ALBERT

Description: In this paper, we explore the emergence of Sheke Network (SKN), an architecture design company whose network platform permits architecture design institutes to seek suitable architects for their architectural design projects. The concepts of sustainable business model (SBM) innovation are used to analyze the SKN’s novel business model, which is built around modern internet technologies, and SKN’s distinct appeal, which centers on cost savings, resource sharing, and the potential for access to more architects’ experiences. Although SBM innovation theory has had a significant impact on management practices and aroused plenty of rich debate within academia, it is still not clearly understood how a company might successfully build a sharing economy (SE) platform using SBM innovation. Thus, this article analyzes the process of SBM innovation in terms of its three essential factors—sustainable value proposition (SVP), sustainable value creation and delivery (SVC&D), and sustainable value capture (SVC)—aiming to disambiguate the black box by examining SKN as a case study. The findings highlight the dynamic evolution of SBM innovation in the development of a sharing economy platform.

Description: Crowdsensing has emerged as an efficient and inexpensive way to perform specialized tasks by leveraging external crowds. In some crowdsensing systems, different tasks may have different requirements, and there may be precedence constraints among them, such as the Unmanned Aerial Vehicle (UAV) crowdsensing systems. Moreover, minimizing the total execution time is a regular target for finishing the crowdsensing tasks with precedence constraints. As far as we know, only a few existing studies consider the precedence constraints among crowdsensing tasks, and none of them can minimize the total execution time simultaneously. To tackle this challenge, an efficient allocation mechanism for tasks with precedence constraints is first proposed, which can minimize the total execution time. Then, a case study is given to show how to fit our mechanism in the UAV crowdsensing system. Finally, the simulation results show that the proposed mechanisms have good approximate optimal ratios under different parameter settings and are efficient for the UAV crowdsensing system as well.

Description: Driving mode switches of hybrid vehicles are significant events. Due to the different dynamic characteristics of the engine, motor, and wet clutch, it is difficult to coordinate torque fluctuations caused by mode switches. This paper focused on a control strategy for driving mode switches of plug-in hybrid electric vehicles (PHEVs) with a multi-disk wet clutch. First, the dynamic model of the PHEV was established, and a rule-based control strategy was proposed to divide the working mode regions and distribute the torque between engine and motor. Second, the dual fuzzy control strategy for a wet clutch and the coordinated torque control strategy for driving mode switches were proposed. The dual fuzzy logic control system consisted of the initial pulse-width modulation (PWM)’s duty cycle control and the changing rate of the PWM’s duty cycle control. Considering the difference in the dynamic characteristics between engine, motor, and wet clutch, a coordinated control strategy for the driving mode switches of PHEVs was put forward. Third, simulations of driving mode switches between pure electric driving mode and only engine driving mode were conducted. The results showed that the proposed control strategy could reduce the torque ripple and the jerk of the vehicle, completely satisfying the requirements of China. Finally, the control strategy for the motor-assisted engine starting process was tested on the bench. The experiment results indicated that the proposed control strategy was effective.

Description: Predictions of prospectivity based on remote sensing were developed using alteration mineral indicative hyperspectral mapping and remote sensing anomaly filtering, combined with geological characteristics and anomalous mineral field verification. Based on the results of the hyperspectral mineral mapping and the actual geological ground conditions, the results of mapping of altered minerals, such as chlorite, muscovite, kaolinite, and iron oxide were validated, and gold, silver, copper, nickel, and other geochemical anomaly areas were identified for verification work. The results of hyperspectral mineral extraction show that the mineral assemblage closely related to gold deposits in shear zones is muscovite + chlorite + epidote + kaolinite. This alteration mineral assemblage can be used as regional search criteria for shear zone gold mineralisation and was the basis for the discovery of mineralised hydrothermal alteration centres and delineation of four prospective targets. Established on a spectral prospectivity model of the study area, prospective ore-bearing areas have been delineated, which indicate the direction for further geological and mineral resource surveys.

