3714 research outputs found
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Artificial intelligence in hepatopancreaticobiliary surgery: a systematic review
No full textAim: The aim of this systematic review was to provide an overview of Machine Learning applications within hepatopancreaticobiliary surgery. The secondary aim was to evaluate the predictive performances of applied Machine Learning models.Methods: A systematic search was conducted in PubMed, EMBASE, Cochrane, and Web of Science. Studies were only eligible for inclusion when they described Machine Learning in hepatopancreaticobiliary surgery. The Cochrane and PROBAST risk of bias tools were used to evaluate the quality of studies and included Machine Learning models.Results: Out of 1821 articles, 52 studies have met the inclusion criteria. The majority of Machine Learning models were developed to predict the course of disease, and postoperative complications. The course of disease has been predicted with accuracies up to 99%, and postoperative complications with accuracies up to 89%. Most studies had a retrospective study design, in which external validation was absent for Machine Learning models.Conclusion: Machine learning models have shown promising accuracies in the prediction of short-term and long-term surgical outcomes after hepatopancreaticobiliary surgery. External validation of Machine Learning models is required to facilitate the clinical introduction of Machine Learning
Reliable metal alloy contact for Mg3+δBi1.5Sb0.5 thermoelectric devices
No full textProper contacts between thermoelectric (TE) materials and electrodes are critical for TE power generation or refrigeration. The Bi-rich n-type Zintl material Mg3+δBi2-xSbx exhibits very good TE performance near room temperature, which makes Mg3+δBi2-xSbx-based compounds highly promising candidates to replace the Bi2Te3-ySey alloys, but ideal contacts that can match their TE performance have not yet been well studied. Here we investigate different metal (Ni and Fe) and metal alloy (NiFe, NiCr, NiCrFe, and stainless steel) contacts on n-type Mg3+δBi1.5Sb0.5. It is first shown that the low Schottky barrier and narrow depletion region resulting from the band degeneracy and high carrier concentration of a heavily doped TE material are beneficial for the formation of a low-resistivity ohmic contact with a metal or a metal alloy. Most fully optimized TE materials can take advantage of this. Second, it is found that the NiFe/Mg3+δBi1.5Sb0.5 contact exhibits excellent thermal stability and the lowest ohmic contact resistivity among those studied after aging for over 2100 h, which is attributed to the formation of metallic NiMgBi between the NiFe and Mg3+δBi1.5Sb0.5 layers. As a buffer phase, NiMgBi can effectively prevent elemental diffusion without negatively affecting the electron transport. Benefiting from such low contact resistance, a Mg3+δBi1.5Sb0.5/Bi0.4Sb1.6Te3 unicouple exhibits competitive conversion efficiency, 6% with a 150 K temperature difference and a hot-side temperature of 448 K
A distributed electricity energy trading strategy under energy shortage environment
No full textThis paper presents a power dispatch strategy combining the main grid and distributed generators based on aggregative game theory and the Cournot price mechanism. Such a dispatch strategy aims to increase the electricity under the power shortage situation. Under the proposed strategy, this paper designs a discrete-time algorithm fusing the estimation technique and the Digging method to solve the power shortage problem in a distributed way. The distributed algorithm can provide privacy protection and information safety and improve the power grid's extendibility. Moreover, the simulation results show that the proposed algorithm has favorable performance and effectiveness in the numerical example
Process parameter optimization of metal additive manufacturing: a review and outlook
No full textThe selection of appropriate process parameters is crucial in metal additive manufacturing (AM) as it directly influences the defect formation and microstructure of the printed part. Over the past decade, research efforts have been devoted to identifying "optimal" processing regimes for different materials to achieve defect-free manufacturing, which mostly involve costly trial-and-error experiments and computationally expensive mechanistic simulations. Hence, it is apropos to critically review the methods used to achieve the optimal process parameters in AM. This work seeks to provide a structured analysis of current methodologies and discuss systematic approaches toward general optimization work in AM and the process parameter optimization of new AM alloys. A brief review of process-induced defects due to process parameter selection is given and the current methods for identifying "optimal processing windows" are summarized. Research works are analyzed under a standard optimization framework, including the design of experiments and characterization, modelling and optimization algorithms. The research gaps that preclude multi-objective optimization in AM are identified and future directions toward optimization work in AM are discussed. With growing capabilities in AM, we should reconsider the definition of the "optimal processing region"
Deep reinforcement learning for real-world quadrupedal locomotion: a comprehensive review
No full textBuilding controllers for legged robots with agility and intelligence has been one of the typical challenges in the pursuit of artificial intelligence (AI). As an important part of the AI field, deep reinforcement learning (DRL) can realize sequential decision making without physical modeling through end-to-end learning and has achieved a series of major breakthroughs in quadrupedal locomotion research. In this review article, we systematically organize and summarize relevant important literature, covering DRL algorithms from problem setting to advanced learning methods. These algorithms alleviate the specific problems encountered in the practical application of robots to a certain extent. We first elaborate on the general development trend in this field from several aspects, such as the DRL algorithms, simulation environments, and hardware platforms. Moreover, core components in the algorithm design, such as state and action spaces, reward functions, and solutions to reality gap problems, are highlighted and summarized. We further discuss open problems and propose promising future research directions to discover new areas of research
