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The problem of the contamination of soils with high background values of heavy metals has attracted increasing attention. In this paper, the contents, spatial distribution characteristics and correlations of five heavy metals in seven types of arable soils with different soil-forming matrices were analyzed by using Kriging spatial interpolation, descriptive statistics and correlation analysis to clarify the spatial distribution of heavy metals in different soil-forming matrices, and to explore the influence of parent rocks on the spatial distribution and concentration of heavy metals. The results showed that the Cd contents of the seven soil-forming parent materials exceeded the background values recorded for soils in Yunnan Province and that metamorphic rocks such as mudstone, argillaceous rock, purple rock, and carbonate rock exceeded the risk screening values. The average Pb, Cu, and As contents were lower than the background values recorded for soils in Yunnan Province and smaller than the risk screening values for agricultural land. Carbonate areas have a large area of contamination, while metamorphic mudstone areas have a relatively small percentage of contamination. The correlations of heavy metals in different soil-forming matrices varies, and the source of each element and its correlation can be further analyzed and verified by means such as the source analysis method. The results of this study are crucial for pollution prevention and the analysis of the source of heavy metal soil contamination. [ABSTRACT FROM AUTHOR]

Many plant secondary metabolites are active and important in the regulation of plant growth. Certain plant-derived diterpenes are known to promote plant growth, but the pathways by which this promotion occurs are still unknown. Activity screening revealed that the plant-derived diterpene isopimaric acid exhibits growth-promoting activity in rice (Oryza sativa L.) seedlings. Furthermore, 25 μg/mL of isopimaric acid promoted the growth of 15 self-incompatible associated populations from different rice lineages to different extents. Quantitative analyses revealed a significant decrease in the concentration of the defense-related phytohormone abscisic acid (ABA) following treatment with isopimaric acid. Correlation analysis of the phytohormone concentrations with growth characteristics revealed that the length of seedling shoots was significantly negatively correlated with concentrations of 3-indole-butyric acid (IBA). Moreover, the total root weight was not only negatively correlated with ABA concentrations but also negatively correlated with concentrations of isopentenyl adenine (iP). These data suggest that isopimaric acid is able to influence the phytohormone pathway to balance energy allocation between growth and defense in rice seedlings and also alter the correlation between the concentrations of phytohormones and traits such as shoot and root length and weight. We provide a theoretical basis for the development and utilization of isopimaric acid as a plant growth regulator for rice.

To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability. We demonstrated our method's capabilities through experiments on both synthetic and real-world datasets, covering scenarios involving grass and rice crops. Moreover, we use random forest and linear regression models to predict water status variables from our Local Biplot-based feature ranking and clusters. Our findings revealed enhanced clustering and prediction capability while emphasizing the importance of input features in precision agriculture. As a result, Local Biplot is a useful tool to visualize, analyze, and compare the intricate underlying patterns and internal structures of complex agricultural datasets. [ABSTRACT FROM AUTHOR]

ABSTRACT: We previously identified a dark blue appearance through the skin of abdomen, especially the colored chicken breeds, called hyperpigmentation of the visceral peritoneum (HVP) which characterized by intense pigmentation of connective tissue in the visceral peritoneum. The HVP has recently garnered increasing attention due to its negative impact on carcass appearance, and been an important concern in the poultry industry, especially for the Chinese yellow-feathered broilers. In this study, we measured the in vivo HVP at different time points, and analyzed the correlation between the HVP in vivo and postmortem. Then, established an accurate and reliable HVP phenotypic measuring method in vivo for early selection in chickens and analyzed the association of phenotypic variations with the in vivo HVP traits with growth traits. The results showed that the in vivo HVP at 21 d of age in chickens have a high heritability (h2 = 0.452) through estimating genetic parameters, and in vivo HVP levels at 21 and 42 d were both significantly associated with those postmortem in chickens, suggesting that directional selection on reducing HVP can be implemented as early as at 21 d in the breeding and production of chickens. Although, we found HVP had no effect on the body weight at 1 d, it could significantly reduce the body weight at 21, 42, 70 d and 91 d in chickens. This suggests HVP not only has a negative effect on carcass traits, but also significantly reduces the production in the poultry industry.

