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    First Affiliated Hospital of Hunan Normal University Reports Findings in Cholang iocarcinoma (Machine learning developed an intratumor heterogeneity signature fo r predicting prognosis and immunotherapy benefits in cholangiocarcinoma)

    22-23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 Oncology - Cholangioca rcinoma is the subject of a report. According to news reporting out of Hunan, Pe ople's Republic of China, by NewsRx editors, research stated, "Cholangiocarcinom a is a kind of epithelial cell malignancy with high mortality. Intratumor hetero geneity (ITH) is involved in tumor progression, aggressiveness, treatment resist ance, and disease recurrence." Our news journalists obtained a quote from the research from the First Affiliate d Hospital of Hunan Normal University, "Integrative machine learning procedure i ncluding 10 methods (random survival forest, elastic network, Lasso, Ridge, step wise Cox, CoxBoost, partial least squares regression for Cox, supervised princip al components, generalized boosted regression modeling, and survival support vec tor machine) was performed to construct an ITH-related signature (IRS) for chola ngiocarcinoma. Single cell analysis was performed to clarify the communication b etween immune cell subtypes. Cellular experiment was used to verify the biologic al function of hub gene. The optimal prognostic IRS developed by Lasso method se rved as an independent risk factor and had a stable and powerful performance in predicting the overall survival rate in cholangiocarcinoma, with the AUC of 2-, 3-, and 4-year ROC curve being 0.955, 0.950 and 1.000 in TCGA cohort. low IRS sc ore indicated with a lower tumor immune dysfunction and exclusion score, lower t umor microsatellite instability, lower immune escape score, lower MATH score, an d higher mutation burden score in cholangiocarcinoma. Single cell analysis revea led a strong communication between fibroblasts, microphage and epithelial cells by specific ligand-receptor pairs, including COL4A1-(ITGAV+ITGB8) and COL1A2-(IT GAV+ITGB8). Down-regulation of BET1L inhibited the proliferation, migration and invasion as well as promoted apoptosis of cholangiocarcinoma cell. Integrative m achine learning analysis was performed to construct a novel IRS in cholangiocarc inoma."
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    Reports from Cairo University Add New Data to Findings in Artificial Intelligenc e (Artificial Intelligence Powered Metaverse: Analysis, Challenges and Future Pe rspectives)

    23-24页
    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 Artificial In telligence have been published. According to news reporting originating from Giz a, Egypt, by NewsRx correspondents, research stated, "The Metaverse, a virtual r eality (VR) space where users can interact with each other and digital objects, is rapidly becoming a reality. As this new world evolves, Artificial Intelligenc e (AI) is playing an increasingly important role in shaping its development." Financial support for this research came from Cairo University. Our news editors obtained a quote from the research from Cairo University, "Inte grating AI with emerging technologies in the Metaverse creates new possibilities for immersive experiences that were previously impossible. This paper explores how AI is integrated with technologies such as the Internet of Things, blockchai n, Natural Language Processing, virtual reality, Augmented Reality, Mixed Realit y, and Extended Reality. One potential benefit of using AI in the Metaverse is t he ability to create personalized experiences for individual users, based on the ir behavior and preferences. Another potential benefit of using AI in the Metave rse is the ability to automate repetitive tasks, freeing up time and resources f or more complex and creative endeavors. However, there are also challenges assoc iated with using AI in the Metaverse, such as ensuring user privacy and addressi ng issues of bias and discrimination. By examining the potential benefits and ch allenges of using AI in the Metaverse, including ethical considerations, we can better prepare for this exciting new era of VR."
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    Reports on Robotics from California Institute of Technology (Caltech) Provide Ne w Insights (Multimodal Soft Robotic Actuation and Locomotion)

