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    University of Sannio Reports Findings in Machine Learning (A reproducible ensemb le machine learning approach to forecast dengue outbreaks)

    38-39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 originating from Benevento, Italy, by N ewsRx correspondents, research stated, "Dengue fever, a prevalent and rapidly sp reading arboviral disease, poses substantial public health and economic challeng es in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas." Our news journalists obtained a quote from the research from the University of S annio, "To address this, we propose a machine learning ensemble model for foreca sting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, provi ding one-month-ahead DIR estimates at the state level. Comparative analyses with a dummy model and ablation studies demonstrate the ensemble model's qualitative and quantitative efficacy across the 27 Brazilian Federal Units. Furthermore, w e showcase the transferability of this approach to Peru, another Latin American country with differing epidemiological characteristics. This timely forecast sys tem can aid local governments in implementing targeted control measures. The stu dy advances climate services for health by identifying factors triggering dengue outbreaks in Brazil and Peru, emphasizing collaborative efforts with intergover nmental organizations and public health institutions. The innovation lies not on ly in the algorithms themselves but in their application to a domain marked by d ata scarcity and operational scalability challenges. We bridge the gap by integr ating well-curated ground data with advanced analytical methods, addressing a si gnificant deficiency in current practices. The successful transfer of the model to Peru and its consistent performance during the 2019 outbreak in Brazil showca se its scalability and practical application. While acknowledging limitations in handling extreme values, especially in regions with low DIR, our approach excel s where accurate predictions are critical. The study not only contributes to adv ancing DIR forecasting but also represents a paradigm shift in integrating advan ced analytics into public health operational frameworks. This work, driven by a collaborative spirit involving intergovernmental organizations and public health institutions, sets a precedent for interdisciplinary collaboration in addressin g global health challenges."
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    Study Findings on Machine Learning Detailed by Researchers at Institut Teknologi Bandung (Machine learning based multi-method interpretation to enhance dissolve d gas analysis for power transformer fault diagnosis)

    39-40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 ar tificial intelligence. According to news originating from Bandung, Indonesia, by NewsRx correspondents, research stated, "Accurate interpretation of dissolved g as analysis (DGA) measurements for power transformers is essential to ensure ove rall power system reliability. Various DGA interpretation techniques have been p roposed in the literature, including the Doernenburg Ratio Method (DRM), Roger R atio Method (RRM), IEC Ratio Method (IRM), Duval Triangle Method (DTM), and Duva l Pentagon Method (DPM)." The news editors obtained a quote from the research from Institut Teknologi Band ung: "While these techniques are well documented and widely used by industry, th ey may lead to different conclusions for the same oil sample. Additionally, the ratio-based methods may result in an out-of-code condition if any of the used ga ses fall outside the specified limits. Incorrect interpretation of DGA measureme nts can lead to mismanagement and may lead to catastrophic consequences for oper ating power transformers. This paper presents a new interpretation technique for DGA aimed at improving its accuracy and consistency. The proposed multi-method approach employs s scoring index and random forest machine learning principles t o integrate existing interpretation methods into one comprehensive technique. Th e robustness of the proposed method is assessed using DGA data collected from se veral transformers under various health conditions."
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    Research Conducted at Koneru Lakshmaiah Education Foundation Has Provided New In formation about Robotics (Deep Reinforcement Learning In Mobile Robotics - a Con cise Review)

