Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model

Hydrological modeling is one of the important subjects in managing water resources and the processes of predicting stochastic behavior. Developing Data-Driven Models (DDMs) to apply to hydrological modeling is a very complex issue because of the stochastic nature of the observed data, like seasonality, periodicities, anomalies, and lack of data. As streamflow is one of the most important components in the hydrological cycle, modeling and estimating streamflow is a crucial aspect. In this study, two models, namely, Optimally Pruned Extreme Learning Machine (OPELM) and Chi-Square Automatic Interaction Detector (CHAID) methods were used to model the deterministic parts of monthly streamflow equations, while Autoregressive Conditional Heteroskedasticity (ARCH) was used in modeling the stochastic parts of monthly streamflow equations. The state of art and innovation of this study is the integration of these models in order to create new hybrid models, ARCH-OPELM and ARCH-CHAID, and increasing the accuracy of models. The study draws on the monthly streamflow data of two different river stations, located in north-western Iran, including Dizaj and Tapik, which are on Nazluchai and Baranduzchai, gathered over 31 years from 1986 to 2016. To ascertain the conclusive accuracy, five evaluation metrics including Correlation Coefficient (R), Root Mean Square Error (RMSE), Nash–Sutcliffe Efficiency (NSE), Mean Absolute Error (MAE), the ratio of RMSE to the Standard Deviation (RSD), scatter plots, time-series plots, and Taylor diagrams were used. Standalone CHAID models have better results than OPELM methods considering sole models. In the case of hybrid models, ARCH-CHAID models in the validation stage performed better than ARCH-OPELM for Dizaj station (R = 0.96, RMSE = 1.289 m3/s, NSE = 0.92, MAE = 0.719 m3/s and RSD = 0.301) and for Tapik station (R = 0.94, RMSE = 2.662 m3/s, NSE = 0.86, MAE = 1.467 m3/s and RSD = 0.419). The results remarkably reveal that ARCH-CHAID models in both stations outperformed all other models. Finally, it is worth mentioning that the new hybrid “ARCH-DDM” models outperformed standalone models in predicting monthly streamflow.
Transition Period Between Vegetation Growth and Senescence Controlling Interannual Variability of C Fluxes in a Mediterranean Reed Wetland

Wetlands are crucial ecosystems modulating climate change due to their great potential to capture carbon dioxide (CO2), emit methane (CH4), and regulate local climate through evapotranspiration (ET). Common reed wetlands are particularly interesting given their high productivity, abundance, and highly efficient internal gas‐transport mechanism. However, little is known about the interannual behavior and dominant controlling factors of Mediterranean reed wetlands, characterized by seasonal flooding and remarkable weather variability. After six years of ecosystem carbon and ET flux measurements by eddy covariance (three years for CH4 fluxes), this study shows the functional vulnerability of such wetlands to climate variability, switching between carbon (CO2 + CH4) sink (660 g CO2‐eq m−2 yr−1, in 2014) and source (360 g CO2‐eq m−2 yr−1, in 2016) in short periods of time. According to our analyses, the great interannual variability appeared to mainly depend on the behavior of reed growth dynamics during the transition to senescence period, what is confirmed through the enhanced vegetation index as a proxy of photosynthetic activity. Additionally, a similar behavior of seasonal and daily patterns of carbon fluxes and ET was found compared with other wetlands under different climates.
Striking Dose- and Time-Dependency of Radiation Cystitis in the Rat: Repeat Cystometric Evaluation with Saline Alone and Potassium Chloride Challenge

Radiation therapy is the first choice for treatment of many pelvic visceral cancers. Two forms are currently employed, external beam radiation and brachytherapy. In the case of the former, ionizing radiation is directed at the afflicted organ and, in the latter, pellets of radioactive material are implanted at cancer lesion sites. The therapeutic mechanism of action is indirect (via reactive oxygen species) and direct damage to DNA of cancer cells. In non-replicating cells, this is not necessarily a lethal event, and DNA repair mechanisms may be utilized to repair the damaged DNA and maintain the terminally differentiated cells. In the case of cancers, where the cells are rapidly replicating and doing so before DNA repair mechanisms are able to correct the radiationinduced damage, this damage results in death of the cells during mitosis and thus elimination of the cancer.
Fuzzy Logic System Application for Detecting SNP-SNP Interaction

