Background Enterotoxigenic Escherichia coli(E.coli)is a threat to humans and animals that causes intestinal dis-orders.Antimicrobial resistance has urged alternatives,including Lactobacillus postbiotics,to mitigate th...Background Enterotoxigenic Escherichia coli(E.coli)is a threat to humans and animals that causes intestinal dis-orders.Antimicrobial resistance has urged alternatives,including Lactobacillus postbiotics,to mitigate the effects of enterotoxigenic E.coli.Methods Forty-eight newly weaned pigs were allotted to NC:no challenge/no supplement;PC:F18^(+)E.coli chal-lenge/no supplement;ATB:F18^(+)E.coli challenge/bacitracin;and LPB:F18^(+)E.coli challenge/postbiotics and fed diets for 28 d.On d 7,pigs were orally inoculated withF18^(+)E.coli.At d 28,the mucosa-associated microbiota,immune and oxidative stress status,intestinal morphology,the gene expression of pattern recognition receptors(PRR),and intestinal barrier function were measured.Data were analyzed using the MIXED procedure in SAS 9.4.Results PC increased(P<0.05)Helicobacter mastomyrinus whereas reduced(P<0.05)Prevotella copri and P.ster-corea compared to NC.The LPB increased(P<0.05)P.stercorea and Dialister succinatiphilus compared with PC.The ATB increased(P<0.05)Propionibacterium acnes,Corynebacterium glutamicum,and Sphingomonas pseudosanguinis compared to PC.The PC tended to reduce(P=0.054)PGLYRP4 and increased(P<0.05)TLR4,CD14,MDA,and crypt cell proliferation compared with NC.The ATB reduced(P<0.05)NOD1 compared with PC.The LPB increased(P<0.05)PGLYRP4,and interferon-γand reduced(P<0.05)NOD1 compared with PC.The ATB and LPB reduced(P<0.05)TNF-αand MDA compared with PC.Conclusions TheF18^(+)E.coli challenge compromised intestinal health.Bacitracin increased beneficial bacteria show-ing a trend towards increasing the intestinal barrier function,possibly by reducing the expression of PRR genes.Lac-tobacillus postbiotics enhanced the immunocompetence of nursery pigs by increasing the expression of interferon-γand PGLYRP4,and by reducing TLR4,NOD1,and CD14.展开更多
The ionosphere is an important component of the near Earth space environment.The three common methods for detecting the ionosphere with high frequency(HF)radio signals are vertical detection,oblique detection,and obli...The ionosphere is an important component of the near Earth space environment.The three common methods for detecting the ionosphere with high frequency(HF)radio signals are vertical detection,oblique detection,and oblique backscatter detection.The ionograms obtained by these detection methods can effectively reflect a large amount of effective information in the ionosphere.The focus of this article is on the oblique backscatter ionogram obtained by oblique backscatter detection.By extracting the leading edge of the oblique backscatter ionogram,effective information in the ionosphere can be inverted.The key issue is how to accurately obtain the leading edge of the oblique backscatter ionogram.In recent years,the application of pattern recognition has become increasingly widespread,and the YOLO model is one of the best fast object detection algorithms in one-stage.Therefore,the core idea of this article is to use the newer YOLOX object detection algorithm in the YOLO family to perform pattern recognition on the F and E_(s) layers echoes in the oblique backscatter ionogram.After image processing,a single-layer oblique backscatter echoes are obtained.It can be found that the leading edge extraction of the oblique backscatter ionogram obtained after pattern recognition and image processing by the YOLOX model is more fitting to the actual oblique backscatter leading edge.展开更多
In recent years,audio pattern recognition has emerged as a key area of research,driven by its applications in human-computer interaction,robotics,and healthcare.Traditional methods,which rely heavily on handcrafted fe...In recent years,audio pattern recognition has emerged as a key area of research,driven by its applications in human-computer interaction,robotics,and healthcare.Traditional methods,which rely heavily on handcrafted features such asMel filters,often suffer frominformation loss and limited feature representation capabilities.To address these limitations,this study proposes an innovative end-to-end audio pattern recognition framework that directly processes raw audio signals,preserving original information and extracting effective classification features.The proposed framework utilizes a dual-branch architecture:a global refinement module that retains channel and temporal details and a multi-scale embedding module that captures high-level semantic information.Additionally,a guided fusion module integrates complementary features from both branches,ensuring a comprehensive representation of audio data.Specifically,the multi-scale audio context embedding module is designed to effectively extract spatiotemporal dependencies,while the global refinement module aggregates multi-scale channel and temporal cues for enhanced modeling.The guided fusion module leverages these features to achieve efficient integration of complementary information,resulting in improved classification accuracy.Experimental results demonstrate the model’s superior performance on multiple datasets,including ESC-50,UrbanSound8K,RAVDESS,and CREMA-D,with classification accuracies of 93.25%,90.91%,92.36%,and 70.50%,respectively.These results highlight the robustness and effectiveness of the proposed framework,which significantly outperforms existing approaches.By addressing critical challenges such as information loss and limited feature representation,thiswork provides newinsights and methodologies for advancing audio classification and multimodal interaction systems.展开更多
