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The properties of unbonded rock–steel interfaces and the characteristics that control this behaviour seem to be an under-researched area in terms of geotechnical application, for example in the design of gravity-based foundation systems or dead weight anchors and the interaction of pipelines on rock. Although basic guidance does exist for rock–rock interfaces or pipeline behaviour, this focuses on macro roughness, with little consideration of micro roughness, relative roughness of the surfaces or their strengths and hardness. Therefore, in order for design and understanding to develop in these areas, there is a need for basic interface friction parameters and understanding of the interface characteristics that control the strength of the interface, such that correct values can be used, but also so that the interface properties can be best manipulated to improve interface interaction. This paper presents interface friction angles for four types of rock sheared against steel interfaces of different roughness at a variety of normal stresses. The rocks themselves have a range of surface roughness, strength and hardness. The results of the testing programme are used to improve a simple analytical approach for predicting the shear strength of rock–steel interfaces that allows input of key controlling parameters.

Understanding what is happening in the surveillance video is important for human-machine interface in transportation systems, where temporal language grounding is one of the key tasks, targeting at localizing the desired moment in an untrimmed video with a given sentence query that is relevant to the moment. This task is challenging due to the following reasons: 1) the requirement of understanding the video contents and query semantics comprehensively, and 2) building the bridge between the cross-modal semantics. To tackle these problems, early methods first sample video clips and then match them with the sentence to find the most relevant one. To reduce the computational complexity associated with video clip sampling, recent methods directly predict the temporal boundaries of the desired moment on the fused features of the sentence and the video frames. However, all the previous methods often learn the word-level or phrase-level features of the sentence, or directly generates the global sentence representation by attention mechanisms or graph network. However, we argue that applying only word-level or phrase-level semantic information and cross-modal interactions is not enough to fully capture the correspondence between the video and the query. To this end, we proposed a novel Multi-level Query Exploration and Interaction (MQEI) model, which explores the semantics in both the word- and phrase-level and captures the multi-level interactions between the video and the query through an attention module. Extensive experiments on two public benchmark datasets ActivityNet Captions and Charades-STA demonstrate that the proposed model can outperform all the state-of-the-art methods consistently.

Based on the connection between the categorical derivation of classical programs from specifications and the category-theoretic approach to quantum physics, this paper contributes to extending the laws of classical program algebra to quantum programming. This aims at building correct-by-construction quantum circuits to be deployed on quantum devices such as those available at the IBM Q Experience. Quantum circuit reversibility is ensured by minimal complements, extended recursively. Measurements are postponed to the end of such recursive computations, termed "quantamorphisms", thus maximising the quantum effect. Quantamorphisms are classical catamorphisms which, extended to ensure quantum reversibility, implement quantum cycles (vulg. for-loops) and quantum folds on lists. By Kleisli correspondence, quantamorphisms can be written as monadic functional programs with quantum parameters. This enables the use of Haskell, a monadic functional programming language, to perform the experimental work. Such calculated quantum programs prepared in Haskell are pushed through Quipper to the Qiskit interface to IBM Q quantum devices. The generated quantum circuits - often quite large - exhibit the predicted behaviour. However, running them on real quantum devices incurs into a significant amount of errors. As quantum devices are constantly evolving, an increase in reliability is likely in the near future, allowing for our programs to run more accurately., 18 pages

Locally available marginal soils are of great interest to use as backfill material in geosynthetic reinforced soil structures due to their cost-effectiveness. The advanced stress–strain analysis of these structures necessitates the correct evaluation and the accurate description of marginal soil-geosynthetic interface behaviour. Therefore, the primary objectives of this study are numerical and analytical analysis of the soil-geosynthetic interface behaviour with application of a hyperbolic nonlinear elastic perfectly plastic constitutive model. In this investigation a series of large-scale direct shear tests was performed to evaluate the shear stress-displacement interface behaviour considering two types of marginal soils in contact with five types of geosynthetic materials in different soil moisture conditions. Accordingly, the hyperbolic model was used to simulate the soil-geosynthetic interface behaviour. Moreover, a three-dimensional finite element model of the direct shear test was developed using ABAQUS software and a user-defined subroutine was implemented to consider the hyperbolic interface model. The results indicated a very good agreement between the experimental data and the predicted finite element simulations of the direct shear tests and analytical solutions. Finally, a numerical simulation of a pullout test is presented in this paper with application of the hyperbolic interface model.

