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University scientific research ability is an important indicator to express the strength of universities. In this paper, the evaluation of university scientific research ability is investigated based on the output of sci-tech papers. Four university alliances from North America, UK, Australia, and China, are selected as the case study of the university scientific research evaluation. Data coming from Thomson Reuters InCites are collected to support the evaluation. The work has contributed new framework to the issue of university scientific research ability evaluation. At first, we have established a hierarchical structure to show the factors that impact the evaluation of university scientific research ability. Then, a new MCDM method called D-AHP model is used to implement the evaluation and ranking of different university alliances, in which a data-driven approach is proposed to automatically generate the D numbers preference relations. Next, a sensitivity analysis has been given to show the impact of weights of factors and sub-factors on the evaluation result. At last, the results obtained by using different methods are compared and discussed to verify the effectiveness and reasonability of this study, and some suggestions are given to promote China’s scientific research ability. [ABSTRACT FROM AUTHOR]

This paper focuses on evaluating the systemic risk in interbank networks, proposing a series of measurements: risk distance, risk degree and m-order risk degree. The proposed measurements are formally proven to have good basic and extended properties that are able to reflect the effect of bank size, liability size, liability distribution, and the discount factor on the default risk, not only of a single bank, but also of the entire system. Additionally, the abovementioned properties and the relationship between risk distance and financial contagion indicate the rationality embodied in the proposed measurements. This paper also provides some implications on how to decrease or prevent the systemic risk in an interbank system. [ABSTRACT FROM AUTHOR]

Classifying bank accounts by using transaction data is encouraging in cracking down on illegal financial activities. However, few research simultaneously use heterogenous features, which are embedded in the time series data. In this paper, a two route convolution neural network TRHD-CNN model, fed with two types of heterogeneous feature matrices, is proposed for classifying the bank accounts. TRHD-CNN adopts divide and conquer strategy to extract characteristics from two types of data source independently. The strategy is proved able in mining complementary classification characteristics. We firstly transfer the original log data into a directed and dynamic transaction network. On the basis of that, two feature generation methods are devised for extracting information from local topological structure and time series transaction respectively. A DirectedWalk method is developed in this paper to learning the network vector of vertices used for embedding the neighbor relationship of bank account. The extensive experimental results, conducted on a real bank transaction dataset that contains illegal pyramid selling accounts, show the significant advantage of TRHD-CNN over the existing methods. TRHD-CNN can provide recall scores up to 5.15% higher than competing methods. In addition, the two-route architecture of TRHD-CNN is easy to extend to multi-route scenarios and other fields. [ABSTRACT FROM AUTHOR]

Background: Detection of pulmonary nodules is an important aspect of an automatic detection system. Incomputer-aided diagnosis (CAD) systems, the ability to detect pulmonary nodules is highly important, which plays an important role in the diagnosis and early treatment of lung cancer. Currently, the detection of pulmonary nodules depends mainly on doctor experience, which varies. This paper aims to address the challenge of pulmonary nodule detection more effectively. Methods: A method for detecting pulmonary nodules based on an improved neural network is presented in this paper. Nodules are clusters of tissue with a diameter of 3 mm to 30 mm in the pulmonary parenchyma. Because pulmonary nodules are similar to other lung structures and have a low density, false positive nodules often occur. Thus, our team proposed an improved convolutional neural network (CNN) framework to detect nodules. First, a nonsharpening mask is used to enhance the nodules in computed tomography (CT) images; then, CT images of 512×512 pixels are segmented into smaller images of 96×96 pixels. Second, in the 96×96 pixel images which contain or exclude pulmonary nodules, the plaques corresponding to positive and negative samples are segmented. Third, CT images segmented into 96×96 pixels are down-sampled to 64×64 and 32×32 size respectively. Fourth, an improved fusion neural network structure is constructed that consists of three three-dimensional convolutional neural networks, designated as CNN-1, CNN-2, and CNN-3, to detect false positive pulmonary nodules. The networks’ input sizes are 32×32×32, 64×64×64, and 96×96×96 and include 5, 7, and 9 layers, respectively. Finally, we use the AdaBoost classifier to fuse the results of CNN-1, CNN-2, and CNN-3. We call this new neural network framework the Amalgamated-Convolutional Neural Network (A-CNN) and use it to detect pulmonary nodules. Findings: Our team trained A-CNN using the LUNA16 and Ali Tianchi datasets and evaluated its performance using the LUNA16 dataset. We discarded nodules less than 5mm in diameter. When the average number of false positives per scan was 0.125 and 0.25, the sensitivity of A-CNN reached as high as 81.7% and 85.1%, respectively. [ABSTRACT FROM AUTHOR]

A Mesh topology is one of the most promising architecture due to its regular and simple structure for on-chip communication. Performance of mesh topology degraded greatly by increasing the network size due to small bisection width and large network diameter. In order to overcome this limitation, many researchers presented modified Mesh design by adding some extra links to improve its performance in terms of network latency and power consumption. The Cross-By-Pass-Mesh was presented by us as an improved version of Mesh topology by intelligent addition of extra links. This paper presents an efficient topology named Cross-By-Pass-Torus for further increase in the performance of the Cross-By-Pass-Mesh topology. The proposed design merges the best features of the Cross-By-Pass-Mesh and Torus, to reduce the network diameter, minimize the average number of hops between nodes, increase the bisection width and to enhance the overall performance of the network. In this paper, the architectural design of the topology is presented and analyzed against similar kind of 2D topologies in terms of average latency, throughput and power consumption. In order to certify the actual behavior of proposed topology, the synthetic traffic trace and five different real embedded application workloads are applied to the proposed as well as other competitor network topologies. The simulation results indicate that Cross-By-Pass-Torus is an efficient candidate among its predecessor’s and competitor topologies due to its less average latency and increased throughput at a slight cost in network power and energy for on-chip communication. [ABSTRACT FROM AUTHOR]

