Miracle-dz/UniFAR-FTUs huggingface dataset:
Financial and Economic Analysis of a 1-Tonne Capacity Mobile Solar-Biomass Hybrid Dryer for Maize Drying

Drawbacks associated with natural convection solar dryers are encouraging the development of low-capacity hybrid drying systems which integrate renewable energy in their operation. However, information on the cost-effectiveness of such drying systems for decision-making by investors is lacking. This study seeks to assess the financial and economic feasibility of a solar-biomass hybrid dryer for maize drying. A cost-benefit analysis was done using net present value (NPV), benefit-cost ratio (BCR), and internal rate of return (IRR) as well as payback period to measure the financial viability of the dryer operation and the profitability of the investment. The results show that the total capital expenditure required to establish the drying system is $ 5,263 with a running cost of $ 1,166 per year. Using an economic utilisation period of 10 years, the investment in the dryer was determined to be viable with an NPV of $ 4,876 and an IRR of 38% at a capital cost of 24%. At a drying charge of $2.11/bag, the results show the initial capital invested can be recouped in a PBP of 2.7 years at a BCR of 1.48. These financial indicators suggest that investment in the dryer is profitable. Further analysis shows that, at a projected 30, 266.80 tonnes of maize produce per season by smallholder maize farmers in the study area, about 210 of the hybrid dryers are needed to provide drying services to the farmers. This translates into about 420 direct job opportunities created for the youth and the potential to prevent economic loss of maize grains equivalent to $ 5,684.2 per year due to post-harvest losses of maize in the study area. The adoption and scale-up of the dryer have implications in addressing maize post-harvest loss at the smallholder level and job creation for the youth in the country.
A Fuzzy Seasonal Long Short-Term Memory Network for Wind Power Forecasting

To protect the environment and achieve the Sustainable Development Goals (SDGs), reducing greenhouse gas emissions has been actively promoted by global governments. Thus, clean energy, such as wind power, has become a very important topic among global governments. However, accurately forecasting wind power output is not a straightforward task. The present study attempts to develop a fuzzy seasonal long short-term memory network (FSLSTM) that includes the fuzzy decomposition method and long short-term memory network (LSTM) to forecast a monthly wind power output dataset. LSTM technology has been successfully applied to forecasting problems, especially time series problems. This study first adopts the fuzzy seasonal index into the fuzzy LSTM model, which effectively extends the traditional LSTM technology. The FSLSTM, LSTM, autoregressive integrated moving average (ARIMA), generalized regression neural network (GRNN), back propagation neural network (BPNN), least square support vector regression (LSSVR), and seasonal autoregressive integrated moving average (SARIMA) models are then used to forecast monthly wind power output datasets in Taiwan. The empirical results indicate that FSLSTM can obtain better performance in terms of forecasting accuracy than the other methods. Therefore, FSLSTM can efficiently provide credible prediction values for Taiwan’s wind power output datasets.
Residue Sum Formula for Pricing Options under the Variance Gamma Model

We present and prove a triple sum series formula for the European call option price in a market model where the underlying asset price is driven by a Variance Gamma process. In order to obtain this formula, we present some concepts and properties of multidimensional complex analysis, with particular emphasis on the multidimensional Jordan Lemma and the application of residue calculus to a Mellin–Barnes integral representation in C3, for the call option price. Moreover, we derive triple sum series formulas for some of the Greeks associated to the call option and we discuss the numerical accuracy and convergence of the main pricing formula.
Combined Application of Experimental Design and Artificial Neural Networks in Modeling and Characterization of Spray Drying Drug: Cyclodextrin Complexes

The aim of this study was to investigate the usefulness of combined application of quality by design tools such as central composite design (CCD), response surface methodology (RSM), and artificial neural networks (ANN) in the characterization, modeling, and optimizaton of spray drying of a poorly soluble drug : cyclodextrin complex. Models were developed by RSM and ANN from different pools of data. The model with best predictability was the ANN multilayer perceptron (MLP)1 model developed from the largest group of data (R 2 for response yield 0.854, moisture content 0.886). On the other hand, analysis of equations derived from the application of RSM contributed in better understanding the complex relationships between input and output variables. By application of a desirability function approach, optimal process parameters that resulted in the best process yield (86%) and minimal moisture content in the powder (3.3%) were established (25% feed concentration, 180°C inlet air temperature, 10% pump speed).
Data Mining Analysis of Gene Prognostic Markers of Metastatic Skin Cancer Based on the Elastic Network Method

