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It is a fact that the performance of most crop genotypes varies across environments due to genotype‐environment interactions. Sharing knowledge about maize genotypes that are well‐adapted and high‐yielding in specific environments is essential for smallholder farmers to boost their productivity. A field trial was established in three locations during the 2019 and 2020 growing seasons to identify and select better‐adapted high‐yielding maize genotypes for production. A total of 10 improved varieties were evaluated using a randomized complete block design with three replications. The AMMI analysis of variance showed grain yield significantly (p ≤ 0.001) influenced by genotype‐environment interaction. This suggests that different genotypes showed varying grain yield performance across environments, implying the opportunity to choose effective genotypes suitable for both specific and broad adaptability. Moreover, the combined analysis of variance showed that genotype‐environment interaction significantly influenced plant height, ear height, dry biomass yield, and hundred‐kernel weight. The sum of squares factor explained that grain yield variation was contributed by the genotype (71.22%), followed by the environment (16.54%) and the genotype‐environment interaction effect (12.24%) of the total variation. Genotypes BH‐661, BH‐549, and BH‐546 demonstrated superior performance and stability, surpassing the national maize mean productivity and indicating broad adaptability. The AMMI analysis, GSI, and GGE biplot confirmed their robustness and high yield potential, especially for BH‐661 and BH‐549, making them optimal for variable conditions. Therefore, BH‐661 and BH‐549 are selected as stable genotypes for production in all agroecologies suitable for maize in the Dawuro zone. [ABSTRACT FROM AUTHOR]

When the thickness of a structure is reduced to decrease weight, it may experience structural vibration and disturbance. The use of passive patches is effective in addressing this issue when the loss factor is small or when space and weight are restricted. The greatest attenuation occurs when passive patches are used across the entire coverage area. However, passive patches of reasonable size must be affixed to ensure that they are effective in terms of cost and design. In this paper, the sum of squares' value for the bending mode shape is used to determine the location of a small passive patch to achieve vibration damping for multiple modes. Under the condition of forced vibration, the modal contribution of each mode is obtained. Using this contribution as a weight, the optimal position of the passive patch is determined as the maximum value obtained in the form of a linear combination multiplied by the curvature of the beam. Simulation and experiment were used to test the efficacy of the location determined for passive patches. It was determined that, depending on the location of the passive patch, the peak amplitude at the natural frequency of each mode decreased significantly, validating the effectiveness of the design method. [ABSTRACT FROM AUTHOR]

The variability, intensity, and distribution of rainfall have drawn a lot of interest globally and especially in nations where rainfed agriculture is the norm. This article uses rainfall data from the Rwanda Meteorology Agency for the years 1981 to 2021 to delineate and analyze rainfall variability and trends in the Kayonza District. The time series were grouped using the K-means clustering technique based on computed Euclidean distance, the total within-cluster sum of squares, and the elbow plot technique to determine the optimal number of clusters. The coefficient of variation measures was employed to analyze rainfall variability, while Sen's slope and the Mann–Kendall (MK) test were used, respectively, to find trends and changes in magnitude. The results indicated four near homogeneous zones named region one to four. The dry seasons indicated higher variability compared to rainy seasons and annual rainfall total with a variability of 128–142% over the southeastern part during June to August (JJA) season, while a variability of 16–48% was observed over most of the district during both annual and rainy seasons. It was further noted that the areas in the central part of the Kayonza District indicated a significant increasing trend at a significance level of 95% and above during January to February (JF), September to December (SOND), and on annual basis, while March to May (MAM) and JJA season exhibited no significant trend. The findings of this study are essential for creating adequate mitigation strategies to lessen climate change's effects on agriculture as well as other socioeconomic sectors. [ABSTRACT FROM AUTHOR]

As a statistical and computational technique, independent component analysis (ICA) is employed to separate the source variables into statistically independent components. ICA methods have received growing attention as effective data mining tools. In this paper, two novel ICA-based approaches are proposed to identify the clusters of variables. The identified clusters reduce the dimensionality of the data in a natural way. The first approach, namely "Estimated Mixing Coefficients," is based on the sum of squares of mixing coefficients, and the second approach, namely "Ranked R ¯ 2 ," uses the ranking pattern of R ¯ 2 of the original and reconstructed series at predefined threshold levels. The proposed techniques are applied to financial time series data to validate their effectiveness. The main focus of the study is on the clustering of multivariate time series datasets using two new proposed approaches based on independent component analysis. The internal and external structures of clusters are also explored using different metrics. Both proposed techniques are compared with some existing clustering techniques. The experimental evaluation results show that the performance of the proposed techniques is better than the existing techniques. [ABSTRACT FROM AUTHOR]

This paper considers the robust nonlinear mixed H2/H∞ output-feedback control problem for a class of uncertain polynomial systems. Generally, the solvable conditions of such nonlinear output-feedback control problems are nonconvex, whose computations are challenging. By using the semidefinite programming relaxation technique based on sum of squares (SOS) decomposition, the solvable conditions of above control problem are formulated in terms of state-dependent linear matrix equality (LMI), which can be effectively solved. This conversion effectively overcomes the computational difficulties caused by the non-convexity of output-feedback H∞ control design for nonlinear systems. Furthermore, the state-observer and the controller can be designed simultaneously through a single-step SOS condition and constructed in a simple analytical form, thus reducing the computational complexity in a certain degree. In the simulation, the nonlinear mass-spring-damper system is considered to illustrate the effectiveness and feasibility of the proposed approach. The results show that the proposed method not only guarantees the stability of the system, but also has good transient performance and robust performance. [ABSTRACT FROM AUTHOR]

