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    Studies from Swiss Federal Laboratories for Materials Science and Technology Add New Findings in the Area of Robotics (Soft Chemiresistive Sensing Shields Soft Robotic Actuators From Mechanical Degradation Due To Critical Solvent Exposure)

    48-49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 originating from Dubendorf, Switzerland, by New sRx correspondents, research stated, “Thermoplastic elastomers (TPEs) are popula r for fabricating soft actuators thanks to their compatibility with thermoplasti c processing methods, like material extrusion additive manufacturing. However, t hese TPEs are susceptible to nonpolar solvents, and upon exposure, the mechanica l properties can diminish significantly.” Financial supporters for this research include Horizon 2020, European Union (EU) .
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    Northwest University Reports Findings in Artificial Intelligence (Vaginal microb iota molecular profiling and diagnostic performance of artificial intelligence-a ssisted multiplex PCR testing in women with bacterial vaginosis: a single-center ...)

    49-50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 originating from Xi’an , People’s Republic of China, by NewsRx correspondents, research stated, “Bacter ial vaginosis (BV) is a most common microbiological syndrome. The use of molecul ar methods, such as multiplex real-time PCR (mPCR) and next-generation sequencin g, has revolutionized our understanding of microbial communities.” Our news editors obtained a quote from the research from Northwest University, “ Here, we aimed to use a novel multiplex PCR test to evaluate the microbial compo sition and dominant lactobacilli in nonpregnant women with BV, and combined wit h machine learning algorithms to determine its diagnostic significance. Residual material of 288 samples of vaginal secretions derived from the vagina from heal thy women and BV patients that were sent for routine diagnostics was collected a nd subjected to the mPCR test. Subsequently, Decision tree (DT), random forest ( RF), and support vector machine (SVM) hybrid diagnostic models were constructed and validated in a cohort of 99 women that included 74 BV patients and 25 health y controls, and a separate cohort of 189 women comprising 75 BV patients, 30 int ermediate vaginal microbiota subjects and 84 healthy controls, respectively. The rate or abundance of and were significantly reduced in BV-affected patients whe n compared with healthy women, while , , BVAB2, 2, and were significantly increa sed. Then the hybrid diagnostic models were constructed and validated by an inde pendent cohort. The model constructed with support vector machine algorithm achi eved excellent prediction performance (Area under curve: 0.969, sensitivity: 90. 4%, specificity: 96.1%). Moreover, for subjects with a Nugent score of 4 to 6, the SVM-BV model might be more robust and sensitive tha n the Nugent scoring method. The application of this mPCR test can be effectivel y used in key vaginal microbiota evaluation in women with BV, intermediate vagin al microbiota, and healthy women.”
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    Champalimaud Foundation Reports Findings in Neural Computation (Approximating No nlinear Functions With Latent Boundaries in Low-Rank Excitatory-Inhibitory Spiki ng Networks)

    50-51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 Computation - Neural C omputation is the subject of a report. According to news originating from Lisbon , Portugal, by NewsRx correspondents, research stated, “Deep feedforward and rec urrent neural networks have become successful functional models of the brain, bu t they neglect obvious biological details such as spikes and Dale’s law. Here we argue that these details are crucial in order to understand how real neural cir cuits operate.” Our news journalists obtained a quote from the research from Champalimaud Founda tion, “Towards this aim, we put forth a new framework for spike-based computatio n in low-rank excitatory-inhibitory spiking networks. By considering populations with rank-1 connectivity, we cast each neuron’s spiking threshold as a boundary in a low-dimensional input-output space. We then show how the combined threshol ds of a population of inhibitory neurons form a stable boundary in this space, a nd those of a population of excitatory neurons form an unstable boundary. Combin ing the two boundaries results in a rank-2 excitatory-inhibitory (EI) network wi th inhibition-stabilized dynamics at the intersection of the two boundaries. The computation of the resulting networks can be understood as the difference of tw o convex functions and is thereby capable of approximating arbitrary non-linear input-output mappings. We demonstrate several properties of these networks, incl uding noise suppression and amplification, irregular activity and synaptic balan ce, as well as how they relate to rate network dynamics in the limit that the bo undary becomes soft. Finally, while our work focuses on small networks (5-50 neu rons), we discuss potential avenues for scaling up to much larger networks.”
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    Studies from Northeastern University Yield New Information about Robotics (A Dou ble-beam Piezoelectric Robot Based On the Principle of Two-mode Excitation)