To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability. We demonstrated our method’s capabilities through experiments on both synthetic and real-world datasets, covering scenarios involving grass and rice crops. Moreover, we use random forest and linear regression models to predict water status variables from our Local Biplot-based feature ranking and clusters. Our findings revealed enhanced clustering and prediction capability while emphasizing the importance of input features in precision agriculture. As a result, Local Biplot is a useful tool to visualize, analyze, and compare the intricate underlying patterns and internal structures of complex agricultural datasets.

[ Objective/Meaning] Based on the theory of industrial correlation, the analysis on the correlation mechanism of agriculture between Fujian and Taiwan could provide theoretical and practical reference for the development of agricultural integration between Fujian and Taiwan. [Methods/Procedures]By taking the Phalaenopsis aphrodite industry in Zhangpu County of Fujian Province as an example, the analysis framework of “ production linkage-institutional linkage-market linkage” was constructed to analyze the correlation mechanism and practical dilemma of agriculture between Fujian and Taiwan through the methods of field investigation and in-depth interview. And then the strategies for promoting the integration of agriculture between Fujian and Taiwan were explored. [ Results/Conclusions] The study found that: (1) The Phalaenopsis aphrodite industry in Zhangpu could provide a typical case for the expansion and extension of industrial relevancy theory. The industrial relevancy theory could provide theoretical support for the further research on the strategy of agricultural integration between Fujian and Taiwan. (2) The agriculture in Fujian and Taiwan has established the linkages of production, institution and market at the regional and enterprise levels, and the knowledge spillover effect based on the technological diffusion has promoted the development of local agriculture. However, there were some practical difficulties in the integration of agriculture between Fujian and Taiwan, such as the lack of key technologies for breeding, the insignificant policy effect and the lock-in of product market evolution path. (3) Comprehensively supporting to carry out the breeding work, exerting the remarkable external policy effect and accelerating the breakthrough of evolution path for product market were the effective measures to get out of the current dilemma of agricultural integrated development between Fujian and Taiwan. [ABSTRACT FROM AUTHOR]

As society evolves as a whole, new demands arise with increasingly demanding prerequisites, consequently requiring more significant effort to be met. Such demands cover emerging applications, such as remote surgeries in Smart Health use cases, whose latency and reliability network requirements cannot be met by current communication systems; or simply improving current applications with more challenging requirements to be achieved, such as increasing the transmission rate in a mobile network, offering Quality of Service (QoS), and consequently, better user experience. Therefore, enabling technologies must be chosen to design an appropriate 6G architecture to address such demands. However, the explosion of emerging applications focused on different scopes and requirements to be met makes choosing these enabling technologies extremely complex and unpredictable. Thus, this article aims to create a methodology for analyzing the relevance of enabling technologies and use it to design an optimal architecture capable of meeting the 6G demands. For this purpose, two methods named as Average (AVG) and Analytic Hierarchy Process (AHP) have been selected, whose objective is to determine the relevance of an enabler for the 6G architecture, taking into account different degrees of influencing variables for this analysis, such as adherence to a certain architectural model; popularity in the research area; degree of innovation; synergy with other enablers; and support for requirements. Each of these methods presents a particular result. In the case of the AVG method, the criteria and variables are evaluated independently, and the arithmetic mean is employed to combine the evaluations into a single measure of suitability. In contrast, the AHP method considers the relative importance of criteria and variables in order to classify an optimal set of enabling technologies capable of fulfilling the key roles to be performed by a 6G architecture, and consequently meeting the main 6G demands. Our evaluation provides a unique perspective on 6G enablers, identifying issues and fostering research for future mobile architectures. The results obtained also provide researchers with the necessary information to stay updated on emerging enabling technologies and their suitability for designing new optimized 6G architectures.

