Search Results (71,735)

This paper presents a classification model for eye diseases utilizing attention mechanisms to learn features from fundus images and structures. The study focuses on diagnosing glaucoma by extracting retinal vessels and the optic disc from fundus images using a ResU-Net-based segmentation model and Hough Circle Transform, respectively. The extracted structures and preprocessed images were inputted into a CNN-based multi-input model for training. Comparative evaluations demonstrated that our model outperformed other research models in classifying glaucoma, even with a smaller dataset. Ablation studies confirmed that using attention mechanisms to learn fundus structures significantly enhanced performance. The study also highlighted the challenges in normal case classification due to potential feature degradation during structure extraction. Future research will focus on incorporating additional fundus structures such as the macula, refining extraction algorithms, and expanding the types of classified eye diseases. Full article

Methylene blue dye in water sources can pose health risks to humans, potentially causing methemoglobinemia, a condition that impairs the blood’s ability to carry oxygen. Hence, the current study investigates the synthesis of novel magnesium borate/magnesium oxide (Mg₃B₂O₆/MgO) nanostructures and their efficiency in removing methylene blue dye from aqueous media. The nanostructures were synthesized using the Pechini sol–gel method, which involves a reaction between magnesium nitrate hexahydrate and boric acid, with citric acid acting as a chelating agent and ethylene glycol as a crosslinker. This method helps in achieving a homogeneous mixture, which, upon calcination at 600 and 800 °C, yields Mg₃B₂O₆/MgO novel nanostructures referred to as MB600 and MB800, respectively. The characterization of these nanostructures involved techniques like X-ray diffraction (XRD), Fourier-transform infrared (FTIR) spectroscopy, N₂ gas analyzer, and field-emission scanning electron microscope (FE-SEM). These analyses confirmed the formation of orthorhombic Mg₃B₂O₆ and cubic MgO phases with distinct features, influenced by the calcination temperature. The mean crystal size of the MB600 and MB800 samples was 64.57 and 79.20 nm, respectively. In addition, the BET surface area of the MB600 and MB800 samples was 74.63 and 64.82 m²/g, respectively. The results indicated that the MB600 sample, with its higher surface area, generally demonstrated better methylene blue dye removal performance (505.05 mg/g) than the MB800 sample (483.09 mg/g). The adsorption process followed the pseudo-second-order model, indicating dependency on available adsorption sites. Also, the adsorption process matched well with the Langmuir isotherm, confirming a homogeneous adsorbent surface. The thermodynamic parameters revealed that the adsorption process was physical, exothermic, and spontaneous. The MB600 and MB800 nanostructures could be effectively regenerated using 6 M HCl and reused across multiple cycles. These findings underscore the potential of these nanostructures as cost-effective and sustainable adsorbents for methylene blue dye removal. Full article

AI language models are increasingly transforming language research in various ways. How can language educators and researchers respond to the challenge posed by these AI models? Specifically, how can we embrace this technology to inform and enhance second language learning and teaching? In order to quantitatively characterize and index second language writing, the current work proposes the use of similarities derived from contextualized meaning representations in AI language models. The computational analysis in this work is hypothesis-driven. The current work predicts how similarities should be distributed in a second language learning setting. The results suggest that similarity metrics are informative of writing proficiency assessment and interlanguage development. Statistically significant effects were found across multiple AI models. Most of the metrics could distinguish language learners’ proficiency levels. Significant correlations were also found between similarity metrics and learners’ writing test scores provided by human experts in the domain. However, not all such effects were strong or interpretable. Several results could not be consistently explained under the proposed second language learning hypotheses. Overall, the current investigation indicates that with careful configuration and systematic metrics design, AI language models can be promising tools in advancing language education. Full article