Why do marketing strategies fail? This persistent question attracts significant attention and resource investments given its associated performance implications. Existing research suggests that most marketing strategies suffer due to poor implementation; however, the nuances of strategy implementation impediments and their accompanying remedies remain underdeveloped. To contribute to this line of inquiry, the authors use multi-level qualitative data to capture cross-functional dynamics that traverse the organizational hierarchy and are critical to marketing strategy implementation. The data collection procedure consisted of depth interviews with 48 informants within eight organizations, constituting sales-marketing dyads at three hierarchical levels (senior, middle, and frontline) within each organization. Findings suggest that sales and marketing personnel’s varying self-interests at different hierarchical levels motivate them to engage in a distinct set of inter- and intra-functional activities that foment implementation impediments. In turn, findings explicate not only the individual effects of these impediments but also their cumulative between-level (trickle-down) and within-level, within-function (trickle-around) impact as the strategy moves through the hierarchy of the sales-marketing interface. The authors then offer prescriptive remedies embedded in the informal organization that may stabilize the implementation process. Viewed holistically, this study contributes to marketing literature by inductively deriving a theory of marketing strategy implementation within the sales-marketing interface.

Existing surveys look at parking solutions from the perspective of sensors, communication protocols, and the hardware-software interface. While this is a worthwhile approach, it suffers from three obvious shortcomings, namely that present-day sensors are likely to become obsolete in a few years, communication protocols get discontinued, and present-day software will almost certainly not run on tomorrow's platforms. Consequently, these approaches are not promising for the Smart Cities of the near future. Unlike previous surveys, we look at parking in Smart Cities through the lens of market-based allocation of goods and services. In competitive markets prices act as signals used to allocate goods to those who value them most. In the case of parking spots, some drivers are willing to pay higher prices for the use of those parking spots that offer them the highest utility. What makes our survey unique is that we are looking at the recent literature with an eye for market-oriented solutions including pricing as an instrument for shaping traffic and for incentivizing socially-desirable driver behavior. We believe that one of the important contributions of any survey paper, over and above being a compendium of known art, is to suggest new lines of research. With this in mind, we have peppered the manuscript with slightly unorthodox perspectives. These perspectives are intended to be thought-provoking and to open new avenues for possible investigations.

With the advancement of secondary batteries, interfacial properties of electrode materials have been recognized as essential factors to their electrochemical performance. However, the majority of investigations are devoted into advanced electrode materials synthesis, while there is insufficient attention paid to regulate their interfaces. In this regard, the solid electrolyte interphase (SEI) at anode part has been studied for 40 years, already achieving remarkable outcomes on improving the stability of anode candidates. Unfortunately, the study on the cathode electrolyte interfaces (CEI) remains in infancy, which constitutes a potential restriction to the capacity contribution, stability and safety of cathodes. In fact, the native CEI generally possesses unfavorable characteristics against structural and compositional stability that requires demanding optimization strategies. Meanwhile, an in–depth understanding of the CEI is of great significance to guide the optimization principles in terms of composition, structure, growth mechanism, and electrochemical properties. In this literature, recent progress and advances of the CEI characterization methods and optimization protocols are summarized, and meanwhile the mutually–reinforced mechanisms between detection and modification are explained. The criteria and the potential development of the CEI characterization are proposed with insights of novel optimization directions.

The introduction to this special issue explores why in an "age of communication" it has become increasingly important to revisit a somewhat lost sense of communication that we describe as the interface of culture and communication. Inspired by Karen Barad's work and the diverse range of contributions to this special issue, we reflect on the fragmented, multiplied, and diffracted sense of communication that has emerged in recent years. We examine this emergent form of communication through three interlinking yet distinct areas of study: "affective communication," "tourism media interface," and "interface of the human and nonhuman." Providing grounded empirical research alongside unique theoretical insights, the eight articles bring together a diverse and complex range of contexts that would otherwise not enter into conversation with one another. And yet in their own ways each contribution challenges how communication has been approached and perceived in specific tourism settings and opens up spaces for understanding communication as diffraction and differentiation rather than a coming together. By revisiting communication in this way, previous relationships embedded in tourism can be seen in new and interesting ways. The introduction to this special issue offers an initial exploratory conceptual framing of what we call the interface of culture and communication in effort to forefront new ways of thinking and engaging with culture and communication in tourism studies and beyond.