The identification of interactions between single-nucleotide polymorphisms (SNP–SNP interactions) is crucial for determining human genetic disease susceptibility. With rapid technological advancements, multiobjective multifactor dimensionality reduction (MOMDR) measurements have achieved high detection success rates. However, the classification of high- or low-risk groups is central to MOMDR and has yet to be extensively studied. To address limitations in binary classification, we propose an improved fuzzy sigmoid (FS) approach that uses membership degrees in MOMDR, thus denoting it as FSMOMDR. For determining the interval of membership, our improved FS approach assesses the distance between the $i^{\mathrm {th}}$ multifactor class and outcome (cases and controls). Thus, the improved FS approach enables MOMDR algorithms to determine the membership degrees of high- and low-risk groups in each multifactor class because the two-element set is extended to a specified membership interval. Moreover, the improved FS approach can handle uncertain information, which thus enables the effective detection of the $m$ -locus combinations with similar distributions. FSMOMDR measurements can also distinguish similar frequencies among genotype combinations, thus enabling the detection of more significant SNP–SNP interactions. On the basis of the classification accuracy rate of MOMDR and results obtained from the analysis of several test data sets, we determined FSMOMDR to be superior to other MDR-based methods with respect to detection success rate. The results indicate that binary and fuzzy classifications involving MOMDR can provide insight into uncertainty in risk classification. Thus, FSMOMDR could successfully detect SNP–SNP interactions in coronary artery disease in a large data set obtained from the Wellcome Trust Case Control Consortium. We could successfully reduce uncertain information in MDR and thus suggest that membership based on the improved sigmoid function can be used to identify SNP–SNP interactions as well as obtain content knowledge.
Nanoscale structural evaluation of 0-3 magnetic nanocomposites fabricated by electro-infiltration

Magnetic nanocomposites with 0-3 connectivity, whereby a 0D magnetic nanoparticle phase is embedded into a 3D magnetic metal matrix phase, have gained increased interest for use in applications ranging from integrated power inductor cores to exchange-spring magnets. The electro-infiltration process, in which a metal phase is electroplated through a nanoparticle film phase, is an inexpensive approach compatible with semiconductor fabrication methods for the formation of these nanocomposites. Past demonstrations of electro-infiltrated nanocomposites have relied on scanning electron microscopy and energy dispersive x-ray spectroscopy to evaluate the 0-3 composite structure. However, a detailed investigation of the boundary between the particle and metal matrix phases cannot be performed with these tools, and it is unknown whether the particle/matrix interfaces are dense and void-free. This detail is critical, as the presence of even nanoscale voids would affect any potential magnetic exchange coupling and hence the overall electromagnetic properties of the material. This work seeks to explore the phase boundary of 0-3 magnetic nanocomposite fabricated by electro-infiltration by using scanning transmission electron microscopy and energy-dispersive x-ray spectroscopy to analyze the nanostructure of two different composites—a nickel/iron-oxide composite and a permalloy/iron-oxide composite. High-resolution imaging indicates that the interface between the particle phase and matrix phase is dense and void-free. These results will help guide future studies on the design and implementation of these magnetic nanocomposites for end applications.Magnetic nanocomposites with 0-3 connectivity, whereby a 0D magnetic nanoparticle phase is embedded into a 3D magnetic metal matrix phase, have gained increased interest for use in applications ranging from integrated power inductor cores to exchange-spring magnets. The electro-infiltration process, in which a metal phase is electroplated through a nanoparticle film phase, is an inexpensive approach compatible with semiconductor fabrication methods for the formation of these nanocomposites. Past demonstrations of electro-infiltrated nanocomposites have relied on scanning electron microscopy and energy dispersive x-ray spectroscopy to evaluate the 0-3 composite structure. However, a detailed investigation of the boundary between the particle and metal matrix phases cannot be performed with these tools, and it is unknown whether the particle/matrix interfaces are dense and void-free. This detail is critical, as the presence of even nanoscale voids would affect any potential magnetic exchange coupling and he.
Comparative Study on Driving Switching Characteristics of GaN-FET and SiC-MOSFET in Transient High Voltage Pulse Discharge Circuit

