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This paper investigates the impact of ammonia (NH3) kinetics on the ignition mechanism of dimethyl ether (DME), a topic minimally addressed in existing literature, by utilizing a hypothetical NH3 representative species with identical thermodynamic properties and atomic mass to actual NH3, yet remaining inert during reactions, thereby distinguishing the kinetic effects from thermal and dilution influences. Kinetic analysis via zero-dimensional (0D) idealized reactor calculations shows that DME ignition in the ammonia-air atmosphere is still primarily governed by peroxy kinetics, yet ammonia kinetics significantly modify the ignition reaction pathways of DME. Specifically, during the low-temperature oxidation preparation stage, ammonia oxidation yields nitrogen-containing species that (e.g., NO2, NO, NH2), through CN reactions, reduce the flux in the keto-hydroperoxides (KET) formation pathway in DME. The NH3 oxidation pathway also competes for OH radicals, which disfavors DME ignition. The rapid decomposition of KET during the low-temperature heat release (LTHR) stage emits a substantial amount of OH radicals, increasing temperature and causing the shift from chain branching to chain propagation pathways in DME oxidation, leading to significant CH2O production and decreased reaction reactivity. This shift also promotes the hydrogen‑oxygen reaction mechanism, transitioning the controlling mechanism from the KET mechanism to the hydrogen peroxide (H2O2)-loop mechanism. The LTHR stage further enhances CN reactions in the CH3 pathway, favoring NO production and increasing the flux of NO and HO2 reactions releasing OH radicals. Moreover, the ammonia oxidation pathway, characterized by HO2 radical consumption and concurrent OH radical and H2O2 generation, significantly influences the H2O2-loop system, resulting in a diminished reaction flux in the H → HO2 → H2O2 mechanism during the thermal ignition preparation stage. In summary, these findings underscore the significance of CN interactions in the NH3/DME ignition process and highlight the necessity of considering CN interactions in mixed fuels between ammonia and other high-reactivity fuels (e.g., diesel with higher carbon atoms), for accurate ignition prediction.

[Introduction] In order to facilitate the design optimisation of foundation for offshore wind turbines, it is necessary to promote application of advanced geotechnical constitutive models that are theoretically sound and capable of modelling realistic soil behaviours in real engineering scenarios. [Method] Firstly, the theoretical framework of the Unified Hardening model (UH model), an advanced constitutive model, was introduced. Then, further modifications oriented to requirements in real engineering applications were presented. Finally, the modified model was applied to the pile-soil analysis of large-diameter monopoles, and the results were analyzed at the macroscopic and element level to verify its effectiveness and practicability. [Result] The UH_G0 model obtained through modification and verification is applicable to addressing issues in real engineering practices. The parameter "initial void ratio" in the original UH model is replaced by "overconsolidation ratio". By incorporating the Andersen empirical formula, the shear modulus is decoupled from the constitutive parameter κ, significantly improving the initial shear stiffness of the original UH model, and thus allowing the UH model to be applicable to the deformation (stiffness) sensitive boundary value problems. [Conclusion] As an advanced constitutive model with a well-defined theoretical framework and physically meaningful parameters, the UH model demonstrates high potential for practical engineering applications. The two modifications tailored to engineering design requirements are necessary, which improve the convenience in application of the UH model to address boundary value problems and the accuracy in calculating initial stiffness, respectively. The modified UH_G0 model is proven to be effective and practical for solving practical engineering problems in the process of finite element simulation.

High resolution imaging is achieved using increasingly larger apertures and successively shorter wavelengths. Optical aperture synthesis is an important high-resolution imaging technology used in astronomy. Conventional long baseline amplitude interferometry is susceptible to uncontrollable phase fluctuations, and the technical difficulty increases rapidly as the wavelength decreases. The intensity interferometry inspired by HBT experiment is essentially insensitive to phase fluctuations, but suffers from a narrow spectral bandwidth which results in a lack of effective photons. In this study, we propose optical synthetic aperture imaging based on spatial intensity interferometry. This not only realizes diffraction-limited optical aperture synthesis in a single shot, but also enables imaging with a wide spectral bandwidth, which greatly improves the optical energy efficiency of intensity interferometry. And this method is insensitive to the optical path difference between the sub-apertures. Simulations and experiments present optical aperture synthesis diffraction-limited imaging through spatial intensity interferometry in a 100 nm spectral width of visible light, whose maximum optical path difference between the sub-apertures reaches 69λ. This technique is expected to provide a solution for optical aperture synthesis over kilometer-long baselines at optical wavelengths.

The aim of this study was to evaluate the effect of conjugated linoleic acid (CLA) on milk fat globule (MFG) size and the ruminal microbiome of goats. Twenty-four mid-lactation Saanen dairy goats weighing 49 ± 4.5 kg (168 ± 27 d in milk, 1.2 ± 0.1 kg milk/d, 2–3 years old) were randomly divided into four groups—a control (CON) group, which was fed a basal diet, and three CLA supplementation groups, in which 30 g CLA (low-dose group, L-CLA), 60 g CLA (medium-dose group, M-CLA), or 90 g CLA (high-dose group, H-CLA) was added to the basal diet daily. The experiment lasted for 21 days, during which time goat milk was collected for composition and MFG size analysis. On day 21 of feeding, ruminal fluid was collected from the CON and H-CLA groups for analysis of the changes in microorganismal abundance. The results showed that CLA supplementation did not affect milk production, milk protein, or lactose content in the dairy goats (p > 0.05), but significantly reduced the milk fat content (p < 0.01) compared with the CON group. The CLA supplementation significantly decreased the D[3,2] and D[4,3] of the MFGs in a dose-dependent manner (p < 0.01). Moreover, dietary CLA inclusion increased the proportion of small-sized MFGs and decreased that of large-sized ones. The results of 16S rRNA gene sequencing showed that CLA-induced milk fat depression in dairy goats was accompanied by significant changes in the relative abundance of ruminal bacterial populations, most of which belonged to the Firmicutes and Bacteroidetes phyla. The relative abundance of Rikenellaceae_RC9_gut_group and Prevolellaceae_UCG-003 in Bacteroidetes and UCG-002, Succiniclasticum, and norank_f__norank_o__Clostridia_vadinBB60_group in Firmicutes was significantly higher in the CON group than in the H-CLA group. In contrast, the relative abundance of norank_f__UCG-011, norank_f_Eubacterium_coprostanoligenes_group, unclassified_f__Lachnospiraceae, and UCG-001 in Firmicutes and norank_f__Muribaculaceae in Bacteroidetes was significantly higher in the H-CLA group than in the CON group. Correlation analysis showed that the milk fat content was negatively correlated with the relative abundance of some bacteria, including members of Firmicutes and Bacteroidetes. Similarly, MFG size (D[3,2] and D[4,3]) was negatively correlated with several members of Firmicutes and Bacteroidetes, including Lachnospiraceae, norank_f__UCG-011, UCG-001, norank_f__Eubacterium_coprostanoligenes_group (Firmicutes), and norank_f__Muribaculaceae (Bacteroidetes), while positively correlated with the relative abundance of some members of Firmicutes and Bacteroidetes, including Mycoplasma, Succiniclasticum, norank_f__norank_o__Clostridia_vadinBB60_group, UCG-002 (Firmicutes), and Rikenellaceae_RC9_gut_group (Bacteroidetes). Overall, our data indicated that CLA treatment affected milk fat content and MFG size in dairy goats, and these effects were correlated with the relative abundance of ruminal bacterial populations. These results provide the first evidence to explain the mechanism underlying diet-induced MFG from the perspective of the ruminal microbiome in dairy goats.