    40-40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 Telangana, India, by NewsRx correspondents, research stated, "Mobile robotics is one of the emerging research area in the robotics. The recently evolving techniques, artificial int elligence and precise hardware controller design gave new scope in the area of m obile robots." Our news editors obtained a quote from the research from Koneru Lakshmaiah Educa tion Foundation, "Initially, deep learning (DL) approach is used for operating r obotics with this approach robots can be operated in fixed pattern. Later to per form autonomous operations researchers used deep reinforcement learning (DRL) ap proach. This DRL approach transformed the face of robotics from conventional poi nt to more precise, modern and self-control robots. This literature review gives the information about different approaches and developments in the area of robo tics using deep reinforcement learning. Furthermore, this paper gives the inform ation about different algorithms to deal with robotics. Moreover, this paper dis cusses about the different sensors and their importance." According to the news editors, the research concluded: "Moreover, this paper giv es the information about developments and the challenges in robotics using deep reinforcement learning."
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    Department of Ophthalmology Reports Findings in Artificial Intelligence (ASSOCIA TION OF TESSELLATION DENSITY WITH PROGRESSION OF AXIAL LENGTH AND REFRACTION IN CHILDREN: An Artificial Intelligence-Assisted 4-Year Study)

    41-42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 Artificial Intelligenc e is the subject of a report. According to news reporting from Kunming, People's Republic of China, by NewsRx journalists, research stated, "To investigate fund us tessellation density (TD) and its association with axial length (AL) elongati on and spherical equivalent (SE) progression in children. The school-based prosp ective cohort study enrolled 1,997 individuals aged 7 to 9 years in 11 elementar y schools in Mojiang, China." Financial supporters for this research include Yunnan Province young and middle- aged academic and technical leaders reserve Talents Project, Basic Research Prog ram of Yunnan Province, Yunnan Province High-level Talent Training Support Progr am special famous doctors, Construction of a 3D digital intelligent prevention a nd control platform for the whole life cycle of highly myopic patients in the Ya ngtze River Delta, Project of Shanghai Xuhui District Science and Technology, Pr oject of Shanghai Xuhui District Science and Technology, Shanghai Rising-Star Pr ogram, Shanghai Yangfan Project, National Natural Science Foundation of China, N ational Natural Science Foundation of China, Natural Science Foundation of Shang hai.
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    New Robotics Findings from Hefei University of Technology Discussed (Calibration of Static Errors and Compensation of Dynamic Errors for Cable-driven Parallel 3 d Printer)

    42-43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 reporting from Hefei, People's Republic of Chi na, by NewsRx journalists, research stated, "As rigid robots suffer from the hig her inertia of their rigid links, cable-driven parallel robots (CDPRs) are more suitable for large-scale three-dimensional (3D) printing tasks due to their outs tanding reconfigurability, high load-to-weight ratio, and extensive workspace. I n this paper, a parallel 3D printing robot is proposed, comprising three pairs o f driving cables to control the platform motion and three pairs of redundant cab les to adjust the cable tension." Financial support for this research came from National Key Research and Developm ent Program of China. The news correspondents obtained a quote from the research from the Hefei Univer sity of Technology, "To improve the motion accuracy of the moving platform, the static kinematic error model is established, and the error sensitivity coefficie nt is determined to reduce the dimensionality of the optimization function. Subs equently, the self-calibration positions are determined based on the maximum cab le length error in the reachable workspace. A self-calibration method is propose d based on the genetic algorithm to solve the kinematic parameter deviations. Ad ditionally, the dynamic errors are effectively reduced by compensating for the e lastic deformation errors of the cable lengths. Furthermore, an experimental pro totype is developed. The results of dynamic error compensation after the self-ca libration indicate a 67.4 % reduction in terms of the maximum error along the Z-axis direction."
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    New Findings Reported from Sungkyunkwan University Describe Advances in Robotics (Development of a Wheel-Type In-Pipe Robot Using Continuously Variable Transmis sion Mechanisms for Pipeline Inspection)

