. Here, we present a generalized framework for fitting scalar couplings with arbitrarily complex multiplet patterns using a weak-coupling model. The method is implemented in FitNMR and applicable to one-dimensional, two-dimensional, and three-dimensional NMR spectra. To gain insight into the proton–proton coupling patterns present in protein side chains, we analyze a set of free amino acid one-dimensional spectra. We show that the weak-coupling assumption is largely sufficient for fitting the majority of resonances, although there are notable exceptions. To enable structural interpretation of all couplings, we extend generalized and self-consistent Karplus equation parameterizations to all χ angles. An enhanced model of side-chain motion incorporating rotamer statistics from the Protein Data Bank (PDB) is developed. Even without stereospecific assignments of the beta hydrogens, we find that two couplings are sufficient to exclude a single-rotamer model for all amino acids except proline. While most free amino acids show rotameric populations consistent with crystal structure statistics, beta-branched valine and isoleucine deviate substantially.

Diels-Alder Cycloaddition of 2,5-Bis(hydroxymethyl)furan (BHMF) and N-Phenylmaleimide Derivatives.

Currently, amidst atmospheric menace where natural calamities such as wildfire and floods are becoming more frequent than ever, biobased derivatives offer a sustainable alternative to conventional ways, for instance, petrochemical commodities. Biobased products, obtained from agricultural waste, including 5-(hydroxymethyl)furfural (HMF), 2,5-bis(hydroxymethyl)furan (BHMF), and 2,5-furandicarboxylic acid (FDCA) are promising chemical platforms in the biorefinery, which is yet to be explored. The Diels-Alder cycloaddition of BHMF and N-phenylmaleimide derivatives under optimal reaction conditions is investigated in this report. First, HMF is reduced to BHMF in the presence of NaBH4, and then the Diels-Alder reaction of BHMF and N-phenylmaleimide derivatives is investigated to produce Diels-Alder adducts. All novel compounds are synthesized in acceptable yields and effectively characterized by employing important techniques such as one-dimensional (1D) NMR spectroscopy (1H, 13C, DEPT-90, and DEPT- 135), two-dimensional (2D) NMR spectroscopy (1H-1H COSY, 1H-13C HSQC, and 1H-13C HMBC), IR spectroscopy, elemental analysis, mass spectrum (QTOF), and single-crystal X-ray diffraction (SC-XRD). Furthermore, this study underlines the necessity of sustainable synthetic methodologies and gives critical insights into the progress of ecologically friendly methodologies, providing a new avenue as a tunable precursor for the challenging functionalized polymer in the future.

A Novel SAR Imaging Method for GEO Satellite–Ground Bistatic SAR System with Severe Azimuth Spectrum Aliasing and 2-D Spatial Variability

The satellite–ground bistatic configuration, which uses geosynchronous synthetic aperture radar (GEO SAR) for illumination and ground equipment for reception, can achieve wide coverage, high revisit, and continuous illumination of interest areas. Based on the analysis of the signal characteristics of GEO satellite–ground bistatic SAR (GEO SG-BiSAR), it is found that the bistatic echo signal has problems of azimuth spectrum aliasing and 2-D spatial variability. Therefore, to overcome those problems, a novel SAR imaging method for a GEO SG-BiSAR system with severe azimuth spectrum aliasing and 2-D spatial variability is proposed. Firstly, based on the geometric configuration of the GEO SG-BiSAR system, the time-domain and frequency-domain expressions of the signal are derived in detail. Secondly, in order to avoid the increasing cost caused by traditional multi-channel reception technology and the processing burden caused by inter-channel errors, the azimuth deramping is executed to solve the azimuth spectrum aliasing of the signal under the special geometric structure of GEO SG-BiSAR. Thirdly, based on the investigation of azimuth and range spatial variability characteristics of GEO SG-BiSAR in the Range Doppler (RD) domain, the azimuth spatial variability correction strategy is proposed. The signal corrected by the correction strategy has the same migration characteristics as monostatic radar. Therefore, the traditional chirp scaling function (CSF) is also modified to solve the range spatial variability of the signal. Finally, the two-dimensional spectrum of GEO SG-BiSAR with modified chirp scaling processing is derived, followed by the SPECAN operation to obtain the focused SAR image. Furthermore, the completed flowchart is also given to display the main composed parts for GEO SG-BiSAR imaging. Both azimuth spectrum aliasing and 2-D spatial variability are taken into account in the imaging method. The simulated data and the real data obtained by the Beidou navigation satellite are used to verify the effectiveness of the proposed method.

