Solar Irradiation Forecasting Technologies: A Review

Renewable energy has received a lot of attention in the previous two decades when it comes to meeting electrical needs in the home, industrial, and agricultural sectors. Solar forecasting is critical for the efficient operation, scheduling, and balancing of energy generation by standalone and grid-connected solar PV systems. A variety of models and methods have been developed in the literature to forecast solar irradiance. This paper provides an analysis of the techniques used in the literature to forecast solar irradiance. The main focus of the study is to investigate the influence of meteorological variables, time horizons, climatic zone, pre-processing technique, optimization & sample size on the complexity and accuracy of the model. Due to their nonlinear complicated problem solving skills, artificial neural network based models outperform other models in the literature. Hybridizing the two models or performing pre-processing on the input data can improve their accuracy even more. It also addresses the various main constituents that influence a model’s accuracy. The paper provides key findings based on studied literature to select the optimal model for a specific site. This paper also discusses the metrics used to measure the efficiency of forecasted model. It has been observed that the proper selection of training and testing period also enhance the accuracy of the model.

Multimodal Approach for Kayaking Performance Analysis and Improvement

Artificial Intelligence (AI) invades fields where sophisticated analytics has not been applied before. Modality refers to how something happens or is experienced. Multimodal datasets are beneficial for solving complex research problems with AI methods. Kayaking technique optimization has been challenging, as there seems to be no gold standard for effective paddling techniques since there are outstanding athletes with profoundly different physical capabilities and kayaking styles.Multimodal analysis can help find the most effective paddling techniques for training and competition based on individuals’ abilities.We describe the characteristics of the output power of kayak athletes and Electromyogram (EMG) measurements collected from the most critical muscles, and the relationship between these modalities. We propose metrics (weighted arithmetic mean difference and variability of power output and stroke duration) suitable for discerning athletes based on how efficiently and correctly they perform particular training tasks. Additionally, the described methods (asymmetry, coactivation, muscle intensity-output power) help athletes and coaches in assessing their performance and compare it with others based on their EMG activities.As the next step, we will apply machine-learning approaches on the synchronized dataset we collect with the described methods to reveal desirable EMG and stroke patterns.

Does SEO Matter for Startups? Identifying Insights from UGC Twitter Communities

In the present study, we analyzed User Generated Content (UGC) to measure the importance of Search Engine Optimization (SEO) for startups. For this purpose, we used several clustering algorithms to identify user communities on Twitter. The dataset contained a total of 67,126 tweets. A three-step UGC analysis process was applied to the data. First, a Latent Dirichlet allocation (LDA) was developed to divide the UGC-sample into topics. Next, a sentiment analysis (SA) with machine-learning was applied to divide the sample of topics into negative, positive, and neutral feelings. Finally, a textual analysis (TA) process with data mining techniques was used to extract indicators related to the SEO technique optimization in startups. The results helped us identify UGC communities in Twitter about SEO for startups and the main optimization indicators according to the feelings expressed in tweets. Our results also demonstrated that Black Hack SEO is not the most relevant strategy of positioning of digital marketing for startups and that, although this strategy is used by the startups, it is predominantly negatively perceived by SEO UGC communities.

Astaxanthin–garlic oil nanoemulsions preparation using spontaneous microemulsification technique: optimization and their physico–chemical properties

Garlic oil in water nanoemulsion was resulted through subcritical water method (temperature of 120 °C and pressure of 1.5 bar, for 2 h), using aponin, as emulsifier. Based on the prepared garlic oil nanoemulsion, astaxanthin–garlic oil nanoemulsions were prepared using spontaneous microemulsification technique. Response surface methodology was employed to evaluate the effects of independent variables namely, amount of garlic oil nanoemulsion (1–9 mL) and amount of provided astaxanthin powder (1–9 g) on particle size and polydispersity index (PDI) of the resulted nanoemulsions. Results of optimization indicated that well dispersed and spherical nanodroplets were formed in the nanoemulsions with minimum particle size (76 nm) and polydispersity index (PDI, 0.358) and maximum zeta potential value (−8.01 mV), using garlic oil nanoemulsion amount of 8.27 mL and 4.15 g of astaxanthin powder. Strong antioxidant activity (>100%) of the prepared astaxanthin–garlic oil nanoemulsion, using obtained optimum amounts of the components, could be related to the highest antioxidant activity of the colloidal astaxanthin (>100%) as compared to that of the garlic oil nanoemulsion (16.4%). However, higher bactericidal activity of the resulted nanoemulsion against Escherichia coli and Staphylococcus aureus, were related to the main sulfur bioactive components of the garlic oil in which their main functional groups were detected by Fourier transform-infrared spectroscopy.