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    Findings from Huazhong University of Science and Technology Provide New Insights into Robotics (Dynamic Balancing of U-shaped Robotic Disassembly Lines Using an Effective Deep Reinforcement Learning Approach)

    76-76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 Robotics is now availab le. According to news reporting from Wuhan,People's Republic of China,by NewsR x journalists,research stated,"Disassembly line balancing (DLB) is used for ef ficient task planning of large-scale end-of-life products,which is a key issue to realize resource recycling and reuse. Robot disassembly and U-shaped station layout can effectively improve disassembly efficiency." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from the Huazhong Uni versity of Science and Technology,"To accurately characterize the problem,a mi xed-integer linear programming model of U-shaped robotic DLB is proposed. The ai m is to minimize the cycle time to shorten the offline time of the product. Sinc e there are many dynamic disturbances in the actual disassembly line,and tradit ional optimization methods are suitable for dealing with static problems,this a rticle develops a deep reinforcement learning approach based on problem characte ristics,namely deep Q network (DQN),to achieve a dynamic balancing of disassem bly lines. Eight state features and ten heuristic action rules are designed in t he proposed DQN to describe the disassembly environment completely. The effectiv eness and superiority of the proposed DQN are verified by numerical experiments. "
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    Pompeu Fabra University Reports Findings in Robotics [Transdu odenal robotic ampullectomy: tips and tricks and strategies for postoperative du odenal fistula management (with video)]

    77-78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 Robotics is the subjec t of a report. According to news reporting out of Barcelona,Spain,by NewsRx ed itors,research stated,"Transduodenal Ampullectomy (TA) is a procedure for rese cting low-malignancy ampullary tumors,with postoperative fistula as a notable c omplication. This study aims to clarify the indications for TA,outline the surg ical robotic technique,and emphasize the importance of comprehensive complicati on management alongside the surgical approach." Our news journalists obtained a quote from the research from Pompeu Fabra Univer sity,"This multimedia article provides a detailed exposition of the robotic TA surgical technique,including the most important steps involved in exposing and reimplanting biliary and pancreatic ducts. The procedure encompasses the mobiliz ation of the hepatic flexure of the colon,an extensive Kocher maneuver for duod enal mobilization,and ampulla exposure through a duodenal incision. Employing r etraction loop sutures enhances surgical field visibility. Reconstruction involv es securing pancreatic and biliary ducts to the duodenal mucosa,each tutored wi th a silicon catheter,and suturing for ampullectomy completion. The total opera tive time was 380 min. Final histopathology disclosed high-grade dysplasia with an isolated focus of adenocarcinoma (pT1),accompanied by clear resection margin s. A postoperative duodenal fistula occurred,managed successfully through conse rvative treatment,utilizing subcutaneous drainage. Despite accurate robotic TA execution,complications may arise."
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    University of Florida Researchers Highlight Research in Machine Learning (Predic ting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods)

    77-77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 from the Unive rsity of Florida by NewsRx journalists,research stated,"Facing the escalating effects of climate change,it is critical to improve the prediction and understa nding of the hurricane evacuation decisions made by households in order to enhan ce emergency management." The news editors obtained a quote from the research from University of Florida: "Current studies in this area often have relied on psychology-driven linear mode ls,which frequently exhibited limitations in practice. The present study propos ed a novel interpretable machine learning approach to predict household-level ev acuation decisions by leveraging easily accessible demographic and resource-rela ted predictors,compared to existing models that mainly rely on psychological fa ctors. An enhanced logistic regression model (that is,an interpretable machine learning approach) was developed for accurate predictions by automatically accou nting for nonlinearities and interactions (that is,univariate and bivariate thr eshold effects). Specifically,nonlinearity and interaction detection were enabl ed by low-depth decision trees,which offer transparent model structure and robu stness. A survey dataset collected in the aftermath of Hurricanes Katrina and Ri ta,two of the most intense tropical storms of the last two decades,was employe d to test the new methodology. The findings show that,when predicting the house holds' evacuation decisions,the enhanced logistic regression model outperformed previous linear models in terms of both model fit and predictive capability."
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    China Medical University Reports Findings in Personalized Medicine (The cell dea th-related genes machine learning model for precise therapy and clinical drug se lection in hepatocellular carcinoma)

