Best-fit Curve Research Articles

Learning curve and complication analysis of oblique lateral interbody fusion in cases with single-segment lumbar tuberculosis: a retrospective single-center consecutive study

ObjectiveOblique lateral interbody fusion (OLIF) is becoming widely used in patients with single-segment spinal tuberculosis (STB). The learning curve and complications associated with OLIF for the treatment of STB are still unknown. This study aims to figure out the learning curve and associated complications experience of OLIF for the treatment of STB.MethodsBetween September 2018 and August 2023, 61 STB patients underwent OLIF plus percutaneous pedicle screw fixation (PPSF) were consecutively included in this research. Cumulative sum analysis (CUSUM) was applied to establish the learning curve of OLIF and determine the cut-off case number. All cases were divided into learning and experienced groups based on the cut-off case number. Clinical characteristics and relating surgical complications were compared and analyzed between the two groups.ResultsThe best-fitting curve was quadratic. The fitting equation was CUSUM (min) = 95.83 + 34.16x − 0.611 x 2 (x means operation case). The cut-off operation case number was 27 cases. The presence of significant differences particularly in the duration of operation, operative blood loss, overall incidence, and severity of postoperative complications was noteworthy between the two groups.ConclusionA total of 27 cases is the minimum number to master OLIF combined with PPSF for the treatment of patients with single-segment STB. When surgeons master this operation, the operative time, operative blood loss, overall incidence, and severity of postoperative complications will improve.

Regression-based model predicting cost contingencies for road network projects

Cost contingencies represent a significant markup percentage for construction projects. Many construction projects experience cost overruns due to unexpected events before or during the construction phase. Determining the cost contingency for construction projects is one of the most frequent and widespread financial problems many countries face. Since the amount included in the estimates is always insufficient to absorb the actual cost overruns of the project. The main objective of this paper is to develop a simple realistic regression model for predicting the appropriate contingency amount for road network projects before the bidding stage. The regression model has been developed based on data from real case studies for 75 road network projects to adequately consider all potential risks may face the project. Good-of-fit tests were used to select the best-fitting statistical distribution for a given dataset. Based on the analysis of selected data, the log-logistic probability distribution function is the best-fit curve representing cost overrun behavior, and the cumulative distribution function has been used to determine the realistic probability of incurred cost overrun. Based on the analysis of real case studies, it is suggested that an average of 59.722% be used as a cost overrun contingency amount for road network projects. The study outcomes will help practitioners and researchers improve future accuracy/calculations of contingency amount for road construction projects and improve the decision-making process.

Evidence for gravitational self-lensing of the central supermassive black hole binary in the Seyfert galaxy NGC 1566

It is generally accepted that all massive galaxies host supermassive black holes (BHs) in their center and that mergers of two galaxies lead to the formation of BH binaries. The most interesting among them comprise the mergers in their final state, that is to say\ with parsec (3.2 light years) or sub-parsec orbital separations. It is possible to detect these systems with binary self-lensing. Here we report the potential detection of a central supermassive BH binary in the active galaxy (AGN) NGC\,1566 based on a microlensing outburst. The light curve of the outburst -- based on observations with the All Sky Automated Survey for SuperNovae -- lasted from the beginning of 2017 until the beginning of 2020. The steep symmetric light curve as well as its shape look very different with respect to normal random variations in AGN. However, the observations could be easily reproduced with a best-fit standard microlensing light curve. Based on the light curve, we derived a characteristic timescale of 155 days. During the outburst, the continuum as well as the broad line intensities varied; however, the narrow emission lines did not. This is an indication that the lensing object orbits the AGN nucleus between the broad line region (BLR) and the narrow line region (NLR), that is, at a distance on the order of 250 light days. The light curve can be reproduced by a lens with a BH mass of $5 M_ odot $. This implies a mass ratio to the central AGN on the order of 1 to 10.

Solar eclipse maps – historical and contemporary methodologies

ABSTRACT The first part of this paper examines the history of solar eclipse maps of the world from the seventeenth century to present. It compares the path of centrality (totality or annularity) on a few antique maps with the corresponding path given by modern astronomy programs. The research examines the connections between cartography, astronomy, ancient history, chronology, and archaeology. Since the uncertainties of Delta-T are accountable for the accuracy of eclipse maps, special attention has been devoted to ancient key eclipses. Through the identification of the earliest solar eclipse traditions in the Near East, it recommends new chronological anchors to help astronomers, mapmakers, and editors of historical atlases. It suggests the acceptance of four eclipses of the Long Chronology between June 2353 B.C. and February 1659 B.C. for the rate of Earth’s rotation. Optimal combined selection of ancient historical eclipse records and traditions is presented, to obtain the best-fitting curve of Delta-T (clock-time error) that is the key for the accuracy of solar eclipse maps. Several early historical solar eclipses are identified with high probability.

