QuNex—An integrative platform for reproducible neuroimaging analytics

Introduction: Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability. Methods: To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a “turnkey” command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features. Results: The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform. Discussion: Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease

QuNex—An integrative platform for reproducible neuroimaging analytics

Introduction: Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability.Methods: To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a “turnkey” command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features.Results: The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform.Discussion: Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease

Lightweight Ship Detection Methods Based on YOLOv3 and DenseNet

Ship detection is one of the most important research contents of ship intelligent navigation and monitoring. As a supplement to classical navigational equipment such as radar and the Automatic Identification System (AIS), target detection based on computer vision and deep learning has become a new important method. A target detector called YOLOv3 has the advantages of detection speed and accuracy and meets the real-time requirements for ship detection. However, YOLOv3 has a large number of backbone network parameters and requires high hardware performance, which is not conducive to the popularization of applications. On the basis of YOLOv3, this paper proposes a lightweight ship detection model (LSDM) in which the backbone network is improved by using dense connection inspired from DenseNet, and the feature pyramid networks are improved by using spatial separation convolution to replace normal convolution. The two improvements reduce parameters and optimize the network structure greatly. The experimental results show that, with only one-third of parameters of YOLOv3, the LSDM has higher accuracy and speed for ship detection. In addition, the LSDM is simplified further by reducing the number of densely connected units to form a model called LSDM-tiny. The experimental results show that, LSDM-tiny has similar detection speed with YOLOv3-tiny, but has a lot higher accuracy

Z-Scheme CuOx/Ag/TiO2 Heterojunction as Promising Photoinduced Anticorrosion and Antifouling Integrated Coating in Seawater

In the marine environment, steel materials usually encounter serious problems with chemical or electrochemical corrosion and fouling by proteins, bacteria, and other marine organisms. In this work, a green bifunctional Z-scheme CuOx/Ag/P25 heterostructure coating material was designed to achieve the coordination of corrosion prevention and antifouling by matching the redox potential of the reactive oxygen species and the corrosion potential of 304SS. When CuOx/Ag/P25 heterostructure was coupled with the protected metal, the open circuit potential under illumination negatively shifted about 240 mV (vs. Ag/AgCl) and the photoinduced current density reached 16.6 μA cm−2. At the same time, more reactive oxygen species were produced by the Z-shape structure, and then the photocatalytic sterilization effect was stronger. Combined with the chemical sterilization of Ag and the oxide of Cu, the bacterial survival rate of CuOx/Ag/P25 was low (0.006%) compared with the blank sample. This design provides a strategy for developing green dual-functional coating materials with photoelectrochemical anticorrosion and antifouling properties

The role of biomass in China’s long-term mitigation toward the Paris climate goals

Biomass is a crucial option of substituting fossil fuels to reduce emissions, and bioenergy with carbon capture and storage (BECCS) allows for obtaining net-negative emissions. We explore the role of biomass in China’s long-term mitigation toward the Paris climate goals in light of three narratives and five mitigation scenarios, modeling by a refined Global Change Assessment Model. While presenting a limited contribution to achieving China’s Nationally Determined Contribution (NDC), biomass plays an important role in China’s post-NDC mitigation toward the Paris climate goals. All the assessed scenarios call for extensive biomass use, accounting for 6.5%–28% of China’s 2100 primary energy in our three 2 °C scenarios and 15%–30% in our two 1.5 °C scenarios. The exact biomass deployment trajectories tend to depend greatly on how China envisages national mitigation paces and BECCS strategies. For either 2 °C or 1.5 °C, a smaller negative-emission narrative, which means a more rapid immediate decarbonization of the energy system toward mid-century, depends on larger bioenergy in medium-to-long-term. Delaying short- and medium-term ambition delays bioenergy applications but requires far more in the second half of the century to create greater negative emissions via BECCS. Moving from 2 °C toward 1.5 °C features higher and earlier bioenergy deployments and meaningfully increasing BECCS volumes and biofuel shares in China’s energy system. Consequently, the Chinese stockholders might be ready to make a decision on to what degree biomass and BECCS enter the sphere of China’s energy and climate policies, which will greatly influence not only national biomass roadmap but also mid-century mitigation targets

Exploring Use Acceptance of Electric Bicycle-Sharing Systems: An Empirical Study Based on PLS-SEM Analysis

The electric bicycle-sharing system (EBSS) is the fourth-generation urban shared bicycle travel system, which effectively improves the travel efficiency of urban residents and solve the problem of urban congestion. This study attempts to use an extended technology acceptance model (TAM) method to study the acceptance of EBSSs. We had introduced four potential variables, including perceived pleasure (PP), perceived environmental value (PEV), perceived cost (PC), and perceived reliability (PR), into the classic TAM to form a new EBSS-TAM. Data were obtained by using a Likert scale questionnaire from 399 citizens in China. Partial least-squares structural equation modeling (PLS-SEM) with reflective constructs was employed as the analysis method. The results showed that: (1) the EBSS-TAM can explain user behaviors regarding the use of EBSSs. PP has a positive impact on behavior attitude (BA) while having no impact on behavior intention (BI). PEV has no impact on BA and BI. PC has a negative impact on BA and has no impact on BI. PR has a positive impact on BA while having no impact on BI. Perceived ease of use (PEU) has a positive impact on PP and PEV. (2) Younger users (under 35 years old) are more likely to change from liking CBSSs to using EBSSs than older users are. Male users are more satisfied with EBSSs because of their ease of use. The users who never used CBSSs are more likely to perceive the environmental protection value of EBSSs. Some managerial implications were proposed for the EBSSs

