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Entrepreneurship is vital for driving innovation, economic development, and sustainability in the agricultural sector, empowering farmers, and ensuring food security. Successful promotion of agri-entrepreneurship demands a nuanced approach that considers both the personal traits of entrepreneurs and the institutional factors. This study employed linear regression analysis and principal component analysis to examine the determinants of entrepreneurial success and identify factors contributing to effective interventions across three distinct entrepreneurial categories i.e., farm-based, off-farm based, and service/tech entrepreneurs. Data was gathered through structured interviews involving two hundred agri-entrepreneurs in Rajasthan and Telangana states. The regression analysis revealed that diverse psycho-personal and socioeconomic variables like marital status, income levels, and achievement motivation were of significant influence. The principal component analysis provided valuable insights into the institutional factors underpinning effective entrepreneurship promotion interventions. Technical factors like tailored project support, financial enablers including government funding and tax incentives, and robust implementation mechanisms involving stakeholder collaboration were highlighted. Operational elements such as; training institute-industry-market-entrepreneur linkages, administrative commitments, and policy consistency, collectively shaped intervention effectiveness across the entrepreneurial ecosystems. This comprehensive examination of individual and institutional determinants offered a holistic perspective on fostering successful agri-enterprises, emphasizing the need for contextualized approaches that align personal attributes with tailored institutional interventions.

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This study was conducted to evaluate genetic variation among 70 sunflower recombinant inbred lines (RIL) derived from the crosses PAC2 × RHA266 together with parents based on seed morphological traits by using a rectangular lattice design with two replications. Seed morphological such as kernel length, kernel width, kernel diameter, 100-kernel weight, percentage of hull, percentage of dehulled kernel and seed yield per plant was measured. Analysis of variance revealed significant differences among lines for the studied traits. The highest coefficient of phenotypic variation was observed for seed yield per plant (23.42) and the lowest one was observed for percentage of dehulled kernel (1.37). The highest heritability was observed for 100-kernel weight (0.995) and kernel width (0.990) and the lowest one was observed for the yield per plant (0.521). The highest correlation coefficients were observed between kernel diameter and kernel width (0.908). Principal component analysis reduced the seed characteristics traits to 2 components explaining 81% accumulative variance. By using Ward clustering method based on seed morphological traits the 72 studied sunflower lines were classified into six groups.

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This study was conducted to investigate the morphological variation of Planiliza abu in the Tireh (Tigris Basin), Kor (Kor River Basin) and Jegin (Hormozgan Basin) rivers using traditional (TM) and geometric morphometric (GM) methods. For this purpose, a total of 62 specimens were collected using electrofishing device and Salik net. In the Lab, 21 morphometric traits were measured. Then, to extract the morphological data in the geometric method, 16 landmark-points were defined and digitized on the photographs taken from the left side of fish using tpsDig2 software. The results showed that the studied populations had significant differences in 7 morphometric traits (P<0.05). The differences in the geometric method were those of the head size, body depth, pectoral fin position and caudal peduncle length. Based on the results, GM method showed higher accuracy to reveal the morphological variations in the generalist species of Planiliza abu, which can inhabit a wide range of habitats.

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An ideal fusion method preserves the spectral information in fused image without spatial distortion. The PCA is believed to be a well-known pan-sharpening approach and being widely used for its efficiency and high spatial resolution. However, it can distort the spectral characteristics of multispectral images. The current paper tries to present a new fusion method based on the same concept. In the conventional standard PCA method, PCA transform is applied to spectral bands of multispectral images, but we applied the PCA transform to pixel blocks instead. Since PCA coefficients are extracted from statistical properties of the image, it is more consistent with type and texture of remotely sensed image compared to other kernels such as wavelets. After that, a new hybrid algorithm is proposed which uses both the spatial PCA and the spectral PCA method to improve the quality of the merged images. Visual and statistical analyses show that the proposed algorithm clearly improves the merging quality in terms of RASE, ERGAS, SAM, correlation coefficient and UIQI; compared to fusion methods such as IHS, Brovey, PCA, HPF, and HPM.

