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    Investigators from University of Carlos III Madrid Have Reported New Data on And roids (Evaluating Users’ Perception of Biologically Inspired Involuntary Behavio r In Human-robot Interaction)

    70-70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 - Androids. According to news reporting originating from Madrid, Spain, by NewsRx correspondents, research stated, “Multimodal communication is a human feature that enables diverse interactions. In human-robot interaction (HRI), ro bots have to communicate using human skills so that they can seem natural and as sist effectively.” Financial supporters for this research include MCIN/AEI, ERDF A way of making Eu rope, European Union (EU), Mejora del nivel de madurez tecnologica del robot Min i (MeNiR) - MCIN/AEI, Portable Social Robot with high level of Engagement (PoSoR o) - MCIN/AEI. Our news editors obtained a quote from the research from the University of Carlo s III Madrid, “Most research uses predefined gestures to equip robots with socia l abilities. However, researchers scarcely consider generating bioinspired invol untary behavior to improve a robot’s expressiveness and communication. Human stu dies revealed that involuntary behavior affects how others perceive communicativ e intentions. Therefore, mimicking human involuntary behavior may positively aff ect HRI. This article extends our previous work on equipping robots with involun tary behavior with a user study that evaluates the use of bioinspiration for com plementing gestures. A preliminary test is conducted with 15 participants to det ermine if they can perceive the intensities of the involuntary processes heart r ate, pupil size, blink rate, breathing rate, and motor activity. 63 new particip ants interacted with a robot with bioinspired behaviors or a robot only showing predefined gestures to evaluate the robots’ warmth, competence, and discomfort. The results show that the preliminary test participants differentiated the inten sities of the involuntary processes. Participants in the second study find the r obot with bioinspired behaviors significantly warmer and more competent than the robot with predefined gestures, with no discomfort difference.”
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    Hefei University of Technology Reports Findings in Nanozymes (Machine learning-a ssisted laccase-like activity nanozyme for intelligently onsite real-time and dy namic analysis of pyrethroid pesticides)

    71-71页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Nanotechnology - Nanozymes is the subject of a report. According to news reporting from Hefei, People’s Republic of China, by NewsRx journalists, research stated, “The intelligently efficient, reliable, economical and portable onsite assay toward pyrethroid pesticides (PPs ) residues is critical for food safety analysis and environmental pollution trac eability. Here, a fluorescent nanozyme Cu- ATP@ [Ru(bpy)] with laccase-like activity was designed to develop a versatile machine learning- assisted colorimetric and fluorescence dual-modal assay for efficient onsite int elligent decision recognition and quantification of PPs residues.” The news correspondents obtained a quote from the research from the Hefei Univer sity of Technology, “In the presence of alkaline phosphatase (ALP), the laccase- like activity of Cu-ATP@ [Ru(bpy)] was enh anced to oxidize colorless o-phenylenediamine (OPD) into dark-yellow 2,3-diamino phenazine (DAP) via electron transfer, appearing a new yellow fluorescence at 55 0 nm. Meanwhile, the red fluorescence of Cu-ATP@ [Ru(bpy)] at 600 nm was quenched due to the internal filter effect (IFE) of DAP towards Cu -ATP@ [Ru(bpy)]. However, the selective in hibition of PPs toward ALP activity enabled to observe a dual-modal response of PPs concentration-dependent decrease in colorimetric signal and enhancement in t he fluorescence intensity ratio of F/F.”
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    University of Liverpool Researchers Detail Research in Artificial Intelligence ( Use of Social Media and Artificial Intelligence Tools by Online Doctoral Student s)

    72-72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 artificial intelligen ce have been presented. According to news reporting from the University of Liver pool by NewsRx journalists, research stated, “Our paper aims to explore how doct oral EdD students in their thesis stage made use of digital technologies, social media (SM), and artificial intelligence (AI) tools.” Our news correspondents obtained a quote from the research from University of Li verpool: “In this study, AI does not involve data on the use of the new generati on of AI, which has been introduced in more recent years after this study took p lace. This paper refers to a 2nd stage qualitative analysis of semistructured i nterviews collected from research undertaken in 2018 into student use of digital technologies in an online professional doctorate programme. The original study utilised an exploratory case study approach, an online survey (n = 28), and a se ries of semi-structured interviews (n = 9). This study will add further qualitat ive findings and perspectives to those that emerged in the previous study. This study will help to provide new insights into the interview data that was used to inform the initial paper resulting from the research in 2018. We argue that the unique characteristics of online doctoral students as both individuals and lear ners determine the popularity of some digital tools and that, in order to make t he best use of the full range available they need to develop new skills and a be tter understanding of the pedagogy associated with those digital tools and the v alue they can add to an educational context.”
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    Reports Summarize Support Vector Machines Findings from Sao Paulo State Universi ty (UNESP) (Flood Susceptibility Mapping In River Basins: a Risk Analysis Using Ahp-topisis-2 N Support and Vector Machine)

