In this study, we successfully designed and synthesized a novel one‐dimensional Cd(II) coordination polymer, [CdL 3 ] n [HL = 4‐acetylphenoxyacetic acid], employing 4‐acetylphenoxyacetic acid as both a chelating and bridging ligand. The structural features of the Cd(II) coordination polymer were analyzed using infrared (IR) spectroscopy, the thermogravimetric and differential thermal analysis, and single‐crystal x‐ray diffraction. The results indicated that the Cd(II) ion adopts a distorted pentagonal pyramidal coordination geometry involving seven oxygen atoms from five different 4‐acetylphenoxyacetate ligands. This configuration forms a one‐dimensional chain structure through the bridging interactions of the oxygen atoms in the ligands. The Hirschfeld surface analysis was implemented to examine intermolecular interactions within the Cd(II) coordination polymer. Furthermore, the photoluminescent properties of the Cd(II) coordination polymer were investigated in ethanol, indicating that the studied material has the potential to exhibit luminescent properties.

In this paper, we systematically established a chirp modulation theoretical model for spectral focusing coherent anti‐Stokes Raman scattering technology (SF‐CARS). Through in‐depth analysis of the dispersion characteristics of fiber optic dispersion and grating pairs, the control mechanism of dispersion effect on the time frequency characteristics of laser pulses is elucidated, and the influence of laser pulse parameters on spectral resolution and signal‐to‐noise ratio (SNR) is revealed. Through theoretical optimization of precise chirp modulation, we improved the spectral resolution of the SF‐CARS system to 6.7 cm −1 and simultaneously enhanced the SNR to 340, achieving a concurrent improvement in both resolution and detection sensitivity. With the improvement of spectral resolution and SNR of SF‐CARS, its application boundaries for studying dynamic processes will be further expanded.

This study sought to obtain information regarding the authorship of two old wooden icons from the “Museikon” collection, a museum dedicated to icons located in the historical city of Alba Iulia. The stylistic and theological analyses indicated a possible author; to support this hypothesis, nondestructive physicochemical investigations were carried out, which were compared with those carried out on an icon with a known author (signed icon) located in the collection of the Metropolitan Museum in Cluj‐Napoca. The nondestructive physical–chemical analyses performed, using energy‐dispersive x‐ray fluorescence and Fourier‐transform infrared spectroscopy, provided information about the materials used to create the works of art. The results indicated the use of similar materials and techniques for the creation of works of art: gold leaf, cinnabar, copper green, lead white, iron red, umber, carbon black—used as pigments, and egg yolk—used as the binder in the tempera technique (and probably, linseed oil, constituting a variation known as tempera grassa) are the common materials observed. The omnipresence of white lead applied over the ground and the brown lead color suggests the appearance of plattnerite as a degradation product, which strengthens the connection between the icons. Thus, the physical–chemical investigations support the hypothesis of the common author (Grigore Ranite) suggested by the theological and stylistic analysis.

Particulate phosphorus (PP) is a major contributor to lakes developing eutrophic. It also serves as one of the most significant sources of phosphorus for primary productivity, serving as a possible source of soluble reactive phosphorus, and contributing a sizable amount of the total phosphorus (TP), so monitoring the spatial and temporal variability of PP is crucial for understanding eutrophication in water bodies. This study aims to propose an algorithm to accurately predict the PP concentration and to assess the performance of the model. Considering Chaohu Lake as a case study, we proposed a random forest algorithm based on the convolutional neural network (CNN‐RF) to investigate the spatial and temporal patterns of PP concentration in the lake using HJ‐2 hyperspectral satellite images. The applicability of backpropagation (BP) neural network, random forest (RF), convolutional neural network (CNN), and CNN‐RF models for remote sensing inversion of PP concentration is assessed through model comparison. The results indicate that the CNN‐RF model has the best prediction performance, with a coefficient of determination (R2) of 0.91, a residual prediction deviation (RPD) of 3.42, a root mean square error (RMSE) of 0.0155 mg/L, and a percentage bias (PBIAS) of 5.01%, which is better than the BP model by 42%, 106%, 51%, and 81%, the RF model by 25%, 77%, 44%, and 59%, and the CNN model by 9.6%, 42%, 30%, and 49%, respectively, and 59% better than the CNN model. The inversion findings suggest that the PP of Chaohu Lake is substantially high in summer and autumn but comparatively low in spring and winter. The spatial distribution demonstrates an arrangement of highest in the western part of the lake, second in the eastern region, and lowest in the central, with obvious spatial and temporal distribution characteristics, and the trend is closely related to the quarterly mean air temperature and mean precipitation. This indicates that based on the differences in phosphorescence scattering signals of different morphologies in water bodies, the use of hyperspectral remote sensing and the CNN‐RF model can effectively extract PP spatiotemporal information, strengthen the learning capability of multiscale characteristics, and contribute to the improvement of the precision of estimating PP concentration, which could provide an innovative approach for determining the degree of eutrophication of lake water bodies.

