Search Results (955)

In this study, we report the synthesis and characterization of a novel photocatalyst composite composed of functionalized carbon nanotubes (f-CNT) and phenyl-modified graphitic carbon nitride (PhCN). The incorporation of the phenyl group extends the absorption range into the visible spectrum compared to pure g-C₃N₄. Additionally, the formation of the heterostructure in the f-CNT/PhCN composite exhibits improved charge transfer efficiency, facilitating the separation and transfer of photogenerated electron-hole pairs and reducing recombination rates. The photocatalytic performance of this composite was evaluated by the degradation of Rhodamine B (RhB) under visible light irradiation. The f-CNT/PhCN composite exhibits remarkable efficiency in degrading RhB, achieving 60% degradation after 4 h, and 100% after 24 h under low-power white LED excitation. This represents a substantial improvement over the non-functionalized CNT/PhCN composite, which shows much lower performance. In contrast, pure PhCN demonstrates very little activity. Structural and optical properties were characterized using X-ray diffraction (XRD), transmission electron microscopy (TEM), Raman spectroscopy, and UV–Vis spectroscopy. Time-resolved photoluminescence measurements were used to study the behavior of photoexcited carriers, confirming that the composite improves charge transfer efficiency for photogenerated carriers by approximately 30%. The results indicate that the functionalization of CNTs significantly enhances the photocatalytic properties of the composite, making f-CNT/PhCN a promising candidate for environmental remediation applications, particularly in the degradation of organic pollutants in wastewater. Full article

Water level forecasting has significant impacts on transportation, agriculture, and flood control measures. Accurate water level values can enhance the safety and efficiency of water conservancy hub operation scheduling, reduce flood risks, and are essential for ensuring sustainable regional development. Addressing the nonlinearity and non-stationarity characteristics of gate-front water level sequences, this paper introduces a gate-front water level forecasting method based on a GRU–TCN–Transformer coupled model and permutation entropy (PE) algorithm. Firstly, an analysis method combining Singular Spectrum Analysis (SSA) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is used to separate the original water level data into different frequency modal components. The PE algorithm subsequently divides each modal component into sequences of high and low frequencies. The GRU model is applied to predict the high-frequency sequence part, while the TCN–Transformer combination model is used for the low-frequency sequence part. The forecasting from both models are combined to obtain the final water level forecasting value. Multiple evaluation metrics are used to assess the forecasting performance. The findings indicate that the combined GRU–TCN–Transformer model achieves a Mean Absolute Error (MAE) of 0.0154, a Root Mean Square Error (RMSE) of 0.0205, and a Coefficient of Determination (R²) of 0.8076. These metrics indicate that the model outperforms machine learning Support Vector Machine (SVM) models, GRU models, Transformer models, and TCN–Transformer combination models in forecasting performance. The forecasting results have high credibility. This model provides a new reference for improving the accuracy of gate-front water level forecasting and offers significant insights for water resource management and flood prevention, demonstrating promising application prospects. Full article

Accurate and reliable bearing-fault diagnosis is important for ensuring the efficiency and safety of industrial machinery. This paper presents a novel method for bearing-fault diagnosis using Mel-transformed scalograms obtained from vibrational signals (VS). The signals are windowed and pass through a Mel filter bank, converting them into a Mel spectrum. These scalograms are subsequently fed into an autoencoder comprising convolutional and pooling layers to extract robust features. The classification is performed using an artificial neural network (ANN) optimized with the FOX optimizer, which replaces traditional backpropagation. The FOX optimizer enhances synaptic weight adjustments, leading to superior classification accuracy, minimal loss, improved generalization, and increased interpretability. The proposed model was validated on a laboratory dataset obtained from a bearing testbed with multiple fault conditions. Experimental results demonstrate that the model achieves perfect precision, recall, F1-scores, and an AUC of 1.00 across all fault categories, significantly outperforming comparison models. The t-SNE plots illustrate clear separability between different fault classes, confirming the model’s robustness and reliability. This approach offers an efficient and highly accurate solution for real-time predictive maintenance in industrial applications. Full article

