Shekhar Rathor, Weidong Xia and Dinesh Batra
Agile principles have been widely used in software development team practice since the creation of the Agile Manifesto. Studies have examined variables related to agile principles…
Abstract
Purpose
Agile principles have been widely used in software development team practice since the creation of the Agile Manifesto. Studies have examined variables related to agile principles without systematically considering the relationships among key team, agile methodology, and process variables underlying the agile principles and how these variables jointly influence the achievement of software development agility. In this study, the authors tested a team/methodology–process–agility model that links team variables (team autonomy and team competence) and methodological variable (iterative development) to process variables (communication and collaborative decision-making), which are in turn linked to software development agility (ability to sense, respond and learn).
Design/methodology/approach
Survey data from one hundred and sixty software development professionals were analyzed using structural equation modeling methods.
Findings
The results support the team/methodology–process–agility model. Process variables (communication and collaborative decision-making) mediated the effects of team (autonomy and competence) and methodological (iterative development) variables on software development agility. In addition, team, methodology and process variables had different effects on the three dimensions of software development agility.
Originality/value
The results contribute to the literature on organizational IT management by establishing a team/methodology–process–agility model that can serve as a basis for developing a core theoretical foundation underlying agile principles and practices. The results also have practical implications for organizations in understanding and managing holistically the different roles that agile methodological, team and process factors play in achieving software development agility.
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Ruth C. King, Weidong Xia, James Campbell Quick and Vikram Sethi
This study examined how six institutionalized socialization tactics affect a particular occupation of knowledge workers – information technology (IT) professionals' role…
Abstract
Purpose
This study examined how six institutionalized socialization tactics affect a particular occupation of knowledge workers – information technology (IT) professionals' role adjustment (role conflict and role ambiguity) and organizational attachment variables (job satisfaction, affective commitment, continuance commitment and intention to quit).
Design/methodology/approach
The research model and hypotheses were tested using path analysis techniques with survey data collected from 187 recently hired IT professionals.
Findings
The results showed that the six socialization tactics affected IT professionals differently. Socialization tactics that recognize employees' values and skills (investiture tactics) and that emphasize the interpersonal and mentoring aspects (serial tactics) had the most significant effects on employees' role adjustment and organizational attachment. The study also revealed complex mediating relationships among socialization tactics, role adjustment and organizational attachment variables.
Originality/value
This study provides new insights about the differential effects of the various socialization tactics on IT professionals' role adjustment and organizational attachment. It also sheds light on the complex mediating relationships among socialization tactics, role adjustment and organizational attachment variables. Without considering the logical relationships between the various variables, studies examining the direct effects of socialization on isolated organizational outcome variables may overlook important linkages that are critical for explaining the inconsistent results in past empirical studies.
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Yuzhen Long, Chunli Yang, Xiangchun Li, Weidong Lu, Qi Zhang and Jiaxing Gao
Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to…
Abstract
Purpose
Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to potential safety risks owing to the complex geologic environment. Effective safety supervision is a vital guarantee for safe production in coal mines. This paper aims to explore the impacts of the internet+ coal mine safety supervision (CMSS) mode that is being emerged in China.
Design/methodology/approach
In this study, the key factors influencing CMSS are identified by social network analysis. They are used to develop a multiple linear regression model of law enforcement frequency for conventional CMSS mode, which is then modified by an analytical hierarchy process to predict the law enforcement frequency of internet+ CMSS mode.
Findings
The regression model demonstrated high accuracy and reliability in predicting law enforcement frequency. Comparative analysis revealed that the law enforcement frequency in the internet+ mode was approximately 40% lower than the conventional mode. This reduction suggests a potential improvement in cost-efficiency, and the difference is expected to become even more significant with an increase in law enforcement frequency.
Originality/value
To the best of the authors’ knowledge, this is one of the few available pieces of research which explore the cost-efficiency of CMSS by forecasting law enforcement frequency. The study results provide a theoretical basis for promoting the internet+ CMSS mode to realize the healthy and sustainable development of the coal mining industry.
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Mingda Ping, Xiangrui Ji, Yan Liu and Weidong Wang
To supply temporary pressure testing devices with favorable performance for emergency environments, this paper aims to present a pressure sensor with a central boss and…
Abstract
Purpose
To supply temporary pressure testing devices with favorable performance for emergency environments, this paper aims to present a pressure sensor with a central boss and straight-annular grooves. The structural feature is modeled and optimized by neural network-based method, and the device prototype is fabricated by 3D printing techniques.
Design/methodology/approach
The study initially compares mechanical properties of the proposed structure with two conventional designs using finite element analysis. The impacts from structural dimensions on sensor performance are modeled using a Backpropagation neural network and optimized through genetic algorithms. The sensing diaphragm is fabricated using stereolithography (SLA) 3D printing, while the piezoresistors and necessary interconnects are realized with screen printing techniques.
Findings
The experimental results demonstrate that the fabricated sensor exhibits a sensitivity of 2.8866 mV/kPa and a nonlinearity of 6.81% within the pressure range of 0–100 kPa. This performance is an improvement of 118% in sensitivity and a decrease of 54% in nonlinearity compared to flat diaphragm structure, highlighting the effectiveness of proposed diaphragm configuration.
Originality/value
This research offers a holistic methodology that encompasses the structural design, optimization and fabrication of pressure sensors. The proposed diaphragm and corresponding modelling method can provide a practical approach to enhance the measurement capabilities of pressure sensors. By leveraging SLA printing for diaphragm and screen printing for circuit, the prototype can be produced in a timely manner.
