Search Results (203)

Postharvest technologies and supply chain management are critical to improving food security, reducing losses, and advancing sustainability in global agri-food systems. Nearly one-third of global food is lost after harvest, particularly in developing regions, underscoring the urgent need for efficient postharvest handling, cold chain integration, and sustainable logistics systems. This paper explores key components of effective postharvest systems, including harvesting, treatments, storage, and value-added processing. It highlights digital innovations IoT sensors, blockchain, AI, and digital twins that enhance traceability, forecasting, and operational efficiency. Case studies from South Asia, Africa, Europe, and North America emphasize region-specific solutions, highlighting low-cost technologies for smallholders and advanced systems for export chains. Sustainable practices such as renewable-powered cold chains, circular economy models, and eco-friendly packaging align with Sustainable Development Goals (SDGs) on zero hunger, responsible consumption, and climate action. This paper concludes that while technology is vital, systemic transformation requires inclusive policies and collaboration among governments, private sectors, researchers, and farming communities to build resilient, equitable food systems. Full article

The global food industry is undergoing a critical shift toward sustainability, driven by high postharvest losses—reaching up to 40% for fruits and vegetables—and the need to reduce environmental impact. Sustainable postharvest innovations focus on improving quality, extending shelf life, and minimizing waste through eco-efficient technologies. Advances in non-thermal and minimal processing, including ultrasound, pulsed electric fields, and edible coatings, support nutrient preservation and food safety while reducing energy consumption. Although integrated postharvest technologies can reduce deterioration and microbial spoilage by 70–92%, significant challenges remain, including global losses of 20–40% and the high implementation costs of certain nanostructured materials. Simultaneously, eco-friendly packaging solutions based on biodegradable biopolymers and bio-composites are replacing petroleum-based plastics and enabling intelligent systems capable of monitoring freshness and detecting spoilage. Energy-efficient storage, smart sensors, and optimized cold-chain logistics further contribute to product integrity across distribution networks. In parallel, the circular bioeconomy promotes the valorization of agro-food by-products through the recovery of bioactive compounds with antioxidant and anti-inflammatory benefits. Together, these integrated strategies represent a promising pathway toward reducing postharvest losses, supporting food security, and building a resilient, environmentally responsible fresh produce system. Full article

Decarbonizing heavy-duty freight transport is essential for achieving climate neutrality targets. Although internal combustion engine (ICE) trucks currently dominate logistics, they contribute substantially to greenhouse gas emissions. Zero-emission alternatives, such as battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (H2), provide different decarbonization pathways; however, their relative roles remain contested, particularly in small economies. While BEVs benefit from technological maturity and declining costs, hydrogen offers advantages for high-payload, long-haul operations, especially within energy-intensive cold supply chains. The aim of this paper is to examine the gradual transition from ICE trucks to hydrogen-powered vehicles with a specific focus on cold-chain logistics, where reliability and energy intensity are critical. The hypothesis is that applying a system dynamics forecasting approach, incorporating investment costs, infrastructure coverage, government support, and technological progress, can more effectively guide transition planning than traditional linear methods. To address this, the study develops a system dynamics economic model tailored to the structural characteristics of a small economy, using a European case context. Small markets face distinct constraints: limited fleet sizes reduce economies of scale, infrastructure deployment is disproportionately costly, and fiscal capacity to support subsidies is restricted. These conditions increase the risk of technology lock-in and emphasize the need for coordinated, adaptive policy design. The model integrates acquisition and maintenance costs, fuel consumption, infrastructure rollout, subsidy schemes, industrial hydrogen demand, and technology learning rates. It incorporates subsystems for fleet renewal, hydrogen refueling network expansion, operating costs, industrial demand linkages, and attractiveness functions weighted by operator decision preferences. Reinforcing and balancing feedback loops capture the dynamic interactions between fleet adoption and infrastructure availability. Inputs combine fixed baseline parameters with variable policy levers such as subsidies, elasticity values, and hydrogen cost reduction rates. Results indicate that BEVs are structurally more favorable in small economies due to lower entry costs and simpler infrastructure requirements. Hydrogen adoption becomes viable only under scenarios with strong, sustained subsidies, accelerated station deployment, and sufficient cross-sectoral demand. Under favorable conditions, hydrogen can approach cost and attractiveness parity with BEVs. Overall, market forces alone are insufficient to ensure a balanced zero-emission transition in small markets; proactive and continuous government intervention is required for hydrogen to complement rather than remain secondary to BEV uptake. The novelty of this study lies in the development of a system dynamics model specifically designed for small-economy conditions, integrating industrial hydrogen demand, policy elasticity, and infrastructure coverage limitations, factors largely absent from the existing literature. Unlike models focused on large markets or single-sector applications, this approach captures cross-sector synergies, small-scale cost dynamics, and subsidy-driven points, offering a more realistic framework for hydrogen truck deployment in small-country environments. The model highlights key leverage points for policymakers and provides a transferable tool for guiding freight decarbonization strategies in comparable small-market contexts. Full article

