Electrospun polymer-based nanofibers are of increasing interest in contemporary applied biomedicine. The challenge regarding modern surgery and tissue engineering is to discover a variety of manufactured scaffolds with improved properties that can replace and regenerate damaged skin and organs. The unique properties of polymer nanofibers, such are submicron and nanoscale diameters, large surface area, flexibility, etc., make them an attractive object for a wide range of applications.

In this study, a combination of chitosan as natural polymer and poly(lactic) acid as synthetic polymer is studied with the aim of improving and accelerating the healing of skin wounds. Chitosan (Chi) is one of the most promising polymers for scaffold design, due to its high biodegradability, non-toxic and antibacterial properties. On the other hand, poly(lactic) acid (PLA) possesses enhanced electrospinability potential and desirable mechanical strength. Therefore, the combination of Chi and PLA enhances the mutually superior properties of both. After optimizing the process parameters, imaging, and determining the diameter of the nanofibers, the scaffold potential for wound healing was investigated by in vitro scratch test on a healthy fibroblast cell line.

The study concludes that ultrafine Chi-PLA nanofiber scaffolds have significant potential to regenerate and restore damaged tissue under in vitro conditions.

Hesperidin is a major phenolic antioxidant of the orange fruits and responsible for their positive health effect. It is used as part of therapy of blood vessel conditions. Methods for hesperidin quantification are of practical interest. Recently, several voltammetric approaches have been developed for hesperidin quantification. Nevertheless, the analytical characteristics could be improved. To solve this problem, glassy carbon electrode modified with multi-walled carbon nanotubes and poly(ferulic acid) has been developed. Polymeric coverage has been obtained electrochemically under potentiodynamic conditions. Their optimization based on the hesperidin voltammetric response has been performed. Poly(ferulic) layer has to be obtained from 250 µM monomer solution in 0.1 M NaOH by fifteen potential scans from -0.2 to 1.0 V with the scan rate of 100 mV s^-1. Hesperidin oxidation currents are 2.8-fold increased at the polymer-modified electrode vs. carbon nanotube-based electrode at the same oxidation potential. Differential pulse voltammetry in phosphate buffer pH 5.5 has been used for the quantification of hesperidin. Linear dynamic ranges of 0.025-1.0 µM and 1.0-10 µM has been achieved with the limits of detection and quantification of 7.0 and 23.4 nM, respectively. The analytical characteristics obtained are the best ones reported to date.

Poor or excessive nutrient management may result in generation of mosquitos in vineyard which is a potential impact of vineyard on residential area. Some species of mosquitos are real threat for human society. For instance, a linkage was observed between vineyards and West Nile virus which spreads via mosquitos [1]. Thus, continuous effective monitoring system is required to ensure mitigation of mosquito borne diseases originated from orchards and vineyards. Numerous image-based machine learning (ML) approaches has been utilized in mosquito systematics, but considering the small body size, these models often required high resolution images and sophisticated pre-processing algorithm to result in high accuracy. Moreover, those classifiers often do not generalize well across different datasets due to a relatively small number of Aedes samples. In this paper, we adopt a one-class perspective for mosquito detection, where the detection classifier is trained with Aedes vigilax mosquito class samples only, which is a major coastal pest species for NSW and more northern areas, and also for parts of coastal SA. Our model employs a BERT-BiLSTM module for feature extraction and a one-class SVM for classification. A comprehensive evaluation with a benchmarking dataset demonstrates the better performance of our model than existing approaches.

Reference

[1] Crowder, David W.,Dykstra, Elizabeth A., Brauner, Jo Marie, Duffy, Anne, Reed, Caitlin, Martin, Emily, Peterson, Wade, Carrière, Yves, Dutilleul, Pierre and Owen, Jeb P. (2013). West Nile Virus Prevalence across Landscapes Is Mediated by Local Effects of Agriculture on Vector and Host Communities. PLOS ONE, 8:1, 1-8.

