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The purpose of this paper was to study some effective evaluation methods for the biocide performance on sulfate-reducing bacteria (SRB) biofilm.

The most probable number method, electrochemical impedance spectroscopy (EIS) measurements, scanning electron microscopy (SEM), three-dimensional (3D) photos and epifluorescent microscopy were used in this study.

The results showed that the minimum inhibitory concentrations and minimum bactericidal concentrations of them to sessile SRB were greatly more than planktonic SRB. The EIS of the two biocides indicated that the biofilm exposed to higher concentrations of biocide were much more compact and flat, which perfectly coincided with the SEM, 3D photos and the epifluorescent microscopies.

In this paper, it, thus, appears that these methods evaluating biocide performance on the SRB biofilm were really effective by comparing the performance of bis-quaternary ammonium salt (BAQS) and tetrakis hydroxymethyl phosphonium sulfate.

Decision theory holds that the ambiguity of decision information affects the choices of decision makers, who have the emotion of “ambiguity aversion” when making fuzzy decisions. The purpose of this paper is to explore the neural mechanism how the information ambiguity of different sales promotion strategies influences consumers’ purchasing decision.

The paper uses the event-related potential (ERP) technique and experiment.

Results indicate that the information ambiguity of sales promotion strategies did influence the purchasing decision of consumers, and there were significant differences in the amplitudes of brain wave P2, N2 and P3 when consumers encountered the sales promotions of different types (discounts and gift-giving). This reflects the difference in perceived risk, decision-making conflict and decision-making attitude. It means that compared with discounts, the perceived risk and difficulty increased while the decision-making confidence plunged when consumers were faced with gift-giving promotions. This finding gives an explanation on the neural level why consumers prefer discounts, rather gift-giving sales promotions.

For the merchants to promote commodities online, it is suggested that the actual benefit from the sales promotion should be specified to reduce the ambiguity of sales promotion information. As the neuromarketing develops, merchants have obtained more effective approaches to study marketing strategies.

One of the theoretical contributions this paper made is that the authors innovatively explored the consumer’s preference to online sales promotion strategies from the perspective of fuzzy decision. Second, the authors adopted the ERP technique to study the influence of the ambiguity of sales promotion information on the consumer’s purchasing behaviors. Third, this study provides an explanation for why consumers prefer the sales promotion type of discounts according to the neural mechanism of decision making.

This paper aims to review the latest applications in terms of three-dimensional printed (3DP) metal implants in orthopedics, and, importantly, the design of 3DP metal implants through a series of cases operated at The Second Hospital of Jilin University were presented.

This paper is available to practitioners who are use 3DP implants in orthopedics. This review began with the deficiency of traditional prostheses and basic concepts of 3DP implants. Then, representative 3DP clinical cases were summarized and compared, and the experiences using customized prostheses and directions for future potential development are also shown.

The results obtained from the follow-up of clinical applications of 3DP implants show that the 3D designed and printed metal implants could exhibit good bone defect matching, quick and safe joint functional rehabilitation as well as saving time in surgery, which achieved high patient satisfaction collectively.

Single center experiences of 3DP metal implants design were shared and the detailed technical points between various regions were compared and analyzed. In conclusion, the 3DP technology is infusive and will present huge potential to reform future orthopedic practice.

The purpose of this paper is to explore suzhi requirements and expectations to double-shouldered academics as middle-level cadres (双肩挑处级干部) in a Chinese higher education institute (HEI) as an initial step to examine the interplays between suzhi requirement and expectations and organizational operational mechanism in the Chinese context.

The study adopted an exploratory single-case approach for the study. In this study, 22 participants composed of middle-level cadres, other stakeholders at the university, college/department and business unit levels were interviewed.

Suzhi requirements for the cadres followed the Communist Party of China’s (CPC) guideline, the required de, neng, qin, ji and lian (德, 能, 勤, 绩, 廉) was abstract and vague. With the parallel systems, the CPC and administrative lines, there were discrepancies between the CPC required suzhi and those expected by the stakeholders. A social phenomenon, “official rank-oriented standard” (ORS, guan ben wei, 官本位), was found significantly intertwining with the self suzhi expectation of the cadres, unveiling a more complex dynamics than most research reported for the Chinese public sector organizations (PSOs).

