Search Results (17,072)

As a cost-effective and environmentally benign photocatalyst, hydrothermal carbonation carbon (HTCC) has been extensively studied in the fields of resource utilization and environmental remediation. In this study, HTCC photocatalysts with efficient photocatalytic performances were prepared from straw using acid modification under hydrothermal conditions. The as-prepared HTCC photocatalysts were applied to the degradation of microcystin-LR and the reduction of aqueous Cr(VI). The critical role of acid modification in the photocatalytic performances of the HTCC photocatalysts was systematically investigated. The results demonstrated that acid-modified photocatalysts exhibited a significantly enhanced removal efficiency for Cr(VI) and microcystin-LR under visible light irradiation. A series of characterization techniques, including Raman spectroscopy and N₂ adsorption–desorption analysis, revealed that the superior photocatalytic activities of acid-modified HTCC could be attributed to its higher aromatization level, enhanced light-harvesting ability, and increased concentration of active sites compared with pristine HTCC. Furthermore, electron spin resonance (ESR) and trapping experiments indicated that hydrogen radicals (·H) served as the primary active species in the photocatalytic Cr(VI) reduction of straw-based HTCC. This work provides both practical and theoretical insights into the resource utilization of agricultural waste and the remediation of environmental pollution. Full article

Major vibrational spectroscopy studies have focused on the preparation of chromium coatings via chemical processes (conversion coatings), and few studies have focused on electrochemical processes (electrodeposition). Initially, the chemical precursors were hexavalent chromium salts, but these compounds are now replaced by less toxic trivalent ions. There is a profound understanding of the process when vibrational spectroscopy is used in combination with other techniques. This is the case for chromium(VI) conversion coatings, and the results of several techniques, such as synchrotron infrared microspectroscopy, have made it possible to understand the structure of the two-layer coating and the chemical composition of each layer. Vibrational spectroscopy confirmed the mechanism for coating formation, in which ferricyanide was a redox mediator. In addition, vibrational spectroscopy was effective in determining the mechanism of corrosion resistance of the coatings. Conversely, there are very few studies on the electrodeposition of trivalent chromium ions, and the mechanics of electrodeposition are unknown. To simplify the use of spectroscopy, spectra of potassium dichromate and chromium(III) sulfate are presented as references for coating studies, and a compilation of $\mathrm{C}\mathrm{r}\left(\mathrm{I}\mathrm{I}\mathrm{I}\right)-\mathrm{O}$ and $\mathrm{C}\mathrm{r}\left(\mathrm{V}\mathrm{I}\right)-\mathrm{O}$ vibrational modes is provided to facilitate band assignment. Our review highlights that spectroscopic techniques have been insufficiently applied in this field; however, the results of vibrational spectroscopy accelerate the transition to safer Cr(III) technology. Full article

The latest exploration developments and discoveries from the eastern Mediterranean documented that Neogene formations can act as source-rocks for hydrocarbon generation and their exploitation delivered large amounts of mostly biogenic gas to the market. Examples of such offshore gas-fields include the Zohr-Egypt, Leviathan/Tamar-Israel, and Aphrodite-Cyprus. Having attracted the oil majors’ attention for hydrocarbons exploration in southern Greece (e.g., Exxon-Mobil, Chevron), by using onshore geologic analogs, we suggest relevant perspectives in the country’s offshore sector. Our study focuses on Miocene marine deposits exploration, from Gavdos Island, southern Greece, evaluating their characteristics as potential source-rocks affected by a paleodepositional framework. By integrating fieldwork, organic (Rock-Eval VI-pyrolysis, CHNS) and inorganic geochemical data (XRF), the current results indicate gas-prone organic matter with variable preservation status, reflecting a few oxidation episodes during deposition under generally dysoxic-to-suboxic conditions. Paleoclimatic weathering indices (CIA, C.I., Sr/Cu, Rb/Sr) suggest predominantly arid to semi-arid regimes punctuated by short-lived humid phases that locally enhance organic accumulation and nutrient supply. Variations in paleosalinity and stratification, particularly within the Messinian section, are interpreted as precursors to the Messinian Salinity Crisis. Our findings highlight the potential for hydrocarbon-prone intervals in the deeper-offshore Eastern Mediterranean basins, where most favorable conditions for organic-carbon preservation and maturation are documented by the discoveries. Full article