In this work, we aimed to develop and validate a new absorption factor spectrophotometric method for the assay of the paracetamol (PRA) and orphenadrine (ORP) combination in bulk and tablet dosage form. The method utilizes mathematical processing of the total absorbance of the PRA‐ORP mixture at two precisely selected wavelengths (203 and 243 nm). Employing the established absorption factor of standard PRA at these wavelengths, the individual absorbance of each drug within the mixture is obtained, eliminating the need for prior separation. The method showed excellent linearity for both drugs within the concentration range of 2–14 μg/mL, and the correlation coefficients (R2) were 0.9985 and 0.9986 for PRA and ORP, respectively. The limit of detection (LOD) values were determined to be 0.14 μg/mL for PRA and 0.16 μg/mL for ORP. Similarly, the limit of quantification (LOQ) values were 0.44 and 0.48 μg/mL for PRA and ORP, respectively. Furthermore, the method exhibited high accuracy, with recoveries ranging from 99% to 102%, and excellent precision, demonstrated by relative standard deviation (%RSD) values below 2%. These findings confirm the validity of our developed method for the routine analysis of PRA and ORP in both bulk and tablet dosage forms, making it a practical and cost‐effective approach for quality control purposes.

This study presents a novel approach to identifying the spatial distribution of geological outcrops using remote sensing, with the Lufeng Dinosaur Valley in China as the focal area. We integrate GF‐5 hyperspectral imagery with the automated morphological endmember extraction–pure pixel index (AMEE–PPI) algorithm to improve endmember extraction accuracy. Additionally, we apply a suite of spectral transformation techniques—log(1/R), continuum removal (CR), and first‐order derivative (FD)—combined with feature band selection algorithms including the successive projection algorithm (SPA), iteratively retained informative variables (IRIVs), and competitive adaptive reweighted sampling (CARS), effectively reducing data dimensionality and enhancing model performance. For classification, we evaluate three machine learning models: random forest (RF), k‐nearest neighbor (KNN), and support vector machine (SVM). Among them, the SVM model demonstrates superior accuracy and stability. The proposed original spectral–SPA–SVM (OS–SPA–SVM) model achieves high precision in identifying outcrops, offering practical value for geological exploration and assessment. Our findings contribute theoretically and methodologically to the application of hyperspectral remote sensing and machine learning in geological research, providing insights into the unique geological characteristics of the Lufeng Dinosaur Valley.

Honey is considered as a premium food produced by honeybees. It is highly appreciated by consumers around the world and raises a major concern nowadays which is ensuring its authenticity in respect to its production and its botanical origin. In Lebanon, honey is mainly multifloral which makes its authentication rather difficult. While mid-infrared (MIR) spectroscopy combined with multivariate analysis has proven to be successful in authenticating unifloral honey, the challenge with Lebanese honey lies in assessing its performance with multifloral honey. Therefore, this work aims to test the performance of common components analysis (CCA) applied on mid-infrared spectra in the authentication of multifloral Lebanese honey. For this purpose, 96 multifloral Lebanese honey samples of different floral sources were collected from different regions of the Lebanese territory and analyzed using MIR spectroscopy. CCA applied to the spectral data, allowed a separation between honeydew honey samples and floral honey samples. In addition, honey samples collected from the Bekaa plain region were differentiated from the other honey samples collected from all the other Lebanese geographical regions. This discrimination between the groups of honey samples is based essentially on their sugar composition.

