Search

Background/objective: The diagnosis and treatment of functional dyspepsia (FD) require a systematic and tailored approach. In traditional Korean medicine (TKM), pattern-identification tools help analyze clinical information and guide treatment. This study aimed to develop a Korean version of the standard tool of pattern identification for functional dyspepsia (STPI-FD) and subsequently assess its reliability and validity. Methods: Common patterns and symptoms were identified through a systematic review of the clinical studies conducted in Korea and China. An importance survey for each pattern and symptom was conducted using the Delphi method and refined through expert consensus. A draft STPI-FD comprising six patterns and 38 items was developed. Ninety-five patients with FD completed the STPI-FD along with the Visual Analog Scale, Total Dyspepsia Symptom scale, Single Dyspepsia Symptom scale, and Functional Dyspepsia-related Quality of Life questionnaire. Results: Two items were excluded from the STPI-FD due to their negative impact on reliability, resulting in a 36-item tool. The revised STPI-FD demonstrated high internal consistency, with an overall Cronbach's alpha of 0.942. In contrast, the Cronbach's alpha values for each pattern ranged from 0.7 to 0.9. To assess its validity, significant differences in the pattern characteristics and associated symptoms were confirmed, indicating its clinical relevance. Statistically significant positive correlations between the revised STPI-FD and other dyspepsia-related scales underscore the differentiation between patterns. Conclusion: The final STPI-FD is a moderately reliable and valid tool for diagnosing specific patterns in FD, supporting the selection of interventions and the evaluation of symptom improvement in patients treated with TKM. [ABSTRACT FROM AUTHOR]

Prolonged exposure to high-intensity noise environments in urban rail transit systems can negatively impact the health and work efficiency of drivers. However, there is a lack of comprehensive understanding of the noise pattern and, therefore, effective mitigation strategies. To control the noise in urban rail transit systems, this study proposes a comprehensive noise assessment framework, including metrics such as average sound pressure level, peak sound pressure level, percentile sound pressure levels, dynamic range, main frequency component, and cumulative time energy to evaluate the noise characteristics. We also employ a density-based spatial clustering of applications with noise (DBSCAN) method to identify the noise patterns with the evaluation of their hazard to urban rail transit drivers. The results have revealed that: (1) The equivalent continuous sound pressure level (Leq) in the cab of Lanzhou Urban Rail Transit Line 1 averages 87.12 dB, with a standard deviation of 8.52 dB, which reveals a high noise intensity with substantial fluctuations. (2) Ten noise patterns were identified, with frequencies varying from 14.47 Hz to 69.70 Hz and Leq varying from 60 dB to 115 dB. (3) The major noise sources from these patterns are inferred to be the train's mechanical systems, wheel–rail interaction, aerodynamic effects, and braking systems. Combined with the noise patterns and urban rail transit's operation environment, this study proposes tailored mitigation strategies for applications aimed at protecting drivers' hearing health, enhancing work efficiency, and ensuring driving safety. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study was to investigate a traditional medicine (TM) classification based on the nature and location of disease for pattern identification (PI) in Parkinson's disease (PD). This study also aimed to present evidence regarding the pathogenesis of PD and provide a basis for establishment of individualized treatment strategies with TM. Methods: An electronic search was conducted in three core databases, three Korean databases, and four Chinese databases. A hierarchical clustering analysis was conducted based on the nature and location of the disease for PD, and the analysis findings were compared with PI in the existing literature. Results: A total of 79 different types of PI were confirmed in 189 PD cases. Cluster analysis using the nature and location of disease disassembled from PI identified 7 groups: group 1 (wind, blood stasis) comprised 15 studies; group 2 (fire–heat, phlegm-retained fluid) comprised 7 studies; group 3 (liver, Qi stagnation) comprised 5 studies; group 4 (kidney, Yang deficiency) comprised 10 studies; group 5 (liver–kidney, Yin deficiency) comprised 93 studies; group 6 (Qi deficiency, blood deficiency) comprised 18 studies; and group 7 (meridian and collateral) comprised 9 studies. Conclusions: The findings provide evidence for the pathogenesis of PD and have the potential to translate into more specific TM patterns for PD. The authors hope that their study results will be used as a foundation for development of traditional medical diagnostic methods and treatments tailored to PD. [ABSTRACT FROM AUTHOR]

