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This study aimed to investigate the diagnostic performance of the histogram array and convolutional neural network (CNN) based on diffusion-weighted imaging (DWI) with multiple b-values under magnetic resonance imaging (MRI) to distinguish pancreatic ductal adenocarcinomas (PDACs) from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine neoplasms (PNENs). This retrospective study consisted of patients diagnosed with PDACs (n = 132), PNENs (n = 45) and SPNs (n = 54). All patients underwent 3.0-T MRI including DWI with 10 b values. The regions of interest (ROIs) of pancreatic tumor were manually drawn using ITK-SNAP software, which included entire tumor at DWI (b = 1500 s/m2). The histogram array was obtained through the ROIs from multiple b-value data. PyTorch (version 1.11) was used to construct a CNN classifier to categorize the histogram array into PDACs, PNENs or SPNs. The area under the curves (AUCs) of the histogram array and the CNN model for differentiating PDACs from PNENs and SPNs were 0.896, 0.846, and 0.839 in the training, validation and testing cohorts, respectively. The accuracy, sensitivity and specificity were 90.22%, 96.23%, and 82.05% in the training cohort, 84.78%, 96.15%, and 70.0% in the validation cohort, and 81.72%, 90.57%, and 70.0% in the testing cohort. The performance of CNN with AUC of 0.865 for this differentiation was significantly higher than that of f with AUC = 0.755 (P = 0.0057) and α with AUC = 0.776 (P = 0.0278) in all patients. The histogram array and CNN based on DWI data with multiple b-values using MRI provided an accurate diagnostic performance to differentiate PDACs from PNENs and SPNs. • CNN is proposed to differentiate PDACs from non-PDACs by converting DWI into an image of histogram array. • AUCs of histogram array and CNN for diagnosing PDACs are 0.896, 0.846, and 0.839 in training, validating and testing cohorts. • The performance of histogram array and CNN for diagnosing PDACs is higher than that of DWI parameters in all patients. [ABSTRACT FROM AUTHOR]

As a data-driven design method, model-free optimal control based on reinforcement learning provides an effective way to find optimal control strategies. The design of model-free optimal control is sensitive to system data because it relies on data rather than detailed dynamic models. A prerequisite for generating applicable data is that the system must be open-loop stable (with a stable equilibrium point), which restricts the data-based control design methods in actual control problems and leads to rare experimental studies or verification in the literature. To improve this situation and enrich its applications, we propose a pre-stabilized mechanism and apply it to the motion control of a mechanical system together with a reinforcement learning-based model-free optimal control method, which constitutes a so-called hierarchical control structure. We design two real-time control experiments on an underactuated system to verify its effectiveness. The control results show that the proposed hierarchical control is quite promising in controlling this mechanical system, even though it is open-loop unstable with unknown dynamics. • Establish a pre-stabilized mechanism to effectively collect training data. • The parameters of near-optimal hierarchical control are tuned by system data. • Find parameter q is insensitive to the training result of weight coefficients of NNs. [ABSTRACT FROM AUTHOR]

To assess the value of radiomics, apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and stretched-exponential (SE) MR imaging in prediction of therapeutic response in patients with spinal metastases before chemotherapy. Thirty-six patients with 190 osteolytic metastatic lesions from breast cancer were prospectively enrolled and underwent MR imaging before and after 6 months' treatment on a 1.5 T MRI. According to MDA criteria, 68 lesions were categorized as progressive disease (PD) and 122 lesions were categorized as stable or improvement (non-PD). The regions of interest (ROIs) were manually drawn on DWI, T1WI, T2WI and FS-T2WI by two radiologists with ITK-SNAP. The ADC all (multiple b-values method), IVIM parameters (D, D* and f) and SE parameters (DDC and α) were generated. The radiomics features were extracted from the ROIs. The mean values of ADC, DDC, and D before treatment were significantly higher in non-PD group than those in PD group (P = 0.001). The radiomics based on ADC all had the highest AUC value (0.852), followed by that of the T2WI (0.829) and FS-T2WI (0.798). The radiomics model using ADC all and FS-T2WI showed excellent efficiency in predicting treatment response with AUCs of 0.905 and 0.873 in training and validation cohorts. The radiomics model had better performance than that of ADC all , D, and DDC for predicting treatment response of bone metastases. Radiomics model based on ADC all and FS-T2WI could predict the treatment response and contribute to assisting clinicians in accurately choosing appropriate management. • The mean values of ADC, DDC, and D values before treatment were significantly higher in non-PD group than those in PD group. • Radiomics ananlysis based on ADC all and FS-T2WI could predict treatment response of bone metastases. • The performance of radiomics model for predicting treatment response was higher than that of DWI parameters. [ABSTRACT FROM AUTHOR]

