Search results

After receiving advertising messages, most consumers rarely purchase the advertised products at once, which results in a delay between advertising exposure and its effect. This paper is devoted to exploring the advertising decision and coordination issues for a supply chain system subject to advertising immediate and delayed effects.

By applying the game theory, the differential game models with delay are constructed for the supply chain to examine the equilibrium advertising efforts, brand goodwill and the optimal profits under the different cooperation situations. A class of transfer payment contracts is designed to achieve the best outcome of the supply chain. Illustrative examples are given to demonstrate the effectiveness of addressed results and provide some managerial perspectives.

It can be found that the complete cooperation situation can stimulate the advertising investment, drive the product demand and improve the economic profit. Also, a class of transfer payment contracts is designed in this paper, such that the supply chain can perfectly realize the profit maximization, and each member can achieve the Pareto improvement.

This work does not address the random market environment, which can be filled in the future. Furthermore, this paper has been done in a single supply chain structure. It is an interesting future line of research when taking competitive behavior (e.g. competition among manufacturers, retailers or supply chains) into account.

This study will help managers make advertising strategies, advise an optimal cooperation way and design the coordination contracts to ensure the economic development of the supply chain. These obtained conclusions may provide a valuable decision-support for marketing management.

For a supply chain, the most previous literature about dynamic advertising models focused on a single advertising effect-immediate effect. This work explores advertising strategy with double advertising effects and investigates the coordinating power of new transfer payment contracts.

This study aims to introduce a multi-configuration, three-finger dexterous hand with integrated high-dimensional sensors and provides an analysis of its design, modeling and kinematics.

A mechanical design scheme of the three-finger dexterous hand with a reconfigurable palm is proposed based on the existing research on dexterous hands. The reconfigurable palm design enables the dexterous hand to achieve four grasping modes to adapt to multiple grasping tasks. To further enhance perception, two six-axis force and torque sensors are integrated into each finger. The forward and inverse kinematics equations of the dexterous hand are derived using the D-H method for kinematics modeling, thus providing a theoretical model for index analysis. The performance is evaluated using three widely applied indicators: workspace, interactivity of fingers and manipulability.

The results of kinematics analysis show that the proposed hand has excellent dexterity. Additionally, three different experiments are conducted based on the proposed hand. The performance of the dexterous hand is also verified by fingertip force, motion accuracy test, grasping and in-hand manipulation experiments based on Feix taxonomy. The results show that the dexterous hand has good grasping ability, reproducing 82% of the natural movement of the human hand in daily grasping activities and achieving in-hand manipulations such as translation and rotation.

A novel three-finger dexterous hand with multi-configuration and integrated high-dimensional sensors is proposed. It performs better than the previously designed dexterous hand in actual experiments and kinematic performance analysis.

Online learning is well-known by its flexibility of learning anytime and anywhere. However, how behavioral patterns tied to learning anytime and anywhere influence learning outcomes are still unknown.

This study proposed concepts of time and location entropy to depict students’ spatial-temporal patterns. A total of 5,221 students with 1,797,677 logs, including 485 on-the-job students and 4,736 full-time students, were analyzed to depict their spatial-temporal learning patterns, including the relationships between identified patterns and students’ learning performance.

Analysis results indicate on-the-job students took more advantage of anytime, anywhere than full-time students. Students with a higher tendency for learning anytime and a lower level of learning anywhere were more likely to have better outcomes. Gender did not show consistent findings on students’ spatial-temporal patterns, but partial findings could be supported by evidence in neural science or by cultural and geographical differences.

A more accurate approach for categorizing position and location might be considered. Some findings need more studies for further validation. Finally, future research can consider connections between other well-known performance predictors (such as financial situation, motivation, personality and major) and the type of learning patterns.

The findings gained from this study can help improve the understandings of students’ learning behavioral patterns and design as well as implement better online education programs.

This study proposed concepts of time and location entropy to identify successful spatial-temporal patterns of on-the-job and full-time students.

For studies in educational data mining or learning Analytics, the prediction of student’s performance or early warning is one of the most popular research topics. However, research gaps indicate a paucity of research using machine learning and deep learning (DL) models in predictive analytics that include both behaviors and text analysis.

This study combined behavioral data and discussion board content to construct early warning models with machine learning and DL algorithms. In total, 680 course sections, 12,869 students and 14,951,368 logs were collected from a K-12 virtual school in the USA. Three rounds of experiments were conducted to demonstrate the effectiveness of the proposed approach.

The DL model performed better than machine learning models and was able to capture 51% of at-risk students in the eighth week with 86.8% overall accuracy. The combination of behavioral and textual data further improved the model’s performance in both recall and accuracy rates. The total word count is a more general indicator than the textual content feature. Successful students showed more words in analytic, and at-risk students showed more words in authentic when text was imported into a linguistic function word analysis tool. The balanced threshold was 0.315, which can capture up to 59% of at-risk students.

The results of this exploratory study indicate that the use of student behaviors and text in a DL approach may improve the predictive power of identifying at-risk learners early enough in the learning process to allow for interventions that can change the course of their trajectory.

A new cutting tool is always well-defined and sharp at the onset of the metal cutting process and gradually losses these properties as the machining process advances. Similarly, at the beginning of the machining process, amplitude of tool vibrations is considerably low and it increases gradually and peaks at the end of the service period of the cutting tool while machining. It is significant to provide a corresponding real-time varying damping to control this chatter, which directly influences accuracy and quality of productivity. This paper aims to review the literature related to the application of smart fluid to control vibration in metal cutting and also focused on the challenges involved in the implementation of active control system during machining process.

