Autocorrelation Values Research Articles

Statistical Models and Evaluation Criteria Used in Poultry Farming

In this thesis study, the statistical models and evaluation criteria used in poultry breeding were examined comprehensively, taking into account live weight gain, egg weight and egg numbers. For this purpose, as an example of live weight increase; 9-week live weight gains of Ross-308 broiler line, as an example of egg weight; The average of 20-week-old egg weights of brown Lohmann laying hens and 9-week egg yields of Japanese quail were used as an example of the number of eggs. In modeling, for live weight gain; 10 different models for egg production; 11 different models and 8 different models for egg weight were considered. In evaluating the models; error mean squares, coefficient of determination, corrected coefficient of determination, Akaike information criterion, corrected Akaike information criterion, Bayesian information criterion, accuracy factor, deviation factor and Durbin-Watson autocorrelation values were taken into consideration. As a result of the study, in terms of live weight gain; The cubic piecewise regression model is the best model in terms of egg yields; It was determined that the modified Compartmental model and the logistic model gave better results than the others in terms of egg weight. The worst models are in live weight gains; Brody, egg yields; It was concluded that there was a quadratic linear and Von Bertalanffy model for egg weight.

Spatio-temporal patterns and driving factors of green development level in alpine meadow regions: A case study in Gannan Tibetan Autonomous Prefecture

Green development is an approach that harmonizes human beings with nature and leads to sustainable social and economic development. However, the factors influencing the level of green development are uncertain due to regional differences, especially in the alpine meadow regions. To explore the factors affecting green development in the alpine meadow region, we took the Gannan Tibetan Autonomous Prefecture as an example. Through a green development level evaluation index system, the DPSIR (Driver, Pressure, State, Impact, and Response) was constructed to comprehensively evaluate and analyze the green development level of the study area from 1990 to 2020. On this basis, the key influencing factors of the green development level was analyzed based on the GeoDetector. The findings indicate that the total level of green development in Gannan has an upward trend within the range of 0.044–0.535, moving towards high quality and sustainable development. Except for 2005, the spatial clustering characteristics of the green development level did not display obvious high values of spatial autocorrelation. The green development space is concentrated and stable, and the centre of gravity is in Zhuoni. The impact and response dimensions make the greatest contribution to green development. Different evaluation indicators have different impacts on green development during the study period. Specifically, people’s productive lives, the impact of social production and positive policy responses are the main factors contributing to green development. The findings will provide theoretical basis and case support for ecological security and green sustainable development in the alpine meadow regions.

Autocorrelation of daily resting heart rate: A novel metric of postoperative recovery

PurposeResting heart rate (RHR) is a sensitive indicator of an individual’s physiologic condition. However, its use in clinical practice has been limited due to the wide variation in baseline RHR based on multiple factors, including age, sex, cardiovascular fitness, and comorbidities. The study aims to develop a novel, clinically meaningful metric that is applicable across these conditions, based on day-by-day changes in RHR—the difference in autocorrelation of daily RHR (ACΔ-RHR). We present ACΔ-RHR in the context of monitoring post-discharge recovery for pediatric appendectomy patients. MethodsChildren 3–17 years old who underwent laparoscopic appendectomy for complicated appendicitis from 2019 to 2022 at a tertiary children’s hospital wore a Fitbit for twenty-one postoperative days (POD). Patients without complications were included to describe normative recovery. Using RHR on POD 1–3 as the baseline, autocorrelation of daily RHR was calculated (fixed lag = 1) for POD 3–21. Then, daily ACΔ-RHR was determined by subtracting autocorrelation values between the current and previous day. Means and standard deviations were calculated for daily RHR to estimate on which POD ACΔ-RHR stabilized at 0, representing general RHR stability and recovery from surgery for all patients. Subgroup analyses were performed by age (3–10 years old vs 11–17 years old) and sex. ResultsThirty-one patients were included (58.1 % 3–10 years old, 41.9 % female, 67.7 % Hispanic). Whereas the mean daily RHR did not demonstrate clear trends, the mean ACΔ-RHR for the cohort first reached 0 on POD 12 and stabilized on POD 14 (95 % confidence interval: POD [11,17]). Subgroup analysis showed that ACΔ-RHR stabilized on POD 9 for age of 3–10 years, POD 12 for age of 11–17 years, POD 12 for females and POD 10 for males. ConclusionsThe ACΔ-RHR is a promising clinical metric that could enhance post-surgical patient monitoring, such as for children following laparoscopic appendectomy for complicated appendicitis.

