Statistical Regression Equations Research Articles

Fuzzy C-Means clustering for physical model calibration and 7-day, 10-year low flow estimation in ungaged basins: comparisons to traditional, statistical estimates

In the northeast U.S., resource managers commonly apply 7-day, 10-year (7Q10) low flow estimates for protecting aquatic species in streams. In this paper, the efficacy of process-based hydrologic models is evaluated for estimating 7Q10s compared to the United States Geological Survey's (USGS) widely applied web-application StreamStats, which uses traditional statistical regression equations for estimating extreme flows. To generate the process-based estimates, the USGS's National Hydrologic Modeling (NHM-PRMS) framework (which relies on traditional rainfall-runoff modeling) is applied with 36 years of forcings from the Daymet climate dataset to a representative sample of ninety-four unimpaired gages in the Northeast and Mid-Atlantic U.S. The rainfall-runoff models are calibrated to the measured streamflow at each gage using the recommended NHM-PRMS calibration procedure and evaluated using Kling-Gupta Efficiency (KGE) for daily streamflow estimation. To evaluate the 7Q10 estimates made by the rainfall-runoff models compared to StreamStats, a multitude of error metrics are applied, including median relative bias (cfs/cfs), Root Mean Square Error (RMSE) (cfs), Relative RMSE (RRMSE) (cfs/cfs), and Unit-Area RMSE (UA-RMSE) (cfs/mi2). The calibrated rainfall-runoff models display both improved daily streamflow estimation (median KGE improving from 0.30 to 0.52) and 7Q10 estimation (smaller median relative bias, RMSE, RRMSE, and UA-RMSE, especially for basins larger than 100 mi2). The success of calibration is extended to ungaged locations using the machine learning algorithm Fuzzy C-Means (FCM) clustering, finding that traditional K-Means clustering (FCM clustering with no fuzzification factor) is the preferred method for model regionalization based on (1) Silhouette Analysis, (2) daily streamflow KGE, and (3) 7Q10 error metrics. The optimal rainfall-runoff models created with clustering show improvement for daily streamflow estimation (a median KGE of 0.48, only slightly below that of the calibrated models at 0.52); however, these models display similar error metrics for 7Q10 estimation compared to the uncalibrated models, neither of which provide improved error compared to the statistical estimates. Results suggest that the rainfall-runoff models calibrated to measured streamflow data provide the best 7Q10 estimation in terms of all error metrics except median relative bias, but for all models applicable to ungaged locations, the statistical estimates from StreamStats display the lowest error metrics in every category.

Influence of Posture on Gait Parameters in Severe Symptomatic Lumbar Stenosis Before and After Decompression Surgery

BackgroundWe searched to quantify the influence of sagittal vertical axis (SVA) on the improvement of spatiotemporal gait parameters using a gait motion analysis (GMA) before and after decompression surgery in patients suffering from lumbar stenosis (LSS). MethodsThirty-nine patients with severe LSS planned for lumbar decompression underwent a full body biplanar radiographs (EOS®) to quantify the SVA, and have benefited from a three-dimensional GMA one month before surgery (M0) and 6 month (M6) after surgery. The first step of this study was to confirm the validation of three dimensional SVA (3D SVA) for posture analysis. An analysis of modification of the 3D SVA and spatio-temporal gait parameters was then carried out in order to identify any correlation. ResultsDecompression surgery did not significantly improve 3D SVA between M0 and M6 (respectively 49.1 (50.3) versus 49.84 (19.02), p=0.42). Concerning spatio-temporal parameters, we found significant difference for all parameters between M0 and M6. A strong correlation (R2>0.65) between static SVA (EOS®) and 3D SVA was demonstrated using a statistical regression equation. There was also a statistically significant correlation between SVA (static and 3D) and improvement in spatiotemporal gait parameters after decompression surgery. ConclusionThis study analyses the relationship between postural change (SVA) and improvement in gait parameters measured during GMA before and after decompression surgery for lumbar spinal stenosis. This specific analysis of gait parameters may represent a prognostic assessment tool for the recovery of patients undergoing surgery for a lumbar spinal stenosis.