An effective and concise electrophilic cyclization of 1,4-butynediols, 4-aminobut-2-yn-1-ols, pent-2-yne-1,5-diols, 2-propynolphenols and 2-propynolanilines with N -halo succinimides and trimethylsilyl halides opens a one-pot synthetic access to five- and six-membered dihalogenated heterocycles in moderate to good yields. The optimized cyclization conditions are tolerated with many functional groups. [ABSTRACT FROM AUTHOR]

An efficient and convenient metal-free [2+2]-cycloaddition of benzyl-2-(3-hydroxypropynyl)-benzoates via allene processes has been developed, which provides impressive access to fused cyclobutanes from easily accessed π-components. This transformation involved the cleavage of two C–O bonds, the formations of two C–O bonds and two C–C bonds and showed some advantages, including mild conditions and wide substrate scope. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a rapid method for preparation of copper metal nanoparticles by reducing CuSO4·5H2O with NaH2PO2·H2O in ethylene glycol under microwave irradiation. The influences of the reaction parameters, such as the concentrations of reducing agent and protective polymer time of microwave irradiation, on the size and agglomeration of copper nanoparticles were investigated by X-ray powder diffraction and transmission electron microscope. Well-dispersed copper nanoparticles with diameter of about 10nm were obtained. The use of microwave irradiation accelerated the reaction rate and benefited the dispersion and the particle size distribution of the nanoparticles. [Copyright &y& Elsevier]

This study investigates the failure probability of steel frame structures against terrorist attack from Vehicle Borne Improvised Explosive Device (VBIED). A two-step approach is used to evaluate the collapse potential of structures against blast loads. In the first step, the damage degree and responses of structural members under blast loads are determined based on an equivalent single–degree of freedom system. In the second step, the post-blast collapse behavior of steel frame structures is investigated using a 3-D nonlinear macro-based numerical model. To improve the computational efficiency, the failure probability is calculated using subset simulation method cooperated with an advanced Delayed Rejection Adaptive Markov Chain Monte Carlo simulation algorithm. The variability of blast load, vertical gravity load and structural material properties are considered. The computational framework is applied to a prototype 10-story steel frame to study the failure risk against VBIED. The results show that the reliability assessment framework used in this study provides an accurate and more efficient prediction of failure risk of structures against blast loads compared with the direct Monte Carlo simulation method. The framework also presents an approach for determination of effective measures in protecting structures against blast loads. [ABSTRACT FROM AUTHOR]

The paper presents a probabilistic analysis of steel-concrete composite floor against progressive collapse considering uncertainties in strength and ductility of steel connections. Using component-based connection model, an analytical framework for developing probabilistic connection models is proposed. The connection models developed are further introduced in probabilistic structure analysis against progressive collapse. Tornado diagram-based sensitivity analysis is performed to determine the influential random variables for structural resistance capacity. Using Latin Hypercube sampling of both random structure variables and external loads, random realizations of structures are generated and progressive collapse analysis is carried out using pseudo-static pushdown method. The proposed framework is applied to study the vulnerability of composite floor. Fragility curves corresponding to three limit states are developed. Discussions on the influences of steel connection and slab continuity on collapse vulnerability are given. Finally, results from the present probabilistic method are compared with those from deterministic approach. [ABSTRACT FROM AUTHOR]

Abstract: This study investigates the hydrodynamic and thermal performance of nanofluids in the region of thermal developing laminar mixed convection. Single-phase approach was employed to evaluate the pumping power and heat transfer coefficient, i.e., no novel mechanism other than the renewing thermophysical properties of nanofluids was involved. An aggregation-mechanism-based methodology was introduced and verified to well unify the rheological and thermal properties of nanofluids. In comparing the pumping power savings, critical differences between forced convection and mixed convection were found: nanofluids are more favorable for application to mixed convection. Criteria for nanofluid forced-mixed transition have been discussed and presented to help the decision of whether a flow can be viewed as mixed flow. [Copyright &y& Elsevier]

