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The Unani Tibb is a medical system of Greek descent that has undergone substantial dissemination since the 11th century and is currently prevalent in modern South and Central Asia, particularly in primary health care. The ingredients of Unani herbal medicines are primarily derived from plants. Our research aimed to address the pressing issues of antibiotic resistance, multi-drug resistance, and the emergence of superbugs by examining the molecular-level effects of Unani ingredients as potential new natural antibiotic candidates. We utilized a machine learning approach to tackle these challenges, employing decision trees, kernels, neural networks, and probability-based methods. We used 12 machine learning algorithms and several techniques for preprocessing data, such as Synthetic Minority Over-sampling Technique (SMOTE), Feature Selection, and Principal Component Analysis (PCA). To ensure that our model was optimal, we conducted grid-search tuning to tune all the hyperparameters of the machine learning models. The application of Multi-Layer Perceptron (MLP) with SMOTE pre-processing techniques resulted in an impressive accuracy precision and recall values. This analysis identified 20 important metabolites as essential components of the formula, which we predicted as natural antibiotics. In the final stage of our investigation, we verified our prediction by conducting a literature search for journal validation or by analyzing the structural similarity with known antibiotics using asymmetric similarity. Full article

Grit particles remaining on the substrate surface after grit blasting are generally considered to impair the thermal performance of thermal barrier coatings (TBCs). However, the specific mechanisms by which these particles degrade the multilayer structure of TBCs during thermal cycling have not yet been fully elucidated. In this study, the superalloy substrate was grit-blasted using various processing parameters, followed by the deposition of thermal barrier coatings (TBCs) consisting of a metallic bond coat (BC) and a ceramic top coat (TC). After thermal shock tests, local thinning or discontinuities in the thermally grown oxide (TGO) layer were observed in TBCs where large grit particles were embedded at the BC/substrate interface. Moreover, cracks originated at the concave positions of the TGO layer and propagated vertically towards BC; these cracks may be associated with additional stress imposed by the foreign grit particles during thermal cycling. At the BC/substrate interface, crack origins were observed in the vicinity of large grit particles (~50 μm). Full article

Coal-bearing strata belong to sedimentary strata, and there are multiple aquifers. The accurate detection of deep aquifers is helpful to the safe mining of the working face. In order to provide guidance for the interpretation of the surface-to-underground transient electromagnetic method (SUTEM) that can be used to detect deep aquifers, we used theoretical analysis and numerical simulation methods in this study. Taking uniform half-spaces, single aquifers, and double aquifers as examples, we systematically studied the data characteristics and degree of influence of SUTEM under the influence of shallow aquifers. The results indicate the following: Under the influence of the primary field distribution, the x or y component of the induced electromotive force received by the underground receiving point has a positive and negative inflection point, which increases the difficulty of data interpretation, and the z component is easier to use for data interpretation. The influence of the aquifer on the early data of the underground receiving point is much greater than that of the ground receiving point, and the late influence is closer to the ground receiving point. The change in resistivity of the shallow aquifer has the greatest influence on the ability of each measuring point to detect the data of the deep aquifer; this influence is followed by change in thickness, and change in depth has the least influence on the detection capability of each measuring point. Full article

The traditional approach to optical design faces limitations as photonic devices grow increasingly complex, requiring advanced functionalities. Recently, machine learning algorithms have gained significant interest for extracting structural designs from customized wavelength spectra, surpassing traditional simulation methods known for their time-consuming nature and resource-demanding computational requirements. This study focuses on the inverse design of a reflectionless multilayer thin-film structure across a specific wavelength region, utilizing a tandem neural network (TNN) approach. The method effectively addresses the non-uniqueness problem in training inverse neural networks. Data generation via the transfer matrix method (TMM) involves simulating the optical behavior of a multilayer structure comprising alternating thin films of silicon dioxide (SiO₂) and silicon (Si). This innovative design considers both reflection and absorption properties to achieve near-zero reflection. We aimed to manipulate the structure’s reflectivity by implementing low-index and high-index layers along with Si absorption layers to attain specific optical properties. Our TNN demonstrated an MSE accuracy of less than 0.0005 and a maximum loss of 0.00781 for predicting the desired spectrum range, offering advanced capabilities for forecasting arbitrary spectra. This approach provides insights into designing multilayer thin-film structures with near-zero reflection and highlights the potential for controlling absorption materials to enhance optical performance. Full article