Abstract Two-dimensional arrays of magnetically coupled nanomagnets provide a mesoscopic platform for exploring collective phenomena as well as realizing a broad range of spintronic devices. In particular, the magnetic coupling plays a critical role in determining the nature of the cooperative behavior and providing new functionalities in nanomagnet-based devices. Here, we create coupled Ising-like nanomagnets in which the coupling between adjacent nanomagnetic regions can be reversibly converted between parallel and antiparallel through solid-state ionic gating. This is achieved with the voltage-control of the magnetic anisotropy in a nanosized region where the symmetric exchange interaction favors parallel alignment and the antisymmetric exchange interaction, namely the Dzyaloshinskii-Moriya interaction, favors antiparallel alignment of the nanomagnet magnetizations. Applying this concept to a two-dimensional lattice, we demonstrate a voltage-controlled phase transition in artificial spin ices. Furthermore, we achieve an addressable control of the individual couplings and realize an electrically programmable Ising network, which opens up new avenues to design nanomagnet-based logic devices and neuromorphic computers.

With the rapid increase in smart grid users and the increasing cost of user data transmission, proposing an encryption method that does not increase the construction cost while increasing the user ceiling has become the focus of many scholars. At the same time, the increase in users will also lead to more security problems, and it is also necessary to solve the privacy protection for users during information transmission. In order to solve the above problems, this paper proposes an aggregated ring encryption scheme based on the SM2 algorithm with special features, referred to as SM2-CLARSC, based on the certificateless ring signcryption mechanism and combining with the aggregate signcryption. SM2-CLARSC is designed to satisfy the basic needs of the smart grid, and it can be resistant to replay attacks, forward security and backward security, etc. It has better security and higher efficiency than existing solutions. Comparing SM2-CLARSC with existing typical solutions through simulation, the result proves that this solution has more comprehensive functions, higher security, and significant computational efficiency improvement.

Abstract Microclimate heterogeneity in Southeast Asia and its implications for biodiversity remain understudied. We investigated microclimate changes between 1982 and 2017 using high‐resolution data and a patch‐mosaic model. Our findings reveal a decline in microclimate heterogeneity (as indicated by Shannon's diversity index) and a significant increase in elevation. Topographical factors (∼50.6%) and climatic factors (∼21.5%) primarily influenced the decline. Spatial heterogeneity weakened with larger moving windows and coarser grid‐scale data sets. Our study suggests that shifting mountainous microclimates play a crucial role in mediating biodiversity in Southeast Asia.

This study establishes and verifies the finite element models (FEM) of MSCCJs to analyze the mechanical performance of modular steel–concrete composite joints (MSCCJs) under monotonic static load without considering the floor slab composite effect (FSCE). Subsequently, 12 MSCCJ models incorporating the FSCE are constructed using ABAQUS. The study evaluates the effects of varying column axial compression ratios (n = 0.15, 0.25, 0.4, 0.6), floor width (w = 800, 1200 mm), floor thickness (t = 90, 110, 120 mm), and floor reinforcement ratio (r = 6 %, 8 %, 12 %) on the bending moment, initial stiffness (k0), ductility, and stress transfer of the joints. The results indicated that as n increases, the shear stress concentration in the joint core module (JCM) and the steel framework at the lower column end become more pronounced. Concurrently, the bearing moment and k0 of the joint also increase. However, once n surpasses 0.4, the joint's plastic properties diminish upon damage. With an increment in w, the k0 and ductility deformation of the joint remain stable and then decline. At w = 1000 mm, the joint's bearing capacity, stress distribution, and deformation performance reach their optimum. Since it significantly impacts stress distribution, its effect on the joint's mechanical performance is minimal. Lastly, the value of r enhances the ductility of MSCCJs and the stress concentration in the reinforcement. It is advisable to maintain r between 1.1 % and 1.7 %.

Abstract NOC2 like nucleolar associated transcriptional repressor (NOC2L) was recently identified as a novel inhibitor of histone acetyltransferase (INHAT). NOC2L is found to have two INHAT function domains and regulates histone acetylation in a histone deacetylases (HDAC) independent manner, which is distinct from other INHATs. In this review, we summarize the biological function of NOC2L in histone acetylation regulation, P53-mediated transcription, ribosome RNA processing, certain development events and carcinogenesis. We propose that NOC2L may be explored as a potential biomarker and a therapeutic target in clinical practice.

Abstract High-resolution optical imaging through or within thick scattering media is a long sought after yet unreached goal. In the past decade, the thriving technique developments in wavefront measurement and manipulation do not significantly push the boundary forward. The optical diffusion limit is still a ceiling. In this work, we propose that a scattering medium can be conceptualized as an assembly of randomly packed pinhole cameras and the corresponding speckle pattern as a superposition of randomly shifted pinhole images. The concept is demonstrated through both simulation and experiments, confirming the new perspective to interpret the mechanism of information transmission through scattering media under incoherent illumination. We also analyze the efficiency of single-pinhole and dual-pinhole channels. While in infancy, the proposed method reveals a new perspective to understand imaging and information transmission through scattering media.