Improvement of field falling-head test and determination of hydraulic conductivity using Darcy’s equation

This study presents a novel permeameter design for field falling-head tests to determine vertical hydraulic conductivity (K) using Darcy's equation. The design features an open-ended standpipe with two ports for simultaneous measurement of hydraulic heads at both ends of the sediment column, enabling direct estimation of flow rate (q) and hydraulic gradient (i). Flow rate is calculated by differentiating the best-fit curve for water level change above the sediment, and the hydraulic gradient is derived from the head difference between the ports. Laboratory and field tests consistently demonstrated a strong linear relationship between q and i (R2 > 0.999), validating the applicability of Darcy's equation for this new permeameter design. The K values obtained using the proposed method matched those obtained using the Hvorslev equation method. Furthermore, the design allows for continuous measurement of heads after the falling-head permeameter test, facilitating the collection of time series data for the hydraulic gradient. When combined with a pre-determined K value, this enables calculation of time series for the seepage rate across the surface water/sediment interface. To demonstrate this capability, preliminary tests were conducted using commercially-available pressure transducers to monitor heads and obtain seepage time series. The results of these tests are also presented.

Extending the Hough transform to recognize and approximate space curves in 3D models

Feature curves are space curves identified by color or curvature variations in a shape, which are crucial for human perception (Biederman, 1995). Detecting these characteristic lines in 3D digital models becomes important for recognition and representation processes. For recognizing plane curves in images, the Hough transform (HT) provided a very good solution to the problem. It selects the best-fitting curve in a dictionary of families of curves through a voting procedure that makes it robust to noise and missing parts. Since 3D digital models are often obtained by scanning real objects and may have many defects, the HT has been extended to recognize and approximate space curves in 3D models that correspond to relevant featuresThis work overviews three HT-based different approaches for identifying and approximating spatial profiles of points extracted from point clouds or meshes. A first attempt at this extension involved projecting the spatial points onto the regression plane, thus reducing the problem to planar recognition and using families of plane curves. A second approach has been proposed to recognize spatial profiles that cannot be projected onto the regression plane, using two types of space curve families. Unfortunately, the main drawback of methods based on traditional HT is that it requires prior knowledge of which family of curves to look for.To overcome this limitation, a third method has been developed that provides a piecewise space curve approximation using specific parametric polynomial curves. Additionally, free-form curves that a parametric or implicit form cannot express can be represented using this technique.In the paper, we also analyze the pros and cons of the various approaches and how they managed and reduced the HT's computational cost, given the large number of parameters introduced when families of space curves are considered.

Unmanned Aerial Vehicle-Assisted Terahertz-Visible Light Communication Systems: An In-Depth Performance Analysis.

This paper investigates the performance of dual-hop unmanned aerial vehicle (UAV)-assisted communication channels, employing a decode-and-forward (DF) relay architecture. The system leverages terahertz (THz) communication for the primary hop and visible light communication (VLC) for the secondary hop. We conduct an in-depth analysis by deriving closed-form expressions for the end-to-end (E2E) bit error rate (BER). Additionally, we use a Monte Carlo simulation approach to generate best-fitting curves, validating our analytical expressions. A performance evaluation through BER and outage probability metrics demonstrates the effectiveness of the proposed system. Specifically, our results indicate that the proposed system outperforms Free-Space Optics (FSO)-VLC and Radio-Frequency (RF)-VLC at a higher signal-to-noise ratio (SNR). The results of this study provide valuable insights into the feasibility and limitations of UAV-assisted THz-VLC communication systems.

COVID-19 Pandemic and Impact on Research Publications in Critical Care.