The photocatalytic activity of silica–titania (S-T) fibers synthesized via sol–gel and electrospinning was evaluated using methyl orange (MO), eriochrome black T (EB), and methylene blue (MB) as model dyes. Characterization by X-ray diffraction confirmed the presence of anatase and rutile TiO₂ phases, while UV-Vis spectroscopy determined a bandgap energy of 3.2 eV. Scanning electron microscopy revealed fibers with an average diameter of 214 nm. Under UV irradiation, nearly complete dye removal (initial concentration: 30 mg/L; catalyst dosage: 0.1 g/L) was achieved within 8 h. The reaction kinetics followed the Langmuir–Hinshelwood model, with significant differences in apparent reaction rates (k_a) among the dyes, attributable to their distinct structural and functional properties. This study establishes silica–titania fibers as a high-performance, highly versatile composite photocatalyst. Achieving 98% degradation efficiency, their key innovation is their fibrous morphology, which solves the critical problem of powder catalyst recovery. This enables a paradigm shift from simple lab efficiency to practical, sustainable application. Full article

Underwater object detection is critical for marine resource utilization, ecological monitoring, and maritime security, yet it remains constrained by optical degradation, high energy consumption, and vulnerability to adversarial perturbations. To address these challenges, this study proposes an Adaptive Vision Transformer (A-ViT)-based detection framework. At the hardware level, a systematic power-modeling and endurance-estimation scheme ensures feasibility across shallow- and deep-water missions. Through the super-resolution reconstruction based on the Hybrid Attention Transformer (HAT) and the staged enhancement with the Deep Initialization and Deep Inception and Channel-wise Attention Module (DICAM), the image quality was significantly improved. Specifically, the Peak Signal-to-Noise Ratio (PSNR) increased by 74.8%, and the Structural Similarity Index (SSIM) improved by 375.8%. Furthermore, the Underwater Image Quality Measure (UIQM) rose from 3.00 to 3.85, while the Underwater Color Image Quality Evaluation (UCIQE) increased from 0.550 to 0.673, demonstrating substantial enhancement in both visual fidelity and color consistency. Detection accuracy is further enhanced by an improved YOLOv11-Coordinate Attention–High-order Spatial Feature Pyramid Network (YOLOv11-CA_HSFPN), which attains a mean Average Precision at Intersection over Union 0.5 (mAP@0.5) of 56.2%, exceeding the baseline YOLOv11 by 1.5 percentage points while maintaining 10.5 ms latency. The proposed A-ViT + ROI reduces inference latency by 27.3% and memory usage by 74.6% when integrated with YOLOv11-CA_HSFPN and achieves up to 48.9% latency reduction and 80.0% VRAM savings in other detectors. An additional Image-stage Attack QuickCheck (IAQ) defense module reduces adversarial-attack-induced latency growth by 33–40%, effectively preventing computational overload. Full article

In this work, two novel nanoscale zero-valent iron (nZVI) composites (nanoscale zero-valent iron and copper-intercalated montmorillonite (MMT-nFe⁰/Cu⁰) and carbon microsphere-supported sulfurized nanoscale zero-valent iron (CMS@S-nFe⁰)) were used to treat soil contaminated with both Cr(VI) and p-nitrophenol (PNP), and added persulfate (PMS). Experiments found that the pollutant removal effect has a great relationship with the ratio of water to soil, the amount of catalyst, the amount of PMS, and the pH value. When the conditions are adjusted to the best (water–soil = 2:1, catalyst 30 g/kg, PMS 15 g/kg, pH 7–9), both materials fix Cr(VI) well and decompose PNP. The removal rates of Cr(VI) and PNP by the MMT-nFe⁰/Cu⁰ system are 90.4% and 72.6%, respectively, while the CMS@ S-nFe⁰ system is even more severe, reaching 94.8% and 81.3%. Soil column leaching experiments also proved that the fixation effect of Cr can last for a long time and PNP can be effectively decomposed. Through detection methods such as X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR) and X-ray photoelectron spectroscopy (XPS), we found that Cr(VI) was effectively reduced to Cr(III) by Fe⁰ and Fe²⁺ ions and subsequently transformed into stable FeCr₂O₄ spinel oxides, and the groups produced after the decomposition of PNP could also help fix the metal. This work provides a way to simultaneously treat Cr(VI) and PNP pollution, and also allows the use of multifunctional nZVI composites in complex soil environments. Full article