Abstract Background In the Diabetes domain, events such as meals and exercises play an important role in the disease management. For that, many studies focus on automatic meal detection, specially as part of the so-called artificial $$\beta$$ β -cell systems. Meals are associated to blood glucose (BG) variations, however such variations are not peculiar to meals, it mostly comes as a combination of external factors. Thus, general approaches such as the ones focused on glucose signal rate of change are not enough to detect personalized influence of such factors. By using a data-driven individualized approach for meal detection, our method is able to fit real data, detecting personalized meal responses even when such external factors are implicitly present. Methods The method is split into model training and selection. In the training phase, we start observing meal responses for each individual, and identifying personalized patterns. Occurrences of such patterns are searched over the BG signal, evaluating the similarity of each pattern to each possible signal subsequence. The most similar occurrences are then selected as possible meal event candidates. For that, we include steps for excluding less relevant neighbors per pattern, and grouping close occurrences in time globally. Each candidate is represented by a set of time and response signal related qualitative variables. These variables are used as input features for different binary classifiers in order to learn to classify a candidate as Meal or Non-Meal. In the model selection phase, we compare all trained classifiers to select the one that performs better with the data of each individual. Results The results show that the method is able to detect daily meals, providing a result with a balanced proportion between detected meals and false alarms. The analysis on multiple patients indicate that the approach achieves good outcomes when there is enough reliable training data, as this is reflected on the testing results. Conclusions The approach aims at personalizing the meal detection task by relying solely on data. The premise is that a model trained with data that contains the implicit influence of external factors is able to recognize the nuances of the individual that generated the data. Besides, the approach can also be used to improve data quality by detecting meals, opening opportunities to possible applications such as detecting and reminding users of missing or wrongly informed meal events.

Prolonged exposure to high-intensity noise environments in urban rail transit systems can negatively impact the health and work efficiency of drivers. However, there is a lack of comprehensive understanding of the noise pattern and, therefore, effective mitigation strategies. To control the noise in urban rail transit systems, this study proposes a comprehensive noise assessment framework, including metrics such as average sound pressure level, peak sound pressure level, percentile sound pressure levels, dynamic range, main frequency component, and cumulative time energy to evaluate the noise characteristics. We also employ a density-based spatial clustering of applications with noise (DBSCAN) method to identify the noise patterns with the evaluation of their hazard to urban rail transit drivers. The results have revealed that: (1) The equivalent continuous sound pressure level (Leq) in the cab of Lanzhou Urban Rail Transit Line 1 averages 87.12 dB, with a standard deviation of 8.52 dB, which reveals a high noise intensity with substantial fluctuations. (2) Ten noise patterns were identified, with frequencies varying from 14.47 Hz to 69.70 Hz and Leq varying from 60 dB to 115 dB. (3) The major noise sources from these patterns are inferred to be the train’s mechanical systems, wheel–rail interaction, aerodynamic effects, and braking systems. Combined with the noise patterns and urban rail transit’s operation environment, this study proposes tailored mitigation strategies for applications aimed at protecting drivers’ hearing health, enhancing work efficiency, and ensuring driving safety.

ABSTRACTAn intersection of two or more roads poses a risk for potential conflicts among vehicles. Often the reasons triggering such conflicts are not clear, as they might be too subtle for the human eye. The environment also plays a part in understanding where, when, and why a particular vehicle interaction has occurred in a certain way. Therefore, it is of paramount importance to dive deeper into the vehicle interaction at a micro-scale within the embedded geographical environment, particularly at the intersections. This would in turn assist in evaluating the association of vehicle interactions with conflict risks and near-miss accidents. Moreover, detection of such micro traffic interactions could also be used to improvise the complexity of the already established transport infrastructure. Conversely, traffic at intersections has been explored mainly for flow estimation, capacity and width measurements, and traffic congestion, etc., whereas the detection of micro-scale traffic interactions at intersections remains relatively under-explored. In this paper, we present a novel approach to retrieve and represent micro-scale traffic movement interactions at a non-signalized T-junction by extending a recently introduced qualitative spatiotemporal Point-Descriptor-Precedence (PDP) representation. We study how the PDP representation offers a fine solution to study the interaction of traffic flows at intersections. This permits tracking the micro-movement of vehicles in much finer detail, which is used later to retrieve movement patterns from a motion dataset. Unlike conventional approaches, we start our approach with the actual movements before modeling the static intersection environment. Additionally, with the aid of illustrative examples, we discuss how the length, width, and speed of the vehicles can be exploited in our approach to detect specific patterns more accurately. Additionally, we address the potential benefits of our approach for traffic safety assessment and how it can be extended to a network of intersections using different transport modes.

Background: In the Diabetes domain, events such as meals and exercises play an important role in the disease management. For that, many studies focus on automatic meal detection, specially as part of the so-called artificial β -cell systems. Meals are associated to blood glucose (BG) variations, however such variations are not peculiar to meals, it mostly comes as a combination of external factors. Thus, general approaches such as the ones focused on glucose signal rate of change are not enough to detect personalized influence of such factors. By using a data-driven individualized approach for meal detection, our method is able to fit real data, detecting personalized meal responses even when such external factors are implicitly present. Methods: The method is split into model training and selection. In the training phase, we start observing meal responses for each individual, and identifying personalized patterns. Occurrences of such patterns are searched over the BG signal, evaluating the similarity of each pattern to each possible signal subsequence. The most similar occurrences are then selected as possible meal event candidates. For that, we include steps for excluding less relevant neighbors per pattern, and grouping close occurrences in time globally. Each candidate is represented by a set of time and response signal related qualitative variables. These variables are used as input features for different binary classifiers in order to learn to classify a candidate as Meal or Non-Meal. In the model selection phase, we compare all trained classifiers to select the one that performs better with the data of each individual. Results: The results show that the method is able to detect daily meals, providing a result with a balanced proportion between detected meals and false alarms. The analysis on multiple patients indicate that the approach achieves good outcomes when there is enough reliable training data, as this is reflected on the testing results. Conclusions: The approach aims at personalizing the meal detection task by relying solely on data. The premise is that a model trained with data that contains the implicit influence of external factors is able to recognize the nuances of the individual that generated the data. Besides, the approach can also be used to improve data quality by detecting meals, opening opportunities to possible applications such as detecting and reminding users of missing or wrongly informed meal events. [ABSTRACT FROM AUTHOR]