Non-self-adjoint quasi-periodic operators with complex spectrum

We give a precise and complete description on the spectrum for a class of non-self-adjoint quasi-periodic operators acting on ℓ2(Zd) which contains the Sarnak's model as a special case. As a consequence, one can see various interesting spectral phenomena including PT symmetric breaking, the non-simply-connected two-dimensional spectrum in this class of operators. Particularly, we provide new examples of non-self-adjoint operator in ℓ2(Z) whose spectra (actually a two-dimensional subset of C) can not be approximated by the spectra of its finite-interval truncations.

Resolution enhanced two-dimensional correlation infrared spectra of biomolecular changes in muscle tissues of Gambusia affinis

The primary objective of this study is to investigate the effects of Zinc toxicity when fishes are exposed in a controlled environment using two-dimensional correlation spectral analysis. This analysis is superior as subtle spectral changes induced by an external perturbation are not readily noticeable in conventional one-dimensional spectra. A higher decrease in protein and carbohydrates and a lesser decrease in lipids (20%–28%) against the control sample occurs due to Zn toxicity. The lowest value of the bioconcentration factor for Zn shows carbohydrate utilization, which was resolved well in our two-dimensional spectral analysis. Synchronous maps show the predominant changes in protein (1668 cm−1), phospholipids (1771 cm−1), and fatty acids (1707 cm−1). The asynchronous two-dimensional spectra show alteration in phosphodiester stretching of glycogen followed by Amide I. In addition, we explored the importance of principal component analysis coupled two-dimensional spectra analysis which shows weak amide III protein changes. The hetero asynchronous shows well-resolved cross peaks of +(1047, 1389), resulting in improved resolution. The study helps in understanding biomolecular changes of muscle tissues of G. affinis under Zn toxicity using two-dimensional correlation infrared spectral analysis.

Assessment of comprehensive PAH index in roasted Tan lamb by two-dimensional correlation spectroscopy combined with hyperspectral imaging.

Thermally processed meat may contain harmful compounds, including polycyclic aromatic hydrocarbons (PAHs). This study constructed, for the first time, the comprehensive PAH index (CPI) concentration (phenanthrene [26.47%], acenaphthene [21.83%], pyrene [18.64%], fluoranthene [17.11%], fluorene [8.49%], and anthracene [7.46%]). A visible near-infrared (Vis-NIR) hyperspectral image (HSI) system was employed to detect CPI in 150 roasted Tan lamb samples. Furthermore, two-dimensional correlation spectra were used to identify spectral features and reveal the order of chemical bond changes under the characteristic peaks at 579-737-631-449nm. The results indicated that competitive adaptive reweighted sampling-multiple linear regression quantitative prediction model worked the best with calibration set coefficient of determination of 0.9161, calibration set coefficient of root mean square error of 2.3426µg/kg, R-squared prediction of 0.8469, and root mean square error of prediction of 2.4119µg/kg. Finally, PAH content distributions were visualized using the best prediction model. This study aimed to propose a feasible method for CPI in roasted Tan lamb detection based on Vis-NIR HSI coupled with multivariate analysis methods.

Combination of real-time and hyphenated mass spectrometry for improved characterisation of exhaled breath biomarkers in clinical research.