Hutton et al. (2016) argued that computational hydrology can only be a proper science if the hydrological community makes sure that hydrological model studies are executed and presented in a reproducible manner. Hut, Drost and van de Giesen replied that to achieve this hydrologists should not “re-invent the water wheel” but rather use existing technology from other fields (such as containers and ESMValTool) and open interfaces (such as the Basic Model Interface, BMI) to do their computational science (Hut et al., 2017). With this paper and the associated release of the eWaterCycle platform and software package (available on Zenodo: https://doi.org/10.5281/zenodo.5119389, Verhoeven et al., 2022), we are putting our money where our mouth is and providing the hydrological community with a “FAIR by design” (FAIR meaning findable, accessible, interoperable, and reproducible) platform to do science. The eWaterCycle platform separates the experiments done on the model from the model code. In eWaterCycle, hydrological models are accessed through a common interface (BMI) in Python and run inside of software containers. In this way all models are accessed in a similar manner facilitating easy switching of models, model comparison and model coupling. Currently the following models and model suites are available through eWaterCycle: PCR-GLOBWB 2.0, wflow, Hype, LISFLOOD, MARRMoT, and WALRUS While these models are written in different programming languages they can all be run and interacted with from the Jupyter notebook environment within eWaterCycle. Furthermore, the pre-processing of input data for these models has been streamlined by making use of ESMValTool. Forcing for the models available in eWaterCycle from well-known datasets such as ERA5 can be generated with a single line of code. To illustrate the type of research that eWaterCycle facilitates, this paper includes five case studies: from a simple “hello world” where only a hydrograph is generated to a complex coupling of models in different languages. In this paper we stipulate the design choices made in building eWaterCycle and provide all the technical details to understand and work with the platform. For system administrators who want to install eWaterCycle on their infrastructure we offer a separate installation guide. For computational hydrologists that want to work with eWaterCycle we also provide a video explaining the platform from a user point of view (https://youtu.be/eE75dtIJ1lk, last access: 28 June 2022)​​​​​​​. With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both Open Science and FAIR science.

Situated at the interface between psychology and linguistics, the work presented here aims to open new perspectives concerning the study of interactional processes at work during “these very particular conversations” which therapy interviews indeed are. Peculiar to my project is the fact that I approach it not as a linguist but as a clinician, that is to say that priority is given to clinical reflection upon the processes observed, with the tools of linguistics and pragmatics being used only to clarify and focus upon the conversational and language mechanisms which give concrete expression to these processes. (I will return to this question at length when I discuss my methodological choices). I wish thus to underline the interest of this type of study in the revival of clinical psychology often criticized as much for its conceptual as for its methodological vagueness.

ON ASSIGNING PRAGMATIC FUNCTIONS IN ENGLISH J. Lachlan Mackenzie and M. Evelien Keizer Abstract This paper presents a discussion of the treatment of the pragmatic functions Topic and Focus in Functional Grammar (Dik: 1989, ch. 1 3 ) . Two questions will be addressed: (a) the theoretical question of how the interface between the static grammar (dealing with a discourse as the product of text-creating activity; and the dynamic theory of verbal interaction (dealing with discourse as the ongoing text-creating process itself) is handled with regard to pragmatic functions; and (b) the practical question whether the reader of Dik (1989) finds a set of proposals that can be operationalized in the analysis of linguistic material. With regard to the former question we conclude that in the present FG treatment of Topic and Focus, the static and the dynamic approaches do not connect and that as a result, the speaker's selection of constituents for Topic or Focus function is left unaccounted for. As for the second question, we show that Dik's proposal can be put into practice, but that the resultant analysis suffers from a number of inconsistencies and unclarities. Finally, we argue that most of these inconsistencies and unclarities can be solved if (1) we accept a different classification of Focus, and (2) assume that Topic assignment is irrelevant in English, as there is no consistent way in which Topic constituents are given special treatment, and P1-placement can be accounted for without having to resort to Topic function.

Pursuing long-term climate stabilisation scenarios subjects power, heat, industry, and transport sectors to undergo fundamental transitions towards fully renewable energy systems. Cost-efficient strategies involve the integration of energy vectors, introducing additional cross-sectoral electricity demand and flexibility for traditional power sectors. When performing cross-sectoral capacity expansion planning (CEP) analyses of low-carbon energy systems for decision making support, the underlying mathematical optimisation problems easily become intractable. To overcome the computational challenges of optimising real-world instances of future energy systems with adequate representations of temporal, spatial, and technological detail, this work investigates different modelling approaches for flexible heat pump systems at the power and heat sector interface of the long-term energy system model SCOPE SD. A comparative case study carried out for a net-neutral decarbonisation scenario in Europe and Germany highlights the implications of using model aggregation techniques. It is shown that the proposed modelling approaches can present computational benefits at only limited cost of result accuracy. However, poorly implemented grouping strategies can lead to high result inaccuracies and even a lack of performance improvements.

This paper presents an experimental investigation and modeling of interface creep behavior of glass fiber-reinforced polymer (GRFP) tendons embedded in cemented soils. Rapid and creep pullout tests were carried out on GRFP tendons embedded in cemented soils using a specially developed pullout setup. Interface creep displacement responses for specimens with two different water–cement ratios were derived under various interface shear stress conditions. A modified Burgers model was developed to characterize the interface creep behavior by incorporating a time-dependent viscosity coefficient. This viscosity coefficient was calibrated using creep rate variation obtained experimentally. Regression fittings on a part of interface creep measurements were conducted to determine the value of the parameters of the interface creep model. Additional interface creep measurements were used to validate the applicability of the presented creep testing protocol and the effectiveness of the rheological modeling was validated.

