Search Results (76)

The global coffee supply chain is a complex multi-stakeholder ecosystem plagued by fragmented records, unverifiable origin claims, and limited real-time visibility. These limitations pose risks to ethical sourcing, product quality, and consumer trust. To address these issues, this paper proposes a blockchain and IoT-enabled framework for secure and transparent coffee supply chain management. The system integrates simulated IoT sensor data such as Radio-Frequency Identification (RFID) identity tags, Global Positioning System (GPS) logs, weight measurements, environmental readings, and mobile validations with Ethereum smart contracts to establish traceability and automate supply chain logic. A Solidity-based Ethereum smart contract is developed and deployed on the Sepolia testnet to register users and log batches and to handle ownership transfers. The Internet of Things (IoT) data stream is simulated using structured datasets to mimic real-world device behavior, ensuring that the system is tested under realistic conditions. Our performance evaluation on 1000 transactions shows that the model incurs low transaction costs and demonstrates predictable efficiency behavior of the smart contract in decentralized conditions. Over 95% of the 1000 simulated transactions incurred a gas fee of less than ETH 0.001. The proposed architecture is also scalable and modular, providing a foundation for future deployment with live IoT integrations and off-chain data storage. Overall, the results highlight the system’s ability to improve transparency and auditability, automate enforcement, and enhance consumer confidence in the origin and handling of coffee products. Full article

Shadow fleets, operating covertly in global maritime commerce, have emerged as a significant challenge to international regulatory frameworks and trade policies. This paper introduces a novel conceptual framework that distinguishes between ‘dark fleets’ and ‘gray fleets’, offering a more nuanced understanding of these clandestine maritime activities. Through a comprehensive methodological approach integrating a literature review, case studies, and data analysis, we examine the characteristics, operational strategies, and implications of shadow fleets. Our research reveals that shadow fleets have expanded rapidly, now accounting for approximately 10% of global seaborne oil transportation. We identify key indicators of shadow fleet operations, including disabled Automatic Identification System (AIS) transmitters, inconsistent vessel information, unusual behavior patterns, obscure ownership structures, and the use of aging vessels. This paper highlights the economic disruptions caused by shadow fleets, their role in circumventing international sanctions, and the significant environmental and safety risks they pose. The study underscores the regulatory challenges in addressing shadow fleets, particularly their exploitation of flags of convenience and complex ownership structures. We propose a multifaceted approach to tackling these challenges, emphasizing the need for advanced technological solutions, enhanced international collaboration, and adaptive ocean governance frameworks. This research contributes to the evolving field of maritime security and policy, offering insights for policymakers, industry stakeholders, and researchers into developing strategies to mitigate the risks posed by shadow fleets in global maritime commerce. Full article

With the increase in ownership of Internet of Things (IoT) devices, there is a bigger demand for stronger implementation of security mechanisms and addressing zero-day vulnerabilities. This work is the first to provide a platform that combines featureless approaches with artificial intelligence (AI) algorithms, which are deep learning and large language models, to uncover IoT security vulnerabilities based on network traffic data directly without manual feature selection. The platform correctly identifies vulnerable and secure IoT devices just by raw network traffic! Experimental results show that the proposed study detects vulnerability with great accuracy by using pre-trained deep learning and LLM models, which facilitates direct extraction of vulnerability features from the dataset and therefore helps speed up the identification process. In addition, the design of the platform ensures that the models are accessible and can be easily applied by users with a user-friendly interface. Furthermore, the models with small sizes, 277.5 MB and 334 MB for the deep learning model and the LLM model, respectively, illustrated the potential use of the detection tool in practical settings. The ability to defend large-scale, diversified IoT ecosystems efficiently and in a scalable way by installing thousands of software manifestations quickly while exposing new applications to growing cyber threats is made possible by this significant advancement in the field of IoT security. Full article