Satellite sensors as an emerging technique for monitoring macro- and microplastics in aquatic ecosystems
No full textPlastic pollution in aquatic ecosystems has been identified as a growing global water pollution threat that is negatively impacting water quality and, as a result, affecting the health of humans, aquatic animals, and wildlife. Therefore, it presents a global environmental catastrophe that requires immediate attention. Plastics in water (in their different forms, macro-, meso-, micro-, and nanoplastics) are contaminants of emerging concerns that have since evolved to be a global environmental threat. Despite increasing levels of pollution in aquatic ecosystems, there are insufficient monitoring data to evaluate the extent of the catastrophe. Traditional methods of monitoring plastics in water are constrained by high sampling costs, intensive labor, and limited temporal and spatial coverage, which results in limited monitoring data. Thus, insufficient monitoring data limit our understanding of the true quantities and persistence of plastic particles in aquatic ecosystems, as well as the extent to which they impact the aquatic environment. There is increasing availability of free big geospatial data (amounting to petabytes/day) from satellite sensors for potentially monitoring plastics. This provides a possible solution to these challenges by minimizing the fieldwork required and therefore reducing the costs and sampling time. The study purpose of this review is to analyze advances in emerging technology such as the use of satellite sensors to monitor the occurrence of macro- and microplastics in freshwater, ultimately aimed at creating new operational monitoring solutions. This review: (1) examines the literature to identify trends, accomplishments, and limitations of using satellite data to monitor plastics in water; (2) identifies and compares traditional, and machine and deep learning satellite image classification methods for monitoring plastics in water; and (3) identifies research gaps and summarizes future perspectives and recommendations to improve monitoring methods
A review of the bioaccumulation and adverse effects of PFAS in free-living organisms from contaminated sites nearby fluorochemical production plants
No full textPer- and polyfluoroalkyl substances (PFAS) encompass a large, heterogeneous group of chemicals of potential concern to human and environmental health. Based on information for some legacy PFAS, such as perfluorooctane sulfonate and perfluorooctanoate, there is an increasing awareness that they can represent a serious environmental risk. Although the environmental occurrence and fate of some legacy PFAS and their toxicity under controlled laboratory conditions have been investigated, to date, there is a dearth of information on the exposure and potential adverse effects of these compounds towards free-living organisms. The present review summarizes the findings of field studies investigating the accumulation and adverse effects induced by the exposure to environmental mixtures of both legacy and emerging PFAS in the wildlife living nearby fluorochemical production plants (FCP). Biomonitoring campaigns performed close to FCP, which can be considered as hotspots of PFAS contamination, can be very useful in exploring the fate and toxicity of these compounds towards free-living organisms. All studies showed that the bioaccumulation of both legacy and emerging PFAS in wildlife living near the FCP is higher compared to control sites and other areas worldwide. However, the investigation on adverse effects returned contrasting results, suggesting the need for further studies to shed light on the toxicity and mechanism(s) of action of PFAS in free-living organisms
Training in robotic pancreatic surgery
No full textThe aim of this narrative review is to discuss current training for the robotic approach to pancreatic surgery and the potential use of machine learning to progress robotic surgical training. A literature search using PubMed and MEDLINE was conducted to investigate training programmes in robotic pancreatic surgery and advances in the use of artificial intelligence for training. The use of virtual reality can assist novice robotic surgeons in learning basic surgical skills. The use of automated video analytics can also improve surgical education to enable self-directed learning both within and outside the operating room. The emerging role and novel applications of machine learning in robotic surgery could shape future training by aiding the autonomous recognition of anatomical structures in the surgical field, instrument tracking and providing feedback on surgical competence. Training should be standardised to ensure the attainment of assessment benchmarks and include virtual simulation basic training in addition to procedural-specific training. Standardised procedural techniques should be used to improve patient safety, theatre efficiency and the continuation of robotic practice
Advances in lithium-ion battery materials for ceramic fuel cells
No full textLithium-ion batteries (LIBs) and ceramic fuel cells (CFCs) are important for energy storage and conversion technologies and their materials are central to developing advanced applications. Although there are many crosslinking research activities, e.g., through materials and some common scientific fundamentals employed for both LIB and CFCs, crosslinking scientific aspects to achieve a comprehensive understanding are missing. There is a lack of such a review to promote and guide further research and development in the crosslinking of LIBs and CFCs. Herein, we review the existing application of LIB materials in CFCs to discover the scientific advances of lithium-ion and proton transport cooperation and identify the new directions of Li-CFCs in the future. This review is the first to propose CFC advances, especially at low temperatures (300-600 °C) by applying LIB materials to practical devices and highlight the material properties and new device functions with enhanced performance, as well as the scientific mechanisms and principles. Furthermore, we seek to deepen the scientific understanding of materials science, ion transport mechanisms and semiconductor electrochemistry to benefit both the battery and fuel cell fields
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