Ecological vulnerability is the main index to evaluate areal environmental stability and monitor the development of ecological environment. Longdong area is a typical Loess Plateau area with complex terrain, serious soil erosion, mineral resource development, and other human activities leading to the ecological vulnerability evolution of the area, but the monitoring of its ecological status and the determination of its factors are still lacking. Based on the ecological characteristics of Longdong area, this study constructed an ecological vulnerability system including natural, social, and economic data and used the fuzzy analytic hierarchy process (FAHP) to study the temporal and spatial evolution of ecological vulnerability from 2006 to 2018. A model for quantitative analysis of the evolution of ecological vulnerability and correlation of influencing factors was ultimately developed. The results showed that (1) from 2006 to 2018, the ecological vulnerability index (EVI) had a minimum value of 0.232 and a maximum value of 0.695. EVI was high in the northeast and southwest of Longdong area and low in the central region. (2) At the same time, the areas of potential vulnerability and mild vulnerability increased, and the areas of slight vulnerability, moderate vulnerability, and severe vulnerability decreased. (3) The correlation coefficient between average annual temperature and EVI exceeded 0.5 in four years, and the correlation coefficient between population density and per capita arable land area and EVI exceeded 0.5 in two years showed significant correlation. The results reflect the spatial pattern and influencing factors of ecological vulnerability in typical arid areas of northern China. Additionally, it served as a resource for researching the interrelationships of the variables affecting ecological vulnerability. [ABSTRACT FROM AUTHOR]

Aggregate stability is considered an essential indicator of changes in the physical properties of soils, and vegetation roots play a crucial role in the stability of shallow soil aggregates in ionic rare earth tailings piles during ecological remediation. In this paper, the influence of the law of ecologically restored vegetation roots on the stability of shallow aggregates of ionic rare earth tailing piles was investigated by means of field investigation tests, indoor experiments and mathematical statistics. The influence of different types of root systems on the stability of the shallow depth range aggregates of tailings piles was investigated; the correlation between vegetation root systems and the main physical parameters of rare earth tailings was clarified; and a mathematical correlation model characterizing the characteristic parameters of vegetation root systems was constructed. The evaluation index of the stability of rare earth tailings piles was constructed, and the influence of the law of the ecological restoration of vegetation root systems on the strength of shallow aggregates of ionic rare earth tailings piles was revealed. The results of the study showed that compared with the RD (root density), the root characteristic parameter with the largest response weight to the rare earth tailings pile is the RL (root length density), and the root characteristic parameter with the largest response weight to the water content is the RV (root volume). Suitable vegetation roots can effectively enhance the content of shallow large aggregates of rare ionic earth tailing piles. With the increase of the depth of a tailing pile, the content of large aggregates continues to decrease, and the content of micro aggregates continues increasing. This indicates that the vegetation root system changed the shallow soil of the rare earth tailing pile from disorderly to orderly through its own growth pattern, which effectively improved the stability of the shallow aggregates of the tailing pile and improved the physical properties of the tailing. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management 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.)

We previously identified a dark blue appearance through the skin of abdomen, especially the colored chicken breeds, called hyperpigmentation of the visceral peritoneum (HVP) which characterized by intense pigmentation of connective tissue in the visceral peritoneum. The HVP has recently garnered increasing attention due to its negative impact on carcass appearance, and been an important concern in the poultry industry, especially for the Chinese yellow-feathered broilers. In this study, we measured the in vivo HVP at different time points, and analyzed the correlation between the HVP in vivo and postmortem. Then, established an accurate and reliable HVP phenotypic measuring method in vivo for early selection in chickens and analyzed the association of phenotypic variations with the in vivo HVP traits with growth traits. The results showed that the in vivo HVP at 21 d of age in chickens have a high heritability (h2 = 0.452) through estimating genetic parameters, and in vivo HVP levels at 21 and 42 d were both significantly associated with those postmortem in chickens, suggesting that directional selection on reducing HVP can be implemented as early as at 21 d in the breeding and production of chickens. Although, we found HVP had no effect on the body weight at 1 d, it could significantly reduce the body weight at 21, 42, 70 d and 91 d in chickens. This suggests HVP not only has a negative effect on carcass traits, but also significantly reduces the production in the poultry industry. [ABSTRACT FROM AUTHOR]

