The purpose of this study was to evaluate trace element contents in wild sour lychee (WSL), a variety characterized by their sour taste and production of large fruit with prominent seeds, in JingXi region. Moreover, the sweet fruit variety “Fei Zi Xiao” (FZX), known for its palatability and small seed size, was selected for a comparative exanimation. Utilizing inductively coupled plasma mass spectrometry (ICP-MS), elemental analyses were performed on both liquid and solid lychee samples. The comprehensive analytical findings revealed distinct variances in the levels of 68 metal elements, including Chromium (Cr), Selenium (Se), Cobalt (Co), Manganese (Mn), Nickel (Ni), Cadmium (Cd), Arsenic (As), among others, in the liquid sample (LS) and solid sample (SS) of the two lychee varieties (measured in mg·kg−1). Specifically, the Cr content in SS of WSL (17.89 mg·kg−1) was 44.76 times higher than that of FZX (0.3997 mg·kg−1). Cr, an essential metal element crucially involved in glucose metabolism, was found to be significantly elevated in WSL. The Ni content in SS of WSL (18.39 mg·kg−1) was 24.40 times higher than that of FZX (0.7538 mg·kg−1), with Ni categorized as a hazardous heavy metal element impacting human health negatively. In conclusion, the analysis identified that WSL comprises beneficial elements such as Cr, Cu, Se, and Mn with potential anti-diabetic properties. However, the presence of toxic heavy metal elements like Nickel and Lead was also detected in WSL.

Hyperspectral imaging technology can simultaneously capture spatial and spectral target information, which provides technical support for the accurate identification of vehicles and camouflage detection. Although traditional RGB images can achieve vehicle category classification, they suffer from the dual limitations of being unable to distinguish genuine targets from camouflage and having poor adaptability to multi-pose variations of targets. To address these challenges, this study proposes a spatial-spectral decoupled dual-branch network and constructs a multi-type, multi-pose hyperspectral dataset of simulated military trucks, tanks, armored vehicles, and their corresponding camouflage models in a darkroom environment. In the spatial branch, RGB pseudo-color images are synthesized by extracting the central bands of red, green, and blue from hyperspectral images, Then, based on the Vision Transformer (ViT) framework combined with transfer learning, the network deeply extracts spatial structural features of multi-pose vehicles, achieving a classification accuracy of 92.54% for the three vehicle categories. In contrast, in the spectral branch, a collaborative model combining 1D convolutional neural network (1D-CNN) with self-attention mechanism is constructed using preprocessed spectral data, achieving precise camouflage recognition with accuracy exceeding 99.50% for all three vehicle categories. The network effectively overcomes the dual effects of multi- pose variations and camouflage interference on target recognition in complex operational environments, establishing a verifiable algorithmic framework for the tactical deployment of hyperspectral reconnaissance equipment.

By employing the general-purpose relativistic atomic structure package (GRASP) based on a fully relativistic multiconfiguration Dirac–Fock (MCDF) method and the pseudo-relativisitic Hartree–Fock code including superposition of configurations with relativistic corrections, we have reported the energy levels and transition parameters such as wavelengths, transition rates, and logarithmic weighted oscillator strengths for allowed transition (electric dipole, E1), and forbidden transitions (electric quadrupole, E2, and magnetic dipole, M1) for five times ionized krypton (Kr VI, Z = 36), and radon (Rn VI, Z = 86) in this work. The calculations agree very well with the existing data. Such a data set for the levels of some excited configurations and their radiative properties in Rn VI are reported for the first time to our knowledge.