Skin cancer is a typical cancer tumor, which occurs all over the world and has a relatively high recurrence rate, including metastatic tumors that occur in other tissues and metastases to the skin, thus jeopardizing the personal life satisfaction and soundness of patients. Due to individual differences, the traditional treatment methods cannot adapt to every patient accurately, so it is difficult to achieve the desired treatment effect for each individual. Nowadays, with the development of gene chip, many new therapies based on gene are more targeted and flexible for the treatment of skin cancer patients. Therefore, it is necessary to mine and analyze appropriate gene biomarkers according to patients' genes. Because of the high cost of gene chip technology and the large number of human genes, there are few samples of gene data and high dimensions. It is a key problem to mine effective genetic biomarkers from the sample data. In this paper, we firstly performed the preliminary analysis using the difference expression analysis and proportional hazards model, then used the elastic network method to reduce the range of genetic data selection, and screened 26 gene prognostic markers closely related to the recurrence of metastatic skin cancer. Finally, the 26 gene biomarkers were analyzed by functional analysis and verified using a test sample. Research findings have shown that the obtained genetic markers have certain value in the clinical prognostic treatment of metastatic skin cancer.
An Economic study for Climate Change Impact on Wheat production in the Northern West Coast Region of Egypt

This paper aimed to examine the impact of climate change on wheat productivity in the five rainsfed districts: El-Negaila, Sidi Barrani, El-Daba'a, Marsa Matrouh, and EL-Alamain in the northern west coast of Egypt in which the cultivated wheat area represents about 41.4% of wheat area in Matrouh Governorate (1990-2019). The productivity is fluctuated between 1.167 to 13.38 Ardab/Fadden accompanied with the fluctuation in precipitation between 24.35 to 115.10 MM/Season, and fluctuation of average difference between Max. Min. temperature from 8.07 to 7 C. Fully Modified Ordinary Least Square (FMOLS) technique was applied to investigate the relationship between wheat productivity and the independent variables (precipitation, temperature, cultivated area, labor and technology). OLS function showed that the model suffers from endogenity and heteroscedasticity. LLC and IPS statistics of panel unit root test proved that the included variables have unit root, i.e. they are non-stationary at level. Pedroni panel residual cointegration test confirmed the long run relationship between the first-order integrated variables [I (1)]. FMOLS function proved that natural climatic variables are the main determinants of wheat productivity, as a 1% increase in annual rainfall improves wheat productivity significantly by 3.3%, while temperature affects the wheat productivity negatively by 5.7%. The far west districts are the most affected by rainfall, as 1% increase of rainfall in EL-Negaila and SidiBarrani districts increase wheat productivity by 8.4%, 5.1% respectively. Results in all districts except EL-Negaila and Sidi Barrani showed the extent of labor intensification to enhance productivity, also all districts showed the importance of technical improvements. It is recommend adopting water policy as rain harvesting, building stone dykes and cisterns to provide: 355.5, 301.7, 287.9, 339.8, and 245.8 MM/Fadden in El-Negaila, Sidi Barrani, EL-Daba'a, Marsa Matrouh, and EL-Alamain districts respectively to improve wheat yield to 12 Ardab/Fadden under drought climate of north coast.
Comprehensive Diagnosis of Selenium and Iodine Deficiencies in Golden Manchurian Quails and Gray-Speckled Guinea Fowls Risen in the Astrakhan Region

| This study presents the results of comprehensive diagnosis of combined selenium-iodine hypomicroelementosis in quails and guinea fowls adapting to new biogeochemical conditions. This study focused on golden Manchurian quails, Coturnix japonica (Temminck and Schlegel, 1849), and gray-speckled guinea fowls, Numida meleagris (Linnaeus, 1758), imported to the Astrakhan region in 2015 from the Krasnodar Krai, Russia. The major components of terrestrial ecosystem (soil, water, and plants) in the Astrakhan region were found to be low in selenium, iodine and cobalt. As a result, quails and guinea fowls received a diet with insufficient amounts of therein. All subjects were examined for microelements, physiological, hematological, and biochemical changes, and pituitary-thyroid hormones. In addition, the study examined antioxidant indicators and markers of free radical oxidation. The comprehensive examination revealed selenium deficiency and iodine deficiency in the body of examined quails and guinea fowls. The experimental birds were found to have elevated levels of blood corpuscles, glucose, diene conjugates, and malonic aldehyde. Levels of antioxidant vitamins (E, A, C) were found to be low. Based on the above, it can be concluded that golden Manchurian quails and gray-speckled guinea fowls raised in conditions of selenium deficiency and iodine deficiency can experience latent symptoms of hypomicroelementosis when fed a diet based on local plants. The consequence is a drop in egg productivity. Thus, farmers specialized at breeding quails and guinea fowls should considers feeds enriched with selenium and iodine when the local ecosystem lacks a sufficient amount of therein.
Thermosensitive alginate–gelatin–nitrogen-doped carbon dots scaffolds as potential injectable hydrogels for cartilage tissue engineering applications