To achieve high efficiency with limitation of energy consumption is a key problem in the system of UAVs. In existing UAV task allocation methods, the relevance between tasks and UAV cruise directions is neglected, which may further influence the energy consumption and delay of task accomplishment. In view of this, this paper proposed an UAV task allocation method based on the relevance analysis between tasks and cruise directions. This method includes two phases: task screening phase and conflict resolution phase based on consensus. In the first phase, this method selects out tasks without turning back for an UAV according to angles between the cruise direction of the UAV and directions of these tasks, then designs an algorithm to further select candidate tasks before interaction from the tasks without turning back according to their energy consumption utility parameters and time urgency parameters. In the second phase, this method solves tasks conflicts between UAVs after exchanging of their candidate tasks according to energy consumption utility parameters and time delay parameters of these tasks in different cruise directions of these UAVs. Simulation results verify that the proposed method can achieve lower average task energy consumption and average task delay. [ABSTRACT FROM AUTHOR]

The economic development of Indonesia and the region has an impact on national maritime security. With these conditions, developments and challenges in the maritime aspect, Indonesia has outlined a policy on the vision of the World Maritime Axis. This vision is a consequence of being a maritime nation with an area that is mostly ocean, as is the policy of Alfred Thayer Mahan (A. T. Mahan). The purpose of this paper is to provide an analysis of the relationship between the vision of the World Maritime Axis (WMA) and the concept of maritime strategy from A.T. Mahan so that it can be seen the relevance of this strategy to the development of President Joko Widodo’s World Maritime Axis (WMA) Vision. Based on the results of analysis, the development of the World Maritime Axis (WMA) Vision is relevant to Alfred Thayer Mahan’s maritime strategy. The relevance, among others, is that Indonesia is between two continents in the world, namely the Asian continent and the Australian continent; Indonesia is also the largest archipelagic country in the world with a very wide maritime area; Demographic bonus is a condition where the productive age population is more than the non-productive age; History records that the Indonesian people have long controlled sea shipping lanes with a fairly formidable fleet; The vision and mission of the government of President Joko Widodo have the ambition to make Indonesia the world’s maritime axis.

The use of machine learning (ML) for diagnosis support has advanced in the field of health. In the present paper, the results of studying ML techniques in a tuberculosis diagnosis loop in a scenario of limited resources are presented. Data are analyzed using a tuberculosis (TB) therapy program at a health institution in a main city of a developing country using five ML models. Logistic regression, classification trees, random forest, support vector machines, and artificial neural networks are trained under physician supervision following physicians' typical daily work. The models are trained on seven main variables collected when patients arrive at the facility. Additionally, the variables applied to train the models are analyzed, and the models' advantages and limitations are discussed in the context of the automated ML techniques. The results show that artificial neural networks obtain the best results in terms of accuracy, sensitivity, and area under the receiver operating curve. These results represent an improvement over smear microscopy, which is commonly used techniques to detect TB for special cases. Findings demonstrate that ML in the TB diagnosis loop can be reinforced with available data to serve as an alternative diagnosis tool based on data processing in places where the health infrastructure is limited.

Copyright of Food Science & Technology & Economy is the property of Grain Science & Technology & Economy 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 objective of this study was to explore the relevance between Autonomous Emergency Braking (AEB) control strategy and occupant pre-impact kinematics among a typical real-world cut-in impact scenario. First, the accident scenario was built with PreScan software after accident analysis. Second, a MADYMO simulation model with Active Human Model (AHM) was built and validated with the volunteer test carried out by our team. Finally, the AEB module and related control strategies were introduced into the main vehicle, and the effects of different strategies on the occupant kinematic were evaluated. The simulation results indicated that it was efficient to evaluate the occupant kinematics during pre-impact phase through vehicle and occupant integrated simulation method. The main vehicle's velocity could be reduced between 5 km/h and 14 km/h respectively after introducing different AEB control strategies, which was less than the one manoeuvred by driver (22 km/h). Earlier activation of the AEB and heavier braking could result in larger up-body displacement, but less final impact velocity, and the maximum head displacement reached 172.56 mm due to the AEB control. Comparing partial braking with detection angle 9° case with 100% braking with detection angle 18° case, the head, thorax and shoulder displacements were increased by 94.8%, 104.1%, and 48.7%. This research is beneficial for the subsequent integrated safety analysis. [ABSTRACT FROM AUTHOR]