    73-73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 Machine Learning - Su pport Vector Machines have been presented. According to news reporting out of So rocaba, Brazil, by NewsRx editors, research stated, “Due to the damage caused by floods, mapping areas susceptible to this natural phenomenon plays a fundamenta l role in environmental planning. Therefore, it becomes essential to understand and map the conditions and factors involved in areas affected by geo-hydro-meteo rological events.” Financial support for this research came from Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES). Our news journalists obtained a quote from the research from Sao Paulo State Uni versity (UNESP), “In this context, we mapped areas susceptible to flooding using the AHP-TOPSIS-2 N, Support Vector Machine (SVM), and a hybrid model, AHP-SVM, the Sorocaba-Medio Tiete basin, that is a subtropical, densely populated river b asin located in Brazilian territory. We considered 11 conditioning factors relat ed to hydrogeomorphological and anthropological characteristics, and 382 histori cal flood and non-flood points. We assessed the accuracy of the modeling using t he Area Under the Curve - AUC. The AHP-SVM model presented the best efficiency a mong the models analyzed (AUC = 0.962). The principal conditioning factors relat ed to flooding were land cover and land use.”
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    Researchers at Universitas Sebelas Maret Have Published New Data on Machine Lear ning (Leveraging machine learning for hydrological drought prediction and mitiga tion)

    74-74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 reporting from the Universitas Sebelas Maret by NewsRx journalists, research stated, “Drought disasters have become a g lobal issue, occurring more frequently due to climate change and increasing wate r usage patterns.” The news editors obtained a quote from the research from Universitas Sebelas Mar et: “Adaptation and mitigation efforts to reduce disaster vulnerability involve effective drought monitoring, such as drought predictions. This study aims to pr edict the hydrological drought index (HDI) for the next 5 years (20242028) in th e Bendung Notog sub-watershed. The HDI prediction modeling is based on machine l earning with an artificial neural network (ANN) algorithm using historical HDI v alues from the past 20 years (2004-2023). The historical HDI was calculated usin g the Threshold Level Method with discharge data transformed by the NRECA method . The drought prediction model demonstrates high accuracy with performance asses sment values of MAE = 0.015, R = 0.91, R2 = 0.82, NSE = 0.82, and RMSE = 0.022. The HDI prediction results indicate that the Bendung Notog sub-watershed experie nces dry conditions annually during the dry season, with the lowest HDI and long est drought duration occurring in 2024.”
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    Researcher from Xihua University Details New Studies and Findings in the Area of Robotics (Coupled error super-twisted sliding mode active fault-tolerant contro l for robotic system with actuator fault)

    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 robotics have been published. According to news originating from Xihua University by NewsRx c orrespondents, research stated, “This paper focuses on a robotic system with act uator fault and presents a sliding mode active fault-tolerant control method bas ed on coupled position error.” Funders for this research include Natural Science Foundation of Sichuan Province ; National Natural Science Foundation of China.The news reporters obtained a quote from the research from Xihua University: “Th e actuator fault is first detected by a fault alerter with a predetermined thres hold. After the fault is successfully detected, the fault is estimated by a faul t observer. Coupling the position error with the weighted position error of diff erent joints, a non-singular fast terminal sliding mode surface is constructed, and a super-twisted algorithm is introduced to design a coupled error super-twis ted sliding mode controller (CESSMC). Furthermore, the fault estimation is incor porated with the CESSMC to accomplish the coupled error super-twisted sliding mo de active fault-tolerant control. The stability and the finite-time convergence of the system are theoretically proved. Simulations and experiments are conducte d to verify the effectiveness of the proposed method.”
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    New Machine Learning Research Has Been Reported by a Researcher at Zhejiang Ocea n University (Water Quality in the Ma’an Archipelago Marine Special Protected Ar ea: Remote Sensing Inversion Based on Machine Learning)

    75-76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 Zhoushan, Peop le’s Republic of China, by NewsRx correspondents, research stated, “Due to the i ncreasing impact of climate change and human activities on marine ecosystems, th ere is an urgent need to study marine water quality. The use of remote sensing f or water quality inversion offers a precise, timely, and comprehensive way to ev aluate the present state and future trajectories of water quality.” Financial supporters for this research include Science And Technology Bureau of Zhejiang. Our news journalists obtained a quote from the research from Zhejiang Ocean Univ ersity: “In this paper, a remote sensing inversion model utilizing machine learn ing was developed to evaluate water quality variations in the Ma’an Archipelago Marine Special Protected Area (MMSPA) over a long-time series of Landsat images. The concentrations of chlorophyll-a (Chl-a), phosphate, and dissolved inorganic nitrogen (DIN) in the sea area from 2002 to 2022 were inverted and analyzed. Th e spatial and temporal characteristics of these variations were investigated. Th e results indicated that the random forest model could reliably predict Chl-a, p hosphate, and DIN concentrations in the MMSPA. Specifically, the inversion resul ts for Chl-a showed the coefficient of determination (R2) of 0.741, the root mea n square error (RMSE) of 3.376 mg/L, and the mean absolute percentage error (MAP E) of 16.219%. Regarding spatial distribution, the concentrations o f these parameters were notably elevated in the nearshore zones, especially in t he northwest, contrasted with lower concentrations in the offshore and southeast areas. Predominantly, the nearshore regions with higher concentrations were in proximity to the aquaculture zones. Additionally, nutrients originating from lan d sources, transported via rivers such as the Yangtze River, as well as influenc ed by human activities, have shaped this nutrient distribution. Over the long te rm, the water quality in the MMSPA has shown considerable interannual fluctuatio ns during the past two decades.”
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    Studies from New York University (NYU) Langone Health Provide New Data on Roboti cs (Discharging Patients Home With a Chest Tube and Digital System After Robotic Lung Resection)