Harnessing Big Data Analytics to Accelerate Innovation: An Empirical Study on Sport-Based Entrepreneurs

The emergence of advanced technologies brings various opportunities for all kinds of business organizations. This topic was selected for research and discussion to figure out the possible impacts of using the ever-increasing development of digitalization, big data analytic capabilities (BDACs) and innovation in the field of sport-based entrepreneurship, which is the main pillar for the economic wellbeing, development and growth of sport activities. This study highlights the function of the BDAC of entrepreneurs in the acceleration of their readiness and innovation activities. Beyond the direct association of BDAC with an entrepreneur’s readiness and innovation performance (IP), we also tested the mediation of entrepreneurial readiness between the BDAC and IP of sport-based entrepreneurs. Moreover, the moderation of entrepreneurial orientation (EO) was also considered for the readiness and IP link. In this study, data were collected from 562 sport-based entrepreneurs. Online questionnaires were used for data collection, and various statistical techniques, including correlation, regression and structural equation modeling (AMOS 7.0), were applied for the analyses of the collected data. The outcomes of this study disclosed that BDAC and entrepreneur readiness positively predicted the IP. The results revealed that entrepreneurial readiness mediated between BDAC and IP. The findings suggested that sport-based entrepreneurs should enhance their BDAC for the execution of sport-related innovative activities. In spite of its valuable findings and suggestions, the current study is subject to some limitations. Firstly, this study is limited to sport-based entrepreneurs only. Secondly, AIT theory was used here, so for future considerations, other pillars of the economy such as the manufacturing sector should also be considered and the many other theories available should be employed.

137 Replacement Heifer Gain, Feed Intake and Efficiency While Consuming an Unprocessed Hay Diet Followed by a Mixed Concentrate/Forage Diet

Abstract The objective of this study was to evaluate weight gain, feed intake, and feed efficiency in beef heifers while consuming two diets divergent in energy density. Fifty-four yearling Angus and Angus cross heifers (initial body weight = 307.5 ± 30.35 kg) were subject to sequential intake trials using SmartFeed individual intake measuring technology (C-Lock Inc., Rapid City, SD) in drylot pens at the Oklahoma State University Range Cow Research Center. The first intake period (HAY) was initiated on July 15 (d -21) with a 21-d adaptation period and a 70-d intake period. Bermudagrass hay (10.3% CP, 2.0 Mcal ME, and 56% TDN) and a salt/mineral/vitamin supplement was offered on an ad libitum basis throughout the experiment. The second intake period was initiated on d-92 and included 14 d of adaption with 42 d of data collection. A mixed diet (12.4% CP, 45% NDF, and 65% TDN) consisting of 50% chopped bermudagrass hay, concentrate feeds, and a liquid supplement containing minerals and vitamins (TMR) was offered on an ad libitum basis. In both periods, weights were recorded every 14 days, and ADG was calculated by regressing body weight over time. Dry matter intake (DMI) and residual feed intake (RFI) were calculated at the end of each feeding period. Statistical regression equations were used to predict RFI for both HAY and TMR, including average metabolic body weight and average daily gain for each of their respective trials. For RFI, cattle were classified as inefficient, average, or efficient based on 0.5 SD away from the mean RFI score. Descriptive statistics for HAY evaluated traits of DMI, ADG, and RFI (means ± SD) were 5.49 ± 0.84 kg, 0.15 ± 0.16 kg, and 0.0 ± 1.01 kg, respectively. Means ± SD for DMI, ADG, and RFI for TMR were 9.54 ±1.26 kg, 1.45 ± 0.38 kg, and 0.0 ± 0.15 kg. Spearman rank correlations for DMI, ADG, and RFI were 0.35 (P = 0.0089), 0.16 (P = 0.2367), and 0.52 (P < 0.0001), respectively. This initial study suggests that intake of hay and a mixed diet may be modestly correlated, while there is no relationship of weight gain when consuming forage and weight gain consuming a mixed, processed diet.