The solution to the job shop scheduling problem (JSSP) is of great significance for improving resource utilization and production efficiency of enterprises. In this paper, in view of its non-deterministic polynomial properties, a multi-agent genetic algorithm based on tabu search (MAGATS) is proposed to solve JSSPs under makespan constraints. Firstly, a multi-agent genetic algorithm (MAGA) is proposed. During the process, a multi-agent grid environment is constructed based on characteristics of multi-agent systems and genetic algorithm (GA), and a corresponding neighbor interaction operator, a mutation operator based on neighborhood structure and a self-learning operator are designed. Then, combining tabu search algorithm with a MAGA, the algorithm MAGATS are presented. Finally, 43 benchmark instances are tested with the new algorithm. Compared with four other algorithms, the optimization performance of it is analyzed based on obtained test results. Effectiveness of the new algorithm is verified by analysis results. [ABSTRACT FROM AUTHOR]

Background: Frailty is a major health issue which impacts the life of older people, posing a significant challenge to the health system. One of the key emerging areas is the development of frailty interventions to halt or reverse the progression of the condition. In many countries, economic evidence is required to inform public funding decisions for such interventions, and cost-effectiveness models are needed to estimate long-term costs and effects. Such models should capture current clinical understanding of frailty, its progression and its health consequences. The objective of this paper is to present a conceptual model of frailty that can be used to inform the development of a cost-effectiveness model to evaluate frailty interventions. Methods: After critical analysis of the clinical and economic literature, a Delphi study consisting of experts from the disciplines of clinical medicine and epidemiology was undertaken to inform the key components of the conceptual model. We also identified relevant databases that can be used to populate and validate the model. Results: A list of significant health states/events for which frailty is a strong independent risk factor was identified (e.g., hip fracture, hospital admission, delirium, death). We also identified a list of important patient attributes that may influence disease progression (e.g., age, gender, previous hospital admissions, depression). A number of large-scale relevant databases were also identified to populate and validate the cost-effectiveness model. Face validity of model structure was confirmed by experts. Discussion and conclusions: The proposed conceptual model is being used as a basis for developing a new cost-effectiveness model to estimate lifetime costs and outcomes associated with a range of frailty interventions. Using an appropriate model structure, which more accurately reflects the natural history of frailty, will improve model transparency and accuracy. This will ultimately lead to better informed public funding decisions around interventions to manage frailty. [ABSTRACT FROM AUTHOR]

People are connected by various social networks, resulting in the interdependence of consumer choice. Therefore, it is very important and realistic to assume choice interdependence in private car ownership modeling. In this paper, we investigate the interdependence of private car ownership choice using a spatial autoregressive binary probit model estimated by the Bayesian Markov chain Monte Carlo (MCMC) method. Constructing the autoregressive matrix demographically shows that the private car ownership choice of a household is dependent on other household choices. Compared with the pure binary probit model estimated by the MCMC method, the spatial autoregressive model achieves a significant improvement both in loglikelihood value and log marginal density value, which are calculated using the importance sampling method of Newton and Raftery, from approximately -202 to approximately -63 and from -208 to -145, respectively. Moreover, the results indicated by the spatial autoregressive probit model suggest that the number of children, the ownership of an apartment or the availability of a parking lot are positively and significantly associated with the private car ownership level. To test the out-of-sample performance of the model, we estimate the model using 600 data points and test it using another 148 data points. The results indicate that the predictive power is greatly improved. Finally, we analyze the augmented parameter and discover that it is associated with the parking variable in addition to the license variable. [ABSTRACT FROM AUTHOR]

The construction of large renewable energy projects is characterized by the great uncertainties associated with their administrative complexity and their constructive characteristics. For proper management, it is necessary to undertake a thorough project risk assessment prior to construction. The work presented in this paper is based on a hierarchical risk structure identified by a group of experts, from which a Probabilistic Fuzzy Sets with Analysis Hierarchy Process (PFSAHP) was applied. This probabilistic analysis approach used expert opinion based on the Monte Carlo Method that allows for extracting more information from the original data. In addition, the coherence of the experts’ opinions is assessed using a novel parameter known as Confidence Level, which allows for adjusting the opinions of experts and weighting their judgments regarding impact and probability according to their coherence. This model has the advantage of offering a risk analysis in the early stages of the management of renewable energy projects in which there is no detailed information. This model is also more accurate than the classic fuzzy methodology when working with complete distribution functions, whilst it avoids the loss of information that results from the traditional mathematical operations with Fuzzy numbers. To test the model, it was applied to a 250 MW photovoltaic solar plant construction project located in southeast of Spain (Region of Murcia). As a result of the application of the proposed method, risk rankings are obtained with respect to the cost, the time, the scope and from a general point of view of the project. [ABSTRACT FROM AUTHOR]

Road Detection is a basic task in automated driving field, in which 3D lidar data is commonly used recently. In this paper, we propose to rearrange 3D lidar data into a new organized form to construct direct spatial relationship among point cloud, and put forward new features for real-time road detection tasks. Our model works based on two prerequisites: (1) Road regions are always flatter than non-road regions. (2) Light travels in straight lines in a uniform medium. Based on prerequisite 1, we put forward difference-between-lines feature, while ScanID density and obstacle radial map are generated based on prerequisite 2. According to our method, we construct an array of structures to store and reorganize 3D input firstly. Then, two novel features, difference-between-lines and ScanID density, are extracted, based on which we construct a consistency map and an obstacle map in Bird Eye View (BEV). Finally, the road region is extracted by fusing these two maps and refinement is used to polish up our outcome. We have carried out experiments on the public KITTI-Road benchmark, achieving one of the best performances among the lidar-based road detection methods. To further prove the efficiency of our method on unstructured road, the visual outcomes in rural areas are also proposed. [ABSTRACT FROM AUTHOR]

