Step In Construction Research Articles

WinoGrande

Commonsense reasoning remains a major challenge in AI, and yet, recent progresses on benchmarks may seem to suggest otherwise. In particular, the recent neural language models have reported above 90% accuracy on the Winograd Schema Challenge (WSC), a commonsense benchmark originally designed to be unsolvable for statistical models that rely simply on word associations. This raises an important question---whether these models have truly acquired robust commonsense capabilities or they rely on spurious biases in the dataset that lead to an overestimation of the true capabilities of machine commonsense. To investigate this question, we introduce WinoGrande, a large-scale dataset of 44k problems, inspired by the original WSC, but adjusted to improve both the scale and the hardness of the dataset. The key steps of the dataset construction consist of (1) large-scale crowdsourcing, followed by (2) systematic bias reduction using a novel AFLITE algorithm that generalizes human-detectable word associations to machine-detectable embedding associations. Our experiments demonstrate that state-of-the-art models achieve considerably lower accuracy (59.4%-79.1%) on WINOGRANDE compared to humans (94%), confirming that the high performance on the original WSC was inflated by spurious biases in the dataset. Furthermore, we report new state-of-the-art results on five related benchmarks with emphasis on their dual implications. On the one hand, they demonstrate the effectiveness of WINOGRANDE when used as a resource for transfer learning. On the other hand, the high performance on all these benchmarks suggests the extent to which spurious biases are prevalent in all such datasets, which motivates further research on algorithmic bias reduction.

Constructing an Individualized Middle Cerebral Artery Model Using 3D Printing and Hydrogel for Bypass Training.

The importance and complexity of cerebral bypass surgery (CBS) highlight the necessity for intense and dedicated training. Several available training models are yet to satisfy this need. In this technical note, we share the steps to construct a digital imaging and communications in medicine (DICOM)-based middle cerebral artery (MCA) model that is anatomically accurate, resembles handling properties of living tissue, and enables trainers to observe the cerebrovascular anatomy, improve and maintain microsurgical dexterity, and train in the essential steps of CBS.The internal and external molds were created from the geometry of DICOM-based MCA using Fusion 360 software (Autodesk, San Rafael, USA). They were then three-dimension (3D) printed using a polylactic acid filament. The 15% w/v solution of polyvinyl alcohol (PVA) was prepared and injected between the molds. Using five freeze-thaw cycles the solution was converted to tissue-mimicking cryo-gel. The model was then placed in a chloroform bath until the internal mold dissolved. To evaluate the accuracy of the MCA model, selected characteristics were measured and compared with the MCA mesh.The DICOM-based MCA model was produced using 3D printing that was available in the lab and the overall cost was less than $5 per model. The external mold required six and a half hours to be 3D printed, while the internal mold only required 23 minutes. Overall, the time required to 3D print the DICOM-based MCA model was just short of seven hours. The greatest statistically significant difference between the virtual MCA model and the DICOM-based MCA model was found in the length of the pre-bifurcation part of the M1 segment and the total length of the superior bifurcation trunk of M1 and superior branch of M2. The smallest statistically significant difference was found at the diameter of the inferior post-bifurcation trunk of the M1 segment and the diameter at the origin of the artery.This technical report aims to show the construction of a CBS training system involving the DICOM-based MCA model that demonstrates the shape of the vascular tree, resembles the handling/suturing properties of living tissue, and helps set up a homemade training station. We believe that our DICOM-based MCA model can serve as a valuable resource for CBS training throughout the world due to its cost-effectiveness and straightforward construction steps. Moreover, once the DICOM-based MCA model is used with our training station, it may offer an option for trainers to gain and maintain CBS skills despite limitations on time, cost, and space.This work was presented in February 2019 at the American Association of Neurological Surgeons/Congress of Neurological Surgeons (AANS/CNS) Cerebrovascular Section Annual Meeting held in Honolulu, Hawaii.

