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Orbital-free density functional theory (DFT) is much more efficient than the orbital-dependent Kohn–Sham DFT due to the avoidance of the auxiliary one-body orbitals. The machine learning approach has been applied to build nuclear orbital-free DFT recently [Wu et al., Phys. Rev. C 105 (2022) L031303] and achieved more precise descriptions for nuclei than existing orbital-free DFTs. Here, improved machine learning nuclear orbital-free density functional is built by including the Thomas–Fermi approach as a basement. Performances of the functional are compared in detail with the ones based on the pure machine learning approach. It is found that with the Thomas–Fermi functional included, the machine-learning-based functional can achieve better performance in directly predicting the kinetic energies and in providing the ground-state properties by the self-consistent procedures. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The aim of the study was to investigate the "Internet + rehabilitation guidance" under the theory of Information- Knowledge-Attitude-Practice (IKAP) in patients with esophageal cancer during the perioperative period and to analyze the influence on the short-term prognosis of patients with esophageal cancer. PATIENTS AND METHODS: From April 2022 to February 2023, 118 patients who underwent radical esophagectomy in the First Hospital of Huai'an Affiliated Hospital of Nanjing Medical University were enrolled using the convenience sampling method. They were divided into the IKAP group (59 cases) and the Control Group (Group C) (59 cases), according to the random number table method. The conventional intervention was performed during the perioperative period, and the IKAP group was also given "Internet + rehabilitation guidance" based on IKAP theory. The first postoperative defecation time, exhaust time, feeding time, discharge time, and postoperative complication rate of the two groups were compared. Meanwhile, blood samples were collected before surgery and 1, 3, 7, and 30 days after surgery (at outpatient review) for the detection of inflammatory factor indexes and nutritional indexes. RESULTS: Patients within the IKAP group showed a shorter first postoperative exhaust and defecation time, eating time, and hospital compared to the control group (p<0.05). Before surgery, there was no significant difference in serum inflammatory factors and nutritional indexes between the two groups (p>0.05). Comparing the levels of serum inflammatory factors in the two groups after surgery, the levels of CRP and IL-6 in the IKAP group were lower than those in the control group on days 1, 3, and 7 after surgery. After 30 days, the serum CRP level was found to be lower than the control group, but no statistical difference with the control level of serum IL-6 (p<0.05) was found. Compared with the serum nutritional index levels in the two groups: 1 d after surgery, the serum HGB, PA, and TRF levels were not different (p>0.05). The serum ALB level in the IKAP group was higher than that in the control group (p<0.05). Postoperative 3 d, 7 d, the serum levels of HGB, ALB, PA, and TRF in the IKAP group were higher than those in the control group (p<0.05). After 30 d, there was no statistical difference in serum HGB levels between the two groups (p<0.05); Serum ALB, PA, and TRF levels in the IKAP group were higher than those in the control group (p<0.05). From preoperative to 30 days after surgery, serum CRP and IL-6 levels in 2 groups were first increased and then decreased, while serum HGB, ALB, PA, and TRF levels were first decreased and then increased. After surgery, the IKAP group showed a greater incidence of complications in patients than in controls (p<0.05). CONCLUSIONS: In patients with esophageal cancer, perioperative "Internet + rehabilitation guidance" based on IKAP theory can effectively shorten the postoperative gastrointestinal function recovery time and rapidly reduce the inflammatory response, improving the nutritional status of the body, thereby reducing the risk of short-term postoperative complications. [ABSTRACT FROM AUTHOR]

