Search Results (272)

In smart last-mile logistics networks, real-time status telemetry from ground logistics nodes, such as automated parcel lockers, cold-chain sensors, and unmanned delivery vehicles, is crucial for safe dispatching and precise delivery tracking. However, during peak delivery hours in urban areas, heavy workloads and traffic may lead to severe congestion at base stations (BSs). To address this, multiple unmanned aerial vehicle (UAV) relays are introduced to offload critical logistics monitoring data to neighboring light-load BSs. To quantify the freshness of the received status updates, we adopt the Peak Age of Information (PAoI) metric. We formulate a multi-UAV-assisted multi-cell logistics communication framework that jointly optimizes logistics data offloading association, routing, and 3D UAV deployment to minimize the average PAoI. To solve the resulting mixed-integer non-convex problem, an alternating optimization algorithm based on Block Coordinate Descent (BCD) is developed. Specifically, the offloading association and routing subproblem is reformulated as an integral single-commodity minimum-cost flow problem, while the horizontal UAV positions and altitudes are optimized using Successive Convex Approximation (SCA). Over five random realizations, the proposed method reduces the average PAoI by approximately $0.6\%$ and the transmission delay by up to $4.2\%$ compared with the benchmark methods under the default setting. The delay reduction reaches $6.6\%$ at $K=60$ and exceeds $28\%$ when the benchmark altitude is fixed at $100\mathrm{m}$, demonstrating its effectiveness under high network loads and the importance of UAV altitude optimization. Full article

Accurately predicting the material loss rate is critical for MRP-driven delivery and inventory control. Inaccurate and delayed data acquisition leads to imprecise master data and poor knowledge of how machines are used in small- and medium-sized manufacturing enterprises (SMEs), but dynamic production environments and data latency are persistent challenges. This paper proposes a two-stage dynamic prediction framework integrating Time Decay Weighted Average (TDWA) and AHP-Gray Markov correction. TDWA establishes a baseline loss rate prioritizing recent process data, while the Analytic Hierarchy Process (AHP) quantifies eleven heterogeneous factors affecting material consumption. A Gray $GM\left(1,1\right)$ model predicts loss-rate overshoot, refined by Markov chain residual correction. Empirical validation using 42 months of production data from a hardware–sanitary ware manufacturer demonstrates a MAPE of 1.97%, outperforming standalone $GM(1,1)$ by 38% and the three-year average by 66.5%. The method effectively balances delivery assurance and inventory costs, maintaining zero stockouts while limiting excess inventory to 1.7%. Unlike ARIMA, XGBoost, or LSTM, which suffer from overfitting or performance degradation with eight or fewer data periods, the proposed model maintains high accuracy and robustness with extremely small samples. Offering a low-threshold “small-sample master data governance” paradigm, this approach may be applied to aerospace spare parts support, cold chain logistics, and customized manufacturing, providing a viable pathway for digital transformation in resource-constrained environments. Full article

Extreme climate events increasingly amplify agricultural trade security risks across interconnected stages: agricultural production, commodity conditions, cold-chain storage, logistics distribution, and trade fulfillment. However, existing approaches predominantly focus on isolated stages and fail to model cross-stage risk propagation, enable real-time edge inference, or provide well-calibrated risk warnings. To address these challenges, a multimodal edge-intelligence framework, termed AgriClimate-EdgeNet, is proposed to jointly model climatic conditions, agricultural production, commodity imagery, cold-chain states, logistics trajectories, and trade records. An extreme-climate-aware cross-modal consistency mechanism is developed to capture normal inter-stage correspondence and identify abnormal information conflicts. Depthwise separable temporal convolutions, gated temporal units, lightweight attention, and Teacher–Student distillation are incorporated for efficient edge inference. Furthermore, dynamic modality reliability estimation and dual predictive uncertainty modeling (decoupling epistemic and heteroscedastic aleatoric uncertainties) are integrated to ensure decision trustworthiness under severe sensory noise and missing observations. On a 38,400-window agricultural trade dataset, AgriClimate-EdgeNet achieves an Accuracy of $0.914$, Recall of $0.896$, Macro-F1 of $0.902$, and AUC of $0.949$, while reducing expected calibration error to $0.028$ in routine single-pass Streaming Mode and $0.021$ under multi-sample Deep Audit Mode. In operational edge deployment on NVIDIA Jetson AGX Orin, the model requires $3.96$ M on-device parameters and $1.21$ G FLOPs, achieving an inference latency of $8.6$ ms ($116.3$ samples/s) in Streaming Mode and $38.4$ ms in Deep Audit Mode. These results demonstrate that AgriClimate-EdgeNet provides an accurate, robust, and low-latency solution for full-chain agricultural trade risk sensing under extreme climate shocks. Full article

