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    Research Study Findings from University of Technology Update Understanding of Ro botics (Kinematic and Dynamic Modeling Based on Trajectory Tracking Control of M obile Robot with Mecanum Wheels)

    67-68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 robotics. According to news reporting out of Baghdad, Iraq, by NewsRx editors, r esearch stated, "The trajectory tracking is important to make the WMR move auton omously from the starting point to the destination along a predefined time. Impl ementing of trajectory tracking control is a fundamental part to accomplish its application tasks." The news journalists obtained a quote from the research from University of Techn ology: "In this article a new method by using a hybrid controller has been prese nted to solve the problem of the trajectory tracking of four mecanum wheeled mob ile robot. Proposed controller is depending on modeling of robot kinematic and d ynamic equations. The novelty in this work is that, an optimal control system se lf-tuning parameters based on an optimization algorithm for these models of the mobile robot is utilized. The optimal control type that is used in this work is the Linear Quadratic Regulator (LQR) controller. LQR is used to control the actu ator torque that is required in each wheel to achieve the robot task. The parame ters of the LQR controller are tuned by using Ant Colony Optimization (ACO). For results simulation, MATLAB/ Simulink is used for circular and infinity shape tr ajectories. Results show that when the robot follows a circular trajectory, the values of position trajectory error values are reduced to smAll value (ex=3.218 *10-5m) and (ey= 2.224*10-5m) in xo and yo directions, respectively and remained almost at these values until the end of the simulation time."
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    School of Business Reports Findings in Machine Learning (Machine learning miscla ssification networks reveal a citation advantage of interdisciplinary publicatio ns only in high-impact journals)

    68-69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 from Bremen, Germ any, by NewsRx correspondents, research stated, "Given a large enough volume of data and precise, meaningful categories, training a statistical model to solve a classification problem is straightforward and has become a standard application of machine learning (ML). If the categories are not precise, but rather fuzzy, as in the case of scientific disciplines, the systematic failures of ML classifi cation can be informative about properties of the underlying categories." Financial support for this research came from Constructor University Bremen gGmb H. Our news editors obtained a quote from the research from the School of Business, "Here we classify a large volume of academic publications using only the abstra ct as information. From the publications that are classified differently by jour nal categories and ML categories (i.e., misclassified publications, when using the journal assignment as ground truth) we construct a network among disciplines. Analysis of these misclassifications provides insight in two topics at the core of the science of science: (1) Mapping out the interplay of disciplines. We sho w that this misclassification network is informative about the interplay of acad emic disciplines and it is similar to, but distinct from, a citation-based map o f science, where nodes are scientific disciplines and an edge indicates a strong co-citation count between publications in these disciplines. (2) Analyzing the success of interdisciplinarity."
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    Hospital Israelita Albert Einstein Reports Findings in Colon Cancer (Oncologic o utcomes for robotic versus laparoscopic colectomy for colon cancer: an ACS-NSQIP analysis)

    69-70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 - Colon Cance r is the subject of a report. According to news reporting originating in Sao Pau lo, Brazil, by NewsRx journalists, research stated, "Robotic colectomy has been associated with comparable or improved short-term morbidity and mortality when c ompared to laparoscopic colectomy, including shorter length of stay. In this stu dy, we sought to understand oncologic advantages for robotic as compared to lapa roscopic colectomy in colon cancer." The news reporters obtained a quote from the research from Hospital Israelita Al bert Einstein, "We analyzed the American College of Surgeons National Surgical Q uality Improvement Program (NSQIP) participant user files for All elective colon cancer cases from 1/2016 through 12/2021 performed with minimAlly invasive surg ical techniques (robotic and laparoscopic). We calculated relative risks (RR) th rough Poisson Regression models and treatment effect coefficients by propensity- score match, after adjusting for age, BMI, ASA scores, mechanical and antibiotic bowel preparation, emergency surgery, race, gender, smoking status, hypertensio n and diabetes mellitus. Analyzed outcomes included rate of chemotherapy initiat ion within 90 days of surgery, number of harvested lymph nodes, any occurrence o f intraoperative or postoperative blood transfusion, and the need for ostomy. Du ring the study period, 44,745 patients underwent minimAlly invasive colectomy fo r colon cancer; 39,614 in the laparoscopic cohort and 7,831 in the robotic cohor t. After adjusting for confounders, robotic colectomy was associated with a sign ificant increase in the likelihood for initating chemotherapy within 90 days (RR 1.98, 95% CI {1.86-2.10} , p<0.001). The robotic-treated patients had a significantly more lymph nodes harve sted, a significant decrease in the need for intraperative or postoperative bloo d transfusion (RR 0.64, 95% CI {0.57-0.71} , p<0.001) and a significant reduction in the need for ost omy formation (RR 0.26, 95% CI {0.22-0.30} , p<0.001). As a retrospective and non-randomized study, r esidual bias and confouding variables are likely to exist. The study is also sub ject to coding incompleteness and inaccuracies. We also do not have additional c ontext on potential factors that might influence time to chemotherapy. In additi on, there is no information on surgeon or hospital volume, which can be associat ed with outcomes. Robotic colectomy for colon cancer was associated with signifi cant improvement in the rate of chemotherapy initiation within 90 days, a signif icant reduction in need for blood transfusions, and a lower likelihood of receiv ing an ostomy when compared to laparoscopic colectomy procedures."
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    Research on Androids Described by Researchers at National Center for Scientific Research (CNRS) (Unraveling the thread: understanding and addressing sequential failures in human-robot interaction)