Background and objective Malignant pleural effusion is one of the common clinical manifestations of patients with lung adenocarcinoma. Patients with pleural effusion at the initial diagnosis of lung adenocarcinoma usually indicate poor prognosis. Epidermal growth factor receptor (EGFR) mutations mainly occur in patients with lung adenocarcinoma. Patients with different mutant subtypes have different prognosis. The clinical characteristics and prognostic factors of patients with EGFR mutated lung adenocarcinoma of different molecular subtypes combined with pleural effusion at initial diagnosis are still unclear. This study was designed to explore the clinical characteristics and prognostic factors of these patients in order to provide management recommendations for them. Methods A retrospective analysis of the clinical characteristics, treatment, outcomes and progression-free survival (PFS) of first-line treatment in patients with EGFR mutated lung adenocarcinoma combined with pleural effusion at initial diagnosis admitted to Department of Medical Oncology and Radiation Sickness, Peking University Third Hospital from January 2012 to June 2021 was performed. Pearson's chi-square test or Fisher's exact test were performed for comparison between groups. Kaplan-Meier method was performed for survival analysis and Cox proportional risk regression model was performed for multivariate analysis. Results 76 patients met the inclusion criteria in this study. The incidences of EGFR classical mutations 19del, 21L858R and non-classical mutations were 46.0%, 38.2% and 15.8%, respectively among these patients. There was no significant difference between the three mutations in terms of gender, age, presence of dyspnea at presentation, whether other distant metastases were combined, site of pleural effusion, volume of pleural effusion, presence of other combined effusions, tumor-node-metastasis (TNM) stage, presence of other gene mutations, and treatment of pleural effusion (P>0.05). In patients with EGFR classical mutations 19del or 21L858R or non-classical mutations subtype, the proportion of chemotherapy in first-line regimens were 17.1%, 20.7% and 58.3%, respectively (P=0.001); and first-line disease control rates were 94.3%, 75.9% and 50%, respectively (P=0.003); pleural effusion control rates were 94.3%, 79.3% and 66.7%, respectively (P=0.04); PFS were 287 d, 327 d and 55 d, respectively (P=0.001). Univariate analysis showed that EGFR mutation subtype, control of pleural effusion, first-line treatment agents, and first-line treatment efficacy were significantly associated with PFS (P

Abstract Immune checkpoint inhibitors (ICIs) have been shown to improve survival in patients with advanced or metastatic esophageal cancer. However, ICI‐based rechallenges after recovery from fatal adverse events (AEs) are equivocal, especially in patients who have already undergone treatment‐related AEs. In this study, we report the case of a patient with advanced esophageal squamous cell cancer (ESCC) who developed a treatment‐related tracheoesophageal fistula (TEF) after two cycles of ICI administration, provided in combination with traditional chemotherapeutics. After spontaneous healing of the TEF, the patient was again treated with ICIs and achieved a durable clinical response without any signs of fistula recurrence. Successful ICI‐based rechallenges after fistula healing have rarely been reported. Therefore, ICI‐based rechallenge in patients with esophageal cancer having an Eastern Cooperative Oncology Group (ECOG) performance status (PS) 0–1 after serious AEs may serve as a clinically viable treatment strategy that should be administered under close monitoring.

Abstract Background Aerobic glycolysis is an emerging hallmark of cancer. Although some studies have constructed glycolysis-related prognostic models of colon adenocarcinoma (COAD) based on The Cancer Genome Atlas (TCGA) database, whether the COAD glycolysis-related prognostic model is appropriate for distinguishing the prognosis of rectal adenocarcinoma (READ) patients remains unknown. Exploring critical and specific glycolytic genes related to READ prognosis may help us discover new potential therapeutic targets for READ patients. Results Three gene sets, HALLMARK_GLYCOLYSIS, REACTOME_GLYCOLYSIS and REACTOME_REGULATION_OF_GLYCOLYSIS_BY_FRUCTOSE_2_6_BISPHOSPHATE_METABOLISM, were both significantly enriched in both COAD and READ through glycolysis-related gene set enrichment analysis (GSEA). We found that six genes (ANKZF1, STC2, SUCLG2P2, P4HA1, GPC1 and PCK1) were independent prognostic genes in COAD, while TSTA3 and PKP2 were independent prognostic genes in READ. Glycolysis-related prognostic model of COAD and READ was, respectively, constructed and assessed in COAD and READ. We found that the glycolysis-related prognostic model of COAD was not appropriate for READ, while glycolysis-related prognostic model of READ was more appropriate for READ than for COAD. PCA and t-SNE analysis confirmed that READ patients in two groups (high and low risk score groups) were distributed in discrete directions based on the glycolysis-related prognostic model of READ. We found that this model was an independent prognostic indicator through multivariate Cox analysis, and it still showed robust effectiveness in different age, gender, M stage, and TNM stage. A nomogram combining the risk model of READ with clinicopathological characteristics was established to provide oncologists with a practical tool to evaluate the rectal cancer outcomes. GO enrichment and KEGG analyses confirmed that differentially expressed genes (DEGs) were enriched in several glycolysis-related molecular functions or pathways based on glycolysis-related prognostic model of READ. Conclusions We found that a glycolysis-related prognostic model of COAD was not appropriate for READ, and we established a novel glycolysis-related two-gene risk model to effectively predict the prognosis of rectal cancer patients.

Background: Postmenopausal osteoporosis (PMOP) is a disease with a high prevalence in postmenopausal women and is characterized by an imbalance in bone metabolism, reduced bone mass, and increased risk of fracture due to estrogen deficiency. Jiangu granules (JG) is a compound prescription used in traditional Chinese medicine to treat PMOP. However, its definitive mechanism in PMOP is unclear. This study used a 4D label-free quantitative proteomics method to explore the potential therapeutic mechanism of JG in an ovariectomy (OVX) rats’ model.Materials and methods: A rat model of PMOP was established by removing the ovaries bilaterally. Nine 3-month-old specific-pathogen-free female SD rats. The nine rats were randomly divided into 3 groups (n = 3 in each group): the sham-operated group (J), the ovariectomy group (NC), and the JG treatment (ZY) group. Proteins extracted from the bone tissue of the lumbar spine (L3, L4) of three groups of rats were analyzed by 4D label-free quantitative proteomics, and proteins differentially expressed after JG treatment and proteins differentially expressed after de-ovulation were intersected to identify proteins associated with the mechanism of PMOP by JG treatment.Result: There were 104 up-regulated and 153 down-regulated differentially expressed proteins (DEPs) in the J group vs. NC group, 107 up-regulated and 113 down-regulated DEPs in the J group vs. ZY group, and 15 up-regulated and 32 down-regulated DEPs in the NC group vs. ZY group. Six potential target proteins for JG regulation of osteoblast differentiation in OVX rats were identified by taking intersections of differential proteins in the J group vs. NC group and NC group vs. ZY group.Conclusion: JG may exert therapeutic effects by modulating the expression levels of target proteins associated with osteoblast differentiation to enhance osteoblast differentiation in OVX rats. These results further uncovered the target proteins and specific mechanisms of JG in treating PMOP, providing an experimental basis for the clinical application of JG in treating PMOP.

