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    Center for Physical Sciences and Technology Reports Findings inThyroid Nodules (Machine learning-based diagnostics of capsularinvasion in thyroid nodules with wide-field second harmonic generationmicroscopy)

    1-2页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Thyroid Diseases and C onditions - Thyroid Nodules is the subjectof a report. According to news origin ating from Vilnius, Lithuania, by NewsRx correspondents, researchstated, “Papil lary thyroid carcinoma (PTC) is one of the most common, well-differentiated carc inomasof the thyroid gland. PTC nodules are often surrounded by a collagen caps ule that prevents the spread ofcancer cells.”Our news journalists obtained a quote from the research from Center for Physical Sciences and Technology,“However, as the malignant tumor progresses, the integ rity of this protective barrier is compromised,and cancer cells invade the surr oundings. The detection of capsular invasion is, therefore, crucial for thediag nosis and the choice of treatment and the development of new approaches aimed at the increase ofdiagnostic performance are of great importance. In the present study, we exploited the wide-field secondharmonic generation (SHG) microscopy i n combination with texture analysis and unsupervised machinelearning (ML) to ex plore the possibility of quantitative characterization of collagen structure in the capsuleand designation of different capsule areas as either intact, disrupt ed by invasion, or apt to invasion.Two-step k-means clustering showed that the collagen capsules in all analyzed tissue sections were highlyheterogeneous and exhibited distinct segments described by characteristic ML parameter sets. The l atterallowed a structural interpretation of the collagen fibers at the sites of overt invasion as fragmented andcurled fibers with rarely formed distributed n etworks. Clustering analysis also distinguished areas in thePTC capsule that we re not categorized as invasion sites by the initial histopathological analysis b ut couldbe recognized as prospective micro-invasions after additional inspectio n. The characteristic features ofsuspicious and invasive sites identified by th e proposed unsupervised ML approach can become a reliablecomplement to existing methods for diagnosing encapsulated PTC, increase the reliability of diagnosis,simplify decision making, and prevent human-related diagnostic errors.”
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    New Artificial Intelligence Findings from University of Cagliari Discussed(A Qu antum-inspired Approach To Pattern Recognition andMachine Learning. Part I)

    2-3页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Ar tificial Intelligence. According to news originatingfrom Cagliari, Italy, by Ne wsRx correspondents, research stated, “How are abstract concepts formed andreco gnized on the basis of a previous experience? It is interesting to compare the b ehavior of humanminds and of artificial intelligences with respect to this prob lem. Generally, a human mind that abstractsa concept (say, table), from a given set of known examples creates a table-Gestalt: a kind of vague andout of focus image that does not fully correspond to a particular table with well determined features.”Funders for this research include Ubiquitous Quantum Reality (UQR): understandin g the naturalprocesses under the light of quantum-like structures, Fondazione B anco di Sardegna, Ministry of Education,Universities and Research (MIUR), TUEV SUED Foundation, Federal Ministry of Education & Research(BMBF), Free State of Bavaria under the Excellence Strategy of the Federal Government an d the Laender,Technical University of Munich-Institute for Advanced Study.
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    Study Findings from Beijing Institute of Technology Provide NewInsights into Ro botics (Ev Charging Fairness Protective ManagementAgainst Charging Demand Uncer tainty for a New '1 To N'Automatic Charging Pile)

    3-4页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Robotics have been published. According to news reportingoriginating in Beijing, People’s Re public of China, by NewsRx journalists, research stated, “Electric vehicles(EVs ) have been popularly adopted and deployed over the past few years. However, the mismatch betweenEVs and charging infrastructure has become one of the major ro adblocks to restricting EV promotion.”Financial support for this research came from National Natural Science Foundatio n of China (NSFC).The news reporters obtained a quote from the research from the Beijing Institute of Technology, “Targetat improve the temporal and spatial utilization rate of charging infrastructure, this paper presents a new‘1 to N’ automatic charging s ystem with the combination of charging pile and special robotic arm. Theconnect ion between the charging pile and arrived EVs can be automatically switched by t he robotic armand the charging demand of EVs parking at the random parking spot s could be satisfied. Based on the ‘1to N’ charging scenario a two-layer iterat ive charging scheduling strategy is proposed benefits of restrainingthe impact of the charging demand uncertainty while realizing the fairness in charging beha vior.”
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    Findings from University of Kentucky in Machine Learning Reported(Coupled Lands lide Analyses Through Dynamic Susceptibility andForecastable Hazard Analysis)

