Piecewise Linear Regression Method Research Articles

A shift in the South China Sea marine radiocarbon reservoir age associated with the opening of the Balabac Strait

Accurate chronologies are crucial when comparing paleo-climate records, especially in the case of terrestrial and marine comparisons. It is essential to understand temporal variability of the radiocarbon reservoir effect to produce an accurate chronology. Here we establish a high-resolution Bayesian chronology for a northern South China Sea (SCS) sediment core. By applying a piece-wise linear regression method, we observe a distinct shift in the marine radiocarbon reservoir age at ~16.9 ka BP from our core. This shift hampers the accuracy of chronology, even though within higher dating resolution. Together with chronologies within different resolution, we show that the piece-wise linear regression approach is potential in producing accurate chronology for marine sediment. Based on comparisons with other sediment records from the region we show that the shift in radiocarbon reservoir age was pervasive throughout the SCS basin, coeval with the opening of the Balabac Strait, as global sea level rose. Hence, we highlight that the opening of the Balabac strait had a basin-wide effect on surface ocean circulation in the SCS.

Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy

The carbon content of different types of coal determines its utility in industries and thermal power generation. The most popular and widely used is the conventional method (ultimate analysis) to determine coal’s carbon content (C, wt.%), along with H, N, and S. In the present study, the authors attempted to analyze the carbon content (C in %) in coals via data from Fourier-transform infrared (FTIR) spectroscopy, which can be a promising alternative. As a reference, the carbon content in the coal samples, referred to as CCHNS (in wt.%), was determined from the ultimate analysis. The mid-infrared FTIR spectroscopic data were used to investigate the response of functional groups associated with carbon or its compounds, which were used to model and estimate the carbon content in coal samples (referred to as CFTIR, in wt.%). FTIR spectral signatures were utilized in specific zones (between wavenumbers 4000 and 400 cm−1) from a total of 18 coal samples from the Johilla coalfield, Umaria district, Madhya Pradesh, India. These 18 coal samples were used to produce 126 Coal+KBr pellets (at seven known dilution factors for each coal sample), and the spectral response (absorbance) from each pellet was recorded. For model development and validation, the training set and test set were formed using a 17:1 split (K-fold cross validation). The carbon content in the coal samples was modeled using the training set data by applying the piecewise linear regression method employing quasi-Newton (QN) with a breakpoint and least squares loss function. The model was validated using an independent test set. A pairwise comparison of estimates of carbon in the laboratory from the CHNS analyzer (CCHNS) and modeled carbon from FTIR data (CFTIR) exhibited a good correlation, relatively low error, and bias (coefficient of determination (R2) up to 0.93, RMSE of 23.71%, and MBE of −0.52%). Further, the significance tests for the mean and variance using the two-tailed t-test and F-test showed that no significant difference occurred between the pair of observed CCHNS and the model’s estimated CFTIR. For high-ash coals from the Johilla coalfield, the model presented here using mid-infrared FTIR spectroscopy data performs well. Thus, FTIR can potentially serve as an important method for quickly determining the carbon content of high-ash coals from various basins and can potentially be extended to soil and shale samples.

Rapid Estimation of Sulfur Content in High-Ash Indian Coal Using Mid-Infrared FTIR Data