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Creating and supporting small and medium-sized industries in economic development programs and the emphasis on improving efficiency and productivity in the policies adopted, shows the vital position of such industries in developed as well as developing economies. In general, the most important role of these industries in the economic development process can be summarized as; effective employment, production and supply chain management, creating value added and reducing dependence on unnecessary commodity imports. Since any improvement in the efficiency of small scale industries will bring more equitable distribution of income, the main purpose of this study is to measure the performance of various technical, management and scale efficiencies in subsectors of small scale industries and provide policy recommendations to the inefficient industries. For this purpose, the principal component analysis (PCA) and factor analysis is used to determine the variables of the model and the DEA is used for evaluation and sensitivity analysis of the factors affecting the efficiency and productivity of small scale industries during the period 2002 - 2007. The results show that out of 22 industries, only 8 ones are found to be perfectly efficient. Productivity measurement of these industries according to the "Malmquist" index reveals that, the trend of productivity enjoys positive growth.

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Road crashes cause more than 20 thousand fatalities each year in Iran. Human factors consisting of driving styles and skills have been recognized as important contribiuting factors in most traffic crashes. Focous on driving behavior has been the subject of many researches. Driver Behavior Questionnaire (DBQ) in this regard is a relatively new, important and widely used instrument, devised to identify the components of the structure of aberrant driving behaviors. Surveys based on DBQ, urges respondents to self-report the frequency of aberrant driving behaviors during a specific period of time. Investigation of driving styles is estimated to be the subject of more than 170 researches since DBQ was first by Reason et al in the 90’s. Since then, many researchers have employed the original DBQ or a modified version, either to explore behavioral components (exploratory approach) or to confirm a given setting based on authors’ theories or observations, in group(s) under the study (confirmatory approach). Lack of exploratory analysis and spatial dispersion of respondents in the previous domestic researches, motivated the authors of this paper to conduct a new survey to investigate aberrant driving behaviors among Iranian drivers applying exploratory approach. The original DBQ was modified, validated and dispersed between Iranian drivers through an internet-based survey. Recent increase in the number of internet users in Iran, more interactions between respondants and the questionnaires, the power of self-administration, massive reductions in cost and time over interviewer-administered surveys, building a database, were among factors yielding hope that the sample would be comprehensive enough to comprise different groups of drivers. Using social networks and email services, the proposed questionnaire was exposed to internet users and a sample of 213 drivers (165 males and 48 females) from 40 cities inside and outside Iran, filled out the 37-item DBQ. Principal Component Analysis (PCA), with Varimax Rotation implied, a five-factor structure: “Push and Speed Violations”, “Disregarding the Regulations”, “Lack of Concentration while driving”, “Aggressive Violation” and “Lapses and Error” for Iranian drivers. These components account for 42.2 percent of the total variance. It is worth noting that the distinction between different kinds of violation and lapses and error support the fact that this new structure is consistent with the previous studies. Moreover, using cell phone while driving (both sending texts and talking), aggressive violations and push and speed violations are the most frequent aberrant driving behaviors. Compared to the other countries, drivers in Iran reported more violations than drivers in industrialized countries and fewer violations than Asian drivers. Results also show that unlike industrialized countries, Iranian drivers reported more aggressive violations than ordinary violations.

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Patients with medial compartment knee osteoarthritis (OA) may exhibit different kinematics during walking according to the disease stage, also most of differences are in the frontal plane. The objective of this study was to compare lower extremity kinematics in frontal plane between medial knee OA patients and control subjects. Three dimensional gait analysis was performed on 25 women (35 to 53 years old): 10 control subjects, 10 mild medial knee OA and 5 moderate medial knee OA patients. Kinematics waveforms were reduced dimensionally by using Principal Component Analysis (PCA). PCA scores were compared between three groups (control, mild OA and moderate OA) with ANOVA and Post-Hoc TukeyHSD statistical analysis. Ankle of mild OA patients had a leaning towards inversion and moderate OA patients had a leaning towards eversion. Patients with mild OA, had smaller range of ankle motion than two other groups (p>0.05). Knee adduction angle increased with progression of OA severity (p>0.05). Range of hip motion in frontal plane decreased with progression of OA severity and this difference was significant between mild and moderate OA groups (p=0.05).

