Search Results (92)

Canthaxanthin is a significant carotenoid that is synthesized by specific microorganisms. It has multiple functions and has been utilized in food and feed supply chains. This research focused on improving canthaxanthin production by Paracoccus bogoriensis PH1, an orange-pigmented bacterium isolated from hot springs. Canthaxanthin production was optimized in flask-scale cultures by varying the pH, temperature, nutritional sources, aeration rates, and agitation techniques. Flask culture cultivation indicated that canthaxanthin production by this strain was influenced by pH stress mechanisms, resulting in the establishment of a two-stage pH control (pH-shift) technique to enhance cell mass and pigment production. The optimum flask conditions were refined for application in a 1 L bioreactor. An optimized cultivation procedure was established utilizing a Polypeptone Sucrose Yeast Extract (PPSYE) medium, with a pH transition from 7 to 11, incubation at 40 °C, agitation at 250 rpm, and aeration at 2 vvm for 48 h. This process resulted in a 3.12-fold increase in total carotenoid content and a 1.61-fold increase in canthaxanthin production, achieving 0.84 ± 0.06 mg/L compared to pre-optimized flask cultures in TSYEB medium (pH 7 at 37 °C, 72 h). Purified canthaxanthin from P. bogoriensis PH1 exhibited antioxidant activity in the ABTS assay. Full article

To address the limitations of traditional single-point detection methods in monitoring the sulfur content of ship fuel (FSC), which are inadequate in meeting the regulatory demands of high-traffic ports, this study proposes an integrated analytical approach based on artificial intelligence. This approach synthesizes multi-source heterogeneous data, including historical fuel testing records, Automatic Identification System (AIS) trajectory data, ship and operator profiles, technical specifications, fuel supply chain documentation, fundamental ship attributes and so on. Following rigorous data cleaning and preprocessing procedures, a refined dataset comprising 3046 records collected between 2017 and 2024 from the Port of Ningbo was utilized. Initially, multiple linear regression analysis was con-ducted to identify key factors influencing sulfur emissions, resulting in an R² value of 0.67. Based on these findings, a deep neural network model was developed using TensorFlow to enable real-time estimation of FSC and classification of compliance risk levels. The results indicate that the proposed method exhibits high estimated accuracy and robustness. An AI-based intelligent monitoring module, developed based on this research, has been integrated into the ship exhaust gas detection system at the Port of Ningbo. This module enables real-time analysis of inbound ships and intelligent identification of potentially non-compliant ships, thereby significantly improving the precision and efficiency of port regulatory operations. This study not only contributes to the theoretical framework for ship fuel compliance monitoring but also provides a practical and scalable technical solution for intelligent port governance. Full article

Rapid depletion of fossil fuel reserves forces the global energy sector to transition to sustainable energy sources. Specifically, distributed energy markets have emerged in the renewable energy sector in recent years, partly because blockchain technology is becoming a successful way to promote secure and transparent transactions. Using its decentralized structure, transparency, and even pseudonymity, blockchain is increasingly adopted worldwide for large-scale energy trading, peer-to-peer exchanges, project financing, supply chain management, and asset tracking. The research comprehensively analyzes blockchain applications across multiple fields related to energy, bibliographically evaluating their transformative potential. In addition, the study explores the architecture of various blockchain systems, assesses critical security and privacy challenges, and discusses how blockchain can enhance operational efficiency, transparency, and reliability in the energy sector. The paper’s findings provide a roadmap for future developments and the strategic adoption of blockchain technologies in the evolving energy landscape for an effective energy transition. Full article

In light of the persistent environmental degradation driven by fossil fuels, developing new energy sources is essential for achieving sustainability. The recent surge in electric vehicle adoption has underscored the significance of new energy batteries. However, the supply chains of new energy battery manufacturers face multiple sustainability risks, which impede sustainable practice adoption. To tackle these challenges, leanness philosophy is an effective tool, and Industry 5.0 enhances its efficacy significantly, further mitigating sustainability risks. This study integrates the supply chain, leanness philosophy, and Industry 5.0 by applying quality function deployment. A novel four-phase hybrid MCDM model integrating the fuzzy Delphi method, DEMATEL, AHP, and fuzzy VIKOR, identified five key sustainability risks five core leanness principles, and eight critical Industry 5.0 enablers. By examining a Chinese new energy battery manufacturer as a case study, the findings aim to assist managers and decision-makers in mitigating sustainability risks within their supply chains. Full article

