Ranking Technique Research Articles

UNVEILING FARMERS’ PERSPECTIVES: A COMPREHENSIVE EXAMINATION OF PRADHAN MANTRI FASAL BIMA YOJANA (PMFBY) IN TIRUCHIRAPPALLI DISTRICT OF TAMIL NADU

Agriculture’s inherent uncertainties threaten farmers' income and food security. The government introduced Pradhan Mantri Fasal Bima Yojana (PMFBY) in 2016 to mitigate risks, aligning with the “One Nation-One Plan” initiative for farmers’ protection. The current investigation aimed to gauge farmers’ perceptions of the Pradhan Mantri Fasal Bima Yojana (PMFBY). Conducted in the year 2022-23, the study focused on deliberately chosen blocks namely Thuraiyur and Uppiliyapuram in Tiruchirappalli district of Tamil Nadu. Respondents, totaling 120, were chosen from three villages in each selected block using a proportionate random sampling method. A thoroughly designed interview schedule, formulated based on expert opinions and literature reviews, was employed for data collection. Subsequently, the acquired data underwent coding, quantification, classification, tabulation and analysis, employing frequency, percentage and mean score ranking techniques to derive meaningful insights. The study's results indicated that a significant proportion of farmers (70.83%) held medium level perception, while 20.00 per cent had low perception, and 9.17 per cent expressed high perception regarding the Pradhan Mantri Fasal Bima Yojana (PMFBY). The assessment revealed that statements such as the enhancement of individual farmers' knowledge and skills under PMFBY (Mean Score - 2.59) ranked highest, followed by its role in enhancing yields and crop protection (Mean Score - 2.58) and improving resource-poor farmers’ access to better crop production (Mean Score - 2.48), which were ranked second and third, respectively. Crop insurance shields farmers from uncertainties like natural disasters. It is deemed essential for all types of farmers due to its significance.

Towards Dimension Reduction: A Balanced Relative Discrimination Feature Ranking Technique for Efficient Text Classification (BRDC)

Towards Dimension Reduction: A Balanced Relative Discrimination Feature Ranking Technique for Efficient Text Classification (BRDC)

Analyzing Optimal Marketing Channels in the Vegetable Supply Chain: Exploring Factors Influencing Marketing Channel Selection

Potato is amongst most important vegetables being used in kitchens of ordinary and common men. The income elasticity of demand of potato is relatively high as compared with other vegetables. In Pakistan, the traditional fruit and vegetable markets and considered to be the places of pure competition and farmers are linked to these markets via different marketing channels. This study delves into the analysis of optimal marketing channels within the vegetable supply chain, with a focus on understanding the factors that influence the selection of these channels. The study used cross-sectional primary data gathered from different supply chain actors, including 120 potato growers, 20 retailers, 20 consumers, 16 commission agents, and 16 wholesalers from two districts of Punjab, namely Okara and Chinote. This research employs a two-fold methodology. First, it involves the identification of the optimal marketing channel among five alternatives using a unique ranking technique methodology. Second, it employs regression analysis with binary endogenous treatment to discern the factors affecting the choice of marketing channels. The study sheds light on the intricacies of marketing channel decisions in the vegetable supply chain, offering insights into the preferences and considerations of stakeholders in the Okara and Chinote districts of Punjab, Pakistan. The findings of the study contribute not only to the understanding of optimal channel selection but also to the broader knowledge of the factors that influence these decisions. These insights are valuable for practitioners, policymakers, and researchers aiming to enhance the efficiency and effectiveness of marketing strategies within the agricultural sector, benefiting farming communities and the country as a whole.

