Natural gas has emerged as one of the preferred alternative fuels for vehicles owing to its advantages of abundant reserves, cleaner combustion and lower cost. At present, the gas supply methods for natural-gas engines are mainly port fuel injection (PFI) and direct injection (DI). The transient injection characteristics of a gas fuel injection device, as the terminal executive component of the PFI or DI mode, will directly affect the key performance of a gas fuel engine. Therefore, gas fuel injection devices have been selected as the research object of this paper, with a focus on the transient injection process. To explore the impacts of valve vibration amplitude, period, frequency and velocity on transient injection characteristics, one transient computational fluid dynamics (CFD) model for gas fuel injection devices was established. The findings thereof demonstrated that there is a linear relationship between the instantaneous mass flow rate and instantaneous lift during the vibration process. However, this relationship is somewhat impacted when the valve speed is high enough. A shorter valve vibration period tends to preclude a shorter period of flow-hysteresis fluctuation. The near-field pressure fluctuation at the throat of an injection device, caused by valve vibration, initiates flow fluctuation.
Trustworthy personal data access control at a semi-trusted or distrusted Cloud Service Provider (CSP) is a practical issue although cloud computing has widely developed. Many existing solutions suffer from high computation and communication costs, and are impractical to deploy in reality due to usability issue. With the rapid growth and popularity of mobile social networking, trust relationships in different contexts can be assessed based on mobile social networking activities, behaviors and experiences. Obviously, such trust cues extracted from social networking are helpful in automatically managing personal data access at the cloud with sound usability. In this paper, we propose a scheme to secure personal data access at CSP according to trust assessed in mobile social networking. Security and performance evaluations show the efficiency and effectiveness of our scheme for practical adoption.
To assess the psychological and physiological benefits of early exercise rehabilitation in patients with acute coronary syndrome (ACS). Among 559 ACS-diagnosed patients at Fuzhou University Affiliated Provincial Hospital from January to December 2021, 200 eligible participants were assigned to two groups. The control group received standard care, while the experimental group received early exercise rehabilitation in addition to standard care. The outcomes measured included changes in depression levels (PHQ-9), fasting blood glucose, and troponin I (TnI) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels. Data were analyzed using SPSS, with t tests and chi-square tests for group comparisons. In comparison to the control group, the experimental group demonstrated significant improvements in PHQ-9 scores (P < 0.001) and lower fasting blood glucose levels before discharge (P = 0.046). Additionally, the experimental group had notably reduced TnI levels at 72 h after admission (P = 0.001), especially among non-diabetic NSTEMI patients over 60 years old, who showed decreased TnI levels at the 48-hour mark (P = 0.016). However, there were no significant differences in NT-ProBNP change values between the two groups (P > 0.05). Subgroup analysis revealed enhanced outcomes in the intervention group for ACS patients without smoking or drinking history and no heart failure (P = 0.025,P = 0.014,P = 0.018). Early exercise rehabilitation has notable benefits for ACS patients, including reduced depression, improved blood glucose control, and enhanced myocardial protection, especially in nondiabetic NSTEMI patients aged 60 and above.
Background: Atmospheric chemistry studies suggest air pollution impedes ultraviolet B photons and thus reduces cutaneous vitamin D synthesis. Biological evidence documents that inhaled pollutants disrupt circulating 25-hydroxyvitamin D [25(OH)D] metabolism and ultimately impact bone health. The hypothesis is that higher air pollution concentrations are associated with a higher risk of fractures, mediated by lower circulating 25(OH)D levels.Methods: The study included participants of the UK Biobank who were free of fractures history at enrollment (2006 to 2010) and analyzed their environmental exposure data (2007 to 2010) based on residential locations. Air pollution measurements included the annual averages of air particulate matter (PM2·5, PM2·5-10, and PM10), nitrogen oxides (NO2and NOx), and a composite air pollution score. Multivariable Cox proportional hazard models were used to assess the associations of the individual pollutants and the score with fracture risks. Mediation analyses were conducted to assess the underlying role of serum 25(OH)D in such associations.Findings: Among 446,395 participants with a median of eight-year follow-up, 12,288 (2·75%) incident fractures were documented. Participants living in places with the highest quintile of air pollution score had a 15·3% increased risk of fractures (hazard ratio [95%CI]: 1·15 [1·09, 1·22]) compared to those in the lowest quintile; and 5·49% of this association was mediated through serum 25(OH)D levels (p for mediation<0·05). Pollutant-specific hazard ratio [95%CI] of top-to-bottom quintiles was 1·16 [1·09, 1·23] for PM2·5, 1·04 [0·98,1·10] for PM2·5-10, 1·05 [0·99, 1·12] for PM10, 1·20 [1·13,1·27] for NO2, and 1·17 [1·11,1·24] for NOx, respectively, with a 4-6% mediation effect of serum 25(OH)D levels. The associations of the air pollution score with fracture risks were weaker among female participants, those who drank less alcohol, and consumed more fresh fruit than their counterparts (all p for interaction <0·05).Interpretation: Our results indicated that long-term exposure to air particulate matter and nitrogen oxides was associated with an increased risk of fractures. These associations were partly mediated through lower serum 25(OH)D levels. A healthy lifestyle (e.g., reducing alcohol consumption and increasing fresh fruit intake) may counteract the harmful effects of air pollution on bone health to some degree.Funding: This work was supported by the National Key R&D Program of China (2020YFC2005000), the National Natural Science Foundation of China (81973032),and the fellowship of the China Postdoctoral Science Foundation (2022M710786).Declaration of Interests: JSJ served as the Asia Editor of The Lancet in 2017. All other authors declare no competing interests.Ethics Approval Statement: The ethics approval of the UK Biobank was declared by the North West Multicenter Research Ethical Committee (11/NW/0382). All participants provided written informed consent.
Trust has been recognized as an important factor for a component software platform. Inside the platform, trust can be controlled according to its evaluation result. Special control modes can be applied into the software platform in order to ensure a trustworthy system. In this paper, we present a methodology for trust control mode prediction and selection in order to support autonomic platform trust management. The methodology is based on Fuzzy Cognitive Maps. The simulation results show this method is effective for predicting and selecting the feasible trust control modes.
Android has stood at a predominant position in mobile operating systems for many years. However, its popularity and openness make it a desirable target of malicious attackers. There is an increasing need for mobile malware detection. Existing analysis methods fall into two categories, i.e., static analysis and dynamic analysis. The dynamic analysis is more effective and timely than the static one, but it incurs a high computational overhead, thus cannot be deployed in resource-constrained mobile devices. Existing studies solve this issue by outsourcing malware detection to the cloud. However, the privacy of mobile app runtime data uploaded to the cloud is not well preserved during both detection model training and malware detection. Numerous efforts have been made to preserve privacy with cryptography, which suffers from high computational overhead and low flexibility. To address these issues, in this paper, we propose an Intel SGX-empowered mobile malware detection scheme called EPMDroid. We also design a probabilistic data structure based on cuckoo filters, named CuckooTable, to effectively fuse features for detection and achieve high space efficiency. We conduct both theoretical analysis and real-world data based tests on EPMDroid performance. Experimental results show that EPMDroid can speed up malware detection by up to 43.8 times and save memory space by up to 3.7 times with the same accuracy, as compared to a baseline method.
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