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Xiaofeng Li, Xiaoxue Liu, Xiangwei Li, Weidong He and Hanfei Guo
The purpose of this paper is to propose an improved method which can shorten the calculation time and improve the calculation efficiency under the premise of ensuring the…
Abstract
Purpose
The purpose of this paper is to propose an improved method which can shorten the calculation time and improve the calculation efficiency under the premise of ensuring the calculation accuracy for calculating the response of dynamic systems with periodic time-varying characteristics.
Design/methodology/approach
An improved method is proposed based on Runge–Kutta method according to the composition characteristics of the state space matrix and the external load vector formed by the reduction of the dynamic equation of the periodic time-varying system. The recursive scheme of the holistic matrix of the system using the Runge–Kutta method is improved to be the sub-block matrix that is divided into the upper and lower parts to reduce the calculation steps and the occupied computer memory.
Findings
The calculation time consumption is reduced to a certain extent about 10–35% by changing the synthesis method of the time-varying matrix of the dynamics system, and the method proposed of paper consumes 43–75% less calculation time in total than the original Runge–Kutta method without affecting the calculation accuracy. When the ode45 command that implements the Runge–Kutta method in the MATLAB software used to solve the system dynamics equation include the time variable which cannot provide its specific analytic function form, so the time variable value corresponding to the solution time needs to be determined by the interpolation method, which causes the calculation efficiency of the ode45 command to be substantially reduced.
Originality/value
The proposed method can be applied to solve dynamic systems with periodic time-varying characteristics, and can consume less calculation time than the original Runge–Kutta method without affecting the calculation accuracy, especially the superiority of the improved method of this paper can be better demonstrated when the degree of freedom of the periodic time-varying dynamics system is greater.
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Luosong Jin, Weidong Liu, Cheng Chen, Wei Wang and Houyin Long
With the advent of the information age, this paper aims to apply risk analysis theories to study the risk prevention mechanism of information disclosure, thus supporting the green…
Abstract
Purpose
With the advent of the information age, this paper aims to apply risk analysis theories to study the risk prevention mechanism of information disclosure, thus supporting the green electricity supply.
Design/methodology/approach
This paper conducts a comprehensive evaluation and analysis of the impact of power market transactions, power market operations and effective government supervision, so as to figure out the core risk content of power market information disclosure. Moreover, AHP-entropy method is adopted to weigh different indicators of information disclosure risks for the participants in the electricity market.
Findings
The potential reasons for information disclosure risk in the electricity market include insufficient information disclosure, high cost of obtaining information, inaccurate information disclosure, untimely information disclosure and unfairness of information disclosure.
Originality/value
Some suggestions and implications on risk prevention mechanism of information disclosure in the electricity market are provided, so as to ensure the green electricity supply and promote the electricity market reform in China.
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Han Wang, Quan Zhang, Zhenquan Fan, Gongcheng Wang, Pengchao Ding and Weidong Wang
To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types…
Abstract
Purpose
To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles.
Design/methodology/approach
The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method.
Findings
The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage.
Originality/value
This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.
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Jun Xiang, Ruru Pan and Weidong Gao
The paper aims to propose a novel method based on deep sparse convolutional neural network (CNN) for clothing recognition. A CNN based on inception module is applied to bridge…
Abstract
Purpose
The paper aims to propose a novel method based on deep sparse convolutional neural network (CNN) for clothing recognition. A CNN based on inception module is applied to bridge pixel-level features and high-level category labels. In order to improve the robustness accuracy of the network, six transformation methods are used to preprocess images. To avoid representational bottlenecks, small-sized convolution kernels are adopted in the network. This method first pretrains the network on ImageNet and then fine-tune the model in clothing data set.
Design/methodology/approach
The paper opts for an exploratory study by using the control variable comparison method. To verify the rationality of the network structure, lateral contrast experiments with common network structures such as VGG, GoogLeNet and AlexNet, and longitudinal contrast tests with different structures from one another are performed on the created clothing image data sets. The indicators of comparison include accuracy, average recall, average precise and F-1 score.
Findings
Compared with common methods, the experimental results show that the proposed network has better performance on clothing recognition. It is also can be found that larger input size can effectively improve accuracy. By analyzing the output structure of the model, the model learns a certain “rules” of human recognition clothing.
Originality/value
Clothing analysis and recognition is a meaningful issue, due to its potential values in many areas, including fashion design, e-commerce and retrieval system. Meanwhile, it is challenging because of the diversity of clothing appearance and background. Thus, this paper raises a network based on deep sparse CNN to realize clothing recognition.
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Rui Wang, Shunjie Zhang, Shengqiang Liu, Weidong Liu and Ao Ding
The purpose is using generative adversarial network (GAN) to solve the problem of sample augmentation in the case of imbalanced bearing fault data sets and improving residual…
Abstract
Purpose
The purpose is using generative adversarial network (GAN) to solve the problem of sample augmentation in the case of imbalanced bearing fault data sets and improving residual network is used to improve the diagnostic accuracy of the bearing fault intelligent diagnosis model in the environment of high signal noise.
Design/methodology/approach
A bearing vibration data generation model based on conditional GAN (CGAN) framework is proposed. The method generates data based on the adversarial mechanism of GANs and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.
Findings
The method proposed in this paper is verified by the western reserve data set and the truck bearing test bench data set, proving that the CGAN-based data generation method can form a high-quality augmented data set, while the CGAN-based and improved residual with attention mechanism. The diagnostic model of the network has better diagnostic accuracy under low signal-to-noise ratio samples.
Originality/value
A bearing vibration data generation model based on CGAN framework is proposed. The method generates data based on the adversarial mechanism of GAN and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.