查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Nervous System Disease s and Conditions - Seizures is the subject of a report. According to news report ing out of London, United Kingdom, by NewsRx editors, research stated, “Function al seizures (FS) look like epileptic seizures but are characterized by a lack of epileptic activity in the brain. Approximately one in five referrals to epileps y clinics are diagnosed with this condition.” Our news journalists obtained a quote from the research from University College London (UCL), “FS are diagnosed by recording a seizure using video-electroenceph alography (EEG), from which an expert inspects the semiology and the EEG. Howeve r, this method can be expensive and inaccessible and can present significant pat ient burden. No single biomarker has been found to diagnose FS. However, the cur rent limitations in FS diagnosis could be improved with machine learning to clas sify signal features extracted from EEG, thus providing a potentially very usefu l aid to clinicians. The current study has investigated the use of seizure-free EEG signals with machine learning to identify subjects with FS from those with e pilepsy. The dataset included interictal and preictal EEG recordings from 48 sub jects with FS (mean age = 34.76 ? 10.55 years, 14 males) and 29 subjects with ep ilepsy (mean age = 38.95 ? 13.93 years, 18 males) from which various statistical , temporal, and spectral features from the five EEG frequency bands were extract ed then analyzed with threshold accuracy, five machine learning classifiers, and two feature importance approaches. The highest classification accuracy reported from thresholding was 60.67%. However, the temporal features were the best performing, with the highest balanced accuracy reported by the machine learning models: 95.71% with all frequency bands combined and a su pport vector machine classifier. Machine learning was much more effective than u sing individual features and could be a powerful aid in FS diagnosis.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting from Sydney, Australia, by NewsRx journalists, research stated, “Spatially explicit prediction of soil o rganic carbon (SOC) serves as a crucial foundation for effective land management strategies aimed at mitigating soil degradation and assessing carbon sequestrat ion potential.” The news editors obtained a quote from the research from ARC Centre of Excellenc e for Climate Extremes: “Here, using more than 1000 in situ observations, we tra ined two machine learning models (a random forest model and a * * k* * -means co upled with multiple linear regression model) and one process-based model (the ve rtically resolved MIcrobial-MIneral Carbon Stabilization, MIMICS, model) to pred ict the SOC stocks of the top 30 cm of soil in Australia. Parameters of MIMICS w ere optimised for different site groupings using two distinct approaches: plant functional types (MIMICS-PFT) and the most influential environmental factors (MI MICS-ENV). All models showed good performance with respect to SOC predictions, w ith an * * R* * 2 value greater than 0.8 during out-of-sample validation, with random forest bein g the most accurate; moreover, it was found that SOC in forests is more predicta ble than that in non-forest soils excluding croplands. The performance of contin ental-scale SOC predictions by MIMICS-ENV is better than that by MIMICS-PFT espe cially in non-forest soils. Digital maps of terrestrial SOC stocks generated usi ng all of the models showed a similar spatial distribution, with higher values i n south-eastern and south-western Australia, but the magnitude of the estimated SOC stocks varied. The mean ensemble estimate of SOC stocks was 30.3 t ha ~(-1), with * * k* * -means coupled with multiple linear regression generating the hi ghest estimate (mean SOC stocks of 38.15 t ha ~(-1)) and MIMICS-PFT generating the lowest estimate (mean SOC stocks of 24.29 t ha ~(-1)).”
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 Dalian, People’s Repub lic of China, by NewsRx editors, research stated, “Scandium-doped aluminum nitri de with a wurtzite structure has emerged as a promising ferroelectric material d ue to its exceptional physical and chemical properties and its compatibility wit h existing processing techniques. However, its high coercive voltage presents a substantial challenge for its potential applications.” Our news journalists obtained a quote from the research from the Dalian Universi ty of Technology, “To effectively reduce this high coercive voltage, it is cruci al to comprehensively understand the factors governing polarization reversal pro cesses. Unfortunately, a unified set of pivotal factors has not yet been identif ied. Herein, machine-learning regression models were developed to predict the un iform polarization reversal barrier () using data sets comprising 41 binary and 113 simple ternary wurtzite materials. Features were extracted based on elementa l properties, crystal parameters, mechanical properties, and electronic properti es. Calculation of and partial feature extraction were performed using first-pri nciples methods. The results revealed that the average cation-ion potential is t he primary intrinsic factor influencing. Additionally, the maximum value of the relative height ratio of cations to anions, cell parameter ratio, and average ca tion Mendeleev number were found to have secondary impacts. This study addresses gaps in the current understanding of , by considering multiple influencing fact ors beyond a single material system.”
