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    Autonomous University Barcelona (UAB) Reports Findings in Artificial Intelligenc e (Enhanced Artificial Intelligence Methods for Liver Steatosis Assessment Using Machine Learning and Color Image Processing: Liver Color Project)

    1-2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 originating from Barcelona, Spai n, by NewsRx correspondents, research stated, “The use of livers with significan t steatosis is associated with worse transplantation outcomes. Brain death donor liver acceptance is mostly based on subjective surgeon assessment of liver appe arance, since steatotic livers acquire a yellowish tone.” Funders for this research include Fundacion Mutua Madrilena, Instituto de Salud Carlos III. Our news journalists obtained a quote from the research from Autonomous Universi ty Barcelona (UAB), “The aim of this study was to develop a rapid, robust, accur ate, and cost-effective method to assess liver steatosis. From June 1, 2018, to November 30, 2023, photographs and tru-cut needle biopsies were taken from adult brain death donor livers at a single university hospital for the study. All the liver photographs were taken by smartphones then color calibrated, segmented, a nd divided into patches. Color and texture features were then extracted and used as input, and the machine learning method was applied. This is a collaborative project between Vall d’Hebron University Hospital and Barcelona MedTech, Pompeu Fabra University, and is referred to as LiverColor. A total of 192 livers (362 p hotographs and 7240 patches) were included. When setting a macrosteatosis thresh old of 30%, the best results were obtained using the random forest classifier, achieving an AUROC = 0.74, with 85% accuracy.”
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    Department of Orthopedics Reports Findings in Artificial Intelligence (Simulatio n of osteotomy in total knee arthroplasty with femoral extra-articular deformity assisted by artificial intelligence: a study based on three-dimensional models)

    2-3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 in Yunnan, People’s Republic of China, by NewsRx journalists, research stated, “The impact of extra-articular deformities (EADs) on lower limb alignment and collateral li gament integrity during total knee arthroplasty (TKA) poses significant challeng es, increasing surgical complexity. Our study aims to evaluate the influence of EADs on mechanical axis alignment and the risk of collateral ligament injury dur ing TKA using an AI-assisted surgical planning system, with the goal of minimizi ng ligament damage through precise and scientific planning.” The news reporters obtained a quote from the research from the Department of Ort hopedics, “A healthy volunteer underwent CT and MRI scans of the lower limbs. Th e scan images were imported into Mimics 20.0 software, and the reconstructed mod els were spatially aligned using 3-maticResearch 11.0 software. Using Unigraphic s NX9.0 software, 50 three-dimensional models of femoral lateral joint deformiti es with varying positions and angles were created. Finally, TKA was simulated us ing the AI JOINT preoperative planning system. The larger the deformity angle an d the closer it is to the knee joint, the more pronounced the deviation of the m echanical axis. During MA-aligned osteotomy, nine types of deformities can damag e the collateral ligaments. After adjusting the varus/valgus of the prosthesis w ithin a safe range of 3° and leaving a residual 3° varus/valgus in the lower lim b alignment, only the 25° varus and 25° valgus deformities located at 90% of the femoral anatomical axis remain uncorrected. For patients with osteoarthri tis and concurrent EAD undergoing TKA, using reconstructed 3D models of the coll ateral ligaments for preoperative planning helps visually assess collateral liga ment damage, providing a practical solution.”
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    Studies from Polytechnic University Cartagena Have Provided New Information abou t Machine Learning (Are We Crossing a Minimum of the Gleissberg Centennial Cycle ? Multivariate Machine Learning-based Prediction of the Sunspot Number Using ... )

    3-4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 Cartagena, Spain, by NewsRx correspondents, research stated, “We propose a new method for predicting the solar cycle in terms of the sunspot number (SN) N ) based on multivariate m achine learning algorithms, various proxies of solar activity, and the spectral analysis of all considered time series via the fast Fourier transform (through t he latter we identify periodicities with which to lag these series and thus gene rate new attributes -predictors- for incorporation in the prediction model).” Funders for this research include Spanish Government, European Union (EU), Unive rsidad de Extremadura, Ministerio de Universidades of the Spanish Government.
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    University of Parma Reports Findings in Colon Cancer (Evaluation of the da Vinci single-port system in colorectal cancer surgery: a scoping review)