    78-80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 or iginating in Liaoning,People's Republic of China,by NewsRx journalists,resear ch stated,"Hepatocellular carcinoma (HCC) is the prevailing subtype of hepatoce llular malignancy. While previous investigations have evidenced a robust link wi th programmed cell death (PCD) and tumorigenesis,a comprehensive inquiry target ing the relationship between multiple PCDs and HCC remains scant."The news reporters obtained a quote from the research from China Medical Univers ity,"Our aim was to develop a predictive model for different PCD patterns in or der to investigate their impact on survival rates,prognosis and drug response r ates in HCC patients. We performed functional annotation and pathway analysis on identified PCD-related genes (PCDRGs) using multiple bioinformatics tools. The prognostic value of these PCDRGs was verified through a dataset obtained from GE O. Consensus clustering analysis was utilized to elucidate the correlation betwe en diverse PCD clusters and pertinent clinical characteristics. To comprehensive ly uncover the distinct PCD regulatory patterns,our analysis integrated gene ex pression profiling,immune cell infiltration and enrichment analysis. To predict survival differences in HCC patients,we established a PCD model. To enhance th e clinical applicability for the model,we developed a highly accurate nomogram. To address the treatment of HCC,we identified several promising chemotherapeut ic agents and novel targeted drugs. These drugs may be effective in treating HCC and could improve patient outcomes. To develop a cell death feature for HCC pat ients,we conducted an analysis of 12 different PCD mechanisms using eligible da ta obtained from public databases. Through this analysis,we were able to identi fy 1254 PCDRGs likely to contribute to cell death on HCC. Further analysis of 12 54 PCDRGs identified 37 genes with prognostic value in HCC patients. These genes were then categorized into two PCD clusters A and B. The categorization was bas ed on the expression patterns of the genes in the different clusters. Patients i n PCD cluster B had better survival probabilities. This suggests that PCD mechan isms,as represented by the genes in cluster B,may have a protective effect aga inst HCC progression. Furthermore,the expression of PCDRGs was significantly hi gher in PCD cluster A,indicating that this cluster may be more closely associat ed with PCD mechanisms. Furthermore,our observations indicate that patients exh ibiting elevated tumour mutation burden (TMB) are at an augmented risk of mortal ity,in comparison to those displaying low TMB and low-risk statuses,who are mo re likely to experience prolonged survival. In addition,we have investigated th e potential distinctions in the susceptibility of diverse risk cohorts towards e merging targeted therapies,designed for the treatment of HCC. Moreover,our inv estigation has shown that AZD2014,SB505124,LJI308 and OSI-207 show a greater e fficacy in patients in the low-risk category. Conversely,for the high-risk grou p patients,PD173074,ZM447439 and CZC24832 exhibit a stronger response. Our fin dings suggest that the identification of risk groups and personalized treatment selection could lead to better clinical outcomes for patients with HCC. Furtherm ore,significant heterogeneity in clinical response to ICI therapy was observed among HCC patients with varying PCD expression patterns. This novel discovery un derscores the prospective usefulness of these expression patterns as prognostic indicators for HCC patients and may aid in tailoring targeted treatment for thos e of distinct risk strata. Our investigation introduces a novel prognostic model for HCC that integrates diverse PCD expression patterns. This innovative model provides a novel approach for forecasting prognosis and assessing drug sensitivi ty in HCC patients,driving a more personalized and efficacious treatment paradi gm,elevating clinical outcomes."
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    New Robotics Study Results Reported from University of Padua (A Novel Step-by-st ep Procedure for the Kinematic Calibration of Robots Using a Single Draw-wire En coder)

    80-80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 out of Padua,Italy,by NewsRx editors,research stated,"Robot positioning accuracy is a key factory when performing high-precis ion manufacturing tasks. To effectively improve the accuracy of a manipulator,o ften up to a value close to its repeatability,calibration plays a crucial role. " Financial support for this research came from Universit degli Studi di Padova. Our news journalists obtained a quote from the research from the University of P adua,"In the literature,various approaches to robot calibration have been prop osed,and they range considerably in the type of measurement system and identifi cation algorithm used. Our aim was to develop a novel step-by-step kinematic cal ibration procedure - where the parameters are subsequently estimated one at a ti me - that only uses 1D distance measurement data obtained through a draw-wire en coder. To pursue this objective,we derived an analytical approach to find,for each unknown parameter,a set of calibration points where the discrepancy betwee n the measured and predicted distances only depends on that unknown parameter. T his reduces the computational burden of the identification process while potenti ally improving its accuracy. Simulations and experimental tests were carried out on a 6 degrees-of-freedom robot arm: the results confirmed the validity of the proposed strategy."
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    New Findings from Thiagarajar College of Engineering Describe Advances in Roboti cs (Simultaneous Allocation and Sequencing of Orders for Robotic Mobile Fulfillm ent System Using Reinforcement Learning Algorithm)

