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A persistent purpose for self-powered and wearable electronic devices is the fabrication of graphene-PVDF piezoelectric nanogenerators with various co-solvents that could provide enhanced levels of durability and stability while generating a higher output. This study resulted in a piezoelectric nanogenerator based on a composite film composed of graphene, and poly (vinylidene fluoride) (PVDF) as a flexible polymer matrix that delivers high performance, flexibility, and cost-effectiveness. By adjusting the co-solvent in the solution, a graphene-PVDF piezoelectric nanogenerator can be created (acetone, THF, water, and EtOH). The solution becomes less viscous and is more diluted the more significant the concentration of co-solvents, such as acetone, THF, and EtOH. Additionally, when the density is low, the thickness will be thinner. The final film thickness for all is ~25 µm. Furthermore, the- crystal phase becomes more apparent when graphene is added and combined with the four co-solvents. Based on the XRD results, the peak changes to the right, which can be inferred to be more dominant with the β-phase. THF is the co-solvent with the highest piezoelectric output among other co-solvents. Most of the output voltages produced are 0.071 V and are more significant than the rest.

Membrane separation has been an efficient and energy saving technique in dealing with greenhouse gases, but the traditional membrane designs might not be able to handle the concomitant environmental pollution due to their fixed properties. Correspondingly, the use of an active-responsive smart membrane appears to be a new trend for membrane development in the coming future, which shows great potential to deal with the obstacles. In this research, we demonstrate a smart graphene-organic framework membrane to enable reversed electrical-switchable permselectivity in CO2 separation. The addition of polydopamine (PDA) to the polyvinylidene fluoride/Graphene (PVDF/G) membranes was done to (i) induce the β-phase of PVDF, since –NH2-functionalized graphene has specific interactions (dipole induced dipole interaction) between graphene-PDA and PVDF; (ii) modify the organic PVDF-inorganic graphene interface; and (iii) facilitate CO2 selective separation. Permeability and permselectivity was increased after applying voltage that resulted in the increase of gas permselectivity in response to the lowest applied voltage range (0–3 V) to the membrane. Digital image correlation method depicted the response of the membrane to voltage and proved that the membrane has high piezoelectric properties that is switchable. Furthermore, PALS studies confirmed the free volume and interlayers in the membrane. This membrane has unique properties because the pore changes from bimodal to single pore distribution.

Taiwan’s rowing athletes have performed well during the Asian Games, but their performance in the Olympics has not been adequate. In addition to their hard work and rigorous and effective training, the skill of the athletes is a key factor for achieving good results. In this study, an artificial intelligence (AI) evaluation algorithm is developed to help rowing athletes excel in the sporting events. The AI algorithm uses the analytic hierarchy process to invite experts and scholars in the rowing field to answer a questionnaire. The technique for order performance by similarity to ideal solution is then applied to calculate the ranking of selection indicators, to construct an evaluation model for rowing athletes. The key findings indicate that physicality (or the body structure) is the highest priority among the four main aspects of talent identification; this is followed, in descending order, by specialism, reaction, and psychological elements. The proposed AI strategy was established as the most beneficial decision model and can be used to identify talented rowers in the future.

Recently, human activity recognition (HAR) techniques have made remarkable developments in the field of machine learning. In this paper, we classify human gestures using data collected from a curved piezoelectric sensor, including elbow movement, wrist turning, wrist bending, coughing, and neck bending. The classification process relies on data collected from a sensor. Machine learning algorithms enabled with K-mer are developed and optimized to perform human gesture recognition (HGR) from the acquired data to achieve the best results. Three machine learning algorithms, namely support vector machine (SVM), random forest (RF), and k-nearest neighbor (k-NN), are performed and analyzed with K-mer. The input parameters such as subsequence length (K), number of cuts, penalty parameter (C), number of trees (n_estimators), maximum depth of the tree (max_depth), and nearest neighbors (k) for the three machine learning algorithms are modified and analyzed for classification accuracy. The proposed model was evaluated using its accuracy percentage, recall score, precision score, and F-score value. We achieve promising results with accuracy of 94.11 ± 0.3%, 97.18 ± 0.4%, and 96.90 ± 0.5% for SVM, RF, and k-NN, respectively. The execution time to run the program with optimal parameters is 19.395 ± 1 s, 5.941 ± 1 s, and 3.832 ± 1 s for SVM, RF, and k-NN, respectively.