Dual permeability modelling of concrete joints

In the case of concrete structures, moisture ingress can induce deterioration of the material weakening the structure. Changes in the hydraulic material properties control the flow speed and flow duration and thus the intensity of deterioration processes. As many concrete structures like dams are built within multiple construction steps characteristic joints are formed at the interfaces. The influence of construction and expansion joints on moisture transport is measured and quantified in this study. A dual permeability based moisture modeling approach is presented in order to describe the moisture transport characteristics of these joints with an efficient numerical solution. It is shown that the presented approach is in good agreement with the measured data even for a coarse mesh. Therefore, it is particularly well suited for modeling the moisture distribution in larger structures.

Highly sensitive and label-free electrochemical biosensor based on gold nanostructures for studying the interaction of prostate cancer gene sequence with epirubicin anti-cancer drug

A label-free electrochemical DNA hybridization biosensor was developed based on gold nanocubes (AuNCs) modified graphite screen-printed electrodes (GSPEs) for investigating the epirubicin (EPI) interaction with the short sequence DNA of prostate cancer gene. The electrochemical behavior of the genosensor was assessed in different steps of construction using cyclic voltammetry and electrochemical impedance spectroscopy methods. The changes in the biosensor responses versus the DNA interactions with the various concentrations of EPI were monitored by the differential pulse voltammetry method. The reduction peak current of EPI linearly increased with the increasing concentration of EPI. Under optimal conditions, two linear ranges were obtained from 0.04 to 0.80 μM and 0.8 to 20.0 μM, respectively with a limit of detection 0.01 μM. The prepared biosensor exhibited a proper selectivity toward the target drugs. The applicability of the sensor was demonstrated by the explored of epirubicin in a human blood serum sample. In addition, the prepared biosensor was used as a reproducible and stable device for EPI determination.

Automated Adsorption Workflow for Semiconductor Surfaces and the Application to Zinc Telluride.

Surface adsorption is a crucial step in numerous processes, including heterogeneous catalysis, where the adsorption of key species is often used as a descriptor of efficiency. We present here an automated adsorption workflow for semiconductors which employs density functional theory calculations to generate adsorption data in a high-throughput manner. Starting from a bulk structure, the workflow performs an exhaustive surface search, followed by an adsorption structure construction step, which generates a minimal energy landscape to determine the optimal adsorbate-surface distance. An extensive set of energy-based, charge-based, geometric, and electronic descriptors tailored toward catalysis research are computed and saved to a personal user database. The application of the workflow to zinc telluride, a promising CO2 reduction photocatalyst, is presented as a case study to illustrate the capabilities of this method and its potential as a material discovery tool.

Sensitivity analysis of Look-up table for satellite-based aerosol optical depth retrieval

Due to the high variation of aerosol in space and time, remote sensing has become an effective tool in aerosol optical depth (AOD) retrieval. Most of the satellite-based AOD retrieval methods using the radiative transfer model (RTM). RTMs help to establish a relation between AOD and the observed top-of-atmosphere (TOA) reflectance. Using look-up table-based approaches is among the commonly used methods for reducing the computational complexity in RTMs. Tuning steps for parameters in the LUTs influences computational cost and accuracy of retrieved satellite-based AOD. However, a comprehensive and quantitative assessment for the construction of LUT based on sensitivity analysis does not exist. In this paper, we present a sensitivity analysis approach called Morris one-at-a-time (MOAT) for identifying parameters that dominate model behaviour in the AOD retrieval process. We also use 8 cases to tune steps for each parameter in the look-up table. We investigate the influence of geometrical and physical parameters in the 6S RT code on radiance and reflectance. The result indicated that ground reflectance, aerosol model, and single scattering albedo are dominant parameters in LUT construction. Other geometrical and physical parameters have less impact on the simulated radiance and reflectance. Finally, we tuned proper steps for LUT construction based on the trend and behaviour of each parameter. The proposed method in this study can improve the accuracy of satellite-based AOD by constructing a LUT with a short processing time.

