Wide Application Research Articles

Bio-inspired algorithm integrated with sequential quadratic programming to analyze the dynamics of hepatitis B virus

BackgroundThere are a variety of lethal infectious diseases that are seriously affecting people's lives worldwide, particularly in developing countries. Hepatitis B, a fatal liver disease, is a contagious disease spreading globally. In this paper, a new hybrid approach of feed forward neural networks is considered to investigate aspects of the SEACTR (susceptible, exposed, acutely infected, chronically infected, treated, and recovered) transmission model of hepatitis B virus disease (HBVD). The combination of genetic algorithms and sequential quadratic programming, namely CGASQP, is applied, where genetic algorithm (GA) is used as the main optimization algorithm and sequential quadratic programming (SQP) is used as a fast-searching algorithm to fine-tune the outcomes obtained by GA. Considering the nature of HBVD, the whole population is divided into six compartments. An activation function based on mean square errors (MSEs) is constructed for the best performance of CGASQP using proposed model.ResultsThe solution's confidence is boosted through comparative analysis with reference to the Adam numerical approach. The results revealed that approximated results of CGASQP overlapped the reference approach up to 3–9 decimal places. The convergence, resilience, and stability characteristics are explored through mean absolute deviation (MAD), Theil’s coefficient (TIC), and root mean square error (RMSE), as well as minimum, semi-interquartile range, and median values with respect to time for the nonlinear proposed model. Most of these values lie around 10−10–10−4 for all classes of the model.ConclusionThe results are extremely encouraging and indicate that the CGASQP framework is very effective and highly feasible for implementation. In addition to excellent reliability and level of precision, the developed CGASQP technique also stands out for its simplicity, wider applicability, and flexibility.

Redesigned Guide DNA Enhanced Clostridium butyricum Argonaute Activity for Amplification-Free and Multiplexed Detection of Pathogens.

Clostridium butyricum (CbAgo)-based bioassays are popular due to their programmability and directional cleavage capabilities. However, the relatively compact protein structure of CbAgo limits its cleavage activity (even at the optimal temperature), thus restricting its wider application. Here, we observed that guide DNA (gDNA) with specific structural features significantly enhanced CbAgo cleavage efficiency. Then, we invented a novel gDNA containing DNAzyme segments (gDNAzyme) that substantially enhanced the CbAgo cleavage efficency (by 100%). Using a molecular dynamics simulation system, we found that the augmented cleavage efficiency might be attributed to the large-scale global movement of the PIWI domain of CbAgo and an increased number of cleavage sites. Moreover, this gDNAzyme feature allowed us to create a biosensor that simultaneously and sensitively detected three pathogenic bacteria without DNA extraction and amplification. Our work not only dramatically expands applications of the CbAgo-based biosensor but also provides unique insight into the protein-DNA interactions.

A Fast Survival Support Vector Regression Approach to Large Scale Credit Scoring via Safe Screening.

Survival models have found wider and wider applications in credit scoring recently due to their ability to estimate the dynamics of risk over time. In this research, we propose a Buckley-James safe sample screening support vector regression (BJS4VR) algorithm to model large-scale survival data by combing the Buckley-James transformation and support vector regression. Different from previous support vector regression survival models, censored samples here are imputed using a censoring unbiased Buckley-James estimator. Safe sample screening is then applied to discard samples that guaranteed to be non-active at the final optimal solution from the original data to improve efficiency. Experimental results on the large-scale real lending club loan data have shown that the proposed BJS4VR model outperforms existing popular survival models such as RSFM, CoxRidge and CoxBoost in terms of both prediction accuracy and time efficiency. Important variables highly correlated with credit risk are also identified with the proposed method.

