Accurate indoor positioning is crucial for many location-based services, but GPS accuracy is significantly reduced due to issues such as signal penetration and accuracy in indoor scenarios. In ...contrast, indoor Wi-Fi positioning is emerging as a promising alternative in the field. This study proposes a model that combines the k-nearest neighbor algorithm with the dynamic time regularization distance metric for indoor Wi-Fi positioning, and investigates methods for optimizing this model. The traditional K-nearest neighbor algorithm usually uses Euclidean distance for distance calculation, which has the disadvantage of being affected by the length of the signal sequence, resulting in inaccurate calculation of the distance between adjacent points with different time intervals. The dynamic time regularization is more suitable for signals of different lengths like Wi-Fi, which can bend the time axis to make the alignment of two Wi-Fi sequences more accurate. Using DTW as the distance measure of KNN is DTW-KNN. In addition, to enhance the model's ability to handle large-scale data sets, We use Gaussian sum matrices instead of the distance matrix of the traditional dynamic time regularization algorithm. Once again, the standard deviation sigma of the Gaussian distribution and the distance hyperparameters of the K-nearest neighbors are optimally chosen for the most suitable values of Wi-Fi signals. Finally, a fast recognition model based on intermittent downsampling and an accurate recognition model with complete sampling are designed to cope with the focus on real-time and accuracy in different scenarios. These two models can achieve 95.3% and 98% accuracy, respectively, on the public dataset (Wireless Indoor Localization) of indoor Wi-Fi localization.
Electric power enterprises are developing rapidly in the era of big data information digitization. At this stage, the total number of substations is gradually increasing, the structure of the power ...engineering system is slowly becoming complicated, and the video monitoring system instantly collects a lot and contains a lot of noisy data information, which affects the power supply system’s access to effective data information and fault detection. To prevent the above phenomenon. This paper selects a decision tree algorithm to obtain and analyze meaningful operation-confirming information from a large amount of data information, and then can quickly and confirm the diagnosis of common fault machines and equipment in substations, reduce the running time of common fault machines, and improve the safety and reliability of primary equipment in substations with automation technology. The paper describes the basic concept of big data mining common algorithm and its data mining algorithm in the automation technology substation primary equipment fault detection, selected the typical alarm signal to start the analysis, and categorization and collocation solution. A decision tree algorithm entity model is built, several classical decision tree algorithms are described, and their data analysis is carried out for each attribute, and then the decision tree algorithm is improved. According to build the decision tree algorithm according to improve the decision tree algorithm under the fuzzy set base theory, mainly by expertise in the four on cannot identify the association, rough set and up close and down close, similar and membership relationship, expertise concise to optimize the calculation method. And the common ID3, C4.5, and CRAT algorithm of each property is compared and analyzed, and the results show that: compared with C4.5 and ID3, the boosted optimization algorithm has higher classification accuracy and can model rate more quickly. The research in this paper can quickly diagnose the automation technology substation primary equipment and fault phenomena, and its establishment of the whole process is easy, the scope of application is relatively high, and it has wide applicability.
The gut microbiota represents a huge community of microorganisms that play essential roles in immune modulation and homeostasis maintenance. Microbiota transplantation is an important approach to ...prevent and treat disease as it can inhibit pathogen colonization and positively modulate bacterial composition. However, the development of oral bacterial therapeutics has been restricted by low bioavailability and limited retention in the gastrointestinal tract. Here, we report a simple yet highly efficient method to coat gut microbes via biointerfacial supramolecular self-assembly. Coating can be performed within 15 min by simply vortexing with biocompatible lipids. Bacteria coated with an extra self-assembled lipid membrane exhibit significantly improved survival against environmental assaults and almost unchanged viability and bioactivity. We demonstrate their enhanced efficacies in oral delivery and treatment using two murine models of colitis. We suggest that biointerfacial supramolecular self-assembly may provide a unique platform to generate advanced bacterial therapeutics for the treatment of various diseases.