    4-5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 - Colon Cance r is the subject of a report. According to news reporting originating from Parma , Italy, by NewsRx correspondents, research stated, “Minimally invasive surgery for the treatment of colon and rectal cancer has gained popularity due to its as sociation with reduced postoperative pain, shorter hospital stays, and quicker r ecovery. The Da Vinci Single-Port (SP) System combines single-port laparoscopy w ith robotic assistance.” Our news editors obtained a quote from the research from the University of Parma , “This scoping review aims to evaluate the safety and short-term postoperative outcomes of utilizing the Da Vinci SP platform in colorectal cancer surgery. A s coping review was conducted adhering to the PRISMA-ScR guidelines. Data were col lected from PubMed, Embase, and the Web of Science Library as of December 22, 20 23. Studies were screened and selected based on predefined criteria, focusing on the application of the SP robotic system in colorectal procedures. Data extract ion included demographics, surgical details, intraoperative and postoperative ou tcomes. A narrative summary of the results was provided due to the heterogeneity in study designs. From an initial 2312 articles, 22 studies were selected for a nalysis, encompassing 465 patients undergoing robotic SP colorectal surgeries. O f these, 384 (82.6%) had a cancer diagnosis. The median age was 65 years, with approximately 60% being male. The median operative tim e was 225 min, with docking times averaging 12-20 min. Conversion to multi-port laparoscopy occurred in 4.2% of cases, with no conversions to open surgery. Mean intraoperative blood loss ranged from 50 to 150 ml. The mean numb er of lymph nodes retrieved ranged from 15 to 28. A diverting ileostomy was cons tructed in 20.3% of patients. Median times to flatus and soft diet were 2.5 and 3 days, respectively, with hospital stays ranging from 3 to 11 day s. Perioperative complications occurred in 15.1% of patients, incl uding wound infections (5.1%), anastomotic leakage (3.7% ), and postoperative ileus (2.8%). Negative margin status (R0 resec tion) was achieved in 95% of cases. The Da Vinci SP robotic platfo rm demonstrates promising safety and effectiveness in colorectal cancer surgery. It achieves high rates of successful oncological resection, adequate lymph node retrieval, and minimal intraoperative blood loss. Postoperative outcomes indica te quicker recovery times and manageable complication rates.”
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    Johns Hopkins University Reports Findings in Gene Therapy (Machine Learning Eluc idates Design Features of Plasmid Deoxyribonucleic Acid Lipid Nanoparticles for Cell Type-Preferential Transfection)

    5-6页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Biotechnology - Gene Therapy is t he subject of a report. According to news reporting originating in Baltimore, Ma ryland, by NewsRx journalists, research stated, “To broaden the accessibility of cell and gene therapies, it is essential to develop and optimize nonviral, cell type-preferential gene carriers such as lipid nanoparticles (LNPs). While high- throughput screening (HTS) approaches have proven effective in accelerating LNP discovery, they are often costly, labor-intensive, and do not consistently yield actionable design rules that direct screening efforts toward the most relevant chemical and formulation parameters.” The news reporters obtained a quote from the research from Johns Hopkins Univers ity, “In this study, we employed a machine learning (ML) workflow, utilizing wel l-curated plasmid DNA LNP transfection data sets across six cell types, to extra ct compositional and chemical insights from HTS studies. Our approach achieved p rediction errors averaging between 5 and 10%, depending on the cell type. By applying SHapley Additive exPlanations to our ML models, we uncovered key composition-function relationships that govern cell type-preferential LNP tr ansfection efficiency. Notably, we identified consistent LNP composition paramet ers that enhance transfection efficiency across diverse cell types, including a helper lipid molar percentage of charged lipids between 9 and 50% and the inclusion of cationic/zwitterionic helper lipids. Additionally, several parameters were found to modulate cell type-preferentiality, such as the total m olar percentage of ionizable and helper lipids, N/P ratio, PEGylated lipid molar percentage of uncharged lipids, and hydrophobicity of the helper lipid.”
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    Jiangsu University Researcher Yields New Data on Machine Learning (Research on O ptimizing English Translation Teaching Methods for College Students Using Machin e Learning Technology)

    6-7页
    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 reporting from Jiangsu, People’s Repu blic of China, by NewsRx journalists, research stated, “With the changes in the market situation for English majors, teaching English translation in colleges an d universities is also facing many challenges.” The news editors obtained a quote from the research from Jiangsu University: “Th is paper proposes an optimization strategy for English translation teaching meth ods by using machine learning technology to automatically identify English trans lation errors and extract text summaries. Pearson coefficient and multi-feature fusion technology are used to prejudge the correctness of English translation re sults, and according to the directed graph of wrong translation results, the aut omatic identification algorithm of English translation errors is constructed to automatically identify translation errors. The unsupervised machine learning Tex tRank algorithm is introduced and applied in text summary extraction, and combin ed with a multi-feature fusion computer system based on similarity relationships , it is improved to enhance the efficiency and quality of text extraction. Inner Mongolia Normal University set up an experimental class and a control class and applied this paper’s technology to practice English translation teaching. After the practice, the total English translation score of students in the experiment al class was 85.74, which was 4.41 higher than that of the control group, showin g a significant difference (P<0.05).”
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    Jiangsu University of Technology Researcher Updates Knowledge of Machine Learnin g (Explaining a Logic Dendritic Neuron Model by Using the Morphology of Decision Trees)

