SSE Values Research Articles

Simultaneous Hypothesis Testing of Multivariable Nonparametric Spline Regression in the GWR Model

In this research, studied multivariable nonparametric geographically weighted regression use truncated spline approach. The model is an expansion of nonparametric truncated spline regression that takes into account geographical or spatial factors. The purpose of this study was to find statistics test and distribution for the simultaneous hypothesis test. This study obtains the statistic test used the maximum likelihood ratio test (MLRT) method. Results of the research obtained statistics test based on the ratio between the maximum of the likelihood function under the set of H_0  and the maximum of the set likelihood function below the population with each have a spatial factor. Distribution of statistical tests has been proven to have a distribution of F. The modeling application used the percentage of the death of Dengue Hemorrhagic Fever (DHF) in 38 districts/cities in East Java Province. The modeling resulted in the determination coefficient of 80.7% and SSE value that is 0.0043.

Developing Multiple-Linear Regression Model to Predict Soil Cation Exchange Capacity for Nagaland Soils

Cation exchange capacity (CEC) is an important parameter to assess the soil health and fertility. The procedure for measuring CEC is complicated and time consuming. To overcome this issue, researchers have developed and tested models to estimate CEC, but no such model has been developed for North-eastern region (NER) of India. In the present study, a training dataset of 198 numbers of soil samples having data of soil texture, bulk density (BD), pH, soil organic carbon (SOC) and CEC was used to develop step-wise regression model for CEC. Correlation analysis was done to extract the influential parameters for predicting CEC. Results showed that basic soil parameters were able to predict CEC and can define 90 % of variability with SSE value of 2.76. The agreement between observed and predicted CEC in validation dataset with R2 value of 0.665 provided a strong basis to identify input variables for predicting CEC in the region.

Developing Multiple-Linear Regression Model to Predict Soil Cation Exchange Capacity for Nagaland Soils

Cation exchange capacity (CEC) is an important parameter to assess the soil health and fertility. The procedure for measuring CEC is complicated and time consuming. To overcome this issue, researchers have developed and tested models to estimate CEC, but no such model has been developed for North-eastern region (NER) of India. In the present study, a training dataset of 198 numbers of soil samples having data of soil texture, bulk density (BD), pH, soil organic carbon (SOC) and CEC was used to develop step-wise regression model for CEC. Correlation analysis was done to extract the influential parameters for predicting CEC. Results showed that basic soil parameters were able to predict CEC and can define 90 % of variability with SSE value of 2.76. The agreement between observed and predicted CEC in validation dataset with R2 value of 0.665 provided a strong basis to identify input variables for predicting CEC in the region.

Treatment of soil washing wastewater via adsorption of lead and zinc using graphene oxide.

In the present work, graphene oxide (GO) was synthesized via the modified Hummers method and utilized in treating real soil washing wastewater via adsorptive removal of lead (Pb) and zinc (Zn). Characterization analysis of GO was performed using X-ray diffraction, Brunauer-Emmett-Teller method, Fourier transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, and zeta potential analysis. The Van't Hoff, Eyring, and Arrhenius equations were applied to determine the activation and thermodynamic parameters namely activation energy (Ea), standard Gibbs energy change (ΔG°), standard enthalpy change (ΔH°), standard entropy change (ΔS°), change in activation Gibbs energy (ΔG#), change in activation enthalpy (ΔH#), and change in activation entropy (ΔS#). Based on the high coefficient of determination values (0.8882 ≥ R2 ≥ 0.9094) and low values of SSE (0.0292 ≤ SSE ≤ 0.0511) and ARE (0.8014 ≤ ARE ≤ 0.8822), equilibrium data agreed well with the Freundlich isotherm. The maximum adsorption capacity for Pb(II) and Zn(II) was determined to be 11.57 and 4.65mg/g, respectively. Kinetic studies revealed that pseudo-second-order equation fitted well with the experimental data, which indicates that chemisorption is the rate-determining step of the adsorption system. Results have shown the possibility of GO as a potential adsorbent material in the treatment of soil washing wastewater.