Functional Magnetic Resonance Imaging (fMRI) is the popular technique where it is possible to capture neural activity in brain regions when subjected to different stimuli. However, due to fMRI datasets' high dimensional and sparse nature, the best features' selection plays an essential role in providing the best classification accuracy in fMRI models. This paper selects the stable feature set from the fMRI dataset using hybrid Fast Fourier Transform with Particle Swarm Optimization and Genetic Algorithm (FFTPSOGA). Fast Fourier Transform (FFT) is used on the extracted features by PSO-GA to convert the magnitude of features into phase values for better performance. Next, the machine learning algorithms of GaussianNB, Support Vector Machine (SVM), and XGboost has been trained based on these extracted features of six subjects of the dataset. The experimental analysis reveals that the proposed algorithm resulted in optimum features that helped extract informative Regions of Interest (ROI) with better classification accuracy. Our implemented algorithm FFTPSOGA extracted the best voxels in six subjects of the dataset by selecting minimum ROIs with a model classification accuracy of 0.98, 0.95, 0.95, 0.95, 0.97, and 0.96 for the SVM classifier. Comparison of the proposed scheme with state-of-the-art techniques show that our algorithm resulted in best voxels and outperformed work in [1, 9, 25] by achieving higher accuracy of 98% and low computational costs with only 127 number of features. Due to its better performance, we believe that it can be used for the pattern identification of brain responses in multi-subject fMRI data. [ABSTRACT FROM AUTHOR]

In this work, a lightweight adaptive hybrid gait detection method with two inertial measurement units (IMUs) on the foot and thigh was developed and preliminarily evaluated. An adaptive detection algorithm is used to eliminate the pre-training phase and to modify parameters according to the changes within a walking trial using an adaptive two-level architecture. The present algorithm has a two-layer structure: a real-time detection algorithm for detecting the current gait pattern and events at 100 Hz., and a short-time online training layer for updating the parameters of gait models for each gait pattern. Three typical walking patterns, including level-ground walking (LGW), stair ascent (SA), and stair descent (SD), and four events/sub-phases of each pattern, can be detected on a portable Raspberry-Pi platform with two IMUs on the thigh and foot in real-time. A preliminary algorithm test was implemented with healthy subjects in common indoor corridors and stairs. The results showed that the on-board model training and event decoding processes took 20 ms and 1 ms, respectively. Motion detection accuracy was 97.8% for LGW, 95.6% for SA, and 97.1% for SD. F1-scores for event detection were over 0.86, and the maximum time delay was steadily below 51 ± 32.4 ms. Some of the events in gait models of SA and SD seemed to be correlated with knee extension and flexion. Given the simple and convenient hardware requirements, this method is suitable for knee assistive device applications. [ABSTRACT FROM AUTHOR]

An intersection of two or more roads poses a risk for potential conflicts among vehicles. Often the reasons triggering such conflicts are not clear, as they might be too subtle for the human eye. The environment also plays a part in understanding where, when, and why a particular vehicle interaction has occurred in a certain way. Therefore, it is of paramount importance to dive deeper into the vehicle interaction at a micro-scale within the embedded geographical environment, particularly at the intersections. This would in turn assist in evaluating the association of vehicle interactions with conflict risks and near-miss accidents. Moreover, detection of such micro traffic interactions could also be used to improvise the complexity of the already established transport infrastructure. Conversely, traffic at intersections has been explored mainly for flow estimation, capacity and width measurements, and traffic congestion, etc., whereas the detection of micro-scale traffic interactions at intersections remains relatively under-explored. In this paper, we present a novel approach to retrieve and represent micro-scale traffic movement interactions at a non-signalized T-junction by extending a recently introduced qualitative spatiotemporal Point-Descriptor-Precedence (PDP) representation. We study how the PDP representation offers a fine solution to study the interaction of traffic flows at intersections. This permits tracking the micro-movement of vehicles in much finer detail, which is used later to retrieve movement patterns from a motion dataset. Unlike conventional approaches, we start our approach with the actual movements before modeling the static intersection environment. Additionally, with the aid of illustrative examples, we discuss how the length, width, and speed of the vehicles can be exploited in our approach to detect specific patterns more accurately. Additionally, we address the potential benefits of our approach for traffic safety assessment and how it can be extended to a network of intersections using different transport modes. [ABSTRACT FROM AUTHOR]

