Multi-Criteria Evaluation in Support of the Decision-Making Process in Highway Construction Projects

The decision-making process in highway construction projects identifies and selects the optimal alternative based on the user requirements and evaluation criteria. The current practice of the decision-making process does not consider all construction impacts in an integrated decision-making process. This dissertation developed a multi-criteria evaluation framework to support the decision-making process in highway construction projects. In addition to the construction cost and mobility impacts, reliability, safety, and emission impacts are assessed at different evaluation levels and used as inputs to the decision-making process. Two levels of analysis, referred to as the planning level and operation level, are proposed in this research to provide input to a Multi-Criteria Decision-Making (MCDM) process that considers user prioritization of the assessed criteria. The planning level analysis provides faster and less detailed assessments of the inputs to the MCDM utilizing analytical tools, mainly in a spreadsheet format. The second level of analysis produces more detailed inputs to the MCDM and utilizes a combination of mesoscopic simulation-based dynamic traffic assignment tool, and microscopic simulation tool, combined with other utilities. The outputs generated from the two levels of analysis are used as inputs to a decision-making process based on present worth analysis and the Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) MCDM method and the results are compared

Diesel engine combustion feature extraction based on time-frequency correlation

The use of diesel cylinder head vibration signal for fault diagnosis must eliminate signal interference non-periodic and random components. The periodic components related to the working cycle of the diesel engine are retained, so as to achieve the purpose of fault feature extraction. A time-frequency correlation-based diesel engine fault feature extraction method is proposed in this paper. First, the Wavelet transform is applied to the vibration signals of diesel engines collected in three continuous working cycles to realize the time-frequency distribution of the signals. Then three time-frequency distributions are estimated by cross-correlation, so as to eliminate noise interference and extract periodic transient characteristics. The experimental results of simulation signals and real signals show that this method can effectively extract the periodic transient impact characteristics of diesel engines

Misfire fault diagnosis of diesel engine based on VMD and XWT

For diesel engine misfire fault diagnosis under strong noise, a new method based on variational mode decomposition (VMD) and cross wavelet transform (XWT) is proposed. Firstly, the vibration signal of cylinder head is processed by stages according to the working cycle of diesel engine, and then the vibration signal of each working cycle is re-sampled according to the same angle. Then the vibration signal of cylinder head is decomposed by VMD to denoise adaptively and reconstruct the signal. Next, XWT is used to analyze the time-frequency correlation of any two continuous working cycle signals, and the non-periodic components and random noise in the vibration signals are further eliminated to extract the combustion characteristics of diesel engines. Finally, the misfire fault of diesel engine is diagnosed by calculating the energy proportion of each cylinder in time-frequency space. The effectiveness of the proposed method is verified by simulation and experiment

Hydrophobic gating in BK channels

The gating mechanism of transmembrane ion channels is crucial for understanding how these proteins control ion flow across membranes in various physiological processes. Big potassium (BK) channels are particularly interesting with large single-channel conductance and dual regulation by membrane voltage and intracellular Ca2+. Recent atomistic structures of BK channels failed to identify structural features that could physically block the ion flow in the closed state. Here, we show that gating of BK channels does not seem to require a physical gate. Instead, changes in the pore shape and surface hydrophobicity in the Ca2+-free state allow the channel to readily undergo hydrophobic dewetting transitions, giving rise to a large free energy barrier for K+permeation. Importantly, the dry pore remains physically open and is readily accessible to quaternary ammonium channel blockers. The hydrophobic gating mechanism is also consistent with scanning mutagenesis studies showing that modulation of pore hydrophobicity is correlated with activation properties

Weak feature extraction for early wear of connecting rod bearings under transient conditions

As we all know, it is difficult to extract weak feature for early wear of connecting rod bearings under transient conditions. In order to solve the problem, a method of extracting wear features for connecting rod bearings based on variational modal decomposition (VMD) adaptive noise reduction and computational order tracking (COT) was proposed. Firstly, the vibration signals of internal combustion engine block under transient operating conditions were collected, and the signals were reordered to satisfy the order tracking condition. Then, interpolation and fitting techniques were used to map the signal from the time domain space to the angular domain space. Next, the VMD was used to decompose the angular domain signal into multiple modal components, and the autocorrelation function (ACF) was used to denoise the modal components adaptively. Finally, the signal was reconstructed to conduct COT analysis, and the wear features of connecting rod bearing were extracted by average COT spectrum. The simulation analysis and the simulation experiment of the connecting rod bearing fault show that the proposed method is effective, and the weak features for early wear of connecting rod bearing of internal combustion engine are extracted

Variational mode decomposition denoising combined with the Euclidean distance for diesel engine vibration signal

Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). There is a lot of background noise in the vibration signal of diesel engine. To solve the problem, a denoising algorithm based on VMD and Euclidean Distance is proposed. Firstly, a multi-component, non-Gauss, and noisy simulation signal is established, and decomposed into a given number K of band-limited intrinsic mode functions by VMD. Then the Euclidean distance between the probability density function of each mode and that of the simulation signal are calculated. The signal is reconstructed using the relevant modes, which are selected on the basis of noticeable similarities between the probability density function of the simulation signal and that of each mode. Finally, the vibration signals of diesel engine connecting rod bearing faults are analyzed by the proposed method. The results show that compared with other denoising algorithms, the proposed method has better denoising effect, and the fault characteristics of vibration signals of diesel engine connecting rod bearings can be effectively enhanced

Disturbance of the OPG/RANK/RANKL pathway and systemic inflammation in COPD patients with emphysema and osteoporosis

<p>Abstract</p> <p>Background</p> <p>Osteoporosis is one of the systemic features of COPD. A correlation between the emphysema phenotype of COPD and reduced bone mineral density (BMD) is suggested by some studies, however, the mechanisms underlying this relationship are unclear. Experimental studies indicate that IL-1β, IL-6 and TNF-α may play important roles in the etiology of both osteoporosis and emphysema. The OPG/RANK/RANKL system is an important regulator of bone metabolism, and participates in the development of post-menopausal osteoporosis. Whether the OPG/RANK/RANKL pathway is involved in the pathogenesis of osteoporosis in COPD has not been studied.</p> <p>Methods</p> <p>Eighty male patients (current or former smokers) completed a chest CT scan, pulmonary function test, dual x-ray absorptiometry measurements and questionnaires. Among these subjects, thirty patients with normal BMD and thirty patients with low BMD were selected randomly for measurement of IL-1β, IL-6, TNF-α (flow cytometry) and OPG/RANK/RANKL (ELISA). Twenty age-matched healthy volunteers were recruited as controls.</p> <p>Results</p> <p>Among these eighty patients, thirty-six had normal BMD and forty-four had low BMD. Age, BMI and CAT score showed significant differences between these two COPD groups (<it>p </it>< 0.05). The low-attenuation area (LAA%) in the lungs of COPD patients was negatively correlated with lumbar vertebral BMD (r = 0.741; <it>p </it>< 0.0001). Forward logistic regression analysis showed that only LAA% (<it>p </it>= 0.005) and BMI (<it>p </it>= 0.009) were selected as explanatory variables. The level of IL-1β was significantly higher in the COPD patients as compared to the normal controls (<it>p </it>< 0.05), but the difference between the two COPD groups did not reach significance. The levels of IL-6 and TNF-α among the three groups were significantly different (<it>p </it>< 0.05). The level of RANKL and the RANKL/OPG ratio were significantly higher in COPD patients with low BMD compared to those with normal BMD and the normal controls (<it>p </it>< 0.05), and correlated negatively with lumbar vertebral BMD, but positively with LAA%.</p> <p>Conclusions</p> <p>Radiographic emphysema is correlated with low BMD in current and former smokers with COPD. IL-1β, IL-6, TNF-α, and the osteoporosis-related protein system OPG/RANK/RANKL may have some synergetic effects on emphysema and bone loss in COPD.</p

An improved variational mode decomposition method and its application in diesel engine fault diagnosis

The diesel engine is a complex mechanical device, with the characteristics of multi-source, multi moving parts, complex work. For the complex multi-component signal, it is usually necessary to decompose it into a number of single-component AM-FM signals, and each component is analyzed to extract amplitude and frequency information. VMD is essentially composed of a plurality of adaptive Wiener filter and has good noise robustness. Compared with EMD, EEMD, CEEMDAN, LMD and ITD, VMD has strong mathematical theory basis. At the same time, VMD rejects the method of recursive screening stripping. So VMD can effectively alleviate or avoid a series of problems which appear in other methods. However, it is a problem how to determine the number of decomposition layers and the penalty factor, because human factors will affect the decomposition results. In order to solve the problem, an improved adaptive genetic algorithm (IAGA) is proposed to optimize the parameters of VMD. Genetic algorithms mainly include 3 genetic operators: selection, crossover and mutation. The cross probability and mutation probability will directly affect the optimization results. In the traditional genetic algorithm, the probability of cross and mutation are fixed, and the genetic algorithm is easy to fall into the local optimal. According to the regulation of hormone regulation, the cross probability and mutation probability in evolution were improved. The permutation entropy is a new method of mutation detection, which mainly aims at the spatial characteristics of the time series itself. Therefore, the entropy of the components obtained by the VMD decomposition is used as the fitness function of the IAGA. The modal number K and penalty factor α of VMD were iteratively optimized by IAGA, and the optimal combination of parameters was obtained. Based on the proposed method, the vibration signals of the crankshaft bearing fault simulation experiment were decomposed into several components. According to the value of the permutation entropy, the fault components were selected and the energy was extracted. The fault pattern is identified by the support vector machine (SVM) successfully. The simulation analysis and the simulation experiment of the crankshaft bearing fault show that the proposed method is effective. For the diagnosis of other engines, a large number of validation experiments are needed for further research