Effectiveness of vegetative filter strips for sediment control from steep construction landscapes

Sediment carried off-site by runoff poses a significant water quality threat to receiving water bodies. Best management practices (BMPs) are commonly applied to reduce the impact of runoff on surface water quality. Vegetative filter strips (VFS) are low-cost BMP that often provide significant runoff mitigation in agricultural and urban settings with moderate slopes (<15 %). However, the runoff mitigation effectiveness of VFS under steep, short slopes like those typical of roadway construction is unknown. This study aimed to systematically investigate the efficacy of short (<5 m) and sparse VFS on steep slopes (>15 %). Field experiments were conducted on a steep hillslope (3H:1V, 33.3 %) where three VFS lengths (1.52 m, 3.05 m, and 4.57 m) were monitored during runoff events (10-y, 6-hr) to measure runoff delivery ratio (RDR) and sediment delivery ratio (SDR). The experimental results showed reductions of 60–98 % in runoff and 73–98 % in sediment, confirming the potential efficiency of this BMP in high-sloped conditions like a roadway construction site under certain conditions. The Vegetative Filter Strips Modeling System (VFSMOD) was successfully tested against the observed field values (NSE = 0.98 and 0.89 for RDR and SDR, respectively) and used to explore more scenarios. A wide range of roadway construction settings (VFS size, slope, vegetation density) was investigated with VFSMOD, and two simplified statistical regression equations and nomographs were developed to quantify RDR and SDR based on the simulation results. The proposed equations simplify the complexity of VFS design and can be readily generalized to other locations (rainfall/runoff, soil, vegetation) after reparametrizing coefficients in regression equations based on VFSMOD simulations to obtain new nomographs as a design aid to determine the minimum buffer length for a particular site.

Ambient background and quality reference values for trace metals in soils from Algeria

The establishment of the reference ambient background concentrations (ABCs) and quality reference values (QRVs) for trace metal (TM) concentrations in soils are required for the environmental assessment and any implementation of a protective action. This information is lacking for soils of the eastern Mitidja plain, which is an important agricultural production area in Algeria. Data for the aqua regia extractable Cd, Cr, Cu, Fe, Ni, Pb and Zn concentrations from 180 composite topsoil samples taken across the Mitidja plain in a stratified random pattern were statistically analysed. Descriptive statistical methods and linear regression equations were applied to determine the upper limit of the ABCs for the TMs. After removal of outliers, the derived QRVs were: Cd 0.24, Cr 62.1, Cu 99.3, Fe 45 590, Ni 47.7, Pb 33 and Zn 115 mg/kg. Iron is a macro element in the soils, but is included as its concentration can be used to normalise the concentrations of the other elements. The derived QRVs are similar or less than those reported for other regions of the world, apart from Cu, where a wide range (36 to 206 mg/kg) is reported. These reference values can be used to identify areas that may require follow-up surveys or to identify priority sites for decision making.

Artificial neural network algorithms to predict the bond strength of reinforced concrete: Coupled effect of corrosion, concrete cover, and compressive strength

• The bond strength between steel and concrete was predicted using artificial neural network (ANN) models. • Softplus activation function provided an accurate prediction in comparison to Sigmoid and ReLU functions. • Bond strength is predominantly influenced by the level of steel corrosion. Degradation of the bond between reinforcement steel bars and concrete poses a huge challenge to the design of sustainable infrastructure. In this study, an initial effort was made to develop and apply Artificial Neural Network (ANN) models to predict the bond strength between steel reinforcement and concrete. To assess the efficiency of ANN under a case of limited experimental data, the ANN models were activated through Softplus, Rectified Linear unit (ReLU), or Sigmoid functions and their results were compared. The experimental/test data used in the modeling study only covered corrosion levels from 0 to 20 % of the reinforcement bars' weight, concrete compressive strengths of 23 and 51 MPa, and concrete covers ranging between 15 and 45 mm. A comparison was made between the bond strength values predicted by the ANN models, linear/non-linear statistical regression equations, and other analytical equations available in the literature. The model results indicated that the bond strength was predominantly affected by the level of corrosion (in comparison to the other parameters). Moreover, the ANN(Softplus) model with a mean squared error (J) of 2.89 and a coefficient of determination (R 2 ) of 96 % demonstrated a more accurate prediction of the bond strength in comparison to the ANN(Sigmoid), ANN(ReLu), and statistical regression models.

