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With the increase in adoption of mobile electronic devices such as smartphones, it becomes more and more important to address the risks associated with their use. As such, this research paper addresses the behavior and habit formation of mobile multitasking, i.e., the use of a mobile IT device while performing a motor task such as walking, and its negative impacts on the individual’s performance and safety. To have a better understanding on how different countermeasures impact the behavior of mobile multitasking in the short term as well as the habit of mobile multitasking in the long term, the present research paper introduces a classification of the different countermeasures that are put in place to curb the risks of mobile IT multitasking in an urban context. Then, it proposes a conceptual framework that explains the mechanisms and the impacts of the deterrent countermeasures as well as the preventive countermeasures on both the behavior of mobile multitasking and the habit formation of mobile multitasking.

In this article, we report about our progress in developing and testing a teaching–learning sequence on thermal phenomena and irreversibility for secondary school students, based on dice and coin toy models which are used both as hands-on game activity and computer simulations. Our approach can be described as laboratory-based, where the term “laboratory” is not intended as referring solely the physics laboratory, but as an integrated laboratory perspective, comprising physical experiments, game with toy models, computer simulation, group discussion, and group tasks. We report the results of a first experimentation with one class of the fourth year of secondary school (17–18 years old).

As quantum computers are becoming real, they have the inherent potential to significantly impact many application domains. In this paper we outline the fundamentals about programming quantum computers and show that quantum programs are typically hybrid consisting of a mixture of classical parts and quantum parts. With the advent of quantum computers in the cloud, the cloud is a fine environment for performing quantum programs. The tool chain available for creating and running such programs is sketched. As an exemplary problem we discuss efforts to implement quantum programs that are hardware independent. A use case from quantum humanities is discussed, hinting which applications in this domain can already be used in the field of (quantum) machine learning. Finally, a collaborative platform for solving problems with quantum computers – that is currently under construction – is presented.

The term selection is a potential challenge in query expansion. It concerns with the inability of query expansion techniques to select appropriate terms for expanding query. Most of the existing term selection for query expansion techniques allows the expansion of terms with synonyms from a single semantic dictionary, and this doesn’t provide an efficient information retrieval system due to low precision results. This paper proposed terms interrelationship of term selection value (TI-TSV) for query expansion where terms are expanded based on synonyms from many semantic dictionaries. The proposed TI-TSV employs existing TSV method of term selection based on the three cases. The three Cases for term selection are based on the retrieved top-ranked documents to increase precision results. The experiments are performing on Yusuf Ali, Sarwar and Arberry English Quran translation datasets collected from Tanzil. The results indicate that the proposed TI-TSV has significantly improved the performance of the search system.

During the COVID-19 pandemic, ‘Chinese Virus’ emerged as a controversial term for coronavirus. To some, it may seem like a neutral term referring to the physical origin of the virus. To many others, however, the term is in fact attaching ethnicity to the virus. While both arguments have justifications of their own, quantitative analysis of how these terms are being used in real life is lacking. In this paper, we attempt to fill this gap with fine-grained analysis. We find that tweets with a controversial term can be easily distinguished from those with neutral terms using state-of-the-art classifiers, that they cover different substantive topics, and that they possess different linguistic features reflecting sentiment and psychological states. All evidence suggests that the real life use of the controversial terms is distinctively different from that of the neutral terms.

Query expansion has changed the information retrieval process to improve search performance. It aimed at improving the performance of information retrieval system to retrieve user information need. However, the term selection process still lacks precision results due to lexical ambiguity challenge. Many researchers have focused on pseudo-relevance feedback to select terms from the top-retrieved documents using some statistical linguistic techniques. However, their methods have limitations. This paper proposed a statistical linguistic terms interrelationship that exploits term selection in query expansion and retrieved relevance results. The proposed approach was tested on Malay, Hausa and Urdu Quran translated datasets and the results indicate that the proposed approach outperforms the previous method in retrieving relevance results. Future work should focus on the weighting score based on terms interrelationship to improve the query expansion performance.

