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    Tampere University Hospital Reports Findings in Endometrial Cancer (Robotic-assi sted versus conventional laparoscopic surgery for endometrial cancer: Long-term results of a randomized controlled trial)

    66-67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 - Endometrial Cancer is the subject of a report. According to news reporting out of Tampere, Finland, by NewsRx editors, research stated, “Robotic-assisted laparoscopic surg ery (RALS) has become a widely and increasingly used modality of minimally invas ive surgery in the treatment of endometrial cancer (EC). Due to its technical ad vantages, RALS offers benefits, such as a lower rate of conversions compared to conventional laparoscopy (CLS).” Our news journalists obtained a quote from the research from Tampere University Hospital, “Yet, data on long-term oncological outcomes after RALS is scarce and based on retrospective cohort studies only. This study aimed to assess overall s urvival (OS), progression-free survival (PFS), and long-term surgical complicati ons in EC patients randomly assigned to RALS or CLS. This randomized controlled trial was conducted at the Department of Gynecology and Obstetrics of Tampere Un iversity Hospital, Finland. Between 2010 and 2013, 101 patients with low-grade E C scheduled for minimally invasive surgery were randomized preoperatively 1:1 ei ther to RALS or CLS. All patients underwent laparoscopic hysterectomy, bilateral salpingo-oophorectomy, and pelvic lymphadenectomy. A total of 97 patients (49 i n the RALS group and 48 in the CLS group) were followed up for a minimum of 10 y ears. Survival was analyzed using Kaplan-Meier curves, log-rank test, and Cox pr oportional hazard models. Binary logistic regression analysis was used to analyz e risk factors for trocar site hernia. In the multivariable regression analysis, OS was favorable in the RALS group (HR 0.39; 95% CI, 0.15-0.99, p =.047) compared to the CLS group. There was no difference in PFS (log-rank test, p=.598). The three-, 5- and 10-year OS were 98.0% (95% CI, 94.0-100) vs. 97.9% (93.8-100), 91.8% (84.2-99.4 ) vs. 93.7% (86.8-100), and 75.5% (64.5-87.5) vs. 85 .4% (75.4-95.4) in the CLS and the RALS group, respectively. Troca r site hernia developed more often in the RALS group compared to the CLS group 1 8.2% vs. 4.1% (OR 5.42, 95% CI, 1.11-2 6.59, p=.028). The incidence of lymphocele, lymphedema, or other long-term compl ications did not differ between the groups. The results of this RCT suggest a mi nor OS benefit in EC after RALS compared to CLS. Hence, the use of RALS in the t reatment of EC seems safe, but larger RCTs are needed to confirm the potential s urvival benefit of RALS.”
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    University of Waterloo Reports Findings in Machine Learning (Computational and M achine Learning Methods for CO2 Capture Using Metal-Organic Frameworks)

    67-67页
    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 reporting originating in Waterloo, Canada, by News Rx journalists, research stated, “Machine learning (ML) using data sets of atomi c and molecular force fields (FFs) has made significant progress and provided be nefits in the fields of chemistry and material science. This work examines the i nteractions between chemistry and materials computational science at the atomic and molecular scales for metal-organic framework (MOF) adsorbent development tow ard carbon dioxide (CO) capture.” The news reporters obtained a quote from the research from the University of Wat erloo, “Herein, a connection will be drawn between atomic forces predicted by ML algorithms and the structures of MOFs for CO adsorption. Our study also takes i nto account the successes of atomic computational screening in the field of mate rials science, especially quantum ML, and its relationship to ML algorithms that clarify advancements in the area of CO adsorption by MOFs. Additionally, we rev iewed the processes for supplying data to ML algorithms for algorithm training, including text mining from scientific articles, and MOF’s formula processing lin ked to the chemical properties of MOFs. To create ML algorithms for future resea rch, we recommend that the digitization of scientific records can help efficient ly synthesize advanced MOFs.”
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    Research from University Teknologi MARA in the Area of Robotics Published (A Rob otic Concepts: Study of Perceived Brand Reputation and Customers’ Perceived Perf ormance in a Restaurant)