High-ash Indian coals are primarily used as thermal coal in power plants and industries. Due to the presence of sulfur in thermal coal, flue gas is a major environmental concern. Conventional methods (Ultimate Analysis of Coal) for sulfur content estimation are time-consuming, relatively costly, and destructive. In this study, Fourier-transform infrared (FTIR) spectroscopy has emerged as a promising alternative method for the rapid and nondestructive analysis of the sulfur content in coal. In the present study, the actual sulfur content in the coal samples was determined using Ultimate Analysis (CHNS analyzer). In contrast, mid-infrared FTIR spectroscopic data (4000–400 cm−1) were used to analyze the functional groups related to sulfur or its compounds in the coal samples to predict the sulfur content. A comparison of sulfur estimated using a CHNS analyzer and predicted using mid-infrared spectroscopy (FTIR) data shows that it can accurately predict sulfur content in high-ash Indian coals using the piecewise linear regression method (Quasi-Newton, QN). The proposed FTIR-based sulfur prediction model showed a coefficient of determination (R2) of up to 0.93, where the total no. of samples (Coal + KBr pellets, n) was 126 (using 17:1 split, K-fold cross validation). The root-mean-square error (RMSE, wt.%) is 0.0035, mean bias error (MBE, wt.%) is −0.0003, MBE (%) is 3.31% and mean absolute error (MAE, wt.%) is 0.0020. The two-tailed t-test and F-test for mean and variance indicated no significant difference between the pair of values of observed sulfur (SCHNS, wt.%) using CHNS data and the model predicted sulfur (SFTIR, wt.%) using FTIR data. The prediction model using mid-infrared FTIR spectroscopy data and the Quasi-Newton method with a breakpoint and loss function performs well for coal samples from the Johilla Coalfield, Umaria. Thus, it can be a valuable tool for analyzing sulfur in other ash-rich coals from various basins worldwide.

Potential changes in cooling degree day under different global warming levels and shared socioeconomic pathways in West Africa

Increasing levels of climatic warming are expected to affect the global development of energy consumption. The cooling degree day (CDD) is one of the climate-driven indices that captures the impact of climate on energy demand. However, little is known about the spatiotemporal trends of CDD in relation to a changing climate and economy in West Africa and its main implications. Hence, in order to analyze how energy demand could evolve, this study aims to assess the changes in CDD under 1.5, 2.0, 2.5, and 3.0 °C global warming levels (GWLs), with and without population exposure and trends under the two representative concentration pathways (RCPs) of RCP4.5 and RCP8.5 for West Africa. A climate-reflective base temperature (T-base) is used and was determined using a piecewise linear regression method. Seasonal electricity consumption was derived using a decomposition feature. An ensemble of seven Global Climate Models (GCMs) were used for the future temperature projections. The future population was based on shared socioeconomic pathway outputs. Based on the analysis, the reported average T-base for the West African region is 24 °C. An increasing CDD trend was identified in all of the RCP scenarios, but is more pronounced in RCP8.5. RCP8.5 departs from the mean historical period of approximately 20% by 2100 with the standardized value. The same trend is observed under different GWLs as the warming level increased and was most striking in the Sahelian zone. Population exposure to CDD (labelled CDDP) increases with warming levels, but is more pronounced in highly agglomerated areas. The CDDP index best captures the spatial representation of areas with high cooling demand potential with respect to the demographic distribution. This study can serve to inform better energy demand assessment scenarios and supply planning against the backdrop of changing climate conditions in West Africa.

The Spatio-Temporal Pattern and Transition Mode of Recessive Cultivated Land Use Morphology in the Huaibei Region of the Jiangsu Province

Examining land use transition is a new way of building on the comprehensive research on Land Use/Cover Change (LUCC). Research on transition law and characteristics is important for improving the theory of land use transition and the practice of land resource management, and for being able to provide a basis and reference for promoting socio-economic transformation. Based on the relevant statistical data concerning cultivated land use in the Huaibei area of the Jiangsu Province from 1995 to 2020, and by understanding the county as a unit to be measured, this paper constructed a multi-dimensional (economic–social–ecological) functional index system of recessive morphology, analyzed the spatio-temporal pattern of the transition of cultivated land use, identified transition point mutations, and established the transition mode by adopting multi-dimensional time series point mutation detection and the piecewise linear regression method. The findings suggest that the index of recessive cultivated land use morphology in the Huaibei region of the Jiangsu Province presents a trend of “slow decline to significant growth to stable growth”. Moreover, the index presented evolutionary characteristics such as “high in the middle and east while low in the west”, as well as “the relatively balanced distribution between counties”, thus indicating that the degree of transition deepened, it showed a homogeneous development trend, and the transition process presented obvious “ladder” stage characteristics; therefore, the authors suggest making scientific use of cultivated land resources, in accordance with local conditions, in order to make the land use transition of cultivated land efficient, green, and sustainable.

