Modeling of electrospun PVDF/LiCl nanogenerator by the energy approach method: determining piezoelectric constant

Increasing demands for harvesting energy from other sources of energy than fossil fuel and oil have led to an upsurge of interest in nano-structured materials such as piezoelectric nanofibers and nanowebs to harvest energy from environmental movements. Previous research introduced a suitable nanogenerator package to have higher output voltage needless of the post-treatment process. Electrospinning of PVDF (polyvinylidene fluoride) with LiCl (lithium chloride) increases the beta-crystalline phase and thus piezoelectric effect. This article presents an FEM (finite element method) model for the electrospun PVDF/LiCl nanogenerator to determine the piezoelectric constant base on the energy approach method. An experimental and analytical procedure has been developed to determine the piezoelectric coupling coefficient and results validation. The nanogenerator package was modeled as a multilayer structure. The excitation method for the nanogenerator package was dropping a ball on the sample from different heights. The determinations of electrospun nanogenerator coupling coefficients are an important factor in the final application of these types of nanogenerators that have not been studied yet.     

Hydrogen recovery from the photovoltaic electroflocculation-flotation process for harvesting Chlorella pyrenoidosa microalgae

In this paper, an integrated process using photovoltaic power to harvest microalgae by electro-flocculation (EF) and hydrogen recovery is presented. It is mainly favorable in regions with high solar radiation. The electro-flocculation efficiency (EFE) of Chlorella pyrenoidosa microalgae was investigated using various types of electrodes (aluminum, iron, zinc, copper and a non-sacrificial electrode of carbon). The best results regarding the EFE, and biomass contamination were achieved with aluminum and carbon electrodes where the electrical energy demand of the process for harvesting 1 kg of algae biomass was 0.28 and 0.34 kWh, respectively, while the energy yield of harvested hydrogen was 0.052 and 0.005 kWh kg^?1, respectively. The highest harvesting efficiency of 95.83 ± 0.87% was obtained with the aluminum electrode.The experimental hydrogen yields obtained were comparable with those calculated from theory. With a low net energy demand, microalgae EF may be a useful and low-cost technology.     

Effects of short dry periods on performance and metabolic status in Holstein dairy cows

To evaluate effects of different dry period lengths on milk yield, milk composition, and energy balance of dairy cows, 122 multiparous and primiparous Holstein dairy cows were used in a completely randomized experimental design with 56-, 42-, and 35-d dry period lengths. Actual dry period lengths for respective treatments (TRT) were 56 ± 5.1 d, 42 ± 2.1 d, and 35 ± 2.7 d. Overall, cows in the 42- and 56-d TRT gained more body condition than those in 35-d TRT during the dry period; however, postpartum body condition score did not change substantially among the TRT. Although from 3 to 210 DIM, differences were not detected in the milk yield of multiparous cows between the 35- and 56-d TRT, primiparous cows in the 35-d TRT produced less milk than those in 56-d TRT. In primiparous cows, the milk production at wk 9, 10, and 11 of lactation was lower in the 35-d compared with the 56-d TRT. Primiparous cows in the 35-d compared with the 56-d TRT produced less milk protein. In the 35-d TRT, serum triglyceride concentration was greater in primiparous cows than in multiparous cows during the peripartum period. Among primiparous cows, those in the 56-d TRT had greater concentrations of nonesterified fatty acids than those in the 35-d TRT during the peripartum period. No significant differences were observed in concentrations of serum glucose, insulin, and insulin-like growth factor-I during early lactation among TRT. There was also no difference among TRT for incidence of metabolic disorders. Thus, this study indicates that shortening the dry period to 35 d may be beneficial in multiparous and overconditioned cows, but not in primiparous cows.     

Thermoelastic vibration of doubly-curved nano-composite shells reinforced by graphene nanoplatelets

Abstract

The thermo-mechanical vibration characteristics of doubly-curved nano-composite shells reinforced by graphene nanoplatelets are investigated by considering a uniform distribution of graphene and a first-order shear deformation theory. The mechanical properties of the nano-composite shells are estimated by using the modified Halpin–Tsai model. The governing equations are first derived by a variational formulation using Hamilton’s principle and are solved using the Galerkin technique. Numerical results are presented for various shell curvatures and compared with those available in the archival literature. Furthermore, parametric studies are offered to highlight the significant influence of graphene nanoplatelets’ weight fraction, dimensions of graphene nanoplatelets, and temperature variation, on the free vibration of the nano-composite shells.     