Ex-ante online risk assessment for building emergency evacuation is essential to protect human life and property. Current risk assessment methods are limited by the tradeoff between accuracy and efficiency. In this paper, we propose an online method that overcomes this tradeoff based on multimedia data (e.g. videos data from surveillance cameras) and deep learning. The method consists of two parts. The first estimates the evacuee position as input for training the assessment model to then perform risk assessment in real scenarios. The second considers a social force model based on the evacuation simulation for the output of training model. We verify the proposed method in simulation and real scenarios. Model sensitivity analyses and large-scale tests demonstrate the usability and superiority of the proposed method. By the method, the computation time of risk assessment could be decreased from 10 minutes (by traditional simulation method) to 2.18 s. [ABSTRACT FROM AUTHOR]

It is evident through biology research that, biological neural network could be implemented through two means: by congenital heredity, or by posteriority learning. However, traditionally, artificial neural network, especially the Deep learning Neural Networks (DNNs) are implemented only through exhaustive training and learning. Fixed structure is built, and then parameters are trained through huge amount of data. In this way, there are a lot of redundancies in the implemented artificial neural network. This redundancy not only requires more effort to train the network, but also costs more computing resources when used. In this paper, we proposed a bionic way to implement artificial neural network through construction rather than training and learning. The hierarchy of the neural network is designed according to analysis of the required functionality, and then module design is carried out to form each hierarchy. We choose the Drosophila’s visual neural network as a test case to verify our method’s validation. The results show that the bionic artificial neural network built through our method could work as a bionic compound eye, which can achieve the detection of the object and their movement, and the results are better on some properties, compared with the Drosophila’s biological compound eyes. [ABSTRACT FROM AUTHOR]

Typical underground water storage facilities consist of reinforced concrete tanks and pipes. Although methods of their analysis are well developed, the use of these methods does not always give unambiguous results, as presented in the paper. An example of underground tank is considered in which cylindrical roof collapsed during construction under soil and excavator loads. The causes of failure are investigated with deterministic and stochastic models. In the first step nonlinear finite element analysis including soil-structure interaction was performed to examine overall level of the structural safety, which was found satisfactory thus not explaining the collapse. In the second step an analytical stochastic model was developed and analysed with emphasis to sensitivity. The last analysis explained the collapse as a complex of unfavourable states for considered variables and the failure was recognised as a mixed construction-geotechnical-structural problem. The key role played backfill properties and its depth. [ABSTRACT FROM AUTHOR]

Archaeologists often need to date and group artifact types to discern typologies, chronologies, and classifications. For over a century, statisticians have been using classification and clustering techniques to infer patterns in data that can be defined by algorithms. In the case of archaeology, linear regression algorithms are often used to chronologically date features and sites, and pattern recognition is used to develop typologies and classifications. However, archaeological data is often expensive to collect, and analyses are often limited by poor sample sizes and datasets. Here we show that recent advances in computation allow archaeologists to use machine learning based on much of the same statistical theory to address more complex problems using increased computing power and larger and incomplete datasets. This paper approaches the problem of predicting the chronology of archaeological sites through a case study of medieval temples in Angkor, Cambodia. For this study, we have a large dataset of temples with known architectural elements and artifacts; however, less than ten percent of the sample of temples have known dates, and much of the attribute data is incomplete. Our results suggest that the algorithms can predict dates for temples from 821–1150 CE with a 49-66-year average absolute error. We find that this method surpasses traditional supervised and unsupervised statistical approaches for under-specified portions of the dataset and is a promising new method for anthropological inquiry. [ABSTRACT FROM AUTHOR]

The aesthetic quality of the built environment is of paramount importance to the quality of life of an increasingly urbanizing population. However, a lack of data has hindered the development of comprehensive measures of perceived architectural beauty. In this paper, we demonstrate that the local frequency of geotagged photos posted by internet users in two photo-sharing websites strongly predict the beauty ratings of buildings. We conduct an independent beauty survey with respondents rating proprietary stock photos of 1,000 buildings across the United States. Buildings with higher ratings were found more likely to be geotagged with user-uploaded photos in both Google Maps and Flickr. This correlation also holds for the beauty rankings of raters who seldom upload materials to the internet. Objective architectural characteristics that predict higher average beauty ratings of buildings also positively covary with their internet photo frequency. These results validate the use of localized user-generated image uploads in photo-sharing sites to measure the aesthetic appeal of the urban environment in the study of architecture, real estate, urbanism, planning, and environmental psychology. [ABSTRACT FROM AUTHOR]

This paper explores a new method for rapid structural damage inspection of steel tube slab (STS) structures along randomly measured paths based on a combination of compressive sampling (CS) and ultrasonic computerized tomography (UCT). In the measurement stage, using fewer randomly selected paths rather than the whole measurement net is proposed to detect the underlying damage of a concrete-filled steel tube. In the imaging stage, the ℓ1-minimization algorithm is employed to recover the information of the microstructures based on the measurement data related to the internal situation of the STS structure. A numerical concrete tube model, with the various level of damage, was studied to demonstrate the performance of the rapid UCT technique. Real-world concrete-filled steel tubes in the Shenyang Metro stations were detected using the proposed UCT technique in a CS framework. Both the numerical and experimental results show the rapid UCT technique has the capability of damage detection in an STS structure with a high level of accuracy and with fewer required measurements, which is more convenient and efficient than the traditional UCT technique. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. [ABSTRACT FROM AUTHOR]