An immune-scaffold relying biosensor for simultaneous detection of nitrofurazone and furazolidone

Here, to realize the simultaneous, simple, and highly sensitive detection for nitrofurazone and furazolidone, an immune-scaffold relying immunochromatographic assay was proposed. In this multi-detection immunochromatographic assay (MDICA), the immune-scaffold is composed of labeled anti-antibody IgG (Ab2) and two anti-analyte monoclonal antibodies (Abs1). A nanocomposite of MIL-101 (Cr)@AuNPs is used as the signal tag, where the unique optical property and biocompatibility of AuNPs and the protective effect of metal-organic framework can enable the nanocomposite possesses powerful signals, conjugating ability with antibodies and enhanced colloidal stability. In the immune-scaffold platform, the labeled Ab2 is the core trestle, on the one hand to exempt Ab1 from labeling requirement and offer strong detection signals, on the other hand to orient Abs1 to expose their antigen-binding sites as well as improve the identification and binding efficiency between antigen and antibody. Compared with randomly fixed antibody in the conventional probe, a 6-fold reduction of applied Ab1 amount can be achieved to produce the similar signal intensity in the immune-scaffold based method. In brief, the proposed MDICA system can effectively simplify the construction steps of MDICA, improve the Abs1 utilization efficiency, heighten analytical performances, and providing an efficient, valuable reference for the establishment of other MDICAs.

Fractional Brownian motion of worms in worm algorithms for frustrated Ising magnets.

We study the distribution of lengths and other statistical properties of worms constructed by Monte Carlo worm algorithms in the power-law three-sublattice ordered phase of frustrated triangular and kagome lattice Ising antiferromagnets. Viewing each step of the worm construction as a position increment (step) of a random walker, we demonstrate that the persistence exponent θ and the dynamical exponent z of this random walk depend only on the universal power-law exponents of the underlying critical phase and not on the details of the worm algorithm or the microscopic Hamiltonian. Further, we argue that the detailed balance condition obeyed by such worm algorithms and the power-law correlations of the underlying equilibrium system together give rise to two related properties of this random walk: First, the steps of the walk are expected to be power-law correlated in time. Second, the position distribution of the walker relative to its starting point is given by the equilibrium position distribution of a particle in an attractive logarithmic central potential of strength η_{m}, where η_{m} is the universal power-law exponent of the equilibrium defect-antidefect correlation function of the underlying spin system. We derive a scaling relation, z=(2-η_{m})/(1-θ), that allows us to express the dynamical exponent z(η_{m}) of this process in terms of its persistence exponent θ(η_{m}). Our measurements of z(η_{m}) and θ(η_{m}) are consistent with this relation over a range of values of the universal equilibrium exponent η_{m} and yield subdiffusive (z>2) values of z in the entire range. Thus, we demonstrate that the worms represent a discrete-time realization of a fractional Brownian motion characterized by these properties.

The construction technology and simulation analysis of a cable-stayed arch composite bridge

In order to explore the changes of mechanical properties of cable-stayed arch composite bridge in different construction stages, this paper takes a 136m tensed-string arch bridge as the engineering background, briefly describes the construction steps of grid lifting, and adopts Madis/Civil finite element analysis software for modeling. The actual construction process of the arch bridge is simulated by classifying different construction stages. The static and dynamic mechanical properties and stability of arch Bridges are studied. Through research and analysis, the main conclusions are drawn that the cables of the tensioned arch bridge bear part of the load of the original arch rib and girder.