As a new type of wide band gap semiconductor device, GaN-FET has faster switching speed and higher power density than SiC-MOSFET. It has a good application prospect in the field of pulse power technology. Aiming at the effect of gate driving circuit (mainly gate on-resistance and off-resistance, gate-source capacitance and driving voltage) on the switching behavior of semiconductor devices, this paper designs a pulse discharge test circuit to build a GaN-FET switching process. The circuit model uses a mathematical model to analyze the typical characteristics of GaN-FET switching behavior. And using SiC-MOSFET with similar parameters in the data sheet for comparative experiments, revealing the difference in switching characteristics of the two semiconductor devices in the transient pulse loop based on driving regulation. It can provide reference for driving design of GaN-FET in high voltage pulse generator. In addition, based on the results of comparative experiments, the driving parameters are designed to build a 4-level Marx generator experimental prototype with GaN-FET and SiC-MOSFET as the main switches. It has been preliminarily verified that GaN-FET can generate faster leading-edge high-voltage nanosecond pulses and have greater stability at high repetition rates.
Improving the bankruptcy prediction by combining some classification models

Bankruptcy prediction problem has been a concerned topic due to its negative effects to areas of life namely economy, society and policy. However, once this problem being solved, it will help to repulse the above effects and helps individuals and organizations to make wise decision. The purpose of predicting bankruptcy is to develop a predictive model that allows to foreseen likelihood of financial distress of company. In this paper, we consider "Polish companies’ bankruptcy dataset "that includes financial ratios and the bankruptcy situation of 43405 Polish companies. In literature, the best result on the dataset reaches a AUC of 95.9%. The difficulties of the dataset come from the huge number of missing values and the imbalance between majority and minority class. In our approach, we used some techniques including random forests regression and Synthetic Minority Oversampling Technique(SMOTG) method to treat he dataset. Some classifiers and a proposed combination of three best classifiers were then applied and evaluated by the cross-validation method. The results show that the proposed method has a good performance with a AUC of 99.78%, a F1 score of 95.12%.
Back stepping-Based-PID-Controller Designed for an Artificial Pancreas model

Abstract Artificial pancreas is simulated to handle Type I diabetic patients under intensive care by automatically controlling the insulin infusion rate. A Backstepping technique is used to apply the effect of PID controller to blood glucose level since there is no direct relation between insulin infusion (the manipulated variable) and glucose level in Bergman’s system model subjected to an oral glucose tolerance test by applying a meal translated into a disturbance. Backstepping technique is usually recommended to stabilize and control the states of Bergman's class of nonlinear systems. The results showed a very satisfactory behavior of glucose deviation to a sudden rise represented by the meal that increase the blood glucose Keywords:- Type I diabetes, Backstepping, Bergman’s model, oral glucose tolerance test. 1. Introduction With more than 246 million affected people worldwide, diabetes mellitus is one of the most widespread diseases and causes 3.8 million deaths per year, similar to HIV/AIDS [1]. Any patient that suffers from diabetes that does properly receives the insulin cure can lead to complications such as nerve damage, brain damage, amputation and eventually death. Diabetes related complications are a worldwide epidemic with high medical, economic and social costs [2]. The normal blood glucose concentration level in the human body varies in a narrow range (70 - 110 mg/dL). If for some reason the human body is unable to control the normal glucose-insulin interaction (e.g. the glucose concentration level is constantly out of the above mentioned range), diabetes is diagnosed. As a result four types of diabetes are known: Type I (also known as insulin-dependent diabetes mellitus), Type II (or insulin- independent diabetes mellitus), gestational diabetes and other special types, like genetic deflections [3]. Type I diabetes mellitus is characterized by theinability of the beta cells of the pancreas islets to produceinsulin, which is essential for the uptake of glucose in themuscles and storage in the liver [1]. As known, insulin dosing is divided into two divisions: basal and bolus insulin. The basal insulin is required for fasting conditions; while bolus insulin is calculated to correct for meals or hyperglycemia condition. Both dosages should be adjusted over time. The development of external insulin infusion pumps as well as the introduction of rapid-acting insulin analogues has made automated intensive insulin therapy feasible [4]. Automatic controllers are needed to adjust the insulin therapy according to the measurements of glucose concentrations as illustrated in figure (1). Automatic control of insulin infusion rate especially for type I diabetes proved its ability to give satisfactory results. Different types of automatic controllers are used in the literature. A. Makroglou et al[5] introduced an overview of some of the mathematical models appearing in the literature for use in the glucose-insulin regulatory system in relation to diabetes, enhanced with a
Response on growth performance of wheat (Triticum aestivum L.) under the rhizospheric management

