Simulation and Identification of Multiscale Falling Morphology of Fresh Tea Leaves at Different Transport Speeds Based on Discrete Element Method

To explore the falling morphology of multiscale fresh tea leaves at different speeds, this study evaluated the multiscale fresh tea leaves (one bud with two leaves, one bud with one leaf, single leaf, and damaged leaf) at different heights (0.7 m, 0.5 m, 0.3 m, and 0.1 m from the ground) during the process of dropping on the conveyor belt at different speeds (0.6 m/s and 1.2 m/s). The motion morphology of fresh tea leaves on multiple scales was analyzed by discrete element simulation, the results showed that the movement patterns of multiscale fresh tea leaves at different positions from the ground were different when the conveyor was dropping at different speeds, and that the multiscale fresh tea leaves all rotated around the long axis, short axis, and root of the fresh tea leaves. When the conveying speed of the conveyor belt was 0.6 m/s, the movement patterns of one bud with two leaves and of one bud with one leaf of fresh tea were near the ground, and the movement patterns of the fresh tea leaves were mostly oriented toward the ground. The leaf tips of the fresh tea leaves were mostly on the side near the ground, the damaged leaves were near the ground, and the movement patterns of the fresh tea leaves were mostly parallel to the ground. When the conveyor belt throwing speed was 1.2 m/s, the roots of one bud with two leaves moved toward the ground when they were close to the ground. When one bud with one leaf was close to the ground, the leaf tip moved toward the ground, and the single leaf and damaged leaf rotated around the root because of the inertia of the conveyor belt throwing

Human Digital Twin: A Survey

Digital twin has recently attracted growing attention, leading to intensive research and applications. Along with this, a new research area, dubbed as "human digital twin" (HDT), has emerged. Similar to the conception of digital twin, HDT is referred to as the replica of a physical-world human in the digital world. Nevertheless, HDT is much more complicated and delicate compared to digital twins of any physical systems and processes, due to humans' dynamic and evolutionary nature, including physical, behavioral, social, physiological, psychological, cognitive, and biological dimensions. Studies on HDT are limited, and the research is still in its infancy. In this paper, we first examine the inception, development, and application of the digital twin concept, providing a context within which we formally define and characterize HDT based on the similarities and differences between digital twin and HDT. Then we conduct an extensive literature review on HDT research, analyzing underpinning technologies and establishing typical frameworks in which the core HDT functions or components are organized. Built upon the findings from the above work, we propose a generic architecture for the HDT system and describe the core function blocks and corresponding technologies. Following this, we present the state of the art of HDT technologies and applications in the healthcare, industry, and daily life domain. Finally, we discuss various issues related to the development of HDT and point out the trends and challenges of future HDT research and development

A nomogram combining thoracic CT and tumor markers to predict the malignant grade of pulmonary nodules ≤3 cm in diameter

BackgroundWith the popularity of computed tomography (CT) of the thorax, the rate of diagnosis for patients with early-stage lung cancer has increased. However, distinguishing high-risk pulmonary nodules (HRPNs) from low-risk pulmonary nodules (LRPNs) before surgery remains challenging.MethodsA retrospective analysis was performed on 1064 patients with pulmonary nodules (PNs) admitted to the Qilu Hospital of Shandong University from April to December 2021. Randomization of all eligible patients to either the training or validation cohort was performed in a 3:1 ratio. Eighty-three PNs patients who visited Qianfoshan Hospital in the Shandong Province from January through April of 2022 were included as an external validation. Univariable and multivariable logistic regression (forward stepwise regression) were used to identify independent risk factors, and a predictive model and dynamic web nomogram were constructed by integrating these risk factors.ResultsA total of 895 patients were included, with an incidence of HRPNs of 47.3% (423/895). Logistic regression analysis identified four independent risk factors: the size, consolidation tumor ratio, CT value of PNs, and carcinoembryonic antigen levels in blood. The area under the ROC curves was 0.895, 0.936, and 0.812 for the training, internal validation, and external validation cohorts, respectively. The Hosmer-Lemeshow test demonstrated excellent calibration capability, and the fit of the calibration curve was good. DCA has shown the nomogram to be clinically useful.ConclusionThe nomogram performed well in predicting the likelihood of HRPNs. In addition, it identified HRPNs in patients with PNs, achieved accurate treatment with HRPNs, and is expected to promote their rapid recovery

Distinguishing EGFR mutant subtypes in stage IA non-small cell lung cancer using the presence status of ground glass opacity and final histologic classification: a systematic review and meta-analysis