Lung cancer is the most diagnosed malignant neoplasm worldwide and it is associated with great mortality. Currently, developing antineoplastic agents is a challenging, time-consuming, and costly process. Computational methods can speed up the early discovery of anti-lung-cancer chemicals. Here, we report a perturbation theory machine learning model based on a multilayer perceptron (PTML-MLP) model for phenotypic early antineoplastic drug discovery, enabling the rational design and prediction of new molecules as virtual versatile inhibitors of multiple lung cancer cell lines. The PTML-MLP model achieved an accuracy above 80%. We applied the fragment-based topological design (FBTD) approach to physicochemically and structurally interpret the PTML-MLP model. This enabled the extraction of suitable fragments with a positive influence on anti-lung-cancer activity against the different lung cancer cell lines. By following the aforementioned interpretations, we could assemble several suitable fragments to design four novel molecules, which were predicted by the PTML-MLP model as versatile anti-lung-cancer agents. Such predictions of potent multi-cellular anticancer activity against diverse lung cancer cell lines were rigorously confirmed by a well-established virtual screening tool reported in the literature. The present work envisages new opportunities for the application of PTML models to accelerate early antineoplastic discovery from phenotypic assays. Full article

A SAW device with a multi-layered piezoelectric substrate has excellent performance due to its high Q value. A multi-layer piezoelectric substrate combined with phononic crystal structures capable of acoustic wave reflection with a very small array can achieve miniaturization and high performance. In this paper, a honeycomb-shaped phononic crystal structure based on 42°Y-X LT/SiO₂/poly-Si/Si-layered substrate is proposed. The analysis of the bandgap distribution under various filling fractions was carried out using dispersion and transmission characteristics. In order to study the application of PnCs in SAW devices, one-port resonators with different reflectors were compared and analyzed. Based on the frequency response curves and Bode-Q value curves, it was found that when the HC-PnC structure is used as a reflector, it can not only improve the transmission loss of the resonator but also reduce the size of the device. Full article

In fields such as manufacturing and aerospace, remaining useful life (RUL) prediction estimates the failure time of high-value assets like industrial equipment and aircraft engines by analyzing time series data collected from various sensors, enabling more effective predictive maintenance. However, significant temporal diversity and operational complexity during equipment operation make it difficult for traditional single-scale, single-dimensional feature extraction methods to effectively capture complex temporal dependencies and multi-dimensional feature interactions. To address this issue, we propose a Dual-Path Interaction Network, integrating the Multiscale Temporal-Feature Convolution Fusion Module (MTF-CFM) and the Dynamic Weight Adaptation Module (DWAM). This approach adaptively extracts information across different temporal and feature scales, enabling effective interaction of multi-dimensional information. Using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset for comprehensive performance evaluation, our method achieved RMSE values of 0.0969, 0.1316, 0.086, and 0.1148; MAPE values of 9.72%, 14.51%, 8.04%, and 11.27%; and Score results of 59.93, 209.39, 67.56, and 215.35 across four different data categories. Furthermore, the MTF-CFM module demonstrated an average improvement of 7.12%, 10.62%, and 7.21% in RMSE, MAPE, and Score across multiple baseline models. These results validate the effectiveness and potential of the proposed model in improving the accuracy and robustness of RUL prediction. Full article

Splenic epidermoid cysts are rare benign congenital tumors. However, if the cyst is not completely removed, it can reoccur. Laparoscopic splenectomy in children is being conducted more often, but it is a therapeutic challenge in cases of a giant cyst. We report a case of a 15-year-old girl who presented with nausea, anorexia and abdominal pain. The ultrasound showed a giant well-defined hypoechoic cyst with diffuse internal echoes. Computed tomography revealed a cystic mass (92/124/102 mm) without contrast enhancement. Anti-Echinococcus ELISA IgG was negative, and serum tumor markers CA 19-9 79.1 U/mL (N < 34) and CA-125 39.6 U/ML (N < 35) were elevated. Before the operation, the girl was vaccinated for Haemophilus influenzae, Pneumococci and Meningococci. Laparoscopic splenectomy was performed. The patient’s postoperative course was uneventful. Histopathology indicated a cyst walled by multilayered squamous epithelium positive for cytokeratin AE1/AE3. The diagnosis epidermoid cyst was confirmed. Full article

As global carbon reduction initiatives progress and the new energy sector rapidly develops, photovoltaic (PV) power generation is playing an increasingly significant role in renewable energy. Accurate PV output forecasting, influenced by meteorological factors, is essential for efficient energy management. This paper presents an optimal hybrid forecasting strategy, integrating bidirectional temporal convolutional networks (BiTCN), dynamic convolution (DC), bidirectional long short-term memory networks (BiLSTM), and a novel mixed-state space model (Mixed-SSM). The mixed-SSM combines the state space model (SSM), multilayer perceptron (MLP), and multi-head self-attention mechanism (MHSA) to capture complementary temporal, nonlinear, and long-term features. Pearson and Spearman correlation analyses are used to select features strongly correlated with PV output, improving the prediction correlation coefficient (R²) by at least 0.87%. The K-Means++ algorithm further enhances input data features, achieving a maximum R² of 86.9% and a positive R² gain of 6.62%. Compared with BiTCN variants such as BiTCN-BiGRU, BiTCN-transformer, and BiTCN-LSTM, the proposed method delivers a mean absolute error (MAE) of 1.1%, root mean squared error (RMSE) of 1.2%, and an R² of 89.1%. These results demonstrate the model’s effectiveness in forecasting PV power and supporting low-carbon, safe grid operation. Full article

