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Real economy has always been a crucial component of China’s economic development, while fictitious economy has experienced rapid growth in past decades. As a result, the connection between the real and fictitious economy has become increasingly complex. This study utilized a hierarchical framework for classifying real economy and conducted a hidden causality test and EEMD method to explore a causal relationship between markets. Monthly data from July 2001 to September 2022 were analyzed using a TVP-SV-VAR model to investigate dynamic relationships among the manufacturing, construction, real estate, and financial industries as well as the mechanisms between the real and fictitious economies. The study outcomes demonstrated that the financial and real estate industries have only short-term positive effects on the manufacturing and construction industries, and in the later period of sample intervals, both industries had negative effects on the construction industry. The construction industry in the real economy has already shown a trend of moving “from Real to Virtual”, while the core manufacturing industry in the real economy has not yet exhibited this trend. To prevent the spread of this trend in the real economy, it is necessary to guide the fictitious economy to serve the real economy by regulating its development appropriately. This study offers a novel perspective for examining the real economy and the fictitious economy in China.

Childhood maltreatment (CM) has a long impact on physical and mental health of children. However, the neural underpinnings of CM are still unclear. In this study, we aimed to establish the associations between functional connectome of large-scale brain networks and influences of CM evaluated through Childhood Trauma Questionnaire (CTQ) at the individual level based on resting-state functional magnetic resonance imaging data of 215 adults. A novel individual functional mapping approach was employed to identify subject-specific functional networks and functional network connectivities (FNCs). A connectome-based predictive modeling (CPM) was used to estimate CM total and subscale scores using individual FNCs. The CPM established with FNCs can well predict CM total scores and subscale scores including emotion abuse, emotion neglect, physical abuse, physical neglect, and sexual abuse. These FNCs primarily involve default mode network, fronto-parietal network, visual network, limbic network, motor network, dorsal and ventral attention networks, and different networks have distinct contributions to predicting CM and subtypes. Moreover, we found that CM showed age and sex effects on individual functional connections. Taken together, the present findings revealed that different types of CM are associated with different atypical neural networks which provide new clues to understand the neurobiological consequences of childhood adversity.

Inspired by the human vision system and learning, we propose a novel cognitive architecture that understands the content of raw videos in terms of objects without using labels. The architecture achieves four objectives: (1) Decomposing raw frames in objects by exploiting foveal vision and memory. (2) Describing the world by projecting objects on an internal canvas. (3) Extracting relevant objects from the canvas by analyzing the causal relation between objects and rewards. (4) Exploiting the information of relevant objects to facilitate the reinforcement learning (RL) process. In order to speed up learning, and better identify objects that produce rewards, the architecture implements learning by causality from the perspective of Wiener and Granger using object trajectories stored in working memory and the time series of external rewards. A novel non-parametric estimator of directed information using Renyi's entropy is designed and tested. Experiments on three environments show that our architecture extracts most of relevant objects. It can be thought of as 'understanding' the world in an object-oriented way. As a consequence, our architecture outperforms state-of-the-art deep reinforcement learning in terms of training speed and transfer learning.

Anastomotic leakage (AL) is a major cause of postoperative morbidity and mortality in the treatment of colorectal cancer. The aim of this study was to investigate an innovative and convenient technique of laparoscopic demucositized suture the overlapping point of the "dog ear" area after the double stapling anastomosis (lds-DSA), as an improved alternative for conventional DSA, and whether it could reduce the AL rate in laparoscopic anterior resection (Lapa-AR). Between January 2018 and December 2020, a total of 245 patients who underwent Lapa-AR for the treatment of adenocarcinoma of the sigmoid colon or rectal cancer were divided into the lsd-DSA group (n = 99) and the DSA group (n = 146). Data were analyzed retrospectively. Morbidity, AL rate and other perioperative outcomes were compared between the two groups. Patient demographics, preoperative comorbidity, preoperative chemoradiotherapy, tumor size, stage, and other operative details were comparable between the two groups. There was no difference in surgical time between the two groups (196.41 ± 76.71 vs. 182.39 ± 49.10 min, p = 0.088). The overall complication rate was also without a difference (11/99, 11.11% vs. 21/146, 14.38%, p = 0.456), but AL rate significantly lower in the lsd-DSA group than in the DSA group (2/99, 2.02% vs. 12/146, 8.22%, p = 0.040). For other perioperative outcomes, the lsd-DSA group shortened the total and postoperative hospital stay, and the time to pull out drainage tube than in the DSA group. Our comparative study demonstrates lds-DSA to have a better short-term outcome in reducing AL compared with DSA. This technique could be an alternative approach to maximize the patients' benefit in Lapa-AR.