Nondisplaced femoral neck fractures are sometimes misdiagnosed by radiographs, which may deteriorate into displaced fractures. However, few efficient artificial intelligent methods have been reported. We developed an automatic detection method using deep learning networks to pinpoint femoral neck fractures on radiographs to assist physicians in making an accurate diagnosis in the first place. Our proposed accurate automatic detection method, called the direction-aware fracture-detection network (DAFDNet), consists of two steps, namely region-of-interest (ROI) segmentation and fracture detection. The first step removes the noise region and pinpoints the femoral neck region. The fracture-detection step uses a direction-aware deep learning algorithm to mark the exact femoral neck fracture location in the region detected in the first step. A total of 3840 femoral neck parts in anterior–posterior (AP) pelvis radiographs collected from the China Medical University Hospital database were used to test our method. The simulation results showed that DAFDNet outperformed the U-Net and DenseNet methods in terms of the IOU value, Dice value, and Jaccard value. Our proposed DAFDNet demonstrated over 94.8% accuracy in differentiating non-displaced Garden type I and type II femoral neck fracture cases. Our DAFDNet method outperformed the diagnostic accuracy of general practitioners and orthopedic surgeons in accurately locating Garden type I and type II fracture locations. This study can determine the feasibility of applying artificial intelligence in a clinical setting and how the use of deep learning networks assists physicians in improving correct diagnoses compared to the current traditional orthopedic manual assessments. [ABSTRACT FROM AUTHOR]

In this study, we develop an innovative method that assists computer-aided diagnosis in the determination process of the exact location of the femoral neck junction in plain radiographs. Our algorithm consists of two phases, i.e., coarse prediction and fine matching, which are implemented by supervised deep learning method and unsupervised clustering, respectively. In coarse prediction, standard masks are first produced by a specialist and trained in our proposed feature propagation network (FPU-Net) with supervised learning on the femoral neck dataset. In fine matching, the standard masks are first classified into different categories using our proposed three parameters with unsupervised learning. The predicted mask from FPU-Net is matched with each category of standard masks by calculating the values of intersection of union (IOU), and finally the predicted mask is substituted by the standard mask with the largest IOU value. A total of 4320 femoral neck parts in anterior–posterior (AP) pelvis radiographs collected from China Medical University Hospital database were used to test our method. Simulation results show that, on the one hand, compared with other segmentation methods, the method proposed in this paper has a larger IOU value and better suppression of noise outside the region of interest; on the other hand, the introduction of unsupervised learning for fine matching can help in the accurate localization segmentation of femoral neck images. Accurate femoral neck segmentation can assist surgeons to diagnose and reduce the misdiagnosis rate and burden. [ABSTRACT FROM AUTHOR]

We present a theoretical study of bichromatic weakly non-collinear Airy-type waves propagating with various amplitudes in water of infinite depth. The coupled nonlinear Schrödinger equations are invoked to study the behavior of self- and cross-interacting Airy wave packets. We demonstrate that bichromatic pulses possess the main properties of single Airy wave packets—shape invariance and self-acceleration or self-deceleration—when propagating in a dispersive medium. Accounting for nonlinearity leads to a strong dependence of the structure, stability, and propagation velocity of wave pulses on their amplitude. We show that interacting pulses of Airy-type waves can form giant accelerating and decelerating waves. [ABSTRACT FROM AUTHOR]

This study aims to design a novel air cleaning facility which conforms to the current situation in China, and moreover can satisfy our demand on air purification under the condition of poor air quality, as well as discuss the development means of a prototype product. Air conditions in the operating room of a hospital were measured as the research subject of this study. First, a suitable turbulence model and boundary conditions were selected and computational fluid dynamics (CFD) software was used to simulate indoor air distribution. The analysis and comparison of the simulation results suggested that increasing the area of air supply outlets and the number of return air inlets would not only increase the area of unidirectional flow region in main flow region, but also avoid an indoor vortex and turbulivity of the operating area. Based on the summary of heat and humidity management methods, the system operation mode and relevant parameter technologies as well as the characteristics of the thermal-humidity load of the operating room were analyzed and compiled. According to the load value and parameters of indoor design obtained after our calculations, the airflow distribution of purifying the air-conditioning system in a clean operating room was designed and checked. The research results suggested that the application of a secondary return air system in the summer could reduce energy consumption and be consistent with the concept of primaiy humidity control. This study analyzed the feasibility and energy conservation properties of cleaning air-conditioning technology in operating rooms, proposed some solutions to the problem, and performed a feasible simulation, which provides a reference for practical engineering.