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    Researchers from Chinese Academy of Sciences Provide Details of New Studies and Findings in the Area of Robotics (Barrier-based Adaptive Line-of-sight 3-d Path- following System for a Multijoint Robotic Fish With Sideslip Compensation)

    86-87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 from Beijing, People's Republic of Ch ina, by NewsRx correspondents, research stated, "This article proposes a novel b arrier-based adaptive line-of-sight (ALOS) three-dimensional (3-D) path-followin g system for an underactuated multijoint robotic fish. The framework of the deve loped path-following system is established based on a detailed dynamic model, in cluding a barrier-based ALOS guidance strategy, three integrated inner-loop cont rollers, and a nonlinear disturbance observer (NDOB)-based sideslip angle compen sation, which is employed to preserve a reliable tracking under a frequently var ying sideslip angle of the robotic fish." Funders for this research include National Natural Science Foundation of China ( NSFC), Beijing Municipal Science & Technology Commission, Youth In novation Promotion Association CAS.
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    Reports from Xiamen University Advance Knowledge in Machine Learning (Synchronou s Fluorescence Spectra-based Machine Learning Algorithm With Quick and Easy Acce ssibility for Simultaneous Quantification of Polycyclic Aromatic Hydrocarbons In ...)

    87-88页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Xiamen, People's Repub lic of China, by NewsRx correspondents, research stated, "Polycyclic aromatic hy drocarbons (PAHs) are one of the leading causes of human cancer. Four typical PA Hs (PAH4) including benzo(a)pyrene (BaP), benzo(b)fluoranthene (BbF), benzo(a)an thracene (BaA), and chrysene (Chr) have been regarded as reasonable indicators f or the occurrence of PAHs in food." Funders for this research include Science and Technology Program of Fujian Provi nce, National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from Xiamen University, "In this study, the constant wavelength synchronous fluorescence (CWSF) spectra of P AH4 mixtures were used as the data sets without preprocessing and directly combi ned with the back propagation neural network (BPNN) algorithm to establish a qua ntitative analysis method of PAH4. This method is capable of predicting the conc entrations of PAH4 in edible oil samples without pre-separation. The detection l imits for BaP, BbF, BaA, and Chr were 0.014, 0.068, 0.026, and 0.013 mu g/kg, re spectively. The recoveries in various oil samples for BaP, BbF, BaA, and Chr wer e 99.5 +/- 2.1, 101.0 +/- 4.6, 98.6 +/- 3.2, and 98.5 +/- 4.9 %, re spectively."
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    Research from University of Sharjah Provides New Study Findings on Artificial In telligence [Artificial Intelligence Islamic Architecture (AII A): What Is Islamic Architecture in the Age of Artificial Intelligence?]

    88-89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 from the Universit y of Sharjah by NewsRx journalists, research stated, "Revisiting the long-debate d question: "What is Islamic architecture?", this research article aims to explo re the identity of "Islamic architecture (IA)" in the context of artificial inte lligence (AI) as well as the novel opportunities and cultural challenges associa ted with applying AI techniques, such as the machine learning of Midjourney in t he context of IA." Financial supporters for this research include University of Sharjah. Our news correspondents obtained a quote from the research from University of Sh arjah: "It investigates the impact factors of AI technologies on the understandi ng and interpretation of traditional Islamic architectural principles, especiall y architectural design processes. This article employs a quantitative research m ethodology, including the observation of works of artists and architectural desi gners appearing in the mass media in light of a literature review and critical a nalysis of scholarly debates on Islamic architecture, spanning from historical p erspectives to contemporary discussions. The article argues for the emergence of a continuous paradigm shift from what is commonly known as "postmodern Islamic architecture" (PMIA) into "artificial intelligence Islamic architecture" (AIIA), as coined by the authors of this article. It identifies the following impact fa ctors of AI on IA: (1) particular requirements and sensitivities, inaccuracies, and biases, (2) human touch, unique craftsmanship, and a deep understanding of c ultural issues, (3) regional variation, (4) translation, (5) biases in sources, (6) previously used terms and expressions, and (7) intangible values. The signif icance of this research in digital heritage lies in the fact that there are no p re-existing theoretical publications on the topic of "Islamic architecture in th e age of artificial intelligence", although an extensive set of publications int erpreting the question of the definition of Islamic architecture, in general, is found."
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    Medical Psychological Center Reports Findings in Major Depressive Disorder (Pred ictive modeling of antidepressant efficacy based on cognitive neuropsychological theory)

