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    Medical School of Chinese People’s Liberation Army (PLA) Reports Findings in Rob otics (Advances in the Application of AI Robots in Critical Care: Scoping Review )

    66-67页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Robotics is the subjec t of a report. According to news reporting originating from Beijing, People’s Re public of China, by NewsRx correspondents, research stated, “In recent epochs, t he field of critical medicine has experienced significant advancements due to th e integration of artificial intelligence (AI). Specifically, AI robots have evol ved from theoretical concepts to being actively implemented in clinical trials a nd applications.” Our news editors obtained a quote from the research from the Medical School of C hinese People’s Liberation Army (PLA), “The intensive care unit (ICU), known for its reliance on a vast amount of medical information, presents a promising aven ue for the deployment of robotic AI, anticipated to bring substantial improvemen ts to patient care. This review aims to comprehensively summarize the current st ate of AI robots in the field of critical care by searching for previous studies , developments, and applications of AI robots related to ICU wards. In addition, it seeks to address the ethical challenges arising from their use, including co ncerns related to safety, patient privacy, responsibility delineation, and cost- benefit analysis. Following the scoping review framework proposed by Arksey and O’Malley and the PRISMA (Preferred Reporting Items for Systematic Reviews and Me ta-Analyses) guidelines, we conducted a scoping review to delineate the breadth of research in this field of AI robots in ICU and reported the findings. The lit erature search was carried out on May 1, 2023, across 3 databases: PubMed, Embas e, and the IEEE Xplore Digital Library. Eligible publications were initially scr eened based on their titles and abstracts. Publications that passed the prelimin ary screening underwent a comprehensive review. Various research characteristics were extracted, summarized, and analyzed from the final publications. Of the 59 08 publications screened, 77 (1.3%) underwent a full review. These studies collectively spanned 21 ICU robotics projects, encompassing their system development and testing, clinical trials, and approval processes. Upon an exper t-reviewed classification framework, these were categorized into 5 main types: t herapeutic assistance robots, nursing assistance robots, rehabilitation assistan ce robots, telepresence robots, and logistics and disinfection robots. Most of t hese are already widely deployed and commercialized in ICUs, although a select f ew remain under testing. All robotic systems and tools are engineered to deliver more personalized, convenient, and intelligent medical services to patients in the ICU, concurrently aiming to reduce the substantial workload on ICU medical s taff and promote therapeutic and care procedures. This review further explored t he prevailing challenges, particularly focusing on ethical and safety concerns, proposing viable solutions or methodologies, and illustrating the prospective ca pabilities and potential of AI-driven robotic technologies in the ICU environmen t. Ultimately, we foresee a pivotal role for robots in a future scenario of a fu lly automated continuum from admission to discharge within the ICU. This review highlights the potential of AI robots to transform ICU care by improving patient treatment, support, and rehabilitation processes.”
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    Department of Surgery Reports Findings in Hernias (Roboticassisted treatment of paraesophageal hernias in the emergency setting: a retrospective study)

    67-68页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Gastroenterology - Her nias is the subject of a report. According to news reporting out of Bellinzona, Switzerland, by NewsRx editors, research stated, “Emergency treatment of paraeso phageal hernias can be carried out through laparotomy or minimally invasive appr oaches, however, evidence in this regard is weak. The aim of our study was to as sess safety and feasibility of the robotic-assisted treatment of paraesophageal hernias in the emergency setting.” Our news journalists obtained a quote from the research from the Department of S urgery, “At the Bellinzona e Valli Regional Hospital, Switzerland, we conducted a retrospective analysis of patients operated on from January 2020 to January 20 24 with robotic surgery for emergency presentation of paraesophageal hernias. De mographic and clinical details, operative techniques, and postoperative outcomes were collected and analyzed. Out of 82 patients who underwent robotic-assisted paraesophageal hernia repair, 17 were treated in the emergency setting. Median a ge was 79 years (IQR 77-85), 3 (17.6%) patients were male, and medi an BMI was 23.9 kg/m (IQR 21.0-26.0). Most frequent presentation symptoms were p ain (100 %), regurgitation (88.2%), and dyspnea (17.6% ). No intraoperative complication, conversion to open surgery or stomach resecti ons were recorded. Two complications of grade 3 according to the Clavien- Dindo c lassification and one of grade 2 occurred; all were successfully treated until r esolution. The median length of hospital stay was 8 days (IQR 5-16). After a mea n follow-up of 15.9 months (IQR 6.5-25.6) only two small axial asymptomatic recu rrences that required no treatment. Despite limitations, our study demonstrated a very low rate of intra- and postoperative complications, likely supporting the safety and feasibility of robotic-assisted treatment for paraesophageal hernias in emergency settings.”
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    Findings from Nanjing University of Science and Technology Provides New Data abo ut Machine Learning (Spatial Sensitivity Synthesis Based On Alternate Projection for the Machine-learningbased Coding Digital Receiving Array)