Purpose We develop a deep learning (DL) radiomics model and integrate it with circulating tumor cell (CTC) counts as a clinically useful prognostic marker for predicting recurrence outcomes of early-stage non-small cell lung cancer (ES-NSCLC) patients treated with stereotactic body radiation therapy (SBRT). Methods and Materials A cohort of 421 NSCLC patients was used to train a DL model for gleaning informative imaging features from computed tomography (CT) data. The learned imaging features were optimized on a cohort of 98 ES-NSCLC patients treated with SBRT for predicting individual patient recurrence risks by building DL models on CT data and clinical measures. These DL models were validated on the third cohort of 60 ES-NSCLC patients treated with SBRT to predict recurrent risks and stratify patients into subgroups with distinct outcomes in conjunction with CTC counts. Results The DL model obtained a concordance-index of 0.880 (95% confidence interval: 0.879, 0.881). Patient subgroups with low and high DL risk scores had significantly different recurrence outcomes (p = 3.5e-04). The integration of DL risk scores and CTC measures identified 4 subgroups of patients with significantly different risks of recurrence (χ2= 20.11, p=1.6e-04). Patients with positive CTC measures post-SBRT were associated with increased risks of recurrence that were significantly different from patients with negative CTC measures (χ2= 4.03, p=0.0447). Conclusion In this first-ever study integrating DL radiomics models and CTC counts, our results suggested that this integration improves patient stratification compared with either imagining data or CTC measures alone in predicting recurrence outcomes for patients treated with SBRT for ES-NSCLC.

AimThis study investigates the clinicopathological features and prognostic genic biomarker factors of primary retroperitoneal extra-gastrointestinal stromal tumors (EGISTs).MethodsThe clinicopathological data of six patients with primary retroperitoneal EGIST were analyzed, including cell type (epithelioid or spindle), mitoses, and the presence of intratumoral necrosis and hemorrhage. Mitoses were counted and summed from 50 high power fields (HPFs). Mutations of exons 9, 10, 11, 13, 14, and 17 of the C-kit genes and those of exons 12 and 18 of the PDGFRA gene were examined. Follow-up was performed via telephone, and all outpatient records were reviewed. The last follow-up date was February 2022, the median follow-up was 27.5m and the postoperative status, medication, and survival of the patients were recorded.ResultThe patients were treated with radical intent. Four cases (patients 3, 4, 5, and 6) underwent multivisceral resection for encroachment on the adjacent viscera. The postoperative pathological results demonstrated that all biopsy specimens were negative for S-100 and desmin, and positive for DOG1 and CD117. Additionally, four patients (case 1, 2, 4, and 5) were positive for CD34, four (case 1, 3, 5, and 6) were positive for SMA, four (case 1, 4, 5, and 6) had >5/50 HPFs, and three (case 1, 4, and 5) had Ki67 >5%. According to the modified National Institutes of Health (NIH) guidelines, all patients were graded as high-risk cases. By exome sequencing, exon11 mutations were detected in the six patients, while exon10 mutations were detected in two cases (patients 4 and 5). The median follow-up time was 30.5 (11–109) months, with only one fatality at 11 months.ConclusionRetroperitoneal EGIST is a rare mesenchymal tumor that is difficult to distinguish from other retroperitoneal tumors. To diagnose this highly malignant tumor, low-threshold suspicion is necessary, and Kit and PDGFRA gene mutations should be routinely tested to confirm the diagnosis and guide subsequent treatment.

Slipping-stretching landslides have long been recognized as a common type of landslide case, but such reactions have rarely been reported. There was a slipping-stretching landslide reaction at Liangtianao, Guangxi Province, China, and the geological background and deformation characteristics of the case were identified by detailed geological survey and long-term monitoring. A FEM model of the case was built using GEO5 to analyze the mechanism of reaction and deformation. The results are as follows. 1) The Liangtianao landslide is a bedding rock ancient landslide, which remained in a creeping state after the landslide occurred in geological history. 2) The new sliding surface in the Liangtianao landslide is basically consistent with the weak interlayers formed by ancient landslides, and the fault-type is a slipping-stretching type with the following deformation process: strain at leading edge by road excavation→ leading edge instability→ middle part shear and creep→ back edge tensile. 3) Micro-geomorphology, rock mass bedding, and weak interlayers are internal causes of the Liangtianao landslide reaction, while excavation unloading and rainfall are the external causes. The inclinometer indicates that the Liangtianao landslide is still highly sensitive to rainfall after the landslide reaction, which may trigger a secondary landslide reaction. 4) The numerical analysis results indicate that the maintenance of a passive state in the anti-sliding section is highly beneficial to maintaining basic stability after the landslide reaction; unloading only 1/20 of the sliding section makes the landslide stable in the long term.

Landslides are frequent in the Nanpeng River fault zone in southwest Yunnan. Based on a Zhen Qing Expressway survey, the geological conditions and characteristics of landslides in this area were studied. The conclusions are as follows: 1) the geological background was the internal cause of local highly steep slopes and broken rock masses. The main landslide types in study area are fault-type and accumulation landslides. 2) The formation of fault-type landslides is related to endogenous causes, such as tectonic movements, but they are less easily triggered than accumulation landslides. Accumulation landslides are related to broken rock masses and are sensitive to external factors such as rainfall and artificial unloading. 3) The coordinated control of permanent support engineering, local deformation control, and overall support reinforcement should be considered as preventive measures.

