The field of 3D printing, also known as additive manufacturing (AM), is developing rapidly in both academic and industrial research environments. New materials and printing technologies, which enable ...rapid and multimaterial printing, have given rise to new applications and utilizations. However, the main bottleneck for achieving many more applications is the lack of materials with new physical properties. Here, some of the recent reports on novel materials in this field, such as ceramics, glass, shape‐memory polymers, and electronics, are reviewed. Although new materials have been reported for all three main printing approaches—fused deposition modeling, binder jetting or laser sintering/melting, and photopolymerization‐based approaches, apparently, most of the novel physicochemical properties are associated with materials printed by photopolymerization approaches. Furthermore, the high resolution that can be achieved using this type of 3D printing, together with the new properties, has resulted in new implementations such as microfluidic, biomedical devices, and soft robotics. Therefore, the focus here is on photopolymerization‐based additive manufacturing including the recent development of new methods, novel monomers, and photoinitiators, which result in previously inaccessible applications such as complex ceramic structures, embedded electronics, and responsive 3D objects.
Photopolymerization‐based 3D printing materials have been reported widely in recent years. Various materials such as hydrogels, ceramics, and responsive polymers, along with new applications of these materials and methods are described.
This paper studies event design in event-triggered feedback systems. A novel event-triggering scheme is presented to ensure exponential stability of the resulting sampled-data system. The scheme ...postpones the triggering of events over previously proposed methods and therefore enlarges the intersampling period. The resulting intersampling periods and deadlines are bounded strictly away from zero when the continuous time system is input-to-state stable with respect to measurement errors.
Feature selection is an important data processing method to reduce dimension of the raw datasets while preserving the information as much as possible. In this paper, an enhanced version of Black ...Widow Optimization Algorithm called SDABWO is proposed to solve the feature selection problem. The Black Widow Optimization Algorithm (BWO) is a new population-based meta-heuristic algorithm inspired by the evolution process of spider population. Three main improvements were included into the BWO to overcome the shortcoming of low accuracy, slow convergence speed and being easy to fall into local optima. Firstly, a novel strategy for selecting spouses by calculating the weight of female spiders and the distance between spiders is proposed. By applying the strategy to the original algorithm, it has faster convergence speed and higher accuracy. The second improvement includes the use of mutation operator of differential evolution at mutation phase of BWO which helps the algorithm escape from the local optima. And then, three key parameters are set to adjust adaptively with the increase of iteration times. To confirm and validate the performance of the improved BWO, other 10 algorithms are used to compared with the SDABWO on 25 benchmark functions. The results show that the proposed algorithm enhances the exploitation ability, improves the convergence speed and is more stable when solving optimization problems. Furthermore, the proposed SDABWO algorithm is employed for feature selection. Twelve standard datasets from UCI repository prove that SDABWO-based method has stronger search ability in the search space of feature selection than the other five popular feature selection methods. These results confirm the capability of the proposed method simultaneously improve the classification accuracy while reducing the dimensions of the original datasets. Therefore, SDABWO-based method was found to be one of the most promising for feature selection problem over other approaches that are currently used in the literature.
In this manuscript, by using undetermined parameter method, an efficient iterative method with eighth-order is designed to solve nonlinear systems. The new method requires one matrix inversion per ...iteration, which means that computational cost of our method is low. The theoretical efficiency of the proposed method is analyzed, which is superior to other methods. Numerical results show that the proposed method can reduce the computational time, remarkably. New method is applied to solve the numerical solution of nonlinear ordinary differential equations (ODEs) and partial differential equations (PDEs). The nonlinear ODEs and PDEs are discretized by finite difference method. The validity of the new method is verified by comparison with analytic solutions.