Parked in the Park: pandemic traffic congestion in Acadia National Park Based on Regression Analysis

Contemporarily, the COVID-19 has strongly affected plenty of fields including travel industry. This paper takes Acadia National Park in the United States as a reference to investigate the impact of the epidemic on the number of visitors to the park as well as analyze the impact of the change in visitor numbers on businesses in and around the park. To be specific, the data from the national park's official website and hotels near the park on AirDNA will be used to determine the impact on the park and the surrounding economy based on statistical methods and regression equations. According to the results, the number of visitors to Acadia National Park has skyrocketed due to the epidemic, which led to an increase in demand for nearby hotels and hired staff. The increase in visitors also had a negative impact on the sustainability of Acadia National Park, visitor experience, and flora and fauna habitat. To address the issue, the corresponding solutions to are proposed. These results shed light on guiding further explorations for outdoor tourism areas in the post-epidemic era.

Analysis of a High-Strength Steel SMAW Database

Recently, Dr. Glyn M. Evans posted a large shielded metal arc (SMA) weld metal (WM) database on the ResearchGate website (researchgate.net). This database contains more than 950 WM compositions, along with their respective WM tensile and Charpy V-notch (CVN) impact properties. In particular, the CVN impact properties list the test temperatures that achieved 28 and 100 J impact energy for each WM composition. While the availability of this SMA WM database is a valuable and rare gift to the welding community, how could the welding community analyze this database to gain valuable insights? This paper utilizes a constraints-based model (CBM) as a simple and effective framework to organize and analyze this very large Fe-C-Mn SMA WM database. A CBM is built on the metallurgical principle that one needs to lower relevant solid-state phase transformation (i.e., austenite decomposition) temperatures to improve WM strength and fracture toughness while simultaneously reducing carbon content and Yurioka’s carbon equivalent number (CEN) to improve the weldability of high-strength steels. To this end, a CBM identifies and simultaneously solves several statistical (regression) equations that relate the chemical composition of high-strength steel WM with Yurioka’s CEN and selected solid-state phase transformation temperatures related to austenite decomposition. The results of the current effort demonstrate that the analysis of Evans’s shielded metal arc welding database using a CBM as a framework reaffirms that controlling carbon content, the value of the CEN, and calculated solid-state phase transformation temperatures, particularly the difference between the calculated Bs (bainite-start) and Ms (martensite-start) temperatures, is critical to developing and identifying high-performance, high-strength steel welding electrodes. A dual approach that manipulates the contents of principal alloy elements such as C, Mn, Ni, Cr, Mo, and Cu, and adds controlled amounts of Ti, B, Al, O, and N, appears to offer the best means to lower relevant solid-state phase transformation temperatures to produce high-strength and high-toughness WMs.

Prediction of bursting strength of the nylon parachute fabrics

PurposeParachutes are equipment that is repeatedly used as and when needed. Some of them are used for as many as 60 jumps. The property of the canopy fabric gets deteriorated with use. It is evaluated by destructive tensile and bursting strength. This study aims to focus on the nondestructive evaluation of the canopy fabric's fitness by testing air permeability and relating it with bursting strength. Predictor equations were developed to determine bursting strength from air permeability values.Design/methodology/approachANOVA techniques and statistical regression equations were formed.FindingsA series of samples containing five parachutes fabrics was used seven times, and their air permeability and bursting strength were determined to find the extent to the effect of reuse of parachute fabrics on their bursting strength and air permeability determination. It was found that there was a progressive drop in bursting strength and an increase in air permeability. An investigation of the extent of determination in terms of bursting strength and an increase in air permeability following the sense of five different types of parachute fabrics is reported.Research limitations/implicationsThe work focuses on the prediction of bursting strength to textile materials only and may not apply to other materials like membranes and sheets. The process of determining air permeability is relatively simpler and faster.Practical implicationsThe bursting strength can be predicted for used parachutes, which are otherwise subjected to destructive testing.Social implicationsThe men using the parachutes can be assured of the superior flawless performance of the parachute as equipment and also contribute to the saving of resources due to nondestructive testing, 100% evaluation of all parachutes is possible.Originality/valueThis article describes the nature of the test procedure and discusses the means of introducing it to users of parachutes. It is accepted that the method must undergo field evaluation and possible modification before it can become a routine tool of parachute using organizations.