This study presents the theoretical design and evaluation of a triple-junction polymer solar cell architecture, incorporating oligomers of PDCBT, PPDT2FBT, and PDPP3T as donor materials and PC₇₁BM as the electron acceptor. Using density functional theory (DFT) simulations and time-dependent DFT (TD-DFT) methods, the investigation covers essential photovoltaic parameters, including molecular geometries, UV-Vis spectra, and charge transport properties. The device is structured to maximize solar energy absorption across the spectrum, featuring front, middle, and back junctions with band gaps of 1.9 eV, 1.63 eV, and 1.33 eV, respectively. Each layer targets different regions of the solar spectrum, optimizing light harvesting and charge separation. This innovative multi-junction design offers a promising pathway to enhanced power conversion efficiencies in polymer solar cells, advancing the integration of renewable energy technologies. Full article

This study investigates the stability, electronic structure, and optical properties of the GaN/g-C₃N₄ heterojunction using the plane wave super-soft pseudopotential method based on first principles. Additionally, an external electric field is employed to modulate the band structure and optical properties of GaN/g-C₃N₄. The computational results demonstrate that this heterojunction possesses a direct band gap and is classified as type II heterojunction, where the intrinsic electric field formed at the interface effectively suppresses carrier recombination. When the external electric field intensity (E) falls below −0.1 V/Å and includes −0.1 V/Å, or exceeds 0.2 V/Å, the heterojunction undergoes a transition from a type II structure to the superior Z-scheme, leading to a significant enhancement in the rate of separation of photogenerated carriers and an augmentation in its redox capability. Furthermore, the introduction of a positive electric field induces a redshift in the absorption spectrum, effectively broadening the light absorption range of the heterojunction. The aforementioned findings demonstrate that the optical properties of GaN/g-C₃N₄ can be precisely tuned by applying an external electric field, thereby facilitating its highly efficient utilization in the field of photocatalysis. Full article

A method for identifying six estrogen preparations by thin-layer chromatography combined with Raman imaging microscopy (TLC-RIM) was established. An appropriate pretreatment method was adopted to extract and purify the six estrogen preparations. After that, each estrogen extraction solution was spotted on a thin-layer chromatography plate. Estriol (E3), estradiol (E2), estradiol valerate (EV), estradiol benzoate (EB), nilestriol (CEE), and ethinylestradiol (EE2) were separated by TLC, and their R_f value and localization were determined under a UV lamp at 254 nm, followed by the in situ enrichment of the drug component. Using a 532 nm laser as the light source, the Raman scattering spectrum of the component was directly collected by micro-Raman imaging. The R_f values after TLC separation of the six estrogens and their Raman spectra can, respectively, reflect differences in polarity and structure, and they are not affected by the excipients of preparation. The detection limits of the six estrogens are 0.636, 1.00, 0.687, 0.497, 0.649, and 0.626 mg/mL. Based on the intensity of the minimum characteristic peak, the stability results within 40 min showed that the RSD of each substance is 1.34, 2.06, 1.65, 3.99, 1.16, and 2.71%, respectively. This method has strong specificity, good stability, and high sensitivity, and it can provide a new reference for improving the identification standards of estrogen preparations. Full article

Cremastrae Pseudobulbus Pleiones Pseudobulbus (CPPP) is derived from the dried pseudobulb of the orchid family plants Cremastra appendiculata (D.Don) Makino, Pleione bulbocodioides (Franch.) Rolfe, or Pleione yunnanensis Rolfe, and has the properties of clearing heat, detoxification, resolving phlegm, and dispersing nodules. It is frequently used for the treatment of various malignant tumors in clinical practice, especially lung cancer. CPPP is divided into two commercial specifications in the market, Maocigu (MCG) and Bingqiuzi (BQZ). However, owing to a lack of appropriate research strategies, the active ingredients and molecular mechanisms involved have not yet been clarified. This study intended to discover the combination of effective anti-lung-cancer ingredients in CPPP and explore their potential mechanisms of action. In this study, UHPLC-MS fingerprints of MCG and BQZ were established separately. Inhibitory effects on the proliferative viability and migratory ability of A459 and H1299 cells were evaluated as pharmacodynamic indicators. GRA and BCA were used to determine spectrum–effect relationships. Next, the identification and analysis of components of drug-containing serum were performed using UHPLC-Q-Exactive Orbitrap MS. Then, the results of the two analyses were combined to jointly screen out the anti-lung-cancer candidate active monomers of CPPP, and their in vitro activities were verified. Afterward, all effective ingredient combinations of MCG (MCGC) and BQZ (BQZC) were prepared according to their contents in the original medicinal materials. Their anti-lung-cancer activities in vitro and in vivo were compared and verified. Finally, we used the human lung cancer cell line A549 and the Lewis tumor xenograft model to investigate how BQZC would influence autophagy and apoptosis processes and the mechanisms involved. Overall, 11 predominant anti-lung-cancer active ingredients from CPPP were screened. Next, MCGC and BQZC were prepared according to their contents in the original medicinal materials, respectively, and their anti-tumor effects were equivalent to those of the original materials in vitro and in vivo. We found that BQZC could inhibit lung cancer cell growth and induce protective autophagy and apoptosis in lung cancer cells by activating the AMPK–mTOR–ULK1/BMF signaling pathway. These results provide important evidence for the clinical application and deep development of CPPP against tumors. Full article