    43-44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 reporting originating from Suwon, South Korea, by NewsRx correspondents, research stated, "Pipelines are embedded in industria l sites and residential environments, and maintaining these pipes is crucial to prevent leakage. Given that most pipelines are buried, the development of robots capable of exploring their interiors is essential." Financial supporters for this research include National Research Foundation of K orea (Nrf) Grant Funded By The Korea Government. The news correspondents obtained a quote from the research from Sungkyunkwan Uni versity: "In this work, we introduce a novel in-pipe robot utilizing Continuousl y Variable Transmission (CVT) mechanisms for navigating various pipes, including vertical and curved pipes. The robot comprises one air motor, three CVT mechani sms, and six wheels at the end of six slider-crank mechanisms, including three a ctive and three idler ones. The slider crank and spring mechanism generate a wal l press force through the wheel to prevent slipping inside the pipe. This capabi lity allows the robot to climb vertical pipes and adapt to various pipe diameter s. Moreover, by combining CVT mechanisms, whose speed ratios between the driver and driven pulleys are passively adjusted by the position of the slider, the rob ot achieves independent and continuous speed control for each wheel. This enable s it to navigate pipes with various geometries, such as straight-curved-straight pipes, using only one motor."
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    Studies from Kyushu Institute of Technology Add New Findings in the Area of Robo tics (Motion Evaluation of Variable-Stiffness Link Based on Shape-Memory Alloy a nd Jamming Transition Phenomenon)

    44-44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 Fukuoka, Japan, by NewsRx cor respondents, research stated, "In rapidly aging societies, the application of ro bots has spread from industry to nursing and social welfare." Financial supporters for this research include Japan Society For The Promotion o f Science. Our news editors obtained a quote from the research from Kyushu Institute of Tec hnology: "As the designs of industrial and non-industrial robots are different, numerous robot components with various shapes and stiffness are required for dif ferent tasks. In this study, we attached a variable-stiffness link based on a sh ape-memory alloy (SMA) and the jamming transition phenomenon to a robot arm and evaluated its pick-and-place motion for various objects with different shapes an d weights. The link can be fixed in an arbitrary shape and then restored to its initial shape via the shape memory effect. The objects were picked up and moved by a prototype link, which consisted of four SMA wires inserted in the jamming m echanism." According to the news editors, the research concluded: "We compared two states o f the link, namely with and without deformation of the link into a shape (the ce nterline and the cross section) to suit the target object using a mold. Experime nts confirmed that changing and fixing the link shape to suit the target object increased both positioning accuracy and weight capacity."
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    New Findings from University of Wisconsin Update Understanding of Artificial Int elligence (Automated Deep Learning Artificial Intelligence Tool for Spleen Segme ntation On Ct: Defining Volume-based Thresholds for Splenomegaly)

    45-46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 reporting from Madison, Wisconsin, by NewsRx journalists, research stated, "Splenomegaly historically has been assessed on i maging by use of potentially inaccurate linear measurements. Prior work tested a deep learning artificial intelligence (AI) tool that automatically segments the spleen to determine splenic volume." Financial support for this research came from National Institutes of Health (NIH ) - USA. The news correspondents obtained a quote from the research from the University o f Wisconsin, "The purpose of this study is to apply the deep learning AI tool in a large screening population to establish volume-based splenomegaly thresholds. This retrospective study included a primary (screening) sample of 8901 patients (4235 men, 4666 women; mean age, 56 +/- 10 [SD] years) who underwent CT colonoscopy (n = 7736) or renal donor CT (n = 1165) from April 2004 to January 2017 and a secondary sample of 104 patients (62 men, 42 w omen; mean age, 56 +/- 8 years) with end-stage liver disease who underwent contr ast-enhanced CT performed as part of evaluation for potential liver transplant f rom January 2011 to May 2013. The automated deep learning AI tool was used for s pleen segmentation, to determine splenic volumes. Two radiologists independently reviewed a subset of segmentations. Weight-based volume thresholds for splenome galy were derived using regression analysis. Performance of linear measurements was assessed. Frequency of splenomegaly in the secondary sample was determined u sing weight-based volumetric thresholds. In the primary sample, both observers c onfirmed splenectomy in 20 patients with an automated splenic volume of 0 mL; co nfirmed incomplete splenic coverage in 28 patients with a tool output error; and confirmed adequate segmentation in 21 patients with low volume (<50 mL), 49 patients with high volume (> 600 mL), and 20 0 additional randomly selected patients. In 8853 patients included in analysis o f splenic volumes (i.e., excluding a value of 0 mL or error values), the mean au tomated splenic volume was 216 +/- 100 [SD] mL. The weight-based volumetric threshold (expressed in milliliters) for splenom egaly was calculated as (3.01 x weight [expressed as kilogram s]) + 127; for weight greater than 125 kg, the splenomegaly t hreshold was constant (503 mL). Sensitivity and specificity for volume-defined s plenomegaly were 13% and 100%, respectively, at a tru e craniocaudal length of 13 cm, and 78% and 88% for a maximum 3D length of 13 cm. In the secondary sample, both observers identified segmentation failure in one patient. The mean automated splenic volume in the 1 03 remaining patients was 796 +/- 457 mL; 84% (87/103) of patients met the weight-based volume-defined splenomegaly threshold. We derived a weight -based volumetric threshold for splenomegaly using an automated AI-based tool. C LINICAL IMPACT."
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    Data on Machine Learning Reported by Danmin Cao and Colleagues (FedEYE: A scalab le and flexible end-to-end federated learning platform for ophthalmology)