Taiwan’s rowing has achieved well performance in the Asian Games, but it has not in the Olympics. To get good results, in addition to the hard work and solid and effective training on weekdays, the qualification of the players is one of the key factors. Therefore, this research attempts to construct an artificial intelligence (AI) evaluation algorithm for rowing athletes to obtain advantages in the sports field, and can start from the athletes. The AI algorithm involves the hierarchical analysis method (AHP) is adopted to invite experts and scholars in the rowing field to answer the expert questionnaire. The TOPSIS (technique for order performance by similarity to ideal solution) method is then applied to calculate the ranking of material selection indicators, aiming to construct an evaluation model for rowers. The main research result shows that physicality is considered as the highest priority among the four main criteria of talent identification, following in a descending order by specialism, reaction and psychological elements. The propose model established by the most beneficial decision model for identification of talented rowers in the future.

The continental crust of southeast Asia underwent from thickening, thinning to almost rifting during the Mesozoic era as the active continental margin transformed into a passive one. Such crustal thinning history is well-preserved in the Kinmen Island, as the lower crustal granitoids retrograded and rapidly exhumed to surface that were crosscutted by mafic dike swarm. Kinmen Island is situated on the SE coast of Asia, featured by the widespread Cretaceous magmatism as the Paleo-Pacific plate subducted and rollbacked underneath the South China block. Although these complex magmatism are well reported and studied, their associated structural evolution and plate kinematics have not been clearly deciphered. Detailed field mapping, structural measurement, and petrographic analysis of the Kinmen Island were conducted. Up to five deformation events accompanied with five relevant magmatic episodes as well as their corresponding kinematic setting are reconstructed. The ∼129 Ma Chenggong Tonalite (G1) preserved all deformation events identified in this study, which marks the lower bound timing of all reported events. D1 formed a gneiss dome with the Taiwushan Granite (∼139 Ma) at the core bounded by moderately dipping gneissic foliation (S1) as crust extended. D2 formed subhorizontal S-tectonite (S2) with further exhumation of D1 gneiss dome due to middle-to-lower crustal flow associated with further crustal thinning. D3 formed a sinistral ENE-WSW striking steeply S dipping shear belts with well-developed S/C/C’ fabrics. The moderately E-plunging lineation on C surface indicates its transtensional nature. Widespread garnet-bearing leucogranite (G2) associated with decompressional melting showed long lasting intrusion prior to D2 until post D3. D4 was the intrusion of biotite-bearing Tienpu Granite (∼100 Ma; G3) that truncated G1, G2, and all fabrics, which was followed by the intrusion of E-W striking, steeply dipping biotite-bearing pegmatite (G4) as the crust further extended. The youngest deformation event (D5) was NE-SW striking subvertical mafic dike swarm (G5; 90–76 Ma) due to mantle upwelling through significantly thinned crust. By integrating the structural evolution and the previously reported strain pattern, we delineate the slab rollback direction of the Paleo-Pacific plate, which changed from northeastward (129∼114 Ma) to southeastward (107∼76 Ma). This plate kinematic movement switched during 114–107 Ma.

