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    New Study Findings from Xidian University Illuminate Research in Computational I ntelligence (A Review of Deep Learning-Based Methods for Road Extraction from Hi gh-Resolution Remote Sensing Images)

    48-49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 computational intelligence have been published. According to news reporting from Xidian Unive rsity by NewsRx journalists, research stated, "Road extraction from high-resolut ion remote sensing images has long been a focal and challenging research topic i n the field of computer vision." Funders for this research include National Science And Technology Major Project; National Natural Science Foundation of China; Guangxi Key Laboratory of Trusted Software; Provincial Key Research And Development Program of Shaanxi; Fundament al Research Funds For The Central Universities. The news editors obtained a quote from the research from Xidian University: "Acc urate extraction of road networks holds extensive practical value in various fie lds, such as urban planning, traffic monitoring, disaster response and environme ntal monitoring. With rapid development in the field of computational intelligen ce, particularly breakthroughs in deep learning technology, road extraction tech nology has made significant progress and innovation. This paper provides a syste matic review of deep learning-based methods for road extraction from remote sens ing images, focusing on analyzing the application of computational intelligence technologies in improving the precision and efficiency of road extraction. Accor ding to the type of annotated data, deep learning-based methods are categorized into fully supervised learning, semi-supervised learning, and unsupervised learn ing approaches, each further divided into more specific subcategories. They are comparatively analyzed based on their principles, advantages, and limitations."
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    Zhejiang University Researcher Reports Recent Findings in Robotics (Lifetime Pre diction of Permanent Magnet Synchronous Motor in Selective Compliance Assembly R obot Arm Considering Insulation Thermal Aging)

    49-50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 robotics have been published. According to news originating from Hangzhou, People's Republic of China, by NewsRx editors, the research stated, "The direct-drive selective co mpliance assembly robot arm (SCARA) is widely used in high-end manufacturing fie lds, as it omits the mechanical transmission structures and has the advantages o f high positioning accuracy and fast movement speed." Financial supporters for this research include Key Research And Development Prog ram of Zhejiang 322 Province; National Natural Science Foundation of China. Our news journalists obtained a quote from the research from Zhejiang University : "However, due to the intensifying dynamic coupling problem of structures in th e direct-drive SCARA, the permanent magnet synchronous motors (PMSMs) located at the joints will take on nonstationary loads, which causes excessive internal te mperature and reduces the lifetime of PMSMs. To address these issues, the lifeti me prediction of PMSMs is studied. The kinematic and dynamic models of the SCARA are established to calculate the torque curve required by the PMSM in specific typical motion tasks. Additionally, considering thermal stress as the main facto r affecting lifetime, accelerated degradation tests are conducted on insulation material. Then, the reliability function of the PMSM is formulated based on the accelerated degradation model. Based on the parameters and working conditions of the PMSM, the temperature field distribution is obtained through simulation."
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    Sichuan University Reports Findings in Lung Cancer (Real-World Survival Comparis ons Between Radiotherapy and Surgery for Metachronous Second Primary Lung Cancer and Predictions of Lung Cancer-Specific Outcomes Using Machine Learning: ...)

    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 Oncology - Lung Cancer is the subject of a report. According to news reporting from Chengdu, People's Republic of China, by NewsRx journalists, research stated, "Metachronous second primary lung cancer (MSPLC) is not that rare but is seldom studied. We aim to co mpare real-world survival outcomes between different surgery strategies and radi otherapy for MSPLC." The news correspondents obtained a quote from the research from Sichuan Universi ty, "This retrospective study analyzed data collected from patients with MSPLC b etween 1988 and 2012 in the Surveillance, Epidemiology, and End Results (SEER) d atabase. Propensity score matching (PSM) analyses and machine learning were perf ormed to compare variables between patients with MSPLC. Survival curves were plo tted using the Kaplan-Meier method and were compared using log-rank tests. A tot al of 2451 MSPLC patients were categorized into the following treatment groups: 864 (35.3%) received radiotherapy, 759 (31 %) underwent surgery, 89 (3.6%) had surgery plus radiotherapy, and 739 (30.2% ) had neither treatment. After PSM, 470 pairs each for radiotherapy and surgery were generated. The surgery group had significantly better survival than the rad iotherapy group (P <.001) and the untreated group (563 pair s; P <.001). Further analysis revealed that both wedge resec tion (85 pairs; P=.004) and lobectomy (71 pairs; P=.002) outperformed radiothera py in overall survival for MSPLC patients. Machine learning models (extreme grad ient boosting, random forest classifier, adaptive boosting) demonstrated high pr edictive performance based on area under the curve (AUC) values. Least absolute shrinkage and selection operator (LASSO) regression analysis identified 9 signif icant variables impacting cancer-specific survival, emphasizing surgery's consis tent influence across 1 year to 10 years. These variables encompassed age at dia gnosis, sex, year of diagnosis, radiotherapy of initial primary lung cancer (IPL C), primary site, histology, surgery, chemotherapy, and radiotherapy of MPSLC. C ompeting risk analysis highlighted lower mortality for female MPSLC patients (ha zard ratio [HR]=0.79, 95% CI 0.71-0.87) and recent IPLC diagnoses (HR=0.79, 95 % CI 0.73-0.85), while radiotherapy for IPLC increased mortality (HR=1.31, 95% CI 1.16-1.50). Surgery alone had the lowest cancer-specific mortality (HR=0.83, 95% CI 0.81-0.85), with sublevel resection having the lowest mortality rate among th e surgical approaches (HR=0.26, 95% CI 0.21-0.31). The findings pr ovide valuable insights into the factors that influence cumulative cancer-specif ic mortality."
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    Istanbul University Reports Findings in Essential Thrombocythemia (Raman Spectro scopy of Blood Serum for Essential Thrombocythemia Diagnosis: Correlation with G enetic Mutations and Optimization of Laser Wavelengths)