    81-82页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Researchers detail new data in Robotics. Accordin g to news originating from Madurai,India,by NewsRx correspondents,research st ated,"Robotic Mobile Fulfillment Systems (RMFS) can benefit large e-commerce wa rehouse operations significantly. To fulfill the orders received,RMFS deploys m obile robots to carry shelves back and forth from the storage area to the pickin g station." Our news journalists obtained a quote from the research from the Thiagarajar Col lege of Engineering,"Order allocation and sequencing for mobile robots is a com plex yet critical task as it influences the distance traveled by mobile robots i n fulfilling the orders,i.e.,appropriately allocating and sequencing the order s. In this paper,a Simultaneous Allocation and Sequencing of Orders Reinforceme nt Learning (SASORL) algorithm is proposed to minimize the distance traveled by mobile robots. Unlike existing methods,the SASORL algorithm optimizes order all ocation and sequencing concurrently,significantly reducing mobile robot travel distance. The proposed SASORL algorithm encompasses three sets,namely state,ac tion,and reward/penalty. The state set comprises the orders fulfilled,whereas the action set contains the orders yet to be fulfilled. The distance traveled by the mobile robot as a result of the orders allocated and sequenced is taken as the penalty for the proposed SASORL algorithm. As the proposed SASORL algorithm simultaneously allocates and sequences the orders to the mobile robots,the acti on set depletes,the state set enlarges,and the penalty updates until the actio n set becomes null. Each episode restarts with the learned experience of the pri or episodes,and after completing a few episodes,the SASORL algorithm is capabl e of generating an optimal order allocation and sequence that commits the minimu m travel distance to the mobile robots. SASORL algorithm is superior to widely a dopted soft computing techniques when orders are randomly distributed. This supe riority is evidenced by a 26% reduction in the maximum distance tr aveled by all mobile robots,a 54% reduction in the standard devia tion of the distance traveled by the mobile robots,and a marginal increase of 7 % in the total distance traveled by all mobile robots."
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    Chengdu University Reports Findings in Inflammatory Bowel Disease (Therapeutic e ffects of epigallocatechin-3-gallate for inflammatory bowel disease: A preclinic al meta-analysis)

    82-83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 Digestive System Disea ses and Conditions - Inflammatory Bowel Disease is the subject of a report. Acco rding to news originating from Chengdu,People's Republic of China,by NewsRx co rrespondents,research stated,"Epigallocatechin-3-gallate (EGCG),the primary a ctive compound in green tea,is recognized for its significant anti-inflammatory properties and potential pharmacological effects on inflammatory bowel disease (IBD). However,comprehensive preclinical evidence supporting the use of EGCG in treating IBD is currently insufficient." Our news journalists obtained a quote from the research from Chengdu University,"To evaluate the efficacy of EGCG in animal models of IBD and explore potential underlying mechanisms,serving as a groundwork for future clinical investigatio ns. A systematic review of pertinent preclinical studies published until Septemb er 1,2023,in databases such as PubMed,Embase,Web of Science,and Cochrane Li brary was conducted,adhering to stringent quality criteria. The potential mecha nisms via which EGCG may address IBD were summarized. STATA v16.0 was used to pe rform a meta-analysis to assess IBD pathology,inflammation,and indicators of o xidative stress. Additionally,dose-response analysis and machine learning model s were utilized to evaluate the dose-effect relationship and determine the optim al dosage of EGCG for IBD treatment. The analysis included 19 studies involving 309 animals. The findings suggest that EGCG can ameliorate IBD-related pathology in animals,with a reduction in inflammatory and oxidative stress indicators. T hese effects were observed through significant changes in histological scores,D isease Activity Index,Colitis Macroscopic Damage Index and colon length; a decr ease in markers such as interleukin (IL)-1b,IL-6 and interferon-g; and alterati ons in malondialdehyde,superoxide dismutase,glutathione,and catalase levels. Subgroup analysis indicated that the oral administration route of EGCG exhibited superior efficacy over other administration routes. Dose-response analysis and machine learning outcomes highlighted an optimal EGCG dosage range of 32-62 mg/k g/day,with an intervention duration of 4.8-13.6 days."
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    Kyoto University Researchers Update Understanding of Robotics (Sequential model based on human cognitive processing to robot acceptance)