In order to comprehensively understand the complex fracture mechanisms in thick and loose sandstone formations, we have carefully developed a coupled finite element numerical model that captures the complex interactions between fluid flow and solid deformation. This model is the cornerstone of our future exploration. Based on this model, the crack propagation problem of hydraulic fracturing under different engineering and geological conditions was studied. In addition, we conducted in-depth research on the key factors that shape the geometry of hydraulic fractures, revealing their subtle differences and complexities. It is worth noting that the sharp contrast between the stress profile and mechanical properties between the production layer and the boundary layer often leads to fascinating phenomena, such as the vertical merging of hydraulic fracture propagation. The convergence of cracks originating from adjacent layers is a recurring theme in these strata. Sensitivity analysis clarified our understanding, revealing that increased elastic modulus promotes longer crack propagation paths. As the elastic modulus increases from 12 GPa to 18 GPa, overall, the maximum crack width slightly decreases, with a less than 10% reduction rate. The increased fluid leakage rate will significantly shorten the length and width of hydraulic fractures (with a maximum decrease of over 70% in fracture width). The increase in viscosity of fracturing fluid causes a change in fracture morphology, with a reduction in length of about 32% and an increase in fracture width of about 25%. It is worth noting that as the leakage rate of fracturing fluid increases, the importance of the viscosity of fracturing fluid decreases relatively. Strategies such as increasing fluid viscosity or adding anti-filtration agents can alleviate these challenges and improve the efficiency of fracturing fluids. In summary, our research findings provide valuable insights that can provide information and optimization for hydraulic fracturing filling and fracturing strategies in loose sandstone formations, promoting more efficient and influential oil and gas extraction work. Full article

Recent observations of superconductivity in low-dimensional systems composed of twisted, untwisted, or rhombohedral graphene have attracted significant attention. One-dimensional moiré superlattices and flat bands have interestingly been identified in collapsed chiral carbon nanotubes (CNTs), opening up new avenues for the tunability of the electronic properties in these systems. The nucleation of hexagonal moiré superlattices and other types of stacking faults has also been demonstrated in partially collapsed and uncollapsed carbon nano-onions (CNOs). Here, we report a novel investigation on the dynamics of stacking fault nucleation within the multilayered lattices of micrometer-scale vertically oriented films of multiwall CNTs (MWCNTs), resulting from the pyrolysis of molecular precursors consisting of ferrocene or dimethyl ferrocene, at low vapor flow rates of ~5–20 mL/min. Interestingly, local nucleation of moiré-like superlattices (as stacking faults) was found when employing dimethyl ferrocene as the pyrolysis precursor. The morphological and structural properties of these systems were investigated with the aid of scanning and transmission electron microscopies, namely SEM, TEM, and HRTEM, as well as X-ray diffraction (XRD) and Raman point/mapping spectroscopy. Deconvolution analyses of the Raman spectra also demonstrated a local surface oxidation, possibly occurring on defect-rich interfaces, frequently identified within or in proximity of bamboo-like graphitic caps. By employing high-temperature Raman spectroscopy, we demonstrate a post-growth re-graphitization, which may also be visualized as an alternative way of depleting the oxygen content within the MWCNTs’ interfaces through recrystallization. Full article

Aflatoxin B1 is a toxic substance in almonds, other nuts, and grains that poses potential serious health risks to humans and animals, particularly in warm, humid climates. Therefore, it is necessary to remove aflatoxin B1 before almonds enter the supply chain to ensure food safety. Hyperspectral imaging (HSI) is a rapid, non-destructive method for detecting aflatoxin B1 by analyzing specific spectral data. However, HSI increases data dimensionality and often includes irrelevant information, complicating the analysis process. These challenges make classification models for detecting aflatoxin B1 complex and less reliable, especially for real-time, in-line applications. This study proposed a novel hybrid spectral band selection algorithm to detect aflatoxin B1 in almonds based on multilayer perceptron (MLP) network weights and spectral refinement (W-SR). In the proposed process, the hyperspectral imaging (HSI) spectral rank was firstly generated based on MLP network weights. The rank was further updated using a spectral confidence matrix. Then, a spectral refinement process identified more important spectra from the lower-ranked ones through iterative processes. An exhaustive search was performed to select an optimal spectral subset, consisting of only the most significant spectral bands, to make the entire process suitable for real-time, in-line aflatoxin B1 detection in industrial environments. The experimental results using the artificially contaminated almonds dataset achieved a cross-validation accuracy of 98.67% with an F1-score of 0.982 for the standard normal variate (SNV) processed data with only four spectral bands. Comparative experiment results showed that the proposed MLPW-SR spectral band selection algorithm outperforms baseline methods. Full article