    68-68页
    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 originating from Selangor, Malaysia, by NewsRx co rrespondents, research stated, “Budget, hygiene factors, and the issue of workfo rce shortages have accelerated the use of robotics technology in restaurant chai ns.” The news journalists obtained a quote from the research from University Teknolog i MARA: “Even though service robots could operate many functions and roles, furt her research is required in hotel settings to ascertain how various combinations of robots and humans at different product/service tiers may affect customers’ v iewpoints and intentions. Design/methodology/approach: This research study addre ssed the issue of data collected from 364 customers in Malaysia. This study exam ines both theoretical and managerial insights.”
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    Loughborough University London Researcher Releases New Study Findings on Machine Learning (Novel Directions for Neuromorphic Machine Intelligence Guided by Func tional Connectivity: A Review)

    69-69页
    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 originating from London, United Kingdom , by NewsRx editors, the research stated, “As we move into the next stages of th e technological revolution, artificial intelligence (AI) that is explainable and sustainable is becoming a key goal for researchers across multiple domains.” Financial supporters for this research include Atract: A Trustworthy Robotic Aut onomous System To Support Casualty Triage. The news editors obtained a quote from the research from Loughborough University London: “Leveraging the concept of functional connectivity (FC) in the human br ain, this paper provides novel research directions for neuromorphic machine inte lligence (NMI) systems that are energy-efficient and humancompatible. This revi ew serves as an accessible review for multidisciplinary researchers introducing a range of concepts inspired by neuroscience and analogous machine learning rese arch. These include possibilities to facilitate network integration and segregat ion in artificial architectures, a novel learning representation framework inspi red by two FC networks utilised in human learning, and we explore the functional connectivity underlying task prioritisation in humans and propose a framework f or neuromorphic machines to improve their task-prioritisation and decision-makin g capabilities.”
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    New Machine Learning Research from Taif University Discussed (FutureCite: Predic ting Research Articles’ Impact Using Machine Learning and Text and Graph Mining Techniques)

    70-70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 artificial in telligence have been published. According to news reporting from Taif University by NewsRx journalists, research stated, “The growth in academic and scientific publications has increased very rapidly. Researchers must choose a representativ e and significant literature for their research, which has become challenging wo rldwide.” The news reporters obtained a quote from the research from Taif University: “Usu ally, the paper citation number indicates this paper’s potential influence and i mportance. However, this standard metric of citation numbers is not suitable to assess the popularity and significance of recently published papers. To address this challenge, this study presents an effective prediction method called Future Cite to predict the future citation level of research articles. FutureCite integ rates machine learning with text and graph mining techniques, leveraging their a bilities in classification, datasets in-depth analysis, and feature extraction. FutureCite aims to predict future citation levels of research articles applying a multilabel classification approach. FutureCite can extract significant semanti c features and capture the interconnection relationships found in scientific art icles during feature extraction using textual content, citation networks, and me tadata as feature resources. This study’s objective is to contribute to the adva ncement of effective approaches impacting the citation counts in scientific publ ications by enhancing the precision of future citations. We conducted several ex periments using a comprehensive publication dataset to evaluate our method and d etermine the impact of using a variety of machine learning algorithms.”
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    Third Affiliated Hospital of Kunming Medical University Reports Findings in Brea st Cancer (Assessing Axillary Lymph Node Burden and Prognosis in cT1-T2 Stage Br east Cancer Using Machine Learning Methods: A Retrospective Dual-Institutional M RI ...)

    71-72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 - Breast Canc er is the subject of a report. According to news reporting from Kunming, People’ s Republic of China, by NewsRx journalists, research stated, “Pathological axill ary lymph node (pALN) burden is an important factor for treatment decision-makin g in clinical T1-T2 (cT1-T2) stage breast cancer. Preoperative assessment of the pALN burden and prognosis aids in the individualized selection of therapeutic a pproaches.” Financial support for this research came from National Natural Science Foundatio n of China.
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    Findings from Chengdu University of Traditional Chinese Medicine Provide New Ins ights into Alzheimer Disease (Bulk-rna and Singlenuclei Rna Seq Analyses Reveal the Role of Lactate Metabolismrelated Genes In Alzheimer’s Disease)

