A Novel Autonomous Robotics System for Aquaculture Environment Monitoring

Implementing fully automatic unmanned surface vehicles (USVs) monitoring water quality is challenging since effectively collecting environmental data while keeping the platform stable and environmental-friendly is hard to approach. To address this problem, we construct a USV that can automatically navigate an efficient path to sample water quality parameters in order to monitor the aquatic environment. The detection device needs to be stable enough to resist a hostile environment or climates while enormous volumes will disturb the aquaculture environment. Meanwhile, planning an efficient path for information collecting needs to deal with the contradiction between the restriction of energy and the amount of information in the coverage region. To tackle with mentioned challenges, we provide a USV platform that can perfectly balance mobility, stability, and portability attributed to its special round-shape structure and redundancy motion design. For informative planning, we combined the TSP and CPP algorithms to construct an optimistic plan for collecting more data within a certain range and limiting energy restrictions.We designed a fish existence prediction scenario to verify the novel system in both simulation experiments and field experiments. The novel aquaculture environment monitoring system significantly reduces the burden of manual operation in the fishery inspection field. Additionally, the simplicity of the sensor setup and the minimal cost of the platform enables its other possible applications in aquatic exploration and commercial utilization

Efficacy mechanisms research progress of the active components in the characteristic woody edible oils

Woody edible oils are a type of vegetable oil. Woody edible oils like olive oil have greater quantities of unsaturated fatty acids (UFAs), particularly essential FAs, as well as vitamin E, phytosterols, and other nutrients that are becoming more vital in human health. As a result, finding high-quality woody oil resource plants is critical to ensuring enough edible oil supply. As six novel woody crops, Paeonia suffruticosa, Plukenetia volubilis, Acer truncatum, Olea europaea, Camellia sinensis, and Camellia oleifera are characterized by high oil production, widespread cultivation, adaptability, and various active ingredients. The six woody crop oils contain UFAs (e.g., α-linolenic acid, oleic acid, and linoleic acid), vitamin E, polyphenols, phytosterols, and so forth. The presence of these active ingredients confers anti-inflammatory, antioxidant, cholesterol and lipid metabolism regulating, blood lipid lowering, immune boosting, memory improving, intestinal flora regulating, and other properties to the oils, which are beneficial to body health. This article examined in depth the seed resources, FA composition, active component kinds, active ingredient efficacy mechanism, and physiological impacts of these six novel woody crop oils. These developments lay a solid platform for further study and development of these woody oil crops.This work was supported by the Key Research and Development Program of Zhejiang Province (No. 2021C02002), Zhejiang Provincial Natural Sciences Foundation of China under Grant (No. LZ22C200006), Top young talents of the ten thousand talents program of Zhejiang Province (ZJWR0308016), Key R&D projects in Zhejiang Province (2023C04010), and Zhejiang Basic Public Welfare Research Project (LGN21C200006). Agusti Romero acknowledges financial support from the CERCA Program from the Generalitat of Catalonia. We would like to thank all contributors of the current study for their concepts, ideas, contribution, and provision.info:eu-repo/semantics/publishedVersio

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Self-supervised learning at the sensor layer in robotics

Modern robots are generally armed with diverse modalities of sensors, for various functionalities and safety redundancy. The recent breakthrough of deep learning (DL) technologies demonstrates superior performance on many high-level tasks, especially with using multi-sensor fusion. While the majority of multi-modal DL methods assume that sensors are well calibrated, synchronized and denoised, such efforts at the sensor layer are non-trivial and get increasingly expensive with the growing complexity of robotic systems. Currently dominant approaches heavily rely on specific hardware setup or high-end sensors, which generally are not cost-effective. This cost concern could be a bottleneck for the potential wide adoption of low-cost robots in the near future. Even though DL has a huge potential at the sensor layer, the difficulty of acquiring sufficient and accurate annotations for related tasks remain a major challenge. This thesis first formulates key problems at the robot sensor layer from the machine learning perspective, and further proposes efficient self-supervised learning approaches systematically. In our work, the popular and representative LiDAR-camera-inertial system is utilized as the study target. Firstly, the challenging LiDAR-camera online extrinsic calibration task is delved into, and we investigate the Riemannian metrics equipped self-supervised learning approach via synthetic data. This was the first work in the literature which demonstrates competing performance of data-driven methods when compared with conventional approaches at the sensor layer. It lays the foundation and shows the potential for later deeper explorations. Secondly, we address several overlooked limitations of the conventional synchronization pipelines and propose the first DL based LiDAR-camera synchronization framework, which is an innovative self-supervised learning schema. Thirdly, the problem of Inertial Measurement Unit (IMU) denoising for navigation is studied, and we propose a self-supervised multi-task framework. This work demonstrates the superiority of data-driven approaches on IMU denoising and presents one realistic self-supervised learning implementation. These explorations initialize the adoption of deep learning for robot sensor layer tasks and show case how self-supervised learning can be applied. Our work helps push the boundary of self-supervised learning at the sensor layer to an usable stage, demonstrate the potential for this direction and shed the lights for future research.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

A 3d printing and moulding method of the fabrication of a miniature voice coil motor actuator

The goal of this project is to apply 3D printing and moulding (3DPM) methods for the fabrication of a miniature magnetic actuator for optical image stabilization (OIS) applications. Polydimethylsiloxane (PDMS) and strontium ferrite (SrFe) nano powder were used as the main structural material. Young’s modulus and the magnetization of the material with SrFe-doping ratios ranging from 20% to 60% by weight were characterized. The actuator, consisting of four coils, an actuating plate, and a base supporter was assembled and tested with a Laser Doppler Velocimetry (LDV) system. A tilting angle of 0.6º was achieved with the application of 500 mA (50 turns/9 mm long coils). A Taguchi’s orthogonal experimental design was used in the finite element analysis (FEA) simulation to examine the effect of dimension variations on the eigenfrequencies. Frequency response of the actuator was characterized and the experimental results matched with the simulation results between 1 and 450 Hz showing less than 5% errors. A series of replica experiments were also performed and analyzed.Applied Science, Faculty ofMechanical Engineering, Department ofGraduat

Research on a User-Centered Evaluation Model for Audience Experience and Display Narrative of Digital Museums

As culture becomes a value dimension of economic and social development worldwide, museums as a social medium are given more missions and expectations. Mobile Internet technology is empowering digital museums in the epidemic context, bringing new public cultural service content to the public. In this paper, we focus on the website quality of user experience in the current construction of digital museums. By analyzing the components of 20 digital museums, three models with different tendencies are abstracted. Then the three models are implemented as prototype websites, and their user experience was evaluated by experiment. Result shows that website content and user identity differences affect website quality, user attitudes, and user intentions. Rich contextual information contributes to the experience, and the “professional group” generally agrees less with the digital museum experience than the “non-professional group”. This research has implications for the study of digital museum user groups, experience analysis, and content construction