Cold chain logistics is a critical process for ensuring product safety and quality assurance; however, existing systems face significant challenges due to centralized data structures, limited transparency, and low reliability. The objective of this study is to develop a blockchain infrastructure based on Hyperledger Fabric, integrated with IoT technologies, to address these issues. In the proposed system, secure collaboration among producers, carrier, and retailer organizations is achieved through role-based access control and authorization mechanisms, while environmental data collected from IoT sensors are immutably recorded on the blockchain. Performance tests conducted with Hyperledger Caliper demonstrated that the system maintained stable operation even under high transaction loads. In particular, query transactions achieved the most efficient results, reaching 442 transactions per second at a send rate of 500 TPS and 818 transactions per second at a send rate of 1000 TPS, with corresponding average latencies of 0.21 and 0.26 s, respectively. The absence of failed transactions further reinforced the reliability of the system. In addition, scalability experiments were conducted to assess how the system performs as the network expands with additional peer nodes across organizations. The results confirmed that the proposed architecture maintains improved latency and throughput under both intra-organizational and network-wide scaling scenarios. The results demonstrate that the proposed system provides a reliable, transparent, and scalable infrastructure even under low hardware configurations, contributing to the rapid and trustworthy verification of product history and environmental conditions in cold chain applications. Full article

Over the past two decades, the meat industry has faced increasing pressure to prevent foodborne outbreaks and reduce economic losses associated with delayed detection of spoilage. This demand has accelerated the development of on-site, real-time sensing tools capable of identifying early signs of contamination. Electrospun nanofiber (NF) platforms have emerged as particularly promising due to their large surface area, tunable porosity, and versatile chemistry, which make them ideal scaffolds for immobilizing enzymes, antibodies, or aptamers while preserving bioactivity under field conditions. These NFs have been integrated into optical, electrochemical, and resistive devices, each enhancing response time and sensitivity for key targets ranging from volatile organic compounds indicating early decay to specific bacterial markers and antibiotic residues. In practical applications, NF matrices enhance signal generation (SERS hotspots), facilitate analyte diffusion through three-dimensional networks, and stabilize delicate biorecognition elements for repeated use. This review summarizes major NF fabrication strategies, representative sensor designs for meat quality monitoring, and performance considerations relevant to industrial deployment, including reproducibility, shelf life, and regulatory compliance. The integration of such platforms with data networks and Internet of Things (IoT) nodes offers a path toward continuous, automated surveillance throughout processing and cold-chain logistics. By addressing current technical and regulatory challenges, NF-based biosensors have the potential to significantly reduce waste and safeguard public health through early detection of contamination before it escalates into costly recalls. Full article