Evaluation of SS 275 Substrate Substitutability in Arc and Wire Additive Manufacturing of Inconel 718

Evaluation of SS 275 Substrate Substitutability in Arc and Wire Additive Manufacturing of Inconel 718

Kinetika konvektivnog sušenja ekstrakcione pogače kikirikija

The groundnut contains valuable edible oil (45-60 %), proteins (20-30 %), carbohydrates (18 -20 %), essential vitamins and minerals. Groundnut seeds are used for the production of imitation milk containing 15.92 g /100 g of total solids, i.e. 5.74 g/100 g of fat, 4.24 g/100 g of carbohydrate, 5.58 g/100 g of protein, and 0.34 g/100 g of ash. The groundnut extraction cake examined in this paper was convectively dried at air temperature 80 °C and 90 °C for different periods of time (0-120 min). The moisture content of the groundnut extraction cake totaled 63.01%. The following models were used to fit the simulation and experimental data obtained for the convective drying kinetics of the groundnut extraction cake under consideration: Page, Modified Page, One- and Two -Term Exponential, Cubic (Sigmoid), Exponential, Vega-Galvez, Logistic and Rational. The Page model had the best goodness of fit at air temperature 80 °C, whereas the Exponential model had the best goodness of fit at 90 °C due to the highest coefficient of determination (R2) and lower Rmse and Sse values.

Research on Decision-Making of Complex Venture Capital Based on Financial Big Data Platform

The prediction of stock premium has always been a hot issue. By predicting stock premiums to provide a way for companies to respond to financial risk investments, companies can avoid investment failures. In this paper, under the financial big data platform, bootstrap resampling technology and long short-term memory (LSTM) are used to predict the value of the stock premium within 20 months. First, using the theme crawler, jsoup page parsing, Solr search, and Hadoop architecture to build a platform for financial big data. Secondly, based on the block bootstrap resampling technology, the existing data information is expanded to make full use of the existing data information. Then, based on the LSTM network, the stock premium in 20 months is predicted and compared with the values predicted by support vector machine regression (SVR), and the SSE and R-square average indicators are calculated, respectively. The calculation results show that the SSE value of LSTM is lower than SVR, and the R-square value of LSTM is higher than SVR, which means that the effect of LSTM prediction is better than SVR. Finally, based on the forecast results and evaluation indicators of the stock premium, we provide countermeasures for the company’s financial risk investment.

Fatigue response evaluation of stainless steel SS 304 L(N) and SS 316 L(N) through cyclic ball indentation studies

This paper presents the results of an experimental investigation of fatigue response of stainless steel SS 304 L(N) and SS 316 L(N) using cyclic ball indentation test method. A Tungsten Carbide (WC) spherical ball of 1.57 mm diameter is used for applying compression-compression fatigue cycling on the test specimen having a nominal thickness of 5 mm; the displacement response is monitored as a function of every cycle of loading. The study focused on cases where the stainless steel specimens were welded by two different welding processes – Activated flux TIG welding and conventional multi-pass TIG welding. Fatigue response was monitored at locations of weld zone, heat affected zone (HAZ) and base metal to identify the effect of microstructure variation on fatigue response. It is observed that there is a steady increase in depth of penetration of the spherical indenter due to fatigue cycling; however, after a number of cycles, there is a sudden increase in depth of penetration which indicates the failure of the material beneath the indenter. The specimens after cyclic ball indentation were examined using a scanning electron microscope and one could observe the presence of secondary cracking in the penetrated region of the specimen.

Electrochemical, mechanical and osseointegration evaluation of NBPC-coated 316L SS by EPD

ABSTRACTThe effect of nano biphasic bioceramic (NBPC) coating on 316L stainless steel (316L SS) was investigated. NBPC coatings were developed on 316L SS by electrophoretic deposition process followed by vacuum sintering. The NBPC coatings were characterised by XRD, FTIR, FESEM with EDAX and AFM studies. The electrochemical performances of the coated samples were carried out by open circuit potential-time measurements, potentiodynamic polarisation and electrochemical impedance studies in Hank’s solution. The electrochemical studies indicate that NBPC-coated samples have improved corrosion resistance and Vickers microhardness test showed enhancement in mechanical strength. Invitro studies with MC3T3-E1 osteoblast cell shows non-toxicity and high proliferation of NBPC-coated samples. These studies confirm the enhanced corrosion resistance, good mechanical strength with enhanced cell attachment and proliferation on the coated samples thus making it an ideal candidate for orthopaedic implants.

Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster

Clustering is a data mining technique used to analyse data that has variations and the number of lots. Clustering was process of grouping data into a cluster, so they contained data that is as similar as possible and different from other cluster objects. SMEs Indonesia has a variety of customers, but SMEs do not have the mapping of these customers so they did not know which customers are loyal or otherwise. Customer mapping is a grouping of customer profiling to facilitate analysis and policy of SMEs in the production of goods, especially batik sales. Researchers will use a combination of K-Means method with elbow to improve efficient and effective k-means performance in processing large amounts of data. K-Means Clustering is a localized optimization method that is sensitive to the selection of the starting position from the midpoint of the cluster. So choosing the starting position from the midpoint of a bad cluster will result in K-Means Clustering algorithm resulting in high errors and poor cluster results. The K-means algorithm has problems in determining the best number of clusters. So Elbow looks for the best number of clusters on the K-means method. Based on the results obtained from the process in determining the best number of clusters with elbow method can produce the same number of clusters K on the amount of different data. The result of determining the best number of clusters with elbow method will be the default for characteristic process based on case study. Measurement of k-means value of k-means has resulted in the best clusters based on SSE values on 500 clusters of batik visitors. The result shows the cluster has a sharp decrease is at K = 3, so K as the cut-off point as the best cluster.

ADVANCED WASTEWATER TREATMENT BY HYDRAULIC AUTOMATIC FLOATING MEDIA FILTER

A pilot scale experiment was carried out in tertiary wastewater treatment using hydraulic automatic floating filter. The hydraulic automatic floating media filter has been working with 120 cm depth of polystyrene bed. Wastewater from secondary biological treatment of WWTP was treated in the pilot filter with filtration velocity of 4 m/h. Oxygen concentration was remained constantly at level 2.0 mg/l by using an ejector. Headloss development and evaluation of SS, COD, NH4+ removals during filtration were objectives of the study. The experiment results showed that concentrations of SS, COD and NH4+ in effluent wastewater were kept lower than 5 mg/l, 30 and 0.5 mg/l respectively for almost filtration cycle. Effects of treatment in SS, COD and NH4+ were 88.27 %, 68.54 % and 5.59 %, respectively.

K-Means Algorithm Performance Analysis With Determining The Value Of Starting Centroid With Random And KD-Tree Method

Clustering methods that have high accuracy and time efficiency are necessary for the filtering process. One method that has been known and applied in clustering is K-Means Clustering. In its application, the determination of the begining value of the cluster center greatly affects the results of the K-Means algorithm. This research discusses the results of K-Means Clustering with starting centroid determination with a random and KD-Tree method. The initial determination of random centroid on the data set of 1000 student academic data to classify the potentially dropout has a sse value of 952972 for the quality variable and 232.48 for the GPA, whereas the initial centroid determination by KD-Tree has a sse value of 504302 for the quality variable and 214,37 for the GPA variable. The smaller sse values indicate that the result of K-Means Clustering with initial KD-Tree centroid selection have better accuracy than K-Means Clustering method with random initial centorid selection.

Thermal modeling and drying kinetics of bitter gourd flakes drying in modified greenhouse dryer

Abstract A modified greenhouse dryers with solar air heating collector were used for bitter gourd flakes drying under natural and forced convection modes till stagnation in their moisture evaporation. Experiments were performed simultaneously for open sun, natural and forced convection modes to validate the thermal modeling and compare drying kinetic results. The predicted temperatures of bitter gourd flakes, room air temperature and moisture evaporation rate show the fair agreement with the experimental observations within the root mean square of percentage deviation ranges from 6.10 to 10.41% and coefficient of correlation ranges from 0.96 to 0.99 under natural convection mode. Similarly, for the forced convection mode, coefficient of correlation ranges from 0.96 to 1 and root mean square of percentage deviation ranges from 3.74 to 7.55%. Logarithmic and Midilli-Kucuk models are selected as best curve fitting techniques for non-linear regression analysis for bitter gourd flakes drying under natural and forced convection modes respectively based on SSE, R-square, adjusted R-square and RMSE values. Proposed thermal models will be helpful in designing energy efficient greenhouse drying system for given crop mass and greenhouse dryer location.