Polymeric g‐C3N4 is a promising visible‐light‐responsive photocatalyst; however, the fast recombination of charge carriers and moderate oxidation ability remarkably restrict its photocatalytic ...oxidation efficiency towards organic pollutants. To overcome these drawbacks, a self‐modification strategy of one‐step formaldehyde‐assisted thermal polycondensation of molten urea to prepare carbon‐deficient and oxygen‐doped g‐C3N4 (VC‐OCN) is developed, and the carbon vacancy concentration is well‐controlled by changing formaldehyde dosage. The VC‐OCN catalysts exhibit interesting carbon vacancy concentration‐dependent photocatalytic removal efficiency to p‐nitrophenol (PNP) and atrazine (ATN), in which VC‐OCN15 with appropriate carbon vacancy concentration displays significantly higher pollutant removal efficiency than bulk g‐C3N4. The apparent first‐order rate constant of VC‐OCN15 for PNP and ATN removal is 4.4 and 5.2 times higher than that of bulk g‐C3N4. A combination of the experimental results and theoretic calculations confirm that the synergetic effect of carbon vacancies and oxygen doping sites can not only delay the recombination of charge carriers but also facilitate adsorption of oxygen molecules on the carbon vacancies, which leads to the generation of plentiful active oxygen species including not only superoxide anion radicals but also indirectly formed hydroxyl radicals and singlet oxygen. These active oxygen species play a dominant role in the removal of target pollutants.
A strategy of one‐step formaldehyde‐assisted thermal polycondensation of molten urea to prepare carbon‐deficient and oxygen‐doped g‐C3N4 (VC‐OCN) is developed, in which carbon vacancy concentration is controllable. At a suitable carbon vacancy concentration, the VC‐OCN exhibits a significantly higher photocatalytic oxidation capacity to organic pollutants than bulk g‐C3N4, attributed to the synergetic effect of carbon vacancies and oxygen doping sites.
This article evaluates learners’ thinking in the complex environment of teaching level and cognitive construct process and examines learners within the framework of cognitive factors, as well as the ...degree of consistency in the training process, in the social practice as the teaching of teachers and students to provide timely and dynamic feedback, first of all to “evidence centered” education evaluation of design patterns and cognitive framework theory as the theoretical basis. An evaluation model based on learners’ cognitive network analysis is designed and constructed by integrating cognitive visualization analysis techniques such as network analysis. Secondly, at the beginning of action research, the teaching framework structure sequence is established under the guidance of the implementation model of flipped classroom, and the investigation results of the current situation are designed under the guidance of operational steps and organizational strategies, and categories and autonomous learning theories are divided, so as to preliminarily construct strategies to improve the ability of autonomous learning. Then through three rounds of iterative action research to improve the flip classroom teaching middle school students’ autonomous learning ability of teaching strategy, the interview method is used; the questionnaire and autonomous learning process to improve the students’ autonomous learning ability training effect evaluation questionnaire is analyzed, and finally a complete set of reverse ascending of classroom teaching is formed to improve students’ autonomous learning ability of effective classroom strategies.
Bacteria have been extensively utilized for bioimaging, diagnosis and therapy given their unique characteristics including genetic manipulation, rapid proliferation and disease site targeting ...specificity. However, clinical translation of bacteria for these applications has been largely restricted by their unavoidable side effects and low treatment efficacies. Engineered bacteria for biomedical applications ideally need to generate only a low inflammatory response, show slow elimination by macrophages, low accumulation in normal organs, and almost unchanged inherent bioactivities. Here we describe a set of stealth bacteria, cell membrane coated bacteria (CMCB), meeting these requirement. Our findings are supported by evaluation in multiple mice models and ultimately demonstrate the potential of CMCB to serve as efficient tumor imaging agents. Stealth bacteria wrapped up with cell membranes have the potential for a myriad of bacterial-mediated biomedical applications.
•A logistics network model under the environment of low-carbon is proposed.•Solution processes with PSO and GA are constructed.•The validity and reliability of the model are verified by a calculating ...case study.•Provides a reference for the design of forward and reverse logistics network.
In view of the rapid development of low-carbon economy, the increasing distribution demand and returned demand which caused by the short shelf life and spoilage of fresh food, network and route planning model of a two-stage forward/reverse logistics is firstly proposed for fresh food e-commerce enterprises under the environment of low-carbon emissions (The objective in the first stage is to minimize the overall cost of the system, and the minimum overall cost of the circulation-type distribution vehicles routing is considered in the second stage). And the validity of the model is verified by adopting genetic algorithm (GA) and particle swarm optimization (PSO) algorithm with the study of the fresh food e-commerce enterprises of Shanghai. Furthermore, a good reference can be provided to build the forward and reverse logistics network and optimal route planning model of fresh food e-commerce enterprises and reduce the carbon emissions during its operation process.