In this paper, we present an automatic system for the analysis and labeling of structural scenes, floor plan drawings in Computer-aided Design (CAD) format. The proposed system applies a fusion strategy to detect and recognize various components of CAD floor plans, such as walls, doors, windows and other ambiguous assets. Technically, a general rule-based filter parsing method is fist adopted to extract effective information from the original floor plan. Then, an image-processing based recovery method is employed to correct information extracted in the first step. Our proposed method is fully automatic and real-time. Such analysis system provides high accuracy and is also evaluated on a public website that, on average, archives more than ten thousands effective uses per day and reaches a relatively high satisfaction rate. [ABSTRACT FROM AUTHOR]

This paper proposes a new DAC BIST (digital-to-analog converter built-in self-test) structure using a resistor loop known as a DDEM ADC (deterministic dynamic element matching analog-to-digital converter). Methods for both switch reduction and switch effect reduction are proposed for solving problems related to area overhead and accuracy of the conventional DAC BIST. The proposed BIST modifies the length of each resistor in the resistor loop via a merging operation and reduces the number of switches and resistors. In addition, the effect of switches is mitigated using the proposed switch effect reduction method. The accuracy of the proposed BIST is demonstrated by the reduction in the switch effect. The experimental results show that the proposed BIST reduces resource usages and the mismatch error caused by the switches. [ABSTRACT FROM AUTHOR]

In this paper, we model the bus networks of six major Indian cities as graphs in L-space, and evaluate their various statistical properties. While airline and railway networks have been extensively studied, a comprehensive study on the structure and growth of bus networks is lacking. In India, where bus transport plays an important role in day-to-day commutation, it is of significant interest to analyze its topological structure and answer basic questions on its evolution, growth, robustness and resiliency. Although the common feature of small-world property is observed, our analysis reveals a wide spectrum of network topologies arising due to significant variation in the degree-distribution patterns in the networks. We also observe that these networks although, robust and resilient to random attacks are particularly degree-sensitive. Unlike real-world networks, such as Internet, WWW and airline, that are virtual, bus networks are physically constrained. Our findings therefore, throw light on the evolution of such geographically and constrained networks that will help us in designing more efficient bus networks in the future. [ABSTRACT FROM AUTHOR]

This paper presents the results of investigations whose aim was to describe the influence of the pressure difference level on the ability of contaminants migration between neighbouring rooms in dynamic conditions associated with door swing. The analysis was based on airflow visualization made with cold smoke, which simulated the heavy contaminants. The test room was pressurized to a specific level and then the door was opened to observe the trail of the smoke plume in the plane of the door. The door was opened in both directions: to the positively and negatively pressurized room. This study focuses on the visualization of smoke plume discharge and an uncertainty analysis is not applicable. Unlike other studies which focus on the analysis of pressure difference, the present study looks at the contaminants which are heavier than air and on “pumping out” the contaminants by means of door swing. Setting the proper level of pressure difference between the contaminated room and the neighbouring rooms can prove instrumental in ensuring protection against toxic contaminants migration. This study helped to establish the threshold of pressure difference necessary to reduce migration of heavy contaminants to neighbouring rooms. [ABSTRACT FROM AUTHOR]

Drosophila suzukii (Matsumura), commonly known as Spotted Wing Drosophila (SWD), is a worldwide serious economic threat to the production of berries and stone fruits. The chemical control widely used against this pest is often not able to preventing yield losses because wild flora offers an abundance of fruits to D. suzukii where the pest is able to reproduce and from where it recolonizes neighbouring cultivated fields. Alternatively, within Integrated Pest Management protocols for D. suzukii, biological control could play a key role by reducing its populations particularly in non-cultivated habitats, thus increasing the effectiveness and reducing the side negative effects of other management strategies. Because of the scarcity and of the low efficiency of autochthonous parasitoids in the new invaded territories, in the last few years, a number of surveys started in the native area of D. suzukii to find parasitoid species to be evaluated in quarantine structures and eventually released in the field, following a classical biological control approach. This paper reports the results of these surveys carried out in South Korea and for the first time in China. Among the parasitoids collected, those belonging to the genus Asobara Foerster resulted dominant both by number and species diversity. By combining morphological characters and the mitochondrial COI gene as a molecular marker, we identified seven species of Asobara, of which two associated with D. suzukii, namely A. japonica and A leveri, and five new to science, namely Asobara brevicauda, A. elongata, A mesocauda, A unicolorata, A. triangulata. Our findings offer new opportunity to find effective parasitoids to be introduced in classical biological control programmes in the territories recently invaded by D. suzukii. [ABSTRACT FROM AUTHOR]

This paper presents responses of an edge circular cantilever beam under the effect of an impact force. The beam is excited by a transverse triangular force impulse modulated by a harmonic motion. The Kelvin–Voigt model for the material of the beam is used. The cracked beam is modelled as an assembly of two sub-beams connected through a massless elastic rotational spring. The considered problem is investigated within the Bernoulli-Euler beam theory by using energy based finite element method. The system of equations of motion is derived by using Lagrange's equations. The obtained system of linear differential equations is reduced to a linear algebraic equation system and solved in the time domain by using Newmark average acceleration method. In the study, the effects of the location of crack, the depth of the crack, on the characteristics of the reflected waves are investigated in detail. Also, the positions of the cracks are calculated by using reflected waves. [ABSTRACT FROM AUTHOR]

Non-invasive fetal electrocardiography (NI-FECG) plays an important role in fetal heart rate (FHR) measurement during the pregnancy. However, despite the large number of methods that have been proposed for adult ECG signal processing, the analysis of NI-FECG remains challenging and largely unexplored. In this study, we propose a prefix tree-based framework, called QRStree, for FHR measurement directly from the abdominal ECG (AECG). The procedure is composed of three stages: Firstly, a preprocessing stage is employed for noise elimination. Secondly, the proposed prefix tree-based method is used for fetal QRS complexes (FQRS) detection. Finally, a correction stage is applied for false positive and false negative correction. The novelty of the framework relies on using the range of FHR to establish the connections between the FQRS. The consecutive FQRS can be considered as strings composed of alphabet items, thus we can use the prefix tree to store them. A vertex of the tree contains an alphabet, thus a path of the tree gives a string. Such that, by storing the connections of the FQRS into the prefix tree structure, the problem of FQRS detection converts to a problem of optimal path selection. Specifically, after selecting the optimal path of the tree, the nodes in the optimal path are collected as detected FQRS. Since the prefix tree can cover every possible combination of the FQRS candidates, it has the potential to reduce the occurrence of miss detections. Results on two different databases show that the proposed method is effective in FHR measurement from single-channel AECG. The focus on single-channel FHR measurement facilitates the long-term monitoring for healthcare at home. [ABSTRACT FROM AUTHOR]

