查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting from Murdoch, Australia, by NewsRx journalists, research stated, "Frost damage significantly reduces global wheat production. Temperature development in wheat crops is a complex and dynamic proc ess." Financial support for this research came from Murdoch University Digital Agricul -ture Connectivity PhD scholarship. The news correspondents obtained a quote from the research from Murdoch Universi ty, "During frost events, a vertical temperature gradient develops from soil to canopy due to the heat loss from the soil and canopy boundary. Understanding the se temperature gradients is essential for improving frost management strategies in wheat crops. We hypothesise that the relationship between the temperatures of the canopy, plant and ground can be an early indicator of frost. We collected i nfrared thermal (IRT) images from fieldgrown wheat crops and extracted the temp eratures from the canopy, plant and ground layers. We analysed these temperature s and applied four machine learning (ML) models to detect coldness scales leadin g to frost nights with different degrees of severity. We implemented a gated rec urrent unit, convolutional neural network, random forest and support vector mach ines to evaluate the classification. Our study shows that in these three layers, temperatures have a relationship that can be used to determine frost early. The patterns of these three temperatures on a frost night differ from a cold no-fro st winter night. On a no-frost night we observed that the canopy is the coldest, plant is warm, and the soil is warmest, and these three temperatures did not co nverge. On the other hand, on a frost night, before the frost event, the canopy and plant temperatures converged as the cold air penetrated through the canopy. These patterns in temperature distribution were translated into an ML problem to detect frost early. We classified coldness scales based on the temperatures con ducive to frost formation of a certain severity degree. Our results show that th e ML models can determine the coldness scales automatically with 93% -98% accuracy across the four models."
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 have been pr esented. According to news originating from Shenzhen, People's Republic of China , by NewsRx correspondents, research stated, "Multi -tote storage and retrieval (MTSR) autonomous mobile robots can carry multiple product totes, store and retr ieve them from different shelf rack tiers, and transport them to a workstation w here the products are picked to fulfill customer orders. In each robot trip, tot es retrieved during the previous trip must be stored." Funders for this research include National Natural Science Foundation of China ( NSFC), Shenzhen Science and Technology Program. Our news journalists obtained a quote from the research from Tsinghua University , "This leads to a mixed storage and retrieval route. We analyze this mixed stor age and retrieval route problem and derive the optimal travel route for a multib lock warehouse by a layered graph algorithm, based on storage first -retrieval s econd and mixed storage and retrieval policies. We also propose an effective heu ristic routing policy, the closest retrieval (CR) sequence policy, based on a lo cal shortest path. Numerical results show that the CR policy leads to shorter tr avel times than the well-known S -shape policy, whereas the gap with the optimal mixed storage and retrieval policy in practical scenarios is small. Based on th e CR policy, we model the stochastic behavior of the system using a semiopen que uing network (SOQN). This model can accurately estimate average tote throughput time and system throughput capacity as a function of the number of robots in the system. We use the SOQN and corresponding closed queuing network models to opti mize the total annual cost as a function of the warehouse shape, the number of r obots, and tote buffer positions on the robots for a given average tote throughp ut time and throughput capacity."
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 out of Weihai, People's Republic of China, by NewsRx editors, research stated, "Based on upper echelon theory, we e mploy machine learning to explore how CEO characteristics influence corporate vi olations using a large-scale dataset of listed firms in China for the period 201 0-2020. Comparing ten machine learning methods, we find that eXtreme Gradient Bo osting (XGBoost) outperforms the other models in predicting corporate violations ." Financial support for this research came from Ministry of Education, China. Our news journalists obtained a quote from the research from Shandong University , "An interpretable model combining XGBoost and SHapley Additive exPlanations (S HAP) indicates that CEO characteristics play a central role in predicting corpor ate violations. Tenure has the strongest predictive power and is negatively asso ciated with corporate violations, followed by marketing experience, education, d uality (i.e., simultaneously holding the position of chairperson), and research and development experience. In contrast, shareholdings, age, and pay are positiv ely related to corporate violations. We also analyze violation severity and viol ation type, confirming the role of tenure in predicting more severe and intentio nal violations."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-A new study on Robotics is now available. Accordi ng to news originating from Liuzhou, People's Republic of China, by NewsRx corre spondents, research stated, "In order to reduce the labor intensity of high-alti tude workers and realize the cleaning and maintenance of high-rise building exte riors, this paper proposes a design for a 4-DOF bipedal wall-climbing bionic rob ot inspired by the inchworm's movement. The robot utilizes vacuum adsorption for vertical wall attachment and legged movement for locomotion." Financial supporters for this research include Guangxi Science and Technology Ba se and Talent Project, Guangxi Science and Technology Base and Talent Project, S pecial fund for centrally guided local science and technology development, Guang xi University of Science and Technology Doctoral Fund.
