Effects Of Influencing Factors Research Articles

CFD simulation on internal flow field of typical hydrocyclone for coal and development of novel hydrocyclone

The work conducted flow field analysis of a typical hydrocyclone (φ500 type) in the Coal Processing Plant using CFD simulation, including hydrocyclone modeling, flow field development, static pressure distribution, three-dimensional velocity, and air column development. Besides, the effects of influential factors on the classification effect of hydrocyclone were studied. On this basis, a novel hydrocyclone model was developed and simulated for the ultrafine classification of coal slurry. The static pressure and velocity in the typical hydrocyclone have good symmetry and certain regularity. Decreasing the overflow pipe diameter and cone angle, while increasing the underflow pipe diameter, cylindrical section height, and feed rate will increase the classification efficiency of hydrocyclone. The novel hydrocyclone designed of annular feeding, small cone angle, and large cone bottom reduces energy consumption, decreases classification size, and improves classification accuracy. CFD simulation results show that the novel hydrocyclone has higher classification efficiency and smaller classification size over the typical hydrocyclone. The novel hydrocyclone develops an excellent ultrafine classification effect of coal slurry and provides a prospective approach for the industrial application of hydrocyclone in the fine coal ultrafine classification process.

New insights into geotribology of non-dilative interfaces from novel experimental studies

The paper presents new insights into the particle kinematics and tribological aspects and their effects on the non-dilative interface shear response from novel experimental investigations. A custom-designed apparatus that enables image analysis of particulate-continuum materials interactions from the bottom of the interface plane while shearing was developed. The effect of influential factors on the frictional mechanism, particle kinematics, and subsequently on the friction coefficient was investigated by performing experiments on three types of sands at different normal stresses with a transparent acrylic sheet and smooth geomembrane. The results demonstrated that the frictional response of the acrylic sheet and geomembrane was comparable, indicating that their particle kinematics at the interface could be similar. However, the critical normal and peak shear stresses differed due to the materials’ hardness. The image and micro-topographical analysis of the tested interfaces revealed that the box fixity, particle shape, and normal stress influence particle kinematics and shear-induced surface changes. The fixed box has shown restricted particle movements compared to the conventional box. Angular and smooth spherical particles exhibited lesser kinematics despite a huge difference in the shape and shear-induced surface changes. Rough spherical particles have larger displacements and shear-induced surface changes than smooth spherical particles.

Community attachment in the context of urban settlement regeneration: Mediating role of resident interaction

Large-scale urban settlement regeneration has created a dissonance between community environment improvement and resident interaction maintenance, posing challenges to community attachment. Relying on the three-pole triangular model, influential factors were identified in terms of resident interaction, community environment, and individual characteristics. Path analysis model was established to examine feasible pathways and effects of influential factors on community attachment, using survey data from Harbin, China. The results demonstrate that resident interaction is a mediation between community environment and community attachment, as well as between individual characteristics and community attachment. Resident interaction could be regarded to consist of three sub-dimensions: interaction depth, interaction breadth, and interaction frequency. Among them, interaction depth plays a crucial mediating role. Increased interaction depth could enhance community attachment, but extensive or frequent interactions do the opposite. Increasing greening directly benefits community attachment. Constructing public space and improving pedestrian walkways could directly and positively affect community attachment and enhance it by affecting resident interaction. Furthermore, residents with higher education attainment, lower annual income or longer residence time were more likely to be attached. Some policy implications and suggestions for local government and developers were proposed, which can encourage residents' identity in the regenerated community and enhance community attachment.

Ambient air pollutants concentration prediction during the COVID-19: A method based on transfer learning

Research on the correlation analysis between COVID-19 and air pollution has attracted increasing attention since the COVID-19 pandemic. While many relevant issues have been widely studied, research into ambient air pollutant concentration prediction (APCP) during COVID-19 is still in its infancy. Most of the existing study on APCP is based on machine learning methods, which are not suitable for APCP during COVID-19 due to the different distribution of historical observations before and after the pandemic. Therefore, to fulfill the predictive task based on the historical observations with a different distribution, this paper proposes an improved transfer learning model combined with machine learning for APCP during COVID-19. Specifically, this paper employs the Gaussian mixture method and an optimization algorithm to obtain a new source domain similar to the target domain for further transfer learning. Then, several commonly used machine learning models are trained in the new source domain, and these well-trained models are transferred to the target domain to obtain APCP results. Based on the real-world dataset, the experimental results suggest that, by using the improved machine learning methods based on transfer learning, our method can achieve the prediction with significantly high accuracy. In terms of managerial insights, the effects of influential factors are analyzed according to the relationship between these influential factors and prediction results, while their importance is ranked through their average marginal contribution and partial dependence plots.