Differentiable and hardware-accelerated waveforms for gravitational wave data analysis

We propose the use of automatic differentiation through the programming framework for accelerating a variety of analysis tasks throughout gravitational wave (GW) science. Firstly, we demonstrate that complete waveforms which cover the inspiral, merger, and ringdown of binary black holes (i.e., IMRPhenomD) can be written in and demonstrate that the serial evaluation speed of the waveform (and its derivative) is similar to the implementation in . Moreover, allows for graphics processing unit–accelerated waveform calls which can be over an order of magnitude faster than serial evaluation on a CPU. We then focus on three applications where efficient and differentiable waveforms are essential. Firstly, we demonstrate how gradient descent can be used to optimize the ∼200 coefficients that are used to calibrate the waveform model. In particular, we demonstrate that the typical with numerical relativity waveforms can be improved by more than 50%. Secondly, we show that Fisher forecasting calculations can be sped up by ∼3–5× (on a CPU) with no loss in accuracy. This increased speed makes Fisher forecasting for a population of events substantially simpler. Finally, we show that gradient-based samplers like Hamiltonian Monte Carlo lead to significantly reduced autocorrelation values when compared to traditional Monte Carlo methods. Since differentiable waveforms have substantial advantages for a variety of tasks throughout GW science, we propose that waveform developers use to build new waveforms moving forward. Our waveform code, , can be found on GitHub website and will continue to be updated with new waveforms as they are implemented. Published by the American Physical Society 2024

Autocorrelation—A Simple Diagnostic for Tropical Precipitation Variability in Global Kilometer‐Scale Climate Models

AbstractWe propose the lag‐1 autocorrelation of daily precipitation as a simple diagnostic of tropical precipitation variability in climate models. This metric generally has a relatively uniform distribution of positive values across the tropics. However, selected land regions are characterized by exceptionally low autocorrelation values. Low values correspond to the dominance of high frequency variance in precipitation, and specifically of high frequency convectively coupled equatorial waves. Consistent with previous work, we show that CMIP6 climate models overestimate the autocorrelation. Global kilometer‐scale models capture the observed autocorrelation when deep convection is explicitly simulated. When a deep convection parameterization is used, though, the autocorrelation increases over land and ocean, suggesting that land surface‐atmosphere interactions are not responsible for the changes in autocorrelation. Furthermore, the metric also tracks the accuracy of the representation of the relative importance of high frequency and low frequency convectively coupled equatorial waves in the models.

On the Pentanomial Power Mapping Classification of 8-bit to 8-bit S-Boxes

Substitution boxes, or S-boxes, are one of the most important mathematical primitives in modern symmetric cryptographic algorithms. Given their importance, in the past decades, they have been thoroughly analyzed and evaluated by the academic world. Thus, a lot of desirable characteristics a given S-box should possess have been found. This includes, as much as possible, higher nonlinearity and algebraic degrees as well as, as much as possible, lower values of differential uniformity, autocorrelation and sum of squares indicator values. In this work, we use power mappings over GF(28) to generate, enumerate and evaluate all bijective S-boxes yielded by pentanomials of the form f(x)=xa+xb+xc+xd+xe given 0<a<b<c<d<e<256. We find a total of 152,320 different bijective S-boxes, which are further classified into 41,458 different groups in terms of the aforementioned characteristics as well as the number of their fixed points. Having this data, an S-box designer can easily generate a bijective substitution S-box with parameters of their choice. By using pentanomials, we show how we can easily construct S-boxes with cryptographic properties similar to those found in some popular S-boxes like the Kuznyechik S-box proposed by the Russian Federation’s standardization agency as well as the Skipjack S-box proposed by the National Security Agency of the USA.

Reconsidering stepped wedge cluster randomized trial designs with implementation periods: Fewer sequences or the parallel-group design with baseline and implementation periods are potentially more efficient.