    24-25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 Ro botics. According to news reporting originating from Pasadena, California, by Ne wsRx correspondents, research stated, "Diverse and adaptable modes of complex mo tion observed at different scales in living creatures are challenging to reprodu ce in robotic systems. Achieving dexterous movement in conventional robots can b e difficult due to the many limitations of applying rigid materials." Financial supporters for this research include National Science Foundation (NSF) , Alfred P. Sloan Foundation, Heritage Medical Research Institute. Our news editors obtained a quote from the research from the California Institut e of Technology (Caltech), "Robots based on soft materials are inherently deform able, compliant, adaptable, and adjustable, making soft robotics conducive to cr eating machines with complicated actuation and motion gaits. This review examine s the mechanisms and modalities of actuation deformation in materials that respo nd to various stimuli. Then, strategies based on composite materials are conside red to build toward actuators that combine multiple actuation modes for sophisti cated movements. Examples across literature illustrate the development of soft a ctuators as free-moving, entirely soft-bodied robots with multiple locomotion ga its via careful manipulation of external stimuli. The review further highlights how the application of soft functional materials into robots with rigid componen ts further enhances their locomotive abilities. Finally, taking advantage of the shape-morphing properties of soft materials, reconfigurable soft robots have sh own the capacity for adaptive gaits that enable transition across environments w ith different locomotive modes for optimal efficiency. Overall, soft materials e nable varied multimodal motion in actuators and robots, positioning soft robotic s to make real-world applications for intricate and challenging tasks. Soft robo ts are a promising direction to create machines capable of complex motion observ ed in living creatures. Designing soft actuators that perform a combination of m ultiple actuation modalities allows them to perform intricate tasks."
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    Researchers' from Norwegian University of Science and Technology (NTNU) Report D etails of New Studies and Findings in the Area of Machine Learning (SODRet: Inst ance retrieval using salient object detection for self-service shopping)

    25-25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 Norwegian University of Scien ce and Technology (NTNU) by NewsRx journalists, research stated, "Selfservice s hopping technologies have become commonplace in modern society. Although various innovative solutions have been adopted, there is still a gap in providing effic ient services to consumers." The news correspondents obtained a quote from the research from Norwegian Univer sity of Science and Technology (NTNU): "Recent developments in mobile applicatio n technologies and internet-of-things devices promote information and knowledge dissemination by integrating innovative services to meet users' needs. We argue that object retrieval applications can be used to provide effective online or se lf-service shopping. Therefore, to fill this technological void, this study aims to propose an object retrieval system using a fusion-based salient object detec tion (SOD) method. The SOD has attracted significant attention, and recently man y heuristic computational models have been developed for object detection. It ha s been widely used in object detection and retrieval applications. This work pro poses an instance retrieval system based on the SOD to find the objects from the commodity datasets. A prediction about the object's position is made using the saliency detection system through a saliency model, and the proposed SODbased r etrieval (SODRet) framework uses saliency maps for retrieving the searched items ."
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    Hohai University Reports Findings in Machine Learning [Uncert ainty-based saltwater intrusion prediction using integrated Bayesian machine lea rning modeling (IBMLM) in a deep aquifer]

    28-28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 from Nanjing, People's Republ ic of China, by NewsRx journalists, research stated, "Data-driven machine learni ng approaches are promising to substitute physically based groundwater numerical models and capture input-output relationships for reducing computational burden . But the performance and reliability are strongly influenced by different sourc es of uncertainty." The news correspondents obtained a quote from the research from Hohai University , "Conventional researches generally rely on a stand-alone machine learning surr ogate approach and fail to account for errors in model outputs resulting from st ructural deficiencies. To overcome this issue, this study proposes a flexible in tegrated Bayesian machine learning modeling (IBMLM) method to explicitly quantif y uncertainties originating from structures and parameters of machine learning s urrogate models. An Expectation-Maximization (EM) algorithm is combined with Bay esian model averaging (BMA) to find out maximum likelihood and construct posteri or predictive distribution. Three machine learning approaches representing diffe rent model complexity are incorporated in the framework, including artificial ne ural network (ANN), support vector machine (SVM) and random forest (RF). The pro posed IBMLM method is demonstrated in a field-scale real-world ‘1500-foot' sand aquifer, Baton Rouge, USA, where overexploitation caused serious saltwater intru sion (SWI) issues. This study adds to the understanding of how chloride concentr ation transport responds to multi-dimensional extraction-injection remediation s trategies in a sophisticated saltwater intrusion model. Results show that most I BMLM exhibit r values above 0.98 and NSE values above 0.93, both slightly higher than individual machine learning, confirming that the IBMLM is well established to provide better model predictions than individual machine learning models, wh ile maintaining the advantage of high computing efficiency. The IBMLM is found u seful to predict saltwater intrusion without running the physically based numeri cal simulation model. We conclude that an explicit consideration of machine lear ning model structure uncertainty along with parameters improves accuracy and rel iability of predictions, and also corrects uncertainty bounds."
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    Shanghai Maritime University Researchers Update Current Data on Support Vector M achines (Rolling bearing fault diagnosis based on RQA with STD and WOA-SVM)

