Hybrid 3D Printing of Synthetic and Cell‐Laden Bioinks for Shape Retaining Soft Tissue Grafts

Despite recent advances in clinical procedures, the repair of soft tissue remains a reconstructive challenge. Current technologies such as synthetic implants and dermal flap autografting result in inefficient shape retention and unpredictable aesthetic outcomes. 3D printing, however, can be leveraged to produce superior soft tissue grafts that allow enhanced host integration and volume retention. Here, a novel dual bioink 3D printing strategy is presented that utilizes synthetic and natural materials to create stable, biomimetic soft tissue constructs. A double network ink composed of covalently cross‐linked poly(ethylene) glycol and ionically cross‐linked alginate acts as a physical support network that promotes cell growth and enables long‐term graft shape retention. This is coupled with a cell‐laden, biodegradable gelatin methacrylate bioink in a hybrid printing technique, and the composite scaffolds are evaluated in their mechanical properties, shape retention, and cytotoxicity. Additionally, a new shape analysis technique utilizing CloudCompare software is developed that expands the available toolbox for assessing scaffold aesthetic properties. With this dynamic 3D bioprinting strategy, complex geometries with robust internal structures can be easily modulated by varying the print ratio of nondegradable to sacrificial strands. The versatility of this hybrid printing fabrication platform can inspire the design of future multimaterial regenerative implants.     

The Stingless Bee <Emphasis Type="Italic">Melipona solani</Emphasis> Deposits a Signature Mixture and Methyl Oleate to Mark Valuable Food Sources

Stingless bees foraging for food improve recruitment by depositing chemical cues on valuable food sites or pheromone marks on vegetation. Using gas chromatography/mass spectrometry and bioassays, we showed that Melipona solani foragers leave a mixture composed mostly of long chain hydrocarbons from their abdominal cuticle plus methyl oleate from the labial gland as a scent mark on rich food sites. The composition of hydrocarbons was highly variable among individuals and varied in proportions, depending on the body part. A wide ratio of compounds present in different body parts of the bees elicited electroantennogram responses from foragers and these responses were dose dependent. Generally, in bioassays, these bees prefer to visit previously visited feeders and feeders marked with extracts from any body part of conspecifics. The mean number of visits to a feeder was enhanced when synthetic methyl oleate was added. We propose that this could be a case of multi-source odor marking, in which hydrocarbons, found in large abundance, act as a signature mixture with attraction enhanced through deposition of methyl oleate, which may indicate a rich food source.     

Economic production quantity with rework process at a single-stage manufacturing system with planned backorders

In traditional inventory models such as the economic order quantity (EOQ) and the economic production quantity (EPQ) the sole objective is to minimize the total inventory-related costs, typically holding cost and ordering cost. These models do not consider the presence of defective products in the lot or rework of them. Recently, Jamal, Sarker, and Mondal (Jamal, A. A. M., Sarker, B. R., & Mondal, S., (2004). Optimal manufacturing batch size with rework process at single-stage production system. Computers and Industrial Engineering, 47(1), 77–89) proposed a model, which dealt with the optimum batch quantity in a single-stage system in which rework is done by addressing two different operational policies to minimize the total system cost, but their models do not consider planned backorders. In this direction, this paper develops an EPQ type inventory model with planned backorders for determining the economic production quantity for a single product, which is manufactured in a single-stage manufacturing system that generates imperfect quality products, and all these defective products are reworked in the same cycle. We also establish the range of real values of the proportion of defective products for which there is an optimal solution, and the close form for the total cost of inventory system. The use of the inventory model is illustrated with numerical examples. The classical EOQ, EPQ inventory models with or without planned backorders and Jamal, Sarker and Mondal’s model (Jamal, A. A. M., Sarker, B. R., & Mondal, S., (2004). Optimal manufacturing batch size with rework process at single-stage production system. Computers and Industrial Engineering, 47(1), 77–89) are shown to be special cases of the EPQ inventory model presented in this paper.     

A traffic-based evolutionary algorithm for network clustering

Network clustering algorithms are typically based only on the topology information of the network. In this paper, we introduce traffic as a quantity representing the intensity of the relationship among nodes in the network, regardless of their connectivity, and propose an evolutionary clustering algorithm, based on the application of genetic operators and capable of exploiting the traffic information. In a comparative evaluation based on synthetic instances and two real world datasets, we show that our approach outperforms a selection of well established evolutionary and non-evolutionary clustering algorithms.     

Extending standard evolutionary programming with self-adaptive stable distributions

Applications in evolutionary programming have suggested the use of further stable probability distributions, such as Cauchy and Lévy, in the random process associated with the mutations, as an alternative to the traditional, also stable, normal distribution. This work goes further along the encouraging results of the latter, by extending them in a self-adaptive way, with algorithms that are in tune with the standard lineage of evolutionary programming. Evaluations that rely upon standard analytical benchmarking functions and comparative performance tests between them were carried out in respect to the baseline defined by the standard evolutionary programming algorithm that relies on normal distribution. Additional comparative studies were made in respect to various self-adaptive approaches, also proposed herein, and a method drawn from the literature. The results lead to numerical and statistical superiority of the more general stable distribution based approach, when compared with the baseline, and is unclear in regard to the method drawn from the literature, possibly due to distinct implementation details.     