    68-69页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting from Nanjing, People’s Republic of China, by NewsRx journalists, research stated, “Recently, a novel low-cost coding digi tal receiving array based on machine learning (ML-CDRA) has been proposed to red uce the required radio frequency channels in modern wireless systems. The spatia l sensitivity of ML-CDRA is studied which describes the spatial accumulation gai n in different directions.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from the Nanjing Univ ersity of Science and Technology, “It is demonstrated that the spatial sensitivi ty is determined by the encoding network, decoding network, and beamforming crit erion. To obtain the desired spatial sensitivity, a spatial sensitivity synthesi s method is proposed based on the alternate projection by optimising the encodin g network with the constraint of amplitude-phase quantisation. Simulation result s show that the proposed method can significantly improve the spatial sensitivit y of ML-CDRA. Furthermore, in the directions of interest, the spatial accumulati on gain of ML-CDRA can exceed the full-channel digital receiving array. The spat ial sensitivity of a machine-learning-based coding digital receiving array (ML-C DRA) is studied which describes the spatial accumulation gain in different direc tions. It is demonstrated that spatial sensitivity is determined by the encoding network, decoding network, and beamforming criterion.”
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    Findings from Forschungszentrum Julich GmbH Provide New Insights into Machine Le arning (Efficient surrogate models for materials science simulations: Machine le arning-based prediction of microstructure properties)

    69-69页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news originating from Julich, Germany, by NewsRx editors, the research stated, “Determining, understanding, and predicting the so -called structure-property relation is an important task in many scientific disc iplines, such as chemistry, biology, meteorology, physics, engineering, and mate rials science.” Financial supporters for this research include Forschungszentrum Julich Gmbh; Eu ropean Research Council. The news journalists obtained a quote from the research from Forschungszentrum J ulich GmbH: “Structure refers to the spatial distribution of, e.g., substances, material, or matter in general, while property is a resulting characteristic tha t usually depends in a non-trivial way on spatial details of the structure. Trad itionally, forward simulations models have been used for such tasks. Recently, s everal machine learning algorithms have been applied in these scientific fields to enhance and accelerate simulation models or as surrogate models. In this work , we develop and investigate the applications of six machine learning techniques based on two different datasets from the domain of materials science: data from a two-dimensional Ising model for predicting the formation of magnetic domains and data representing the evolution of dual-phase microstructures from the Cahn- Hilliard model. We analyze the accuracy and robustness of all models and elucida te the reasons for the differences in their performances.”
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    Recent Findings from Polytechnic University Bari Provides New Insights into Mach ine Learning (Neuralpmg: a Neural Polyphonic Music Generation System Based On Ma chine Learning Algorithms)