    29-29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 are presented in a new r eport. According to news reporting from Shanghai, People's Republic of China, by NewsRx journalists, research stated, "A rolling bearing fault diagnosis method based on Recursive Quantitative Analysis (RQA) combined with time domain feature extraction and Whale Optimization Algorithm Support Vector Machine (WOA-SVM) is proposed." Our news editors obtained a quote from the research from Shanghai Maritime Unive rsity: "Firstly, the recurrence graph of the vibration signal is drawn, and the nonlinear feature parameters in the recurrence graph combined with Standard Devi ation (STD) are extracted by recursive quantitative analysis method to generate feature vectors; after that, in order to construct the optimal support vector ma chine model, the Whale Optimization Algorithm is used to optimize the c and g pa rameters. Finally, both Recursive Quantitative Analysis and standard deviation a re combined with the WOA-SVM model to perform fault diagnosis of rolling bearing s. The rolling bearing datasets from Case Western Reserve University and Jiangna n University were used for example analysis, and the fault identification accura cy reached 100 % and 95.00%, respectively. Compared to other methods, the method proposed in this paper has higher diagnostic accuracy and wide practical applicability, and the risk of accidents can be reduced thro ugh accurate fault diagnosis, which is also important for safety and environment al policies." According to the news editors, the research concluded: "This research originated in the field of mechanical fault diagnosis to solve the problem of fault diagno sis of rolling bearings in industrial production, it builds on previous research and explores new methods and techniques to fill some gaps in the field of mecha nical fault diagnosis."
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    Reports from Departamento de Informatica y Ciencias de la Computacion Advance Kn owledge in Machine Learning (Real-time impulse response: a methodology based on Machine Learning approaches for a rapid impulse response generation for real-tim e ...)

    30-31页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news originating from Quito, Ecuador, by NewsRx correspondents, research stated, "Simulation of high-definition binaural room im pulse responses using conventional approaches involves a significant amount of c omputational resources, resulting in high computational time, making these appro aches incapable of performing realtime high quality acoustic virtual reality. T his research implemented a methodology for the rapid impulse response generation using the position of a moving listener inside a fixed sound field." Our news correspondents obtained a quote from the research from Departamento de Informatica y Ciencias de la Computacion: "The rapid generation of the impulse r esponse is performed using its representative compressed dimension, with a small er dimension than the original impulse response, learned by variational autoenco ders and long short-term memory neural networks. First, the methodology selects a representative number of impulse responses covering the area of interest using a reliable room acoustic simulator. Second, it generates a dataset with suffici ent impulse responses uniformly distributed through a data augmentation approach using a modified bilinear interpolation from the impulse responses previously s imulated. Third, it applies an unsupervised model to positionally cluster the im pulse responses to reduce the variability of the impulse responses in the given environment. Fourth, it splits the impulse response into time segments and gener ates a dataset per segment and cluster. Fifth, it trains a variational autocoder with a long short-term memory neural network model for each time segment cluste r of impulse responses to infer the correspondent compressed impulse response pa rt. In summary, the impulse response is generated by assigning the current liste ner position to the corresponding cluster and executing the decoders of the vari ational autoencoders with long short-term memory, trained previously. The findin gs are encouraging; the normalized mean absolute error of the impulse responses gathered by the interpolator and the impulse responses generated by the proposed model is less than 15% in the 88% of impulse respon ses reserved for testing."
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    New Findings from Linyi University Describe Advances in Machine Learning (An Ada ptive Machine Learning Framework for Multi-Scenes Road Surface Weather Condition Monitoring)

