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 out of Leeuwarden, Netherla nds, by NewsRx editors, research stated, “Despite the lack of consensus on an of ficial definition of Big Data, research and studies have continued to progress b ased on this ‘no consensus’ stance over the years. However, the lack of a clear definition and scope for Big Data results in scientific research and communicati on lacking a common ground.” Our news journalists obtained a quote from the research from University of Groni ngen: “Even with the popular ‘V’ characteristics, Big Data remains elusive. The term is broad and is used differently in research, often referring to entirely d ifferent concepts, which is rarely stated explicitly in papers. While many studi es and reviews attempt to draw a comprehensive understanding of Big Data, there has been little systematic research on the position and practical implications o f the term Big Data in research environments. To address this gap, this paper pr esents a Systematic Literature Review (SLR) on secondary studies to provide a co mprehensive overview of how Big Data is used and understood across different sci entific domains. Our objective was to monitor the application of the Big Data co ncept in science, identify which technologies are prevalent in which fields, and investigate the discrepancies between the theoretical understanding and practic al usage of the term. Our study found that various Big Data technologies are bei ng used in different scientific fields, including machine learning algorithms, d istributed computing frameworks, and other tools. These manifestations of Big Da ta can be classified into four major categories: abstract concepts, large datase ts, machine learning techniques, and the Big Data ecosystem. This study revealed that despite the general agreement on the ‘V’ characteristics, researchers in d ifferent scientific fields have varied implicit understandings of Big Data.”
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 originating in Eugene, Orego n, by NewsRx journalists, research stated, “The transformative impact of generat ive AI (GenAI), extending beyond traditional AI, raises numerous concerns includ ing the replacement of human roles and AI misuse in an array of industries. This article introduces blockchain technology as a complementary technological safeg uard to address some of these challenges.” The news reporters obtained a quote from the research from the University of Ore gon, “We emphasize blockchain’s role in promoting transparency, verifiability, a nd decentralization in AI development and usage, thereby offering potential solu tions for four distinct challenges: (1) AI toxicity, biases, hallucinations, (2) AI interest misalignment, (3) AI as a black box, and (4) AI misuse. This articl e proposes ways to ensure responsible and transparent AI usage through the integ ration of block- chain. We position the convergence of AI and blockchain as a me ans to manage AI’s societal impact and unlock its benefitsdcontingent upon colla borative efforts among various stakeholders such as businesses, developers, and regulatory bodies.”
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 Dublin, Irel and, by NewsRx correspondents, research stated, “Air pollution from transport hu bs is a recognised health concern for local urban inhabitants. Within the domain of transport hubs, significant attention has been given to larger airport and p ort settings, however concerns have been raised about emissions from urban railw ay hubs, especially those with diesel trains.” Our news editors obtained a quote from the research from the University of Dubli n, “This paper presents an approach that adopts low-cost monitoring (LCM) for fi xed site monitoring (FSM) to quantify and disaggregate PM and NO contributions f rom railway station and road traffic on air quality in the vicinity of railway s tation in Dublin, Ireland. The NO sensor showed larger discrepancies than the PM sensor when compared to the reference monitor. Machine learning models (XGBoost and Random Forest (RF) regression) were applied to calibrate the LCM devices, w ith the XGBoost model (NO R = 0.8 and RSME = 9.1 mg/m & PM, R = 0. 92 and RSME = 2.2 mg/m) deemed more appropriate than the RF model. Local wind co nditions, pressure, PM concentrations, and road traffic significantly impacted N O model results, while raw PM sensor readings greatly influenced the PM model ou tput. This highlights that the NO sensor requires more input data for accurate c alibration, unlike the PM sensor. The monitoring results from the one-month moni toring campaign from May 25, 2023 to June 25, 2023 presented elevated NO and PM concentrations measured at the railway station, which translated to exceedances of the annual WHO limits (PM = 5 mg/m, NO = 10 mg/m) by 1.6-1.8 and 3.2-5.2 time s respectively at the study site. A subsequent data filtering technique based on wind orientation, revealed that the railway station was the main PM source and road traffic was the main NO source when winds come from the railway station.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting originating from Rajshahi, B angladesh, by NewsRx correspondents, research stated, “In this work, AuCuSe4 4 d irect bandgap (Eg g = 0.52 eV) semiconductor has been examined as a thermophotov oltaic (TPV) material. Initially, the electronic structure of AuCuSe4 4 compound has been investigated using first- principles calculations.” Our news editors obtained a quote from the research from the University of Rajsh ahi, “The study reveals a semiconducting nature of AuCuSe4 4 with a narrow bandg ap computed by DFT-MBJ (Density Functional Theory with Modified Becke-Johnson) m ethod which is lower than the experimental value. The optical properties show si gnificant aniso-tropic by AuCuSe4 4 absorption in the solar spectrum, favoring T PV growth orientation. Subsequently, a single-junction AuCuSe4 4 TPV cell has be en presented employing a device transport model with an n-p structure, operating at the black body and cell temperatures of 1538 K and 300 K, respectively. Nota bly, machine learning (ML) techniques are utilized to investigate the significan ce of each parameter in the model, enhancing the understanding of the system’s b ehavior and design optimization.”