As information acquisition terminals for artificial olfaction,chemiresistive gas sensors are often troubled by their cross-sensitivity,and reducing their cross-response to ambient gases has always been a difficult and...As information acquisition terminals for artificial olfaction,chemiresistive gas sensors are often troubled by their cross-sensitivity,and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area.Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors.It is crucial to choose an appropriate pattern recognition method for enhancing data analysis,reducing errors and improving system reliability,obtaining better classification or gas concentration prediction results.In this review,we analyze the sensing mechanism of crosssensitivity for chemiresistive gas sensors.We further examine the types,working principles,characteristics,and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays.Additionally,we report,summarize,and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification.At the same time,this work showcases the recent advancements in utilizing these methods for gas identification,particularly within three crucial domains:ensuring food safety,monitoring the environment,and aiding in medical diagnosis.In conclusion,this study anticipates future research prospects by considering the existing landscape and challenges.It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.展开更多
Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system.Fast and accurate measurements of the locations,intensity,and width of the near-surface changes are n...Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system.Fast and accurate measurements of the locations,intensity,and width of the near-surface changes are necessary but highly difficult due to the complicated environmental conditions and instrumental issues.This paper develops a spatial pattern recognition method to measure the near-surface high temperature increase(NSHTI),one of the lesser-attended changes.First,raster window measurement was proposed to calculate the temperature lapse rate using MODIS land surface temperature and SRTM DEM data.It fully considers the terrain heights of two neighboring cells on opposite or adjacent slopes with a moving window of 3×3 cell size.Second,a threshold selection was performed to identify the NSHTI cells using a threshold of-0.65℃/100 m.Then,the NSHTI strips were parameterized through raster vectorization and spatial analysis.Taking Yunnan,a mountainous province in southwestern China,as the study area,the results indicate that the NSHTI cells concentrate in a strip-like pattern along the mountains and valleys,and the strips are almost parallel to the altitude contours with a slight northward uplift.Also,they are located mostly at a 3/5 height of high mountains or within 400 m from the valley floors,where the controlling topographic index is the altitude of the terrain trend surface but not the absolute elevation and the topographic uplift height and cutting depth.Additionally,the NSHTI intensity varies with the geographic locations and the proportions increase with an exponential trend,and the horizontal width has a mean of about 1000 m and a maximum of over 5000 m.The result demonstrates that the proposed method can effectively recognize NSHTI boundaries over mountains,providing support for the modeling of weather and climate systems and the development of mountain resources.展开更多
The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant par...The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant identification.Leaf images,however,stand out as the preferred and easily accessible source of information.Manual plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human perception.Artificial intelligence(AI)techniques offer a solution by automating plant recognition processes.This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned repositories.This paper critically summarizes relevant literature based on AI algorithms,extracted features,and results achieved.Additionally,it analyzes extensively used datasets in automated plant classification research.It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition.Moreover,this rigorous review study discusses opportunities and challenges in employing these AI-based approaches.Furthermore,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions.This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.展开更多
Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key mot...Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.展开更多
Objective:To explore which pattern recognition receptors(PRRs)play a key role in the development of hand,foot,and mouth disease(HFMD)by analyzing PRR-associated genes.Methods:We conducted a comparative analysis of PRR...Objective:To explore which pattern recognition receptors(PRRs)play a key role in the development of hand,foot,and mouth disease(HFMD)by analyzing PRR-associated genes.Methods:We conducted a comparative analysis of PRR-associated gene expression in human peripheral blood mononuclear cells(PBMCs)infected with enterovirus 71(EV-A71)which were derived from patients with HFMD of different severities and at different stages.A total of 30 PRR-associated genes were identified as significantly upregulated both over time and across different EV-A71 isolates.Subsequently,ELISA was employed to quantify the expression of the six most prominent genes among these 30 identified genes,specifically,BST2,IRF7,IFI16,TRIM21,MX1,and DDX58.Results:Compared with those at the recovery stage,the