Traditional Techniques Research Articles

Molecular authentication of surimi-based products (fish cakes, 鱼糕) sold on the Chinese e-commerce: Traditional (DNA barcoding) and innovative techniques (metabarcoding) to tackle seafood fraud

Molecular authentication of surimi-based products (fish cakes, 鱼糕) sold on the Chinese e-commerce: Traditional (DNA barcoding) and innovative techniques (metabarcoding) to tackle seafood fraud

An innovative network intrusion detection system (NIDS): Hierarchical deep learning model based on Unsw-Nb15 dataset

With the increasing prevalence of network intrusions, the development of effective network intrusion detection systems (NIDS) has become crucial. In this study, we propose a novel NIDS approach that combines the power of long short-term memory (LSTM) and attention mechanisms to analyze the spatial and temporal features of network traffic data. We utilize the benchmark UNSW-NB15 dataset, which exhibits a diverse distribution of patterns, including a significant disparity in the size of the training and testing sets. Unlike traditional machine learning techniques like support vector machines (SVM) and k-nearest neighbors (KNN) that often struggle with limited feature sets and lower accuracy, our proposed model overcomes these limitations. Notably, existing models applied to this dataset typically require manual feature selection and extraction, which can be time-consuming and less precise. In contrast, our model achieves superior results in binary classification by leveraging the advantages of LSTM and attention mechanisms. Through extensive experiments and evaluations with state-of-the-art ML/DL models, we demonstrate the effectiveness and superiority of our proposed approach. Our findings highlight the potential of combining LSTM and attention mechanisms for enhanced network intrusion detection.

A comparative analysis of traditional meat processing methods

This comparative analysis examines traditional meat preservation techniques, including drying, curing, fermentation, smoking, fat preservation, air drying, fermented fish sauce, pickling and traditional sausage making. Rooted in different cultures, these methods use specific processes and ingredients to preserve meat, enhance flavour and extend shelf life. Some different variations and adaptations showcase techniques such as marinating, braising, and incorporating herbs or probiotics. Studies highlight advances in reducing salt and nitrite levels during pickling, improving brine handling methods, and optimizing smoking techniques for safety. Factors affecting preservation include the microbial community in fermented cured meats, temperature control during smoking and moisture content during air drying. Recent research findings observed that fermentation-enhanced sausage flavour and certain microorganisms declined histamine levels in fish sauce. New technologies and traditional methods combined facilitate conservation, nutrition, and the sensory quality of meat.

Research on Dynamic Tracking and Choreographic Techniques of Modern Dance Based on Computer 3D Modeling Technology

Abstract In this paper, we propose an improved 3D modeling method based on RecurDyn dynamics Theory to address the poor efficiency of traditional modeling techniques in dance dynamics tracking. Solid manipulation of the virtual scene is realized by geometric transformation, and singular value decomposition is applied to solve the transformation. Simulation experiments were designed to explore the tracking effect. When the MS moves at a more significant speed, the larger the Doppler frequency corresponding to the dynamic tracking and choreography technique of modern dance, the more rapidly the channel changes, and the more quickly the channel correlation decreases, such as when the MS moves at a speed of 13m/s and the correlation coefficient drops to 0.1, which corresponds to the number of sampling points within 6. From the Analysis of the ablation experiments, the errors of the three models are reduced by 9.6%, 11.2%, and 5.9%, respectively. From the first qualitative test analysis, frames 1-13 should be less spatial and 14-24 should be more spatial. From the Analysis of the first qualitative test, according to the numerical value, the average moving speed of the arm in the 53rd-68th frames is the largest, and the average moving speed of the arm in the 87th-100th frames is the smallest. Therefore, the improved algorithm can be accurately applied to modern dance dynamic tracking and choreography techniques.

Global Model Selection via Solution Paths for Robust Support Vector Machine.

Robust support vector machine (RSVM) using ramp loss provides a better generalization performance than traditional support vector machine (SVM) using hinge loss. However, the good performance of RSVM heavily depends on the proper values of regularization parameter and ramp parameter. Traditional model selection technique with gird search has extremely high computational cost especially for fine-grained search. To address this challenging problem, in this paper, we first propose solution paths of RSVM (SPRSVM) based on the concave-convex procedure (CCCP) which can track the solutions of the non-convex RSVM with respect to regularization parameter and ramp parameter respectively. Specifically, we use incremental and decremental learning algorithms to deal with the Karush-Khun-Tucker violating samples in the process of tracking the solutions. Based on the solution paths of RSVM and the piecewise linearity of model function, we can compute the error paths of RSVM and find the values of regularization parameter and ramp parameter, respectively, which corresponds to the minimum cross validation error. We prove the finite convergence of SPRSVM and analyze the computational complexity of SPRSVM. Experimental results on a variety of benchmark datasets not only verify that our SPRSVM can globally search the regularization and ramp parameters respectively, but also show a huge reduction of computational time compared with the grid search approach.