Geographically Weighted Multinomial Logit Models for Modelling the Spatial Heterogeneity in the Bike-Sharing Renting-Returning Imbalance: A Case Study on Nanjing, China

• GWMNL models were proposed to model bike-sharing renting-returning imbalance. • GWMNL models outperformed MNL models in the case study of Nanjing • Built environment factors could significantly impact the bike-sharing imbalance. • Effects of influential factors were discussed considering spatial heterogeneity. Bike-sharing systems have promoted transportation sustainability in cities. Shared bikes can be used for short-distance trips or accessing public transport systems. However, the asymmetric spatiotemporal distribution of bikes across stations has become a major barrier that hinders bike-sharing adoption in cities. Before implementing policies to tackle this issue effectively, it is necessary to understand the unevenly distributed shared bikes across stations. This study proposed methods to identify the renting-returning imbalance at the station level using Nanjing bike-sharing trip dataset. Next, this study compared the spatiotemporal characteristics of imbalanced bike-sharing usage during different periods (morning peak, evening peak, and non-peak). Finally, geographically weighted multinomial logit models (GWMNL) were established to reveal the influence of exogenous variables on the renting-returning imbalance. Model results suggested that work-related points of interest (POI) could positively impact the renting-returning imbalance during peak periods. Higher population density could result in the bike-sharing renting-returning imbalance in the morning peak, and it contributed to the balance of bike-sharing in the evening. In the morning peak, higher land use mix and older people could ease the imbalance of bike-sharing. In the evening peak, longer travel time has negative impacts in most places in the study area. Based on these findings, this study provided implications for transportation planners and policymakers to balance bike-sharing systems and improve the overall service quality.

A comprehensive review of nano‐phase change materials with a focus on the effects of influential factors

AbstractConservation of energy and reduction of carbon dioxide footprint are among the most crucial global challenges faced by humanity. Dozens of solutions have been so far introduced to address this issue. Nano‐phase change materials (nano‐PCMs) are new emerging technology that are vastly being studied which are believed to minimize energy waste. This study focuses on many aspects of nano‐PCMs with applications in the building industry. It was found that carbon‐based nanoparticles have the greatest thermal properties to be integrated into the PCMs. In this work, a summary of the previous review articles regarding PCM and nano‐PCM following the comparison of various types of nano‐particles in terms of their impact on thermal conductivity enhancement of PCMs is reported. Moreover, other influencial thermophyical properties of nanoparticles such as size, concentration, dispersion, shape, and inclination angle and their effect on thermal properties of PCMs are discussed.

Elementary behavior of dual-particle composites cemented by self-compacting mortar: Experimental and constitutive modelling

Artificial cementation can effectively increase the modulus and strength properties of granular materials. To characterize and simulate the cementation effect between granules is of great significance for the analysis of cemented granular materials (CGM). The aim of this study is to investigate the elementary behavior of dual-particle composites cemented by self-compacting mortar (SCM) and provide a unit model for the numerical representation of CGM. A specimen preparation procedure of dual-particle composites was proposed to simulate the filling process of SCM through rockfills in practice. A series of uniaxial compression and tension tests were carried out and the effects of influential factors, including the particle size, particle distance and SCM fluidity, were considered. Based on analyses of the test results, a uniaxial constitutive model was proposed for the targeted dual-particle composites. Finally, through finite element implementation of the model and simulation studies of uniaxial compression tests on CGM, the established constitutive model has been effectively verified to be reliable in predicting the deformation and failure properties of CGM.

دور الذكاء الاستراتيجي في تعزيز متطلبات الجامعة الريادية دراسة استطلاعية لأصحاب القرار في عينة من الجامعات الحكومية في محافظة دهوك

هدف البحث هو التعرف على دور الذكاء الاستراتيجي في تعزيز متطلبات الجامعة الريادية لعينة من الجامعات الحكومية في محافظة دهوك، منطلقا من مخطط فرضي يأخذ بنظر الاعتبار اتجاه العلاقة بين المتغيرين، وتم اتباع المنهج الوصفي التحليلي، وصممت استبانة لجمع البيانات، وتم توزيع (70) استبانة، وتم استرداد (65)، واستخدمت بعض الأساليب الإحصائية لتحليل النتائج واختبار الفرضيات تمثلت اهم الاستنتاجات باهتمام الجامعات بالذكاء الاستراتيجي ومتطلبات الجامعة الريادية، وقدم البحث مجموعة من التوصيات تمثلت أهمها بمنح الحوافز لكونها تدفع الافراد العاملين ويشجع الجهود المبدعة لدى العاملين.