expression levels of BST2(P=0.027),IFI16(P=0.016),MX1(P=0.046)and DDX58(P=0.008)in the acute stage of infection were significantly upregulated,while no significant difference in the expression levels of IRF7(P=0.495)and TRIM21(P=0.071)was found between different stages of the disease.The expression levels of BST2,IRF7,IFI16 and MX1 were significantly higher in children infected with single pathogen than those infected with mixed pathogens,and BST2,IRF7,IFI16 and MX1 expression levels were significantly lower in coxsackie B virus(COXB)positive patients than the negative patients.Expression levels of one or more of BST2,IRF7,IFI16,TRIM21,MX1 and DDX58 genes were correlated with PCT levels,various white blood cell counts,and serum antibody levels that reflect disease course of HFMD.Aspartate aminotransferase was correlated with BST2,MX1 and DDX58 expression levels.Conclusions:PRR-associated genes likely initiate the immune response in patients at the acute stage of HFMD.展开更多
The performance of photovoltaic(PV)systems is in-fluenced by various factors,including atmospheric conditions,geographical locations,and spatial and temporal characteristics.Consequently,the optimization of PV systems...The performance of photovoltaic(PV)systems is in-fluenced by various factors,including atmospheric conditions,geographical locations,and spatial and temporal characteristics.Consequently,the optimization of PV systems relies heavily on the global maximum power point tracking(GMPPT)methods.In this paper,we adopt virtual reality(VR)technology to visual-ize PV entities and simulate their performances.The integra-tion of VR technology introduces a novel spatial and temporal dimension to the shading pattern recognition(SPR)of PV sys-tems,thereby enhancing their descriptive capabilities.Further-more,we introduce an interactive GMPPT(IGMPPT)method based on VR technology.This method leverages interactive search techniques to narrow down search regions,thereby en-hancing the search efficiency.Experimental results demonstrate the effectiveness of the proposed IGMPPT in representing the spatial and temporal characteristics of PV systems and improv-ing the efficiency of GMPPT.展开更多
Polythiophenes(PTs)with flexible backbones possess inherent polymer behaviors,including molecular wire effects and dynamic structural changes inπ-conjugated systems.The chemical sensing at the functionalized side cha...Polythiophenes(PTs)with flexible backbones possess inherent polymer behaviors,including molecular wire effects and dynamic structural changes inπ-conjugated systems.The chemical sensing at the functionalized side chains can manipulate such polymer characteristics,resulting in various optical patterns depending on the analyte structures and their concentrations.The unique optical patterns derived from polymer properties contribute to group categorization over a wide concentration range for pattern recognition.This review aims to provide a concise overview of the potential of PT chemosensor arrays using actual sensing examples in environmental monitoring,medical diagnostics,and food analysis.Furthermore,this review summarizes the methodologies that use polymer gels to realize practical chemosensor array chips for onsite analysis.展开更多
The combination of pyrolysis high resolution gas chromatography and pat- tern recognition techniques is a powerful tool for the classification of traditional Chinese drug.A study has been completed on 55 Beimu samples...The combination of pyrolysis high resolution gas chromatography and pat- tern recognition techniques is a powerful tool for the classification of traditional Chinese drug.A study has been completed on 55 Beimu samples of five different geographic origins: Eastern China.Central China.South-western China,North-western China and North-eastern China.Principal component analysis and SIMCA are applied to effectively classifying the samples according to the origin of the plants.The chemical information contained in the high resolution gas chromatographic data is sufficient to characterize the geographic origin of sam- pies.展开更多
The concept of the degree of similarity between interval-valued intuitionistic fuzzy sets （IVIFSs） is introduced, and some distance measures between IVIFSs are defined based on the Hamming distance, the normalized H...The concept of the degree of similarity between interval-valued intuitionistic fuzzy sets （IVIFSs） is introduced, and some distance measures between IVIFSs are defined based on the Hamming distance, the normalized Hamming distance, the weighted Hamming distance, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance, etc. Then, by combining the Hausdorff metric with the Hamming distance, the Euclidean distance and their weighted versions, two other similarity measures between IVIFSs, i. e., the weighted Hamming distance based on the Hausdorff metric and the weighted Euclidean distance based on the Hausdorff metric, are defined, and then some of their properties are studied. Finally, based on these distance measures, some similarity measures between IVIFSs are defined, and the similarity measures are applied to pattern recognitions with interval-valued intuitionistic fuzzy information.展开更多
A hybrid network is presented for spatio-temporal feature detecting, which is called TS-LM-SOFM. Its top layer is a novel single layer temporal sequence recognizer called TS which can transform sparse temporal sequen...A hybrid network is presented for spatio-temporal feature detecting, which is called TS-LM-SOFM. Its top layer is a novel single layer temporal sequence recognizer called TS which can transform sparse temporal sequential pattern into abstract spatial feature representations. The bottom layer of TS-LM-SOFM, a modified self-organizing feature map, is used as a spatial feature detector. A learning matrix connects the two layers. Experiments show that the hybrid network can well capture the spatio-temporal features of input signals.展开更多
Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we deve...Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we develop a pure mechanical sensor architecture for intent pattern recognition of lower-limb motion. The sensor system is composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. To compensate the delay in the control of prosthesis, the signals in the stance phase are used to predict the terrain and speed in the swing phase. Specifically, the intent pattern recognizer utilizes intraclass correlation coefficient(ICC) according to the Cartesian product of walking speed and terrain. Moreover, the sensor data are fused via DempsterShafer's theory. And hidden Markov model(HMM) is used to recognize the realtime motion state with the reference of the prior step. The proposed method can infer the prosthesis user's intent of walking on different terrain, which includes level ground,stair ascent, stair descent, up and down ramp. The experiments demonstrate that the intent pattern recognizer is capable of identifying five typical terrain-modes with the rate of 95.8%. The outcome of this investigation is expected to substantially improve the control performance of powered above-knee prosthesis.展开更多
In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purpos...In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purposes by applying three methods： the support vector machine （SVM） model, the radial basis function neural network （RBFNN） model and the multinomial logit （MNL） model. The effect of explanatory factors on trip chaining behaviors and their contribution to model performace were investigated by sensitivity analysis. Results show that the SVM model has a better performance than the RBFNN model and the MNL model due to its higher overall and partial accuracy, indicating its recognition advantage under a smai sample size scenario. It is also proved that the SVM model is capable of estimating the effect of multi-category factors on trip chaining behaviors more accurately. The different contribution of explanatory, factors to trip chaining pattern recognition reflects the importance of refining trip chaining patterns ad exploring factors that are specific to each pattern. It is shown that the SVM technology in travel demand forecast modeling and analysis of explanatory variable effects is practical.展开更多
In this paper, the feasibility and advantages of employing high performance liquid chromatographic （HPLC） fingerprints combined with pattern recognition techniques for quality control of Shenmai injection were inves...In this paper, the feasibility and advantages of employing high performance liquid chromatographic （HPLC） fingerprints combined with pattern recognition techniques for quality control of Shenmai injection were investigated and demonstrated. The Similarity Evaluation System was employed to evaluate the similarities of samples of Shenmai injection, and the HPLC generated chromatographic data were analyzed using hierarchical clustering analysis （HCA） and soft independent modeling of class analogy （SIMCA）. Consistent results were obtained to show that the authentic samples and the blended samples were successfully classified by SIMCA, which could be applied to accurate discrimination and quality control of Shenmai injection. Furthermore, samples could also be grouped in accordance with manufacturers. Our results revealed that the developed method has potential perspective for the original discrimination and quality control of Shenmai injection.展开更多
We present a new pattern recognition system based on moving average and linear discriminant analysis （LDA）, which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensit...We present a new pattern recognition system based on moving average and linear discriminant analysis （LDA）, which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensitive sensor system we designed, called the new e-nose. Using the new e-nose, we obtain the template datum of Chinese spirits via a new pattern recognition system. To verify the effectiveness of the new pattern recognition system, we select three kinds of Chinese spirits to test, our results confirm that the new pattern recognition system can perfectly identify and distinguish between the Chinese spirits.展开更多
In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of...In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.展开更多
In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occu...In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.展开更多
This paper develops a fuzzy pattern recognition model for group decision making to solve the problem of lectotype optimization of offshore platforms. The lack of data and the inexact or incomplete information for crit...This paper develops a fuzzy pattern recognition model for group decision making to solve the problem of lectotype optimization of offshore platforms. The lack of data and the inexact or incomplete information for criteria are the main cause of uncertainty in the evaluation process, therefore it is necessary to integrate the judgments from different decision makers with different experience, knowledge and preference. This paper first uses a complementary principle based pairwise comparison method to obtain the subjective weight of the criteria from each decision maker. A fuzzy pattern recognition model is then developed to integrate the judgments from all the decision makers and the information from the criteria, under the supervision of the subjective weights. Finally a case study is given to show the efficiency and robustness of the proposed model.展开更多
