Function Of Decay Time Research Articles

A hybrid user-based collaborative filtering algorithm with topic model

Currently available Collaborative Filtering(CF) algorithms often utilize user behavior data to generate recommendations. The similarity calculation between users is mostly based on the scores, without considering the explicit attributes of the users with profiles, as these are difficult to generate, or their evolution of preferences over time. This paper proposes a collaborative filtering algorithm named T-LDA (Time-decay Dirichlet Allocation), which is based on the topic model. In this method, we generate a hybrid score for similarity calculation with topic model. However, most topic models ignore the attribute of time order. In order to further improve the prediction accuracy, a time-decay function is introduced in topic model. The experimental results show that this algorithm has better performance than currently available algorithms on the MovieLens dataset, Netflix dataset and la.fm dataset.

Global formation control of nonholonomic unicycle vehicles with guaranteed rate of convergence

AbstractThis paper studies the full‐state formation control problem of multiple nonholonomic unicycle vehicles. Firstly, the formation error model is transformed into standard nonholonomic chained form, including orientation and position error subsystems. For orientation subsystem, an angular velocity control law is designed to be a cooperative term plus an exponential decay function of time, ensuring orientations reach an agreement on a common value. For position error subsystem, a time‐varying output‐like variable is carefully constructed to ensure a stable zero dynamics, that is, the consensus of these output variables guarantees position formation, overcoming the intrinsic underactuated control difficulty. Then, a linear velocity control law is derived to realize consensus of outputs. It is proved that the proposed distributed time‐varying controller is capable of steering the vehicles to globally exponentially make into desired geometric position shape with the same orientations and vanishing velocities, provided that the communication topology is directed and having a spanning tree. The calculated convergence rates are shown dependent on controller coefficients; thus, the controller is modified to additionally reach a prescribed rate of convergence by redesigning conditions of controller parameters. Finally, two numerical simulations are implemented for five unicycle vehicles, demonstrating the effectiveness of the proposed control scheme.

Research on Energy Efficiency Application of Wireless Communication Energy Saving and Emission Reduction Based on Self-organizing Network Game Technology

Energy efficiency is a very important performance index in wireless ad hoc networks. So far, there is no universally accepted definition of the energy efficiency of self-organizing networks, and most of the conclusions drawn are asymptotic or qualitative, and its practicality is very limited. Aiming at this problem, a trust model based on game theory is proposed. The model introduces a time decay function to improve the accuracy and dynamic adaptability of trust evaluation, and introduces a node transaction density function in the recommendation trust calculation to calculate the recommendation performance of the recommended node. Reliability: In order to effectively encourage nodes to actively provide high-quality services and punish bad behaviour nodes, game theory is also introduced to analyse the trust value of nodes and decide whether to forward messages according to the analysis results. The simulation results show that the algorithm can adaptively deploy network nodes according to the distribution of task requirements, and quickly achieve the optimal layout.

An adaptive trust model based on recommendation filtering algorithm for the Internet of Things systems

The Internet of Things (IoT) is growing rapidly and brings great convenience to humans. But it also causes some security issues which may have negative impacts on humans. Trust management is an effective method to solve these problems by establishing trust relationships among interconnected IoT objects. In this paper, we propose an adaptive trust model based on recommendation filtering algorithm for the IoT systems. The utilization of sliding window and time decay function when calculating direct trust can greatly accelerate the convergence rate of trust evaluation.We design a recommendation filtering algorithm to effectively filter out bad recommendations and minimize the impact of malicious objects. An adaptive weight is developed to better combine direct trust and recommendation trust into synthesis trust so as to adapt to the dynamically hostile environment. In the simulation experiments, we compare our adaptive trust model with three related models: TBSM, NRB and NTM. The experimental results indicate that our trust model converges fast and the mean absolute error is always less than 0.05 when the proportion of malicious nodes is from 10% to 70%. The comparative experiments further verify the effectiveness of our trust model in terms of accuracy, convergence rate and resistance to trust related attacks.

