查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Cadiz, Spain, by NewsR x correspondents, research stated, "At present, transmission electron microscopy is regarded as the main option when dealing with phase characterization for mat erials at a nanometric scale. The development and improvement of complementary t echniques such as energydispersive X-ray spectroscopy (EDS), electron energy lo ss spectroscopy (EELS), imaging detectors and associated computational methods p rovide a huge variety of choices to determine the composition and crystal struct ure at any region of a specimen." Financial supporters for this research include Spanish Government, Ministerio de Ciencia e Innovacion MCIN/AEI/FEDER UE, European Union "NextGenerationEU"/PRTR.
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 originating from Bad Oeynhausen, Germany, by NewsRx correspondents, research stated, "Baseline right ventricular (RV) function derived from 3-dimensional analyses has been demonstrated to be p redictive in patients undergoing transcatheter tricuspid valve repair (TTVR). Th e complex nature of these cumbersome analyses makes patient selection based on e stablished imaging methods challenging." Our news journalists obtained a quote from the research from Ruhr-Universitat Bo chum, "Artificial intelligence (AI)-driven computed tomography (CT) segmentation of the RV might serve as a fast and predictive tool for evaluating patients pri or to TTVR. Patients suffering from severe tricuspid regurgitation underwent ful l cycle cardiac CT. AI-driven analyses were compared to conventional CT analyses . Outcome measures were correlated with survival free of rehospitalization for h eart-failure or death after TTVR as the primary endpoint. Automated AI-based ima ge CT-analysis from 100 patients (mean age 77 ± 8 years, 63% femal e) showed excellent correlation for chamber quantification compared to conventio nal, core-lab evaluated CT analysis (R 0.963-0.966; p<0.00 1). At 1 year (mean follow-up 229 ± 134 days) the primary endpoint occurred sign ificantly more frequently in patients with reduced RV ejection fraction (EF) <50 % (36.6% vs. 13.7%; HR 2.864, CI 1.21 2-6.763; p = 0.016). Furthermore, patients with dysfunctional RVs defined as end -diastolic RV volume > 210 ml and RV EF <50% demonstrated worse outcome than patients with functional RVs ( 43.7% vs. 12.2%; HR 3.753, CI 1.621-8.693; p = 0.002) . Derived RVEF and dysfunctional RV were predictors for death and hospitalizatio n after TTVR."
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 new report. According to news originating from Debrecen, H ungary, by NewsRx correspondents, research stated, "Honeybees (Apis mellifera L. ) are important for agriculture and ecosystems; however, they are threatened by the changing climate." Our news editors obtained a quote from the research from University of Debrecen: "In order to adapt and respond to emerging difficulties, beekeepers require the ability to continuously monitor their beehives. To carry out this, the utilizat ion of advanced machine learning techniques proves to be an exceptional tool. Th is review provides a comprehensive analysis of the available research on the dif ferent applications of artificial intelligence (AI) in beekeeping that are relev ant to climate change. Presented studies have shown that AI can be used in vario us scientific aspects of beekeeping and can work with several data types (e.g., sound, sensor readings, images) to investigate, model, predict, and help make de cisions in apiaries. Research articles related to various aspects of apiculture, e.g., managing hives, maintaining their health, detecting pests and diseases, a nd climate and habitat management, were analyzed."
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 reporting from Seattle, Washington, by NewsRx journal ists, research stated, "Highly articulated organisms serve as blueprints for inc redibly dexterous mechanisms, but building similarly capable robotic counterpart s has been hindered by the difficulties of developing electromechanical actuator s with both the high strength and compactness of biological muscle." Financial supporters for this research include Honda Research Institute, Usa; Ar my Research Laboratory; Amazon Robotics; National Science Foundation; Office of Naval Research. Our news journalists obtained a quote from the research from University of Washi ngton: "We develop a stackable electrostatic brake that has comparable specific tension and weight to that of muscles and integrate it into a robotic joint. Hig h degree-of-freedom mechanisms composed of such electrostatic brake enabled join ts can then employ established control algorithms to achieve hybrid motor-brake actuated dexterous manipulation. Specifically, our joint design enables a ten de gree-of-freedom robot equipped with only one motor to manipulate multiple object s simultaneously."
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 - Prostate Ca ncer is the subject of a report. According to news reporting from Milan, Italy, by NewsRx journalists, research stated, "Prostate-specific membrane antigen radi oguided surgery (PSMA-RGS) might identify lymph node invasion (LNI) in prostate cancer (PCa) patients undergoing extended pelvic lymph node dissection (ePLND). The optimal target-to-background (TtB) ratio to define RGS positivity is still u nknown." Financial support for this research came from Italian Ministry of Health.