Solving the supply–demand imbalance is the most crucial issue for stable implementation of a public bike-sharing system. This gap can be reduced by increasing the accuracy of demand prediction by considering spatial and temporal properties of bike demand. However, only a few attempts have been made to account for both features simultaneously. Therefore, we propose a prediction framework based on graph convolutional networks. Our framework reflects not only spatial dependencies among stations, but also various temporal patterns over different periods. Additionally, we consider the influence of global variables, such as weather and weekday/weekend to reflect non-station-level changes. We compare our framework to other baseline models using the data from Seoul’s bike-sharing system. Results show that our approach has better performance than existing prediction models. [ABSTRACT FROM AUTHOR]

Investigations at the Maya site of Ceibal, Guatemala, documented an artificial plateau, measuring 600 x 340 m in horizontal dimensions and 6 to 15 m in height. Unlike highly visible pyramids, such horizontally extensive constructions covered by the rainforest are difficult to recognize on the ground, but airborne laser scanning (LiDAR) revealed its planned form. Excavations carried out over many years provided data on its construction sequence, fill volumes, and labor investments. The initial construction of the plateau occurred around 950 B.C. when a formal ceremonial complex was built in its center. This was the period when the inhabitants of the Maya lowlands were adopting a new way of life with greater reliance on maize agriculture, full sedentism, and ceramic use. The inhabitants of areas surrounding Ceibal, who retained certain levels of residential mobility, probably participated in the construction of the plateau. In this regard, the Ceibal plateau is comparable to monumental constructions that emerged before or during the transition to agriculture or sedentism in other parts of the world. The data from Ceibal compel researchers to examine the social implications of monumental constructions in the Maya lowlands before the establishment of centralized polities with hereditary rulers. Unlike pyramids, where access to the summits may have been limited to privileged individuals, the horizontal monumentality of the plateau was probably more conducive to inclusive interaction. The Ceibal plateau continued to be built up during the Preclassic period (1000 B.C.-A.D. 175), and its fill volume substantially surpassed those of pyramids. Large-scale construction projects likely promoted organizational and managerial innovations among participants, which may have set the stage for later administrative centralization. [ABSTRACT FROM AUTHOR]

Neighborhood relationship plays an important role in spatial analysis, map generalization, co-location data mining and other applications. From the perspective of computation, the formal model of neighborhood representation is a challenging question. This study presents a formal spatial data model for representing the planar spatial field with the support of Delaunay triangulation. Based on the three geometric elements in a triangle of the vertex, edge, and triangle area, the constructed data model describes the spatial objects of a point, line, and region respectively, as well as the neighborhood relationships among them. Three types of operators based on the model are formally defined, expanding, compressing and skeletonizing. For practical applications, three complex operators are extended by continuous and conditional operation. Through the application example of urban building generalization, this study illustrates the analysis of a neighborhood relationship and the detection of spatial conflicts, which is a crucial pre-process during map generalization. With the support of the proposed formal model of neighborhood representation, the generalization method uses the three basic operations of grouping, displacement and aggregation to perform decision making and detailed operation. The generalized result can retain the balance of built-up area better than that of other similar building generalization methods. [ABSTRACT FROM AUTHOR]

Trial design: During electrical stimulation in the lower urinary tract for the purpose of current perception threshold and sensory evoked potential recording, we observed that bladder volume increased rapidly. The aim of this prospective randomised comparative proof-of-concept study was to quantify urine production per time during stimulation of the lower urinary tract using different stimulation frequencies. Methods: Ninety healthy subjects (18 to 36 years old) were included. Forty females and 50 males were randomly assigned to one of the following study groups: dome, trigone or proximal, membranous (males only) or distal urethra. Starting from 60mL prefilling, stimulation was performed at two separate visits with a 14 French custom-made catheter using randomly applied frequencies of 0.5Hz, 1.1Hz, 1.6Hz (each with 500 stimuli). After each stimulation cycle per frequency, urine production was assessed. Main outcome measures represented urine production during stimulation, daily life and their ratio. Results: Lower urinary tract electrical stimulation increased urine production per time compared to bladder diary baseline values. Linear mixed model showed that frequency (p<0.001), stimulation order (p = 0.003), intensity (p = 0.042), and gender (p = 0.047) had a significant influence on urine production. Location, visit and age had no significant influence. Conclusions: Urine production is increased during electrical stimulation with a bigger impact of higher frequencies. This might be relevant for methodological aspects in the assessment of lower urinary tract afferent function and for patients with impaired renal urine output. Inhibition of renal sympathetic nerve activity by vagal afferents may be the underlying mechanism. [ABSTRACT FROM AUTHOR]

