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To analyze the influence of hydrous ethanol on the performance of the direct-injection engine, the three-dimensional simulation is carried out by using converge software coupled with the combustion mechanism of hydrous ethanol gasoline and the soot model. The combustion and soot generation characteristics of a direct-injection gasoline engine burning hydrous ethanol gasoline using exhaust gas recirculation (EGR) technology are investigated. It is found that the increase of the blending ratio of the hydrous ethanol can accelerate the flame propagation speed, shorten the combustion duration, and improve the combustion isovolumetric. The nucleation and growth of soot are jointly controlled by polycyclic aromatic hydrocarbons (PAHs) and the small molecular components such as C2H2. The oxygen content properties and high reactive OH of the hydrous ethanol-containing gasoline inhibit soot formation. Compared with pure gasoline, the carbon soot precursor mass is reduced by 60%, 54.5%, 73.3%, and 52.4% for 20% anhydrous ethanol blended with gasoline, A1, A2, A3, and A4, respectively, and the carbon soot mass is reduced by 63.6% and the carbon soot volume density is reduced by 40%. The introduction of EGR exhaust reduces the burning rate and leads to an increase in the production of carbon monoxide, hydrocarbon, and soot. However, the combination of EGR with hydrous ethanol gasoline can significantly improve the engine combustion environment and significantly reduce soot and PAHs concentrations. The impact of EGR also includes the ability to reduce combustion chamber temperatures and reduce NOx emissions from hydrous ethanol gasoline by 75%.

Objectives To share our initial experience with modified vein clamping technique for the treatment of renal cell carcinoma complicated with the level I-II IVC thrombus Methods From March 2018 to April 2021, 11 patients with renal cell carcinoma (RCC) involving the IVC tumor thrombus were admitted to our hospital. Then, they all underwent laparoscopic radical nephrectomy and IVC thrombectomy (LRN-IVCTE) using modified vein clamping technique. Results All procedures were successfully completed without conversion to open surgery. The median operative time was 185 min (range 125–229 min); the median estimated blood loss was 200 ml (range 150–300 ml), and four patients received an intraoperative transfusion. Besides, the median IVC clamping time was 18 min (range 10–24 min); the median postoperative hospital stay was 6 days (range 4–8 days), while the median follow-up period was 28 months (range 2–36 months). Conclusions Modified vein clamping technique for the treatment of renal cell carcinoma complicated with the level I-II IVC thrombus may be a safe and technically feasible alternative technic.

The combination dimethyl carbonate (DMC)/diesel-blended fuels and the exhaust gas recirculation (EGR) can decrease nitrogen oxide (NOX) and soot emissions simultaneously emitted from the compressio...

Numerous researches have been conducted on developing effective intelligent fault diagnosis systems. As a commonly used deep learning technique, stacked autoencoders (SAEs) have shown the ability of automatic feature extraction and classification. However, the traditional SAEs have two deficiencies: (1) The multi-layer structure and too many epoch number always require plenty of time for training. (2) The internal covariate shift problem exists in deep networks, leading to that it is hard to train the model with saturating nonlinearities. To overcome the aforementioned deficiencies, a recently developed optimization method called batch normalization is introduced into deep neural networks (DNNs). The method is employed in every layer of DNNs to obtain a steady distribution of activation values during training. Besides, it applies normalization technique on every mini-batch training. As a result, it offers an easy starting condition for training, and the training epoch number can also be reduced. Thus, fault features can be extracted rapidly in an elegant way. A bearing and a gearbox datasets are adopted to conform the effectiveness of the proposed method. The experimental results show that the proposed method can not only solve the two deficiencies of SAEs, but also achieve a superior performance to the existing methods.