Solving the forward kinematics problem in parallel robots using Support Vector Regression

The Stewart platform, a representative of the class of parallel manipulators, has been successfully used in a wide variety of fields and industries, from medicine to automotive. Parallel robots have key benefits over serial structures regarding stability and positioning capability. At the same time, they present challenges and open problems which need to be addressed in order to take full advantage of their utility. In this paper, we propose a new approach for solving one of these key aspects: the solution to the forward kinematics in real-time, an under-defined problem with a high-degree nonlinear formulation, using a popular machine learning method for classification and regression, the Support Vector Machines. Instead of solving a numerical problem, the proposed method involves applying Support Vector Regression to model the behavior of a platform in a given region or partition of the pose space. It consists of two phases, an off-line preprocessing step and a fast on-line evaluation phase. The experiments made have yielded a good approximation to the analytical solution, and have shown its suitability for real-time application.     

Multicriteria‐Based Computer‐Aided Pronunciation Quality Evaluation of Sentences

The problem of the sentence‐based pronunciation evaluation task is defined in the context of subjective criteria. Three subjective criteria (that is, the minimum subjective word score, the mean subjective word score, and first impression) are proposed and modeled with the combination of word‐based assessment. Then, the subjective criteria are approximated with objective sentence pronunciation scores obtained with the combination of word‐based metrics. No a priori studies of common mistakes are required, and class‐based language models are used to incorporate incorrect and correct pronunciations. Incorrect pronunciations are automatically incorporated by making use of a competitive lexicon and the phonetic rules of students' mother and target languages. This procedure is applicable to any second language learning context, and subjective‐objective sentence score correlations greater than or equal to 0.5 can be achieved when the proposed sentence‐based pronunciation criteria are approximated with combinations of word‐based scores. Finally, the subjective‐objective sentence score correlations reported here are very comparable with those published elsewhere resulting from methods that require a priori studies of pronunciation errors.     

Boosting sustainability and financial performance: the role of supply chain controversies

In recent years, numerous scandals concerning working conditions in Asia have threatened the reputation of large companies like Wal-Mart and Inditex. Since such scandals call attention to activities harmful to sustainable development, it is very important to study the relationships between concerns for sustainable supply chain management (SSCM), sustainability, and organisations’ market value. The added value of our study is, first, to explore SSCM controversies as drivers of or pressures for adopting and applying sustainability practices; and, second, to contribute to the recent but growing literature that analyses the channels through which SSCM and sustainability practices influence the firm’s value. We introduce sustainability environmental, social, and governance (ESG) practices as a mediator between SSCM controversies and market value to examine whether SSCM controversies with firms’ stakeholders lead to improvements in the ESG dimensions of organisational sustainability. We also examine the relationships between the ESG dimensions and market value, measured as Tobin’s Q ratio. The results confirm both the positive relationships of SSCM controversies to the ESG dimensions two years later and a positive relationship of the dimension of governance to Tobin’s Q. We also confirm a negative and significant relationship of the social dimension to Tobin’s Q, and a non-significant relationship of the environmental dimension to Tobin’s Q.     

Tunable uptake of poly(ethylene oxide) by graphite-oxide-based materials

We investigate the role of structure and chemical composition on the uptake of poly(ethylene oxide) by a series of graphite oxides (GOs) and thermally reduced GOs, leading to the formation of polymer-intercalated GO and polymer-adsorbed graphene nanostructures. To this end, a series of poly(ethylene oxide) (PEO) - GO hybrid materials exhibiting a variable degree of GO oxidation and exfoliation has been investigated in detail using a combination of techniques including X-ray photoelectron spectroscopy, X-ray diffraction, thermogravimetry, scanning-electron microscopy, and nitrogen adsorption. Intercalation of the polymer phase into well-defined GO galleries is found to correlate well with both the degree of GO oxidation and with the presence of hydroxyl groups. The latter feature is an essential prerequisite to optimize polymer uptake owing to the predominance of hydrogen-bonding interactions between intercalant and host. Unlike the bulk polymer, these intercalation compounds show neither crystallisation nor glass-transition associated with the polymer phase. Exfoliation and reduction of GO result in high-surface-area graphene layers exhibiting the highest polymer uptake in these GO-based materials. In this case, PEO undergoes surface adsorption, where we observe the recovery of glass and melting transitions associated with the polymer phase albeit at significantly lower temperatures than the bulk.     

Effects of etching on zircon grains and its implications for the fission track method

Studies of zircon grains using optical microscopy, micro-Raman spectroscopy, and scanning electron microscopy (SEM) have been carried out to characterize the surface of natural zircon as a function of etching time. According to the surface characteristics observed using an optical microscope after etching, the zircon grains were classified as: (i) homogeneous; (ii) anomalous, and (iii) hybrid. Micro-Raman results showed that, as etching time increases, the crystal lattice is slightly altered for homogeneous grains, it is completely damaged for anomalous grains, and it is altered in some areas for hybrid grains. The SEM (energy dispersive X-ray spectroscopy, EDS) results indicated that, independent of the grain types, where the crystallinity remains after etching, the chemical composition of zircon is approximately 33% SiO(2):65% ZrO(2) (standard natural zircon), and for areas where the grain does not have a crystalline structure, there are variations of ZrO(2) and, mainly, SiO(2). In addition, it is possible to observe a uniform surface density of fission tracks in grain areas where the determined crystal lattice and chemical composition are those of zircon. Regarding hybrid grains, we discuss whether the areas slightly altered by the chemical etching can be analyzed by the fission track method (FTM) or not. Results of zircon fission track and U-Pb dating show that hybrid and homogeneous grains can be used for dating, and not only homogeneous grains. More than 50 sedimentary samples from the Bauru Basin (southeast Brazil) were analyzed and show that only a small amount of grains are homogeneous (10%), questioning the validity of the rest of the grains for thermo-chronological evolution studies using zircon FTM dating.