Fabrication of lightweight polyethersulfone foams with enhanced surface quality and high specific flexural properties using micro‐opening microcellular injection molding

AbstractPolyethersulfone (PES) is distinguished by its exceptional comprehensive properties, making it a highly valued engineering plastic employed in various cutting‐edge fields. However, the high cost of PES limits its wider application in industrial fields. Considering the cost‐reduction advantages of polymer foaming, PES foams were successfully prepared in this work using microcellular injection molding (MIM) and micro‐opening microcellular injection molding (MOMIM) foaming methods. Compared with the MIM foaming method, the PES foam samples prepared by the MOMIM foaming method have better microcellular structure and surface quality. As the micro‐opening distance increased, the cell density of the PES foam samples showed a gradual rise before plateauing, with the cell size remaining relatively constant. As a result, the density of the PES foam sample dropped to 1.07 g/cm3, which is 80.5% of the solid PES sample. Meanwhile, the mechanical properties of the PES foam samples remained robust, with both the specific flexural modulus and the specific flexural strength showing increases as the micro‐opening distance increased. The knowledge obtained from this study provides a method for preparing high‐performance PES foam materials, which will facilitate the application of PES in more civilian fields.

Rosemary oil - a remedy for various conditions?

Introduction and Purpose: The use of rosemary oil has become a new trend in cosmetology and medicine. The purpose of the study is to systematize information on the composition of rosemary oil and its effects on specific health problems. Materials and methods: A literature search was conducted using the medical databases PubMed and Google Scholar. Articles were retrieved in English, employing the key words: “rosemary oil”, “Rosmarinus officinalis”, „rosmarinic acid” and „carnosolin” appropriate configurations. Conclusions: Rosemary oil exhibits a wide range of potential therapeutic and cosmetic applications, making it an extremely versatile and valuable ingredient. Its anti-inflammatory, antioxidant, antibacterial, antifungal, antiviral and anticancer properties confirm its status as a powerful tool in natural medicine. Nonetheless, in order to fully realize its potential, further clinical and experimental research is needed to thoroughly understand its mechanisms of action and to develop effective and safe therapies. In this way, rosemary oil can gain even wider application and become a key element in the treatment of many diseases and in daily health and beauty care.

Variable hyperparameterized Gaussian kernel using displaced squeezed vacuum state

There are machine learning schemes for realizing different types of kernels by quantum states of light. It is particularly interesting to realize a Gaussian kernel due to its wider applicability. A multimode coherent state can generate the Gaussian kernel with a constant value of hyperparameter. However, this constancy has limited the application of the Gaussian kernel when it is applied to complex learning problems. We improve it by introducing a variable hyperparameterized Gaussian kernel with a multimode-displaced squeezed vacuum state. The learning capacity of this kernel is tested with the support vector machines over some synthesized data sets, as well as public benchmark data sets. We establish that the proposed variable hyperparameterized Gaussian kernel offers better accuracy over the constant Gaussian kernel.

Identification of High Erucic Acid Brassica carinata Genotypes through Multi-Trait Stability Index

Brassica carinata is an important and native oilseed crop in Ethiopia. The seed oil from B.carinata attracts global attention for its various industrial applications, mainly due to its high erucic acid levels and its superior agronomic traits. Since the demand for high erucic acid from oilseed brassica has been increasing in the world market due to its wider applications in bio-industries, the breeding target of B. carinata has recently been focused on enhancing its erucic acid. Several high erucic acid B. carinata genotypes have been screened from the pre-breeding activities. Such genotypes, however, need to be tested for their stable performance, for their erucic acid level, and other desirable traits under different environments. The aim of this study was to identify high erucic acid B. carinata genotypes with stable performance in multiple desirable traits. Thirty-two B. carinata genotypes were grown in a randomized complete block design with three replications at three locations for two years. The genotypes were evaluated for nine desirable traits related to seed oil quality (erucic acid and oil content), seed yield, and other agronomic traits. The results showed that the proportion of genotype by environment interaction (GEI) was clearly observed in erucic acid, which led to a stability and mean performance analysis for selecting the most stable and best-performing genotypes for the desired traits. For such an analysis, we used the multi-trait stability index (MTSI) along with the weighted average of absolute score BLUPs (WAASB). As revealed from the MTSI, five genotypes (G13, G18, G10, G22 and G5) were identified as the most stable in erucic acid, oil content, seed yield, and other agronomic traits. The selected genotypes showed on average 45.7% erucic acid, 3185 kg ha−1 seed yield and 45.1% oil content with 4.3%, 25.8% and 6.9% positive selection gain, respectively. The negative selection gain of phonological traits and the plant height of the selected genotypes revealed their early maturity and their lower probability of being affected by lodging. Our findings demonstrated MTSI can be used to select high erucic acid B. carinata with a set of desirable traits, which would facilitate breeding efforts in developing novel and high erucic acid B. carinata varieties. Our results also showed that MTSI is an effective tool for selecting genotypes across different environments due to its unique ability to select multiple traits simultaneously.