T‐RBAC Model Based on Two‐Dimensional Dynamic Trust Evaluation under Medical Big Data

The professionalism and complexity of medical big data and the expensiveness of acquiring medical knowledge make it difficult for policymakers to judge whether the information accessed by doctors is necessary from a professional perspective and to formulate accurate access control strategies. To solve the above problems, this paper proposes a T‐RBAC (trust‐role based access control) model based on two‐dimensional dynamic trust assessment, Using AHP and Grey theory to quantify the role attribute trust in the dimension of the doctor’s own attributes, Using Euler’s measurement method and probability statistics to quantify doctors’ behavioral trust in the dimension of historical behavior, then, the trust rule base performs hierarchical authorization based on the comprehensive trust value obtained by the weighted average. Multiattribute trust comprehensive evaluation makes the access control model have finer access granularity and higher security. At the same time, the introduction of time decay function and penalty function enhances the model’s sensitivity, dynamics, and resistance to bleaching attacks.

An Improved Dynamic Collaborative Filtering Algorithm Based on LDA

Currently, available collaborative filtering (CF) algorithms often utilize user behavior data to generate recommendations. The similarity calculation between users is mostly based on the scores, without considering the explicit attributes of the users with profiles, as these are difficult to generate, or their preferences over time evolve. This paper proposes a collaborative filtering algorithm named hybrid dynamic collaborative filtering (HDCF), which is based on the topic model. Considering that the user's evaluation of an item will change over time, we add a time-decay function to the subject model and give its variational inference model. In the collaborative filtering score, we generate a hybrid score for similarity calculation with the topic model. The experimental results show that this algorithm has better performance than currently available algorithms on the MovieLens dataset, Netflix dataset and la.fm dataset.

Link prediction based on feature representation and fusion

Link prediction is one of the core problems in social network analysis. Considering the complexity of features in social networks, we propose a link prediction method based on feature representation and fusion. Firstly, based on the sparseness and high-dimensionality of network structure, network embedding is applied to represent the network structure as low-dimensional vectors, which identifies the spatial relationships and discovers the relevance among users. Second, owing to the diversity and complexity of text semantics, the user text is converted into vectors by word embedding models. As user behaviors can reflect the dynamic change of links, a time decay function is introduced to process the text vector to quantify the impact of user text on link establishment. Meanwhile, to simplify the complexity, we choose the top-k relevant users for each user. Finally, due to the attention mechanism can improve the expression of user’s interests in text information, a link prediction method with attention-based convolutional neural network is proposed. By fusing and mining structural and text features, the purpose of synthetically predict link is finally achieved. Experimental results show that the proposed model can effectively improve the performance of link prediction.

A Time Dependent Process Zone Model for Slow Crack Growth of Polyethylene Pipe Material

Polyethylene (PE) pipe is widely used in water supply and gas transportation engineering. Its fracture behavior is very important for the safe operation of the pipeline. The objective of this work is to simulate the slow crack growth (SCG) process of PE pipe material via a time dependent crack-tip process zone model. To achieve this end, the crazing damage caused by stress concentration at crack tip of PE material is taken into account, the stress on the bulk/craze interface layer is supposed to be uniformly distributed due to the micro necking mechanism of the layer, and a linear crack growth kinetics is used. In order to describe the delay characteristics of the fracture process of PE materials, the fracture surface energy is considered as an exponential decay function of time, which is attributed to the creep damage of the craze fibrils. A simulation algorithm of the proposed process zone model is suggested using iterative method. The model is verified by comparing the simulation with the discontinuous SCG behavior reported in the literature.

An observation of the evolution of equilibrium stress on poly(lactic acid) and poly(lactic acid)/hydroxyapatite nanocomposites

Relaxation behavior provides specific information that indicates stress development with elapsed time for the determination of material characterization and constituting of material modeling. In addition, anomalous relaxation behavior of polymeric materials enables experimental analysis of some theoretical variables in constitutive equation. Equilibrium stress is one of the most prevailing variables in material modeling and it is generally considered as unmeasurable. In this study, evolution of equilibrium stress was examined with relaxation tests of two semi crystalline polymer materials, one of which was found to reach precise equilibrium stress levels. In accordance with this purpose, extensive relaxation tests were conducted on poly(lactic acid) and poly(lactic acid)/hydroxyapatite nanocomposites specimens at a wide range of temperatures from 23 ℃ and 55 ℃, and stress levels from 1 MPa to 50 MPa for poly(lactic acid) and 51 MPa for poly(lactic acid)/hydroxyapatite nanocomposites. All the specimens were subjected tensile loading–unloading and partial retraction process. The starting points of the relaxation test were chosen on unloading segment of stress–strain curves. Evolution of stress may decay, increase or decay then increase depending on the test point on unloading curves. Relaxation behavior of poly(lactic acid) and poly(lactic acid)/hydroxyapatite nanocomposites was simulated using viscoplasticity theory based on overstress for polymeric materials. Experimental results of poly(lactic acid) and poly(lactic acid)/hydroxyapatite nanocomposites were matched with numerical results of viscoplasticity theory based on overstress for polymeric materials, and viscoplasticity theory based on overstress for polymeric material model was found to have an aptitude for predicting anomalous relaxation behavior of poly(lactic acid) and poly(lactic acid)/hydroxyapatite nanocomposites. Additionally, the effect of temperature on relaxation time was investigated with using Kohlraush–Williams–Watts time-decay function. Modeling capability of Kohlrausch–Williams–Watts model was reasonable to predict short-term simple relaxation of poly(lactic acid) and poly(lactic acid)/hydroxyapatite nanocomposites. Responses of the model showed that interaction between relaxation time and environmental temperature was related to transition from glass state to rubbery state.

