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Reports on Machine Learning Findings from University Hospital Provide New Insights (Predictive Model for Vitamin C Levels In Hyperinsulinemic Individuals Based On Age, Sex, Waist Circumference,Low-density Lipoprotein, and Immune-associated ...)

96-97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 Machine Learning have been presented. According to news reporting originating from Quebec City, Canada, by NewsRx correspondents, research stated, "Vitamin C (ascorbic acid) is an important water-soluble antioxidant associated with decreased oxidative stress in type 2 diabetes (T2D) patients. A previous targeted plasma proteomic study has indicated that ascorbic acid is associated with markers of the immune system in healthy subjects." Funders for this research include Canadian Institutes of Health Research (CIHR), New Initiative Funds from the Endocrinology/Nephrology axis at the Centre de Recherche du CHU de Quebec, Fondation du CHU de Quebec, Fonds de recherche du Quebec-Sante (FRQ-S). Our news editors obtained a quote from the research from University Hospital, "However, the association between the levels of ascorbic acid and blood biomarkers in subjects at risk of developing T2D is still unknown. Serum ascorbic acid was measured by ultra-performance liquid chromatography and serum proteins were quantified by untargeted liquid-chromatography mass spectrometry in 25 hyperinsulinemia subjects that were randomly assigned a high dairy intake diet or an adequate dairy intake diet for 6 weeks, then crossed-over after a 6-week washout period. Spearman correlation followed by gene ontology analyses were performed to identify biological pathways associated with ascorbic acid. Finally, machine learning analysis was performed to obtain a specific serum protein signature that could predict ascorbic acid levels. After adjustments for waist circumference, LDL, HDL, fasting insulin, fasting blood glucose, age, gender, and dairy intake; serum ascorbic acid correlated positively with different aspects of the immune system. Machine learning analysis indicated that a signature composed of 21 features that included 17 proteins (mainly from the immune system), age, sex, waist circumference, and LDL could predict serum ascorbic acid levels in hyperinsulinemia subjects."

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Studies from University of Granada Further Understanding of Artificial Intelligence [General Purpose Artificial Intelligence Systems (Gpais): Properties, Definition, Taxonomy, Societal Implications and Responsible Governance]

97-98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 Artificial Intelligence. According to news reporting originating from Granada, Spain, by NewsRx correspondents, research stated, "Most applications of Artificial Intelligence (AI) are designed for a confined and specific task. However, there are many scenarios that call for a more general AI, capable of solving a wide array of tasks without being specifically designed for them." Funders for this research include Maria Zambrano Senior Fellowship at the University of Granada, R&D and Innovation project, Spanish Government, European Union (EU), Basque Government, Department of Education of this institution.

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Studies from Heriot-Watt University Further Understanding of Machine Learning (Comparison of Machine Learning Approaches for Robust and Timely Detection of PPE in Construction Sites)

98-99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 intelligence. According to news originating from Edinburgh, United Kingdom, by NewsRx correspondents, research stated, "Globally, workplace safety is a critical concern, and statistics highlight the widespread impact of occupational hazards. According to the International Labour Organization (ILO), an estimated 2.78 million work-related fatalities occur worldwide each year, with an additional 374 million non-fatal workplace injuries and illnesses." The news correspondents obtained a quote from the research from Heriot-Watt University: "These incidents result in significant economic and social costs, emphasizing the urgent need for effective safety measures across industries. The construction sector in particular faces substantial challenges, contributing a notable share to these statistics due to the nature of its operations. As technology, including machine vision algorithms and robotics, continues to advance, there is a growing opportunity to enhance global workplace safety standards and mitigate the human toll of occupational hazards on a broader scale. This paper explores the development and evaluation of two distinct algorithms designed for the accurate detection of safety equipment on construction sites. The first algorithm leverages the Faster R-CNN architecture, employing ResNet-50 as its backbone for robust object detection. Subsequently, the results obtained from Faster R-CNN are compared with those of the second algorithm, Few-Shot Object Detection (FsDet)."

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New Machine Learning Findings Has Been Reported by Investigators at Arizona State University (Feasibility Study To Identify Machine Learning Predictors for a Virtual Environment Grocery Store)

99-100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 originating in Tempe, Arizona, by NewsRx journalists, research stated, "Virtual reality-based assessment and training platforms proffer the potential for higher-dimensional stimulus presentations (dynamic; three dimensional) than those found with many low-dimensional stimulus presentations (static; two-dimensional) found in pen-and-paper measures of cognition. Studies have investigated the psychometric validity and reliability of a virtual reality-based multiple errands task called the Virtual Environment Grocery Store (VEGS)." The news reporters obtained a quote from the research from Arizona State University, "While advances in virtual reality-based assessments provide potential for increasing evaluation of cognitive processes, less has been done to develop these simulations into adaptive virtual environments for improved cognitive assessment. Adaptive assessments offer the potential for dynamically adjusting the difficulty level of tasks specific to the user's knowledge or ability. Former iterations of the VEGS did not adapt to user performance. Therefore, this study aimed to develop performance classifiers from participants (N = 75) using three classification techniques: Support Vector Machines (SVM), Naive Bayes (NB), and k-Nearest Neighbors (kNN). Participants were categorized as either high performing or low performing based upon the number items they were able to successfully find and add to their grocery cart. The predictors utilized for the classification focused on the times to complete tasks in the virtual environment. Results revealed that the SVM (88% correct classification) classifier was the most robust classifier for identifying cognitive performance followed closely by kNN (86.7%); however, NB tended to perform poorly (76%)."