    89-90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 Major Depressive Disor der is the subject of a report. According to news originating from Changsha, Peo ple's Republic of China, by NewsRx correspondents, research stated, "We aimed to develop a clinical predictive model based on the cognitive neuropsychological ( CNP) theory and machine-learning to examine SSRI efficacy in the treatment of MD D. Baseline assessments including clinical symptoms (HAMD, HAMA, BDI, and TEPS s cores), negative biases (NEO-PI-R-N and NCPBQ scores), sociodemographic characte ristics (social support and SES), and a 5-min eye-opening resting-state EEG were completed by 69 participants with first-episode major depressive disorder (MDD) and 36 healthy controls." Our news journalists obtained a quote from the research from Medical Psychologic al Center, "The clinical symptoms and negative bias were again assessed after an 8-week treatment of depression with selective serotonin reuptake inhibitors (SS RIs). A multi-modality machine-learning model was developed to predict the effec tiveness of SSRI antidepressants. At baseline, we observed significant differenc es between MDD patients and healthy controls in terms of social support, clinica l symptoms, and negative bias characteristics (p <0.001). A negative association was found (p <0.05) between neuroti cism and alpha asymmetry in both the central and central-parietal areas, as well as between negative cognitive processing bias and alpha asymmetry in the pariet al region. Compared to responders, non-responders exhibited less negative cognit ive processing bias and greater alpha asymmetry in both central and centralpari etal regions. Importantly, we developed a multi-modality machine-learning model with 83 % specificity using the above salient features. Research r esults support the CNP theory of depression treatment. To some extent, the multi modal clinical model constructed based on the CNP theory effectively predicted t he efficacy of this treatment in this population."
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    U.S. Environmental Protection Agency (EPA) Reports Findings in Machine Learning (Systematic Approaches for the Encoding of Chemical Groups: A Case Study)

    90-91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 originating from Research Triangle Park , North Carolina, by NewsRx correspondents, research stated, "Regulatory authori ties aim to organize substances into groups to facilitate prioritization within hazard and risk assessment processes. Often, such chemical groupings are not exp licitly defined by structural rules or physicochemical property information." Our news journalists obtained a quote from the research from U.S. Environmental Protection Agency (EPA), "This is largely due to how these groupings are develop ed, namely, a manual expert curation process, which in turn makes updating and r efining groupings, as new substances are evaluated, a practical challenge. Herei n, machine learning methods were leveraged to build models that could preliminar ily assign substances to predefined groups. A set of 86 groupings containing 2,1 84 substances as published on the European Chemicals Agency (ECHA) website were mapped to the U.S. Environmental Protection Agency (EPA) Distributed Toxicity St ructure Database (DSSTox) content to extract chemical and structural information . Substances were represented using Morgan fingerprints, and two machine learnin g approaches were used to classify test substances into 56 groups containing at least 10 substances with a structural representation in the data set: k-nearest neighbor (kNN) and random forest (RF), that led to mean 5- fold cross-validation test accuracies (average F1 scores) of 0.781 and 0.853, respectively. With a 9% improvement, the RF classifier was significantly more accurate than KNN (-value = 0.001). The approach offers promise as a means of the initial profiling of new substances into predefined groups to facilitate prioritization efforts and stre amline the assessment of new substances when earlier groupings are available." According to the news editors, the research concluded: "The algorithm to fit and use these models has been made available in the accompanying repository, thereb y enabling both use of the produced models and refitting of these models, as new groupings become available by regulatory authorities or industry." This research has been peer-reviewed.
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    Reports from Texas A&M University Highlight Recent Findings in Robo tics (Investigating Stakeholder Perception and Developing a Decision Framework f or Robot Adoption In Construction)

    91-92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 College Station, Texas, by NewsRx journalis ts, research stated, "Considering the benefits of using robots in the manufactur ing and automobile sectors, robots are currently being developed for the constru ction industry; however, the rate of adopting robots in this industry is still i n its nascent stage. As robots are introduced to project sites, safety, producti vity, ease of use, level of robot autonomy, etc. become important parameters for wide-scale adoption; however, research that analyzes the point of view of const ruction industry stakeholders on adopting robots, to our best knowledge, is stil l limited." Financial support for this research came from US Department of Transportation's University Transportation Centers Program through the Transportation Consortium of South-Central States.
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    Findings from Dundalk Institute of Technology Advance Knowledge in Artificial In telligence [The Potential Utilisation of Artificial Intellige nce (AI) in Enterprises]