The importance of decellularized extracellular matrix (dECM) as a natural biomaterial in tissue engineering and regenerative medicine is rapidly growing. The core objective of the decellularization process is to eliminate cellular components while maximizing the preservation of the ECM’s primary structure and components. Establishing a rapid, effective, and minimally destructive decellularization technique is essential for obtaining high-quality dECM to construct regenerative organs. This study focused on human umbilical cord tissue, designing different reagent combinations for decellularization protocols while maintaining a consistent processing time. The impact of these protocols on the decellularization efficiency of human umbilical cord tissue was evaluated. The results suggested that the composite decellularization strategy utilizing trypsin/EDTA + Triton X-100 + sodium deoxycholate was the optimal approach in this study for preparing decellularized human umbilical cord dECM. After 5 h of decellularization treatment, most cellular components were eliminated, confirmed through dsDNA quantitative detection, hematoxylin and eosin (HE) staining, and DAPI staining. Meanwhile, Masson staining, periodic acid-silver methenamine (PASM) staining, periodic acid-Schiff (PAS) staining, and immunofluorescent tissue section staining results revealed that the decellularized scaffold retained extracellular matrix components, including collagen and glycosaminoglycans (GAGs). Compared to native umbilical cord tissue, electron microscopy results demonstrated that the microstructure of the extracellular matrix was well preserved after decellularization. Furthermore, Fourier-transform infrared spectroscopy (FTIR) findings indicated that the decellularization process successfully retained the main functional group structures of extracellular matrix (ECM) components. The quantitative analysis of collagen, elastin, and GAG content validated the advantages of this decellularization process in preserving and purifying ECM components. Additionally, it was confirmed that this decellularized matrix exhibited no cytotoxicity in vitro. This study achieved short-term decellularization preparation for umbilical cord tissue through a combined decellularization strategy. Full article

With the advantages of new technologies and rising demand from customers, it is necessary to improve the manufacturing process. This necessity was recognized by the industry; therefore, the concept of Industry 4.0 has been implemented in various areas of manufacturing and services. The backbone and main aspect of Industry 4.0 is digitalization and the implementation of technologies into processes. While this concept helps manufacturers with the modernization and optimization of many attributes of the processes, Industry 5.0 takes a step further and brings importance to the human factor of industry practice, together with sustainability and resilience. The concept of Industry 5.0 contributes to the idea of creating a sustainable, prosperous, and human-friendly environment within companies. The main focus of the article is to analyze the existing literature regarding what is missing from the successful implementation of human centricity into industry practice, namely in small and medium-sized factories (SMEs). These findings are then presented in the form of requirements and barriers for the implementation of human centricity into SME factories, which can serve as guidelines for implementing human-centered manufacturing using axiomatic design theory in SMEs, which can serve as a roadmap for practitioners. Full article

Background: Liver resection is a critical surgical procedure for treating various hepatic pathologies. Minimally invasive approaches have gradually gained importance, and, in recent years, the introduction of robotic surgery has transformed the surgical landscape, providing potential advantages such as enhanced precision and stable ergonomic vision. Among robotic techniques, the single-site approach has garnered increasing attention due to its potential to minimize surgical trauma and improve cosmetic outcomes. However, the full extent of its utility and efficacy in liver resection has yet to be thoroughly explored. Methods: We conducted a comprehensive systematic review to evaluate the current role of the single-site robotic approach in liver resection. A detailed search of PubMed was performed to identify relevant studies published up to January 2024. Eligible studies were critically appraised, and data concerning surgical outcomes, perioperative parameters, and post-operative complications were extracted and analyzed. Results: Our review synthesizes evidence from six studies, encompassing a total of seven cases undergoing robotic single-site hepatic resection (SSHR) using various versions of the da Vinci© system. Specifically, the procedures included five left lateral segmentectomy, one right hepatectomy, and one caudate lobe resection. We provide a summary of the surgical techniques, indications, selection criteria, and outcomes associated with this approach. Conclusion: The single-site robotic approach represents an option among the minimally invasive approaches in liver surgery. However, although the feasibility has been demonstrated, further studies are needed to elucidate its optimal utilization, long-term outcomes, and comparative effectiveness against the other techniques. This systematic review provides valuable insights into the current state of single-site robotic liver resection and underscores the need for continued research in this rapidly evolving field. Full article