Background: Respiratory syncytial virus (RSV) is a major pathogen of acute lower respiratory tract infection (LRTI) in infants, the elderly, and immunocompromised individuals. This review focuses on the progress of RSV vaccine development, especially subunit vaccines targeting the fusion protein (F) and attachment glycoprotein (G), aiming to summarize key strategies, challenges, and future directions in the field. Methods: The review is based on a comprehensive literature search and analysis of recent studies on RSV vaccine development, with a specific focus on subunit vaccines and related technologies. Results: Approved vaccines such as Abrysvo and Arexvy utilize structural engineering to stabilize the prefusion conformation of the F protein (PreF), thereby exposing neutralizing epitopes. Subunit vaccine candidates such as DS-Cav1 and DT-PreF enhance stability through disulfide bonds and dityrosine linkages, while ADV110 targets the conserved domain of the G protein to elicit cross-strain immunity. Virus-like particle (VLP) vaccines like IVX-A12 combine RSV and human metapneumovirus antigens to provide broad-spectrum immunity. However, challenges exist, including maintaining PreF stability, overcoming immunosenescence in the elderly, and addressing safety concerns like Guillain-Barré syndrome (GBS). Conclusions: Future RSV vaccine development should center on combined PreF-G protein vaccines, VLP technology, and optimizing cold-chain logistics to improve global accessibility and overcome existing challenges, thereby providing more effective prevention and control of RSV infections. Full article

Balancing the development of agricultural cold chain logistics with ecological conservation remains a critical challenge for green cold chain logistics in China’s three northeastern provinces. This study evaluates the efficiency of green cold chain logistics to promote synergy between logistics development and ecological sustainability. Using CiteSpace for keyword co-occurrence analysis and literature extraction, an evaluation index system comprising eight input and output indicators was constructed. The super-efficiency Slacks-Based Measure (SBM) model and the Malmquist–Luenberger (ML) productivity index were employed to assess efficiency from static and dynamic perspectives, respectively. Kernel density estimation was used to examine spatial distribution patterns, and the Dagum Gini coefficient was applied to decompose regional disparities. The results indicate that (1) overall efficiency remains relatively low, with ML index changes primarily driven by technological progress; (2) substantial regional differences exist among the three provinces in terms of distribution location, shape, and degree of polarization; and (3) inter-regional disparities are the main source of variation. A Tobit model further identified the key influencing factors, indicating that the level of economic development, growth of the tertiary industry, and informatization are the main drivers. These findings provide valuable insights for optimizing regional green cold chain logistics and promoting sustainable agricultural development. Full article

Blockchain-based cold chain logistics (BCCL) systems establish a new logistics data-sharing mechanism with blockchain technology, which destroys the traditional data island problem and promotes cross-institutional data interoperability. However, security vulnerabilities, risks of data loss, exposure of private information, and particularly the emergence of quantum-based attacks pose heightened threats to the existing BCCL framework. This paper first introduces a transaction privacy preserving (TPP) model for BCCLS that aggregates the blockchain and ring signcryption scheme together to strengthen the security of the data exchange process. Then, a lattice-based ring signcryption (LRSC) scheme is proposed. This LRSC utilizes the lattice assumption to enhance resistance against quantum attacks while employing ring mechanisms to safeguard the anonymity and privacy of the actual signer. It also executes signature and encryption algorithms simultaneously to improve algorithm execution efficiency. Moreover, the formal security proof results show that this LRSC can capture the signer’s confidentiality and unforgeability. Experimental findings indicate that the LRSC scheme achieves higher efficiency compared with comparable approaches. The proposed TPP model and LRSC scheme effectively facilitate cross-institutional logistics data exchange and enhance the utilization of logistics information via the BCCL system. Full article

In fresh produce cold chain last-mile delivery, the highly dispersed customer base leads to exorbitant delivery costs, posing the greatest challenge for cold chain enterprises. Achieving a symmetrical balance between cost-efficiency, environmental sustainability, and service quality is a fundamental pursuit in logistics system optimization. This paper proposes integrating the crowd-shipping logistics model—characterized by internet platform sharing and flexibility—into the delivery service. It incorporates and extends features such as cold chain delivery, mixed fleets using gasoline and diesel vehicles (GDVs), electric vehicles (EVs), partial charging strategies for EVs, and time-of-use electricity pricing into the crowd-shipping model. A joint delivery mode combining traditional professional delivery (using GDVs and EVs) with crowd-shipping is proposed, creating a symmetrical collaboration between centralized fleet management and distributed social resources. The challenges associated with utilizing occasional drivers (ODs) are analyzed, along with the corresponding compensation decisions and allocation-related constraints. A route optimization model is constructed with the objective of minimizing total cost. To solve this model, an Improved Whale Optimization Algorithm (IWOA) is proposed. To further enhance the algorithm’s performance, an adaptive variable neighborhood search is embedded within the proposed algorithm, and four local search operators are applied. Using a case study of 100 customer nodes, the joint delivery mode with OD participation reduces total delivery costs by an average of 24.94% compared to the traditional professional vehicle delivery mode, demonstrating a more symmetrical allocation of logistical resources. The experiments fully demonstrate the effectiveness of the joint delivery model and the proposed algorithm. Full article