Cybersecurity represents a critical challenge for data-sharing platforms involving multiple stakeholders, particularly within complex and decentralized systems such as livestock supply chain networks. These systems demand novel approaches, robust security protocols, and advanced data management strategies to address key challenges such as data consistency, transparency, ownership, controlled access or exposure, and privacy-preserving analytics for value-added services. In this paper, we introduced the Framework for Livestock Empowerment and Decentralized Secure Data eXchange (FLEX), as a comprehensive solution grounded on five core design principles: (i) enhanced security and privacy, (ii) human-centric approach, (iii) decentralized and trusted infrastructure, (iv) system resilience, and (v) seamless collaboration across the supply chain. FLEX integrates interdisciplinary innovations, leveraging decentralized infrastructure-based protocols to ensure trust, traceability, and integrity. It employs secure data-sharing protocols and cryptographic techniques to enable controlled information exchange with authorized entities. Additionally, the use of data anonymization techniques ensures privacy. FLEX is designed and implemented using a microservices architecture and edge computing to support modularity and scalable deployment. These components collectively serve as a foundational pillar of the development of a digital product passport. The FLEX architecture adopts a layered design and incorporates robust security controls to mitigate threats identified using the STRIDE threat modeling framework. The evaluation results demonstrate the framework’s effectiveness in countering well-known cyberattacks while fulfilling its intended objectives. The performance evaluation of the implementation further validates its feasibility and stability, particularly as the volume of evidence associated with animal identities increases. All the infrastructure components, along with detailed deployment instructions, are publicly available as open-source libraries on GitHub, promoting transparency and community-driven development for wider public benefit. Full article

In this article, we explored how open-source software serves as a strategic enabler for safety and security in the digital transformation of logistics systems. Open source is examined across multiple dimensions, including transparency, community collaboration, digital sovereignty, and long-term infrastructure resilience. The analysis focuses on the logistics domain, where interoperability, critical infrastructure protection, and supply chain continuity are essential. Key elements of open-source development—such as modular architectures, legal and licensing frameworks, and peer-reviewed codebases—support rapid vulnerability management, increased transparency, and the creation of sustainable digital ecosystems. Emphasis is placed on the role of open-source models in strengthening institutional trust, reducing dependency on proprietary vendors, and enhancing responsiveness to cyber threats. Our findings indicate that open source is not merely a technical alternative, but a strategic decision with legal, economic, and political implications, shaping secure, sovereign, and adaptive digital environments—particularly in mission-critical sectors. Full article

In the face of increasingly frequent natural disasters resulting from climate change and disruptions in the supply chains of energy resources, the demand for energy carriers based on locally sourced renewable resources is growing. Biomethane, derived from biomass and having multiple uses in the energy sector, fully meets these conditions. Analyses of the development and spatial distribution of biomethane production plants, the prevalence of methods of its production, and directions of applications, made on the basis of the data gained from official databases and research papers, are the main subjects of the paper. Additionally, the advantages and disadvantages of biomethane production, taking into account the results of the life cycle assessments, and the prospects for development of the biomethane market, facing regulatory and policy challenges, are considered. The results of the review indicate that biomethane production is currently concentrated in Europe and North America, which together generate over 80% of the globally produced biomethane. An exponential growth of the number of biomethane plants and their production capacities has been observed over the last decade. Assuming that the global strategies currently adopted and the resulting regional and national regulations on environmental and socio-economic policies are maintained, the further intensive development of the biomethane market will be expected in the near future. Full article

Seafood plays a vital role in human diets worldwide, serving as an important source of high-quality protein, omega-3 fatty acids, and essential vitamins and minerals that promote health and prevent various chronic conditions. The health benefits of seafood consumption are well documented, including a reduced risk of cardiovascular diseases, improved cognitive function, and anti-inflammatory effects. However, the safety of seafood is compromised by multiple hazards that can pose significant health risks. Pathogenic microorganisms, including bacteria, viruses, and parasites, in addition to microbial metabolites, are prominent causes of the foodborne diseases linked to seafood consumption, necessitating reliable detection and monitoring systems. Molecular biology and digital techniques have emerged as essential tools for the rapid and accurate identification of these foodborne pathogens, enhancing seafood safety protocols. Additionally, the presence of chemical contaminants such as heavy metals (e.g., mercury and lead), microplastics, and per- and polyfluoroalkyl substances (PFASs) in seafood is of increasing concern due to their potential to accumulate in the food chain and adversely affect human health. The biogenic amines formed during the microbial degradation of the proteins and allergens present in certain seafood species also contribute to food safety challenges. This review aims to address the nutritional value and health-promoting effects of seafood while exploring the multifaceted risks associated with microbial contamination, chemical pollutants, and naturally occurring substances. Emphasis is placed on enhanced surveillance, seafood traceability, sustainable aquaculture practices, and regulatory harmonization as effective strategies for controlling the risks associated with seafood consumption and thereby contributing to a safer seafood supply chain. Full article