The study investigates accuracy of two machine learning methods, neuro-fuzzy system with grid partition (ANFIS-GP) and multivariate adaptive regression spline (MARS) in prediction of 1-day- to 6-day-ahead groundwater levels (GWLs) using data from two wells, USA. The outcomes indicate that the ANFIS-GP provides inferior results compared to regression-based simple MARS method. The MARS method which is much simpler than the ANFIS-GP is recommended for short-term GWL prediction.

Artificial’ ability the term artificial intelligence (AI) was first coined by John McCarthy in 1956 when he had the initial theoretical meeting on this topic. But this journey to see if machines will really believe started much before this. Five years later Alan Turing published the article on this idea of machines being able to imitate human race and the ability to do intelligent things, e.g., play chess. Yet, most people be uncertain about the purpose of AI. What if he produces the AI that has the only aim of estimating the amount of Pi, but has decided that by getting earthly death and giving human lives, it could gain another digit to its collection of digits efficiently? Therefore, the entire idea of knowing in machines is unlikely. The only plausible explanation of the power to philosophize, emotionally interact with humans and reason motivation is artificial awareness.

The rolling horizons procedure is widely used both in industry and in scientific research for the resolution of mathematical programming models. It allows reducing the size of the models to be solved in the times allowed with the available computational capacities. It takes into consideration the closest information with less uncertainty. But programming models can have symmetries, when they have variables that can be permuted without changing the structure of the problem. These symmetries increase the search spaces for possible solutions, increasing the need for computation and presenting alternative solutions with equivalent results in the objective function. The symmetry can generate different solutions with equivalent values in the objective function but that can imply very different results in the long term with the rolling horizons procedure. This paper presents the proposed actions to symmetry break that have been applied to the rolling horizons procedure. The work provides the different proposals identified to break the symmetry.

Hospital information system stores all clinical information, whose major part is electronic patient records written by doctors, nurses and other medical staff. Since records are described by medical experts, they are rich in knowledge about medical decision making. This paper proposes an approach to extract clinical knowledge from the texts of clinical records. The method consists of the following three steps. First, discharge summaries, which include all clinical processes during the hospitalization, are extracted from hospital information system. Second, morphological and correspondence analysis generates a term matrix from text data. Then, finally, machine learning methods are applied to a term matrix in order to acquire classification knowledge. We compared several machine learning methods by using discharge summaries stored in hospital information system. The experimental results show that random forest is the best classifier, compared with deep learning, SVM and decision tree. Furthermore, random forest gains more than 90% classification accuracy.

Bilingual term resources are helpful on collaborative translation tasks. Firstly, we build an English-Chinese parallel term resource of social sciences (SocialSciTerm) based on the bilingual big data and advanced theories of information extraction and alignment. Secondly, we verify the SocialSciTerm’s accuracy and practicality through a translation task of social sciences. The statistical results indicate that the coverage rate is high in common entity names and terms of social sciences disciplines while that of text-specific word still needs to be improved. Finally, some advice is proposed for the future study of information extraction and alignment.

Unsupervised image anomaly detection and segmentation is challenging but important in many fields, such as the defect of product inspection in intelligent manufacturing. The challenge is that, the labeled anomalous data is few and only normal data is available, causing the distribution of the anomaly unknowable. Unsupervised methods based on image-reconstruction and feature-embedding have been recently studied for anomaly detection and segmentation, while there still exists the problems in term of the reconstruction quality and testing performance. Inspired by the application of pre-trained feature-embedding in anomaly detection, this paper proposes a novel framework called Partial Distillation of Deep Feature (PDDF) for unsupervised image anomaly detection and segmentation. Only normal images are used to partially distill the knowledge of the deep features extracted by the pre-trained network at training. The difference (dissimilarity) between the distilled feature with the pre-trained one is applied to score the uncertainty of anomaly at testing. The PDDF achieves the comparing results compared with the state-of-the-art methods on MVTec AD dataset, with the area under the receiver operating characteristic curve (ROCAUC) of 96.7% for unsupervised anomaly detection, 96.9% and the area under the per-region-overlap curve (PROAUC) of 91.8% for unsupervised anomaly segmentation.