Particle Swarm Optimization for Predicting the Development Effort of Software Projects

Software project planning includes as one of its main activities software development effort prediction (SDEP). Effort (measured in person-hours) is useful to budget and bidding the projects. It corresponds to one of the variables most predicted, actually, hundreds of studies on SDEP have been published. Therefore, we propose the application of the Particle Swarm Optimization (PSO) metaheuristic for optimizing the parameters of statistical regression equations (SRE) applied to SDEP. Our proposal incorporates two elements in PSO: the selection of the SDEP model, and the automatic adjustment of its parameters. The prediction accuracy of the SRE optimized through PSO (PSO-SRE) was compared to that of a SRE model. These models were trained and tested using eight data sets of new and enhancement software projects obtained from an international public repository of projects. Results based on statistically significance showed that the PSO-SRE was better than the SRE in six data sets at 99% of confidence, in one data set at 95%, and statistically equal than SRE in the remaining data set. We can conclude that the PSO can be used for optimizing SDEP equations taking into account the type of development, development platform, and programming language type of the projects.

Optimized process parameters for production of Chlorella pyrenoidosa biomass and lipids using response surface methodology

To utilize microalgae as a valuable bioresource, the effect of growth conditions on their yield and biochemical product production must be determined. We employed response surface methodology in the function of a Box-Behnken design to derive the functional relationship between environmental conditions and the microalgal biomass and lipid content of Chlorella pyrenoidosa. C. pyrenoidosa was grown in BG-11 medium from 20 to 30 °C, 100 to 200 μmol photons m−2 s−1 light intensity, and 7.5 to 9.5 pH. A total of 17 experimental runs were analyzed for the effect of three parameters of interest (temperature, light intensity, and pH). The statistical model and regression equation of the program predicted the optimal conditions for accumulating the highest biomass and lipid content. As predicted by the model, the optimized conditions of 24.70 °C, 178 μmol photons m−2 s−1 of light intensity, and a pH of 8.74 produced the maximal experimental results of C. pyrenoidosa algal biomass and lipid content, 2.90 g L−1 and 27.12%, respectively. Under optimal conditions, C. pyrenoidosa synthesized 53.25% of total polyunsaturated fatty acids, 23.53% of total monounsaturated fatty acids, and 23.07% of saturated fatty acids; α-linolenic acid was the most abundant fatty acid (39.93%) and is an essential ω-3 polyunsaturated fatty acid for commercial algal products. The optimized process parameters obtained in this work have provided a basis for further large volume photobioreactor culturing for biomass and lipid content.

A morphometric evaluation of the humerus in relation to shoulder arthroplasty

BackgroundThe shoulder prosthesis design resulted in significant reductions in loss of work force and mortality if industries manufacturing prostheses design and manufacture prostheses according to the morphometric data of the population of their own country.ObjectivesThis morphometric evaluation was proposed to find a correspondence of the anthropometric parameters measured at the proximal and distal humeral parts of humerus.Materials and MethodsThe claims of this study were appraised on radiographs of dry bones. In this study, (20) dry bones available in the anatomy museum of the department of human anatomy/College of medicine/ Al-Nahrain University were used. The parameters measured on the X-ray of dry bones at the proximal and the distal parts of humerus were statistically compared and evaluated.ResultsSignificant and high significant correlations found for the parameters provide regression equations for them; these equations allow extraction of missing limit from an available one. Accordingly, statistical regression equations has been concluded for the parameters of the proximal part of the x-ray of dry bone from the values obtained from comparable parameter of the distal part of the x-ray of dry bone.ConclusionsThe statistical evaluation upraised the theory of "recapitulation" and introducing a scheme for mathematic reprocessing of approximate estimation for the proper choice of humeral head arthroplasty depending on plain X-imaging to the proximal and distal part of the humerus.