When designing a cluster randomized trial, advantages and disadvantages of tentative designs must be weighed. The stepped wedge design is popular for multiple reasons, including its potential to increase power via improved efficiency relative to a parallel-group design. In many realistic settings, it will take time for clusters to fully implement the intervention. When designing the HEALing (Helping to End Addiction Long-termSM) Communities Study, implementation time was a major consideration, and we examined the efficiency and practicality of three designs. Specifically, a three-sequence stepped wedge design with implementation periods, a corresponding two-sequence modified design that is created by removing the middle sequence, and a parallel-group design with baseline and implementation periods. In this article, we study the relative efficiencies of these specific designs. More generally, we study the relative efficiencies of modified designs when the stepped wedge design with implementation periods has three or more sequences. We also consider different correlation structures. We compare efficiencies of stepped wedge designs with implementation periods consisting of three to nine sequences with a variety of corresponding designs. The three-sequence design is compared to the two-sequence modified design and to the parallel-group design with baseline and implementation periods analysed via analysis of covariance. Stepped wedge designs with implementation periods consisting of four or more sequences are compared to modified designs that remove all or a subset of 'middle' sequences. Efficiencies are based on the use of linear mixed effects models. In the studied settings, the modified design is more efficient than the three-sequence stepped wedge design with implementation periods. The parallel-group design with baseline and implementation periods with analysis of covariance-based analysis is often more efficient than the three-sequence design. With respect to stepped wedge designs with implementation periods that are comprised of more sequences, there are often corresponding modified designs that improve efficiency. However, use of only the first and last sequences has the potential to be either relatively efficient or inefficient. Relative efficiency is impacted by the strength of the statistical correlation among outcomes from the same cluster; for example, the relative efficiencies of modified designs tend to be greater for smaller cluster auto-correlation values. If a three-sequence stepped wedge design with implementation periods is being considered for a future cluster randomized trial, then a corresponding modified design using only the first and last sequences should be considered if sole focus is on efficiency. However, a parallel-group design with baseline and implementation periods and analysis of covariance-based analysis can be a practical, efficient alternative. For stepped wedge designs with implementation periods and a larger number of sequences, modified versions that remove 'middle' sequences should be considered. Due to the potential sensitivity of design efficiencies, statistical correlation should be carefully considered.

Early warning signals of grassland ecosystem degradation: A case study from the northeast Qinghai-Tibetan Plateau

As a typical vulnerable ecosystem, alpine steppes can be subject to nonlinear accelerations in degradation, once tipping points are reached or exceeded, and thus far more effort and investment will be needed, to reverse the degraded status of such ecosystems, if effective preventive measures or interventions are not adopted sufficiently early. Thus, detecting early warning signals of tipping events is extremely important for ecosystem protection and recovery, but remains highly challenging. For alpine steppes, coverage or biomass loss and species invasion are both key transitions. Using multi-source data, including ground surveys and remote sensing images with different resolutions, we tried to screen and identify potential early-warning signals for alpine grassland degradation events and analyze their efficacy. The results showed that the degradation levels derived from spatial heterogeneity information combined with a vegetation index were reliable, as was verified by ground surveys, and could illustrate the degradation process of a grassland ecosystem. Six common early-warning signals (variance, skewness, and autocorrelation, from both the spatial and temporal domains) were tested according to degradation degree, and spatial autocorrelation and temporal autocorrelation had the best performances, as they could indicate different degradation levels, and the change trends of temporal autocorrelation could provide early-warning functions. A waved change of spatial variance could reflect a vegetation growth dynamic that might indicate species invasion. In this study, the threshold values of spatial and temporal autocorrelation were 0.7 and 0.2, respectively, and from these, the thresholds of vegetation coverage and biomass were then obtained. However, these signals and thresholds had some limitations during application. These findings provide insights into better understanding of the alpine grassland degradation process, and contribute to a better management approach for vulnerable ecosystems using early-warning methods.