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Abrasive water jet cutting process can produce tapered edges on cutting kerf. This problem can limit the applications of abrasive water jet cutting process and in some cases it is necessary another edge preparation process. In this paper, an experimental investigation kerf characteristics of Ti-6Al-4V titanium alloy under abrasive water jet cutting is presented. In this regards, it is shown how to use the hybrid approach of Taguchi method and principal component analysis to optimize abrasive water jet cutting are used in this paper. The abrasive water jet cutting process input parameters effect on material removal rate and the characteristics of the surface. A considerable effort was made in understanding the influence of the system operational process parameters such as water jet pressure, traverse speed, abrasive flow rate, and standoff distance. Due to appropriate selecting abrasive water jet cutting process parameters leads to optimizing of kerf characteristics include top kerf width, kerf tapper and kerf deviation, therefore it is important to select appropriate input parameters. The obtained results from this method show that the hybrid approach of Taguchi method and principal component analysis is a suitable solution for optimizing of abrasive water jet cutting process.

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Detection of tool wear and breakage during machining operations is one of the major problems in control and optimization of the automatic machining process. In this study, the relationship between tool wear with vibration in the two directions, one in the machining direction and the other perpendicular to machining direction was investigated during face milling. For this purpose, a series of experiment were conducted in a vertical milling machine. An indexable sandvik insert and ck45 work piece were used in the experiments. Tool wear was measured by a microscope. It was observed that there was an increase in vibration amplitude with increasing tool wear. In this study adaptive neuro - fuzzy inference systems (ANFIS) and multi-layer perceptron neural network (MLPNN) were implemented for classification of tool wear. In this study for the first time, five different states of tool wear was used for accurate tool wear classification. Also to accuracy and speed of the network Principle Component Analysis (PCA) was implemented. Using PCA, the input matrix size was reduced to an acceptable order causing more efficient networks. ANFIS and MLP were trained using feature vectors extracted from the spectrum frequency and time signals. The results showed that for 86 final measurements, the ANFIS and MLP networks were successful in classifying different tool wear state correctly for 91 and 82 percent, respectively. ANFIS due to its high efficiency in diagnosing tool wear and breakage can be proposed as proper technique for intelligent fault classification.

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Besides economic factors affecting economic growth, some cultural, political and social factors influence economic growth and development too; inter alia, social components play important roles. Social instability originating from social threats is one of the most important social components, which affects economic growth. This study aims to investigate the consequences of social instability on economic growth in Iran during 1981-2011. For this purpose, the Auto- Regressive Distributed LagModel (ARDL) and Error Correction Model (ECM) are estimated by Eviews.5 and Microfit 4.1. Using Principal Component Analysis (PCA), an index for social instability (absence of social capital) is made. The results show that physical capital, labor and social instability have the highest effectiveness on economic growth, respectively. Paying attention of policymakers to improving social conditions and reducing social instabilities may lead to higher economic growth.  

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Selecting within local pomegranate accessions is the main method used to identify new cultivars. Total of 76 pomegranate accessions from Hatay, Turkey, were collected and their morpho-pomological and chemical characteristics were determined. The results showed that there was significant diversity among the accessions in terms of fruit quality parameters. Several accessions were notable for their various characteristics. For example, ‘Ekşi 5’, ‘Ekşiliknar’, ‘Kara Mehmet 1’, ‘Lifani 5’ and ‘Ekşi 3’ accessions could be used for extracted aril and juice as they had dark red arils and juice, good taste, and large arils. In addition, the sweet accessions ‘Tatlı 3’, ‘Tatlı 13’, and ‘Tatlı 16’ with soft seeds, rosy peel, and red aril colors were very promising for fresh consumption. Our study demonstrated that there was great morpho-pomological variability among the local pomegranates grown in eastern Mediterranean region of Turkey, making them a valuable genetic source for incorporation into potential breeding programs, especially for different fruit quality characteristics.

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Data analysis is the most complicated and ambiguous step in food and drink sensory evaluation projects. Accordingly, the least systematic and contemplativeness discussions have been made in scientific articles. The present article is the result of studying, teaching and research activities of the author in the past 10 years, in which a number of other valid sources have been used. This paper is intended to describe the stages of sensory data analysis and the use of the principal components in the visualization of sensory data and to present practical examples. The review of scientific literature indicates that many studies have used this approach for analyzing sensory data. But the analysis and interpretation of such data have not been described in details. Such information has not yet been published in Persian in scientific form. So, presenting further details on the PCA method provides scholars and students with access to the necessary information and makes a practical guide available for them in future researches. Therefore, describing the process of sensory data analysis based on the use of PCA and the related criteria for their interpretation is essential and discussed in this paper.