    51-52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 Myeloproliferative Dis eases and Conditions - Essential Thrombocythemia is the subject of a report. Acc ording to news reporting originating in Elazig, Turkey, by NewsRx journalists, r esearch stated, "Essential thrombocythemia (ET) is a type of myeloproliferative neoplasm that increases the risk of thrombosis. To diagnose this disease, the an alysis of mutations in the Janus Kinase 2 (JAK2), thrombopoietin receptor (MPL), or calreticulin (CALR) gene is recommended." The news reporters obtained a quote from the research from Istanbul University, "Disease poses diagnostic challenges due to overlapping mutations with other neo plasms and the presence of triple-negative cases. This study explores the potent ial of Raman spectroscopy combined with machine learning for ET diagnosis. We as sessed two laser wavelengths (785, 1064 nm) to differentiate between ET patients and healthy controls. The PCR results indicate that approximately 50% of patients in our group have a mutation in the JAK2 gene, while only 5% of patients harbor a mutation in the ASXL1 gene. Additionally, only one patient had a mutation in the IDH1 and one had a mutation in IDH2 gene. Consequently, pa tients having no mutations were also observed in our group, making diagnosis cha llenging. Raman spectra at 1064 nm showed lower amide, polysaccharide, and lipid vibrations in ET patients, while 785 nm spectra indicated significant decreases in amide II and C-H lipid vibrations. Principal Component Analysis (PCA) confir med that both wavelengths could distinguish ET from healthy subjects. Support Ve ctor Machine (SVM) analysis revealed that the 800-1800 cm range provided the hig hest diagnostic accuracy, with 89% for 785 nm and 72% for 1064 nm. These findings suggest that FT-Raman spectroscopy, paired with mult ivariate and machine learning analyses, offers a promising method for diagnosing ET with high accuracy by detecting specific molecular changes in serum. Princip al Component Analysis (PCA) confirmed that both wavelengths could distinguish ET from healthy subjects. Support Vector Machine (SVM) analysis revealed that the 800-1800 cm range provided the highest diagnostic accuracy, with 89% for 785 nm and 72% for 1064 nm."
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    University of Tunku Abdul Rahman Reports Findings in Personalized Medicine (Iden tifying miRNA as biomarker for breast cancer subtyping using association rule)

    53-53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 Drugs and Therapies - Personalized Medicine is the subject of a report. According to news reporting ou t of Selangor, Malaysia, by NewsRx editors, research stated, "- This paper prese nts a comprehensive study focused on breast cancer subtyping, utilizing a multif aceted approach that integrates feature selection, machine learning classifiers, and miRNA regulatory networks. The feature selection process begins with the CF S algorithm, followed by the Apriori algorithm for association rule generation, resulting in the identification of significant features tailored to Luminal A, L uminal B, HER-2 enriched, and Basal-like subtypes." Our news journalists obtained a quote from the research from the University of T unku Abdul Rahman, "The subsequent application of Random Forest (RF) and Support Vector Machine (SVM) classifiers yielded promising results, with the SVM model achieving an overall accuracy of 76.60 % and the RF model demonstr ating robust performance at 80.85 %. Detailed accuracy metrics reve aled strengths and areas for refinement, emphasizing the potential for optimizin g subtype-specific recall. To explore the regulatory landscape in depth, an anal ysis of selected miRNAs was conducted using MIENTURNET, a tool for visualizing m iRNA-target interactions. While FDR analysis raised concerns for HER-2 and Basal -like subtypes, Luminal A and Luminal B subtypes showcased significant miRNA-gen e interactions. Functional enrichment analysis for Luminal A highlighted the rol e of Ovarian steroidogenesis, implicating specific miRNAs such as hsa-let-7c-5p and hsa-miR-125b-5p as potential diagnostic biomarkers and regulators of Luminal A breast cancer. Luminal B analysis uncovered associations with the MAPK signal ing pathway, with miRNAs like hsa-miR-203a-3p and hsa-miR-19a-3p exhibiting pote ntial diagnostic and therapeutic significance."
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    Findings from Hebei University of Technology Has Provided New Data on Machine Le arning (Determination of Hardness and Young's Modulus In Fcc Cu-ni-sn-al Alloys Via High-throughput Experiments, Calphad Approach and Machine Learning)