A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation

With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user’s behaviour, which means a user’s interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn’t consider the time effect; next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method.

Towards an optimal dose-response relationship in gene electrotransfer protocols

In search of an optimal gene electrotransfer (GET) protocol, an electroporation-based (EP) tumor treatment with great potential as a non-viral gene-delivery system, the concept of the dose-response relationship is introduced. It is shown that a reliable dose parameter is the pulse dosage and reliable response parameters are the reversibly electroporated tissue area as well as the unwanted damaged tissue area and plasmid damage due to pH. The standard stationary EP model consists in computing the reversibly electroporated tissue area in the first pulse as the region of tissue subjected to an electric field distribution higher than an electric field threshold for EP, where the electric field threshold comes from an experimental measurement and the electric field distribution from the solution of the nonlinear stationary Laplace equation for the electrostatic potential. The extended standard EP model introduced here consists in replicating for n consecutive pulses the standard EP model, via the experimental measurement in time of the successive thresholds. Because experimental data of this threshold variation is lacking, an exponential time decay function is assumed based on experimental measurements. The damage induced by pH fronts is defined as the tissue area subjected to pH abrupt changes above a basic threshold or below an acid threshold, where these changes come from numerical solutions via the electrolytic ablation (EA) model for EP-based protocols and the basic and acid thresholds from experiments. An optimal dose-response relationship in a GET protocol, for the range of pulse intensities with fixed pulse length and frequency, tested here, is predicted as the critical pulse dosage yielding maximum reversibly electroporated tissue area with minimal tissue area damage induced by pH fronts. Moreover, since damage induced by pH changes is proportional to the Coulomb dosage, damage induced by pH fronts is negligible in typical EP-based tumor protocols such as in electrochemotherapy (ECT) and irreversible electroporation (IRE) but not in GET, due to the most often longer pulses applied/used, i.e. higher dosage applied.

An Improved Real-Time Recommendation for Microblogs Based on Topic

With the rapid development of the Internet, people are confronted with information overload. Many recommendation methods are designed to solve this problem. The main contributions of recommendation methods proposed in this paper are as follows: (1) An improved collaborative filtering recommendation algorithm based on user clustering is proposed. Clustering is performed according to user similarity based on the user-item rating matrix. So the search space of recommendation algorithm is reduced. (2) Considering the factor that user’s interests may dynamically change over time, a time decay function is introduced. (3) A method of real-time recommendation based on topic for microblogs is designed to realize real-time recommendation effectively by preserving intermediate variables of user similarity. Experiments show that the proposed algorithms have been improved in terms of running time and accuracy.

A service satisfaction-based trust evaluation model for cloud manufacturing

ABSTRACT In the cloud manufacturing service model, a good trust relationship among trading entities including manufacturing service providers and consumers is the basis for service transactions. To characterize the cooperative trust effectively, the evaluation index system was built to quantize the service satisfaction by monitoring the data of historical service evaluation, and then a service satisfaction-based trust evaluation model was proposed. The direct satisfaction, the friend recommendation satisfaction and the platform satisfaction were integrated into the comprehensive trust. To improve the reliability of the proposed model, the time decay function was utilized to characterize the change of service satisfaction over time. Meanwhile, the comprehensive trust was corrected by using the service satisfaction volatility, which was taken as a mechanism of reward and punishment in the long-term transaction process. The experimental results verified that the model reflected the subjective preference of consumers and showed good accuracy and efficiency, especially in the case of big transaction data.