文摘Background Enterotoxigenic Escherichia coli(E.coli)is a threat to humans and animals that causes intestinal dis-orders.Antimicrobial resistance has urged alternatives,including Lactobacillus postbiotics,to mitigate the effects of enterotoxigenic E.coli.Methods Forty-eight newly weaned pigs were allotted to NC:no challenge/no supplement;PC:F18^(+)E.coli chal-lenge/no supplement;ATB:F18^(+)E.coli challenge/bacitracin;and LPB:F18^(+)E.coli challenge/postbiotics and fed diets for 28 d.On d 7,pigs were orally inoculated withF18^(+)E.coli.At d 28,the mucosa-associated microbiota,immune and oxidative stress status,intestinal morphology,the gene expression of pattern recognition receptors(PRR),and intestinal barrier function were measured.Data were analyzed using the MIXED procedure in SAS 9.4.Results PC increased(P<0.05)Helicobacter mastomyrinus whereas reduced(P<0.05)Prevotella copri and P.ster-corea compared to NC.The LPB increased(P<0.05)P.stercorea and Dialister succinatiphilus compared with PC.The ATB increased(P<0.05)Propionibacterium acnes,Corynebacterium glutamicum,and Sphingomonas pseudosanguinis compared to PC.The PC tended to reduce(P=0.054)PGLYRP4 and increased(P<0.05)TLR4,CD14,MDA,and crypt cell proliferation compared with NC.The ATB reduced(P<0.05)NOD1 compared with PC.The LPB increased(P<0.05)PGLYRP4,and interferon-γand reduced(P<0.05)NOD1 compared with PC.The ATB and LPB reduced(P<0.05)TNF-αand MDA compared with PC.Conclusions TheF18^(+)E.coli challenge compromised intestinal health.Bacitracin increased beneficial bacteria show-ing a trend towards increasing the intestinal barrier function,possibly by reducing the expression of PRR genes.Lac-tobacillus postbiotics enhanced the immunocompetence of nursery pigs by increasing the expression of interferon-γand PGLYRP4,and by reducing TLR4,NOD1,and CD14.
基金Supported by the National Natural Science Foundation of China(42104151,42074184,42188101,41727804)。
文摘The ionosphere is an important component of the near Earth space environment.The three common methods for detecting the ionosphere with high frequency(HF)radio signals are vertical detection,oblique detection,and oblique backscatter detection.The ionograms obtained by these detection methods can effectively reflect a large amount of effective information in the ionosphere.The focus of this article is on the oblique backscatter ionogram obtained by oblique backscatter detection.By extracting the leading edge of the oblique backscatter ionogram,effective information in the ionosphere can be inverted.The key issue is how to accurately obtain the leading edge of the oblique backscatter ionogram.In recent years,the application of pattern recognition has become increasingly widespread,and the YOLO model is one of the best fast object detection algorithms in one-stage.Therefore,the core idea of this article is to use the newer YOLOX object detection algorithm in the YOLO family to perform pattern recognition on the F and E_(s) layers echoes in the oblique backscatter ionogram.After image processing,a single-layer oblique backscatter echoes are obtained.It can be found that the leading edge extraction of the oblique backscatter ionogram obtained after pattern recognition and image processing by the YOLOX model is more fitting to the actual oblique backscatter leading edge.
基金supported by the National Natural Science Foundation of China(62106214)the Hebei Natural Science Foundation(D2024203008)the Provincial Key Laboratory Performance Subsidy Project(22567612H).
文摘In recent years,audio pattern recognition has emerged as a key area of research,driven by its applications in human-computer interaction,robotics,and healthcare.Traditional methods,which rely heavily on handcrafted features such asMel filters,often suffer frominformation loss and limited feature representation capabilities.To address these limitations,this study proposes an innovative end-to-end audio pattern recognition framework that directly processes raw audio signals,preserving original information and extracting effective classification features.The proposed framework utilizes a dual-branch architecture:a global refinement module that retains channel and temporal details and a multi-scale embedding module that captures high-level semantic information.Additionally,a guided fusion module integrates complementary features from both branches,ensuring a comprehensive representation of audio data.Specifically,the multi-scale audio context embedding module is designed to effectively extract spatiotemporal dependencies,while the global refinement module aggregates multi-scale channel and temporal cues for enhanced modeling.The guided fusion module leverages these features to achieve efficient integration of complementary information,resulting in improved classification accuracy.Experimental results demonstrate the model’s superior performance on multiple datasets,including ESC-50,UrbanSound8K,RAVDESS,and CREMA-D,with classification accuracies of 93.25%,90.91%,92.36%,and 70.50%,respectively.These results highlight the robustness and effectiveness of the proposed framework,which significantly outperforms existing approaches.By addressing critical challenges such as information loss and limited feature representation,thiswork provides newinsights and methodologies for advancing audio classification and multimodal interaction systems.