Critical evaluation of HOMA and MBL as local aromaticity indices

AbstractThe harmonic oscillator model of aromaticity (HOMA) index is an excellent structural indicator of aromaticity. We found that HOMA values for internal benzene rings in large pericondensed polycyclic aromatic hydrocarbons are often overestimated because of the local aromaticity of adjacent benzene rings. The occurrence of this phenomenon was confirmed by comparing the HOMA with the corresponding SSE(LA) values; SSE(LA) is a graph‐theoretical index of local aromaticity not disturbed by the aromaticity of the adjacent benzene rings. Mean bond length values were likewise assessed by comparing them with the corresponding HOMA and SSE(LA) values.

Determination of diagnostic standards on saturated soil extracts for cut roses grown in greenhouses.

This work comprises the theoretical determination and validation of diagnostic standards for the analysis of saturated soil extracts for cut rose flower crops (Rosa spp.) growing in the Bogota Plateau, Colombia. The data included 684 plant tissue analyses and 684 corresponding analyses of saturated soil extracts, all collected between January 2009 and June 2013. The tissue and soil samples were selected from 13 rose farms, and from cultivars grafted on the 'Natal Briar' rootstock. These concurrent samples of soil and plant tissues represented 251 production units (locations) of approximately 10,000 m2 distributed across the study area. The standards were conceived as a tool to improve the nutritional balance in the leaf tissue of rose plants and thereby define the norms for expressing optimum productive potential relative to nutritional conditions in the soil. To this end, previously determined diagnostic standard for rose leaf tissues were employed to obtain rates of foliar nutritional balance at each analyzed location and as criteria for determining the diagnostic norms for saturated soil extracts. Implementing this methodology to foliar analysis, showed a higher significant correlation for diagnostic indices. A similar behavior was observed in saturated soil extracts analysis, becoming a powerful tool for integrated nutritional diagnosis. Leaf analyses determine the most limiting nutrients for high yield and analyses of saturated soil extracts facilitate the possibility of correcting the fertigation formulations applied to soils or substrates. Recommendations are proposed to improve the balance in soil-plant system with which the possibility of yield increase becomes more probable. The main recommendations to increase and improve rose crop flower yields would be: continuously check pH values of SSE, reduce the amounts of P, Fe, Zn and Cu in fertigation solutions and carefully analyze the situation of Mn in the soil-plant system.

Removal of antibiotic compounds by adsorption using glycerol-based carbon materials

This study is focused on the synthesis and application of glycerol-based carbon materials (GBCM200, GBCM300 and GBCM350) as adsorbents for the removal of the antibiotic compounds flumequine and tetracycline from aqueous solution. The synthesis enrolled the partial carbonization of a glycerol-sulfuric acid mixture, followed by thermal treatments under inert conditions and further thermal activation under oxidative atmosphere. The textural properties were investigated through N2 adsorption–desorption isotherms, and the presence of oxygenated groups was discussed based on zeta potential and Fourier transform infrared (FTIR) data. The kinetic data revealed that the equilibrium time for flumequine adsorption was achieved within 96h, while for tetracycline, it was reached after 120h. Several kinetic models, i.e., pseudo-first order, pseudo-second order, fractional power, Elovich and Weber–Morris models, were applied, finding that the pseudo-second order model was the most suitable for the fitting of the experimental kinetic data. The estimated surface diffusion coefficient values, Ds, of 3.88 and 5.06×10−14m2s−1, suggests that the pore diffusion is the rate limiting step of the adsorption process. Finally, as it is based on SSE values, Sips model well-fitted the experimental FLQ and TCN adsorption isotherm data, followed by Freundlich equation. The maximum adsorption capacities for flumequine and tetracycline was of 41.5 and 58.2mgg−1 by GBCM350 activated carbon.