Recently, the requirement for cooling capacity decreased when the driving energy changed from liquid fuel to lithium batteries. Therefore, the structure and location of the forecabin could be adjusted based on the aerodynamic performance. The current study conducted a significant number of simulations in order to find out the effects of the internal flow through forecabin in an Ahmed body. The following conclusions have been identified:1, The flow through the forecabin would always increase the resistance of the entire body, and the drag coefficient increases, on average, by approximately 85%. 2, When the aspect ratio is higher or the position of the inlet opening is lower, the total drag coefficient is lower due to a weaker vortex strength, a simpler vortex structure and a relatively simple flow. 3, The existence of the forecabin will largely increase the oscillation frequency of the flow field by approximately 15 times compared to the original Ahmed model. Finally, the high drag coefficient moment always appears to be due to the formation of more complex or intense vortex motion. These conclusions can offer useful results and references for the structural design of the front cabin for new energy vehicles.

As a result of their cell structures, elastomeric foams exhibit high compressibility and are frequently used as buffer cushions in energy absorption. Foam pads between two surfaces typically withstand uniaxial loads. In this paper, we considered the effects of porosity and cell size on the mechanical behavior of random elastomeric foams, and proposed a constitutive model based on an artificial neural network (ANN). Uniform cell size distribution was used to represent monodisperse foam. The constitutive relationship between Cauchy stress and the four input variables of axial stretch λU, lateral stretch λL, porosity φ, and cell size θ was given by con-ANN. The mechanical responses of 500 different foam structures (20% < φ < 60%, 0.1 mm < θ < 0.5 mm) under compression and tension loads (0.4 < λU < 3) were simulated, and a dataset containing 100,000 samples was constructed. We also introduced a pre-ANN to predict lateral stretch to address the issue of missing lateral strain data in practical applications. By combining physical experience, we chose appropriate input forms and activation functions to improve ANN’s extrapolation capability. The results showed that pre-ANN and con-ANN could provide reasonable predictions for λU outside the dataset. We can obtain accurate lateral stretch and axial stress predictions from two ANNs. The porosity affects the stress and λL, while the cell size only affects the stress during foam compression.

The forestry industry is a low-carbon green industry with great potential for development, but its current development model is facing multiple dilemmas that urgently require a shift to high-quality forestry development. Neoclassical theory and endogenous growth theory consider technology innovation as the foundation of economic growth. In order to explore the impact of forestry technology innovation (FTI) on the economic growth of forestry industry, this paper uses the entropy method, spatial Durbin model, and threshold model to explore the spatial effect of forestry technology innovation on forestry industry economy for analysis and exploration of the non-linear relationship between the two, and the panel data from 31 provinces in China from 2002 to 2020 are used as the sample for empirical study. Results show that: (1) Forestry technology innovation has a significant role in promoting the economic growth of the forestry industry and has obvious spatial spillover effects, which also promotes the economic growth of forestry in neighboring regions. (2) There is a threshold effect of forestry technology innovation on the economic growth of the forestry industry. When the forestry technology innovation exceeds its optimal interval, its effect on the economic growth of forestry industry shows diminishing marginal benefits. (3) Forestry technology innovation has industrial heterogeneity on the economic growth of the forestry industry. Therefore, managers should take advantage of the growth effect of forestry technology innovation in the forestry industry at the level of sustainability, and further make full use of the spatial effect resulting from the flow of technology to establish a system of communication and learning to form a virtuous coordination of the forestry economic environment for the high-quality development of the forestry industry.

In the study of consumer behavior, we believe that a distinction should be made between the subjective mental activity of consumption and the objective process of consumption experience, and that the deviation and fluctuation of “decision utility” and the randomness of “experience utility” have essential effects on consumer behavior. For one thing, purchase often precedes experience, and consumers cannot precisely predict utility and its distribution but can only make decisions based on adaptive expectations. Secondly, there is often uncertainty in the experience of goods, making predictions more difficult. Thirdly, the revision of consumer decision utility is carried out through memory in an adaptive process. We introduce two stochastic forms of decision and experience utility functions and build a two-period model. In the model, consumers make decisions based on a pre-determined decision utility function in the first period and a randomly realized experience utility function in the second period. The analysis shows that the volatility of “decision utility” and “experience utility” affects consumers in opposite directions; the former may trigger expansionary consumption, while the latter makes consumers more cautious. Finally, the consumption behaviors in the model can be divided into 24 categories based on the dimensions of chance, systematicity, luck, and deviance, corresponding to various scenarios. The total number of consumer behavior categories is a full ranking of the size relationship of the four factors mentioned above, thus 24 categories. For example, when good luck is accompanied by chance underestimation versus systematic underestimation, it leads to a better process experience for the consumer.

GDI (Gasoline direct injection) technology is used in downsized engines for its economy and low emissions. However, if the injection strategy is not set properly, GDI will produce more emissions than conventional gasoline engines. In this paper, the effect of injection timing on GDI engine emissions under optimal phasing conditions was analyzed by using a three-dimensional (3D) computational fluid dynamics (CFD) GDI engine numerical model. The results showed that at medium engine speed and medium load advancing the injection timing from −300 to −290 CAD ATDC resulted in a more efficient and cleaner combustion process, as evidenced by the higher power output, the increased thermal and combustion efficiencies, and the reduced CO, UHC, soot emissions. The raised NO x emissions at advanced injection timing operation corresponded to the high combustion quality. There was a trade-off relation for advancing injection timing strategy. Specifically, an advanced injection timing operation would increase the amount of liquid film formed on the piston and liner, which is not favorable to clean combustion. However, advancing injection timing also provides more time for fuel-air mixing, which is beneficial for the formation of a more homogeneous mixture. The numerical simulations suggested that the advantages of earlier injection timing outweighed the disadvantages and improved engine efficiency, at least for the conditions and the engine investigated here. Moreover, the comparison indicated that changing injection timing also altered the chemical reaction pathways for pollutant species formation. Overall, all of these findings demonstrated that more fundamental work is still needed to understand the effect of injection timing on engine performance.

Background A precise map of the metabolic fluxome, the closest surrogate to the physiological phenotype, is becoming progressively more important in the metabolic engineering of photosynthetic organisms for biofuel and biomass production. For photosynthetic organisms, the state-of-the-art method for this purpose is instationary 13C fluxomics, which has arisen as a sibling of transcriptomics or proteomics. Instationary 13C data processing requires solving high-dimensional nonlinear differential equations and leads to large computational and time costs when its scope is expanded to a genome-scale metabolic network. Result Here, we present a parallelized method to model instationary 13C labeling data. The elementary metabolite unit (EMU) framework is reorganized to allow treating individual mass isotopomers and breaking up of their networks into strongly connected components (SCCs). A variable domain parallel algorithm is introduced to process ordinary differential equations in a parallel way. 15-fold acceleration is achieved for constant-step-size modeling and ~ fivefold acceleration for adaptive-step-size modeling. Conclusion This algorithm is universally applicable to isotope granules such as EMUs and cumomers and can substantially accelerate instationary 13C fluxomics modeling. It thus has great potential to be widely adopted in any instationary 13C fluxomics modeling.