    83-83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 ro botics. According to news originating from Kyoto,Japan,by NewsRx editors,the research stated,"Robots have tremendous potential,and have recently been intro duced not only for simple operations in factories,but also in workplaces where customer service communication is required." The news journalists obtained a quote from the research from Kyoto University: " However,communication robots have not always been accepted. This study proposes a three-stage (first contact,interaction,and decision) model for robot accept ance based on the human cognitive process flow to design preferred robots and cl arifies the elements of the robot and the processes that affect robot acceptance decisionmaking. Unlike previous robot acceptance models,the current model foc uses on a sequential account of how people decide to accept,considering the int eraction (or carry-over) effect between impressions established at each stage. A ccording to the model,this study conducted a scenario-based experiment focusing on the impression of the first contact (a robot's appearance) and that formed d uring the interaction with robot (politeness of its conversation and behavior) o n robot acceptance in both successful and slightly failed situations. The better the appearance of the robot and the more polite its behavior,the greater the a cceptance rate. Importantly,there was no interaction between these two factors. "
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    Study Results from Hebei Normal University Broaden Understanding of Robotics (Ap eriodically Synchronization of Multi-links Delayed Complex Networks With Semi-ma rkov Jump and Their Numerical Simulations To Single-link Robot Arms)

    84-84页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Robotics have been publi shed. According to news reporting originating from Shijiazhuang,People's Republ ic of China,by NewsRx correspondents,research stated,"This paper investigates the pth moment exponential synchronization problem of multi -links stochastic d elayed complex networks with semi-Markov jump via aperiodically intermittent con trol. Combining random disturbances,time -varying delay and semi-Markov jump wi th multi -links systems,our work is more relevant than previous work." Financial supporters for this research include National Natural Science Foundati on of China (NSFC),Science and Technology Project of Hebei Education Department ,Science Foundation of Hebei Normal University. Our news editors obtained a quote from the research from Hebei Normal University ,"Based on Lyapunov method and graph theory,a novel inequality for disposing o f the problem of pth exponential synchronization is established under the aperio dically intermittent control and some sufficient criteria are derived. The theor etical results supply a new perspective showing the synchronization criterion an d the topological structure of multi -links systems related closely. Furthermore ,the value of our results is exhibited by applying them to the single -link rob ot arms in engineering." According to the news editors,the research concluded: "Eventually,a numerical simulation is provided to demonstrate the validity of our results."
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    Researchers at Wuhan University Release New Data on Machine Learning (A Methodol ogy for Stress-strain Behavior Characterization and Mixture Optimization of Recy cled Aggregate Concrete Based On Machine Learning)

    85-85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 Ma chine Learning. According to news reporting originating in Wuhan,People's Repub lic of China,by NewsRx journalists,research stated,"The characterization of s tress-strain behaviors of recycled aggregate concrete (RAC) excluding physical p roperties of recycled aggregates (RAs) may result in an inaccurate prediction of mechanical responses in practical applications. In this study,a data-driven mo del using a refined long short-term memory (LSTM) network is established based o n the Bayesian optimization algorithm,with the motivation to accurately predict the uniaxial compressive stress-strain behaviors of RAC,including the stress-s train relation,elastic modulus,peak stress,and the peak strain." Funders for this research include National Natural Science Foundation of China ( NSFC),Key Research and Development Program of Hubei Province,China,Guangdong Basic and Applied Basic Research Foundation. The news reporters obtained a quote from the research from Wuhan University,"Tr aining and testing of the proposed model require the integration of the mixture content and the fundamental physical properties of RAs with the stress-strain re lation of ordinary concrete featured by prominent sequential attributes. Upon a dataset containing 100 experimental samples from independent studies,covering a wide range of RA substitution rates,the superior prediction capability of the proposed LSTM network is demonstrated in comparison with the analytical results of three empirical mechanics-driven models. Finally,the trained LSTM network is further employed to optimize the mixture for RAC using the Bayesian optimizatio n technique innovatively,to achieve a balance between the mechanical performanc e and requirement to the quality of RAs."
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