基于SIVA理论的科普自媒体营销模式探析——以“混知”自媒体为例

数字技术的纵深发展和公众科学意识的不断觉醒为科普类自媒体平台提供了有利的发展条件。文章以SIVA理论为基础分析“混知”自媒体在发展过程中采取的营销策略,指出科普平台须从用户角度出发提供解决策略、信息、价值、渠道四方面的措施,旨在为科普自媒体坚守科学内容普及的初心、优化营销模式提供建议。

Design of Multi-attribute Knowledge Base Based on Hybrid Knowledge Representation

Based on the knowledge representation and knowledge reasoning, this paper addresses the creation of the multi-attribute knowledge base on the basis of hybrid knowledge representation, with the help of object-oriented programming language and relational database. Compared with general knowledge base, multi-attribute knowledge base can enhance the ability of knowledge processing and application; integrate the heterogeneous knowledge, such as model, symbol, case-based sample knowledge; and support the whole decision process by integrated reasoning.

Confounding factors affect the pathophysiology of eosinophilic esophagitis

Eosinophilic esophagitis is a newly diagnosed esophageal disease in adult and children. The clinical and pathological characteristics of this disease have been established and were recently summarized in the expert clinical guideline published in 2011. In spite of the wide knowledge accumulated on this disease, there are many areas where scientific data are missing, especially in regard to the disease's pathophysiology. Recent publications have suggested that other confounding factors modify the disease and may affect its clinicalphenotypic presentation. Those factors may include place of living, air pollution, race, genetic factors and other. In the present report we discussed and review those confounding factors, the new developments, and what direction we should go to further advance our knowledge of this disease.

A Knowledge-reuse Based Intelligent Reasoning Model for Worsted Process Optimization

The textile process planning is a knowledge reuse process in nature, which depends on the expert’s knowledge and experience. It seems to be very difficult to build up an integral mathematical model to optimize hundreds of the processing parameters. In fact, the existing process cases which were recorded to ensure the ability to trace production steps can also be used to optimize the process itself. This paper presents a novel knowledge-reuse based hybrid intelligent reasoning model (HIRM) for worsted process optimization. The model architecture and reasoning mechanism are respectively described. An applied case with HIRM is given to demonstrate that the best process decision can be made, and important processing parameters such as for raw material optimized.

Research of IDSS Architecture Based on Hybrid Systems

This paper discusses the necessity of building IDSS on hybrid systems, and adopts XML technology to manage isomeric knowledge in hybrid systems. The paper proposes a new architecture of hybrid systems based IDSS whose core system is isomeric knowledge system. The architecture is composed of knowledge component, problems processing system, data component and intelligent user interface. This new architecture aims to enhance the capability of integrating hybrid systems, to improve the supporting effectiveness of decision-making and the intelligent level of IDSS, and tries a new way to elevate the system’s ability of handling and learning knowledge.

Constraints in Using Manufactured Sands in Concrete Pavements in Australia

Due to the shortages of natural sands along the east coast of Australia in particular and the need to fully utilise fines produced in quarry operations, progress has been made in utilising blends of manufactured sands and natural sands in concrete pavements. This paper documents some of the constraints in utilising larger proportions of manufactured sands in concrete pavements. These constraints are mainly caused by the current level of knowledge regarding the impact of manufactured sands on skid and abrasion resistance of concrete pavements. This paper presents a brief review of literature on this subject in the USA, France and UK. It also briefly documents work recently carried out in Australia by CCAA （Cement Concrete and Aggregates Australia）, referring to the skid and abrasion resistance of concrete pavements using manufactured sands. The paper concludes that there is no relationship between the free silica content and the skid resistance. With regard to the abrasion resistance, it is rather the curing conditions and the compressive strength that are more important in achieving good results.

A relay selection scheme based on geometric optimum in cooperative cognitive systems over mixed channels

This paper proposes a relay selection scheme based on geometric optimum principle to maximize the cognitive link＇ s connectivity with limited interference to primary user in cooperative cognitive systems. A dual-hop cognitive relay system is considered, in which the channel impulse response follows independent non-identical distribution （i. n. d. ） each hop, such as Rician, Rayleigh and Nakagami-m distribution. Then, closed-form expressions in terms of outage probability and average bit error rate （ABER） are obtained using amplify-and-forward （AF） relaying protocol over such mixed fading channels. Furthermore, the best range of the relay is derived. Extensive simulation re- suits are conducted to verify the theoretical analysis, which is useful to the network optimal design.

Mapping Soil Organic Carbon Stocks of Northeastern China Using Expert Knowledge and GIS-based Methods

The main aim of this paper was to calculate soil organic carbon stock(SOCS) with consideration of the pedogenetic horizons using expert knowledge and GIS-based methods in northeastern China.A novel prediction process was presented and was referred to as model-then-calculate with respect to the variable thicknesses of soil horizons(MCV).The model-then-calculate with fixed-thickness(MCF),soil profile statistics(SPS),pedological professional knowledge-based(PKB) and vegetation type-based(Veg) methods were carried out for comparison.With respect to the similar pedological information,nine common layers from topsoil to bedrock were grouped in the MCV.Validation results suggested that the MCV method generated better performance than the other methods considered.For the comparison of polygon based approaches,the Veg method generated better accuracy than both SPS and PKB,as limited soil data were incorporated.Additional prediction of the pedogenetic horizons within MCV benefitted the regional SOCS estimation and provided information for future soil classification and understanding of soil functions.The intermediate product,that is,horizon thickness maps were fluctuant enough and reflected many details in space.The linear mixed model indicated that mean annual air temperature(MAAT) was the most important predictor for the SOCS simulation.The minimal residual of the linear mixed models was achieved in the vegetation type-based model,whereas the maximal residual was fitted in the soil type-based model.About 95% of SOCS could be found in Argosols,Cambosols and Isohumosols.The largest SOCS was found in the croplands with vegetation of Triticum aestivum L.,Sorghum bicolor(L.) Moench,Glycine max(L.) Merr.,Zea mays L.and Setaria italica(L.) P.Beauv.

Approximate Gaussian conjugacy： parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov-Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture （GM） prior to a Gaussian/GM posterior （termed ＇Gaussian conjugacy＇ in this paper）, form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality （including target maneuver）, intractable uncertainties （such as unknown inputs and/or non-Gaussian noises） and constraints （including circular quantities）, etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity.

Hybrid-augmented intelligence： collaboration and cognition

The long-term goal of artificial intelligence （AI） is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. Hybrid-augmented intelligence can be divided into two basic models： one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.