Low-temperature antiferromagnetism in quaternary Mn2FeSi0.5Al0.5 alloys

In this work, the quaternary Mn2FeSi0.5Al0.5 alloys are prepared for the first time in the form of cylinder-shaped ingots by traditional induction melting technique followed by homogenization annealing at 773 K for 100 h. The microstructural and magnetic properties of as-cast and annealed Mn2FeSi0.5Al0.5 samples are analyzed in detail and compared to the Mn2FeSi and Mn2FeAl ternary alloys. The Mn2FeSi0.5Al0.5 ingots are two-phase both before and after annealing, and their diffractograms correspond to the primitive cubic β-Mn structure. Obtained lattice constant of 0.6274 nm is only slightly lower than that of Mn2FeAl alloy (0.6339 nm) and different from Mn2FeSi (0.5672 nm). The existence of both phases enriched in Si at the expense of Al and Mn was confirmed by differential thermal analysis showing two endothermic and exothermic peaks at temperatures of 1363 K and 1407 K. The magnetic properties of both quaternary samples studied in wide temperature range from 5 K to 573 K indicate paramagnetic behavior at room and elevated temperatures. The annealed system has the values of Curie temperature and effective paramagnetic moment comparable to the ternary Mn2FeAl alloy. The transition to antiferromagnetic state occurring at Néel temperatures of 34 K (as-cast sample) and 37 K (annealed sample) is caused by strong geometric frustration of β-Mn structure. The magnetic transitions observed in both samples between Néel and room temperature are discussed in terms of the existence of Griffiths phase.

Bacterial community structure of rice wine from China's Xiaogan and Dazhou regions: Correlation with taste characteristics

To investigate the bacterial profiles and taste characteristics of rice wine from the Xiaogan and Dazhou regions, this study employed a combination of High-throughput sequencing, traditional pure culture techniques, and bionics equipment. The findings revealed a significant difference (P < 0.05) in bacterial diversity and community structure between the rice wine from Xiaogan and Dazhou. In particular, Leuconostoc, Lactococcus, Lacticaseibacillus, Exiguobacterium, Gluconobacter, Streptococcus, and Pantoea were significantly enriched in Xiaogan rice wine, Limosilactobacillus, Pediococcus, and Lactobacillus were significantly enriched in Dazhou rice wine. Notably, the most dominant group of bacteria in rice wine was lactic acid bacteria (LAB), and the main culturable isolates were Limosilactobacillus fermentum and Pediococcus pentosaceus. Additionally, the dominant bacterial genera showed a significant correlation with the taste characteristics of rice wine. Limosilactobacillus, Pediococcus, Lactobacillus, and Pseudomonas were positively correlated with the lactic acid, citric acid, pH value, Ala, Ile, and Leu of rice wine. Meanwhile, Exiguobacterium, Lacticaseibacillus, and Streptococcus were positively correlated with the richness of rice wine. This study provides insights into the bacterial diversity and taste characteristics of rice wine from different regions of the Yangtze River Basin and highlights the potential of traditional fermented foods as resources for LAB.

Image-Based Process Monitoring using Projective Nonnegative-Tensor Factorization

In this study, a novel process monitoring method using image data is presented. The new method is referred to as projective nonnegative-tensor factorization (PNTF). Video monitoring systems are increasingly being deployed in complex industrial processes such as manu-facturing processes, food industries, and pharmaceutical process, for extracting information from images to achieve the purpose of process monitoring. Traditional process-monitoring techniques fail to take full advantage of the rich information from color images due to the complex data characteristics including the high-dimensionality and correlation structures. The proposed PNTF is capable of modeling the high-dimensionality structure of red-green-blue data of color images using a tensor analysis technique. Moreover, in contrast to the traditional data-processing algorithms such as principal component analysis, linear discriminant analysis and so on, the PNTF is more applicable to analyzing image data for its nonnegative constraints on matrix factors. In addition, the PNTF allows us to extract features from both grayscale and color images, making process monitoring using multivariate control charts achiev- able. At last, based on the PNTF, methods for detecting and isolating process faults are developed and evaluated through a case study on a production process of electrically melting magnesia.