Prestress is an important index of the mechanical performance of prestressed structures, which will have a certain impact on the natural vibration frequency of the structure. In this paper, based on the action mechanism of the prestressing system, the externally prestressed simply supported steel beams are classified, and the energy method is used to establish the dynamic equations of the prestressed steel beams with three forms of cable distribution, with a view to exploring the relationship between the magnitude of prestressing force, cable distribution form, and cable section size and the natural vibration frequency of externally prestressed simply-supported steel beams. Through a numerical example, the theoretical calculation results are compared with the finite element simulation results to verify the theoretical calculation formula derived in this paper and reveal the action law of the prestressed system on the natural vibration frequency of the simply supported steel beam.

As the main load-bearing component of the bridge structure, the cable is damaged by the impact from time to time, so it is very important to clarify its impact resistance capacity. Based on the method of the drop-weight test, this article mainly studies the degradation law of the mechanical properties of epoxy-coated steel strand (ECSS) under the impact load. Through the impact test of ECSS under different working conditions, the influence law of prestress, impact energy, initial defects and other factors on the impact resistance of the steel strand was revealed. Then, the difference of the impact resistance of ECSS and ordinary steel strand (OSS) was verified under the same impact conditions. Finally, the failure mechanism and the residual-bearing capacity of ECSS were analyzed through the secondary impact test. The results show that the initial prestress is helpful to improve its impact resistance when it is less than 0.11 f u {f}_{{\rm{u}}} , and exceeding this value will accelerate its failure process. The effect of impact height on the strain at the impact point of ECSS is significantly greater than that near the anchor end. ECSS has better energy absorption characteristics than OSS. The impact resistance of ECSS with initial defects is very sensitive to the impact energy. The influence on outer strand in the secondary impact is significantly higher than the central strand, and its residual tensile capacity still has 0.85 f u {f}_{{\rm{u}}} .

Sparse learning methods have been powerful tools for learning compact representations of functional brain networks consisting of a set of brain network nodes and a connectivity matrix measuring functional coherence between the nodes. However, these tools typically focus on the functional connectivity measures alone, ignoring the brain network nodal information that is complementary to the functional connectivity measures for comprehensively characterizing the functional brain networks. In order to provide a comprehensive delineation of the functional brain networks, we develop a new data fusion method for heterogeneous data, aiming at learning sparse network patterns to characterize both the functional connectivity measures and their complementary network nodal information within a unified framework. Experimental results have demonstrated that our method outperforms the best alternative method under comparison in terms of accuracy on simulated data as well as both reproducibility and prediction performance of brain age on real resting state functional magnetic resonance imaging data.

Background The purpose of this study was to compare the “caudal to cranial” (CC) versus “medial to lateral” (ML) approach for laparoscopic right hemicolectomy. Methods Pertinent data from all patients with stage II and III between January 2015 and August 2017 were entered into a retrospective database. Results A total of 175 patients underwent the ML (n = 109) or CC approach (n = 66). Patient characteristics were equivalent between groups. The CC group showed a shorter surgical time 170.00 (145.00, 210.00) vs. (206.50 (178.75, 226.25) min) than the ML group (p＜0.001). The time to oral intake was shorter in the CC group than in the ML group ((3.00 (1.00, 4.00) vs. 3.00 (2.00, 5.00) days; p=0.007). For the total harvested lymph nodes, there was no statistical significance between the CC group 16.50 (14.00, 21.25) and the ML group 18.00 (15.00, 22.00) (p = 0.327), and no difference was found in the positive harvested lymph nodes (0 (0, 2.00) vs. 0 (0, 1.50); p=0.753). Meanwhile, no differences were found in other perioperative or pathological outcomes, including blood loss and complications. For 5-year prognosis, overall survival rates were 75.76% in the CC group and 82.57% in the ML group (HR 0.654, 95% CI 0.336–1.273, p = 0.207); progress-free survival rates were 80.30% in the CC group and 85.32% in the ML group (HR 0.683, 95% CI 0.328–1.422, p = 0.305). Conclusions Both approaches were safe and feasible and resulted in excellent survival. The CC approach was beneficial in terms of the surgical time and rapid recovery after operation.

Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain’s functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive DevelopmentSMStudy. Across matched discovery (n=3,525) and replication (n=3,447) samples, the total cortical representation of fronto-parietal PFNs positively correlated with general cognition. Cross-validated ridge regressions trained on PFN topography predicted cognition across domains, with prediction accuracy increasing along the cortex’s sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.

In this paper, the nonmetallic Si[Formula: see text]-doped modified lanthanum aluminate (LaAlO3) solid electrolyte for the first time, which has particular scientific significance, and LaAl[Formula: see text][Formula: see text]Si[Formula: see text]O[Formula: see text][Formula: see text] material was successfully prepared by the sol–gel method. LaAl[Formula: see text]Si[Formula: see text]O[Formula: see text] is a ceramic material sintered at 1100[Formula: see text]C for 2 h in an air atmosphere. The surface microstructure and electrochemical properties of the samples were tested by multiple characterization methods. The results show that the LaAl[Formula: see text]Si[Formula: see text]O[Formula: see text] has good sintering activity, high crystallinity, and a grain radius of approximately 25 nm. At 800[Formula: see text]C, the ionic conductivity is above 1.05E− 04 S⋅cm[Formula: see text] and the activation energy is lower than 1.19 eV.