    76-77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 originating in New York City, New York, by NewsR x journalists, research stated, “Our objective is to assess the feasibility, saf ety, and outcomes for patients discharged home with a chest tube connected to a digital drainage system after robotic pulmonary resection. This was a retrospect ive analysis of a prospectively collected database as a quality improvement init iative.” The news reporters obtained a quote from the research from New York University ( NYU) Langone Health, “All patients had planned discharge on postoperative day on e (POD1) after robotic pulmonary resection. Those with an air leak were discharg e home with a chest tube connected to a digital drainage system with daily commu nication with the surgeon. From January 2019 to February 2023 there were 580 con secutive robotic resections, of which 69 (12%) patients had an air leak on POD1; 38 of 276 (14%) after lobectomy, 24 of 226 (11% ) after segmentectomy, and 7 of 78 (9%) after wedge resection. Of t hese 69 patients, 52 patients (75%) were discharged on POD1, 15 pat ients (22%) on POD2, and 2 patients (3%) on POD3. Ches t tubes were removed a median outpatient chest tube duration was 4 days (interqu artile range, 3-5 days). Of the 69 patients sent home with a digital drainage sy stem, there was 1 complication requiring readmission for increasing subcutaneous emphysema. Five patients (7%) had system malfunctions that require d return to our clinic for problem-solving. There were no 30- or 90-day mortalit ies.”
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    Investigators at University of Ghent Describe Findings in Machine Learning (Expl ainable Real-time Predictive Analytics On Employee Workload In Digital Railway C ontrol Rooms)

    77-78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 Machine Learning are discussed in a new report. According to news reporting out of Ghent, Belgium , by NewsRx editors, research stated, “Both workload peaks and lows contribute t o lower employee well-being. Predictive employee workload analytics can empower management to undertake proactive prevention.” Funders for this research include VT (Virginia Tech) -INFORMS Student Chapter, A gence nationale pour le developpement de la recherche en sante (ANDRS). Our news journalists obtained a quote from the research from the University of G hent, “For this purpose, we develop a real-time machine learning framework to pr edict and explain future workload in a challenging environment with variable imb alanced workload: the digital control rooms for railway traffic management of In frabel, Belgium’s railway infrastructure company. The proposed two-stage methodo logy leverages granular data of workload categories that are very different in n ature and separates the effects of workload presence and magnitude. In this way, set-up addresses the changing workload mix over 15-minute intervals. We extensi vely benchmark machine learning and deep learning models within this context, le ading to LightGBM (Light Gradient Boosting Machine) as the best-performing model . SHAP (SHapley Additive exPlanations) values highlight the benefits of disentan gling presence and magnitude and reveal associations with human-machine interact ion and team exposure. As a proof of concept, our implemented predictive model o ffers tailored decision support to the traffic supervisor in an explainable way. ”
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    Beijing University of Technology Reports Findings in Machine Learning (Unravelin g the determinants of traffic incident duration: A causal investigation using th e framework of causal forests with debiased machine learning)

    78-79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 from Beijing, People’s Republ ic of China, by NewsRx journalists, research stated, “Predicting the duration of traffic incidents is challenging due to their stochastic nature. Accurate predi ctions can greatly benefit end-users by informing their route choices and safety warnings, while helping traffic operation managers more effectively manage non- recurrent traffic congestion and enhance road safety.” The news correspondents obtained a quote from the research from the Beijing Univ ersity of Technology, “This study conducts a comprehensive causal analysis of tr affic incident duration using a data collected over a long time and including di fferent types of roads across the city of Tianjin, China. Employing the innovati ve framework of causal forests with biased machine learning (CF-DML) techniques, this study advances beyond traditional methods by focusing on interpreting the causal relationships between various factors and incident duration, emphasizing the role of heterogeneity among these factors. The CF-DML framework enables the assessment of the average treatment effects (ATEs) of various factors on inciden t duration. Notably, the significant influence of road type and suburban setting on treatment effects is underscored, which is generally consistent with the res ults obtained through classical methods. Second, to look more closely at the imp ortant factors such as road and collision types, a conditional average treatment effects (CATE) analysis is conducted, explaining heterogeneity through a causal heterogeneity tree. Third, based on insights from causal analysis, policies rel ated to lane configurations are explored, emphasizing the necessity of consideri ng causal effects in traffic management decisions.”
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