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New Robotics Data Have Been Reported by Researchers at Shanghai Jiao Tong University (A Calibration and Compensation Method for an Industrial Robot With High Accuracy Harmonic Reducers)

100-100页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Robotics. According to news originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "Industrial serial robots need high stiffness to keep absolute pose accuracy and meet the requirements in practical applications. However, the weak stiffness feature of robot joints and the payloads affected on robot end-effector, which will also increase the pose error of robot." Financial support for this research came from National Key Research and Development Program for Robotics Serialized Harmonic Reducer Fatigue Performance Analysis and Prediction and Life Enhancement Technology Research. Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, "Especially, the existing calibration methods often consider under no-payload condition without discussing the payload state. In this paper, we report a new industrial serial robot composed by a new harmonic reducer: Model-Y, based on high accuracy and high stiffness, and a kinematic parameter calibration algorithm which is based on a harmonic reducer force-deformation model. To decrease the accuracy effects of payload, an iterative calibration method for kinematic parameters with payload situation was proposed. Simulation and experiments are conducted to verify the effectiveness of the proposed calibration method using the self-developed industrial serial robot. The results show a remarkably improved accuracy in absolute position and orientation with the robot's payload range. The position mean error has 70% decreased to 0.1 mm and the orientation mean error diminished to less than 0.01 degrees after calibration with compensation. Additionally, online linear and circular tests are carried out to evaluate the position error of the robot during large-scale spatial and low-speed continuous movement."

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Research on Robotics and Mechatronics Published by a Researcher at Hirosaki University (Demonstration of Autonomous Driving Control for a Retrofitted Wheel Loader)

101-101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 robotics and mechatronics have been presented. According to news reporting out of Aomori, Japan, by NewsRx editors, research stated, "Most research on automating gravel pile transport using wheel loaders has been performed primarily through simulations." Funders for this research include Japan Society For The Promotion of Science; Japan Science And Technology Agency. The news editors obtained a quote from the research from Hirosaki University: "Thus, studies should evaluate the usefulness of automatic gravel pile transportation by demonstrating it with an actual wheel loader. This study demonstrates automatic driving control using a retrofitted 3-ton wheel loader for gravel pile transportation. The driving model of a retrofitted wheel loader, in which multiple control systems are interlocked, is considered a simple control model with one input and one output for the pedal and vehicle velocity as well as for the steering wheel and steering angular velocity. In this study, we propose a simple and practical method for constructing a driving model via simple response analysis using an actual machine by constructing a feedforward control model based on control input/output using step responses. In this study, feedforward control was applied to the translation of the vehicle, which has a large dead time."

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Researchers at Delhi Technological University Target Machine Learning (Comparative Analysis of Slope Stability for Kalimpong Region Under Dynamic Loading Using Limit Equilibrium Method and Machine Benchmark Learning Classifiers)

102-103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 originating from Delhi, India, by NewsRx correspondents, research stated, "Significant slope destabilisation may become more likely due to the speed at which urbanisation is occurring, as well as the growing necessity for geoengineering initiatives or the growth of the road network. Slope stability analysis is done to lower the risk of landslides and slope failures." Our news editors obtained a quote from the research from Delhi Technological University, "The study area, Kalimpong, is well-known for its lush greenery and stunning views and is situated in the Eastern Himalayas. However, it also constantly confronts the risk of landslides because of its rugged topography, potential seismic zone, and heavy monsoon rains. In this study, the results of the factor of safety computed by limit equilibrium (conventional) method have been compared analytically using computational intelligence and machine learning methodologies for both dry and saturated conditions under dynamic loading. Conventional machine learning techniques are combined with seven prediction models. The following algorithms have been chosen for slope stability analysis: support vector machine, k-nearest neighbours, decision tree, random forest, logistic regression, AdaBoost, and gradient boosting. Random cross-validation is used to assess each model's dependability. The stability condition is the result of the random selection of seven parameters: cohesiveness, unit weight, slope height, angle of the slope, internal friction angle, horizontal and vertical pseudo-static coefficient. Moreover, the coefficient of variation method is employed to assess the importance of every indicator in forecasting slope stability. As per the sensitivity analysis, slope stability is primarily affected by cohesiveness. With an average classification accuracy of 0.878, ensembling approach SVM-Boost demonstrates the best prediction abilities among the models tested using multifold cross-validation. The accuracy ratings of SVM and AdaBoost were 0.865 and 0.834, respectively. When combined with SLOPE/W advances, novel SVM-Boost exhibits the highest exactitude, hegemony, and best outcomes in slope stability prediction. Future earthquakes, strong rainfall, and human activity could cause the slope to collapse."