A field experiment entitled "Response of rhizospheric management on soil health and nutrient use efficiency in wheat (Triticum aestivum L.)" was conducted during rabi season of 2017-2018 at G.B Pant University of Agriculture and Technology, Pantnagar, District, Udham Singh Nagar (Uttarakhand). The experiment was conducted with 19 treatment combinations and 3 replications in a factorial randomized block design. The treatments were consisted of 3 different placement methods viz: Deep placement, surface application and Band placement, Six nutrient management options viz: 100% RDF (150:60:40 kg/ha NPK), 75% RDF (112.5:45:30 kg/ha NPK), 75% RDF +Vermicompost (2 q/ha), 75% RDF +Vermicompost (2 q/ha) +PSB (10 kg/ha), 75% RDF + Poultry manure (2 q/ha), 75% RDF + Poultry manure (2 q/ha) +PSB (10 kg/ha) with one absolute control. Results suggested that among the different placement methods; Deep placement performed significantly better over rest of the treatments in all the growth performance viz: Plant height (cm) and dry matter production (g/m). However, it was found at par with Band placement. Among the nutrient management treatment, 75% RDF + Vermicompost+ PSB found to be significantly superior in growth of wheat to the rest of the nutrient management options. It was also found at par with 100% RDF and 75 % RDF+ Poultry manure+ PSB. Absolute control plot recorded the lowest improvement in growth. Combined application of Deep placement and 75% RDF+ Vermicompost +PSB or 75% RDF + poultry manure + PSB was found to be the best treatment as they resulted in growth enhancement. Considering widespread low nutrient use efficiency of fertilizers, rhizospheric manipulation with fertilizer placement and nutrient management approach can help in improving growth performance with a reduction in the rate of fertilizers. The addition of organic matter into the soil maximized the rhizospheric efficiency and nutrient use efficiency of wheat.
Wt1 positive neurons in the hindbrain are essential for respiration

Neuronal networks commonly referred to as central pattern generator (CPG) networks coordinate the generation of rhythmic activity like locomotion and respiration. These networks are proposed to exhibit a high degree of homology in their development. Their establishment is influenced by a variety of transcription factors. One of them is the Wilms tumor protein Wt1 that is present in dI6 neurons of the ventral spinal cord, which are involved in the coordination of locomotion. Here we report about the so far undescribed presence of Wt1 in neurons of the caudoventral medulla oblongata and their impact on respiration. By performing marker analyses, we were able to characterize these Wt1 positive (+) cells as dB4 neurons. The temporal pattern of Wt1 occurrence suggests a role for Wt1 in the differentiation of dB4 neurons during embryonic and postnatal development. Conditional knockout of Wt1 in these cells caused an altered population size of V0 neurons already in the developing hindbrain leading to a decline in the respiration rate in the adults. Thereby, we confirmed and extended the so far proposed homology between neurons of the dB4 domain in the hindbrain and dI6 neurons of the spinal cord in terms of development and function. Ablation of Wt1+ dB4 neurons resulted in the death of neonates due to the inability to initiate respiration suggesting a vital role for Wt1+ dB4 neurons in breathing. These results extend the role of Wt1 in the CNS and show that in addition to its function in differentiation of dI6 neurons it also contributes to the development of dB4 neurons in the hindbrain that are critically involved in the regulation of respiration.
Isolation and characterization of aerial bacteria from a microbiology laboratory environment