The detection of tomatoes is of vital importance for enhancing production efficiency, with image recognition-based tomato detection methods being the primary approach. However, these methods face challenges such as the difficulty in extracting small targets, low detection accuracy, and slow processing speeds. Therefore, this paper proposes an improved RT-DETR-Tomato model for efficient tomato detection under complex environmental conditions. The model mainly consists of a Swin Transformer block, a BiFormer module, path merging, multi-scale convolutional layers, and fully connected layers. In this proposed model, Swin Transformer is chosen as the new backbone network to replace ResNet50 because of its superior ability to capture broader global dependency relationships and contextual information. Meanwhile, a lightweight BiFormer block is adopted in Swin Transformer to reduce computational complexity through content-aware flexible computation allocation. Experimental results show that the average accuracy of the final RT-DETR-Tomato model is greatly improved compared to the original model, and the model training time is greatly reduced, demonstrating better environmental adaptability. In the future, the RT-DETR-Tomato model can be integrated with intelligent patrol and picking robots, enabling precise identification of crops and ensuring the safety of crops and the smooth progress of agricultural production. Full article

A method for the reduction of aldehydes with pinacolborane catalyzed by pincer cobalt complexes based on a triazine backbone is developed in this paper. The presented methodology allows for the transformation of several aldehydes bearing a wide range of electron-withdrawing and electron-donating groups under mild conditions. The presented procedure allows for the direct one-step hydrolysis of the obtained intermediates to the corresponding primary alcohols. A plausible reaction mechanism is proposed. Full article

Convolutional neural networks (CNNs) face challenges in capturing long-distance text correlations, and Bidirectional Long Short-Term Memory (BiLSTM) networks exhibit limited feature extraction capabilities for text classification of public service requests. To address the abovementioned problems, this work utilizes an ensemble learning approach to integrate model elements efficiently. This study presents a method for classifying public service request text using a hybrid neural network model called BERT-BiLSTM-CNN. First, BERT (Bidirectional Encoder Representations from Transformers) is used for preprocessing to obtain text vector representations. Then, context and process sequence information are captured through BiLSTM. Next, local features in the text are captured through CNN. Finally, classification results are obtained through Softmax. Through comparative analysis, the method of fusing these three models is superior to other hybrid neural network model architectures in multiple classification tasks. It has a significant effect on public service request text classification. Full article

As a vital pigment for photosynthesis in rice, chlorophyll content is closely correlated with growth status and photosynthetic capacity. The estimation of chlorophyll content allows for the monitoring of rice growth and facilitates precise management in the field, such as the application of fertilizers and irrigation. The advancement of hyperspectral remote sensing technology has made it possible to estimate chlorophyll content non-destructively, quickly, and effectively, offering technical support for managing and monitoring rice growth across wide areas. Although hyperspectral data have a fine spectral resolution, they also cause a large amount of information redundancy and noise. This study focuses on the issues of unstable input variables and the estimation model’s poor applicability to various periods when predicting rice chlorophyll content. By introducing the theory of harmonic analysis and the time-frequency conversion method, a deep neural network (DNN) model framework based on wavelet packet transform-first order differential-harmonic analysis (WPT-FD-HA) was proposed, which avoids the uncertainty in the calculation of spectral parameters. The accuracy of estimating rice chlorophyll content based on WPT-FD and WPT-FD-HA variables was compared at seedling, tillering, jointing, heading, grain filling, milk, and complete periods to evaluate the validity and generalizability of the suggested framework. The results demonstrated that all of the WPT-FD-HA models’ single-period validation accuracy had coefficients of determination (R²) values greater than 0.9 and RMSE values less than 1. The multi-period validation model had a root mean square error (RMSE) of 1.664 and an R² of 0.971. Even with independent data splitting validation, the multi-period model accuracy can still achieve R² = 0.95 and RMSE = 1.4. The WPT-FD-HA-based deep learning framework exhibited strong stability. The outcome of this study deserves to be used to monitor rice growth on a broad scale using hyperspectral data. Full article

