Multifunctional probes integrating accurate multidiagnosis and efficient therapy hold great prospects in biomedical research. However, the sophisticated construction and difficulties in matching the ratios of doses and laser triggers of probes for each modal imaging and therapy still hinder the extensive practice of multifunctional probes in biomedicine. We herein rationally designed an organic dye SY1080 with intrinsic multifunction by introducing both 3,4-ethylenedioxy thiophene (EDOT) and the selenium containing acceptor unit into the backbone to balance the fluorescent brightness and emission wavelength. Under single dose and 808 nm laser irradiation conditions, SY1080 not only carried out NIR-II fluorescence/ photoacoustic imaging of real-time and noninvasive tumor delineation with excellent contrast, but also effectively ablated tumors with laser irradiation to perform photothermal therapy under the guidance of dual-modal imaging. These exciting results highlight SY1080 as a multifunctional and universal phototheranostic platform for potential applications.

The construction business is always changing, and with the introduction of artificial intelligence (AI) technology it is undergoing substantial modifications in a variety of areas. The purpose of this research paper is to investigate the function of AI tools in the construction industry using a hybrid multi-criteria decision-making (MCDM) framework based on the Delphi method, analytic network process (ANP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) under a fuzzy scenario. The ANP framework offers a systematic approach to quantifying the relative importance of AI technologies based on expert opinions gathered during the Delphi process, whereas the fuzzy TOPSIS methodology is used to rank and select the most appropriate AI technologies for the construction industry. The final results from the ANP revealed that the technological factors are the most crucial, followed by the environmental factors, which highly influence the AI environment. In addition, TOPSIS identified robotics and automation as the best AI alternative among the three options, followed by building information modeling (BIM), whereas computer vision was the least preferred among the list. The proposed hybrid MCDM framework enables a comprehensive evaluation and selection process that takes into account the interdependencies between AI technologies and uncertainties in decision-making.

Optical coherence tomography (OCT) reconstruction by using frequency measurements in the wavelength domain is presented in this paper. The method directly recovers the axial scan by formulating the frequency domain OCT (FD-OCT) into an algebraic reconstruction problem. In this way, the need for interpolation is removed. Then by solving the problem with ℓ1 optimization, the computational load is significantly reduced. It is demonstrated by experiment and simulation that the proposed method can achieve high resolution and longer imaging depth compared to the FD-OCT method.

This study investigates the fluence rate effect, an essential modulating mechanism of photodynamic therapy (PDT), by using photoacoustic imaging method. To the best of our knowledge, this is the first time that the fluence rate dependence is investigated at a microscopic scale, as opposed to previous studies that are based on tumor growth/necrosis or animal surviving rate. This micro-scale examination enables subtle biological responses, including the vascular damage and the self-healing response, to be studied. Our results reveal the correlations between fluence rate and PDT efficacy/self-healing magnitude, indicating that vascular injuries induced by high fluence rates are more likely to recover and by low fluence rates (≤126 mW/cm2) are more likely to be permanent. There exists a turning point of fluence rate (314 mW/cm2), above which PDT practically produces no permanent therapeutic effect and damaged vessels can fully recover. These findings have practical significance in clinical setting. For cancer-related diseases, the ‘effective fluence rate’ is useful to provoke permanent destruction of tumor vasculature. Likewise, the ‘non effective range’ can be applied when PDT is used in applications such as opening the blood brain barrier to avoid permanent brain damage.

With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS) to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model, whose functionality rests on three pillars: (i) an adaptive dropout deep learning model to learn features from each type of data, (ii) a feature tensor model to capture the correlations of heterogeneous data, and (iii) a tensor distance-based high-order CFS algorithm to cluster heterogeneous data. Furthermore, we verify our proposed algorithm on different datasets, by comparison with other two clustering schemes, that is, HOPCM and CFS. Results confirm the effectiveness of the proposed algorithm in clustering heterogeneous data.