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User-oriented seamless service handoff is prosperous in the ubiquitous computing environment. This paper aims to present an open architecture for the user-oriented seamless service handoff across heterogeneous platforms. The migration of service environment is the key to implement the user-oriented service handoff. A distributed service handoff scheme based on the binary sensor network and context-aware middleware has been proposed.

To validate this scheme, a test-bed has been constructed in the lab environment.

The experimental results show that the proposed scheme can reduce the service handoff delay compared to the centralized scheme.

The experimental results show that the scheme can reduce the delay of service handoff compared to the traditional centralized scheme.

The purpose of this paper is to provide energy-efficient task scheduling and resource allocation (RA) in cloud data centers (CDC).

Task scheduling and RA is proposed in this paper for cloud environment, which schedules the user’s seasonal requests and allocates resources in an optimized manner. The proposed study does the following operations: data collection, feature extraction, feature reduction and RA. Initially, the online streaming data of seasonal requests of multiple users were gathered. After that, the features are extracted based on user requests along with the cloud server, and the extracted features are lessened using modified principal component analysis. For RA, the split data of the user request is identified and that data is pre-processed by computing closed frequent itemset along with entropy values. After that, the user requests are scheduled using the normalized K-means algorithm (NKMA) centered on the entropy values. Finally, the apt resources are allotted to that scheduled task using the Cauchy mutation-genetic algorithm (CM-GA). The investigational outcomes exhibit that the proposed study outruns other existing algorithms in respect to response time, execution time, clustering accuracy, precision and recall.

The proposed NKMA and CM-GA technique’s performance is analyzed by comparing them with the existing techniques. The NKMA performance is analyzed with KMA and Fuzzy C-means regarding P_rc (Precision), R_ca (Recall), F ms (f measure), Acr (Accuracy)and C_t (Clustering Time). The performance is compared to about 500 numbers of tasks. For all tasks, the NKMA provides the highest values for P_rc, R_ca, F_ms and A_cr, takes the lowest time (C_t) for clustering the data. Then, the CM-GA optimization for optimally allocating the resource in the cloud is contrasted with the GA and particle swarm optimization with respect to R_t (Response Time), P_t (Process Time), A_wt (Average Waiting Time), A_tat (Average Turnaround Time), L_cy (Latency) and T_p (Throughput). For all number of tasks, the proposed CM-GA gives the lowest values for R_t, P_t, A_wt, A_tat and L_cy and also provides the highest values for T_p. So, from the results, it is known that the proposed technique for seasonal requests RA works well and the method optimally allocates the resources in the cloud.

The proposed approach provides energy-efficient task scheduling and RA and it paves the way for the development of effective CDC.

This paper aims to design a new bandgap reference circuit with complementary metal–oxide–semiconductor (CMOS) technology.

Different from the conventional bandgap reference circuit with operational amplifiers, this design directly connects the two bases of the transistors with both the ends of the resistor. The transistor acts as an amplifier to amplify the change of voltage, which is convenient for the feedback regulation of low dropout regulator (LDO) regulator circuit, at last to realize the temperature control. In addition, introducing the depletion-type metal–oxide–semiconductor transistor and the transistor operating in the saturation region through the connection of the novel circuit structure makes a further improvement on the performance of the whole circuit.

This design is base on the 0.18?m process of BCD, and the new bandgap reference circuit is verified. The results show that the circuit design not only is simple and novel but also can effectively improve the performance of the circuit. Bandgap voltage reference is an important module in integrated circuits and electronic systems. To improve the stability and performance of the whole circuit, simple structure of the bandgap reference voltage source is essential for a chip.

This paper adopts a new circuit structure, which directly connects the two base voltages of the transistors with the resistor. And the transistor acts as an amplifier to amplify the change of voltage, which is convenient for the feedback regulation of LDO regulator circuit, at last to realize the temperature control.

This study aims to explore how marketers can use text mining to analyze actors, actions and performance effects of service encounters by building on the role theory. This enables hotel managers to use introduced methodology to measure and monitor frontline employees’ role behavior and optimize their service.

The authors’ approach links text mining and importance-performance analysis with role theory’s conceptual foundations taking into account the hotel industry’s specifics to assess the effect of frontline hotel employees’ actions on consumer satisfaction and to derive specific management implications for the hospitality sector.

This study identifies different actors involved in hotel frontline interactions revealing distinct role behaviors that characterize consumers’ perspectives of service encounters with different role types associated with front-office employees. This research also identifies role performance related to role behavior to improve service encounters.

Customer–employee interactions can be assessed by user-generated contents (UGC). Performance evaluations relate to frontline employee roles associated with distinct role scripts, whereby different hotel segments require tailored role designs. Insights of this study can be used for service optimization, market positioning as well as for improving human resource management practices in the hotel industry.

This study contributes to the service encounter literature by applying role theory in the text mining of UGC to assess frontline employees as actors and the effects of their actions on service quality delivery.

Water-based graphite lubricants have good lubricity in the process of metal forming, especially for hot-rolling seamless pipe. Although the use of water as a working fluid system instead of conventional mineral oil has many advantages for the fuel consumption, post cleaning and a new type of lubricant, the graphite contaminated the machine and workers for its physical properties. From the global environmental protection viewpoint, it is urgent to develop a kind of benign material.

Magnesium hydroxide which has the average particle size of 10 μm was chosen as a base material without further modification and pretreatments. On the HT-1000 high-temperature tribometer, the influence of temperature and lubricant materials on the friction coefficient was studied. The tribological performance at 900^°C provided evidence under high temperature for exploring a new lubricant material.