Identification of High Erucic Acid Brassica carinata Genotypes through Multi-Trait Stability Index

Brassica carinata is an important and native oilseed crop in Ethiopia. The seed oil from B.carinata attracts global attention for its various industrial applications, mainly due to its high erucic acid levels and its superior agronomic traits. Since the demand for high erucic acid from oilseed brassica has been increasing in the world market due to its wider applications in bio-industries, the breeding target of B. carinata has recently been focused on enhancing its erucic acid. Several high erucic acid B. carinata genotypes have been screened from the pre-breeding activities. Such genotypes, however, need to be tested for their stable performance, for their erucic acid level, and other desirable traits under different environments. The aim of this study was to identify high erucic acid B. carinata genotypes with stable performance in multiple desirable traits. Thirty-two B. carinata genotypes were grown in a randomized complete block design with three replications at three locations for two years. The genotypes were evaluated for nine desirable traits related to seed oil quality (erucic acid and oil content), seed yield, and other agronomic traits. The results showed that the proportion of genotype by environment interaction (GEI) was clearly observed in erucic acid, which led to a stability and mean performance analysis for selecting the most stable and best-performing genotypes for the desired traits. For such an analysis, we used the multi-trait stability index (MTSI) along with the weighted average of absolute score BLUPs (WAASB). As revealed from the MTSI, five genotypes (G13, G18, G10, G22 and G5) were identified as the most stable in erucic acid, oil content, seed yield, and other agronomic traits. The selected genotypes showed on average 45.7% erucic acid, 3185 kg ha−1 seed yield and 45.1% oil content with 4.3%, 25.8% and 6.9% positive selection gain, respectively. The negative selection gain of phonological traits and the plant height of the selected genotypes revealed their early maturity and their lower probability of being affected by lodging. Our findings demonstrated MTSI can be used to select high erucic acid B. carinata with a set of desirable traits, which would facilitate breeding efforts in developing novel and high erucic acid B. carinata varieties. Our results also showed that MTSI is an effective tool for selecting genotypes across different environments due to its unique ability to select multiple traits simultaneously.

The enhancement and impact of Se doping on the electrochemical properties of LiMn2O4 cathode material for aqueous Li-ion batteries

Spinel LiMn2O4 is a promising cathode material for lithium-ion batteries. However, due to the Jahn-Teller effect and Mn dissolution, its capacity decreases seriously in the cycle process, which hinders its wider application. In the course of our research, we discovered that a modest infusion of Se-doping is capable of significantly augmenting the oxygen vacancy within the material, improve the conductivity of the LiMn2O4 material and promotes superior battery performance. Moreover, due to Se-doped the average valence state of Mn is raised, which effectively mitigates the Jahn-Teller effect. The optimized sample exhibits high capacity, long cycle life, and superior rate capability, with an initial discharge specific capacity of 101.0 mAh·g−1 at 7 C and capacity retention of 87.3 % after 200 cycles. The maximum specific discharge capacity of the full battery during the cycle was 97.9 mAh·g−1, the capacity retention rate after 200 cycles was 78.2 %, and the maximum energy density during the cycle is 145.1 Wh·Kg−1, which was nearly twice that of the single lithium-ion battery in this paper. The effect of Se-doped on the properties of LiMn2O4 cathode materials provides a new idea for the development of high-performance spinel lithium manganese oxide cathode materials.

Expanding the Structural Diversity of Tubulin-Targeting Agents: Development of Highly Potent Benzimidazoles for Treating Fungal Diseases.