Renal cell cancers typically exhibit high metastasis and recurrence, and this is thought to be due to renal cancer stem cells (CSCs). Meanwhile, aberrant activation of Sonic hedgehog (Shh) signaling is linked with CSCs. Resveratrol has direct or indirect impacts on the pathological activities of CSCs. However, the effects of resveratrol on renal CSCs remain to be elucidated.We cultured renal CSCs in serum-free medium. Western blotting was used to analyze the expression levels of related proteins. The mRNA changes were detected by qRT-PCR after resveratrol treatment. The CD133Renal CSCs were enriched by tumorsphere formation assays of ACHN and 786-O cells. Resveratrol treatments markedly decreased the size and number of cell spheres and downregulated the expression of the Shh pathway-related proteins and CSCs markers. Moreover, we observed that resveratrol inhibited cell proliferation and induced cell apoptosis, while purmorphamine upregulated the Shh pathway and weakened the effects of resveratrol on renal CSCs.These results suggested that resveratrol is a potential novel therapeutic agent that targets inactivation of renal CSCs by affecting the function of the Shh pathway.

Traditional intelligent fault diagnosis methods take advantage of diagnostic expertise but are labor-intensive and time-consuming. Among various unsupervised feature extraction methods, sparse filtering computes fast and has less hyperparameters. However, the standard sparse filtering has poor generalization ability and the extracted features are not so discriminative by only constraining the sparsity of the feature matrix. Therefore, an improved sparse filtering with L1 regularization (L1SF) is proposed to improve the generalization ability by improving the sparsity of the weight matrix, which can extract more discriminative features. Based on Fourier transformation (FFT), L1SF, softmax regression, a new three-stage intelligent fault diagnosis method of rotating machinery is developed. It first transforms time-domain samples into frequency-domain samples by FFT, then extracts features in L1-regularized sparse filtering and finally identifies the health condition in softmax regression. Meanwhile, we propose employing different activation functions in the optimization of L1SF and feedforward for considering their different requirements of the non-saturating and anti-noise properties. Furthermore, the effectiveness of the proposed method is verified by a bearing dataset and a gearbox dataset respectively. Through comparisons with the standard sparse filtering and L2-regularized sparse filtering, the superiority of the proposed method is verified. Finally, an interpretation of the weight matrix is given and two useful sparse properties of weight matrix are defined, which explain the effectiveness of L1SF.

Intelligent fault diagnosis methods are promising in dealing with mechanical big data owing to its efficiency in extracting discriminative features automatically. Sparse filtering (SF) is a simple and effective unsupervised feature extraction method aiming at optimizing the feature sparsity. However, the sparsity realized by SF is irregular and the features are unnecessarily discriminative for further classification. Hence, a simple and fast supervised feature extraction algorithm called supervised regularized sparse filtering (SRSF) is proposed, which explores a new way to optimize for sparsity. The supervised feature extraction is realized through fusing a novel parameterized sparse label matrix (PSLM) into the feature matrix to regular the sparsity. Meanwhile, a new objective function is developed together with it, and they work together to quicken the network convergence. In addition, SRSF can find out the specific frequencies from the learned weight matrix for each health condition innovatively, which connects the proposed method with traditional signal processing techniques. Furthermore, based on SRSF, a three-stage fault diagnosis network is developed. Experiments on a bearing case and a gearbox case are conducted separately to verify its effectiveness, and comparisons with the state of the art confirm its superiority.

Effective data-driven rotating machine fault diagnosis has recently been a research topic in the diagnosis and health management of machinery systems owing to the benefits, including safety guarantee, labor saving, and reliability improvement. However, in vast real-world applications, the classifier trained on one dataset will be extended to datasets under variant working conditions. Meanwhile, the deviation between datasets can be triggered easily by rotating speed oscillation and load variation, and it will highly degenerate the performance of machine learning-based fault diagnosis methods. Hence, a novel dataset distribution discrepancy measuring algorithm called high-order Kullback–Leibler (HKL) divergence is proposed. Based on HKL divergence and transfer learning, a new fault diagnosis network which is robust to working condition variation is constructed in this paper. In feature extraction, sparse filtering with HKL divergence is proposed to learn sharing and discriminative features of the source and target domains. In feature classification, HKL divergence is introduced into softmax regression to link the domain adaptation with health conditions. Its effectiveness is verified by experiments on a rolling bearing dataset and a gearbox dataset, which include 18 transfer learning cases. Furthermore, the asymmetrical performance phenomenon found in experiments is also analyzed.