Mutations that confer herbicide resistance are a primary concern for herbicide-based chemical control of invasive plants and are often under-characterized structurally and functionally. As the outcome of selection pressure, resistance mutations usually result from repeated long-term applications of herbicides with the same mode of action and are discovered through extensive field trials. Here we used acetohydroxyacid synthase (AHAS) of Kochia scoparia (KsAHAS) as an example to demonstrate that, given the sequence of a target protein, the impact of genetic mutations on ligand binding could be evaluated and resistance mutations could be identified using a biophysics-based computational approach. Briefly, the 3D structures of wild-type (WT) and mutated KsAHAS-herbicide complexes were constructed by homology modeling, docking and molecular dynamics simulation. The resistance profile of two AHAS-inhibiting herbicides, tribenuron methyl and thifensulfuron methyl, was obtained by estimating their binding affinity with 29 KsAHAS (1 WT and 28 mutated) using 6 molecular mechanical (MM) and 18 hybrid quantum mechanical/molecular mechanical (QM/MM) methods in combination with three structure sampling strategies. By comparing predicted resistance with experimentally determined resistance in the 29 biotypes of K. scoparia field populations, we identified the best method (i.e., MM-PBSA with single structure) out of all tested methods for the herbicide-KsAHAS system, which exhibited the highest accuracy (up to 100%) in discerning mutations conferring resistance or susceptibility to the two AHAS inhibitors. Our results suggest that the in silico approach has the potential to be widely adopted for assessing mutation-endowed herbicide resistance on a case-by-case basis. [ABSTRACT FROM AUTHOR]

The forces that arise from the actin cortex play a crucial role in determining the membrane deformation. These include protrusive forces due to actin polymerization, pulling forces due to transient attachment of actin filaments to the membrane, retrograde flow powered by contraction of actomyosin network, and adhesion to the extracellular matrix. Here we present a theoretical model for membrane deformation resulting from the feedback between the membrane shape and the forces acting on the membrane. We model the membrane as a series of beads connected by springs and determine the final steady-state shape of the membrane arising from the interplay between pushing/pulling forces of the actin network and the resisting membrane tension. We specifically investigate the effect of the gel dynamics on the spatio-temporal deformation of the membrane until a stable lamellipodium is formed. We show that the retrograde flow and the cross-linking velocity play an essential role in the final elongation of the membrane. Interestingly, in the simulations where motor-induced contractility is switched off, reduced retrograde flow results in an increase in the rate and amplitude of membrane protrusion. These simulations are consistent with experimental observations that report an enhancement in protrusion efficiency as myosin II molecular motors are inhibited. [ABSTRACT FROM AUTHOR]

Research with adults and older adolescents suggests a general factor may underlie both mental health difficulties and wellbeing. However, the classical bifactor model commonly used to demonstrate this general trait has recently been criticised when a unidimensional structure is not supported. Furthermore, research is lacking in this area with children and early adolescents. We present confirmatory factor analysis models to explore the structure of psychopathology and wellbeing in early adolescents, using secondary data from a large U.K. sample (N = 1982). A simple correlated factors structure fitted the data well and revealed that wellbeing was just as related to internalising as this was to externalising symptoms. The classical bifactor solution also fitted the data well but was rejected as the general factor explained only 55% of the total common variance. S-1 models were therefore used to explore general covariance in a more robust way, and revealed that a general internalising distress factor could play an important role in all item responses. Gender and income differences in mental health were also explored through invariance testing and correlations. Our findings demonstrate the importance of considering mental health difficulties and wellbeing items together, and suggestions are made for how their correspondence could be controlled for. [ABSTRACT FROM AUTHOR]

The accurate location of induced seismicity is a problem of major interest in the safety monitoring of underground mines. Complexities in the seismic velocity structure, particularly changes in velocity caused by the progression of mining excavations, can cause systematic event mislocations. To address this problem, we present a novel construction method for an arbitrary 3D velocity model and a targeted hypocenter determination method based on this velocity model in underground mining. The method constructs a velocity model from 3D geological objects that can accurately express the interfaces of geologic units. Based on this model, the block corresponding to the minimum difference between the observed arrival times and the theoretical arrival times computed by the Fast Marching Method is located. Finally, a relocation procedure is carried out within the targeted block by heuristic algorithms to improve the performance. The accuracy and efficiency of the proposed method are demonstrated by the source localization results of both synthetic data and on-site data from Dongguashan Copper Mine. The results show that our proposed method significantly improves the location accuracy compared with the widely used Simplex and Particle Swarm Optimization methods. [ABSTRACT FROM AUTHOR]

Based on the apparent energy consumption data, a systematic and comprehensive city-level total carbon accounting approach was established and applied in Guangzhou, China. A newly extended LMDI method based on the Kaya identity was adopted to examine the main drivers for the carbon emissions increments both at the industrial sector and the residential sector. Research results are listed as follow: (1) Carbon emissions embodied in the imported electricity played a significant important role in emissions mitigation in Guangzhou. (2) The influences and impacts of various driving factors on industrial and residential carbon emissions are different in the three different development periods, namely, the 10th five-year plan period (2003–2005), the 11th five-year plan period (2005–2010), and the 12th five-year plan period (2010–2013). The main reasons underlying these influencing mechanisms were different policy measures announced by the central and local government during the different five-year plan periods. (3) The affluence effect (g-effect) was the dominant positive effect in driving emissions increase, while the energy intensity effect of production (e-effect-Production), the economic structure effect (s-effect) and the carbon intensity effect of production (f-effect-Production) were the main contributing factors suppressing emissions growth at the industrial sector. (4) The affluence effect of urban (g-effect-AUI) was the most dominant positive driving factor on emissions increment, while the energy intensity effect of urban (e-effect-Urban) played the most important role in curbing emissions growth at the residential sector. [ABSTRACT FROM AUTHOR]

Worries about possible harmful effects of new technologies (modern health worries) have intensely been investigated in the last decade. However, the comparability of translated self-report measures across countries is often problematic. This study aimed to overcome this problem by developing psychometrically sound brief versions of the widely used 25-item Modern Health Worries Scale (MHWS) suitable for multi-country use. Based on data of overall 5,176 individuals from four European countries (England, Germany, Hungary, Sweden), Ant Colony Optimization was used to identify the indicators that optimize model fit and measurement invariance across countries. Two scales were developed. A short (12-item) version of the MHWS that represents the four-factor structure of the original version and an ultra-short (4-item) scale that only measures the general construct. Both scales show that overall levels of health worries were highest in England and Hungary, but that the main reason for concern (e.g. electromagnetic radiation or food related fears) differs considerably between these countries. This study also shows that even if measurement invariance of translated self-report instruments across countries is problematic, it can be optimized by using adequate item selection procedures. Differences of modern health worries across countries and recommendations for cross-cultural research are discussed. [ABSTRACT FROM AUTHOR]