Soil water content is a critical environmental parameter in research and practice, though various technological and contextual constraints limit its estimation in arid areas with vegetation cover. This study combined the multitemporal remote sensing data of Sentinel-1 and Landsat 8 to conduct an inversion study on surface soil water content under low vegetation cover in Nagqu, central Tibetan Plateau. Four vegetation indices (NDVI, ARVI, EVI, and RVI) were extracted from optical remote sensing data. A water cloud model was used to eliminate the influence of the vegetation layer on the backscattering coefficient associated with vegetation cover, and a predictive model suitable for the Nagqu area was constructed. The water cloud model effectively incorporated a vegetation index instead of vegetation water content. We found that VV polarization was more suitable for soil water content inversion than VH polarization. Among the four vegetation indices, the soil water content inversion model constructed with RVI under VV polarization had the best fit (R2 = 0.8212; RMSE = 6.30). The second-best fit was observed for vegetation water content-NDVI (R2 = 0.8201). The soil water content inversion models all had an R2 > 0.6, regardless of the vegetation index used, though the RVI had the best fitting effect, indicating that this vegetation index is highly applicable in the water cloud model, as a substitute for vegetation water content, and is expected to perform well in similar study sites.

Acanthospermum hispidum DC. is a plant with extensive traditional use in folk medicine for treatment of various infections in Sudan including jaundice, stomach discomfort, constipation, and viral infections. This research was carried out to explore the biological and chemical properties of the crude extract of A. hispidum. Ethanol crude extract was prepared from the aerial parts of the plant and investigated for antibacterial action against standard monoderm and diderm bacteria using the cup plate method. Free radical scavenging was investigated out by using the DPPH method. Cytotoxicity assay was performed against human breast cancer cell line MCF7 using the MTT assay. Bioactive compounds were identified using GC‐MS. The ethanolic extract of A. hispidum revealed antibacterial activity against all the tested bacteria. Also, the extract exhibited potent antioxidant activity at 0.5 mg/ml using DPPH (83% ± 00.09). The cytotoxicity against MCF7 revealed potent growth inhibition activity (IC50 75.8 µg/ml). GC‐MS analysis revealed the presence of ethyl α‐d‐glucopyranoside, hexadecanoic acid, stigmasterol, β‐sitosterol, undecanoic acid, phytol, alloaromadendrene oxide, 3‐cyclopentylpropionic acid, 2‐dimethylaminoethyl ester, and longifolenaldehyde in aerial parts of A. hispidum, all of which support antibacterial, antioxidant, and cytotoxic properties.

The investigation of pathological diseases largely relies on laboratory examinations. The ability to identify and characterise cells is an essential process for clinicians to reach an accurate diagnosis and inform appropriate treatments. There is currently a gap between the advancement of scientific knowledge on cellular and molecular pathways and the development of novel techniques capable of detecting subtle cellular changes associated with disease. Biospectroscopy is the use of spectroscopy techniques to investigate biological materials. Within a biological sample, important molecules such as lipids, carbohydrates, nucleic acids, and proteins are held together by chemical bonds; these bonds will vibrate following excitation with infrared light. By measuring the vibrational energy of each molecule present in a biological sample, a unique spectrum, known as the “molecular fingerprint” is generated. As disease-related changes in biological samples will be reflected in the molecular fingerprint, biospectroscopy is a well-placed candidate for the investigation of disease. Biospectroscopy has been gaining wider acceptance and application in the clinical setting over the past decade; however, it has yet to reach diagnostic laboratories and healthcare clinics as a routine platform for clinical assessment. Immunological disorders are complex, often demonstrating interaction across multiple molecular pathways which results in delayed diagnosis. Vibrational spectroscopy is being applied in many fields, and here we present a review of its use in cellular immunology. Potential benefits, including an enhanced definition of molecular processes and the use of spectroscopy in disease diagnosis, monitoring, and treatment response, are discussed. The translation of vibrational spectroscopic techniques into clinical practice offers rapid, noninvasive, and inexpensive methods to obtain information on the molecular composition of biological samples. The potential clinical benefits of biospectroscopy include providing a more prompt and accurate disease diagnosis, thus improving patient care and resulting in better health outcomes.