Leaf water content (LWC) is a vital physiological indicator reflecting crop water status, crucial for precision irrigation and water management. Traditional monitoring methods are labor-intensive and costly, while unmanned aerial vehicle (UAV) remote sensing offers an efficient alternative with high spatiotemporal resolution. This study developed an inversion model for winter wheat LWC based on a stacking ensemble learning framework integrating multispectral and texture features to improve estimation accuracy. UAV multispectral images collected at different growth stages were used to extract 17 vegetation indices (VIs) and 32 texture features (TFs). The top 10 features most correlated with LWC were selected to construct a fused dataset, and five machine learning models (SVM, RF, XGB, PLSR, RR) were combined within a base–meta stacking architecture. Results showed that: (1) Using only multispectral features yielded R² values of 0.526–0.718 and rRMSE of 22.795–29.536%, while texture-only models performed worse (R² = 0.273–0.425, rRMSE = 34.7–36.6%), indicating that single data sources cannot fully represent LWC variability. (2) Combining multispectral and texture features notably improved accuracy (R² = 0.748–0.815; rRMSE = 18.5–21.6%), demonstrating the complementary advantages of spectral and spatial information. (3) Stacking ensemble learning outperformed all single models, achieving the highest precision under fused features (R² = 0.865; rRMSE = 16.3%). (4) LWC distribution maps derived from the stacking model effectively revealed field-scale moisture differences and spatial heterogeneity during different periods. This study confirms that multi-source feature fusion combined with ensemble learning enhances UAV-based crop water estimation, offering a reliable and scalable approach for precision agricultural water monitoring. Full article

Background and Objective: Accurate classification of standard fetal ultrasound planes is a critical step in prenatal diagnostics, enabling reliable biometric measurements and anomaly detection. Conventional deep learning approaches, particularly convolutional neural networks (CNNs) and transformers, often face challenges such as domain variability, noise artifacts, class imbalance, and poor calibration, which limit their clinical utility. This study proposes a hybrid state–space and vision transformer framework designed to address these limitations by integrating sequential dynamics and global contextual reasoning. Methods: The proposed framework comprises five stages: (i) preprocessing for ultrasound harmonization using intensity normalization, anisotropic diffusion filtering, and affine alignment; (ii) hybrid feature encoding with a state–space model (SSM) for sequential dependency modeling and a vision transformer (ViT) for global self-attention; (iii) multi-task learning (MTL) with anatomical regularization leveraging classification, segmentation, and biometric regression objectives; (iv) gated decision fusion for balancing local sequential and global contextual features; and (v) calibration strategies using temperature scaling and entropy regularization to ensure reliable confidence estimation. The framework was comprehensively evaluated on three publicly available datasets: FETAL_PLANES_DB, HC18, and a large-scale fetal head dataset. Results: The hybrid framework consistently outperformed baseline CNN, SSM-only, and ViT-only models across all tasks. On FETAL_PLANES_DB, it achieved an accuracy of 95.8%, a macro-F1 of 94.9%, and an ECE of 1.5%. On the Fetal Head dataset, the model achieved 94.1% accuracy and a macro-F1 score of 92.8%, along with superior calibration metrics. For HC18, it achieved a Dice score of 95.7%, an IoU of 91.7%, and a mean absolute error of 2.30 mm for head circumference estimation. Cross-dataset evaluations confirmed the model’s robustness and generalization capability. Ablation studies further demonstrated the critical role of SSM, ViT, fusion gating, and anatomical regularization in achieving optimal performance. Conclusions: By combining state–space dynamics and transformer-based global reasoning, the proposed framework delivers accurate, calibrated, and clinically meaningful predictions for fetal ultrasound plane classification and biometric estimation. The results highlight its potential for deployment in real-time prenatal screening and diagnostic systems. Full article

Mechanical fault samples of disconnectors are scarce, the fault types vary, and the self-evidence is weak, which leads to a lack of perfect fault diagnosis methods, and hidden defects cannot be found in time. To solve this problem, a mechanical fault diagnosis method for disconnectors based on a parallel dual-channel feature fusion model is proposed. Firstly, the optimal parameters for variational mode decomposition (VMD) are obtained using the black-winged kite algorithm (BKA). After the signal decomposition, the kurtosis values of each intrinsic mode function (IMF) are calculated, screened, and reconstructed. The reconstructed signal is input into the gated recurrent unit (GRU) to capture its time-series characteristics. Then, the vibration signal is generated by the recurrence plot (RP) to generate the atlas set and input into the vision Transformer (ViT) to extract its spatial characteristics. Finally, the time-series and spatial characteristics are fused, the multi-head self-attention mechanism is used for training, and softmax is used for fault classification. The measured data results show that the diagnostic accuracy of the model for mechanical fault types reaches 97.9%, which is 3.2%, 4.3%, 1.0%, 2.4%, 2.9%, 1.8%, 2.1%, 9%, and 7.5% higher than the other nine models numbered #2–#10, respectively, verifying its effectiveness and adaptability. Full article