Constraints faced by farmers in energy consumption in crop production in Belagavi and Gadag districts of north Karnataka

Energy is a critical input in all forms of economic activity on adaptability basis and plays a significant role in daily living from cooking to production activities. A farm is both consumer of energy and also producer. Farming through production process converts input energy into output energy on various forms of outputs produced in agriculture. The pattern in which energy inputs are used in crop production is closely related to the nature of cropping pattern and crop intensities. Therefore, there is a need to understand the constraints faced by farmers in energy consumption during crop production. In this context, the present study was conducted in Belagavi and Gadag districts of North Karnataka. The primary data pertaining to the crop year 2021-22 were collected from 72 farmers from irrigated condition and equal number of farmers from rainfed condition from two districts, comprising 144 farmers. The collected data on constraints were analysed using Garrett ranking technique. The major findings of the study revealed that non-availability of labour during peak operations in crop production observed in both Belagavi and Gadag districts (with Garrett score of 76.55 and 67.69 respectively). The second major constraint in Belagavi district was non-availability of draft animals (65.44) while, in Gadag district it was lack of accessibility of inputs like seeds, fertilizers and FYM (62.90), respectively. The third constraint in Belagavi district was non-availability of required machines at custom hiring centers (Garrett score 61.25) while, in Gadag district it was high cost of using mechanical power during farm operations (Garrett score 59.63) and the last constraint in both districts was given to Govt. support through subsidy to purchase of machinery (with score of 18.55 and 15.28).

Constraints and strategies of smallholder farmers for successful protected cultivation of capsicum: A critical appraisal

The study was framed to assess the challenges/constraints in protected cultivation of capsicum by small holder farmers in Karnataka state, India and provide strategies for sustained profitability. Ex-post-facto research design was followed for conducting study in three districts (Bangalore Rural, Bangalore Urban and Chickballapur) of Karnataka. From each district, 50 smallholders capsicum cultivation farmers under protected cultivation were selected through purposive random sampling, constituting 150 respondents. Garrett’s ranking technique was adopted to analyse the constraints faced by the farmers in the study area. The various constraints experienced by the farmers were broadly grouped in to production constraints, market constraints, financial constraints, technological constraints, institutional constraints, weather-based constraints, health and labour constraints. These finding demonstrated urgent need to intervene towards the constraints experienced by the smallholder capsicum grows under protected cultivation, which not only ensures to get stable income but also sustain their livelihoods.

Using leave-one-out cross validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale.

In recent decades, multilevel regression and poststratification (MRP) has surged in popularity for population inference. However, the validity of the estimates can depend on details of the model, and there is currently little research on validation. We explore how leave-one-out cross validation (LOO) can be used to compare Bayesian models for MRP. We investigate two approximate calculations of LOO: Pareto smoothed importance sampling (PSIS-LOO) and a survey-weighted alternative (WTD-PSIS-LOO). Using two simulation designs, we examine how accurately these two criteria recover the correct ordering of model goodness at predicting population and small-area estimands. Focusing first on variable selection, we find that neither PSIS-LOO nor WTD-PSIS-LOO correctly recovers the models' order for an MRP population estimand, although both criteria correctly identify the best and worst model. When considering small-area estimation, the best model differs for different small areas, highlighting the complexity of MRP validation. When considering different priors, the models' order seems slightly better at smaller-area levels. These findings suggest that, while not terrible, PSIS-LOO-based ranking techniques may not be suitable to evaluate MRP as a method. We suggest this is due to the aggregation stage of MRP, where individual-level prediction errors average out. We validate these results by applying to the real world National Health and Nutrition Examination Survey (NHANES) data in the United States. Altogether, these results show that PSIS-LOO-based model validation tools need to be used with caution and might not convey the full story when validating MRP as a method.