Objective: To evaluate retroperitoneoscopic renal pedicle lymphatic disconnection (RRPLD) for chyluria in the setting of complex renal vasculature and compare those outcomes with the RRPLD outcomes of patients with normal renal vasculature. Materials and Methods: From December 2002 to December 2011, RRPLD was performed in 14 patients with complex renal vasculature and 64 patients with normal renal vasculature. Preoperative multislice spiral computed tomography angiography for renal vessels was done on 5 patients with complex vasculature. The demographic and perioperative data were collected to assess critical outcomes. Results: The abnormal vasculature was identified using preoperative multislice spiral computed tomography angiography in 5 patients and surgical exploration in 9 patients. RRPLD was successfully completed in all patients without conversion to open surgery or vascular injury. The mean operative time was significantly longer in those with complex renal vasculature than those with normal renal vasculature (105.4 ± 18.7 vs 84.5 ± 15.6 minutes; P = .000). The outcomes were similar in the 2 groups in terms of intraoperative blood loss (P = .060), mean hospital stay (P = .478), and intraoperative complications (P = .660). The occurrence of postoperative gross hematuria was significantly greater in those with complex renal vasculature than in those with normal renal vasculature (4 of 14 vs 2 of 64; P = .008). The event was resolved uneventfully. Conclusion: Although it is technically challenging, RRPLD is feasible and safe for patients in the presence of complex renal vasculature. Preoperative evaluation of the renal vasculature with multislice spiral computed tomography angiography is beneficial for managing abnormal renal vessels. [ABSTRACT FROM AUTHOR]

To assess the MRI performance in differentiating pancreatic ductal adenocarcinomas (PDACs), from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine tumors (PNETs) using non-gaussian diffusion-weighted imaging models. This was a retrospective study of patients diagnosed with PDACs (01/2015–06/2019) or with PNETs or SPNs diagnosed (01/2011–12/2019) at our hospital. The lesions were randomized 1:1 to the primary and validation cohorts. The regions of interest (ROIs) were manually drawn on each slice at DWI (b = 1500 s/mm2) from 3 T MRI. D (diffusion coefficient), D* (pseudodiffusion coefficient), f (perfusion fraction), distributed diffusion coefficient (DDC), α (diffusion heterogeneity index), mean diffusivity (MD) and mean kurtosis (MK) were obtained. The parameters with largest performance for differentiation were used to establish a diagnostic model. There were 148, 56, and 60 patients with PDAC, PNET, and SPN, respectively. For differentiating PDACs from SPNs, f and MK values were used to establish a diagnostic model with areas under the receiver operating characteristic curves (AUCs) of 0.92 and 0.89 in the primary and validation groups, respectively. For distinguishing PDACs from PNETs, α and MK values were used to establish a diagnostic model with AUCs of 0.87 and 0.86 in the primary and validation groups, respectively. The accuracy rate of the subjective evaluation with the assistance of non-gaussian DWI models for differentiating PDAC from SPNs and PNETs were higher than that of subjective diagnosis alone (P < 0.05). The non-gaussian DWI models could assist radiologists in accurately differentiating PDACs from PNETs and SPNs. • The DWI model yielded AUCs of 0.87 and 0.86 for distinguishing PDACs from PNETs in primary and validation groups. • This DWI model achieved AUCs of 0.916 and 0.891 for differentiating PDACs from SPNs in primary and validation groups. • The error rates were reduced when radiologists were assisted by the DWI models. [ABSTRACT FROM AUTHOR]

Abstract An experimental investigation was conducted for flow boiling inside three horizontal stainless-steel tubes with a series of newly-developed three-dimensional enhanced surfaces. Both the inner and outer surfaces of the 1EHT tubes were composed of dimples in different sizes and depths, and with an array of rectangular cavities for the 3EHT tube. These enhanced tubes have the same outer and inner diameters of 9.52 mm and of 8.32 mm, respectively. The evaporation experiments were performed using R410A as the working fluid at a constant saturation temperature of 279 K. The mass fluxes varied from 160 to 390 kg/m2s, with inlet and outlet vapor qualities of 0.2 and 0.8, respectively. As the mass velocity increased, the heat transfer coefficient and pressure drop penalty increased accordingly. Experimental results showed that the pressure drop of the 1EHT-1 tube is larger than those of the 1EHT-2 and 3EHT tubes, while the 1EHT-2 tube has the highest evaporation heat transfer coefficient. [ABSTRACT FROM AUTHOR]

Utilizing simple and readily available 2-hydroxy benzaldehydes and 1,3-diketones or 1,3-cyclohexanediones as raw materials, complex 2,8-dioxabicyclo [3.3.1] nonane derivatives were synthesized in one-step via classical Knoevenagel condensation. The reaction was simply conducted under solvent-free and mild conditions, which showed good tolerance to a variety of functional groups. This atom-economical approach delivers an attractive synthetic protocol for 2,8-dioxabicyclo [3.3.1] nonane derivatives. [ABSTRACT FROM AUTHOR]