Tap water-based brucite lubricant will open a new chapter in the industrial lubrication, effectively avoiding many unfavorable factors caused by graphite lubrication, such as conductivity, pollution and energy loss. Meanwhile, it expanded the application of brucite as flame-retardant agent, catalyst, water treatment agent and so on.

It is a new and environmental lubricant to tap water-based brucite lubricant. And specially, the preparation process of lubricant is simple and economical.

This study aims to understand the practices and perceptions of education for sustainable development (ESD) in the university classrooms of Malaysia, exploring how holistic, pluralistic and action-oriented approaches to ESD were linked to their knowingness, attitudes and behavior regarding sustainability.

The perceptions of 2,678 students and 1,013 teachers from a range of undergraduate and postgraduate courses at four Malaysian universities were surveyed. Their views of ESD and sustainability consciousness (SC) were recorded on five-point Likert-type scales, and the resulting data was investigated using descriptive and analytical statistics.

Holistic, pluralistic and action-oriented approaches to ESD teaching and learning were widespread in Malaysian universities, with significant associations found between ESD approaches and SC. While all three approaches enhanced each aspect of SC, the results indicated the need to develop action-oriented approaches more fully.

This research underlines the importance of sustainability education in the context of higher education. It provides empirical evidence for developing action-oriented approaches to ESD and confirms the utility of holistic content and pluralistic pedagogy to the teaching and learning of sustainability.

In contrast to existing studies, this paper aims to propose that digital transformation does not depend on a single condition; rather, it depends on the interaction between internal and external factors of a firm. Therefore, the aim of this paper is to examine the effect of a combination of internal and external factors on a firm's digital transformation intention.

An empirical analysis on a sample of 112 Chinese small- and medium-sized firms was conducted by applying smart-PLS and fuzzy set qualitative comparative analysis (fsQCA).

The results of smart PLS show that external pressures (institutional and market pressures) and human capital have a positive impact on corporate digital transformation intentions. From a combination perspective, the results of the fsQCA show that there are five causal conditions that lead to high digital transformation intention. In contrast to the net effect, the results of fsQCA show that different combinations of states of internal (human capital, organizational culture and technological capital) and external elements (institutional and market pressures) of the firm are likely to stimulate digital transformation intention.

This study provides empirically based insights into firms' digital transformation intentions and advances the current understanding of the drivers and inhibitors of digital transformation. Unlike most current research, which tends to focus on the net effect of factors influencing the digital transformation of enterprises, this study focuses on identifying the core elements influencing enterprises' digital transformation intention, especially the joint effect of different factors, both internal and external to the enterprise. The combined SEM and fsQCA findings of this paper not only enrich the existing theories on digital transformation but also have high value in guiding the digital transformation of firms.

Understandings of house prices and their interrelationships have undoubtedly drawn a great amount of attention from various market participants. This study aims to investigate the monthly newly-built residential house price indices of seventy Chinese cities during a 10-year period spanning January 2011–December 2020 for understandings of issues related to their interdependence and synchronizations.

Analysis here is facilitated through network analysis together with topological and hierarchical characterizations of price comovements.

This study determines eight sectoral groups of cities whose house price indices are directly connected and the price synchronization within each group is higher than that at the national level, although each shows rather idiosyncratic patterns. Degrees of house price comovements are generally lower starting from 2018 at the national level and for the eight sectoral groups. Similarly, this study finds that the synchronization intensity associated with the house price index of each city generally switches to a lower level starting from early 2019.

Results here should be of use to policy design and analysis aiming at housing market evaluations and monitoring.

Previous researches on credit scoring mainly focussed on static modeling on panel sample data set in a certain period of time, and did not pay enough attention on dynamic incremental modeling. The purpose of this paper is to address the integration of branch and bound algorithm with incremental support vector machine (SVM) ensemble to make dynamic modeling of credit scoring.

This new model hybridizes support vectors of old data with incremental financial data of corporate in the process of dynamic ensemble modeling based on bagged SVM. In the incremental stage, multiple base SVM models are dynamically adjusted according to bagged new updated information for credit scoring. These updated base models are further combined to generate a dynamic credit scoring. In the empirical experiment, the new method was compared with the traditional model of non-incremental SVM ensemble for credit scoring.

The results show that the new model is able to continuously and dynamically adjust credit scoring according to corporate incremental information, which helps produce better evaluation ability than the traditional model.

This research pioneered on dynamic modeling for credit scoring with incremental SVM ensemble. As time pasts, new incremental samples will be combined with support vectors of old samples to construct SVM ensemble credit scoring model. The incremental model will continuously adjust itself to keep good evaluation performance.

The tactile sensor with array structure normally has the defects of existing nondetection zone, complex and nonstretchable structure. It is difficult to seamlessly attach to the surface of the robot. For this reason, this paper proposes a method to prepare nonarray structure tactile sensor directly on the surface of the robot by spraying process.

Based on the principle of gradient potential distribution, the potential fields are constructed in two different directions over the conductive film in time-sharing. The potentials at touching position in the two directions are detected to determine the coordinate of the touching point. The designed tactile sensor based on this principle consists of only three layers. Its bottom layer is designed as a weak conductive film made of graphite coating and used to construct the potential field. It can be sprayed either on PET substrate or directly on robot surface.

The radial basis function neural network is used for remodeling the potential distribution, which can effectively solve the problem of nonlinear potential distribution caused by irregular sensor shape, and uneven conductivity at different points of the spraying coating. The simulation and experimental results show that the principle of the proposed tactile sensor used for touching position detection is feasible to be applied to complex surfaces of the robot.

This paper proposed a nonarray customizable tactile sensor based on the spraying process. The sensor has a simple structure, and only five lead wires are needed to realize the coordinate detection of the touch position.