CRP Albumin Ratio: A novel noninvasive and cost-effective method for assessing the severity of acute pancreatitis.

Acute pancreatitis is a significant and potentially life-threatening gastrointestinal disorder that requires prompt and accurate diagnosis for effective treatment. Current diagnostic methods often involve expensive and inaccessible imaging studies, such as CT scans, limiting their utility in emergency settings and underserved areas. This correspondence discusses an innovative and cost-effective approach to assessing the severity of acute pancreatitis, focusing on the C-Reactive Protein-Albumin Ratio (CAR). We searched relevant articles and studies from 2015 till date on PubMed, Web of Science, and Google Scholar using these keywords: "C-Reactive Protein", "Albumin Ratio", "Acute pancreatitis", "Cost-effective", "Non-invasive", and "Severity". The CAR diagnostic method involves a simple blood test that measures the levels of C-Reactive Protein (CRP) and albumin, both commonly used markers for assessing inflammation. Elevated CRP and decreased albumin levels are indicative of inflammation, and the CAR has shown a strong positive correlation with the severity of acute pancreatitis. This method offers a non-invasive, time-efficient, and cost-friendly alternative to traditional diagnostic techniques. The potential of CAR as an assessment tool for the severity of acute pancreatitis is highlighted, especially in resource-limited settings. This innovation holds promise for improving the timely and accurate diagnosis of acute pancreatitis, ultimately enhancing patient outcomes and reducing mortality rates.

NIR-II fluorescence imaging in liver tumor surgery: A narrative review

In liver tumor surgery, the recognition of tumor margin and radical resection of microcancer focis have always been the crucial points to reduce postoperative recurrence of tumor. However, naked-eye inspection and palpation have limited effectiveness in identifying tumor boundaries, and traditional imaging techniques cannot consistently locate tumors in real time. As an intraoperative real-time navigation imaging method, NIR fluorescence imaging has been extensively studied for its simplicity, reliable safety, and superior sensitivity, and is expected to improve the accuracy of liver tumor surgery. In recent years, the research focus of NIR fluorescence has gradually shifted from the first near-infrared window (NIR-I, 700–900 nm) to the second near-infrared window (NIR-II, 1000–1700[Formula: see text]nm). Fluorescence imaging in NIR-II reduces the scattering effect of deep tissue, providing a preferable detection depth and spatial resolution while significantly eliminating liver autofluorescence background to clarify tumor margin. Developing fluorophores combined with tumor antibodies will further improve the precision of fluorescence-guided surgical navigation. With the development of a bunch of fluorophores with phototherapy ability, NIR-II can integrate tumor detection and treatment to explore a new therapeutic strategy for liver cancer. Here, we review the recent progress of NIR-II fluorescence technology in liver tumor surgery and discuss its challenges and potential development direction.

Exploring freshwater snail diversity and community structure in china's largest lake using eDNA technology

Freshwater snails are a crucial benthic animal group that plays a pivotal role in algae regulation and ecosystems while serving as intermediate hosts for certain parasites. Environmental DNA (eDNA) technology offers numerous advantages for biodiversity assessment. However, the utilization of eDNA techniques for freshwater snail monitoring remains limited. In this study, we screened primers for assessing freshwater snail diversity using eDNA technology and constructed a comprehensive database for Poyang Lake. Using eDNA technology, a total of 21 species of snails were detected in Poyang Lake, while traditional methods detected only 11 species. Notably, all species collected by traditional methods were also detected by eDNA technology. Additionally, environmental DNA methods detected the invasive snail Pomacea canaliculata, while traditional methods did not. In this study, eleven intermediate host snail species for parasitic disease were identified using eDNA methods, while six were detected through traditional sampling techniques. The eDNA metabarcoding analysis showed that the α and β diversity levels of snails in Poyang Lake did not vary significantly, indicating that results obtained by eDNA sampling are not influenced by the season in which samples were collected. The correlation analysis between the community structure of snails and environmental factors in Poyang Lake revealed significant impacts of total nitrogen, chlorophyll a, water depth, and water temperature on the snail community structure. These results confirm that eDNA technology can be exploited to monitor freshwater snail biodiversity.

Moduli: A Disaggregated Data Management Architecture for Data-Intensive Workflows

As companies store, process, and analyse bigger and bigger volumes of highly heterogeneous data, novel research and technological challenges are emerging. Traditional and rigid data integration and processing techniques become inadequate for a new class of data-intensive applications. There is a need for new architectural, software, and hardware solutions that are capable of providing dynamic data integration, assuring high data quality, and offering safety and security mechanisms, while facilitating online data analysis. In this context, we propose moduli , a novel disaggregated data management reference architecture for data-intensive applications that organizes data processing in various zones. Working on moduli allowed us also to identify open research and technological challenges.