BACKGROUND Opinions vary on the medial border of D3 lymphadenectomy for right colon cancer. Most surgeons place the medial border along the left side of the superior mesenteric vein, but some consider the left side of the superior mesenteric artery as the medial border. OBJECTIVES This study investigated the clinical outcomes of laparoscopic D3 lymphadenectomy for right colon cancer with the medial border along the left side of superior mesenteric artery. DESIGN This was a retrospective study. SETTINGS The study was conducted in specialized colorectal cancer department of 5 tertiary hospitals. PATIENTS Patients receiving laparoscopic D3 lymphadenectomy for right colon cancer from January 2013 to December 2018 were included. MAIN OUTCOME MEASURES After propensity score matching, 307 patients receiving laparoscopic D3 lymphadenectomy along the left side of the superior mesenteric artery were assigned to the superior mesenteric artery group and 614 patients were assigned to the superior mesenteric vein group. Univariate, multivariate, and Kaplan-Meier analyses were performed to assess the clinical data. RESULTS The short-term outcomes were similar between the 2 groups; however, the superior mesenteric artery group had a higher rate of chylous leakage (p < 0.001). More lymph nodes were harvested from the superior mesenteric artery group than from the superior mesenteric vein group (p = 0.001). The number (p = 0.005) of metastatic lymph nodes and the lymph node ratio (p = 0.041) in main nodes were both higher in the superior mesenteric artery group. The 2 groups had similar long-term survival, but the superior mesenteric artery group tended to show better disease-free survival in patients with stage disease III (p = 0.056). LIMITATIONS This was a retrospective, nonrandomized study. CONCLUSION Laparoscopic D3 lymphadenectomy along the left side of the superior mesenteric artery, except for a higher rate of chylous leakage, had short-term outcomes comparable to the superior mesenteric vein group. The superior mesenteric artery group tended to achieve better disease-free survival in patients with stage III disease, but further study is required to better elucidate differences in these approaches because risks/benefits do exist.

This study aimed to observe the protective effect of momordicine I, a triterpenoid compound extracted from momordica charantia L., on isoproterenol (ISO)-induced hypertrophy in rat H9c2 cardiomyocytes and investigate its potential mechanism. Treatment with 10 μM ISO induced cardiomyocyte hypertrophy as evidenced by increased cell surface area and protein content as well as pronounced upregulation of fetal genes including atrial natriuretic peptide, β-myosin heavy chain, and α-skeletal actin; however, those responses were markedly attenuated by treatment with 12.5 μg/ml momordicine I. Transcriptome experiment results showed that there were 381 and 447 differentially expressed genes expressed in comparisons of model/control and momordicine I intervention/model, respectively. GO enrichment analysis suggested that the anti-cardiomyocyte hypertrophic effect of momordicine I may be mainly associated with the regulation of metabolic processes. Based on our transcriptome experiment results as well as literature reports, we selected glycerophospholipid metabolizing enzymes group VI phospholipase A

Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy ( P < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography.

With the development of various network technologies and the spread of coronavirus disease 2019, many online learning platforms have been built. However, some of them may negatively impact student learning outcomes. Therefore, this study aims to improve the online learning effect of students by comprehensively evaluating their learning behavior by using deep learning algorithms. On this basis, new teaching strategies are proposed. According to the structured deep network embedding model, a network representation learning algorithm is proposed with the help of auto-encoders under deep learning. This study elaborates the concept and structure of the encoder model and tests its performance. After the node labels and dataset are trained, the applicable parameter λ2 of the model is 0.3. During the teaching process, the model’s reliability in distinguishing users is examined. Therefore, this model can be applied to network teaching, is an innovative teaching strategy, and provides a theoretical basis for improving teaching methods.

Purpose To predict overall survival of patients receiving stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (ES-NSCLC), we developed a radiomic model that integrates risk of death estimates and changes based on pre- and posttreatment computed tomography (CT) scans. We hypothesize this innovation will improve our ability to stratify patients into various oncologic outcomes with greater accuracy. Methods and Materials Two cohorts of patients with ES-NSCLC uniformly treated with SBRT (a median dose of 50 Gy in 4-5 fractions) were studied. Prediction models were built on a discovery cohort of 100 patients with treatment planning CT scans, and then were applied to a separate validation cohort of 60 patients with pre- and posttreatment CT scans for evaluating their performance. Results Prediction models achieved a c-index up to 0.734 in predicting survival outcomes of the validation cohort. The integration of the pretreatment risk of survival measures (risk-high vs risk-low) and changes (risk-increase vs risk-decrease) in risk of survival measures between the pretreatment and posttreatment scans further stratified the patients into 4 subgroups (risk: high, increase; risk: high, decrease; risk: low, increase; risk: low, decrease) with significant difference (χ2 = 18.549, P = .0003, log-rank test). There was also a significant difference between the risk-increase and risk-decrease groups (χ2 = 6.80, P = .0091, log-rank test). In addition, a significant difference (χ2 = 7.493, P = .0062, log-rank test) was observed between the risk-high and risk-low groups obtained based on the pretreatment risk of survival measures. Conclusion The integration of risk of survival measures estimated from pre- and posttreatment CT scans can help differentiate patients with good expected survival from those who will do more poorly following SBRT. The analysis of these radiomics-based longitudinal risk measures may help identify patients with early-stage NSCLC who will benefit from adjuvant treatment after lung SBRT, such as immunotherapy.