Performance Evaluation of Deep Dense Layer Neural Network for Diabetes Prediction

Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer lives. Early prediction of diabetes is crucial to taking precautionary steps to avoid or delay its onset. In this study, we proposed a Deep Dense Layer Neural Network (DDLNN) for diabetes prediction using a dataset with 768 instances and nine variables. We also applied a combination of classical machine learning (ML) algorithms and ensemble learning algorithms for the effective prediction of the disease. The classical ML algorithms used were Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), and Naïve Bayes (NB). We also constructed ensemble models such as bagging (Random Forest) and boosting like AdaBoost and Extreme Gradient Boosting (XGBoost) to evaluate the performance of prediction models. The proposed DDLNN model and ensemble learning models were trained and tested using hyperparameter tuning and K-Fold cross-validation to determine the best parameters for predicting the disease. The combined ML models used majority voting to select the best outcomes among the models. The efficacy of the proposed and other models was evaluated for effective diabetes prediction. The investigation concluded that the proposed model, after hyperparameter tuning, outperformed other learning models with an accuracy of 84.42%, a precision of 85.12%, a recall rate of 65.40%, and a specificity of 94.11%.     

Effect of Acidic and Basic Solutions on Electron Beam Irradiated Epoxy Nanocomposites Containing Nanoclay,CaCO3 and TiO2 Nanoparticles

High chemical resistance is the main prerequisites for materials that are intended to be utilized in usages such as chemicals storage containers production. Nanocomposites of epoxy resin containing nanoclay, CaCO₃ and TiO₂ nanoparticles were prepared and their chemical resistance was studied. Moreover, the effect of electron beam irradiation was explored. TEM micrographs proved the dispersion of nano-size particles in the polymeric matrix. XRD patterns showed an exfoliated structure for nanocomposite containing 1 % nanoclay and intercalated structures for nanocomposites with higher nanoclay contents. SEM showed the pits that appeared in epoxy/nanoclay structure due to chemical corrosion. Weight loss measurements revealed that an addition of 1 % nanoclay to the epoxy matrix is effective for improving the chemical properties of the polymer. Desirable effect of 100 kGy irradiation on chemical resistance properties of the samples was also observed in both acidic and basic environments.     

Smart Bubble Sort: A Novel and Dynamic Variant of Bubble Sort Algorithm

In the present era, a very huge volume of data is being stored in online and offline databases. Enterprise houses, research, medical as well as healthcare organizations, and academic institutions store data in databases and their subsequent retrievals are performed for further processing. Finding the required data from a given database within the minimum possible time is one of the key factors in achieving the best possible performance of any computer-based application. If the data is already sorted, finding or searching is comparatively faster. In real-life scenarios, the data collected from different sources may not be in sorted order. Sorting algorithms are required to arrange the data in some order in the least possible time. In this paper, I propose an intelligent approach towards designing a smart variant of the bubble sort algorithm. I call it Smart Bubble sort that exhibits dynamic footprint: The capability of adapting itself from the average-case to the best-case scenario. It is an in-place sorting algorithm and its best-case time complexity is Ω(n). It is linear and better than bubble sort, selection sort, and merge sort. In average-case and worst-case analyses, the complexity estimates are based on its static footprint analyses. Its complexity in worst-case is O(n²) and in average-case is Θ(n²). Smart Bubble sort is capable of adapting itself to the best-case scenario from the average-case scenario at any subsequent stages due to its dynamic and intelligent nature. The Smart Bubble sort outperforms bubble sort, selection sort, and merge sort in the best-case scenario whereas it outperforms bubble sort in the average-case scenario.     

Effect of frequency and duty cycle on corrosion and wear resistance of functionally graded Zn–Ni nanocomposite coating

In this study, functionally graded Zn–Ni nanocomposite coatings with a nanoparticle mix of 80?wt-% alumina, 5?wt-% yttria and 15?wt-% graphene were electrodeposited on a mild steel substrate by the pulsed current method. The effects of pulse parameters such as frequency and duty cycle on chemical composition, microstructure, corrosion resistance and tribological properties of coated specimens were evaluated. The coatings were formed through changing the duty cycle from 10% to 90% in five steps during the coating process and different frequencies of 500, 1000, 3000 and 5000?Hz. A continuous decrease in the duty cycle led to an increase of the nanoparticles embedded in the coating structure from the interface to the surface, so that most increments corresponded to the sample with a 500?Hz frequency and was 1.41?wt-%. By increasing the frequency, Ni and nanoparticle content in the coating surfaces increased up to 3.6?wt-% and 1.82?vol.-%, respectively. In particular, by increasing the frequency and nanoparticle content in the coatings, corrosion and wear resistance of coatings improved.     

Reality and danger in psychoanalytic treatment

In recent papers there has been considerable interest in the disclosure of the analyst's erotic countertransference. In our view this discussion touches a more fundamental issue: must something "real" take place between analyst and patient in order for real change to occur? And if what takes place is "real," will it not be dangerous and potentially destructive? Tracing the history of psychoanalytic understandings of what is "real" in the patient's life and what is "real" in the transference, we explore these questions in a clinical vignette and discuss the implications of this issue for our understanding of the process of psychoanalytic treatment.