Exploring the value chain of organic pineapple of Assam, India

PurposeThe global organic market is expanding, and India is in an advantageous position with the highest number of organic producers worldwide. Although many articles have been published on the value chain of organic products from India, no significant studies were found related to the value chain analysis of organic pineapple. This study aims to know the various aspects of the organic pineapple value chain, i.e. network structure, value addition at various stages of chain actors, value chain upgradation and governance structure.Design/methodology/approachThe study is explorative in nature, and primary data from various actors involved in the chain is collected and analyzed. Primary data through a structured schedule and interviews are collected from farmers and traders. A multistage sampling plan has been adopted. A sample of 75 farmers was randomly selected from the study area. For traders, snowball sampling is used due to the nonavailability of the sampling frame. A total of 10 commission agents, 10 wholesalers and 20 retailers were thus selected for the study. For objectives 1 and 4, descriptive statistics are used. For objective 2, a modified formula described by (Murthy et al., 2007) is used to calculate farmer’s net price and marketing margin. For objective 3, Garrett’s ranking technique is used to identify various constraints in upgrading the organic pineapple value chain in Assam.FindingsThis study shows that the value chain of organic pineapple is in the initial stage and proper value addition is required to have a complete regulated value chain. Six marketing channel is identified, and products are sold through farmer producer company only in case of export and trade with distant buyers. The marketing efficiency for channels II and III is 1.69 and 0.99, respectively. The degree of value addition for channel II in the hands of the commission agent, wholesaler and retailer is 11.65%, 4.56% and 12.60%, respectively. In the various constraints in upgrading the value chain, farmers rank “policy support” as a major constraint. In the governance structure, trade with distant traders and exports is done formally and through written contracts.Research limitations/implicationsThe study performs value chain analysis of organic pineapple in Cachar district of Assam, India for the year January 2022–January 2023. Future studies are encouraged related to various aspects of the supply chain and value chain of organic pineapple from various northeastern states of India and other states.Practical implicationsThe study will help policymakers and key actors to know the existing chain and frame a well-coordinated and regulated value chain.Originality/valueThis study is one of the first study to explore the value chain of organic pineapple of Cachar district of Assam, India. Implementation of these findings can help various actors to strengthen the existing value chain.

Pre and post impact of GST on medicines: Retailers’ perspective

GST is known as the Goods and Service Tax levied to improve economic condition of nation. The implementations of GST have impact on the entire sector including the pharmaceutical industries. The GST Rates for medicines were decided by the GST council. GST is levied under 5 different rates, namely NIL, 5%, 12%, 18% and 28% based on the HSN code of the item. The general statement is GST is positively impact on the Indian Pharmaceutical Industries via the four different taxes levied on the medicines and it reduced the cost of production and it helps to business daily operations. Retailers are supplying medicine to the ultimate customers from manufacturers; but the medicine retailers’ survival depends on Government rules and regulation. To trace out the reliability of the statement “GST makes some positive impact and some negative impacts” the researchers have chosen the research area entitled “Pre and Post Impact of GST on Medicines: Retailers’ Perspective”. The researchers to attain the objectives, the primary data collected from 67 retailers by adopting Convenient Sampling method with help of structural questionnaire. Garrett Ranking Techniques has been adopted to prove the result qualitatively as well as quantitatively.

EVALUATION OF CONSUMERS’ PURCHASING BEHAVIOR FOR PROCESSED RICE IN MINNA, NIGER STATE, NIGERIA

The study evaluate consumers purchasing behaviour for processed rice in Minna, Niger State Nigeria. Data were collected from 114 respondent’s rice consumers across markets in Minna and this was done to understand the consumer preferences for rice variety for desired attributes. The study used multinomial logistic, garret ranking techniques and budget share index as analytical tools. The study revealed from the result that for the category of local rice only the coefficient of education is statistically significant at P< 0.01 and has a negative effect on the consumer preference for local rice. Result indicates that among the factors that consumers consider when they are purchasing rice in the study area, rice purity was the most important factor and ranked first with Garrett Mean Score of 61.04 followed by price ranked second with a garret mean score of 58.48 and the third factor was food safety with mean score of 56.53 while the least factor to be consider by the consumer was rice aroma with the mean score of 36.61. It was also revealed that the household’s monthly budget share on rice (local and foreign) in Minna was 0.35 which implies that 35% of the household’s monthly food expenditure were spent on rice consumption. The budget share on rice, beans, semovita/maize, yam and garri were estimated as 34%, 5%, 15%, 10% and 8% respectively and this clearly shows that rice constitutes a larger proportion of the household’s monthly food expenditure relative to beans, maize, yam and garri. It was concluded that as the marketing process becomes more complex and competitive, understanding consumers’ preference for rice attributes is becoming very important in order to improve the rice quality for marketability. Hence, paying attention to attributes of locally produced rice is therefore important right from production and through the entire market chain for improving competitiveness as well as securing future rice markets.