With the initial qualitative findings unveiling suzhi as an organizational construct, this study informs future empirical research in the indigenous suzhi phenomenon in organizational setting. The conceptualized results of our study offer new insight for future indigenous Chinese management research in all PSOs including state-owned enterprises (SOEs).

As an initial step, this study endeavored to explore suzhi as an organizational construct in a Chinese HEI. The paper contributes to the literature by unveiling the complexity of PSOs in the interplays of dual management systems and ORS coupled with dual-role suzhi requirements for the cadres.

The purpose of this paper is to analyze the reasons that plagiarism in online literature is so hard to control in China, and it will conclude with a clear solution for the future.

This paper begins its research with the statistics and analysis of plagiarism data and a review of expert interviews regarding online literature publishing. All of these data materials were collected from anti-plagiarism platforms, online literature websites, news report websites and judiciary office websites.

The paper provides empirical insights into why the plagiarism is so rampant in the publishing of online literature in China. It suggests that the current task of controlling network literature plagiarism is arguably created by the literary production platform, which leads to the problem of the validity of the “self-monitoring model.” In fact, controlling plagiarism must be emphasized by means of external monitoring, because strict supervision and various external punitive measurements for committing plagiarism can force literature-generating platforms to strengthen their own internal monitoring.

Online plagiarism occurs almost constantly, but it rarely results in court cases over copyright because of the lack of a robust copyright ecology in China. This paper considers large amounts of data and cases from self-publishing media platforms.

The paper includes implications for the development of plagiarism management in online literature publishing from the publishing Association, media and government.

This paper suggests to online literature users that plagiarism will be controlled when certain active measures against it are taken. The authors hope that this view will promote the development of original online literature.

This paper points out that China must strengthen supervision that comes from outside the online literature generate platforms to control the current rampant plagiarism that occurs on these platforms.

The prerequisite for most traditional visual simultaneous localization and mapping (V-SLAM) algorithms is that most objects in the environment should be static or in low-speed locomotion. These algorithms rely on geometric information of the environment and restrict the application scenarios with dynamic objects. Semantic segmentation can be used to extract deep features from images to identify dynamic objects in the real world. Therefore, V-SLAM fused with semantic information can reduce the influence from dynamic objects and achieve higher accuracy. This paper aims to present a new semantic stereo V-SLAM method toward outdoor dynamic environments for more accurate pose estimation.

First, the Deeplabv3+ semantic segmentation model is adopted to recognize semantic information about dynamic objects in the outdoor scenes. Second, an approach that combines prior knowledge to determine the dynamic hierarchy of moveable objects is proposed, which depends on the pixel movement between frames. Finally, a semantic stereo V-SLAM based on ORB-SLAM2 to calculate accurate trajectory in dynamic environments is presented, which selects corresponding feature points on static regions and eliminates useless feature points on dynamic regions.

The proposed method is successfully verified on the public data set KITTI and ZED2 self-collected data set in the real world. The proposed V-SLAM system can extract the semantic information and track feature points steadily in dynamic environments. Absolute pose error and relative pose error are used to evaluate the feasibility of the proposed method. Experimental results show significant improvements in root mean square error and standard deviation error on both the KITTI data set and an unmanned aerial vehicle. That indicates this method can be effectively applied to outdoor environments.

The main contribution of this study is that a new semantic stereo V-SLAM method is proposed with greater robustness and stability, which reduces the impact of moving objects in dynamic scenes.

Aiming at the problem that quadruped crawling robot is easy to collide and overturn when facing obstacles and bulges in the process of complex slope movement, this paper aims to propose an obstacle avoidance gait planning of quadruped crawling robot based on slope terrain recognition.

First, considering the problem of low uniformity of feature points in terrain recognition images under complex slopes, which leads to too long feature point extraction time, an improved ORB (Oriented FAST and Rotated BRIEF) feature point extraction method is proposed; second, when the robot avoids obstacles or climbs over bumps, aiming at the problem that the robustness of a single step cannot satisfy the above two motions at the same time, the crawling gait is planned according to the complex slope terrain, and a robot obstacle avoidance gait planning based on the artificial potential field method is proposed. Finally, the slope walking experiment is carried out in the Robot Operating System.

