Visco-Elastic Properties of Semi-Solid Alloys

Oscillation and creep experiments have been performed with Semi-Solid Material (SSM) AlSi7 with 35% solid fraction to investigate the early visco-elastic properties after shearing of the material in a Searle Rheometer. The preparation of the SSM has been done in situ using a standard procedure to guarantee for all experiments the same initial properties of the material. First, oscillation experiments at low amplitude allowed to study the evolution of material structure with time. Subsequently, creep experiments have been performed changing the resting period based on previous results. Creep experiments are characterized by exposing the material to a sudden increase of shear stress. The resolution in time has been 0.01 seconds, which allows observing the dynamics of the development of visco-elastic properties.The material exhibits viscoelastic properties that are becoming more pronounced with longer resting time. This is in accordance with previous experiments where the ratio between elastic and viscous properties increases with increasing resting time. The development of the elastic properties follows the increase of the yield stress due to the creation of an internal structure of the material, which starts immediately after stopping shearing. The investigation of the short-term response of SSM can be particularly relevant for industrial practice, where material deformation during die filling is very fast and the material flow does not take place in steady-state condition.

Dynamic Changes in Glutenin Macropolymer during Different Dough Mixing and Resting Processes

The glutenin macropolymer (GMP), which is an important component of the glutenin protein in wheat flour, plays a prominent role in governing dough properties and breadmaking quality. This study investigated the changes in GMP properties during the mixing and resting stages of dough processing. The results show that the GMP content decreases by about 20.20% when the mixing time increases from 3 to 5 min, while increasing the resting time can lead to restoration of some GMP contents. Resting promotes greater formation of large-sized GMP particles, which is likely related to the increased disulfide bond content in the GMP during this process. In contrast, the mechanical force of mixing causes GMP depolymerization and formation of smaller particles. Furthermore, after mixing, the protein secondary structure tends to be disordered, the protein morphology becomes irregular, and the protein subunit ratio changes. Thus, mixing has many of the opposite effects to resting, although resting can (to some extent) restore the properties of the GMP after mixing. However, excessive resting time can lead to negative results, reflected in lower disulfide bond (SS) and GMP contents, and more irregular particle sizes. The presented results suggest that dough mixing induces rearrangement of the dough’s protein structure, and resting somewhat restores the chemical bonds and internal protein structure.

Towards an AI-Based Tailored Training Planning for Road Cyclists: A Case Study

In a world where the data is a central piece, we provide a novel technique to design training plans for road cyclists. This study exposes an in-depth review of a virtual coach based on state-of-the-art artificial intelligence techniques to schedule road cycling training sessions. Together with a dozen of road cycling participants’ training data, we were able to create and verify an e-coach dedicated to any level of road cyclists. The system can provide near-human coaching advice on the training of cycling athletes based on their past capabilities. In this case study, we extend the tests of our empirical research project and analyze the results provided by experts. Results of the conducted experiments show that the computational intelligence of our system can compete with human coaches at training planification. In this case study, we evaluate the system we previously developed and provide new insights and paths of amelioration for systems based on artificial intelligence for athletes. We observe that our system performs equal or better than the control training plans in 14 and 24 week training periods where it was evaluated as better in 4 of our 5 test components. We also report a higher statistical difference in the results of the experts’ evaluations between the control and virtual coach training plan (24 weeks; training load: X2 = 4.751; resting time quantity: X2 = 3.040; resting time distance: X2 = 2.550; efficiency: X2 = 2.142).

Machine learning to reveal hidden risk combinations for the trajectory of posttraumatic stress disorder symptoms

The nature of the recovery process of posttraumatic stress disorder (PTSD) symptoms is multifactorial. The Massive Parallel Limitless-Arity Multiple-testing Procedure (MP-LAMP), which was developed to detect significant combinational risk factors comprehensively, was utilized to reveal hidden combinational risk factors to explain the long-term trajectory of the PTSD symptoms. In 624 population-based subjects severely affected by the Great East Japan Earthquake, 61 potential risk factors encompassing sociodemographics, lifestyle, and traumatic experiences were analyzed by MP-LAMP regarding combinational associations with the trajectory of PTSD symptoms, as evaluated by the Impact of Event Scale-Revised score after eight years adjusted by the baseline score. The comprehensive combinational analysis detected 56 significant combinational risk factors, including 15 independent variables, although the conventional bivariate analysis between single risk factors and the trajectory detected no significant risk factors. The strongest association was observed with the combination of short resting time, short walking time, unemployment, and evacuation without preparation (adjusted P value = 2.2 × 10−4, and raw P value = 3.1 × 10−9). Although short resting time had no association with the poor trajectory, it had a significant interaction with short walking time (P value = 1.2 × 10−3), which was further strengthened by the other two components (P value = 9.7 × 10−5). Likewise, components that were not associated with a poor trajectory in bivariate analysis were included in every observed significant risk combination due to their interactions with other components. Comprehensive combination detection by MP-LAMP is essential for explaining multifactorial psychiatric symptoms by revealing the hidden combinations of risk factors.