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Wound age estimation is the core content in the practice of forensic medicine. Accurate estimation of wound age is a scientific question that needs to be urgently solved by forensic scientists at home and abroad. Metabolomics techniques can effectively detect endogenous metabolites produced by internal or external stimulating factors and describe the dynamic changes of metabolites in vivo . It has the advantages of strong operability, high detection efficiency and accurate quantitative results. Machine learning algorithm has special advantages in processing high-dimensional data sets, which can effectively mine biological information and truly reflect the physiological, disease or injury state of the body. It is a new technical means for efficiently processing high-throughput big data. This paper reviews the status and advantages of metabolomic techniques combined with machine learning algorithm in the research of wound age estimation, and provides new ideas for this research.

In terms of water quality monitoring based on the ultraviolet-visible spectroscopy, different optical path lengths of spectrometer probe need to be set to maintain higher signal-to-noise ratio of the spectra when the water body measured is complex and changeable. However, large numbers of experiments always have to be undertaken to get the appropriate optical path length, which is difficult to meet the demand of real-time, accurate, sensitive and stable online monitoring system. In this paper, an optimized spectra fusion algorithm was developed to improve the signal-to-noise ratio of fused spectra from two independent spectra that were acquired using two different optical path lengths. In the fusion algorithm, the sliding-pane method was applied to obtain the distribution of noise variance of the spectra, so the region of strong noise in the spectra could be determined. Due to different signal intensity of spectra with long and short optical path length, genetic algorithm was used to calculate the optimal gain matching rate of fusion. Finally, according to the distribution of noise variance, piecewise weighted method is applied to achieve a fusion spectrum with higher signal-to-noise ratio. The experimental results showed that the fusion algorithm could effectively enhance the signal-to-noise ratio of the fused spectra for each sample without altering the optical path length; the noise within 200~250 nm was suppressed and the low-noise and high-sensitivity spectra in the visible band were preserved; Zero interference was moved to the left of 220 nm of the spectrum. This means the fusion algorithm not only shows improvements in both signal-to-noise ratio and the detailed characteristics of the spectrum, but also reduces the excessive number of experiments in order to optimize optical path length and minimize noise in spectra. It has important practical significance to broaden the application range of the ultraviolet-visible spectroscopy based water quality monitoring system.

That artificial intelligence (AI), algorithms, and related technologies could use a few good booster shots of “humanism” is widely apparent. In both program code and implementation, AI and algorithms have been accused of harbouring deep-seated flaws that conflict with human values. They are prime examples of the skew towards white, Western, men and demonstrate the bankruptcy in the face of neoliberalist, profit- and market-oriented social paradigms that this special issue seeks to address. Currently, computer scientists and AI researchers who are looking to remedy these problems are often in favour of more data, more powerful machines, more complex algorithms—in short, that we should fix problems with AI by building better AI. In this view human beings and the world can be modelled in code—our lives, interactions, society, and our very selves can be broken down into data points which can be assessed by highly advanced technologies. When these scientists and researchers seek to broaden their approach they often look to philosophy. However, the philosophy they look to is overwhelmingly Anglo-analytic, which views the world in extremely similar ways. Both AI and Anglo-analytic philosophy argue for solutions to humanistic problems which are essentially mathematical. They share in seeing important concepts, such as persons, emotions, agency, and ethics, as mechanistic, atomistic, and calculable. In this paper I will argue that Classical Chinese philosophy offer insightful resources for addressing the humanist problems in AI. Rather than arguing for mathematical solutions, or envisioning persons, emotions, agency, and ethics, as other rigid, atomistic, and mechanistic approaches, Chinese philosophy emphasizes transformation, interrelatedness, and correlative developments. Accordingly, it offers tools for appreciating the world, society, and ourselves as spontaneous, complex, and full of tension. AI can be programmed and used in ways that do not reduce the complexity and conflict in the world, but provide us instead with tools to make sense of it—tools that are humanistic in nature. To this end, Chinese philosophy can be a helpful collaborative partner.