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    Shulan (Hangzhou) Hospital Reports Findings in Rectal Cancer (Comparison of shor t-term outcomes of laparoscopic surgery, robot-assisted laparoscopic surgery, an d open surgery for lateral lymph-node dissection for rectal cancer: a network .. .)

    11-12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 from Hang zhou, People's Republic of China, by NewsRx correspondents, research stated, "Th is study attempted to compare short-term outcomes of laparoscopic surgery (LS), robot-assisted laparoscopic surgery (RS), and open surgery (OS) for lateral lymp h-node dissection (LLND) in treatment of rectal cancer through network meta-anal ysis. Embase, Web of Science, PubMed, and The Cochrane Library databases were se arched to collect cohort studies on outcomes of LS, RS, and OS for LLND for rect al cancer." Our news editors obtained a quote from the research from Shulan (Hangzhou) Hospi tal, "Newcastle- Ottawa Scale (NOS) was utilized to evaluate the quality of cohor t studies. Primary outcomes should at least include one of the following clinica l outcome measures: operative time, blood loss, total lymphnode harvest, positi ve resection margin rate, postoperative complications, and postoperative hospita l stay. A network meta-analysis was conducted using STATA software. Fourteen coh ort studies including 8612 patients were eligible for inclusion. The network met a-analysis results showed that, in terms of intraoperative outcomes, the RS grou p had the longest operative time, while the OS group had the shortest; the LS an d RS groups had significantly less blood loss than the OS group. In terms of his tological outcomes, there were no significant differences in the total number of lymph nodes harvested and the positive margin rate among the LS, RS, and OS gro ups (P > 0.05). Regarding postoperative outcomes, the OS group had the highest probability of postoperative complications and the longes t hospital stay, followed by the LS group, with the RS group being the lowest. R S was the best method in blood loss, postoperative complication rate, and postop erative hospital stay, followed by LS."
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    Research from University of Texas Arlington Reveals New Findings on Machine Lear ning (Estimation of Suspended Sediment Concentration along the Lower Brazos Rive r Using Satellite Imagery and Machine Learning)

    12-12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 Arlington, Texas, b y NewsRx journalists, research stated, "This article focuses on developing model s that estimate suspended sediment concentrations (SSCs) for the Lower Brazos Ri ver, Texas, U.S." The news correspondents obtained a quote from the research from University of Te xas Arlington: "Historical samples of SSCs from gauge stations and satellite ima gery from Landsat Missions and Sentinel Mission 2 were utilized to develop model s to estimate SSCs for the Lower Brazos River. The models used in this study to accomplish this goal include support vector machines (SVMs), artificial neural n etworks (ANNs), extreme learning machines (ELMs), and exponential relationships. In addition, flow measurements were used to develop rating curves to estimate S SCs for the Brazos River as a baseline comparison of the models that used satell ite imagery to estimate SSCs. The models were evaluated using a Taylor Diagram a nalysis on the test data set developed for the Brazos River data."
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    Studies Conducted at Saveetha University on Machine Learning Recently Reported ( An Integrated Energy Storage Framework With Significant Energy Management and Ab sorption Mechanism for Machine Learning Assisted Electric Vehicle Application)

    13-14页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in Machine Lea rning. According to news originating from Tamil Nadu, India, by NewsRx correspon dents, research stated, "Regarding environmental friendliness, low maintenance n eeds, and statuses as a renewable technology, Hybrid Electric Vehicles (HEV) hav e become more and more popular around the world. In this, the energy management system is crucial for the effective storage of power and regulation of the energ y flow system." Our news journalists obtained a quote from the research from Saveetha University , "As a result, Hybrid Energy Storage Systems (HESS) has increased interest due to their superior capabilities in system performance and battery capacity when c ompared to solo energy sources. Additionally, the primary problem interaction ap plications, including such battery electric vehicles, are the energy storage sys tem. Multiple energy storage technologies, including battery packs, flywheels, s uper-capacitors and fuel cells, are combined into a HESS due to their complement ing properties. The goal of this setup is to make renewable energy sources more reliable by storing power generated from intermittent sources or by providing ba ckup energy generation from traditional energy sources. A HESS could be utilized as an alternate energy storage system to help them make up for their lack of po wer density. HESS needs a smart Energy Management System (EMS) to function prope rly since it combines the dynamic characteristics of a battery and a SuperCapaci tor (SC). The motive of the study is to suggest an actual power management contr ol system to accomplish these objectives. The plan is built using a wavelet tran sform, deep learning mechanism, and fuzzy logic together. A useful tool for sepa rating the various frequency elements of a load's power requirements to reflect the properties of a battery or supercapacitor is the wavelet transform. It is ch allenging to immediately apply it in a system, though. Because of this, the trad itional optimizationmodel- based facility energy management system encounters su bstantial difficulties with online forecast and calculation. To solve this probl em, the paper proposes a ML technique dependent on a Long ShortTerm Memory (LSTM ). The suggested control system structure allows for the separation of the offli ne and online stages of the LSTM technique. The LSTM is being used to map states (inputs) to decisions (outputs) based on system training during the offline sta ge. As a result, the supercapacitor receives an online calculation and distribut ion of the high-frequency power requirement. The SOC of the supercapacitor is ke pt within the appropriate range via fuzzy logic control."
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    Data on Machine Learning Described by Researchers at University of Johannesburg (Analyzing the evolution of machine learning integration in educational research : a bibliometric perspective)