The COVID-19 pandemic precipitated a significant transformation of scientific journals. Our aim was to determine how critical care (CC) journals and their impact may have evolved during the COVID-19 pandemic. We hypothesized that the impact, as measured by citations and publications, from the field of CC would increase. Observational study of journal publications, citations, and retractions status. All work was done electronically and retrospectively. The top 18 CC journals broadly concerning CC, and the top 5 most productive CC journals on the SCImago list. None. For the top 18 CC journals and specifically Critical Care Medicine (CCM), time series analysis was used to estimate the trends of total citations, citations per publication, and publications per year by using the best-fit curve. We used PubMed and Retraction Watch to determine the number of COVID-19 publications and retractions. The average total citations and citations per publication for all journals was an upward quadratic trend with inflection points in 2020, whereas publications per year spiked in 2020 before returning to prepandemic values in 2021. For CCM total publications trend downward while total citations and citations per publication generally trend up from 2017 onward. CCM had the lowest percentage of COVID-related publications (15.7%) during the pandemic and no reported retractions. Two COVID-19 retractions were noted in our top five journals. Citation activity across top CC journals underwent a dramatic increase during the COVID-19 pandemic without significant retraction data. These trends suggest that the impact of CC has grown significantly since the onset of COVID-19 while maintaining adherence to a high-quality peer-review process.

Robotic intersphincteric resection for low rectal cancer: a cumulative sum analysis for the learning curve.

This study aimed to assess the learning curve of robot-assisted intersphincteric resection for low rectal cancer. We retrospectively analyzed the clinical data of 89 patients who underwent robot-assisted intersphincteric resection. All surgeries were performed by the same group of surgeons at our institution between June 2016 and April 2021. The learning curve was evaluated using a cumulative sum analysis and the best-fit curve. The different stages of the learning curve were compared based on patient characteristics and short-term clinical outcomes to evaluate their impact on clinical efficacy. The minimum number of cases required to overcome the learning curve was 47. The learning curve was divided into the learning improvement and proficiency stages. Significant differences were observed in the operation time and the number of lymph nodes between the two stages (P < 0.05), whereas no significant differences were found in intraoperative blood loss, first postoperative exhaust time, postoperative complications, 3-year progression-free survival, overall survival, and local recurrence-free survival (P > 0.05). Robotic-assisted intersphincteric resection for low rectal cancer exhibits a learning curve that can be divided into two stages: namely, learning improvement and proficiency. Achieving proficiency requires a minimum of 47 surgical cases.

Correlating Measured SPT-N, Shear Wave Velocity and Liquid Limit Values in Melekli Region, Igdır (Türkiye)

Characterization of soil layers underneath or having interaction with structures is substantially critical for the overall stability of structures under static and dynamic conditions. The main objectives in characterizing soil are mainly to determine ultimate bearing capacity, settlement, and liquefaction potential. Additionally, the dynamic behavior of soil during seismic excitation, as well as its interaction with structures, should be determined. In these regards, Standard Penetration Test blow counts (SPT-N) and shear wave velocity (Vs) values of soils obtained directly through field tests are known to reflect the soil characteristics, strongly. Therefore, any correlation between these two soil parameters is always in utmost interest. This study assesses the correlation between Vs and SPT-N values measured in Melekli region, Igdır (Türkiye). Moreover, four existing correlations in the literature are presented. The best-fit curve for the measured data is shown to divert from the existing correlation curves, which are also significantly different from each other, for all soils, sand, and clay soils. This can be attributed to the uniqueness of correlation to the study site as geological conditions at one site differ extensively from another site. There seems to be valuable correlation between Vs and water content and liquid limit in the studied area.

Learning curve of ultrasound-guided percutaneous nephrolithotripsy in the treatment of complex renal calculi.

This study was performed to investigate the learning curve of ultrasound-guided percutaneous nephrolithotripsy (PCNL) for the treatment of complex renal calculi. Seventy-two patients with complex renal stones who underwent ultrasound-guided PCNL by the same operator from November 2016 to September 2020 were retrospectively analyzed. The learning curve of PCNL for complex renal stones was analyzed using multifactorial cumulative sum (CUSUM) analysis and best-fit curves. The CUSUM best-fit curve peaked at Case 36, which represented the minimum number of cumulative cases required to cross the learning curve for this surgical procedure. Accordingly, the learning curve was divided into a learning improvement period (36 cases) and a proficiency phase (36 cases). Compared with the learning improvement period, the proficiency phase was characterized by a shorter puncture time and operation time, a smaller drop in the hemoglobin level, and a lower postoperative complication rate. The learning curve of PCNL for the treatment of complex renal calculi can be divided into a learning improvement period and a proficiency phase, and the minimum cumulative number of cases is 36.

