Miracle-dz/UniFAR-FTUs huggingface dataset:
A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.
A Comparative Study of Different Data Mining Algorithms with Different Oversampling Techniques in Predicting Online Shopper Behavior

Nowadays buying over the internet has become very popular among online users. This action or attitude has increased due to the facilities and features that e-commerce sites offer to users, such as availability, accessibility, no crowds, easy price comparisons, etc. However, the number of actual buyers is still very low compared to the number of total visitors of these sites. Therefore, this paper will study the behavior of online shoppers to predict whether they will buy a product or not. The study first compares several classification algorithms against each other, then tries to enhance the results using different oversampling techniques. Results show that the RF algorithm achieved the best results and regarding oversampling, the SVMSMOTE exceeded the other methods.
ANTIOXIDANT, ANTIMICROBIAL AND ANTI-PROLIFERATIVE ACTIVITY OF SUILLUS LUTEUS (L.) ROUSSEL EXTRACTS

Amac: Bircok ilac kesfinde doga, dogal urunler benzeri bilesik siniflarinin tasarimina ilham kaynagi olarak kullanmistir. Eski zamanlardan beri yenilebilir mantarlar hem gida hem de ilac olarak kullanilmistir. Turkiye'de yasayan insanlar Suillus luteus (L.) Roussel yabani yenilebilir mantarlarini yaygin olarak tuketmektedir. Bu calismada, cesitli S.luteus ekstraktlarinin antioksidan, antimikrobiyal ve anti-proliferatif aktiviteleri arastirildi. Gerec ve Yontem: S.luteus'un antioksidan aktivitesi, DPHH serbest radikal supurme yontemi ve demir iyonu selatlama kabiliyeti saptandi. Ek olarak, toplam fenoller, β-karoten ve likopen gibi antioksidan ozelliklere sahip bilesenlerin konsantrasyonu spektrofotometrik yontemlerle belirlenmistir. Antimikrobiyal potansiyel, 14 mikroorganizma uzerinde agar difuzyon yontemi ile gosterilmistir. Son olarak, S. luteus metanol ekstresinin MCF-7 kanser hucre hatlari uzerindeki sitotoksik etkisi MTT yontemi kullanilarak degerlendirildi. Sonuc ve Tartisma: Sonuclar S.luteus metanolik ve etanolik ozutlerin daha bol fenollere sahip oldugunu gosterdi. (sirasiyla 153, 49.33 mg GAE/g ekstre,) Ayrica β-karoten ve likopen icerigi saptandi (0.120 ile 0.606 µg/mL arasi) S. luteus’un metanol ve etanol ozutlerinin, DPPH radikaline karsi antioksidan aktiviteleri ayni konsantrasyondaki standart antioksidanlar olan BHT’den daha yuksek aktivite gostermistir (Metanol ozutu (IC50: 63.72µg/mL) > Etanol ozutu (IC50: 80.72 µg/mL) > BHT (IC50: 96.47µg/mL). Ek olarak, metanol ektresinin etanol ekstresine gore daha yuksek demir iyonu selatlama kabiliyetine sahiptir (sirasiyla 2.72, 3.45 µg/mL). Genel olarak, test edilen mantar ekstreleri test edilen mikroorganizmalara karsi nispeten dusuk antimikrobiyal aktiviteye sahiptir (9 ve 10mm zoncapi) Ayrica, S. luteus’un metanol ekstresinin 1mg/mL konsantrasyonda kanser hucrelerinin tamamini oldurdugu tespit edildi. Bu calismanin sonuclari incelendiginde S.luteus'un ozellikle metanol ekstresinin potansiyel medikal ozelliklere sahip oldugu gosterilmistir.
Enhancement of the Productivity of Groundnut in Malawi through the Combined Use of Inoculants and Foliar Application of Nutrients