In model-based system engineering (MBSE), reuse of existing models in the development of a new system can be advantageous. Automatic assignment of existing models to each design task within a design task set has been proven to be feasible. However, while several studies have discussed the significance of models in MBSE and methodologies for models reuse, solving the model reusability problem through a model assignment method has not been discussed. Additionally, a significant challenge in model assignment is to address the conflict between the maximization of the model value summations, which are yielded by assigning the models to a design task set, and the minimization of the execution cycle of the task set. This study (a) proposes a design-task-oriented model assignment method that establishes a multiobjective model, based on a model assignment integration framework, and (b) designs a differential-evolution-combined adaptive nondominated sorting genetic algorithm-II to provide an optimal tradeoff between maximizing the total model values and minimizing the execution cycle of the task set. By comparing the performance of the algorithm in resolving the assignment of models to a design task set with those of two conventional algorithms in a phased-array radar development project, the algorithm’s performance and promotion of system development are verified to be superior. The new method can be applied for developing model scheduling software for MBSE-compliant product development projects to improve using effects of the models and development cycle.

Intelligent algorithm is widely used in the field of device health monitoring because of its good feature extraction ability. However, the fault features for rotating parts of mechanical equipment often change with the change of rotating speed. Failure labels at various speeds are impossible to obtain. In this case, the traditional deep learning model often fails to achieve good diagnostic results. In this paper, a multilayer transfer network relied on the CNN module named MTCNN is proposed to overcome the above difficulties. The GAP layer is employed in the CNN architecture. The Maximum Mean Discrepancy (MMD) transfer function is applied to the second last layer and the deep coral transfer function is used on the last layer. Finally, a set of test data from the test bench is applied to verify the manifestation of the MTCNN architecture. The performance shows that it has good diagnostic capability and strong domain adaptive capability.

Since the technology of n-butanol/diesel blending fuel coupled with post-injection (PI) strategy increases the complexity of engine combustion process, the disputes that the interpretation of the formation mechanism of pollutants are still existed. Moreover, studies on the effects of this coupling-strategy on fine-particles (DP

Recent years, cross-domain fault diagnosis of rotating machinery has been a hot topic, and various kinds of methods taking advantage of transfer learning are proposed correspondingly. Despite their success, they mainly focus on marginal distribution alignments, which ignore weighing between marginal and conditional distributions in network training. However, this kind of weighting can boost diagnosis network performance further and make it more robust. Hence, a novel transfer learning method called discriminative feature-based adaptive distribution alignment (DFADA) is proposed, which can extract discriminative features and conduct a two-stage adaptive distribution alignment on L2 ball. In DFADA, maximum mean discrepancy (MMD) and graph Laplacian regularization are fused to extract discriminative and task-specific features. Meanwhile, for comprehensive and adaptive distribution alignments, the distributions of datasets are pre-matched via MMD and further matched in feature classifier via dynamic distribution alignment (DDA), which can not only reduce both marginal and conditional distribution discrepancies but also weigh their importance adaptively. Finally, a DFADA-based fault diagnosis method for rotating machinery with volatile working conditions is constructed correspondingly. The validity of the proposed method is also confirmed by extensive experiments and comparisons with some state of the arts on 18 transfer learning cases.

Recently, although vast intelligent fault diagnosis methods are proposed, their validities are mostly confirmed via balanced datasets, which cannot always hold for the class-imbalance problem prevails among datasets in real-world applications. Hence, a class imbalance-robust network is proposed for bearing fault diagnosis, which tackles class imbalance both in the feature extraction and classification stages. For feature extraction, balanced sparse filtering (BSF) is proposed, which innovatively introduces kurtosis into balancing the discriminative feature extraction capabilities of different classes. Meanwhile, the balancing matrix is also proposed in BSF to remedy the parameter updating imbalance caused by class imbalance. For feature classification, the balancing matrix is also embedded into softmax regression to enhance the balancing capability. Furthermore, extensive experiments on bearing vibration signal datasets are conducted in validity confirmation. Additionally, an interesting property of BSF is investigated, and the phenomenon that class imbalance is actually a two-edge sword is interpreted.