Dynamic logistics disruption risk model for offshore supply vessel operations in Arctic waters

In harsh environments, offshore oil and gas support operations are subjected to frequent logistics and supply chain operational disruption, due to harsh environmental factors with their associated risks. To capture these stochastic influential factors and support related decision making, it is helpful to develop a robust and dynamic probabilistic model.The current study presents a proactive methodology that integrates the Pure-Birth Markovian process (PBMP) with the Bayesian network (BN) for the effective analysis of offshore logistics disruption risk. The PBMP captures the stochasticity in the failure characteristics of the engineering systems for estimating the time-evolution degradation probability. The BN explores the dynamic interactions among the most important offshore logistics influential factors to analyze the disruption risk in a harsh environment. The effects of influential factors’ non-linear dependencies are propagated and updated, given evidence on the degree of disruption. The level of logistics disruption is further assessed using cost aggregation-based expectation theory. The theory explores the incurred cost/economic risk under different operational scenarios. The proposed methodology is tested on an offshore supply vessel operation to estimate the likely operational disruption risk in terms of financial loss in a harsh operating environment. The most critical influential functions are assessed to establish their degree of impact on the logistics disruption. At the upper bound probability of disruption occurrence, an economic risk/additional incurred cost of US$2.38E+05 with a variance (σ2) of 3.05×109 was predicted. The result obtained suggests that the proposed methodology is adaptive and effective for dynamic logistics disruption risk analysis in harsh offshore environments.

Herders’ willingness-to-participate in the Grassland Ecological Compensation and Award Policy in China: a meta-analysis

Since implementation of its first phase, the Grassland Ecological Compensation and Award Policy (GECAP; 2011–2015) has significantly influenced participants’ livelihoods in China. Willingness-to-participate (WTP) is an important component of successful policy implementation. The effects of influential factors on herders’ WTP have received considerable research effort, although no systematic literature review or quantitative analysis has been conducted to provide evidence-based policy insights. Focusing on 3405 observations extracted from 13 empirical studies, this research conducted a meta-analysis and aggregated the effects of factors affecting Chinese herders’ WTP in the GECAP. The study also analysed the heterogeneity in influential factors and its sources. Factors increasing herders’ WTP included a higher formal education level, a higher farm income from raising livestock, access to larger grassland area, improved grassland condition, and better policy understanding. The cumulative effects of the number of livestock and grassland area on herders’ WTP had increased since the policy was implemented. We examined publication bias that may arise from research with favourable results being more likely to be published and found that publication bias was statistically insignificant for the selected case studies. However, we found significant heterogeneity in household income and number of livestock. The sources of heterogeneity included regional differences, publication year, and sampling method. Future policy modification and formulation should better incentivise active participation of herders by targeting specific pastoral regions and herder groups at certain income levels and/or herd size.

THE MODERATING EFFECTS OF GENDER, CAREER, MORAL MINDSET ON THE RELATIONSHIP BETWEEN THE GRADUATE ATTRIBUTES AND READINESS FOR EMPLOYABILITY AMONG ENGINEERING COLLEGES GRADUATES IN OMAN

