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    Study Data from Kazan State Power Engineering University Update Knowledge of Mac hine Learning (Forecasting Peak Hours for Energy Consumption in Regional Power S ystems)

    49-50页
    查看更多>>摘要:Current study results on artificial in telligence have been published. According to news reporting originating from Kaz an State Power Engineering University by NewsRx correspondents, research stated, ". Electrical power is the second most important commodity in electrical energy markets." The news journalists obtained a quote from the research from Kazan State Power E ngineering University: "For consumers, the charged amount of ‘generator' power i s determined as the average value of hourly consumption amounts on working days during peak hours in the region. The cost of power in some regions can reach 40 % of the final tariff, so reducing the load during peak hours by 1 0 % can lead to a decrease in monthly consumer payments by 3 % . However, such a way of saving money is not available to the consumer since the commercial operator of the wholesale market of electricity and capacity publish es the peak hours of the regions after the 10th day of the next month, when this information is no longer relevant. Timely forecasting of peak hours will make i t possible, on the one hand, to reduce consumer costs for payments for electric power, and on the other hand, to smooth out the daily schedule of electric load of the power system, thereby optimizing the operation of generating equipment of stations and networks of the system operator. The article presents a study of t he effectiveness of machine learning methods in the context of forecasting the p eak hour of a regional power system. The study concerns the period from November 2011 to October 2023, covers 76 regions of the Russian Federation, including su bjects of price (1st and 2nd) and non-price zones and includes 10 machine-learni ng methods. The results of the study showed that statistically, the K-nearest ne ighbors clustering method turns out to be the most accurate, although not univer sal."

    Shandong University Reports Findings in Personalized Medicine (Machine Learning-Enabled Fuhrman Grade in Clear-cell Renal Carcinoma Prediction Using Two-dimensi onal Ultrasound Images)

    50-51页
    查看更多>>摘要:New research on Drugs and Therapies -Personalized Medicine is the subject of a report. According to news reporting fr om Jinan, People's Republic of China, by NewsRx journalists, research stated, "A ccurate assessment of Fuhrman grade is crucial for optimal clinical management a nd personalized treatment strategies in patients with clear cell renal cell carc inoma (CCRCC). In this study, we developed a predictive model using ultrasound ( US) images to accurately predict the Fuhrman grade." The news correspondents obtained a quote from the research from Shandong Univers ity, "Between March 2013 and July 2023, a retrospective analysis was conducted o n the US imaging and clinical data of 235 patients with pathologically confirmed CCRCC, including 67 with Fuhrman grades III and IV. This study included 201 pat ients from Hospital A who were divided into training set (n = 161) and an intern al validation set (n = 40) in an 8:2 ratio. Additionally, 34 patients from Hospi tal B were included for external validation. US images were delineated using ITK software, and radiomics features were extracted using PyRadiomics software. Sub sequently, separate models for clinical factors, radiomics features, and their c ombinations were constructed. The model's performance was assessed by calculatin g the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA). In total, 235 patients diagnosed with C CRCC, comprising 168 low-grade and 67 high-grade tumors, were included in this s tudy. A comparison of the predictive performances of different models revealed t hat the logistic regression model exhibited relatively good stability and robust ness. The AUC of the combined model for the training, internal validation and ex ternal validation sets were 0.871, 0.785 and 0.826, respectively, which were hig her than those of the clinical and imaging histology models. Furthermore, the ca libration curve demonstrated excellent concordance between the predicted Fuhrman grade probability of CCRCC using the combined model and the observed values in both the training and validation sets. Additionally, within the threshold range of 0-0.93, the combined model demonstrated substantial clinical utility, as evid enced by DCA. The application of US radiomics techniques enabled objective predi ction of Fuhrman grading in patients with CCRCC. Nevertheless, certain clinical indicators remain indispensable, underscoring the pressing need for their integr ated use in clinical practice. Previous studies have predominantly focused on us ing computed tomography or magnetic resonance imaging modalities to predict the Fuhrman grade of CCRCC. Our findings demonstrate that a prediction model based o n US images is more cost-effective, easily accessible and exhibits commendable p erformance."

