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This paper aims to provide an overview of the application of blockchain to agri-food supply chains, including key issues and trends. It examines the state of the art and conceptual structure of the field and proposes an agenda to guide future research.

This article performs a bibliometric analysis using VOSviewer software on a sample of 205 articles from the WoS database to identify research trend topics.

The number of publications in this area has increased since 2020, which shows a growing research interest. The research hotspots are related to the integration of blockchain technology in the agri-food supply chain for traceability, coordination between all actors involved, transparency of operations and improvement of food safety. Furthermore, this is linked to sustainability and the achievement of the sustainable development gtoals (SDGs), while addressing key challenges in the implementation of blockchain-based technologies in the agri-food supply chain.

The application of blockchain in the agri-food supply chain may consider four key aspects. Firstly, the implementation of blockchain can improve the traceability of food products. Secondly, this technology supports sustainability issues and could avoid disruptions in the agri-food supply chain. Third, blockchain improves food quality and safety control throughout the supply chain. Fourthly, the findings show that regulation is needed to improve trust between stakeholders.

The paper provides a comprehensive overview of the blockchain phenomenon in the agri-food supply chain by optimising the search criteria. Moreover, it serves to bridge to future research by identifying gaps in the field.

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.

This study aims to investigate blockchain technology (BT) and its opportunities and weaknesses in Iran's tax system; it addresses the opportunities and challenges of BT when incorporated into Iran's tax system.

The statistical population consists of all the employees and managers working in tax administration, and 674 participants were selected as the sample size via Cochran sampling. The partial least square tests are used to investigate the impact of the independent variable on dependent ones.

The results show that BT positively affects three components of tax, including value-added tax, tax on shipping goods and income tax. BT’s advantages and opportunities positively affect these taxation types, while its threats negatively affect the opportunities and challenges in Iran’s tax system; this study provides helpful insights and develops the knowledge. Furthermore, this is among the initiatives addressing BT’s opportunities and challenges in three discriminative taxation sectors, including value-added tax, tax on shipping goods and payroll tax.

Since no study has addressed BT’s opportunities and weaknesses in Iran’s tax system, it addresses the opportunities and challenges of BT when incorporated into Iran’s tax system.

The debate around automation through digital technologies has gathered traction in line with the advancement of Industry 4.0. Blockchain-powered construction progress payment has emerged as an area that can benefit from such automation. However, the challenges inherent in real-time construction payment processes cannot be solely mitigated by blockchain. Including building information modeling (BIM)-based schedule information stored in decentralized storage linked with a smart contract (SC) can allow the efficient administration of payments. Accordingly, this study aims to present an integrated BIM-blockchain system (BBS) to administer decentralized progress payments in construction projects.

A mixed-method approach is adopted, including an extensive literature review, development of the integrated BBS, and a case study with 13 respondents to test and validate the BBS. This study proposes a BBS that extracts the invoices from BIM and pushes them to the decentralized app (dApp) for digital payment to the contractor through the Ethereum blockchain. The Solc npm package was used to compile the backend SC. Next.js was used to create the front end of the dApp. The Web3 npm package is paramount in developing a dApp. A total of 13 construction professionals working on the case study project were engaged through a questionnaire survey to comment on and validate the proposed BBS. A descriptive analysis was conducted on the case study data to apprehend the responses of expert professionals.

The proposed BBS creates an SC, enables sender verification, checks contract complaints, verifies bills, and processes the currency flow based on a coded payment logic. After passing the initial checks, the bill amount is processed and made available for the contractor to claim. Every activity on dApp leaves its trace on the blockchain ledger. A control mechanism for accepting or rejecting the invoice is also incorporated into the system. The case study-based validation confirmed that the proposed BBS could increase payment efficiency (92.3%), tackle financial misconduct (84.6%), ensure transparency and audibility (92.4%), and ensure payment security (61%) in construction projects. A total of 46.2% of respondents were skeptical of the BBS because of its dependency on cryptocurrencies. A further 23.1% of respondents indicated that the price fluctuation of cryptocurrencies is a major barrier to BBS adoption. Others highlighted the absence of legal frameworks for cryptocurrencies’ usage.

This study opens the avenue for the application of dApp for autonomous contract management and progress payments, which is flexible with applications across various construction processes. Overall, it is a potential solution to the endemic problem of cash flow that has devastating consequences for all project stakeholders. This is also aligned with the goals of Industry 4.0, where process automation is a key focus. The study provides a practice application for automated progress payments that can be leveraged in construction projects across the globe.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

Empirical literature is the primary source of understanding how policing can effectively reduce criminal activities. Spatial analyses can identify particular effects that can explain and assist in constructing appropriate regional strategies and policies; nevertheless, studies that use spatial regression methods are more limited and can provide a perspective on specific effects in a more disaggregated regional context.

This research aims to conduct a systematic literature review (SLR) to understand the relationship between crime indicators and police production using spatial regression models. We consider a combination of Kitchenham and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols as a methodological strategy in five bibliographic databases for collecting scientific articles.