The piston pump is the significant source of motive force in a hydraulic transmission system. Owing to the changeable working conditions and complex structural characteristics, multiple friction pairs in the piston pump are prone to wear and failure. An accurate fault diagnosis method is a crucial guarantee for system reliability. Deep learning provides a great insight into the intelligent exploration of machinery fault diagnosis. Hyperparameters are very important to construct an effective deep model with good performance. This research fully mines the feature component from vibration signals, and converts the failure recognition into a classification issue via establishing a deep model. Furthermore, Bayesian algorithm is introduced for hyperparameter optimization as it considers prior information. An adaptive convolutional neural network is established for typical failure pattern recognition of an axial piston pump. The proposed method can automatically complete fault classification and represents a higher accuracy by experimental verification. Typical failures of an axial piston pump are intelligently diagnosed with reduced subjectivity and preprocessing knowledge. The proposed method achieves an identification accuracy of more than 98% for five typical conditions of an axial piston pump. [ABSTRACT FROM AUTHOR]

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The agricultural sector is undergoing a revolution that requires sustainable solutions to the challenges that arise from traditional farming methods. To address these challenges, technical and sustainable support is needed to develop projects that improve crop performance. This study focuses on onion crops and the challenges presented throughout its phenological cycle. Unmanned aerial vehicles (UAVs) and digital image processing were used to monitor the crop and identify patterns such as humid areas, weed growth, vegetation deficits, and decreased harvest performance. An algorithm was developed to identify the patterns that most affected crop growth, as the average local production reported was 40.166 tons/ha. However, only 25.00 tons/ha were reached due to blight caused by constant humidity and limited sunlight. This resulted in the death of leaves and poor development of bulbs, with 50% of the production being medium-sized. Approximately 20% of the production was lost due to blight and unfavorable weather conditions. [ABSTRACT FROM AUTHOR]

Background: Current treatment and management options for functional dyspepsia (FD) often fail to alleviate symptoms. Naesohwajung-tang (NHT) is a herbal formula frequently used to treat functional dyspepsia in traditional Korean medicine. However, few animal and case reports on the use of Naesohwajungtang for functional dyspepsia treatment exist, and the clinical evidence remains deficient. Objectives: This study aimed to evaluate the efficacy of Naesohwajung-tang in patients with functional dyspepsia. Methods: We enrolled 116 patients with functional dyspepsia at two study sites in this 4 weeks, randomized, double-blind, placebo-controlled trial and randomly assigned them to either the Naesohwajung-tang or placebo group. To evaluate the efficacy of Naesohwajung-tang, the primary endpoint was a score on the total dyspepsia symptom (TDS) scale after treatment. The overall treatment effect (OTE), single dyspepsia symptom (SDS) scale, food retention questionnaire (FRQ), Damum questionnaire (DQ), functional dyspepsia-related quality of life (FD-QoL) questionnaire, and gastric myoelectrical activity measured using electrogastrography were evaluated as secondary outcomes. Laboratory tests were performed to confirm the safety of the intervention. Results: The 4 weeks administration of Naesohwajung-tang granules demonstrated a significantly higher reduction in the total dyspepsia symptom (p < 0.05) and a higher degree of improvement in the total dyspepsia symptom (p < 0.01) than the placebo group. Patients who underwent Naesohwajung-tang had a significantly higher overall treatment effect and a greater increase in the degree of improvement in scores such as epigastric burning, postprandial fullness, early satiation, functional dyspepsia-related quality of life, and Damum questionnaire (p < 0.05). Additionally, the Naesohwajung-tang group showed a greater effect in preventing a decrease in the percentage of normal gastric slow waves after meals than the placebo group. As a result of subgroup analyses using the degree of improvement in total dyspepsia symptom, Naesohwajung-tang was found to be more effective than placebo in female, younger patients (<65 years), with a high body-mass index (=22), overlap type, food retention type, and Dampness and heat in the spleen and stomach systems pattern. There was no significant difference in the incidence of adverse events between the two groups. Conclusion: This is the first randomized clinical trial to verify that Naesohwajungtang leads on symptom relief in patients with functional dyspepsia. [ABSTRACT FROM AUTHOR]

The overexploitation of groundwater due to increasing irrigation, domestic demands, and industry has led to the degradation of groundwater resources worldwide. Therefore, rigorous statistical techniques are required to simplify groundwater evaluation and monitoring. In this paper, we consider the application of Principal Component Analysis (PCA) to identify sources of ions in groundwater. Two data sets were derived from the Western Sokoto Basin, comprising hydrochemical data from shallow and deep aquifers. We show how PCA can be applied to identify sources of ions. Along with Hierarchical Cluster Analysis, PCA enables groundwater classification based on the mechanisms controlling the hydrochemistry of groundwater. The exploratory PCA and associated PCs and Biplots offer insights that can provide water quality analysts with an understanding of the experiential water composition influenced by natural and anthropogenic processes. PCA offers a comprehensive, straightforward, and cost-effective tool for standard and innovative analysis that helps water quality analysts discover, recognize, and analyze the accumulating volumes of water quality data by reducing data and retaining the major components characterizing the hydrochemistry of aquifers or surface water. Therefore, this study provides the basis for PCA application in groundwater quality analysis and monitoring for pollutant source identification, especially in data-poor regions where historical data is lacking. Theoretically, this review further enriches the literature on the systematic approach to assessing groundwater quality analysis, monitoring, and management using PCA. Practically, PCA is a robust statistical tool for identifying complex relationships between different physicochemical parameters and their links to natural and human processes. This review presents a new approach to hydrogeochemical analysis different from the conventional PCA application in the existing literature, which concerns hydrochemical data reduction. Thus, the PCA model used in this study can be used by environmental agencies seeking long-lasting and cost-effective improvement of their water quality assessment and monitoring. [ABSTRACT FROM AUTHOR]