Aiming at the problems of the traditional K-means clustering algorithm, such as the local optimal solution and the slow clustering speed caused by the uncertainty of k value and the randomness of the initial cluster center selection, this paper proposes an improved KMeans clustering method. The algorithm first uses the idea of the elbow rule based on the sum of squares of errors to obtain the appropriate number of clusters k, then uses the variance as a measure of the degree of dispersion of the samples, and selects k data points with the smallest variance and the distance greater than the average distance of the samples as the initial clustering center of the data set. Finally, combined with the "triangular inequality principle," the unnecessary distance calculation of the KMeans algorithm in the iterative process is reduced, and the algorithm operation efficiency is improved. The results show that the improved KMeans clustering algorithm is tested on the UCI data set. Compared with the traditional k-means algorithm and Canopy-KMeans algorithm, the accuracy and speedup ratio of the improved KMeans clustering algorithm are significantly improved, and the clustering quality is improved. [ABSTRACT FROM AUTHOR]

How to start from the fitness needs of people and effectively improve the precision of the supply of public fitness services everyone is an important issue that needs to be solved first at the current stage. This requires us to proceed from the reality, conduct accurate research, and find a method that can match the current problem. In this paper, taking basketball projects in national fitness as an example, by introducing a proposition about the development of small basketball events, the corresponding big data modeling and analysis methods are studied. The research methods and research objectives involved in this paper are based on the relevant parameters of the multivariate statistical model. First, the article introduces the calculation principle of the multiple linear regression model. We introduce the concept of variance inflation factor involved in this principle and carry out the modeling and analysis of big data based on this variable. In order to illustrate the application effect of big data in this kind of research, this paper introduces three different big data technologies, including immune selection optimization algorithm, particle swarm optimization algorithm, and Elman neural network, to predict and analyze the variance inflation factor (VIF) corresponding to the small basketball project. The analysis results show that the Elman network exhibits certain advantages in terms of computing convergence time. And, as the number of calculation steps increases, the superiority of the Elman network is more obvious. As far as the prediction performance is concerned, the square of the correlation coefficient corresponding to the immune selection optimization algorithm is the largest and the sum of the squares of the residuals is the smallest, showing superior prediction performance. [ABSTRACT FROM AUTHOR]

The rough-strips energy dissipator (R-SED) is applied to the bottom of the spillway bend and can play the role of energy dissipation and flow stabilization. In this study, based on 18 sets of orthogonal tests and the principle of dimensional analysis, a multifactor influence model of R-SED's energy dissipation rate was proposed. A dimensionless factor k was introduced, which can reflect the comprehensive characteristics of the geometric dimensions of R-SEDs. The multifactor influence model of the energy dissipation rate considered nine factors, including bend radius of curvature Rc, bend width B, flow velocity of the bend inlet v , R-SED's average height hL, R-SED's arrangement angle θ, R-SED's arrangement spacing ∆L, fluid density ρ, dynamic viscosity coefficient μ, and gravitational acceleration g. The residual sum of squares of the model (RSS) was 6.6% and the correlation coefficient R was 83.2% (>80%), indicating the universality and feasibility of the model. The independent variables of the multifactor model of the energy dissipation rate were ranked according to the Pearson value in descending order: (ΔL/Rc) > (θ) > (B/Rc) > (hL/Rc) > (1/Fr2). This indicates that R-SEDs' layout parameters showed larger effects on the multifactor model of the energy dissipation rate, compared with the engineering layout parameters of the spillway. The maximum relative error between the predicted value of the multifactor model and the measured value of the validation group was 6.28%, indicating good agreement. In the orthogonal tests, scenario 5 had the highest energy dissipation rate (44.83%) with k = 0.023; scenario 16 had the largest k value (0.043), with an energy dissipation rate of 40.78%. The multifactor influence model of R-SEDs' energy dissipation rate proposed in this paper was a semi-theoretical and semi-empirical calculation formula, which can provide reference and support for similar practical engineering designs. [ABSTRACT FROM AUTHOR]

Aiming at the problem that online video learning resources of business English are scattered and the learners are inefficient in acquiring learning resources, this paper designed a business English learning system based on the EDIPT model. In addition, aiming at the problem of multifeature fusion between low-level features and high-level semantic features in video scenes, this paper proposes a multi-modal video scene segmentation algorithm based on a deep network. By minimizing the square sum of distances in the time period, the shots are clustered, and finally, the semantic scene is obtained. The experimental results show that the algorithm has good performance in classification accuracy and can effectively segment video scenes, which is helpful for users to improve their comprehensive business English skills. [ABSTRACT FROM AUTHOR]

The variation law of satellite clock bias (SCB) can be regarded as a grey system because the spaceborne atomic clock is very sensitive and vulnerable to many factors. GM (1,1) model is the core and foundation of the grey system, which has been highly valued and successfully applied in SCB prediction since its production. However, there are still some problems to be further studied such as the lack of stability of its prediction effect in practical application. In view of this, an improved GM (1,1) model by optimizing the initial condition has been proposed in this paper so as to increase the prediction performance. The new initial condition is obtained by the weighted combination of the latest and oldest components of the original clock bias sequence. And the weight values of these two components are acquired from a method of minimizing the sum of squares of fitting errors. We adopt GPS rapid precision SCB data provided by the International GNSS Service (IGS) for 15 mins, 30 mins, 1 h, 3 h, 6 h, 12 h, and 24 h prediction experiments. The results show that the improved GM (1,1) model is effective and feasible, and its prediction accuracy and stability are significantly better than those of the traditional GM (1,1) model, ARIMA model, and QP model, even for the SCB signal with obvious fluctuation. [ABSTRACT FROM AUTHOR]