Dynamic Analysis of Arbor Species Germinated from Soil Seed Bank under Watering Strategies Driving

The watering gradient is an important factor affecting the composition and structure of the soil seed bank community. The germplasm resources in the soil seed bank have their corresponding water germination intervals. The soil seed bank of Tianjin Guangang Forest Park was selected as the research object and different watering gradients were set to test. Through statistical analysis and regression equation model, we could analyse and evaluate the morphological features of arbor species in soil seed bank under different watering gradients and find out the soil moisture interval which was most suitable for the restoration effect of soil seed bank in Tianjin. The study results showed that: (1) the watering gradient was set to 1000 mL, the arbor population in the experimental dish had the highest plant height, the largest canopy cover and the best arbor community; when the water content was less than 1000 mL, it couldn’t meet the water demand of plant growth and the number of seed germination was greatly reduced; while above 1000mL, the soil was in flooded state, inhibited the germination of some seeds; (2) under the same watering gradients, the average height and canopy cover of Fraxinus chinensis species showed great advantages compared with Robinia pseudoacacia L. and Koelreuteria paniculate species; (3) the average height and canopy cover of three arbor species were positively correlated by regression analysis, and when the watering gradient was set to 1000mL, its correlation was best.

Assessment of the risk of osteoporotic vertebral fractures of the 2nd and 3rd degree in women in reproductive age and men under 50 with type 1 diabetes mellitus

Rationale : Patients with type 1 diabetes mellitus (DM1) have higher rates of any fractures than those without diabetes. Aim : To identify significant predictors of the osteoporotic vertebral fractures of the 2nd and 3rd degrees and to develop a statistical model for the risk assessment in DM1 patients. Materials and methods : 97 DM1 patients and 77 control aged below 50 participated in this cross-sectional study. Multiple logistic regressions were used to assess the risk of osteoporotic vertebral fractures of the 2nd and 3rd degrees in DM1 patients. Results : Patients with DM1 had a statistically significantly lower bone mineral density (BMD) (Z-criterion) in all areas of the axial skeleton, compared to that in the control group (p&lt; 0.001). Osteoporotic vertebral fractures were significantly more frequent (p=0.0385) in DM1 patients than in the controls. The subgroup of DM1 patients with osteoporotic vertebral fractures (n=8) had higher daily insulin requirement per kg of bodyweight compared to that in the subgroup of the patients without fractures (n=89; р=0.029), as well as lower axial BMD (Z-criterion L1–L4, p=0.002; femoral neck Z-criterion, p&lt; 0.001; proximal femoral Z-criterion, p=0.002). We developed a statistical regression equation model including two parameters: 1) presence or absence of low femoral neck BMD based on the dual X-ray absorptiometry, 2) daily insulin requirement per kg of bodyweight. Conclusion : The statistical model has the sensitivity of 87% and specificity of 75% to predict that the DM1 patients would have low or high risk of osteoporotic vertebral fractures of the 2nd and 3rd degree.

Evaluation and Analysis of Life Quality of Residents in Yunnan Province

This paper evaluates the life quality of residents in Yunnan from six aspects: economic development, social harmony, living standard, democracy and legal system, culture and education, resources and environment, and compares with the life quality of Chinese residents. Finally, according to the statistical regression equation, this paper analyzes the impact of economic development on the life quality of residents in Yunnan province.

Implementation Practices of Public Procurement Plans and Policies

Public procurement issues have drawn many concerns and became a public debate because the rules and regulations are constantly reformed and restructured. Public procurement contributions in the world economy are very significant. In Indonesia, according to the Ministry of Finance, public procurement spending for 2009 was 33.4 percent of total government spending. This study aims to determine implementation practices of Public Procurement Plans or Rencana Umum Pengadaan (RUP) in Bitung City. The multiple statistical regression equation is used to process data from 47 respondents from regional apparatus task forces (SKPD) and the results show that the influence of independent variables (X) on dependent variable (Y) is 87.3 percent (R2), while 12.7 percent is influenced by other factors. The result of F-test shows 36.231&gt; F- table 3.21. The calculated probability value is 0.000 which is smaller than 0.05. The partial test (t-test) from 9 independent variables (X) identifies 7 variables that have significant effects on the scores of RUP implementation (Y). The overall results of t-test show that the implementation practices of public procurement plans have significant effects of the higher compliance rates on the output of goods and services.