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One of important steps to quality improvement of activities and increasing productivity in manufacturing systems, is noticing and emphasizing on evaluation factors that be Caused to personnel's efficiency reduction. Correct and timely recognition of factors that reduce to personnel's efficiency, especially managers, meets to efficiency improvement. One of major reasons of managers' efficiency reduction is dawdle. in this paper using descriptive statistics, Factor Analysis method and DEMATEL technique, has been purposed a novel model to analyzing existent similarities and diversities between opinions of different groups' managers of one company about reasons of dawdle, specifying and classifying most important areas that need to improvement, and specifying cause and effect relation between them. Findings this model show the most effective factor of dawdle and finally managers' efficiency reduction in office is very much visits and sessions with very little results, and least effective factor is be inordinate social. Also most causal factors are job incentive shortage, expert personnel shortage, mistake or incomplete information, and self assurance shortage.

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Spatial patterns are useful descriptors of the horizontal structure in a plant population and may change over time as the individual components of the population grow or die out. But, whether this is the case for desert woody annuals is largely unknown. In the present investigation, the variations in spatial patterns of Tribulus terrestris during different pulse events in semi-arid area of the Thar Desert, India, was quantified. Further ordination technique and path analysis were utilized to link the pattern and process of spatial distribution of T. terrestris. Dispersal indices like index of dispersal (I_D), index of clumping (I_C), Green’s Index, Lloyd’s mean crowding and Morisita’s index of dispersion (I_δ) revealed uniform distribution pattern during non-pulse events, showing intense competition among plants for limited resources. Kaiser-Meyer-Olkin (KMO) and Bartlett’s test of sphericity indicated the appropriate use of factor analysis and the significant relationships between variables. Principal Component Analysis (PCA) exhibited the significant correlation of the index of dispersion with the index of clumping and with the Lloyd index, while the Lloyd index correlated with the index of clumping and with the Morisita index. Path analysis suggested the association of soil organic carbon, nitrogen, and C/N ratio with the transition from clumped to uniform pattern. Further, lower soil phosphorus also supported the uniform distribution of this plant. Diversity indices like evenness and Simpson index are associated with uniform and clumped distribution patterns. Higher and intermediate level of percent cover and seed out-put of T. terrestris were also related to uniform and clumped patterns. Path analysis also indicated that salinity tolerance capacity of the species could be utilized for reclamation programme.

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Targeted extension for heterogeneous farming systems is a challenge in developing countries. Farm type identification and characterization based on estimates of income from different farm components allows simplifying diversity in farming systems. Use of multivariate statistical techniques, such as principal component analysis (PCA) and cluster analysis (CA), help in such farm typology delineation. Using this methodological approach, the present study conducted in West Bengal, India, identified four distinct farm types, namely, farms growing food grain and jute, farms with animal husbandry and fishery based diversification with high off-farm income, farms with crop based diversification with off-farm income, and farms growing vegetables and fruits. Such typology delineation helps in differentiated, holistic, and broad-based extension intervention to address the need of different identified farm types and a reduced transaction cost in the agricultural research and extension system. inbred lines, and 9 hybrids). A total of 94 and 262 loci were amplified using 5 IRAP and 15 REMAP primers, respectively. The percentage of polymorphic loci (PPL) in populations ranged from 39% (Zivari Shahrood) to 48% (Shadegani E). The Mantel test between IRAP and REMAP cophenetic matrices evidenced no significant correlation (r= 0.29). IRAP+REMAP-based cluster analysis using UPGMA algorithm and Dice similarity coefficient depicted 6 groups among 100 melon genotypes. AMOVA revealed the higher level of genetic variation within populations (67%) compared to among populations (33%). The mean Fst values of all groups, except for group VI, were more than 0.20, demonstrating differentiation among the populations and genetic structure of the studied melon collection. 