Geomorphic Imprints of Active Tectonics of the Bikaner-Nagaur Petroliferous Rift Basin and its Surroundings (Western Rajasthan, India)

Abstract Geology of sedimentary rift-basins require strong geomorphic input for a proper interpretation of active tectonics. Rift-related sedimentation took place in western Rajasthan of the Indian shield, which includes the Bikaner-Nagaur basin (BNB) and a few other adjacent basins. The sedimentation history of the BNB includes Proterozoic, Cambrian, Permo-Carboniferous and from Paleocene to the Recent. This study analyses river profiles with the best-fit curve (R2) model for the BNB and the surrounding regions. The research shows that the watershed 3 within the study area is most active tectonically, through which multiple faults and lineaments pass. Hypsometric Curves (HCs) of watersheds 1, 2 and 3 indicate that these watersheds are tectonically active. This inference is based on the concave profiles of HCs at the head, and convex profiles of HCs at the body and toe sections. Clustering of sixty segments (S) of the considered rivers based on linear-scale morphometric parameters, e.g., sinuosity index (SI), stream length gradient index (SL) and concavity (θ) enabled segment-wise comparison of river profiles with similar values. These segments were compared pair-wise, and Euclidean-based dissimilarity (dR) values were calculated between each such pair. The findings too imply that tectonic activeness exists in parts of watersheds 1, 2 and 3. The channel flow lines are controlled by faults/lineaments as per the micro-scale examination of the drainage network and faults/ lineaments analysis. Under structural control, nine major geomorphic units emerged with distinct erosional surfaces, denudational hillocks, dissected hills and inselbergs. Detailed geomorphic map with micro-scale studies revealed a slope retreat process that resulted in landforms viz., pediment, pediment slope and active flood plains.

The sensory evaluation and antimicrobial efficacy of Lactobacillus acidophilus supernatant on Salmonella enteritidis in milk.

Postbiotics are metabolites derived from living probiotic bacteria like Lactobacillus strains, during the fermentation process and/or produced in pure form on laboratory scales. These compounds, depending on the type of probiotic from which they are prepared, have specific antibacterial agents such as: organic acids, bacteriocins, short-chain fatty acids, and peptides. The objective of this study was to investigate the effect of Lactobacillus acidophilus supernatant (LAS) on the growth pattern of Salmonella enteritidis at fluctuating temperatures and the sensory evaluation of milk that contains this probiotic. Baranyi and Roberts's model determined the best-fit curve for the microbial growth. According to mathematical equations, the highest and lowest specific growth (μ max) rates of S. enteritidis were obtained at 0.055 h-1 and 0.0059 h-1 and also highest and lowest maximum generation time (MGT) values were obtained at 20.06 h and 8.85 h, respectively. Sensory evaluation by the Triangel test reveals that LAS could not establish a significant (p > .05) adverse effect on milk perceptible. Regarding the results obtained in the present study, LAS, without causing adverse sensory change, could act as a safe food additive for the control of bacterial pathogens and reducing food waste, particularly in milk and milk-containing food products.

Validation of fibre stress utilization model for modified ring spun yarns

To model the fibre stress utilization in modified ring spun yarns, we developed an analytical formula from the experimental data. The development of empirical formulae is carried out by using two different techniques, i.e., Cubic Spline and Artificial Neural Network methods. The experimental data of stress-strain curves of fibre and yarn has a large variation. To cope this variability, we used the smoothing spline technique to find the best-fit curve with respect to a reasonable smoothness. The best nonlinear smooth fitting can be used to extrapolate the experimental data beyond the breaking point. The modified ring spun yarns (compact, SIRO and SIRO-compact) with 20/1, 30/1 and 40/1 English count, produced from viscose staple fibre, were used to predict fibre stress utilization up to the yarn break by extrapolating the mean stress-strain curves of fibre and yarn by using the artificial neural network. Moreover, a new distribution function of fibre distribution in yarn has been proposed and successfully implemented for the prediction of fibre stress utilization in yarn. The new formulation helps to compute the fibre stress utilization in the yarn analytically. The validation of the proposed methodology is presented by comparing the numerical results with the experimental data. The predicted fibre stress utilization was in good agreement with the experimental fibre stress utilization for all types of modified ring spun yarns. It has been observed that SIRO-compact yarn exhibits improved fibre stress utilization as compared to SIRO and compact yarns. Moreover, the new distribution functions Gamma and Gaussian distribution were introduced in parallel with the Dirac delta function. In previous similar studies on ring, rotor and air-jet spun yarns, the proposed model can only predict the fibre stress utilization before the breakage point whereas the modified model, in this study, can predict the fibre stress utilization up to the breaking point.