Exhaled breath volatilomics is a powerful non-invasive tool for biomarker discovery in medical applications, but compound annotation is essential for pathophysiological insights and technology transfer. This study was aimed at investigating the interest of a hybrid approach combining real-time proton transfer reaction-time-of-flight mass spectrometry (PTR-TOF-MS) with comprehensive thermal desorption-two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (TD-GCxGC-TOF-MS) to enhance the analysis and characterization of VOCs in clinical research, using COVID-19 as a use case. VOC biomarker candidates were selected from clinical research using PTR-TOF-MS fingerprinting in patients with COVID-19 and matched to the Human Breathomic Database. Corresponding analytical standards were analysed using both a liquid calibration unit coupled to PTR-TOF-MS and TD-GCxGC-TOF-MS, together with confirmation on new clinical samples with TD-GCxGC-TOF-MS. From 26 potential VOC biomarkers, 23 were successfully detected with PTR-TOF-MS. All VOCs were successfully detected using TD-GCxGC-TOF-MS, providing effective separation of highly chemically related compounds, including isomers, and enabling high-confidence annotation based on two-dimensional chromatographic separation and mass spectra. Four VOCs were identified with a level 1 annotation in the clinical samples. For future applications, the combination of real-time PTR-TOF-MS and comprehensive TD-GCxGC-TOF-MS, at least on a subset of samples from a whole study, would enhance the performance of VOC annotation, offering potential advancements in biomarker discovery for clinical research.

Singing voice separation based on U-NET neural network and its application

Singing Voice separation is a very important method that helps people to obtain a vocal or accompaniment for further usage like music composing. In this paper, I developed a Song accompaniment separation application based on Convolutional neural network Unet model and Deep learning (DL). Basically, the U-net convolutional network is applied to the two-dimensional spectrum of a music audio in order to achieve a more precise separation effect. During this deep learning process, a significant amount of data was inserted into the model to get the best fit parameters. Then, after all, the model can automatically generate a mask that filters out the accompaniment or vocal part that users want.. This completed model is then transmitted to the terminal of a internet server to enable users to get access to the model through internet. After the test of both quantitative evaluation and subjective assessment, the application impressively achieved the Song accompaniment Separation that provides a good quality of both vocal and song.

Two-dimensional coherent spectrum of high-spin models via a quantum computing approach

We present and benchmark a quantum computing approach to calculate the two-dimensional coherent spectrum (2DCS) of high-spin models. Our approach is based on simulating their real-time dynamics in the presence of several magnetic field pulses, which are spaced in time. We utilize the adaptive variational quantum dynamics simulation algorithm for the study due to its compact circuits, which enables simulations over sufficiently long times to achieve the required resolution in frequency space. Specifically, we consider an antiferromagnetic quantum spin model that incorporates Dzyaloshinskii-Moriya interactions and single-ion anisotropy. The obtained 2DCS spectra exhibit distinct peaks at multiples of the magnon frequency, arising from transitions between different eigenstates of the unperturbed Hamiltonian. By comparing the one-dimensional coherent spectrum with 2DCS, we demonstrate that 2DCS provides a higher resolution of the energy spectrum. We further investigate how the quantum resources scale with the magnitude of the spin using two different binary encodings of the high-spin operators: the standard binary encoding and the Gray code. At low magnetic fields both encodings require comparable quantum resources, but at larger field strengths the Gray code is advantageous. Numerical simulations for spin models with increasing number of sites indicate a polynomial system-size scaling for quantum resources. Lastly, we compare the numerical 2DCS with experimental results on a rare-earth orthoferrite system. The observed strength of the magnonic high-harmonic generation signals in the 2DCS of the quantum high-spin model aligns well with the experimental data, showing significant improvement over the corresponding mean-field results.

Mechanochemical Tailoring of Lignin Structure: Influence of Different Particle Sizes in the Organosolv Process.