基金supported by the National Key Research and Development Program of China(2021YFB3200400)the National Natural Science Foundation of China(62371299,62301314,and 62020106006)the China Postdoctoral Science Foundation(2023M732198).
文摘As information acquisition terminals for artificial olfaction,chemiresistive gas sensors are often troubled by their cross-sensitivity,and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area.Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors.It is crucial to choose an appropriate pattern recognition method for enhancing data analysis,reducing errors and improving system reliability,obtaining better classification or gas concentration prediction results.In this review,we analyze the sensing mechanism of crosssensitivity for chemiresistive gas sensors.We further examine the types,working principles,characteristics,and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays.Additionally,we report,summarize,and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification.At the same time,this work showcases the recent advancements in utilizing these methods for gas identification,particularly within three crucial domains:ensuring food safety,monitoring the environment,and aiding in medical diagnosis.In conclusion,this study anticipates future research prospects by considering the existing landscape and challenges.It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.
基金supported by the National Natural Science Foundation of China (Grant No. 42061004)the Joint Special Project of Agricultural Basic Research of Yunnan Province (Grant No. 202101BD070001093)the Youth Special Project of Xingdian Talent Support Program of Yunnan Province
文摘Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system.Fast and accurate measurements of the locations,intensity,and width of the near-surface changes are necessary but highly difficult due to the complicated environmental conditions and instrumental issues.This paper develops a spatial pattern recognition method to measure the near-surface high temperature increase(NSHTI),one of the lesser-attended changes.First,raster window measurement was proposed to calculate the temperature lapse rate using MODIS land surface temperature and SRTM DEM data.It fully considers the terrain heights of two neighboring cells on opposite or adjacent slopes with a moving window of 3×3 cell size.Second,a threshold selection was performed to identify the NSHTI cells using a threshold of-0.65℃/100 m.Then,the NSHTI strips were parameterized through raster vectorization and spatial analysis.Taking Yunnan,a mountainous province in southwestern China,as the study area,the results indicate that the NSHTI cells concentrate in a strip-like pattern along the mountains and valleys,and the strips are almost parallel to the altitude contours with a slight northward uplift.Also,they are located mostly at a 3/5 height of high mountains or within 400 m from the valley floors,where the controlling topographic index is the altitude of the terrain trend surface but not the absolute elevation and the topographic uplift height and cutting depth.Additionally,the NSHTI intensity varies with the geographic locations and the proportions increase with an exponential trend,and the horizontal width has a mean of about 1000 m and a maximum of over 5000 m.The result demonstrates that the proposed method can effectively recognize NSHTI boundaries over mountains,providing support for the modeling of weather and climate systems and the development of mountain resources.
文摘The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant identification.Leaf images,however,stand out as the preferred and easily accessible source of information.Manual plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human perception.Artificial intelligence(AI)techniques offer a solution by automating plant recognition processes.This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned repositories.This paper critically summarizes relevant literature based on AI algorithms,extracted features,and results achieved.Additionally,it analyzes extensively used datasets in automated plant classification research.It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition.Moreover,this rigorous review study discusses opportunities and challenges in employing these AI-based approaches.Furthermore,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions.This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.
文摘Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.
文摘Objective:To explore which pattern recognition receptors(PRRs)play a key role in the development of hand,foot,and mouth disease(HFMD)by analyzing PRR-associated genes.Methods:We conducted a comparative analysis of PRR-associated gene expression in human peripheral blood mononuclear cells(PBMCs)infected with enterovirus 71(EV-A71)which were derived from patients with HFMD of different severities and at different stages.A total of 30 PRR-associated genes were identified as significantly upregulated both over time and across different EV-A71 isolates.Subsequently,ELISA was employed to quantify the expression of the six most prominent genes among these 30 identified genes,specifically,BST2,IRF7,IFI16,TRIM21,MX1,and DDX58.Results:Compared with those at the recovery stage,the expression levels of BST2(P=0.027),IFI16(P=0.016),MX1(P=0.046)and DDX58(P=0.008)in the acute stage of infection were significantly upregulated,while no significant difference in the expression levels of IRF7(P=0.495)and TRIM21(P=0.071)was found between different stages of the disease.The expression levels of BST2,IRF7,IFI16 and MX1 were significantly higher in children infected with single pathogen than those infected with mixed pathogens,and BST2,IRF7,IFI16 and MX1 expression levels were significantly lower in coxsackie B virus(COXB)positive patients than the negative patients.Expression levels of one or more of BST2,IRF7,IFI16,TRIM21,MX1 and DDX58 genes were correlated with PCT levels,various white blood cell counts,and serum antibody levels that reflect disease course of HFMD.Aspartate aminotransferase was correlated with BST2,MX1 and DDX58 expression levels.Conclusions:PRR-associated genes likely initiate the immune response in patients at the acute stage of HFMD.