Study on the Application of Improved Deep Convolutional Neural Network Algorithm in Broken Information Recovery

Abstract This study on the fusion of deep convolutional neural network (CNN) and extended short-term memory network (LSTM) aims to improve the efficiency and accuracy of broken information recovery. The challenges faced by traditional information recovery techniques are addressed through improved algorithms. The research methodology includes constructing CNN models to automatically extract features and combining LSTM networks to process complex time-series data. We conducted a detailed experimental evaluation of the CNN-LSTM fusion algorithm, including recovery of different types of corrupted data, and compared it with other algorithms. The results show that the CNN-LSTM fusion algorithm has the highest structural similarity (0.9545) and the most minor normalized mean square error (0.0016) for recovering broken video information, outperforming the methods using CNN or LSTM alone. The fusion algorithm dramatically reduces computation time and resource consumption for processing complex datasets. The combination of CNN and LSTM significantly improves the performance of broken information recovery, especially in processing video and audio data, and provides new ideas for future information processing techniques.

One-Shot Domain-Adaptive Imitation Learning via Progressive Learning Applied to Robotic Pouring

Traditional deep learning-based visual imitation learning techniques require a large amount of demonstration data for model training, and the pre-trained models are difficult to adapt to new scenarios. To address these limitations, we propose a unified framework using a novel progressive learning approach comprised of three phases: i) a coarse learning phase for concept representation, ii) a fine learning phase for action generation, and iii) an imaginary learning phase for domain adaptation. Overall, this approach leads to a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one-shot domain-adaptive imitation learning</i> framework. We use robotic pouring as an example task to evaluate its effectiveness. Our results show that the method has several advantages over contemporary end-to-end imitation learning approaches, including an improved success rate for task execution and more efficient training for deep imitation learning. In addition, the generalizability to new domains is improved, as demonstrated here with novel backgrounds, target containers, and granule combinations in the experiment. We believe that the proposed method is broadly applicable to various industrial or domestic applications that involve deep imitation learning for robotic manipulation, and where the target scenarios are diverse and human demonstration data is limited. For project video, please check our website:. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to develop a progressive learning framework, which can be used for both service and industrial robots to learn from human demonstrations, and then transfer the learned skill to different scenarios with ease. We use the robotic pouring task as an example to demonstrate the effectiveness of our proposed method, since pouring is an essential skill for service robots to assist humans’ daily lives, and can benefit robot automation in wet-lab industries. The aim of this research is to enable robots to obtain visuomotor skills (such as the pouring skill), and accomplish the tasks with a high success rate using our proposed progressive learning method. We conducted experiments to show that the proposed method has good performance, high data efficiency and evident generalizability. This is significant for intelligent robots working in various practical applications.

Reduction-responsive and tumor-targeted polyprodrug nanocarriers for targeting therapy of hepatocellular carcinoma

DOX@HA-SS-PFA NPs, a targeted and reduction-responsive nano-delivery platform for polyprodrug, could effectively boost the therapeutic effect of the antitumor drug and improve the therapeutic efficiency.

A systematic review on powder-mixed electrical discharge machining (PMEDM) technique for machining of difficult-to-machine materials

The area of metallurgy has witnessed many advancements in the development of novel electrically conductive materials that shows exceptional mechanical as well as thermal properties. Nonetheless, traditional machining techniques encounter difficulties while machining hard materials. In order to address this limitation, electrical discharge machining (EDM) has emerged as a widely utilised method for machining of intricate geometries and the hard materials. EDM is a category of thermo-electric process that employs rapid recurring sparks between the electrode and work material, eroding the material without direct contact. As there is no contact between the electrode and work material, the issues related to machining defects such as mechanical stresses, clattering, and vibration eliminates. However, EDM have some limitations like poor surface finish and low volumetric material removal. To overcome these kind of limitations, the introduction of metallic powder into the dielectric fluid has been explored in powder-mixed electric discharge machining (PMEDM). This introduction of powder during the process leads to enhance the conductive strength of the fluid and increases the spark gap distance between the electrode and counter material. The inclusion of powder has a significant impact on the performance of the EDM process. Hence, this review aims to facilitate researchers in comprehending the concept of PMEDM and to examine the process parameters required to achieve improved levels of quality.