The orifice flow model is generally established based on the turbulent state, and the flow discharge is considered to be a constant value. In fact, the flow discharge has obvious nonlinearity under the condition of small opening and small pressure difference of the valve port, which makes the electro-hydraulic servo system more difficult to control. In order to improve the nonlinearity of the flow discharge, the paper designed different spool profile, such as the arc combined spool profile, the power curve combined spool profile, the index curve combined spool profile, and the mixed curve combined spool profile, and analyzed the flow discharge through CFD simulation and experiment. The results show that the flow discharge of the mixed curve combined spool is more linear and the residual sum of squares is the smallest, which is 2.56 × 10−3 under CFD numerical simulation and 3.26 × 10−4 under experiment. The combined spool has better linear characteristics, which can improve the control performance of the electro-hydraulic servo valve. [ABSTRACT FROM AUTHOR]

Due to the complex dynamic interactions of the vehicle-track system in the turnout of high-speed railway stations on a ballasted track, the geometric position of the line changes rapidly. In order to improve the stability of the line, the ballasted track in the turnout area of the stations can be replaced by a ballastless track. Based on the engineering background of laying ballastless track in the turnout area of Zhonglan's high-speed railway station, this paper studies the subgrade reinforcement scheme, subgrade settlement prediction, and ballasted-ballastless track transition section setting. The result shows that, in order to meet the requirements of laying ballastless track in the turnout area, a reinforcement scheme of subgrade impact rolling, surcharge preloading, and closed water isolation on the subgrade surface is proposed. Based on the commonness and individuality of the grey Verhulst, hyperbola, and Deng Yinger prediction model, this paper optimizes the combination of the three models. The sum of squares of the minimum errors of the combined model is used as the objective function to solve the optimal weighting coefficient, and in this way the combined prediction model is constructed. Through the prediction comparison, we found that the accuracy of the three-model combination prediction model is better than any single- or two-model combination, and its adaptability and reliability are stronger and more reliable. This dynamic analysis model of vehicle tracks was established to carry out the dynamic response analysis under the conditions of fastener stiffness grading transition and ballast glue grading curing ballast bed transition. The results show that the dynamic response indexes under the two transition modes meet the requirements for the smooth running of trains. Due to the convenience of fastener stiffness transition construction and easier replacement, this measure is recommended. [ABSTRACT FROM AUTHOR]

The dynamic balance is a significant issue for the nonlinear dynamic characteristics of the spindle rotor system. However, there is a problem that the dynamic balance is lacking detailed study on optimization method. In the paper, a modal dynamic balance optimization model of spindle rotor system is proposed, which can intend to improve the accuracy of spindle rotor system modal dynamic balance. Because the multiorder unbalance components are the main spindle rotor system mode shapes, the particle swarm optimization (PSO) method is adopted. The sum of squares of residual vibration after balancing is taken as the optimization objective, and the correction is presented as the optimization variable in the optimization model. The optimal correction weight of every unbalance component is calculated through a modal matrix equation of PSO. The vibration amplitude that is greatly reduced after optimization balance is presented under different conditions. The balancing effect shows a better dynamic characteristic than that of traditional methods. And the fluctuation range of the axis track of the rotor system also shows reductive phenomenon. The proposed optimization spindle rotor system model is well verified through experiments. It can contribute a theoretical optimization foundation for available dynamic balance in spindle rotor system. [ABSTRACT FROM AUTHOR]

The limited instantaneous overload available and the curved trajectory lead to adaptivity problems for the proportional navigation guidance (PNG) of a guided mortar with a fixed-canard trajectory correction fuze. In this paper, the optimization of a PNG law with gravity compensation is established. Instead of using the traditional empirical method, the selection of the proportional navigation constants is formulated as an optimization problem, which is solved using an intelligent optimization algorithm. Two optimization schemes are proposed for constructing corresponding optimization models. In schemes 1 and 2, the sum squared error between the impact point and target and the circular error probability (CEP), respectively, are taken as the objective function. Monte Carlo simulations are conducted to verify the effectiveness of the two optimization schemes, and their guidance performance is compared through trajectory simulations. The simulation results show that the impact point dispersion can be efficiently reduced under both proposed schemes. Scheme 2 achieves a lower CEP, which is approximately 2.9 m and 2.4 times smaller than that achieved by scheme 1. Moreover, the mean impact point is closer to the target. [ABSTRACT FROM AUTHOR]

Since fare discounts have been regarded as an effective economical measure to increase passenger flow, it is helpful for local governments and transit operators to understand its impact on ridership. Taking Xiamen, China, as an example, this study uses transaction data to analyze the changes of weekday daily metro ridership after the opening of Xiamen Metro Line 1. At the initial stage of operation of Xiamen Metro Line 1, there are three preferential schemes: discount per trip, money reduced per trip, and discount after reaching the accumulated fare. Therefore, the algorithm of the iterated cumulative sums of squares is introduced to identify structural change points of the time series of daily ridership, which varies according to the type of ticket. The effects of different fare discounts on total ridership and ridership varied by ticket types are analyzed by the regression discontinuity method. The results show that the dates of structural change points are well-matched with the start and end dates of preferential schemes. Each preferential scheme has its own benefited groups. During the fare discount period, the number of passengers gradually increased. But after the cancellation of the favorable preferential scheme, the number of passengers decreased sharply. By understanding the impact of fare discounts on ridership in Xiamen, China, several metro ticketing policy recommendations are put forward, including raising the focus on E-Tickets, formulating more attractive preferential measures to promote the mode conversion of private cars and vehicles to metro, paying attention to high-frequency passengers, and seeking common subsidies from the financial industry to achieve a win-win situation. In addition, the analytical framework proposed in this study can be used to evaluate the effectiveness of other transportation policies in the future. [ABSTRACT FROM AUTHOR]