Microbial community succession contributes to quality changes in fresh freshwater fish during cold-chain transportation. In this study, bighead carp (Hypophthalmichthys nobilis) was used as the research material. Using 16S rRNA high-throughput sequencing, we characterized microbial community dynamics in fish muscle after 12 h and 24 h of simulated transportation under combined low-temperature (0 °C, 4 °C, 8 °C) and gradient CO₂ modified atmosphere packaging (MAP) treatments. We further identified key environmental factors associated with shifts in bacterial communities during cold-chain logistics. The results showed that transportation temperature was the primary factor structuring microbial communities in bighead carp muscle, with stronger effects than MAP composition and transportation duration. Under 8 °C temperature conditions, low-to-moderate CO₂ MAP corresponded with elevated microbial diversity and spoilage-associated succession, alongside enrichment of spoilage taxa including Pseudomonas. High-CO₂ (80%) MAP altered community composition and reduced community complexity under low-temperature conditions, and the community changes exhibited a time-dependent pattern during extended transport. The combined treatment of 0 °C and 80% CO₂ MAP generated distinct microbial succession profiles within 24 h and lowered the relative abundance of Pseudomonas. This study described the response patterns of bighead carp muscle microbiota to coupled low-temperature and MAP treatments and characterized community succession during cold-chain transport. The findings provide microbiota-based references for understanding microbial variation in freshwater fish cold-chain logistics. Full article

This study aims to investigate the factors influencing kombucha purchase intention among online consumers in Northern Thailand by addressing the “perception–execution gap” between consumer preferences and entrepreneurial logistics and green supply chain practices in the rapidly growing functional beverage market in Southeast Asia. This study integrates an adapted Theory of Planned Behavior (TPB) perspective with the Stimulus–Organism–Response (S-O-R) framework. A quantitative research design was employed, collecting primary data from 400 online consumers from Chiang Mai, Chiang Rai, and Lamphun provinces in Northern Thailand, complemented by qualitative insights from five local kombucha entrepreneurs. The study conceptualizes consumer belief, subjective norm, lifestyle, and last-mile delivery as external stimuli influencing consumer attitude, which is subsequently associated with purchase intention. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that consumer attitude serves as a significant mediating mechanism linking external stimuli to purchase intention. Among the antecedents, lifestyle and last-mile delivery emerged as important factors associated with positive consumer attitudes. Additionally, health consciousness significantly moderates the relationship between consumer belief and purchase intention, with the negative interaction indicating that this relationship becomes weaker as health consciousness increases. From an entrepreneurial perspective, the findings highlight the importance of last-mile accessibility, while the qualitative interviews additionally identify cold-chain management as a relevant operational consideration for maintaining product quality. This study contributes to the literature by offering an integrated theoretical framework that combines an adapted TPB perspective with the S-O-R framework to explain psychological and logistical determinants of consumer behavior in the functional beverage context. It provides a dual-perspective roadmap that bridges consumer psychology and entrepreneurial practice, delivering actionable insights for kombucha businesses to align distribution strategies with the needs of health-conscious consumers in Northern Thailand. Full article