A novel self-supervised deep learning (DL) method is developed for computing bias-free, personalized brain functional networks (FNs) that provide unique opportunities to better understand brain function, behavior, and disease. Specifically, convolutional neural networks with an encoder-decoder architecture are employed to compute personalized FNs from resting-state fMRI data without utilizing any external supervision by optimizing functional homogeneity of personalized FNs in a self-supervised setting. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify canonical FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, self-supervised DL allows for rapid, generalizable computation of personalized FNs.

In the context of a PRMT5 inhibitor program, we describe our efforts to develop a flexible and robust strategy to access tetrahydrofuro[3,4-b]furan nucleoside analogues. Ultimately, it was found that a Wolfe type carboetherification from an alkenol derived from d-glucofuranose diacetonide was capable of furnishing the B-ring and installing the desired heteroaryl group in a single step. Using this approach, key intermediate 1.3-A was delivered on a gram scale in a 62% yield and 9.1:1 dr in favor of the desired S-isomer. After deprotection of 1.3-A, a late-stage glycosylation was performed under Mitsunobu conditions to install the pyrrolopyrimidine base. This provided serviceable yields of nucleoside analogues in the range of 31-48% yield. Compound 1.1-C was profiled in biochemical and cellular assays and was demonstrated to be a potent and cellularly active PRMT5 inhibitor, with a PRMT5-MEP50 biochemical IC50 of 0.8 nM, a MCF-7 target engagement EC50 of 3 nM, and a Z138 cell proliferation EC50 of 15 nM. This work sets the stage for the development of new inhibitors of PRMT5 and novel nucleoside chemical matter for alternate drug discovery programs.

Providing exclusive right of way for trams in road sections is one of management strategies to improve the operation efficiency of urban public transportation systems. However, the availability of ...

There are many films and televisions (FATs) on the Internet, but the quality is uneven. This study explores the ability of college students to screen good films and resist bad films in television works in such a large environment. In the deep learning model of FAT, the ability of college students to think about the ideas expressed and the degree of influence on college students’ values are analyzed. Based on this conceptual basis, a questionnaire is designed for the intention and influencing factors of college students’ FAT innovation and entrepreneurship. It reflects the influence of concentration on FAT learning, the cognitive level of deep learning, the ability to process deep learning ideas, the feeling of the teaching process, and the process of self-learning, which all positively impact college students’ FAT entrepreneurial intentions. The importance of innovative deep learning is highlighted, which proves that a good deep learning course guidance method can improve students’ interest and ability and provide a reference for relevant colleges and universities to cultivate pertinent talents of the field of FAT.

SUMMARYThe brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derived personalized functional networks across 29 scales in a large sample of youths (n=693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling systematically adhered to and strengthened a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks dissociably mediated the emergence of executive function. These results delineate maturation of multi-scale brain networks, which varies according to a functional hierarchy and impacts cognitive development.

The purpose of the study is to promote college students to actively respond to the national “Public Entrepreneurship and Mass Innovation” policies and calls, improve college students’ entrepreneurial enthusiasm and their entrepreneurial ability, and cultivate their good entrepreneurial psychological states. First, the relevant content of entrepreneurship psychology and causal attribution theory is displayed. Second, the questionnaire of college students’ entrepreneurship education is formulated and a questionnaire survey is conducted on University N based on the relevant content of entrepreneurship psychology. Subsequently, the management system of new venture A is taken as the research object to construct the management strategy of new ventures and simulate the implementation process. Finally, the questionnaire survey results of college students’ entrepreneurship education are analyzed and the corresponding entrepreneurship education path is formulated. Meanwhile, the implementation effect of the management strategy of new ventures is evaluated. After the questionnaire is sorted out, it is found that there are some problems in college students’ entrepreneurship education, such as weak awareness of entrepreneurship, insufficient publicity, outdated curriculum, and unqualified teachers. The reasons for these problems are the constraints of traditional concepts, insufficient attention, and incomplete system construction. Therefore, a plan is made for overall entrepreneurship education, the publicity of the concept of entrepreneurship education is strengthened, and the setting of entrepreneurship education curriculum and the ability of the teachers for entrepreneurship education are improved. Through the evaluation of the simulation implementation of a new enterprise management strategy, it is found that the new management strategy can achieve the expected effect. Therefore, this study provides some references for the development of college students’ entrepreneurship education and the management strategy of new ventures.