The study was conducted to estimate the effects of genotype, environment, and genotype × environment interaction on grain yield and yield-related traits and to identify stability genotype. At six environments, twenty-four cowpea landraces and one check were evaluated in a 5 × 5 triple lattice during the 2019 cropping season. Data were collected on yield and yield-related traits. The analysis of variance for each environment and across environments showed significant differences among genotypes, environments, and GEI for most traits including yield. Environment, genotype, and GEI showed 27.45%, 20.9%, and 49.55% contribution to the total sum of squares, respectively, for grain yield. This indicated that the environments were diverse and most of the variation in grain yield was caused due to interaction and environmental means. G24 (2632 kg ha−1) and G16 (2290 kg ha−1) were the highest yielder and stable genotypes with mean grain yields above the grand mean (2049.28 kg ha−1) and standard check (2273 kg ha−1). G24 and G16 were the most stable genotypes according to cultivar superiority, Wricke's ecovalence, regression coefficient, and devotion from regression stability models. [ABSTRACT FROM AUTHOR]

The notion of spherical fuzzy sets (SFSs) is one of the most effective ways to model the fuzzy information in decision-making processes. The sum of squares of membership, neutral, and nonmembership degrees in SFSs lies in the interval [0, 1] and accommodates more uncertainties. Henceforth, in this article, the idea of spherical cubic fuzzy sets (SCFSs) is introduced, which is the generalization of SFSs. Spherical cubic fuzzy set is the combination of spherical fuzzy sets and interval-valued spherical fuzzy sets. The membership, neutral, and nonmembership degrees in an SCFS are cubic fuzzy numbers (CFNs). Consequently, this set outperforms the pre-existing structures of fuzzy set theory. Moreover, some fundamental operations for the comparison of two spherical CFNs are defined such as score function and accuracy function. Further, several new operations through Dombi t-norm and Dombi t-conorms are characterized to get the best results during the decision criteria. Furthermore, spherical cubic fuzzy Dombi weighted averaging (SCFDWA), SCFD ordered weighted averaging (SCFDOWA), SCFD hybrid weighted averaging (SCFDHWA), SCFD weighted geometric (SCFDWG), SCFD ordered weighted geometric (SCFDOWG), and the SCFD hybrid weighted geometric (SCFDHWG) aggregated operators are discussed, and their characteristics are examined. In addition, some of the operational laws of these operators are defined. Also, a decision-making approach based on these operators is proposed. Since the proposed methods and operators are the generalizations of the existing methods and operators, therefore, these techniques produce more general, accurate, and precise results as compared with existing ones. Finally, a descriptive example is given in order to describe the validity, practicality, and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In recent years, aquaculture of the African catfish Clarias gariepinus (Burchell 1882) has experienced rapid growth in Africa, Asia and Europe. In order to improve its rearing systems, it is imperative to determine the individual growth patterns of the species. Individual fish growth is relevant when evaluating commercial viability, planning the time of harvest or projecting profits. In this study, we analysed the growth data of African catfish juveniles reared in an indoor freshwater recirculation aquaculture system and fed five different commercial aquafeeds over a period of 111 days. We fitted five nonlinear growth models—von Bertalanffy, Gompertz, Brody, Richards and logistic—to the weight data and applied the Akaike information criterion, the sum of squares and the maximum likelihood to determine the model that best describes the growth data of the species under the controlled culture conditions. According to the goodness‐of‐fit criteria, the Gompertz model was consistently the one that best described the growth of African catfish. The F‐test showed significant differences (p = 0.05) for one of the experimental feeds (T1,2) that produced the superior growth in terms of rate and biomass. [ABSTRACT FROM AUTHOR]

A Pythagorean fuzzy set is the superset of fuzzy and intuitionistic fuzzy sets, respectively. Yager proposed the concept of Pythagorean fuzzy sets in which he relaxed the condition that sum of square of both membership degree and nonmembership degree of an element of a set must not be greater than 1. This paper introduces two new techniques to solve L R -type fully Pythagorean fuzzy linear programming problems with mixed constraints having unrestricted L R -type Pythagorean fuzzy numbers as variables and parameters by introducing unknown variables and using a ranking function. Furthermore, we show the equivalence of both the proposed methods and compare the solutions obtained by the two techniques. Besides this, we solve an already existing practical model using proposed techniques and compare the result. [ABSTRACT FROM AUTHOR]

Due to many nondamage factors such as temperature, humidity, carbonation, and corrosion effects on natural frequency, the key problem of the application frequency-based method to detect damage is to reveal the rules of these factors affect natural frequency and further to eliminate their effects. The long-term characteristics of reinforced concrete structures require a lot of attention, especially in corrosive environment. In this paper, an experimental investigation was conducted to study the deflection and natural frequency of reinforced concrete beam in a marine environmental chamber for six corrosion stages (accelerated corrosion for 0, 20, 40, 70, 100, and 140 days). The experimental results demonstrated that deflection increases with corrosion time, while natural frequency decreases with corrosion time. Based on the accelerate corrosion test data of reinforced concrete beams, the general expression of the relationship between corrosion depth and natural frequency has been established through the fitting curve method. The polynomial model has been selected for establishing the relationship between steel corrosion depth (including the main reinforcement and stirrup) and natural frequency. The reason for selecting the polynomial model is that the sum of squares due to error (SSE) is closer to 0 and the coefficient of multiple determination (R-square) is closer to 1. This investigations help to discriminate the cause of reinforced concrete beams natural frequency change, to eliminate nondamage factors affects, and to apply many structural damage identification methods effectively. [ABSTRACT FROM AUTHOR]