This paper introduces a parameter-efficient transformer-based model designed for scientific literature classification. By optimizing the transformer architecture, the proposed model significantly reduces memory usage, training time, inference time, and the carbon footprint associated with large language models. The proposed approach is evaluated against various deep learning models and demonstrates superior performance in classifying scientific literature. Comprehensive experiments conducted on datasets from Web of Science, ArXiv, Nature, Springer, and Wiley reveal that the proposed model’s multi-headed attention mechanism and enhanced embeddings contribute to its high accuracy and efficiency, making it a robust solution for text classification tasks. Full article

Digital twin technology, a new type of digital technology emerging in recent years, realizes real-time simulation, prediction and optimization by digitally modeling the physical world, providing a new idea and method for the design, operation and management of water conservancy projects, which is of great significance for the realization of the transformation of water conservancy informatization to intelligent water conservancy. In view of this, this paper systematically discusses the concept and development history of digital twin smart water conservancy, compares its differences with traditional water conservancy models, and further proposes the digital twin smart water conservancy five-dimensional model. Based on the five-dimensional model of digital twin water conservancy, the research progress of digital twin smart water conservancy is summarized by focusing on six aspects, namely digital twin water conservancy data perception, data transmission, data analysis and processing, digital twin water conservancy model construction, digital twin water conservancy interaction and collaboration and digital twin water conservancy service application, and the challenges and problems of digital twin technology in the application of smart water conservancy. Finally, the development trend of digital twin technology and the direction of technological breakthroughs are envisioned, aiming to provide reference and guidance for the research on digital twin technology in the field of smart water conservancy and to promote the further development of the field. Full article

The accurate extraction of agricultural parcels from remote sensing images is crucial for advanced agricultural management and monitoring systems. Existing methods primarily emphasize regional accuracy over boundary quality, often resulting in fragmented outputs due to uniform crop types, diverse agricultural practices, and environmental variations. To address these issues, this paper proposes DSTBA-Net, an end-to-end encoder–decoder architecture. Initially, we introduce a Dual-Stream Feature Extraction (DSFE) mechanism within the encoder, which consists of Residual Blocks and Boundary Feature Guidance (BFG) to separately process image and boundary data. The extracted features are then fused in the Global Feature Fusion Module (GFFM), utilizing Transformer technology to further integrate global and detailed information. In the decoder, we employ Feature Compensation Recovery (FCR) to restore critical information lost during the encoding process. Additionally, the network is optimized using a boundary-aware weighted loss strategy. DSTBA-Net aims to achieve high precision in agricultural parcel segmentation and accurate boundary extraction. To evaluate the model’s effectiveness, we conducted experiments on agricultural parcel extraction in Denmark (Europe) and Shandong (Asia). Both quantitative and qualitative analyses show that DSTBA-Net outperforms comparative methods, offering significant advantages in agricultural parcel extraction. Full article