Abstract Objective To investigate how the ulcerative colitis (UC) be treated with Chinese herbal medicines (CHM), using Chinese medicine (CM) pattern (zheng) identification, in the current clinical practice. Methods A total of 7 electronic databases were systematically searched for UC clinical studies with CHM interventions (including single herbs and CHM formulas) published in English and Chinese from the date of their inception to November 25, 2020. Descriptive statistics were adopted to demonstrate the characteristics of study design, and to collate the commonly CM patterns of UC and frequently used CHM herbs and formulas. Further, IBM SPSS Modeler 18.0 and Cytoscape 3.7.1 software were used to analyze and visualize the associations between different categories of CHM and their zheng indications. Results A total of 2311 articles were included in this study, of which most (> 90%) were RCTs with CHM formulas. The most common zheng of UC was Large intestine dampness-heat, while the basic type of CM patten was Spleen deficiency. The most frequently used classical formula was Bai-Tou-Weng-Tang, followed by Shen-Ling-Bai-Zhu-San, and the commonly used proprietary CHM was Xi-Lei-San (enema). Sulfasalazine and Mesalazine are commonly used as concomitant western medicines. The most frequently used single medicinals were Huang Lian and Bai Zhu, which also identified as the core herbs for different CM patterns. Conclusion This study examined the application of CHM interventions for UC and summarized their characteristics in clinical practice. These data indicated there were limited information about the safety assessment of CHM formulas and further RCTs including CM pattern(s) with strict design are necessary.

While pattern identification (PI) is an essential process in traditional medicine (TM), it is difficult to objectify since it relies heavily on implicit knowledge. Therefore, this study aimed to propose a machine learning (ML)-based analysis tool to evaluate the clinical decision-making process of PI in terms of explicit and implicit knowledge, and to observe the actual process by which this knowledge affects the choice of diagnosis and treatment in individual TM doctors. Clinical data for the development of the analysis tool were collected using a questionnaire administered to allergic rhinitis (AR) patients and the diagnosis and prescription results of TM doctors based on the completed AR questionnaires. Explicit knowledge and implicit knowledge were defined based on the doctors' explicit scoring and feature evaluations of ML models, respectively. There were many differences between the explicit and implicit importance scores in this study. Implicit importance is more closely related to explicit importance in prescription than in diagnosis. The analysis results for eight doctors showed that our tool could successfully identify explicit and implicit knowledge in the PI process. This is the first study to evaluate the actual process by which explicit and implicit knowledge affect the choice of individual TM doctors and to identify assessment tools for the definition of the decision-making process in diagnosing PI and prescribing herbal treatments by TM clinicians. The assessment tool suggested in this study could be broadly used for the standardization of precision medicine, including TM therapeutics. [ABSTRACT FROM AUTHOR]

Human fingertip tactile perception relies on the activation of densely distributed tactile receptors to identify contact patterns in the brain. Despite significant efforts to integrate tactile sensors with machine learning algorithms for recognizing physical patterns on object surfaces, developing a tactile sensing system that emulates human fingertip capabilities for identifying contact patterns with a high spatiotemporal resolution remains a formidable challenge. In this study, we present the development of an artificial tactile finger for accurate contact pattern identification, achieved through the integration of a high spatiotemporal piezoresistive sensor array (PRSA) and a convolutional neural network (CNN) model. Spatiotemporal characterization tests reveal that the artificial finger exhibits a fast temporal resolution of approximately 7 ms and achieves a two-point threshold of 1.5 mm, surpassing that of the human fingertip. To compare the performance of the artificial finger with the human finger in recognizing different patterns, we acquired pressure images by pressing the artificial finger, coated with a flexible PRSA film, onto both simple embossed and complex curved patterns while also recording human recognition results of perceiving these patterns. Experimental findings demonstrate that the artificial finger achieves higher classification accuracy in recognizing both simple and complex patterns (99.0 and 96.1%, respectively) compared to the human fingertip (69.1 and 22.7%). This artificial finger serves as a promising platform with great potential for various robotic tactile sensing applications including prosthetics, skin electronics, and robotic surgery.

In this paper, we develop a liveness detection of iris present in the study to reduce various spoofing attacks using gray-level co-occurrence matrix (GLCM) and Deep Learning (DL). The input images of iris are been given to this technique for the extraction of texture and colour features with fine details. The details are fused finally and given to a DL classifier for the classification of liveness detection. The simulation is conducted to test the efficacy of the model and the results of simulation shows that the proposed method achieves higher level of accuracy than existing methods.

