Combustion aided by a glow plug in diesel engines under cold idling conditions

Glow plugs are widely used to promote the desired cold start and post-cold start combustion characteristics of light duty diesel engines. The importance of the glow plug becomes more apparent when the compression ratio is low. An experimental investigation of combustion initiation and development aided by the glow plug has been carried out on a single cylinder HPCR DI diesel engine with a low compression ratio of 15.5:1. High speed imaging of combustion initiated by the glow plug in a combustion bomb has been used to add understanding of initiation process. Complementary CFD studies have been carried out using ANSYS Fluent 14.0 to explore the interactions between the glow plug and the spray behavior. Observation of successful combustion initiation show that two conditions must be met, compression heating and heat transfer from the glow plug must raise temperature of gas nearby to at least 413ºC and the vapour/air equivalence ratio no lower than 0.15-0.35. The initiation site was at spray edge close to the glow plug, the flame grew locally before expanding downstream in direction of spray penetration after the end of the main injection. Experimental studies carried out on the engine indicated that the engine IMEP, heat release and combustion stability were continuously improved by using the glow plug at ambient temperatures higher than the temperature requiring the glow plug for initiation of combustion. A rapid development of premixed combustion was achieved associated with improved engine work output, heat release rate and cycle-by-cycle stability. The premixed combustion was enhanced by strengthening spray vaporization through the glow plug. In this study, the combustion behavior was enhanced by the glow plug up to ambient temperature of 20ºC. Initiation delay was shortened by a rapid development of combustion aided by the glow plug. An initiation delay model was developed to account for both physical part (transport delay) and chemical part (chemical delay). The transport delay (ms) is equivalent to the time for spray to transport to the vicinity of the glow plug, dictated by parameters including S, distance between the glow plug tip and the injector tip (mm)

Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection

In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology and framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) algorithms. In PROPEDEUTICA, all software processes in the system start execution subjected to a conventional ML detector for fast classification. If a piece of software receives a borderline classification, it is subjected to further analysis via more performance expensive and more accurate DL methods, via our newly proposed DL algorithm DEEPMALWARE. Further, we introduce delays to the execution of software subjected to deep learning analysis as a way to "buy time" for DL analysis and to rate-limit the impact of possible malware in the system. We evaluated PROPEDEUTICA with a set of 9,115 malware samples and 877 commonly used benign software samples from various categories for the Windows OS. Our results show that the false positive rate for conventional ML methods can reach 20%, and for modern DL methods it is usually below 6%. However, the classification time for DL can be 100X longer than conventional ML methods. PROPEDEUTICA improved the detection F1-score from 77.54% (conventional ML method) to 90.25%, and reduced the detection time by 54.86%. Further, the percentage of software subjected to DL analysis was approximately 40% on average. Further, the application of delays in software subjected to ML reduced the detection time by approximately 10%. Finally, we found and discussed a discrepancy between the detection accuracy offline (analysis after all traces are collected) and on-the-fly (analysis in tandem with trace collection). Our insights show that conventional ML and modern DL-based malware detectors in isolation cannot meet the needs of efficient and effective malware detection: high accuracy, low false positive rate, and short classification time.Comment: 17 pages, 7 figure

Explicating the microfoundation of SME pro-environmental operations: The role of top-managers

By recognizing the decisive role of top-managers (TMs) of small and medium-sized enterprises (SMEs), this study attempts to explicate the microfoundation of pro-environmental operations of SMEs by examining the influence of institutional pressure on managerial cognition and subsequent SME pro-environmental operations. This study highlights the personal ethics of TMs, so as to examine the moderating effect of TMs’ place attachment on SMEs’ pro-environmental operations. Empirical data is collected from a questionnaire survey of 509 SMEs in China. Hierarchical regression results are subject to cross-validation using secondary public data. This study demonstrates that coercive and mimetic pressures have inverted U-shaped effects, whilst normative pressure has a U-shaped effect on the threat cognition of TMs. The results also show that TMs’ threat cognition (as opposed to opportunity cognition) positively influence SMEs’ pro-environmental operations. Moreover, both the emotional (place identity) and functional (place dependence) dimensions of place attachment have positive moderating effects on the relationship between threat cognition and SMEs’ pro-environmental operations. Practical implications – Findings of this study lead to important implications for practitioners such as regulators, policy makers and trade associations. Enabling better understanding of the nature of SMEs’ pro-environmental operations, they allow for more targeted development and the provision of optimal institutional tools to promote such operations. This study allows some important factors that differentiate SMEs from large firms to surface. These factors (i.e., institutional pressures, managerial cognition and place attachment) and the interactions between them form important constituents of the microfoundations of SMEs’ pro-environmental operations.Shanghai planning program of philosophy and social science, 2018BGL02