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Pomegranate is one of the important and an oldest fruit that is grown in vast regions. In recent years pomegranate juice is became a popular beverage. This study was carried out to investigate the mineral elements and some biochemical properties of juice in eight local Iranian pomegranate cultivars. The results showed a significant difference in the studied traits in all cultivars. The potassium content in pomegranate juice was higher than other elements, followed by calcium, sodium, phosphorus, magnesium, iron, manganese, zinc and copper. Potassium, calcium and iron was found the highest in chr('39')Golabichr('39'), meanwhile the highest amount of  magnesium and sodium measured in chr('39')Sorahichr('39') cultivars. chr('39')Aliakbarichr('39') had the highest amount of manganese and zinc and chr('39')Lopsorkhichr('39') and chr('39')Mirzaeichr('39') had the highest value of phosphorus and copper, respectively. The lowest amount of potassium and iron was observed in chr('39')Lopsorkhichr('39') and chr('39')Aliakbarichr('39') cultivars, respectively. For other elements, chr('39')Garchr('39') had the lowest concentration. In terms of biochemical properties such as total soluble solids (TSS), maturity index (MI) and acidity (pH) of juice, chr('39')Golabi, Lopsorkhi and Sorahichr('39') cultivars had the highest value. chr('39')Sorahichr('39') and chr('39')Aliakbarichr('39') cultivars with the highest amount of titrable acid were sour and suitable for industries pomegranate production, which require sour taste. Other cultivars were introduced due to the proper amounts of mineral elements and sweet and sour taste for fresh consumption and pomegranate juice production industry. The two-dimensional graph, based on the principal components analysis, confirmed the results of grouping the cultivars based on the comparison of the mean. 

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 The species of Dioscorea (yam) are regarded as a staple food crop for millions of people in the tropical and subtropical regions of the world. It is regarded as an important food crop next to cereals and grains due to high yield storage of carbohydrates. Economically, only few species are recognized for cultivation from agricultural point of view, in spite of its large species diversity. The species of Dioscorea also represents great morphological variability in nature. However, very little research has been done on it. Hence, in the present study, an attempt was made to establish genetic variability and relationships among 50 accessions of Dioscorea spp. growing naturally in Meghalaya. Principal Component Analysis (PCA) for the first nine components indicates 91.5% observed variability. Morphological characters or traits with discriminating values were stem color, leaf type, number of leaflet in compound leaf, leaf color, leaf shape, inner petal shape, staminode absent or present, length and width of mature leaf. Agglomerative Hierarchical Cluster Analysis clearly separated the 50 accessions based on their close association.

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Linseed is an important oilseed and fibre crop predominantly grown in India. The aim of the present research was to evaluate genetic diversity and patterns of relationships among the 58 genotypes through 10 morphological traits and 12 polymorphic microsatellite (SSR) markers. Euclidean analysis of agro-morphological traits grouped the 58 genotypes into four clusters of which cluster I was the largest with 20 accessions while clusters II and IV were most genetically diverse due to maximum inter-cluster distance. Principal component analysis revealed three traits accounted for more than 86% of the total variation. A total of 41 alleles were amplified with 12 SSRs having an average of 5.71 alleles per primer locus. The Polymorphic Information Content (PIC) varied between 0.18 to 0.78. Based on Jaccard's similarity coefficient, the genetic distance varied from 0.07 to 0.89 with an average of 0.54±0.10. The genotypes RKY-14, KL-213, LC-185 and Kartika were found to be the most divergent among all the genotypes studied on the basis of genetic distance. The most diverse genotypes identified in this study can be used in breeding programs to broaden the genetic base of the linseed germplasm.

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Knowledge of buffalo growth curves is essential for improving reproductive management, nutritional strategies and identifying the best slaughter age. We provided the first joint study comparing growth curves of the three major buffalo breeds. Additionally, we used principal component analysis and Biplot graphics to evaluate the degree of similarity between the groups (breed by sex) and their relationship with mature weight, maturation rate and weight at different ages. The dataset included 8,550 weight records from 1,391 Jaffarabadi, Mediterranean and Murrah buffaloes. The Bertalanffy model had the best fit. The mature weights were 696.64±8.50 and 678.53±9.44 kg (Mediterranean), 716.26±48.54 and 629.28±32.11 kg (Jaffarabadi) and 694.69±17.97 and 556.53±15.49 kg (Murrah) male and female, respectively, by Bertalanffy model. All breeds reaching 75% of mature weight in less than two years. Murrah females were particularly productive, having high precocity and low weight maturity - important biotypes for milk production. Murrah males showed intermediate characteristics, and high potential for meat production in dairy herds. Mediterranean animals showed high weight gain, median precocity and medium to high weight at maturity, supporting its status as the main breed for beef production in Brazil. Jaffarabadi males had high mature weight, slow growth in the first year of life followed by high growth thereafter. Female Jaffarabadi were smaller and showed a similar level of precocity to Mediterranean animals. Buffaloes in Brazil have traditionally been used for milk production; however, our study clearly demonstrates that all three breeds have appropriate characteristics for meat production.