Analysis of the spread of dengue hemorrhagic fever with the moran index (Case study of Sukoharjo Regency in 2019)

One of the endemic diseases that can be found in tropical areas is DHF or Dengue Hemorrhagic Fever (DHF). Dengue fever can be transmitted through Aedes aegypti mosquitoes that have been infected with the dengue virus. It is estimated that the level of dependence on DHF is always related to the infected area around it. DHF can spread to other people through mosquito bites. Sukoharjo Regency in 2018 recorded 35 cases of DHF sufferers and zero patients died. Increased up to 90% in 2019, there were 317 cases of dengue fever and 10 patients died. Therefore, the purpose of this study is to determine the autocorrelation of the spread of DHF in Sukoharjo Regency in 2019. This study used data on the number of DHF sufferers per subdistrict in Sukoharjo Regency in 2019. The research method used the calculation of the Moran Index. Calculation of Moran’s Index produced a value I = -0.181. Moran’s index value is in the range - 1≤I< 0 so that it indicates a negative autocorrelation. The correlation is classified to be a weak relationship. Negative spatial autocorrelation values indicate locations that are close to each other have values that are not close together. Subdistricts with a high number of sufferers tended to be irregular.

Machine learning approaches to evaluate infants’ general movements in the writhing stage—a pilot study

The goals of this study are to describe machine learning techniques employing computer-vision movement algorithms to automatically evaluate infants’ general movements (GMs) in the writhing stage. This is a retrospective study of infants admitted 07/2019 to 11/2021 to a level IV neonatal intensive care unit (NICU). Infant GMs, classified by certified expert, were analyzed in two-steps (1) determination of anatomic key point location using a NICU-trained pose estimation model [accuracy determined using object key point similarity (OKS)]; (2) development of a preliminary movement model to distinguish normal versus cramped-synchronized (CS) GMs using cosine similarity and autocorrelation of major joints. GMs were analyzed using 85 videos from 74 infants; gestational age at birth 28.9 ± 4.1 weeks and postmenstrual age (PMA) at time of video 35.9 ± 4.6 weeks The NICU-trained pose estimation model was more accurate (0.91 ± 0.008 OKS) than a generic model (0.83 ± 0.032 OKS, p < 0.001). Autocorrelation values in the lower limbs were significantly different between normal (5 videos) and CS GMs (5 videos, p < 0.05). These data indicate that automated pose estimation of anatomical key points is feasible in NICU patients and that a NICU-trained model can distinguish between normal and CS GMs. These preliminary data indicate that machine learning techniques may represent a promising tool for earlier CP risk assessment in the writhing stage and prior to hospital discharge.

Solar Energy Prediction Based on Intelligent Predictive Controller Algorithm

The technological advancement in all countries leads to massive energy demand. The energy trading companies struggle daily to meet their customers’ power demands. For a good quality, disturbance-free, and reliable power supply, one must balance electricity generation and consumption at the grid level. There is a profound change in distribution networks due to the intervention of renewable energy generation and grid interactions. Renewable energy sources like solar and wind depend on environmental factors and are subject to unpredictable variations. Earlier, energy distribution companies faced a significant challenge in demand forecasting since it is often unpredictable. With the prediction of the ever-varying power from renewable sources, the power generation and distribution agencies are facing a challenge in supply-side predictions. Several forecasting techniques have evolved, and machine learning techniques like the model predictive controller are suitable for arduous tasks like predicting weather-dependent power generation in advance. This paper employs a Model Predictive Controller (MPC) to predict the solar array’s power. The proposed method also includes a system identification algorithm, which helps acquire, format, validate, and identify the pattern based on the raw data obtained from a PV system. Autocorrelation and cross-correlation value between input and predicted output 0.02 and 0.15. The model predictive controller helps to recognize the future response of the corresponding PV plant over a specific prediction horizon. The error variation of the predicted values from the actual values for the proposed system is 0.8. The performance analysis of the developed model is compared with the former existing techniques, and the role and aptness of the proposed system in smart grid digitization is also discussed.