A geotextile mattress with sloping curtain is a newly proposed countermeasure against river and estuarine scour. In previous laboratory experiments, a geotextile mattress with sloping curtain was capable of protecting the bed downstream from scour and stimulating sediment deposition on both sides. However, the seepage scour under its geotextile mattress is inadequately researched at present. In this study, the Geotextile Mattress with Sloping Plate (GMSP) is proposed based on the simplification of the geotextile mattress with sloping curtain with the construction feasibility considered. A series of experiments was conducted to investigate the pressure distribution around the GMSP and the averaged seepage hydraulic gradient beneath its mattress. The results indicate remarkable pressure difference on two sides of the GMSP. The minimum bed pressure appears about 1.3 times the plate height downstream to the GMSP. The averaged seepage hydraulic gradient beneath the mattress increases with the sloping angle increasing from 35° to 60° in general. The averaged hydraulic gradient also ascends as the relative plate height increases, but reduces as the opening ratio increases at opening ratios greater than 0.143. The safety boundary for the averaged hydraulic gradient under the geotextile mattress of the GMSP could get much smaller than the critical hydraulic gradient of piping and can easily be overwhelmed. This phenomenon can mainly be attributed to the discontinuous contact between the mattress and the seabed. A suggestion for the parametric design of the GMSP is to extend the width of the mattress to reduce the risk of seepage failure. [ABSTRACT FROM AUTHOR]

The accumulation of scientific knowledge on calling is limited by the absence of a common theoretical and measurement framework. Many different models of calling have been proposed, and we do not know how much research results that refer to a specific model are generalizable to different theoretical accounts of calling. In this article, we investigate whether two leading models of calling tackle the same construct. The two models were merged into a comprehensive framework that measures calling across seven facets: Passion, Purposefulness, Sacrifice, Pervasiveness, Prosocial Orientation, Transcendent Summons, and Identity. We then developed the Unified Multidimensional Calling Scale (UMCS) drawing from previous published items. Across two surveys involving college students (N = 5886) and adult employees (N = 205) the UMCS was proved to be valid and reliable. We also observed that the UMCS is invariant across time and calling domains. Finally, we found that facets of calling have very different relationships with outcomes and concurrent measures, suggesting that results obtained with a smaller set of facets are not generalizable to the higher-order construct of calling or to a different model that does not share the same facets. [ABSTRACT FROM AUTHOR]

It has been demonstrated that nurturing and affiliative touch is essential for human emotional and physical well-being throughout our entire life. Within the last 30 years a system of low-threshold mechanosensitive C fibers innervating the hairy skin was discovered and described; this system is hypothesized to represent the neurobiological substrate for the affective and rewarding properties of touch. This discovery opens new perspectives for multidisciplinary research of the role of affiliative social touch in health and disease, and calls for establishing novel psychometric tools assessing individual differences in the domain of affective touch. The main objective of the study was to construct and validate a Russian version of the Touch Experiences and Attitudes Questionnaire (TEAQ), a self-report measure recently developed to quantify individual experience and attitude to social and affective touch. A pool of 117 items was translated into Russian and all the items were assessed for appropriateness for Russian culture (232 participants). After exploring the factor structure (468 participants), we composed a 37-item questionnaire (TEAQ-37 Rus) characterized by good reliability and a clear 5-factor structure, covering the aspects of attitude to intimate touch, attitude to friendly touch, attitude to self-care, current intimate touch experiences, and childhood touch experiences. Confirmatory factor analysis (551 participants) has demonstrated good consistency and reliability of the 5-factor structure of the TEAQ-37 Rus. Cross-validation research demonstrated moderate positive correlations between predisposition to social touch and emotional intelligence; positive correlations with extraversion and openness facets of the Big Five personality model were also found. As predicted, participants with higher TEAQ-37 Rus scores rated all observed kinds of touch as more pleasant, with a particular preference for slow touch. We anticipate that this questionnaire will be a valuable tool for researchers of social touch, touch perception abnormalities, and the importance of touch experiences for emotional and mental health. [ABSTRACT FROM AUTHOR]

We aimed to describe the development of an inference model for computer-aided diagnosis of lung nodules that could provide valid reasoning for any inferences, thereby improving the interpretability and performance of the system. An automatic construction method was used that considered explanation adequacy and inference accuracy. In addition, we evaluated the usefulness of prior experts’ (radiologists’) knowledge while constructing the models. In total, 179 patients with lung nodules were included and divided into 79 and 100 cases for training and test data, respectively. F-measure and accuracy were used to assess explanation adequacy and inference accuracy, respectively. For F-measure, reasons were defined as proper subsets of Evidence that had a strong influence on the inference result. The inference models were automatically constructed using the Bayesian network and Markov chain Monte Carlo methods, selecting only those models that met the predefined criteria. During model constructions, we examined the effect of including radiologist’s knowledge in the initial Bayesian network models. Performance of the best models in terms of F-measure, accuracy, and evaluation metric were as follows: 0.411, 72.0%, and 0.566, respectively, with prior knowledge, and 0.274, 65.0%, and 0.462, respectively, without prior knowledge. The best models with prior knowledge were then subjectively and independently evaluated by two radiologists using a 5-point scale, with 5, 3, and 1 representing beneficial, appropriate, and detrimental, respectively. The average scores by the two radiologists were 3.97 and 3.76 for the test data, indicating that the proposed computer-aided diagnosis system was acceptable to them. In conclusion, the proposed method incorporating radiologists’ knowledge could help in eliminating radiologists’ distrust of computer-aided diagnosis and improving its performance. [ABSTRACT FROM AUTHOR]