    7-7页
    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 reporting out of Changzhou, People’s Republic of China, by NewsRx editors, research stated, “The development of expla inable machine learning methods is attracting increasing attention.” Funders for this research include National Natural Science Foundation of China; Natural Science Foundation of Jiangsu Province of China; Qingpu District Industr y University Research Cooperation Development Foundation of Shanghai.
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    Researchers from Harbin Institute of Technology Discuss Findings in Machine Lear ning (Exploring Shear Nonlinearity of Plain-woven Composites At Various Temperat ures Based On Machine Learning)

    8-8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 Harbin, People’s Rep ublic of China, by NewsRx correspondents, research stated, “Plainwoven composit es are extensively utilized across various fields; however, it exhibits signific ant shear nonlinearity, especially at high temperatures. This study aims to prop ose a machine learning (ML) based constitutive model using Gaussian Process Regr ession (GPR), which is able to effectively characterize the shear nonlinearity o f plain-woven composites at different temperatures.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Heilongjiang Province, Heilong jiang Touyan Innovation Team Program.
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    Louisiana State University Researcher Yields New Study Findings on Machine Learn ing (Deep Learning-Based Eddy Viscosity Modeling for Improved RANS Simulations o f Wind Pressures on Bluff Bodies)

    9-9页
    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 Baton Rouge, Louisia na, by NewsRx correspondents, research stated, “Accurate prediction of wind pres sures on buildings is crucial for designing safe and efficient structures. Exist ing computational methods, like Reynolds-averaged Navier-Stokes (RANS) simulatio ns, often fail to predict pressures accurately in separation zones.” Our news editors obtained a quote from the research from Louisiana State Univers ity: “This study proposes a novel deep-learning methodology to enhance the accur acy and performance of eddy viscosity modeling within RANS turbulence closures, particularly improving predictions for bluff body aerodynamics. A deep learning model, trained on large eddy simulation (LES) data for various bluff body geomet ries, including a flat-roof building and forward/backward facing steps, was used to adjust eddy viscosity in RANS equations. The results show that incorporating the machine learning-predicted eddy viscosity significantly improves agreement with LES results and experimental data, particularly in the separation bubble an d shear layer. The deep learning model employed a neural network architecture wi th four hidden layers, 32 neurons, and tanh activation functions, trained using the Adam optimizer with a learning rate of 0.001. The training data consisted of LES simulations for forward/backward facing steps with width-to-height ratios r anging from 0.2 to 6. The study reveals that the machine learning model achieves a balance in eddy viscosity that delays flow reattachment, leading to more accu rate pressure and velocity predictions than traditional turbulence closures like k-o SST and k-e. A sensitivity analysis demonstrated the pivotal role of eddy v iscosity in governing flow separation, reattachment, and pressure distributions. ”
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    University of Glasgow Reports Findings in Robotics (Algorithm- Driven Robotic Dis covery of Polyoxometalate-Scaffolding Metal- Organic Frameworks)

    10-10页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Robotics is the subject of a repo rt. According to news reporting originating in Glasgow, United Kingdom, by NewsR x journalists, research stated, “The experimental exploration of the chemical sp ace of crystalline materials, especially metal-organic frameworks (MOFs), requir es multiparameter control of a large set of reactions, which is unavoidably time -consuming and labor-intensive when performed manually. To accelerate the rate o f material discovery while maintaining high reproducibility, we developed a mach ine learning algorithm integrated with a robotic synthesis platform for closed-l oop exploration of the chemical space for polyoxometalate-scaffolding metal-orga nic frameworks (POMOFs).” The news reporters obtained a quote from the research from the University of Gla sgow, “The eXtreme Gradient Boosting (XGBoost) model was optimized by using upda ting data obtained from the uncertainty feedback experiments and a multiclass cl assification extension based on the POMOF classification from their chemical con stitution. The digital signatures for the robotic synthesis of POMOFs were repre sented by the universal chemical description language (chDL) to precisely record the synthetic steps and enhance the reproducibility. Nine novel POMOFs includin g one with mixed ligands derived from individual ligands through the imidization reaction of POM amine derivatives with various aldehydes have been discovered w ith a good repeatability. In addition, chemical space maps were plotted based on the XGBoost models whose F1 scores are above 0.8.”
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