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    Fondazione Policlinico Universitario A. Gemelli IRCCS Reports Findings in Ovaria n Cancer [Robotic Recto-Sigmoid Resection with Total Intracor poreal Colorectal Anastomosis (TICA) in Recurrent Ovarian Cancer]

    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 Oncology - Ovarian Can cer is the subject of a report. According to news reporting originating from Rom e, Italy, by NewsRx correspondents, research stated, “About 70 % of women affected by ovarian cancer experience relapse within 2 years of diagnosis . Traditionally, the standard treatment for recurrent ovarian cancer (ROC) has b een represented by systemic chemotherapy.” Financial support for this research came from Universita Cattolica del Sacro Cuo re.
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    Studies in the Area of Machine Learning Reported from Nanjing Normal University (Optimal operation strategy predictive control for an integrated radiant cooling with fresh air system based on machine learning)

    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 study results on artificial intell igence have been published. According to news reporting originating from Nanjing , People’s Republic of China, by NewsRx correspondents, research stated, “Radian t cooling systems are widely valued for their great comfort and energy-saving po tential. However, they still face the risk of condensation in the early stages o f operation, especially in case of random occupancy and intermittent operation.” Our news reporters obtained a quote from the research from Nanjing Normal Univer sity: “This study aims to avoid unacceptable discomfort durations in randomly oc cupied rooms that installed integrated radiant cooling and fresh air system whil e consuming as little energy as possible. This paper firstly compares the effect s of adopting only the setback or standby cooling strategy in a randomly occupie d conference room by simulation. The simulation results demonstrate the necessit y for predicting optimal operation strategy. Subsequently, optimal operation str ategy predictive models were built using three machine learning algorithms on th ree datasets. The evaluation results of the models indicate the feasibility of u sing data from neighbouring cities to improve the generalisation ability of the target city model. Finally, the best one of models was used to predict optimal o peration strategy and achieved good results: discomfort durations of 97.56% of the conferences were within the acceptable range. Additionally, compared to o nly adopting the standby cooling strategy, the radiant cooling system operating time was reduced by 8.88%, and the total energy consumption was red uced by 28.85 kWh.”
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    University of Belgrade Reports Findings in Machine Learning (Unveiling the poten tial of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights )

    68-69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 Machine Learning is th e subject of a report. According to news reporting originating in Belgrade, Serb ia, by NewsRx journalists, research stated, “Although low-cost air quality senso rs facilitate the implementation of denser air quality monitoring networks, enab ling a more realistic assessment of individual exposure to airborne pollutants, their sensitivity to multifaceted field conditions is often overlooked in labora tory testing. This gap was addressed by introducing an in-field calibration and validation of three PAQMON 1.0 mobile sensing low-cost platforms developed at th e Mining and Metallurgy Institute in Bor, Republic of Serbia.” The news reporters obtained a quote from the research from the University of Bel grade, “A configuration tailored for monitoring PM and PM mass concentrations al ong with meteorological parameters was employed for outdoor measurement campaign s in Bor, spanning heating (HS) and non-heating (NHS) seasons. A statistically s ignificant positive linear correlation between raw PM and PM measurements during both campaigns (R > 0.90, p 0.001) was observed. Measur ements obtained from the uncalibrated NOVA SDS011 sensors integrated into the PA QMON 1.0 platforms exhibited a substantial and statistically significant correla tion with the GRIMM EDM180 monitor (R > 0.60, p 0.001). The calibration models based on linear and Random Forest (RF) regression were co mpared. RF models provided more accurate descriptions of air quality, with avera ge adjR values for air quality variables in the range of 0.70 to 0.80 and averag e NRMSE values between 0.35 and 0.77. RF-calibrated PAQMON 1.0 platforms display ed divergent levels of accuracy across different pollutant concentration ranges, achieving a data quality objective of 50% during both measurement campaigns.”
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    Reports from Robert Bosch GmbH Advance Knowledge in Machine Learning (Comparison of Hybrid Machine Learning Approaches for Surrogate Modeling Part Shrinkage in Injection Molding)

    69-70页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news reporting originating from Renningen, Germa ny, by NewsRx correspondents, research stated, “Machine learning (ML) methods pr esent a valuable opportunity for modeling the non-linear behavior of the injecti on molding process.” The news journalists obtained a quote from the research from Robert Bosch GmbH: “They have the potential to predict how various process and material parameters affect the quality of the resulting parts. However, the dynamic nature of the in jection molding process and the challenges associated with collecting process da ta remain significant obstacles for the application of ML methods. To address th is, within this study, hybrid approaches are compared that combine process data with additional process knowledge, such as constitutive equations and high-fidel ity numerical simulations. The hybrid modeling approaches include feature learni ng, fine-tuning, delta-modeling, preprocessing, and using physical constraints, as well as combinations of the individual approaches. To train and validate the hybrid models, both the experimental and simulated shrinkage data of an injectio n-molded part are utilized.”
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    Researchers from Shenzhen University Discuss Findings in Artificial Intelligence (Does Artificial Intelligence Deter Greenwashing?)