Most existing aspect-term level sentiment analysis (ATSA) approaches combined neural networks with attention mechanisms built upon given aspect to generate refined sentence representation for better predictions. In these methods, aspect terms are always provided in both training and testing process which may degrade aspect-level analysis into sentence-level prediction. However, the annotated aspect term might be unavailable in real-world scenarios which may challenge the applicability of the existing methods. In this paper, we aim to improve ATSA by discovering the potential aspect terms of the predicted sentiment polarity when the aspect terms of a test sentence are unknown. We access this goal by proposing a capsule network based model named CAPSAR. In CAPSAR, sentiment categories are denoted by capsules and aspect term information is injected into sentiment capsules through a sentiment-aspect reconstruction procedure during the training. As a result, coherent patterns between aspects and sentimental expressions are encapsulated by these sentiment capsules. Experiments on three widely used benchmarks demonstrate these patterns have potential in exploring aspect terms from test sentence when only feeding the sentence to the model. Meanwhile, the proposed CAPSAR can clearly outperform SOTA methods in standard ATSA tasks.

Those who do not study history are doomed to repeat it, but much the same can be said of those who fail to prepare for the future. In this chapter, both the long and short view of the future are taken by our panel of contributors: short term outlooks often revolve around the media titans of today—GAFAM (Google, Apple, Facebook. Amazon, Microsoft), which according to Benhamou, cannot be ignored. Long term outlooks include both positivity—Curien—and pessimism, via Bernisson. Of course, these forecasting exercises will only meet reality halfway. The future is as unpredictable as the past is unbelievable.

This chapter investigates how the National Science Foundation’s (NSF) Long Term Ecological Research (LTER) Program has changed from 1980 to 2018. The LTER program is designed to balance persistence with response to change in science, society, and ecosystems through renewable 6-year grants subjected to peer review at the midterm and at renewal. The LTER program had an initial period of rapid growth with some terminations (1980s), a middle period of slower growth with no terminations (1990–2010), and a third period of no net growth, with added and terminated sites and an accelerated rate of site probations (2010s). Changes in the character and composition of the LTER program are associated with changes in leadership and research directions within individual LTER sites, as well as changes in the sources of funding for the LTER program within NSF, turnover in NSF program officers, and changes in review criteria used to renew LTER site funding. In the past decade, a focus on conceptual frameworks as a tool for integrating LTER research emerged from the LTER renewal review process. Given the accelerated pace of environmental change, the need for long-term ecological research is even more urgent today than when NSF established the pioneering LTER program. The LTER Program history reveals important lessons for how to structure and manage long-term ecological research.

Renewable energy infrastructure are multimillion long-term investments that involve dozens of extensive studies of all sort, numerous stakeholders, public and private, each of them bund to the project company and to each other through one to several agreements. One could prudently say that the complexity of the agreements goes with the importance of what is at stake in the agreement, in other words, the bigger the stake, the more complex the contract. In Project Finance, these contracts are one of the most relevant pieces of the financing puzzle, since they represent together with the obtained permits and licenses the “asset” of the project company. If a power purchase agreement (PPA), for instance, has good conditions relative to the average market conditions, e.g., a higher offtake price or a longer term, it will create more “asset” value to the project. This complexity requires the participation of experts, advisors, lawyers, financiers, promoters among others who will discuss and negotiate the contract terms from their perspective. All agreements are relevant, however, some of them, because of the high impact they might have on the future revenues stream of the project, require special attention and care from the contracting parties. The offtake contract, the EPC and O&M contracts, the shareholders agreement, and the share purchase agreement as well as the facility agreement clearly belong to this category and will be thoroughly analyzed.