With the gradual adoption of smart hardware such as the Internet of Things (IoT) in warehousing and logistics, the efficiency bottlenecks and resource wastage inherent in traditional storage management models are now poised for breakthrough through digital and intelligent transformation. This study focuses on the cross-border cold chain storage scenario for Malaysia’s Musang King durians. Influenced by the fruit’s extremely short 3–5-day shelf life and the concentrated harvesting period in primary production areas, the issue of delayed dynamic demand response is particularly acute. Utilizing actual sales order data for Mao Shan Wang durians from Beigang Logistics in Guangxi, this study constructs a demand forecasting model integrating Bayesian optimization with bidirectional long short-term memory networks (BO-BiLSTM). This aims to achieve precise forecasting and optimization of cold chain storage inventory. Experimental results demonstrate that the BO-BiLSTM model achieved an R² of 0.6937 on the test set, with the RMSE reduced to 19.1841. This represents significant improvement over the baseline LSTM model (R² = 0.5630, RMSE = 22.9127). The bidirectional Bayesian optimization mechanism effectively enhances model stability. This study provides a solution for forecasting inventory demand of fresh durians in cold chain storage, offering technical support for optimizing the operation of backbone hub cold storage facilities along the New Western Land–Sea Trade Corridor. Full article

The COVID-19 pandemic exposed the urgent need for innovative tools to strengthen pandemic preparedness and health defense, especially in low- and middle-income countries (LMICs). While vaccination has been the cornerstone of the defense strategy against many infectious agents, there is a critical gap in vaccine equity, ensuring it is accessible to all, especially among the most vulnerable populations. The conventional vaccine delivery platforms, through parenteral administration, face notable limitations, including reliance on trained personnel, sterile conditions, and cold chain logistics. The parenteral vaccines often fail to induce robust mucosal immunity, which is critical for preventing infections at mucosal surfaces, the primary entry point for many pathogens. Bacillus subtilis, a Gram-positive, spore-forming bacterium, has emerged as a promising platform for mucosal vaccine delivery owing to its Generally Recognized as Safe (GRAS) status. Its robust spores are highly resilient to harsh environmental conditions, which may eliminate the need for cold chain storage and further facilitate distribution in LMICs. This review explores the potential of B. subtilis as a next-generation vaccine delivery platform, focusing on its unique characteristics, mechanisms of action, and applications in addressing global health challenges. This review also examines existing research demonstrating the safety, immunogenicity, and efficacy of B. subtilis spore-based vaccines while identifying limitations and future directions for optimization as a scalable and adaptable solution for resilient health defense, particularly in LMICs. Full article

The quality change process of table grapes during cold chain logistics is complex and highly susceptible to vibration-induced damage. Traditional monitoring techniques not only consume significant human and material resources but also cause destructive effects on the fruit structure of table grapes, making them difficult to apply in practical scenarios. Based on this, this paper focuses on table grapes in cold chain business processes and designs a flexible wireless vibration sensor for monitoring the quality of table grapes during cold chain transportation. The hardware component of the system fabricates a flexible wireless vibration sensing for monitoring the quality of the table grape cold chain. In contrast, the software component develops corresponding data acquisition and processing functionalities. Using Summer Black table grapes purchased from Tianjin Hongqi Agricultural Market as the research subject, correlation and quality monitoring models for the cold chain process of table grapes were constructed. After Z-score standardization, the prediction results based on the MLR model achieved R² values all greater than 0.87 and RPD values all exceeding 2.7. Comparisons with other regression models demonstrated its optimal fitting performance for monitoring the quality of the cold chain for table grapes. This achieves non-destructive and high-precision data acquisition and processing during the cold chain process of table grapes, wirelessly transmitting results to terminal devices for real-time visual monitoring. Full article