Phenols removal from industrial effluents through novel polymeric resins: Kinetics and equilibrium studies

Abstract The efficient removal of phenols from wastewaters has increasingly become a significant environmental concern due to its high toxicity even at low concentrations. In this research work, phenols removal from aqueous solution was evaluated by using Amberlyst A26, a strong-base anion exchange resin, and Amberlite IRA-67, a weak-base anion exchange resin. The influence of phenols concentration in the feedstream was investigated as well as the effect of recirculation time. Equilibrium data fitted to the Langmuir, Freundlich and Temkin isotherms revealed Langmuir isotherm provided the best correlation for both resins ( R 2 = 0.99). Kinetic studies performed based on pseudo-first order, pseudo-second order and intraparticle diffusion models showed that although the correlation coefficients of the second-order model were the highest ( R 2 = 1) and also the SSE values were the lowest ( G ° = −1.55 kJ mol −1 ), whereas it is not spontaneous for Amberlite IRA-67 (Δ G ° = 3.06 kJ mol −1 ). Finally, Amberlyst A26 was confirmed to be considerably more efficient than Amberlite IRA-67 for the potential removal of phenols from industrial effluents.

RosettaTMH: a method for membrane protein structure elucidation combining EPR distance restraints with assembly of transmembrane helices

Membrane proteins make up approximately one third of all proteins, and they play key roles in a plethora of physiological processes. However, membrane proteins make up less than 2% of experimentally determined structures, despite significant advances in structure determination methods, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy. One potential alternative means of structure elucidation is to combine computational methods with experimental EPR data. In 2011, Hirst and others introduced RosettaEPR and demonstrated that this approach could be successfully applied to fold soluble proteins. Furthermore, few computational methods for <em>de novo </em>folding of integral membrane proteins have been presented. In this work, we present RosettaTMH, a novel algorithm for structure prediction of helical membrane proteins. A benchmark set of 34 proteins, in which the proteins ranged in size from 91 to 565 residues, was used to compare RosettaTMH to Rosetta’s two existing membrane protein folding protocols: the published RosettaMembrane folding protocol (“MembraneAbinitio”) and folding from an extended chain (“ExtendedChain”). When EPR distance restraints are used, RosettaTMH+EPR outperforms ExtendedChain+EPR for 11 proteins, including the largest six proteins tested. RosettaTMH+EPR is capable of achieving native-like folds for 30 of 34 proteins tested, including receptors and transporters. For example, the average RMSD₁₀₀SSE relative to the crystal structure for rhodopsin was 6.1 ± 0.4 Å and 6.5 ± 0.6 Å for the 449-residue nitric oxide reductase subunit B, where the standard deviation reflects variance in RMSD₁₀₀SSE values across ten different EPR distance restraint sets. The addition of RosettaTMH and RosettaTMH+EPR to the Rosetta family of <em>de novo </em>folding methods broadens the scope of helical membrane proteins that can be accurately modeled with this software suite.

INVESTIGATION ON THE EFFECT OF SHAPES ON THE DRYING KINETICS AND SENSORY EVALUATION STUDY OF DRIED JACKFRUIT

Jackfruits are seasonal and highly nutritional fruits indigenous to the Southwestern rainforests of India. However much of the produce are spoilt annually due to poor preservation techniques. Minimal studies have been conducted on the drying kinetics of jackfruit and the effect of shapes on the drying kinetics. In this research, drying curves of three different shaped jackfruit slices were obtained using a convective oven at 40 o C, 50 o C, 60 o C and 70 o C. Modified Midilli-Kucuk Model was found to be the best kinetic model for drying of jackfruits. At all temperatures, effective moisture diffusivity values and activation energy varied from 2.66 x 10 -10 - 4.85 x 10 -10 m 2 /s and 16.08 - 20.07 kJ/mol respectively. Drying was found to be most efficient at 50 o C using the square shaped slices with a R 2 , RMSE and SSE value of 0.9984, 0.01127 and 0.002668 respectively. Sensory evaluation of untreated and additive-added dried jackfruit slices was conducted by 40 untrained sensory panelists. Jackfruit with ascorbic acid and sugar coating had highest aesthetics value due to better retention of colour by ascorbic acid. However sugar coated jackfruit had the most favorable taste and smell. Further optimization must be done to satisfy consumers collectively to enable a highly marketable product.

Parameter Estimation and Comparison for SRGMs and ARIMA Model in Software Failure Data

As the requirement on the quality of the system has increased, the reliability is very important part in terms of enhance stability and to provide high quality services to customers. Many statistical models have been developed in the past years for the estimation of software reliability. We consider the functions for NHPP software reliability model and time series model in software failure data. We estimate parameters for the proposed models from three data sets. The values of SSE and MSE is presented from three data sets. We compare the predicted number of faults with the actual three data sets using the NHPP software reliability model and time series model.