The term “smart city” is usually associated with eco-friendliness, resource efficiency and environmental sustainability. Also, Information and Communication Technology (ICT) is vastly used within these developments as a catalyst for solving social and business needs during the operation of smart cities, with user-friendly solutions, optimized infrastructure and more sustainable approaches.

In this chapter, which paves the way for our systematic discussion of the problem of the a priori in the Logical Investigations, we will reconstruct Husserl’s late retrospective assessment of the way in which he himself had understood the concept of a priori from his early works on. We will show that according to Husserl’s narrative, the Greek term eidos was introduced for two different reasons: to avoid some of the ambiguities of the expressions a priori-a posteriori as well as to have a more fine-grained terminology to account for the many meanings implied by the German term Wesen.

Correcting functional terms using data driven techniques is one recent approach to improve the predictive accuracy of turbulence models. We present a multitask learning framework that can be used to generalize functional corrections of turbulence models to different flow conditions and test cases. The approach is developed with a view to generalize the correction of turbulence models using data from different flow conditions. The machine learning model first learns a compressed latent representation of flow variables that are most correlated with the correction term and has enhanced predictive accuracy for new test cases. The approach is shown to improve the performance of Spalart-Allmaras turbulence model in massively separated flow regimes.

In this chapter, we argue that the literature has added considerable complexity around the term New Way(s) of Working (NWW) by producing multiple and conflicting definitions of NWW. Several scholars imprecisely and indiscriminately refer to NWW as a “philosophy”, a “phenomenon”, a “concept”, a “mix of practices”, or a “type of work organization”. This chapter aims to provide support for five ways of conceptualizing NWW: (1) as a management fashion disseminated across organizational fields, (2) as a set of discourses and narratives, (3) as an organizational change project, (4) as a material workspace, and (5) as a set of work practices and behaviors.

Thinking about the future city the way food-energy-water generation, distribution and supply is organised may well make a difference for urban dwellers’ quality of life. The urban context is unprecedented and cannot be predicted very well. When uncertainties increase the demand for simple responses seems to be the preferred way of treatment. This is however an implicit flaw because when the complexity of the problems is getting higher the responses can no longer be simple. Responding as if the city is stable while it is increasingly dynamic would only bring fake solutions that last for a short time. The opposite is the way forward: when problems are wicked, self-organising processes and responses that do not bring definite solutions are preferable as they can adjust themselves should the problem change along the way. For FEW-nexuses this implies that a moveable approach, in which the solutions are flexible, and benefit from all other components in the system, will decrease uncertainty, in particular on the longer term. In this context a design-led approach is extremely useful, as it is able to create something out of nothing that was before, presenting opportunities to be continuously adaptive. As a city, as a landscape and as a society.

Ricoeur’s readers usually assume they understand his view of Hegel, since he talks about Hegel often and likes to characterize various aspects of his work as “Hegelian.” What often goes unnoticed is that Ricoeur does not always use this term in the same way. This chapter argues that Ricoeur uses the term “Hegelian” in three distinct senses: a methodological, an ontological, and a metaphilosophical sense. These senses overlap, but they are also in tension, and this fact greatly complicates the task of identifying Ricoeur’s attitude toward German Idealism—especially his claim in Time and Narrative that contemporary philosophers should “renounce Hegel.”

In the nonprofit world, many organizations claim they are working to end the challenge they are dedicated to solve, no matter how limited their resources or reach are. In the fight against homelessness, the term “solving homelessness” is a common one used by nonprofits. There was always a hope that homelessness could be solved. The fact that there are one million homeless Americans living in the U.S. is hard to process. The fact that every city in the U.S. has dozens or hundreds of families, mostly singles moms, living in their cars tonight with their children is shocking. 20,000 children age out of foster care every year when they turn 18 years old and 40–50% of them will become homeless.

A short term study abroad may be preferable to students who do not wish to go abroad for an extended period of time or not have the flexibility within their major for an entire semester abroad. In the summer 2015 I organized a short term study abroad trip to study renewable energy while traveling through Germany and Switzerland. The goal of the course was to provide students with an introduction to energy markets with a specific emphasis on renewable energy and applications to the German Renewable Energy Act. This paper details the process of developing the course and experiences while traveling abroad with a group of 10 students.

