A straightforward spectral emissivity estimating method based on constructing random rough surfaces.

Spectral emissivity is an essential and sensitive parameter to characterize the radiative capacity of the solid surface in scientific and engineering applications, which would be non-negligibly affected by surface morphology. However, there is a lack of assessment of the effect of roughness on emissivity and a straightforward method for estimating the emissivity of rough surfaces. This paper established an estimating method based on constructing random rough surfaces to predict rough surface (Geometric region) emissivity for metal solids. Based on this method, the emissivity of ideal gray and non-gray body surfaces was calculated and analyzed. The calculated and measured spectral emissivities of GH3044, K465, DD6, and TC4 alloys with different roughness were compared. The results show that the emissivity increases with the roughness degree, and the enhancement effect weakens with the increase of roughness or emissivity due to the existing limit (emissivity ε = 1.0). At the same time, the roughness would not change the overall spectral distribution characteristics but may attenuate the local features of the spectral emissivity. The estimated results are in good agreement with the experimental data for the above alloys' rough surfaces. This study provides a new reliable approach to obtaining the spectral emissivity of rough surfaces. This approach is especially beneficial for measuring objects in extreme environments where emissivity is difficult to obtain. Meanwhile, this study promotes an understanding of surface morphology's effect mechanism on emissivity.

Structural Anomalies Detection from Electrocardiogram (ECG) with Spectrogram and Handcrafted Features.

Cardiovascular diseases are the leading cause of death globally, causing nearly 17.9 million deaths per year. Therefore, early detection and treatment are critical to help improve this situation. Many manufacturers have developed products to monitor patients' heart conditions as they perform their daily activities. However, very few can diagnose complex heart anomalies beyond detecting rhythm fluctuation. This paper proposes a new method that combines a Short-Time Fourier Transform (STFT) spectrogram of the ECG signal with handcrafted features to detect heart anomalies beyond commercial product capabilities. Using the proposed Convolutional Neural Network, the algorithm can detect 16 different rhythm anomalies with an accuracy of 99.79% with 0.15% false-alarm rate and 99.74% sensitivity. Additionally, the same algorithm can also detect 13 heartbeat anomalies with 99.18% accuracy with 0.45% false-alarm rate and 98.80% sensitivity.

An Automatic Method to Reduce Baseline Wander and Motion Artifacts on Ambulatory Electrocardiogram Signals.

Today's wearable medical devices are becoming popular because of their price and ease of use. Most wearable medical devices allow users to continuously collect and check their health data, such as electrocardiograms (ECG). Therefore, many of these devices have been used to monitor patients with potential heart pathology as they perform their daily activities. However, one major challenge of collecting heart data using mobile ECG is baseline wander and motion artifacts created by the patient's daily activities, resulting in false diagnoses. This paper proposes a new algorithm that automatically removes the baseline wander and suppresses most motion artifacts in mobile ECG recordings. This algorithm clearly shows a significant improvement compared to the conventional noise removal method. Two signal quality metrics are used to compare a reference ECG with its noisy version: correlation coefficients and mean squared error. For both metrics, the experimental results demonstrate that the noisy signal filtered by our algorithm is improved by a factor of ten.

Salutaxel, a Conjugate of Docetaxel and a Muramyl Dipeptide (MDP) Analogue, Acts as Multifunctional Prodrug That Inhibits Tumor Growth and Metastasis.

Salutaxel (3) is a conjugate of docetaxel (7) and a muramyl dipeptide (MDP) analogue. Docetaxel (7) has been recognized as a highly active chemotherapeutic agent against various cancers. MDP and its analogues are powerful potentiators of the antitumor actions of various tumor-necrotizing agents. This article documents the discovery of compound 3 and presents pharmacological proof of its biological function in tumor-bearing mice. Drug candidate 3 was superior to compound 7 in its ability to prevent tumor growth and metastasis. Compound 3 suppressed myeloid-derived suppressor cell (MDSC) accumulation in the spleens of tumor-bearing mice and decreased various serum inflammatory cytokines levels. Furthermore, compound 3 antagonized the nucleotide-binding oligomerization domain-like receptor 1 (NOD1) signaling pathway both in vitro and in vivo.