The security strength of the traditional one-time-pad encryption system depends on the randomness of the secret key. However, It can hardly to generatea truerandom key by using the existing technologies and methods, and it is also difficult to issue and store the random keywhich is at least as long as the plaintext. Therefore, we pay more attention to the logical operation used in the encryption and decryption but not to how to generate the random key. The calculator, a three-dimensional DNA self-assembly pyramid structure, is designed to construct four common logical operations (AND, OR, NOT, XOR) by programming DNA interactions. And two novel one-time-pad cryptography schemes, a single-bit one-time-pad algorithm and improved double-bit one-time-pad algorithm, are proposed based on the calculator. The security fragments, used to construct the three-dimensional DNA self-assembly pyramid structure, are intercepted from a reference chain which is selected from the DNA database. All of the interception parameters are transmitted to recipient by hiding in DNA sequences. Only the authorized user can get all secret parameters to reconstruct the structure. The secret random key sequences for the two one-time-pad cryptography algorithms are generated by using logistic map. It only needs to share two parameters and thresholding function in sender and recipient without code books. The simulation results and security analysis show that the encryption algorithms are effective and can provide higher computational complexity as well as a reduced cracking probability except for the difficult of biological experiments. [ABSTRACT FROM AUTHOR]

The current study contributes to previous work on measuring the social phenotype in Autism Spectrum Disorder (ASD) by validating a multidimensional test of social competence developed for use with individuals with and without ASD. The “Multidimensional Social Competence Scale” (MSCS) was previously validated as a parent-rating scale with youth 11–18 years with ASD without intellectual disability and typically developing adolescents of comparable age. The current study presents a validation of a self-report version of the MSCS in a non-clinical young adult population (N = 1178, males = 360, females = 817, age range = 17–25 years). The MSCS consists of seven domains that represent social competence: social motivation, social inferencing, demonstrating empathic concern, social knowledge, verbal conversation skills, nonverbal sending skills, and emotion regulation. These domains are theorized to be indicative of the higher-order construct of social competence. A second higher-order theorization of the MSCS structure posits that 3 of these factors are indicative of social responsiveness, and the remaining 4 factors are indicative of social understanding and emotion regulation. Our findings indicated support for each of the theorized multidimensional factor structures. Reliability, optimal scoring, convergent and discriminant validity of the measure, as well as implications for future research are discussed. [ABSTRACT FROM AUTHOR]

With the development of outsourcing data services, data security has become an urgent problem that needs to be solved. Attribute-based encryption is a valid solution to data security in cloud storage. There is no existing scheme that can guarantee the privacy of access structures and achieve attribute-based encryption with keyword search and attribute revocation. In this article, we propose a new searchable and revocable multi-data owner attribute-based encryption scheme with a hidden policy in cloud storage. In the new scheme, the same access policy is used in both the keyword index and message encryption. The advantage of keyword index with access policy is that as long as a user’s attributes satisfy the access policy, the searched ciphertext can be correctly decrypted. This property improves the accuracy of the search results. The hidden policy is used in both the ciphertext and the keyword index to protect users’ privacy. The new scheme contains attribute revocation, which is suitable for the actual situation that a user’s attributes maybe changed over time. In the general bilinear group model, the security of the scheme is demonstrated, and the efficiency of the scheme is analyzed. [ABSTRACT FROM AUTHOR]

Honeybees construct nests that consist of regularly arrayed hexagonal cylinders. In the first stage of honeycomb construction, they build a linear sequence of tetrapod structures that form the basis of the comb. However, considering their physiological limitations, it is unknown how honeybees produce that initial pattern. Herein, in an attempt to understand the mechanisms of honeycomb construction, we propose an agent-based model, the attachment-excavation model, in which worker honeybees are classified into attachers who secrete and attach wax, and excavators who excise the attached wax. The model assumes that workers instinctively refrain from digging through the thin parts of a wax cluster. We then conduct two-dimensional (2D) simulations that show how a tripod pattern can be seen as a projection of tetrapods onto a plane. The simulation results show that the tripod pattern emerges due to competition between the attachers and excavators. As time advances, the isotropic wax growth causes the tripods to connect planarly. Because the homogeneously broadened structures do not match that of a natural comb, we employ anisotropic wax growth to obtain a linear sequence of constructed tripods, thus suggesting that anisotropy is a significant contributor to the first stage of honeycomb construction. From our simulation results, we conclude that honeybees utilize self-organization to achieve complexity during the first stage of honeycomb construction. It is anticipated that the results of our study will provide insights into how complexity can be achieved within a hierarchy. [ABSTRACT FROM AUTHOR]

Throughout the Angkor period (9th to 15th centuries CE), the Khmer kingdom maintained a series of interconnected cities and smaller settlements across its territory on mainland Southeast Asia. One such city was Koh Ker, which for a brief period in the 10th century CE even served as a royal capital. The complexity of the political landscape meant the Khmer kings and the elite were particularly mobile through the Angkor period, and rupture in royal houses was common. However, while the historical record chronicles the 10th century migration of the royal seat from Koh Ker back to Angkor, the fate of Koh Ker’s domestic population has remained unknown. In this article, we reconstruct the settlement history of Koh Ker, using palaeoecological and geoarchaeological techniques, and show that human activity and land use persisted in the city for several centuries beyond the city’s abandonment by the royal court. These results highlight the utility of multi-proxy environmental reconstructions of Khmer urban settlements for re-evaluating prevailing assumptions regarding the use and occupation of Angkor-period cities. [ABSTRACT FROM AUTHOR]