The subject of the study was the recognition of sounds of critical situations in the audio signal. The term “critical situation” is understood as an event, the characteristic sound signs of which can speak about acoustic artifacts as a shot, a scream, a glass crash, an explosion, a siren, etc.. The paper considers the scope of audio analytics, its advantages, the history of spectral analysis, as well as analyzes and selects tools for further development of system components. In the paper, we propose our dataset that consists of 14 classes that contains 1000 sounds of each, and a model to detect emergency situations using audio processing and analytics.

Aiming at the problem that the traditional dictionary system cannot use the big data mining technology for term calculation, which leads to the long response time of the dictionary system retrieval, a philosophy and social science term dictionary system based on big data mining is designed. The hardware part designs the system controller and connects the single-chip microcomputer connection circuit. The software part first divides the term dimensions according to the characteristics of philosophy and social science terms, mines the corpus according to different dimensions, completes the calculation of philosophy and social science terms, sets up the term database structure, and finally completes the software design of the dictionary system. The experimental results show that: Compared with the traditional dictionary system, the search time of philosophy and Social Sciences terminology dictionary system based on big data mining is the shortest, and the detection accuracy of philosophy and social science terms is higher, which is suitable for all applications.

Atmospheric fine particulate pollutant affects seriously the human health but also the passage of light through the lower atmosphere, reducing the solar radiation reaching the ground as well as the PV panels. In this study, the solar insolation reduction due to air pollution has been investigated in the city of Naples (Italy). Analyzing local long term field data, we have obtained that the solar insolation reduction is exponentially correlated to the PM2.5 concentration. By using the derived empirical relation, for Naples it was estimated that the solar insolation was reduced around 5% or 66.20 kWh/m−2 within one-year period (May 2018–May 2019), due to air fine particulate pollution. This study provides the theoretical basis to design successful solar PV systems to be mounted on building rooftops or in other suitable sites, taking into account also the local air pollution condition.

Carbon dioxide (CO2) emissions is one of the recent global issues where the negative influence and effect on the environment is high. Enhancing the degree of awareness among public and concerned authorities and developing forecasting methods and techniques form a vital solution to this issue. The aim of this research is to enhance the forecasting efficiency of the traditional GM(1,N) model by proposing and modifying background values of GM(1,N) using a new algorithms. This paper presents the Artificial Bee Colony (ABC) to select the optimal weight of background values for a traditional GM(1,N) model. The data of CO2 emissions, GDP per capita, the amount invested in Malaysia, population, total energy consumption and number of registered motor vehicles during the period from 2000 to 2016 is used to verify the applicability and effectiveness of the model. The numerical example results indicate that the new model is performing well compared to the traditional GM(1,N) model.

Many patients recover following even a severe brain injury and go on to make a good physical recovery such that to an outside observer they would appear to be “normal.”

Statistical methods have been systematically adopted in efforts to improve process efficiency and semiconductor manufacturing quality. Some modeling advances have been provided to meet business peculiarities and specific characteristics of this industry. This paper presents an approach to address long-term Pp (Ppk) indices using a modified control chart. The idea is to apply this model to dynamically obtain the ppm (parts per million) and the acceptance limits given some parameters: long-term target, the probability of rejecting a good item, and the upper and lower specification limits. In a way that the cost of identifying and correcting special causes should be compatible with the cost of off-target products and with the corporate quality policy. Such an approach was developed to integrate a statistical module for monitoring and controlling of equipment set-up of a Brazilian semiconductor manufacturing company.