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 originating from Yogyakarta, Indonesia, by NewsRx correspondents, research stated, “Purpose - The study aims to explore how integrating recent fundamental values (RFVs) from conventional ac counting studies enhances the accuracy of a machine learning (ML) model for pred icting stock return movement in Indonesia. Design/methodology/approach - The stu dy uses multilayer perceptron (MLP) analysis, a deep learning model subset of th e ML method.” The news correspondents obtained a quote from the research from Gadjah Mada Univ ersity: “The model utilizes findings from conventional accounting studies from 2 019 to 2021 and samples from 10 firms in the Indonesian stock market from Septem ber 2018 to August 2019. Findings - Incorporating RFVs improves predictive accur acy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs . Research limitations/ implications - The study uses Indonesian firms as its sam ple. We believe our findings apply to other emerging Asian markets and add to th e existing ML literature on stock prediction. Nevertheless, expanding to differe nt samples could strengthen the results of this study. Practical implications - Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, pract itioners, analysts and investors can be inspired to develop RFV-based AI tools.”
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 artificial intelligence is now available. According to news originating from Lexington, Kentucky, by Ne wsRx correspondents, research stated, “Non-ionic deep eutectic solvents (DESs) a re non-ionic designer solvents with various applications in catalysis, extractio n, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES form ation.” The news editors obtained a quote from the research from University of Kentucky: “The search for DES relies heavily on intuition or trial-and-error processes, l eading to low success rates or missed opportunities. Recognizing that hydrogen b onds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learni ng (ML) models to discover new DES systems. We first analyze the HB properties o f 38 known DES and 111 known non-DES systems using their molecular dynamics (MD) simulation trajectories. The analysis reveals that DES systems have two unique features compared to non-DES systems: The DESs have more imbalance between the n umbers of the two intra-component HBs and more and stronger inter-component HBs. Based on these results, we develop 30 ML models using ten algorithms and three types of HB-based descriptors. The model performance is first benchmarked using the average and minimal receiver operating characteristic (ROC)-area under the c urve (AUC) values. We also analyze the importance of individual features in the models, and the results are consistent with the simulation-based statistical ana lysis. Finally, we validate the models using the experimental data of 34 systems .”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on robotics are disc ussed in a new report. According to news reporting out of Marquette University b y NewsRx editors, research stated, “This paper presents an approach to passively realize any specified object spatial compliance using the grasp of a robotic ha nd.” The news journalists obtained a quote from the research from Marquette Universit y: “The kinematically anthropomorphic hands considered have multiple 4-joint fin gers making hard point contact with the held object, and the joints of each fing er have selectable passive elastic behavior. It is shown that the space of passi vely realizable compliances is restricted by the kinematic structure of the anth ropomorphic hand. To achieve an arbitrary compliant behavior, fingers must be ab le to adjust their orientation.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on Transportation - Intelligent Tra nsportation Systems have been presented. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research st ated, “Autonomous intersection management systems aim to efficiently control con nected and autonomous vehicles at urban intersections. However, current driving behavior models face challenges in accurately capturing the distinctive human dr iver characteristics specific to intersection interactions.” Financial supporters for this research include National Key Research & Development Program of China, China Postdoctoral Science Foundation. Our news editors obtained a quote from the research from the Beijing University of Technology, “This article introduces a human-like driving behavior model base d on the driver’s risk field (DRF) for intersection scenarios. The DRF represent s the driver’s belief regarding the likelihood of an event occurring, and the as sociated cost function is determined by the consequences of said event. A drivin g simulation experiment was conducted at a signalized intersection to evaluate t he model, and the results were compared with a human-like driving behavior model . The results show that the proposed model has a high degree of fit.”
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 the University of Pittsburgh by NewsRx correspondents, research stated, “We h ere introduce Ensemble Optimizer (EnOpt), a machine-learning tool to improve the accuracy and interpretability of ensemble virtual screening (VS).” Funders for this research include National Institute of General Medical Sciences . Our news journalists obtained a quote from the research from University of Pitts burgh: “Ensemble VS is an established method for predicting protein/small-molecu le (ligand) binding. Unlike traditional VS, which focuses on a single protein co nformation, ensemble VS better accounts for protein flexibility by predicting bi nding to multiple protein conformations. Each compound is thus associated with a spectrum of scores (one score per protein conformation) rather than a single sc ore. To effectively rank and prioritize the molecules for further evaluation (in cluding experimental testing), researchers must select which protein conformatio ns to consider and how best to map each compound’s spectrum of scores to a singl e value, decisions that are system-specific. EnOpt uses machine learning to addr ess these challenges. We perform benchmark VS to show that for many systems, EnO pt ranking distinguishes active compounds from inactive or decoy molecules more effectively than traditional ensemble VS methods.”
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 Machine Learn ing have been published. According to news reporting out of Fredericton, Canada, by NewsRx editors, research stated, “Hot rolling is a critical thermomechanical processing step for nonoriented electrical steel (NOES) to achieve optimal mech anical and magnetic properties. Depending on the silicon and carbon contents, th e electrical steel may or may not undergo austenite-ferrite phase transformation during hot rolling, which requires different process controls as the austenite and ferrite show different flow stresses at high temperatures.” Funders for this research include Natural Resources Canada through the Office fo r Energy Research and Development (OERD), Transport Canada’s Clean Transportatio n System - Research and Development Program (CTS-RD), Natural Sciences and Engin eering Research Council of Canada (NSERC), Canada Foundation for Innovation, Atl antic Canada Opportunities Agency (ACOA), New Brunswick Innovation Foundation (N BIF), Natural Resources Canada library.
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