This paper examines event-triggered data transmission in distributed networked control systems with packet loss and transmission delays. We propose a distributed event-triggering scheme, where a ...subsystem broadcasts its state information to its neighbors only when the subsystem's local state error exceeds a specified threshold. In this scheme, a subsystem is able to make broadcast decisions using its locally sampled data. It can also locally predict the maximal allowable number of successive data dropouts (MANSD) and the state-based deadlines for transmission delays. Moreover, the designer's selection of the local event for a subsystem only requires information on that individual subsystem. Our analysis applies to both linear and nonlinear subsystems. Designing local events for a nonlinear subsystem requires us to find a controller that ensures that subsystem to be input-to-state stable. For linear subsystems, the design problem becomes a linear matrix inequality feasibility problem. With the assumption that the number of each subsystem's successive data dropouts is less than its MANSD, we show that if the transmission delays are zero, the resulting system is finite-gain Lp stable. If the delays are bounded by given deadlines, the system is asymptotically stable. We also show that those state-based deadlines for transmission delays are always greater than a positive constant.
Human biomechanical energy is characterized by fluctuating amplitudes and variable low frequency, and an effective utilization of such energy cannot be achieved by classical energy-harvesting ...technologies. Here we report a high-efficient self-charging power system for sustainable operation of mobile electronics exploiting exclusively human biomechanical energy, which consists of a high-output triboelectric nanogenerator, a power management circuit to convert the random a.c. energy to d.c. electricity at 60% efficiency, and an energy storage device. With palm tapping as the only energy source, this power unit provides a continuous d.c. electricity of 1.044 mW (7.34 W m(-3)) in a regulated and managed manner. This self-charging unit can be universally applied as a standard 'infinite-lifetime' power source for continuously driving numerous conventional electronics, such as thermometers, electrocardiograph system, pedometers, wearable watches, scientific calculators and wireless radio-frequency communication system, which indicates the immediate and broad applications in personal sensor systems and internet of things.
Computer-aided drug design uses high-performance computers to simulate the tasks in drug design, which is a promising research area. Drug-target affinity (DTA) prediction is the most important step ...of computer-aided drug design, which could speed up drug development and reduce resource consumption. With the development of deep learning, the introduction of deep learning to DTA prediction and improving the accuracy have become a focus of research. In this paper, utilizing the structural information of molecules and proteins, two graphs of drug molecules and proteins are built up respectively. Graph neural networks are introduced to obtain their representations, and a method called DGraphDTA is proposed for DTA prediction. Specifically, the protein graph is constructed based on the contact map output from the prediction method, which could predict the structural characteristics of the protein according to its sequence. It can be seen from the test of various metrics on benchmark datasets that the method proposed in this paper has strong robustness and generalizability.
Prediction of drug-target affinity by constructing both molecule and protein graphs.
Harvesting biomechanical energy is an important route for providing electricity to sustainably drive wearable electronics, which currently still use batteries and therefore need to be charged or ...replaced/disposed frequently. Here we report an approach that can continuously power wearable electronics only by human motion, realized through a triboelectric nanogenerator (TENG) with optimized materials and structural design. Fabricated by elastomeric materials and a helix inner electrode sticking on a tube with the dielectric layer and outer electrode, the TENG has desirable features including flexibility, stretchability, isotropy, weavability, water-resistance and a high surface charge density of 250 μC m
. With only the energy extracted from walking or jogging by the TENG that is built in outsoles, wearable electronics such as an electronic watch and fitness tracker can be immediately and continuously powered.
•Particle generation and packing algorithms for heterogeneous meso-structures.•Intra- and inter-phase cohesive zones for failure initiation and propagation.•Monte Carlo analysis of meso-structure ...effects on emergent behaviour.•Damage evolution, macro-crack patterns and failure energy of concrete.
Methodology for analysis of meso-structure effects on longer-scale mechanical response of concrete is developed. Efficient algorithms for particle generation and packing are proposed to represent 3D meso-structures as collections of discrete features distributed randomly in a continuous phase. Specialised to concrete, the continuous phase represents mortar, while the features are aggregates and voids. Intra- and inter-phase cohesive zones are used for failure initiation and crack propagation. A Monte Carlo approach is proposed to analyse the effects of meso-structure geometrical (volume density, size distribution and shape of features) and physical (strength and energy of cohesive zones) properties, whereas a number of model realisations with identical properties are used for statistical analysis. The results present the relative significance of each meso-structure parameter for the emergent load capacity (tensile strength), damage evolution via micro-crack coalescence and macro-crack patterns, and failure energy density (toughness) of concrete. The proposed methodology is an effective tool for meso-structure optimisation in the design of concrete structures with prescribed requirements for strength and toughness.
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