Canine cognitive dysfunction syndrome (CDS) is a neurodegenerative disease that causes cognitive and behavioral disorders and reduces the quality of life in dogs and their guardians. This study reviewed the complementary and alternative medicine (CAM) for CDS and compared the diagnosis and therapy of CAM between CDS in canines and dementia in humans. The evaluation tools for the diagnosis of CDS and dementia were similar in the neurological and neuropsychiatric examinations, daily life activity, cognitive tests, and neuroimaging, but the evaluation for dementia was further subdivided. In CAM, pattern identification is a diagnostic method for accurate, personalized treatment, such as herbal medicine. For herbal medicine treatment of cognitive impairment in canines and humans, a similar pattern identification classified as deficiency (Qi, blood, and Yin) and Excess (phlegm, Qi stagnation, and blood stasis) is being used. However, the veterinary clinical basis for verifying the efficacy and safety of CAM therapies for CDS is limited. Therefore, based on CAM evidence in dementia, it is necessary to establish CDS-targeted CAM diagnostic methods and therapeutic techniques considering the anatomical, physiological, and pathological characteristics of dogs. [ABSTRACT FROM AUTHOR]

The use of electroencephalography to recognize human emotions is a key technology for advancing human–computer interactions. This study proposes an improved deep convolutional neural network model for emotion classification using a non-end-to-end training method that combines bottom-, middle-, and top-layer convolution features. Four sets of experiments using 4500 samples were conducted to verify model performance. Simultaneously, feature visualization technology was used to extract the three-layer features obtained by the model, and a scatterplot analysis was performed. The proposed model achieved a very high accuracy of 93.7%, and the extracted features exhibited the best separability among the tested models. We found that adding redundant layers did not improve model performance, and removing the data of specific channels did not significantly reduce the classification effect of the model. These results indicate that the proposed model allows for emotion recognition with a higher accuracy and speed than the previously reported models. We believe that our approach can be implemented in various applications that require the quick and accurate identification of human emotions. [ABSTRACT FROM AUTHOR]

Aims and Introduction. In the period of communication, future's communication, communication policies and planning have a special necessity; Because they are necessary to achieve and maintain a competitive advantages. In practice, an organization's communication with a particular product can occur in four general ways: in-house communication, ex-organizational communication with suppliers, ex-organizational communication with customers, and ex-organizational communication with other stakeholders. These connections must be planned and implemented in a way that strengthens the image and brand of the organization and the product. Insufficient ability of companies to communicate seamlessly with the internal and external environment leads to loss of opportunities and loss of competitiveness. With the advent of e-commerce, the fourth wave created the motivation to move from accessing customer summary information (to clarify the basic patterns in the second wave) to acquiring and working with customer information. This strategy developed relationships with individual customers and raised the need to pay more attention to marketing integration. Thus, the term IMC was extended to integrated marketing. Thus, the main purpose of this study is to identify and design a basic integrated marketing pattern.Methodology. This study, with a pragmatic philosophical approach, surveyes the evolution of integrated marketing through a gradual process and seeks to know what integrated marketing patterns are and what practical effects and results these patterns can have. Considering Hunt's three-pronged pattern, integrated marketing and its basic concepts, namely marketing communications and integrated marketing communications (both at the micro levels of the marketing domain and the sum of the elements of the marketing mix and at the macro levels of the organization), even occasionally, recommended in the for-profit sector of small companies, market corners, and service organizations, as well as in the non-profit sector.In terms of the nature of research data, the need to study the definitions and descriptions in the disciplines and scientific concepts of communication, marketing, organizational communication, managerial communication, marketing communication, management of marketing communication tools, integrated communication, integrated marketing communication, integrated brand communication, communication and public relations management so that it can achieve a common language of integration approach. This research tends more towards qualitative exploratory research. Regarding the degree of coordination between the content of the measurement tool and the purpose of the research, the validity of the content was also examined using the opinion of experts. To evaluate the content validity of the obtained codes quantitatively, two relative content validity coefficients (CVR) of Lawshe and content validity index (CVI) of Waltz and Basel are used.Findings. Content analysis of 46 integrated marketing definitions and similar concepts performed using content analysis method. In order to examine the validity of codings, the opinions and suggestions of five experts familiar with the concepts of coding and marketing communications used. The relative validity coefficient of the core and selective content codes was 0.99, which indicates sufficient validity of the codes. Content validity index of core and selective codes were 0.93 and 0.88, respectively. The consensus of experts on the validity of pivotal and selective codings and the lack of code changes in revisions performed over a period of one month indicates the stability, representation, and accuracy of the codes and thus the reliability of the results. One of the research findings is the identification of three basic patterns of integrated marketing: thinking, speech and behavior. These three basic patterns used to design the core of an integrated marketing model. Identifying and designing an integrated marketing model can provide the necessary basis for presenting an integrated marketing theory.Discussion and results. Integrated marketing has emerged as a natural evolutionary process in marketing communications that has undergone fundamental changes in at least three main areas: markets, media and communications, and consumers. These changes have taken place primarily with advances in information technology and have led to a major shift from product-centric mass marketing theories in the 1950s and 1960s to more customer-oriented, interactive, and measurable databases. Is. But integrated marketing shows that there is a need for more accurate identification of patterns associated with this phenomenon.The basic model of integrated marketing can combine the concept and process of integrated marketing with more coherence and form the basis of theorizing in this field in different frameworks.As marketing enters a new era in which consumers determine when, where, and how to use media, marketing needs to adapt and change. In the new marketing renaissance, when and where do customers and stakeholders want to hear it? Therefore, marketers must first understand the customer and then develop interdisciplinary plans to get the best customers at the time and place where the brands and messages are most appropriate to build long-term profitable relationships and build customer trust. Get and keep. The organization with long-term and strategic planning of its communications and by integrating the communications process in the environment inside and outside its borders must create and transfer values, production and innovation in any type of products, speech and behavior before performing any communication activities. Coordinate and make a sound to increase the level of trust and confidence in the organization in the business environment through interaction with stakeholders.The core of integrated marketing lies in the principles of developing customer-oriented and data-driven communication programs. The strategic advantage of transferring consumer data to customer applied knowledge requires an understanding of what the data represents and how the database managed as a strategic communication tool. Using the proposed model (thinking, speech, and behavior), the organization expected to be able to solve these problems by adopting a knowledge-based market-based approach: 1) know itself well, 2) identify all its communication channels 3) Identify the reasons for its relationship and its levels (who and why?), 4) identify its audience and stakeholders (for whom); 5) Specify its direct and clear content and message (what); 6) Define his / her communication tools accurately (how); And finally 7) follow up and analyze the message sent and the results obtained (feedback).