Exploring fair and ambitious mitigation contributions under the Paris Agreement goals

In order to achieve the Paris Agreement goals of keeping the temperature rise well below 2 °C or even 1.5 °C, all countries would need to make fair and ambitious contributions to reducing emissions. A vast majority of countries have adopted reduction targets by 2030 in their Nationally Determined Contributions (NDCs). There are many alternative ways to analyze the fairness of national mitigation contributions. This article uses a model framework based on six equity principles of effort-sharing, to allocate countries’ reduction targets under global emissions scenarios consistent with meeting the Paris climate goals. It further compares these allocations with the NDCs. The analysis shows that most countries need to adopt more ambitious reduction targets by 2030 to meet 2 °C, and even more for 1.5 °C. In the context of 2 °C, the NDCs of the United States of America and the European Union lack ambition with respect to the approaches that emphasize responsibility; China's NDC projection falls short of satisfying any approach in 2030. In the context of 1.5 °C, only India, by implementing its most ambitious efforts by 2030, could be in line with most equity principles. For most countries, the NDCs would use most of their allowed emissions space for the entire 21 st century by 2030, posing a major challenge to transform to a pathway consistent with their fair contributions in the long-term

Exploring fair and ambitious mitigation contributions under the Paris Agreement goals

In order to achieve the Paris Agreement goals of keeping the temperature rise well below 2 °C or even 1.5 °C, all countries would need to make fair and ambitious contributions to reducing emissions. A vast majority of countries have adopted reduction targets by 2030 in their Nationally Determined Contributions (NDCs). There are many alternative ways to analyze the fairness of national mitigation contributions. This article uses a model framework based on six equity principles of effort-sharing, to allocate countries’ reduction targets under global emissions scenarios consistent with meeting the Paris climate goals. It further compares these allocations with the NDCs. The analysis shows that most countries need to adopt more ambitious reduction targets by 2030 to meet 2 °C, and even more for 1.5 °C. In the context of 2 °C, the NDCs of the United States of America and the European Union lack ambition with respect to the approaches that emphasize responsibility; China's NDC projection falls short of satisfying any approach in 2030. In the context of 1.5 °C, only India, by implementing its most ambitious efforts by 2030, could be in line with most equity principles. For most countries, the NDCs would use most of their allowed emissions space for the entire 21 st century by 2030, posing a major challenge to transform to a pathway consistent with their fair contributions in the long-term

Aminoalkyldisiloxane as effective electrolyte additive for improving high temperature cycle life of nickel-rich LiNi0.6Co0.2Mn0.2O2/graphite batteries

A novel aminoakyldisiloxane compound, (3-(N,N-dimethylamino)diethoxypropyl) pentamethyldisiloxane (DSON), is reported as an effective electrolyte additive for improving the electrochemical performances of high energy density power batteries of nickel-rich LiNi0.6Co0.2Mn0.2O2 (NCM622)/graphite. The NCM622/graphite cells using DSON addition in the carbonate-based reference electrolyte exhibit enhanced electrochemical performances, especially high temperature performances including long cycle life and high temperature storage. The pouch cell (1 Ah) with 0.2 wt% DSON retained a capacity of 88% as compared with 84% for that without DSON in electrolyte after 200 cycles at 45 degrees C. The mechanism investigation reveals that DSON acts as a film-forming additive for constructing uniform conductive cathode electrolyte interphase (CEI) layer upon NCM622 particles, which suppresses the internal cracks and prohibits the irreversible phase transformation of NCM622. DSON also serves as an effective water/acid scavenger and inhibits the hydrolysis of LiPF6, thus effectively blocking the occurrence of side reactions and the dissolution of transition metal ions from cathode. Therefore, NCM622 cathode sheet maintains a better integrity surface morphology after 100 cycles. This work demonstrates that aminoakyldisiloxane is promising for practical use as effective electrolyte additive for high energy density power batteries of nickel-rich NCM/graphite

Manipulation of Microobjects Based on Dynamic Adhesion Control

Due to scale effects, microoperation, especially the releasing of microobjects, has been a long-standing challenge in micromanipulation applications. In this paper a micromanipulation method is presented based on dynamic adhesion control with compound vibration. This adhesion control technique employs inertia force to overcome adhesion force achieving 100% repeatability with releasing accuracy of 4± 0.5μm, which was experimentally quantified through the manipulation of 20–100μm polystyrene spheres under an optical microscope. The micromanipulation system consists of a microgripper and a piezoelectric ceramics module. The compound vibration comes from the electrostatic actuator and the piezoelectrically driven actuator. Surface and bulk micromachining technology is employed to fabricate the microgripper used in the system from a single crystal silicon wafer. Experimental results confirmed that this adhesion control technique is independent of substrate. Theoretical analyses were conducted to understand the picking up and releasing mechanism. Based on this preliminary study, the micromanipulation system proved to be an effective solution for active picking up and releasing of micromanipulation