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The purpose of this paper is to improve the design of a solid square canopy of a parachute. The design improvements are brought out by providing minor slits in the canopy area. Proper designing of the parachute was carried out using theoretical investigation coupled with experimentation. This parachute is designed for launch of sonobuoy from fixed wing aircraft.

Literature review was carried out on the design of such parachutes for the launch of a sonobuoy from a high altitude to the water entry. Computational fluid dynamics (CFD) analysis provided the value of the coefficient of drag for the slit-cut square canopy parachute, with and without sonobuoy for different lengths of the slit. Besides the theoretical investigation, experimentation was also carried out to validate the design.

The experimentation was carried out on 58 and 75 gsm fabric canopies with the slit edge plain-cut with thermally sealed edges, stitched and strengthened. In the case of plain-cut slits on the canopy made of 75 gsm fabric, no tearing of the slit edge was observed in dynamic and flight tests.

The present work has been carried out considering various assumptions and limited trial data specific to precision drop of 9 kg payload. The work can be adopted for bigger parachute for dropping of higher payloads.

Lab strength test, track dynamic and flight trials were conducted to acquire useful data for the present analysis. Besides the theoretical investigations and CFD analysis inherently based on numerous assumptions, experimentation was carried out as the sonobuoy deployment conditions are full of uncertainty. Dynamic and airdrop tests were conducted for this reason to determine design changes in the slits, both at the material level and on improvisations.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

This study investigates the various themes around the demand-driven approach of vocational education and training (VET). The study focuses on investigating two major aspects of VET. Firstly, the extant literature has been systematically classified based on seven parameters: Geography, Focus on Vocational Education, Research Methods, Economic Sector, Origin of Study, Type of Training and Level of Skill. Secondly, this study has explored various themes around the VET implementation, performance gaps and road ahead.

Thematic analysis with the systematic literature review (SLR) method is applied to 50 research papers on VET, published between 2011 and 2022.

This systematic review has found that experiential compliments VET and VET is the medium of skill development. Further, it was extracted through themes that VET may help ensure alternate employment in rural areas and contribute to micro- and macro-level economic development. Also, there is a requirement to adopt a demand-driven approach to VET by localizing VT content and delivery.

The study's findings are directed towards the need for demand-driven and customized VET. This study also explores many potential areas for further empirical research through various themes.

This study is a novel effort that extracts the themes constituting the effect of VETs along with the descriptive analysis of the extant literature using the SLR approach. The study has rationalized the findings by providing due coding to various parameters in the previous studies under investigation.

The purpose of this paper is to design and manufacture an intelligent 3D printed sensor to monitor the re-occurrence of diaphragmatic hernia (DH; after surgery) in bovines as an Internet of Things (IOT)-based solution.

The approach used in this study is based on a bibliographic analysis for the re-occurrence of DH in the bovine after surgery. Using SolidWorks and ANSYS, the computer-aided design model of the implant was 3D printed based on literature and discussions on surgical techniques with a veterinarian. To ensure the error-proof design, load test and strain–stress rate analyses with boundary distortion have been carried out for the implant sub-assembly.

An innovative IOT-based additive manufacturing solution has been presented for the construction of a mesh-type sensor (for the health monitoring of bovine after surgery).

An innovative mesh-type sensor has been fabricated by integration of metal and polymer 3D printing (comprising 17–4 precipitate hardened stainless steel and polyvinylidene fluoride-hydroxyapatite-chitosan) without sacrificing strength and specific absorption ratio value.

The impact of COVID-19 has caused a recession in economies all over the world. In this context, the current study aims to analyze the prevailing economic scenario using a machine learning approach and suggest sustainable measures to recover the global economy taking the case of Make in India (MII) initiative of developing the economy as a base for the study.

A well-known topic modeling technique – Latent Dirichlet allocation (LDA) algorithm has been employed to extract useful information characterizing the existing state of selected sectors under the MII initiative alongside catalytic policies that have been implemented for the same. The textual data acts as the base of the study upon which suggestions are provided.