    70-70页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Artificial Intelligen ce have been presented. According to news reporting originating from Shenzhen, P eople’s Republic of China, by NewsRx correspondents, research stated, “Employing 6,940 observations of 1,205 Chinese-listed firms from 2012 to 2022, we provide robust evidence that Artificial Intelligence (hereafter AI) inhibits greenwashin g.” Funders for this research include National Natural Science Foundation of China ( NSFC), Start-up Research Grant in Shenzhen University. Our news editors obtained a quote from the research from Shenzhen University, “W e further find that AI achieves this effect by mitigating agency problems, easin g financing constraints, and increasing external attention. In addition, the pos itive impact of AI in curbing greenwashing is more notable in politically unaffi liated firms, those with fewer female directors, or those with weaker equity inc entives.”
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    New Robotics and Automation Findings from KTH Royal Institute of Technology Repo rted (Bathymetric Surveying With Imaging Sonar Using Neural Volume Rendering)

    71-71页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Robotics - Robotics and Automation are presented in a new report. According to news reporting out of Sto ckholm, Sweden, by NewsRx editors, research stated, “This research addresses the challenge of estimating bathymetry from imaging sonars where the state-of-the-a rt works have primarily relied on either supervised learning with ground-truth l abels or surface rendering based on the Lambertian assumption. In this letter, w e propose a novel, self-supervised framework based on volume rendering for recon structing bathymetry using forward-looking sonar (FLS) data collected during sta ndard surveys.” Financial supporters for this research include Knut & Alice Wallen berg Foundation, Stiftelsenfur Strategisk Forskning (SSF) through Swedish Mariti me Robotics Centre(SMaRC).
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    Researchers from Hang Seng University of Hong Kong Publish Findings in Machine L earning (Deterministic modelling of implied volatility in cryptocurrency options with underlying multiple resolution momentum indicator and non-linear machine . ..)

    72-72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 artificial in telligence. According to news reporting from Hang Seng University of Hong Kong b y NewsRx journalists, research stated, “Modeling implied volatility (IV) is impo rtant for option pricing, hedging, and risk management. Previous studies of dete rministic implied volatility functions (DIVFs) propose two parameters, moneyness and time to maturity, to estimate implied volatility.” Our news reporters obtained a quote from the research from Hang Seng University of Hong Kong: “Recent DIVF models have included factors such as a moving average ratio and relative bid-ask spread but fail to enhance modeling accuracy. The cu rrent study offers a generalized DIVF model by including a momentum indicator fo r the underlying asset using a relative strength index (RSI) covering multiple t ime resolutions as a factor, as momentum is often used by investors and speculat ors in their trading decisions, and in contrast to volatility, RSI can distingui sh between bull and bear markets. To the best of our knowledge, prior studies ha ve not included RSI as a predictive factor in modeling IV. Instead of using a si mple linear regression as in previous studies, we use a machine learning regress ion algorithm, namely random forest, to model a nonlinear IV. Previous studies a pply DVIF modeling to options on traditional financial assets, such as stock and foreign exchange markets. Here, we study options on the largest cryptocurrency, Bitcoin, which poses greater modeling challenges due to its extreme volatility and the fact that it is not as well studied as traditional financial assets. Rec ent Bitcoin option chain data were collected from a leading cryptocurrency optio n exchange over a four-month period for model development and validation. Our da taset includes short-maturity options with expiry in less than six days, as well as a full range of moneyness, both of which are often excluded in existing stud ies as prices for options with these characteristics are often highly volatile a nd pose challenges to model building. Our in-sample and out-sample results indic ate that including our proposed momentum indicator significantly enhances the mo del’s accuracy in pricing options. The nonlinear machine learning random forest algorithm also performed better than a simple linear regression. Compared to pre vailing option pricing models that employ stochastic variables, our DIVF model d oes not include stochastic factors but exhibits reasonably good performance.”
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    Taiyuan University of Technology Researchers Add New Findings in the Area of Rob otics (Path planning of wheeled coal mine rescue robot based on improved A* and potential field algorithm)