Low productivity characterizes the production of groundnut among smallholder farmers in Malawi. There is a need to explore options capable of increasing the productivity of the crop sustainably more especially under the changing climate. Against this background, experiments were conducted during the 2016/17 cropping season to investigate the potential to enhance the productivity of groundnut in Malawi through the combined use of inoculants (Graph-Ex and Histick-BASF) and foliar application of nutrients using Allwin fertilizer (legumes). The experiments were established at Bvumbwe and Chitala Agricultural research Stations and were laid in a randomized complete block design (RCBD) replicated four times. Data collected were analyzed in Genstat Discovery Edition 4 and were subjected to analysis of variance (ANOVA) at a 95% level of confidence. Means were separated by the least significant difference (LSD0.05). In general, foliar application of Allwin fertilizer alone particularly when conducted twice at two and four weeks after emergence produced a positive significant (p<0.05) groundnut grain yield response (97.8-170.8%) above the control. The yield increase is attributable to enhanced growth and development of the groundnut through the foliar Original Research Article Phiri et al.; APRJ, 5(1): 13-28, 2020; Article no.APRJ. 56750 14 supply of nutrients. In general, under the changing climate and amidst other constraints foliar feeding of nutrients using Allwin fertilizer alone particularly when conducted twice can increase significantly groundnut productivity in Malawi.
Design, Fabrication and Characterization of Optical Biosensors Based on (Bloch) Long Range Surface Plasmon Waveguides

A novel, cost effective, label free and real time biosensor based on straight gold waveguides supporting long range surface plasmon polaritons (LRSPPs) is used to measure the kinetics constants of protein–protein interactions. Bovine serum albumin (BSA) and anti-bovine albumin antibody (anti-BSA) produced in rabbit are used as the bio-pair for the purposes of demonstrating and investigating the extraction of binding kinetics. BSA protein is immobilized on the surface using thiol coupling. The output power of the system is recorded continuously over time, and then converted into surface mass concentration. eywords: inetics iosensors RSPP PR SA The kinetics constants are extracted from binding curves using the integrated rate equation. Linear least squares and non-linear least squares analysis are employed to obtain the constants and the results are compared. © 2016 Elsevier B.V. All rights reserved.
Financial Decisions and Sustainable Cash Flows in Nigerian Manufacturing Companies

This study aimed to identify the effect of some financial decisions that revolve around the assets of the firm on sustainable cash flows. For the financial performance of companies to be sustainable, they ought to generate sustainable cash flows over time because dividends and other appropriations are often paid from net cash flows. Sustainable cash flow was measured using the free cash flow yield. Data on leverage, revenue from asset utilization, and dividend policy of companies were extracted and their relationships with the free cash flow yield were examined. The fully modified OLS (FMOLS) was used to analyze a panel data set of 17 firms listed on the Nigerian Stock Exchange from 2008 to 2016. Asset turnover was found to be positive and significant while debt-to-equity ratio and dividend payout were both found to be negative and significant indicators of sustainable cash flows. Shareholders were advised to moderate their appetite for dividends to make sustainable funds more available. Managers were also advised to promote investment policies that generate positive net cash flows and avoid excessive use of debt to cover deficits in asset financing.
Carbapenem-Resistant Enterobacteriaceae among In-Patients of Tertiary Hospitals in Southwest, Nigeria

The study assessed the incidence and risk factor associated with the occurrence of carbapenem-resistant Enterobacteriaceae (CRE) among hospitalized patients at three tertiary hospitals in Southwest, Nigeria. A cross-sectional study was performed over a six-month surveillance period in the locations with a total of 300 blood and urine samples. A structured close-ended questionnaire was also administered to all subjects for review of demographics and potential risk factors. Fifty-nine isolates belonging to genera Escherichia (52.5%), Enterobacter (23.8%), Klebsiella (10.2%) and Proteus (13.6%) were isolated. All the isolates were multi-drug resistant with a notable resistance (100%) to cephalosporins and significant sensitivity to nitrofurantoin. A total of 23 isolates including Escherichia coli (n = 8), Enterobacter aerogenes (n = 9), Klebsiella pneumoniae (n = 1) and Proteus mirabilis (n = 5) exhibited resistance to one or both of imipenem and meropenem. The overall incidence of CRE in the three locations at the time of study was 7.7%. Age (p = 0.01) and exposure to invasive devices were significant risk factors for CRE colonization. Although at low incidence, the occurrence of CRE among this group calls for active monitoring because of its implication fatality of infections as well as the propensity to spread.
Digital Entrepreneurship and Digital Entrepreneurship Approach in Turkey: Ankara Case