This paper presents a sum of squares (SOS) approach for active fault tolerant control (AFTC) of nonlinear polynomial systems. A polynomial adaptive fault estimation algorithm for polynomial systems is firstly proposed. Then, sufficient conditions for the existence of the fault estimator are given in terms of SOS which can be numerically (partially symbolically) solved via the recently developed SOSTOOLS. Based on the obtained online fault estimation information, a fault-tolerant control strategy is designed for both compensating the effect of actuator faults in real time and stabilizing the closed-loop system. Finally, tunnel diode circuit and mass-spring-damper systems are used to demonstrate the applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Split-type steel box girders are widely used in long-span bridges because of their good wind-resistance performance. In the design stage, a simple finite element model is usually established based on the beam element for wind-resistance design. However, since the irregular cross-beams and diaphragms in the split steel box girder cannot be virtually established, the stiffness of the girder will be underestimated. To improve the accuracy in simulating stiffness of the split-type triple-box steel box girder (STSBG) with the beam element model (BEM), a correction is made to the initial beam element model (IBEM) based on the result of a more refined finite element model. ANSYS is adopted to make a refined model (RM) of a bridge with STSBG as its girder and to calculate its aerostatic responses and dynamic characteristics in 3 typical construction states and 1 finished state. With the reference value, an objective function of the overall residual sum of squares is constructed for the torsion angle of the girder and the frequency of the bridge. Then, the beam element is used for conventional modelling of the bridge, and artificial bee colony (ABC) algorithm is adopted for the optimization and correction of structure parameters of the BEM of the girder. Finally, static and dynamic characteristics of the IBEM and the corrected beam element model (CBEM) are compared with values of the corresponding RM to evaluate the validity of the correction of the model. The results show that the aerostatic responses and dynamic characteristics of the CBEM are close to calculated values of the RM. In more detail, the relative error between the torsion angle of the girder in the middle span of the BEM and the corresponding reference value in the finished state is decreased from +61.71% to +4.94%, and the relative error of torsional fundamental frequency is decreased from −17.43% to +3.66%. According to the calculated value of the RM, ABC algorithm would satisfactorily improve the accuracy in simulating torsional stiffness of the STSBG with the IBEM. This research is expected to provide reference for beam element modelling, which is conducive to accurately simulating torsional stiffness of the STSBG. [ABSTRACT FROM AUTHOR]

This study establishes a more reasonable and effective rutting prediction model called the quadratic modified Burgers rheological model by considering dynamic loads. Use ABAQUS to simulate the rutting depths through the existing Burgers model and the quadratic modified model and compare with the measured values of the multitemperature and load rutting tests and triaxial repeated load tests. The real tests were conducted on four asphalt mixtures, including SK-90 asphalt mixture, styrene-butadiene-styrene (SBS) modified asphalt mixture, direct coal liquefaction residue (DCLR) modified asphalt mixture, and compound DCLR modified asphalt mixture. The results showed that the range of error ratio and residual sum of squares between simulated and measured rutting depth based on the two different models are 5–35%/5.0–8.74% and 3–15%/0.9–3.1%, respectively, which show that the quadratic modified Burgers rheological model has a more accurate prediction. [ABSTRACT FROM AUTHOR]

Combining resistance to grain mold with high grain yield in tannin-free white-grained photoperiod-insensitive sorghum is of major interest for farmers in Senegal. In this study, GGE biplot analysis was used to assess the performance, adaptability, and stability of eleven sorghum parental lines and their hybrid combinations for yield and grain mold resistance under Senegalese environments. Eleven inbred lines along with their 22 hybrid combinations and one check were evaluated across three sites during the 2015 and 2016 rainy seasons under natural grain mold infestation. The results of this study showed strong genetic variability among studied genotypes for all measured traits. The highly significant G × E interaction effects for grain yield and panicle grain mold rating score (PGMR) indicated that both traits are influenced by genetics and to some extent by environment. Broad-sense heritability computed was high for all these traits except PGMR, showing a high environmental pressure on this later. The study showed that grain mold pressure in the studied sites decreased following a South-North gradient similar to the rainfall pattern, with the south region wetter, explaining the high disease pressure in Darou and Sinthiou Maleme contrary to Bambey. The GGE biplot analysis performed showed that the first two principal components explained 85.84% of the total variation of GGE sum of squares for grain yield. The which-won-where view of the GGE biplot for grain yield showed that the hybrid HB16 was the most adapted for Bambey area. The ranking of genotypes based on both yield performance and stability showed that HB16, HB5, HB21, HB18, and HB7 were the best hybrids combining high grain yield, high stability performance, and tolerance to grain mold disease across the test environments. These hybrids outperformed the best yielding inbred line P29 (2196.7 kg ha−1) with grain yield advantages ranging 17–60%. Therefore, these hybrids could be recommended to farmers in order to improve sorghum yield in Senegal. [ABSTRACT FROM AUTHOR]

