Dual Path Research Articles

Is It Influence or Pressure? A Study on the Dual Path Impact of Self-Sacrificial Leadership on Employee Organizational Citizenship Behavior

Is It Influence or Pressure? A Study on the Dual Path Impact of Self-Sacrificial Leadership on Employee Organizational Citizenship Behavior

数字化转型对财务绩效的影响研究——基于融资约束与技术创新的双重路径

数字化转型对财务绩效的影响研究——基于融资约束与技术创新的双重路径

The dual path effect mechanism study of digital-HRM on employee innovative performance and cyberloafing.

In recent years, an increasing number of companies have begun implementing digital-HRM. However, much of the existing research primarily discusses digital-HRM from a "thing" perspective or explores its consequences at the organizational level. There has been limited research focusing on individual employees, particularly on how digital-HRM impacts their psychological states and performance. Drawing on job demands-resources theory, this study examines the relationship between digital-HRM and employee innovative performance, as well as cyberloafing. We conducted a time-lagged study involving 487 employees across various industries in China and employed partial least squares path modeling. The results suggest that digital-HRM enhances employee innovative performance by increasing the sense of work gain, while it reduces cyberloafing by decreasing relative deprivation. Perceived ease of technology use was found to positively moderate these relationships. By rigorously investigating the critical psychological mechanisms of the sense of work gain and relative deprivation, and the essential boundary condition of perceived ease of technology use, this study aims to develop a comprehensive conceptual framework that deepens our understanding of how digital-HRM, as an emerging job resource in the digital era, influences employee behavior. Adopting a human-centered approach, the research theoretically extends the study of digital-HRM's impact at the individual level and finds that digital-HRM influences employee performance in a mutually beneficial manner. These findings provide practical insights for organizations to actively implement digital-HRM and maximize its benefits.

The dual influence path of decent work perception on employee innovative behavior.

The goal of decent work (DW) is a win-win situation for both employees and employers. It promotes an individual's employability and enhances the competitiveness of the organization. Based on the conservation of resources theory (COR), this paper conducted survey on knowledge workers and analyzed the data by hierarchical linear model (HLM). This paper aims to examine how decent work perception (DWP) influences employee innovation behavior through the mediating effect of job engagement and burnout and the moderating effect of authoritarian leadership. Based on the results of statistical analyses conducted on 489 valid knowledge workers, it was demonstrated that DWP positively influence employee innovative behavior. Job engagement has a full mediating effect on the relationship between DWP and employee innovative behavior. The study did not support the mediating effect of job burnout, however. There is a positive moderating effect of authoritarian leadership on the relationship between DWP and job engagement and a negative moderating effect on the relationship between DWP and job burnout. In addition to contributing to theoretical studies on DW and work behavior, this paper also contributes to practice on employee motivation and leadership.

Dual path sub-type diagnostic model based on the anatomic morphological feature

Intracranial hematoma can compress the brain tissue, resulting in increased cranial pressure and a high fatality rate. Therefore, providing a rapid and accurate clinical diagnosis and treatment plan is a crucial step. Intracranial hematoma can be classified into clinical sub-types such as cerebral lobes, ventricular, basal ganglia, pons, and cerebellum hemorrhage based on the location of the hemorrhage. To accurately identify different sub-types and provide targeted treatment, this paper proposes a dual path sub-type diagnosis model based on the anatomic morphological feature. It extracts anatomic morphological features of hematoma based on clinical prior knowledge to achieve prior feature enhancement. Then, a dual-path input model is constructed, and the dual-path module is used to analyze and integrate various features of different sub-types of intracranial hematoma. The model is validated on the clinical data set of the Second Affiliated Hospital of Dalian Medical University, and the corresponding accuracy can reach 0.993. This model can automatically recognize the sub-type of the clinical intracranial hematoma cases and improve diagnostic efficiency. Besides, it further aid in image analysis and enable specific treatment planning.

Barrier Lyapunov function-based dual event-triggered prescribed performance path following control for marine surface vessel under input saturation