    31-32页
    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 artificial in telligence have been published. According to news reporting from Shandong, Peopl e's Republic of China, by NewsRx journalists, research stated, "Timely road surf ace condition (RSC) monitoring and maintenance significantly influences road saf ety." Our news editors obtained a quote from the research from Linyi University: "The current RSC relies on fixed road surveillance cameras and in-vehicle cameras. Ho wever, the fixed camera demands higher precision, while the in-vehicle camera re quires higher timeliness. To address these challenges, this paper introduces an adaptive machine learning framework for simultaneous road surface detection on b oth device types. Initially, a convolutional neural network -based differentiati on module identifies image sources. Subsequently, an adaptive algorithm switchin g mechanism leads to the development of two algorithms improved upon the real-ti me object detection algorithms. At last, extensive experiments with datasets col lected from Ontario, Canada and Iowa U.S. validate the framework."
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    Findings on Machine Learning Reported by Investigators at Northeastern Universit y (Rapid Detection of Iron Ore Grades Based On Fractional-order Derivative Spect roscopy and Machine Learning)

    32-33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 Ma chine Learning. According to news reporting originating from Shenyang, People's Republic of China, by NewsRx correspondents, research stated, "The time-consumin g nature of chemical testing techniques makes them lag behind mineral processing . Therefore, this article combines visible-infrared reflectance spectroscopy wit h machine learning (ML) algorithms to achieve rapid detection of iron ore grades and meet the requirements of mining production." 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 Northeastern University , "First, the standard normal variate (SNV) and de-trending (DT) are used to eli minate noise and baseline drift in the original spectral data. Then, extraneous signals are removed using direct orthogonal signal correction (DOSC). In additio n, fractional-order derivative (FOD) is performed on the DOSC spectrum to furthe r amplify the spectral details. To extract spectral features and reduce the spec tral dimension, a multilayer incremental extreme learning machine autoencoder (M IELM-AE) is proposed in this article. MIELM-AE can automatically match the optim al number of network nodes and network layers to minimize the reconstruction err or. The experimental results show that the Pearson correlation coefficient ( R-2 ) of the extreme learning machine (ELM) built using MIELM-AE improves from 0.71 5 to 0.821, compared with the ELM built without the dimensionality reduction met hod. To increase the measurement accuracy, this article uses Tikhonov regulariza tion and truncated singular value decomposition (TSVD) to alleviate the ill-cond itioned matrix of the hidden layer of the ELM and uses the incremental method to match the optimal network nodes. Finally, double-regularization incremental ELM (DRIELM) is proposed in this article."
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    New Artificial Intelligence Study Findings Have Been Reported from La Trobe Univ ersity (Learning from artificial intelligence researchers about international bu siness implications)

    34-35页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting out of La Trob e University by NewsRx editors, research stated, "Artificial intelligence is a d ynamic and emerging form of technological innovation that has numerous ramificat ions for international business managers." The news reporters obtained a quote from the research from La Trobe University: "The aim of this article is to obtain commentary from researchers about the role artificial intelligence will play in the global arena. This includes asking que stions about how it will affect internationalization processes and whether it wi ll lead to more international collaboration." According to the news editors, the research concluded: "Well-known researchers p rovide advice on what international business managers should do in terms of stay ing competitive but also how they can integrate learning from artificial intelli gence into their business operations. Lastly, suggestions for future research re garding the interplay between international business and artificial intelligence are provided." For more information on this research see: Learning from artificial intelligence researchers about international business implications. Thunderbird International Business Review, 2024. The publisher for Thunderbird International Business Review is Wiley.
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