    14-14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 artificial intell igence are discussed in a new report. According to news reporting out of the Uni versity of Johannesburg by NewsRx editors, research stated, "Machine learning, a subset of artificial intelligence, has experienced rapid advancements and appli cations across various domains. In education, its integration holds great potent ial to revolutionize teaching, learning, and educational outcomes." The news reporters obtained a quote from the research from University of Johanne sburg: "Despite the growing interest, there needs to be more comprehensive bibli ometric analyses that track the trajectory of machine learning's integration int o educational research. This study addresses this gap by providing a nuanced per spective derived from bibliometric insights. Using a dataset from 1986 to 2022, consisting of 449 documents from 145 sources retrieved from the Web of Science ( WoS), the research employs network analysis to unveil collaborative clusters and identify influential authors. A temporal analysis of annual research output she ds light on evolving trends, while a thematic content analysis explores prevalen t research themes through keyword frequency. The findings reveal that co-authors hip network analysis exposes distinct clusters and influential figures shaping t he landscape of machine learning in educational research. Scientific production over time reveals a significant surge in research output, indicating the field's maturation. The co-occurrence analysis emphasizes a collective focus on student -centric outcomes and technology integration, with terms like ‘online' and ‘anal ytics' prevailing. This study provides a nuanced understanding of the collaborat ive and thematic fabric characterizing machine learning in educational research. The implications derived from the findings guide strategic collaborations, emph asizing the importance of cross-disciplinary engagement."
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    Affiliated Hospital of Xuzhou Medical University Reports Findings in Liver Cance r (Identification of key molecules in the formation of portal vein tumor thrombu s in hepatocellular carcinoma based on single cell transcriptomics and in vitro ...)

    15-16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 - Liver Cance r is the subject of a report. According to news reporting originating from Xuzho u, People's Republic of China, by NewsRx correspondents, research stated, "The p resence of portal vein tumor thrombus (PVTT) is a significant indicator of advan ced-stage hepatocellular carcinoma (HCC). Unfortunately, the prediction of PVTT occurrence remains challenging, and there is a lack of comprehensive research ex ploring the underlying mechanisms of PVTT formation and its association with imm une infiltration." Our news editors obtained a quote from the research from the Affiliated Hospital of Xuzhou Medical University, "Our approach involved analyzing single-cell sequ encing data, applying high dimensional weighted gene co-expression network analy sis (hdWGCNA), and identifying key genes associated with PVTT development. Furth ermore, we constructed competing endogenous RNA (ceRNA) networks and employed we ighted gene co-expression network analysis (WGCNA), as well as three machine-lea rning techniques, to identify the upstream regulatory microRNAs (miRNAs) and lon g non-coding RNAs (lncRNAs) of the crucial mRNAs. We employed fuzzy clustering o f time series gene expression data (Mfuzz), gene set variation analysis (GSVA), and cell communication analysis to uncover significant signaling pathways involv ed in the activation of these important mRNAs during PVTT development. In additi on, we conducted immune infiltration analysis, survival typing, and drug sensiti vity analysis using The Cancer Genome Atlas (TCGA) cohort to gain insights into the two patient groups under study. Through the implementation of hdWGCNA, we id entified 110 genes that was closely associated with PVTT. Among these genes, eme rged as a crucial candidate, and we further investigated its significance using COX regression analysis. Furthermore, through machine learning techniques and su rvival analysis, we successfully identified the upstream regulatory miRNA () and lncRNA () that targeted. These findings shed light on the complex regulatory ne twork surrounding in the context of PVTT. Moreover, we conducted CIBERSORT analy sis, which unveiled correlations between and immune infiltration in HCC patients . Specifically, exhibited associations with various immune cell populations, inc luding memory B cells and CD8 T cells. Additionally, we observed implications fo r immune function, particularly in relation to immune checkpoints, within the co ntext of HCC. The regulatory axis involving , and emerges as a crucial determina nt in the development of PVTT in HCC patients, and it holds significant implicat ions for prognosis."
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    Research on Machine Learning Described by Researchers at China University of Geo sciences (Crop Phenology Estimation in Rice Fields Using Sentinel-1 GRD SAR Data and Machine Learning- Aided Particle Filtering Approach)

