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The purpose of the paper is to present research trends in the food supply chain in the context of changes in food systems due to globalization, urbanization, environmental concerns, technological changes and changes in food consumption patterns in the world.

The present investigation was performed by bibliometric analysis using the VOSviewer software, visualization software developed by Nees and Waltman (2020). In this work we performed co-citation, bibliographic coupling and keyword evolution analyses.

The results show that research in the food supply chain is rapidly changing and growing. By applying co-citation analysis, The authors found that the intellectual structure of the food supply chain has evolved around six clusters, namely, (a) collaboration and integration in the supply chain (b) sustainable supply chain management, (c) food supply chain management (FSCM), (d) models for decision-making in the food supply chain, (e) risk management in the supply chain and (g) quality and food logistics in the supply chain. However, based on bibliographic coupling analysis, The authors find that new or emerging research niches are moving toward food supply market access, innovation and technology, food waste management and halal FSCM. Nevertheless, the authors found that the existing research in each of the thematic clusters is not exhaustive.

The limitation of the research is that the analysis mainly relates only to the bibliometric approach and only one database, namely, Scopus. Broader inclusion of databases and deeper application of content analysis could expand the results of this research.

There are limited studies that have examined research trends in food supply chains in both developed and developing countries using bibliometric analysis. The present investigation is novel in identifying the thematic research clusters in the food supply chain, emerging issues and likely future research directions. This is important given the dynamics, consumer demand for quality food, technological changes and environmental sustainability issues in food systems.

This study aims to examine the influence of regulatory framework (RF), market dynamism (MDY), competitive intensity (CIN) and environmental public concern (EPC) on responsible innovation (RI) in the manufacturing SMEs of a developing country. Furthermore, the study examines the influence of RI on sustainable competitive advantage (SCA). Moreover, big data adoption (BDA) is used as a moderator between RI and SCA.

The study’s hypotheses are evaluated using the structural equation modeling methodology. The study collected data from a sample of 487 owners/managers of manufacturing SMEs using simple random sampling technique.

The results explain that RF, MDY, CIN and EPC significantly determine RI. Moreover, RI significantly determines SCA. Besides, BDA significantly moderates between RI and SCA.

For manufacturing SMEs to obtain SCA, RI is essential, and BDA is crucial to meet changing consumer demands for environmentally friendly products. With more environment-friendly initiatives, the natural environment will become more sustainable. Moreover, this research offers a comprehensive perspective to the public regarding the extent to which manufacturing enterprises are adopting RI and BDA.

The majority of earlier research on RI and BDA has been carried out in the context of large businesses. Institutional theory was used to look into the drivers of RI. Furthermore, BDA is used as moderating variable between RI and SCA.

Surface acoustic wave (SAW) sensors have attracted great attention worldwide for a variety of applications in measuring physical, chemical and biological parameters. However, stability has been one of the key issues which have limited their effective commercial applications. To fully understand this challenge of operation stability, this paper aims to systematically review mechanisms, stability issues and future challenges of SAW sensors for various applications.

This review paper starts with different types of SAWs, advantages and disadvantages of different types of SAW sensors and then the stability issues of SAW sensors. Subsequently, recent efforts made by researchers for improving working stability of SAW sensors are reviewed. Finally, it discusses the existing challenges and future prospects of SAW sensors in the rapidly growing Internet of Things-enabled application market.

A large number of scientific articles related to SAW technologies were found, and a number of opportunities for future researchers were identified. Over the past 20 years, SAW-related research has gained a growing interest of researchers. SAW sensors have attracted more and more researchers worldwide over the years, but the research topics of SAW sensor stability only own an extremely poor percentage in the total researc topics of SAWs or SAW sensors.

Although SAW sensors have been attracting researchers worldwide for decades, researchers mainly focused on the new materials and design strategies for SAW sensors to achieve good sensitivity and selectivity, and little work can be found on the stability issues of SAW sensors, which are so important for SAW sensor industries and one of the key factors to be mature products. Therefore, this paper systematically reviewed the SAW sensors from their fundamental mechanisms to stability issues and indicated their future challenges for various applications.

Some studies have been reported in the past on diaphragmatic hernia (DH) surgery techniques using additive manufacturing (AM) technologies, symptoms of a hernia and post-surgery complications. But hitherto little has been reported on bibliographic analysis (BA) for health monitoring of bovine post-DH surgery for long-term management. Based on BA, this study aims to explore the sensor fabrication integrated with innovative AM technologies for health monitoring assistance of bovines post-DH surgery.

A BA based on the data extracted through the Web of Science database was performed using bibliometric tools (R-Studio and Biblioshiny).

After going through the BA and a case study, this review provides information on various 3D-printed meshes used over the sutured site and available Internet of Things-based solutions to prevent the recurrence of DH.

Research gaps exist for 3D-printed conformal sensors for health monitoring of bovine post-DH surgery.

Blockchain’s potential is so significant that business activities across all industries can be drastically altered. Furthermore, the characteristics of blockchain appear to be well-suited to accounting requirements. However, accounting professionals’ attitude and intention toward blockchain adoption are not clear, particularly in India. Thus, this study aims to investigate and evaluate accountants’ intention to adopt blockchain technology in accounting activities.