The autocatalyzed ethanolic organosolv process is gaining increasing attention for the sulfur-free isolation of lignin, which is subsequently used as a renewable substitute for various fossil-based applications. For the first time, the mechanochemical influence of seven different particle sizes of two different biomasses on the respective organosolv lignin structure is examined. Wine pruning (Pinot Noir) and wine pomace (Accent) are used for organosolv process with particle sizes ranging from 2.0-1.6mm to less than 0.25mm. As particle size decreases, the weight-average molecular weight increases, while the total phenol content decreases significantly. Additionally, the distribution of the lignin-typical monolignols and relevant substructures, as determined by two-dimensional heteronuclear nuclear magnetic resonance spectra single quantum coherence (HSQC), is observed. The degree of grinding of the biomass has a clear chemical-structural influence on the isolated HG and HGS organosolv lignins. Therefore, it is crucial to understand this influence to apply organosolv lignins in a targeted manner. In the future, particle size specifications in the context of the organosolv process should be expressed in terms of distribution densities rather than in terms of a smaller than specification.

An Effective Model for Capturing the Role of Excitonic Interactions in the Wave-Packet Dynamics of DNA Nucleobases

Investigating exciton dynamics within DNA nucleobases is essential for comprehensively understanding how inherent photostability mechanisms function at the molecular level, particularly in the context of life’s resilience to solar radiation. In this paper, we introduce a mathematical model that effectively simulates the photoexcitation and deactivation dynamics of nucleobases within an ultrafast timeframe, particularly focusing on wave-packet dynamics under conditions of strong nonadiabatic coupling. Employing the hierarchy equation of motion, we simulate two-dimensional electronic spectra (2DES) and calibrate our model by comparing it with experimentally obtained spectra. This study also explores the effects of base stacking on the photo-deactivation dynamics in DNA. Our results demonstrate that, while strong excitonic interactions between nucleobases are present, they have a minimal impact on the deactivation dynamics of the wave packet in the electronic excited states. We further observe that the longevity of electronic excited states increases with additional base stacking and pairing, a phenomenon accurately depicted by our excitonic model. This model enables a detailed examination of the wave packet’s motion on electronic excited states and its rapid transition to the ground state. Additionally, using this model, we studied base stacks in DNA hairpins to effectively capture the primary exciton dynamics at a reasonable computational scale. Overall, this work provides a valuable framework for studying exciton dynamics from single nucleobases to complex structures such as DNA hairpins.

Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine

To tackle the difficulty of extracting features from one-dimensional spectral signals using traditional spectral analysis, a metabolomics analysis method is proposed to locate two-dimensional correlated spectral feature bands and combine it with deep learning classification for wine origin traceability. Metabolomics analysis was performed on 180 wine samples from 6 different wine regions using UPLC-Q-TOF-MS. Indole, Sulfacetamide, and caffeine were selected as the main differential components. By analyzing the molecular structure of these components and referring to the main functional groups on the infrared spectrum, characteristic band regions with wavelengths in the range of 1000–1400 nm and 1500–1800 nm were selected. Draw two-dimensional correlation spectra (2D-COS) separately, generate synchronous correlation spectra and asynchronous correlation spectra, establish convolutional neural network (CNN) classification models, and achieve the purpose of wine origin traceability. The experimental results demonstrate that combining two segments of two-dimensional characteristic spectra determined by metabolomics screening with convolutional neural networks yields optimal classification results. This validates the effectiveness of using metabolomics screening to determine spectral feature regions in tracing wine origin. This approach effectively removes irrelevant variables while retaining crucial chemical information, enhancing spectral resolution. This integrated approach strengthens the classification model's understanding of samples, significantly increasing accuracy.