Concentrated on the marine surface vessel (MSV) with input saturation, model uncertainties and unknown disturbances, this paper proposes the error-constrained line-of-sight guidance-based prescribed performance dual event-triggered path following control (ECLOS-PPDETPFC) strategy which applies the dual relative threshold event-triggered mechanism (ETM) to both guidance and controller of the MSV path following. We apply the prescribed performance function (PPF) as the bounds of asymmetric modified barrier Lyapunov function (AMBLF) to construct the prescribed performance asymmetric modified barrier Lyapunov function (PPAMBLF). In guidance, event-triggered error-constrained line-of-sight (ETECLOS) is constructed by relative threshold ETM and AMBLF to reduce the calculation pressure and keep position errors within the constraint range; In controller, prescribed performance event-triggered path following control (PPETPFC) is based on the relative threshold ETM and PPAMBLF, which makes the heading error meet the prescribed performance requirements, while reducing the loss of actuator. At the same time, the adaptive auxiliary systems are used to compensate the influence of input saturation, radial basis function neural networks (RBFNNs) and adaptive laws are used to approximate the model uncertainties and composite disturbances. According to the Lyapunov stability theory, all signals are semi-globally uniformly ultimately bounded (SGUUB) and the Zeno phenomenon is avoided. Finally, the comparative experiment shows the effectiveness and superiority of the designed control strategy.

Semantic land cover change detection using harDNet and dual path coronet

ABSTRACT Monitoring the spatial and temporal trends of land use land cover (LULC) change is significant for protecting land assets sustainably. However, complicated scenarios in Remote Sensing (RS) images lead to unsatisfactory outcomes, particularly for some uneven and obstructed objects. Furthermore, there is still a hurdle in efficiently extracting and fusing the complex characteristics of RS images to increase the precision of change detection. Therefore, to get better results while detecting changes in LULC, this research implements effective deep learning approaches for segmentation and feature extraction. In the beginning, a HarDNet-based technique is used to efficiently segment the edge details of the land cover data. After that, the segmented information is sent to the Dual Path Coronet to extract spatial and spectral properties and fuse them to create the feature map of the input. After that, it is sent to the Enhanced Change Prediction Module, which generates a change prediction map with precise information. Last but not least, the suggested method has been tested on the three complex remote sensing change detection datasets, Sun Yat-Sen University (SYSU-CD), High-Resolution Semantic Change Detection (HRSCD), and LEVIR-CD. The quantitative and qualitative analysis findings on the datasets reveal that the suggested technique works better than other standard change detection techniques.

Continuous solar-driven saturated brine separation using sawtooth photothermal fabrics with asymmetrical wetting property

Eco-friendly solar-driven water evaporation is emerging as a promising strategy for saline wastewater separation. However, due to the severe decline in evaporation performance caused by salt fouling, complete separation of water and solute for saturated brine from industrial sectors calls for alternative efficient strategies. Here, the present study proposes a sawtooth photothermal fabric with asymmetrical wetting properties for high-efficiency and continuous solar-driven saturated brine separation. The proposed sawtooth photothermal fabric comprises a hydrophobic upper layer and a hydrophilic lower layer, which together enable efficient salt rejection and continuous water transport. In addition, the sawtooth structure of the photothermal fabric creates a dual escape path for the vapor to travel from the absorber to the environment, which improves the evaporation rate and provides appropriate conditions for salt collection. The experimental results revealed that the sawtooth photothermal fabric with asymmetrical wetting properties achieved an evaporation rate of 1.43 kg m–2 h−1 for 25 wt% brine under 1 sun illumination and recovery of a considerable amount of salt. Notably, in the outdoor experiment, the evaporation rate achieved using the fabric did not decline throughout the eight days of continuous solar-driven saturated brine separation. The present study has therefore provided a simple and economical method for the treatment of high-concentration waste brine with considerable salt recovery.

Mercy does not hold the army: a study on the dark side effect of benevolent leadership

PurposeThe purpose of this study is to examine the mechanism of benevolent leadership on employee cheating behavior through two paths – employee uncertainty and perceived acceptability of norm violation – and also reveal the possible dark side of benevolent leadership. Meanwhile, the moderating effects of leader behavioral integrity in the cognition dual path process are also discussed.Design/methodology/approachThis study invites 383 employees and their superiors in seven Chinese enterprises as the research objects and conducts a paired survey at three time points, and then Mplus 7.4 software is used to analyze the empirical data.FindingsThe results are shown as follows. Benevolent leadership plays a positive role on uncertainty and perceived acceptability of norm violation. Uncertainty and perceived acceptability of norm violation mediate the relationship between benevolent leadership and cheating behavior, respectively. Leader behavioral integrity moderates the positive role of benevolent leadership on uncertainty and perceived acceptability of norm violation. Leader behavioral integrity moderates the indirect effect of benevolent leadership on employees' cheating behavior through uncertainty and perceived acceptability of norm violation.Originality/valueThis study reveals the mechanism behind the negative role of benevolent leadership through the cognition reaction of employees to benevolent leadership and broadens the research scope of benevolent leadership. Meanwhile, it provides some practical inspiration for leaders to effectively use the benevolent leadership style and restrain employees' cheating behavior.