Innovative Multi-Criteria Decision-Making Approach for Supplier Evaluation: Combining TLF, Fuzzy BWM, and VIKOR

When confronted with underperforming suppliers, the need to evaluate and improve supplier performance becomes apparent. However, the inherent inaccuracies in information introduce complexity, especially when subjective human judgment is involved in the supplier evaluation process. Associated with such problem, this study presents a novel methodology for supplier performance evaluation in the crumb rubber industry, integrating the Taguchi Loss Function (TLF), fuzzy Best-Worst Method (BWM), and VIKOR technique in group decision-making environment. Aimed at addressing the challenges in industries with variable supplier quality and performance, such as the crumb rubber industry in Indonesia, the methodology was empirically tested to demonstrate its practical utility. The process involved identifying evaluation criteria through literature review tailored to the needs of decision makers (DMs), applying TLF to quantify losses from supplier performance deviations, using fuzzy BWM to determine criteria weights based on the DMs judgment, and employing the VIKOR technique for comprehensive supplier ranking. The findings underscore the methodology's effectiveness in enhancing decision-making, offering a unified metric that accommodates diverse criteria and balances precise data with subjective assessments. This approach simplifies the evaluation process, particularly in situations with conflicting interests among decision-makers. Demonstrating its practical application in the crumb rubber industry, the study highlights the methodology's potential for broader industrial applicability. Future research could explore comparative analyses with other analytical methods, further establishing the methodology's robustness and adaptability in different management contexts.

Investigating machine learning and ensemble learning models in groundwater potential mapping in arid region: case study from Tan-Tan water-scarce region, Morocco

Groundwater resource management in arid regions has a critical importance for sustaining human activities and ecological systems. Accurate mapping of groundwater potential plays a vital role in effective water resource planning. This study investigates the effectiveness of machine learning models, including Random Forest (RF), Adaboost, K-Nearest Neighbors (KNN), and Gaussian Process in groundwater potential mapping (GWPM) in the Tan-Tan arid region, Morocco. Fourteen groundwater conditional factors were considered following multicollinearity test, including topographical, hydrological, climatic, and geological factors. Additionally, point data with 174 sites indicative of groundwater occurrences were incorporated. The groundwater inventory data underwent random partitioning into training and testing datasets at three different ratios: 55/45%, 65/35%, and 75/25%. Ultimately, a comprehensive ranking of the 13 models, encompassing both individual and ensemble models, was determined using the prioritization rank technique. The results revealed that ensemble learning (EL) models, particularly RF and Adaboost (RF-Adaboost), outperformed individual models in groundwater potential mapping. Based on accuracy assessment using the validation dataset, the RF-Adaboost EL results yielded an Area Under the Receiver Operating characteristic Curve (AUROC) and Overall Accuracy (OA) of 94.02 and 94%, respectively. Ensemble models have been effectively applied to integrate 14 factors, capturing their intricate interrelationships, and thereby enhancing the accuracy and robustness of groundwater prediction in the Tan-Tan water-scarce region. Among the natural factors, the current study identified lithology, structural elements (such as faults and tectonic lineaments), and land use as significant contributors to groundwater potential. However, the critical characteristics of the study area showing a coastal position as well as a low background in groundwater prospectivity (low borehole points) are challenging in GWPM. The findings highlight the importance of the significant factors in assessing and managing groundwater resources in arid regions. Moreover, this study makes a contribution to the management of groundwater resources by demonstrating the effectiveness of ensemble learning algorithms in the groundwater potential mapping (GWPM) in arid regions.

Exploring the Challenges and Constraints Encountered by Mushroom Growers in Uttarakhand: A Comprehensive Study