Insights on the roles of climate and human activities to vegetation degradation and restoration in Beijing-Tianjin sandstorm source region

Evaluating climate and human-induced vegetation change could support managing the sustainability of ecological environment. Based on the Normalized Difference Vegetation Index (NDVI) dataset, meteorological data (temperature, precipitation), land use data and statistical data, this study used a trend quantitative analysis to distinguish the relative roles of climate change and human activities on vegetation degradation and restoration from 1982 to 2015 in Beijing-Tianjin sandstorm source region. Our results demonstrated that the increased vegetation reached 63.8% of the study area during 1982–2015. Using piecewise linear regression method, we identified two turning points within the period 1982–2015 were 1994 and 2004, respectively. By considering the turning points, we found the increasing, decreasing, and increasing trend during 1982–1994, 1995–2004 and 2005–2015, respectively. In 1982–2015, 68.1% of the vegetation change was dominated by the combined effects of climate and human activities, whereas 25.4% was caused by human activities. During 1982–1994, the increasing trend of vegetation was due to favorable climate factors (26.8%) and the combined effects of both (45.4%). In 1995–2004, the vegetation degradation was driven by unreasonable human activities (13%) and the combined effects of unfavorable climate factors and unreasonable human activities (33.1%). However, with the implementation of ecological engineering, the positive human activities (8%) and the combined effects of both (59.1%) were responsible for vegetation restoration during 2005–2015. We noticed that precipitation was the main climate factor limiting regional vegetation change, the sensitivity of vegetation to precipitation was increasing over the study period. Nevertheless, human activities played an increasingly important role in regional vegetation. In particularly, the ecological engineering and increasing precipitation were the main factors for regional vegetation restoration in the past 10 years.

Recent changes in the drought of China from 1960 to 2014

AbstractIn recent years, extreme droughts have been frequent and widespread, and understanding the spatiotemporal variations in drought events in China plays an important role in drought risk management. In this study, using monthly meteorological data at 618 stations, we computed the Standardized Precipitation Evapotranspiration Index with potential evapotranspiration based on the Penman–Monteith equation (SPEI_PM) for drought assessment. Furthermore, we analysed the trend, affected area, frequency and duration of drought in China over the period of 1960–2014 by the piecewise linear regression method (LRM) and Mann–Kendall (MK) method. The results showed that (a) the slight wetting trend reflected during 1960–2014 by the SPEI_PM (0.03/10a (p > .05). This trend was allocated to most parts of northern and southeastern China and was prone to arrive in winter. In contrast, the most significant drying trend was in the Loess Plateau and Yunnan–Kweichow Plateau. (b) The turning point (TP) year of drought trend was 1993. The SPEI_PM was increased in most parts of northern China before 1993 according to the LRM and MK method. However, it decreased after 1993, especially in the south and northwest of China; (c) Over the past 55 years, the areas affected by drought decreased at a rate of −1.23%/10a (p < .05). In detail, these areas decreased at a rate of −3.41%/10a before 1993 (p < .05) and increased at a rate of 2.71%/10a after 1993 (p > .05). (d) The drought frequency in most parts of China was between 15 and 20 during 1960–2014. Furthermore, compared with that in 1960–1977, the drought frequency was decreased in 1978–1995 and then increased in 1996–2014. These findings suggested that the long‐term trend in drought events during 1960–2014 was not significant in China. However, the severity, affected area and frequency of droughts increase after 1993. These results allowed us to understand the changes in the drought across China over the last 55 years, which is important for guiding relevant agricultural activities.

Performance Evaluation of Techniques for Identifying Abnormal Energy Consumption in Buildings