Improving traceability and transparency of table grapes cold chain logistics by integrating WSN and correlation analysis

Effective and efficient measurement and determination of critical quality parameter(s) is the key to improve the traceability and transparency of the table grapes quality as well as the sustainability performance of the table grapes cold chain logistics, and ensure the table grapes quality and safety. This paper is to determine the critical quality parameter(s) in the cold chain logistics through the real time monitoring of the temperature fluctuation implemented with the Wireless Sensor Network (WSN), and the correlation analysis among the various quality parameters. The assessment was conducted through three experiments. Experiment I indicated that the temperature have a large fluctuation from 0 °C to 30 °C, and the critical temperatures could be determined as 0 °C, 5 °C, 10 °C, 15 °C, 20 °C, 25 °C and 30 °C. Experiment II described that the firmness and moisture loss rate, whose Pearson correlation coefficient with the sensory evaluation were all greater than 0.9 at the critical temperatures determined in Experiment I, could be the critical quality parameters. Experiment III illustrated that the critical quality parameters, firmness and moisture loss rate, could be reliable indicators of table grapes quality by the Arrhenius kinetic equation, and results showed that the evaluation model based on the firmness is better to predict the shelf life than that based on the moisture loss rate. The best quality table grapes could be provided for the consumers via the easily and directly tracing and controlling the critical quality parameters in real time in actual cold chain logistics.National Natural Science Foundation of Chin

Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferring

Taking an answer and its context as input, sequence-to-sequence models have made considerable progress on question generation. However, we observe that these approaches often generate wrong question words or keywords and copy answer-irrelevant words from the input. We believe that lacking global question semantics and exploiting answer position-awareness not well are the key root causes. In this paper, we propose a neural question generation model with two concrete modules: sentence-level semantic matching and answer position inferring. Further, we enhance the initial state of the decoder by leveraging the answer-aware gated fusion mechanism. Experimental results demonstrate that our model outperforms the state-of-the-art (SOTA) models on SQuAD and MARCO datasets. Owing to its generality, our work also improves the existing models significantly.Comment: Revised version of paper accepted to Thirty-fourth AAAI Conference on Artificial Intelligenc

Multi-species Ion Acceleration in 3D Magnetic Reconnection

Magnetic reconnection drives explosive particle acceleration in a wide range of space and astrophysical applications. The energized particles often include multiple species (electrons, protons, heavy ions), but the underlying acceleration mechanism is poorly understood. In-situ observations of these minority heavy ions offer a more stringent test of acceleration mechanisms, but the multi-scale nature of reconnection hinders studies on heavy-ion acceleration. Here we employ hybrid simulations (fluid electron, kinetic ions) to capture 3D reconnection over an unprecedented range of scales. For the first time, our simulations demonstrate nonthermal acceleration of all available ion species into power-law spectra. The reconnection layers consist of fragmented kinking flux ropes as part of the reconnection-driven turbulence, which produces field-line chaos critical for accelerating all species. The upstream ion velocities influence the first Fermi reflection for injection. Then lower charge/mass species initiate Fermi acceleration at later times as they interact with growing flux ropes. The resulting spectra have similar power-law indices $(p\sim4.5)$, but different maximum energy/nucleon $\propto($charge/mass$)^\alpha$, with $\alpha\sim0.6$ for low plasma $\beta$, and with $p$ and $\alpha$ increasing as $\beta$ approaches unity. These findings are consistent with observations at heliospheric current sheets and the magnetotail, and provide strong evidence suggesting Fermi acceleration as the dominant ion-acceleration mechanism.Comment: 9 pages, 5 figure