基金This research was supported by the Suzhou Science and Technology Project-Key Industrial Technology Innovation(No.SYG202122)the XJTLU Postgraduate Research Scholarship(No.PGRS1906004)+1 种基金the XJTLU AI University Research CentreJiangsu(Provincial)Data Science and Cognitive Computational Engineering Research Centre.
文摘The performance of photovoltaic(PV)systems is in-fluenced by various factors,including atmospheric conditions,geographical locations,and spatial and temporal characteristics.Consequently,the optimization of PV systems relies heavily on the global maximum power point tracking(GMPPT)methods.In this paper,we adopt virtual reality(VR)technology to visual-ize PV entities and simulate their performances.The integra-tion of VR technology introduces a novel spatial and temporal dimension to the shading pattern recognition(SPR)of PV sys-tems,thereby enhancing their descriptive capabilities.Further-more,we introduce an interactive GMPPT(IGMPPT)method based on VR technology.This method leverages interactive search techniques to narrow down search regions,thereby en-hancing the search efficiency.Experimental results demonstrate the effectiveness of the proposed IGMPPT in representing the spatial and temporal characteristics of PV systems and improv-ing the efficiency of GMPPT.
基金support from the Japan Society for the Promotion of Science(JSPS)KAKENHI(Grant Nos.JP23H03864 and JP24K01315)JST CREST(Grant No.JPMJCR2011)+1 种基金Y.Sasaki thanks JSPS KAKENHI(Grant No.JP24K17667)JST PRESTO(JPMJPR23H2).
文摘Polythiophenes(PTs)with flexible backbones possess inherent polymer behaviors,including molecular wire effects and dynamic structural changes inπ-conjugated systems.The chemical sensing at the functionalized side chains can manipulate such polymer characteristics,resulting in various optical patterns depending on the analyte structures and their concentrations.The unique optical patterns derived from polymer properties contribute to group categorization over a wide concentration range for pattern recognition.This review aims to provide a concise overview of the potential of PT chemosensor arrays using actual sensing examples in environmental monitoring,medical diagnostics,and food analysis.Furthermore,this review summarizes the methodologies that use polymer gels to realize practical chemosensor array chips for onsite analysis.
文摘The combination of pyrolysis high resolution gas chromatography and pat- tern recognition techniques is a powerful tool for the classification of traditional Chinese drug.A study has been completed on 55 Beimu samples of five different geographic origins: Eastern China.Central China.South-western China,North-western China and North-eastern China.Principal component analysis and SIMCA are applied to effectively classifying the samples according to the origin of the plants.The chemical information contained in the high resolution gas chromatographic data is sufficient to characterize the geographic origin of sam- pies.
基金The National Natural Science Foundation of China (No70571087)the National Science Fund for Distinguished Young Scholarsof China (No70625005)
文摘The concept of the degree of similarity between interval-valued intuitionistic fuzzy sets （IVIFSs） is introduced, and some distance measures between IVIFSs are defined based on the Hamming distance, the normalized Hamming distance, the weighted Hamming distance, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance, etc. Then, by combining the Hausdorff metric with the Hamming distance, the Euclidean distance and their weighted versions, two other similarity measures between IVIFSs, i. e., the weighted Hamming distance based on the Hausdorff metric and the weighted Euclidean distance based on the Hausdorff metric, are defined, and then some of their properties are studied. Finally, based on these distance measures, some similarity measures between IVIFSs are defined, and the similarity measures are applied to pattern recognitions with interval-valued intuitionistic fuzzy information.
文摘A hybrid network is presented for spatio-temporal feature detecting, which is called TS-LM-SOFM. Its top layer is a novel single layer temporal sequence recognizer called TS which can transform sparse temporal sequential pattern into abstract spatial feature representations. The bottom layer of TS-LM-SOFM, a modified self-organizing feature map, is used as a spatial feature detector. A learning matrix connects the two layers. Experiments show that the hybrid network can well capture the spatio-temporal features of input signals.