    73-73页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New study results on robotics have been published . According to news reporting originating from Taiyuan, People’s Republic of Chi na, by NewsRx correspondents, research stated, “Coal mine rescue robots perform search and rescue tasks in unstructured underground tunnel environments. Traditi onal path planning algorithms may encounter issues such as low efficiency, non-o ptimal paths, and poor smoothness when applied to search spaces that are large o r complex.” Our news journalists obtained a quote from the research from Taiyuan University of Technology: “Additionally, tunnels feature complex environmental characterist ics such as intersections, where robots are prone to deviating from preset route s or scraping against tunnel walls. To address these challenges and enhance the navigation accuracy of robots, improvements to the path planning algorithm for w heeled coal mine rescue robots are proposed: The heuristic global path planning A* algorithm is enhanced by employing layered neighborhood search and pruning te chniques to optimize the search process. The cost function is refined to better balance the influence of actual cost and heuristic cost, thus more accurately as sessing the cost of each node, adapting to real situations, reducing computation al complexity, and smoothing the path using B-spline methods. The Random Sample Consensus (RANSAC) fitting algorithm is utilized to construct a geometric model of coal mine tunnel walls, facilitating the extraction of feature point coordina tes of intersections for inclusion in the planning system. The path is optimized using the local support property of B-spline basis functions. When additional p ath optimization points are added subsequently, only the shape of the curve in t he corresponding interval is affected, leaving the rest of the path unaffected. A comprehensive local force field is established based on the constructed enviro nmental geometric model and extracted feature points. Adjustment coefficients ar e introduced to optimize the distribution of the force field, and motion control is achieved using the Particle Swarm Optimization (PSO) optimized PID (Proporti on Integral Differential) algorithm, enhancing the robot’s adaptability to compl ex environments such as tunnel intersections.”
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    University of Lubeck Reports Findings in Robotics (SonoBox: development of a rob otic ultrasound tomograph for the ultrasound diagnosis of paediatric forearm fra ctures)

    74-74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 in Lubeck, Germany, by Ne wsRx journalists, research stated, “Paediatric forearm fractures are a prevalent reason for medical consultation, often requiring diagnostic X-rays that present a risk due to ionising radiation, especially concerning given the sensitivity o f children’s tissues. This paper explores the efficacy of ultrasound imaging, pa rticularly through the development of the SonoBox system, as a safer, non-ionisi ng alternative.” The news reporters obtained a quote from the research from the University of Lub eck, “With emerging evidence supporting ultrasound as a viable method for fractu re assessment, innovations like SonoBox will become increasingly important. In o ur project, we want to advance ultrasound-based, contact-free, and automated cro ss-sectional imaging for diagnosing paediatric forearm fractures. To this end, w e are building a technical platform that navigates a commercially available ultr asound probe around the extremity within a water-filled tank, utilising intellig ent robot control and image processing methods to generate a comprehensive ultra sound tomogram. Safety and hygiene considerations, gender and diversity relevanc e, and the potential reduction of radiation exposure and examination pain are pi votal aspects of this endeavour. Preliminary experiments have demonstrated the f easibility of rapidly generating ultrasound tomographies in a water bath, overco ming challenges such as water turbulence during probe movement. The SonoBox prot otype has shown promising results in transmitting position data for ultrasound i maging, indicating potential for autonomous, accurate, and potentially painless fracture diagnosis. The project outlines further goals, including the constructi on of prototypes, validation through patient studies, and development of a hygie ne concept for clinical application. The SonoBox project represents a significan t step forward in paediatric fracture diagnostics, offering a safer, more comfor table alternative to traditional X-ray imaging. By automating the imaging proces s and removing the need for direct contact, SonoBox has the potential to improve clinical efficiency, reduce patient discomfort, and broaden the scope of ultras ound applications.”
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    Research Data from North University of China Update Understanding of Robotics (R esearch On Trajectory Tracking Control of Delta High-speed Parallel Robot Based On Ptntsmc)

    75-75页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Robotics are presented i n a new report. According to news originating from Taiyuan, People’s Republic of China, by NewsRx correspondents, research stated, “Effective trajectory trackin g control is a crucial assurance for the optimal vibration suppression results o f trajectory optimization. Based on the dynamic model of the Delta robot, the tr ajectory tracking strategy of the Delta high-speed parallel robot was investigat ed to cater to the rapid response requirements during high-speed operations.” Financial supporters for this research include Key Science Foundation of Taiyuan Institute of Technology, Science and Technology Major Special Program of Shanxi Provincial, Taiyuan Institute of Technology Science Research Initial Funding, R esearch Program of Shanxi Province.
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