Research Article Purpose – The main purpose of this study is to examine the relationship between the personality traits of digital entrepreneurs participating in the survey, their level of use of information technologies and their innovation skills. Digital entrepreneurship activities play an important role in bringing new products and services developed by businesses to large audiences. In this context, it is assumed that as the digital entrepreneurship usage levels of the enterprises increase, the profitability rates and innovation levels of the enterprises increase.
Network Fundamental Diagram Based Routing of Vehicle Fleets in Dynamic Traffic Simulations

The growing popularity of mobility-on-demand fleets increases the importance to understand the impact of mobility-on-demand fleets on transportation networks and how to regulate them. For this purpose, transportation network simulations are required to contain corresponding routing methods. We study the trade-off between computational efficiency and routing accuracy of different approaches to routing fleets in a dynamic network simulation with endogenous edge travel times: a computationally cheap but less accurate Network Fundamental Diagram (NFD) based method and a more typical Dynamic Traffic Assignment (DTA) based method. The NFD-based approach models network dynamics with a network travel time factor that is determined by the current average network speed and scales free-flow travel times. We analyze the different computational costs of the approaches in a case study for 10,000 origin-destination (OD) pairs in a network of the city of Munich, Germany that reveals speedup factors in the range of 100. The trade-off for this is less accurate travel time estimations for individual OD pairs. Results indicate that the NFD-based approach overestimates the DTA-based travel times, especially when the network is congested. Adjusting the network travel time factor based on pre-processed DTA results, the NFD-based routing approach represents a computationally very efficient methodology that also captures traffic dynamics in an aggregated way.
Dynamic compression and expansion in a classifying recurrent network

Recordings of neural circuits in the brain reveal extraordinary dynamical richness and high variability. At the same time, dimensionality reduction techniques generally uncover low-dimensional structures underlying these dynamics when tasks are performed. In general, it is still an open question what determines the dimensionality of activity in neural circuits, and what the functional role of this dimensionality in task learning is. In this work we probe these issues using a recurrent artificial neural network (RNN) model trained by stochastic gradient descent to discriminate inputs. The RNN family of models has recently shown promise in revealing principles behind brain function. Through simulations and mathematical analysis, we show how the dimensionality of RNN activity depends on the task parameters and evolves over time and over stages of learning. We find that common solutions produced by the network naturally compress dimensionality, while variability-inducing chaos can expand it. We show how chaotic networks balance these two factors to solve the discrimination task with high accuracy and good generalization properties. These findings shed light on mechanisms by which artificial neural networks solve tasks while forming compact representations that may generalize well.
The changing nature of consumer animosity and cosmopolitanism among young, individualistic consumers in emerging Asia: evidence from China

PurposeThis study is designed to address how rising individualism in emerging Asia changes consumer values and subsequent consumer behavior toward foreign brands. For this, we investigate consumer animosity (CA) and consumer cosmopolitanism (COS) in China and their impacts on consumer dispositions toward Japanese brands.Design/methodology/approachThe study hypothesizes that CA and COS mediate the effects of consumer individualism and age on brand attitudes and purchase intentions. Online surveys were conducted with 421 consumers in China.FindingsThe results indicate that consumers with individualistic values show favorable dispositions toward Japanese brands because of weak CA and strong COS values. In addition, the study found that younger consumers also harbor weak CA and do not avoid Japanese brands because of it. However, younger consumers were not found to be more cosmopolitan than their older counterparts.Originality/valueOur findings suggest that the modernization and individualization of a society can have impacts on consumer values in emerging Asia and that changes in consumer values among young and individualistic consumers can lead to increased preferences for foreign brands, especially brands from a country with historical animosity.
An Observation‐Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model