Improved channel attention methods via hierarchical pooling and reducing information loss

Channel attention has been demonstrated to improve performance of convolutional neural networks. Most existing channel attention methods lower channel dimension for reducing computational complexity. However, the dimension reduction causes information loss, thus resulting in performance loss. To alleviate the paradox of complexity and performance trade-off, we propose two novel channel attention methods named Grouping-Shuffle-Aggregation Channel Attention (GSACA) method and Mixed Encoding Channel Attention (MECA) method, respectively. Our GSACA method partitions channel variables into several groups and performs the independent matrix multiplication without the dimension reduction to each group. Our GSACA method enables interaction between all groups using a ”channel shuffle” operator. After these, our GSACA method performs the independent matrix multiplication each group again and aggregates all channel correlations. Our MECA method encodes channel information through dual path architectures to benefit from both path topology where one uses the multilayer perception with dimension reduction to encode channel information and the other uses channel information encoding method without dimension reduction. Furthermore, a novel pooling operator named hierarchical pooling is presented and applied to our GSACA and MECA methods. The experimental results showed that our GSACA method almost consistently outperformed most existing channel attention methods and that our MECA method consistently outperformed the existing channel attention methods.

Influencing factors and improvement paths of manufacturing innovation performance: Configuration analysis based on TOE framework.

Innovation is the first driving force to lead development, how to improve manufacturing innovation performance has become a hot topic. Based on 47 listed companies in the computer, communication and other electronic equipment manufacturing industry in the A-share market, this paper adopted the Fuzzy set qualitative comparative analysis (fsQCA) to explore the influencing factors of technology, organization and environment on the innovation performance of manufacturing industry and the improvement path. The findings are as follows: (1) A single condition is not a necessary condition for high innovation performance in manufacturing industry, but government support plays a key role in improving innovation performance in manufacturing industry. (2) There are two improvement paths for high innovation performance in manufacturing industry, which are specifically explained as "technology-environment dual improvement path" and "technology-organization-environment collaborative improvement path". (3) The improvement of innovation performance in the manufacturing industry is the result of multiple factors, showing the characteristics of "all paths lead to the same destination". Different manufacturing enterprises have different paths to improve innovation performance based on their actual conditions. Based on these findings, this study may provide some implications for the effective improvement of manufacturing innovation performance.

Dual Path Attention Network (DPANet) for Intelligent Identification of Wenchuan Landslides

Currently, the application of remote sensing technology in landslide identification and investigation is an important research direction in the field of landslides. To address the errors arising from the inaccurate extraction of texture and location information in landslide intelligent recognition, we developed a new network, the dual path attention network (DPANet), and performed experiments in a typical alpine canyon area (Wenchuan County). The results show that the new network recognizes landslide areas with an overall accuracy (OA) and pixel accuracy (PA) of 0.93 and 0.87, respectively, constituting an overall improvement of 4% and 18% compared to the base pyramid scene parsing network (PSPNet). We applied our knowledge of the landslide image features to other areas in the upper reaches of the Minjiang River to enrich the landslide database for this region. Our evaluation of the results shows that the proposed network framework has good robustness and can accurately identify some complex landslides, providing an excellent contribution to the intelligent recognition of landslides.

Breast Cancer Detection Based on Simplified Deep Learning Technique With Histopathological Image Using BreaKHis Database

AbstractPresented here are the results of an investigation conducted to determine the effectiveness of deep learning (DL)‐based systems utilizing the power of transfer learning for detecting breast cancer in histopathological images. It is shown that DL models that are not specifically developed for breast cancer detection can be trained using transfer learning to effectively detect breast cancer in histopathological images. The outcome of the analysis enables the selection of the best DL architecture for detecting cancer with high accuracy. This should facilitate pathologists to achieve early diagnoses of breast cancer and administer appropriate treatment to the patient. The experimental work here used the BreaKHis database consisting of 7909 histopathological pictures from 82 clinical breast cancer patients. The strategy presented for DL training uses various image processing techniques for extracting various feature patterns. This is followed by applying transfer learning techniques in the deep convolutional networks like ResNet, ResNeXt, SENet, Dual Path Net, DenseNet, NASNet, and Wide ResNet. Comparison with recent literature shows that ResNext‐50, ResNext‐101, DPN131, DenseNet‐169 and NASNet‐A provide an accuracy of 99.8%, 99.5%, 99.675%, 99.725%, and 99.4%, respectively, and outperform previous studies.