The Omani higher education system has enlarged noticeably since 1970 both in the number of students and in the infrastructure. As a result, there has been a wide investment to provide quality higher educational institutes capable of providing suitable graduates to fulfill the requirements of both public and private sectors with a focus on females to assure equal gender educational and career opportunities chances. Therefore, graduates’ readiness for employability has become a major issue for Higher Education Institutes (HEIs) in Oman due to the growing concern from governments and industries on the quality of the graduates. Therefore, the main objective of the current study is to develop a conceptual framework for the graduate readiness for employability and incorporates the graduate attributes including human capital attributes, social capital attributes, individual attributes, institution-related attributes, and active learning attributes moderated by gender, career, and moral mindset among Engineering Graduate in Oman. The study uses available sources of existing literature based on the four main keywords ‘higher education’, ‘graduate attributes ‘or ‘readiness for employability, and ‘skills gaps in Oman’. The study also justified by the following theories which are human capital; education and economy development; behavior theory; and motivation theory. The literature utilized for this study covers the latest literature (from 2013 to 2020) extracted from Google Scholar, ProQuest, and Scopus, and other online resources. Based on the summary findings, the study develops a framework that analyses the effects of influential factors on the graduate readiness for employability that will be considered as an empirical study on the graduates of the engineering colleges in Oman. Moreover, the study also concluded that there are moderating effects of gender, moral mindset, and career mindset on the influence factors and the graduate readiness for employability among Omani engineering colleges’ students. The results of the study will fill the gap in understanding the main factors affecting the attainment of graduates’ readiness for employability skills in Oman. The study recommends justifying this conceptual framework by empirical data from the Engineering Graduate in the Higher Education Institutes (HEIs) and draw a policy guideline for ensuring of readiness of engineering graduates for employability in the HEIs in Oman.

Improved Performance for Acyclovir Sensing in the Presence of Deep Eutectic Solvent and Nanostructures and Polymer

Acyclovir is a synthetic deoxyguanosine analog that is used for the treatment of herpes simplex as well as varicella zoster virus infections. In this work, by incorporation of different deep eutectic solvents (DESs), ZnO nanoparticles, and multi-walled carbon nanotubes (MWCNTs) into the carbon paste matrix, and electropolymerization of arginine at the resulted electrode, new sensor was designed for improved sensing of acyclovir. The effect of influential factors on the electrode quality was investigated. As a result, the carbon paste electrode modified with 5% ZnO, 5% MWCNTs, 5% DES (choline chloride-urea) and applying10 cycles in polymeric condition exerted remarkable enhancement for acyclovir in physiological media (pH = 7). Two linear ranges have been obtained for the acyclovir concentration within the ranges of 0.01–0.33 and 0.33- $1.0~\mu \text{M}$ , with a detection limit of $0.007~\mu \text{M}$ . The excellent selectivity of sensor for the acyclovir determination was confirmed by discrimination of peaks for interferences (e.g. ascorbic acid, and guanine). Suitability of the fabricated sensor for acyclovir determination in pharmaceutical formulations and human fluid was confirmed with satisfactory results.

Temporal Feature Selection for Multi-Step Ahead Reheater Temperature Prediction

Accurately predicting the reheater steam temperature over both short and medium time periods is crucial for the efficiency and safety of operations. With regard to the diverse temporal effects of influential factors, the accurate identification of delay orders allows effective temperature predictions for the reheater system. In this paper, a deep neural network (DNN) and a genetic algorithm (GA)-based optimal multi-step temporal feature selection model for reheater temperature is proposed. In the proposed model, DNN is used to establish a steam temperature predictor for future time steps, and GA is used to find the optimal delay orders, while fully considering the balance between modeling accuracy and computational complexity. The experimental results for two ultra-super-critical 1000 MW power plants show that the optimal delay orders calculated using this method achieve high forecasting accuracy and low computational overhead. Moreover, it is argued that the similarities of the two reheater experiments reflect the common physical properties of different reheaters, so the proposed algorithms could be generalized to guide temporal feature selection for other reheaters.

Experimental investigation on perforation of shale with ultra-high pressure abrasive water jet: Shape, mechanism and sensitivity

Perforation is critical to create a flow path between a shale reservoir and the production tubing to ultimately achieve supercritical carbon dioxide (SC–CO2) hydro-jet fracturing and natural gas production. However, the mechanisms of ultra-high pressure abrasive water jet (UHP-AWJ) perforation in shale remain unclear. To address this concern, experiments are designed using the Taguchi method to simulate UHP-AWJ perforating in practice. The UHP-AWJ perforation mechanisms are investigated by measuring the reflection angle, the length of perforation, testing strain, and perforation pressure. The results indicate that there are two typical perforation tunnel shapes, depending on jetting flow, back flow, and abrasive action. During UHP-AWJ perforating, the reflection angle decreased from 180° to 40° (by 77.4%), while the length of perforation increased following an exponential trend, and the perforation pressure rose with fluctuations owing to dynamical jetting. In addition, the effects of influential factors, including AWJ pressure, AWJ distance, AWJ time, and casing thickness on the evaluation indexes (such as length of perforation, the maximum diameter of perforation, mass loss of shale, perforating speed, and effective diameter of perforation) are discussed thoroughly. Moreover, the axial pressure is more than twice the radial pressure due to the strong impingement of the water jet. The experimental error of the perforation pressure is only 1.09%, indicating the reliability of our results. Three main stages of UHP-AWJ perforation based on the above results are outlined and summarized.