    4-5页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According to newsreporting out of Lexington, Kentucky, by NewsRx editors, research stated, “Landslides, specifically thosetriggered t hrough an increase of soil moisture either during or after a rainfall event, pos e severe threatsto surrounding infrastructure. Herein, the term ‘landslide’ ref ers primarily to translational movements ofshallow colluvial soil upon a hillsl ope.”Our news journalists obtained a quote from the research from the University of K entucky, “Theselandslides are assumed to adhere to infinite slope approximation s. Potential landslide occurrences aremonitored through identification of areas susceptible to occurrence, through susceptibility analyses, or areaslikely to experience a landslide at a given time, through hazard analyses. Traditional lan dslide susceptibilitysystems are created as a function of static geomorphologic data. This is to say that, while spatially differing,susceptibility via this s ystem does not change with time. Landslide hazard analyses consider dynamic data, such as that of precipitation, and provide warnings of when landslide occurren ces are likely. However,these hazard analysis systems typically only provide wa rnings in near real time (i.e., over the next fewdays). Therefore, dynamic susc eptibility (susceptibility that is seen to change with time rather than remains tatic) as well as the ability to forecast landslide hazard analyses beyond real time is desired. The studyherein presents a novel workflow for the creation of dynamic landslide susceptibility and forecastable hazardanalyses over a domain within Eastern Kentucky. Dynamic susceptibility was developed through inclusionof static geomorphic parameters and dynamic vegetation levels over sites of inte rest. These susceptibilitydata were used in the development of a logistic regre ssion classification machine learning approach whichyielded susceptibility clas sifications with an accuracy of 89%. Forecastable hazard analyses w ere developedas a function of forecasted soil moisture, assumed to be a control ling factor in landslide occurrence, overa site. Forecasting of soil moisture w as conducted through development of a Long Short-Term Memory(LSTM) forecasting machine learning system. Forecasts of soil moisture were then assimilated into a ninfinite slope stability equation to provide forecasts of hazard analyses. The se forecasted hazard analyseswere investigated over known landslides with satis factory results obtained.”
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    Pontifical University Javeriana Reports Findings in Malaria (CAM:a novel aid sy stem to analyse the coloration quality of thick bloodsmears using image process ing and machine learning techniques)

    5-6页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Mosquito-Borne Disease s - Malaria is the subject of a report.According to news reporting out of Bogot a, Colombia, by NewsRx editors, research stated, “Battlingmalaria’s morbidity a nd mortality rates demands innovative methods related to malaria diagnosis. Thic kblood smears (TBS) are the gold standard for diagnosing malaria, but their col oration quality is dependenton supplies and adherence to standard protocols.”Funders for this research include Facebook Inc., CV4GC 2019 RFP Research Award, Pontificia UniversidadJaveriana, M.Sc. program in Bioengineering at Pontificia Universidad Javeriana.Our news journalists obtained a quote from the research from Pontifical Universi ty Javeriana, “Machinelearning has been proposed to automate diagnosis, but the impact of smear coloration on parasite detectionhas not yet been fully explore d. To develop Coloration Analysis in Malaria (CAM), an image databasecontaining 600 images was created. The database was randomly divided into training (70% ), validation (15%), and test (15%) sets. Nineteen fea ture vectors were studied based on variances, correlation coefficients,and hist ograms (specific variables from histograms, full histograms, and principal compo nents from thehistograms). The Machine Learning Matlab Toolbox was used to sele ct the best candidate featurevectors and machine learning classifiers. The cand idate classifiers were then tuned for validation andtested to ultimately select the best one. This work introduces CAM, a machine learning system designedfor automatic TBS image quality analysis. The results demonstrated that the cubic SV M classifieroutperformed others in classifying coloration quality in TBS, achie ving a true negative rate of 95% anda true positive rate of 97% . An image-based approach was developed to automatically evaluate thecoloration quality of TBS. This finding highlights the potential of image-based analysis t o assess TBScoloration quality.”
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    Reports from Rio de Janeiro State University Describe Recent Advancesin Computa tional Intelligence (Electricity Energy DemandPrediction Using Computational In telligence Techniques)