Hate speech is abusive or stereotyping speech against a group of people, based on characteristics such as race, religion, sexual orientation and gender. Internet and social media have made it possible to spread hatred easily, fast and anonymously. The large scale of data produced through social media platforms requires the development of effective automatic methods to detect such content. Hate speech detection in short text on social media becomes an active research topic in recent years as it differs from traditional information retrieval for documents. My research is to develop a method to effectively detect hate speech based on deep learning techniques. I have proposed a novel feature based on lexicon for short text. Experiments have shown that proposed deep neural network based model improves performance when novel feature combines with CNN and SVM. A comprehensive evaluation with two benchmarking datasets demonstrates the better performance of our model than existing approaches.

Perovskite solar cells consisting of CH₃NH₃PbI₃ are expected to be alternative photovoltaic devices of silicon solar cells, because of their high conversion efficiency, easy fabrication process and low cost. On the other hand, they have a serious problem of low durability. To improve the stability and conversion efficiencies of the devices, one of the effective methods is to introduce additives into the perovskite photoactive layer. The purpose of this study is to improve the stability and conversion efficiency of the perovskite solar cells by incorporating cesium (Cs) or formamidinium (FA) at the CH₃NH₃ site. Additive effects on the photovoltaic properties and crystalline structures were investigated by current-voltage measurements, X-ray diffraction, and scanning electron microscopy. The simultaneous co-addition of Cs and FA to the CH₃NH₃PbI₃ perovskite crystal improved the photovoltaic properties, which would be due to the suppression of decomposition of the perovskite crystals.

Currently, artificial intelligence is getting more and more different applications in practice. One of the areas of artificial intelligence that has seen significant improvement in recent years is natural language processing. Natural language processing is a discipline with characteristics of linguistics and computer science. This field studies applying various mathematical and computational methods to natural language processing. The application areas can be diverse and include text reading and voicing, automatic translation, which everyone often uses, automatic text correction, information search and many other areas. This can be applied in economic process forecasting as well. Most often, the country's economic activity is characterised by such indicators as the gross domestic product, the level of employment or unemployment of the population, the price level in the country, inflation and other frequently used economic indicators. This work aims to use the information in the Lithuanian mass media and machine learning methods to assess whether these data can be used to evaluate economic activity. The aim of using these data is to determine the correlation between the usual indicators of economic activity assessment and media sentiments and to perform forecasting of traditional indicators. When evaluating consumer confidence, it is observed that the forecasting of this economic activity indicator is better based on the general index of negative sentiment (comparisons with univariate time series). In this case, the average absolute percentage error is 1.3% lower. However, if all sentiments are included in the forecasting instead of the best one, the forecasting is worse, and in this case, the MAPE is 5.9% higher. It is noticeable that forecasting the monthly and annual inflation rate is thus best when the overall negative sentiment is used. The MAPE of the monthly inflation rate is as much as 8.5% lower, while the MAPE of the annual inflation rate is 1.5% lower.

Breast cancer is the type of cancer that affects women the most frequently in the world. Additionally, it is the biggest cause of death for women. For the detection and treatment of breast cancer, there are numerous imaging techniques. For medical image analysts, making a diagnosis is arduous, time-, routine, consuming and tedious. Additionally, the growing volume of ultrasounds to interpret has overloaded practitioners and analysts. In the past, researches have been done with mammogram images. The research aims to take a different approach. The hypothesis is that by using artificial intelligence (AI) for ultrasound analysis, the process of computer-aided diagnosis (CAD) can be made, effective, interesting and free from subjectivity. Research's purpose is to classify benign (non-cancerous), malignant (cancerous), and normal samples. The dataset contains 780 images in total. Data were split %70 for training and %30 for validation. In this dataset, data augmentation and data preprocessing are also applied. Three models are used to classify samples. While ResNet50 scores %85.4 accuracy, ResNeXt50 scores %85.83, VGG16 scores %81.11. Making the diagnosis by artificial intelligence will provide relief in the field of medicine. Computer vision models may be used in medicine. Therefore, providing more data and testing data more broadly will improve the model.