Coffee is the stimulant crop plant that belongs to the genus Coffea, in the family Rubiaceae. The yield variation and pattern of the crop varied within short geographic distance and thus attributed to low productivity and unpredictable production. The objective of this study was to determine the extent of genotype × environment interaction on yields of promising Teppi coffee genotype. In this study, seventeen Arabica coffee genotypes, representing coffee growing areas of Teppi, southwestern Ethiopia were evaluated at six environments. This study was conducted by using completely randomized block design with two replications. The extent of genotype × environment interaction was analyzed by using additive main effect and multiplicative interaction model and genotype main effect and genotype × environment interaction biplot analysis model. The analysis of variance for yield revealed the presence of highly significant differences (P < 0.01) within genotypes, environments and genotype by environment interaction. From the total sum of squares of genotype × environment interaction of yield, environment accounted for 51% while genotypic and GEI effects were 20.5% and 25.32%, respectively. Generally, genotypes with superior performance and suitable environments for coffee were identified. However, further researches have to target hybridization of those superior genotypes with elite lines in feature Arabica coffee breeding program. [ABSTRACT FROM AUTHOR]

Infiltration is a vital phenomenon in the water cycle, and consequently, estimation of infiltration rate is important for many hydrologic studies. In the present paper, different data-driven models including Multiple Linear Regression (MLR), Generalized Reduced Gradient (GRG), two Artificial Intelligence (AI) techniques (Artificial Neural Network (ANN) and Multigene Genetic Programming (MGGP)), and the hybrid MGGP-GRG have been applied to estimate the infiltration rates. The estimated infiltration rates were compared with those obtained by empirical infiltration models (Horton's model, Philip's model, and modified Kostiakov's model) for the published infiltration data. Among the conventional models considered, Philip's model provided the best estimates of infiltration rate. It was observed that the application of the hybrid MGGP-GRG model and MGGP improved the estimates of infiltration rates as compared to conventional infiltration model, while ANN provided the best prediction of infiltration rates. To be more specific, the application of ANN and the hybrid MGGP-GRG reduced the sum of square of errors by 97.86% and 81.53%, respectively. Finally, based on the comparative analysis, implementation of AI-based models, as a more accurate alternative, is suggested for estimating infiltration rates in hydrological models. [ABSTRACT FROM AUTHOR]

This study aimed to provide effective methods for the identification of surgeries with high cancellation risk based on machine learning models and analyze the key factors that affect the identification performance. The data covered the period from January 1, 2013, to December 31, 2014, at West China Hospital in China, which focus on elective urologic surgeries. All surgeries were scheduled one day in advance, and all cancellations were of institutional resource- and capacity-related types. Feature selection strategies, machine learning models, and sampling methods are the most discussed topic in general machine learning researches and have a direct impact on the performance of machine learning models. Hence, they were considered to systematically generate complete schemes in machine learning-based identification of surgery cancellations. The results proved the feasibility and robustness of identifying surgeries with high cancellation risk, with the considerable maximum of area under the curve (AUC) (0.7199) for random forest model with original sampling using backward selection strategy. In addition, one-side Delong test and sum of square error analysis were conducted to measure the effects of feature selection strategy, machine learning model, and sampling method on the identification of surgeries with high cancellation risk, and the selection of machine learning model was identified as the key factors that affect the identification of surgeries with high cancellation risk. This study offers methodology and insights for identifying the key experimental factors for identifying surgery cancellations, and it is helpful to further research on machine learning-based identification of surgeries with high cancellation risk. [ABSTRACT FROM AUTHOR]

The gray prediction model, based on the GM(1,1) method, is an important branch of gray theory with the most active research and the most fruitful results, and it is the most widely used because of its small sample size, simple modeling process, and easy to use. Such advantages have been successfully applied in many fields such as transportation, agriculture, energy, medicine, and environment and have been gradually developed into a mainstream predictive modeling method. This study combines the Three-parameter Whitenization Grey Model (TWGM(1,1)), which fits the inhomogeneous exponential law sequence, and the Particle Swarm Algorithm (PSA) to optimize the order and background value coefficients under the condition of the minimum sum of squares of simulation errors, and hence, to solve the problem that the cumulative order is fixed to "1" and the background value coefficient value is fixed to "0.5." As a result, a parameter-optimized gray system model with flexibility, adaptability, and dynamic adjustment is designed to simulate and predict China's higher education gross enrollment rate. The application shows that the model has better overall simulation and prediction performance than others. On the one hand, the parametric optimization model significantly improves its own performance, and on the other hand, its intelligent and adjustable adaptivity improves the accuracy and further extends its application. [ABSTRACT FROM AUTHOR]

Metabolic syndrome (MetS) is a combination of factors which, collectively, increase cardiovascular risk to a greater extent than each of them separately. Previous studies showed high cardiovascular risk to be associated with autonomic nervous system dysfunction. The purpose of this study was to assess the effects of antihypertensive treatment on heart rate variability (HRV) in patients with hypertension (HTN), depending on cooccurrence of MetS. 118 patients with uncontrolled HTN were enrolled to the study. HRV was compared among patients with and without MetS (MetS [+], n = 70) at baseline and following 12 months antihypertensive treatment. The HRV indices measured from RR intervals recorded form using 24-hour ambulatory electrocardiography. The measured HRV domains were the standard deviation of the average of NN intervals [SDNN], square root of the mean of the sum of the squares of differences between adjacent NN intervals [rMSSD], percentage of NN50 [pNN50], low frequency [LF], high frequency [HF], total power of variance of all NN intervals [TP], and LF/HF ratio. Baseline parameters: SDNN, rMSSD, pNN50, and HF were significantly lower in the MetS[+] compared to the MetS[-] subgroup (p < 0.05). After a 12-month antihypertensive treatment, MetS[+] patients achieved a significant improvement in parameters: SDNN, rMSSD, pNN50, and TP (p < 0.05), while the changes in HRV observed in the MetS[-] subgroup were not statistically significant. The cooccurrence of HTN and other components of MetS is associated with disturbances of the autonomic balance. HTN control has a beneficial effect on HRV, with the effect being more evident in patients with MetS. [ABSTRACT FROM AUTHOR]