Mushroom cultivation has emerged as a promising and sustainable agricultural enterprise in many regions offering economic opportunities and nutritional benefits. Uttarakhand, a hilly state in Northern Himalayas of India, is known for its ecological diversity and rich biodiversity making it an ideal location for mushroom cultivation. This paper attempts to identify the challenges and constraints faced by mushroom growers in Uttarakhand, thereby shedding light on the unique circumstances that can influence the success of this industry in the region. The current investigation was conducted in four purposively chosen districts of Uttarakhand state renowned for their substantial mushroom production. Two blocks were purposefully selected from each district, resulting in a total of eight blocks chosen deliberately due to their high concentration of mushroom growers. Subsequently, two villages were randomly selected from each block, culminating in a total of sixteen villages and 280 respondents for the study. Employing the Garrett ranking technique to assess and rank the ten statements (constraints) gleaned through extensive literature review coupled with consultations with subject matter experts. The study findings revealed that "pest and disease problem" was the most common constraint (Rank-1) faced by the mushroom growers highlighting its profound impact on mushroom cultivation. Following closely, "market accessibility" (Rank II) and "non-availability/delay of inputs" (Rank III) emerged as crucial factors that shape the mushroom cultivation landscape. On the other end of the spectrum, the constraint of "lack of transport" (Rank X) was surprisingly identified as a comparatively lesser impediment. These rankings offer strategic insights into the essential aspects that necessitate targeted interventions and resource allocation within the realm of mushroom cultivation. The study reiterates the multifaceted advantages of mushroom cultivation while acknowledging the challenges that hinder its profitable and sustainable progression. The findings will be of immense significance to policymakers, agricultural development agencies and mushroom growers in the region as they provide valuable insights into the opportunities and constraints in promoting the sustainable mushroom cultivation in Uttarakhand.

Kinetics of Phosphate Ions and Phytase Activity Production for Lactic Acid-Producing Bacteria Utilizing Milling and Whitening Stages Rice Bran as Biopolymer Substrates.

A study evaluated nine kinetic data and four kinetic parameters related to growth, production of various phytase activities (PEact), and released phosphate ion concentration ([Pi]) from five lactic acid bacteria (LAB) strains cultivated in three types of media: phytate (IP6), milling stage rice bran (MsRB), and whitening stage rice bran (WsRB). Score ranking techniques were used, combining these kinetic data and parameters to select the most suitable LAB strain for each medium across three cultivation time periods (24, 48, and 72 h). In the IP6 medium, Lacticaseibacillus casei TISTR 1500 exhibited statistically significant highest (p ≤ 0.05) normalized summation scores using a 2:1 weighting between kinetic and parameter data sets. This strain also had the statistically highest levels (p ≤ 0.05) of produced phosphate ion concentration ([Pi]) (0.55 g/L) at 72 h and produced extracellular specific phytase activity (ExSp-PEact) (0.278 U/mgprotein) at 48 h. For the MsRB and WsRB media, Lactiplantibacillus plantarum TISTR 877 performed exceptionally well after 72 h of cultivation. It produced ([Pi], ExSp-PEact) pairs of (0.53 g/L, 0.0790 U/mgprotein) in MsRB and (0.85 g/L, 0.0593 U/mgprotein) in WsRB, respectively. Overall, these findings indicate the most promising LAB strains for each medium and cultivation time based on their ability to produce phosphate ions and extracellular specific phytase activity. The selection process utilized a combination of kinetic data and parameter analysis.

Constraints Analysis in Procurement and Processing of Sesame in Northern Telangana Zone, Telangana, India

Sesame is one of the most commonly consumed foods either in fresh as raw seeds or processed. Sesame seeds contain 50-60 per cent oil. The study was aimed to document the constraints in procurement and processing of sesame in the study region. Three districts Jagtial, Nirmal and Nizamabad were selected from Northern Telangana Zone where sesame production was highest. Random sampling method was used to select the sesame processors. A total of 30 sesame oil units were selected from three districts for collecting data. The collected data has been analysed through Garette Ranking Technique. The major constraints faced by the processors at different stages of processing of sesame in the study region were high cost of machinery, insufficient funds from the financial institutions, lack of quality raw material, high cost for machinery repairs and maintenance, lack of efficient market for the final product and lack of awareness about the product.