Harm-focused offender triage and prioritization: a Philadelphia case study

PurposeThe purpose of this paper is to describe and test a quantitative harm-focused approach to offender selection for investigation and surveillance. The approach incorporates a measure of crime harm as well as a time-decay function that adjusts the score downward for offenders who desist from crime.Design/methodology/approachAcross 10 of 21 high-crime police districts in the city of Philadelphia, the authors compare the mean harm scores of 60 prolific offenders selected by district analysts, 60 prolific offenders selected citywide by detectives assigned to the Gun Violence Reduction Task Force and the top 60 prolific offenders chosen from a harm-score generated list of known offenders in the ten high-crime districts.FindingsThe offenders on the harm-focused list have significantly greater mean harm scores than the offenders identified by the crime analysts or task force personnel. They have a significantly greater mean number of gun crime episodes in their offending history as well.Research limitations/implicationsThe harm-focused approach uses arrest data that may not accurately reflect convictions and which miss undetected criminal activity. A leader of a criminal organization who orchestrates criminal activities but does not engage directly may have a low harm score. Arrest data may also suffer from some inherent bias. The approach also requires the creation of a crime harm index. Determining the operational impact on overall crime reduction by focusing on offenders with higher harm scores will require further research.Practical implicationsClinical methods of target selection based on officer intuition, opinion and experience may have limitations in terms of effectiveness and accuracy. They also lack transparency and may incorporate bias, a critical consideration given the current crisis in police-community trust and legitimacy. The actuarial method of weighing the harm of past offending with a crime harm score may be more acceptable and defendable to the community. It also identifies offenders with a higher frequency of involvement in gun crimes. Until methodological limitations are better understood, a compromise may be to start with the harm-score method (data-driven) and supplement this initial list through intelligence and investigative information.Originality/valueThe paper expands crime harm indices to quantify offender triage lists. The authors also empirically demonstrate through a case study that the approach is more effective at identifying harmful offenders than methods that solely rely on the experience or intuition of either crime analysts or detectives.

Research on Topic Detection and Tracking for Online News Texts

With the rapid development of the Internet, the amount of data has grown exponentially. On the one hand, the accumulation of big data provides the basic support for artificial intelligence. On the other hand, in the face of such huge data information, how to extract the knowledge of interest from it has become a matter of general concern. Topic tracking can help people to explore the process of topic development from the huge and complex network texts information. By effectively organizing large-scale news documents, a method for the evolution of news topics over time is proposed in this paper to realize the tracking and evolution of topics in the news text set. First, the LDA (latent Dirichlet allocation) model is used to extract topics from news texts and the Gibbs Sampling method is used to speculate parameters. The topic mining using the K-means method is compared to highlight the advantages of using LDA for topic discovery. Second, the improved single-pass algorithm is used to track news topics. The JS (Jensen-Shannon) divergence is used to measure the topic similarity, and the time decay function is introduced to improve the similarity between topics with the similar time. Finally, the strength of the news topic and the content change of the topic in different time windows are analyzed. The experiments show that the proposed method can effectively detect and track the topic and clearly reflect the trend of topic evolution.

Accessibility across transport modes and residential developments in Nairobi

A key goal of urban transportation planning is to provide people with access to a greater number of opportunities for interaction with people and places. Measures of accessibility are gaining attention globally for use in planning, yet few studies measure accessibility in cities in low-income countries, and even fewer incorporate semi-formal bus systems, also called paratransit. Drawing on rich datasets available for Nairobi, Kenya this analysis quantifies place-based accessibility for walking, paratransit, and driving using three different measures: a mobility measure quantifying how many other locations in Nairobi can be reached in 60 min, a contour measure quantifying the number of health facilities that can be reached in 60 min, and a gravity measure quantifying the number of health facilities weighted by a time-decay function. Health facilities are used because they are an essential service that people need physical access to and as a representation of the spatial distribution of activities more broadly. The findings show that place-based accessibility is highest for driving, then paratransit, then walking, and that there are high levels of access to health facilities near the Central Business District (CBD) for all modes. Additionally, paratransit accessibility is comparatively better in the contour and gravity measures, which may mean that paratransit is efficiently providing access based on the spatial distribution of services. The contour measure results are also compared across different residential levels, which are grouped based on neighborhood characteristics and ordered by income. Counterintuitively, the wealthiest areas have very low levels of place-based accessibility for all modes, while poor areas have comparatively better walking access to health facilities. Interestingly, the medium low residential level, characterized in part by tenement apartment buildings, has significantly higher accessibility than other residential types. One way to reduce inequality in access across income groups is to increase spatial accessibility for the modes used by low- and middle-income households, for example with policies that prioritize public transport and non-motorized travel, integrate paratransit with land use development, and provide safe, efficient, and affordable options.

