Artificial Intelligence-Based, Wavelet-Aided Prediction of Long-Term Outdoor Performance of Perovskite Solar Cells.

The commercial development of perovskite solar cells (PSCs) has been significantly delayed by the constraint of performing time-consuming degradation studies under real outdoor conditions. These are necessary steps to determine the device lifetime, an area where PSCs traditionally suffer. In this work, we demonstrate that the outdoor degradation behavior of PSCs can be predicted by employing accelerated indoor stability analyses. The prediction was possible using a swift and accurate pipeline of machine learning algorithms and mathematical decompositions. By training the algorithms with different indoor stability data sets, we can determine the most relevant stress factors, thereby shedding light on the outdoor degradation pathways. Our methodology is not specific to PSCs and can be extended to other PV technologies where degradation and its mechanisms are crucial elements of their widespread adoption.

Metasurface-enhanced light detection and ranging technology.

Deploying advanced imaging solutions to robotic and autonomous systems by mimicking human vision requires simultaneous acquisition of multiple fields of views, named the peripheral and fovea regions. Among 3D computer vision techniques, LiDAR is currently considered at the industrial level for robotic vision. Notwithstanding the efforts on LiDAR integration and optimization, commercially available devices have slow frame rate and low resolution, notably limited by the performance of mechanical or solid-state deflection systems. Metasurfaces are versatile optical components that can distribute the optical power in desired regions of space. Here, we report on an advanced LiDAR technology that leverages from ultrafast low FoV deflectors cascaded with large area metasurfaces to achieve large FoV (150°) and high framerate (kHz) which can provide simultaneous peripheral and central imaging zones. The use of our disruptive LiDAR technology with advanced learning algorithms offers perspectives to improve perception and decision-making process of ADAS and robotic systems.

Tumor markers used in monitoring the tumor recurrence in patients with colorectal cancer.

BACKGROUND AND AIMS: The aim of this study was to investigate the value of serum carcinoembryonic antigen (CEA) and carbohydrate antigen (CA 19-9) correlated with some tissue molecules as predictive markers for recurrence in colon cancer. METHODS: A total of 30 patients diagnosed with colon cancer stage II or III who underwent optimal surgery were enrolled in study. Tumor markers CEA and CA 19-9 were determined before surgery. Tumor samples were prepared using tissue microarray kit (TMA) then stained for different cellular markers (Ki 67, HER2, BCL2, CD56, CD4, CD8) and analyzed using Inforatio programme for quantitative determination. All patients received standard adjuvant treatment, which consisted of eight cycles chemotherapy type XELOX. The patients were followed up for 3 years. RESULTS: Upon 3 years follow-up, 67% of patients developed tumor relapse, the most common site of metastasis being the liver. No correlations were observed between either serum or tissue tumor markers and the risk of tumor relapse. CONCLUSION: Over 50% of patients with colon cancer who had optimal treatment developed metastasis. No statistically significant predictive value for investigated molecules was found. Future studies are needed to confirm the use of molecular markers in monitoring patients with colorectal cancer.