We present Arianna+, a framework to design networks of ontologies for representing knowledge enabling smart homes to perform human activity recognition online. In the network, nodes are ontologies allowing for various data contextualisation, while edges are general-purpose computational procedures elaborating data. Arianna+ provides a flexible interface between the inputs and outputs of procedures and statements, which are atomic representations of ontological knowledge. Arianna+ schedules procedures on the basis of events by employing logic-based reasoning, i.e., by checking the classification of certain statements in the ontologies. Each procedure involves input and output statements that are differently contextualised in the ontologies based on specific prior knowledge. Arianna+ allows to design networks that encode data within multiple contexts and, as a reference scenario, we present a modular network based on a spatial context shared among all activities and a temporal context specialised for each activity to be recognised. In the paper, we argue that a network of small ontologies is more intelligible and has a reduced computational load than a single ontology encoding the same knowledge. Arianna+ integrates in the same architecture heterogeneous data processing techniques, which may be better suited to different contexts. Thus, we do not propose a new algorithmic approach to activity recognition, instead, we focus on the architectural aspects for accommodating logic-based and data-driven activity models in a context-oriented way. Also, we discuss how to leverage data contextualisation and reasoning for activity recognition, and to support an iterative development process driven by domain experts., The paper has been accepted for publication in the IEEE Transactions on Cybernetics journal on April 2021 and with DOI 10.1109/TCYB.2021.3073539. It is an extension of arXiv:1707.03988v1 and it is related to the arXiv:1809.08208v1 article. It contains 20 pages, 6 figures, 4 tables and 2 Appendices

In recent years, the association between synonymous mutations (SMs) and human diseases has been uncovered in many studies. It is a challenge for identifying deleterious SMs in the field of medical genomics. Although there are several computational methods proposed in the past years, the precise prediction of deleterious SMs is still challenging. In this work, we proposed a predictor named as EnDSM, which is an accurate method based on the ensemble framework. We explored multimodal features across four groups including functional score, conservation, splicing, and sequence features, and we then trained eight conceptually different machine learning classifiers for each of them, resulting in 32 base classification models. We further selected four base models referring to their prediction performance and the predictive probabilities of these base classification models were subsequently used as the input feature vectors of logistic regression classifier to construct the ensemble learning model. The results suggested that EnDSM achieved better performance comparing with other state-of-the-art predictors on the training and independent test datasets. We anticipate that our ensemble predictor EnDSM will become a valuable tool for deleterious SM prediction. The EnDSM server interface along with the benchmarking data sets are freely available at http://bioinfo.ahu.edu.cn/EnDSM.

Machine Learning has emerged in the last years as the main solution to many of nowadays’ data-based decision problems. However, while new and more powerful algorithms and the increasing availability of computational resources contributed to a widespread use of Machine Learning, significant challenges still remain. Two of the most significant nowadays are the need to explain a model’s predictions, and the significant costs of training and re-training models, especially with large datasets or in streaming scenarios. In this paper we address both issues by proposing an approach we deem predictive and user-centric. It is predictive in the sense that it estimates the benefit of re-training a model with new data, and it is user-centric in the sense that it implements an explainable interface that produces interpretable explanations that accompany predictions. The former allows to reduce necessary resources (e.g. time, costs) spent on retraining models when no improvements are expected, while the latter allows for human users to have additional information to support decision-making. We validate the proposed approach with a group of public datasets and present a real application scenario.

Pursuing personally valued goals in work and family is important for many people, yet research has only partially addressed how individuals can actively manage the work–family interface. We examined the role of action regulation at the work–family interface (AR-WF) as an integrated individual-level approach to attain favorable work–family outcomes through the selection and pursuit of goals at the work–family interface. We investigated the relation of AR-WF to theoretically derived correlates and outcomes in two time-lagged studies with samples from the USA and Germany, based on a newly developed and validated measure to assess AR-WF. Overall, results showed that AR-WF is positively related to dispositional self-regulation, work and family role commitment, work and family goal regulation, and work and family social support. In contrast, AR-WF was largely unrelated to work and family role demands and segmentation or integration boundary enactment. AR-WF further positively related to work and family goal attainment, as well as work–family enrichment beyond related constructs. However, AR-WF was also positively related to increased work-to-family conflict. We discuss how a focus on action regulation can be useful for attaining a better understanding of the active role that people play in managing multiple role demands at the work–family interface.

