Search Results (22,800)

Mastitis caused by Escherichia coli and Streptococcus uberis remains one of the leading causes of antimicrobial use in dairy cattle, contributing to resistance development and economic losses. This study evaluated the therapeutic potential of the natural flavonoid morin in clinical mastitis in dairy cows. The in vitro antimicrobial activity of morin (1–3% w/v) was assessed by disk diffusion, and the 3% formulation was selected for an in vivo field trial. Seventy-two Holstein–Friesian cows with mastitis caused by E. coli or S. uberis were randomly assigned to one of three intramammary treatments: 3% morin, phosphate-buffered saline, or an antibiotic, serving as a positive control. Somatic cell count (SCC) and clinical scores were monitored for seven days. In E. coli infections, morin significantly reduced somatic cell scores at 144 h and 168 h and improved clinical scores from 48 h onward, showing efficacy comparable to antibiotics. In S. uberis mastitis, morin induced clinical improvement at 96–168 h but resulted in slower and smaller SCC reduction than antibiotic control therapy. Phosphate-buffered saline produced no significant changes. These results indicate that morin exerts anti-inflammatory and supportive effects in bovine mastitis, particularly in Gram-negative infections, but is less effective against S. uberis. Further studies on pharmacokinetics, bacteriological cure rates, and optimized formulations are warranted to confirm its clinical utility. 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

To address the issue of material spillage and equipment wear caused by conveyor belt deviation in complex industrial scenarios, this study proposes a detection method based on an improved U-Net. The approach adopts U-Net as the backbone network, with a ResNet34 encoder to enhance feature extraction capability. At the skip connections, a Multi-scale Adaptive Guidance Attention (MASAG) module is embedded to strengthen the fusion of semantic and detailed features. In the loss function design, a boundary loss is incorporated to improve edge segmentation accuracy. Furthermore, the segmentation results are refined via edge detection and RANSAC regression, and a reference line is constructed based on the physical stability of rollers in the image to enable quantitative measurement of deviation. Experiments on a self-constructed dataset demonstrate that the proposed method achieves higher accuracy (99.77%) compared with the baseline U-Net (99.65%) and also surpasses other categories of approaches, including detection-based (YOLOv5s), anchor-point-based (UFLD), and segmentation-based approaches represented by SEU-Net and DeepLabV3+, thereby exhibiting strong robustness and real-time performance across diverse complex operating conditions. The results validate the effectiveness of this method in practical applications and provide a reliable technical pathway for the development of intelligent monitoring systems for mining conveyor belts. Full article

The operational stability of organic solar cells critically depends on the excited-state characteristics of electron acceptor materials. Through systematic quantum chemical calculations on four representative acceptors (PCBM, ITIC, Y6, and TBT-26), this study reveals fundamental spectra–stability relationships. Non-fullerene acceptors demonstrate superior light-harvesting with systematically tuned energy levels and significantly lower exciton binding energies (2.05–2.12 eV) compared to PCBM (2.97 eV), facilitating efficient charge separation. Structural dynamics analysis uncovers distinct stability mechanisms: ITIC maintains exceptional structural integrity (anionic RMSD = 0.023, S₁ RMSD = 0.134) with superior bond preservation, ensuring balanced performance–stability. Y6 exhibits substantial structural relaxation in excited states (S₁ RMSD = 0.307, T₁ RMSD = 0.262) despite its low exciton binding energy, indicating significant non-radiative losses. TBT-26 employs selective bond stabilization, preserving acceptor–proximal bonding despite considerable anionic flexibility. These findings establish that optimal molecular design requires both favorable electronic properties and structural preservation in photoactive states, providing crucial guidance for developing efficient and stable organic photovoltaics. Full article

The accurate detection of high-impedance faults (HIFs) in distribution systems is fundamentally dependent on the extraction of weak fault signatures. However, these features are often obscured by complex and high-level noise present in current transformer (CT) measurement data. To address this challenge, an energy-proportion-guided channel-wise attention stacked denoising autoencoder (EPGCA-SDAE) model is proposed. In this model, wavelet decomposition is employed to transform the signal into informative frequency band components. A channel attention mechanism is utilized to adaptively assign weights to each component, thereby enhancing model interpretability. Furthermore, a physics-informed prior, based on energy distribution, is introduced to guide the loss function and regulate the attention learning process. Extensive simulations using both synthetic and real-world $10\mathrm{kV}$ distribution network data are conducted. The superiority of the EPGCA-SDAE over traditional wavelet-based methods, stacked denoising autoencoders (SDAE), denoising convolutional neural network (DnCNN), and Transformer-based networks across various noise conditions is demonstrated. The lowest average mean squared error (MSE) is achieved by the proposed model (simulated: $50.60\times {10}^{-5}\mathrm{p}.\mathrm{u}.$; real: $76.45\times {10}^{-5}\mathrm{p}.\mathrm{u}.$), along with enhanced noise robustness, generalization capability, and physical interpretability. These results verify the method’s feasibility within the tested 10 kV distribution system, providing a reliable data recovery framework for fault diagnosis in noise-contaminated distribution network environments. Full article