Analysis of the Technique and Morphological Language of Modern Painting Creation Based on Generative Adversarial Networks

Abstract With the development and progress of society, traditional painting creation techniques cannot realize the development needs of modern society for paintings, and it is necessary to constantly innovate and improve the painting creation techniques. Based on the structure of the generative adversarial network, this paper utilizes the one-dimensional midpoint substitution method and dichotomous method to generate the rock outline in painting creation and combines the generative adversarial network to establish the style migration model of modern painting creation techniques and morphological language. Unity was used to construct the validation dataset, and for the style migration of painting creation techniques, we verified it in terms of stroke curvature, FID value, and peak signal-to-noise ratio, and analyzed the evolution of painting creation techniques and morphology language. The results show that the difference in stroke curvature before and after the contour migration of painting creation techniques is 3.27, the peak signal-to-noise ratio reaches 25.43 dB, and the evolution of comprehensive painting in creation techniques is in an upward trend, with an average annual growth rate of 13.07% from 2012 to 2020. Generative adversarial networks can be used in modern painting creation techniques to increase the richness of paintings and establish a spiritual connection between painters and audiences.

Detection of Prostate Cancer using Ensemble based Bi-directional Long Short Term Memory Network

Aim and Background: In recent periods, micro-array data analysis using soft computing and machine learning techniques gained more interest among researchers to detect prostate cancer. Due to the small sample size of micro-array data with a larger number of attributes, traditional machine learning techniques face difficulty detecting prostate cancer. Methodology: The selection of relevant genes exploits useful information about micro-array data, which enhances the accuracy of detection. In this research, the samples are acquired from the gene expression omnibus database, particularly related to the prostate cancer GEO IDs such as GSE 21034, GSE 15484 and GSE 3325/GSE 3998. In addition, ensemble feature optimization technique and Bidirectional Long Short Term Memory (Bi-LSTM) network are employed for detecting prostate cancer from the microarray data of gene expression. Results: The ensemble feature optimization technique includes 4 metaheuristic optimizers that select the top 2000 genes from each GEO IDs, which are relevant to prostate cancer. Next, the selected genes are given to the Bi-LSTM network for classifying the normal and prostate cancer subjects. Conclusion: The simulation analysis revealed that the ensemble based Bi-LSTM network obtained 99.13%, 98.97%, and 94.12% of accuracy on the GEO IDs like GSE 3325/GSE 3998, GSE 21034, and GSE 15484.

Enhancing English Writing Proficiency in TESOL: Integrating Traditional and Technological Approaches for a Multifaceted Learning Experience

This scholarly article delves into the latest advancements within the Teaching English to Speakers of Other Languages (TESOL) arena, specifically concentrating on their impact on teaching English writing. The study critically analyses a variety of pioneering methods, tactics, and tools currently employed in TESOL, with a special emphasis on refining students’ English writing capabilities. It explores the intersection of traditional and modern pedagogical techniques, highlighting how blended approaches can significantly enhance the writing skills of learners. The paper provides in-depth insights into how these innovative strategies not only improve writing proficiency but also foster a deeper understanding of the English language among students. It argues that the effectiveness of these teaching methodologies is pivotal in equipping students with the skills necessary to excel in English writing, thereby contributing to their overall academic success. The research emphasizes the role of tailored instruction, feedback mechanisms, and the integration of digital tools in creating an engaging and effective learning environment.

Application of linguistic fuzzy neural network to landing control

Most aircraft accidents occurred during the final approach. Wind disturbance is one of the significant factors in these accidents. During the landing phase, the Automatic Landing System (ALS) can help aircraft land safely and significantly reduce the pilot’s work loading. Control schemes of the conventional ALS usually use gain-scheduling and traditional PID control techniques. A traditional controller cannot control the aircraft if the weather conditions are beyond the allowed limits. To improve the performance of the landing control, this study applies a linguistic fuzzy neural network (LFNN) to replace the conventional controller of ALS. Adaptive learning rules are proposed to enhance the LFNN control ability. The method used to obtain adaptive learning rules is the Lyapunov stability theory. Moreover, the convergence of the system performance error is proved by the Lyapunov theory. This study also compares previously proposed control schemes in aircraft landing control. Different turbulence strengths are implemented into the flight simulation to make the proposed controller more robust and adaptive to various wind disturbance conditions. The LFNN controller can successfully overcome 75 ft/s wind speed, while the adaptive LFNN can reach 80 ft/s with optimal learning rates. Using optimal convergence theorems, the proposed controller performs better than the controllers trained by a fixed learning rate.