    6-6页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on computational intellig ence is the subject of a new report. Accordingto news reporting out of Rio de J aneiro State University by NewsRx editors, research stated, “Energy isan import ant pillar for the economic development of a country.”The news reporters obtained a quote from the research from Rio de Janeiro State University: “Thedemand for electricity is something that continues to grow, one of the contributing factors is the emergenceof various technological equipment and the consequent use by the population. There are several resourcesthat can be exploited to generate electricity, with hydroelectric power stations being on e of the most usedresources. As electrical energy cannot be stored, there is a need to estimate its consumption, looking fora way to meet this energy demand. In this context, this study seeks to apply machine learning techniques,using th e Grey Wolf Optimization (GWO) meta-heuristic to optimize regression models, to predict thedemand for electricity in Brazil, and it aims to estimate how much e nergy should be produced. For thepredictions, the period between the years 2017 to 2022 was used, totaling around 2,190 samples. Themethodology involves pre-p rocessing, crossvalidation, parameters optimization and regression.”
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    New Machine Learning Findings Reported from Huazhong Universityof Science and T echnology (Machine Learning-assisted Fluorescence/fluorescence Colorimetric Sens or Array for DiscriminatingAmyloid Fibrils)

    7-7页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in Machine Learning. According to news reportingoriginating in Wuhan, People’s Rep ublic of China, by NewsRx journalists, research stated, “Misfoldingand aggregat ion of proteins often lead to the development of diseases, and amyloid has gaine dwidespread attention as a biomarker for a variety of diseases. In this study, we developed a fluorescence/fluorescence colorimetric dual-mode sensor array for the detection of amyloid fibrils using severalcommercially available organic s mall molecular dyes and alkaloids, including Thioflavin T, Congo Red,8-anilino- 1-naphthalenesulfonic acid, Safranine T, berberine and coptisine, as the element s.”Financial supporters for this research include Program for HUST Academic Frontie r Youth Team,National Natural Science Foundation of China (NSFC).The news reporters obtained a quote from the research from the Huazhong Universi ty of Scienceand Technology, “Herein, the array could not only use the fluoresc ence intensities change before andafter protein interaction as a pattern recogn ition signal, but also read the Delta R/Delta G/Delta Bvalues of the photos tak en in the UV dark box on a smartphone-based platform, which converted thechroma ticity information into intuitive data. Five studied amyloid fibrils, i.e. insul in, lysozyme, bovineserum albumin, amyloid-8 42 and alpha-synuclein fibrils, we re properly distinguished with data processingassisted by machine learning algo rithms, i.e. linear discriminant analysis, principal component analysis andhier archical cluster analysis. After reducing the number of elements by principal co mponent analysis, asimplified array quantified individual amyloid fibrils at 0. 05-5 mu M and 0.5-10 mu M with fluorescenceand fluorescence colorimetric signal s, respectively, and successfully identified 25 unknown samples withhigh accura cy in diluted human plasma matrix and artificial cerebrospinal fluid.”
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    University of Maryland Reports Findings in Proteome (Developmentand Validation of RoboCap, a Robotic Capillary Platform to AutomateCapillary Electrophoresis M ass Spectrometry En Route toHigh-Throughput Single-Cell Proteomics)