Data-Driven Circular Economy of Biowaste to Bioenergy with Conventional Prediction Modelling and Machine Learning

Rapid population growth has not only increased energy demand, but waste generation that has increased and introduced emerging pollutants into waste streams, posing sanitary and environmental risks. The purpose of this research was to investigate waste to energy (anaerobic digestion-bioenergy process) on integrated waste management and digitalization of biomethane production as a data-driven circular economy model across the value chain of the carbon cycle (transition from take-make-dispose to use-make-return) through waste quantification, characterization, biomethane potential test (BMP) to prediction of the biomethane production. Anaerobic digestion experiment was conducted at a laboratory scale to analyze biomethane production from diverse substrates such as food waste, cow manure, sewage sludge, and chicken manure, with an average pH of 7.58. The experimental results obtained were then modelled and simulated with Modified Gompertz, Logistic, and Richards models and compared to machine learning simulation using Python with Gompertz, Logistics, and Richards models. Modified Logistic model was shown to be the best-fit curve, with a coefficient of determination (R2) > 0.9 validating the conventional mathematical modeling and simulation performance. During simulation with machine learning (Python), the experimental results obtained from the cow manure substrate provided the best fitting curve to the training curve compared to other substrates with the highest average of R2 as 1.0 for training, validation, and test data. Cow manure had the best validation performance at MSE (mean squared error) of 25.36 at epoch 1.Graphical

Predictive mechanistic modeling of loading and elution in protein A chromatography

Protein A chromatography is an enabling technology in current manufacturing processes of monoclonal antibodies (mAbs) and mAb derivatives, largely due to its ability to reduce the levels of process-related impurities by several orders of magnitude. Despite its widespread application, the use of mathematical modeling capable of accurately predicting the full protein A chromatographic process, including loading, post-loading wash and elution stages, has been limited. This work describes a mechanistic modeling approach utilizing the general rate model (GRM), the capabilities of which are explored and optimized using two isotherm models. Isotherm parameters were estimated by inverse-fitting simulated breakthrough curves to experimental data at various pH values. The parameter values so obtained were interpolated across the relevant pH range using a best-fit curve, thus enabling their use in predictive modeling, including of elution over a range of pH. The model provides accurate predictions (< 3% mean error in 10% dynamic binding capacity predictions and ∼ 5% mean error in elution mass and pool volume predictions, both on scale-up) for various residence times, buffer conditions and elution schemes and its effectiveness for use in scale-up and process development is shown by applying the same parameters to larger columns and a wider range of residence times.

Characterization of cranial growth patterns using craniometric parameters and best-fit logarithmic growth curves

Few studies have reported a complete quantitative database of cranial growth, from infancy to adulthood, as a reference through three-dimensional analysis. Our study aimed to characterize cranial growth patterns using craniometric parameters by establishing sex- and age-specific norms.In total, 1009 Korean patients (male-to-female ratio, 2:1; age range, 0–18 years) who underwent thin-slice computed tomography (CT) scans for head trauma were divided into 20 age groups, with a 6-month interval for those under 2 years and a 1-year interval for those over 2 years. After four reference planes [Frankfurt horizontal (FH), midsagittal, and two coronal planes passing the sella (S) and basion (B)] had been established, intracranial volume (ICV), anteroposterior diameter (APD), biparietal diameter (BPD), cranial heights (CHs), cephalic index (CI, BPD/APD), and height index (HI, CH–B/APD) were measured using Mimics software. Best-fit logarithmic curves were derived using a linear regression model.The best-fit curves for ICV (cm3) were y = 785.6 + 157*ln(age) for males (R2 = 0.5752) and y = 702 + 150.5*ln(age) for females (R2 = 0.6517). After adjustment for age, males had higher values of ICV, APD, BPD, and CHs than females (all p < 0.0001). ICV, APD, BPD, and CHs demonstrated a rapid increase during the first few months of life, reaching 90–95% of the adult size by 5–6 years of age, while CI and HI showed a continuous decline by 4%, regardless of sex.This study presented cranial growth references for more than 1000 of the Korean population aged up to 18 years. This might help to provide guidelines for diagnosis and treatment (including timing, amount, and direction) for cranial reconstruction in pediatric patients with craniosynostosis.

Estimating functions for visual field progression in newly diagnosed exfoliation glaucoma patients in Sweden.