    Recent Studies from University of Science and Technology Beijing Add New Data to Robotics and Automation (Seg-net: Deep Learning Grasping With a Soft Enveloping Gripper)

    51-52页
    查看更多>>摘要:Research findings on Robotics -Roboti cs and Automation are discussed in a new report. According to news reporting ori ginating from Beijing, People's Republic of China, by NewsRx correspondents, res earch stated, "The emergence of non-fingered soft bioinspired grippers poses a c hallenge for learning-based grasping control due to the lack of a model describi ng grasping robustness and a dataset for training. In this letter, we propose a comprehensive pipeline encompassing grasping evaluation, dataset generation, dee p neural network construction and training, as well as experimental verification for a soft enveloping gripper to investigate its learning-based grasping method s." Funders for this research include National Key Research & Developm ent Program of China, National Natural Science Foundation of China (NSFC), Beiji ng Natural Science Foundation, National Natural Science Foundation of China Regi onal Innovation and Development Joint Fund (Anhui).

    Nanjing University Reports Findings in Machine Learning (Integration of interpre table machine learning and environmental magnetism elucidates reduction mechanis m of bioavailable potentially toxic elements in lakes after monsoon)

    52-53页
    查看更多>>摘要:New research on Machine Learning is th e subject of a report. According to news reporting out of Nanjing, People's Repu blic of China, by NewsRx editors, research stated, "Little information is availa ble on the influence of substantial precipitation and particulate matter enterin g during the monsoon process on the release of potentially toxic elements (PTEs) into lake sediments. Sediments from a typical subtropical lake across three per iods, pre-monsoon, monsoon, and post-monsoon, were collected to determine the ch emical forms of 12 PTEs (As, Cd, Co, Cr, Cu, Fe, Hg, Pb, Mn, Ni, Sb, and Zn), ma gnetic properties, and physicochemical indicators." Our news journalists obtained a quote from the research from Nanjing University, "Feature importance, Shapley additive explanations, and partial dependence plot s were used to explore the factors influencing bioavailable PTEs. The proportion of bioavailable forms of PTEs decreased from 3.85 % (Cd) to 87.84 % (Hg) after the monsoon. Gradient extreme boosting demonstrated robust fitting accuracy for the prediction of the bioavailable forms of the 12 P TEs (R > 0.84). Shapley additive explanations identified that the bioavailable forms were influenced by the total PTE concentrations, wi nd, shortwave radiation, and particle inputs (25.1 %-88.5 % for total importance), either individually or in combination. The partial depend ence plots highlighted the influence thresholds of background values and anthrop ogenic factors on the bioavailable forms of PTEs. Changes in environmental prope rties could indicate the process of external sediment influx into lakes."

    Studies from Norwegian University of Science and Technology (NTNU) Have Provided New Data on Machine Learning (Toward Solving a Puzzle of Fragmented Archeologic al Textiles)

    53-53页
    查看更多>>摘要:Fresh data on Machine Learning are pre sented in a new report. According to news reporting out of Gjovik, Norway, by Ne wsRx editors, research stated, "Archeological textiles can provide invaluable in sight into the past. However, they are often highly fragmented, and a puzzle has to be solved to re-assemble the object and recover the original motifs." Our news journalists obtained a quote from the research from the Norwegian Unive rsity of Science and Technology (NTNU), "Unlike common jigsaw puzzles, archeolog ical fragments are highly damaged, and no correct solution to the puzzle is know n. Although automatic puzzle solving has fascinated computer scientists for a lo ng time, this work is one of the first attempts to apply modern machine learning solutions to archeological textile re-assembly. First and foremost, it is impor tant to know which fragments belong to the same object. Therefore, features are extracted from digital images of textile fragments using color statistics, class ical texture descriptors, and deep learning methods. These features are used to conduct clustering and identify similar fragments. Four different case studies w ith increasing complexity are discussed in this article: from well-preserved tex tiles with available ground truth to an actual open problem of Oseberg archeolog ical tapestry with unknown solution."

    New Robotics Findings from University of Shanghai for Science and Technology Des cribed (End-of-life Electric Vehicle Battery Disassembly Enabled By Intelligent and Human-robot Collaboration Technologies: a Review)

    54-54页
    查看更多>>摘要:Data detailed on Robotics have been pr esented. According to news originating from Shanghai, People's Republic of China , by NewsRx correspondents, research stated, "Electric vehicles (EVs) have been experiencing radical growth to embrace the ambitious targets of decarbonisation and circular economies. The trend has led to a significant surge in the number o f lithium-ion batteries (LIBs) that will soon reach the end-of-life (EoL) stage. " Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Ministry of Science and Technology, China, Science & Technology Commission of Shanghai Municipality (STCSM), China Scholarship Counci l, Engineering & Physical Sciences Research Council (EPSRC).