This study examined and assessed accountants’ intention to use blockchain in accounting. To effectively measure usage intention, this study extended the unified theory of acceptance and use of technology (UTAUT) model by including context-specific constructs. To empirically test and validate the proposed model, data were collected from “369” professional accountants in India.

The findings revealed that facilitating conditions, performance expectancy and initial trust had a significant impact on adoption. Furthermore, the regulatory framework materially moderated the association between usage intention and its predictors.

These findings provide new empirical evidence about the impact of different predictors of usage intention by extending the UTAUT model. Relevant stakeholders can refer to this pioneering study to increase the adoption of blockchain as an efficient and trustworthy system among professional accountants, particularly in developing countries such as India.

This paper aims to present the design development and measurement of two aerodynamic slotted X-bands back-to-back planer substrate-integrated rectangular waveguide (SIRWG/SIW) to Microstrip (MS) line transition for satellite and RADAR applications. It facilitates the realization of nonplanar (waveguide-based) circuits into planar form for easy integration with other planar (microstrip) devices, circuits and systems. This paper describes the design of a SIW to microstrip transition. The transition is broadband covering the frequency range of 8–12 GHz. The design and interconnection of microwave components like filters, power dividers, resonators, satellite dishes, sensors, transmitters and transponders are further aided by these transitions. A common planar interconnect is designed with better reflection coefficient/return loss (RL) (S₁₁/S₂₂ ≤ 10 dB), transmission coefficient/insertion loss (IL) (S₁₂/S₂₁: 0–3.0 dB) and ultra-wideband bandwidth on low profile FR-4 substrate for X-band and Ku-band functioning to interconnect modern era MIC/MMIC circuits, components and devices.

Two series of metal via (6 via/row) have been used so that all surface current and electric field vectors are confined within the metallic via-wall in SIW length. Introduced aerodynamic slots in tapered portions achieve excellent impedance matching and tapered junctions with SIW are mitered for fine tuning to achieve minimum reflections and improved transmissions at X-band center frequency.

Using this method, the measured IL and RLs are found in concord with simulated results in full X-band (8.22–12.4 GHz). RLC T-equivalent and p-equivalent electrical circuits of the proposed design are presented at the end.

The measurement of the prototype has been carried out by an available low-cost X-band microwave bench and with a Keysight E4416A power meter in the microwave laboratory.

The transition is fabricated on FR-4 substrate with compact size 14 mm × 21.35 mm × 1.6 mm and hence economical with IL lie within limits 0.6–1 dB and RL is lower than −10 dB in bandwidth 7.05–17.10 GHz. Because of such outstanding fractional bandwidth (FBW: 100.5%), the transition could also be useful for Ku-band with IL close to 1.6 dB.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

Near-fault pulse-like ground motions have distinct and very severe effects on reinforced concrete (RC) structures. However, there is a paucity of recorded data from Near-Fault Ground Motions (NFGMs), and thus forecasting the dynamic seismic response of structures, using conventional techniques, under such intense ground motions has remained a challenge.

The present study utilizes a 2D finite element model of an RC structure subjected to near-fault pulse-like ground motions with a focus on the storey drift ratio (SDR) as the key demand parameter. Five machine learning classifiers (MLCs), namely decision tree, k-nearest neighbor, random forest, support vector machine and Naïve Bayes classifier , were evaluated to classify the damage states of the RC structure.

The results such as confusion matrix, accuracy and mean square error indicate that the Naïve Bayes classifier model outperforms other MLCs with 80.0% accuracy. Furthermore, three MLC models with accuracy greater than 75% were trained using a voting classifier to enhance the performance score of the models. Finally, a sensitivity analysis was performed to evaluate the model's resilience and dependability.

The objective of the current study is to predict the nonlinear storey drift demand for low-rise RC structures using machine learning techniques, instead of labor-intensive nonlinear dynamic analysis.

This conceptual paper aims to develop the vision of a novel policy tool called the digital industrial cluster (DIC) thought for a future context shaped by digital economies and Industry 4.0. The DIC departs from the industrial cluster’s precepts and take them into the digital agglomeration phenomenon.

This research uses a discursive and theoretical approach. To create a clear picture related to the research topic, this paper reviews the literature on how industrial clusters deal with digital transformation. The review takes a particular interest in industrial clusters, information and communication technologies, Industry 4.0 and policy tools for digital agglomeration.

The research develops the vision of the DIC as a new policy tool, building its foundations on both industrial clusters and digital economies; furthermore, the DIC was compared with other policy tools based on digital agglomeration to understand the similarities, differences and advantages of the former. Finally, this paper discussed where the DIC could find its way toward implementation and what externalities could be expected from doing so.

This research could be useful for policymakers and cluster organizations looking to deploy policy tools that take advantage of industrial clusters and digital transformation. Besides, the theoretical foundations presented could lead researchers to empirical identification of early incarnations of the DIC.

This paper develops theoretical principles for a new policy tool that connects industrial clusters, digital agglomeration and Industry 4.0 for the first time.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.