Archeological evidence shows that dairy farming dates to the early Neolithic era in Europe, the Middle East, Asia, and Africa. Over time, it has evolved from domestication to intensive dairy farms with large, high-tech processing units. Dairy farming has contributed to economic growth, food production, employment, and processing industries. Nonetheless, it has been identified as a major contributor to climate change. This study explores the literature on dairy farming and sustainable development goals (SDGs) to identify current scholarly developments since the formulation and adoption of the SDGs in 2015 and themes for future research. This paper argues that sustainability shortfalls in dairy farming are primarily driven by human processes associated with commercialization and industrialization rather than the animals themselves, although biological emissions remain an inherent factor. Data were analyzed using R package, Excel, NVIVO, and VoS Viewer. A review of the literature showed that dairy farming and its contribution to sustainability has gained more scientific interest since 2015. Moreover, livestock management, feed production and management, stakeholder management, logistics and supply chain management, and waste management are the sources of environmental adversities associated with dairy farming. Notably, these are human processes developed from the commercialization of dairy farming and involve multiple stakeholders across the supply chain. While solutions are embedded within these processes, innovation emerges as a key driver of sustainability and a source of opportunities to strengthen sustainability in the dairy farming sector and achieve SDGs. Sustainability strategies, such as sustainable intensification, multifunctional agriculture, and agro-ecology should be implemented to improve sustainability in the dairy sector. Full article

Accurate demand forecasting is crucial for modern supply chain management, forming the foundation for inventory optimization, cost control, and service level improvement. However, demand time series data often exhibit high volatility and diverse patterns, further complicated by the rapid expansion and heterogeneity of data sources. These challenges can result in significant degradation in predictive accuracy when traditional models are applied to complex demand datasets. To address these challenges, this study proposes an end-to-end demand forecasting framework leveraging Variational Mode Decomposition (VMD) and attention mechanisms. The framework first employs VMD to decompose raw demand time series into multiple modes to extract hierarchical features, including trends, seasonal patterns, and short-term variations. Subsequently, an attention mechanism is introduced to dynamically capture and integrate demand sequences alongside contextual information, enhancing the focus on critical features and improving predictive performance. Experimental results demonstrate that the proposed method achieves superior predictive accuracy compared to conventional approaches, with a 37% reduction in Mean Absolute Error (MAE) relative to baseline models. This substantial improvement in demand forecasting accuracy provides actionable insights for decision-makers, enabling more efficient inventory control, production planning, and overall supply chain optimization. Full article

The publishing industry, a major contributor to greenhouse gas emissions, produced approximately 730 Mt CO₂eq globally in 2020 during the paper production phase alone. Unlike other sectors, decarbonization in publishing requires systematic reforms across the supply chain, production efficiency, energy transitions, consumption patterns, and recycling processes, as reliance on renewable energy alone is insufficient. This study focuses on China’s physical publishing industry, developing a comprehensive, high-resolution carbon emissions dataset that spans multiple publication types, stages, and processes. It reveals the emission characteristics across the life cycle, aiming to quantify the emissions accurately and address the lack of life-cycle-based research. This study explores efficient, replicable, and scalable strategies to facilitate the industry’s low-carbon transformation and sustainable development. The findings are as follows. (1) Books are the primary carbon emissions source, contributing approximately 77.05% of the total emissions, while journals and newspapers account for 13.20% and 9.75%, respectively. (2) Annual carbon accounting across the life-cycle identifies paper production and printing as the most carbon-intensive stages, responsible for about 85% of the total emissions. (3) In terms of recycling efforts, carbon reductions of approximately 347,000 t CO₂eq per year can be achieved through measures such as waste paper and plastic packaging recycling, second-hand publication exchanges, and energy recovery from incineration. Full article