This study aimed to determine whether glaucoma progression was linear or not in newly diagnosed exfoliation glaucoma patients. A total of 96 patients with newly diagnosed exfoliation glaucoma were included. These patients were required to undergo at least seven visual field tests within 3years (± 1month), and all were treated at the time of inclusion. The study was a non-randomized, prospective cohort study. The outcome of the study was visual field progression. Progression was assessed based on mean deviation (MD), visual field index (VFI), and "Guided Progression Analysis". The MD and VFI values were plotted against time, and distribution and curve fit were calculated. The results showed that the general rate of progression of the cohort was - 3.84 (± 2.61) dB for the MD values and 9.66 (± 6.25)% for the VFI values over 3years. The best-fitted curve for MD and VFI values in the 36months period was significant for both linear and exponential curves (p ≤ 0.001; p ≤ 0.001). However, in the MD group, the F and the R2 values were higher for exponential than for linear function (linear: F = 42.60, R2 = 0.059; exponential: F = 53.26, R2 = 0.073). The opposite results were found among VFI values. The F and the R2 values were slightly better for linear than for exponential (linear: F = 37.22, R2 = 0.052; exponential: F = 35.55, R2 = 0.050). In conclusion, the study found that visual field progression between diagnosis and 18months seemed to be exponential. However, after 18months, the IOP reduction effects probably ameliorated progression, making the curve linear.

Swiss deep drilling campaign 2019–2022: Geological overview and rock properties with focus on porosity and pore-space architecture

A recently completed deep drilling campaign, comprising 9 deep boreholes penetrating the Mesozoic sedimentary sequence of northern Switzerland, yielded >6 km of drillcore. One of the main objectives was to characterise the low-permeability sequence with the lower Jurassic Opalinus Clay in its centre, motivated by the site selection for a deep geological repository for radioactive waste. In this context, the chemical and isotopic composition of the porewater, as well as the mineralogical and petrophysical properties of the rocks, were among the main study targets. In this paper, the main objective was the characterisation of the lithologically diverse Jurassic rock sequence with focus on mineralogy, porosity and pore-space architecture. Given the large amount of data, a well constrained relationship between clay content (relating to fine-grained sheet-silicate minerals) and porosity could be established. Porosity increases with clay content along a concave curve, but in detail minor formation-specific differences of the positions relative to the best-fit curve were identified. These are attributed to the highly variable deposition rates that resulted in different times available for early diagenesis to alter the rock fabric by mineral dissolution and cementation, thereby affecting the compaction behaviour. Pore-size distributions were obtained from N2 ad-/desorption isotherms. A distinct peak at a radius of 3 nm can be clearly correlated with clay minerals, whereas limestones are dominated by pore sizes in the range of 40–100 nm, and marls show intermediate distributions. A conceptual framework is proposed distinguishing a nanometric porosity that is proportional to the clay content and a contribution of larger pores that are related to the geometric incompatibility between platy clay minerals and isometric calcite or quartz. The contribution of these larger pore is thought to explain the curvature of the clay content-porosity relationship and the more limited compaction of the clay in pressure shadows adjacent to the larger grains. A number of outliers towards high porosity at a given clay content were identified in oolites (most strongly in Fe-rich oolites), sandstones and a unit containing coral-reef material. All these units have in common the presence of competent calcite or quartz grains at the time of deposition, leading to grain-supported fabrics and therefore to a more limited compaction in the interstitial pore space.

Determination of the effect of the dam age, birth type, and sex on growth curve traits of hair goat kids.

This study was conducted to investigate the best-fit growth curve and dam age, sex, and birth type effect on growth curve traits of hair goat kids. Monthly 3858 test day body weight (BW) records of 643 hair goat kids from birth to 150days of age were used to determine the best-fit growth curve and estimate growth curve parameters with Gompertz and Von Bertalanffy models. The BW records were assigned to three groups: dam age (3, 4, 5, 6, 7years), sex (female, male), and birth type (single, twin). The Gompertz model gave more consistent results than the Von Bertalanffy model according to the goodness of fit criteria. Dam age had no significant effect on any of the growth curve traits. Sex of kids showed a significant effect on maturity index (parameter K) (P < 0.001), estimated mature body weight (parameter A), and weight at point of inflection (IPW) (P < 0.01). Also, birth type had a significant effect on initial/birth weight (parameter B) and parameter K (P < 0.001). Age at point of inflection (IPT) was not affected by any of the factors. Twin kids had a higher maturity index than singles while females higher than males. In conclusion, the Gompertz model was the most suitable model for hair goat kids for selection strategies. For proper selection, the effect of sex and birth type on growth curve traits should be considered by hair goat breeders.