查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting from Hoboken, New Jersey, by NewsRx journalists, research stated, “Understanding the hydrogen-brine transport properties and the hydrogen trapping rate (i.e., ratio of residual hydrogen saturation after recovery to initial hydrogen saturation) is critical to the site selection for underground hydrogen storage (UHS). In this study, a three-dimensional pore network model (PNM) was used to simulate hydrogen-brine two phase flow in various porous media, including sandstone, carbonate, and sand packs.” Financial support for this research came from Stevens Institute of Technology. The news correspondents obtained a quote from the research from the Stevens Institute of Technology, “Surface contact angles measured from previous experiments were used to study the influence of the wettability on hydrogen transport in porous media. Many studies have investigated the impact of these factors on carbon dioxide sequestration. However, because of the difference in the thermal dynamic properties of the fluids and the purpose of UHS and carbon dioxide sequestration, it is still essential to analyze the UHS performance under the different rock and fluid properties. PNM simulations showed that a relatively larger contact angle with low water affinity was more suitable for UHS due to its low hydrogen trapping rate. Two machine learning methods, the least square fitting and the support vector machine (SVM), were developed to classify the ability of a rock to trap hydrogen and to predict hydrogen trapping rates. Hydrogen trapping rates simulated using the PNM were used as the training data in the machine learning models. The SVM classified rock samples into two groups which had high hydrogen trapping rates (>50 %) and low hydrogen trapping rates (<50 %). The machine learning results showed that rock samples with a low ratio of pore size to throat size and high pore connectivity (i.e., average number of throats connected to a given pore) were favorable for a low hydrogen trapping rate. This study illustrated that the impact of both rock surface wettability and pore structural geometry should be accounted for when evaluating a hydrogen-brine two-fluid system in porous media.”
查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “This study developed a machine learning model to accurately predict the isothermal magnetic entropy change (-SM) in amorphous alloys, a key parameter for evaluating magnetocaloric performance. Four machine learning algorithms were compared, and the (Extremely Randomized Trees) ETR algorithm demonstrated exceptional performance with an (R-squared) R2 value of 0.90 and a (Mean Absolute Percentage Error) MAPE of 13.31 % on the test set.” Financial support for this research came from National Key Research and Development Program of China.
查看更多>>摘要：News – Current study results on robotics have been published. According to news originating from Lublin, Poland, by NewsRx editors, the research stated, “The article focuses on the role of modern logistics 4.0 technologies and lean management in optimizing ancillary processes in intralogistics. The literature review presents critical aspects of intralogistics, including using autonomous mobile robots (AMR) and the challenges associated with their successful implementation.” Our news journalists obtained a quote from the research from Lublin University of Technology: “The article also discusses the concepts of Industry 4.0 and Industry 5.0. highlighting the importance of synergies between workers and advanced technologies. In optimizing logistics processes, the authors emphasize the importance of lean management and tools such as 5S and Kaizen. The authors analyze the research gap related to the organization of auxiliary processes, intralogistics, and the introduction of modern technologies. The lack of good practices and strategies for implementing new technologies for ancillary processes makes this a critical issue for managers and production engineers. The article provides practical strategies that can be implemented in companies. It is a valuable resource for managers seeking to manage intralogistics and effectively improve support processes in manufacturing plants.”
查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news reporting from Taipei, Taiwan, by NewsRx journalists, research stated, “Preterm infants are at high risk of neuromotor disorders. Recent advances in digital technology and machine learning algorithms have enabled the tracking and recognition of anatomical key points of the human body.” Financial supporters for this research include National Science And Technology Council. The news correspondents obtained a quote from the research from National Taiwan University Hospital and School of Medicine: “It remains unclear whether the proposed pose estimation model and the skeletonbased action recognition model for adult movement classification are applicable and accurate for infant motor assessment. Therefore, this study aimed to develop and validate an artificial intelligence (AI) model framework for movement recognition in full-term and preterm infants. This observational study prospectively assessed 30 full-term infants and 54 preterm infants using the Alberta Infant Motor Scale (58 movements) from 4 to 18 months of age with their movements recorded by 5 video cameras simultaneously in a standardized clinical setup. The movement videos were annotated for the start/end times and presence of movements by 3 pediatric physical therapists. The annotated videos were used for the development and testing of an AI algorithm that consisted of a 17-point human pose estimation model and a skeleton-based action recognition model. The infants contributed 153 sessions of Alberta Infant Motor Scale assessment that yielded 13,139 videos of movements for data processing. The intra and interrater reliabilities for movement annotation of videos by the therapists showed high agreements (88%-100%). Thirty-one of the 58 movements were selected for machine learning because of sufficient data samples and developmental significance. Using the annotated results as the standards, the AI algorithm showed satisfactory agreement in classifying the 31 movements (accuracy = 0.91, recall = 0.91, precision = 0.91, and F1 score = 0.91).”
查看更多>>摘要：Fresh data on robotics are presented in a new report. According to news reporting out of Auckland, New Zealand, by NewsRx editors, research stated, “Visible light positioning or VLP has been identified as a promising technique for accurate indoor localization utilizing pre-existing lighting infrastructure.” Our news reporters obtained a quote from the research from Massey University: “Robot navigation is one of the many potential applications of VLP. Recent literature shows a small number of works on robots being controlled by fusing location information acquired via VLP that uses a rolling shutter effect camera as a receiver with other sensor data. This paper, in contrast, reports on the experimental performance of a cartesian robot that was controlled solely by a VLP system using a cheap photodiode-based receiver rigidly attached to the robot’s end-effector. The receiver’s position was computed using an inverse-Lambertian function for ranging followed by multi-lateration. We developed two novel methods to leverage the VLP as an online navigation system to control the robot. The position acquired from the VLP was used by the algorithms to determine the direction the robot needed to move.”