Benzimidazoles, the representative pharmacophore of fungicides, have excellent antifungal potency, but their simple structure and single site of action have hindered their wider application in agriculture. In order to extend the structural diversity of tubulin-targeted benzimidazoles, novel benzimidazole derivatives were prepared by introducing the attractive pyrimidine pharmacophore. 2-((6-(4-(trifluoromethyl)phenoxy)pyrimidin-4-yl)thio)-1H-benzo[d]imidazole (A25) exhibited optimal antifungal activity against Sclerotinia sclerotiorum (S. s.), affording an excellent half-maximal effective concentration (EC50) of 0.158 μg/mL, which was higher than that of the reference agent carbendazim (EC50 = 0.594 μg/mL). Pot experiments revealed that compound A25 (200 μg/mL) had acceptable protective activity (84.7%) and curative activity (78.1%), which were comparable with that of carbendazim (protective activity: 90.8%; curative activity: 69.9%). Molecular docking displayed that multiple hydrogen bonds and π-π interactions could be formed between A25 and β-tubulin, resulting in a stronger bonding effect than carbendazim. Fluorescence imaging revealed that the structure of intracellular microtubules can be changed significantly after A25 treatment. Overall, these remarkable antifungal profiles of constructed novel benzimidazole derivatives could facilitate the application of novel microtubule-targeting agents.

Hyaluronic acid modified indocyanine green nanoparticles: a novel targeted strategy for NIR-II fluorescence lymphatic imaging.

The lymphatic system, alongside blood circulation, is crucial for maintaining bodily equilibrium and immune surveillance. Despite its importance, lymphatic imaging techniques lag behind those for blood circulation. Fluorescence imaging, particularly in the near-infrared-II (NIR-II) region, offers promising capabilities with centimeter-scale tissue penetration and micron-scale spatial resolution, sparking interest in visualizing the lymphatic system. Although indocyanine green (ICG) has been approved by the Food and Drug Administration (FDA) for use as a near-infrared-I (NIR-I) region fluorescent dye, its limitations include shallow penetration depth and low signal-to-noise ratio. Research suggests that ICG's fluorescence emission tail in the second near-infrared window holds potential for high-quality NIR-II imaging. However, challenges like short circulation half-life and concentration-dependent aggregation hinder its wider application. Here we developed HA@ICG nanoparticles (NPs), a superior ICG-based NIR-II fluorescent probe with excellent biocompatibility, prolonging in vivo imaging, and enhancing photostability compared to ICG alone. Leveraging LYVE-1, a prominent lymphatic endothelial cell receptor that binds specifically to hyaluronic acid (HA), our nanoprobes exhibit exceptional performance in targeting lymphatic system imaging. Moreover, our findings demonstrate the capability of HA@ICG NPs for capillary imaging, offering a means to assess local microcirculatory blood supply. These compelling results underscore the promising potential of HA@ICG NPs for achieving high-resolution bioimaging of nanomedicines in the NIR-II window.

A Review on the Deep Learning-based Surface Reconstruction from the Point Clouds

Background: Point cloud has become one of the most important data formats for 3D presentation because of the increased availability of acquisition devices and its wide applications. Deep learning has the most powerful ability to capture features from data and has successfully solved various problems in the field of image, such as classification, segmentation, and generation. Deep learning is commonly used to process data with a structured grid, while point cloud is irregular and unstructured. The irregularity of point clouds makes it difficult to use deep learning to solve the problems represented by point clouds. Recently, numerous approaches have been proposed to process point clouds with deep learning to solve various problems. Objective: The objective of this study is to serve as a guide to new scholars in the field of deep learning on 3D surface reconstruction from point clouds as it presents the recent progress in deep learning-based surface reconstruction for point clouds. It helps scholars to grasp the current research situation better and further explore the search direction. Method: This study reviews the recent progress in deep learning-based methods used for surface reconstruction from point clouds and large-scale 3D point cloud benchmark datasets commonly used. Results: Several relevant articles on deep learning used for surface reconstruction from point clouds and some recent patents on deep learning applications are collected and reviewed in this paper. The difficulty of irregularity of point clouds can be overcome by deep learning methods, thus achieving remarkable progress in surface reconstruction. Conclusion: Deep learning for 3D surface reconstruction from point clouds is becoming a research hotspot due to its performance in terms of anti-interference and generalization. Although the advance is remarkable, there are still some challenges that need to be further studied.