The proposed method provides a solution for the efficient walking of robot under slope. The experimental results show that the extraction time of the improved ORB extraction algorithm is 12.61% less than the original ORB extraction algorithm. The vibration amplitude of the robot’s centroid motion curve is significantly reduced, and the contact force is reduced by 7.76%. The time it takes for the foot contact force to stabilize has been shortened by 0.25 s. This fact is verified by simulation and test.

The method proposed in this paper uses the improved feature point recognition algorithm and obstacle avoidance gait planning to realize the efficient walking of quadruped crawling robot on the slope. The walking stability of quadruped crawling robot is tested by prototype.

The purpose of this paper is to explore the contemporary paradigm of business leadership vis-à-vis China’s reform and transitional context.

The paper employs an evidence-based approach to explore the business leadership issues influenced by economic reform and within the context of societal transition in China. A qualitative research method was adopted based on in-depth interviews with a number of middle managers from a variety of Chinese enterprises, including state-owned, domestic-private and foreign-invested enterprises. Content analysis of several rounds of interviews added depth to the data analysis.

The findings complement existing thoughts and illustrate concepts, issues, and characteristics not yet emphasized in mainstream literature. General patterns and associated characteristics of business leadership in China, as well as specific patterns associated with different forms of enterprise ownerships, are identified.

The study makes a timely and necessary contribution that enriches context-specific understandings of business leadership against the backdrop of surrounding economic, social, and cultural changes.

The study enriches understandings of commonalities and differences in leadership across the globe, facilitating working collaboratively to achieve common goals in a global community.

The study offers new insights into business leadership by linking contextual, personal, and cognitional factors together and demonstrates some unique characteristics of leadership styles in transitional economies like China.

Although digitalization in the workplace is burgeoning, tools are needed to facilitate personalized learning in informal learning settings. Existing knowledge recommendation techniques do not account for dynamic and task-oriented user preferences. The purpose of this paper is to propose a new design of a knowledge recommender system (RS) to fill this research gap and provide guidance for practitioners on how to enhance the effectiveness of workplace learning.

This study employs the design science research approach. A novel hybrid knowledge recommendation technique is proposed. An experiment was carried out in a case company to demonstrate the effectiveness of the proposed system design. Quantitative data were collected to investigate the influence of personalized knowledge service on users’ learning attitude.

The proposed personalized knowledge RS obtained satisfactory user feedback. The results also show that providing personalized knowledge service can positively influence users’ perceived usefulness of learning.

This research highlights the importance of providing digital support for workplace learners. The proposed new knowledge recommendation technique would be useful for practitioners and developers to harness information technology to facilitate workplace learning and effect organization learning strategies.

This study expands the scope of research on RS and workplace learning. This research also draws scholarly attention to the effective utilization of digital techniques, such as a RS, to support user decision making in the workplace.

With the fierce competition in the cold chain logistics market, achieving and maintaining excellent customer satisfaction is the key to an enterprise's ability to stand out. This research aims to determine the factors that affect customer satisfaction in cold chain logistics, which helps cold chain logistics enterprises identify the main aspects of the problem. Further, the suggestions are provided for cold chain logistics enterprises to improve customer satisfaction.

This research uses the text mining approach, including topic modeling and sentiment analysis, to analyze the information implicit in customer-generated reviews. First, latent Dirichlet allocation (LDA) model is used to identify the topics that customers focus on. Furthermore, to explore the sentiment polarity of different topics, bi-directional long short-term memory (Bi-LSTM), a type of deep learning model, is adopted to quantify the sentiment score. Last, regression analysis is performed to identify the significant factors that affect positive, neutral and negative sentiment.

The results show that eight topics that customer focus are determined, namely, speed, price, cold chain transportation, package, quality, error handling, service staff and logistics information. Among them, speed, price, transportation and product quality significantly affect customer positive sentiment, and error handling and service staff are significant factors affecting customer neutral and negative sentiment, respectively.

The data of the customer-generated reviews in this research are in Chinese. In the future, multi-lingual research can be conducted to obtain more comprehensive insights.

Prior studies on customer satisfaction in cold chain logistics predominantly used questionnaire method, and the disadvantage of which is that interviewees may fill out the questionnaire arbitrarily, which leads to inaccurate data. For this reason, it is more scientific to discover customer satisfaction from real behavioral data. In response, customer-generated reviews that reflect true emotions are used as the data source for this research.