Hybrid injectable and biodegradable hydrogels based on oxidized alginate/gelatin and containing nitrogen-doped carbon dots (NCDs) as a reinforcement have been fabricated and crosslinked by 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC)/N-hydroxysuccinimide (NHS) as the chemical crosslinking agents in the hydrogel system. The idea of composite hydrogels relies on the assumption that they supply a microenvironment that is convenient for the exchange of nutrients via a porous structure and cell proliferation and have mechanical characteristics that approximately match natural tissue. The effect of the NCD content on the morphology structure, mechanical strength, swelling ratio, and biodegradation has been investigated. The results indicate that nanocomposite hydrogels containing a higher content of NCDs have smaller pore sizes and higher mechanical properties. The in vitro biodegradation and swelling behavior demonstrated that increasing the amount of NCDs up to 0.06% decreased the swelling ratio and weight loss of the hydrogels. The composite hydrogels are biocompatible, as verified by the MTT assay of MG-63 cells. The N-doped graphene quantum dots considerably affect degradation and interaction within the cells and hydrogels.
Fine‐grain beta diversity of Palaearctic grassland vegetation

range of different grassland and other open habitat types. We derived extensive environmental and structural information for these series. For each series and four taxonomic groups (vascular plants, bryophytes, lichens, all), we calculated the slope parameter ( z - value) of the power law species– area relationship (SAR), as a beta diversity measure. We tested whether z - values differed among taxonomic groups and with respect to biogeographic gradients (latitude, elevation, macroclimate), ecological (site) characteristics (several stress– productivity, disturbance and heterogeneity measures, including land use) and alpha diversity ( c - value of the power law SAR). Results: Mean z - values were highest for lichens, intermediate for vascular plants and lowest for bryophytes. Bivariate regressions of z- values against environmental variables had rather low predictive power (mean R ² = 0.07 for vascular plants, less for other taxa). For vascular plants, the strongest predictors of z - values were herb layer cover (negative), elevation (positive), rock and stone cover (positive) and the c value (U- shaped). All tested metrics related to land use (fertilization, livestock grazing, mowing, burning, decrease in naturalness) led to a decrease in z - values. Other predictors had little or no impact on z - values. The patterns for bryophytes, lichens and all taxa combined were similar but weaker than those for vascular plants. Conclusions: We conclude that productivity has negative and heterogeneity posi tive effects on z - values, while the effect of disturbance varies depending on type and intensity. These patterns and the differences among taxonomic groups can be explained via the effects of these drivers on the mean occupancy of species, which is mathematically linked to beta diversity.
CEOs’ Regulatory Focus and Risk-Taking When Firms Perform Below and Above the Bar

Both upper echelons theory and the behavioral theory of the firm (BTOF) have made important contributions to our understanding of the drivers of managerial risk-taking. Yet, how the individual- and organizational-level mechanisms espoused by these theories act in concert to determine risk-taking is not well understood. In this paper, we draw on regulatory focus theory as a fulcrum to bridge these two levels of analyses and integrate insights from both theoretical traditions. Consistent with the BTOF, we argue that promotion-focused CEOs engage in increased (decreased) risk-taking under conditions of performance below (above) aspirations. In contrast to the predictions of the BTOF, however, we predict that prevention-focused CEOs engage in increased (decreased) risk-taking under conditions of performance above (below) aspirations. We find support for these arguments in a large sample of CEOs from publicly listed U.S. firms and discuss the implications of our findings for both theory and practice.
Computationally Efficient Optimal Switching Sequence Model Predictive Control for Three-Phase Vienna Rectifier Under Balanced and Unbalanced DC Links

Unbalanced dc links usually occur when a Vienna rectifier is used as a charger for series-connected battery packs. The redundant vector preselection optimal switching sequence model predictive control (OSS-MPC) scheme does not require weighting factor tuning and is applicable to unbalanced dc links. However, its use is complicated for switching sequence selections and duty cycle calculations. Therefore, this article proposes a computationally efficient OSS-MPC scheme for three-phase Vienna rectifier operating under balanced and unbalanced dc links. The scheme achieves the same performance as that achieved by the redundant vector preselection OSS-MPC scheme, however, its execution is significantly more efficient. In the proposed method, an unbalanced factor is proposed by considering unbalanced dc links, and a look-up table is derived to describe the equivalence relation among the six feasible switching sequences. Then, duty cycles are calculated for only one switching sequence, and the OSS and its duty cycles are reconstructed based on the predefined table. Unlike the redundant vector preselection OSS-MPC scheme, the enumeration process is eliminated in the proposed method, thus, the computational burden is significantly reduced. Experimental results are provided to validate the effectiveness of the proposed method. This article is accompanied by a PDF demonstrating the look-up tables.
Determination of warranty length for one-shot devices with Rayleigh lifetime distribution