An Analysis of the Challenges Faced by Small Scale Backyard Chicken Farmers of Dhemaji District of Assam

Aims: The present study was conducted to know the challenges faced by the small scale backyard chicken farmers of Dhemaji district of Assam.
 Study Design: The data obtained on various challenges faced by the farmers under the study were tabulated and analyzed statistically by using Garrett’s Ranking Technique.
 Place and Duration of Study: The study was conducted at Krishi Vigyan Kendra, Dhemaji, Assam Agricultural University, Simen Chapori, Dhemaji, Assam during the year, 2020-21.
 Methodology: For the purpose, a questionnaire was prepared for surveying and data were collected about various challenges in backyard chicken rearing faced by the farmers of the district. The farmers were asked to identify the challenges he or she and the fellow farmers of their villages faced with respect to rearing of local chickens in their locality and to rank the challenges from 1 to 20 according to the seriousness of the challenges faced by them.
 Results: Lack of suitable germplasm was found to be the major challenge of poultry farmers with mean score of 74.89 and ranked first among the constraints in backyard chicken rearing. Higher incidence of diseases (68.95), unavailability of feed and feed ingredients (66.55) and mortality of young chicks (63.00) were ranked second, third and fourth major challenges, respectively. On the other hand, non availability of vaccine, higher cost of feeds and feed ingredients, lack of extension training or service, high cost of farm equipments were ranked fifth, sixth, seventh and eighth, respectively, as challenges in backyard chicken rearing. Lack of organized marketing facilities, lack of knowledge on scientific poultry farming, high cost of chicks, presence of middle man, higher mortality during brooding period, inability to diagnose sick birds, inadequate veterinary facilities, seasonal price fluctuations, high cost of medicaments, lack of credit facilities, social problems like damaging of the standing crops by the birds and complaint by neighbours, loss of birds due to predators or thieves were some other constraints of lower importance as indicated by the respondents for the backyard chicken farmers surveyed during the study.
 Conclusion: From the study, it can be concluded that the small scale backyard poultry farmers of Dhemaji district of Assam have a lot many challenges in chicken rearing in village conditions, the major being the lack of suitable germplasm and higher incidence of diseases. To address such problems there should be proper initiatives from government level as well as in farmers’ collaborative approach to introduce some newly developed improved chicken breeds and awareness about proper disease prevention and bio-security measures and creation of other facilities.

Utilizing deep learning, feature ranking, and selection strategies to classify diverse information technology ticketing data effectively

In today's internet world, information technology (IT) ticketing services are potentially increasing across many corporations. Therefore, the automatic classification of IT tickets becomes a significant challenge. Feature selection becomes most important, particularly in data sets with several variables and features. However, enhance classification's precision and performance by stopping insignificant variables. Through our earlier research, we have categorized the unsupervised ticket dataset. As a result, we have converted the dataset into a supervised dataset. In this article, the classification of different IT tickets on Machine learning algorithms, Feature ranking, and feature selection techniques are used to improve the efficiency of machine learning algorithms. However, compared to the machine learning (ML) algorithms, the convolutional neural network (CNN) algorithm provides a better classification of the token IDs and provide better accuracy.

Youth Participation in Rural Development in the Jabalpur District of Madhya Pradesh, India

This study elicits and understands rural youth’s participation in different rural development activities that are being conducted in the Jabalpur district of Madhya Pradesh. Data was collected by using a pre-tested and structured schedule. The demographic composition of youth revealed that majority of them were educated up to higher secondary school, belonged to Other Backward Class (OBC) and had a nuclear family. Majority of them were unmarried and opted farming as their major occupation, had low level of annual income, medium level of use of source of information, achievement motivation, social participation, extension contact and had moderately favorable attitude towards rural development activities. Results revealed that majority (50.00%) of the youth had a medium level of participation in rural development activities. Through NYKS many activities were listed out which were undertaken in respect to rural development. Based on the responses activities were arranged in sequence using the Garett ranking technique to identify and rank which of the following activity is performed regularly and at high extent and vice-versa by the youths. Activities like sanitising village surroundings (Garett score= 65.36), agricultural activities (Garett score=62.51) and plantation (Garett score=61.88) were found to be the major activities performed by the youths.