This paper presents an improved algorithm for retrieving ocean surface currents from X-band marine radar images. The original polar current shell (PCS) method begins with a 3D fast Fourier transform (FFT) of the radar image sequence, followed by the extraction of the dispersion shell from the 3D image spectrum, which is then transformed into a PCS using polar coordinates. Building on this foundation, the improved approach is to analyze all data points corresponding to different wavenumber magnitudes in the PCS domain rather than analyzing each specific wavenumber magnitude separately. In addition, kernel density estimation (KDE) to identify high-density directions, interquartile range filtering to remove outliers, and symmetry-based filtering to further reduce noise by comparing data from opposite directions are also utilized for further improvement. Finally, a single curve fitting is applied to the filtered data rather than conducting multiple curve fittings as in the original method. The algorithm is validated using simulated data and real radar data from both the Decca radar, established in 2008, and the Koden radar, established in 2017. For the 2008 Decca radar data, the improved PCS method reduced the root-mean-square deviation (RMSD) for speed estimation by 0.06 m/s and for direction estimation by 3.8° while improving the correlation coefficients (CCs) for current speed by 0.06 and direction by 0.07 compared to the original PCS method. For the 2017 Koden radar data, the improved PCS method reduced the RMSD for speed by 0.02 m/s and for direction by 4.6°, with CCs being improved for current speed by 0.03 and direction by 0.05 compared to the original PCS method. Full article

We consider the wideband spectrum sensing within a multi-path propagation environment, where a multi-antenna base station (BS) is tasked with identifying the frequency positions of multiple narrowband transmissions distributed across a broad range of frequencies. To tackle this, we propose a sub-Nyquist sampling structure that incorporates a phased array system. Specifically, each antenna is connected to two separate sampling channels, i.e., one for direct sampling and another for delayed sampling, with the latter incorporating a specified time delay factor. The cross-correlation matrices associated with the samples, which are characterized by different time lags, are calculated. These matrices are represented in tensor form, and the factor matrices are extracted through CANDECOMP/PARAFAC (CP) decomposition. By these factor matrices, the carrier frequencies and the power spectra of the far-field signals of interest are estimated. Numerical simulations are conducted to evaluate the performance of the proposed method, and the results reveal the feasibility and effectiveness of the approach, demonstrating its potential for accurate and efficient wideband spectrum sensing in complex multi-path propagation environments. Full article

Background: Sleep disorders are common in youths with autism spectrum disorders. Inflammatory cytokines such as Il-1 beta and Il-6 in saliva have been associated with alterations in sleep quality in various conditions. We assessed whether there were associations between the salivary concentration of IL-1 beta and IL-6 and sleep quality in youths with ASD versus typically developing (TD) age- and gender-matched youths. Method: Forty children and adolescents with ASD or TD participated in this study (20% females). Their parents answered the items of a validated questionnaire on sleep quality (Pittsburgh Sleep Quality Index). Results: The mean Pittsburgh score was significantly higher (i.e., the quality of sleep was poorer) in the ASD group (8.68 ± 0.35 (SEM), ranging from 7 to 12 points), compared to the TD group (7.35 ± 0.54 (SEM), ranging from 2 to 12 points) (p = 0.02, Mann–Whitney U test). There were no significant differences in the salivary concentration of Il-1 beta and IL-6 receptor between the two groups, but salivary IL-1 beta concentration was inversely associated with poor sleep quality in the ASD group. No associations between the salivary Il-6 concentration and sleep quality were found in either group. Linear regression analysis by separate groups revealed significant associations between the sleep quality score and the concentration of IL-1 beta in the ASD group (p = 0.01, OR = −0.53, 95% CI −0.008–0.001). In contrast, no significant associations were observed in the TD group, or for IL-6 in either group. No significant effects of sex, age, or use of psychotropic medications were found. Conclusions: Children and adolescents with ASD showed significantly poorer sleep quality based on their parents’ reports compared to the TD group, and the salivary IL-1 beta concentration was inversely associated with sleep quality only in the ASD group. Further studies on the associations between inflammatory cytokines and sleep in ASD are needed. Full article