Eyebox extension for holographic Maxwellian near-eye display by using a real grating and a virtual grating

The Maxwellian display presents always-focused images to the viewer, alleviating the vergence-accommodation conflict (VAC) in near-eye displays (NEDs). However, the limited eyebox of the conventional Maxwellian display prevents it from wider applications. In this paper, an exebox-extended holographic Maxwellian near-eye display is proposed by using a real grating and a virtual grating. A diffraction grating is used to duplicate multiple beams and produce duplicate viewpoints. A virtual grating phase is added in the hologram to steer the beams and deflect the viewpoints. The proposed method combines a slight steering with a large duplicated eyebox, realizing that the viewpoint position can be flexibly controlled. The optical experiment realizes a 49.2° FOV and 12.1 mm eyebox which contains 5 moveable viewpoints. This proposed system shows good display capabilities and has potential applications.

Smart contract vulnerability detection using wide and deep neural network

Smart contracts, integral to blockchain technology, automate agreements without intermediaries, ensuring transparency and security across various sectors. However, the immutable nature of blockchain exposes deployed contracts to potential risks if they contain vulnerabilities. Current approaches, including symbolic execution and graph-based machine learning, aim to ensure smart contract security. However, these methods suffer from limitations such as high false positive rates, heavy reliance on trained data, and over-generalization.The goal of this paper is to investigate the application of Wide and Deep Neural Networks in identifying vulnerabilities within smart contracts. We introduce WIDENNET, a method based on deep neural networks, designed to detect reentrancy and timestamp dependence vulnerabilities in smart contracts. Our approach involves extracting bytecodes from the contracts and converting them into Operational Codes (OPCODES), which are then transformed into distinct vector representations. These vectors are subsequently fed into the neural network to extract both complex and simple patterns for vulnerability detection. Testing on real-world datasets yielded an average accuracy of 83.07% and a precision of 83.13%. Our method offers a potential solution to mitigate vulnerabilities in blockchain applications.

A lightweight network architecture for traffic sign recognition based on enhanced LeNet-5 network.

As an important part of the unmanned driving system, the detection and recognition of traffic sign need to have the characteristics of excellent recognition accuracy, fast execution speed and easy deployment. Researchers have applied the techniques of machine learning, deep learning and image processing to traffic sign recognition successfully. Considering the hardware conditions of the terminal equipment in the unmanned driving system, in this research work, the goal was to achieve a convolutional neural network (CNN) architecture that is lightweight and easily implemented for an embedded application and with excellent recognition accuracy and execution speed. As a classical CNN architecture, LeNet-5 network model was chosen to be improved, including image preprocessing, improving spatial pool convolutional neural network, optimizing neurons, optimizing activation function, etc. The test experiment of the improved network architecture was carried out on German Traffic Sign Recognition Benchmark (GTSRB) database. The experimental results show that the improved network architecture can obtain higher recognition accuracy in a short interference time, and the algorithm loss is significantly reduced with the progress of training. At the same time, compared with other lightweight network models, this network architecture gives a good recognition result, with a recognition accuracy of 97.53%. The network structure is simple, the algorithm complexity is low, and it is suitable for all kinds of terminal equipment, which can have a wider application in unmanned driving system.

Synergistic Solvation as the Enhancement of Local Mixing.

Mixing two solvents can sometimes make a much better solvent than expected from their weighted mean. This phenomenon, called synergistic solvation, has commonly been explained via the Hildebrand and Hansen solubility parameters, yet their inability in other solubilization phenomena, most notably hydrotropy, necessitates an alternative route to elucidating solubilization. While, recently, the universal theory of solubilization was founded on the statistical thermodynamic fluctuation theory (as a generalization of the Kirkwood-Buff theory), its demand for experimental data processing has been a hindrance for its wider application. This can be overcome by the solubility isotherm theory, which is founded on the fluctuation theory yet reduces experimental data processing significantly to the level of isotherm analysis in sorption. The isotherm analysis identifies the driving force of synergistic solvation as the enhancement of solvent mixing around the solute, opposite in behavior to hydrotropy (characterized by the enhancement of demixing or self-association around the solute). Thus, the fluctuation theory, including its solubility isotherms, provides a universal language for solubilization across the historic subcategorization of solubilizers, for which different (and often contradictory) mechanistic models have been proposed.