Gasoline direct injection (GDI) engine are widely adopted in the automobile industry since its advantage in the fuel economy. Injection Timing (IT) is an important parameter for the GDI engine, having a great impact on the spray atomization, mixture evenness, combustion characteristics, and therefore performance of the GDI engine. With the motive of IT optimization, a three-dimensional CFD model of a single cylinder 4-stroke spark ignition GDI engine with bore of 84 mm and compression ratio of 10.3 was utilized to analyze the detailed process at different IT (270, 280, 290, 300-degree CA BTDC), while the other conditions were invariant like rotate speed at 2000 RPM. The spray, turbulence, G-equation combustion were included. The result indicated that delayed IT tended to reduce drop-wall impingement significantly but still intensified unevenness of mixture concertation severely, resulting in fuel-rich region appeared around cylinder. Because the duration available for mixing was shortened, which dominantly intensified the unevenness of the mixture. The combustion was deteriorated as the IT delayed because the excessive equivalence ratio region severely slowed flame propagation and frozen at the most uneven region, which finally degraded thermal efficiency and engine performance. In conclusion, this paper demonstrated the whole process from injection to combustion, revealing that droplet-wall impingement and available duration for mixing are dominant trade-off factors for mixture formation and following combustion process, as the IT changes.

The reliability of combustion chamber components is mainly determined by the thermal load of diesel engines. Under the plateau operation condition, diesel engine performance degradation and ablation area appear. Therefore, it is crucial to study the engine heat transfer phenomenon at different altitudes, of which the Woschni formula cannot meet the accuracy requirement. With the motive of modifying and calibrating the Woschni formula at different altitudes, a modified conjugate heat transfer (CHT) model of the combustion chamber and the cooling medium was proposed to analyze the temperature distribution of the cylinder head. The results indicated that relative errors were controlled within 5% under variant altitudes, comparing the temperature field of the numerical simulation with the single-cylinder engine experiment test data. Therefore, the modified in-cylinder conjugate heat transfer model can be used to predict the thermal load of diesel engine combustion chamber components under different altitude operating conditions.

The emphasis on environmental protection and energy security has promoted automobile engine technology toward low emission and economy. While the traditional port fuel injection engine can hardly meet the latest regulations and requirements, the gasoline direct injection (GDI) engine becomes a hot research topic because of its potential to reduce fuel consumption and emissions. Since injection timing has a determining effect on overall engine performance, this paper aimed to investigate the injection timing effects on mixture formation and emissions. A validated three-dimensional numerical simulation model of a 4-stroke GDI engine combustion chamber was adopted. In a previous work (Part A), the in-cylinder combustion process has been discussed. In this study, the simulation results demonstrate that with the injection timing advancing, the fuel–gas mixture was more uniformly distributed and combusted more completely; the CO, UHC, and soot had decreased sharply by 80.6, 99.2, and 97.5%; the NOx emission increased by 151.7%. The optimized injection timing for this case was 300 CA BTDC. Moreover, this paper studied the in-cylinder views of injection timing effects on mixture formation and emission, providing reference to optimize injection of GDI engine.

Objective: The occurrence, development, and prognosis of serious adverse events (SAEs) associated with anticancer drugs in clinical trials have important guiding significance for real-world clinical applications. However, to date, there have been no studies investigating SAEs reporting in randomized clinical trials of colorectal cancer treatments. This article systematically reviewed the SAEs reporting of phase III randomized clinical trials of colorectal cancer treatments and analyzed the influencing factors.Methods: We reviewed all articles about phase III randomized clinical trials of colorectal cancer treatments published in the PubMed, Embase, Medline, and New England Journal of Medicine databases from January 1, 1993, to December 31, 2018, and searched the registration information of clinical trials via the internet sites such as “clinicaltrials.gov”. We analyzed the correlation between the reported proportion (RP) of SAEs in the literature and nine elements, including the clinical trial sponsor and the publication time. Chi-square tests and binary logistic regression were used to identify the factors associated with improved SAEs reports. This study was registered on PROSPERO.Results: Of 1560 articles identified, 160 were eligible, with an RP of SAEs of 25.5% (41/160). In forty-one publications reporting SAEs, only 14.6% (6/41) described the pattern of SAEs in detail. In clinical trials sponsored by pharmaceutical companies, the RP of SAEs was significantly higher than that in those sponsored by investigators (57.6 versus 20.7%, p < 0.001). From 1993 to 2018, the RP of SAEs gradually increased (none (0/6) before 2000, 17.1% (12/70) from 2000 to 2009, and 34.5% (29/84) after 2009). The average RP of SAEs published in the New England Journal of Medicine (N Engl J Med), the Lancet, the Journal of the American Medical Association (JAMA), the Lancet Oncology (Lancet Oncol), and the Journal of Clinical Oncology (J Clin Oncol) was significantly higher than that published in other journals (31.9 versus 16.7%, p = 0.030). In the clinical trials referenced by clinical guidelines, the RP of SAEs was higher than that in non-referenced clinical trials (32.0 versus 15.9%, p = 0.023). Binary logistic regression analysis showed that pharmaceutical company sponsorship, new drug research, and sample size greater than 1000 were positive influencing factors for SAEs reporting.Conclusion: Although the RP of SAEs increased over time, SAEs reporting in clinical trials needs to be further improved. The performance, outcomes and prognosis of SAEs should be reported in detail to guide clinical practice in the real world.

Although microfiber (MF) loaded polypeptides have been widely studied in the field of medicine, load control and slow release remain significant challenges. Herein, polybutylene adipate terephthalate (PBAT) hollow porous MFs (HPMFs) were prepared by improving and regulating the groups and structures of the MFs to address these challenges. The capacity of the HPMFs for the loading of polypeptide can be improved. Measurements involving the use of X-ray photoelectron, energy-dispersive, and ultraviolet (UV) spectroscopies in conjunction with scanning electron microscopy measurements showed that the amount of polypeptide (arginylglycylaspartic acid, RGD) loaded on the HPMFs was significantly higher than that loaded on the MFs. Animal cell experiments revealed that PBAT grafted MAH has good bioactivity. By loading RGD onto HPMFs and MFs, RGD@HPMFs and RGD@MFs were obtained. A study on HeLa and A549 cancer cells showed that the inhibition rates of RGD@HPMFs were higher than that of RGD@MFs by 14.1% and 6.9%, respectively. The results obtained herein show that HPMF scaffold preparation by improving the material groups and regulating the structure of MFs can address the challenges associated with control of the load and sustained release of polypeptides and other drugs.