Reachability in big graphs: A distributed indexing and querying approach

The advent of Big graphs characterized by their enormous number of nodes, with multiple edges between them makes the existing reachability query indexing approaches unable to guarantee a reasonable time for both the index construction and query steps. Therefore a novel approach that takes into account these new characteristics during the graph processing is needed. In this paper, we propose an Overlay Graph-based Distributed Reachability Indexing approach (ODRI), an indexing scheme through which the index construction and reachability query are processed in a parallel and distributed manner. The key idea of ODRI is to process a Big graph as a set of smaller subgraphs (partitions) interconnected to each other through an overlay graph. In this way, the partitions can be indexed in parallel and, at the same time, the reachability information can also be extracted. Hence, the index construction and query processing time will be reduced significantly. Therefore, ODRI ensures the scalability of Big graphs, which is a challenge for the existing reachability approaches. Besides, we formally prove that this strategy preserves the reachability properties. Using real-life data, we experimentally verify that our approach outperforms the state-of-the-art methods, and is scalable in terms of the number of partitions, regardless of how graphs are distributed.

Synthetic construction of the Hopf fibration in a double orthogonal projection of 4-space

Abstract The Hopf fibration mapping circles on a 3-sphere to points on a 2-sphere is well known to topologists. While the 2-sphere is embedded in 3-space, four-dimensional Euclidean space is needed to visualize the 3-sphere. Visualizing objects in 4-space using computer graphics based on their analytical representations has become popular in recent decades. For purely synthetic constructions, we apply the recently introduced method of visualization of 4-space by its double orthogonal projection onto two mutually perpendicular 3-spaces to investigate the Hopf fibration as a four-dimensional relation without analogy in lower dimensions. In this paper, the method of double orthogonal projection is used for a direct synthetic construction of the fibers of a 3-sphere from the corresponding points on a 2-sphere. The fibers of great circles on the 2-sphere create nested tori visualized in a stereographic projection onto the modeling 3-space. The step-by-step construction is supplemented by dynamic three-dimensional models showing simultaneously the 3-sphere, 2-sphere, and stereographic images of the fibers and mutual interrelations. Each step of the synthetic construction is supported by its analytical representation to highlight connections between the two interpretations.

Meso-Scale Kinematic Responses of Asphalt Mixture in Both Field and Laboratory Compaction

Compaction is one of the most critical steps in asphalt pavement construction. Traditional compaction relies heavily on engineering experience and post-construction quality control and can lead to under/over compaction problems. The emerging intelligent compaction technology has improved compaction quality but is still not successful in obtaining mixture properties of a single pavement layer. Besides, very few studies have discussed the internal material responses during field and laboratory compaction to explain the meso-scale (i.e., particle scale) compaction mechanism. Knowledge in those areas may greatly promote the development of smart compaction. Therefore, this study aims to investigate the kinematic behavior of the asphalt mixture particles (translation and rotation) under six types of field and laboratory compaction methods and establish the relationship between the field and the laboratory compaction by using a real-time particle motion sensor, SmartRock. It was found that particle movement pattern was mainly affected by the compaction mode. At the meso-scale where particle behavior is the focus, the kneading effects of a pneumatic-tire roller can be simulated by laboratory gyratory and rolling wheel compaction, and the vibrating effects of a vibratory roller can be simulated by Marshall compaction. However, none of those laboratory compaction methods can completely simulate the field compaction. Under vibratory rolling, particle acceleration decreased fast in the breakdown rolling stage. Under pneumatic-tire rolling, particle angular position change was related to aggregate skeleton, and particle relative rotation showed a decreasing trend that was consistent with the laboratory gyratory compaction results. Those kinematic responses can potentially be used to monitor density change in field compaction.

