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BACKGROUND/OBJECTIVES: Since the initial approach towards the clinical presentation of soft-tissue masses is challenging for frontline physicians, some countries use clinical practice guidelines. Proper imaging work-up is crucial to differentiate between soft-tissue tumors. Recently, ultrasonography and magnetic resonance imaging (MRI) have been widely used. But there is some controversy about whether pre-referral evaluations are being conducted properly. Thus, this study aims to assess whether the ultrasonography investigation of soft-tissue masses prior to referral to the musculoskeletal tumor center is being performed adequately in terms of indications, diagnostic accuracy, and referral interval, and to evaluate whether the pre-referral MRI for soft-tissue masses is being conducted reasonably concerning indications, imaging protocol, reporting, diagnostic accuracy, and cost-effectiveness. METHODS: The study protocol was registered. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. We performed a database search of the MEDLINE, Embase, and Cochrane Library. Then, two authors reviewed the studies, and the third author resolved any disagreement between them. A total of nine studies were included. The Risk Of Bias In Non-Randomized Studies-of Interventions was applied. Relevant data were extracted by two authors. RESULTS: This review showed that the way that the imaging investigations are performed in non-specialized centers before referral was generally considered inappropriate. CONCLUSIONS: Frontline physicians should regard the alarm symptom as an indication for advanced imaging evaluation. Education and certification may be required for ultrasonography. MRI should be performed and interpreted in a specialized center or by a specialist with relevant expertise. Guidance may help reduce inappropriate imaging.

To reduce production costs, environmental effects, and crop losses, tomato leaf disease recognition must be accurate and fast. Early diagnosis and treatment are necessary to cure and control illnesses and ensure tomato output and quality. The YOLOv5m was improved by using C3NN modules and Bidirectional Feature Pyramid Network (BiFPN) architecture. The C3NN modules were designed by integrating several soft attention modules into the C3 module: the Convolutional Block Attention Module (CBAM), Squeeze and Excitation Network (SE), Efficient Channel Attention (ECA), and Coordinate Attention (CA). The C3 modules in the Backbone and Head of YOLOv5 model were replaced with the C3NN to improve feature representation and object detection accuracy. The BiFPN architecture was implemented in the Neck of the YOLOv5 model to effectively merge multi-scale features and improve the accuracy of object detection. Among the various combinations for the improved YOLOv5m model, the C3ECA-BiFPN-C3ECA-YOLOv5m achieved a precision (P) of 87.764%, a recall (R) of 87.201%, an F1 of 87.482, an mAP.5 of 90.401%, and an mAP.5:.95 of 68.803%. In comparison with the YOLOv5m and Faster-RCNN models, the improved models showed improvement in P by 1.36% and 7.80%, R by 4.99% and 5.51%, F1 by 3.18% and 6.86%, mAP.5 by 1.74% and 2.90%, and mAP.5:.95 by 3.26% and 4.84%, respectively. These results demonstrate that the improved models have effective tomato leaf disease recognition capabilities and are expected to contribute significantly to the development of plant disease detection technology. [ABSTRACT FROM AUTHOR]

The authors examine the impact of industrial robots on US labor markets between 2005 and 2016. Because some industries adopt robots more intensively, growth in robot stocks more heavily affect local labor markets with larger employment shares in those industries. This robot exposure variation occurs across 722 commuting zones in the continental United States. Analyzing the five-year intervals within this period, the authors find that robot exposure reduces employment in the earlier periods but augments employment in the more recent periods. Similarly, the effect of robot exposure on the local wage is initially negative but gradually rebounds and turns positive in more recent years. The evolving influence of robots is primarily driven by the automotive industry, in which digitization and automation have not only increased labor productivity but also created new tasks. Findings show evidence of spillover effects on other industries within and outside of manufacturing, which may be explained by input-output linkages and aggregate demand effects. [ABSTRACT FROM AUTHOR]

We examine the impacts of potential artificial intelligence (AI) regulations on managers' perceptions on ethical issues related to AI and their intentions to adopt AI technologies. We conduct a randomized online survey experiment on more than a thousand managers in the United States. We randomly present managers with different proposed AI regulations, and ask about ethical issues related to AI and their intentions related to AI adoption. We find that information about AI regulation increases manager perception of the importance of safety, privacy, bias/discrimination, and transparency issues related to AI. However, there is a tradeoff; regulation information reduces manager intent to adopt AI technologies. Moreover, information about regulation increases manager intent to spend on developing AI strategy including ethical issues at the cost of investing in AI adoption, such as providing AI training to current employees or purchasing AI software packages. (JEL: K24, L21, L51, O33, O38) [ABSTRACT FROM AUTHOR]