This study investigates the mechanical properties of carbon fiber reinforced polymer (CFRP) cable at the anchoring region where arrange the adhesive material with different mediums aiming at reducing the high stress concentration. The 3D finite element (FE) model was established based on cable force of 100t and 1000t, and its accuracy was verified by experimental results. The effects of geometrical dimensions and mediums stiffness on anchoring capacity of the cable was studied. The results show that the proposed anchorage gets high anchoring efficiency. The gradient variation of the stiffness of the adhesive material can effectively reduce the stress crest of the cable at the anchorage region. The distributions of the radial and shear stress of the cable at the anchorage region become comparatively flat with the increase in the number of segments. The variation stiffness and length ratio have the main influence on the stress of the cable in three orientations. Friction coefficient and internal taper have the secondary effects on the radial stress and the axial displacement. Anchor length and thickness of adhesive material only affect the radial stress. Anchorage design should primarily focus on the stiffness and length ratio of the adhesive material. The anchoring method proposed in this paper is helpful to anchor multi-tendon cables efficiently, and promote the application of high-capacity FRP cables in large-span spatial structure.

The application of bicyclo[1.1.1]pentanes (BCPs) as phenyl bioisosteres has garnered significant attention, as these structural motifs can improve the physiochemical profiles of drug candidates. De...

Feature dimensionality reduction plays an important role in radiomic studies with a large number of features. However, conventional radiomic approaches may suffer from noise, and feature dimensionality reduction techniques are not equipped to utilize latent supervision information of patient data under study, such as differences in patients, to learn discriminative low dimensional representations. To achieve robustness to noise and feature dimensionality reduction with improved discriminative power, we develop a robust collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively under adaptive sparse regularization. Our method is built upon matrix tri-factorization enhanced by adaptive sparsity regularization for simultaneous feature dimensionality reduction and denoising. Particularly, latent grouping information of patients with distinct radiomic features is learned and utilized as supervision information to guide the feature dimensionality reduction, and noise in radiomic features is adaptively isolated in a Bayesian framework under a general assumption of Laplacian distributions of transform-domain coefficients. Experiments on synthetic data have demonstrated the effectiveness of the proposed approach in data clustering, and evaluation results on an fDg-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method outperforms alternative methods in terms of both patient stratification and prediction of patient clinical outcomes.

Inspired by the human brain, we propose an external memory-augmented decision-making architecture for video processing. A self-organizing object detector is employed as a frontend to deconstruct the environment. This is done by extracting events from the flow of time and detecting objects within the frames. By employing an extra working memory where objects are temporarily stored, the system can extract properties of the stored objects related to the task. We propose a deep reinforcement learning (RL) neural network to learn affordances, i.e., a sequence of actions to manipulate these objects. The RL network and object detector are trained alternatively. After both the network and detector are trained, the objects and their affordances are transferred to an external memory. They are then utilized when the same objects are detected in input frames. Here, we use a combination of a dictionary and a linked list for the external memory that can be accessed by either content or temporal order. This dual access is motivated by the temporal property of human procedural memory. The proposed memory-augmented RL framework brings advantages of transferability, explainability and computational efficiency with respect to conventional deep learning architectures. We validate the framework on the video game Super Mario Brothers to show superiority to some classical deep RL architectures and exemplify these three advantages.

OBJECTIVE To reliably and quickly diagnose children with posterior urethral valves (PUV), we developed a multi-instance deep learning method to automate image analysis. METHODS We built a robust pattern classifier to distinguish 86 children with PUV from 71 children with mild unilateral hydronephrosis based on ultrasound images (3504 in sagittal view and 2558 in transverse view) obtained during routine clinical care. RESULTS The multi-instance deep learning classifier performed better than classifiers built on either single sagittal images or single transverse images. Particularly, the deep learning classifiers built on single images in the sagittal view and single images in the transverse view obtained area under the receiver operating characteristic curve (AUC) values of 0.796 ± 0.064 and 0.815 ± 0.071, respectively. AUC values of the multi-instance deep learning classifiers built on images in the sagittal and transverse views with mean pooling operation were 0.949 ± 0.035 and 0.954 ± 0.033, respectively. The multi-instance deep learning classifiers built on images in both the sagittal and transverse views with a mean pooling operation obtained an AUC of 0.961 ± 0.026 with a classification rate of 0.925 ± 0.060, specificity of 0.986 ± 0.032, and sensitivity of 0.873 ± 0.120, respectively. Discriminative regions of the kidney located using classification activation mapping demonstrated that the deep learning techniques could identify meaningful anatomical features from ultrasound images. CONCLUSION The multi-instance deep learning method provides an automatic and accurate means to extract informative features from ultrasound images and discriminate infants with PUV from male children with unilateral hydronephrosis.

This study reported novel eco-friendly Ziegler-Natta catalysts containing bio-derived maleic rosinate tri-n-butyl and maleic rosinate tri-n-octyl as internal electron donors for propylene polymeriz...