Energy consumption in buildings has steadily increased. Buildings consume more energy than necessary due to suboptimal design and operation. Apart from retro-fitting, not much can be done with the design of the existing building, but the operation of the building can be improved. Ignoring or failing to fix the faults can lead to problems like the higher cost in excess energy usage or premature component failure. At the same time understanding, identifying, and addressing abnormal energy consumption in buildings can lead to energy savings and detection of faulty appliances. This paper investigates two key challenges found in energy anomaly detection research: 1) the lack of labeled ground truth and 2) the lack of consistent performance accuracy metrics. In the first challenge, labeled ground truth is imperative for training and benchmarking algorithms to detect anomalies. In the second challenge, consistent performance accuracy metrics are crucial to quantifying how well algorithms perform against each other. There exists no publicly available energy consumption dataset with labeled anomaly events. Therefore, we propose two approaches that help in the automatic annotation of the ground truth data from publicly available datasets: a statistical approach for short-term data and a piecewise linear regression method for long-term data. We demonstrate these approaches using two publicly available datasets called Dataport (Pecan Street) and HUE. Using different existing accuracy metrics, we run a series of experiments on anomaly detection algorithms and discuss what metrics can be best used for consistent accuracy testing amongst researchers. In addition, while providing the source code, we also release an anomaly annotated dataset produced by this source code.

Piecewise Linear Regression Based on Plane Clustering

Piecewise linear regressions have shown many successful applications in image denoising, signal process, and data mining fields. In essence, they attempt to seek multiple linear functions (piecewise/stepwise function) to fit the given scatter data points by various methodologies, typically point-centered clustering methods, such as ${k}$ -means or fuzzy c means. Obviously, it is reasonable that plane-centered clustering is more suitable for capturing the linearities in data. In this paper, we propose an efficient piecewise linear regression method based on ${k}$ -plane clustering, termed as PlrPC. The proposed method first partitions the data into multiple plane-centered clusters and then analytically compute corresponding piecewise linear functions. Compared with the state-to-the-art linear regressors, the advantages of the PlrPC lie in fourfold: 1) it is generated from plane clustering, which is truly coincident with geometrical intuition; 2) to fuse the linear characteristics into plane clustering, a new implicit regression method is proposed; 3) a new plane jump method is proposed to detect the number of clusters, and; 4) the leading problem can be solved by ordinary eigenvalue problems. The experimental results will show the aforesaid characters on some artificial and some benchmark datasets.

High temperature properties of high viscosity asphalt based on rheological methods

This research aims to comparatively evaluate the high temperature properties of modified high viscosity asphalt (SHVA) by temperature and frequency sweep testing as well as repeated creep and recovery testing. Wheel-tracking testing was also introduced to verify anti-rutting properties of asphalt mixtures. The results indicated that the phase angles (δ) of high viscosity asphalts decreased with increased temperature. The lowest decrease rate of the complex modulus (G∗) reflected that SHAV had the lowest temperature susceptibility. The δ of neat asphalts were nearly unchanged with increased frequency, while the δ of modified asphalts clearly increased. Both temperature and frequency sweep tests indicated that improvements in anti-rutting performance (i.e., smaller δ and bigger G∗) of high viscosity asphalt were noticeable. Burgers model and a piecewise linear regression method showed high accuracy in simulating asphalt’s creep process. Changes in binder creep stiffness and accumulated strain (i.e. 2.8, 2.6, and 2.4% for SBS, TPS and SHAV, compared to 96.4 and 102.9% for two neat asphalts) demonstrated that the polymer used in these modified asphalts significantly enhanced their stiffness and elastic properties. From the analysis of creep compliance, SHVA had the maximum elastic and minimum viscous proportions in comparison with the other four asphalts. Test results of the mixtures showed that the asphalt mixture with SHVA possessed the best high-temperature stability (9265 cycles/mm). Good agreement was found between the high temperature properties of the asphalts and mixtures.

Queue discharge patterns at signalized intersections with green signal countdown device and long cycle length

SummaryTwo apparent features that prevail at signalized intersections in China are green signal countdown device and long cycle lengths. The objective of this study is to investigate the impacts of green signal countdown device and long cycle length on queue discharge patterns and to discuss its implications on capacity estimation in the context of China's traffic. At five typical large intersections in Shanghai and Tianjin, 11 through lanes were observed, and 9251 saturation headways were obtained as valid samples. Statistical analyses indicate that the discharge process of queuing vehicles can be divided into three distinct stages according to the discharge flow rate: a start‐up stage, a steady stage, and a rush stage. The average time for queuing vehicles to reach a stationary saturation flow rate, that is, the start‐up stage, was found to be approximately 20–30 seconds; the rush stage usually occurs during the phase transition period. The finding is contrary to the conventional assumption that the discharge rate reaches a maximum value after the fourth vehicle is discharged and then remains constant during the green time until the queue is completely dissolved. The capacity estimation errors that might arise from the conventional methods are discussed through a comparative study and a sensitivity analysis that are based on the identified queue discharge patterns. In addition, a piecewise linear regression method was proposed in order to reduce such errors. The proposed method can be used for capacity estimation at signalized intersections with the identified queue discharge patterns. Copyright © 2017 John Wiley & Sons, Ltd.