The need for a sustainable economy has grown as technological advancements increasingly influence economic and social structures. This study investigates the role of FinTech and artificial intelligence (AI) in fostering sustainable development by facilitating green initiatives and promoting social responsibility. The research hypothesis posits that FinTech enables better access to financing for economic and social development projects, while AI enhances decision-making processes critical to the implementation of these initiatives. Through a qualitative approach, this study analyzes the interactions between FinTech, AI, and the Sustainable Development Goals (SDGs), exploring whether their relationship is bilateral or unidirectional. Using a quantitative approach, this study employs Principal Component Analysis (PCA) and Analysis of Variance (ANOVA) to examine the key factors influencing bank account ownership across different demographic groups and time periods. PCA is utilized to reduce data dimensionality while preserving the most significant variance, enabling the identification of underlying patterns in financial inclusion determinants. Meanwhile, ANOVA is applied to assess statistical differences in bank account ownership across demographic categories and the pre-pandemic, during-pandemic, and post-pandemic periods, highlighting the impact of digital financial services on financial inclusion trends in Europe. The findings suggest that both technologies play a significant role in supporting sustainability, with FinTech providing the necessary financial tools and AI optimizing decision-making. Furthermore, this study identifies barriers, such as regulatory challenges and technological gaps, that hinder the full integration of these technologies into sustainable development practices. It also highlights facilitators, such as policy support and technological innovation, that accelerate their adoption. The conclusions emphasize the transformative potential of FinTech and AI in achieving robust economic growth, reducing inequalities, and fostering a new cultural approach to resource management and societal responsibility. Full article

Free-roaming cats and dogs impact biodiversity, public health, and the welfare of other animals. Attitudes towards free-roaming animals can influence their population dynamics and management success. We conducted an online survey to evaluate social perceptions and attitudes towards free-roaming animals among self-selected Portuguese residents aged 18 or older with internet access. The survey focused on responsible ownership, perceptions and attitudes, and management practices, and allowed the collection and analysis of 1083 responses (607 for dogs and 476 for cats). Our results identified needs for improvement in pet ownership: increasing pet cat identification, reducing unsupervised outdoor access, and promoting pet dog sterilization. In terms of management strategies, we found strong support for trap–neuter–release, sheltering, sanctions on abandonment, and educational campaigns. We also found limited support for lethal control methods and fear of culling and long-term caging as barriers to reporting free-roaming animals. While our findings are based on a self-selected online sample, they establish a foundation for future research while also offering valuable guidance for policymakers and stakeholders. Full article

Numerous scales have been developed to assess pet–owner relationship quality. One commonly used measure is the Monash Dog Owner Relationship Scale (MDORS) and its various derivatives. Since this scale was published in 2006, many social changes have occurred, necessitating a review and, if necessary, refinement of this measure. We sought to investigate the internal consistency and structure of the existing instrument, as well as an expanded and modified version of the scale, in a contemporary adult sample of over 350 adults, recruited to be potentially less dog-centric than previous samples. The existing three-factor structure of the MDORS appeared reasonably sound, but a Principal Components Analysis with modified items resulted in identification of a four-component structure. Two of the components approximated existing MDORS measures: the Perceived Costs of Dog Ownership and the owner’s Emotional Reliance on their pet. Rather than replicating the existing Pet Owner Interaction subscale, however, we identified two different types of engagement: Affectionate Engagement and Active Engagement. The subscale scores and the total score were, as expected, intercorrelated with each other and with the Lexington Attachment to Pets Scale. Perhaps surprisingly, however, they were statistically associated with very few demographic variables, such as owner gender, age, education, or residence location, and they were rarely associated with dog variables such as age, sex, or source. This may speak to the current near-universality of strong human–dog relationships, which we propose can now be assessed using two new measures: the Dog Owner Relationship Scale 28 (DORS28) and a shortened version, the Dog Owner Relationship Scale 12 (DORS12). Full article