查看更多>>摘要：New research on Mental Health Diseases and Conditions is the subject of a report. According to news reporting from Toronto, Canada, by NewsRx journalists, research stated, “Substance use is increasing among sexual and gender minority youth (SGMY). This increase may be due to changes in social norms and socialisation, or due to SGMY exploring the potential therapeutic value of drugs such as psychedelics.” Financial support for this research came from Canadian Institutes of Health Research. The news correspondents obtained a quote from the research from the University of Toronto Dalla Lana School of Public Health, “We identified predictors of psychedelics, MDMA and ketamine use. Data were obtained from 1414 SGMY participants who completed the ongoing longitudinal 2SLGBTQ+ Tobacco Project in Canada between November 2020 to January 2021. We examined the association between 80 potential features (including sociodemographic factors, mental health-related factors and substance userelated factors) with the use of psychedelics, MDMA and ketamine in the past year. Random forest classifier was used to identify the predictors most associated with reported use of these drugs. 18.1% of participants have used psychedelics in the past year; 21.9% used at least one of the three drugs. Cannabis and cocaine use were the predictors most strongly associated with any of these drugs, while cannabis, but not cocaine use, was the one most associated with psychedelic use. Other mental health and 2SLGBTQ+ stigmarelated factors were also associated with the use of these drugs. The use of psychedelics, MDMA and ketamine among 2SLGBTQ+ individuals appeared to be largely driven by those who used them together with other drugs. Depression scores also appeared in the top 10 factors associated with these illicit drugs, suggesting that there were individuals who may benefit from the potential therapeutic value of these drugs.”
查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting originating from Harbin, People’s Republic of China, by NewsRx correspondents, research stated, “This paper addresses the issue of safety in reinforcement learning (RL) with disturbances and its application in the safety-constrained motion control of autonomous robots. To tackle this problem, a robust Lyapunov value function (rLVF) is proposed.” Funders for this research include National Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central Universities, State Key Laboratory of Robotics and System, Heilongjiang Touyan Team.
查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting out of Gujarat Technological University by NewsRx editors, research stated, “Modern web application development involves handling enormous amounts of sensitive and consequential data. Security is, therefore, a crucial component of developing web applications.” Our news journalists obtained a quote from the research from Gujarat Technological University: “A web application’s security is concerned with safeguarding the data it processes. The web application framework must have safeguards to stop and find application vulnerabilities. Among all web application attacks, SQL injection and XSS attacks are common, which may lead to severe damage to Web application data or web functionalities. Currently, there are many solutions provided by various study for SQLi and XSS attack detection, but most of the work shown have used either SQL/XSS payload-based detection or HTTP request-based detection. Few solutions available can detect SQLi and XSS attacks, but these methods provide very high false positive rates, and the accuracy of these models can further be improved. We proposed a novel approach for securing web applications from both cross-site scripting attacks and SQL injection attacks using decoding and standardization of SQL and XSS payloads and HTTP requests and trained our model using hybrid deep learning networks in this paper. The proposed hybrid DL model combines the strengths of CNNs in extracting features from input data and LSTMs in capturing temporal dependencies in sequential data. The soundness of our approach lies in the use of deep learning techniques that can identify subtle patterns in the data that traditional machine learning-based methods might miss. We have created a testbed dataset of Normal and SQLi/XSS HTTP requests and evaluated the performance of our model on this dataset. We have also trained and evaluated the proposed model on the Benchmark dataset HTTP CSIC 2010 and another SQL/XSS payload dataset. The experimental findings show that our proposed approach effectively identifies these attacks with high accuracy and a low percentage of false positives. Additionally, our model performed better than traditional machine learning-based methods.”
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting from Urbana, Illinois, by NewsRx journalists, research stated, “Construction activities during airport expansion projects disrupt air traffic operations and often need to be performed in phases to minimize their disruptive impacts. This paper presents a machine learning methodology for quantifying the impact of alternative construction phasing plans on air traffic operations.” The news correspondents obtained a quote from the research from the Department of Civil and Environmental Engineering, “The methodology is implemented in four stages: data collection, data preprocessing, model training, and evaluation stages. A case study is analyzed to highlight the original contributions of the methodology that include (1) development of five machine learning models for accurately and efficiently quantifying the impact of construction-related airport closures on flights ground movement time, (2) comparison of the performance and prediction accuracy of the developed models, and (3) efficient assessment of the impact of alternative construction phasing plans on airport operations without the need for time-consuming simulations.”
查看更多>>摘要：New study results on robotics have been published. According to news reporting originating from Tokyo, Japan, by NewsRx correspondents, research stated, “This study proposes a method to automatically generate paths for multiple autonomous agents to collectively form a sequence of consecutive patterns.” Financial supporters for this research include Japan Society For The Promotion of Science. Our news reporters obtained a quote from the research from Waseda University: “Several studies have considered minimizing the total travel distances of all agents for formation transitions in applications with multiple self-driving robots, such as unmanned aerial vehicle shows by drones or group actions in which self-propelled robots synchronously move together, consecutively transforming the patterns without collisions. However, few studies consider fairness in travel distance between agents, which can lead to battery exhaustion for certain agents and thereafter reduced operating time. Furthermore, because these group actions are usually performed with a large number of agents, they can have only small batteries to reduce cost and weight, but their performance time depends on the battery duration.”
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