The response theory applied to study climate change has its origins in a couple of papers published by Cecil Leith in the 1970s. It is based on the Fluctuations Dissipation Theorem (FDT) whose classical application is to the Brownian motion. In this case, the random motion of a particle, in the fluid, is forced by the thermal movement of the molecules. During the motion the energy of the particle is dissipated by the viscosity of the fluid and converted to heat that contribute to maintain the temperature of the fluid. Following the words of Leith, “In this analogy the detailed motions of gas molecules correspond to the weather and the statistical properties of a gas such as temperature and pressure correspond to the climate” with some additional problems. For practical purposes climate is defined as the average of weather in some location and this must include the mean but also the standard deviation from the mean. Beside the mean must be carried out over some time interval and this is a crucial point. As a matter of fact, climate changes with time so that the interval must be long enough but not too long that would eliminate long term variations. In statistical mechanics such a problem is circumvented by recurring to the concept of ensemble mean. The ensemble is constituted by a large number (infinity?) of identical systems. This concept is not very practical to define climate because it would require studying a large number of Earths, subject to the same conditions including forcing. There is an alternative definition of climate as the distribution of the different variables (temperature, precipitation, etc.) with the weather being a sample from that distribution.

Aspect-Based Opinion Mining (ABOM) mainly focuses on mining the aspect terms (product’s features) and related opinion polarities (e.g., Positive, Negative, and Neutral) from user’s reviews. The most prominent neural network-based methods to perform ABOM tasks include BERT-based approaches, such as BERT-PT and BAT. These approaches build separate models to complete each ABOM subtasks, such as aspect term extraction (e.g., pizza, staff member) and aspect sentiment classification. Both approaches use different training algorithms, such as Post-Training and Adversarial Training. Also, the BERT-LSTM/Attention approach uses different pooling strategies on the intermediate layers of the BERT model to achieve better results. Moreover, they do not consider the subtasks of aspect categories (e.g., a category of aspect pizza in a review is food) and related opinion polarity. This paper proposes a new system for ABOM, called BERT-MTL, which uses Multi-Task Learning (MTL) approach and differentiates from these previous approaches by solving two tasks such as aspect terms and categories extraction simultaneously by taking advantage of similarities between tasks and enhancing the model’s accuracy as well as reduce the training time. Our proposed system also builds models to identify user’s opinions for aspect terms and aspect categories by applying different pooling strategies on the last layer of the BERT model. To evaluate our model’s performance, we have used the SemEval-14 task 4 restaurant dataset. Our model outperforms previous models in several ABOM tasks, and the experimental results support its validity.

Up to date, various neural-based methods have been proposed for joint mention span detection and coreference resolution. However, existing studies on coreference resolution mainly depend on mention representations, while the rest spans in the text are largely ignored and directly eliminated. In this paper, we aim at investigating whether those eliminated spans are totally worthless, or to what extent they can help improve the performance of coreference resolution. To achieve this goal, we propose to refine the representation of mentions with global spans including these eliminated ones leveraged. On this basis, we further introduce an additional loss term in this work to encourage the diversity between different entity clusters. Experimental results on the document-level CoNLL-2012 Shared Task English dataset show that the eliminated spans are indeed useful and our proposed approaches show promising results in coreference resolution.

Globally, sustainable product design is an issue that has been receiving more academic attention around the world. Understanding the sustainability-related design decisions seems to be important to ensuring sustainable design. Based on a stance, this study attempted to quantify sustainability factors in the product design through a hybrid method which combines the fuzzy cognitive mapping (FCM) and the system dynamics (SD) approaches. Designer behavior with regard to sustainability criteria was simulated in Vensim and sourced through sessions of two groups who deal with sustainable design. The results provide an easy-to-understand model of sustainable design to help designers analyze the dynamics of the cause-and-effect relationships between factors of sustainable design and long term scenarios, therefore, different scenarios at the inter and intra-cluster levels were built to determine the impacts of variable changes on the model developed.