Pattern identification (PI) is a diagnostic method used in Traditional East Asian medicine (TEAM) to select appropriate and personalized acupuncture points and herbal medicines for individual patients. Developing a reproducible PI model using clinical information is important as it would reflect the actual clinical setting and improve the effectiveness of TEAM treatment. In this paper, we suggest a novel deep learning-based PI model with feature extraction using a deep autoencoder and k-means clustering through a cross-sectional study of sleep disturbance patient data. The data were obtained from an anonymous electronic survey in the Republic of Korea Army (ROKA) members from August 16, 2021, to September 20, 2021. The survey instrument consisted of six sections: demographics, medical history, military duty, sleep-related assessments (Pittsburgh sleep quality index (PSQI), Berlin questionnaire, and sleeping environment), diet/nutrition-related assessments [dietary habit survey questionnaire and nutrition quotient (NQ)], and gastrointestinal-related assessments [gastrointestinal symptom rating scale (GSRS) and Bristol stool scale]. Principal component analysis (PCA) and a deep autoencoder were used to extract features, which were then clustered using the k-means clustering method. The Calinski-Harabasz index, silhouette coefficient, and within-cluster sum of squares were used for internal cluster validation and the final PSQI, Berlin questionnaire, GSRS, and NQ scores were used for external cluster validation. One-way analysis of variance followed by the Tukey test and chi-squared test were used for between-cluster comparisons. Among 4,869 survey responders, 2,579 patients with sleep disturbances were obtained after filtering using a PSQI score of >5. When comparing clustering performance using raw data and extracted features by PCA and the deep autoencoder, the best feature extraction method for clustering was the deep autoencoder (16 nodes for the first and third hidden layers, and two nodes for the second hidden layer). Our model could cluster three different PI types because the optimal number of clusters was determined to be three via the elbow method. After external cluster validation, three PI types were differentiated by changes in sleep quality, dietary habits, and concomitant gastrointestinal symptoms. This model may be applied to the development of artificial intelligence-based clinical decision support systems through electronic medical records and clinical trial protocols for evaluating the effectiveness of TEAM treatment.

The piston pump is the significant source of motive force in a hydraulic transmission system. Owing to the changeable working conditions and complex structural characteristics, multiple friction pairs in the piston pump are prone to wear and failure. An accurate fault diagnosis method is a crucial guarantee for system reliability. Deep learning provides a great insight into the intelligent exploration of machinery fault diagnosis. Hyperparameters are very important to construct an effective deep model with good performance. This research fully mines the feature component from vibration signals, and converts the failure recognition into a classification issue via establishing a deep model. Furthermore, Bayesian algorithm is introduced for hyperparameter optimization as it considers prior information. An adaptive convolutional neural network is established for typical failure pattern recognition of an axial piston pump. The proposed method can automatically complete fault classification and represents a higher accuracy by experimental verification. Typical failures of an axial piston pump are intelligently diagnosed with reduced subjectivity and preprocessing knowledge. The proposed method achieves an identification accuracy of more than 98% for five typical conditions of an axial piston pump.