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 robotics have bee n published. According to news reporting from the Department of Jurisprudence by NewsRx journalists, research stated, "Our society, in general, and health care, in particular, faces notable challenges due to the emergence of innovative digi tal technologies." Our news reporters obtained a quote from the research from Department of Jurispr udence: "The use of socially assistive robots in aged care is a particular digit al application that provokes ethical reflection. The answers we give to the ethi cal questions associated with socially assistive robots are framed by ontologica l and anthropological considerations of what constitutes human beings and how th e meaning of being human relates to how these robots are conceived. Religious be liefs and secular worldviews, each of which may participate fully in pluralist s ocieties, have an important responsibility in this foundational debate, as anthr opological theories can be inspired by religious and secular viewpoints. This ar ticle identifies seven anthropological considerations grounded in the synthesis of biblical scriptures, Roman Catholic documents, and recent research literature ."
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 originating in Rome, Italy, by NewsRx journalists, research stated, "Amplitude and phase scintillation inde xes (S4 and sigma(phi)) provided by Ionospheric Scintillation Monitoring (ISM) r eceivers are the most used GNSS-based indicators of the signal fluctuations indu ced by the presence of ionospheric irregularities. These indexes are available o nly from ISM receivers which are not as abundant as other types of professional GNSS receivers, resulting in limited geographic distribution." Financial support for this research came from Swarm Space Weather Variability of Ionospheric Plasma (Swarm VIP) project - European Space Agency.
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 Morogoro, Tanzania, by NewsRx journ alists, research stated, "Study region: This study refers to the Wami river sub- catchments in Eastern Tanzania." The news journalists obtained a quote from the research from Sokoine University of Agriculture: "Study Focus: The five-machine learning (ML) algorithms, includi ng long short-term memory (LSTM), multivariate adaptive regression spline (MARS) , support vector machine (SVM), extreme learning machine (ELM), and M5 Tree, wer e used to predict the most widely used drought index, the standard precipitation index (SPI), at six and nine months. Algorithms were established using monthly rainfall data for the period from 1990 to 2022 at five meteorological stations d istributed across the Wami River sub-catchment: Barega, Dakawa, Dodoma, Kongwa, and Mandera stations. New hydrological insights for the region. The predicted re sults of all five ML algorithms were evaluated using several statistical metrics , including Pearson's correlation coefficient ® mean absolute error (MAE), root mean square error (RMSE), and Nash Sutcliffe efficiency (NSE). The prediction r esults revealed that LSTM perform better in predicting drought conditions using SPI6 (6-month SPI) and SPI9 (9-month SPI) with the highest NSE of 0.99 in all fi ve stations, and R of 0.99 in four stations except at Kongwa station, where R ra nge from 0.75 to 0.99."
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 artificial intelligence. According to news originating from Hong Kong Polytechni c University by NewsRx correspondents, research stated, "The amplitude scintilla tion detection is typically achieved by using the scintillation index generated by dedicated and costly ionospheric scintillation monitoring receivers (ISMRs)." Funders for this research include Research Grants Council, University Grants Com mittee; National Natural Science Foundation of China. Our news reporters obtained a quote from the research from Hong Kong Polytechnic University: "Considering the large volume of common Global Navigation Satellite System (GNSS) receivers, this paper presents a strategy to accurately identify the ionospheric amplitude scintillation events utilizing the measurements collec ted with geodetic GNSS receivers. The proposed detection method relies on a pre- trained machine learning decision tree algorithm, leveraging the scintillation i ndex computed from the carrier-tonoise data and elevation angles collected at 1 -Hz. The experimental results using real data demonstrate a 99% ac curacy in scintillation detection can be achieved."
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 - Computational Intelligence have been published. According to news reportin g originating from Beijing, People's Republic of China, by NewsRx correspondents , research stated, "The rapidly evolving Industrial Internet of Things (IIoT) is driving the transition from conventional manufacturing to intelligent manufactu ring. Intelligent shop scheduling, as one of the essential components of intelli gent manufacturing in IIoT, is desired to allocate jobs on different machines to achieve specific production targets." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news editors obtained a quote from the research from the Beijing University of Posts and Telecommunications, "The flow-shop scheduling problem with batch pr ocessing machines (FSSP-BPM), which widely exists in real-world manufacturing, r equires two distinct but interdependent decisions: batch formation and job sched uling. Existing approaches rely on fixed search paradigms that utilize expert kn owledge to find satisfactory solutions. However, these methods struggle to ensur e solution quality under real-time constraints due to the varying data distribut ion and the complexity of large-scale practical problems. To address this challe nge, we propose a deep reinforcement learning (DRL) based method. First, we form ulate the FSSP-BPM decision process as a Markov Decision Process (MDP) and desig n the corresponding state, action, and reward. Second, we propose a basic schedu ling framework based on an encoder-decoder model with the attention mechanism. F inally, we design a batch formation module and a scheduling module trained on un labeled multi-dimensional data."
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