Windmills are one of the virtually limitless sources of energy that may be used to generate electricity. It is regarded as a renewable source, but more investigation is indeed required to design the scientific knowledge and techniques that guarantee homogeneity in creation, increasing the contribution of this origin to the electricity sector. This is because the wind exhibits sudden variants in speed, surface area, and other crucial factors. Comprehensive data collection methods of wind speed and phase are required for the assessment of wind resources in a location. Wind energy happens when the wind makes contact well with the wind turbine's rotors. These rotor rotates, converting wind speed into kinetic energy that powers the wind generator's rotor and produces energy. In addition to assessing the energy production for the coming periods, which is valuable knowledge in the deployment of the producing units and the regulation of the power system, it is crucial to estimate the forecasts of wind activity a minimum of one day in advance. This study creates a wind speed forecasting model for the ultra-short, short, medium, as well as long-term development of computational techniques. Utilizing wavelet-based prediction, artificial neural network approaches, Autoregressive Integrated Moving Average (ARIMA), and other hybrid models.

The inner surface of engine cylinder liners has a plateau structure because of being required excellent sliding properties. To improve the tribological properties, the plateau surface consists of a smooth plateau region and valley region which serves as an oil reservoir for improving lubrication. The roughness of the plateau surface is measured and evaluated for improving fuel economy of engines in manufacturing job sites. For highly valid roughness evaluation of the plateau surface, filtering method is important. Therefore, ISO 21920-1 has stipulated that the plateau surface should be processed with the Gaussian regression filter (GRF) of ISO 16610-31. In addition, in previous research, the fast M-estimation Gaussian filter (FMGF) was proposed as a filter that overcomes the shortcomings of GRF. The proposed the FMGF is expected to be a better filter than the GRF because of including the robustness and the characteristic becoming equal output of the Gaussian filter. On the other hand, since the parameters of the robust profile filter have different suitable values for the normal surface or the plateau surface, their setting require human judgement. Therefore, the robust profile filters are not practical in manufacturing job sites because the parameters of the robust profile filters need to be set an optimum parameter manually, which takes time and effort. In this paper, we aim to improve the convenience of the robust profile filters in manufacturing job site by establishing guidelines for the selection of optimum parameters.

Hydrogen could become one of the energetic vectors widely used in the future. However, hydrogen is normally produced from non-renewable sources, which is a problem regarding the decarbonization of energy generation. Green hydrogen i.e., hydrogen obtain from renewable sources, has only been 1% of total hydrogen generated in the last years [1]. As a means of incrementing this percentage, the generation of H₂ from the dissociation of the water molecule (through electrolysis, photocatalysis, or thermolysis) has emerged as an adequate solution.

Materials that are able to catalyze water-splitting reaction through sunlight absorption has been widely studied [2]. Among the proposed oxide materials, Sr₂Ta₂O₇ displays photocatalytic activity, although its large band gap (4.6 eV) restricts the light absorbance to the ultraviolet region [3]. Several works point to a decrease in the band gap thanks to compositional modifications [4]. Density Functional Theory (DFT) is a powerful tool that allows us to acknowledge the factors behind the named modifications without the necessity of synthesizing the studied materials. In this work, DFT calculations have been performed to study new phases with general formula Sr_2-xA_xTa₂O₇ (A = Ba, Ca). Structural, energetic, and electronic features have been examined through these calculations.

References

[1] Z. Abdin, A. Zafaranloo, A. Rafiee, W. Mérida, W. Lipiński, y K. R. Khalilpour, Renew. Sustain. Energy Rev. 2020, 120, 109620.

[2] F. E. Osterloh, Chem. Mater. 2008, 20, 35.

[3] A. Kudo, H. Kato, y S. Nakagawa, J. Phys. Chem. B 2000, 104, 571.

[4] K. Y. Kim, T. H. Eun, S.-S. Lee, y U. Chon, Resour. Process. 2009, 56, 3, 138.