The findings obtained suggest that digital transformation will play a key role in concerned sectors to optimize the performance of manufacturing organizations. Additionally, inter-relationship between Key Performance Indicators for the economy's revival is crucial for effective utilization of foreign direct investment resources.

The novel efforts to utilize MII initiative as a case present crucial information which can be used by policy makers and various other stakeholders across the globe to enhance decision-making and draft legislation across different sectors to empower the economy.

The study presents a novel approach to utilize the MII initiative by identifying important measures for crucial sectors and associated policies that have been presented by employing a text mining approach which in itself makes it unique in its contribution to research literature.

The present study seeks to evaluate the role of village bamboo management in the rural landscape of North East India in global climate change mitigation.

A set of 100 home gardens and 40 bamboo groves were selected from Irongmara and Dargakona village, in Cachar district, Assam, North East India through random sampling. Sampling was done mostly for smallholders. Culm growth, carbon storage, carbon sequestration and carbon in litter floor mass and soil of bamboo growing areas in homegarden was explored from 2003‐2007.

Culm growth extension revealed the brief periodicity of culm growth in a single growth period. Of the total carbon storage soil contributed 84.6 per cent of the total (50.1 Mg ha⁻¹) followed by carbon in above ground vegetation 15 per cent (9 Mg ha⁻¹) and carbon in litter floor mass 0.4 per cent (0.2 Mg ha⁻¹).

Bamboo plantation development and its management in home gardens has social, ecological and economical benefits for the rural life in North East India and its promotion can become an effective choice for climate change mitigation strategy.

Bamboo forms an important component in the traditional home garden system of North East India where the practice of bamboo cultivation and management provides an important sink for CO₂. Village bamboos play an important role in local economics, societies and environments and, considering its potential to mitigate global climate change, the authors recommend the promotion of bamboo in agroforestry expanding practices and rehabilitation of degraded lands. Management of village bamboos in rural landscape is highlighted in context to environmental sustainability and as a sink measure under the Clean Development Mechanism (CDM) of Kyoto Protocol.

The purpose of this paper is to focus on studying the concept of “Smart Organization” and providing a comprehensive framework for the various factors as barriers for the smart organization, identifying and classifying the key criterion of these factors based on their direct and indirect relationships.

In this paper an extensive literature survey and experts’ opinion have been used to identify major barriers of smart organization. These barriers are then modeled using interpretative structural modeling (ISM) methodology. The model so developed has been further improved and an integrated model has been developed using fuzzy-MICMAC.

Various barriers of smart organization have been identified and a structural model has been developed for barriers using the ISM methodology. The critical barriers have been found out by fuzzy-MICMAC analysis. The driver power and dependence graph has been plotted for barriers. The barriers are classified into four categories which are, autonomous, linkage, dependent and independent according to their driver power and dependence. From the ISM model and the integrated model, and from further discussions with the experts, it has been found that the barriers “(B1) organizational structure” and “(B6) Managerial actions” are the two most important barriers, every other barrier is directly or indirectly driven by these.

The basis of developing the ISM model, i.e, the structural self-interaction matrix is based on experts’ opinion, therefore the result may get influenced if there is any biasing in judging the barriers. The future research scope for this paper will be to test the model generated in this paper. The testing of the model can be done by applying structural equation modeling technique, it has the capability of testing the hypothetical model. Further a framework of smart organizations can be created to find out the smartness of different organizations.

The paper can be used by organizations in understanding the barriers in becoming “smart” on the basis of their inter-relationships. This model can help manufacturing organization of North India in understanding the barriers which needs to be worked upon and the inter-relationship among these factors. This model-based study may be helpful in understanding and implementing the practices of smart organization by removing the possible critical barriers.

This is the first study to identify the barriers of smart organizations and to develop a model of these barriers using ISM and fuzzy-MICMAC.

