查看更多>>摘要：New study results on artificial intelligence have been published. According to news originating from Moulay Ismail University by NewsRx correspondents, research stated, "Accurate precipitation forecasting is paramount for various sectors. Traditional methods for rainfall prediction involve understanding physical processes, historical weather data, and statistical models." The news reporters obtained a quote from the research from Moulay Ismail University: "These methods utilize observations from ground-based weather stations, satellites, and weather radars to assess current conditions and predict future precipitation. However, accurate precipitation prediction remains challenging due to the intricate and non-linear characteristics of rainfall. Over the past few years, machine learning (ML) algorithms have shown promise in improving precipitation prediction accuracy. This research provides an overview of both traditional methods and advanced ML models applicable to rainfall prediction, including regression, classification, and time series models. We conducted a comprehensive review of related works that explore the impact of using ML algorithms for rainfall estimation. Through this analysis, we identified the strengths and limitations of ML models in this context and highlighted advancements in rainfall prediction using these algorithms. We possess a comprehensive dataset, spanning data from 1996 to 2015, comprising historical weather data from the Ziz basin, our designated study area. This dataset contains five key meteorological features: precipitation, humidity, wind, temperature, and evaporation."
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Basel, Switzerland, by NewsRx correspondents, research stated, "Elderly patients often have more than one disease that affects walking behavior. An objective tool to identify which disease is the main cause of functional limitations may aid clinical decision making." Our news journalists obtained a quote from the research from University Hospital Basel, "Therefore, we investigated whether gait patterns could be used to identify degenerative diseases using machine learning. Data were extracted from a clinical database that included sagittal joint angles and spatiotemporal parameters measured using seven inertial sensors, and anthropometric data of patients with unilateral knee or hip osteoarthritis, lumbar or cervical spinal stenosis, and healthy controls. Various classification models were explored using the MATLAB Classification Learner app, and the optimizable Support Vector Machine was chosen as the best performing model. The accuracy of discrimination between healthy and pathologic gait was 82.3%, indicating that it is possible to distinguish pathological from healthy gait. The accuracy of discrimination between the different degenerative diseases was 51.4%, indicating the similarities in gait patterns between diseases need to be further explored."
查看更多>>摘要：Investigators publish new report on Artificial Intelligence. According to news reporting from Hershey, Pennsylvania, by NewsRx journalists, research stated, "Review articles play a critical role in informing medical decisions and identifying avenues for future research. With the introduction of artificial intelligence (AI), there has been a growing interest in the potential of this technology to transform the synthesis of medical literature." The news correspondents obtained a quote from the research from the Pennsylvania State University (Penn State) College of Medicine, "Open AI's Generative Pre -trained Transformer (GPT-4) (Open AI Inc, San Francisco, CA) tool provides access to advanced AI that is able to quickly produce medical literature following only simple prompts. The accuracy of the generated articles requires review, especially in subspecialty fields like Allergy/Immunology. To critically appraise AI -synthesized allergyfocused minireviews. We tasked the GPT-4 Chatbot with generating 2 1,000 -word reviews on the topics of hereditary angioedema and eosinophilic esophagitis. Authors critically appraised these articles using the Joanna Briggs Institute (JBI) tool for text and opinion and additionally evaluated domains of interest such as language, reference quality, and accuracy of the content. The language of the AI -generated minireviews was carefully articulated and logically focused on the topic of interest; however, reviewers of the AI -generated articles indicated that the AI -generated content lacked depth, did not appear to be the result of an analytical process, missed critical information, and contained inaccurate information. Despite being provided instruction to utilize scientific references, the AI chatbot relied mainly on freely available resources, and the AI chatbot fabricated references."
查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, "This study illustrates the considerable improvement in accuracy achievable for long-lead forecasts (18 months) of the Ocean Nino Index (ONI) through the utilization of a long short-term memory (LSTM) machine learning algorithm. The research assesses the predictive potential of eight predictors from both tropical and extratropical regions constructed based on sea surface temperature, outgoing longwave radiation, sea surface height and zonal and meridional wind anomalies." Funders for this research include National Natural Science Foundation of China (NSFC), NSF's Climate and Large-Scale Dynamics Program of USA, Open Fund of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography.
查看更多>>摘要：Data detailed on Liquid State Machines have been presented. According to news reporting originating in Changsha, People's Republic of China, by NewsRx journalists, research stated, "Event-based computation has recently gained increasing research interest for applications of vision recognition due to its intrinsic advantages on efficiency and speed. However, the existing event-based models for vision recognition are faced with several issues, such as large network complexity and expensive training cost." Funders for this research include National Natural Science Foundation of China (NSFC), Key Laboratory of Advanced Microprocessor Chips and System. The news reporters obtained a quote from the research from the National University of Defense Technology, "In this paper, we propose an improved multi-liquid state machine (M-LSM) method for highperformance vision recognition. Specifically, we introduce two methods, namely multi-state fusion and multi-liquid search, to optimize the liquid state machine (LSM). Multistate fusion by sampling the liquid state at multiple timesteps could reserve richer spatiotemporal information. We adapt network architecture search (NAS) to find the potential optimal architecture of the multi-liquid state machine. We also train the M-LSM through an unsupervised learning rule spike-timing dependent plasticity (STDP)."
