Co-precipitation synthesis of CeVO4 nanoparticles for electrochemical hydrogen storage

Journal of Materials Science: Materials in Electronics - In this study, cerium vanadate (CeVO4) nanoparticles were synthesized using ammonium metavanadate and cerium (III) nitrate hexahydrate as...     

Tricalcium phosphate and tricalcium phosphate/polycaprolactone particulate composite for controlled release of protein

β-Tricalcium phosphate (β-TCP) with three different particle size ranges was used to study the effects of particle size and surface area on protein adsorption and release. Polycaprolactone (PCL) coating was applied on the particle systems to investigate its effect on particulate system properties from both structural and application aspects. The maximum loading of 27 mg/g was achieved for 100 nm particles. Bovine serum albumin (BSA) loading amount was controlled by varying the BSA loading solution concentration, as well as the sample powder's surface area. Increasing the surface area of the delivery powder significantly increased loading and release yield. Unlike the samples with low surface area, the lowest particle size samples showed sigmoidal release profile. This indicated that release was governed by different mechanisms for particles with different sizes. While the majority of samples showed no more than 50% release, the 550 nm particles demonstrated 100% release. PCL coating showed no significant ability to attenuate burst release in PBS. However, it led to a steadier release profile as compared to the bare TCP particles. FTIR analysis also proved that the secondary structure of BSA did not change significantly during the adsorption; however, minor denaturation was found during the release. The same results were found when PCL coating was applied on the TCP particles. We envision potential use of TCP and TCP + PCL systems in bone growth factor or orthopedic drug delivery applications in future bone tissue engineering application.     

A hybrid particle swarm optimisation for scheduling just-in-time single machine with preemption,machine idle time and unequal release times

This paper addresses preemption in just-in-time (JIT) single–machine-scheduling problem with unequal release times and allowable unforced machine idle time as realistic assumptions occur in the manufacturing environments aiming to minimise the total weighted earliness and tardiness costs. Delay in production systems is a vital item to be focussed to counteract lost sale and back order. Thus, JIT concept is targeted including the elements required such as machine preemption, machine idle time and unequal release times. We proposed a new mathematical model and as the problem is proven to be NP-hard, three meta-heuristic approaches namely hybrid particle swarm optimisation (HPSO), genetic algorithm and imperialist competitive algorithm are employed to solve the problem in larger sizes. In HPSO, cloud theory-based simulated annealing is employed with a certain probability to avoid being trapped in a local optimum. Taguchi method is applied to calibrate the parameters of the proposed algorithms. A number of numerical examples are solved to demonstrate the effectiveness of the proposed approach. The performance of the proposed algorithms is evaluated in terms of relative percent deviation and computational time where the computational results clarify better performance of HPSO than other algorithms in quality of solutions and computational time.     

Maximization of Photonic Bandgaps in Two-Dimensional Superconductor Photonic Crystals

In this paper, we investigate the properties of photonic band structures in two-dimensional superconductor photonic crystals (2D-SCPCs) using the frequency dependent plane wave expansion method. We consider two types of 2D-SCPCs, which are composed of superconductor (dielectric) rods embedded into a dielectric (superconductor) background, named type I (type II) SCPCs. We target maximization of the gap-to-mid-gap ratio by varying many parameters, namely, shape of the rods, the operating temperature, the permittivity of the dielectric material, and the threshold frequency of the superconductor. We show that the type II SCPCs have a higher gap-to-mid-gap ratio than the type I SCPCs. In addition, the PBGs can be tuned efficiently by the operating temperature. Moreover, the photonic band structures can be tailored by changing the dielectric constant of the background (rods) in the type I (type II) SCPCs.     

Alginate hydrogel as a promising scaffold for dental-derived stem cells: an in vitro study

The objectives of this study were to: (1) develop an injectable and biodegradable scaffold based on oxidized alginate microbeads encapsulating periodontal ligament (PDLSCs) and gingival mesenchymal stem cells (GMSCs); and (2) investigate the stem cell viability, and osteogenic differentiation of the stem cells in vitro. Stem cells were encapsulated using alginate hydrogel. The stem cell viability, proliferation and differentiation to adipogenic and osteogenic tissues were studied. To investigate the expression of both adipogenesis and ontogenesis related genes, the RNA was extracted and RT-PCR was performed. The degradation behavior of hydrogel based on oxidized sodium alginate with different degrees of oxidation was studied in PBS at 37?°C as a function of time by monitoring the changes in weight loss. The swelling kinetics of alginate hydrogel was also investigated. The results showed that alginate is a promising candidate as a non-toxic scaffold for PDLSCs and GMSCs. It also has the ability to direct the differentiation of these stem cells to osteogenic and adipogenic tissues as compared to the control group in vitro. The encapsulated stem cells remained viable in vitro and both osteo-differentiated and adipo-differentiated after 4?weeks of culturing in the induction media. It was found that the degradation profile and swelling kinetics of alginate hydrogel strongly depends on the degree of oxidation showing its tunable chemistry and degradation rate. These findings demonstrate for the first time that immobilization of PDLSCs and GMSCs in the alginate microspheres provides a promising strategy for bone tissue engineering.     

Supply chain risk management and artificial intelligence: state of the art and future research directions

Supply chain risk management (SCRM) encompasses a wide variety of strategies aiming to identify, assess, mitigate and monitor unexpected events or conditions which might have an impact, mostly adverse, on any part of a supply chain. SCRM strategies often depend on rapid and adaptive decision-making based on potentially large, multidimensional data sources. These characteristics make SCRM a suitable application area for artificial intelligence (AI) techniques. The aim of this paper is to provide a comprehensive review of supply chain literature that addresses problems relevant to SCRM using approaches that fall within the AI spectrum. To that end, an investigation is conducted on the various definitions and classifications of supply chain risk and related notions such as uncertainty. Then, a mapping study is performed to categorise existing literature according to the AI methodology used, ranging from mathematical programming to Machine Learning and Big Data Analytics, and the specific SCRM task they address (identification, assessment or response). Finally, a comprehensive analysis of each category is provided to identify missing aspects and unexplored areas and propose directions for future research at the confluence of SCRM and AI.