Background. Reference interval (RI) research is to make it a concise, effective, and practical diagnostic tool. This study aimed to establish sex- and age-specific RI for myocardial enzyme activity in population aged 1–<18 years old in Changchun, China. Methods. Healthy subjects (n = 6,322, 1–<18 years old) were recruited from communities and schools. Aspartate aminotransferase (AST), lactate dehydrogenase (LDH), creatine kinase (CK), and creatine kinase isoenzyme (CKMB) were measured using an automatic biochemical analyzer. Fisher's optimal segmentation method was used to partition by including percentiles as impact factors, aiming at minimizing the sum of the squares of the total dispersion into groups as splitting sequence of ordered data. Results. AST decreased gradually and was partitioned as 1, 2∼<10 and 10∼<18 years old. LDH presented disparate descending rate among 1∼<4, 4∼<12, and 12∼<18 years old. CK stood quite stable with the same RI in all ages. CKMB began to differ at 6 years of age sexually and then remained stable during 6∼<14 years old for male while it continued to decline in female. Cardiac development was partitioned as 1∼<6, 6∼<13, and 13∼<18 years old using multiple percentiles from massive data that reflect characteristics of totality as impact factors. Conclusions. Fisher's optimal segmentation method excelled for multidimensionality, continuity, and loop calculating as dealing with RIs for myocardial enzymes activity and cardiac development process despite limitations. In future, impact of partition on the overall interval should be delved into. [ABSTRACT FROM AUTHOR]

The linguistic intuitionistic fuzzy sets (LIFSs) and linguistic Pythagorean fuzzy sets (LPFSs) are two linguistic orthopair fuzzy sets whose membership grades are pairs of linguistic terms from the predefined linguistic term sets (LTSs). One linguistic term indicates the membership degree (MD), while the other one gives the nonmembership degree (NMD). In each LIFS, the sum of the subscripts of MD and NMD is less than the cardinality of LTS. In the LPFSs, the sum of the squares of the subscripts of MD and NMD is less than the square of the cardinality of LTS. In this paper, we propose a general form of these two linguistic orthopair fuzzy sets, which can be named linguistic q‐rung orthopair fuzzy sets. We devise the operational laws, based on which, the linguistic q‐rung orthopair fuzzy weighted averaging (LqROFWA) operator and linguistic q‐rung orthopair fuzzy weighted geometric (LqROFWG) operator are developed to aggregate the linguistic q‐rung orthopair fuzzy numbers (LqROFNs). Then, the novel interactional operational laws that consider the interactions between the MD and NMD from different LqROFNs are given. The partitioned geometric Heronian mean (PGHM) operator can effectively solve the decision‐making problems in which the attributes grouped into the same clusters have interrelationships and the attributes belonging to different clusters have no interrelationship. Based on these novel operational laws and PGHM operator, the linguistic q‐rung orthopair fuzzy interactional PGHM (LqROFIPGHM) operator and linguistic q‐rung orthopair fuzzy interactional weighted PGHM (LqROFIWPGHM) operator are proposed and their properties are discussed. Based on the LqROFIWPGHM operator, an efficient multiattribute group decision‐making model is given to deal with the linguistic q‐rung orthopair fuzzy information. Finally, the superiorities of the interactional operational laws and LqROFIWPGHM operator are tested using some illustrative examples. [ABSTRACT FROM AUTHOR]

The Hamilton–Jacobi–Issacs (HJI) inequality is the most basic relation in nonlinear H∞ design, to which no effective analytical solution is currently available. The sum of squares (SOS) method can numerically solve nonlinear problems that are not easy to solve analytically, but it still cannot solve HJI inequalities directly. In this paper, an HJI inequality suitable for SOS is firstly derived to solve the problem of nonconvex optimization. Then, the problems of SOS in nonlinear H∞ design are analyzed in detail. Finally, a two-step iterative design method for solving nonlinear H∞ control is presented. The first step is to design an adjustable nonlinear state feedback of the gain array of the system using SOS. The second step is to solve the L2 gain of the system; the optimization problem is solved by a graphical analytical method. In the iterative design, a diagonally dominant design idea is proposed to reduce the numerical error of SOS. The nonlinear H∞ control design of a polynomial system for large satellite attitude maneuvers is taken as our example. Simulation results show that the SOS method is comparable to the LMI method used for linear systems, and it is expected to find a broad range of applications in the analysis and design of nonlinear systems. [ABSTRACT FROM AUTHOR]

The Pythagorean fuzzy set (PFS) is characterized by two functions expressing the degree of membership and the degree of nonmembership, which square sum of them is equal or less than 1. It was proposed as a generalization of a fuzzy set to deal with indeterminate and inconsistent information. In this study, we shall present some novel Dice similarity measures of PFSs and the generalized Dice similarity measures of PFSs and indicates that the Dice similarity measures and asymmetric measures (projection measures) are the special cases of the generalized Dice similarity measures in some parameter values. Then, we propose the generalized Dice similarity measures‐based multiple attribute group decision‐making models with Pythagorean fuzzy information. Then, we apply the generalized Dice similarity measures between PFSs to multiple attribute group decision making. Finally, an illustrative example is given to demonstrate the efficiency of the similarity measures for selecting the desirable ERP system. [ABSTRACT FROM AUTHOR]