A coupled thermal-mechanical numerical model of underground coal gasification (UCG) including spontaneous coal combustion and its effects

Underground coal gasification (UCG) is a promising option for extracting energy from coal in unworked or hard to access areas of the subsurface. From a geotechnical perspective, UCG involves various complex phenomena resulting from the elevated temperatures induced within the rock surrounding the UCG burn. This paper presents a coupled thermal-mechanical numerical model developed to represent a UCG trial in Wieczorek, Poland. Temperature dependent mechanical properties were assigned according to results obtained from laboratory experiments and data available in the literature. The coal burning process was simulated by modifying the energy balance equation with an additional term related to the calorific value of coal as a source. This source term was described using a time decay function to reflect the fact that the energy release from coal gradually decreases with time. The mechanical degradation of coal due to burning was simulated by removing the burned zone from the calculation after a specific time, which depended on zone size and type of coal. In this study, it was found that the maximum temperature at the burning zone was always <1000 °C, which agrees with previous research carried out for other UCG trials. The size of the burning zone was predicted to spread about 15 m laterally after 20 days of burning. Ground subsidence was evaluated for single and multiple (parallel) panel simulations; subsidence at the top of the numerical mesh, corresponding to a depth of 395 m below the surface, ranged from 23 mm for a single panel to 85 mm for seven panels. The degradation of mechanical properties of the rock surrounding the burned zone due to heating was found to have a marginal effect on the ground subsidence when parallel burning was carried out. The numerical modelling results obtained from this study may provide guidance for the design and operation of UCG processes.

Velocity time correlation function of a rouse chain

In this paper, with the inclusion of inertia term in the equation of motion, the expression of the velocity time correlation of a Rouse chain, for which hydrodynamic interactions are neglected, is analytically derived. For long time scale mode of motion, the velocity time correlation function is an exponential decay function of time, whereas for short time scale mode of motion, it is a damped oscillatory function of time. This was confirmed by our numerical simulation results.

Experimental study of the moisture content influence on CH4 adsorption and deformation characteristics of cylindrical bituminous coal core

Moisture content in coal is an important factor affecting the coal seam gas extraction. It directly affects the storage and flow of gas in bituminous coal. In this paper, the cylindrical bituminous coal cores of Xutuan coal mine in Huaibei coal mine group were studied as experimental objects, using the laboratory self-designed experimental device Gas Adsorption and Strain Testing Apparatus system. The influence of the bituminous coal moisture content on gas adsorption characteristics was studied. Drying experiments of coal samples showed that they lose the original moisture content following the exponential decay function of time. At wetting, the saturated moisture content in coal samples increased following the Exponential Association function of time. The experimental results show that the average original moisture content and average saturated moisture content of raw coal samples are 1.3 and 2.4%, respectively. On this basis, the gas adsorption experiments on samples with different moisture contents under different gas pressures were carried out. With the moisture content increase, the gas adsorption capacity and saturation value decreased and the decrease rate gradually reduced. The single exponential decay function describes the gas adsorption capacity dependence on moisture content. Moisture content also affects the adsorption deformation of bituminous coal. At high moisture content, the adsorption deformation of bituminous coal is less.

Fuzzy Evaluation Mechanism of Trust Chain under Embedded Trusted Computing

This paper proposes a trust chain evaluation mechanism based on fuzzy theory under embedded trusted computing. Firstly, this method described the trusted computing, the trusted computing platform, embedded system, which showed the important position of the trust chain in dependable computing. Then, in the process of the entity evaluation of trust chain, according to the influencing factors of trust chain in the trusted computing, considering the indirect trust degree, completed the establishment of evaluation method of trusted computing model based on the fuzzy set theory. Finally, combined the fuzzy logic inference with the trust transitivity, we proposed a trust chain evaluation mechanism based on similarity, and introduced the time decay function and adaptive weight coefficient to give the trust updating model. Simulation experiment shows that the proposed method can effectively improve the reliability of trust chain evaluation in embedded trusted computing, which has good application value.