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Several industries have recently seen the replacement of human labor by automated machinery and equipment. Across the globe, farmers’ attitudes on the use of technology in agriculture are divergent. However, although some people are excited and ready to embrace technology, others are cautious and wary of trying new technologies for the first time. The third category is particularly prevalent in underdeveloped nations such as India, owing to a lack of competence, a lack of effective translation, and most crucially, a lack of financial resources. It is fruitless for the government to attempt to resolve these difficulties due to the fact that they do not take into consideration the changing circumstances and input needs of each agricultural group. Smart Tillage is a cutting-edge framework that was developed to solve the challenges listed above. In India, a decision-based smart engine for the rental and sharing of tools and equipment has been developed, which leverages machine learning methods to proceed towards a selection of tools and equipment. The option is entirely reliant on a variety of input variables, including crop kind, harvest time/month, crop equipment needed, harvest type, and the amount of money available for rental. Additionally, an ideal recommendation engine driven by content and collaborative-based filtering will provide the farmer’s requirements depending on their requirements. In terms of escalation, the proposals would be cost-effective and excellent since they would need little changes in training, technique improvements, and resource management via a new rent-share model similar to that used by Uber. In this work, demand and supply algorithms are used to define market equilibrium, and the results are shown in graphs. This includes discussion of a variety of demand and supply parameters, their impact on market equilibrium prices and quantities, and their effect on shifting demand and supply curves. The many sorts of elasticities (demand, cross-price, supply, income, and so on) are examined, as well as the ramifications for pricing systems that may result from these elasticities.

In an agriculture sector, the quality of several raw crops depends upon the time factor. After harvesting, it is necessary to bring the crops either to cold storage or directly to customer though wholesale dealers. Keeping crops in cold storage decrease the nutrition value and also increase the overall cost of the crop leading to price hike. So, it will be best if the crops are put in the market as soon as possible. This can only be possible if the logistic system is updated and can handle the real-time requirement of agriculture product transport. This study investigates and highlights the possible IoT-based logistic support using artificial intelligence for the farmers such that a fast corridor is created between farmers’ lands to end-user customers. This will benefit the farmers in two-fold; first, it will increase the revenue of farmers by decreasing the time span avoiding cold storage fees, and second, it will maintain the quality of crops.

Adolescents and young adults face elevated risks of sexual and interpersonal violence (SIV) compared to adults. Technology has been used to help mitigate this issue by mapping incidents, supporting victims, and promoting behavior change to prevent re-occurrence. However, there is a risk that technology could facilitate abuse. Ensuring technology use is safe and ethical in the context of SIV is critical. This protocol provides a roadmap for a scoping review to synthesize global literature on how technology has been used to address SIV among adolescents and young adults. It aims to identify evidence and strategies for enhancing the safety, privacy, and effectiveness of technologies to prevent and respond to SIV. A scoping review was chosen because the evidence in this field is still emerging and fragmented. This approach allows us to map the breadth of existing research, highlight gaps, and make recommendations on the pathways forward. A search strategy was developed to identify English-language studies published since 2008, the year of the emergence of smartphones in the United States, in databases such as PubMed, EMBASE, Web of Science, and Scopus. We will include qualitative, quantitative, and mixed-methods studies that use technology (e.g., mobile apps, websites) to address SIV among youth. This scoping review protocol was prepared following the Joanna Briggs Institute guidelines and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P), which will be adhered to during content analysis review process. Covidence, a web-based platform for systematic review management, will be used to manage screening and data extraction. Two independent reviewers will screen and extract data, with disagreements resolved by a third reviewer. The findings of the proposed scoping review have the potential to contribute to improving technology safety, data privacy, and ethical considerations in the context of reporting and tracking SIV, alongside informing future metrics, policies, guidelines, and platform designs, contributing to the creation of more secure and effective tools for SIV reporting and tracking. This protocol has been registered in the Open Science Framework (OSF): https://doi.org/10.17605/OSF.IO/WUNT9.

PurposeThis study aims to utilize DMAIC methodology along with value stream mapping and other Lean Six Sigma tools in a major automobile light manufacturing industry to reduce defect rates and increase production capacity in their manufacturing line. The study also proposes a modified framework based on lean principles and FlexSim to identify and reduce waste in the selected industry.Design/methodology/approachA Lean Six Sigma modified framework has been deployed with DMAIC to reduce the defect rate and increase the production rate. Various tools like value stream mapping, brainstorming, Pareto charts, 5S, kanban, etc. have been used at different phases of DMAIC targeting wastes and inventory in the production line. Also, a simulation model has been utilized for the automobile light manufacturing industry to improve the machine utilization time with varying batch sizes.FindingsThe results of the study indicated a 53% reduction in defect rates. Thus, there would be an expected improvement in sigma value from 3.78 to 3.89 and a reduction in defects per million opportunities (DPMO) from 11,244 to 8,493. Additionally, simulation model using FlexSim was developed, and the optimum ordering batch size of raw material was obtained. It was also analyzed that idle time for various stations could be reduced by up to 30%.Practical implicationsThe utilized framework helps identify defects for managers to increase production efficiency. The workers, operators and supervisors on the production line also need to be trained regularly for identifying the areas of improvement.Originality/valueThe modified Lean Six Sigma framework used in this study includes FlexSim simulation to make the framework robust, which has not been used with LSS tools in the literature studied. Also, the LSS finds very less application in the manufacturing domain, considering which this study tends to add value in existing literature taking a case of an automobile light manufacturing industry.