On the basis of the existing test data of high temperature mechanical properties of 1860 grade steel strands, a regression model of high temperature performance of steel strands that can be used for theoretical analysis and numerical calculation is obtained. Based on the non-stationary temperature field model in the fire of tall and large space buildings, the non-linear finite element numerical analysis method considering the time integral effect is used to establish the fire resistance numerical model of the large-span prestressed steel structure. Through the fire-resistance calculation example of Beam String Structure, the influence of different fire source positions on the fire-resistance of prestressed steel structures is discussed.

Medical diagnostic imaging is essential for the differential diagnosis of cervical lymphadenopathy. Here we develop an ultrasound radiomics method for accurately differentiating cervical lymph node tuberculosis (LNTB), cervical lymphoma, reactive lymph node hyperplasia, and metastatic lymph nodes especially in the multi-operator, cross-machine, multicenter context. The inter-observer and intra-observer consistency of radiomics parameters from the region of interest were 0.8245 and 0.9228, respectively. The radiomics model showed good and repeatable diagnostic performance for multiple classification diagnosis of cervical lymphadenopathy, especially in LNTB (area under the curve, AUC: 0.673, 0.662, and 0.626) and cervical lymphoma (AUC: 0.623, 0.644, and 0.602) in the whole set, training set, and test set, respectively. However, the diagnostic performance of lymphadenopathy among skilled radiologists was varied (Kappa coefficient: 0.108, *p < 0.001). The diagnostic performance of radiomics is comparable and more reproducible compared with those of skilled radiologists. Our study offers a more comprehensive method for differentiating LNTB, cervical lymphoma, reactive lymph node hyperplasia, and metastatic LN.

Roadside unit (RSU) cloud and its vehicle-to-infrastructure (V2I) connectivity can enable various security, entertainment, and shared mobility applications for vehicles in intelligent transportation systems (ITS) through wireless communications. In this article, the deep programmability of software-defined networking (SDN) is employed to dynamically reconfigure network hosting services and their data forwarding information for effectively meeting the basic shared mobility applications’ needs in vehicle ad hoc networks (VANETs). Multipath is also enabled to forward data flow for balancing network links utilization rate and SDN is thus utilized to achieve the minimum cloud delay with the least number of hosts, which can be summarized as a mixed-integer linear programming (MILP) problem. The joint optimization (JO) algorithm is proposed and in contrast to the two single-objective algorithms which are the delay optimization (DO) algorithm and host optimization (HO) algorithm, respectively. Results show that, for the single-threading instance, the JO and DO algorithms are the same in essence. For the multithreading instance, the JO algorithm generally outperforms the two single-objective optimization algorithms, respectively, under given demands. Furthermore, results also demonstrate that the services should be deployed globally in a distributed manner rather than in the centralized manner for achieving the minimized cloud delay in designing an RSU cloud.

This paper mainly expounds the research hotspots and trends of computer-aided teaching platforms in view of particle swarm optimization algorithm and introduces current situation of computer-aided teaching platforms. This paper analyzes the realization of the core business system of the computer help education platform. And it puts forward the optimization idea of multimedia compute help education platform in view of particle swarm optimization algorithm. The experimental results illustrate Za = 1.42 when students in two classes use the computer-aided teaching platform before optimization. It shows that, at the 0.05 obvious level, there is no important objection in the scores of the two groups of students in the test. When the students in the experimental class used the optimized computer-aided teaching platform, Zb = 3.67, indicating that, at the 0.01 significant level, the performance of the students in the control class was significantly lower than that of the students in the experimental class.

We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks (FCNs) under a self-supervised learning setting. Particularly, a deep neural network is trained to estimate diffeomorphic spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and warped moving images, similar to those adopted in conventional image registration algorithms. The network is implemented in a multi-resolution image registration framework to optimize and learn spatial transformations at different image resolutions jointly and incrementally with deep self-supervision in order to better handle large deformation between images. A spatial Gaussian smoothing kernel is integrated with the FCNs to yield sufficiently smooth deformation fields for diffeomorphic image registration. The spatial transformations learned at coarser resolutions are utilized to warp the moving image, which is subsequently used as input to the network for learning incremental transformations at finer resolutions. This procedure proceeds recursively to the full image resolution and the accumulated transformations serve as the final transformation to warp the moving image at the finest resolution. Experimental results for registering high-resolution 3D structural brain magnetic resonance (MR) images have demonstrated that image registration networks trained by our method obtain robust, diffeomorphic image registration results within seconds with improved accuracy compared with state-of-the-art image registration algorithms.