查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news originating from Hangzhou, People's Republic of China, by NewsRx correspondents, research stated, "The geographical origin of Baishao (Radix Paeoniae Alba) affects the components and content, which in turn affects its pharmacological action. Laser-induced breakdown spectroscopy (LIBS) was combined with conventional machine learning and deep learning methods to rapidly discriminate the geographical origins of Baishao slices without sample preparation." Funders for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Zhejiang Province. Our news journalists obtained a quote from the research from the Zhejiang University of Technology, "The influence of spatial variation of Baishao slices on the LIBS signal was investigated. The spectra that were averaged using 16-point spectra showed the best origin identification performance, with an accuracy of 96.7% as determined by partial least squares-discriminant analysis (PLS-DA). Meanwhile, the spectra obtained from a single point after voting showed the best origin identification performance using ResNet, with an accuracy of 95.0%."
查看更多>>摘要：Fresh data on self-driving cars are presented in a new report. According to news originating fromFargo, North Dakota, by NewsRx correspondents, research stated, "The emergence of autonomous vehicles(AVs) presents a transformative shift in transportation, promising enhanced safety and economic efficiency."Funders for this research include United States' Department of Transportation.The news correspondents obtained a quote from the research from North Dakota State University:"However, a fragmented legislative landscape across the United States hampers AV deployment. Thisfragmentation creates significant challenges for AV manufacturers and stakeholders. This research contributesby employing advanced machine learning (ML) techniques to analyze state data, aiming to identifyfactors associated with the likelihood of passing AV-friendly legislation, particularly regarding the requirementfor human backup drivers. The findings reveal a nuanced interplay of socio-economic, political,demographic, and safety-related factors influencing the nature of AV legislation. Key variables such asdemocratic electoral college votes per capita, port tons per capita, population density, road fatalities percapita, and transit agency needs significantly impact legislative outcomes."
查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news reporting originating in Shaanxi, People's Republic of China, by NewsRx journalists, research stated, "Shale gas plays a pivotal role in the global energy landscape, emphasizing the need for accurate shale gas-in-place (GIP) prediction to facilitate effective production planning. Adsorbed gas in shale, the primary form of gas storage under reservoir conditions, is a critical aspect of this prediction." Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC), Innovative Talent Promotion Plan of Shaanxi Province-Scientific and Technological Innovation Team, Zhuhai Innovation and Entrepreneurship Team Project, Key Technologies and Industrialization of Solar Powered Multi-Energy Conversion and Complementary Integrated Electricity, Heating and Hydrogen Energy System.
查看更多>>摘要：New research on Robotics - Androids is the subject of a report. According to news reporting originating in Jiangsu, People's Republic of China, by NewsRx journalists, research stated, "Shape memory alloy (SMA) actuators are attractive options for robotic applications due to their salient features. So far, achieving precise control of SMA actuators and applying them to human-robot interaction scenarios remains a challenge." The news reporters obtained a quote from the research from the Affiliated Hospital of Yangzhou University, "This paper proposes a novel approach to deal with the control problem of a SMA actuator. Departing from conventional mechanism models, we attempt to describe this nonlinear plant using a gray-box model, in which only the input current and the output displacement are measured. The control scheme consists of the model parameters updating and the control law calculation. The adaptation algorithm is founded on the multi-innovation concept and incorporates a dead-zone weighted factor, aiming to concurrently reduce computational complexities and enhance robustness properties. The control law is based on a PI controller, the gains of which are designed by the pole assignment technique. Theoretical analysis proves that the closed-loop performance can be ensured under mild conditions. The experiments are first conducted through the Beckhoff controller. The comparative results suggest that the proposed adaptive PI control strategy exhibits broad applicability, particularly under load variations. Subsequently, the SMA actuator is designed and incorporated into the hand rehabilitation robot. System position tracking experiments and passive rehabilitation training experiments for various gestures are then conducted. The experimental outcomes demonstrate that the hand rehabilitation robot, utilizing the SMA actuator, achieves higher position tracking accuracy and a more stable system under the adaptive control strategy proposed in this paper. Simultaneously, it successfully accommodates hand rehabilitation movements for multiple gestures."
查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting originating in Surrey, United Kingdom, by NewsRx journalists, research stated, "We use supervised machine learning to approximate the expectations typically contained in the optimality conditions of an economic model in the spirit of the parameterized expectations algorithm (PEA) with stochastic simulation. When the set of state variables is generated by a stochastic simulation, it is likely to suffer from multicollinearity." Financial support for this research came from Society of Computational Economics. The news reporters obtained a quote from the research from the University of Surrey, "We show that a neural network-based expectations algorithm can deal efficiently with multicollinearity by extending the optimal debt management problem studied by Faraglia, Marcet, Oikonomou, and Scott (2019) to four maturities. We find that the optimal policy prescribes an active role for the newly added medium-term maturities, enabling the planner to raise financial income without increasing its total borrowing in response to expenditure shocks."
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