Description: As a part of vehicle thermal management, water-cooled intercoolers play an important role in engine efficiency. The incompressible simulation model was usually applied to estimate the performance of water-cooled intercoolers. In this paper, the computational fluid dynamics (CFD) compressible model is taken to analyze more accurate prediction models. The rate of section change, heat exchange, and the surface friction coefficient are used as the comparison basis of the compressible flow model and incompressible model on the pressurized air side of the water-cooled intercooler. By comparing the simulation results of the air side, it was found that the compressible simulation is closer to the experimental value than the incompressible simulation. Compared with the experiment, the compressible model heat transfer maximum value of deviation is 6.5%, and the pressure loss maximum deviation is 7.5%. This provides guidance to optimize the design of heat exchangers, in order to save on costs and shorten development times.

Description: The effects of three kinds of oxygenated fuel blends—i.e., ethanol-gasoline, n-butanol-gasoline, and 2,5-dimethylfuran (DMF)-gasoline-on fuel consumption, emissions, and acceleration performance were investigated in a passenger car with a chassis dynamometer. The engine mounted in the vehicle was a four-cylinder, four-stroke, turbocharging gasoline direct injection (GDI) engine with a displacement of 1.395 L. The test fuels include ethanol-gasoline, n-butanol-gasoline, and DMF-gasoline with four blending ratios of 20%, 50%, 75%, and 100%, and pure gasoline was also tested for comparison. The original contribution of this article is to systemically study the steady-state, transient-state, cold-start, and acceleration performance of the tested fuels under a wide range of blending ratios, especially at high blending ratios. It provides new insight and knowledge of the emission alleviation technique in terms of tailoring the biofuels in GDI turbocharged engines. The results of our works showed that operation with ethanol–gasoline, n-butanol–gasoline, and DMF–gasoline at high blending ratios could be realized in the GDI vehicle without any modification to its engine and the control system at the steady state. At steady-state operation, as compared with pure gasoline, the results indicated that blending n-butanol could reduce CO2, CO, total hydrocarbon (THC), and NOX emissions, which were also decreased by employing a higher blending ratio of n-butanol. However, a high fraction of n-butanol increased the volumetric fuel consumption, and so did the DMF–gasoline and ethanol–gasoline blends. A large fraction of DMF reduced THC emissions, but increased CO2 and NOX emissions. Blending n-butanol can improve the equivalent fuel consumption. Moreover, the particle number (PN) emissions were significantly decreased when using the high blending ratios of the three kinds of oxygenated fuels. According to the results of the New European Drive Cycle (NEDC) cycle, blending 20% of n-butanol with gasoline decreased CO2 emissions by 5.7% compared with pure gasoline and simultaneously reduced CO, THC, NOX emissions, while blending ethanol only reduced NOX emissions. PN and particulate matter (PM) emissions decreased significantly in all stages of the NEDC cycle with the oxygenated fuel blends; the highest reduction ratio in PN was 72.87% upon blending 20% ethanol at the NEDC cycle. The high proportion of n-butanol and DMF improved the acceleration performance of the vehicle.

Description: Change detection (CD) remains an important issue in remote sensing applications, especially for high spatial resolution (HSR) images, but it has yet to be fully resolved. This work proposes a novel object-based change detection (OBCD) method for HSR images that is based on region–line primitive association analysis and evidence fusion. In the proposed method, bitemporal images are separately segmented, and the segmentation results are overlapped to obtain the temporal region primitives (TRPs). The temporal line primitives (TLPs) are obtained by straight line detection on bitemporal images. In the initial CD stage, Dempster–Shafer evidence theory fuses the multiple items of evidence of the TRPs’ spectrum, edge, and gradient changes, and obtains the initial changed areas. In the refining CD stage, the association between the TRPs and their contacting TLPs in the unchanged areas is established on the basis of the region–line primitive association framework, and the TRPs’ main line directions (MLDs) are calculated. Some changed TRPs omitted in the initial CD stage are recovered by their MLD changes, thereby refining the initial CD results. Different from common OBCD methods, the proposed method considers the change evidence of TRPs’ internal and boundary information simultaneously via information complementation between TRPs and TLPs. The proposed method can significantly reduce missed alarms while maintaining a low level of false alarms in OBCD, thereby improving total accuracy. In our experiments, our method is superior to common CD methods, including change vector analysis (CVA), PCA-k-means, and iterative reweighted multivariate alteration detection (IRMAD), in terms of overall accuracy, missed alarms, and Kappa coefficient.