This study focuses on the synthesis and structural characterization of new compounds that integrate thiazolidine-2,4-dione, acridine moiety, and an acetamide linker, aiming to leverage the synergistic effects of these pharmacophores for enhanced therapeutic potential. The newly designed molecules were efficiently synthesized through a multi-step process and subsequently transformed into their hydrochloride salts. Comprehensive spectroscopic techniques, including nuclear magnetic resonance (NMR), high-resolution mass spectrometry (HRMS), infrared (IR) spectroscopy, and elemental analysis, were employed to determine the molecular structures of the synthesized compounds. Biological evaluations were conducted to assess the therapeutic potential of the new compounds. The influence of these derivatives on the metabolic activity of various cancer cell lines was assessed, with IC₅₀ values determined via MTT assays. An in-depth analysis of the structure–activity relationship (SAR) revealed intriguing insights into their cytotoxic profiles. Compounds with electron-withdrawing groups generally exhibited lower IC₅₀ values, indicating higher potency. The presence of the methoxy group at the linking phenyl ring modulated both the potency and selectivity of the compounds. The variation in the acridine core at the nitrogen atom of the thiazolidine-2,4-dione core significantly affects the activity against cancer cell lines, with the acridin-9-yl substituent enhancing the compounds’ antiproliferative activity. Furthermore, compounds in their hydrochloride salt forms demonstrated better activity against cancer cell lines compared to their free base forms. Compounds 12c·2HCl (IC₅₀ = 5.4 ± 2.4 μM), 13d (IC₅₀ = 4.9 ± 2.9 μM), and 12f·2HCl (IC₅₀ = 4.98 ± 2.9 μM) demonstrated excellent activity against the HCT116 cancer cell line, and compound 7d·2HCl (IC₅₀ = 4.55 ± 0.35 μM) demonstrated excellent activity against the HeLa cancer cell line. Notably, only a few tested compounds, including 7e·2HCl (IC₅₀ = 11.00 ± 2.2 μM), 7f (IC₅₀ = 11.54 ± 2.06 μM), and 7f·2HCl (IC₅₀ = 9.82 ± 1.92 μM), showed activity against pancreatic PATU cells. This type of cancer has a very high mortality due to asymptomatic early stages, the occurrence of metastases, and frequent resistance to chemotherapy. Four derivatives, namely, 7e·2HCl, 12d·2HCl, 13c·HCl, and 13d, were tested for their interaction properties with BSA using fluorescence spectroscopic studies. The values for the quenching constant (K_sv) ranged from 9.59 × 10⁴ to 10.74 × 10⁴ M⁻¹, indicating a good affinity to the BSA protein. Full article

Poly(3,4-ethylenedioxythiophene) (PEDOT) and PEDOT-functionalized carbon nanoparticles (f-CNPs) were synthesized by in situ chemical oxidative polymerization and pyrolysis methods. f-CNP-PEDOT nanocomposites were prepared by varying the concentration of PEDOT from 1 to 20% by weight (i.e., 1, 2.5, 5, 10, and 20 wt%). Several characterization techniques, such as field-emission scanning electron microscopy (FESEM), attenuated total reflectance-Fourier transform infrared (ATR-FTIR), X-ray diffraction (XRD), N₂ Brunauer–Emmett–Teller (BET) and Barrett–Joyner–Halenda (BJH) analyses, as well as cyclic voltammetry (CV), galvanostatic charge discharge (GCD), and electrochemical impedance spectroscopy (EIS), were applied to investigate the morphology, the crystalline structure, the N₂ adsorption/desorption capability, as well as the electrochemical properties of these new synthesized nanocomposite materials. FESEM analysis showed that these nanocomposites have defined porous structures, and BET surface area analysis showed that the standalone f-CNP exhibited the largest surface area of 801.6 m²/g, whereas the f-CNP-PEDOT with 20 wt% exhibited the smallest surface area of 116 m²/g. The BJH method showed that the nanocomposites were predominantly mesoporous. CV, GCD, and EIS measurements showed that f-CNP functionalized with 5 wt% PEDOT had a higher capacitive performance compared to the individual f-CNPs and PEDOT constituents, exhibiting an extraordinary specific capacitance of 258.7 F/g, at a current density of 0.25 A/g, due to the combined advantage of enhanced electrochemical activity induced by PEDOT doping, and highly developed porosity of f-CNPs. Symmetric aqueous supercapacitor devices were fabricated using the optimized f-CNP-PEDOT doped with 5 wt% of PEDOT as active material, exhibiting a high capacitance of 96.7 F/g at 1.4 V, holding practically their full charge, after 10,000 charge–discharge cycles at 2 A/g, thus providing the highest electrical electrodes performance. Hereafter, this work paves the way for the potential use of f-CNP-PEDOT nanocomposites in the development of high-energy-density supercapacitors. Full article