Revealing turning points in ecosystem functioning over the Northern Eurasian agricultural frontier.

The collapse of the Soviet Union in 1991 has been a turning point in the World history that left a unique footprint on the Northern Eurasian ecosystems. Conducting large scale mapping of environmental change and separating between naturogenic and anthropogenic drivers is a difficult endeavor in such highly complex systems. In this research a piece-wise linear regression method was used for breakpoint detection in Rain-Use Efficiency (RUE) time series and a classification of ecosystem response types was produced. Supported by earth observation data, field data, and expert knowledge, this study provides empirical evidence regarding the occurrence of drastic changes in RUE (assessment of the timing, the direction and the significance of these changes) in Northern Eurasian ecosystems between 1982 and 2011. About 36% of the study area (3.4 million km(2) ) showed significant (P<0.05) trends and/or turning points in RUE during the observation period. A large proportion of detected turning points in RUE occurred around the fall of the Soviet Union in 1991 and in the following years which were attributed to widespread agricultural land abandonment. Our study also showed that recurrent droughts deeply affected vegetation productivity throughout the observation period, with a general worsening of the drought conditions in recent years. Moreover, recent human-induced turning points in ecosystem functioning were detected and attributed to ongoing recultivation and change in irrigation practices in the Volgograd region, and to increased salinization and increased grazing intensity around Lake Balkhash. The ecosystem-state assessment method introduced here proved to be a valuable support that highlighted hotspots of potentially altered ecosystems and allowed for disentangling human from climatic disturbances.

Mathematical programming for piecewise linear regression analysis

In data mining, regression analysis is a computational tool that predicts continuous output variables from a number of independent input variables, by approximating their complex inner relationship. A large number of methods have been successfully proposed, based on various methodologies, including linear regression, support vector regression, neural network, piece-wise regression, etc. In terms of piece-wise regression, the existing methods in literature are usually restricted to problems of very small scale, due to their inherent non-linear nature. In this work, a more efficient piece-wise linear regression method is introduced based on a novel integer linear programming formulation. The proposed method partitions one input variable into multiple mutually exclusive segments, and fits one multivariate linear regression function per segment to minimise the total absolute error. Assuming both the single partition feature and the number of regions are known, the mixed integer linear model is proposed to simultaneously determine the locations of multiple break-points and regression coefficients for each segment. Furthermore, an efficient heuristic procedure is presented to identify the key partition feature and final number of break-points. 7 real world problems covering several application domains have been used to demonstrate the efficiency of our proposed method. It is shown that our proposed piece-wise regression method can be solved to global optimality for datasets of thousands samples, which also consistently achieves higher prediction accuracy than a number of state-of-the-art regression methods. Another advantage of the proposed method is that the learned model can be conveniently expressed as a small number of if-then rules that are easily interpretable. Overall, this work proposes an efficient rule-based multivariate regression method based on piece-wise functions and achieves better prediction performance than state-of-the-arts approaches. This novel method can benefit expert systems in various applications by automatically acquiring knowledge from databases to improve the quality of knowledge base.

