Design and Simulation of Flow Field for Bone Tissue Engineering Sca old Based on Triply Periodic Minimal Surface

A novel method was proposed to design the structure of a bone tissue engineering scafold based on triply periodic minimal surface.In this method,reverse engineering software was used to reconstruct the surface from point cloud data.This method overcomes the limitations of commercially available software packages that prevent them from generating models with complex surfaces used for bone tissue engineering scafolds.Additionally,the fluid feld of the scafolds was simulated through a numerical method based on fnite volume and the cell proliferation performance was evaluated via an in vitro experiment.The cell proliferation and the mass flow evaluated in a bioreactor further verifed the flow feld simulated using computational fluid dynamics.The result of this study illustrates that the pressure value drops rapidly from 0.103 Pa to 0.011 Pa in the y-axis direction and the mass flow is unevenly distributed in the outlets.The mass flow in the side outlets is observed to be approximately 24.3 times higher thanthe bottom.Importantly,although the mean value of wall shear stress is signifcantly more than 0.05 Pa,there is stil a large area with a suitable shear stress below 0.05 Pa where most cells can proliferate well.The result shows that th inlet velocity 0.0075 m/s is suitable for cell proliferation in the scafold.This study provides an insight into the design analysis,and in vitro experiment of a bone tissue engineering scafold.

Viral-mediated gene therapy for spinal cord injury(SCI) from a translational neuroanatomical perspective

Repairing spinal cord injury（SCI）is one of the most challenging endeavours currently faced by neuroscientists.One promising therapeutic avenue to reverse this once considered permanent condition is gene therapy,however progress has been hampered by the anatomica intricacy of the spinal cord itself as well as by the sheer complexity of the molecular cascades of events that take place in the injured cord.

Reconstruction of an Expert＇s Decision Making Expertise in Concrete Dispatching by Machine Learning

Finding the optimum solution for dispatching in concrete delivery is computationally intractable because it is a NP-hard （non-deterministic polynomial-time hard） problem. Heuristic methods are required to obtain satisfactory solutions. Inefficiencies in mathematical modeling still make concrete dispatching difficult to solve. In reality, complex dispatching systems are mostly handled by human experts, who are able to manage the assigned tasks well. However, the high dependency on human expertise is a considerable challenge for RMC （ready mixed concrete） companies. In this paper, a logical reconstruction of an expert＇s decision making is achieved by two machine learning techniques： decision tree and rule induction. This paper focuses on the expert dispatcher＇s prioritization of customer orders. The proposed method has been tested on a simulation model consisting of a batch plant and three customers per day. The scenarios generated by the simulation model were given to a dispatch manager who was asked to prioritize the customers in each day. The scenarios and the decisions were then input to the machine learning programs, which created generalizations of the expert＇s decisions. Both decision trees and rules approach 80% accuracy in reproducing the human performance.

An evaluation of the engineering suitability of extensive brackishwater ponds in Barru, South Sulawesi Province, Indonesia

In brackishwater aquaculture, pond engineering is essential to meet the cultured species’ bio-physical requirements and potentially minimizing social and environmental impacts. This study evaluated the suitability of pond engineering designs and pond, dyke, and canal construction at family-run, extensive brackishwater aquaculture farms in Barru regency of South Sulawesi, Indonesia. Soil properties, topography, hydrological data, field surveys, and high resolution (0.6 m) Quickbird imagery were used to assess the suitability of local pond engineering designs and the efficiency of canals. The study shows that in 752 of the evaluated pond units (430 ha), about 70% of pond beds were not constructed at the optimal pond elevation. Basic engineering requirements for pond layout and dyke and canal designs were not addressed in more than 70% of the pond units. Cease-to-flow conditions commonly occur due to the sedimentation of canals. Flows are also attenuated due to collapsed dykes. Farmers disregarded soil limitations and the impacts of tidal conditions and wave regimes. It is unlikely that shrimp and fish production in the region can increase without significant improvements in pond engineering. The problems identified by the study are not unique to Barru;they also occur in other extensive aquaculture areas in Indonesia and the region. Increased awareness of the need for improved pond engineering techniques is urgently needed to safeguard the economic and environmental sustainability of Indonesia’s valuable aquaculture industry.

