The fusion of machine learning, biomedical, and bioengineering research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical and bioengineering data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. We introduce a new Automatic Semantic Machine Learning Microservice Framework (AIMS) that addresses these challenges by automating key aspects of the machine learning pipeline, from data preprocessing to model evaluation. Our proposed framework emphasizes model interpretability, the integration of domain knowledge, and the efficient handling of biomedical data. Furthermore, it incorporates self-supervised knowledge learning strategies, enabling the automatic generation, optimization, and continuous adaptation of machine learning models in response to new tasks and data. We illustrate the potential of our framework through three case studies in biomedical research, showcasing its capacity to streamline research processes, improve scientific exploration quality, and provide a foundation for significant breakthroughs.

Group-IV GeSn photodetectors (PDs) compatible with standard complementary metal-oxide-semiconductor (CMOS) processing have emerged as a new and non-toxic infrared detection technology to enable a wide range of infrared applications. The performance of GeSn PDs is highly dependent on the Sn composition and operation temperature. Here, we develop theoretical models to establish a simple rule of thumb, namely “GeSn-rule 23”, to describe GeSn PDs’ dark current density in terms of operation temperature, cutoff wavelength, and Sn composition. In addition, analysis of GeSn PDs’ performance shows that the responsivity, detectivity, and bandwidth are highly dependent on operation temperature. This rule provides a simple and convenient indicator for device developers to estimate the device performance at various conditions for practical applications.

Newly build offshore wind farms (OWFs) render a collision risk between ships and installations. The paper proposed a real-time traffic monitoring method based on machine vision and deep learning technology to improve the efficiency and accuracy of the traffic monitoring system in the vicinity of offshore wind farms. Specifically, the method employs real automatic identification system (AIS) data to train a machine vision model, which is then used to identify passing ships in OWF waters. Furthermore, the system utilizes stereo vision techniques to track and locate the positions of passing ships. The method is tested in offshore water in China to validate its reliability. The results prove that the system sensitively detects the dynamic information of the passing ships, such as the distance between ships and OWFs, ship speed and course. Overall, this study provides a novel approach to enhancing the safety of OWFs, which is increasingly important as the number of such installations continues to grow. By employing advanced machine vision and deep learning techniques, the proposed monitoring system offers an effective means of improving the accuracy and efficiency of ship monitoring in challenging offshore environments.

Synthesis and antitumour activity of a palladium compound, [Bis (quinoline) palladium (II) chloride] (coded as NH1) alone and in combination along with its proposed mechanism of action has been described in the present study. Antitumour activity has been performed in seven different cancer cell lines using colorimetric 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay. The investigated palladium compound demonstrated superiority over clinical standard cisplatin against drug resistant ovarian cancer cell lines. NH1 also demonstrates 4.4 times greater activity than cisplatin against breast cancer model. Binary sequenced combination study showed that NH1 in combination with curcumin is mostly additive to antagonistic. However, combination of NH1 with 6-shogaol demonstrated synergism depending on dose and sequence. Proteomic study revealed that changes in expression of 14 proteins were significantly associated with anticancer mechanism of NH1. In vivo anticancer activity of NH1 at a dose of 0.75 mg/kg was also evaluated using Erlich Ascites Carcinoma (EAC) cell lines in Swiss-albino mice model. Preliminary toxicity study in Swiss-albino mice proved that NH1 is comparatively less toxic at both of the administered doses, either 2.5 mg/kg or 5 mg/kg. However, cisplatin demonstrated significant toxicity at both of the administered doses. Biochemical investigations also supported that NH1 demonstrated lesser toxicity profile towards liver or kidney compared to clinical standard cisplatin.

Germanium films were grown on c-plane sapphire with a 10 nm AlAs buffer layer using molecular beam epitaxy. The effects of Ge film thickness on the surface morphology and crystal structure were investigated by ex-situ characterization techniques. The nucleation of Ge proceeds by forming (111) oriented three-dimensional islands with two rotational twin domains about the growth axis. The boundaries between the twin grains are the origin of 0.2% strain and tilt grains. The transition to a single-grain orientation reduces the strain and results in a better-quality Ge buffer. Understanding the role of thickness on material quality during the Ge(111)/Al2O3(0001) epitaxy is vital for achieving device quality when using group IV material on the sapphire platform.