We perform a global sensitivity analysis of the binding energy and the charge radius of the nucleus ^{16}O to identify the most influential low-energy constants in the next-to-next-to-leading order ...chiral Hamiltonian with two- and three-nucleon forces. For this purpose, we develop a subspace-projected coupled-cluster method using eigenvector continuation Frame D. et al., Phys. Rev. Lett. 121, 032501 (2018)PRLTAO0031-900710.1103/PhysRevLett.121.032501. With this method, we compute the binding energy and charge radius of ^{16}O at more than 10^{6} different values of the 16 low-energy constants in one hour on a standard laptop computer. For relatively small subspace projections, the root-mean-square error is about 1% compared to full-space coupled-cluster results. We find that 58(1)% of the variance in energy can be apportioned to a single contact term in the ^{3}S_{1} wave, whereas the radius depends sensitively on several low-energy constants and their higher-order correlations. The results identify the most important parameters for describing nuclear saturation and help prioritize efforts for uncertainty reduction of theoretical predictions. The achieved acceleration opens up an array of computational statistics analyses of the underlying description of the strong nuclear interaction in nuclei across the Segrè chart.
The Birth of the Lithium-Ion Battery Yoshino, Akira
Angewandte Chemie (International ed.),
June 11, 2012, Letnik:
51, Številka:
24
Journal Article
Recenzirano
The moment of truth: The lithium‐ion battery is currently the predominant power source for mobile phones, laptop computers, and many other portable electronic devices, and is being used increasingly ...in electric vehicles. Its inventor, A. Yoshino, describes the process by which the lithium‐ion battery was first developed (picture shows the first test‐tube cell) and made commercially practical. Successful safety tests marked the turning point in this work.
There is no formal system in place for household e-waste management although e-waste from the industries were controlled and regulated according to Natural Resources and Environment Ministry in ...Malaysia. In fact, e-wastes are collected by buyers, non-governmental organizations or collectors; but many are improperly dismantled which can cause environmental and health hazards. Malaysia was estimated to generate 53 million pieces of e-waste in the year 2020 and therefore a proper system is required to control hazardous substances such as cadmium, mercury, chromium, zinc, lead, silver and copper found in e-wastes which should not be released into the environment. The aim of the present study is to find out the individual conviction on laptop disposal practices. Data were obtained from 123 respondents through structured questionnaire and open-ended questions from individuals owning laptop. The findings highlight that individual awareness on laptop disposal practice and laptop usage are positively influencing on the conviction of laptop disposal practices. Knowledge on computer literacy moderates the relationship between social consequences and the conviction of laptop disposal practices. It is recommended in the present study an extensive e-waste management model that resolves some of the major challenges aroused due to e-waste crisis. In particular, the proposed model acts as a guide for upstream and downstream reduction of e-waste generation through green design and cleaner engenderment to succeed for e-waste environmentally sound management system.
Two studies were conducted to examine what undergraduate students do on their laptops during class time and the extent to which laptop usage behaviors are associated with academic success. In Study ...1, a sample of 1129 students from a Canadian university completed a survey measuring prototypical behaviors emitted on laptops during class time. Results of factor analyses indicated that laptop behaviors can be regrouped in two dimensions: School related and school unrelated laptop utilization. School unrelated laptop behaviors were significantly associated with lower levels of self-reported academic achievement and satisfaction. School related laptop behaviors were positively associated with academic satisfaction. These results were invariant across different faculties on campus. In Study 2, another sample of 88 students was recruited to examine the longitudinal association between laptop behaviors and semester grade point average obtained at the end of the semester. Results of Study 2 showed that school unrelated laptop behaviors were prospectively associated with lower semester grade point average, even after controlling for a series of potentially confounding influences (i.e., self-regulation failure, motivational deficit, disorganized learning, internet addiction, and school disenchantment). Overall, these results provide theoretically important support to suggest that in-class laptop utilization is a unique and contemporary mode of learning that should not be treated as an epiphenomenon merely accountable and reducible to other sources of psychological influences.
•Students emit school related and unrelated behaviors on laptops during class.•School unrelated laptop behaviors predict lower semester grade point average.•School related laptop behaviors are associated with higher academic satisfaction.•Results replicated across four faculties on campus.•Laptop explains unique variance beyond other sources of psychological influence.
The leading causes of construction fatalities include traumatic brain injuries (resulted from fall and electrocution) and collisions (resulted from struck by objects). As a preventive step, the U.S. ...Occupational Safety and Health Administration (OSHA) requires that contractors enforce and monitor appropriate usage of personal protective equipment (PPE) of workers (e.g., hard hat and vest) at all times. This paper presents three deep learning (DL) models built on You-Only-Look-Once (YOLO) architecture to verify PPE compliance of workers; i.e., if a worker is wearing hard hat, vest, or both, from image/video in real-time. In the first approach, the algorithm detects workers, hats, and vests and then, a machine learning model (e.g., neural network and decision tree) verifies if each detected worker is properly wearing hat or vest. In the second approach, the algorithm simultaneously detects individual workers and verifies PPE compliance with a single convolutional neural network (CNN) framework. In the third approach, the algorithm first detects only the workers in the input image which are then cropped and classified by CNN-based classifiers (i.e., VGG-16, ResNet-50, and Xception) according to the presence of PPE attire. All models are trained on an in-house image dataset that is created using crowd-sourcing and web-mining. The dataset, named Pictor-v3, contains ~1,500 annotated images and ~4,700 instances of workers wearing various combinations of PPE components. It is found that the second approach achieves the best performance, i.e., 72.3% mean average precision (mAP), in real-world settings, and can process 11 frames per second (FPS) on a laptop computer which makes it suitable for real-time detection, as well as a good candidate for running on light-weight mobile devices. The closest alternative in terms of performance (67.93% mAP) is the third approach where VGG-16, ResNet-50, and Xception classifiers are assembled in a Bayesian framework. However, the first approach is the fastest among all and can process 13 FPS with 63.1% mAP. The crowed-sourced Pictor-v3 dataset and all trained models are publicly available to support the design and testing of other innovative applications for monitoring safety compliance, and advancing future research in automation in construction.