An empirical model calibration approach is presented that aims to approximate missing biosphere processes in a global land surface model without the need for substantial model structural changes. The strategy is implemented here using the NASA Catchment‐CN land surface model and Moderate Resolution Imaging Spectroradiometer (MODIS) observations of the fraction of absorbed photosynthetically active radiation (FPAR). Existing plant functional types (PFTs) of the Catchment‐CN model are divided into three subtypes, based on the bias between the model‐simulated and MODIS‐observed FPAR. Separate sets of vegetation parameters for each subtype are then calibrated at a small number of grid cells with homogeneous, single‐PFT land cover, using MODIS FPAR reference observations from 2003 to 2009. The effectiveness of the empirical approach at improving the realism of modeled vegetation dynamics is investigated with two global model simulations for the period 2010–2016, one using the newly calibrated parameter values and the other using the original values. Globally, the calibrated parameters reduce the root mean square error (RMSE) of the modeled FPAR with respect to MODIS by 0.029 (∼10%) on average. In some regions, substantially larger RMSE reductions are achieved. RMSE reductions are primarily driven by model bias reductions, with neutral effects on the temporal correlation skill. While the empirical approach is suitable for achieving consistent model improvements, it is shown to be sensitive to the characteristics of the model error, specifically a dominance of the bias component in the case of Catchment‐CN. Ultimately, more fundamental model structural changes may be required to achieve better improvements.
Phylogenetic relationships based on nuclear and plastid DNA sequences reveal recent diversification and discordant patterns of morphological evolution of the Chilean genera of Gilliesieae (Amaryllidaceae: Allioideae)

Gilliesieae are a South American tribe of Amaryllidaceae characterized by high floral diversity. Given different taxonomic interpretations and proposals for generic and specific relationships, a representative phylogenetic analysis is required to clarify the systematics of this group. The present study provides a framework for understanding phylogenetic relationships and contributing to the development of an appropriate taxonomic treatment of Gilliesieae. Molecular analyses, based on nuclear (ITS) and plastid DNA sequences (trnL-F and rbcL), resolve with strong support the monophyly of the tribe and the differentiation of two major clades. Clade I comprises the genera Gilliesia, Gethyum and Solaria and Clade II includes Miersia and Speea. These well-supported clades are mostly congruent with vegetative and karyotype characters rather than, e.g., floral symmetry. At the generic level, all molecular analyses reveal the paraphyly of Gilliesia and Miersia. Gethyum was found to be paraphyletic, resulting in the confirmation of Ancrumia as a distinct genus. Several instances of incongruent phylogenetic signals were found among data sets. The calibrated tree suggests a recent diversification of the tribe (Pliocene–Pleistocene), a contemporary process of speciation in which instances of hybridization and incomplete lineage sorting could explain patterns of paraphyly and incongruence of floral morphology.
Stochastic Assessment of Harmonic Propagation and Amplification in Power Systems Under Uncertainty

The time-varying harmonics in power systems are not only caused by the stochastic harmonic emissions from different types of harmonic sources, but also the uncertain harmonic response of the system. This paper aims to reveal the impact of the latter and presents a stochastic assessment technique to study the harmonic propagation and amplifications in power systems. To achieve this, systematic indices are defined to quantify the harmonic propagation and amplification characteristics of the system. Potential system uncertainties are then categorized using aleatory and epistemic uncertainties. As a result, both the time-varying and epistemic randomness can be characterized. Finally, the stochastic behavior of the harmonic propagation and amplification is studied using the Monte Carlo simulation technique. Moreover, the impact of load modelling method on the randomness of harmonic propagation and amplification has been investigated, raising awareness to the importance of careful selection and validation of the load modelling on harmonic studies. Extensive case studies confirm the usefulness and effectiveness of the proposed method.
Short-Term Wind Speed Prediction Based on Principal Component Analysis and LSTM