    70-71页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on androids have been pr esented. According to news originating from Lyon, France, by NewsRx corresponden ts, research stated, "Interaction is a dynamic process that evolves in real time ." Financial supporters for this research include Labex Aslan. The news correspondents obtained a quote from the research from National Center for Scientific Research (CNRS): "Participants interpret and orient themselves to wards turns of speech based on expectations of relevance and social/conversation al norms (that have been extensively studied in the field of Conversation analys is). A true chAllenge to Human Robot Interaction (HRI) is to develop a system ca pable of understanding and adapting to the changing context, where the meaning o f a turn is construed based on the turns that have come before. In this work, we identify issues arising from the inadequate handling of the sequential flow wit hin a corpus of in-the-wild HRIs in an open-world university library setting. Th e insights gained from this analysis can be used to guide the design of better s ystems capable of handling complex situations."
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    Korea Photonics Technology Institute (KOPTI) Researcher Updates Knowledge of Mac hine Learning (Discrimination of Explosive Residues by Standoff Sensing Using An odic Aluminum Oxide Microcantilever Laser Absorption Spectroscopy with Kernel-Ba sed ...)

    71-72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 Gwangju, Sout h Korea, by NewsRx correspondents, research stated, "Standoff laser absorption s pectroscopy (LAS) has attracted considerable interest across many applications f or environmental safety." Funders for this research include Korea Planning & Evaluation Inst itute of Industrial Technology Funded By The Ministry of The Interior And Safety ; National Research Foundation of Korea (Nrf) Grant Funded By The Korea Governme nt; Chonnam National University (Smart Plant Reliability Center) Grant Funded By The Ministry of Education. Our news correspondents obtained a quote from the research from Korea Photonics Technology Institute (KOPTI): "Herein, we propose an anodic aluminum oxide (AAO) microcantilever LAS combined with machine learning (ML) for sensitive and selec tive standoff discrimination of explosive residues. A nanoporous AAO microcantil ever with a thickness of <1 mm was fabricated using a micro machining process; its spring constant (18.95 mN/m) was approximately one-third of that of a typical Si microcantilever (53.41 mN/m) with the same dimensions. the standoff infrared (IR) spectra of pentaerythritol tetranitrate, cyclotrimethy lene trinitramine, and trinitrotoluene were measured using our AAO microcantilev er LAS over a wide range of wavelengths, and they closely matched the spectra ob tained using standard Fourier transform infrared spectroscopy. The standoff IR s pectra were fed into ML models, such as kernel extreme learning machines (KELMs) , support vector machines (SVMs), random forest (RF), and backpropagation neural networks (BPNNs)."
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    Studies from Department of Mathematics Yield New Information about Robotics (Fra ctional Semi-infinite Programming Problems: Optimality Conditions and Duality Vi a Tangential Subdifferentials)

    72-73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 Robotic s. According to news reporting originating in Gujarat, India, by NewsRx journali sts, research stated, "In this paper, we have focused on a multiobjective fract ional semi-infinite programming problems in which the constraints and objective functions are tangentiAlly convex. A result has been established to find the tan gential subdifferential of a fractional function, assuming the numerator and the negative of the denominator being tangentiAlly convex functions." Financial support for this research came from Sardar VAllabhbhai National Instit ute of Technology, Surat, India. The news reporters obtained a quote from the research from the Department of Mat hematics, "With this, optimality conditions have been derived using a non-parame tric approach under F-convexity assumption. Further, a Mond-Weir type dual has b een considered and weak and strong duality relations have been developed. Moreov er, an application in robot trajectory planning has been considered and solved u sing MATLAB. In addition, considering the same trajectory as in Vaz et al. (Eur J Oper Res 153(3):607-617, 2004), we have compared the results obtained in MATLA B with the results available in Vaz et al. (Eur J Oper Res 153(3):607-617, 2004) and Haaren-Retagne (A semi-infinite programming algorithm for robot trajectory planning, 1992), where the authors have solved using AMPL. It has been observed that our results are more efficient than the previously available results, with the implementation of MATLAB as it substantiAlly reduces the computational time. "
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    New Machine Learning Data Has Been Reported by a Researcher at Cranfield Univers ity (Aircraft Skin Machine Learning-Based Defect Detection and Size Estimation i n Visual Inspections)