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 Rabat, Morocco, by NewsRx editors, research stated, “Rabies is a major zoonotic disease legally notifiable in Morocco and elsewhere. Given the burden of rabies and its impact on public health, several national control programs have been implemented since 1986, without achieving their expected objectives.” Our news journalists obtained a quote from the research from Institut Agronomiqu e et Veterinaire Hassan II, “The aim of this study was to design a predictive an alysis of rabies in Morocco. The expected outcome was the construction of probab ilistic diagrams that can guide actions for the integrated control of this disea se, involving all stakeholders, in the country. Such modeling is an essential st ep in operational epidemiology to optimize expenditure of public funds allocated to the integrated strategy for fighting this disease. The methodology employed combined the use of geospatial analysis tools (kriging) and artificial intellige nce models (Machine Learning). In order to investigate the link between the risk of rabies within a territorial municipality (commune) and its socio-economic si tuation, the following data were analyzed: (1) health data: reported animal case s of rabies between 2004 and 2021 and data obtained through the ArcGIS kriging t ool (Geospatial data); (2) demographic and socio-economic data. We compared seve ral Machine Learning models. Of these, the ‘Imbalanced-Xgboost’ model associated with kriging yielded the best results. After optimizing this model, we mapped o ur results for better visualization. The obtained results complement and consoli date previous study in this field with greater accuracy, showing a strong correl ation between a commune’s socio-economic status, its geographical location and i ts risk level of rabies. From this, 399 out of the 1546 communes have been ident ified as high-risk areas, accounting for 25.8% of the total number of communes. Under this risk-based approach, the results of these analyses make it practical to take targeted decisions for rabies prevention and control, as w ell as canine population control, in a territorial commune according to its risk level. Such an approach allows obvious optimized distribution of financial reso urces and adaptation of the control actions to be taken. The study highlights al so the importance of using innovative technologies to refine epidemiological app roaches and fill gaps in field data.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news originating from Beijing, Peopl e’s Republic of China, by NewsRx correspondents, research stated, “The flavor pr ofile is an important factor that affects consumer preference and purchase inten tion and plays a vital role in food production as well as in food science resear ch.” The news correspondents obtained a quote from the research from Beijing Technolo gy and Business University: “Traditional methods for determining food flavor are based on sensory evaluation, instrumental analysis, or a combination of the two . With the rapid development of computer technology, machine learning technology with high predictive ability and accuracy has been widely used for food flavor analysis and prediction. This method overcomes the limitations of traditional me thods in food flavor evaluation, which are time-consuming and cannot be used to process large quantities of data. This review discusses the latest progress in f ood flavor analysis technology and machine learning technology in food flavor re search and presents commonly used food flavor analysis techniques and machine le arning algorithms. Based on an overview of common machine learning models, this review systematically summarizes the application of machine learning in the high -throughput screening of food flavor substances, flavor perception, and flavor q uality control. The challenges and future research trends in the application of machine learning to food flavor analysis and prediction are summarized and discu ssed.”