Wireless, satellite, and mobile networks are increasingly used in application scenarios to provide advanced services to mobile or nomadic devices. For example, to authenticate mobile users while obtaining access to remote services, a two-factor authentication mechanism is typically used, e.g., based on the ownership of a personal mobile phone, device, or (smart)card and the knowledge of a (static) username and password. Nevertheless, two-factor authentication is considered roughly “adequate” for security problems encountered today on the Internet and even less for ubiquitous or mobile environments. To increase the authentication level, several authentication methods of different classes may be combined to achieve more reliable user identification. In particular, location technologies allow ubiquitous applications to better exploit the (physical) location information in the authentication process. Consequently, in security applications based on multiple authentication factors, an additional authentication factor could be the location information protected for integrity against undesired modification. We present the SAM-PAY authentication method, which combines different authentication factors to obtain a more reliable user identification. The mechanism is based on the use of a (location-aware) device, the location information certified by a trusted external party, such as a component or element in a telecom network, and the knowledge of data, like a static PIN and a dynamically generated one-time password. We also describe the design and implementation of a real case scenario exploiting our SAM-PAY method, namely the refueling service at a self-service gas station. The test-bed put in place for this service demonstrates the feasibility and effectiveness of the SAM-PAY method in open mobile environments. Full article

This paper explores how to promote consumer identification and acceptance of green products in the field of sustainable consumption and green marketing. Specifically, this paper examines how green factors affect consumers’ willingness to purchase new energy vehicles (NEVs) and focuses on the mediating role played by perceived price equity in this process. It is found that consumers’ green self-identity, green product experience, and green product innovation have a significant positive impact on their willingness to purchase NEVs, while perceived price fairness plays an important mediating role in this process. When consumers perceive that the pricing of NEVs is fair, they are more inclined to purchase them. Through the analysis of China’s new energy vehicle market, this paper puts forward suggestions to optimize the promotion strategy of green products from the perspective of price fairness, with a view to providing theoretical support and practical guidance for relevant enterprises. As China is the world’s number one country in terms of NEV ownership, studying its market consumption willingness not only reveals the unique characteristics of the Chinese market but also provides lessons and references for the future development of the new energy vehicle market in other countries, which is of great exemplary significance. Full article

At the global level, significant efforts have been made to address societal challenges and improve the lives of people and restore the planet’s ecosystems through sustainability and resilience programs. These programs, however, tend to be driven by governments, private sectors, and financial institutions, and therefore often lack a process of empowerment to ensure that the local communities can participate actively in co-designing and implementing these programs. More knowledge is needed on how to develop such programs and how the process of empowerment can be organized so that it supports in the long run sustainability transformation. Against this background, the paper explores the role of community empowerment programs as a critical tool for sustainability management strategies and practices. A semi-systematic review of 21 community empowerment programs for sustainability and resilience is conducted. The analysis reveals that the programs mostly aimed to address challenges such as the lack of education and capacity, limited access to basic services and resources, and poor governance and management. The programs initiators involve a diverse set of actors, especially through established partnerships and networks. Most of the programs address the specific needs of vulnerable or marginalized groups or communities. The structure of the programs typically follows a phased methodological approach, beginning with awareness-raising and problem identification, followed by capacity building that allows for making decisions collaboratively and for co-creating innovative solutions based on local knowledge and values. Also, monitoring and evaluation of transformative impact are mentioned as important structural elements. Specifically, the analysis highlights four main focus areas of empowerment: (1) capacity building, (2) self-reliance, control, ownership, responsibility, and independence, (3) participation, engagement, and collective action, and (4) integration of local knowledge and values. However, there is no one-size-fits-all approach to such programs. Instead, successful empowerment programs towards sustainability depend on a deep understanding of local contexts and the ability to tailor strategies to meet specific community needs. The review also identified knowledge gaps that require further investigation to enhance the effectiveness of empowerment programs for both people and nature. Full article