PICO recognition is an information extraction task for detecting parts of text describing Participant (P), Intervention (I), Comparator (C), and Outcome (O) (PICO elements) in clinical trial literature. Each PICO description is further decomposed into finer semantic units. For example, in the sentence ‘The study involved 242 adult men with back pain.’, the phrase ‘242 adult men with back pain’ describes the participant, but this coarse-grained description is further divided into finer semantic units. The term ‘242’ shows “sample size” of the participants, ‘adult’ shows “age”, ‘men’ shows “sex”, and ‘back pain’ show the participant “condition”. Recognizing these fine-grained PICO entities in health literature is a challenging named-entity recognition (NER) task but it can help to fully automate systematic reviews (SR). Previous approaches concentrated on coarse-grained PICO recognition but focus on the fine-grained recognition still lacks. We revisit the previously unfruitful neural approaches to improve recognition performance for the fine-grained entities. In this paper, we test the feasibility and quality of multitask learning (MTL) to improve fine-grained PICO recognition using a related auxiliary task and compare it with single-task learning (STL). As a consequence, our end-to-end neural approach improves the state-of-the-art (SOTA) F1 score from 0.45 to 0.54 for the “participant” entity and from 0.48 to 0.57 for the “outcome” entity without any handcrafted features. We inspect the models to identify where they fail and how some of these failures are linked to the current benchmark data.

Retrieving information through World Wide Web searches is part of daily life. Many people prefer using short search strings/queries which can be ambiguous because of their brevity. This ambiguity often causes search engines to return thousands of irrelevant results which can cause frustration with the particular search engine. Consequently, users might rate the particular search engine unfavourably. We conducted a randomised controlled cross-over trial with a Graeco-Latin Square design to measure various user emotions (Frustration, Excitement, Meditation and Engagement) with a Brain-Computer Interface while participants performed ambiguous Internet searches in Google, Yahoo! and Bing. The study results suggest that emotion data captured with a Brain-Computer Interface together with the pre-test and post-test questionnaire feedback can be used to characterise the user experience of different search engines when searches are conducted using ambiguous search terms. In particular, the effect of Search Engine and Search Term had a significant outcome on the measured emotions, while the effect of Occasion was not significant.

The support of modelling in school is a common issue in investigations and in the relevant literature on modelling competence. In this chapter, research is presented on constructing a test instrument for assessing metacognitive knowledge of modelling. Based on a theoretical definition of the term “metacognitive knowledge” and its domain-specific connection to mathematical modelling, a large number of items were developed. The scalability and possible reduction of items are analysed in this chapter. The process of item construction and evaluation is described in detail. With the help of a one-parameter Rasch analysis, it can be deduced that a selection of items is suitable for measuring at least some aspects of metacognitive knowledge of mathematical modelling.

We consider Holmgren-John’s uniqueness theorem for a partial differential equation with a memory term when the coefficients of the equation are analytic. This is a special case of the general unique continuation property (UCP) for the equation if its coefficients are analytic. As in the case in the absence of a memory term, the Cauchy-Kowalevski theorem is the key to prove this. The UCP is an important tool in the analysis of related inverse problems. A typical partial differential equation with memory term is the equation describing viscoelastic behavior. Here, we prove the UCP for the viscoelastic equation when the relaxation tensor is analytic and allowed to be fully anisotropic.

Many critically ill patients admitted to an intensive care unit (ICU) experience moderate-to-severe pain during their admission. This includes pain at rest and pain experienced during common ICU procedures.

The term roto-linear actuator is used to describe a solution able to control at the same time two degree of freedom, a linear motion along an axis and a rotation around the same one, realizing a controlled helicoidal motion with variable pitch. This kind of actuation systems widely diffused in robotics are encountering a growing diffusion also applications associated to relatively small production series such as packaging assembly machines. In this work some applications related to common industrial applications developed by authors are introduced to show how the increased accessibility of this mature technology can be useful to improve design and performances of existing machines even considering very small production scales.

