The detection of tiny defects in industrial products is important for improving the quality of industrial products and maintaining production safety. Currently, image-based defect detection methods are ineffective in detecting tiny and variously shaped defects. Therefore, this paper proposes a tiny defect detection network (TD-Net) for industrial products to improve the effectiveness of tiny defect detection. TD-Net improves the overall defect detection effect, especially the detection effect of tiny defects, by solving the problems of downsampling of tiny defects, pre-filtering of conflicting deep and shallow semantic information, and cascading fusion of multi-scale information. Specifically, this paper proposes the Defect Downsampling (DD) module to realize the defect information supplementation during the backbone downsampling process and improve the problem that the stepwise convolution easily misses the detection of tiny defects. Meanwhile, the Semantic Information Interaction Module (SIIM) is proposed, which fuses deep and shallow semantic features, and is designed to interact the fused features with shallow features to optimize the detection of tiny defects. Finally, the Scale Information Fusion Module (SIFM) is proposed to improve the Path Aggregation Network (PANet) for cascading fusion and information focus on different scale information, which enables further improvement of defect detection performance of TD-Net. Extensive experimental results on the NEU–DET data set (76.8 $$\%$$% mAP), the Peking University PCB defect data set (96.2 $$\%$$% mAP) and the GC10-DET data set (71.5 $$\%$$% mAP) show that the proposed TD-Net achieves competitive results compared with SOTA methods with the equivalent parameter quantity.

Although employee green creativity is recognized as the key to the innovation in green enterprises, few studies explores the measurement of green creativity for employees. To address the gap, the present study identifies the major dimensions of employee green creativity and develops a comprehensive, reliable, and valid measurement instrument. According to the 4P’s model of creativity, four core dimensions of employee green creativity are identified, namely, green creative motivation, thinking, behavior, and outcome. Strictly adhering to the process of scale development, employee green creativity scale (EGCS) is constructed and validated. We first develop the items of employee green creativity based on literature review and expertise from academics and practitioners. Next, we examine the validation of EGCS through exploratory and confirmatory factor analysis using a sample from three large-scale green enterprises (N = 460). Further, we also check the nomological validity of EGCS by testing the effects of determinants (e.g., green transformational leadership, shared vision, and green self-efficacy) on employee green creativity using a new sample from another two green enterprises (N = 169). Results reveal that EGCS is a reliable and valid instrument for capturing employee green creativity in multiple contexts. Theoretical and practical implications are discussed.

Background: The breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis. Methods: This work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time. Results: Our initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively. Discussion: Our architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising. Conclusions: Experimental results on the apnea data in University College Dublin (UCD) Database have proven the efficiency and effectiveness of our methodology. This work is a pilot project and still under development. There is no clinical validation and no support. In addition, the Internet of Things (IoT) architecture enables real-time monitoring of human physiological parameters, combined with diagnostic algorithms to provide early warning of abnormal data.