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This paper aims to identify the effects of two types of teaching approaches, namely, project-based learning and place-based learning, on the development of pro-environmental behaviors among university students.

This study used a pre-test–post-test experimental design to evaluate the efficacy of project- and placed-based learning in promoting pro-environmental behaviors among university students. Participants were randomly allocated to either the project-based learning cohort (consisting of 50 participants) or the place-based learning cohort (also consisting of 50 participants). The pre-test assessments evaluated the initial pro-environmental behaviors of the participants. Then, interventions were conducted with the help of instructors. The project-based learning intervention necessitated participants to collectively take part in real-life problem-solving endeavors about environmental matters. On the other hand, the place-based learning intervention prompted participants to record and contemplate their interactions with the surrounding environment. Both interventions sought to augment participants’ understanding, beliefs and actions related to the environment. After the interventions, post-test assessments were carried out to assess any alterations in participants’ pro-environmental behaviors. Mean analysis and paired sample t-test were performed to examine the differences among the pre-test and post-test for both project- and place-based learning.

The findings of this study indicate that the participants have exhibited improved pro-environmental behaviors, including environmental activism, non-activist behaviors and private sphere green behaviors.

This research is original in its analysis of project- and place-based learning approaches for fostering pro-environmental behaviors. By using a pre-test–post-test experimental design, this study provides empirical evidence on the effectiveness of these active learning strategies in shaping environmental attitudes and actions.

This study aims to investigate the effect of powder recycling on the microstructure of plasma-sprayed Ni625-WC composite coating and to verify the feasibility of Ni625-WC powder recycling by comparing the corrosion resistance of the coatings in high-temperature and pressure CO₂ environment.

Recycling powder is an efficient way to improve the utilization rate of metal powder during plasma spraying. The plasma-sprayed Ni625-WC composite coatings with original powder (OC) and recovered powder (RC) were analytically compared by using scanning electron microscope (SEM) equipped with an energy-dispersive spectroscopy, X-ray diffractometer, and X-ray photoelectron spectroscopy. The corrosion resistance of the Ni625-WC composite coatings was characterized in a self-designed high-temperature and pressure autoclave by an electrochemical workstation.

The results showed that there is massive M₂₃C₆ in OC and acicular M₂₃C₆ in RC. The WC particles in RC are more uniformly distributed, and the area ratios of WC particles to Inconel 625 matrix are 2.37% higher than OC. RC showed high corrosion resistance, and the recycling of Ni625-WC powder is feasible.

The feasibility of Ni625-WC powder recycling was verified from the microstructure evolution and electrochemical behavior of the coatings.

Purpose: The study aims to explore the wider acceptance of blockchain technology and growing faith in this technology among all business domains to mitigate the chances of fraud in various sectors.

Design/Methodology/Approach: The authors focus on studies conducted during 2015–2022 using keywords such as blockchain, fraud detection and financial domain for Systematic Literature Review (SLR). The SLR approach entails two databases, namely, Scopus and IEEE Xplore, to seek relevant articles covering the effectiveness of blockchain technology in controlling financial fraud.

Findings: The findings of the research explored different types of business domains using blockchains in detecting fraud. They examined their effectiveness in other sectors such as insurance, banks, online transactions, real estate, credit card usage, etc.

Practical Implications: The results of this research highlight (1) the real-life applications of blockchain technology to secure the gateway for online transactions; (2) people from diverse backgrounds with different business objectives can strongly rely on blockchains to prevent fraud.

Originality/Value: The SLR conducted in this study assists in the identification of future avenues with practical implications, making researchers aware of the work so far carried out for checking the effectiveness of blockchain; however, it does not ignore the possibility of zero to less effectiveness in some businesses which is yet to be explored.

Partial alignment for 3 D point sets is a challenging problem for laser calibration and robot calibration due to the unbalance of data sets, especially when the overlap of data sets is low. Geometric features can promote the accuracy of alignment. However, the corresponding feature extraction methods are time consuming. The purpose of this paper is to find a framework for partial alignment by an adaptive trimmed strategy.

First, the authors propose an adaptive trimmed strategy based on point feature histograms (PFH) coding. Second, they obtain an initial transformation based on this partition, which improves the accuracy of the normal direction weighted trimmed iterative closest point (ICP) method. Third, they conduct a series of GPU parallel implementations for time efficiency.

The initial partition based on PFH feature improves the accuracy of the partial registration significantly. Moreover, the parallel GPU algorithms accelerate the alignment process.

This study is applicable to rigid transformation so far. It could be extended to non-rigid transformation.

In practice, point set alignment for calibration is a technique widely used in the fields of aircraft assembly, industry examination, simultaneous localization and mapping and surgery navigation.