An optimized evaluation method of an urban ecological network: The case of the Minhang District of Shanghai

Obtaining the characteristics of an urban ecological network structure and connectivity plays an important guiding role in the concrete implementation of ecological construction in a space. Resistance assignment is a key step in ecological network construction. However, most research ignores the diversity of land use in an urbanization environment and the difference between ecological space centres and boundaries. Taking Minhang District of Shanghai as an example, this paper puts forward an optimal evaluation method for an urban ecological network. By combining the morphological spatial pattern analysis method and graph theory, the resistance value of different urban land uses was evaluated, and the ecological network was constructed by comprehensively considering the land use types, land area, forms, and positions. The connectivity and the importance of individual nodes at four different thresholds were quantified. The results revealed that the core and the edge areas of the morphological spatial pattern analysis occupied the highest amount of space, 33.34 % and 21.92 %, respectively. In addition, there were relatively few large and high-quality ecological spaces, and the green space was seriously fragmented in the study area. The degree of structural connectivity was related not only to the area proportion of the ecological space but also to the area of connected patches. The contribution value of each node to the overall network connectivity was affected by the mobility of the species, the node area, the spatial location, and the surrounding matrix environment. This study provided a framework designed to optimize the evaluation method of an urban ecological network. The influence of the type and structure of urban land use on the resistance value was analysed in a more detailed way, and the results of the connectivity and the important nodes were obtained more accurately.

Immobilized Antibodies on Mercaptophenylboronic Acid Monolayers for Dual-Strategy Detection of 20S Proteasome.

A dual strategy for the electrochemical detection for 20S proteasome (20S) is proposed, based on the oriented immobilization of a capture monoclonal antibody (Abβ) on a self-assembled monolayer of 4-mercaptophenylboronic acid (4-MPBA) on gold electrodes, which led to the Au/4-MPBA/Abβ immunosensor. The methodology comprises the correlation of 20S concentration with (i) its proteolytic activity toward the Z-LLE-AMC substrate, using the Au/4-MPBA/Abβ/20S, and (ii) the enzymatic activity of an alkaline phosphatase (AlkP) from the AlkP-labeled secondary antibody (Abcore-AlkP), which involves the conversion of aminophenylphosphate to the electroactive aminophenol using Au/4-MPBA/Abβ/20S/Abcore-AlkP. The step-by-step construction of the immunosensor and the interactions at its surface were evaluated by surface plasmon resonance and gravimetric analysis with quartz crystal microbalance, showing a high affinity between both antibodies and 20S. Morphological analysis by scanning electron microscopy demonstrated a pattern of parallel lines upon immobilization of Abβ on 4-MPBA and morphological changes to a well-organized granular structure upon binding of 20S. A voltametric and impedimetric characterization was performed after each step in the immunosensor construction. The two detection strategies were evaluated. It was shown that the immunosensor responds linearly with 20S concentration in the range between 5 and 100 µg mL−1, which corresponds to proteasome levels in serum in the case of diverse pathological situations, and LoD values of 1.4 and 0.2 µg mL−1 were calculated for the detection strategies. The immunosensor was applied to the detection of 20S in serum samples with recovery values ranging from 101 to 103%.

A new empirical chart for rockburst analysis in tunnelling: Tunnel rockburst classification (TRC)

Rockburst is defined as a phenomenon with immediate dynamic instability under excavation unloading conditions of deep or high geostress areas. Inadequate knowledge and lack of characterizing information prevent engineers and experts from achieving appropriate prediction results related to the rockburst behaviour. In this study, a data set including 220 rockburst instances was collected for rockburst classification via the geostatistical method. An update of the 2D graph, the tunnel rockburst classification (TRC) chart, was introduced based on analysing three indicators, namely, elastic energy index (Wet), tangential stress in rock mass (σθ), and uniaxial compressive strength (σc). Distribution and correlation of data were drawn on 2D plot, and the boundaries of rockburst were distinguished according to the achieved interpolate points by kriging method. Hierarchically, the validation phase was performed using an additional set of 28 case histories obtained from several projects around the world. The results showed that the TRC chart with an average error percentage of 3.6% in the prediction of rockburst had a significant and effective implementation in comparison to the exiting heuristic systems. Despite the initial character of the prediction, the described chart may be a helpful tool in the first steps of design and construction.