    51-52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 Robotic s. According to news reporting from Shenyang, People’s Republic of China, by New sRx journalists, research stated, “In this paper, a brand-new doublebeam piezoe lectric robot (DBPR) is developed. The configuration is designed as a combinatio n of piezoelectric drive units and joint beams.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Fundamental Research Funds for the Central Universities.
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    Researchers from University of Bologna Describe Findings in Machine Learning (Co mparison of nine machine learning regression models in predicting hospital lengt h of stay for patients admitted to a general medicine department)

    52-53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 new report. According to news originating from Bologna, It aly, by NewsRx correspondents, research stated, “The General Medicine (GM) depar tment has the highest patient volume and heterogeneity among other hospital spec ialties. Closely examining hospitalization data is crucial because patients come with various conditions or traits. Length of stay (LoS) in hospitals is often u sed as an efficiency indicator.” Our news correspondents obtained a quote from the research from University of Bo logna: “It is influenced by various factors, including the patient’s medical bac kground, demographics, and type of diseases/signs/symptoms at the triage. LoS is a variable that can vary widely, making it difficult to estimate it promptly an d accurately, but doing so is highly beneficial. Moreover, efficiently grouping and managing patients based on their expected LoS remains a significant challeng e for healthcare organizations. This study aimed to compare the predictive abili ty of nine Machine Learning (ML) regression models in estimating the actual numb er of LoS days using demographics and clinical information recorded at admission as independent variables. We analyzed data collected on patients hospitalized a t the GM department of the Sant’Orsola-Malpighi University Hospital in Bologna, Italy, who were admitted through the Emergency Department. The data were collect ed from January 1, 2022, to October 26, 2022. Nine ML regression models were use d to predict LoS by analyzing historical data and patient information. The model s’ performance was assessed through root mean squared prediction error (RMSPE) a nd mean absolute prediction error (MAPE). Moreover, we used K-means clustering t o group patients’ medical and organizational criticalities (such as diseases, si gns, symptoms, and administrative problems) into four clusters. Feature Importan ce plots and SHAP (SHapley Additive exPlanations) values were employed to identi fy the more essential features and enhance the interpretability of the results. We analyzed the LoS of 3757 eligible patients, which showed an average of 13 day s and a standard deviation of 11.8 days. We randomly divided patients into a tra ining cohort of 2630 (70 %) and a test cohort of 1127 (30 % ). The predictive performance of the different models was between 11.00 and 16.1 6 days for RMSPE and between 7.52 and 10.78 days for MAPE. The eXtreme Gradient Boosting Regression (XGBR) model had the lowest prediction error, both in terms of RMSPE (11.00 days) and MAE (7.52 days). Sex, arrival via own vehicle/walk-in, ambulance arrival, light blue risk category, age 70 or older, and orange risk c ategory are some of the top features.”
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    Julius-Maximilians-University Wurzburg Reports Findings in Machine Learning (Pra ctical approaches in evaluating validation and biases of machine learning applie d to mobile health studies)