This study highlights the differences in the metabolomes of cow milk yoghurt (CY) and goat milk yoghurt (GY) using a nuclear magnetic resonance (NMR)-based metabolomic approach. The 1H HRMAS-NMR spectrum displayed 21 metabolites comprising organic acids, sugars, amino acids, amino acid derivatives and phospholipids. The orthogonal partial least squares discriminant analysis model clearly separated CY and GY groups, implying differences in metabolite composition. The corresponding Variable Importance in Projection (VIP) plot revealed that choline, sn-glycero-3-phosphocholine, O-phosphocholine, fucose, citrate, sucrose, glucose and lactose mainly contributed to the group separation (VIP > 1). Hierarchical cluster analysis further confirmed the metabolome similarities and differences between CY and GY. Additionally, 12 significantly differential metabolites (with a fold change > 1.5 and p-value < 0.05) were identified, with 1 downregulated and 11 upregulated. Pathway impact analysis revealed the correlation of significant metabolites with starch and sucrose metabolism, galactose metabolism, and the citrate cycle. Furthermore, receiver operating characteristic curve analysis identified eight metabolites (choline, sn-glycero-3-phosphocholine, fucose, O-phosphocholine, glucose, citrate, 2-oxoglutarate, lactose and sucrose) as candidate biomarkers. This study represents the first utilization of HRMAS-NMR to analyze the metabolomic profiles of yoghurt made from cow and goat milk. In conclusion, these findings provide preliminary information on how NMR-based metabolomics can discriminate the metabolomes of yoghurt prepared from the milk of two different animals, which may be valuable for authenticity and adulteration assessments. Full article

The production of cost-effective novel materials for PV solar cells with long-term stability, high energy conversion efficiency, enhanced photon absorption, and easy electron transport has stimulated great interest in the research community over the last decades. In the presented work, Cu/Cu₂S-MWCNTs nanocomposites were produced and analyzed in the framework of potential applications for PV solar cells. Firstly, the surface of the produced one-dimensional Cu was covered by Cu₂S nanoflake. XRD data prove the formation of both Cu and Cu₂S structures. The length and diameter of the one-dimensional Cu wire were 5–15 µm and 80–200 nm, respectively. The thickness of the Cu₂S nanoflake layer on the surface of the Cu was up to 100 nm. In addition, the Cu/Cu₂S system was enriched with MWCNTs. MWCNs with a diameter of 50 nm interact by forming a conductive network around the Cu/Cu₂S system and facilitate quick electron transport. Raman spectra also prove good interfacial coupling between the Cu/Cu₂S system and MWCNTs, which is crucial for charge separation and electron transfer in PV solar cells. Furthermore, UV studies show that Cu/Cu₂S-MWCNTs nanocomposites have a wide absorption band. Thus, MWCNTs, Cu, and Cu₂S exhibit an intense absorption spectrum at 260 nm, 590 nm, and 972 nm, respectively. With a broad absorption band spanning the visible–infrared spectrum, the Cu/Cu₂S-MWCNTs combination can significantly boost PV solar cells’ power conversion efficiency. Furthermore, UV research demonstrates that the plasmonic character of the material is altered fundamentally when CuS covers the Cu surface. Additionally, MWCN-Cu/Cu2S nanocomposite exhibits hybrid plasmonic phenomena. The bandgap of Cu/Cu₂S NWs was found to be approximately 1.3 eV. Regarding electron transfer and electromagnetic radiation absorption, the collective oscillations in plasmonic metal-p-type semiconductor–conductor MWCNT contacts can thus greatly increase energy conversion efficiency. The Cu/Cu₂S-MWCNTs nanocomposite is therefore a promising new material for PV solar cell application. Full article