An accurate prediction of wind speed is crucial for the economic and resilient operation of power systems with a high penetration level of wind power. Meteorological information such as temperature, humidity, air pressure, and wind level has a significant influence on wind speed, which makes it difficult to predict wind speed accurately. This paper proposes a wind speed prediction method through an effective combination of principal component analysis (PCA) and long short-term memory (LSTM) network. Firstly, PCA is employed to reduce the dimensions of the original multidimensional meteorological data which affect the wind speed. Further, differential evolution (DE) algorithm is presented to optimize the learning rate, number of hidden layer nodes, and batch size of the LSTM network. Finally, the reduced feature data from PCA and the wind speed data are merged together as an input to the LSTM network for wind speed prediction. In order to show the merits of the proposed method, several prevailing prediction methods, such as Gaussian process regression (GPR), support vector regression (SVR), recurrent neural network (RNN), and other forecasting techniques, are introduced for comparative purposes. Numerical results show that the proposed method performs best in prediction accuracy.
PENGEMBANGAN MEDIA DAN STRATEGI PEMBELAJARAN UNTUK MENGATASI PERMASALAHAN PEMBELAJARAN JARAK JAUH DI PANDEMI COVID- 19

Abstract: Media Development and Learning Strategies to Overcome Distance Learning Problems in the Covid-19 Pandemic. The emergence of the Covid-19 pandemic has an impact on the emergence of new problems in life, not only in the economic world but also in the world of education which has been very significantly affected. Large-Scale Restrictions Policies that result in face-to-face learning in the classroom being moved home with a distance learning system using the internet, learning is continued online / online. On the one hand, this effective learning becomes one of the efforts to spread the epidemic, but on the other hand it creates new problems in the world of education. The problem of online distance learning in this pandemic is related to the many unpreparedness between students and teachers, this is because students must be able to understand the material by themselves without direct explanation from the teacher, likewise in this pandemic state students have to prepare extra costs to prepare quotas so they can follow online learning with a tight schedule. Whereas the teacher must be able to convey the material in an effective way so that it is able to be accessed remotely.Keywords: Development, Learning Media, Learning Strategies, Distance Education Problems, and the Covid-19 Pandemic.Abstrak: Pengembangan Media dan Strategi Pembelajaran untuk Mengatasi Permasalahan Pembelajaran Jarak Jauh di Pandemi Covid-19. Munculnya Pandemi Covid- 19 berdampak pada munculnya permasalahan baru dalam kehidupan, tidak hanya pada dunia ekonomi namun dunia pendidikan pun terdampak dengan sangat signifikan. Kebijakan Pembatasan Skala Besar yang mengakibatkan pembelajaran tatapmuka di dalam kelas dipindahkan ke rumah dengan sistem pembelajaran jarak jauh menggunakan internet, pembelajaran pun dilanjutkan secara daring/ online. Dalam satu sisi pembelajaran ini efaktif menjadi salah satu upaya penyebaran wabah, namun di sisi lain menimbulkan persoalan baru dalam dunia pendidikan. Permasalahan pembelajaran jarak jauh secara online di pandemi ini berkaitan dengan banyaknya ketidaksiapan antara siswa dengan guru, hal tersebut dikarenakan siswa harus dapat memahami materi dengan sendirinya tanpa penjelasan langsung dari guru, begitu pula di keadaan pandemi ini siswa harus mempersiapkan biaya ekstra untuk menyiapkan kuota agar dapat mengikuti pembelajaran secara online dengan jadwal yang padat. Sedangkan guru harus mampu menyampaikan materi dengan cara yang efektif sehingga mampu diakses melalui jarak jauh.Kata Kunci: Pengembangan, Media Pembelajaran, Strategi Pembelajaran, Permasalahan Pendidikan Jarak Jauh, dan Pandemi Covid- 19.