Reducing CPU Utilization & Improving Failover Time in Dual Controller SDN (Software Defined Network) Environment

A salient component of the network is failover. Largely, customers are accessing dual path controller software defined network (SDN) environment. Subsequently, when one controller fizzles out the network communication can persist as a result of the other controller. There will be no interruption in the network communication due to the failover setup which will be effective in case of failure of a controller. In the absence of failover, the network communication will be obstructed. This could be by cause of the system resources, system itself or routing policies or Distributed Denial of Services. Generally, controller will support many switches. When SDN controller collapses controller will not be in sync with switches. After few attempts switches will failover to the secondary SDN controller. During this shutdown time the network communication will be obstructed. Failover algorithm is the key to solve this issue and will facilitate the synchronization of the information with SDN controllers and enhance failover time. Apparently, communication between both SDN controllers and switches is enhanced as well as reinforces the time of synchronization due to multiple SDN controllers. CPU utilization is another problem in SDN environment as the new inflow packets need to be forwarded to control plane to find the action that need to be taken. Processing at both ends and having one to one mapping between new incoming connection requests and the packet from data plane to control plane increases the CPU utilization and there by more power consumption.

An Efficient Detector with Auxiliary Network for Remote Sensing Object Detection

Over the past few decades, advances in satellite and aerial imaging technology have made it possible to acquire high-quality remote sensing images. As one of the most popular research directions of computer vision, remote sensing object detection is widely researched due to the wide application in military and civil fields. The algorithms based on convolutional neural network have made great achievements in the field of object detection. However, plenty of small and densely distributed remote sensing objects against complex background pose some challenges to object detection. In this work, an efficient anchor-free based remote sensing object detector based on YOLO (You Only Look Once) is constructed. Firstly, the backbone network is simplified for the high efficiency of detection. In order to extract the features of densely distributed objects effectively, the detection scales are adjusted based on the backbone network. Secondly, aiming at the shortcomings of CBAM, the improved CJAM (Coordinate Joint Attention Mechanism) is proposed to deal with object detection under complex background. In addition, feature enhancement modules DPFE (Dual Path Feature Enhancement) and IRFE (Inception-ResNet-Feature Enhancement) as well as PRes2Net (Parallel Res2Net) are proposed. We combine CJAM with the above modules to create DC-CSP_n, CSP-CJAM-IRFE, and CJAM-PRes2Net for better feature extraction. Thirdly, a lightweight auxiliary network is constructed to integrate the low-level and intermediate information extracted from remote sensing images into the high-level semantic information of the backbone network. The auxiliary network allows the detector to locate the target efficiently. Fourthly, Swin Transformer is introduced into the ‘Neck’ part of the network so that the network can effectively grasp the global information. The mAP on DOTA1.5 and VEDAI datasets, which both contain a large number of small objects, reached 77.07% and 63.83%, respectively. Compared with advanced algorithms such as YOLO V4, YOLO V5s, YOLO V5l, and YOLO V7, our approach achieves the highest mAP.

Predicting spontaneous termination of atrial fibrillation based on dual path network and feature selection

The termination mechanism of Atrial Fibrillation (AF) is still unclear. In order to avoid the potential incomprehensiveness in feature extraction via manual techniques, a Dual Path Network-SVM (DPNet-SVM) was constructed in this study for predicting the spontaneous termination of AF. The dual paths of the network extracted time and time–frequency domain features, respectively. Different sizes of depthwise separable convolution (DS-Conv) were used in both paths, which enhanced feature extraction via exploration of different sensory fields. Then four methods were used for feature selection. Finally, SVM was employed for AF termination prediction. After validating the model performance based on 1-min fragments, we conducted experiments on 10-second fragments. On the AFTDB public dataset, the accuracy, sensitivity and specificity of 95.6%, 93.3% and 91.8% were obtained, respectively. On the clinical data from Shandong Provincial Hospital (SPHD), the three metrics were 94.3%, 92.9% and 95.4%, respectively. The subsequent cross-database experiment proved the model's good generalization capability, and also substantiated its feasibility in practical applications involving wearable ECG data.