The purpose of this paper is to investigate the significant supply chain issues in the small and medium-sized enterprise (SME) food industry. The objectives are to identify the major themes and the dynamic evolution of SME food supply chain (FSC) issues, the current research trends, the different modelling approaches used in SME FSC, and the most addressed SME food sector.

In all, 3,733 published articles from 2002 to 2018 in the Clarivate Analytics Web of Science database were collected, from which 1,091 articles were shortlisted for the review. The authors used bibliographic coupling combined with co-word analysis to identify the historical relations of the research themes that emerged during the periods 2002–2014 and 2002–2018.

This research identified five major research themes such as production and distribution in alternative food networks, relationship, safety and standards in the FSC, greenhouse gas (GHG) emission impact of the farm food system, traceability and product quality in FSC and asymmetric price transmission in the FSC. Among the identified themes, GHG emission impact of the farm food system and traceability and product quality in the FSC have received increasing attention in recent years. The dairy sector is the most addressed sector (36 per cent), followed by fruits and vegetables (27 per cent), meat and poultry (18 per cent), seafood (10 per cent) and grains and oilseed (8 per cent). It is also identified that the dairy sector has received significant attention in the “GHG Emission impact of farm food system” theme. Similarly, meat and poultry sectors have received much attention in the “Traceability and product quality in the food supply chain” theme. Also, the authors identified that the empirical modelling approaches are the most commonly used solution methodology, followed by the conceptual/qualitative methods in the SME FSC.

This study maps and summarizes the existing knowledge base of supply chain issues in the SME food sector. The results of this review provide the major research areas, most commonly used approaches and food sectors addressed. This study also highlights the research gaps and potential future research direction.

Flowers and fruits of Madhuca longifolia (Koenig) (mahua) tree are edible and used as traditional Indian medicines. The physicochemical properties of different parts of mahua are investigated. This study aims to estimate the different mineral contents, polyphenols compounds and antioxidant activities by 2,2-diphenyl-1-picrylhydrazyl inhibition, reducing power, free radical scavenging activity using 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) and ferric reducing antioxidant power assays of mahua flower, ripe and unripe fruit.

Flavonoids were identified and quantified in yellow flowers and fruits of M. longifolia tree by high-performance liquid chromatography with diode array detector. Low molecular weight carbohydrates were determined by the ICBio scan, a specific method for determining of carbohydrates. Mineral content is determined by laser-induced breakdown spectroscopy (LIBS) and atomic absorption spectroscopy. Physicochemical, nutritional and mineral properties of mahua flower, ripe and unripe fruit were investigated by the statistical approach of principal component analysis (PCA).

Ascorbic acid, gallic acid (GA), quercetin and myrcetin were the phenolic compounds identified and quantified in mahua flower and fruit extracts. Sugar profiling of mahua flowers and fruits confirmed the presence of inositol, sorbitol, mannitol, dextrose, fructose, sucrose, raffinose and maltose. The mineral content of Na, K, Mg and Ca was present in quite a good amount in all samples. Total phenolic content (TPC) was significantly high in mahua flower (25.3 ± 1.0 mg GA equivalent/g FW) followed by mahua unripe (15.8 ± 1.0 mg GA equivalent/g FW) and ripe fruit (14.3 ± 1.0 mg GA equivalent/g FW) at p = 5%. In contrast, total flavonoid contents (TFCs) were highest in ripe fruit, then mahua flower and unripe fruit. Positive correlations were predicted by PCA for mahua flower with TPC, antioxidant activity assays and minerals except for Na; ripe fruit with TFC and Na; and unripe fruit with maltose and sorbitol.

This study demonstrates the application of LIBS for the determination of elements present in the mahua flowers and fruits and reveals that mahua can be a good source of nutrients. Sugar profiling of mahua flower showed that it is a rich source of reducing and non-reducing sugar, proving that mahua flower juice can be used as a natural sweetener in the development of different food products, namely, biscuits, cookies, cake, jam, jelly, juice and squash.