Performance analysis of the mechanical behaviour of seams with various sewing parameters for nylon canopy fabrics

PurposeThe purpose of this paper is to make available to the parachute industry tools to predict behaviour of certain textile materials. In addition to this, it is desired to reveal and explain the basic requirement criteria for proper textile material selection. The strength of an assembly as a whole is directly dependent on the strengths of the various joints and seams required to assemble the larger structure. Keeping in mind the complex problem of parachute construction, this research seeks to enlighten the industry about the performance of seams in nylon woven canopy fabrics. Five factors have been studied: different types of weave (plain, rip-stop and twill), density (number of stitches per centimetre), different rows of stitches with lapped seams, different types of stitches (lock stitch, chain stitch and zig-zag) and seam direction (warp, weft and bias direction). Two responses have been analysed, the seam breaking force and the seam efficiency (per cent ratio of seam strength to fabric strength). The test results were subjected to an analysis of variance and the seam strength proved to vary significantly not only with the primary parameters, but with the interactions of the primary parameters as well. That is seam strength (and seam efficiency) changes with each primary parameter but it changes in a different manner when other parameters change. Multiple regressions have been used to construct preliminary predictor equations for seam strength and efficiency, and investigations to provide better equations are in progress.Design/methodology/approachANOVA techniques and statistical regression equations were formed.FindingsThe work has concluded that twill weave 9 with chain stitch has the maximum seam strength, which makes canopies made with 2/1 twill weave and stitched with lapped seam with four rows of chain stitch optimum for heavy supply droppings with a single use parachute(s). It is evident from the results that twill weave with lock stich has the maximum seam efficiency. This makes the canopies stitched with twill fabric, constructed with lapped seams and four rows of stitches ideal for parachutes to be used multiple times. The brake parachutes on aircrafts and parachutes used by sky divers and air combat soldiers can use parachutes whose canopies can be used many times made out of the above mentioned weave and stitch specification.Originality/valueOriginal work was conducted from the woven fabrics.

Probabilistic innovative solar irradiation estimation

Often, solar irradiation estimation from simply measureable sunshine duration data is achieved by linear and nonlinear regression expressions, which depicts the statistical regression equation between the sunshine duration and solar irradiation data. These models are based on statistical parameters only, and therefore, the estimations do not take into consideration deviations from the averages. In this paper, an innovative probability methodology is proposed for solar irradiation estimation, which is based on the cumulative probability distribution functions (CDFs) of the sunshine duration and solar irradiation data. For this purpose, the sunshine duration data are converted to match the solar irradiation data cumulative distribution (CDF), which is then employed for the solar irradiation estimation. Hence, the CDFs of sunshine duration and solar irradiation data are preserved, and rather than global average, pointwise solar irradiation estimations are achieved. The application of the innovative probabilistic methodology is presented for seven solar irradiation measurements stations from different climate regions in Turkey. It is shown that the suggested method over performs other methodologies with very high improvements. Copyright © 2016 John Wiley & Sons, Ltd.

Performance of classic multiple factor analysis and model fitting in crop modeling

Multivariate statistical analysis and regression, which are typical methods for crop modeling, have direct influence on the accuracy of model, but the applications of these methods usually depend on experiences. In this research, the performances of some common methods of statistical analysis and regression model were compared and verified, in order to avoid the blindness in crop modeling. The monitoring data of growth environment and photosynthesis of tomato, pumpkin and cucumber were obtained by PTM-48A. For the object variable of CO2 exchange rate, selectivity on the main environmental factors by correlation analysis and path analysis were quantitatively compared. The performances of four kinds of multivariate binomial regression equations were compared using a comprehensive aggregative indicator, and the effectiveness of modeling was verified with the selected optimized multivariate statistical analysis and regression equation. Results showed that path analysis was more comprehensive and effective than correlation to discrimination of the variables, especially the path analysis ruled out some suspected independent variables which were not really independent, and the pure quadratic was more suitable to crop modeling because of its simple structure and high accuracy when the data set was small. The conclusion of this research has a general applicability, and offers a useful reference and guide for the other study and application of crops’ modeling. Keywords: crop model, multivariate statistical analysis, path analysis, regression, comparison DOI: 10.3965/j.ijabe.20160902.1023 Citation: Jiang Z H, Zhang J, Yang C H, Rao Y, Li S W. Performance of classic multiple factor analysis and model fitting in crop modeling. Int J Agric & Biol Eng, 2016; 9(2): 119－126.