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Patent Issued for Methods and systems to dynamically adjust a playlist based on cumulative mood score (USPTO 11902623)

103-106页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Rovi Guides Inc. (San Jose, California, United States) has been issued patent number 11902623, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors. The patent's inventors are Gupta, Vikram Makam (Bangalore, IN), Panchaksharaiah, Vishwas Sharadanagar (Tiptur, IN). This patent was filed on August 3, 2022 and was published online on February 13, 2024. From the background information supplied by the inventors, news correspondents obtained the following quote: "The present disclosure relates to methods and systems for adjusting a playlist based on a mood score and, more particularly, to methods and systems for tracking a user profile's cumulative mood score and to dynamically adjust media assets for presentation on a consumer device based on the mood score." Supplementing the background information on this patent, NewsRx reporters also obtained the inventors' summary information for this patent: "Many content items are shared and consumed on social networking platforms like Facebook, WhatsApp, Instagram, and YouTube. The content shared may leave a consumer of the content in a particular mood or feeling; for example, the consumer may be sad, happy or fearful, surprised, agitated, pensive, awed. The variety of subject material available makes it challenging to connect pieces of content. This is mainly because navigating content, particularly content segments such as video, is burdensome and lacks cohesiveness between the content and may inadvertently lead to a large number of items with a similar mood. Thus, after watching a number of such content items, a user may lose interest because the content has made them sad. For example, in the middle of a pandemic, there are a lot of content items (e.g., videos) related to the health crisis, death, illness, isolation, suffering, etc. The consumer may stream an online channel of short clips that follow one after another, in many circumstances. Each short clip is randomly selected and may focus on the health crisis. If a consumer watches many of such content items (e.g., videos) in one or more sessions, the consumer's mood may be impacted by the short clips that he or she has consumed. For example, consuming many short clips with negative, sad or depressing storylines may cause a lot of stress and unhappiness for the consumer.

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Patent Issued for Automated packing system (USPTO 11897701)

106-110页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting originating from Alexandria, Virginia, by NewsRx journalists, a patent by the inventors Faulkner, Mario (Wesley Chapel, FL, US), Harris, Calem (Tampa, FL, US), Jagannathan, Arun Kumar Ranganathan (Shrewsbury, MA, US), Mora, Sebastian (Davenport, FL, US), Skantze, Carl (Tampa, FL, US), Tsoka, Arnold (Orlando, FL, US), Tucker, Grant (Orlando, FL, US), filed on June 30, 2022, was published online on February 13, 2024. The assignee for this patent, patent number 11897701, is Staples Inc. (Framingham, Massachusetts, United States). Reporters obtained the following quote from the background information supplied by the inventors: "This application relates to warehouse fulfillment systems. For example, this application relates to a transfer station that transfers objects from a first location into a second location, for example, into a carton, which may be used to automatedly package the cartons. "Some current fulfillment systems use drag-along carts onto which items are placed by pickers. The pickers may place the items into shipping cartons to be shipped to customers. Other fulfillment systems may use robots to bring items to pickers, who then manually place the items into shipping cartons. Some fulfillment systems divide inventory into a series of zones and use carts, robots, or conveyor belts to move items between zones, but many of the operations are performed manually. Such manual processes require human pickers to follow many instructions, which leads to significant errors by the human pickers and fatigue.

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Patent Issued for Image-based drive-thru management system (USPTO 11900702)

110-113页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting originating from Alexandria, Virginia, by NewsRx journalists, a patent by the inventors DeSantola, Evan (Pittsburgh, PA, US), Litzenberger, Alex (Pittsburgh, PA, US), Mesbah, Rassoul (Aukland, NZ), Sar, Prashasti (Pittsburgh, PA, US), filed on February 17, 2023, was published online on February 13, 2024. The assignee for this patent, patent number 11900702, is Agot Co. (Pittsburgh, Pennsylvania, United States). Reporters obtained the following quote from the background information supplied by the inventors: "Restaurants, or eateries, are businesses that prepare and serve meals (e.g., food and/or drinks) to customers. Meals can be served and eaten on-site of a restaurant, however some restaurants offer a take-out (e.g., such as by implementing a drive-thru) and/or food delivery services. Restaurant food preparation can involve developing systems for taking orders, cooking, and/or serving a collection of items typically organized on a menu. Some food preparation systems involve preparing some ingredients in advance (e.g., cooking sauces and/or chopping vegetables), and completing the final steps when a customer orders an item (e.g., assembly of an order). Menu items are often associated with a series of preparation steps that involve ingredients and actions to be performed in association with those ingredients (e.g., cook a hamburger or apply salt to the French fries). Food preparation systems can depend on knowing precisely how long it takes to prepare each menu item and planning tasks so that the menu items are prepared efficiently and accurately."

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