    70-70页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in Machine Learning. According to news reporting originating from Bari, Italy, by NewsRx corresponden ts, research stated, “The realm of music composition, augmented by technological advancements such as computers and related equipment, has undergone significant evolution since the 1970s. In the field algorithmic composition, however, the i ncorporation of artificial intelligence (AI) in sound generation and combination has been limited.” Funders for this research include Politecnico di Bari, SECURE SAFE APULIA, CTEMT - “Casa delle Tecnologie Emergenti di Matera. Our news editors obtained a quote from the research from Polytechnic University Bari, “Existing approaches predominantly emphasize sound synthesis techniques, w ith no music composition systems currently employing Nicolas Slonimsky’s theoret ical framework. This article introduce NeuralPMG, a computer-assisted polyphonic music generation framework based on a Leap Motion (LM) device, machine learning (ML) algorithms, and brain-computer interface (BCI). ML algorithms are employed to classify user’s mental states into two categories: focused and relaxed. Inte raction with the LM device allows users to define a melodic pattern, which is el aborated in conjunction with the user’s mental state as detected by the BCI to g enerate polyphonic music. NeuralPMG was evaluated through a user study that invo lved 19 students of Electronic Music Laboratory at a music conservatory, all of whom are active in the music composition field. The study encompassed a comprehe nsive analysis of participant interaction with NeuralPMG. The compositions they created during the study were also evaluated by two domain experts who addressed their aesthetics, innovativeness, elaboration level, practical applicability, a nd emotional impact.”
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    Findings from School of Civil Engineering in the Area of Machine Learning Report ed (Machine Learning Assisted Prediction and Analysis of In-plane Elastic Modulu s of Hybrid Hierarchical Square Honeycombs)

    71-71页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting originating from Hunan, Peo ple’s Republic of China, by NewsRx correspondents, research stated, “In this stu dy, experimental, finite element (FE) simulation, machine learning (ML), and the oretical techniques are employed to investigate the in -plane elastic modulus ( E HHSH ) of hybrid hierarchical square honeycombs (HHSHs). First, HHSHs with dif ferent configurations were fabricated using a 3D printer, and in -plane quasi -s tatic compression tests were conducted on them.” Our news editors obtained a quote from the research from the School of Civil Eng ineering, “Then, 234 FE models are simulated to determine the E HHSH of HHSHs wi th various configurations, and the results are used to train 11 ML models. Compa rative analysis demonstrates that the Extreme Gradient Boosting (XGBoost) model has the best predictive capability. Moreover, a modified theory for E HHSH is es tablished based on the XGBoost model and existing theory, and its exceptional pr edictive capability is verified by comparing with experimental, FE, and existing theoretical results. Finally, the upper and lower bounds of E HHSH are determin ed by the modified theory, and the Shapley Additive Explanation (SHAP) method is used to identify the importance of different geometric parameters on tailoring E HHSH.”
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    New Machine Learning Study Findings Reported from Karlsruhe Institute of Technol ogy (KIT) (Machine Learning for Robust Structural Uncertainty Quantification In Fractured Reservoirs)

    72-72页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting out of Karlsruhe, Germany, by NewsRx edit ors, research stated, “Including uncertainty is essential for accurate decision- making in underground applications. We propose a novel approach to consider stru ctural uncertainty in two enhanced geothermal systems (EGSs) using machine learn ing (ML) models.” Financial support for this research came from Deutscher Akademischer Austausch D ienst (DAAD). Our news journalists obtained a quote from the research from the Karlsruhe Insti tute of Technology (KIT), “The results of numerical simulations show that a smal l change in the structural model can cause a significant variation in the tracer breakthrough curves (BTCs). To develop a more robust method for including struc tural uncertainty, we train three different ML models: decision tree regression (DTR), random forest regression (RFR), and gradient boosting regression (GBR). D TR and RFR predict the entire BTC at once, but they are susceptible to overfitti ng and underfitting. In contrast, GBR predicts each time step of the BTC as a se parate target variable, considering the possible correlation between consecutive time steps. This approach is implemented using a chain of regression models. Th e chain model achieves an acceptable increase in RMSE from train to test data, c onfirming its ability to capture both the general trend and small-scale heteroge neities of the BTCs. Additionally, using the ML model instead of the numerical s olver reduces the computational time by six orders of magnitude. This time effic iency allows us to calculate BTCs for 2 ‘ 000 different reservoir models, enabli ng a more comprehensive structural uncertainty quantification for EGS cases.”
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    Shenzhen University Reports Findings in Robotics (Fast and Efficient Motion Plan ning Algorithm for Cfetr Multipurpose Overload Robot In Narrow Workspace)