BackgroundThe progression of early stage non-small cell lung cancer (NSCLC) is closely related to epidermal growth factor receptor (EGFR) mutation status. The purpose of this study was to systematically investigate the relationship between EGFR mutation status and demographic, imaging, and ultimately pathologic features in patients with NSCLC.MethodsA complete literature search was conducted using the PubMed, Web of Science, EMBASE, and Cochrane Library databases to discover articles published by May 15, 2023 that were eligible. The relationship between EGFR mutation status and specific demographic, imaging, and ultimately pathologic features in patients with NSCLC was evaluated using pooled odds ratios (ORs) and their 95% confidence intervals (CIs). The standardized mean difference (SMD) with 95% CIs was the appropriate statistic to summarize standard deviations (SDs) means for continuous variables.ResultsA total of 9 studies with 1789 patients were included in this analysis. The final findings suggested that patients with a greater age, female gender, and non-smoking status would have a relatively higher incidence of EGFR mutations. Additionally, the risk of EGFR mutations increased with larger tumor diameter, tumor imaging presentation of mixed ground glass opacity (mGGO), and tumor pathological findings of minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IAC). Significantly, malignancies presenting as MIA are more likely to contain L858R point mutations (OR = 1.80; 95% CI: 1.04–3.13; p = 0.04) rather than exon 19 deletions (OR = 1.81; 95% CI: 0.95–3.44; p = 0.07).ConclusionThis meta-analysis showed that imaging parameters and histological classifications of pulmonary nodules may be able to predict stage IA NSCLC genetic changes

Nomogram combining clinical and radiological characteristics for predicting the malignant probability of solitary pulmonary nodules measuring ≤ 2 cm

BackgroundAt present, how to identify the benign or malignant nature of small (≤ 2 cm) solitary pulmonary nodules (SPN) are an urgent clinical challenge. This retrospective study aimed to develop a clinical prediction model combining clinical and radiological characteristics for assessing the probability of malignancy in SPNs measuring ≤ 2 cm.MethodIn this study, we included patients with SPNs measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to December 2021. Clinical features, preoperative biomarker results, and computed tomography characteristics were collected. The enrolled patients were randomized at a ratio of 7:3 into a training cohort of 775 and a validation cohort of 331. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. The receiver operating characteristic (ROC) curve was used to evaluate the identification ability of the model. The calibration power was evaluated using the Hosmer–Lemeshow test and calibration curve. The clinical utility of the nomogram was also assessed by decision curve analysis (DCA).ResultA total of 1,106 patients were included in this study. Among them, the malignancy rate of SPNs was 85.08% (941/1,106). We finally identified the following six independent risk factors by logistic regression: age, carcinoembryonic antigen, nodule shape, calcification, maximum diameter, and consolidation-to-tumor ratio. The area under the ROC curve (AUC) for the training cohort was 0.764 (95% confidence interval [CI]: 0.714–0.814), and the AUC for the validation cohort was 0.729 (95% CI: 0.647–0.811), indicating that the prediction accuracy of nomogram was relatively good. The calibration curve of the predictive model also demonstrated a good calibration in both cohorts. DCA proved that the clinical prediction model was useful in clinical practice.ConclusionWe developed and validated a predictive model and nomogram for estimating the probability of malignancy in SPNs measuring ≤ 2 cm. With the application of predictive models, thoracic surgeons can make more rational clinical decisions while avoiding overtreatment and wasting medical resources

Fourier Descriptors Based Expert Decision Classification of Plug Seedlings

To enable automatic transplantation of plug seedlings and improve identification accuracy, an algorithm to identify ideal seedling leaf sets based on Fourier descriptors is developed, and a classification method based on expert system is adopted to improve the identification rate of the plug seedlings. First, the image of the plug seedlings is captured by image acquisition system, followed by application of K-means clustering for image segmentation and binary processing and identification of the ideal seedling leaf set by Fourier descriptors. Then we obtain feature vectors, such as gray scale (R+B+G)/3, hue H, and rectangularity. After that the knowledge model of the plug seedlings is defined, and the inference engine based on knowledge is designed. Finally, the recognizing test is carried out. The success rate of the identification of 10 varieties of plug seedlings from 190 plates is 98.5%. For the same sample, the recognizing rate of support vector machine (SVM) is 85%, the recognizing rate of particle-swarm optimization SVM (PSOSVM) is 87%, the recognizing rate of back propagation neural network (BP) is 63%, and the recognizing rate of Fourier descriptors SVM (FDSVM) is 87%. These results show that our recognition method based on an expert system satisfies the requirements of automatic transplanting

Experimental and simulation investigation on acetone deoxygenation with Ce/Fe-based oxygen carrier

Bio-oil is a renewable energy and a promising alternative to oil, but its disadvantages such as high oxygen content, low higher heating value (HHV), and instability are needed upgrading. For ketones account for a relatively large proportion of bio-oil, acetone is chosen as a single model compound for hydrodeoxygenation (HDO) in this work. The acetone HDO investigation was carried out in a high pressure reactor, using the reduced Fe-based oxygen carrier1 (OC1) and Ce-doping reduced Fe/Al oxygen carrier2 (OC2). The OCs’ evolution was analyzed by XRD, and the liquid phase products were tested by GC–MS. The results showed the OCs played the dual role of carrying oxygen and catalysis and the HDO capability of OC2 on acetone was better than that of OC1. In addition, two molecular structure models of OC1* (reduced iron-based oxygen carrier) and OC2* (Ce-modified Fe/Al reduced oxygen carrier) were established, and the acetone adsorption on OCs* and the H2O dissociation process were simulated with density functional theory (DFT). The analysis showed that the oxygen vacancies generated by Ce-doping and the Al2O3 carrier changed the optimal adsorption sites of acetone and H2O, then promoted the acetone adsorption and H2O dissociation reaction, improved the OCs performance, verifying that OC2 had better HDO performance on acetone. This work provides guidance for the improvement of crude bio-oil upgrading