The cold chain sector is responsible for approximately 4% of global GHG–greenhouse gas emissions. Its passive thermal packaging, which has historically been dominated by expanded polystyrene (EPS), is both a crucial functional component and a significant environmental liability. Regulation (EU) 2025/40 on packaging and packaging waste (PPWR), along with the Single-Use Plastics Directive, the Ecodesign for Sustainable Products Regulation, and the Digital Product Passport, creates cumulative regulatory pressure on EPS and opens the market to alternative passive solutions. This systematic literature review, conducted according to the PRISMA 2020 protocol, addresses three research questions regarding the maturity of EPS alternatives such as phase change materials, vacuum insulated panels, mycelium composites, moulded pulp, dry-moulded fibre, and reusable pooled systems. It examines the cumulative effects of the EU regulatory framework and the systemic interventions needed for a circular transition, interpreted through the ten-R hierarchy. The review introduces the Cold Chain Packaging Circular Transition Framework (CCP-CTF), which is articulated across four dimensions and implemented as a composite indicator across five transition regimes, in addition to a residual EPS scenario. This framework is visualised as a Cold Chain Sustainability Thermometer—a calibrated assessment tool for researchers, policymakers, and industry operators navigating the 2026–2040 PPWR implementation timeline. The review identifies key knowledge gaps, including limited integration of decarbonisation accounting at the packaging-logistics interface, fragmentation of Extended Producer Responsibility schemes in the pharmaceutical sector, and the lack of harmonised eco-modulation criteria for cold chain packaging at the European Union level. Full article

Button mushrooms (Agaricus bisporus) are highly perishable, making rapid and automated postharvest freshness evaluation crucial for cold-chain logistics. Visible-near-infrared (Vis-NIR) hyperspectral imaging is promising for non-destructive food quality assessment, yet mining highly redundant spatial–spectral data remains challenging. This study proposes a novel artificial intelligence approach, the patch-aligned multimodal interaction fusion network (PAMIF-Net), to monitor postharvest mushroom freshness. Using a Vis-NIR dataset of 400 A. bisporus caps over a 9-day refrigerated storage period, this deep learning architecture dynamically fuses global spectral features (indicating internal physicochemical shifts) with localized spatial morphological features (capturing surface deterioration) using a gated attention mechanism. Extensive evaluations across 10 independent trials demonstrated that PAMIF-Net achieved optimal classification accuracy (up to 100% on the current test set) and a minimal mean absolute error for five-class storage time recognition. Furthermore, it exhibited superior computational efficiency and significantly lower inference latency compared to classical machine learning and standard deep learning backbones. This multimodal spatial–spectral deep learning framework demonstrates the feasibility of combining HSI and deep learning for the specific task of automated storage time recognition of A. bisporus under controlled refrigerated conditions. Full article

The cold supply chain (CSC) reverse logistics segment remains underexplored in the risk assessment literature. Existing frameworks apply traditional failure mode and effects analysis (FMEA) with a three-criterion structure (severity, occurrence, detectability), which inadequately captures the time-sensitive and cost-intensive risk environment of CSC reverse logistics. This study proposes an integrated framework combining hazard analysis and critical control points (HACCP)-based node partitioning, an extended five-criterion FMEA incorporating timeliness and economic cost, triangular interval-valued fuzzy number (TIVFN) aggregation via the Aczél–Alsina operator, and cumulative prospect theory (CPT) to prioritize risk modes. Applied to a food-sector reverse CSC, 16 failure modes were identified across six HACCP-based process nodes. Severity and economic cost jointly account for 47.5% of total criterion weight. Monitoring and data management (node P6) consistently emerge as the dominant risk cluster, with data completeness (FM14), sensor accuracy (FM15), and early-warning functionality (FM16) occupying the top three positions across all weight scenarios. Relative to conventional models and the extended RPN, the TIVFN-CPT model assigns FM16 a substantially higher priority, demonstrating that CPT captures asymmetric, loss-averse risk perception that conventional methods fail to encode. Sensitivity analysis across five weight scenarios confirms the structural robustness of the rankings. By extending FMEA to a five-criterion behavioral framework and introducing TIVFNs as an uncertainty-preserving linguistic scale, this study indicates that information quality constitutes a risk factor of comparable priority to physical temperature maintenance, supporting a tiered resource allocation strategy that prioritizes investment at node P6 and targeted upgrades at nodes P1 and P4. Full article