Electron beam melting (EBM) is an additive manufacturing method that enables the manufacturing of metallic parts. EBM-printed parts require post-processing to meet the surface quality and dimensional accuracy requirements. Machining is one approach that is beneficial for achieving these requirements. However, during machining, particles are emitted and can affect the environment and the operator’s health. This study aims to investigate the concentration of particles emitted during the milling of 3D-printed Ti6Al4V alloy produced by EBM. First, the influence of machining speed and cutting fluids, namely flood and minimum quantity lubricant (MQL), on particle emissions was statistically investigated. Then, the standby time required for the operator to safely open the machine door and interact with the machine within the machining area was studied. In this regard, two scenarios were proposed. In the first scenario, the machine door is open immediately after machining, and the operator waits until the particle concentration is acceptable. In the second, the machine door will be opened only when the particle concentration is acceptable. Statistical findings revealed that cutting fluids have a significant impact on particle emissions, exhibiting distinct patterns for both fine and coarse particles. Irrespective of the scenario, MQL results in higher particle concentration peaks and larger particle sizes, and the operator needs a longer standby time before interacting with the machine. For instance, the standby time in MQL is 328% more than that of the flood system. This study provides insight into sustainable manufacturing by taking into account social factors such as worker health and safety. 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

The Ostwald ripening process in 3D and 2D systems has been studied in great detail over decades. In the application to surface nanoislands and nanodroplets, it is usually assumed that the capture coefficients of adatoms by supercritical nanoparticles of size $s$ scale as $s^{\alpha }$, where the growth index $\alpha$ is smaller than unity. Here, we study theoretically the Ostwald ripening of 3D and 2D nanoparticles whose capture coefficients scale linearly with $s$. This case includes submonolayer surface islands that compete for the flux of highly diffusive adatoms upon termination of the material influx. We obtain analytical solutions for the size distributions using the Lifshitz–Slezov scaled variables. The distributions over size $s$ and radius $R$ are monotonically decreasing, and satisfy the normalization condition for different values of the Lifshitz–Slezov constant $c$. The obtained size distributions satisfy the Family–Vicsek scaling hypothesis, although the material influx is switched off. The model is validated by fitting the monotonically decreasing size distributions of Au nanoparticles that serve as catalysts for the vapor–liquid–solid growth of III-V nanowires on silicon substrates. Full article

Ion channels are protein pores that regulate ionic flow across cell membranes, enabling vital processes such as nerve signaling. They often conduct multiple ionic species simultaneously, leading to complex nonlinear transport phenomena. Because experimental techniques provide only indirect measurements of ion channel currents, mathematical models—particularly Poisson–Nernst–Planck (PNP) equations—are indispensable for analyzing the underlying transport mechanisms. In this work, we examine ionic transport through a one-dimensional steady-state PNP model of a narrow membrane channel containing multiple cation species of different valences. The model incorporates a small fixed charge distribution along the channel and imposes relaxed electroneutrality boundary conditions, allowing for a slight charge imbalance in the baths. Using singular perturbation analysis, we first derive approximate solutions that capture the boundary-layer structure at the channel—reservoir interfaces. We then perform a regular perturbation expansion around the neutral reference state (zero fixed charge with electroneutral boundary conditions) to obtain explicit formulas for the steady-state ion fluxes in terms of the system parameters. Through this analytical approach, we identify several critical applied potential values—denoted $V_k^a$ (for each cation species k), $V^b$, and $V^c$—that delineate distinct transport regimes. These critical potentials govern the sign of the fixed charge’s influence on each ion’s flux: depending on whether the applied voltage lies below or above these thresholds, a small positive permanent charge will either enhance or reduce the flux of each ion species. Our findings thus characterize how a nominal fixed charge can nonlinearly modulate multi-ion currents. This insight deepens the theoretical understanding of nonlinear ion transport in channels and may inform the interpretation of current–voltage relations, rectification effects, and selective ionic conduction in multi-ion channel experiments. Full article