    46-46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 Wuhan, People' s Republic of China, by NewsRx journalists, research stated, "Datadriven machin e learning, as a promising approach, possesses the capability to build high-qual ity, exact, and robust models from ophthalmic medical data. Ophthalmic medical d ata, however, presently exist across disparate data silos with privacy limitatio ns, making centralized training challenging." The news reporters obtained a quote from the research, "While ophthalmologists m ay not specialize in machine learning and artificial intelligence (AI), consider able impediments arise in the associated realm of research. To address these iss ues, we design and develop FedEYE, a scalable and flexible end-to-end ophthalmic federated learning platform. During FedEYE design, we adhere to four fundamenta l design principles, ensuring that ophthalmologists can effortlessly create inde pendent and federated AI research tasks. Benefiting from the design principles a nd architecture of FedEYE, it encloses numerous key features, including rich and customizable capabilities, separation of concerns, scalability, and flexible de ployment." According to the news reporters, the research concluded: "We also validated the applicability of FedEYE by employing several prevalent neural networks on ophtha lmic disease image classification tasks." For more information on this research see: FedEYE: A scalable and flexible end-t o-end federated learning platform for ophthalmology. Patterns, 2024;5(2):100928.
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    Reports from University of Florence Provide New Insights into Machine Learning ( Revealing the Structural Behaviour of Brunelleschi's Dome With Machine Learning Techniques)

    47-47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 out of Florence, Italy, by NewsRx e ditors, research stated, "The Brunelleschi's Dome is one of the most iconic symb ols of the Renaissance and is among the largest masonry domes ever constructed. Since the late 17th century, first masonry cracks appeared on the Dome, giving t he start to a monitoring activity." Financial supporters for this research include Universita degli Studi di Firenze within the CRUI-CARE Agreement, National Research Center in High Performance Co mputing, Big Data and Quantum Computing foreseen within Mission 4 (Education and Research) of the "National Recovery and Resilience Plan" (NRRP), Next Generatio n EU (NGEU) program. Our news journalists obtained a quote from the research from the University of F lorence, "In modern times, since 1988 a monitoring system comprised of 166 elect ronic sensors, including deformometers and thermometers, has been in operation, providing a valuable source of real-time data on the monument's health status. W ith the deformometers taking measurements at least four times per day, a vast am ount of data is now available to explore the potential of the latest Artificial Intelligence and Machine Learning techniques in the field of historical-architec tural heritage conservation. The objective of this contribution is twofold. Firs tly, for the first time ever, we aim to unveil the overall structural behaviour of the Dome as a whole, as well as that of its specific sections (known as webs) . We achieve this by evaluating the effectiveness of certain dimensionality redu ction techniques on the extensive daily detections generated by the monitoring s ystem, while also accounting for fluctuations in temperature over time. Secondly , we estimate a number of recurrent and convolutional neural network models to v erify their capability for medium- and long-term prediction of the structural ev olution of the Dome."
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