    16-17页
    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 Wuhan, People' s Republic of China, by NewsRx editors, the research stated, "Monitoring crop ph enology is essential for managing field disasters, protecting the environment, a nd making decisions about agricultural productivity. Because of its high timelin ess, high resolution, great penetration, and sensitivity to specific structural elements, synthetic aperture radar (SAR) is a valuable technique for crop phenol ogy estimation." The news reporters obtained a quote from the research from China University of G eosciences: "Particle filtering (PF) belongs to the family of dynamical approach and has the ability to predict crop phenology with SAR data in real time. The o bservation equation is a key factor affecting the accuracy of particle filtering estimation and depends on fitting. Compared to the common polynomial fitting (P OLY), machine learning methods can automatically learn features and handle compl ex data structures, offering greater flexibility and generalization capabilities . Therefore, incorporating two ensemble learning algorithms consisting of suppor t vector machine regression (SVR), random forest regression (RFR), respectively, we proposed two machine learning-aided particle filtering approaches (PF-SVR, P F-RFR) to estimate crop phenology. One year of time-series Sentinel-1 GRD SAR da ta in 2017 covering rice fields in Sevilla region in Spain was used for establis hing the observation and prediction equations, and the other year of data in 201 8 was used for validating the prediction accuracy of PF methods. Four polarizati on features (VV, VH, VH/VV and Radar Vegetation Index (RVI)) were exploited as t he observations in modeling. Experimental results reveals that the machine learn ing-aided methods are superior than the PF-POLY method. The PF-SVR exhibited bet ter performance than the PF-RFR and PF-POLY methods. The optimal outcome from PF -SVR yielded a root-mean-square error (RMSE) of 7.79, compared to 7.94 for PF-RF R and 9.1 for PF-POLY."
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    University of Agriculture and Forestry Researcher Details Research in Machine Le arning (Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learni ng, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoo n)

    17-18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 originating from Hue City, Vietnam, by NewsRx correspondents, research stated, "Bathymetry data is indispen sable for a variety of aquatic field studies and benthic resource inventories." Funders for this research include Hue University. Our news editors obtained a quote from the research from University of Agricultu re and Forestry: "Determining water depth can be accomplished through an echo so unding system or remote estimation utilizing space-borne and air-borne data acro ss diverse environments, such as lakes, rivers, seas, or lagoons. Despite being a common option for bathymetry mapping, the use of satellite imagery faces chall enges due to the complex inherent optical properties of water bodies (e.g., turb id water), satellite spatial resolution limitations, and constraints in the perf ormance of retrieval models. This study focuses on advancing the remote sensing based method by harnessing the non-linear learning capabilities of the machine l earning (ML) model, employing advanced feature selection through a meta-heuristi c algorithm, and using image extraction techniques (i.e., band ratio, gray scale morphological operation, and morphological multi-scale decomposition). Herein, we validate the predictive capabilities of six ML models: Random Forest (RF), Su pport Vector Machine (SVM), CatBoost (CB), Extreme Gradient Boost (XGB), Light G radient Boosting Machine (LGBM), and KTBoost (KTB) models, both with and without the application of meta-heuristic optimization (i.e., Dragon Fly, Particle Swar m Optimization, and Grey Wolf Optimization), to accurately ascertain water depth ."
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    University Hospital Rey Juan Carlos Reports Findings in Pelvic Organ Prolapse (R obotic sacrocolpopexy for the treatment of pelvic organ prolapse in elderly wome n: comparative analysis of safety and efficacy versus younger women)