A gasoline compression ignition (GCI) engine would cause difficulties of ignition stability or even misfired phenomenon at low loads. Internal exhaust gas recirculation (IEGR) technology is used to broaden the operating range and provide a GCI engine with combustion stability. In this paper, the effects of the IEGR rate (10–90%) and excess air ratio (EAR) (1.0–4.0) on the combustion performance and emissions from a GCI were investigated on a fully variable valve engine, with a particular focus on fuel economy and emissions from a GCI within less than or equal to 5% of the coefficient of variation of the indicated mean effective pressure (COVIMEP). And based on the condition of COVIMEP = 5%, the difference values between maximum and minimum cycle pressure and knock are analyzed in detail. The study finds that excessive EGR hinders combustion but improves the ignition stability of a GCI. With the EGR rate from 10% to 60%, and the EAR is 1.5 to 3.0, the combustion efficiency that greater than 90% can be obtained. Moreover, the different values of the peak of maximum and minimum pressure from the 200 continuous cycles decrease as the EGR increases. In addition, the frequency of knock occurred is further greater than that of misfire even though the low loads with the COVIMEP ranges around 5%.

Objective: To evaluate the efficacy and safety of early unclamping laparoscopic partial nephrectomy (LPN) for complex renal tumor relative to the standard artery clamping technique (SCT). Methods: Sixty-one patients with complex renal tumor (RENAL score ≥7) underwent LPN at our institution from January 2013 to April 2017. LPN was performed via SCT in 32 patients and via the early unclamping technique (EUT) in 29 patients. Operation time, warm ischemia time (WIT), blood loss, bleeding requiring transfusion, tumor volume, excisional volume loss (EVL), complications, and renal function before and after operation of the affected kidney were compared between the groups. Results: All surgeries were successful without conversion to open or nephrectomy. EUT reduced the WIT (p < 0.001) but did not increase the complication rate (p = 0.322). Although the tumor volume and EVL were larger in the EUT than in the SCT group (p = 0.011, p = 0.001), glomerular filtration rate (GFR) reduction in the affected kidney did not significantly differ between the groups (p = 0.120). Conclusion: Early unclamping LPN for complex renal tumor is safe and efficient. Additionally, the EUT could expand the application of LPN in complex renal tumors, and make this challenging surgery easier and safer.

Machine learning is promising in vibration signalbased fault diagnosis because of its full use of big data and nonlinearity extracting capability. However, in real-world application, the network trained by a vibration signal dataset will be applied to target signal datasets with different distributions, which can be triggered easily by rotating speed oscillation and load variation. Hence, based on transfer learning, some vibration signal-based methods which are robust to working conditions have been proposed to address this problem. Nevertheless, most of them need target datasets in network training, and the network should be trained whenever it meets a new vibration signal dataset. So we construct a three-stage deep fault diagnosis network utilizing adaptive batch normalization (AdaBN), which is highly efficient for free of target datasets in training and does not need repeated training in its application. In the first stage, the vibration signal samples are processed into more regular and discriminative frequency spectra. In the second stage, a fourlayer AdaBN based deep neural network (DNN) is pre-trained by stacked autoencoders (SAE) and then finely tuned only using the source dataset. In the final step, the trained network is modified to diagnose samples from the target dataset. Extensive experiments on a gearbox and a bearing dataset, and comparisons with some other fault diagnosis methods verify its effectiveness.

Vinorelbine (VIN) is a semi-synthetic vinca alkaloid and is one of the most active agents for the treatment of solid tumors. The drug is commonly administered through a peripheral vein. Although VIN is known to cause local venous toxicity, such as drug-induced phlebitis, the mechanism responsible for the toxicity remains unclear. To investigate the role of Toll-like receptor 4 (TLR4) in VIN-induced vascular endothelial injury, human umbilical vein endothelial cells (HUVECs) were prepared from umbilical cords recovered with the written informed consent of the parents and treated with VIN for 60 min. Following the washing away of the VIN, the cells were cultured for a further 6 and 12 h, and the changes in TLR4 expression in the HUVECs were detected by quantitative polymerase chain reaction and western blot analyses. Finally, the effects of VIN on the translocation of nuclear factor κB (NF-κB) were determined by immunofluorescence and confocal analysis. The VIN-treated cells were stretched, extended and irregular while the control cells exhibited normal morphology. The TLR4 mRNA and protein levels were significantly higher in the VIN-treated HUVECs than those in the control group (P