Aggregate stability is considered an essential indicator of changes in the physical properties of soils, and vegetation roots play a crucial role in the stability of shallow soil aggregates in ionic rare earth tailings piles during ecological remediation. In this paper, the influence of the law of ecologically restored vegetation roots on the stability of shallow aggregates of ionic rare earth tailing piles was investigated by means of field investigation tests, indoor experiments and mathematical statistics. The influence of different types of root systems on the stability of the shallow depth range aggregates of tailings piles was investigated; the correlation between vegetation root systems and the main physical parameters of rare earth tailings was clarified; and a mathematical correlation model characterizing the characteristic parameters of vegetation root systems was constructed. The evaluation index of the stability of rare earth tailings piles was constructed, and the influence of the law of the ecological restoration of vegetation root systems on the strength of shallow aggregates of ionic rare earth tailings piles was revealed. The results of the study showed that compared with the RD (root density), the root characteristic parameter with the largest response weight to the rare earth tailings pile is the RL (root length density), and the root characteristic parameter with the largest response weight to the water content is the RV (root volume). Suitable vegetation roots can effectively enhance the content of shallow large aggregates of rare ionic earth tailing piles. With the increase of the depth of a tailing pile, the content of large aggregates continues to decrease, and the content of micro aggregates continues increasing. This indicates that the vegetation root system changed the shallow soil of the rare earth tailing pile from disorderly to orderly through its own growth pattern, which effectively improved the stability of the shallow aggregates of the tailing pile and improved the physical properties of the tailing.

Many plant secondary metabolites are active and important in the regulation of plant growth. Certain plant-derived diterpenes are known to promote plant growth, but the pathways by which this promotion occurs are still unknown. Activity screening revealed that the plant-derived diterpene isopimaric acid exhibits growth-promoting activity in rice ( Oryza sativa L.) seedlings. Furthermore, 25 μg/mL of isopimaric acid promoted the growth of 15 self-incompatible associated populations from different rice lineages to different extents. Quantitative analyses revealed a significant decrease in the concentration of the defense-related phytohormone abscisic acid (ABA) following treatment with isopimaric acid. Correlation analysis of the phytohormone concentrations with growth characteristics revealed that the length of seedling shoots was significantly negatively correlated with concentrations of 3-indole-butyric acid (IBA). Moreover, the total root weight was not only negatively correlated with ABA concentrations but also negatively correlated with concentrations of isopentenyl adenine (iP). These data suggest that isopimaric acid is able to influence the phytohormone pathway to balance energy allocation between growth and defense in rice seedlings and also alter the correlation between the concentrations of phytohormones and traits such as shoot and root length and weight. We provide a theoretical basis for the development and utilization of isopimaric acid as a plant growth regulator for rice.

The economic development of Indonesia and the region has an impact on national maritime security. With these conditions, developments and challenges in the maritime aspect, Indonesia has outlined a policy on the vision of the World Maritime Axis. This vision is a consequence of being a maritime nation with an area that is mostly ocean, as is the policy of Alfred Thayer Mahan (A. T. Mahan). The purpose of this paper is to provide an analysis of the relationship between the vision of the World Maritime Axis (WMA) and the concept of maritime strategy from A.T. Mahan so that it can be seen the relevance of this strategy to the development of President Joko Widodo's World Maritime Axis (WMA) Vision. Based on the results of analysis, the development of the World Maritime Axis (WMA) Vision is relevant to Alfred Thayer Mahan's maritime strategy. The relevance, among others, is that Indonesia is between two continents in the world, namely the Asian continent and the Australian continent; Indonesia is also the largest archipelagic country in the world with a very wide maritime area; Demographic bonus is a condition where the productive age population is more than the non-productive age; History records that the Indonesian people have long controlled sea shipping lanes with a fairly formidable fleet; The vision and mission of the government of President Joko Widodo have the ambition to make Indonesia the world's maritime axis. [ABSTRACT FROM AUTHOR]