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 Ma chine Learning. According to news originating from Hunan, People's Republic of C hina, by NewsRx correspondents, research stated, "A green and efficient approach for the co-extraction of essential oil and pectin from Citrus aurantium L. var. amara Engl. was achieved by combining deep eutectic solvents (DES) with steam d istillation. The optimal technological parameters were precisely identified thro ugh single-factor, response surface, and machine learning, with the DES concentr ation of 11.2%, the liquid-solid ratio of 10.6 mL/g, and the distil lation time of 56.0 min." Financial supporters for this research include National Key Research and Develop ment Program of China, China Guangxi Key Laboratory of Chemistry and Engineering of Forest Products, Postgraduate Scientific Research Innovation Project of Huna n Province.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Hip Fracture is the su bject of a report. According to news originating from Zurich, Switzerland, by Ne wsRx correspondents, research stated, "Fracture prediction is essential in manag ing patients with osteoporosis, and is an integral component of many fracture pr evention guidelines. We aimed to identify the most relevant clinical fracture ri sk factors in contemporary populations by training and validating short- and lon g-term fracture risk prediction models in two cohorts." Our news journalists obtained a quote from the research from the Swiss Federal I nstitute of Technology Zurich (ETH), "We used traditional and machine learning s urvival models to predict risks of vertebral, hip and any fractures on the basis of clinical risk factors, T-scores and treatment history among participants in a nationwide Swiss osteoporosis registry (N = 5944 postmenopausal women, median follow-up of 4.1 years between January 2015 and October 2022; a total of 1190 fr actures during follow-up). The independent validation cohort comprised 5474 post menopausal women from the UK Biobank with 290 incident fractures during follow-u p. Uno's C-index and the time-dependent area under the receiver operating charac teristics curve were calculated to evaluate the performance of different machine learning models (Random survival forests and eXtreme Gradient Boosting). In the independent validation set, the C-index was 0.74 [0.58, 0.86 ] for vertebral fractures, 0.83 [0.7, 0.94 ] for hip fractures and 0.63 [0.58, 0.69] for any fractures at year 2, and these values further increased for longer estim ations of up to 7 years. In comparison, the 10- year fracture probability calcula ted with FRAX? Switzerland was 0.60 [0.55, 0.64] for major osteoporotic fractures and 0.62 [0.49, 0.74] for hip fractures. The most important variables identified with Shapley additive explanations (SHAP) values were age, T-scores and prior fractures, while number of falls was an important predictor of hip fractures. Performances of both trad itional and machine learning models showed similar C-indices."
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 Chongqing, People's Republi c of China, by NewsRx correspondents, research stated, "Within the evolving land scape of rehabilitation robotics, integrating cyber-physical systems with digita l twin generation presents a singular paradigm for enhancing the effectiveness a nd personalisation in interaction applications. This paper introduces an interac tion mechanism for artificial intelligence (AI) rehabilitation robots that syner gises visible cognition and movement management within a Cyber-physical device f ramework underpinned through a digital twin-based approach."
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 out of Jyvaskyla, Finland, by NewsRx editors, research stated, "The key component of designing sustainable, enriching, and inclusive cities is public participation. The soundscape is an i ntegral part of an immersive environment in cities, and it should be considered as a resource that creates the acoustic image for an urban environment." Funders for this research include Finnish Cultural Foundation, Ellen and Artturi Nyyssonen Foundation, City of Helsinki Research Grants.
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 Ma chine Learning - Computational Intelligence. According to news reporting out of Chongqing, People's Republic of China, by NewsRx editors, research stated, "Roug h set theory, as an academic hotspot in the field of artificial intelligence, ha s provided a solid theoretical foundation for feature selection. However, with t he continuous updating of large datasets, classical rough set theory is no longe r applicable." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from Southwest Universit y, "Multi-granulation rough set theory is an extension of rough set theory that can better handle complex datasets. Therefore, this paper proposes a generalized multi-granulation dominance neighborhood rough set model based on weight distri bution and discusses some relevant properties of this model. Furthermore, a new information entropy is constructed based on this model to handle uncertainty in data. This approach enhances the ability to describe uncertainty and enables mor e effective feature selection. As a result, a forward heuristic feature selectio n algorithm is developed to find the optimal feature subset."
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 out of Tianjin, People's Repu blic of China, by NewsRx editors, research stated, "Oceans are ultimately a sink of plastic waste. Complex artificial coastlines pose remarkable challenges for coastal plastic waste monitoring." Our news journalists obtained a quote from the research from the Tianjin Univers ity of Technology, "With the development of machine learning methods, high detec tion accuracy can be achieved; however, many false positives have been noted in various network models used for plastic waste investigation. In this study, exte nsive surveys of artificial coastlines were conducted using drones along the Don gjiang Port artificial coastline in the Binhai District, Tianjin, China. The dee p learning model YOLOv8 was enhanced by integrating the InceptionNeXt and LSK mo dules into the network to improve its detection accuracy for plastic waste and r educe instances of tourists being misidentified as plastic. In total, 553 high-r esolution coastline images with 3488 items of detected plastic waste were compar ed using the original and improved YOLOv8 models. The improved YOLOv8s-IL model achieved a detection rate of 64.9%, a notable increase of 11.5% compared with that of the original model. The number of false positives in the i mproved YOLOv8s-IL model was reduced to 32.3%, the multi-class F-sc ore reached 76.5%, and the average detection time per image was onl y 2.7 s."
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