In this paper, a new approach for synthesizing a decentralized observer is proposed to estimate the unmeasurable states of a discrete-time large-scale nonlinear system. The large-scale nonlinear system in this study is modeled in terms of a set of discrete-time polynomial Takagi-Sugeno (T-S) fuzzy subsystems and interconnection terms. This modeling method will assist to reduce significantly the number of fuzzy rules. The interconnection terms are considered as the unknown inputs; then, the unknown input method is employed to design observer for this system. It should be emphasized that the interconnection parts in this paper are arbitrary and their effects are eliminated completely. On the basis of the Lyapunov methodology and SOS (Sum of Square) technique, the conditions for observer design expressed under the framework of SOS are derived in the main theorems. Finally, an illustrative example is presented to show the effectiveness and merit of the proposed method. [ABSTRACT FROM AUTHOR]

Twelve field pea genotypes were evaluated in seven environments in Eastern Amhara in main production season (2010-2012). The objective of this trial was to identify stable and high yielding field pea genotype for production in Eastern Amhara. The trial was conducted using randomized complete block design with three replications. Combined analysis of variance for grain yield revealed that genotypes, environments, and genotype by environment interaction effect were highly significant (P ≤ 0.01). The environments, GEI, and genotypes were accounted for 77.47%, 13.83%, and 4.37%, of the total sum squares, respectively, indicating that field pea grain yield was significantly affected by the changes in the environment, followed by GEI and genotypic effect. The candidate genotype, EH-03-002, showed 14.42% and 44.87% yield advantage over the standard and local checks, respectively. Considering the seven environments data and field performance evaluation during the variety verification trial, the National Variety Releasing Committee has approved the official release of EH-03-002 with the vernacular name of "Yewaginesh" for moisture deficit areas of Wag Lasta and similar agroecologies. [ABSTRACT FROM AUTHOR]

A proper Hamilton-Jacobi-Isaacs (HJI) inequality must be solved in a nonlinear H∞ control problem. The sum of squares (SOS) method can now be used to solve an analytically unsolvable nonlinear problem. A HJI inequality suitable for SOS approach is derived in the paper. The SOS algorithm for solving the HJI inequality is also provided. Conservativeness of the SOS method is then discussed in the paper. The conservativeness of the SOS approach is caused by the method itself, because it is really a synthesis method over the entire state space. To reduce the conservativeness, a local H∞ design on a restricted state-space region is proposed. But the SOS approach for the local H∞ design also suffers from the conservativeness problem, because the S-procedure for solving the set-containment constraint provides only a sufficient condition. The above-mentioned sources of conservativeness are peculiar for the SOS approaches. So a proper approach must be carefully selected in the design process to get a reasonable result. A design example is also given in the paper. [ABSTRACT FROM AUTHOR]

Finite-time stability and stabilization problem is first investigated for continuous-time polynomial fuzzy systems. The concept of finite-time stability and stabilization is given for polynomial fuzzy systems based on the idea of classical references. A sum-of-squares- (SOS-) based approach is used to obtain the finite-time stability and stabilization conditions, which include some classical results as special cases. The proposed conditions can be solved with the help of powerful Matlab toolbox SOSTOOLS and a semidefinite-program (SDP) solver. Finally, two numerical examples and one practical example are employed to illustrate the validity and effectiveness of the provided conditions. [ABSTRACT FROM AUTHOR]

Forecasting the tendencies of time series is a challenging task which gives better understanding. The purpose of this paper is to present the hybrid model of support vector regression associated with Autoregressive Integrated Moving Average which is formulated by hybrid methodology. The proposed model is more convenient for practical usage. The tendencies modeling of time series for Thailand’s south insurgency is of interest in this research article. The empirical results using the time series of monthly number of deaths, injuries, and incidents for Thailand’s south insurgency indicate that the proposed hybrid model is an effective way to construct an estimated hybrid model which is better than the classical time series model or support vector regression. The best forecast accuracy is performed by using mean square error. [ABSTRACT FROM AUTHOR]

This article investigates the [ABSTRACT FROM AUTHOR]

This article is concerned with the stabilization problem for nonlinear networked control systems which are represented by polynomial fuzzy models. Two communication features including signal transmission delays and data missing are taken into account in a network environment. To solve the network-induced communication problems, a novel sampled-data fuzzy controller is designed to guarantee that the closed-loop system is asymptotically stable. The stability and stabilization conditions are presented in terms of sum of squares (SOS), which can be numerically solved via SOSTOOLS. Finally, a simulation example is provided to demonstrate the feasibility of the proposed method. © 2014 Wiley Periodicals, Inc. Complexity 21: 74-81, 2015 [ABSTRACT FROM AUTHOR]

We address the problem of asymptotic stability and region-of-attraction analysis of nonlinear dynamical systems. A hybrid symbolic-numeric method is presented to compute exact Lyapunov functions and exact estimates of regions of attraction of nonlinear systems efficiently. A numerical Lyapunov function and an estimate of region of attraction can be obtained by solving an (bilinear) SOS programming via BMI solver, then the modified Newton refinement and rational vector recovery techniques are applied to obtain exact Lyapunov functions and verified estimates of regions of attraction with rational coefficients. Experiments on some benchmarks are given to illustrate the efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

Summary: This research studies structural model updating through sum of squares (SOS) optimization to minimize modal dynamic residuals. In the past few decades, many model updating algorithms have been studied to improve the similitude between a numerical model and the as‐built structure. Structural model updating usually requires solving nonconvex optimization problems, whereas most off‐the‐shelf optimization solvers can only find local optima. To improve the model updating performance, this paper proposes the SOS global optimization method for minimizing modal dynamic residuals of the generalized eigenvalue equations in structural dynamics. The proposed method is validated through both numerical simulation and experimental study of a four‐story shear frame structure. [ABSTRACT FROM AUTHOR]