    92-92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 out of Dundalk, Ireland, by N ewsRx editors, research stated, "Artificial intelligence (AI) is a part of compu ter science that aims to create and develop intelligent machines." Our news editors obtained a quote from the research from Dundalk Institute of Te chnology: "For AI to function and perform, it involves the development of algori thms, models, and systems that enable computers to learn from the data, identify patterns, and make predictions. Research indicates that AI can increase private industry output, decision-making, and effectiveness. By identifying how enterpr ises utilize AI, what is the impact of implementing AI in enterprise operations, and how does it affect the efficiency, productivity, and overall performance of the business? This exploratory research begins with insight into understanding AI and addresses how AI has been utilized and implemented in businesses, ethical and societal considerations, and potential benefits and challenges that busines ses may confront."
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    Research in the Area of Artificial Intelligence Reported from University of Tabr iz (Combining artificial intelligence and computational fluid dynamics for optim al design of laterally perforated finned heat sinks)

    93-94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 originating fr om Tabriz, Iran, by NewsRx correspondents, research stated, "The efficient desig n of heat sinks is a severe challenge in thermo-fluid engineering. A creative an d innovative way is applying lateral perforations to parallel finned heat sinks. " Our news journalists obtained a quote from the research from University of Tabri z: "The significance of achieving an optimal design for perforated finned heat s inks (PFHSs) has inspired the present authors to introduce a novel hybrid design ing approach that combines computational fluid dynamics (CFD), machine learning (ML), multi-objective optimization (MOO), and multi-criteria decision-making (MC DM). The design variables considered include the size (0.25<ph<0.5) and shape (square, circular, and hexagonal) of the perforations, as well as the airflow Reynolds number (2000<Re<5000). The design objectives have been redefined as dime nsionless parameters to assess heat dissipation, pressure drop, and heat sink we ight. These modified objectives encompass thermal performance (TP), thermo-hydra ulic performance (THP), and thermo-volumetric performance (TVP). The modeling pr ocess showed that both stepwise mixed selection (SMS) and GMDH-NN techniques exh ibited comparable performance in most modeling scenarios. Nevertheless, the SMS approach demonstrated more reliability in modeling diverse objectives. Furthermo re, the optimization results demonstrated that the optimal size of the perforati ons is strongly dependent on their shapes. In PFHSs with square perforations, ap proximately 54% of the Pareto points had a ph-value greater than 0 .45."
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    Researcher at Guangdong Ocean University Targets Artificial Intelligence (A Comp rehensive Review of Assessing Storm Surge Disasters: From Traditional Statistica l Methods to Artificial Intelligence-Based Techniques)

    93-93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 originating from Zhanjiang, People's R epublic of China, by NewsRx correspondents, research stated, "In the context of global climate change and rising sea levels, the adverse impacts of storm surges on the environment, economy, and society of affected areas are becoming increas ingly significant." Funders for this research include National Key Research And Development Program of China; National Natural Science Foundation of China; Innovative Team Plan For Department of Education of Guangdong Province; Guangdong Science And Technology Plan Project; Independent Research Project of Southern Ocean Laboratory; Guangd ong Ocean University Scientific Research Program. Our news correspondents obtained a quote from the research from Guangdong Ocean University: "However, due to differences in geography, climate, and other condit ions among the affected areas, a single method for assessing the risk of storm s urge disasters cannot be fully applicable to all regions. To address this issue, an increasing number of new methods and models are being applied in the field o f storm surge disaster risk assessment. This paper introduces representative tra ditional statistical methods, numerical simulation methods, and artificial intel ligence-based techniques in this field."
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    New Findings on Machine Learning Described by Investigators at Center for AgriBi osciences (An Initial Investigation Into the Use of Machine Learning Methods for Prediction of Carcass Component Yields In F2 Broiler Chickens)

    94-95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 originating from Bundoora, Australia, by NewsR x correspondents, research stated, "As evaluation of carcass components is costl y and time consuming, models for prediction of broiler carcass components are us eful.Aims The aim was to investigate the feasibility of machine learning methods in the prediction of carcass components from measurements on live birds during the rearing period.Methods Three machine learning methods,including regression tree, random forest and gradient-boosting trees, were applied to predict carcass yields, and benchmarked against classical linear regression. Two scenarios were defined for prediction."
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