The oxygen reduction reaction (ORR) is a pivotal electrochemical process in energy technologies and in the generation of hydrogen peroxide (H₂O₂), which serves as both an effective agent for dye degradation and a fuel in H₂O₂-based fuel cells. In this regard, a titanium (Ti) sheet was anodized to generate a TiO₂ layer, and then the oxide layer was modified with gold (presented as Au/TiO₂/Ti) via electrodeposition. The developed electrocatalyst was confirmed by X-ray photoelectron spectroscopy (XPS), which showed characteristic binding energies for Ti⁴⁺ in TiO₂ and metallic Au. In addition, the Nyquist plot verified the electrode modification process, since the diameter of the semicircular arc, corresponding to charge transfer resistance, significantly decreased due to Au deposition. Voltametric studies revealed that the TiO₂ layer with a Ti surface exhibited a good synergistic effect on Au and the ORR in a bicarbonate medium (0.1 M KHCO₃) by lowering the overpotential, enhancing current density, and boosting durability. The scan rate-dependent study of the ORR produced by the developed electrocatalyst showed a Tafel slope of 180 ± 2 mV dec⁻¹ over a scan rate range of 0.05–0.4 V s⁻¹, thereby indicating a 2e⁻ transfer process in which the initial electron transfer process was the rate-limiting step. The study also revealed that the Au/TiO₂/Ti electrode caused oxygen electro-reduction with a heterogenous rate constant (k⁰) of $4.40\times {10}^{-3}$ cm s⁻¹ at a formal potential (E⁰′) of 0.54 V vs. RHE. Full article

Plant disease recognition is a critical technology for ensuring food security and advancing precision agriculture. However, challenges such as class imbalance, heterogeneous image quality, and limited model interpretability remain unresolved. In this study, we propose a Synergistic Dual-Augmentation and Class-Aware Hybrid (SDA-CAH) model designed to achieve robust and interpretable recognition of plant diseases. Our approach introduces two innovative augmentation strategies: (1) an optimized MixUp method that dynamically integrates class-specific features to enhance the representation of minority classes; and (2) a customized augmentation pipeline that combines geometric transformations with photometric perturbations to strengthen the model’s resilience against image variability. To address class imbalance, we further design a class-aware hybrid sampling mechanism that incorporates weighted random sampling, effectively improving the learning of underrepresented categories and optimizing feature distribution. Additionally, a Grad-CAM–based visualization module is integrated to explicitly localize lesion regions, thereby enhancing the transparency and trustworthiness of the predictions. We evaluate SDA-CAH on the PlantVillage dataset using a pretrained EfficientNet-B0 as the backbone network. Systematic experiments demonstrate that our model achieves 99.95% accuracy, 99.89% F1-score, and 99.89% recall, outperforming several strong baselines, including an optimized Xception (99.42% accuracy, 99.39% F1-score, 99.41% recall), standard EfficientNet-B0 (99.35%, 99.32%, 99.33%), and MobileNetV2 (95.77%, 94.52%, 94.77%). For practical deployment, we developed a web-based diagnostic system that integrates automated recognition with treatment recommendations, offering user-friendly access for farmers. Experimental evaluations indicate that SDA-CAH outperforms existing approaches in predictive accuracy and simultaneously defines a new paradigm for interpretable and scalable plant disease recognition, paving the way for next-generation precision agriculture. Full article

Background and Objectives: COVID-19 has been associated with prolonged inactivity and reduced physical performance, even in mild and moderate cases. This study aimed to evaluate changes in functional mobility and gait speed, assessed with the Timed Up and Go (TUG) and 10-Meter Walk Test (10MWT), in patients with mild to moderate post-COVID-19 conditions undergoing a structured rehabilitation program. Materials and Methods: A controlled observational study was conducted on 193 patients (115 women, 78 men) who had recovered from mild to moderate COVID-19. Participants were divided into a rehabilitation group (n = 160) and a control group (n = 33) who did not undergo structured physical therapy. Functional performance was assessed with TUG and 10MWT at admission and at one-year follow-up. Results: Both tests showed significant improvements following rehabilitation. In the rehabilitation group, the proportion of patients classified as functionally independent increased significantly for both the TUG (Cramér’s V = 0.468, p < 0.001) and 10MWT (Cramér’s V = 0.500, p < 0.001). The McNemar test confirmed a moderate within-group improvement for 10MWT (p = 0.001). Older adults (≥60 years) exhibited functional gains comparable to younger participants. A strong association between final TUG and 10MWT categories (Cramér’s V = 0.40, p < 0.001) confirmed the consistency of outcomes. Conclusions: Structured rehabilitation significantly improves balance, gait speed, and functional independence in mild-to-moderate post-COVID-19 patients. These findings highlight that rehabilitation should be integrated into the continuum of post-COVID care, as meaningful recovery is achievable even outside severe cases. Full article