    Studies from Huazhong University of Science and Technology Have Provided New Inf ormation about Robotics (An Analytical Tool Path Smoothing Algorithm for Robotic Machining With the Consideration of Redundant Kinematics)

    55-56页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily NewsResearchers detail new data in Robotics. Accordin g to news reporting from Wuhan, People's Republic of China, by NewsRx journalist s, research stated, "In the machining of complex parts with free -formed surface s, robots are widely employed due to their advantages of a large operating space and high flexibility. The industrial robot with 6 degrees -of -freedom (DOF) ha s an extra redundant degree of freedom around the tool axis, which does not affe ct the tool pose related to the workpieces but influences the robot ‘ s joint co nfiguration." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Hubei Province.

    Findings on Robotics and Automation Discussed by Investigators at Northwestern P olytechnic University (Collaborative Constrained Target-reaching Control In a Mu ltiplayer Reach-avoid Game)

    56-56页
    查看更多>>摘要:Researchers detail new data in Robotic s -Robotics and Automation. According to news originating from Xi'an, People's Republic of China, by NewsRx correspondents, research stated, "For a high-value attacker, relocating it is more valuable than destroying it. This relocation iss ue involves luring an adversarial agent along a predefined path to reach a goal in a non-convex domain." Funders for this research include National Natural Science Foundation of China ( NSFC), Shaanxi Innovative Talent Pandeng Program for Young Science and Technolog y Rising Star Project. Our news journalists obtained a quote from the research from Northwestern Polyte chnic University, "Here, we define it as a collaborative constrained target-reac hing (CTR) problem. We introduce a novel virtual defense channel to define a sym metric dynamic extend target set, enabling us to treat the CTR problem as an agg regation of individual two-player reach-avoid (RA) games and obtain analytical s trategies for defenders. First, we describe the partition of the game space and construct barriers using explicit policy methods and geometric analysis. This al lows us to determine if a solution to the game exists based on players' initial conditions. Second, we develop nonlinear state feedback strategies using a suita ble risk metric. These strategies are based on prescribed performance control, o ffering a viable framework for practical scenarios with control errors."

    Studies from Massachusetts Institute of Technology Update Current Data on Roboti cs (Diverse Controllable Diffusion Policy With Signal Temporal Logic)

    57-57页
    查看更多>>摘要:Investigators publish new report on Ro botics. According to news reporting originating from Cambridge, Massachusetts, b y NewsRx correspondents, research stated, "Generating realistic simulations is c ritical for autonomous system applications such as self-driving and human-robot interactions. However, driving simulators nowadays still have difficulty in gene rating controllable, diverse, and rule-compliant behaviors for road participants : Rule-based models cannot produce diverse behaviors and require careful tuning, whereas learning-based methods imitate the policy from data but are not designe d to follow the rules explicitly." Financial supporters for this research include National Science Foundation (NSF) , MIT-Ford Alliance Program.

    Studies from Beijing Institute of Technology in the Area of Robotics and Automat ion Described (Multi-step Continuous Decision Making and Planning In Uncertain D ynamic Scenarios Through Parallel Spatio-temporal Trajectory Searching)

    58-58页
    查看更多>>摘要:Researchers detail new data in Robotic s -Robotics and Automation. According to news reporting originating from Beijin g, People's Republic of China, by NewsRx correspondents, research stated, "Auton omous driving in urban scenarios faces uncertain dynamic changes, especially in China, where a dense mixture of cars, cyclists and pedestrians travel together o n roads with random uncertain behaviors and high-risk road crossing. This letter proposes a Multi-step Continuous Decision Making and Spatiotemporal Trajectory Planning framework to achieve stable continuous decision making and high-qualit y trajectory planning in such uncertain and highly dynamic environments." Financial support for this research came from Huawei Technologies. Our news editors obtained a quote from the research from the Beijing Institute o f Technology, "Firstly, a 3D spatio-temporal probabilistic map is constructed to represent the uncertain future driving environment. Based on the map, parallel spatio-temporal trajectory search is performed to obtain multi-strategy feasible spatio-temporal trajectories that satisfy the short-term deterministic and long -term uncertain environmental constraints. Then considering the continuity and c onsistency of decision making, risk-aware rolling-fusion of trajectory sequences is proposed, achieving efficient and exploratory far-end planning with a stable and safe near-end driving trajectory. To validate the proposed framework, we co llected the Hard Case data from real Chinese urban roads, containing challenging scenarios such as dense traffic flows, mixed vehicle-pedestrian roads, and comp lex intersections, which are widely recognized barriers to the successful real-w orld deployment of autonomous driving. Moreover, the SMARTS simulator is used to build closed-loop simulation scenarios to verify the effectiveness of the frame work."