Abstract An important goal for the producers is to stimulate the purchase intention of consumers so that the profit can be increased. An attractive warranty policy can not only represent the quality of product but also enhance the purchase desire of consumers. A long warranty length usually stands for high quality. However, it is not suitable for the producers to provide an unlimited warranty length. The main purpose of this paper is to establish an approach to determine the optimal warranty length based on one-shot device sample. The one-shot device data are collected under a multiple-level constant-stress accelerated life test. The lifetime distribution at each stress level is assumed to be Rayleigh. A utility function is provided to measure the monetary profit based on a free-replacement warranty policy. The optimization problem is studied under the Bayesian framework. A numerical example is studied for illustrating purpose. Finally, the sensitivity analysis is performed to see the influence of some parameters on the optimal warranty length.
The interdependence of coffee futures and spot markets - An econometric analysis

Coffee is an export-oriented commodity for producing countries, and it is actively traded at international commodity exchange platforms viz., Intercontinental Exchange (ICE), New York and ICE, Europe. This study examines the interdependence of futures and spot markets for coffee in the price discovery mechanism, particularly in the Indian context. The study has considered both the International Coffee Organization (ICO) indicator prices and producers’ prices in India’s spot prices. The study confirms the existence of a stable long-run relationship between ICE coffee futures and ICO spot prices, implying that both prices react to the same set of market information. While there is an indication of equilibrium or long-run relationship between ICE Coffee futures (New York) and Arabica producer prices (at farm gate level) in India, the same was not true for Robusta coffee. The absence of co-integration between ICE futures prices (London) and Robusta producer prices in India suggested only a short-run relationship between them. The findings of the study conclude with strong evidence that the farm gate prices in India have been caused by the ICE futures markets, declining the contrary.
Liposomes with Caffeic Acid: Morphological and Structural Characterisation, Their Properties and Stability in Time

Medical and pharmaceutical research has shown that liposomes are very efficient in transporting drugs to targets. In this study, we prepared six liposome formulas, three in which we entrapped caffeic acid (CA), and three with only phospholipids and without CA. Determination of entrapment efficiency (EE) showed that regardless of the phospholipids used, the percentage of CA entrapment was up to 76%. The characterization of the liposomes was performed using Dynamic Light Scattering (DLS), Atomic Force Microscopy (AFM), zeta potential and polydispersity and showed that about 75–99% of the liposomes had dimensions between 40 ± 0.55–500 ± 1.45 nm. The size and zeta potential of liposomes were influenced by the type of phospholipid used to obtain them. CA release from liposomes was performed using a six-cell Franz diffusion system, and it was observed that the release of entrapped CA occurs gradually, the highest amount occurring in the first eight hours (over 80%), after which the release is much reduced. Additionally, the time stability of the obtained liposomes was analysed using univariate and multivariate statistical analysis. Therefore, liposomes offer great potential in CA entrapment.
Community-based transmission of hepatitis C in India: a systematic review

Hepatitis C virus (HCV) infection is a leading community health problem globally. The virus infects approximately 3% of the world population, placing approximately 170 million people at risk for developing liver cirrhosis and hepatocellular carcinoma. World Health Organization (WHO) has estimated that there are 10-24 million populations living with active HCV infection in India and seroprevalence of HCV among healthy population estimated, 0.09 to 2.02% in India. HCV is the Principle cause of chronic necroinflammatory and neoplastic liver disease in the developing countries of Asia and Africa; it’s emerging rapidly as an infection warranting attention. According to the WHO, red cross and red crescent societies, two-thirds of developing countries have not followed screening of donated blood and blood products for the presence of HCV.
Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.
A Modular Multiport Converter to Integrate Multiple Solar Photo-Voltaic (PV) Modules With a Battery Storage System and a DC Microgrid

A modular multiport converter (MMPC), which is capable of interfacing multiple solar Photo-Voltaic (PV) modules, a battery bank, and a dc microgrid, is proposed in this article. The number of solar PV modules that can be interfaced with the proposed MMPC is equal to the number of converter modules present in the system. Furthermore, all these solar PV modules can operate at their respective maximum power point, thus increasing the power extraction capability. A high-frequency transformer facilitates the interfacing of low-voltage solar PV modules and the battery bank with a 380-V microgrid. However, the power from the solar PV modules to the battery bank does not flow through this transformer. The variation in the transformer utilization with changes in operating voltages of the solar PV modules is analyzed. Detailed simulation studies are carried out on a system having five solar PV modules to verify the performance of the proposed MMPC. A laboratory prototype of the proposed MMPC consisting of four numbers of 400-W converter modules is fabricated. The performance of the MMPC is analyzed and the experimental results are presented.