•Personal protective equipment (PPE) can prevent workplace injuries and fatalities.•Three AI models are proposed to visually detect if workers are wearing hard hat and vest.•The dataset consists of ~1500 annotated images and ~4700 instances of workers.•The best model achieves ~72% accuracy in detecting PPE in real-world jobsite images.•The model processes >10 images per second (real-time) in uncontrolled environments.
The purpose of this research was to investigate preservice teachers' perceptions about using m‐phones and laptops in education as mobile learning tools. A total of 1087 preservice teachers ...participated in the study. The results indicated that preservice teachers perceived laptops potentially stronger than m‐phones as m‐learning tools. In terms of limitations the situation was balanced for laptops and m‐phones. Generally, the attitudes towards using laptops in education were not exceedingly positive but significantly more positive than m‐phones. It was also found that such variables as program/department, grade, gender and possessing a laptop are neutral in causing a practically significant difference in preservice teachers' views. The results imply an urgent need to grow awareness among participating student teachers towards the concept of m‐learning, especially m‐learning through m‐phones.
There has been a shift in college classrooms from students recording lecture notes using a longhand pencil-paper medium to using laptops. The present study investigated whether note-taking medium ...(laptop, longhand) influenced note taking and achievement when notes were recorded but not reviewed (note taking's process function) and when notes were recorded and reviewed (note taking's product function). One unique aspect of the study was determining how laptop and longhand note taking influence the recording of lecture images in notes and image-related achievement. Note-taking results showed that laptop note takers recorded more notes (idea units and words) and more verbatim lecture strings than did longhand note takers who, in turn, recorded more visual notes (signals and images) than did laptop note takers. Achievement results showed that when taking laptop notes, the process function of note taking was more beneficial than the product function of note taking (i.e., better image-related learning and similar text-related learning). When taking longhand notes, the product function of note taking was more beneficial than the process function of note taking (i.e., better text-related learning and similar image-related learning). Achievement findings suggest that the optimal note-taking medium depends on the nature of the lecture and whether notes are reviewed.
•Synergistic features of commercial reuse: use extension and steering to recycling.•Preparation for reuse is environmentally negligible compared to benefits.•Recycling offsets significant shares of ...laptops’ metal resource use.•Impact reduction varies depending on LCIA-methods’ valuation of recycled metals.•Several metal resource use perspectives useful for assessment of CE applied to ICT.
The circular economy is proposed to reduce environmental impact, but as yet, there is limited empirical evidence of this sort from studying real, commercial circular economy business cases. This study investigates the environmental impacts of using second-hand laptops, mediated by a commercial reuse operation, instead of new ones. The method used is life cycle assessment (LCA) and special attention is given to laptops’ metal resource use by using several complementary life cycle impact assessment methods. The results show that all activities required to enable reuse of laptops are negligible, despite the reuse company’s large geographical scope. Two principal features of reuse reduce environmental impacts. Firstly, use extension reduces all impacts considerably since there are large embedded impacts in components. Secondly, the reuse company steers non-reusable laptops into state-of-the-art recycling. This provides additional impact reductions, especially with regards to toxicity and metal resource use. The results for metal resource use however diverge between LCIA methods in terms of highlighted metals which, in turn, affects the degree of impact reduction. LCIA methods that characterise functionally recycled metals as important, result in larger impact reduction, since these emphasise the merits of steering flows into state-of-the-art recycling. The study thus demonstrates how using second-hand laptops, mediated by a commercial reuse operation, compared to new ones, in practice, reduces different types of environmental impact through synergistic relationships between reuse and recycling. Moreover, it illustrates how the choice of LCIA method can influence interpretations of metal resource use impacts when applying circular economy measures to information and communication technologies (ICT).
Appearance-based gaze estimation in the wild Xucong Zhang; Sugano, Yusuke; Fritz, Mario ...
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
06/2015
Conference Proceeding
Odprti dostop
Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated ...across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks that significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithms on three current datasets, including our own. This evaluation provides clear insights and allows us to identify key research challenges of gaze estimation in the wild.
Descriptions of moment-by-moment changes in attention contribute critical elements to theory and practice about how people process media. We introduce a new concept called screenertia and use new ...screen-capture methodology to empirically evaluate its occurrence. We unobtrusively obtained 400,000+ screenshots of 30 participants’ laptop screens every 5 seconds for 4 days to examine individuals’ attention to their screens and how the distribution of attention differs across media content. All individuals’ screen segments were best described by a log-normal survival function—evidence of screenertia. Consistent with the literature on uses and gratifications of media, news/entertainment activities were the most “sticky.” These findings indicate that screenertia is not only related to the level of interactivity of media content but is also related to its modality and agency. Discussion of the findings highlights the importance of theorizing, examining, and modeling the specific time scales at which media behaviors manifest and evolve.
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