This paper explores the transformation from the traditional consulting mode to the whole process engineering consulting mode based on the current consulting mode of power grid project and the understanding of the Whole Process Engineering Consulting, and analyzes the path of implementing the whole process engineering consulting mode in power grid projects of Provincial Power Grid Corporation. Through contrastive analysis, three aspects will change caused by mode, then discuss how to change used by deductive reasoning. Based on the realistic, proposing Province electric power company and consulting companies should do some changes in short-term/medium- term/ long -term, conjecturing Whole Process Engineering Consulting Mode will be used in Power Grid Project successfully, which has a positive role in promoting the full process engineering consultation of power grid projects.

The term big datarefers to the easy collection and storage of large amounts of data that by virtue of its scale causes rethinking linearly CPU-bound computational approaches and analysis methodologies. These challenges, and the development of approaches to handle them, were in many ways a necessary precursor to the data science revolution. For complex phenomena, with small data, the central limit theorem may not reach convergence, and there may not be enough data points to successfully hold out data to faithfully cross-validate. With small data, abstract analysis models are less essential: low-dimensional data does not need its dimension reduced, and data may be clustered or classified by hand (perhaps with the help of plotting).

The potential of carbon recycling through CO2 utilization is “sized” here by merging all options discussed in previous chapters. It is interesting to note that in the medium-long term some 7–9 GtCO2/y can be avoided, and this is a very interesting news: coupled to the use of biomass and utilization of perennial energies (SWHG) will make possible the reduction of the extraction of fossil fuels to, most likely, one-third–one-fourth with respect to today. This will be a great achievement.

In this chapter, the problems of the 13th chapter are fully solved, in detail, step-by-step, and with different methods.

Recently, flood damage has become a social problem owing to unexperienced weather conditions arising from climate change. An immediate response to heavy rain is important for the mitigation of economic losses and also for rapid recovery. Spatiotemporal precipitation forecasts may enhance the accuracy of dam inflow prediction, more than 6 h forward for flood damage mitigation. However, the ordinary ConvLSTM has the limitation of predictable range more than 3-timesteps in real-world precipitation forecasting owing to the irreducible bias between target prediction and ground-truth value. This paper proposes a rain-code approach for spatiotemporal precipitation code-to-code forecasting. We propose a novel rainy feature that represents a temporal rainy process using multi-frame fusion for the timestep reduction. We perform rain-code studies with various term ranges based on the standard ConvLSTM. We applied to a dam region within the Japanese rainy term hourly precipitation data, under 2006 to 2019 approximately 127 thousands hours, every year from May to October. We apply the radar analysis hourly data on the central broader region with an area of 136 × 148 km2. Finally we have provided sensitivity studies between the rain-code size and hourly accuracy within the several forecasting range.

Equating test scores from nonequivalent groups requires to separate the effect of differences in ability from differences in test form difficulty between the test forms. The gold standard for this purpose is to use common items, however, not all testing programs have such items available. There might be other useful information available, and this paper studies the use of covariates in equating as a substitute for common items. To gain further insight about how to best incorporate covariates in equating this paper studies the propensity score, a scalar function of the covariates. The sensitivity of the propensity score as a proxy for ability is studied to investigate if bias is introduced in the estimated equated scores if the propensity score estimation model is misspecified. By considering an incorrect link function, leaving out a covariate and leaving out a second-order term from the estimation model, common misspecifications are studied. The results show that equating with respect to the propensity score is relatively robust to model misspecification, especially modeling nonlinearity with a linear model and leaving out second-order terms.

Exploration is an essential part of reinforcement learning, which restricts the quality of learned policy. Hard-exploration environments are defined by huge state space and sparse rewards. In such conditions, an exhaustive exploration of the environment is often impossible, and the successful training of an agent requires a lot of interaction steps. In this paper, we propose an exploration method called Rollback-Explore (RbExplore), which utilizes the concept of the persistent Markov decision process, in which agents during training can roll back to visited states. We test our algorithm in the hard-exploration Prince of Persia game, without rewards and domain knowledge. At all used levels of the game, our agent outperforms or shows comparable results with state-of-the-art curiosity methods with knowledge-based intrinsic motivation: ICM and RND. An implementation of RbExplore can be found at https://github.com/cds-mipt/RbExplore.