Effective fault diagnosis method has long been a hot topic in the field of prognosis and health management of rotary machinery. This paper investigates an effective deep learning method known as sparse filtering, which is used to extract features from fault signal directly. And then, the supervised learning method softmax regression is applied to classify the fault types. The training samples are the vibration signals under a certain rotational speed and the test samples are in different rotational speeds. The key parameters of the model are optimized and analyzed through orthogonal experiments and single factor experiment. The diagnosis results show that the sparse filtering model has strong robustness for rotating machinery fault diagnosis in the case of rotating speed fluctuations.

In order to solve the problem of weak discernibility relation between the demands of knowledge in the process of product design, a knowledge organisation model based on granularity principle is put forward. The paper applies knowledge unit and knowledge point to describe product design knowledge, adopts granularity principle to perform the granulation tissue of product design knowledge, monitors the classification, association and inference of knowledge points according to task requirements, structures and formalises the related knowledge, and ultimately provides knowledge service in the form of knowledge unit. Through the analysis of a case, the method is proven to be effective to improve the relevance of knowledge and to improve the efficiency of knowledge service.

Machine learning-based intelligent fault diagnosis methods have gained extensive popularity and been widely investigated. However, in previous works, a major assumption accepted by default is that the training and testing datasets share the same distribution. Unfortunately, this assumption is mostly invalid in real-world applications for working condition variation of rotating machine can cause the distribution discrepancy between datasets easily, which results in performance degeneration of traditional diagnosis methods. Aiming at it, although some deep learning and transfer learning-based methods are proposed and validated effective recently, the dataset distribution alignments of them mainly focus on marginal distribution alignments, which are not powerful enough in some scenarios. Hence, a novel distribution discrepancy evaluating method called auto-balanced high-order Kullback–Leibler (AHKL) divergence is proposed, which can evaluate both the first and higher-order moment discrepancies and adapt the weights between them dimensionally and automatically. Meanwhile, smooth conditional distribution alignment (SCDA) is also developed, which performs excellently in aligning the conditional distributions through introducing soft labels instead of adopting widely-used pseudo labels. Furthermore, based on AHKL divergence and SCDA, weighted joint distribution alignment (WJDA) is developed for comprehensive joint distribution alignments. Finally, built on WJDA, we construct a novel deep transfer network (DTN) for rotating machine fault diagnosis with working condition variation. Extensive experimental evaluations through 18 transfer learning cases demonstrate its validity, and further comparisons with the state of the arts also validate its superiority.

Dimethyl carbonate (DMC) has high oxygen content (53.3%) and a non-toxic preparation, which is a promising additive for diesel fuel. Exhaust gas recirculation (EGR) technology inhibits the formation of NOX. The combination of DMC with EGR is considered to be a practical means of meeting emissions regulations. In this study, a four-cylinder diesel engine was used to study the effects of DMC and EGR on engine emissions and performance. The three test fuels included a pure diesel (D100), 10% DMC and 90% diesel (DMC10), and 20% DMC and 80% diesel (DMC20). The results show that the DMC20 mixtures created a longer ignition delay, and resulted in the highest pressure and heat release rate. However, the use of DMC shortened the duration of the combustion. The brake specific fuel consumption of the DMC blends was higher than that of diesel, and the brake thermal efficiency of the DMC10 was higher than diesel at the same EGR. Through an energy balance analysis, DMC20 has a significant potential for exhaust gas energy recovery. Overall, DMC20 had the best combustion performance when it was used in combination with EGR rates of 20%−30%. When EGR = 40%, the NOX emission of DMC20 was reduced by 64.9%, and the soot emission was reduced by 80%. The nucleation mode and aggregation mode particulate matter (PM) emissions were less affected by EGR, the total PM concentration of DMC20 reduced to less than 20% of D100, and the mass concentration was reduced by approximately 70%.