Discussions on rockburst and dynamic ground support in deep mines

The paper is a summary of discussions on four topics in rockburst and dynamic ground support.Topic 1 is the mechanisms of rockburst.Rockburst events are classified into two categories in accordance with the triggering mechanisms,i.e.strain burst and fault-slip burst.Strain burst occurs on rock surfaces when the tangential stress exceeds the rock strength in hard and brittle rocks.Fault-slip burst is triggered by fault-slip induced seismicity.Topic 2 is prediction and forecasting of rockburst events.Prediction for a rockburst event must tell the location,timing and magnitude of the event.Forecasting could simply foresee the probability of some of the three parameters.It is extremely challenging to predict rockbursts and large seismic events with current knowledge and technologies,but forecasting is possible,for example the possible locations of strain burst in an underground opening.At present,the approach using seismic monitoring and numerical modelling is a promising forecasting method.Topic 3 is preconditioning methods.The current preconditioning methods are blasting,relief-hole drilling and hydrofracturing.Defusing fault-slip seismicity is difficult and challenging but has been achieved.In very deep locations(>3000 m),the fracturing could extend from the excavation face to a deep location ahead of the face and therefore preconditioning is usually not required.Topic 4 is dynamic ground support against rockburst.Dynamic ground support requires that the support system be strong enough to sustain the momentum of the ejecting rock on one hand and tough enough on the other hand to absorb the strain and seismic energies released from the rock mass.The current dynamic support systems in underground mining are composed of yielding tendons and flexible surface retaining elements like mesh/screen and straps.Yielding props and engineered timber props are also used for dynamic support.

Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

During the actual high-speed machining process,it is necessary to reduce the energy consumption and improve the machined surface quality.However,the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining environments.Herein,a novel intelligent system is proposed for prediction and optimization.A novel adaptive neuro-fuzzy inference system(NANFIS)is proposed to predict the energy consumption and surface quality.In the NANFIS model,the membership functions of the inputs are expanded into:membership superior and membership inferior.The membership functions are varied based on the machining theory.The inputs of the NANFIS model are cutting parameters,and the outputs are the machining performances.For optimization,the optimal cutting parameters are obtained using the improved particle swarm optimization(IPSO)algorithm and NANFIS models.Additionally,the IPSO algorithm as a learning algorithm is used to train the NANFIS models.The proposed intelligent system is applied to the high-speed milling process of compacted graphite iron.The experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2%and 93.4%,respectively.The NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent models.Based on the IPSO algorithm and NANFIS models,the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling efficiency.It is demonstrated that the proposed intelligent system is applicable to actual high-speed milling processes,thereby enabling sustainable and intelligent manufacturing.

Laser additive manufacturing of strong and ductile Al-12Si alloy under static magnetic field

Rapid cooling and solidification during laser additive manufacturing(LAM)can produce ultra-fine microstructure with higher strength.However,the non-uniform cell/grain structure can easily result in early stress concentration and fracture during deformation,which remains a major challenge for the LAM field.Using Al-12Si as the model alloy,we employed the external static magnetic field(SMF)to modulate the laser powder bed fusion process(L-PBF),demonstrating a uniform microstructure with a refined cell structure.The mechanical properties show that the SMF can produce a combination of high tensile strength of 451.4±0.5 MPa and large uniform elongation of 10.4%±0.79%,which are superior to those of previously-reported Al-Si alloys with post-treatment or element alloying.The mechanism analysis based on multi-scale simulation reveals the determining role of SMF in rapid solidification,and this method is applicable to the microstructure control of other metallic materials during LAM.

Oxygen-deficient bismuth tungstate and bismuth oxide composite photoanode with improved photostability

A homogeneous layer of Bi_2O_3-Bi_(14)WO_(24) composite(BWO/Bi_2O_3) thin film was fabricated using a combination of electrodeposition and thermal treatment. The evenly distributed Bi14 WO24 component within the Bi_2O_3 layer was found to be important in stabilising the photoelectrochemical performances of Bi_2O_3 photoanode by promoting the photoelectron transport. The unmodified Bi_2O_3 suffered from severe photocorrosion as proven by X-ray diffraction(XRD) and inductively coupled plasma(ICP) analyses while the composite thin film was active without noticeable activity decay for at least 3 h of illumination. This strategy might be applicable to other photocatalysts with stability issues.