    53-54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 out of Wurzburg, Germany, by NewsRx editors, research stated, “Machine learning (ML) models are evaluated in a test set to estimate model performance after deployment. The design of the tes t set is therefore of importance because if the data distribution after deployme nt differs too much, the model performance decreases.” Our news journalists obtained a quote from the research from Julius-Maximilians- University Wurzburg, “At the same time, the data often contains undetected group s. For example, multiple assessments from one user may constitute a group, which is usually the case in mHealth scenarios. In this work, we evaluate a model’s p erformance using several cross-validation train-test-split approaches, in some c ases deliberately ignoring the groups. By sorting the groups (in our case: Users ) by time, we additionally simulate a concept drift scenario for better external validity. For this evaluation, we use 7 longitudinal mHealth datasets, all cont aining Ecological Momentary Assessments (EMA). Further, we compared the model pe rformance with baseline heuristics, questioning the essential utility of a compl ex ML model. Hidden groups in the dataset leads to overestimation of ML performa nce after deployment. For prediction, a user’s last completed questionnaire is a reasonable heuristic for the next response, and potentially outperforms a compl ex ML model. Because we included 7 studies, low variance appears to be a more fu ndamental phenomenon of mHealth datasets. The way mHealth-based data are generat ed by EMA leads to questions of user and assessment level and appropriate valida tion of ML models. Our analysis shows that further research needs to follow to o btain robust ML models. In addition, simple heuristics can be considered as an a lternative for ML.”
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    Investigators from Guizhou Education University Release New Data on Machine Lear ning (Theoretical Calculation Assisted By Machine Learning Accelerate Optimal El ectrocatalyst Finding for Hydrogen Evolution Reaction)

    54-55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 Machine Learn ing have been published. According to news reporting originating in Guiyang, Peo ple’s Republic of China, by NewsRx journalists, research stated, “Electrocatalyt ic hydrogen evolution reaction (HER) is a promising strategy to solve and mitiga te the coming energy shortage and global environmental pollution. Searching for efficient electrocatalysts for HER remains challenging through traditional trial -and-error methods from numerous potential material candidates.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Guizhou Provincial Basic Research Program (Natural Science), Top scientific and technological talents in Guizhou Province, Functional Materi als and Devices Technology Innovation Team of Guizhou Province University, Guizh ou Normal University Academic New Talent Fund, Guizhou Normal New Talent.
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    Data on Machine Learning Reported by Researchers at University of Salvador (Surv ey On Machine Learning-enabled Network Slicing: Covering the Entire Life Cycle)

    55-56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 in Salvador, Brazil, by NewsRx journalists, research stated, “Network slicing (NS) is becoming an essent ial element of service management and orchestration in communication networks, s tarting from mobile cellular networks and extending to a global initiative. NS c an reshape the deployment and operation of traditional services, support the int roduction of new ones, vastly advance how resource allocation performs in networ ks, and notably change the user experience.” Financial support for this research came from Fundacao de Apoio a Pesquisa do Di strito Federal (FAPDF).
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    Studies from Federal Reserve Board in the Area of Machine Learning Reported (Mis sing Values Handling for Machine Learning Portfolios)

    56-56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 Machine Learn ing have been published. According to news originating from Washington, District of Columbia, by NewsRx correspondents, research stated, “We characterize the st ructure and origins of missingness for 159 cross-sectional return predictors and study missing value handling for portfolios constructed using machine learning. Simply imputing with crosssectional means performs well compared to rigorous e xpectation -maximization methods.” Our news journalists obtained a quote from the research from Federal Reserve Boa rd, “This stems from three facts about predictor data: (1) missingness occurs in large blocks organized by time, (2) crosssectional correlations are small, and (3) missingness tends to occur in blocks organized by the underlying data sourc e. As a result, observed data provide little information about missing data.”
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    Reports Outline Machine Learning Study Results from Islamic University of Techno logy (IUT) (Waste Heat Recuperation in Advanced Supercritical CO2 Power Cycles w ith Organic Rankine Cycle Integration & Optimization Using Machine Learning Methods)

    57-58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 reporting originating from Islamic Univ ersity of Technology (IUT) by NewsRx correspondents, research stated, “Supercrit ical CO2 (sCO2) stands out for concentrating solar power (CSP) due to its superi or thermophysical and chemical properties, promising higher cycle efficiency com pared to superheated or supercritical steam. Leveraging the waste heat from sCO2 cycles through the organic Rankine cycle (ORC) as a low-grade energy source enh ances overall thermal efficiency.”
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