Sparfloxacin/Gold Nanoparticles Modified Electrochemical Sensor for the Determination of Dopamine

Background: The development of facile, reliable, and sensitive electrochemical sensors for neurotransmitter detection is of utmost importance in biomedical research. Objectives: In this study, we proposed a novel approach for the synthesis of gold nanoparticles (AuNPs) using sparfloxacin as a reducing and stabilizing agent and further utilized as-prepared AuNPs for the construction of an electrochemical sensor to detect dopamine. Methods: The synthesis of AuNPs was achieved through a simple method where sparfloxacin acted as both a reducing and stabilizing agent. The AuNPs were investigated by using UV-visible spectroscopy, field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDS), elemental mapping (Emap), and X-ray diffraction (XRD). Results: The synthesised AuNPs were used to develop an electrochemical sensor for the detection of dopamine molecules. The fabricated electrochemical sensor demonstrated remarkable sensitivity and selectivity toward dopamine sensing. This sensor exhibited a good linear detection of dopamine in micromolar concentrations. Moreover, the sensor showed excellent stability and reproducibility. Conclusion: This novel electrochemical sensor holds significant potential for practical applications in neuroscience and clinical diagnosis, offering a rapid and sensitive method for dopamine detection. The utilization of sparfloxacin/AuNPs provides a promising avenue for the development of advanced electrochemical sensors with enhanced performance and wider applications in the detection of other neurotransmitters and biomolecules.

Multi-Dimensional Manifolds Consistency Regularization for semi-supervised remote sensing semantic segmentation

Semi-supervised semantic segmentation in remote sensing is critical for urban planning, environmental monitoring and disaster response. The high cost and time required for high-quality data annotation limits its wider application. Traditional semi-supervised deep learning methods, which operate in a single dimension, limit model robustness and generalization. Our study addresses this issue by proposing an effective semi-supervised learning method. This method improves model robustness and generalization in remote sensing semantic segmentation. We introduce the Multi-Dimensional Manifolds Consistency Regularization (MDMCR) approach. It applies multi-dimensional perturbations to input images and features, expanding the sample library and improving learning efficiency. Our method has been rigorously tested on various datasets. With only 1/8 of the data labeled, it achieved mean Intersection over Union (mIoU) scores of 74.48% on ISPRS Vaihingen and 78.80% on Potsdam. With only 5% labeled data, it reached 49.93% mIoU on DeepGlobe Roads and 57.90% on Massachusetts Roads. These results show the superiority of our method over existing techniques.

Preparation and Characterization of Prickly Ash Peel Oleoresin Microcapsules and Flavor Retention Analysis.

Prickly ash peel oleoresin (PPO) is a highly concentrated oil of Prickly ash essential oil and has a stronger aroma. However, its low water solubility, high volatility, difficulty in transport and storage, and decomposition by light, heat, and oxygen limit its wider application. To solve this problem, this study used freeze-drying or spray-drying, with soybean protein isolate (SPI) or gum Arabic (GA), combined with aqueous maltodextrin (MD) as the encapsulating agents to prepare four types of PPO microcapsules (POMs). Spray-dried microcapsules with GA as the encapsulating agent achieved a high encapsulation efficiency (EE) of 92.31 ± 0.31%, improved the thermal stability of the PPO, and had spherical morphology. (Headspace solid-phase microextraction/gas chromatography-mass spectrometry) HS-SPME/GC-MS detected 41 volatile compounds in PPO; of these, linalool, β-myrcene, sabinene, and D-limonene were identified as key flavor components. Principal component analysis (PCA) effectively distinguished the significant differences in flavor between PPO, spray-dried SPI/MD microcapsules (SS), and spray-dried GA/MD microcapsules (SG). During 15 days of air-exposure, the loss of flavor from SG (54.62 ± 0.54%) was significantly lower than PPO (79.45 ± 1.45%) and SS (57.55 ± 0.36%). During the air-exposure period, SG consistently had the highest antioxidant capacity, making it desirable for PPO packaging, and expanding its potential applications within the food industry.

Clinical implications of haemodynamics in symptomatic intracranial atherosclerotic stenosis by computational fluid dynamics modelling: a systematic review

BackgroundRecently, computational fluid dynamics (CFD) has been used to simulate blood flow of symptomatic intracranial atherosclerotic stenosis (sICAS) and investigate the clinical implications of its haemodynamic features, which were systematically...