Point set calibration is a building block in the field of intelligent manufacturing.

The contributions are as follows: first, the authors introduce a novel coarse alignment as an initial calibration by PFH descriptor similarity, which can be viewed as a coarse trimmed process by partitioning the data to the almost overlap part and the rest part; second, they reduce the computation time by GPU parallel coding during the acquisition of feature descriptor; finally, they use the weighted trimmed ICP method to refine the transformation.

This study aims to investigate the impact of supply chain risk (SCR) information processing capabilities (e.g. SCR information sharing and SCR information analysis) and supply chain finance (SCF) on supply chain resilience, as well as the moderating effect of environmental uncertainty in the relationship between SCF and supply chain resilience.

This paper proposes a theoretical model grounded on the information processing theory. Data collected from 216 Chinese firms are used to test the theoretical model by employing structural equation modelling.

The findings reveal that SCR information processing capabilities have a significant impact on both SCF and supply chain resilience. SCF plays a partial mediating role in the relationship between SCR information processing capabilities and supply chain resilience. In addition, environmental uncertainty moderates the relationship between SCF and supply chain resilience.

First, this paper enriches the knowledge of how information processing capability affects SCF and supply chain resilience as the study considers the more granular SCR information rather than general information that has been discussed in previous studies. Second, this is one of the first papers to establish the relationship between SCF and supply chain resilience in emerging economies. Next, the paper extends the theoretical framework of the antecedents and consequences of SCF. Moreover, the study further facilitates the understanding of the role of the external environment in SCR and SCF management.

The purpose of this paper is to report on the findings and implications associated with the millions of financial and other fraud complaints that are reported to the Federal Trade Commission and published in the Consumer Sentinel Network Data Book each year since 2002. Based on the three dimensions, namely, the number of complaints, growth rates and geographic locations of those crimes, this study found similar as well as unique trends that are new and are critical for addressing the rise of cybercrimes in the USA. The trends and patterns identified may also have implications for addressing cybercrimes in other parts of the world.

This research is a cross-sectional time-series study that covers frauds and cybercrimes in the USA from 2002 to 2015. The observed cases included the number of total complaints, complaints categories and payment amount or loss incurred both at the national and state levels. First, aggregate fraud totals, categories, payments and payment methods were analyzed and ranked. Second, state data for fraud categories, payments and filing rate per capita were organized into panel data for analysis, comparison and ranking. This cross-sectional and longitudinal approach of the different dimensions of financial and other frauds generate new rankings and more robust results.

The key findings are related to the long-term occurrences and trends of financial and online frauds in the USA. While some general trends are consistent with prior studies, the cross-sectional and longitudinal panel analysis produced some unique results. States that reported the most complaints do not necessarily rank high when examined with their growth per capital or their rates of growth. Their rankings could change dramatically due to other factors. In addition, eight of the top ten crime categories are the same both at the national and state levels, indicating that law enforcement could target the same crime categories.

The panel data analysis is new (first attempt at using this technique on the data set) and robust because it allows cross-sectional and longitudinally analysis of the various financial and online fraud crimes, in aggregate and by state, for a more comprehensive and comparative examination of the fraud behavioral trends. This research can be viewed as an improvement over earlier studies because the panel analysis identifies what fraud trends, scam types and payment amount exist on the national and state levels. The rate of fraud growth in the respective states provides a better understanding about future development of this problem.

The purpose of this paper is to report on the findings and lessons that were learned from the many cases of internet fraud complaints that are gathered by the Federal Trade Commission in the USA. The implications that are contained in the behavior of the complaints and trends identified are critical for addressing all types of online criminal activities in the increasing world of cybercrimes.

Data for this paper are extracted from the Consumer Sentinel Network Data Handbook covering a period of 13 years. Using the raw data, the occurrences were plotted and trends (number of cycles, time between cycles, and leading state and lagging states) were identified. Descriptive statistics covering the 13 years were examined and discussed. Using 20002 as the base year, the rate of growth of each of the states were ranked annually and were tested for stability and predictability using non-parametric approaches.

The key findings are indigenous to the occurrences of online fraud complaints in the USA. However, as the leading nation with the best database on fraud complaints, the findings are mission critical to fraud prevention across the globe. Specifically, this study found that since 2002, there have been four distinct cycles. Each cycle is clearly noticeable because there is a rapid growth in the number of crime complaints in the beginning half of the period followed by a slowing down period. However, the speed of change from one cycle to the next is steeper and the time gap is faster. While it is true that from the perspective of all the 50 states, the ranks appear to change every year and thus are not stable. However, the majority of the changes are relatively small so their relative positions by rank are still predictable.