Application of wavelet transform in anti-Compton phoswich detector for gamma spectrum

The response of an anti-Compton phoswich detector to gamma rays was investigated using Monte-Carlo method, and the pulses from different crystal cases, including gamma deposition only in the LaBr3(Ce) or CsI(Tl) crystal and coincidence in both crystals, were analyzed. A novel pulse discrimination method for gamma deposition events based on wavelet transform analysis, called SSD (Scale Shape Discrimination), was developed in this study. Compared to the traditional PSD (Pulse Shape Discrimination) method, SSD has the advantage of transforming one-dimensional pulses in the time-domain into two-dimensional time-frequency spectra, providing the more useful features for pulse discrimination. The performances of the Compton suppression and Full-energy peak loss using PSD and SSD methods was studied. The results show that the Compton suppression factor IPSD = 5.12 and ISSD = 5.32, and FEP loss factor PLPSD = 0.0554 and PLSSD = 0.0587. Meanwhile, the influences of the cutoff values for pulse discrimination on the results of I and PL with different method were analyzed.

Accurate Range Modeling for High-Resolution Spaceborne Synthetic Aperture Radar.

Spaceborne synthetic aperture radar (SAR) is an advanced microwave imaging technology that provides all-weather and all-day target information. However, as spaceborne SAR resolution improves, traditional echo signal models based on airborne SAR design become inadequate due to the curved orbit, Earth rotation, and increased propagation distance. In this study, we propose an accurate range model for high-resolution spaceborne SAR by analyzing motion trajectory and Doppler parameters from the perspective of the space geometry of spaceborne SAR. We evaluate the accuracy of existing range models and propose an advanced equivalent squint range model (AESRM) that accurately fits the actual range history and compensates for high-order term errors by introducing third-order and fourth-order error terms while maintaining the simplicity of the traditional model. The proposed AESRM's concise two-dimensional frequency spectrum form facilitates the design of imaging algorithms. Point target simulations confirm the effectiveness of the proposed AESRM, demonstrating significant improvements in fitting accuracy for range histories characterized by nonlinear trajectories. The developed AESRM provides a robust foundation for designing imaging algorithms and enables higher resolution and more accurate radar imaging.

FT-Raman and FTIR spectroscopy as a tools showing marker of platinum-resistant phenomena in women suffering from ovarian cancer

Platinum-resistant phenomena in ovarian cancer is very dangerous for women suffering from this disease, because reduces the chances of complete recovery. Unfortunately, until now there are no methods to verify whether a woman with ovarian cancer is platinum-resistant. Importantly, histopathology images also were not shown differences in the ovarian cancer between platinum-resistant and platinum-sensitive tissues. Therefore, in this study, Fourier Transform InfraRed (FTIR) and FT-Raman spectroscopy techniques were used to find chemical differences between platinum-resistant and platinum-sensitive ovarian cancer tissues. Furthermore, Principal Component Analysis (PCA) and machine learning methods were performed to show if it possible to differentiate these two kind of tissues as well as to propose spectroscopy marker of platinum-resistant. Indeed, obtained results showed, that in platinum-resistant ovarian cancer tissues higher amount of phospholipids, proteins and lipids were visible, however when the ratio between intensities of peaks at 1637 cm−1 (FTIR) and at 2944 cm−1 (Raman) and every peaks in spectra was calculated, difference between groups of samples were not noticed. Moreover, structural changes visible as a shift of peaks were noticed for C–O–C, C–H bending and amide II bonds. PCA clearly showed, that PC1 can be used to differentiate platinum-resistant and platinum-sensitive ovarian cancer tissues, while two-trace two-dimensional correlation spectra (2T2D-COS) showed, that only in amide II, amide I and asymmetric CH lipids vibrations correlation between two analyzed types of tissues were noticed. Finally, machine learning algorithms showed, that values of accuracy, sensitivity and specificity were near to 100% for FTIR and around 95% for FT-Raman spectroscopy. Using decision tree peaks at 1777 cm−1, 2974 cm−1 (FTIR) and 1714 cm−1, 2817 cm−1 (FT-Raman) were proposed as spectroscopy marker of platinum-resistant.