The kinematic method of limit analysis theory was adopted in this paper to calculate the seismic bearing capacity of the shallow strip foundation on a rock mass obeying the non-linear modified Hoek-Brown failure criterion. The generalized Prandtl failure mechanism was chosen, which is different from the multi-wedge failure mechanism assumption commonly used in previous research. Three angle parameters were used to control the mechanism shapes, and the equivalent friction angle and equivalent cohesive were adopted to faithfully reflect the shape characteristics of the failure mechanism. The seismic action was considered using the pseudo-static method, which is simplified to the inertial force determined by the horizontal seismic coefficient. The validation of the present method was carried out by comparing with previous analytical results and the finite element model. Subsequently, the influences of the surface overload, the properties of the rock mass, and the seismic action on the shape and ultimate bearing capacity of the failure mechanism were investigated. For the convenience of practical engineering, this paper gives the ultimate bearing capacity of strip foundations on five representative rock foundations, and the variation trend of bearing capacity with the unit weight of rock mass, surface overload, and horizontal seismic coefficient.

Background Delanzomib, a novel proteasome inhibitor, has demonstrated promising efficacy and antitumor ability in human multiple myeloma cell lines and patient‐derived cells. However, the potential therapeutic effects of delanzomib on breast cancer remain unknown. In this study, we show that delanzomib has antitumor effects and synergizes with doxorubicin (Dox) in human breast cancer cell lines. Methods Cell proliferation assay and flow cytometry were used to evaluate cell viability and apoptosis in eight human breast cancer cell lines after treatment with delanzomib or Dox. Essential molecules of the p53, MAPK, and apoptosis signaling pathways were analyzed by Western blotting. Results Delanzomib induced cell death and demonstrated synergism with Dox in all tested breast cancer cell lines. In addition, delanzomib enhanced the Dox‐induced phosphorylation of p38/JNK and the expression of transcriptional target proteins of p53, such as p21, p27, NOXA, and PUMA. Conclusion The combined regimen of the proteasome inhibitor delanzomib with Dox chemotherapy may become an effective strategy for breast cancer therapy.

In this paper, a color image encryption is proposed based on the dynamic DNA and the 4-D memristive hyper-chaos. First, chaotic matrices are generated from the 4-D memristive hyper-chaos by using the plain image, the salt key, and control parameters. Second, the dynamic encoding process is performed on three components of the plain image to obtain three DNA matrices. Third, to enhance both the security and robustness of encrypted image, dynamic confusion and diffusion are applied to the encoded DNA matrices. Finally, the encrypted image is generated by DNA decoding and components combining. The main feature of our proposed algorithm is that dynamic DNA mechanism based on hyper-chaos is performed on the processes of encoding, confusion, and diffusion. Simulation results and security analysis further demonstrate that it has a strong resistance against various attacks and outperforms other methods in the literature.

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

The indicated mean effective pressure (IMEP) is a key parameter for measuring the power output of an internal combustion engine (ICE). This indicator can be used to locate the high efficiency regions of engines. Therefore, it makes sense to predict the IMEP based on the machine learning (ML) approaches. However, different ML models are applicable to different scenarios, so it is important to choose the right model for prediction. The objective of this paper was to compare three ML models’ (ANN, SVR, RF) predictive performance in forecasting IMEP indicator with the input parameters spark timing (ST), speed and load. A validated one-dimensional (1D) computational fluid dynamics (CFD) model was employed to provide 756 sets of data for the training, validation, and testing of the model. The results indicated that the random forest (RF) model had the worst prediction performance, and support vector regression (SVR) had a slightly better prediction performance than the artificial neural network (ANN), at least for the investigations in this study. Overall, the ANN and SVR models showed good predictive performance for IMEP, as the coefficient of determination (R2) was close to unity, and the root mean squared error (RMSE) was close to zero. Whereas the overall prediction results of the RF model are acceptable, the RF model does not learn well for some internal engine laws.

Abstract Background No approved targeted agents are available for esophageal squamous cell carcinoma (ESCC). Informative genomic analysis and mouse patient-derived xenografts (PDX) also called mouse avatar can greatly expedite drug discovery. Methods Six ESCC cell lines and 7 out of 25 PDX models derived from 188 biopsies with clear molecular features were employed to evaluate the sensitivity of several EGFR blockers in vitro and in vivo, as well as the underlying antitumor mechanisms of the most promising EGFR-TKI afatinib. Mechanisms involved in acquired resistance of afatinib were explored based on established resistant cell lines and PDX models followed by an attempt to reverse resistance. Results Compared with other EGFR blockers, the second-generation EGFR-TKI afatinib exerted superior antitumor effects in ESCC, and EGFR copy number gain (CNG) or overexpression was proposed to be predictive biomarkers. Afatinib played its antitumor effects by inhibiting EGFR downstream pathways, as well as inducing apoptosis and cell cycle arrest at G1. It was increased phosphorylation of Src family kinases (SFKs), rather than MET upregulation, that conferred to acquired resistance of afatinib. Dual blockade of EGFR and SFKs could overcome afatinib resistance and warrants validation in clinical practice. Conclusion Both ESCC cell lines and PDXs with EGFR CNG or overexpression are potential candidates for afatinib, and concomitant EGFR/SFKs inhibition could reverse afatinib-acquired resistance caused by SFKs activation in ESCC.

Abstract Background Patient-derived xenograft (PDX) models with definite molecular signature are attractive preclinical models for development of novel targeted drugs. Here, we profiled and explored potential therapeutic targets based on characterized PDX models for advanced gastric cancer (AGC). Methods The genomic variation and molecular profile of 50 PDX models from AGC patients were analyzed by targeted next-generation sequencing, in situ hybridization, and immunohistochemistry. The antitumor activities of several targeted drugs were investigated in the PDX models. Furthermore, response biomarkers were explored. Results Each PDX model had individual histopathological and molecular features, and recurrent alterations in the MAPK, ErbB, VEGF, mTOR, and cell cycle signaling pathways were major events in these PDX models. Several potential drug targets, such as EGFR, MET, and CCNE1, were selected and validated in this study. Volitinib demonstrated strong antitumor activity in PDX models with MET and phosphorylated MET (pMET) overexpression. The EGFR monoclonal antibodies BK011 and cetuximab inhibited tumor growth in a PDX model with EGFR amplification. Afatinib inhibited tumor growth in the PDX models with EGFR amplification, EGFR overexpression, or HER2 amplification. Apatinib was more sensitive in the PDX models with high microvessel density. The CDK1/2/9 inhibitor AZD5438 had superior anti-tumor activity in two models with higher copy number of CCNE1. Conclusions PDX models with defined molecular signature are useful for preclinical studies with targeted drugs, and the results should be validated in larger studies with PDX models or in clinical trials.

