4,218 publications from this institution
Immune checkpoint inhibitors have emerged as the treatment landscape of advanced non-small cell lung cancer (NSCLC) in recent years. However, approximately 80% of NSCLC patients do not benefit from ICIs due to primary resistance (no initial response) or acquired resistance (tumor relapse after an initial response). In this review, we highlight the mechanisms of primary and secondary resistance. Furthermore, we provide a future direction of the potential predictive biomarkers and the tumor microenvironmental landscape and suggest treatment strategies to overcome these mechanisms.
LFSR reseeding forms the basis for many test compression solutions. A seed can be computed for each test cube by solving a system of linear equations based on the feedback polynomial of the LFSR. Despite the availability of numerous LFSR-reseeding-based compression methods in the literature, relatively little is known about the effectiveness of these seeds for unmodeled defects. We use the recently proposed output deviation measure of the resulting patterns as a metric to select appropriate LFSR seeds. Experimental results are reported using test patterns for stuck-at faults derived from selected seeds. These patterns achieve higher coverage for stuck-open and transition faults than patterns obtained using other methods.
In this study, we designed a spiral-shaped milli-reactor with a T-junction microchannel to generate digital droplets for studying and observing the digital freezing process of droplets. During the study of the recalescence and solidification processes of digital droplets dynamically moving in microchannels, we found that although the digital generation of droplets in our channel aligns well with the literature, achieving the digitalization of the droplet freezing process is very challenging. Even the initial phase of freezing (the recalescence process) exhibits significant randomness. A key feature of the randomness in the freezing process is the nucleation position of droplets within the channel, which significantly impacts the digital characteristics and hinders digital freezing. During the investigation of freezing randomness, we identified five distinct nucleation profiles, which largely determine the evolution of the freezing front and the duration of the recalescence phase. However, upon studying the motion velocity of the freezing front, we found that these velocities are temperature-dependent. This aligns with the results of our phase-field simulations and experimental findings, indicating that the release of latent heat during the recalescence process is stable. Additionally, the randomness in freezing may also stem from the deformation of droplets during the solidification process. In this study, we identified two distinct solidification modes during the freezing phase: one initiating from the droplet's head or tail and the other starting from the middle, with the latter causing significant droplet deformation. Through statistical analysis, we further explored the influence of flow rate variation on the digital clustering of droplet freezing and discovered flow rate parameters that optimize freezing digitalization.
Droplet-based triboelectric nanogenerator (D-TENG) using liquid -solid contact electrification has been widely studied for harvesting raindrop energy and ocean energy due to its unique ability to effectively reduce friction between solids and improve durability. In 2020, a new-generation D-TENG with a "transistor-like" structure was proposed, which first achieved ultra-high instantaneous power output(50 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Subsequently, D-TENG has been rapidly developed and is widely used in ocean wave energy power generation and liquid-solid triboelectric power generation. Currently, although we have excellent D-TENGs to harvest energy from a single water droplet, but if multiple D-TENGs are simply connected in parallel for a large area usage, the output power of the whole power generation module will drop significantly due to the mutual influence between different individual generation units, which severely limits their applications in practice. Therefore, finding a reasonable topology to relieve the inherent constraints of D-TENGs is of great significance for realizing the large-scale raindrop energy harvesting.
A novel low-complexity iterative receiver for coded multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is proposed in this letter. The iterative receiver uses the parallel interference cancellation (PIC)-maximum ratio combining (MRC) detector for MIMO-OFDM detection, which is a popular alternative to the minimum mean square error (MMSE) detector due to its lower computational complexity. However, we have found that the conventional PIC-MRC detector tends to underestimate the magnitude of its output log likelihood ratios (LLRs). Based on this discovery, we propose to multiply these LLRs by a constant factor, which is optimized according to the extrinsic information transfer (EXIT) chart of the soft-in soft-out (SISO) detector. Simulation results show that the proposed scheme significantly improves the performance of the PIC-MRC-based receiver with little additional cost in computational complexity, allowing it to closely approach the performance of receiver using the much more complex MMSE detector.