The paper extends existing understanding of online fraud occurrences in the USA. The findings are timely and based on longitudinal data that span over a decade. The four new cycles identified are critical to the body of knowledge. The observed change in time from one cycle to another and its changes from a mathematic to a geometric one are also new. The findings are invaluable to persons working in law enforcement related occupations (auditors, lawyers, forensic experts, among others) and who must deal with the increasing problem of online fraud and cybercrimes.

This study investigates the causal relationship and mechanisms between the development of digital finance and household carbon emissions. Its objective is to explore how digital finance can influence the carbon footprint at the household level, aiming to contribute to the broader understanding of financial innovations' environmental impacts.

The research combines macro and micro data, employing input-output analysis to utilize data from the China Household Finance Survey (CHFS) for the years 2013, 2015, 2017, and 2019, national input-output tables, and Energy Statistical Yearbooks. This approach calculated CO2 emissions at the household level, including the growth rate of household carbon emissions and per capita emissions. It further integrates the Peking University Digital Financial Inclusion Index of China (PKU-DFIIC) for 2012–2018 and corresponding urban economic data, resulting in panel data for 7,191 households across 151 cities over four years. A fixed effects model was employed to examine the impact of digital finance development on household carbon emissions.

The findings reveal that digital finance significantly lowers household carbon emissions. Further investigation shows that digital transformation, consumption structure upgrades, and improved household financial literacy enhance the restraining effect of digital finance on carbon emissions. Heterogeneity analysis indicates that this mitigating effect is more pronounced in households during the nurturing phase, those using convenient payment methods, small-scale, and urban households. Sub-index tests suggest that the broadening coverage and deepening usage of digital finance primarily drive its impact on reducing household carbon emissions.

The paper recommends that China should continue to strengthen the layout of digital infrastructure, leverage the advantages of digital finance, promote digital financial education, and facilitate household-level carbon emission management to support the achievement of China's dual carbon goals.

The originality of this paper lies in its detailed examination of the carbon reduction effects of digital finance at the micro (household) level. Unlike previous studies on carbon emissions that focused on absolute emissions, this research investigates the marginal impact of digital finance on relative increases in emissions. This method provides a robust assessment of the net effects of digital finance and offers a novel perspective for examining household carbon reduction measures. The study underscores the importance of considering heterogeneity when formulating targeted policies for households with different characteristics.

The purpose of this paper is to illuminate concerned companies to develop a better understanding of customer needs through reference of Kano model.

This paper facilitates decision-making process for the productive use of strategy management through a case study approach for corrugated industries in India. A hybrid approach is employed by calculating coefficients of satisfaction with S-CR (customer requirements and customer satisfaction (CS)) relationship functions and self-stated importance evaluation.

Kano’s model provides an effective approach for both industries and academic research in classifying different customer requirements into different categories based on their impact on CS. It empowers to obtain competitive and factual information about customer needs.

This study is limited in terms of sample size, domain of the study and the coverage of participants.

This paper suggests a valuable Kano approach for concerned organizations and practitioners, to correctly identify customer requirements and channelize their resources in right direction. Fulfilling customer requirements by providing them satisfaction and delight timely is only golden rule for sustaining in this competitive world.

This study addresses the growing cyber risks of banks by proposing an innovative, end-to-end dual-layer blockchain-based cyber fraud (CF) response system that integrates Safeguard (SG) and Block guard (BG) mechanisms. The comprehensive solution offers an actionable framework for bank managers to mitigate CFs by prioritizing fraud detection, leveraging early warning signals (EWS), and implementing tailored, need-based control measures before, during, and after a fraud event.

The study uses a multi-method approach, beginning with an extensive literature review on fraud identification, assessment, and prevention strategies. A theoretical framework is constructed to support the proposed SG and BG measures. Machine learning-based data analysis, using Artificial Neural Networks, is employed to dynamically assess the severity of CFs in real time. A managerial action plan for each phase of the fraud lifecycle is presented.

The research underscores the necessity for an adaptable, dual-layered response system that transitions from reactive to proactive and predictive mitigation strategies. The study introduces a novel approach incorporating SG and BG mitigation measures, enabling managers to detect early warning signals and implement robust post-fraud interventions.

The dual-layer approach enhances the sector's resilience to CFs by providing a robust, adaptive framework for fraud prevention and mitigation. This approach helps maintain stability, SG the bank's reputation, and improve overall risk management practices.

This study is unique in its development of an integrated SG and BG response system, combining machine learning, blockchain technology, early warning signals, and a structured before-during-after fraud control model. The research also highlights the critical role of bank managers in implementing and overseeing this innovative response system.