Convenient labeling of proteins is important for observing its function under physiological conditions. In tissues particularly, heptamethine cyanine dyes (Cy-7) are valuable because they absorb in near infrared (NIR) region (750 – 900 nm) where light penetration is maximal. In this work, we found Cy-7 dyes with a meso-Cl functionality covalently binding to proteins with free Cys residues under physiological conditions (aqueous environments, at near neutral pH, and 37 °C). It transpired that the meso-Cl of the dye was displaced by free thiols in protein, while nucleophilic side-chains from amino acids like Tyr, Lys, and Ser did not react. This finding shows a new possibility for convenient and selective labeling of proteins with near-IR fluorescent probes.

In the field of engineering, tracked vehicles play a significant role in accessing challenging terrains. Maintaining trafficability is of the utmost importance when it comes to tracked vehicles traversing rugged terrains; it provides stability, traction, and adaptability for a wide range of applications, including deep-sea exploration. The terrain characteristics pertain to the interactions that determine the mobility of tracked vehicles. Traditional Terramechanics models are in-adequate in depicting the correlation between tracks and soil. An important issue is the lack of consideration for the moisture content in the soil and the rate at which sinking occurs in these models. Examining sinkage speed helps describe the ability of tracked vehicles to move at high speeds. The focus is on the critical factors influencing the mobility of tracked vehicles, precisely the sinkage speed and its relationship with pressure. Experimental tests in a soil bin, utilizing Bentonite/Diatom sediment soil, aim to measure these terrain factors. The study introduces a rubber-tracked vehicle, pressure, and moisture sensors to monitor pressure sinkage and moisture, evaluating cohesive soils (Bentonite/Diatom) in combination with sand and gravel mixtures. Findings reveal that higher moisture content in Bentonite soil leads to increased track slippage and sinkage compared to Diatom soil. Regarding soil behavior, diatom soil exhibited notable compaction and sinkage characteristics in contrast to bentonite soil. The distinct behaviors of these soils underscore the complexity of terrains, emphasizing the need for a meticulous understanding of soil properties before applying them in natural environments. Furthermore, this study contributes precision in assessing natural terrains, enhancing tracked vehicle design, and advancing terrain mechanics comprehension for off-road exploration. This knowledge contributes to better vehicle design and a deeper comprehension of terrain mechanics.

Tracked vehicles play a vital role in accessing challenging terrains, offering stability, traction, and versatility for various applications, including deep-sea exploration. However, the seafloor and sediment contain valuable but poorly understood resources, making it a significant challenge for tracked vehicles during exploration. Consequently, it is imperative to conduct comprehensive studies before considering these terrains for off-road purposes. In this study, we developed a rigid, flexible track vehicle equipped with various sensors, focusing on monitoring pressure sinkage. Cohesive soils like Bentonite and Diatom soil, particularly when combined with sand and gravel mixtures, were used for the investigation and assessed for ground pressure, bearing capacity, and sinkage in rigid-flexible tracked vehicles on these characterized terrains under varying moisture conditions. Our findings indicated that increased moisture content in bentonite soil resulted in significantly higher track slippage and sinkage compared to diatomaceous earth soil. Regarding soil behavior, diatom soil exhibited notable compaction and sinkage characteristics compared to bentonite soil. These significant variations between these two soil types under changing moisture conditions highlight the complexity of such terrains. Therefore, understanding the soil and its physical properties is crucial before applying them to natural terrains. Furthermore, this study emphasizes the importance of precision in assessing the physical properties of natural terrains for the development of tracked vehicles for off-road exploration. This knowledge contributes to better vehicle design and a deeper comprehension of terrain mechanics.

This paper proposes two fully-automatic machine-extracted linguistic features from an unlimited text input for Mandarin prosody generation. One is the punctuation confidence (PC) which measures the likelihood of inserting a major punctuation mark (PM) at a word boundary. Another is the quotation confidence (QC) which measures the likelihood of a word string to be quoted as a meaningful or emphasized unit in text. Because a major PM in a text is highly correlated with a prosodic break, and a quoted word string plays an important role in human language understanding, the two features potentially could provide useful information for prosody generation. The idea is first realized by employing conditional random field (CRF)-based models to predict major PMs, quoted word string locations, and their associated confidences, i.e., the PC and the QC, for each word boundary. Then, the predicted punctuations and their confidences are combined with traditional contextual linguistic features to predict prosodic-acoustic features. Both objective and subjective tests showed that the prosody generation with the proposed linguistic features performed better than the one without the proposed features. So, the proposed PC and QC are promising features for Mandarin prosody generation.