As an important pillar of the national economy, the green transformation of SMEs is the key to promoting sustainable economic development. However, SMEs generally face issues such as information opacity and high operational risks, which make it difficult for them to obtain traditional financing support, thereby hindering green development. Green Supply Chain Finance has opened up new financing channels for SMEs, but the accuracy of credit risk evaluation remains a bottleneck that limits its widespread application. This paper constructs a credit risk evaluation index system that integrates multiple sources of information, covering factors such as the situations of SMEs themselves, stakeholder feedback, and expert ratings. It compares and analyzes the performance of the genetic algorithm-optimized random forest model (GA-RF), the BP neural network, the support vector machine, and the logistic regression model in credit risk evaluation. The empirical results indicate that the GA-RF model is significantly better than the other models in terms of accuracy, precision, and F1 score, and has the highest AUC value, making it more effective in identifying credit risk. In addition, the GA-RF model reveals that the asset–liability ratio, the time of establishment, the growth rate of operating revenue, the time of collection of accounts receivable, the return on net assets, and daily shipments are the key indicators affecting the credit risk assessment. Full article

The food system has been in focus as one of the major drivers behind the environmental and climate crisis. In this context, there is a growing need for more transparent and reliable information on the environmental impacts of food production and consumption as part of the transition process towards more sustainable food systems. Stakeholders along the food supply chain are confronted with multiple requirements and systems as the demand for environmental reporting at the product, company, and country level increases all at the same time. Simultaneously, consumers are often more interested in the sustainability of the food products they consume. While there is currently a lack of coherent supranational or even national legislation regulating methodological procedures, private initiatives for the environmental and carbon labelling of food products have developed rapidly. This article finds that most labels are characterised by a lack of transparency, clarity, and comprehensibility. Examining 14 labels, mainly from the German food retail market, we found a puzzling variety of data sources and methodologies used to calculate the values and claims displayed. We highlight this variety in data sources and footprint values by looking at milk and beef as case studies. Full article

Enzyme catalysis was traditionally used by various human cultures to create value long before its basic concepts were uncovered. This was achieved by transforming the raw materials available from natural resources into useful products. Tremendous scientific and technological progress has been made globally in understanding what constitutes an enzyme; what reactions enzymes can catalyze; and how to search, develop, apply, and improve enzymes to make desired products. The useful properties of enzymes as nature’s preferred catalysts, such as their high selectivity, diversity, and adaptability, enable their optimal function, whether in single or multiple reactions. Excellent opportunities for the resource-efficient manufacturing of compounds are provided by the actions of enzymes working in reaction cascades and pathways within the same reaction space, like molecular robots along a production line. Enzyme catalysis plays an increasingly prominent role in industrial innovation and responsible production in various areas, such as green and sustainable chemistry and industrial or white biotechnology. Sources of inspiration include current manufacturing or supply chain challenges, the treasure of natural enzymes, and opportunities to engineer tailor-made enzymes. Making the best use of the power of enzyme catalysis is essential for changing how current products are manufactured; how renewable biobased resources can replace fossil-based resources; and improving the safety, health, and environmental aspects of manufacturing processes to support cleaner and more sustainable production. Full article

The accuracy of demand forecasting is critical for supply chain management and strategic business decisions. However, as data volumes grow and demand patterns become increasingly complex, traditional forecasting methods encounter significant challenges in processing intricate multi-dimensional data and achieving a satisfactory predictive accuracy. To address these challenges, this paper proposed an end-to-end multi-model demand forecasting framework based on attention mechanisms. The framework employs a dual attention mechanism to dynamically extract features from both the temporal and product dimensions, while integrating conditional information captured through convolutional neural networks, thereby enhancing its ability to model complex demand patterns. Additionally, a channel attention mechanism is introduced to perform the weighted fusion of outputs from multiple predictive models, thereby overcoming the limitations of single-model approaches and improving adaptability to varying demand patterns across diverse scenarios. The experimental results demonstrate that the proposed method outperforms conventional approaches across several evaluation metrics, achieving a 42% reduction in Mean Squared Error (MSE) compared to the baseline model. This notable improvement enhances both the accuracy and stability of demand forecasting. The framework offers valuable insights for addressing large-scale and complex demand patterns, providing guidance for precise decision-making and resource optimization within supply chain management. Future research will concentrate on further enhancing the model’s generalization capability to manage missing data and demand fluctuations. Additionally, efforts will focus on integrating diverse heterogeneous data sources to assess its performance in various practical scenarios, ultimately improving the model’s accuracy and flexibility. Full article