    73-73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 artificial intelligenc e is the subject of a new report. According to news reporting originating from B edford, United Kingdom, by NewsRx correspondents, research stated, "Aircraft mai ntenance is a complex process that requires a highly trained, qualified, and exp erienced team." Funders for this research include British Engineering And Physics Sciences Resea rch Council; Boeing Company. Our news correspondents obtained a quote from the research from Cranfield Univer sity: "The most frequent task in this process is the visual inspection of the ai rframe structure and engine for surface and sub-surface cracks, impact damage, c orrosion, and other irregularities. Automated defect detection is a valuable too l for maintenance engineers to ensure safety and condition monitoring. The propo sed approach is to process the captured feedback using various deep learning arc hitectures to achieve the highest performance defect detections. AdditionAlly, a n algorithm is proposed to estimate the size of the detected defect. The team co llaborated with TUI's Airline Maintenance Team at Luton Airport, Allowing us to fly a drone inside the hangar and use handheld cameras to collect representative data from their aircraft fleet. After a comprehensive dataset was constructed, multiple deep-learning architectures were developed and evaluated."
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    University of Nebraska Medical Center Reports Findings in Artificial Intelligenc e (Multi-omics based artificial intelligence for cancer research)

    74-75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 Artificial Intelligenc e is the subject of a report. According to news originating from Omaha, Nebraska , by NewsRx correspondents, research stated, "With significant advancements of n ext generation sequencing technologies, large amounts of multi-omics data, inclu ding genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have been accumulated, offering an unprecedented opportunity to explore the heterogen eity and complexity of cancer across various molecular levels and scales. One of the promising aspects of multi-omics lies in its capacity to offer a holistic v iew of the biological networks and pathways underpinning cancer, facilitating a deeper understanding of its development, progression, and response to treatment. " Our news journalists obtained a quote from the research from the University of N ebraska Medical Center, "However, the exponential growth of data generated by mu lti-omics studies present significant analytical chAllenges. Processing, analyzi ng, integrating, and interpreting these multi-omics datasets to extract meaningf ul insights is an ambitious task that stands at the forefront of current cancer research. The application of artificial intelligence (AI) has emerged as a power ful solution to these chAllenges, demonstrating exceptional capabilities in deci phering complex patterns and extracting valuable information from large-scale, i ntricate omics datasets. This review delves into the synergy of AI and multi-omi cs, highlighting its revolutionary impact on oncology. We dissect how this confl uence is reshaping the landscape of cancer research and clinical practice, parti cularly in the realms of early detection, diagnosis, prognosis, treatment and pa thology. AdditionAlly, we elaborate the latest AI methods for multi-omics integr ation to provide a comprehensive insight of the complex biological mechanisms and inherent heterogeneity of cancer. FinAlly, we discuss the current chAllenges o f data harmonization, algorithm interpretability, and ethical considerations."
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    Study Findings on Artificial Intelligence Described by Researchers at Massachuse tts Institute of Technology (Ethical debates amidst flawed healthcare artificial intelligence metrics)

    74-74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 artificial intelligenc e is the subject of a new report. According to news originating from the Massach usetts Institute of Technology by NewsRx editors, the research stated, "Healthca re AI faces an ethical dilemma between selective and equitable deployment, exace rbated by flawed performance metrics." The news reporters obtained a quote from the research from Massachusetts Institu te of Technology: "These metrics inadequately capture real-world complexities and biases, leading to premature assertions of effectiveness. Improved evaluation practices, including continuous monitoring and silent evaluation periods, are cr ucial." According to the news editors, the research concluded: "To address these fundame ntal shortcomings, a paradigm shift in AI assessment is needed, prioritizing act ual patient outcomes over conventional benchmarking."
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    New Research on Intelligent Systems from Guangdong University of Finance and Eco nomics Summarized (An adaptive trimming approach to Bayesian additive regression trees)

    75-76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 intelligent syste ms are discussed in a new report. According to news originating from Guangdong U niversity of Finance and Economics by NewsRx correspondents, research stated, "A machine learning technique merging Bayesian method cAlled Bayesian Additive Reg ression Trees (BART) provides a nonparametric Bayesian approach that further nee ds improved forecasting accuracy in the presence of outliers, especiAlly when de aling with potential nonlinear relationships and complex interactions among the response and explanatory variables, which poses a major chAllenge in forecasting ." Our news reporters obtained a quote from the research from Guangdong University of Finance and Economics: "This study proposes an adaptive trimmed regression me thod using BART, dubbed BART(Atr) to improve forecasting accuracy by identifying suspected outliers effectively and removing these outliers in the analysis. Thr ough extensive simulations across various scenarios, the effectiveness of BART(A tr) is evaluated against three alternative methods: default BART, robust linear modeling with Huber's loss function, and data-driven robust regression with Hube r's loss function. The simulation results consistently show BART(Atr) outperform ing the other three methods. To demonstrate its practical application, BART(Atr) is applied to the well-known Boston Housing Price dataset, a standard regressio n analysis example."
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