A novel treatment method and construction technology of the pipeline gushing water geohazards in karst region

Due to the complex geological structure and abundant groundwater system in karst areas, the gushing water disasters of mines in karst areas are characterized by large flow rate, high velocity and frequent occurrence, which have become an unavoidable social problem, further caused huge economic losses and casualties. In this paper, a novel treatment method and construction technology were summarized and put scientifically forward a set of management procedure for karst gushing water treatment to guarantee security production during mine construction in karst region. The construction procedure includes safety assessment policy, precise exploration method, collaborative grouting mode, curtain closure technology and effectiveness inspection system, furthermore the precise exploration method, grouting material, construction steps and effectiveness check for the karst gushing water treatment are introduced in detail. Finally, the engineering application was successful conducted using the karst pipeline gushing water procedure in treatment object of water inrush of mine belonged to China Resources Cement (Pingnan Country) limestone mine co., which the total quantity of gushing water reached to 117700 m3/d. The engineering trial demonstrates the applicability of this treatment process and accumulated for the similar projects so as to further perfect the treatment method and technology of water inrush in karst conduit.

Application of Artificial Intelligence Technology in Traffic Flow Forecast

In recent years, China’s urbanization process has been accelerating, the number of motor vehicles has been increasing, and the problems of traffic congestion, traffic noise and environmental pollution have become increasingly prominent. The application of artificial intelligence system in the field of transportation provides a good idea to solve the above problems. Traffic flow prediction is to estimate the traffic flow in a period of time according to historical traffic data. This technology can provide decision-making basis for traffic guidance and path planning. In this paper, the deep learning theory of artificial intelligence technology is used to predict the short-term traffic flow, and the LSTM prediction model is constructed. On the basis of preprocessing the original data, through correlation analysis, compression matrix construction and other steps, a more accurate short-term traffic flow prediction is realized. According to the model, this paper also discriminates the actual road congestion, and the prediction results are basically consistent with the actual situation.

Enhancing students\u2019 language in collective processes of knowledge construction in group work: the case of enlarging figures

Against the background of providing learning opportunities for meaningful mathematics for all students, the presented Design Research study had the goal of investigating how to support students facing language barriers while learning mathematics in phases of unmoderated group work. For this purpose, learning meaningful mathematics and demanding discourse practices are connected with quality interaction characteristics identified in research on productive group work. This theoretical consideration was implemented in a learning arrangement on similarity by means of four design principles for enhancing language in group work on meaningful mathematics. The empirical insights show to what extent the intended demanding discourse practices and quality interaction characteristics can be identified in different steps of knowledge construction, and to what extent they are supported by the design element ‘role cards’. Instead of calling for less unmoderated group work for students facing language barriers, the results of the study suggest that more research is needed to design scaffolds matching students’ needs, aside from whole-class discussions.

Towards elliptic deformation of q,t-matrix models

As a necessary step in construction of elliptic matrix models, which preserve the superintegrability property <char>∼char, we suggest an elliptic deformation of the peculiar loci pkΔn, which play an important role in precise formulation of this property. The suggestion is to define the pkΔn-loci as elliptic functions with the right asymptotics at τ→i∞. If this hypothesis is correct, one can move to substituting the Schur and Macdonald functions in the role of characters by the elliptic GNS and study their behavior at the deformed topological and Δ loci.

Moving average EWMA chart for the Weibull distribution

A new control chart is being presented using an exponentially weighted moving average (EWMA) statistic and moving average (MA) statistic to monitor the number of defective counts before specified time which follows Weibull distribution. Steps for construction of the control charts are presented, and the efficiency of the control chart is measured using average run length (ARL). To find ARL values and standard deviation of run length (SDRL) values, Monte Carlo simulations are performed by setting different parameters of the control chart. The proposed control chart is applied to simulated data and real-life data. The efficiency is compared with the popular Shewhart control chart and usual EWMA control chart, and found that the proposed MA-EWMA control chart better detects shifts in an ongoing process.