The evaluation of algal bloom forecasting models typically relies on error metrics that quantify the forecasting performance over the whole test set as a single number. Furthermore, the comparison with simple baseline methods is often omitted. To address this, we introduce a novel framework for Model performance Analysis and Visualization of time series forecasting (MAVts). MAVts incorporates novel algorithms for the automatic identification and visualization of time series periods of interest where the forecasting models are evaluated and compared with simple baseline methods. The application of MAVts on evaluating algal bloom forecasting models composed of sophisticated machine learning (ML) methods, reveals that in 85% of experiments a single error metric is not enough and only in 12.5% of experiments a ML model outperforms all baselines on all metrics and periods of interest. Thus, MAVts emerges as a valuable tool for analyzing and comparing ML models, advancing environmental management and protection. • Introducing MAVts, a framework for analysis of time series forecasting performance. • MAVts provides novel time series period identification and visualization algorithms. • Performance evaluation at various identified time series patterns is informative. • MAVts provides simple baselines with comparable performance to deep learning models. • Past algae concentration is most useful data for algae forecasting. [ABSTRACT FROM AUTHOR]

The agricultural sector is undergoing a revolution that requires sustainable solutions to the challenges that arise from traditional farming methods. To address these challenges, technical and sustainable support is needed to develop projects that improve crop performance. This study focuses on onion crops and the challenges presented throughout its phenological cycle. Unmanned aerial vehicles (UAVs) and digital image processing were used to monitor the crop and identify patterns such as humid areas, weed growth, vegetation deficits, and decreased harvest performance. An algorithm was developed to identify the patterns that most affected crop growth, as the average local production reported was 40.166 tons/ha. However, only 25.00 tons/ha were reached due to blight caused by constant humidity and limited sunlight. This resulted in the death of leaves and poor development of bulbs, with 50% of the production being medium-sized. Approximately 20% of the production was lost due to blight and unfavorable weather conditions.

Objective: To investigate how the ulcerative colitis (UC) be treated with Chinese herbal medicines (CHM), using Chinese medicine (CM) pattern (zheng) identification, in the current clinical practice. Methods: A total of 7 electronic databases were systematically searched for UC clinical studies with CHM interventions (including single herbs and CHM formulas) published in English and Chinese from the date of their inception to November 25, 2020. Descriptive statistics were adopted to demonstrate the characteristics of study design, and to collate the commonly CM patterns of UC and frequently used CHM herbs and formulas. Further, IBM SPSS Modeler 18.0 and Cytoscape 3.7.1 software were used to analyze and visualize the associations between different categories of CHM and their zheng indications. Results: A total of 2311 articles were included in this study, of which most (> 90%) were RCTs with CHM formulas. The most common zheng of UC was Large intestine dampness-heat, while the basic type of CM patten was Spleen deficiency. The most frequently used classical formula was Bai-Tou-Weng-Tang, followed by Shen-Ling-Bai-Zhu-San, and the commonly used proprietary CHM was Xi-Lei-San (enema). Sulfasalazine and Mesalazine are commonly used as concomitant western medicines. The most frequently used single medicinals were Huang Lian and Bai Zhu, which also identified as the core herbs for different CM patterns. Conclusion: This study examined the application of CHM interventions for UC and summarized their characteristics in clinical practice. These data indicated there were limited information about the safety assessment of CHM formulas and further RCTs including CM pattern(s) with strict design are necessary. [ABSTRACT FROM AUTHOR]

Complex turbomachinery systems produce a wide range of noise components. The goal is to identify noise source categories, determine their characteristic noise patterns and locations. Researchers can then use this information to quantify the impact of these noise sources, based on which new design guidelines can be proposed. Phased array microphone measurements processed with acoustic beamforming technology provide noise source maps for pre-determined frequency bands (i.e., bins) of the investigated spectrum. However, multiple noise generation mechanisms can be active in any given frequency bin. Therefore, the identification of individual noise sources is difficult and time consuming when using conventional methods, such as manual sorting. This study presents a method for combining beamforming with Principal Component Analysis (PCA) methods in order to identify and separate apart turbomachinery noise sources with strong harmonics. The method is presented through the investigation of Counter-Rotating Open Rotor (CROR) noise sources. It has been found that the proposed semi-automatic method was able to extract even weak noise source patterns that repeat throughout the data set of the beamforming maps. The analysis yields results that are easy to comprehend without special prior knowledge and is an effective tool for identifying and localizing noise sources for the acoustic investigation of various turbomachinery applications. [ABSTRACT FROM AUTHOR]

Abstract Background Insomnia seriously affects people’s normal lives and work. However, effective treatment strategies are scarce. The purpose of this study is to explore the efficacy and safety of Jiao-tai-wan (JTW) for ameliorating insomnia symptoms caused by disharmony of the heart and kidney. Design This is a randomized, double-blind, placebo-controlled pilot clinical trial. A total of 124 participants suffering from insomnia symptoms will be randomly assigned to the JTW or placebo group in an equal ratio. The participants will be asked to take JTW or placebo granules twice a day for 1 week. All data will be gathered at baseline and at the end of the drug intervention. The primary outcome measures will be the mean change in the Pittsburgh Sleep Quality Index (PSQI) from baseline to the end of the drug intervention. Secondary outcome measures will include the altered sleep parameters in polysomnography, 1H-magnetic resonance spectroscopy (1H-MRS) evaluation, the Disharmony of Heart and Kidney Scoring System score, and blood tests, including the levels of serum adenosine and melatonin. A laboratory test will be taken before and after treatment to assess the safety of JTW. Discussion The outcomes of this study will confirm the efficacy of JTW for the treatment of insomnia symptoms and will also be used to monitor the safety of JTW. Trial registration Chinese Clinical Trial Registry, ChiCTR1 800019239 . Registered on 1st November 2018.