    54-54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 Machine Learning. According to news reporting originating from Tianjin, People's Republ ic of China, by NewsRx correspondents, research stated, "Hardness and Young's mo dulus are critical indicators in the design of innovative Cu-Ni-Sn-Al alloys wit h desired elastic and strength properties. In this study, the composition-depend ent hardness and Young's modulus in the fcc Cu-Ni-Sn-Al alloys were determined u sing high-throughput experiments, the CALPHAD (CALculation of PHAse Diagrams) ap proach, and machine learning (ML) model." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Hebei Province, National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials .
      原文链接:
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    Nanjing University of Aeronautics and Astronautics Researcher Has Provided New D ata on Robotics (Research on Collaboration Motion Planning Method for a Dual-Arm Robot Based on Closed-Chain Kinematics)

    55-55页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on robotics are disc ussed in a new report. According to news reporting from Nanjing, People's Republ ic of China, by NewsRx journalists, research stated, "Aiming to address challeng es in the motion coordination of dual-arm robot engineering applications, a comp rehensive set of planning methods is devised." The news correspondents obtained a quote from the research from Nanjing Universi ty of Aeronautics and Astronautics: "This paper takes a dual-arm system composed of two six-degrees-of-freedom industrial robots as the research object. Initial ly, a transformation model is established for the characteristic trajectories be tween the workpiece coordinate system and various other coordinate systems. Subs equently, the position and orientation curves of the working trajectory are disc retized to facilitate the controller's execution. Furthermore, an analysis is co nducted of the closed-chain kinematics relationship between two arms of the robo t and a pose-calibration method based on a reference coordinate system is introd uced. Finally, constraints to the collaborative motion of the dual-arm robot are analyzed, leading to the establishment of a motion collaboration planning metho dology."
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    First Affiliated Hospital of Xiamen University Reports Findings in Sepsis (The n asal microbiota is a potential diagnostic biomarker for sepsis in critical care units)

    55-56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 Blood Diseases and Con ditions - Sepsis is the subject of a report.According to news reporting out of Xiamen, People's Republic of China, by NewsRx editors, research stated, "This st udy aimed to characterize the composition of intestinal and nasal microbiota in septic patients and identify potential microbial biomarkers for diagnosis. A tot al of 157 subjects, including 89 with sepsis, were enrolled from the affiliated hospital." Funders for this research include GDSTC | Basic and Applied Basic Research Found ation of Guangdong Province, MOST | National Natural Science Foundation of China , MOST | National Natural Science Foundation of China.
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    Hospital Israelita Albert Einstein Reports Findings in Rectal Cancer (Robot-assi sted transanal minimally invasive surgery for the treatment of rectal adenocarci noma)

    57-58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 - Rectal Canc er is the subject of a report. According to news reporting originating in Sao Pa ulo, Brazil, by NewsRx journalists, research stated, "This is a video vignette o f a 57-year-old asymptomatic female patient. The patient underwent a screening c olonoscopy which revealed a 10 mm scar in the rectum." The news reporters obtained a quote from the research from Hospital Israelita Al bert Einstein, "Biopsy resulted in a well-differentiated tubular adenocarcinoma. Computed tomography and pelvic magnetic resonance imaging confirmed tumor chara cteristics without distant or lymph nodal metastasis. A minimally invasive robot ic transanal resection using the Da Vinci Xi platform was performed, achieving f ull-thickness lesion excision with uneventful recovery. Histopathology revealed intramucosal adenocarcinoma with free margins. Local resection is advocated for selected T1 lesions and demands a thorough preoperative assessment." According to the news reporters, the research concluded: "Robotic-assisted surge ry presents a valuable alternative for early rectal adenocarcinoma management." This research has been peer-reviewed.
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    Princess Alexandra Hospital Reports Findings in Machine Learning (Evaluating aut omated machine learning platforms for use in healthcare)

    57-57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 originating in Brisbane, Aust ralia, by NewsRx journalists, research stated, "To describe development and appl ication of a checklist of criteria for selecting an automated machine learning ( Auto ML) platform for use in creating clinical ML models. Evaluation criteria fo r selecting an Auto ML platform suited to ML needs of a local health district we re developed in 3 steps: (1) identification of key requirements, (2) a market sc an, and (3) an assessment process with desired outcomes." The news reporters obtained a quote from the research from Princess Alexandra Ho spital, "The final checklist comprising 21 functional and 6 non-functional crite ria was applied to vendor submissions in selecting a platform for creating a ML heparin dosing model as a use case. A team of clinicians, data scientists, and k ey stakeholders developed a checklist which can be adapted to ML needs of health care organizations, the use case providing a relevant example."
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