Transfer learning and ensemble learning are the new trends for solving the problem that training data and test data have different distributions. In this paper, we design an ensemble transfer learning framework to improve the classification accuracy when the training data are insufficient. First, a weightedresampling method for transfer learning is proposed, which is named TrResampling. In each iteration, the data with heavy weights in the source domain are resampled, and the TrAdaBoost algorithm is used to adjust the weights of the source data and target data. Second, three classic machine learning algorithms, namely, naive Bayes, decision tree, and SVM, are used as the base learners of TrResampling, where the base learner with the best performance is chosen for transfer learning. To illustrate the performance of TrResampling, the TrAdaBoost and decision tree are used for evaluation and comparison on 15 UCI data sets, TrAdaBoost, ARTL, and SVM are used for evaluation and comparison on five text data sets. According to the experimental results, our proposed TrResampling is superior to the state-of-the-art learning methods on UCI data sets and text data sets. In addition, TrResampling, bagging-based transfer learning algorithm, and MultiBoosting-based transfer learning algorithm (TrMultiBoosting) are assembled in the framework, and we compare the three ensemble transfer learning algorithms with TrAdaBoost to illustrate the framework's effective transfer ability.

The spectral camera based on ghost imaging via sparsity constraints (GISC spectral camera) is a phase modulated compressive snapshot spectral imager. It makes use of the second-order intensity correlation of the light field to resolve the spatial and spectral information. In this paper, an optical design for GISC spectral camera which aims to obtain the desired spatial and spectral resolution is presented. A system calibration strategy based on few-mode optical fiber and monochrometer is developed. The snapshot spectral imaging experiments for the test targets and natural scenes are conducted using the prototype of GISC spectral camera loaded on the tethered balloon. The result of the spatial resolution, linearity, and spectra reconstruction error of the prototype is quantitatively evaluated. The distinguishable size at the distance of 1 km is around 0.34 m. The linearity is higher than 0.99 among the wavelength channels from 410 to 640 nm. The reconstructed spectra of eight color targets are compared with those measured by a commercial spectroradiometer. The average relative root mean squared error of the reconstructed spectra is 0.65.

Abstract Background Squamous cell carcinoma is the dominant type of esophageal cancer in China with many patients initially diagnosed at advanced stage. Patient-derived xenografts (PDX) models have been developed to be an important platform for preclinical research. This study aims to establish and characterize PDX models using biopsy tissue from advanced esophageal cancer patients to lay the foundation of preclinical application. Methods Fresh endoscopic biopsy tissues were harvested from patients with advanced esophageal cancer and implanted subcutaneously into NOD/SCID mice. Then, the PDXs were serially passaged for up to four generations. Transplantation was analyzed and genomic characteristics of xenografts were profiled using next-generation sequencing. Results Twenty-five PDX models were established (13.3%, 25/188). The latency period was 75.12 ± 19.87 days (50–120 days) for the first passage and it decreased with increasing passaging. Other than tumor stages, no differences were found between transplantations of xenografts and patient characteristics, irrespective of chemotherapy. Histopathological features and chemosensitivity of PDXs were in great accordance with primary patient tumors. Each PDX was assessed for molecular characteristics including copy number variations, somatic mutations, and signaling pathway abnormalities and these were similar to patient results. Conclusions Our PDX models were established from real time biopsies and molecularly profiled. They might be promising for drug development and individualized therapy.

Abstract Background We investigated antitumor activity and underlying mechanisms of DNA topoisomerase I (TopI) inhibitor gimatecan and irinotecan in gastric cancer (GC) in vitro cell lines and in vivo patient-derived xenograft (PDX) models. Methods GC cell lines SNU-1, HGC27, MGC803 and NCI-N87 were used to evaluate cell viability and apoptosis after gimatecan or irinotecan treatment, using a cell proliferation assay and flow cytometry, respectively. DNA TopI expression and critical molecules of PI3K/AKT, MAPK and apoptosis signaling pathways were analyzed with western blot. For in vivo studies, five PDXs models were treated with gimatecan or irinotecan to assess its antitumor activity. Immunohistochemistry staining of Ki-67 was performed after mice were sacrificed. Results Gimatecan inhibited the proliferation of GC cells in vitro in a dose- and time-dependent manner by inducing apoptosis, and gimatecan had greater inhibitory effects than irinotecan. In addition, both gimatecan and irinotecan demonstrated significant tumor growth inhibition in in vivo PDX models. Gimatecan treatment significantly inhibited the expression of DNA TopI, phosphorylated AKT (pAKT), phosphorylated MEK (pMEK) and phosphorylated ERK (pERK). Meanwhile, gimatecan could also activate the JNK2 and p38 MAPK pathway as indicated by upregulation of phosphorylated p38 MAPK (p-p38) and phosphorylated JNK2 (pJNK2). Conclusions For the first time, we have shown that the antitumor activity of gimatecan in GC via suppressing AKT and ERK pathway and activating JNK2 and p38 MAPK pathway, which indicated that gimatecan might be an alternative to irinotecan in the treatment of GC.

BACKGROUND AND OBJECTIVE: Aurora A, as a member of serine/threonine kinase family and a common characteristic of epithelial cancers, plays a critical role in cell mitosis. However, the clinical significance of Aurora A in non–small cell lung cancer (NSCLC) remains undetermined. METHODS: The expression of Aurora A in NSCLC and paired normal adjacent lung tissues was determined by immunohistochemistry, Western blot, and reverse transcriptase polymerase chain reaction. Receiver operating characteristic (ROC) curve analysis was employed to determine a cutoff score for Aurora A expression in a training set (n = 135). For validation, the ROC-derived cutoff score was subjected to analysis of the association of Aurora A expression with patient outcome and clinicopathological characteristics in a testing set (n = 128) and overall patients (n = 263). The correlation of Aurora A with cisplatin resistance and epithelial-mesenchymal transition (EMT) was examined in vitro in NSCLC cells by overexpression or knockdown of Aurora A. RESULTS: Aurora A expression was significantly upregulated in tumor tissues compared with paired normal tissues (P

