Brewer’s spent grain (BSG) and brewer’s spent hops (BSH) are the major solid by-products of the brewing industry. The present work evaluated their potential as an alternative source of natural antioxidants. The efficacy of different solvents (MilliQ water, 0.75% NaOH, 50% MeOH, 50% MeOH + 0.3% HCl and 50% Acetone) for extracting polyphenols of these by-products was firstly evaluated, with NaOH showing the best results. The extraction conditions were optimized using the response surface methodology, and were determined to be 1.45% NaOH and 80 °C. BSG extracts showed the highest total polyphenol content (24.84–38.83 µmol GAE/g), whereas the BSH showed the lowest value (24.84 ± 1.55 µmol GAE/g). In general, BSG extracts presented significantly higher antioxidant capacity (ABTS, ORAC). Ferulic acid was the main polyphenol in all BSG extracts (156.55–290.88 mg/100 g), whereas in BSH, this compound was not detected. The addition of 10% BSG extract in o/w emulsions (stored 14 days) showed a reduction in the formation of primary oxidation products of 97%. In the emulsions covered with polylactic acid active films (1% BSG), this reduction corresponded to 35%. Hence, this study demonstrates the potential of these by-products as natural antioxidant sources for protecting food systems against oxidation.

(1) Background: Suprascapular neuropathy is an important factor contributing to shoulder pain. Given the prevalence of nerve injury and nerve block in the suprascapular notch region, as well as the frequency of arthroscopic procedures on the suprascapular notch, which are recommended in shoulder pain management, its morphology is relevant from a clinical perspective. (2) Methods: Suprascapular nerve course was studied in twelve shoulders by dissection. Its diameter was measured at omohyoid level, proximal to the suprascapular notch and distal to the spinoglenoid notch. A multi-vari chart was used in order to descriptively visualize the results. The variations found were analyzed with a mixed linear model. (3) Results: In two of the six subjects, the suprascapular nerve was divided into two motor branches proximal to the superior transverse scapular ligament. An increase in diameter around the suprascapular notch was detected, with an estimated difference between diameter means of 2.008 mm at the suprascapular notch level and 2.047 mm at the spinoglenoid notch level. (4) Conclusions: A difference in the estimated diameter detected and the fact that the motor branches, which innervate supraspinatus and infraspinatus muscle, were divided proximal to the suprascapular notch may be relevant in the diagnosis and treatment of suprascapular neuropathy and arthroscopic procedures.

In the present work, a new methodology is presented to translate roughness results from a test machine to different industrial machines without the need to stop production for a long time. First, mathematical models were searched for average roughness Ra in finish honing processes, in both a test and an industrial machine. Regression analysis was employed for obtaining quadratic models. Main factor influencing average roughness Ra was grain size, followed by pressure. Afterwards, several experiments were simulated in the common range of variables for the two machines using the models for average roughness Ra. A new variable DifRa corresponding to the difference between roughness values from the test machine and the industrial machine was defined and a quadratic model was obtained. Once DifRa is modeled, it is possible to predict roughness in a different industrial honing machine from results of the test machine by performing a few experiments in the industrial machine and translating the curves. This will reduce the number of tests to be performed in industrial machines. The suggested new methodology has been tested with two more roughness parameters, maximum height of profile Rz and core roughness depth Rk, proving its validity.Postprint (author's final draft

Honing processes are currently employed to obtain a cross-hatched pattern on the internal surfaces of cylinders that favors oil flow in combustion engines or hydraulic cylinders. The main aim of the present paper is to optimize the machining conditions in honing processes with respect to surface roughness, material removal rate and tool wear by means of the desirability function. Five process variables are considered: grain size, density, pressure, linear speed and tangential speed. Later, a sensitivity analysis is performed to determine the effect of the variation of the importance given to each response on the results of the optimization process. In the rough and semi-finish honing steps, variations of less than 5% of the importance value do not cause substantial changes in the optimization process. On the contrary, in the finish honing step, small changes in the importance values lead to modifications in the optimization process, mainly regarding pressure. Thus, the finish honing phase is more sensitive to changes in the optimization process than the rough and the semi-finish honing phases. The present paper will help users of honing machines to select proper values for the process variables.

Artificial neural network (ANN) models have been used in the past to model surface roughness in manufacturing processes. Specifically, different parameters influence surface roughness in fused filament fabrication (FFF) processes. In addition, the characteristics of the networks have a direct impact on the performance of the models. In this work, a study about the use of ANN to model surface roughness in FFF processes is presented. The main objective of the paper is discovering how key ANN parameters (specifically, the number of neurons, the training algorithm, and the percentage of training and validation datasets) affect the accuracy of surface roughness predictions. To address this question, 125 3D printing experiments were conducted changing orientation angle, layer height and printing temperature, and measuring average roughness Ra as response. A multilayer perceptron neural network model with backpropagation algorithm was used. The study evaluates the effect of three ANN parameters: (1) number of neurons in the hidden layer (4, 5, 6 or 7), (2) training algorithm (Levenberg–Marquardt, Resilient Backpropagation or Scaled Conjugate Gradient), and (3) data splitting ratios (70%–15%–15% vs. 55%–15%–30%). Mean Absolute Error (MAE) was used as the performance metric. The Resilient Backpropagation algorithm, 7 neurons, and using 55% of training data yielded the best predictive performance, minimizing the MAE. Additionally, the impact of the dataset size on prediction accuracy was analysed. It was observed that the performance of the ANN gets worse as the number of datasets is reduced, emphasizing the importance of having sufficient data. This study will help to select appropriate values for the printing parameters in FFF processes, as well as to define the characteristics of the ANN to be used to model surface roughness.