This study investigates the effect of grain size on the deformability of CP2 Titanium to form the mini spur gear in hot squeezing forming process. Experimental specimens are rods with a diameter of 5 mm and annealed with temperature in a range of 500 to 1000°C to precipitate the different initial grain sizes individually. The mechanical properties and hardness of annealed specimens are measured. A mini spur gear die with a modulus of 0.915 and 8 teeth, made of H13 steel, is prepared. The annealed specimens are inserted in die and hot squeezed to form mini spur gear with a temperature of 500°C. The experimental results show that the α-phase microstructure precipitates when the annealing temperature and holding time are 700°C and 3 hours. The specimen with average initial grain size of 154.59 μm results in the maximal elongation and die cavity-filling rate. Harnesses of addendum and dedendum circle are about 215 and 180 MPa that are higher than that of initial grain (107 MPa). The β-phase microstructure precipitates when annealing temperature reaches to 900°C resulting in brittle behavior.

The aim of this research is to investigate the effect of annealing treatment on mechanical properties and deformability of titanium alloy to form mini gear in squeezing process. Diameter of experimental specimen was 5mm of Grade 2 Ti alloy. It was annealed with temperature in a range of 500 to 1000 °C, resulting in different initial grain sizes. The mechanical properties and hardness of annealed specimens were obtained by means of tensile and micro-hardness test. In addition, a modulus of mini gear die was 0.92 and made of h13 steel. The annealed specimens were inserted in die and squeezed into mini gear. The experimental results show that microstructure of α-phase precipitates when annealing temperature reaches to 600 °C and hold time was 3 hours. The specimen which annealed with a temperature of 700 °C has the maximal elongation and die filling rate. The microstructural of β-phase precipitates when annealing temperature reaches to 1000 °C resulting in brittle behavior and the lowest die-filling rate.

The purpose of this paper is to present the use of uniform design of experiments method in improving the permanent deformation and mass of an on-road bicycle frame which undergoes the drop-mass and drop-frame impact tests. Six system parameters of the bicycle frame are selected as the control factors to be improved. Uniform design of experiment is applied to create a set of simulation experiments. Using ANSYS/LS-DYNA software, the dynamic finite element modeling is investigated and the permanent deformations of bicycle frame under the drop-mass and drop-frame impact testing. From the numerical results, the best frame of all the experiments which causes the smaller permanent deformation and lighter mass of bicycle frame is selected as the improved version of design.

Membrane bioreactors (MBRs) have been widely used from biotechnological fermentation to municipal and industrial wastewater treatment. However, membrane fouling is the major obstacle that hinders faster commercialisation of MBRs. Extracellular polymeric substances (EPS) secreted by bacteria are the main fouling components, which are mainly composed of proteins and carbohydrates. Although it is clear that proteins play major roles in the fouling formation mechanisms, very little is known about fouling protein characteristics. This study focused on the identification and characterization of different proteins present in EPS and their relationship with three different membrane materials, polyacrylonitrile (PAN), polyvinylidene fluoride (PVDF), polytetrafluoroethylene (PTFE) in a laboratory-scale MBR setup. Results showed that the largest and the most hydrophilic (average MW = 56.14 kDa, GRAVY = −0.191) proteins deposited on hydrophilic PAN membrane, whereas the smallest and the most hydrophobic (average MW = 46.67 kDa, GRAVY = −0.001) proteins deposited on hydrophobic PTFE membranes. Smaller proteins were responsible for the fouling of membranes with large pores, and vice versa. Characteristics of chaperonin and outer membrane proteins, identified in all membranes, may facilitate the design of anti-fouling membranes.

In this paper, the finite element method (FEM) is used to study the incremental sheet forming process of pyramidal shape. The material used is aluminium alloy 5052. The tool, a hemispherical ball-head with a diameter (d = 4 mm) made of HSS tool steel, is used to press down on the sheet metal causing locally plastic deformation. The comparison between spiral tool path, spiral-step tool path and z-level tool path is carried out. Moreover, the final thickness distribution is investigated. The results indicate that the minimal thickness can be found on the corner of wall angle in SPIF process. Under the same step over, spiral-step tool path can obtain the deepest depth for pyramidal shape. The maximum formability for successful forming of the pyramidal shape with depth 60 mm is wall angles 65 ∘ .