Experimental study on shale fracturing enhancement by using multi-times pulse supercritical carbon dioxide (SC-CO2) jet

Supercritical Carbon Dioxide (SC-CO2) fracturing can increase shale gas production and reduce carbon dioxide (CO2) emission. In order to enhance shale fracturing, a new method of multi-times pulse SC-CO2 jet fracturing is proposed and comprehensively studied. The results demonstrate there are more initiation positions and more complex fracture network after pulse SC-CO2 jet fracturing due to strong impingement and pressurization. Furthermore, three types of main fracture and four types of fracture branch are observed. The main fractures mostly initiate from perforation and propagate with irregular path to the specimen edge. CO2 absorption and fracture volume increase 77.72% and 2283%, respectively, more than that of single-pulse SC-CO2 jet fracturing. The comparison indicates that SC-CO2 creates more complex fracture morphology, larger fracture volume and CO2 absorption than that of water. Moreover, the effect of influential factors, including pulse jet frequency, pulse jet pressure and pulse jet distance, are discussed thoroughly. Thus, this study provides a method of jet fracturing enhancement, and is instructive for understanding and tackling pulse jet fracturing issue related to CO2 utilization and fracturing of shale reservoir.

Abortion and other risk factors for mastitis in Iranian dairy herds

This paper forms a part of a series of studies aiming to estimate the costs of abortion in Iranian dairy herds. In previous studies we have determined mastitis as a significant risk factor for abortion. In order to provide a more reliable estimation of the costs associated with abortion in Iranian dairy herds, the risk of a cow getting infected with mastitis needs to be included. Data from 6 commercial herds and 32,191 cows was assigned to 3-weeks in milk (3-WIM) records from 1 to 567 d after calving (1st–27th 3-WIM). The effect of herd, parity, calving season, past incidence of abortion, cumulative FCM yield level (CFCML), past incidence of mastitis in previous 3-WIM periods (EMAS), days in milk (DIM) and their significant 2-way interactions on mastitis in current 3-WIM period (MAS) were evaluated using a logistic regression model. Mastitis rate (MR) in studied herds was on average 28.3%. Results show that herd, parity, EMAS, CFCML, calving season, and lactation stage significantly (P < 0.01) influenced the risk of MAS. The risk of MAS increased with lactation number. Cows with EMAS had 4.98 times greater risk of MAS compared to cows with no EMAS. Additionally, cows with medium-level CFCML (i.e. 2, 3, and 4) had a higher risk of MAS compared to cows on level 1 and 5. Calving during the spring significantly (P < 0.01) increased the risk of MAS compared to other seasons. Past incidence of abortion, however, was not significantly associated with MAS, but remained in the final model because of the interaction with other factors.It can be concluded that a risk factor analysis with all significant interactions is more informative than a model without the interactions, especially when making the optimal decision for a cow with given characteristics. Moreover, knowledge on the effect of influential factors on mastitis will be useful when designing mastitis control programs at herd or national level.

Investigation of carriers’ ability to transfer toll increases: an empirical analysis of freight agents’ relative market power

Design of effective freight policies requires a comprehensive understanding of freight agents’ interactions. A critical component of the interaction is the freight agents’ relative market power in determining delivery price, time, and frequency. However, empirical studies of freight agents’ market power are limited. This study explores freight carriers’ market power relative to receivers’ by investigating carriers’ ability to transfer toll increases. A stated preference survey is conducted to understand carriers’ willingness to transfer hypothetical toll increases to receivers. The relationship of various factors and the ability to transfer toll increases is first examined by descriptive statistics. Then, regression models are estimated as a function of respondents’ socioeconomic factors, market conditions, hypothetical toll increases, and other factors. Marginal effects of influential factors are obtained to reveal policy implications.