    72-73页
    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 Neurodegenerative Disea ses and Conditions - Alzheimer Disease is now available. According to news repor ting from Sichuan, People’s Republic of China, by NewsRx journalists, research s tated, “Dysfunctional lactate metabolism in the brain has been implicated in neu roinflammation, A beta deposition, and cell disturbance, all of which play a sig nificant role in the pathogenesis of Alzheimer’s disease (AD). In this study, we aimed to investigate the lactate metabolism-related genes (LMRGs) in AD via an integrated bulk RNA and single-nuclei RNA sequencing (snRNA-seq) analysis, with a specific focus on microglia.” Financial support for this research came from National Undergraduate Innovation and Entrepreneurship Training Project.
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    Reports from General Atomics Add New Data to Findings in Machine Learning (Augme nting Machine Learning of Grad-shafranov Equilibrium Reconstruction With Green’s Functions)

    73-74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 reporting originating from San Diego, California, by NewsRx correspondents, research stated, “This work presents a me thod for predicting plasma equilibria in tokamak fusion experiments and reactors . The approach involves representing the plasma current as a linear combination of basis functions using principal component analysis of plasma toroidal current densities (J(t)) from the EFIT-AI equilibrium database.” Funders for this research include U.S. Department of Energy10.13039/100000015, U nited States Department of Energy (DOE). Our news editors obtained a quote from the research from General Atomics, “Then utilizing EFIT’s Green’s function tables, basis functions are created for the po loidal flux ( psi) and diagnostics generated from the toroidal current (J(t)). S imilar to the idea of a physics-informed neural network (NN), this physically en forces consistency between psi, J(t), and the synthetic diagnostics. First, the predictive capability of a least squares technique to minimize the error on the synthetic diagnostics is employed. The results show that the method achieves hig h accuracy in predicting psi and moderate accuracy in predicting J(t) with media n R-2 = 0.9993 and R-2 = 0.978, respectively. A comprehensive NN using a network architecture search is also employed to predict the coefficients of the basis f unctions. The NN demonstrates significantly better performance compared to the l east squares method with median R-2 = 0.9997 and 0.9916 for J(t) and psi, respec tively. The robustness of the method is evaluated by handling missing or incorre ct data through the least squares filling of missing data, which shows that the NN prediction remains strong even with a reduced number of diagnostics.”
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    Faculty of Applied Sciences Researchers Discuss Research in Machine Learning (Ad vanced Machine Learning Based Malware Detection Systems)

    74-75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 artificial in telligence have been published. According to news originating from Macau, People ’s Republic of China, by NewsRx editors, the research stated, “In the area of ma chine learning (ML) training data optimization through the construction of compa ct data, the focus of this paper is presented.” Funders for this research include Macao Polytechnic University. Our news journalists obtained a quote from the research from Faculty of Applied Sciences: “The concept of compact data design, aimed at creating an optimized da taset that maximizes benefits without the need to manage a vast amount of comple x data, is introduced. Improvements in the methods for optimizing ML training ha ve been incorporated into the development of artificial intelligence (AI) system s. The introduction of understanding ML training datasets as a facet of Explaina ble AI (XAI), comprehensible to humans, has been made. Among the methods of XAI, the evaluation of input feature importance stands out as a way to enhance the a ccuracy of complex ML models. The innovative method of compact data design for o ptimizing ML training through dataset reduction is proposed. The performance of an ML-based malware detection system, along with its variant utilizing compact d ata, has been assessed, demonstrating the maintenance of 99% accur acy.”
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    Reports Outline Machine Learning Study Findings from Islamic Azad University [Comparative study of long short-term memory (LSTM), bidirectional LSTM, and trad itional machine learning approaches for energy consumption prediction]

    75-76页
    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 Qazvin, Iran, by Ne wsRx journalists, research stated, “Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring t he reliability of the modern electricity grid.” Our news editors obtained a quote from the research from Islamic Azad University : “This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series d ata often exhibit complex, non-linear patterns. Our approach advocates for utili zing long short-term memory (LSTM) and bidirectional long short-term memory (Bi- LSTM) models for precise time series forecasting. To ensure a fair evaluation, w e compare the performance of our proposed approach with traditional neural netwo rks, time-series forecasting methods, and conventional decline curves. Additiona lly, individual models based on LSTM, Bi-LSTM, and other machine learning method s are implemented for a comprehensive assessment. Experimental results consisten tly demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets.”
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