    8-8页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Peptides and Proteins - Proteome is the subject of a report. Accordingto news reporting out of Colleg e Park, Maryland, by NewsRx editors, research stated, “Currentdevelopments in s ingle-cell mass spectrometry (MS) aim to deepen proteome coverage while enhancin ganalytical speed to study entire cell populations, one cell at a time. Custom- built microanalytical capillaryelectrophoresis (mCE) played a critical role in the foundation of discovery single-cell MS proteomics.”Our news journalists obtained a quote from the research from the University of M aryland, “However,requirements for manual operation, substantial expertise, and low measurement throughput have so farhindered mCE-based single-cell studies o n large numbers of cells. Here, we design and construct a roboticcapillary (Rob oCap) platform that grants single-cell CE-MS with automation for proteomes limit ed to lessthan 100 nL. RoboCap remotely controls precision actuators to transla te the sample to the fused silicaseparation capillary, using vials in this work . The platform is hermetically enclosed and actively pressurizedto inject 1-250 nL of the sample into a CE separation capillary, with errors below 5% relative standarddeviation (RSD). The platform and supporting equipment were op erated and monitored remotely on acustom-written Virtual Instrument (LabView). Detection performance was validated empirically on 5-250nL portions of the HeLa proteome digest using a trapped ion mobility mass spectrometer (timsTOF PRO).R oboCap improved CE-ESI sample utilization to 20% from 3% on the manual mCE, the closest referencetechnology. Proof-of-principle experime nts found proteome identification and quantification to robustlyreturn 1,800 pr oteins ( 13% RSD) from 20 ng of the HeLa proteome digest on this e arlier-generationdetector.”
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    Queensland University of Technology Reports Findings in Nephrectomy(Cost-effect iveness analysis of microwave ablation versusrobot-assisted partial nephrectomy for patients with small renalmasses in Australia)

    9-10页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Surgery - Nephrectomy is the subject of a report. According to newsreporting out of Brisbane, Austral ia, by NewsRx editors, research stated, “Microwave ablation (MWA)has gained att ention as a minimally invasive and safe alternative to surgical intervention for patients withsmall renal masses; however, its cost-effectiveness in Australia remains unclear. This study conducteda cost-effectiveness analysis to evaluate the relative clinical and economic merits of MWA compared torobotic-assisted pa rtial nephrectomy (RA-PN) in the treatment of small renal masses.”Our news journalists obtained a quote from the research from the Queensland Univ ersity of Technology,“A Markov state-transition model was constructed to simula te the progression of Australian patients withsmall renal masses treated with M WA versus RA-PN over a 10-year horizon. Transition probabilities andutility dat a were sourced from comprehensive literature reviews, and cost data were estimat ed from theAustralian health system perspective. Life-years, quality-adjusted l ife-years (QALYs), and lifetime costswere estimated. Modelled uncertainty was a ssessed using both deterministic and probabilistic sensitivityanalyses. A willi ngness-to-pay (WTP) threshold of $50,000 per QALY was adopted. All costs are expressedin 2022 Australian dollars and discounted at 3% annually. To assess the broader applicability of our findings,a validated cost- adaptation method was employed to extend the analysis to 8 other high-income cou ntries.Both the base case and cost-adaptation analyses revealed that MWA domina ted RA-PN, producing bothlower costs and greater effectiveness over 10 years. T he cost-effectiveness outcome was robust acrossall model parameters. Probabilis tic sensitivity analyses confirmed that MWA was dominant in 98.3%of simulations at the designated WTP threshold, underscoring the reliability of the model under varyingassumptions.”
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    Investigators from Fudan University Release New Data on ArtificialIntelligence (Artificial Intelligence Products and Their Influence OnIndividuals’ Objectific ation: a Narrative Review)

    10-10页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Artificial In telligence have been published. According to newsreporting originating from Sha nghai, People’s Republic of China, by NewsRx correspondents, researchstated, “W ith the advancement of technology, artificial intelligence (AI) has permeated va rious aspects ofour social lives. AI products exhibit significant gender biases that may contribute to both self-objectificationand the sexual objectification of others.”Our news editors obtained a quote from the research from Fudan University, “Desp ite this, the impactof AI products on individuals’ objectification and self-obj ectification has not been thoroughly examined,with the mechanisms involved and potential moderating factors remaining unclear. This paper offers aconcise synt hesis and review of the extensive literature on gender bias in AI and sexual obj ectification,covering research up to October 2023. Synthetic analysis indicates that AI products exhibit pronouncedgender biases and cues of sexual objectific ation in both internal attributes, such as algorithms, and externalattributes, such as voice and appearance. These biases may affect an individual’s self-objec tification andthe sexual objectification of others. Internalization, social com parison, and dehumanization are identified askey mechanisms through which AI pr oducts affect individuals’ objectification. Factors like the performancecharact eristics of AI products, individual psychological traits, and human-AI interacti on characteristics mayplay a moderating role. Finally, this paper suggests futu re research directions, including focusing on howto reduce gender bias in AI de sign, exploring the mechanisms through which AI influences objectification,and identifying protective factors involved in this process, as well as addressing t he limitations of existingresearch.”
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