Dataset of Panda sperm proteome.

The giant panda (), a vulnerable species and an icon of wildlife conservation, is still at risk due to habitat fragmentation and a low reproductive rate. To further safeguard the giant panda from extinction, a captive breeding program was established in the mid 1980's, however the growth of this population has been hindered by the poor reproductive ability of captive male giant pandas. To address this, we investigated the sperm proteome of the giant panda as detailed information on sperm proteome is unavailable, as it is a highly specialized area of study.

Effects of Cryptorchidism on the Semen Quality of Giant Pandas from the Perspective of Seminal Plasma Proteomics.

Giant pandas are an endangered species with low reproductive rates. Cryptorchidism, which can negatively affect reproduction, is also often found in pandas. Seminal plasma plays a crucial role in sperm-environment interactions, and its properties are closely linked to conception potential in both natural and assisted reproduction.

A machine learning-based approach to assess impacts of autonomous vehicles on pavement roughness.

Studies have been initiated to investigate the potential impact of connected and automated vehicles (CAVs) on transportation infrastructure. However, most existing research only focuses on the wandering patterns of CAVs. To bridge this gap, an apple-to-apple comparison is first performed to systematically reveal the behavioural differences between the human-driven vehicle (HDV) and CAV trajectory patterns for the first time, with the data collected from the camera-based next generation simulation dataset and autonomous driving co-simulation platform, CARLA and SUMO, respectively.

Consensus Seeking in Large-Scale Multiagent Systems With Hierarchical Switching-Backbone Topology.

Recent developments in multiagent consensus problems have heightened the role of network topology when the agent number increases largely. The existing works assume that the convergence evolution typically proceeds over a peer-to-peer architecture where agents are treated equally and communicate directly with perceived one-hop neighbors, thus resulting in slower convergence speed. In this article, we first extract the backbone network topology to provide a hierarchical organization over the original multiagent system (MAS).

A Bayesian deep learning method for freeway incident detection with uncertainty quantification.

Incident detection is fundamental for freeway management to reduce non-recurrent congestions and secondary incidents. Recently, machine learning technologies have made considerable progress in the incident detection field, but many still face challenges in uncertainty quantification due to the aleatoric uncertainty of traffic data and the epistemic uncertainty of model deviations. In this study, a Bayesian deep learning method was proposed for freeway incident detection with uncertainty quantification.

Spatiotemporal analysis of urban road congestion during and post COVID-19 pandemic in Shanghai, China.

Coronavirus Disease 2019 (COVID-19) has become one of the most serious global health crises in decades and tremendously influence the human mobility. Many residents changed their travel behavior during and after the pandemic, especially for a certain percentage of public transport users who chose to drive their owned vehicles. Thus, urban roadway congestion has been getting worse, and the spatiotemporal congestion patterns has changed significantly.

Taxicab crashes modeling with informative spatial autocorrelation.

Maintaining taxi safety is one of the important goals of operating urban transportation systems. Taxicabs are often prone to higher crash risk due to their long-time exposure to the complicated and dynamic traffic environments in urban areas. Despite existing efforts in understanding the safety issues associated with these vehicles, there were still few attempts that have specifically examined the relationship between taxi-involved crashes and other multifaceted contributing factors.

Direct growth of vertically aligned ReSe nanosheets on conductive electrode for electro-catalytic hydrogen production.

Two-dimensional electrocatalysts with high-density active sites together with a good charge transfer to the conductive substrates can improve their hydrogen evolution reaction performances. Typically, a good contact between catalyst and electrode to enhance charge transfer can be achieved by way of direct growth. However, a controllable growth of vertically aligned two-dimensional ReSe directly on conductive substrates as working electrodes is not reported.

Transparent Glass with the Growth of Pyramid-Type MoS for Highly Efficient Water Disinfection under Visible-Light Irradiation.

Water disinfection is of great importance for human health and daily life. Photocatalysts with high efficiency, environmental protection, and narrow bandgaps are critical for practical water treatment. Here, a general approach is reported for the direct growth of pyramid-type MoS (pyramid MoS) on transparent glass by chemical vapor deposition (CVD).