China is a significant global producer and consumer of pesticides and antibiotics, with their excessive use leading to substantial water pollution that poses challenges for subsequent treatment. Photocatalytic degradation, leveraging renewable solar energy, presents an effective approach for decomposing organic pollutants and reducing residual contaminant levels in water bodies. This approach represents one effective way for addressing environmental challenges. This paper classifies representative photocatalytic materials by structural design and degradation principles including MOFs (Metal–Organic Frameworks), metal- and nonmetal-doped, mesoporous material-loaded, carbon quantum dot-modified, floatation-based, and heterojunction photocatalysts. We also discuss research on degradation pathways and reaction mechanisms for antibiotics. Of particular importance are several key factors influencing degradation efficiency, which are summarized within this work. These include the separation and charge transfer rate of catalyst surface carriers, and the wide-spectrum response capabilities of photocatalysts, as well as persulfate activation efficiency. Furthermore, emphasis is placed on the significant role played by intrinsic driving forces such as built-in electric fields within catalytic systems. Moreover, this paper introduces several promising composite-structure photocatalytic technologies from both composite-structure perspectives (e.g., Aerogel-based composites) and composite-method perspectives (e.g., the molecularly imprinted synthesis method). We also discuss their latest development status, along with future prospects, presenting valuable insights for pollutant degradation targets. This work aims to facilitate the design of efficient photocatalytic materials, while providing valuable theoretical references for environmental governance technologies. Full article

Near-infrared spectroscopy (NIR) is an efficient and accurate method for fat content detection in walnuts. ‘Wen 185’ walnut is grown in large quantities in southern Xinjiang, and its fat content is an important indicator for evaluating the intrinsic quality. The excessive pursuit of yield efficiency, combined with the neglect of quality, agricultural product safety and other factors, has led to the production of poor-quality walnuts. Moreover, research on predicting walnut kernel fat content based on near-infrared spectroscopy technology is rarely reported. Therefore, a technical framework for walnut kernel detection based on near-infrared spectroscopy and the technical standards for ‘Wen 185’ are urgently needed. After first optimizing the initial spectrum data using five preprocessing methods, we established separate prediction models for walnut kernel fat content based on either a back propagation neural network or a support vector regression (SVR) algorithm. The results show that the correction set and validation set coefficients of determination of the walnut kernel fat content prediction model using the back propagation neural network algorithm were 0.86 and 0.89, respectively, with root mean square errors of 1.56 and 1.58, and an RPD value of 2.57; the coefficients of determination for the calibration and validation sets of SVR were 0.90 and 0.83, respectively, with root mean square errors of 1.76 and 1.70, respectively, and an RPD value of 1.70. Thus, near-infrared spectroscopy can be used to establish a foundation for realizing the rapid detection of walnut kernel fat content. Full article

Asymptomatic primary hyperparathyroidism (PHPT) involves 80–90% of the parathyroid tumor-associated cases of PHPT in the modern medical era, while normocalcemic PHPT (NPHPT) has a prevalence of 0.1–11%. We aimed to analyze the bone status and mineral metabolism in NPHPT amid conservative or surgical management. In this narrative review, we searched PubMed (between January 2020 and July 2024) via different keywords. Fourteen studies from the final analysis (388 patients with NPHPT; 1188 with PHPT; and 803 controls) showed that mean serum calcium levels varied between 2.57 and 2.26 mmol/L in NPHPT. Ten studies identified a similar 24 h urinary calcium in NPHPT versus hypercalcemic PHPT (HPHPT). Except for one study, a mandatory vitamin D analysis was performed, but the 25-hydroxyvitamin D cut-offs varied. Osteoporosis (n = 6 studies; N = 172 with NPHPT) was confirmed in 41.7–100% of NPHPT subjects. In surgery candidates, this rate might be overestimated. A DXA analysis was performed in eight studies (235 subjects with NPHPT, and 455 patients with HPHPT); two studies identified a lower BMD in HPHPT < NPHPT, but the results were not homogenous. A single study analyzed the TBS and found similar results in NPHPT. The prevalence of fractures (n = 9) varied between 7.4% and 42.8% in NPHPT. Bone turnover markers (N = 262 patients, n = 8 studies) showed lower bone formation markers in NPHPT versus PHPT (n = 3). Two studies analyzed the BMD and bone turnover markers following parathyroidectomy (161 patients, including 30 patients with NPHPT; mean ages over 60 years). To conclude, given the wide spectrum of complications associated with PHPT, an early diagnosis and proper management is essential. A more extensive screening in patients with osteoporosis and kidney stones might lead to the discovery of NPHPT, a more recently described form of PHPT. While it is still unclear whether NPHPT is an early stage of HPHPT or a separate entity, recent findings show similar osteoporosis and fracture occurrence, and an improvement in bone metabolism, following parathyroidectomy. More extensive prospective studies are crucial to understand the natural course of the disease, to reach a consensus regarding parathyroidectomy indications and surgery candidates’ selection, and to ensure proper personalized management for these patients. With the evolving diagnosis methods, PHPT has become a condition with a changing clinical presentation, which now requires modern evaluation and treatment approaches. Full article