Introduction: Artificial intelligence is emerging in dental education, but its use in preclinical assessment remains limited. Large language models like ChatGPT^® V4.5 enable non-programmers to build AI models through real-time guidance, addressing the coding barrier. Aim: This study aims to empower clinician-led, low-cost, AI-driven assessment models in preclinical restorative dentistry and to evaluate the technical feasibility of using a neural network to score 3D cavity preparations. Methods: Twenty mandibular molars (tooth 46), each with two carious lesions, were prepared and scored by two expert examiners using a 20-point rubric. The teeth were scanned with a Medit i700^® and exported as .OBJ files. Using Open3D, the models were processed into point clouds. With ChatGPT’s guidance, the clinician built a PointNet-based neural model in PyTorch, training it on 20 cases and testing it on 10 unseen preparations. Results: In training, the model achieved an MAE of 0.82, RMSE of 1.02, and Pearson’s r = 0.88, with 66.7% and 93.3% of the predictions within ±5% and ±10% of the examiner scores, respectively. On the test set, it achieved an MAE of 0.97, RMSE of 1.16, and r = 0.92, with 50% and 100% of scores within ±5% and ±10%, respectively. These results show a strong alignment with examiner scores and an early generalizability for scoring preclinical cavity preparations. Conclusions: This study confirms the feasibility of clinician-led, low-cost AI development for 3D cavity assessment using ChatGPT, even without prior coding expertise. Full article

This study presents the design and application of an electrochemical sensor for selective detection of lead ions (Pb²⁺) based on ionophore-modified raspberry-like Fe₃O₄–Au nanostructures. The material was engineered with a magnetic Fe₃O₄ core, coated with polyethyleneimine (PEI) to facilitate nucleation, and subsequently decorated with Au nanoparticles, providing a raspberry-like (Fe₃O₄@PEI@AuNPs) nanostructure with high surface area and excellent electrochemical conductivity. Surface functionalization with Lead Ionophore IV (ionophore thiol) introduced Pb²⁺-selective binding sites, whose presence and structural evolution were verified by TEM and Raman spectroscopy. The Fe₃O₄ core endowed strong magnetic properties, enabling facile manipulation and immobilization onto screen-printed carbon electrodes (SPCEs) via physical adsorption, while the Au nanoparticles enhanced electron transfer, supplied thiol-binding sites for stable ionophore anchoring, and increased the effective electroactive surface area. Operational conditions were systematically optimized, with acetate buffer (HAc/NaAc, pH 5.7) and chronoamperometric preconcentration (CA) at −1.0 V for 175 s identified as optimal for differential pulse voltammetry (DPV) measurements. Under these conditions, the sensor exhibited a linear response toward Pb²⁺ from 0.025 mM to 2.00 mM with superior sensitivity and reproducibility compared to conventional AuNP-modified SPCEs. Furthermore, the ionophore-modified Fe₃O₄–Au nanostructure-based sensor demonstrated outstanding selectivity for Pb²⁺ over competing heavy metal ions (Cd²⁺, Hg²⁺, Cr³⁺), owing to the specific coordination interaction of Lead Ionophore IV with target ions. These findings highlight the potential of raspberry-like Fe₃O₄@PEI@AuNP nanostructures as a robust and efficient electrochemical platform for the sensitive and selective detection of toxic heavy metal ions. Full article

The article describes innovative procedures for determining In(III) using anodic stripping voltammetry (ASV) and adsorptive stripping voltammetry (AdSV) with cupferron as a chelating agent. In both procedures, an environmentally friendly solid bismuth microelectrode (SBiµE) with a diameter of 25 µm was used as the working electrode. In both procedures, 0.1 mol L⁻¹ acetate buffer with a pH of 3.0 ± 0.05 was used as the supporting electrolyte. The electrochemical measurement conditions were as follows: −2.4 V for a 20 s activation step and −1.2 V for a 20 s accumulation step for ASV, and −2.5 V for a 45 s activation step and −0.65 V for a 10 s accumulation step for AdSV. The signal was recorded as a result of a positive potential change from −1.0 to −0.3 V in the case of the ASV procedure and as a result of a negative potential change from −0.4 to −1.0 V in the case of the AdSV procedure. The calibration graph was linear from 5 × 10⁻⁹ mol L⁻¹ to 5 × 10⁻⁷ mol L⁻¹ with a detection limit of 1.4 × 10⁻⁹ mol L⁻¹ for ASV and from 1 × 10⁻⁹ mol L⁻¹ to 1 × 10⁻⁷ mol L⁻¹ with a detection limit of 3.9 × 10⁻¹⁰ mol L⁻¹ for AdSV. The effect of interferents such as surfactants, humic substances and EDTA on the analytical signal was compared in the case of signal recording using the ASV technique with the signal recorded using the AdSV technique. Based on the results obtained, it was determined how the charge of interferents affects the signal depending on the technique used. To validate the practical application of the developed procedures, an analysis of In(III) recovery from samples of the Baltic Sea and Synthetic Sea Water was performed. Full article