Following Forrelation – quantum algorithms in exploring Boolean functions' spectra

&lt;p style='text-indent:20px;'&gt;Here we revisit the quantum algorithms for obtaining Forrelation [Aaronson et al., 2015] values to evaluate some of the well-known cryptographically significant spectra of Boolean functions, namely the Walsh spectrum, the cross-correlation spectrum, and the autocorrelation spectrum. We introduce the existing 2-fold Forrelation formulation with bent duality-based promise problems as desirable instantiations. Next, we concentrate on the 3-fold version through two approaches. First, we judiciously set up some of the functions in 3-fold Forrelation so that given oracle access, one can sample from the Walsh Spectrum of &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ f $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. Using this, we obtain improved results than what one can achieve by exploiting the Deutsch-Jozsa algorithm. In turn, it has implications in resiliency checking. Furthermore, we use a similar idea to obtain a technique in estimating the cross-correlation (and thus autocorrelation) value at any point, improving upon the existing algorithms. Finally, we tweak the quantum algorithm with the superposition of linear functions to obtain a cross-correlation sampling technique. This is the first cross-correlation sampling algorithm with constant query complexity to the best of our knowledge. This also provides a strategy to check if two functions are uncorrelated of degree &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ m $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. We further modify this using Dicke states so that the time complexity reduces, particularly for constant values of &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ m $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;.&lt;/p&gt;

Spatial effects of township health centers' health resource allocation efficiency in China.

China is a large agricultural nation with the majority of the population residing in rural areas. The allocation of health resources in rural areas significantly affects the basic rights to life and health for rural residents. Despite the progress made by the Chinese government in improving rural healthcare, there is still room for improvement. This study aims to assess the spatial spillover effects of rural health resource allocation efficiency in China, particularly focusing on township health centers (THCs), and examine the factors influencing this efficiency to provide recommendations to optimize the allocation of health resources in rural China. This study analyzed health resource allocation efficiency in Chinese rural areas from 2012 to 2021 by using the super-efficiency SBM model and the global Malmquist model. Additionally, the spatial auto-correlation of THC health resource allocation efficiency was verified through Moran test, and three spatial econometric models were constructed to further analyze the factors influencing efficiency. The key findings are: firstly, the average efficiency of health resource allocation in THCs was 0.676, suggesting a generally inefficient allocation of health resources over the decade. Secondly, the average Malmquist productivity index of THCs was 0.968, indicating a downward trend in efficiency with both non-scale and non-technical efficient features. Thirdly, Moran's Index analysis revealed that efficiency has a significant spatial auto-correlation and most provinces' values are located in the spatial agglomeration quadrant. Fourthly, the SDM model identified several factors that impact THC health resource allocation efficiency to varying degrees, including the efficiency of total health resource allocation, population density, PGDP, urban unemployment rate, per capita disposable income, per capita healthcare expenditure ratio, public health budget, and passenger traffic volume. To enhance the efficiency of THC healthcare resource allocation in China, the government should not only manage the investment of health resources to align with the actual demand for health services but also make use of the spatial spillover effect of efficiency. This involves focusing on factors such as total healthcare resource allocation efficiency, population density, etc. to effectively enhance the efficiency of health resource allocation and ensure the health of rural residents.

Chaotic time delay feature cancellation and bandwidth enhancement in cascaded-coupled nanolasers

As an important part of optical sources, nanolasers have a prominent influence in photonic circuit integration, and their nonlinear dynamics has become one of the research hotspots in recent years. In this work, we investigate the time-delay signature and bandwidth characteristics in a cascade-coupled nanolaser system, in which the master nanolaser is connected to an external feedback cavity and injected into the intermediate nanolaser and the slave nanolaser sequentially. The 0-1 chaos test is introduced to quantify the dynamics of the nanolaser, which can accurately distinguish whether the laser is in a chaotic state, and the autocorrelation function is used to analyze the time-delay characteristics in the laser output signal. This type of calculation has the advantages of fast operation speed, high accuracy and anti-noise robustness. The lower the autocorrelation value, the more difficult it is to extract useful information from the chaotic random sequence. The bandwidth is defined as a value where the range between DC and frequency contains 80% of the spectral power, a value that is only applicable to chaotic states. In the simulation, we compare and analyze the two cases of whether the intermediate nanolaser has a peak with obvious time-delay signature. The research results show that by selecting appropriate system parameters, the slave nanolaser can always output a broadband chaotic signal without obvious time-delay signature. Under the condition of a certain injection intensity, by changing the frequency detuning parameter, the intermediate nanolaser has an obvious time-delay signature, and then the slave nano-laser can output chaotic signals which can suppress time-delay signature and enhance bandwidth in a small parameter interval. When the time-delay signal of the intermediate nanolaser is completely hidden, the slave nanolaser can achieve the suppression of the time-delay signature in a larger parameter plane, meanwhile the bandwidth is significantly enhanced. In addition, by plotting the two-dimensional spatial distribution diagram and bandwidth line diagram of the output from the nanolaser under frequency detuning and injection intensity, it is determined that the nanolaser can simultaneously suppress the delay characteristics and enhance the bandwidth under chaotic signals. This provides an important theoretical basis for realizing the practical applications in secrecy-enhanced chaotic optical communication.