The SLR suggests a limited amount of evidence that meets the criteria defined in the research strategy. Several particularities are observed regarding police and criminal production metrics, either in terms of aggregation level, indicator transformations or scope of analysis. A broader time perspective did not necessarily indicate statistical significance compared to models with a single-period sample.

The findings suggest the possibility of expanding efforts by the public sector to provide policing data with the intention of conducting appropriate research using spatial analysis. This step could allow for a more robust integration between the public sector and researchers, strengthening policing strategies, evaluating the effectiveness of public security policies and assisting in the development of strategies for future policy actions.

Limited empirical evidence meets the criteria of spatial regression models with temporal components considering police production and criminality indicators. Constructing an SLR with this scope is an unprecedented contribution to the literature. The discussion can enhance the understanding of approaches for studying the relationship between police efforts and crime prevention.

This study explores the consequences of digitalization in the field of education, particularly in relation to teachers’ course processes in higher education institutions. It emphasizes the importance of understanding how information systems (IS) support not only individual tasks but also processes as a whole. The results reveal that process practices have not been considered comprehensively and even core processes may be unseen.

A systematic literature review was conducted to explore the extent to which teachers’ processes are discussed in the literature. A qualitative case study was then conducted at a Finnish higher education institution to identify course processes and their relationships to IS.

Teachers’ processes have scarcely been discussed in the literature, and the process support provided by ISs is remarkably limited. It seems that course processes, which are core to education, are a blind spot in education digitalization. To support evaluating the level of support by IS, novel course process indicators were introduced.

Developing core processes, teachers’ course processes and thesis processes in education field, supports improving service quality. In all industries, organizations should consider whether processes are properly recognized and whether IS support not only individual tasks but also processes as a whole. We recommend recognizing and applying business process management practices to better support teachers’ work and to improve overall efficiency in education.

To the best of our knowledge, this is the first education sector study that attends to teacher’s work as a comprehensive process.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

This study delves into the challenges obstructing the integration of blockchain-enabled smart contracts (BESC) in the construction industry. Its primary objective is to identify these barriers and propose a roadmap to streamline BESC adoption, thereby promoting sustainability and resilience in building engineering.

Employing a unique approach, this study combines the Technology-Organization-Environment-Social (TOE + S) framework with the IF-Delphi-HF-DEMATEL-IFISM methodology. Data is collected through surveys and expert interviews, enabling a comprehensive analysis of BESC implementation barriers.

The analysis reveals significant hindrances in the construction industry’s adoption of BESC. Key obstacles include economic and market conditions, insufficient awareness and education about blockchain technology among stakeholders, and limited digital technology integration in specific cultural and societal contexts. These findings shed light on the complexities faced by the industry in embracing blockchain solutions.

The research makes a significant contribution by combining the TOE + S framework with the IF-Delphi-HF-DEMATEL-IFISM methodology, resulting in a comprehensive roadmap to address barriers in implementing BESC in Sustainable Construction Projects. Noteworthy for its practicality, this roadmap provides valuable guidance for construction stakeholders. Its impact extends beyond the industry, influencing both academic discourse and practical applications.

This study aims to assess the household food insecurity in Malaysia during the initial phase of the movement control order (MCO) to provide insights into the prevalence and predictors of food insecurity in this context.

The research used an online cross-sectional survey between March 28 and April 28, 2020. The study collected data from the Radimer/Cornell Hunger Scale and a food insecurity instrument. Analytical tools included chi-square and logistic regression models.

Of the 411 participating households, 54.3% were food-secure, while 45.7% experienced varying food insecurity. Among these, 29.9% reported mild hunger-associated food insecurity, 8.5% experienced individual food insecurity and 7.3% reported child hunger. The study identified predictors for food insecurity, including household income, as those with total income of < RM 2,300 had 13 times greater odds (odds ratio [OR] 13.8; confidence interval [CI] 5.9–32.1; p < 0.001) than those with income of RM 5,600, marital status as divorced (OR 4.4; 95% CI 1.0–19.9; p-value = 0.05) or married (OR 1.04; 95% CI 0.52–2.1) compared to those who are single. Self-employed respondents had three times greater odds of living in a household experiencing food insecurity (OR 3.58; 95% CI 1.6–7.7; p-value = 0.001) than those in the private sector (OR 1.48; 95% CI 0.85–2.61) or experiencing job loss (OR 1.39; 95% CI 0.62–3.1) compared with those who reported being in full-time government employment.

This study acknowledged limitations, such as not considering various dimensions of food insecurity, such as coping strategies, nutritional support, diet quality and well-being, due to the complexity of the issue.

The study underscores the importance of targeted support for vulnerable groups and fostering collaborative efforts to address household food insecurity during crises like the MCOs.

The research offers insights into how to address household food insecurity and its impact on society.

It identifies predictors, quantifies increased odds and emphasizes the necessity of targeted policies and collaborative approaches for fostering resilient recovery and promoting well-being in vulnerable populations.