This study evaluated the performances of various autocorrelation function (ACF) models in predicting the geological interface using a well-known conditional random field method. Prediction accuracies and uncertainties were compared between a flexible Matérn model and two classical ACF models: the Gaussian model and the single exponential model. The rockhead data of Bukit Timah granite from boreholes at two sites in Singapore as well as simulated data were used for the comparisons. The results showed that the classical models produce a reasonable prediction uncertainty only when its smoothness coefficient is consistent with that of the geological data. Otherwise, the classical models may produce prediction errors much larger than that of the Matérn model. On the other hand, the prediction accuracy of the Matérn model is affected by the spacing of the boreholes. When the borehole spacing is relatively small (< 0.4 × scale of fluctuation), the Matérn model can reasonably quantify the prediction uncertainty. However, when the borehole spacing is large, the prediction by the Matérn model becomes less accurate as compared with the prediction using the classical models with the right value of smoothness coefficient due to the large estimation error of the smoothness coefficient.

We introduce a new mobile money interface that permits Sri Lankans to deposit mobile airtime balances directly into a formal bank account. Randomizing access and prices, we find a small increase in savings deposits with the partner institution and formal banks more generally, but no change in overall savings. When the deposit transaction costs are completely removed, only 26% use the mobile deposit service and only 7% use it frequently. Our results imply that deposit transaction costs are not a significant barrier to increasing savings, limiting the potential gains of mobile-linked savings products for financial inclusion.

In April 2020, after decades of discussions and controversy, the Lebanese parliament voted a law legalizing the cultivation, production, and sale of cannabis for medicinal purposes. Although the law leaves several unanswered questions and awaits implementation, the symbolic nature of this step in recognizing a positive role of cannabis in the local economy is significant on a regional level. The Arab world has traditionally been conservative when it comes to all drugs-related policies. Cannabis is largely demonized with heavy sentences served to anyone suspected of using selling, let alone planting cannabis. Despite a few countries considered producers and consumers of substances, governing authorities have remained immune to the liberalization trend encountered in western countries. The social experiment taking place in Lebanon is fraught with risks, given the unstable political situation and chronic economic challenges. The reactions to the law have been mixed with several scientific bodies such as the Lebanese Psychiatric Society criticizing the absence of proper consultation of stakeholders. The absence of consistency in enforcing established drugs policies or seriously debating the decriminalization of cannabis use raises concerns over the establishment of a two-tier approach toward drugs, driven solely by economic imperatives.

The result of a typical web search is often overwhelming. It is very difficult to explore the textual listing of the resulting documents, which may be in the thousands. In order to improve the utility of the search experience, we explore presenting search results through clustering and a zoomable two-dimensional map (zoomable treemap). Furthermore, we apply the fisheye view technique to this map of web search clusters to provide details in context. In this study, we report on our evaluation of these presentation features. The particular interfaces evaluated were: (1) a textual list, (2) a zoomable two-dimensional map of the clustered results, and (3) a fisheye version of the zoomable two dimensional map where the results were clustered. We found that subjects completed search tasks faster with the visual interfaces than with the textual interface, and faster with the fisheye interface than just the zoomable interface. Based on the findings, we conclude that there is promise in the use of clustering and visualization with a fisheye zooming capability in the exploration of web search results.

Herein, we report a computational algorithm that follows a spectroscopist-driven elucidation process of the structure of an organic molecule based on IR, $^1$H and $^{13}$C NMR, and MS tabular data. The algorithm is independent from database searching and is based on a bottom-up approach, building the molecular structure from small structural fragments visible in spectra. It employs an analytical combinatorial approach with a graph search technique to determine the connectivity of structural fragments that is based on the analysis of the NMR spectra, to connect the identified structural fragments into a molecular structure. After the process is completed, the interface lists the compound candidates, which are visualized by the WolframAlpha computational knowledge engine within the interface. The candidates are ranked according to the predefined rules for analyzing the spectral data. The developed elucidator has a user-friendly web interface and is publicly available (http://schmarnica. si).

In this article, a comparative study was carried out on two types of thermosetting and thermoplastic matrices to study the effect of the fiber-matrix interface damage on the behavior of thermosetting and thermoplastic composites reinforced by the same natural alfa and wood fibers. The genetic modeling was based on the probabilistic formalism of Weibull. The results have been compared with those obtained by the nonlinear acoustic technique, the two results found to coincide perfectly. The numerical simulation also shows a good concordance with the real behavior of the materials studied, and shows that thermosetting composites are the most resistant to applied thermal stress by 21% compared to thermoplastic composites. Statistical analysis demonstrates that the correlation coefficient values found are very close to 1 (0.964 and 0.973), these values are very satisfactory, and confirm that the results obtained by the genetic model and the nonlinear acoustic technique are in very good agreement with the statistical analysis data. The experimental work presented by Antoine Le Duigou et al. and the work of Bodros et al. have shown that the use of natural fibers greatly improves the mechanical properties of composite materials.