Land subsidence is an environmental hazard occurring gradually over time, potentially posing significant threats to the structural stability of civilian buildings and essential infrastructures. This study presented a workflow using the SBAS-PSInSAR approach to analyze surface deformation in the Choushui River Fluvial Plain (CRFP) based on Sentinel-1 SAR images and validated against precise leveling. Integrating the InSAR results with hydrogeological data, such as groundwater levels (GWLS), multilayer compactions, and borehole loggings, a straightforward model was proposed to estimate appropriate groundwater level drops to minimize further subsidence. The results showed a huge subsidence bowl centered in Yunlin, with maximal sinking at an average 60 mm/year rate. High-resolution subsidence maps enable the quantitative analyses of safety issues for Taiwan High-Speed Rail (THSR) across the areas with considerable subsidence. In addition, the analysis of hydrogeological data revealed that half of the major compaction in the study area occurred at shallow depths that mainly included the first and second aquifers. Based on a maximal subsidence control rate of 40 mm/year specified in the CRFP, the model results indicated that the groundwater level drops from wet to dry seasons needed to be maintained from 3 to 5 m for the shallowest aquifer and 4–6 m for Aquifers 3 and 4. The workflow demonstrated the compatibility of InSAR with traditional geodetic methods and the effectiveness of integrating multiple data sources to assess the complex nature of land subsidence in the CRFP. Full article

The ability to trace the origin of eggs from backyard-raised hens is important due to their higher market value compared to barn-raised eggs. This study aimed to differentiate eggs from these two rearing systems using isotopic, elemental, and fatty acid profiles of egg yolks. A total of 90 egg yolk samples were analyzed, analytical results being followed by statistical tests (Student’s t-test) showing significant differences in δ¹⁸O, several elements (Mg, K, Sc, Mn, Fe, Ni, Cu, Zn, As, Cd, Ba, Pb), and fatty acids compositions (C23:0, C17:0, C18:0, C16:1n7, C18:1n9, C18:2n6, C20:1n7, C20:4n6, C20:5n3, C22:6n3), as well as in the ratios of SFA, PUFA, and UFA. The results indicated a nutritional advantage in backyard eggs due to their lower n-6 polyunsaturated fatty acid content and a more favorable n-6 to n-3 ratio, linked to differences in the hens’ diet and rearing systems. To classify the production system (backyard vs. barn), three pattern recognition methods were applied: linear discriminant analysis (LDA), k–nearest neighbor (k–NN), and multilayer perceptron artificial neural networks (MLP–ANN). LDA provided perfect initial separation, achieving 98.9% accuracy in cross-validation. k-NN yielded classification rates of 98.4% for the training set and 85.7% for the test set, while MLP–ANN achieved 100% accuracy in training and 92.3% in testing, with minor misclassification. These results demonstrate the effectiveness of fusion among isotopic, elemental, and fatty acid profiles in distinguishing backyard eggs from barn eggs and highlight the nutritional benefits of the backyard-rearing system. Full article

Metasurfaces are opening promising flexibilities to reshape the wavefront of electromagnetic waves. Notable optical phenomena are observed with the tailored surface plasmon, which is excited by metallic components in the visible spectrum. However, metamaterial or metasurface devices utilizing metallic materials encounter the challenge of low transmission efficiency, particularly within the visible spectrum. This study proposes a multilayer design strategy to enhance their transmission efficiency. By incorporating additional metal layers for improvements in the transmission efficiency and dielectric layers as spacers, cavities are formed along the propagation direction, enabling the modulation of transmittance and reflection through a process mimicking destructive interference. An analytical model simplified with the assumption of deep-subwavelength-thick metal layers is proposed to predict the structural parameters with optimized transmittance. Numerical studies employing the rigorous coupled wave analysis method confirmed that the additional metal layers significantly improve the transmittance. The introduction of the extra metal and dielectric layers enhances the transmission efficiency in specific spectral regions, maintaining a controllable passband and transmittance. The results indicate that the precise control over the layers’ thicknesses facilitates the modulation of peak-to-valley ratios and the creation of comb-like filters, which can be further refined through controlled random variation in the thickness. Furthermore, when the thickness of the silver layer followed an arithmetic sequence, a multilayer structure with a transmittance of approximately 80% covering the entire visible spectrum could be achieved. Significantly, the polarization extinction ratio and the phase delay of the incident beams could still be modulated by adjusting the geometrical structure and parameters of the multilayer metamaterial for diversified functionalities. Full article