查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject o f a report. According to news originating from Chongqing, People's Republic of C hina, by NewsRx correspondents, research stated, "Fibre reinforced polymers (FRP ) are widely used for the strengthening of beams, slabs and columns due to their light weight and high strength. Compared to the traditional methods like steel cages or steel jackets, it reduces the time and economic cost significantly." Funders for this research include National Natural Science Foundation of China ( NSFC), Chongqing Natural Science Founda- tion of China.
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 from Santa Barbar a, California, by NewsRx correspondents, research stated, "By accelerating time- consuming processes with high efficiency, computing has become an essential part of many modern chemical pipelines. Machine learning is a class of computing met hods that can discover patterns within chemical data and utilize this knowledge for a wide variety of downstream tasks, such as property prediction or substance generation."
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 Machine Learning is now available. According to news reporting out of Bari, Italy, by NewsRx editors, r esearch stated, "The use of voice search is proliferating and expected to grow i nto the foreseeable future; this is why websites increasingly optimize their con tent associated with voice-based search to improve their ranking. In this era of rapid growth in voice search technology, it is a topical matter that needs rese arch." Financial support for this research came from FAIR-Future AI Research - NextGene rationEU.
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 from Sakaka, Saudi Arabia, by NewsRx journalists, research stated, "The impact of artificial intelligence (AI ) on unemployment is a subject of debate among researchers and policymakers." The news editors obtained a quote from the research from Jouf University: "This study investigates how AI affects unemployment among educated people with disabi lities in 33 countries from 2004 to 2021. Several conclusions have been reached. First, both static and dynamic panel data estimators show that AI reduces aggre gate unemployment and unemployment among educated men with disabilities. In cont rast, there is no significant impact on the unemployment of educated women with disabilities. Second, the panel smooth transition regression model provides comp elling evidence for the existence of two regimes and a nonlinear impact of AI on unemployment among educated women with disabilities. The impact is not signific ant when AI is low (first regime), but the situation changes when AI exceeds a g iven threshold level (second regime)."
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 out of Hyderabad, Indi a, by NewsRx editors, research stated, "In recent years, the integration ofArtif icial intelligence (AI) into various fields of medicine including Gynaecology, h as shown promising potential. Surgical treatment of fibroid is myomectomy if ute rine preservation and fertility are the primary aims." Our news journalists obtained a quote from the research from the Department of O bstetrics and Gynaecology, "AI usage begins with the involvement of LLM (Large L anguage Model) from the point when a patient visits a gynecologist, from identif ying signs and symptoms to reaching a diagnosis, providing treatment plans, and patient counseling. Use of AI (ChatGPT versus Google Bard) in the surgical manag ement of fibroid. Identifyingthe patient's problems using LLMs like ChatGPT and Google Bard and giving a treatment optionin 8 clinical scenarios of fibroid. Dat a entry was done using M.S. Excel and was statistically analyzed using Statistic al Package for Social Sciences (SPSS Version 26) for M.S. Windows 2010. All resu lts were presented in tabular form. Data were analyzed using nonparametric tests Chi-square tests or Fisher exact test.pvalues <0.05 were considered statistically significant. The sensitivity of both techniques was cal culated. We have used Cohen's Kappa to know the degree of agreement. We found th at on the first attempt, ChatGPT gave general answers in 62.5 % of cases and specific answers in 37.5 % of cases. ChatGPT showed imp roved sensitivity on successive prompts 37.5 % to 62.5 % on the third prompt. Google Bard could not identify the clinical question in 50 % of cases and gave incorrect answers in 12.5 % of c ases (p = 0.04). Google Bard showed the same sensitivity of 25 % o n all prompts. AI helps to reduce the time to diagnose and plan a treatment stra tegy for fibroid and acts as a powerful tool in the hands of a gynecologist."
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 originating from Atlanta, Georgia, by NewsRx correspo ndents, research stated, "We present IndoorSim-to-OutdoorReal (I2O), an end-to-e nd learned visual navigation approach, trained solely in simulated short-range i ndoor environments, and demonstrate zero-shot sim-to-real transfer to the outdoo rs for long-range navigation on the Spot robot. Our method uses zero real-world experience (indoor or outdoor), and requires the simulator to model no predomina ntly-outdoor phenomenon (sloped grounds, sidewalks, etc)." Financial support for this research came from Apple Scholars in AI/ML PhD Fellow ship.