Reinforced concrete (RC) structures subjected to cyclic fatigue loading are prone to progressive damage. Among the types of structural damage, those leading to shear deficiencies can result in sudden rupture of structures without warning. Hence, RC structures deficient in shear urgently need retrofitting. The use of externally bonded (EB) fiber-reinforced polymer (FRP) composites presents many advantages and is a very promising technology for shear strengthening of RC structures. This paper encompasses a wide range of research findings related to the interaction between concrete and FRP under fatigue loading. The behavior of the bond between FRP and concrete plays a major role in the failure mode of FRP shear-strengthened structures especially under fatigue. Therefore, it is of interest to characterize the FRP/concrete interaction using appropriate models with respect to the influencing parameters. The paper will first discuss existing design guidelines and considerations related to the fatigue behavior of RC structures. A thorough review of available literature on EB-FRP/concrete bond in shear under cyclic fatigue loading will then be presented, with a focus on proposed bond-slip models and finite element studies of the FRP/concrete interface under fatigue loading.

Objectives Growing interest in the applications of artificial intelligence (AI) and, in particular, deep learning (DL) in nuclear medicine and radiology partitions the professional community. At one end of the spectrum are our expert DL wizards developing potion-like code and waving the DL capabilities like a wand across our professions. On the opposite side of the spectrum are our muggle colleagues who lack the wizardry of DL and may be largely oblivious to the entire magical realm. Key findings As crafted by Arthur C Clark, any sufficiently advanced technology is indistinguishable from magic. DL is not only an important technology in the future of medical imaging, but its application lives in the capabilities of medical imaging technologists. This may be incidental through application of techniques at the patient interface, through role expansion in data curation and management, or as active members of DL projects and development. Understanding the rudimentary principles of DL is emerging as requisite in medical imaging. Conclusion AI and DL are valuable tools in advancing capabilities and outcomes in medical imaging. A working knowledge of the technology and techniques is important and achievable for the medical imaging technologist even when capability in application of DL to research and clinical practice is not within one's interests or scope of practice. Implications for practice While there is no requisite for all of the professional community to be tutored in the wizardry of DL, there are benefits for the profession and our patients for all to have a rudimentary understanding of the language and landscape. The breadth of DL literature assumes a level of understanding not evident for the bulk of our professions. This manuscript provides a simplified primer on DL with the aim of arming the muggles among us with sufficient insight to navigate the magical realm of DL without transferring any wizardry capability itself.

We study a semilinear parabolic problem that models the evolution of gene frequencies under the joint action of migration and selection with a geographical barrier. The barrier locates at an embedded interface and is of Kedem-Katchalsky type. Our main goal is to investigate the effects of the barrier on the global dynamics and on the existence, stability, and profile of spatially nonconstant equilibria.

Web services are moving toward a new emerging technology lead to the migration of SOAP to RESTful API, which is an Architectural Style that holds Lightweight, Stateless, Uniform Interface, etc., as its constraints. Various sources of clusters of resources, entity, database relations are access throughout the distributed environment across the internet. Generative Power of the RESTful API witnesses the emergence of many companies whose whole business process is based upon the building applications. Since the Syntactic essentials of RESTful Web Services are mainly concerned with the RESTful API, there is a need for evaluation of those essentials whether they are computable or not. The proposed work is carried out on taking resources as simple and effective using Primitive Recursive Function (PRF). Primitive Recursive Function (PRF) uses Turing Machine for REST API capability evaluation and the Service Invocation along with the Application Logic and AppState Logic in order to handle manageability of the RESTful resources via computability evaluation with or without security. To demonstrate the effectiveness of our evaluation process, we conduct a case study on the available REST web services using Primitive Recursive Resources (PRR). The results of our case study show that our evaluation process achieves greater portability, reliability, scalability, etc., which in turn results in high performance.

The effects of increased temperature on the stress–strain response of geothermal piles and supporting soil were studied in this work. Geothermal piles with different pile end supports were investigated numerically using the finite-element software Abaqus. A critical-state-based constitutive model (clay and sand model) was adopted for simulating the elasto-plastic thermomechanical behaviour of soil using two user-defined material subroutines (UMAT and UMATHT). In order to understand the additional stresses and strains in piles and soil due to thermal loading of piles, a set of parametric investigations was carried out for different values of the restraint at the pile base, the relative density of the sand, pile length, pile diameter and the coefficient of lateral earth pressure of the soil. An increased coefficient of lateral earth pressure in the soil at the pile–soil interface was observed due to circumferential thermal radial strains over the pile length. Furthermore, the thermal response of the piles and soil was found to depend on the soil's relative density and the magnitude of thermal load experienced by the piles. Cyclic thermal loading resulted in increased axial stresses in the piles.