Abstract Background Cell cycle dysregulation is common in human malignancies, and CDK4/6 inhibitors targeting cell cycle have potential antitumor activity. SHR6390 is a novel small molecule inhibitor specifically targeting the CDK4/6 pathway. However, the role of SHR6390 in esophageal squamous cell carcinoma (ESCC) remains unknown, which will be investigated in our study. Methods Eca 109, Eca 9706, and KYSE-510 ESCC cell lines were chosen for further analysis. The effect of SHR6390 on cell viability, cell cycle and cell apoptosis, the status of kinases in Cyclin D1-CDK4/6-Rb pathway were determined by MTS assay, flow cytometry, and western blotting, respectively. Cell-derived and patient-derived xenografts were established to investigate the effects of drugs in vivo. Results SHR6390 could suppress cell proliferation in vitro cell lines and inhibit tumor growth in vivo PDX models with different drug susceptibility. The effective treatment of SHR6390 induced the inhibition of phosphorylated Rb and cell cycle arrest at G1 phase both in cell lines and in xenografts. SHR6390 combined with paclitaxel or cisplatin offered synergistic inhibitory effects in cell-derived xenografts especially in Eca 9706 xenografts which showed relative lower sensitivity of SHR6390 single. Moreover, low expression of CDK6 and/or high expression of Cyclin D1 might be associated with high sensitivity of SHR6390, which would be validated in the future. Conclusions CDK4/6 inhibitor-SHR6390 exerted potential antitumor activity against ESCC cell lines and xenografts, and evaluation of CDK6 and Cyclin D1 expressions might be helpful to select patients beneficial from SHR6390, which provided evidences for future clinical trials.

Long non‐coding RNAs (lncRNAs) play important roles in tumor progression. Whereas the roles and underlying mechanisms of LINC00909 (a newly discovered lncRNA) are still unclear in glioma progression. In the present study, we identified that LINC00909 expression was significantly elevated in glioma tissues and cell lines. High LINC00909 expression was associated with advanced WHO grade, high Karnofsky Performance Score (KPS), and poor prognosis in patients with glioma. Function assays showed that LINC00909 depletion inhibited glioma cells proliferation, invasion in vitro and reduced tumor growth in vivo. In the mechanism, we found that LINC00909 could act as a ceRNA to interact with miR-194 and thereby up‐regulate the expression of MUC1-C, thus promoting the proliferation and invasion of glioma cells. Collectively, these data demonstrated that LINC00909/miR-194/MUC1-C axis regulated glioma progression and might act as a novel therapeutic target.

Service robots are becoming a part of today's life. To realise friendly human–robot interaction, an initiative service model for service robot is proposed, which is composed of three layers, i.e. information acquisition layer, in which communication information and environmental information are collected by sensors; decision layer, in which personal intention and emotional states are obtained by multi-modal emotion recognition and intention understanding, and users' demands are obtained by demand analysis model; initiative service execution layer, in which the task execution and emotion adaption of robot are carried out. Initiative service can sense motive of people, by which a humanised and real-time service could be realised. Drinking service experiments are performed, including passive service and initiative service of robots. Experimental results show that the drinking time of passive service lags behind the initiative service 30 min on average, and the initiative service renders drinking service 2 times more than passive service on average, besides, the water quantity provided by initiative service is more reasonable, which demonstrates the feasibility of the proposal. In prospect, initiative service model could be applied to many occasions in people's daily life, e.g. family service, caring for the elderly, and medical rehabilitation.

The development of a sustainable powertrain requires improved thermal efficiency. Reducing frictional power losses through the use of ultra-low viscosity oil is one of the most effective and economical ways. To assess the potential for efficiency enhancement in a new generation of future engines using low-viscosity oils, a technical analysis was conducted based on numerical simulation and theoretical analysis. This study proposes a numerical method coupling the whole multi-dynamics model and lubrication model under mixed lubrication regimes. Then, load distribution was calculated numerically and verified experimentally. Finally, this paper compares the bearing load and frictional energy loss of the main bearings when using The Society of Automotive Engineers (SAE) 15W40 and SAE 0W20 oil. The results indicate that the application of ultralow-viscosity lubricant can reduce the hydraulic friction loss up to 24%, but the asperity friction loss would increase due to the reduction in load capacity. As a result, the design of a new generation of high efficiency internal combustion engines requires careful calculation and design to balance the trade-off relations between hydraulic friction and asperity friction.

Flow forecasting is an essential topic for flood prevention and mitigation. This study utilizes a data-driven approach, the Long Short-Term Memory neural network (LSTM), to simulate rainfall−runoff relationships for catchments with different climate conditions. The LSTM method presented was tested in three catchments with distinct climate zones in China. The recurrent neural network (RNN) was adopted for comparison to verify the superiority of the LSTM model in terms of time series prediction problems. The results of LSTM were also compared with a widely used process-based model, the Xinanjiang model (XAJ), as a benchmark to test the applicability of this novel method. The results suggest that LSTM could provide comparable quality predictions as the XAJ model and can be considered an efficient hydrology modeling approach. A real-time forecasting approach coupled with the k-nearest neighbor (KNN) algorithm as an updating method was proposed in this study to generalize the plausibility of the LSTM method for flood forecasting in a decision support system. We compared the simulation results of the LSTM and the LSTM-KNN model, which demonstrated the effectiveness of the LSTM-KNN model in the study areas and underscored the potential of the proposed model for real-time flood forecasting.

For the critical engine parts such as the crankshaft, the fatigue limit load is one of the most important parameters involved the design and manufacture stage. In previous engineering applications, this parameter has always been obtained by experiment, which is expensive and time-consuming. This paper, based on the theory of critical distance (TCD), first analyzes the stress distribution of a crankshaft under its limit load. In this way, the length of the critical distance can be obtained. Then a certain load is applied to a new crankshaft made of the same material and the effective stress is calculated based on the critical distance above. Finally, the fatigue limit load of the new crankshaft can be obtained by comparing the effective stress and the fatigue limit of the material. Comparison between the prediction and the corresponding experimental data shows that the traditional TCD may result in bigger errors on some occasions, while the modified TCD proposed in this paper can provide a more satisfactory result in terms of the fatigue limit for a quick engineering prediction.

Different from conventional imaging methods, which are based on the first-order field correlation, ghost imaging (GI) obtains the image information through high-order mutual-correlation of light fields from two paths with an object appearing in only one path. As a new optical imaging technology, GI not only provides us new capabilities beyond the conventional imaging methods, but also gives out a new viewpoint of imaging physical mechanism. It may be applied to many potential applications, such as remote sensing, snap-shot spectral imaging, thermal X-ray diffraction imaging and imaging through scattering media. In this paper, we reviewed mainly our research work of ghost imaging via sparsity constraints (GISC) and discussed the application and theory prospect of GISC concisely.

Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN) is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL) algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA) is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI) datasets and two well-known databases (Japanese Female Facial Expression, Cohn–Kanade). The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.