Plug‐and‐Play Fiber‐Coupled Quantum Dot Single‐Photon Source via Photonic Wire Bonding

AbstractThe collection of single‐photon emission from a quantum dot (QD) in a Bragg waveguide through a photonic wire bond (PWB) via free‐space resonant frequency pumping at 1.6 K is demonstrated. The in‐fiber single photons show a small multiphoton contribution, quantified by a low second order photon autocorrelation value of (background‐corrected) or (raw data). The decay time of the QD is measured to be ps. The PWB obviates the need for in‐cryostat alignment of the single‐photon source with an optical fiber and thus offers a route to scalable integration of quantum photonic devices in a cryogenic environment. Uniquely, the approach combines the QD‐waveguide technique, enabling resonant driving of individual QDs without the need for cross‐polarization filtering, and the PWB for deterministic, alignment‐free coupling of single‐photon sources to optical fibers.

Families of multi-level Legendre-like arrays

AbstractFamilies of new, multi-level integer 2D arrays are introduced here as an extension of the well-known binary Legendre sequences that are derived from quadratic residues. We present a construction, based on Fourier and Finite Radon Transforms, for families of periodic perfect arrays, each of size $$p\times p$$ p × p for many prime values p. Previously delta functions were used as the discrete projections which, when back-projected, build 2D perfect arrays. Here we employ perfect sequences as the discrete projected views. The base family size is $$p+1$$ p + 1 . All members of these multi-level array families have perfect autocorrelation and constant, minimal cross-correlation. Proofs are given for four useful and general properties of these new arrays. 1) They are comprised of odd integers, with values between at most $$-p$$ - p and $$+p$$ + p , with a zero value at just one location. 2) They have the property of ‘conjugate’ spatial symmetry, where the value at location (i, j) is always the negative of the value at location $$(p-i, p-j)$$ ( p - i , p - j ) . 3) Any change in the value assigned to the array’s origin leaves all of its off-peak autocorrelation values unchanged. 4) A family of $$p+1$$ p + 1 , $$p\times p$$ p × p arrays can be compressed to size $$(p+1)^2$$ ( p + 1 ) 2 and each family member can be exactly and rapidly unpacked in a single $$p\times p$$ p × p decompression pass.

Indexing and partitioning the spatial linear model for large data sets.

We consider four main goals when fitting spatial linear models: 1) estimating covariance parameters, 2) estimating fixed effects, 3) kriging (making point predictions), and 4) block-kriging (predicting the average value over a region). Each of these goals can present different challenges when analyzing large spatial data sets. Current research uses a variety of methods, including spatial basis functions (reduced rank), covariance tapering, etc, to achieve these goals. However, spatial indexing, which is very similar to composite likelihood, offers some advantages. We develop a simple framework for all four goals listed above by using indexing to create a block covariance structure and nearest-neighbor predictions while maintaining a coherent linear model. We show exact inference for fixed effects under this block covariance construction. Spatial indexing is very fast, and simulations are used to validate methods and compare to another popular method. We study various sample designs for indexing and our simulations showed that indexing leading to spatially compact partitions are best over a range of sample sizes, autocorrelation values, and generating processes. Partitions can be kept small, on the order of 50 samples per partition. We use nearest-neighbors for kriging and block kriging, finding that 50 nearest-neighbors is sufficient. In all cases, confidence intervals for fixed effects, and prediction intervals for (block) kriging, have appropriate coverage. Some advantages of spatial indexing are that it is available for any valid covariance matrix, can take advantage of parallel computing, and easily extends to non-Euclidean topologies, such as stream networks. We use stream networks to show how spatial indexing can achieve all four goals, listed above, for very large data sets, in a matter of minutes, rather than days, for an example data set.

Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows.

Modeling stream flows is vital for water resource planning and flood and drought management. In this study, the performance of hybrid models constructed by combining least square support vector machines (LSSVM), empirical model decomposition (EMD), and particle swarm optimization (PSO) methods in modeling monthly streamflow was evaluated. For establishing the models, 42years of monthly average streamflow data was used in two hydrometer stations located in the Konya Closed Basin, covering 1964 to 2005. Lagged streamflow values ​​were selected as inputs according to partial autocorrelation values ​​in establishing the models. The dataset was divided into 70% training and 30% testing. Model performances were evaluated according to mean square error, root mean square error, correlation coefficients, scatter plot, and Taylor and Violin diagrams. As a result of the analysis, it was determined that the PSO-LSSVM and EMD-LSSVM models were slightly more successful than the single LSSVM model, and the best model was obtained with the EMD-PSO-LSSVM. In addition, in estimating monthly stream flows, 1-, 9-, 10-, 11-, and 12-month lagged streamflow values were the input combination that gave the best results in semi-arid climatic regions. This result demonstrated that EMD improved the performance of both LSSVM and PSO-LSSVM models by 1% to 5% based on correlation coefficient (R) values.

Front Crawl and Backstroke Sprint Swimming have Distinct Differences along with Similar Patterns Regarding Trunk Rotations

Background: Front crawl and backstroke share similar trunk rotating characteristics and tempt coaches to transfer teaching parts from one stroke to the other intuitively. However, the degree of similarity has yet to be determined. The coordination of the pelvis and the 7th cervical vertebrae (C7), during yaw and roll rotation, when sprint swimming front crawl, and backstroke was studied. Methods: Thirty-four swimmers were assessed on their performance in25m-sprint of each stroke. Using inertial sensors, each segment’s time series of angular displacement was calculated. Their amplitudes, mean autocorrelation values, max cross-correlation coefficient, phase lag, and relative power at the main frequency were analyzed. For all comparisons, the p-value was set to 0.05. Results: Pelvis yaw and roll and C7 roll amplitudes were greater at backstroke, C7 yaw was greater at front crawl. Autocorrelations ranged from 0.79 to 0.82 except for the pelvis at front crawl in yaw which was 0.72±0.16. Relative power at the main frequency ranged from 47% to 52% except for the yaw pelvis’ at the front crawl which was lower (32.81±14.09%). Backstroke had larger mean values in all cases and roll had larger mean values than yaw. Cross-correlation between the two segments yielded higher values at roll. At roll direction, the leading segment in the front crawl was the pelvis while in backstroke, it was the C7 which was true in all cases. In all cases, the coupling was slightly deviating from in-phase mode except from backstroke yaw which yield phase lag values of -13.35±1.14% of stroke cycle time. Conclusions: Although both strokes share similar characteristics their intersegmental coupling differs. The findings of the study imply that proper focus should be given to enhance only a positive transfer of learning between the two strokes.

Extended statistical linearization approach for estimating non-stationary response statistics of systems comprising fractional derivative elements

An efficient technique to analyze non-stationary nonlinear random vibrations of dynamic systems endowed with a fractional derivative term is presented. The technique itself represents an extension of the concept of statistical linearization to this kind of systems, and it is applicable for both analytic and hysteretic system nonlinearities. The technique first resorts to harmonic balancing in deriving response-amplitude dependent equivalent damping and stiffness. This enables representation of the fractional derivative term as a linear combination of the system response displacement and velocity with amplitude dependent coefficients. Then, the expected values of these parameters are considered in proceeding to formulate a statistical linearization solution scheme. In this context, the solution procedure is completed by integrating in time the covariance Lyapunov equation associated with the derived equivalent linear system. The reliability of the proposed technique is tested by a series of germane Monte Carlo studies. This juxtaposition is also used to elucidate salient features of the technique, by varying the order of the fractional derivative term, and of the degree of the nonlinearity in the system. It also points out the versatility of the technique in determining the non-stationary values of auto-correlation and cross-correlations response parameters involving even the fractional derivative term.