基金supported in part by the National Nature Science Fundation(61174009,61203323)Youth Foundation of Hebei Province(F2016202327)+3 种基金the Colleges and Universities in Hebei Province Science and Technology Research Project(ZC2016020)supported in part by Key Project of NSFC(61533009)111 Project(B08015)Research Project(JCYJ20150403161923519)
文摘Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we develop a pure mechanical sensor architecture for intent pattern recognition of lower-limb motion. The sensor system is composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. To compensate the delay in the control of prosthesis, the signals in the stance phase are used to predict the terrain and speed in the swing phase. Specifically, the intent pattern recognizer utilizes intraclass correlation coefficient(ICC) according to the Cartesian product of walking speed and terrain. Moreover, the sensor data are fused via DempsterShafer's theory. And hidden Markov model(HMM) is used to recognize the realtime motion state with the reference of the prior step. The proposed method can infer the prosthesis user's intent of walking on different terrain, which includes level ground,stair ascent, stair descent, up and down ramp. The experiments demonstrate that the intent pattern recognizer is capable of identifying five typical terrain-modes with the rate of 95.8%. The outcome of this investigation is expected to substantially improve the control performance of powered above-knee prosthesis.
基金The Fundamental Research Funds for the Central Universities,the Scientific Innovation Research of College Graduates in Jiangsu Province(No.KYLX_0177)
文摘In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purposes by applying three methods： the support vector machine （SVM） model, the radial basis function neural network （RBFNN） model and the multinomial logit （MNL） model. The effect of explanatory factors on trip chaining behaviors and their contribution to model performace were investigated by sensitivity analysis. Results show that the SVM model has a better performance than the RBFNN model and the MNL model due to its higher overall and partial accuracy, indicating its recognition advantage under a smai sample size scenario. It is also proved that the SVM model is capable of estimating the effect of multi-category factors on trip chaining behaviors more accurately. The different contribution of explanatory, factors to trip chaining pattern recognition reflects the importance of refining trip chaining patterns ad exploring factors that are specific to each pattern. It is shown that the SVM technology in travel demand forecast modeling and analysis of explanatory variable effects is practical.
基金supported by National Key Scientific Project for New Drug Discovery and Development of China (Grant no. 2009ZX09301-012)
文摘In this paper, the feasibility and advantages of employing high performance liquid chromatographic （HPLC） fingerprints combined with pattern recognition techniques for quality control of Shenmai injection were investigated and demonstrated. The Similarity Evaluation System was employed to evaluate the similarities of samples of Shenmai injection, and the HPLC generated chromatographic data were analyzed using hierarchical clustering analysis （HCA） and soft independent modeling of class analogy （SIMCA）. Consistent results were obtained to show that the authentic samples and the blended samples were successfully classified by SIMCA, which could be applied to accurate discrimination and quality control of Shenmai injection. Furthermore, samples could also be grouped in accordance with manufacturers. Our results revealed that the developed method has potential perspective for the original discrimination and quality control of Shenmai injection.
基金Project supported by the National High Technology Research and Development Program of China(Grant No.2013AA030901)
文摘We present a new pattern recognition system based on moving average and linear discriminant analysis （LDA）, which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensitive sensor system we designed, called the new e-nose. Using the new e-nose, we obtain the template datum of Chinese spirits via a new pattern recognition system. To verify the effectiveness of the new pattern recognition system, we select three kinds of Chinese spirits to test, our results confirm that the new pattern recognition system can perfectly identify and distinguish between the Chinese spirits.
基金Project(107021) supported by the Key Foundation of Chinese Ministry of Education Project(2009643013) supported by China Scholarship Fund
文摘In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.
基金Dr. Steve Jones, Scientific Advisor of the Canon Foundation for Scientific Research (7200 The Quorum, Oxford Business Park, Oxford OX4 2JZ, England). Canon Foundation for Scientific Research funded the UPC 2013 tuition fees of the corresponding author during her writing this article
文摘In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.
文摘This paper develops a fuzzy pattern recognition model for group decision making to solve the problem of lectotype optimization of offshore platforms. The lack of data and the inexact or incomplete information for criteria are the main cause of uncertainty in the evaluation process, therefore it is necessary to integrate the judgments from different decision makers with different experience, knowledge and preference. This paper first uses a complementary principle based pairwise comparison method to obtain the subjective weight of the criteria from each decision maker. A fuzzy pattern recognition model is then developed to integrate the judgments from all the decision makers and the information from the criteria, under the supervision of the subjective weights. Finally a case study is given to show the efficiency and robustness of the proposed model.