Application of piecewise linear regression in the detection of vegetation greenness trends on the Tibetan Plateau

Vegetation plays an important role in regulating the climate system and terrestrial carbon cycle. In recent decades, numerous studies have focused on monitoring vegetation greenness changes at different scales with long-term remote-sensing data. Most previous studies adopted a monotonic linear regression approach (one-piece linear regression) to detect vegetation greenness trends at global and regional scales. However, the monotonic linear regression approach is limited in its ability to detect abrupt changes in vegetation greenness trends because non-monotonic phenomena may exist in these trends. This study applied a piecewise linear regression (PLR) method to detect the non-monotonic trends of vegetation greenness in the Tibetan Plateau from 1982 to 2006. The vegetation greenness was indicated by the normalized difference vegetation index (NDVI) data derived from Global Inventory Modelling and Mapping Studies (GIMMS) Advanced Very High Resolution Radiometer (AVHRR) data. The results implied that the PLR method could detect an abrupt change in the trend with a break point (i.e. the year when the vegetation greenness trend changed abruptly) mainly concentrated in 1989. We chose three typical regions to compare the PLR and monotonic linear regression approach for vegetation trend detection to verify the effectiveness of the PLR method. The PLR method can detect different trends during different periods, but the one-piece linear regression method can only detect monotonic trends. After comparing the results of the two methods for three climatic zones, we found that the greenness trend detected by PLR can be better explained by temperature and precipitation variations. Our results illustrate that the PLR method is superior to the one-piece linear regression method due to its ability to detect non-monotonic trends. It can, therefore, delineate vegetation greenness trends in detail and be applied to other, similar studies.

The Estimation of Base Temperature for Heating and Cooling Degree-Days for South Korea

AbstractIn South Korea, heating degree-days (HDD) and cooling degree-days (CDD) have been widely used as climatic indicators for the assessment of the impact of climate change, but arbitrary or customary base temperatures have been used for calculation of HDD and CDD. The purpose of this study is to determine real base temperatures to accurately calculate HDD and CDD for South Korea, using monthly electric energy consumption and mean temperature data from 2001 to 2010. The results reveal that the regional electricity demand generally depends on air temperature in a V-shaped curve in urban settings but in an L-shaped curve in rural settings, indicating that the sensitivity of the electricity demand to the temperature change is affected by the size of cities. The South Korean regional base temperatures, defined by a piecewise linear regression method, range from 14.7° to 19.4°C. These results suggest that the assessment of climate change impacts on the energy sector in South Korea should be carried out on a regional scale.

The effect of temperature on growth of early life stages of the common squid Loligo vulgaris

The squid Loligo vulgaris has an extended spawning season within the upwelling system off north-west Portugal, and its paralarvae may thus develop under a wide range of environmental conditions. Both temperature and salinity are expected to affect the metabolism of young squid and we tested their effects on growth during the embryonic and post-hatching phase, based on measurements of growth increments in statoliths of juveniles and adults, using generalized additive models. There was no evidence that statolith increments representing early growth become unreadable in adult statoliths. Variability in the statolith size at hatching was weakly but significantly related to the variables in the model. On the other hand, the effects on statolith growth of both sea surface temperature and of sea bottom temperature were significant during early post-hatching life. Thicker increments are deposited in the statoliths of squid living under higher temperatures, which results in summer hatchers having larger statoliths at the age of 90 days. Inspection of the statolith accretion pattern, using a piecewise linear regression method, revealed an ontogenetic shift in increment width, which may be an indication of the age of transition from paralarva to juvenile. On this basis, it is suggested that the planktonic stage lasts 60 or 90 days, depending on whether the paralarvae lived at higher (&gt;15°C) or lower (&lt;15°C) sea surface temperatures. The life strategy under warmer conditions potentially favours survival by reducing the duration of the vulnerable planktonic phase.

SOC Estimation of Ni-MH Battery Pack Based on Approved HPPC Test and EKF Algorithm for HEV

In this paper, Methods of SOC estimation of Extended Kalman Filter (EKF) is studied based on the characteristics of Nickel-Metal Hydride (Ni-MH) battery pack with 120 cells in series and 8Ah capacity for HEV. In the study of EKF-based SOC estimation, the improved Thevenin circuit model is adopted, and a new hybrid pulse power characterization （HPPC） test is designed to identify the model parameters by using piecewise linear regression method. In this way, the precision of the circuit model is improved. In addition, The Kalman gain matrix is optimized for EKF iterative algorithm by two ways: a constant gain is increased taking into account the entire process; a dynamic gain which increases at the beginning of abrupt change and decreases rapidly after abrupt change is set up. The improvement achieves a good tracing prediction.