During high wind events with dry weather conditions, electric power systems can be the cause of catastrophic wildfires. In particular, conductor-vegetation contact has been recognized as the major ...ignition cause of utility-related wildfires. There is a urgent need for accurate wildfire risk analysis in support of operational decision making, such as vegetation management or preventive power shutoffs. This work studies the ignition mechanism caused by transmission conductor swaying out to nearby vegetation and resulting in flashover. Specifically, the studied limit state is defined as the conductor encroaching into prescribed minimum vegetation clearance. The stochastic characteristics of the dynamic displacement response of a multi-span transmission line are derived through efficient spectral analysis in the frequency domain. The encroachment probability at a specified location is estimated by solving a classical first-excursion problem. These problems are often addressed using static-equivalent models. However, the results show that the contribution of random wind buffeting to the conductor dynamic displacement is appreciable under turbulent strong winds. Neglecting this random and dynamic component can lead to an erroneous estimation of the risk of ignition. The forecast duration of the strong wind event is an important parameter to determine the risk of ignition. In addition, the encroachment probability is found highly sensitive to vegetation clearance and wind intensity, which highlights the need of high resolution data for these quantities. The proposed methodology offers a potential avenue for accurate and efficient ignition probability prediction, which is an important step in wildfire risk analysis.
Civil aviation transport is an important source of global respiratory disease spread due to the closely-spaced environment. In order to reduce the probability of infection of passengers, an improved ...Wells-Riley model for cabin passenger risk assessment have been given in this work, the cabin ventilation and passenger nose and mouth orientation were considered. The model's effectiveness has been verified with published data. Finally, how the load factor and use of an empty seat scheme are associated with the number of infected people was assessed. The results demonstrated that the number of infected people positively correlates with the passenger load factor, and the most suitable load factor can be determined by controlling the final number of infected people with the condition of the epidemic situation in the departure city. Additionally, infection risk was found to be lower among passengers in window seats than in those in aisle seats and middle seats, and keeping empty seats in the middle or aisle could reduce the cabin average probability of infection by up to 37.47%. Using the model developed here, airlines can determine the optimal load factor threshold and seating arrangement strategy to improve economic benefits and reduce the probability of passenger infection.
Clouds constitute a key component of weather and climate systems, whereas the uniform retrieval of cloud properties, such as cloud top height (CTH) and cloud optical thickness (COT), requires ...accuracy and computational efficiency improvements. In this study, an image-based deep neural network (DNN) model for cloud identification and simultaneous retrieval of CTH and ice-COT is developed for Himawari-8 satellite infrared measurements. The DNN model is trained with brightness temperature data from four months in 2016 as the input, and cloud properties of an active remote sensing product from CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) as the target truth. Supplementary variables, including the vertical temperature profile, the surface elevation, and the geometrical parameters, are added as the input data. DNN model performance is first tested with an independent dataset, and then cases over a CloudSat track and a Himawari-8 granule (85°E–205°E, 60°S–60°N) are selected for further validation of the model by comparing its results with those from two physics-based models. For both the water- and ice-CTH estimates, the DNN model shows high consistency with the target values, with an overall CTH correlation coefficient of 0.90 for high ice clouds with COT ≥0.3. Notably, as an infrared method in nature, the DNN extends the predictable ice-COT to ~200, with relative biases of ~20% for high ice clouds with COT >1. The strong accuracy of the DNN model is primarily derived from its ability to learn from the spatial features imprinted on the input brightness temperature image, and its integration of information from neighboring pixels in a three-dimensional space. A single full disk estimation with the DNN model takes about 20 min using one processor; therefore, near-real-time cloud property retrieval that is uniformly available over 24 h can be obtained for severe weather monitoring and mesoscale cloud-system studies.
•A deep neural network is developed for cloud property retrieval.•The retrieval accuracy is significantly increased by using the spatial features.•The retrieval has high consistency with target truth from active remote sensing.•Thicker cloud can be yielded when compared to the physics-based models.
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