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Effects of Social Capital and Microcredit on Profitability of Grain Traders in Southwestern Nigeria
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Move-Biased Monte Carlo Simulation Method for Protein Native Structure Prediction
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Page will reload when a filter is selected or excluded.- Coarse-grained model 1 results 1
- Credit sources 1 results 1
- Grain business expansion 1 results 1
- Grain marketing requires considerable investment of fund but traders are often plagued with inadequate capital to run their enterprises. The inadequacy of fund prevents traders from expanding their businesses resulting in low profit margin. However, social capital is increasingly recognised as a bridge for the gap in credit availability which can help in business expansion and profitability. There is little empirical evidence on the extent of the effectiveness of social capital and microcredit delivery in profitability of traders. The study was designed to investigate the effects of social capital and microcredit on profitability of grain traders in southwestern Nigeria. Multistage sampling procedure was employed for the study with random selection of Oyo and Ogun states from the six states in southwestern Nigeria. Two Local Government Areas (LGAs) were then randomly selected from the states. Eleven rural and twelve urban markets were randomly chosen in each of the LGAs based on Probability Proportionate to Size (PPS). Finally, 500 grain traders were sampled using PPS, with 492 traders having detailed information used for the analysis. Data were collected on grain traders’ socio-economic characteristics, membership density, Meeting Attendance (MA), heterogeneity, Decision Making (DM), Cash Contribution (CC), Labour Contribution (LC), trust, social cohesion, Time Lag (TL), Payback Period (PP), credit distance as well as costs and returns. Data were analysed using descriptive statistics, multinomial logit, budgetary analysis, ordinary least square and two- stage least square regression models at α0.05. Age and household size were 43.3 ± 9.4 years and 6.0 ± 2.9 respectively. Density of membership in associations was 3.0 ± 0.1. Average MA by traders was four out of five. Membership of the association was diversified with heterogeneity index of 69.9%. Members participated in three out of five decisions made by the associations. The six microcredit sources identified were Traders’ Association (TA); community association; cooperative society; Rotating Savings and Credit Association (ROSCAS); Friends and Relatives (FR) and Microfinance Bank (MB). Total revenue was N496, 135.80 while net revenue was N12, 359.00. Average amount of credit granted from the six identified sources was N67, 480.13 ±6, 764.80 representing only 46.0% of the total credit needs of the traders. The TL for credit was 2.13 ± 2.00 weeks with a PP of 6.51 ±4.17 months. Payback period decreased the likelihood of access to credit in TA, ROSCAS, FR and MB ranging from 61.5% to 84.5%. Credit distance increased credit access in TA (2.81) and ROSCAS (1.93). Interest charged decreased credit access in TA (-2.40) and RF (-3.38). Trust and heterogeneity indices increased credit access in ROSCAS by 77.5% and 99.2% respectively. Increase in time lag reduced profitability of the grain traders (-0.0235) while social capital increased profitability by 12.1%. Social capital increased access to, and the amount of credit available, which improved profitability of grain traders. Therefore, social capital formation with its attendant implications for improved access to microcredit should be encouraged. 1 results 1
- Grain traders 1 results 1
- Hydrophobic-Polar lattice model 1 results 1
- Microcredit access 1 results 1
- Monte Carlo simulation 1 results 1
- Protein primary structure 1 results 1
- Proteins are polymers of Amino Acid (AA) which are constructed after translation of genetic code in DNA of organisms, and have functionality that depends on their Native Structure (NS). Experimental methods for protein NS determination are complicated, expensive and time-consuming. Consequently, Computational Methods (CM), including Monte Carlo (MC), aim to circumvent these challenges. However, the MC is complex and inconsistent in NS Prediction (NSP). This study was designed to develop a Move-Biased MC (MBMC) simulation algorithm that may simplify the complexity of existing MC and makes it consistent for NSP. Protein was described as a coarse-grained structure and folding as Self-Avoiding Walks (SAW) on square lattices. Relative Probability Parameters (RPP) were introduced to determine natural probabilities of protein conformations from SAW and to simulate the desired sequence length from RPP optimal combination. Thereafter, a graphical algorithm was developed to group the SAW steps into hydrophobic and polar AA units according to the Hydrophobic-Polar (HP) model. The MBMC method was developed as a coupling of diagonal-pull Move-Biased (MB) on the lowest energy SAW conformation. The materials for testing the MBMC method included eight Benchmark Sequences (BMS) from the protein data bank such as SI-1, SI-2, SI-3, SI-4, SI-5, SI-6, SI-7, and SI-8 with sequence lengths 20, 24, 25, 36, 48, 60, 64, and 85 nm, respectively. The lowest energy (consistency in prediction of NS), computation time and algorithmic steps of the MBMC method was compared with some existing methods [such as Conventional MC (CMC), Genetic Algorithm (GA), Evolutionary MC (EMC), Ant Colony Optimization (ACO), Hybrid Elastic Net Algorithm (ENA)]. Data were analysed using inferential statistics. The optimal combination of the RPP for the MBMC algorithm were 0.71, 0.02, 0.25 and 0.02 for up, down, left and right orientations, respectively. The energies of the NS obtained from the MBMC method were -9, -9, -8, -14, -23, -35, -42 and -52 J for the BMS, respectively. In contrast, for GA energies derived were -9, -9, -8, -12, -22, -34, -37, and no record for eighth BMS; for ACO they were -9, -9, -8, -14, -23, -34, -32, -53; for EMC they were -9, -9, -8, -14, -23, -35, -39 and -52; for ENA they were -9, -9, -8, -14, -23, -36, -39, and no record for eighth BMS; for CMC they were -9, -9, -7, -12, -20, -33, -35, and no record for the eighth BMS. Also, MBMC method consistently predicted the NS of the BMS in 8.90, 8.51, 8.37, 9.14, 9.45, 9.46, 9.52, and 12.85 seconds, respectively. In contrast the computation times for GA were only reported for the first four BMS as 5.60, 6.00, 3.66, 54.60 seconds, and no record of computational time for the CMC and EMC Benchmark sequences, respectively. Moreover, MBMC has fewer algorithmic steps and simpler simulation procedure than CMC, GA, EMC and ENA methods. The developed Move-Biased Monte Carlo method had simpler algorithmic steps than the existing Monte Carlo methods and consistently predicted the native structure of proteins faster than existing algorithms. 1 results 1
- Social capital 1 results 1
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