Production Rate Analysis of Fractured Horizontal Well considering Multitransport Mechanisms in Shale Gas Reservoir

Shale gas reservoir has been aggressively exploited around the world, which has complex pore structure with multiple transport mechanisms according to the reservoir characteristics. In this paper, a new comprehensive mathematical model is established to analyze the production performance of multiple fractured horizontal well (MFHW) in box-shaped shale gas reservoir considering multiscaled flow mechanisms (ad/desorption and Fick diffusion). In the model, the adsorbed gas is assumed not directly diffused into the natural macrofractures but into the macropores of matrix first and then flows into the natural fractures. The ad/desorption phenomenon of shale gas on the matrix particles is described by a combination of the Langmuir’s isothermal adsorption equation, continuity equation, gas state equation, and the motion equation in matrix system. On the basis of the Green’s function theory, the point source solution is derived under the assumption that gas flow from macropores into natural fractures follows transient interporosity and absorbed gas diffused into macropores from nanopores follows unsteady-state diffusion. The production rate expression of a MFHW producing at constant bottomhole pressure is obtained by using Duhamel’s principle. Moreover, the curves of well production rate and cumulative production vs. time are plotted by Stehfest numerical inversion algorithm and also the effects of influential factors on well production performance are analyzed. The results derived in this paper have significance to the guidance of shale gas reservoir development.

Effects of key factors on the heat insulation performance of a hollow block ventilated wall

A hollow block ventilated wall can be cooled down in the summer and warmed in the winter by utilizing the air that flows through cavities. The heat transferred between the outdoor and indoor environments can also be removed. However, neither simulations nor experimental studies on the hollow block ventilated wall have been complete, and factors that affect the thermal insulation performance of hollow block ventilated walls need to be identified. In this paper, the frequency-domain finite-difference method and the Number of Transfer Units method are adopted to build the coupled three-dimensional heat transfer model for investigating the temperature distribution on wall surfaces and the exhaust airflow. Experiments are carried out to validate the three-dimensional model, and comparisons prove that the model has considerable accuracy. In addition, both the numerical and experimental studies show that the hollow block ventilated wall can significantly reduce the heat transferred from outdoor environments to indoor rooms through ventilation in the summer. The effects of influential factors, including the airflow velocity, the inlet temperature of the airflow and the cavity size, on the thermal insulation performance of the hollow block ventilated wall are investigated based on the heat transfer model. Results show that the temperature of the inner surface of the hollow block ventilated wall at four orientations is reduced and ranges from 24.8 to 27.0 °C during a typical summer day. The heat flux through the inner surface is reduced by 55.08%, 55.07%, 56.03% and 55.19% respectively for the east, west, south, and north hollow block ventilated walls, indicating the energy saving potential of hollow block ventilated walls. The most critical factor is the cavity size. When the cavity dimension is constant, the influence of the airflow velocity on thermal insulation is much larger compared to the airflow temperature, and the suggested airflow rate is 1.3 m/s. Results show that a higher airflow rate, higher inlet airflow temperature, and larger cavity size, enable the hollow block ventilated wall to achieve better thermal insulation performance.

Genetic parameters for neonatal mortality in lambs at semi-arid region of Rajasthan India

Lamb survival is essential for successful rearing of lambs for sale and replacement of breeding stock given a profitable sheep enterprise. The present study was conducted using the data on 4694 Avikalin, 5015 Chokla and 5718 Malpura sheep recorded from 1991 to 2016 for studying the neonatal mortality in lambs. The effect of influential factors on neonatal mortality of lambs was ascertained and the genetic parameters for lamb survival till first 28 days of life using Gibbs sampling were estimated. Incidence of neonatal mortality was 4.29%, 5.17% and 4.07% in Avikalin, Chokla and Malpura sheep, respectively. Logistic regression analysis revealed significant impact of year of birth and lamb's birth weight on neonatal mortality for all the breeds. Year (1997, 2011–12) negatively affected the survival due to sudden shift in policy decision. Dams with low body weight and in their first parity were also found to affect the lamb survival negatively. Due care of the lambs born with low birth weight (< 2.00 kg) and for primi-parous dams with low weight (< 20.00 kg) can improve the chances of lamb survival. Genetic analysis revealed that the direct heritability for Avikalin sheep was 0.17 ± 0.02, indicating scope for genetic improvement through selection. However, for Chokla and Malpura sheep the direct heritability were 0.05 ± 0.00 and 0.06 ± 0.01, respectively. Maternal permanent environmental effect was important in Avikalin and Chokla sheep with maternal heritability estimates of 0.10 and 0.11, respectively. However, for Malpura sheep, with little genetic variance, only direct genetic effect was important. Apart from care and management, accounting for both direct and maternal effects in breeding program can improve the lamb survival.