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 Bristol, United Kingdom, by NewsRx journalis ts, research stated, “This paper reviews how multifunctioning joints produce hig hly agile limbs in animals with lessons for robotics. One of the key reasons why animals are so fast and agile is that they have multifunctioning joints in thei r limbs.” Financial supporters for this research include Biologic Institute.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Liver Diseases and Con ditions - Non-Alcoholic Fatty Liver Disease is the subject of a report. Accordin g to news originating from Harbin, People’s Republic of China, by NewsRx corresp ondents, research stated, “Non-alcoholic Fatty Liver Disease (NAFLD), noted for its widespread prevalence among adults, has become the leading chronic liver con dition globally. Simultaneously, the annual disease burden, particularly liver c irrhosis caused by NAFLD, has increased significantly.” Our news journalists obtained a quote from the research from the Fourth Affiliat ed Hospital of Harbin Medical University, “Neutrophil Extracellular Traps (NETs) play a crucial role in the progression of this disease and are key to the patho genesis of NAFLD. However, research into the specific roles of NETs-related gene s in NAFLD is still a field requiring thorough investigation. Utilizing techniqu es like AddModuleScore, ssGSEA, and WGCNA, our team conducted gene screening to identify the genes linked to NETs in both single-cell and bulk transcriptomics. Using algorithms including Random Forest, Support Vector Machine, Least Absolute Shrinkage, and Selection Operator, we identified ZFP36L2 and PHLDA1 as key hub genes. The pivotal role of these genes in NAFLD diagnosis was confirmed using th e training dataset GSE164760. This study identified 116 genes linked to NETs acr oss single-cell and bulk transcriptomic analyses. These genes demonstrated enric hment in immune and metabolic pathways. Additionally, two NETs-related hub genes , PHLDA1 and ZFP36L2, were selected through machine learning for integration int o a prognostic model. These hub genes play roles in inflammatory and metabolic p rocesses. scRNA-seq results showed variations in cellular communication among ce lls with different expression patterns of these key genes.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Cancer is the subject of a report. According to news originating from Lishui, People’s Republic of Chi na, by NewsRx correspondents, research stated, “Robotic surgery has been widely used in surgical gastric cancer treatments, including proximal gastrectomy. Sing le-port robotic system is gaining more popularity in robotic surgery, but there has been no report on its application in robotic proximal gastrectomy with right -sided overlap and single-flap valvuloplasty (RPG-ROSF).” Financial support for this research came from Health Commission of Zhejiang Prov ince. Our news journalists obtained a quote from the research from Lishui Central Hosp ital, “Here, we report an RPG-ROSF using a novel single-port robotic system in a 51-year-old male patient with an early-stage gastroesophageal cancer detected b y gastroscopy. It took 90 min for robotic setup, 143 min for dissection, and 161 min for digestive tract reconstruction. There was no complication during and af ter the surgery. The patient was discharged in 8 days postsurgery. The pathologi cal staging of the adenocarcinoma was pT1aN0M0.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Foodborne Diseases and Conditions is the subject of a report. According to news reporting originating from Storrs, Connecticut, by NewsRx correspondents, research stated, “Ensuring f ood safety through rapid and accurate detection of pathogenic bacteria in food p roducts is a critical challenge in the food supply chain. In this study, a non-s pecific optical sensor array was proposed for the identification of multiple pat hogenic bacteria in contaminated milk samples.” Our news editors obtained a quote from the research from the University of Conne cticut, “Fluorescencelabeled single-stranded DNA was efficiently quenched by tw o-dimensional nanoparticles and subsequently recovered by foreign biomolecules. The recovered fluorescence generated a unique fingerprint for each bacterial spe cies, enabling the sensor array to identify eight bacteria (pathogenic and spoil age) within a few hours. Four traditional machine learning models and two artifi cial neural networks were applied for classification. The neural network showed a 93.8 % accuracy with a 30-min incubation. Extending the incubati on to 120 min increased the accuracy of the multiplayer perceptron to 98.4 % .”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on robotics have bee n published. According to news reporting out of Chakdara, Pakistan, by NewsRx ed itors, research stated, “Conventional patient monitoring methods require skin-to -skin contact, continuous observation, and long working shifts, causing physical and mental stress for medical professionals. Remote patient monitoring (RPM) as sists healthcare workers in monitoring patients distantly using various wearable sensors, reducing stress and infection risk.” Funders for this research include King Salman Center For Disability Research.
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