This study aims to evaluate the practical application value of the teaching method under the guidance of educational psychology and artificial intelligence (AI) design, taking the deep learning theory as the basis of teaching design. The research objects of this study involve all the teachers, students, and students' parents of Ningbo Middle School. The questionnaires are developed to survey the changes in the performance of students before and after the implementation of the teaching design and the satisfaction of all teachers, students, and parents to different teaching methods by comparing the two results and the satisfaction ratings. All objects in this study volunteer to participate in the questionnaire survey. The results suggest the following: (1) the effective return rates of the questionnaires to teachers, students, and parents are 97, 99, and 95%, respectively, before implementation; whereas those after implementation are 98, 99, and 99%, respectively. Comparison of the two return results suggests that there was no significant difference statistically (P > 0.05). (2) Proportion of scoring results before and after implementation is given as follows: the proportions of levels A, B, C, and D are 35, 40, 15, and 10% before implementation, respectively; while those after implementation are 47, 36, 12, and 5%, respectively. After the implementation, the proportion of level A is obviously higher than that before the implementation, and the proportions of other levels decreased in contrast to those before the implementation, showing statistically obvious differences (P < 0.05). (3) The change in the performance of each subject after 1 year implementation is significantly higher than that before the implementation, and the change in the average performance of each subject shows an upward trend. In summary, (1) the comparison on the effective return rate of the satisfaction survey questionnaire proves the feasibility of its scoring results. (2) The comparison of the survey scoring results shows that people are more satisfied with the new educational design teaching method. (3) The comparison of the change in the performance of each subject before and after the implementation indirectly reflects the drawbacks of partial subject education, indicating that the school should pay the same equal attention to every subject. (4) Due to various objective and subjective factors, the results of this study may be different from the actual situation slightly, and its accuracy has to be further explored in the future.

Motivated by the call of the International Maritime Organization to meet the emission targets of 2030, this study considers two important practical aspects of quay crane scheduling: efficiency and energy consumption. More precisely, we introduce the bi-objective quay crane scheduling problem where the objective is to minimize the vessel’s completion time and the crane’s energy consumption. This is done by formulating a bi-objective mixed-integer programming model. A branch-and-bound algorithm was developed as the exact solution approach to find the full set of Pareto-optimal solutions. We consider (i) various lower bounds for both objectives, (ii) specific upper bounds, (iii) additional branching criteria, and (iv) fathoming criteria to detect Pareto-optimal solutions. Numerical experiments on benchmark instances show that the branch-and-bound algorithm can efficiently solve small- and medium-sized problems.

Electric fields (E-fields) induced by transcranial magnetic stimulation (TMS) can be modeled using partial differential equations (PDEs). Using state-of-the-art finite-element methods (FEM), it often takes tens of seconds to solve the PDEs for computing a high-resolution E-field, hampering the wide application of the E-field modeling in practice and research. To improve the E-field modeling’s computational efficiency, we developed a self-supervised deep learning (DL) method to compute precise TMS E-fields. Given a head model and the primary E-field generated by TMS coils, a DL model was built to generate a E-field by minimizing a loss function that measures how well the generated E-field fits the governing PDE. The DL model was trained in a self-supervised manner, which does not require any external supervision. We evaluated the DL model using both a simulated sphere head model and realistic head models of 125 individuals and compared the accuracy and computational speed of the DL model with a state-of-the-art FEM. In realistic head models, the DL model obtained accurate E-fields that were significantly correlated with the FEM solutions. The DL model could obtain precise E-fields within seconds for whole head models at a high spatial resolution, faster than the FEM. The DL model built for the simulated sphere head model also obtained an accurate E-field whose average difference from the analytical E-fields was 0.0054, comparable to the FEM solution. These results demonstrated that the self-supervised DL method could obtain precise E-fields comparable to the FEM solutions with improved computational speed.

The connected multi road side unit (RSU) environment can be envisioned as the RSU cloud. In this paper, the Software-Defined Networking (SDN) framework is utilized to dynamically reconfigure the RSU clouds for the mixed traffic flows with energy restrictions, which are composed of five categories of vehicles with distinctive communication demands. An environmentally sustainable SDN data dissemination method for safer and greener transportation solutions is thus proposed, aiming to achieve the lowest overall SDN cloud delay with the least working hosts and minimum energy consumption, which is a mixed integer linear programming problem (MILP). To solve the problem, Joint optimization algorithms with Finite resources (JF) in three hyperparameters versions, JF (DW = 0.3, HW = 0.7), JF (DW = 0.5, HW = 0.5) and JF (DW = 0.7, HW = 0.3), were proposed, which are in contrast with single-objective optimization algorithms, the Host Optimization (H) algorithm, and the Delay optimization (D) algorithm. Results show that JF (DW = 0.3, HW = 0.7) and JF (DW = 0.5, HW = 0.5), when compared with the D algorithm, usually had slightly larger cloud delays, but fewer working hosts and energy consumptions, which has vital significance for enhancing energy efficiency and environmental protection, and shows the superiority of JFs over the D algorithm. Meanwhile, the H algorithm had the least working hosts and fewest energy consumptions under the same conditions, but completely ignored the explosive surge of delay, which is not desirable for most cases of the SDN RSU cloud. Further analysis showed that the larger the network topology of the SDN cloud, the harder it was to find a feasible network configuration. Therefore, when designing an environmentally sustainable SDN RSU cloud for the greener future mobility of intelligent transportation systems, its size should be limited or partitioned into a relatively small topology.