    73-73页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Robotics is the subject of a repo rt. According to news reporting out of Shenzhen, People’s Republic of China, by NewsRx editors, research stated, “The China Fusion Engineering Test Reactor (CFE TR) multipurpose overload robot (CMOR) has nine degrees of freedom (DOF), and th e redundant DOF contributes to its ability to avoid collisions in the narrow wor kspace. However, the redundant DOF and narrow workspace poses significant challe nges in the calculation of collision-free trajectories.” Financial support for this research came from Comprehensive Research Facility fo r Fusion Technology Program of China.
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    Researchers from Beijing University of Technology Report Findings in Machine Lea rning (Multi-objective Optimization Design of Recycled Aggregate Concrete Mixtur e Proportions Based On Machine Learning and Nsga-ii Algorithm)

    74-74页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on Machine Learn ing. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “This paper employs Support Vector Regression ( SVR), Random Forest Regression (RF), Gradient Boosting (GB), and Extreme Gradien t Boosting (XGB) algorithms to establish the compressive strength prediction mod els for Recycled Aggregate Concrete (RAC) and analyze the influence of ten input s on RAC compressive strength. Combined with the best prediction model, the Non- dominated Sorting Genetic Algorithm II (NSGA-II) is applied for multiobjective optimization of mixture proportions in RAC addressing cost, carbon emissions, an d compressive strength as key objectives.” Financial supporters for this research include National Natural Science Foundati on of China-China National Railway Group Co., Ltd. Railway Basic Research Joint Fund Project, Beijing Natural Science Foundation.
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    University College London (UCL) Hospitals NHS Foundation Trust Reports Findings in Osteoarthritis (Two-Dimensional Versus Three- Dimensional Preoperative Plannin g In Total Hip Arthroplasty)

    75-76页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Musculoskeletal Diseas es and Conditions - Osteoarthritis is the subject of a report. According to news reporting originating from London, United Kingdom, by NewsRx correspondents, re search stated, “Pre-operative planning in total hip arthroplasty (THA), involves utilizing radiographs or advanced imaging modalities, including computerized to mography (CT) scans, for precise prediction of implant sizing and positioning. T his study aimed to compare Three-Dimensional (3D) versus Two-Dimensional (2D) pr e-operative planning in primary THA with respect to key surgical metrics, includ ing restoration of the horizontal and vertical Center of Rotation (COR), combine d offset, and leg length.” Our news editors obtained a quote from the research from University College Lond on (UCL) Hospitals NHS Foundation Trust, “This study included 60 patients underg oing primary THA for symptomatic hip osteoarthritis, randomly allocated to eithe r robotic-arm-assisted or conventional THA. Digital 2D templating and 3D plannin g using the robotic software were performed for all patients. All measurements t o evaluate the accuracy of templating methods were conducted on the pre-operativ e CT scanogram, using the contralateral hip as a reference. Sensitivity analyses explored differences between 2D and 3D planning in patients who had supero-late ral or medial osteoarthritis patterns. Compared to 2D templating, 3D templating was associated with less medialization of the horizontal COR (-1.2 versus -0.2 m m, P = 0.002) and more accurate restoration of the vertical COR (1.63 versus 0.3 mm, P<0.001) with respect to the contralateral side. Furt hermore, 3D templating was superior for planned restoration of leg length (+0.23 versus -0.74 mm, P = 0.019). Sensitivity analyses demonstrated that in patients who had medial osteoarthritis, 3D planning resulted in less medialization of ho rizontal COR and less offset reduction. Conversely, in patients who had supero-l ateral osteoarthritis, there was less lateralization of horizontal COR and less offset increase using 3D planning. Additionally, 3D planning showed superior rep roducibility for stem, acetabular cup sizes, and neck angle, while 2D planning o ften led to smaller stem and cup sizes. Our findings indicated higher accuracy i n the planned restoration of native joint mechanics using 3D planning.”
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