Assam Flood and Financial Loss in Agricultural Production: A Study on Mitigation Strategy Adopted by the Farmers of Dhemaji District

Background: Assam is considered as a flood prone state, as this natural disaster creates devastating situation almost every year. The study was conducted in Dhemaji district of Assam which is in the list of highly flood hazard index. The district was selected to know the impact of flood on financial loss for agricultural production and to study the mitigation strategy adopted by the flood affected farmers in the district. Methods: For that a multistage random sampling technique was adopted to select flood affected farmers from the district. Financial loss for agricultural production due to flood was estimated by calculating financial loss for crop loss, agricultural asset loss and livestock loss for the study area. Flood situation and Non flood situation was compared while going for calculation of financial loss for crop due to flood. Henry Garret ranking technique was adopted to rank the problems face by the farmers. Result: The result reflected that net return from crop production was recorded to be less (Group I 1032.44Rs/ha and Group II 3830.99 Rs/ha) in flood situation than the non-flood situation (Group I 7051.30 Rs/ha and Group II 8330.08 Rs/ha).Total financial loss due to crop loss, asset loss and livestock loss was found to be high for size group II farmers (Rs 39094.33) then the size group I (Rs 36643.40). Majority of the respondents (79.17 per cent) preferred migration as the best coping mechanism to protect them from flood loss.

Constraint Analysis of Paddy Seed and Grain Production in Karimnagar District of Telangana Using Garrett’s Ranking Technique

The present study was conducted in Karimnagar purposively selected district of Telangana to identify the constraints perceived by farmers in grain and seed production of hybrid and open pollinated varities of paddy . From this district three mandals i.e Vinavanka mandal, Manakondur mandal and Shankarpatnam mandal, were selected randomly among seed growing mandals for collecting primary data. From each mandal, one village was selected as a sample i.e Elbaka village (Vinavanka mandal), Utoor village (Manakondur mandal) and Kannapur village (Shankarpatnam mandal). Sampling was done covering all farm size groups. In every chosen village, a total of 60 farmers were selected for sampling. This selection was designed to encompass 20 farmers engaged in the production of Hybrid seeds, another 20 involved in the production of OPV seeds, and an additional 20 farmers focused on grain production. Consequently, the complete sample consisted of 180 farmers distributed among the three villages. Garrett’ ranking technique was adopted to analyze the constraints as perceived by the respondents in Grain and seed production of paddy. The results revealed that major constraints perceived by rice growers were unfavourable weather conditions which ranked first with Garrett mean score around 85, followed by higher labour wage.

Improving the useful life of tools using active vibration control through data-driven approaches: A systematic literature review

In the present era of sustainable smart manufacturing within the industry 4.0 framework, industries thrive to achieve sustainable development. Machining processes play a substantial role in smart manufacturing. At the same time, the cutting tool is the most significant element of any machining process. The excessive tool wear or sudden failure of the cutting tool causes unplanned downtime, and it also affects the quality of finished products, economics, and effectiveness of the process. Among all the aspects, the vibrations that occur during machining and cutting forces are the most critical parameters, which accelerates the rate of tool wear. Active Vibration Control (AVC) techniques have emerged as promising approaches for mitigating the detrimental effects of vibration on tool performance. To realise the maximum potential of AVC, however, requires a methodical and exhaustive understanding of the existing literature. This study demonstrates the significance of conducting a systematic literature review on improving the Useful Life of cutting tools employing AVC and estimating through data-driven methods. A systematic literature review on AVC and remaining useful life (RUL) estimation of a cutting tool is performed using the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) methodology. However, the study primarily highlights the active vibration control through MR fluid and its characteristics, modelling, and control techniques. Moreover, the data-driven approach for the RUL prediction is discussed briefly through data acquisition, data processing, feature extraction and ranking techniques together with decision-making algorithms. This review presents a structured method for identifying, evaluating, and synthesising relevant studies, thus providing a comprehensive overview of the current state of research in the field. This review seeks to identify gaps, trends, and research directions in the application of AVC for tool longevity by analysing a wide variety of literature, including peer-reviewed journal articles, conference proceedings, and technical reports. Researchers, engineers, and practitioners engaged in tool design, maintenance, and optimization will benefit from the findings of this systematic literature review. The findings will provide a consolidated knowledge base for informed decision-making, allowing for the identification of knowledge deficits, research opportunities, and avenues for further study. The ultimate objective of this review is to contribute to the advancement of AVC techniques for extending the RUL of tools, nurturing innovation, and promoting sustainable and efficient practises across a variety of industrial sectors.