The education is a fundamental law that promotes the social and economic development of a nation. Also, it is how a country can achieve its sustainable development objectives. Because of this, various international organizations (Unicef-UNESCO-Banco Mundial-OECD) are promoting the coverage of education in underdeveloped countries. On the other hand, projects such as PISA that aim to carry out annual evaluations in more than 30 countries that serve as a reference of the educational level and encourages nations to improve the quality of education. Therefore, the objective of this work is to implement a methodology to predict the results of ICFES tests SABER 11 °. that can correct the educational problems that Colombia has presented in secondary education. For the development of this methodology, the ICFES repository database was used. Data were pre-processed using MATLAB software. Also, combined tests were carried out with the new Chi-Square metric developed by the researchers of the automation group of the Technological University of Pereira, obtaining results that were 20% higher compared with conventional classification techniques. In this project, the most influential characteristics were found in the students, who are responsible for the loss of the ICFES SABER 11° exam in Risaralda. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management 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.)

Electronic documents often contain personal or confidential information, which can be used as valuable evidence in criminal investigations. In the digital investigation, special techniques are required for grouping and screening electronic documents, because it is challenging to analyze relationships between numerous documents in storage devices manually. To this end, although techniques such as keyword search, similarity search, topic modeling, metadata analysis, and document clustering are continually being studied, there are still limitations for revealing the relevance of documents. Specifically, metadata used in previous research are not always values present in the documents, and clustering methods with specific keywords may be incomplete because text‐based contents (including metadata) can be easily modified or deleted by users. In this work, we propose a novel method to efficiently group Microsoft Office Word 2007+ (MS Word) files by using revision identifier (RSID). Through a thorough understanding of the RSID, examiners can predict organizations to which a specific user belongs, and further, it is likely to discover unexpected interpersonal relationships. An experiment with a public dataset (GovDocs) provides that it is possible to categorize documents more effectively by combining our proposal with previously studied methods. Furthermore, we introduce a new document tracking method to understand the editing history and movement of a file, and then demonstrate its usefulness through an experiment with documents from a real case. [ABSTRACT FROM AUTHOR]

This paper presents a kernel-based relevance analysis for video data to support social behavior recognition. Our approach, termed KRAV, is twofold: (i) A feature ranking based on centered kernel alignment (CKA) is carried out to match social semantic features with the output labels (individual and group behaviors). The employed method is an extension of the conventional CKA to mitigate the imbalance effect of unusual human behaviors. (ii) A classification stage to perform the behavior prediction. For concrete testing, the Israel Institute of Technology social behavior database is employed to assess the KRAV under a tenfold cross-validation scheme. Attained results show that the proposed approach for the individual recognition task obtains 0.5925 F 1 measure using 50 relevant features. Likewise, for the group recognition task obtains 0.8094 F 1 measure using 12 relevant features, which in both cases outperforms state-of-the-art results concerning the classification performance and number of employed features. Also, our video-based approach would assist further social behavior analysis from the set of features selected regarding the recognition of individual profiles and group behaviors. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-feature fusion based propagation scene recognition model for high-speed railway (HSR) channels and presents the channel relevance analysis of HSR scenes. Extensive field measurement data in typical HSR scenes, including rural, station, suburban and multi-link scenes, are collected with the assist of railway long-term evolution (LTE) networks. The datasets of space-time-frequency channel features, involving Ricean K-factor, root mean square delay spread, Doppler spread, and angle spread, are generated for the model training and testing as well as the relevance analysis. The proposed model merges a weighted score fusion scheme into the deep neural network (DNN) in order to adaptively determine the optimal weights for each feature stream. This weighted score fusion based DNN model is implemented and evaluated in terms of accuracy, confusion matrix, F-score, and receiver operating characteristic (ROC) curve, which exhibits better performance than other machine learning models like random forest, support vector machine (SVM), $k$ -nearest neighbor (KNN), and weighted KNN. In addition, the channel relevance of HSR scenes is analyzed from perspectives of high-dimensional distribution distance and joint correlation of multiple features. Two metrics, Wasserstein distance and correlation matrix collinearity, are used in the analysis. Statistical results are provided, which reveals the relatively strong channel relevance between the multi-link and suburban scenes. [ABSTRACT FROM AUTHOR]

Nowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries' energy demand has a growing tendency, interest for efficient maintenance in electric motors is decisive. Vibration signals from bearings are employed commonly as a non-invasive approach to support fault diagnosis and severity evaluation of rotating machinery. However, vibration-based diagnosis poses a challenge concerning the signal properties, e.g., highly dynamic and non-stationary. Here, we introduce a knowledge-based tool to analyze multiple health conditions in bearings. Our approach includes a stochastic feature selection method, termed Stochastic Feature Selection (SFS), highlighting and interpreting relevant multi-domain attributes (time, frequency, and time–frequency) related to the bearing faults discriminability. In particular, a relief-F-based ranking and a Hidden Markov Model are trained under a windowing scheme to achieve our SFS. Obtained results in a public database demonstrate that our proposal is competitive compared to state-of-the-art algorithms concerning both the number of features selected and the classification accuracy. [ABSTRACT FROM AUTHOR]

In recent years, deep learning achieves remarkable results in the field of artificial intelligence. However, the training process of deep neural networks may cause the leakage of individual privacy. Given the model and some background information of the target individual, the adversary can maliciously infer the sensitive feature of the target individual. Therefore, it is imperative to preserve the sensitive information in the training data. Differential privacy is a state-of-the-art paradigm for providing the privacy guarantee of datasets, which protects the private and sensitive information from the attack of adversaries significantly. However, the existing privacy-preserving models based on differential privacy are less than satisfactory since traditional approaches always inject the same amount of noise into parameters to preserve the sensitive information, which may impact the trade-off between the model utility and the privacy guarantee of training data. In this paper, we present a general differentially private deep neural networks learning framework based on relevance analysis, which aims to bridge the gap between private and non-private models while providing an effective privacy guarantee of sensitive information. The proposed model perturbs gradients according to the relevance between neurons in different layers and the model output. Specifically, during the process of backward propagation, more noise is added to gradients of neurons that have less relevance to the model output, and vice-versa. Experiments on five real datasets demonstrate that our mechanism not only bridges the gap between private and non-private models, but also prevents the disclosure of sensitive information effectively. [ABSTRACT FROM AUTHOR]

Feature selection plays an important role in preprocessing in pattern recognition and data mining, especially in large scale image, digital text, and biological data. Specifically, class label information is unavailable for conducting the selection of a minimal feature subset in unsupervised learning, which is full of challenging and interesting problems. In this paper, we present an unsupervised REPresentative Feature Selection (REPFS) algorithm based on information entropy and relevance analysis. The proposed method seeks to find a high-quality feature subset through feature clustering without using any learning algorithms. More importantly, the features' relevance will be computed based on an information metric of the relevance gain, which provides an information theoretical foundation for finding a minimum of the redundancy between features. Our results on nine benchmark data sets demonstrate that REPFS can significantly improve upon state-of-the-art unsupervised algorithms.

Muslim religious leaders play very important role in speeding the progress of education and knowledge in Aceh. One of the roles done by them is writing historical script. One of the manuscript, Akhbarul Karim (nobel news), created by the most famous muslim religious leader and used to be wellknown for one decade 1860-1960 was composed by Teungku Syekh in Seumatang Geudong. The study of the manuscript of Akhbarul Karim is needed in order to bring up all information about value of Islamic religion education. In general, the problems that will be solved on this research are as following. Firstly, what kind of education value is contained in manuscript Akhbarul Karim? Secondly, what is the relevance between the education value which is contained in manuscript Akhbarul Karim and 2013 curriculum material?. Technical analysis used by the writer is content analysis. Based on the analysis carried on, the writer found several Islamic education value contained in Akhbarul Karim manuscript created by Teungku Seumatang: (a) faith value (b) act of devotion value (c) moral value. The three values are extended on each sentences in that manuscript and they have relevance with faith, moral and religious service in 2013 curriculum material. [ABSTRACT FROM AUTHOR]