Machine learning (ML) revolutionized traditional machine fault detection and identification (FDI), as complex-structured models with well-designed unsupervised learning strategies can detect abnormal patterns from abundant data, which significantly reduces the total cost of ownership. However, their opaqueness raised human concern and intrigued the eXplainable artificial intelligence (XAI) concept. Furthermore, the development of ML-based FDI models can be improved fundamentally with machine learning operations (MLOps) guidelines, enhancing reproducibility and operational quality. This study proposes a framework for the continuous development of ML-based FDI solutions, which contains a general structure to simultaneously visualize and check the performance of the ML model while directing the resource-efficient development process. A use case is conducted on sensor data of a hydraulic system with a simple long short-term memory (LSTM) network. Proposed XAI principles and tools supported the model engineering and monitoring, while additional system optimization can be made regarding input data preparation, feature selection, and model usage. Suggested MLOps principles help developers create a minimum viable solution and involve it in a continuous improvement loop. The promising result motivates further adoption of XAI and MLOps while endorsing the generalization of modern ML-based FDI applications with the HITL concept. Full article

In the past, mill races were part of the urban structure of many towns in Slovakia. As regulated and artificially created waterways, they served to drive mills, rollers, or hammers. With the use of new sources of energy, they lost their functions, and most of them were dried, filled, or buried underground. In our research, we examine the former mill race corridor in Podolínec (Prešov region, Slovakia) and its potential to contribute to urban regeneration strategies. The research steps included the following, namely 1. Identification of the route of the mill race corridor according to historical sources, 2. survey of its current state and its spatial preservation in the urban structure, 3. evaluation of the possibilities of its new uses, which could increase the quality and attractiveness of urban public spaces. The research results show that the fragments of the corridor of the extinct mill race are still identifiable in the urban fabric, and in the cadaster, they are in public ownership and suitable for new uses. The fragments of the corridor of the extinct mill race in Podolínec represent a potential for strengthening the blue and green infrastructure, pedestrian and cycling greenways, and a potential for the presentation of cultural heritage values, which could contribute to the improvement of the qualities of the urban environment. Full article

Coagulase-negative staphylococci (CoNS) species in healthy dogs and their owners could be transferred between these hosts and carry diverse antimicrobial resistance (AMR) genes of public health concern. This study determined the frequency, diversity, and AMR genes of nasal CoNS from healthy dogs and in-contact people as well as the rate of intra-household (between healthy dogs and dog-owners) transmission of CoNS. Nasal samples were collected and processed from 34 dogs and 41 humans from 27 households, and CoNS identification was done by MALDI-TOF-MS. The AMR determinants and genetic lineages were determined by PCR/sequencing. A total of 216 CoNS isolates were initially obtained and identified, and the AMR phenotypes were determined. From these, 130 non-repetitive CoNS were selected (one isolate of each species per sample or more than one if they presented different AMR phenotypes) and further characterized. The predominant species from dog carriers were S. epidermidis (26.5%), S. hominis (8.8%), and S. cohnii (8.8%), whereas in the human carriers, the predominant ones were S. epidermidis (80.4%), S. lugdunensis (9.8%), and S. hominis (9.8%). Intra-host species diversity (>one CoNS species) was detected in 37.5% of dogs and 21.6% of dog-owners. Conversely, 50% of dogs and 70.3% of dog-owners had intra-species AMR diversity (2–4 AMR-CoNS profiles). About 20% were susceptible to all antimicrobial agents tested, 31.5% displayed a multidrug resistance phenotype, and 17.4% were mecA-positive, located in SCCmec type V (24.2%), III (18.1%), IVc (12.1%), and II (6.1%). The other mec-A positive CoNS isolates (39.5%) had non-typeable SCCmec. The highest AMR rates were found against erythromycin (32.3%/mph(C), msr(A)) and mupirocin (20.8%/mupA), but the resistance rates for other antimicrobial agents were <10% each. Remarkably, one linezolid-resistant S. epidermidis-ST35 isolate was identified and mediated by four amino acid substitutions in L3 and one in L4 ribosomal proteins. Dogs and dog-owners as carriers of S. epidermidis with similar AMR patterns and genetic lineages (ST59, ST61, ST166 and ST278) were detected in four households (14.8%). Diverse CoNS carriage and moderate level of AMR were obtained from this study. The detection of CoNS carrying diverse SCCmec elements and intra-species AMR diversity highlights the roles of dog ownership in the potential transmission of antimicrobial-resistant CoNS in either direction. Full article

