Methodological Proposal for Recognition Systems in Sustainable Freight Transport

Establishing a recognition system for sustainable freight transport is a process of change that requires the commitment of those involved and application of good practices. The pressures from consumers and competitors do not allow greater engagement with economic, social, and environmental aspects, and the maintenance stages require a long-term effort to obtain credibility, because the participation of external members of society in the evaluation contributes to reduce the influences and uncertainties of the recognition criterion. In light of the difficulties of establishing a green supply chain, we propose a method with multiple approaches with an emphasis on fuzzy logic. The purpose is to reduce the indecision of judgment, to debug the qualitative variables and to reduce interference from competitors. The procedure was applied to six companies that have carbon reduction programs, but we found that firms with a core business in transport had greater success in the transition to low carbon operations than contractors or shippers. The certification levels allowed visual contact between consumers and the company, making it possible to monitor sustainability actions in freight transportation operations and the competitiveness to achieve higher labeling levels.

Effects of Secondary Sources of Underground Nuclear Explosions on the mb : Ms Criterion and Implications for Discrimination of the DPRK’s Nuclear Tests

ABSTRACT The mb : Ms (mb vs. Ms) relationship is an important criterion for screening explosions from earthquakes and has been widely adopted in seismological monitoring by the Comprehensive Nuclear-Test-Ban Treaty Organization. In general, the earthquakes have larger Ms than the underground explosions with equivalent mb. However, it has been reported that this recognition criterion failed to identify some explosions at the North Korea nuclear test site. In this study, we investigate the potential effects of secondary source components, including the compensated linear vector dipole (CLVD) and double-couple (DC) sources, on mb and Ms magnitude measurements and the physical mechanism of the mb : Ms recognition criterion by calculating synthetic seismograms. The results show an apparent critical body-wave magnitude of 5 when using the mb : Ms method to discriminate North Korean underground nuclear explosions. The Ms measurements decrease as the CLVD components increase, whereas the effects from the DC source can be neglected. Small events, such as the first five North Korean nuclear tests, generate weak CLVD components, leading to the failure of mb : Ms-based discrimination, whereas the last event, with a larger magnitude, caused extensive damage and hence can be successfully discriminated. In addition, the large difference between the source spectrum of explosions and those of earthquakes might be another important factor in the successful mb : Ms-based discrimination of the sixth North Korean nuclear test.

Deep Learning Method on Target Echo Signal Recognition for Obscurant Penetrating Lidar Detection in Degraded Visual Environments

With the rapid development of autonomous vehicles and mobile robotics, the desire to advance robust light detection and ranging (Lidar) detection methods for real world applications is increasing. However, this task still suffers in degraded visual environments (DVE), including smoke, dust, fog, and rain, as the aerosols lead to false alarm and dysfunction. Therefore, a novel Lidar target echo signal recognition method, based on a multi-distance measurement and deep learning algorithm is presented in this paper; neither the backscatter suppression nor the denoise functions are required. The 2-D spectrogram images are constructed by using the frequency-distance relation derived from the 1-D echo signals of the Lidar sensor individual cell in the course of approaching target. The characteristics of the target echo signal and noise in the spectrogram images are analyzed and determined; thus, the target recognition criterion is established accordingly. A customized deep learning algorithm is subsequently developed to perform the recognition. The simulation and experimental results demonstrate that the proposed method can significantly improve the Lidar detection performance in DVE.

The Recognition of the Bifurcation Problem with Trivial Solutions

This paper studies the recognition criterion of the bifurcation problem with trivial solution. The t-equivalence is different from the strong equivalence studied by Golubitsky et al. The difference is that the second component of the differential homeomorphism is not identical. Consider the normal subgroup of t-equivalence group, we obtain the characterization of higher order terms P ( h ) . In addition, we also explore the properties of intrinsic submodules and the finite determinacy of the bifurcation problem.

Motives over S

This chapter shows that the operation φ‎ 𝑉 of the definition introduced in the previous chapter extends to a cohomology operation over 𝑘, and that it satisfies the recognition criterion of a theorem, so that φ‎ 𝑉 must be β‎𝑃𝑏. This construction of the cohomology operation utilizes the machinery of motives over a simplicial noetherian scheme. The chapter first presents this scheme in three parts, initially summarizing the basic theory of motives over a scheme 𝑆 before discussing motives over a simplicial scheme and over a smooth simplicial scheme. It then presents the slice filtration and generalizes from simplicial scheme 𝔛 to embedded schemes. Finally, this chapter defines the operations φ‎ 𝑖 and φ‎ 𝑉.

An Improved Uncertainty Measure Theory Based on Game Theory Weighting

In the application of uncertainty measure theory, the determination method of index weight mainly includes the subjective weight determination method and the objective weight determination method. The subjective weight determination method has the disadvantages affected by the subjective preference of the decision-maker. The objective weight determination method often ignores the participation degree of the decision-maker, and when using the uncertainty measure evaluation model to perform multi-index classification evaluation, the credible degree recognition criterion is often used as the attribute recognition of the object to be measured, because the credible degree is taken by the subjective people, and the different values of different people have a great influence on the evaluation results. In order to solve the above problems in the uncertainty measure theory, this paper used the combination weighting of game theory to determine the optimal weight. At the same time, the credible degree recognition criterion was improved on the basis of the concept of minimum uncertainty measure distance, and a game theory-improved uncertainty measure optimization model was proposed. Finally, the validity of the model was proven by a case.

Quantitative Evaluation and Case Study of Risk Degree for Underground Goafs with Multiple Indexes considering Uncertain Factors in Mines

The accidents caused by underground goafs are frequent and destructive due to irregular geometric shapes and complex spatial distributions, which caused severe damage to the environment and public health. Based on the theories of uncertainty measurement evaluation (WME) and analytic hierarchy process (AHP), the comprehensive risk evaluation of underground goafs was carried out using multiple indexes. Considering the hydrogeological conditions, mining status, and engineering parameters of underground goafs, the evaluation index system was established to evaluate the risk degrees considering quantified uncertain factors. The single index measurement values were solved by the semiridge measurement function. The weights for evaluation vectors were calculated through the entropy theory and AHP. Finally, the risk level was evaluated according to the credible degree recognition criterion (CDRC) and the maximum membership principle. The risk levels of 37 underground goafs in Dabaoshan mine were evaluated using 4 coupled methods. The order for underground goafs risk degrees was ranked and classified on account of the uncertainty important degree. According to the ranked order, the reasonability of 4 coupled methods was evaluated quantitatively. Results show that the UME-CDRC can be applied in the practical engineering, which provides an efficient guidance to both reduce the accident risk and improve the mining environment.