Small object detection, as an important research topic in computer vision, has been widely applied in aerial visual tasks such as remote sensing and UAV imagery. However, due to challenges such as small object size, large-scale variations, and complex backgrounds, existing detection models often struggle to capture fine-grained semantics and high-resolution texture information in aerial scenes, leading to limited performance. To address these issues, this paper proposes an efficient aerial small object detection model, EABI-DETR (Efficient Attention and Bi-level Integration DETR), based on the RT-DETR framework. The proposed model introduces systematic enhancements from three aspects: (1) A lightweight backbone network, C2f-EMA, is developed by integrating the C2f structure with an efficient multi-scale attention (EMA) mechanism. This design jointly models channel semantics and spatial details with minimal computational overhead, thereby strengthening the perception of small objects. (2) A P2-BiFPN bi-directional multi-scale fusion module is further designed to incorporate shallow high-resolution features. Through top-down and bottom-up feature interactions, this module enhances cross-scale information flow and effectively preserves the fine details and textures of small objects. (3) To improve localization robustness, a Focaler-MPDIoU loss function is introduced to better handle hard samples during regression optimization. Experiments conducted on the VisDrone2019 dataset demonstrate that EABI-DETR achieves 53.4% mAP@0.5 and 34.1% mAP@0.5:0.95, outperforming RT-DETR by 6.2% and 5.1%, respectively, while maintaining high inference efficiency. These results confirm the effectiveness of integrating lightweight attention mechanisms and shallow feature fusion for aerial small object detection, offering a new paradigm for efficient UAV-based visual perception. Full article

Background/Objectives: The cerebellum contributes to both motor and cognitive functions. As basketball requires the integration of these abilities, basketball athletes provide an ideal model for exploring cerebellar adaptations. This study aimed to examine multidimensional cerebellar adaptations in basketball athletes and their associations with physical performance. Methods: In this study, 55 high-level basketball athletes and 55 non-athletes matched for age and gender were recruited for multimodal magnetic resonance imaging data collection and physical fitness tests. We compared the structural and functional differences in the brain between the two groups and analyzed the correlations between regional brain indices and physical fitness test outcomes. Results: Basketball athletes exhibited increased gray matter volume in Crus I, alongside heightened ALFF signal in Crus I and improved regional homogeneity in Crus II and VII b compared to non-athletes. Diffusion kurtosis imaging analysis demonstrated that athletes perform elevated kurtosis fractional anisotropy and decreased radial kurtosis within the cerebellar cortex and peduncles, with cortical modifications mainly localized around Crus I and lobule VI. Notably, both kurtosis fractional anisotropy and the amplitude of low-frequency fluctuations displayed positive correlations with vertical jump performance, an indicator specific to basketball ability. Conclusions: Basketball athletes exhibit structural, microstructural, and functional cerebellar adaptations, especially in Crus I. These modifications involve regions associated with motor and cognitive representations within the cerebellum, and part of the indexes are linked to the athletes’ physical performance. This study enhances our understanding of cerebellar adaptive changes in athletes, providing new insights for future research aimed at fully elucidating the role of the cerebellum in these individuals. Full article

Implant-associated infections remain a major clinical challenge, often leading to implant failure, revision surgery, and increased healthcare burden. Systemic antibiotic administration is limited by poor local bioavailability and systemic side effects, highlighting the need for localized drug-delivery systems that can simultaneously support tissue integration and prevent bacterial colonization. This study aimed to develop and characterize a novel generation of chitosan membranes loaded with hydroxyapatite–clindamycin phosphate (CS/HA-CLY) for localized infection prevention at implantation sites. The composite membranes’ physicochemical characteristics were analyzed using ATR FT-IR, XPS, SEM, XRD, and contact angle measurements. Furthermore, the in vitro biomineralization potential was assessed employing the Taguchi method, while the in vitro release of clindamycin phosphate was examined through UV-Vis spectrophotometry. The CS/HA-CLY membranes exhibited improved wettability, drug release behavior, and biomineralization ability compared to neat CS. These results suggest that the developed composite membranes could successfully combine antibacterial efficacy and biocompatibility, supporting their potential as multifunctional biomaterials for preventing implant-related infections while promoting tissue integration. These findings provide a promising basis for further biological assays and in vitro evaluation. Full article