Nanotechnology is driving a revolution by eliminating the boundaries of bio-regenerative medicine and offering new hope in the fight against debilitating pathogen infections, particularly those leads to vulnerabilities. Candida albicans is an opportunistic pathogen, found in the digestive system, skin, and other parts of human body and pose a significant challenge to human health, by causing mucosal and systematic infections. With its revolutionary applications in medicine, nanotechnology offers promising strategies for combating fungal infections. The alarming rise of drug-resistant Candida infections, especially in hospitalized and immunocompromised patients, highlights the urgent need for safer and more effective antifungal alternatives. Limitations of current therapies, such as toxicity, resistance, and poor bioavailability, underscore the potential of novel agents like biogenic silver nanoparticles (Ag NPs) as promising next-generation antifungals. This study explores the bio synthesis of Ag NPs from Saraca asoca (Roxb.) Wild leaf extract, a medicinal plant, and analyzes how it conquers the pathogen Candida albicans. The green synthesized Ag NPs were characterized by using UV–visible spectroscopy, Fourier Transform Infrared Spectroscopy (FT-IR), X-ray powder diffraction (XRPD), and Scanning Electron Microscopy (SEM). The minimum inhibitory concentration (MIC) was determined as 362.152 µg/mL. The antifungal efficacy of green-synthesized Ag NPs against Candida albicans was clearly demonstrated through optical density (OD) measurements and percentage inhibition analysis. The assessments revealed a strong pathogen-suppressing potential, affirming the Ag NP’s promise as an effective antifungal agent.

Identification of ancient wood is crucial for historical research and the preservation of cultural heritage. This study focuses on identifying three ancient wood samples from the Ming Dynasty of China using terahertz (THz) technology and a convolutional neural network (CNN). A comparative analysis was conducted to identify these unknown ancient woods by comparing them with three types of modern pine and three types of modern fir. The terahertz absorption coefficients of nine types of wood samples were first calculated, followed by an analysis of their frequency characteristics within the 0.01–2.5 THz spectral range. The THz spectra were then preprocessed using wavelet denoising (WD) and low-pass filtering (LPF). Dimensionality reduction was subsequently applied to the spectral data based on a cumulative variance contribution threshold of 95%. Finally, the CNN model was developed to identify ancient wood species by minimizing root mean square error (RMSE). Results demonstrate that ancient wood samples THKM7 and NTGSMO1 share five characteristic frequencies with modern pine and fir and a consistent upward trend in absorption coefficients. In addition, the absorption coefficient of ancient wood NTSO3 shows significant deviations in frequency points and amplitudes. Furthermore, the CNN prediction results reveal the minimal RMSE values between the ancient wood THKM7 sample and modern pine XS sample (RMSE = 0.0143) and between the ancient wood NTGSMO1 sample and modern fir LS sample (RMSE = 0.0265). Finally, the accuracy of the prediction results was verified by Generalized Regression Neural Network (GRNN) and Random Forest (RF) classifiers. The study integrating THz technology with deep learning provides a research idea for ancient wood identification and can advance scientific research in cultural heritage conservation.

In this study, a nanocomposite sensor film was developed to identify arsenic metal ions (As3+) in a solution. Polyvinyl alcohol (PVA) nanocomposite films containing in-situ produced gold nanoparticles (AuNPs) were studied for optical, structural, and electrical conductivity, as well as As3+ ion sensing. UV-visible (UV-Vis) spectroscopy was used to corroborate the genesis of AuNPs and showed a band in the wavelength range λ = 526–535 nm. Fourier transform infrared (FT-IR) spectroscopy revealed structural changes in PVA due to AuNP incorporation. The X-ray diffraction (XRD) analysis confirmed the face centered cubic (FCC) structure of the developed AuNPs. Morphological features studied using scanning electron microscopy (SEM) and transmission electron microscopy (TEM) revealed a spherical form of AuNPs with an average size of 7.9 nm. The samples exhibited improved electrical conductivity and thermal stability at higher AuNP concentrations. The produced films were evaluated for sensing As3+ ions using UV-Vis spectroscopy under aqueous conditions. The system exhibited a linear response to As3+ ion concentrations ranging from 1 ppb to 200 ppb. A real water analysis was also performed to ensure the reliability of the developed nanocomposite strips. The detection of As3+ in spiked tap water yielded favorable results, with a detection limit (LOD) 6.38 parts per billion (ppb). These findings demonstrate that this research could be a practical and highly useful tool for detecting As3+ ions under aqueous conditions.