Featured Application: The novel approach, together with the resulting higher-order beam model, possess both efficiency and accuracy in capturing three-dimensional behaviours, which makes it an alternative to more sophisticated shell theories in dynamic analyses of thin-walled structures. This paper presents a novel approach to identify cross-section deformation modes for thin-walled structures by assembling preliminary deformation modes (PDM) considering their participation in free vibration modes. These PDM, defined over the cross-section through kinematic concepts, are integrated in the governing equations of a higher order model and then uncoupled in the form of generalized eigenvectors. The eigenvectors are deemed to inherit the attributes of structural behaviours and can serve as the basis to assemble PDM. Accordingly, a criterion was developed to handle the eigenvectors, pursuing (i) the clustering of PDM that participate in a same structural behaviour, (ii) the assignation of the corresponding weights that indicate their participation and (iii) the decomposition of an amplitude function when it is related to several structural behaviours. Moreover, a numbering system was proposed to hierarchically organize the deformation modes, which is conducive to a reduced higher order model. The main features of this approach are found in its ability to be performed in a more operational way and its nature to give deformation modes physical interpretation inherited from the dynamic behaviours. The versatility of the approach was validated through both numerical examples and comparisons with other theories. [ABSTRACT FROM AUTHOR]

In order to improve the decision-making level for public opinion responses and realize the semantic fusion of multi-level and multi-source heterogeneous public opinion information, an ontology-based public opinion information fusion method is proposed. Firstly, aiming at quick response decision-making, the situation assessment model of public opinion information fusion is studied, and the information fusion system is constructed. The multi-level evaluation model of situation recognition, situation understanding, and situation prediction is formed. Then, the multi-indicator ontology model and method for public opinion decision-making are constructed, and the public opinion data fusion model based on ontology semantics is proposed, which realizes the relevance analysis and semantic fusion of domain knowledge. Finally, a multi-level public opinion data fusion model is constructed, and the construction of the underlying emergency information knowledge base to support the above functions is deeply studied. The simulation results show that the feasibility and efficiency of the situation assessment problem are solved by this method, the time complexity and space complexity of attribute reduction and value reduction are reduced, and the matching efficiency of situation assessment rules is improved. [ABSTRACT FROM AUTHOR]

In order to understand the living conditions and quality of life of peritoneal dialysis patients as well as the status of peer support, a cross-sectional survey method was conducted. Besides, the correlation between quality of life of peritoneal dialysis patients and peer support was explored in this research. A total of 125 peritoneal dialysis patients were selected by random sampling method for questionnaire survey. The questionnaires used in this experiment included general information questionnaire, dialysis-related quality of life scale and peer support rating scale. The database was built using the SPSS19.0 software package. Statistical methods of descriptive statistical analysis, t-test comparison of two mean values, analysis of variance comparison of multiple mean values, multiple linear stepwise regression analysis and Spearman rank correlation were employed. The results showed that in the sub-dimension of quality of life of peritoneal dialysis patients, the scores of physical role function, emotional role function and sexual function were all 0. The scores of male patients in general health, social function, kidney disease and dialysis total score, symptoms, nephropathy, cognitive function, social quality and satisfaction were lower than those of female patients. In peer support, the scores of both subjective support and objective support of female patients were higher than those of male patients, while male patients had higher support utilization scores than female patients. In the correlation analysis of quality of life and peer support in peritoneal dialysis patients, the quality of life dimensions related to the total score of peer support included SF, somatic function, somatic role function, pain, general pain, mental health, emotional role function, social function, nephropathy impact, nephropathy burden and social quality. The factors influencing the quality of life of peritoneal dialysis patients were gender, age and family monthly income. The quality of life of peritoneal dialysis patients was positively correlated with peer support. [ABSTRACT FROM AUTHOR]