Background/Objectives: Appropriate biomarkers are necessary for early diagnosis and multidisciplinary treatment of pancreatic ductal adenocarcinoma (PDAC). In recent years, the clinical utility of circulating tumor cells (CTC) as biomarkers for various can-cers has been reported; however, their detection rate in PDAC remains low, and clinical evidence is not yet established. CTC detection methods with high reliability and per-formance are essential for clarifying the importance of CTC in patients with PDAC. Methods: A total of 5 mL peripheral blood samples were collected from 38 patients newly diagnosed with PDAC and 17 healthy controls. Negatively enriched cells were immunofluorescently stained with EpCAM-phycoerythrin and cell surface vi-mentin-fluorescein isothiocyanate (CSV). Images were automatically captured using an all-in-one fluorescence microscope. Cellular regions were detected from these images, and the average luminance of the cellular regions was calculated. A total of 9086 and 1071 cell images were obtained from patients with PDAC and healthy controls, respec-tively. Results: In the EpCAM assay, a threshold that included 95% of healthy individuals was optimal for distinguishing patients with PDAC from healthy controls, with a sensi-tivity, specificity, and area under the curve of 0.74, 0.76, and 0.84, respectively. At this threshold, the CTC-positivity rate in patients with PDAC was 76.3%. Conversely, the CSV assay failed to demonstrate a valid threshold to distinguish patients with PDAC from healthy controls. No significant differences were found between CTC and clini-copathological features among patients with PDAC. Conclusions: The method using high-resolution image scanning has the potential to identify CTC with greater objectiv-ity by quantifying cell luminance values. Full article

The aim of this study is to evaluate the usefulness of UV-Vis-NIR spectroscopy as a tool for detecting the adulteration of cold-pressed walnut oil and other edible oils (rapeseed, sunflower, and soybean oils) at varying percentages. The spectra were recorded between 200 and 1800 nm, but the analyses focused on 350–1650 nm due to high UV and NIR absorption. Color was determined in CIEL*a*b* coordinates to achieve the differences among the samples. The spectra were submitted to several pre-treatment (none, normalization, SNV, MSC, baseline/detrend, first/second derivative, and 1st-order smoothing) to improve the statistical model’s parameters. The differentiation of the samples was carried out using an unsupervised method (principal component analysis—PCA) and two supervised methods (linear discriminant analysis—LDA and partial least squares linear discriminant analysis—PLS-DA). Partial least squares regression (PLS-R) was used for predicting the degree of adulteration. Separation between the authentic and adulterated samples was visible in the PCA scores plot, primarily along the spectral regions of 420–500 nm (pigment-related absorption band) and 1150–1450 nm (lipid-associated band). PLS-DA was superior to DA for the discrimination of authentic/adulterated samples, with baseline spectra of 350–1650 nm yielding a 100% overall accuracy and near-perfect accuracy with MSC (98.48%). PLS-R was able to predict the adulteration level, depending on the pre-treatment applied. Full article

As artificial intelligence (AI) advances, deep learning models are shifting from convolutional architectures to transformer-based structures, highlighting the importance of accurate floating-point (FP) calculations. Compute-in-memory (CIM) enhances matrix multiplication performance by breaking down the von Neumann architecture. However, many FPCIMs struggle to maintain high precision while achieving efficiency. This work proposes a high-precision hybrid floating-point compute-in-memory (Hy-FPCIM) architecture for Vision Transformer (ViT) through post-alignment with two different CIM macros: Bit-wise Exponent Macro (BEM) and Booth Mantissa Macro (BMM). The high-parallelism BEM efficiently implements exponent calculations in-memory with the Bit-Separated Exponent Summation Unit (BSESU) and the routing-efficient Bit-wise Max Finder (BMF). The high-precision BMM achieves nearly lossless mantissa computation in-memory with efficient Booth 4 encoding and the sensitivity-amplifier-free Flying Mantissa Lookup Table based on 12T Triple Port SRAM. The proposed Hy-FPCIM architecture achieves 23.7 TFLOPS/W energy efficiency and 0.754 TFLOPS/mm² area efficiency, with 617 Kb/mm² memory density in 28 nm technology. With almost lossless architectures, the proposed Hy-FPCIM achieves an accuracy of 81.04% in recognition tasks on the ImageNet dataset using ViT, representing a 0.03% decrease compared to the software baseline. This research presents significant advantages in both accuracy and energy efficiency, providing critical technology for complex deep learning applications. Full article