Supercritical carbon dioxide Brayton cycle is considered one of the most promising systems for waste heat recovery of engines because of its compactness and high energy efficiency. To further improve the fuel utilization ratio and solve the difficulties of waste heat recovery of high temperature exhaust gas, a regenerative supercritical carbon dioxide Brayton cycle/organic Rankine cycle dual loop is proposed for cascade utilization of exhaust heat from a dual-fuel engine. The regenerative supercritical carbon dioxide Brayton cycle of the proposed system is powered by the waste heat contained in the exhaust gas. The working fluid in the organic Rankine cycle is pre-heated by CO2 exiting the regenerator and then further heated by the residual heat of the exhaust gas. The flow rates of the working fluids in both sub cycles are adjusted to match the waste heat recovery system to respond to the changing conditions of the dual-fuel engine. The results revealed that the maximum net power output of this system is up to 40.88 kW, thus improving the dual-fuel engine power output by 6.78%. Therefore, such a regenerative supercritical carbon dioxide Brayton cycle/organic Rankine cycle dual loop system design enables the thorough recovery of high temperature exhaust heat, leading to higher energy efficiency and lower fuel consumption of the engine.

Intelligent fault diagnosis methods are promising in dealing with mechanical big data owing to their efficiency in extracting representative features. However, there is always an undesirable shift variant property embedded in raw vibration signals, which hinders the direct use of raw signals in fault diagnosis networks. A convolutional neural network (CNN) is a widely used and efficient method to extract features in various fields for its excellent sparse connectivity, equivalent representation and weight sharing properties. However, raw CNN is time-consuming and has a marginal problem. Heuristically, we construct a fault diagnosis framework called adaptive overlapping CNN (AOCNN) to deal with one dimension (1D) raw vibration signals directly. The shift variant problem is dealt with by the adaptive convolutional layer and the root-mean-square (RMS) pooling layer, and the marginal problem embedded in the CNN is relieved by employing the overlapping layer. Meanwhile, the AOCNN is also characterized by adopting different convolutional strides and diverse activation functions in feature extraction network training and usage. Furthermore, sparse filtering is embedded into the AOCNN, and experiments on a bearing dataset and a gearbox dataset are conducted to verify the validity of the proposed method separately. When compared with other state-of-the-art methods this method reveals its superiority.

Started from electronic information service connotation, this paper advances the new concept of the electronic information service based on quantization of user experience. Then, the user experience was classified into four levels. Furthermore, combining the three dimensions with the four experience levels, it built a user experience quantitative index system of electronic information services, and ultimately set up a user experience quantitative concept model of electronic information services. It can provide necessary theoretical basis and practical guidance for further empirical research.

Virtually all mail sorting machines currently used in China only recognize post code and ignore the useful destination address information on the envelopes. This paper discusses how to efficiently utilize such important information on handwritten Chinese envelopes in order to improve the sorting performance. For this purpose, two particular problems are addressed, respectively. One is the location of destination address block (DAB) on the envelope, and a new bottom-up location method is described in detail. The other is the interpretation of handwritten Chinese destination address strings. We present our effort on using as many geometric constraints as possible in the string segmentation. Then a novel address interpretation algorithm with global optimization is proposed. It combines the segmentation, recognition and address context information by the best-path search. The effectiveness of the proposed algorithms is fully demonstrated by our experiments on real envelopes.

Considering the effect of the transmitting data, this paper analyzes the performance of circular SA with 8 array elements (AE) in different application scenarios. Double code- channels pointing to the same direction is the basic mode of TDD- CDMA when transmitting voice signals. However, our research has discovered that the system working in that mode is unstable. If more code-channels pointing to different direction are used, then, according to the combination of the transmitting data, the direction for the major beam-lobe will change greatly, which is hard to forecast. Moreover, when the working code-channels rise to 8 ones, the directional diagram of the beam will be similar with that of an un-directional antenna. Anyway, when using circular SA with 8 AE in TDD-CDMA system, it will be difficult to ensure the stability of directional transmission.