This paper presents an experimental and analytical evaluation of factors affecting the interface pullout resistance of polymeric strips embedded in marginal soils, with a particular interest in supporting the partial replacement of natural sands by intensely weathered tropical soils in reinforced soil structures, which have often been considered marginal fills in design guidelines. Large-scale pullout tests were conducted to evaluate the soil-geosynthetic interface pullout resistance, which also provided quantitative insight into the local increases in vertical stresses acting on the reinforcements due to pullout. Based on the experiments, analytical models were developed and calibrated to establish the relationship between confinement and soil-geosynthetic interface pullout resistance. The relationship between actual and initial stresses could then be represented in terms of a linear model in which the angular coefficient corresponds to the ratio between the apparent and actual friction coefficients (f*/f). This analytical relationship was found to represent a useful design tool since it directly correlates with soil geotechnical properties. The use of lateritic soils to partially replace coarse-grained soils in reinforced soil structures was found to be feasible for mixtures involving up to 25% of lateritic soils, with higher fractions affecting the interface resistance significantly.

In this paper, a meshless discrete scheme based on the generalized finite difference method (GFDM) is proposed to solve the biharmonic interface problem. This scheme turns the interface problem to be two non-interface subproblems coupled with the interface conditions. The interface conditions act as the boundary conditions of the subproblems because the interface plays the role of their boundary. Therefore, the proposed method has the advantage of dealing with problems with complex geometrical interfaces. Due to the GFDM approximates the derivatives of the unknown variables by using a linear combination of the values for nearby nodes, the proposed method also has the advantage in dealing with the interface condition with the jump of derivatives. Four numerical examples are provided to verify the accuracy and the stability of the GFDM for biharmonic interface problems. The numerical results show that the H1 errors can reach almost the same convergence rate of the L∞ and L2 errors. The convergence rates are almost and even higher than fourth-order when the fourth-order Taylor Series expansion is adopted in the GFDM.

The sea-surface interface between ocean and air is time varying and can be spatially rough as a result of wind, tides, and currents; the shape of this interface changes over time under the influence of wind, tides, etc. As a result, waves impinging on the sea surface are continuously scattered. In the case of marine seismic, the multiple-scattered waves propagate downward into the underwater formation and result in complex seismic responses. To understand the structure of the responses, we have adopted a multistage algorithm for computing the scattered waves at the sea surface. Specifically, we first extrapolate the upgoing incident waves stepwise using thin-slab approximation from the scattering theory based on the De Wolf approximation of the Lippmann-Schwinger equation. Then, we implement the air-water boundary condition at the sea surface. Finally, we use the irregular boundary processing technique to compute the time-varying undulating sea-surface scattered waves from different scattering stages. To overcome the angular limitation of the original parabolic approximation, we introduce a multidirectional parabolic approximation based on computational electromagnetics. Numerical tests indicate that the multistage algorithm presented here can accurately calculate sea-surface scattered waves and should be useful in investigating the structure of marine seismic responses.

We present a free student-facing tool for creating 3D plots and smartphone-based virtual reality (VR) visualizations for STEM courses. Visualizations are created through an in-browser interface usi...

Frequently, the design of physicochemical processes requires screening of large numbers of alternative designs with complex geometries. These geometries may result in conformal meshes which introduce stability issues, significant computational complexity, and require user-interaction for their creation. In this work, a method for simulation of heat transfer using the diffuse interface method to capture complex geometry is presented as an alternative to a conformal meshing, with analysis and comparisons given. The methods presented include automated non-iterative generation of phase fields from CAD geometries and an extension of the diffuse interface method for mixed boundary conditions. Simple measures of diffuse interface quality are presented and found provide predictions of performance. The method is applied to a realistic heat transfer problem and compared to the traditional conformal mesh approach. It is found to enable reasonable accuracy at an order-of-magnitude reduction in simulation time or comparable accuracy for equivalent simulation times.

In this letter, we address the problem of road extraction in Wildland-urban interface (WUI) areas. In recent years, with the great success of convolutional neural networks (CNNs) in various vision-related tasks, researchers have developed many CNN-based methods for road extraction on remote sensing images. Nevertheless, these methods mostly treat road extraction as a binary classification problem on semantic labeling. In WUI areas, the road is narrower and tends to be occluded by trees, which may result in the serious discontinuous problem of inferred road maps. To address this issue, we propose transforming the input representation of the binary classification map into a continuous signed distance map. In this way, our model is forced to predict the continuous distance representations and, thus, improve the spatial continuities of inferred roads. In addition, a real-value regression task is designed to train along with the original binary classification task to generate spatially continuous and semantically accurate road maps. Then, we conduct experiments on the public Massachusetts road data set and a homemade data set collected from Yajishan Mountain, Beijing, China. Finally, our proposed method achieves intersection-over-unions (IoUs) of 64.11% and 65.92% for the Massachusetts and WUI-Yajishan data sets, respectively, without any postprocessing. In addition, the ablation analysis shows that introducing the regression task on the proposed signed distance representation can effectively alleviate the problem of discontinuous road prediction. Furthermore, comparing with the state-of-the-art methods demonstrates the superiority of our method for road extraction in WUI areas.