“Ethical leadership: a dual path model for fostering ethical voice through relational identification, psychological safety, organizational identification and psychological ownership”

Purpose This study aims to identify the impact of ethical leadership on ethical voice by determining two paths covering relational identification and psychological safety. The first path focused on relational identification and psychological safety. Alternatively, the second path focused on organizational identification and psychological ownership leading to ethical voice. The specific objective of the study is to develop and test an integrated model of ethical leadership. Design/methodology/approach The objectives were achieved through the adoption of quantitative research techniques. Two hundred forty-eight samples were collected from the banking sector using quantitative research techniques, and data was gathered through a self-administrated questionnaire. Exploratory and confirmatory factor analyses were used through AMOS to generate the results and test hypotheses. Findings The results suggested a significant impact of ethical leadership on ethical voice, while the other paths’ results, such as relational identification, psychological safety, organizational identification and psychological ownership, suggested partial mediation. The study result adds new insights into ethical leadership and social exchange theory since it tested overlooked paths in the literature, such as relational identification and psychological safety. Research limitations/implications The research highlights the significance of ethical voice as a desirable organizational behavior. Ethical voice contributes to a culture of accountability, transparency and ethical decision-making. Organizations should establish channels and platforms for employees to voice ethical concerns and suggestions. This may involve regular feedback sessions, anonymous reporting mechanisms and protection policies for whistleblowers. Leaders should actively encourage and value ethical voices as a valuable contribution to the ethical climate of the organization. Practical implications The study found that ethical leaders influence their followers in such a way that they adopt ethical behavior. It is also validated that organizational ethics are shared by employees who interact with ethical leaders. So, departments should train such leaders because ethical leadership positively affects followers’ attitudes and behaviors, and organizations should encourage ethical behavior in supervisors and subordinates. The study also found that relational and organizational identification helps employees develop psychological capabilities, which leads to reporting workplace misconduct. The current study tested these mechanisms collectively and found that ethical leadership significantly contributes to ethical voice. Social implications The current study highlighted the role of ethical leaders in promoting ethical behavior, improving employee well-being and engagement, cultivating collaboration and inclusion, and making a contribution to the overall ethical climate within organizations and society as a whole. Organizations can have a positive impact on the social fabric by cultivating a culture of ethics, respect and social responsibility if they make these considerations their top priorities. Originality/value The current study is unique since it is intended to develop and test an integrated model of ethical leadership and ethical voice. This research combines an integrated model, focusing on employees’ identities and self-concepts and examining ethical voice as a behavioral outcome.

Feasibility and impact of online HIV/STI screening addressed to men who have sex with men and transgender women users of pre-exposure prophylaxis (PrEP) in Spain (TESTATE PrEP): a study protocol for a non-blinded randomised controlled trial

IntroductionThe objectives of the study are: to design and implement a pilot intervention to offer self-sampling kits to detect HIV, Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG) and Treponema pallidum (TP)...

Novel solar forecasting scheme modelled by mixer dual path network and based on sky images

The prediction of global horizontal irradiance has become an effective technique to address the intermittence issue of photovoltaic (PV) power generation. This article proposes a novel deep neural network(DNN), named Mixer Dual Path Network (Mixer-DPN), for promising solar forecasting. It shares common features of cloud images and maintains the flexibility to explore new features through dual-path architecture by combining the Mixer layer and Dual Path Network. Therefore, the proposed model can provide more accurate prediction results compared to the classical DNN-based predictors. Moreover, the proposed model shows a faster convergence speed and smaller model size, which makes it suitable for a practical global horizontal irradiance. The merits of the proposed model are verified by testing it with the data from National Renewable Energy Laboratory comparing it with other DNN-based prediction models. Studies have shown that the new model has achieved excellent results in MSE, MAE and other indicators, and the R2 prediction accuracy rate has increased by 14% compared with the baseline model.

DP-FishNet: Dual-path Pyramid Vision Transformer-based underwater fish detection network

The detection of underwater fish targets is critical for ecological monitoring and marine biodiversity research. However, underwater fish detection is typically constrained by problems including low image quality and variable underwater surroundings. On behalf of further improving the underwater fish detection accuracy in complex underwater environments, this paper proposes a dual-path (DP) Pyramid Vision Transformer (PVT) feature extraction network named DP-FishNet. The backbone network DP-PVT composed from the PVT Network is made up of two feature extraction paths. The first represents the Vision Transformer path, which extracts global features to enhance the distinction between the foreground and background of underwater images. The second is the convolutional neural network path, which enhances the accuracy of detecting small targets by extracting local features. Additionally, to more effectively utilize the feature information extracted by the network, this paper provides a promising solution to employ the content-aware reassembly of features (Carafe) in the feature pyramid network (FPN). The seesaw loss is utilized as a classification loss to address the problem of unbalanced samples caused by the gap in the number of fish populations. According to the experimental findings, the AP and AP50 of the DP-FishNet are 76.0% and 95.2%, respectively. In comparison to currently available advanced two-stage detection algorithms, the quantity of computation and parameters is reduced by approximately 40%. DP-FishNet strengthens the ability to extract global and local features from underwater images and enhances feature reuse. DP-FishNet can be utilized to detect fish targets in actual and complicated underwater habitats.