Purpose: To explore the value of parameters of the on hepatobiliary phase (HBP) of pre-treatment gadoxetic acid-enhanced magnetic resonance imaging (MRI) for predicting pathological response to systemic therapy in colorectal liver metastases (CRLMs), compared with response evaluation criteria in solid tumors, version 1.1 (RECIST 1.1).Methods: A total of 96 patients with CRLMs who underwent gadoxetic acid-enhanced MRI prior to treatment and then liver resection from January 2017 to December 2021 were enrolled. The pathological response was assessed by the percentage of residual tumors (RTs), and CRLMs were classified into two groups according to the pathological response grade (PRG): (1) strong response (including PRG2 and PRG3, RTs ≤ 10%), and weak response (PRG1, RTs > 10%). Two radiologists evaluated the enhancement pattern and degree of CRLMs on the HBP. The diameter, mean and standard deviation (SD) value of signal intensity (SI) of CRLMs on pre-contrast and HBP images were recorded. Relative tumor enhancement (RTE) and the SD ratio (SDR) were calculated. These parameters were analyzed in terms of pathological response on a lesion-by-lesion basis.Results: Totally, 263 CRLMs were classified into: the strong response group (PRG2, n = 57; PRG3, n = 7) and the weak response group (PRG1, n = 199). RTE and SDR values were significantly higher in the strong response group than in the weak response group (P < 0.001). RTE values (P < 0.001) and SDR values (P = 0.031) were independent factors for predicting strong response. The area under curve (AUC) of RTE and SDR values were 0.725 and 0.652, respectively. The combination of these parameters was 0.750, which performed better than RECIST 1.1 (0.750 vs 0.531; P < 0.001).Conclusions: RTE and SDR values on HBP are potential features in predicting pathological response to systemic therapy in CRLMs. [ABSTRACT FROM AUTHOR]

This paper presents a study on the seismic damage evolution and failure process of steel–concrete hybrid structures through simulation and tests. For steel members, the Krieg–Key constitutive model with a plasticity damage model is used to simulate the damage of steel. For concrete members, the improved Faria–Oliver model is adopted to analyze the damage of concrete. After that, these material models are assigned to fiber elements. The fiber element is adopted to establish a finite element model of steel–concrete hybrid structures. In order to evaluate the effectiveness of this modeling method, shaking table tests are conducted on a scaled test model of a three-storey steel–concrete hybrid structure. The test result shows that the proposed damage model and fiber elements are effective to simulate the seismic damage evolution and failure process of steel–concrete structures. [ABSTRACT FROM AUTHOR]

This study experimentally investigated convective heat transfer performances of China RP-3 kerosene flowing in a vertical upward tube under supercritical pressures. Effects of mass flux, heat flux, pressure and inlet temperature on the heat transfer performance were given in detail. The influences of buoyancy and flow acceleration under different flow conditions were discussed as well. It was found that the inner wall temperature varies non-linearly at different mass fluxes. Heat transfer is improved when the fuel temperature is around the critical temperature. The heat transfer coefficient increases as heat flux or inlet temperature increases, while increase in inlet pressure reduces heat transfer coefficient. Besides, as nanofluids generally have higher thermal conductivity compared to their corresponding base fluids (i.e. kerosene), the heat transfer characteristics of Fe 3 O 4 -kerosene nanofluid was also investigated. It was found that the addition of nanoparticles tends to deteriorate the heat transfer performance of nanofluids flowing in a vertical tube under supercritical pressure. As the particle content increases, the heat transfer coefficient decreases due to the modification of the inner wall surface by the nanoparticles. [ABSTRACT FROM AUTHOR]

Regenerative cooling system at supercritical conditions can accommodate high heat fluxes effectively in aerospace applications. The potential of nanofluids as regenerative coolants at supercritical pressures was evaluated in this work. Experiments were carried out to study the heat transfer characteristics of Al 2 O 3 -kerosene nanofluids flowing upward in a vertical minitube at supercritical pressures. Parametric effects of mass flow rate, heat flux, pressure and particle content on the heat transfer performance are presented. Results show that increasing the mass flow rate or pressure enhances heat transfer, while higher heat fluxes lead to poorer heat transfer performance. Nanofluids tend to deteriorate heat transfer at supercritical pressures because deposition of the nanoparticles smoothens the wall roughness and presents an additional thermal resistance. As the particle content increases, the heat transfer performance becomes worse. Based on the experimental data, a heat transfer correlation was established for Al 2 O 3 -kerosene nanofluids at supercritical pressures and the correlation shows good predictive ability. [ABSTRACT FROM AUTHOR]