Sterile nutrient agar (NA) plates were exposed to the inner and outer environment of the laboratory for 15 minutes before the commencement of laboratory work in the morning and after closure of activities in the evening after which the plates were incubated at 37oC for 24 hours. Antibiotic susceptibility of the bacterial isolates was done using agar well diffusion technique. The aerial bacterial load outside the laboratory was found to be higher than inside the laboratory. Bacteria isolated include members of the genus Staphylococcus, Bacillus, Lactobacillus, Salmonella, Micrococcus, Streptococcus, Proteus and Escherichia. Bacillus subtilis had the highest frequency of occurrence (31.0%) followed by alpha haemolytic streptococcus (19.0%) while the least frequency of occurrence was shown by Proteus bulgaricus and Proteus species (1.7%). All the bacteria were sensitive to antibiotics tested except Micrococcus luteus. They were equally found to be sensitive to the anti-microbial agent (antiseptic and disinfectant) tested except for the hand sanitizer to which most isolates displayed high resistance. The presence of a wide variety of pathogenic microorganisms in the laboratory shows that the environment requires regular cleaning and disinfection to enhance the safety of staff and students using the laboratory. J. bio-sci. 26: 41-48, 2018
Spectrum sensing in optical wireless communication network

The main focus of this paper is on the issue of spectrum sensing or channel awareness in an optical wireless scattering communication network. We propose two spectrum sensing algorithms to detect M‐ary pulse‐position modulation signal under Poisson channel statistic. The first algorithm premises on symbol‐by‐symbol block processing, in which the photon‐counting receiver collects the received photons during a symbol interval and a Neyman‐Pearson decision rule is developed to determine the optimum decision threshold, whereas the second algorithm is based on chip‐level sequential as well as diversity detection. Performances of both algorithms are extensively analyzed in terms of the detection probability, the false alarm probability, the average detection delay, and the average false alarm run length. Simulation results demonstrate that scheme 2 outperforms scheme 1 under different scenarios. We also analyze the existing algorithm on the considered signal model and verify the effectiveness of the proposed algorithms over existing spectrum sensing algorithm.
Semantic validation of biological maps in SBGN

SEMANTIC VALIDATION OF BIOLOGICAL MAPS IN SBGN Umut Utku ÇALIŞ M.S. in Computer Engineering Advisor: Uğur Doğrusöz September 2019 Graph visualization is a research field where relational information is graphically represented in the form of graphs or networks. It is applicable in numerous areas from computer network systems, to biology, to software engineering. In such areas, graph visualization techniques provide effective visual analysis of graph based data. Systems Biology Graphical Notation (SBGN) facilitates a standard model for representing biological entities and their interactions by using graph visualization. SBGN-ML is an XML based format for keeping information about SBGN maps. libSBGN enables writing and reading SBGN-ML files in an easy manner and is meant to bring syntactic and semantic validation to SBGN maps. It is currently available in Java/C++ (libSBGN) and JavaScript (libSBGN.js) programming languages with varying support for aforementioned. libSBGN enables important syntactic and semantic correctness concepts for manipulating SBGN maps and converting SBGN-ML files into several other formats. Syntactic validation of SBGN-ML files involves using a simple XML Schema Definition (XSD) file. This validation checks whether files are in correct form or not. However, this XSD file does not enable checking against semantic rules. For semantic validation of such files, the Schematron language was developed providing higher level semantic rule controls. With this thesis, we first enabled high level semantic validation (schematron validation) of SBGN maps in libSBGN.js, which uses XSLT and transformation of process description maps in SBGN-ML files. By using Schematron rules which are written in XPath syntax and enabling human-readable messages of validation errors and source of errors, we developed an XSLT stylesheet. We obtained validation result report by transforming SBGN-ML files using this XSLT stylesheet. In the JavaScript version of libSBGN library, we used a web based XSLT processor iii