Smart dampers, which are used as semi-active and active dampers in metal cutting, were reviewed and the research studies carried out in the field of the magnetorheological (MR) damper were concentrated. In smart materials, MR fluids possess some disadvantages because of their sedimentation of iron particles, leakage and slow response time. To overcome these drawbacks, new MR materials such as MR foam, MR elastomers, MR gels and MR plastomers have been recommended and suggested. This review intents to throw light into available literature which exclusively deals with controlling chatter in metal cutting with the help of MR damping methods.

Using an MR damper popularly known for its semi-active damping characteristics is very adaptable and flexible in controlling chatter by providing damping to real-time amplitudes of tool vibration. In the past, many researchers have attempted to implement MR damper in metal cutting to control vibration and were successful. Various methods with the help of MR fluid are illustrated.

A new cutting tool is always well-defined and sharp at the onset of metal cutting process and gradually losses these properties as the machining process advances. Similarly, at the beginning of the machining process, amplitude of tool vibrations is considerably low and it increases gradually and peaks at the end of service period of cutting tool while machining. Application of MR damper along with the working methodology in metal cutting is presented, challenges met are analyzed and a scope for development is reviewed.

This study provides corresponding real-time varying damping to control tool vibration which directly influences accuracy and quality of productivity. Using an MR damper popularly known for its semi-active damping characteristics is very adaptable and flexible in controlling chatter by providing damping to real-time amplitudes of tool vibration.

This study attempts to implement smart damper in metal cutting to control vibrations.

It is significant to provide corresponding real-time varying damping to control tool vibration which directly influences accuracy and quality of productivity.

The purpose of this study is to, first, provide an overview of the previously conducted works related to thermal analysis of space equipment, including battery packages, especially lithium (Li)-ion ones. Second, the need for a reduced thermal mathematical model (RTMM) and a procedure devising it is defined. Finally, an experimental steady-state temperature distribution test is conducted to finalize the RTMM study.

This study was carried out as part of a development project for thermal analysis of Li-ion battery packages used in a space equipment. The study presents certain stages of the design of the battery pack in parallel with battery technology development. Following a literature review, a numerical thermal analysis is conducted; then interface thermal conductance values are found out by means of the first law of thermodynamics; and the study is completed with the help of an experimental test.

The study provides key aspects for a successful battery-package thermal design for a space equipment. Additionally, the study summarizes the experimental results used in the RTMM process and the computed thermal conductance values between node couples.

Thermal analysis is important and vital in space equipment considering their harsh working conditions and environments. Hence, the study provides a RTMM for the thermal analysis of Li-ion battery packages, instead of a full finite element model, to save computational time and CPU usage. The findings are supported by experimental results. Hence, presented details can be used as guidelines for enterprises having a goal of battery package technology achievement, including design and manufacturing.

After providing a literature review of studies conducted on satellite subsystems including Li-ion batteries, this study presents a clear, complete and verified process of a RTMM for a Li-ion battery package in aero/space structures design. It presents details of building up a model and calculation methodology through an iterative procedure in which an optimization algorithm known as particle swarm optimization (PSO) was benefitted. In the RTMM, additionally, experimental temperature distributions obtained through thermal vacuum test were presented. It has been shown that the model can be used reliably in designing space equipments.

Despite the prevalent use of professional status and service feedback in online healthcare markets, the potential interaction relationship between two types of information is still unknown. This study used the signaling theory to examine the substitute relationship between professional status and service feedback in patients' doctor choice, as well as the moderating effect of illness severity.

To test the paper's hypotheses, we constructed a panel data model using 418 doctors' data collected over a period of six months from an online healthcare market in China. Then, according to the results of the Hausman test, we estimated a fixed-effects model of patients' choice in online healthcare markets.

The empirical results showed that the effect of a doctor's professional status and service feedback on a patient's doctor choice was substitutable. Moreover, patients' illness severity played a moderating role, in that the influence of professional status on a patient with high-severity illness was higher than that on a patient with low-severity illness, whereas the influence of service feedback on a patient with low-severity illness was higher than that of a patient with high-severity illness. In addition, we found that illness severity negatively moderated the substitute relationship between professional status and service feedback on a patient's choice.

These findings not only contribute to signaling theory and research on online healthcare markets, but also help us understand the importance of professional status and service feedback on a patient's choice when seeking a doctor online.

This study aims to analyze the tradeoff between two potential marketing strategies for service providers, namely, market segmentation on the basis of reservation lead time and cooperation with third parties.

This paper proposes an optimization model to describe market segmentation strategy after cooperating with third parties by taking hotels for example.

The results show that the profitability of adopting two strategies simultaneously is lower than that with market segmentation alone under some cases, which is relevant with attributes of travel agencies, such as switch rate and market share.

This study indicates that cooperation with third parties has a negative impact on profitability of hotels using market segmentation in some cases. However, randomness of demand, customer loyalty and existence of cancellation should be considered in further research.

In an e-commerce era, hotels with market segmentation based on reservation lead time, are not required to cooperate with third parties under a number of situations (e.g. high switch rates and small market sizes of travel agencies). In addition, hotels should revise the segmentation strategy based on the change rate of potential demand of individual customers. Furthermore, hotels should enhance customer loyalty, strengthen cooperation with travel agencies that possess large market shares or small switch rates.

The study preliminarily formulates the optimal market segmentation strategy on the basis of reservation lead time after cooperating with third parties, which contributes to the revenue management.