The copper particle size and the interface of Cu and ZnO showed strong impacts on the formation of methanol and CO in CO2 hydrogenation over Cu@ZnO catalysts.

Institutional review boards (IRBs) exist to protect the rights and welfare of research participants. Critical care nurses and researchers may interface with IRBs directly or indirectly. Having an understanding of how the IRB process works will help nurses advocate for patients who may also be research participants and will help researchers facilitate approval of their research studies.

International audience; Tensor data analysis is the evolutionary step of data analysis to more than two dimensions. Dealing with tensor data is often based on tensor decomposition methods. The present paper focuses on unsupervised learning and provides a python package referred to as TensorClus including novel co-clustering algorithms of three-way data. All proposed algorithms are based on the latent block models and suitable to different types of data, sparse or not. They are successfully evaluated on challenges in text mining, recommender systems, and hyperspectral image clustering. TensorClus is an open-source Python package that allows easy interaction with other python packages such as NumPy and TensorFlow; it also offers an interface with some tensor decomposition packages namely Tensorly and TensorD on the one hand, and on the other, the co-clustering package Coclust. Finally, it provides CPU and GPU compatibility. The TensorClus library is available at https://pypi.org/project/TensorClus/ 1 .

Multi-agent reinforcement learning algorithms have achieved satisfactory performances in various scenarios, but many of them encounter difficulties in partially observable environments. In partially observable environments, the inability to perceive environment states results in unsteadiness and misconvergence, especially in large-scale multi-agent environments. To improve interactions among homogeneous agents in a partially observable environment, we propose a novel multi-agent actor-critic model with a visual attention interface to solve this problem. First, a recurrent visual attention interface is used to extract a latent state from each agent’s partial observation. These latent states allow agents to focus on several local environments, in which each agent has a complete perception of a local environment and the intricate multi-agent environment is teased out by the interaction among several agents in the same local environment. The proposed method trains multi-agent systems with a centralized training and decentralized execution mechanism. The joint action of agents is approximated by the mean-field theory because the number of agents in a local environment is uncertain. Experimental results on the simulation platform suggest that our model performs better when training large-scale multi-agent systems in partially observable environments than baselines.

Wildland urban interface (WUI) communities are situated at the interface between human development and wildland fuel. In addition to their proximity to susceptible regions, routes of evacuation in WUIs are often limited, posing great risks to these communities in the event of a natural disaster. Considering the abundance of WUI interfaces in Canada, Canadian-based research is vital for developing national wildfire regulations. However, the limited amount of Canadian research forces authorities to seek wildfire evacuation techniques from foreign sources, which may not be proven to be effective in northern boreal forests. To begin the research herein, a Canadian WUI community in central Canada was selected as a case study to investigate assembly and evacuation patterns during a fire evacuation to illustrate the complexity of the situation and the current research needs required. First-stage simulations of evacuations were performed in the traffic simulation software PTV VISSIM, which extracted useful data, including evacuation times and related parameters. The results demonstrated that the addition of an extra highway access road reduces evacuation times by up to an hour and 20 minutes, which can determine whether a resident evacuates or not. However, the predictive power of the software is limited by its ability to incorporate the effects of human behaviour and the fire behaviour itself. Thus, extending these findings to include the need for evacuee behaviour and fire dynamics is a crucial second stage in the formation of a more complete strategic evacuation plan for communities at risk of wildfires. This study is vital for creating a Canadian-based approach to WUI wildfire evacuations and to expand the knowledge base in the field.

The present article discovers the role of Internet of Things (IoT) in the field of supply chain management (SCM) and Logistics through an extensive literature review. The study offers numerous insights into significant facets of IoT in SCM and Logistics through Bibliometric analysis. A brief account of key IoT technology enablers and their role in numerous SCM operations and executions has been presented. Categorisation of the existent literature, founded on number of bibliometric parameters like year, region, author, institution, and source was made. The studies are further clustered on the basis of keyword co-occurrence. Five clusters represent the application and implementation issues of IoT in SCM and Logistics. The IoT research work in the arena of SCM and logistics is focused on radio-frequency identification technology, blockchain, wireless sensor networks, industry 4.0 technologies, IoT interface between the user and devices and Industrial applications such as the food, retail, healthcare sector etc. The comprehensive review covering the time frame of 2000 to 2021 may prove advantageous to the practitioners to stay up to date with the applications of IoT in varied industries. The study also finds the prospective areas in IoT research in SCM and logistics to endorse IoT implementation.