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 originating from Jiangsu, People's Republic of China, by NewsRx correspondents, research stated, "In response to the limited e ffectiveness of existing weight loss food products, we sought to apply machine l earning-based virtual screening methods to identify potential anti-obesity funct ional compounds from medicinal and edible plants and validate their in vitro act ivities. Firstly, we construct and evaluate the machine learning (ML) screening models using Multilayer Perceptron (MLP) and Random Forest (RF) algorithms." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from Jiangnan University , "The receiver operating characteristic (ROC) curve demonstrates the high accur acy of MLP and RF models in screening for obese-related targets PL (pancreatic l ipase) and AMPK (Adenosine 5 '-monophosphate activated protein kinase). Subseque ntly, the tested ML models are employed to screen the constructed database, and Gypenoside LXVI (GYP) and alisol-b-23-acetate (ALI) are identified as compounds exhibiting favorable activity against both targets. The hit compounds are tested for their impact on lipase activity and lipid accumulation. The test results sh ow that GYP and ALI have favorable inhibitory effects on pancreatic lipase (PL), with IC50 of 359.7 and 433.8 mu g/mL. Furthermore, both GYP and ALI significant ly reduced cellular lipid accumulation by 72.89% and 79.01% with the concentration increase to 40 mu g/mL. The molecular docking results ind icate that GYP and ALI can interact with several amino acid residues on the two target proteins, thereby affecting the activity of the target proteins."
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 Machine Learning. According to news originating from London, United Kingdom, by NewsRx correspondents, research stated, "Machine learning (ML) has been harnesse d as a promising modelling tool for materials research. However, small data, or data scarcity, is a bottleneck when incorporating ML in studies involving experi mentation." Financial support for this research came from Engineering and Physical Sciences Research Council. Our news journalists obtained a quote from the research from University College London (UCL), "Current experiment planning methods show several disadvantages: o ne-factor-at-a-time (OFAT) experimentation became impractical due to limited lab oratory resources; conventional design of experiments (DoE) failed to incorporat e high-dimensional features in ML; Surrogate-based or Bayesian optimization (BO) shifted the goal to optimize material properties rather than guiding training d ata accumulation. The present research proposes leveraging active learning (AL) to strategically select critical data for experimentation. Two AL strategies, qu ery-by-Committee (QBC) algorithm and Greedy method, are benchmarked against rand om query baseline on various materials datasets. AL is shown to efficiently redu ce model prediction errors with minimal additional experiment data. Investigatio n of hyperparameters revealed benefits of applying AL at an early stage of exper imental dataset construction. Moreover, AL is implemented and validated for an i n-house materials development task - electrospray modelling. AL exploration as a paradigm is highlighted to guide experiment design for efficient data accumulat ion purposes, and its potential for further ML modelling. In doing so, the power of ML is expected to be fully unleashed to experimental researchers. Small data is a prevalent bottleneck in machine learning for materials research. This stud y suggests active learning (AL) as a new paradigm for data acquisition. Through strategical selection, AL recommends information-rich datapoints for laboratory investigation."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in hybrid inte lligent systems. According to news originating from the Department of Computer S cience and Engineering by NewsRx correspondents, research stated, "Efforts in ca rdiovascular disorder detection demand immediate attention as they hold the pote ntial to revolutionize patient outcomes through early detection systems. The exp loration of diseases and treatments, coupled with the potential of artifical int elligence to reshape healthcare, highlights a promising avenue for innovation." The news journalists obtained a quote from the research from Department of Compu ter Science and Engineering: "AI-driven early detection systems offer substantia l benefits by improving quality of life and extending longevity through timely i nterventions for chronic diseases. The evolving landscape of healthcare algorith ms presents vast possibilities, particularly in the application of metaheuristic s to address complex challenges. An exemplary approach involves employing metahe uristic solutions such as PSO, FA, GA, WOA, and SCA to optimize an RNN for anoma ly detection using ECG systems. Despite commendable outcomes in the best and med ian case scenarios, the study acknowledges limitations, focusing on a narrow com parison of optimization algorithms and exploring RNN capabilities for a specific problem. Computational constraints led to the use of smaller populations and li mited rounds, emphasizing the need for future research to transcend these bounda ries. Significantly, the introduction of attention layers emerges as a transform ative element, enhancing neural network performance."
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 out of Richmond, Virginia, by NewsRx editors, research stated, "In this work, we utilize our recently developed mach ine learning (ML)-corrected ab initio dispersion (aiD) potential, known as D3-ML , which is based on the comprehensive SAPT10K dataset and relies solely on Carte sian coordinates as input, to address the dispersion deficiencies in second-orde r M & oslash;ller-Plesset perturbation theory (MP2) by replacing i ts problematic dispersion and exchange-dispersion terms with D3-ML. This leads t o the development of a new dispersion-corrected MP2 method, MP2+aiD(CCD), which outperforms other spin-componentscaled and dispersion-corrected MP2 methods as well as popular ML models for predicting noncovalent interactions across various datasets, including S66 x 8, NAP6 (containing 6 naphthalene dimers), L7, S12L, DNA-ellipticine, the C-60 dimer, and C-60[6] CPPA." Financial supporters for this research include American Chemical Society, VCU Gr aduate School Dissertation Assistantship.
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