In the post-pandemic era, the need for accessible urban green open spaces has increased. There is an urgent need to accurately identify large-scale unit-affiliated green spaces and focus on the potential for open sharing. Therefore, using POI data from the Gaode map of Shanghai obtained via web crawler, combined with remote sensing image data and the current green space data, the subjects of unit-affiliated green spaces in the main urban area and five new towns of Shanghai were identified in 2021. On this basis, in-depth explorations were carried out in terms of the type and number of subjects, the overall layout, and the grading of potential open sharing. A new application path for identifying subjects of unit-affiliated green spaces based on the POI data was established. The analysis of the potential openness of the subjects strongly supports the open sharing of unit-affiliated green spaces; the open sharing of unit-affiliated green spaces can compensate for the deficiencies in the fairness and efficiency of urban green spaces. Full article

Climate change exerts a substantial influence on global livelihood security. This research aims to elucidate the risk faced by agroforestry managers of urban and rural areas. Adhering to the IPCC risk framework, we structured the experimental design and adopted an indicator-based methodology to delineate the risk dimensions. Altogether, 105 households from 7 villages in Aizawl district, Mizoram, India, were considered for the study. For indicator identification, we conducted a comprehensive literature review and subsequently employed principal component analysis to select relevant indicators. Finally, risk was determined using the index value of hazard, exposure, and vulnerability. Additionally, we also developed a regression model and integrated it into ArcGIS to generate a spatial risk map. Out of 69 indicators identified, 52 were selected for final assessment after PCA analysis. Our findings underscore the higher susceptibility of urban agroforestry managers to climate change which was in agreement to our hypothesis that the risk index of agroforestry households increases with altitude while it decreases with the distance from Aizawl headquarter. Furthermore, we observed that households residing at higher altitudes exhibit greater vulnerability. Key determinants contributing to elevated risk in the region encompass land ownership constraints, diminished yields, traditional farming practices with no institutional help, and a dearth of available labour resources. The study advocates the implementation of climate smart agroforestry practices integrated with agricultural credit schemes and an educational policy designed to enrol dropout youths. Full article

With the rapid growth in wireless communication and IoT technologies, Radio Frequency Identification (RFID) is applied to the Internet of Vehicles (IoV) to ensure the security of private data and the accuracy of identification and tracking. However, in traffic congestion scenarios, frequent mutual authentication increases the overall computing and communication overhead of the network. For this reason, in this work, we propose a lightweight RFID security fast authentication protocol for traffic congestion scenarios, designing an ownership transfer protocol to transfer access rights to vehicle tags in non-congestion scenarios. The edge server is used for authentication, and the elliptic curve cryptography (ECC) algorithm and the hash function are combined to ensure the security of vehicles’ private data. The Scyther tool is used for the formal analysis of the proposed scheme, and this analysis shows that the proposed scheme can resist typical attacks in mobile communication of the IoV. Experimental results show that, compared to other RFID authentication protocols, the calculation and communication overheads of the tags proposed in this work are reduced by 66.35% in congested scenarios and 66.67% in non-congested scenarios, while the lowest are reduced by 32.71% and 50%, respectively. The results of this study demonstrate a significant reduction in the computational and communication overhead of tags while ensuring security. Full article