Early development and allometric growth in the armoured catfish Corydoras aeneus (Gill, 1858)

An ontogenetic series of in-captivity bred Corydoras aeneus was used, in order to study the developmental changes in the external morphology. Allometric growth of several body parts was studied, attempting to reveal important steps in the species’ early life history. Based on the external morphology, the different stages during early development of C. aeneus were identified, according to Balon (Journal of the Fisheries Research Board of Canada 32:1663–1670, 1975). After hatching, at a SL of 3.5 mm, the developmental state corresponded to an eleutherembryonic phase, followed by the protopterygiolarval phase (4.4–5.7 mm SL), the pterygiolarval phase (5.7–14.0 mm SL) and the juvenile period. In addition, an overall growth curve and inflexion points were determined. As such, ontogenetic changes in growth coefficients k (in SL = b age k ) were determined. Log transformed data were used for a piecewise linear regression method, as per regression spline smoothing procedures. This way, the growth curve could be divided into six different intervals of growth rate. Initially, the slope was 0.05 until 0.7 dph, then increasing to 0.18 until 4 dph, and 0.36 until 10 dph. After this, growth rate reached a maximum of 0.76 until 24 dph, slowed down to 0.47 until 37 dph and then finally again slowed down to 0.36. A similar growth analysis was also done on the different body parts and these results were compared to both morphological and data from literature. This led to the conclusion that the inflexion points found during the early development of C. aeneus matched the different key-events known in teleost early life history and development. The transition from endo- to exogenous feeding, at the moment a functional branchial respiratory system becomes increasingly important, was the first point at which allometries changed together with functional demands. A second, similar congruence occurred at the transition to the pterygiolarval phase, when priorities shift towards locomotory needs. Finally, our results also indicated a transition to a carangiform swimming mode at approximately 8 mm SL.

Norwegian spring‐spawning herring as the test case of piecewise linear regression method for detecting maturation from growth patterns

Data from Norwegian spring‐spawning herring Clupea harengus, were used empirically to assess the performance of a regression method aimed at detecting the time of the onset of maturation from growth trajectories. At the level of the whole dataset, the piecewise linear‐regression method was accurate and showed only a minor bias (−0·17 years) relative to age at maturation visually read from scales. The method, however, was relatively imprecise and provided an estimate of age at maturation equal to the one read from the scale in less than half of the cases (47·6%). Moreover, bias was strongly dependent on age at maturation: the age at maturation of early maturing fish was often overestimated, whereas the opposite was true for late‐maturing fish. Accuracy and precision of the regression method relative to visual readings also depended on the growth type (determined by the nursery area of young C. harengus) and cohort but not on sex. Modifying the original regression method enabled marginally improving the precision of the approach but a strong maturation age‐dependent bias persisted. The results with C. harengus suggested that age‐at‐maturation estimates from the piecewise linear regression method should be treated with caution.

Crop yield estimation model for Iowa using remote sensing and surface parameters

Numerous efforts have been made to develop various indices using remote sensing data such as normalized difference vegetation index (NDVI), vegetation condition index (VCI) and temperature condition index (TCI) for mapping and monitoring of drought and assessment of vegetation health and productivity. NDVI, soil moisture, surface temperature and rainfall are valuable sources of information for the estimation and prediction of crop conditions. In the present paper, we have considered NDVI, soil moisture, surface temperature and rainfall data of Iowa state, US, for 19 years for crop yield assessment and prediction using piecewise linear regression method with breakpoint. Crop production environment consists of inherent sources of heterogeneity and their non-linear behavior. A non-linear Quasi-Newton multi-variate optimization method is utilized, which reasonably minimizes inconsistency and errors in yield prediction.Minimization of least square loss function has been carried out through iterative convergence using pre-defined empirical equation that provided acceptable lower residual values with predicted values very close to observed ones (R2=0.78) for Corn and Soybean crop (R2=0.86) for Iowa state. The crop yield prediction model discussed in the present paper will further improve in future with the use of long period dataset. Similar model can be developed for different crops of other locations.