The development of nano-photocatalytic composites has shown great potential to circumvent the bottlenecks in industrial wastewater treatment. However, their implications have not yet been fully understood. In this study, paramagnetic zinc ferrite was synthesized and used as a photocatalyst for the photocatalytic degradation of Fast Yellow AB (FY-AB). The synthesized nano photocatalyst was characterized using various analytical techniques such as Energy Dispersive X-ray (EDX) analysis, band gap, Differential Scanning Calorimetry (DSC), Scanning Electron Microscopy (SEM), X-Rays Diffraction (XRD) and Thermogravimetric analysis (TGA). Different operational parameters, such as pH, initial pollutant concentration, photocatalyst dosage, and scavengers, were investigated concerning the photocatalytic degradation of FY-AB. The results showed over 80% photocatalytic degradation at pH 3.0 using 7 mM hydrogen peroxide (H2O2) for 20 µg/mL of FY-AB within 40 min. The total organic carbon removal efficiency was more than 78% under the same operating conditions. Furthermore, after three cycles, the zinc ferrite catalyst maintained 69% of its initial photocatalytic activity and performed well for each reuse. The kinetic modeling was piloted, and results revealed that the photocatalytic degradation of industrial dye followed a pseudo-first-order kinetic model with R2 of 0.945. These findings revealed that integrating paramagnetic zinc ferrite under visible-light-sensitive photocatalytic degradation of emerging contaminants holds excellent potential for wastewater treatment, catering to human consumption and industrial applications for diverse wastewater treatment needs.

This study confirms the effectiveness of LED-based transmittance measurements, particularly at 460 nm, as a rapid, cost-efficient, and portable method for assessing edible oil quality and detecting adulteration. Among the four tested wavelengths (697, 630, 565, and 460 nm), the blue LED (460 nm) showed the greatest sensitivity to pigment and antioxidant variations, with olive oils exhibiting transmittance between 6% and 75.6%, and mustard oil as low as 1.7%. These differences, supported by ANOVA (p < 0.001) and Tukey’s HSD test, indicate the method’s high discriminatory power. Comparative literature supports the correlation between transmittance and compositional factors such as chlorophyll, tocopherols, and fatty acids. While not a substitute for detailed compositional analyses like GC–MS, this approach offers a practical alternative to more complex techniques like FTIR and UV–Vis, especially for field or decentralized applications. The study highlights the potential for multi-wavelength integration and standardization, supporting future developments in quality control, authenticity verification, and regulatory monitoring within the edible oil industry.

This study investigates the structural, morphological, and photoluminescent properties of Yb³+ doped Y2O3 phosphors synthesized via the solution combustion method. The structural analysis confirmed the cubic phase of Y2O3, with high crystallinity and successful incorporation of Yb³+ ions, as evidenced by X-ray diffraction (XRD). Scanning electron microscopy (SEM) revealed uniform morphology with nanoparticles averaging ∼50 nm. Photoluminescence (PL) studies highlighted strong near-infrared (NIR) emission at 980 nm, attributed to the 2F5/2 → 2F7/2 transition of Yb³+ under ultraviolet and visible excitation. The emission intensity exhibited a dependence on Yb³+ concentration, with a peak at 10 mol%, followed by quenching at higher concentrations. Energy transfer mechanisms, quantum yield, and the impact of host lattice properties on emission efficiency were analyzed in detail. The material demonstrates significant potential for enhancing the efficiency of silicon-based photovoltaic devices and for applications in bio-imaging due to its efficient NIR emission and compatibility with biological systems. This research establishes Yb³+ doped Y2O3 as a versatile phosphor for advanced optical applications.