This study leverages multi-source remote sensing data—Nighttime Light (NTL) imagery and POI (Point of Interest) datasets—to quantify the spatiotemporal interaction between urban spatial restructuring and logistics industry evolution in Zhengzhou, China. Using calibrated NPP/VIIRS NTL data (2012–2022) and fine-grained POI data, we (1) identified urban functional spaces through kernel density-based spatial grids weighted by public awareness parameters; (2) extracted built-up areas via the dynamic adaptive threshold segmentation of NTL gradients; (3) analyzed logistics agglomeration dynamics using emerging spatiotemporal hotspot analysis (ESTH) and space–time cube models. The results show that Zhengzhou’s urban form transitioned from a monocentric to a polycentric structure, with NTL trajectories revealing logistics hotspots expanding along air–rail multimodal corridors. POI-derived functional spaces shifted from single-dominant to composite patterns, while ESTH detected policy-driven clusters in Airport Economic Zones and market-driven suburban cold chain hubs. Bivariate LISA confirmed the spatial synergy between logistics growth and urban expansion, validating the “policy–space–industry” interaction framework. This research demonstrates how integrated NTL-POI remote sensing techniques can monitor policy impacts on urban systems, providing a replicable methodology for sustainable logistics planning. Full article

Dry ice, the solid form of carbon dioxide (CO₂), is widely used in cold chain logistics, industrial cleaning, and biomedical preservation. Its production, however, is closely linked to carbon capture, energy-intensive liquefaction, and solidification processes. This review critically evaluates and compares the existing methods of CO₂ capture, including chemical absorption, physical absorption, adsorption, and membrane-based separation as they pertain to dry ice production. This study further assesses liquefaction cycles using refrigerants such as ammonia and R744, highlighting thermodynamic and environmental trade-offs. Solidification techniques are examined in the context of energy consumption, process integration, and product quality. The comparative analysis is supported by extensive tabulated data on operating conditions, CO₂ purity, and sustainability metrics. This review identifies key technical and environmental challenges, such as solvent regeneration, CO₂ leakage, and energy recovery. Thus, it also explores emerging innovations, including hybrid cycles and renewable energy integration, to advance the sustainability of dry ice production. This, in turn, offers strategic insight for optimizing dry ice manufacturing in alignment with low-carbon industrial goals. Full article

This study examines the adoption of real-time visibility (RTV) technologies in the Australian meat cold supply chain, a sector where sustainability challenges such as food spoilage, energy inefficiency, and waste are acute. RTV technologies offer promising solutions by enhancing traceability, operational efficiency, and decision-making across supply chain stages. However, adoption remains uneven due to a range of contextual, organisational, and perceptual factors. Through a nationally distributed quantitative survey targeting stakeholders across inventory, logistics, and retail operations, we identify key drivers and barriers influencing RTV adoption. We explore how demographic factors (e.g., age, role), perceived usefulness and ease of use, and supply chain characteristics interact to shape adoption outcomes. Importantly, the study investigates how horizontal collaboration and data-sharing practices moderate these relationships, especially within the transport and logistics stages where cold chain vulnerabilities are highest. Spearman and partial correlation analyses, alongside binary logistic regression, reveal that perceived ease of use and usefulness are significant predictors of adoption, while barriers such as cost and technical complexity impede it. However, strong collaboration and data-sharing networks can mitigate these barriers and enhance adoption likelihood. Our findings suggest that targeted digital infrastructure investment, workforce training, and policy support for cross-organisational collaboration are essential for advancing sustainability in meat cold chains. This research contributes to a growing body of knowledge that connects technological innovation with food system resilience and waste minimisation. Full article