    18-19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 Pelvic Organ Prolapse is the subject of a report. According to news reporting originating in Madrid, S pain, by NewsRx journalists, research stated, "Pelvic organ prolapse is a condit ion with high prevalence in elderly women. With increasing life expectancy and a desire for improved quality of life, a rise in the frequency of surgical treatm ents for these women is anticipated." The news reporters obtained a quote from the research from University Hospital R ey Juan Carlos, "The aim is to compare complication, success, and satisfaction r ates among elderly patients (aged >70 years) in comparis on to younger women undergoing robotic sacrocolpopexy, thereby assessing the saf ety and efficacy of this surgery in this group of patients. A prospective observ ational comparative study of 123 robotic sacrocolpopexies conducted between Dece mber 2016 and June 2022. Patients were stratified by age (cutoff point: 70 years ). Baseline characteristics, type, and grade of prolapse, intra and postoperativ e data, complications, functional and anatomical outcomes, and satisfaction leve ls were collected. Among the 123 patients, 62.6% were under 70 yea rs old, while 37.4% were 70 years or older, exhibiting similar bas eline characteristics, prolapse grade, and type. The percentages of intraoperati ve (6.5%) and postoperative complications (4.4-9%) wer e comparable in both age groups. Furthermore, success and satisfaction rates exc eeded 90%, with no significant differences between women under and over 70 years during a two-year follow-up."
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    Capital Medical University Reports Findings in Pituitary Adenoma (Concomitant Pr ediction of the Ki67 and PIT-1 Expression in Pituitary Adenoma Using Different R adiomics Models)

    19-20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 - Pituitary A denoma is the subject of a report. According to news originating from Beijing, P eople's Republic of China, by NewsRx correspondents, research stated, "To preope ratively predict the high expression of Ki67 and positive pituitary transcriptio n factor 1 (PIT-1) simultaneously in pituitary adenoma (PA) using three differen t radiomics models. A total of 247 patients with PA (training set: n = 198; test set: n = 49) were included in this retrospective study." Our news journalists obtained a quote from the research from Capital Medical Uni versity, "The imaging features were extracted from preoperative contrast-enhance d T1WI (T1CE), T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI). Featu re selection was performed using Spearman's rank correlation coefficient and lea st absolute shrinkage and selection operator (LASSO). The classic machine learni ng (CML), deep learning (DL), and deep learning radiomics (DLR) models were cons tructed using logistic regression (LR), support vector machine (SVM), and multi- layer perceptron (MLP) algorithms. The area under the receiver operating charact eristic (ROC) curve (AUC), sensitivity, specificity, accuracy, negative predicti ve value (NPV) and positive predictive value (PPV) were calculated for the train ing and test sets. In addition, combined with clinical characteristics, the best CML and the best DL models (SVM classifier), the DL radiomics nomogram (DLRN) w as constructed to aid clinical decision-making. Seven CML features, 96 DL featur es, and 107 DLR features were selected to construct CML, DL and DLR models. Comp ared to CML and DL model, the DLR model had the best performance. The AUC, sensi tivity, specificity, accuracy, NPV and PPV were 0.827, 0.792, 0.800, 0.796, 0.80 0 and 0.792 in the test set, respectively."
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    Investigators from Department of Computer Sciences and Engineering Report New Da ta on Artificial Intelligence (Semantic Segmentation Based On Enhanced Gated Pyr amid Network With Lightweight Attention Module)

    20-20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 originating from Hyderabad, India, by NewsRx correspondents, research stated, "Semantic segmentation has ma de tremendous progress in recent years. The development of large datasets and th e regression of convolutional models have enabled effective training of very lar ge semantic model." Our news editors obtained a quote from the research from the Department of Compu ter Sciences and Engineering, "Nevertheless, higher capacity indicates a higher computational problem, thus preventing realtime operation. Yet, due to the limi ted annotations, the models may have relied heavily on the available contexts in the training data, resulting in poor generalization to previously unseen scenes . Therefore, to resolve these issues, Enhanced Gated Pyramid network (GPNet) wit h Lightweight Attention Module (LAM) is proposed in this paper. GPNet is used fo r semantic feature extraction and GPNet is enhanced by the pre-trained dilated D etNet and Dense Connection Block (DCB). LAM approach is applied to habitually re scale the different feature channels weights. LAM module can increase the accura cy and effectiveness of the proposed methodology. The performance of proposed me thod is validated using Google Colab environment with different datasets such as Cityscapes, CamVid and ADE20K. The experimental results are compared with vario us methods like GPNet-ResNet-101 and GPNet-ResNet-50 in terms of IoU, precision, accuracy, F1 score and recall."
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