Background: This study examines the relationships among resource reduction awareness (RRA), AIoT-enabled Cold Chain System (CCS) implementation, supply–demand control (SDC), perceived operational performance (OPF), and ISO certification from a Lean Logistics perspective. Method: Data were collected from 250 Korean agricultural and marine product firms. PLS-SEM was used to examine the hypothesized relationships and indirect associations, while MICOM and multi-group analysis assessed differences between firms holding both ISO 9001 and ISO 14001 certifications and other firms. Result: RRA was positively associated with ACI and OPF. ACI was positively associated with SDC and OPF, and SDC was positively related to OPF. The indirect association between ACI and OPF through SDC was also significant. The ACI–OPF relationship differed significantly across ISO certification groups and was stronger in the high-certification group. Conclusions: The findings suggest that AIoT-enabled cold chain implementation is associated with perceived operational performance directly and through supply–demand control, while standardized management practices may be relevant to the ACI–OPF relationship. This study therefore clarifies organizational and operational conditions associated with digital cold chain practices and provides implications for research. Full article

Food cold chains rely on refrigeration, cold storage, refrigerated transport, packaging and monitoring systems that consume electricity and fuel while preserving food safety, quality and shelf life. Although many studies propose energy-saving technologies or optimization models for individual cold-chain operations, less is known about how energy-efficiency actions are distributed across refrigeration, logistics and digital monitoring domains, which actors must collaborate to implement them, and which benefits and barriers shape adoption. This paper presents a systematic literature review supported by bibliometric and structured content analysis. Searches in Scopus and Web of Science identified 3930 records before deduplication. After removing out-of-year records and duplicates, 2368 unique records were screened; 896 reports were sought for full-text assessment; 751 reports were retrieved and assessed; 466 studies were included in the final review corpus; and 408 were coded as an applied/action corpus. The synthesis identifies ten energy-efficiency action families, seven cold-chain stage classes, multi-actor configurations, evidence types, collaboration-intensity levels, energy benefits, non-energy benefits and implementation barriers. Transport, routing and distribution is the largest action family (134 records), followed by cold storage and refrigeration technology (66), digital monitoring and information sharing (58), life-cycle assessment, energy assessment and decision support (36), energy systems and renewable cooling (34), packaging and thermal insulation (33), and inventory, and planning and coordination (27). The findings show that food cold-chain energy efficiency is not only a technical refrigeration problem but also a collaborative implementation challenge: many actions require information sharing, coordinated operating decisions, joint investment, data governance or cost/benefit-sharing mechanisms. The review contributes an action-oriented framework that links energy-saving actions to stages, actors, collaboration requirements, benefits and barriers, and it identifies future research priorities on comparable energy metrics, measured savings, renewable cooling, digital twins, demand-side flexibility and governance of collaborative energy-efficiency investments. Full article

Last-mile delivery is constrained by manual cargo handling, which consumes a large share of the operating window and limits the number of delivery rounds per shift. This study evaluates the operational and economic effects of the Automated Horizontal Loading and Unloading System (AHLUS), a retrofittable in-vehicle technology that converts the cargo bed into an active handling platform using a belt conveyor and a movable bulkhead. Using daily records from two AHLUS-equipped one-ton trucks operated in a South Korean fresh-food network over four months, an interrupted time-series regression with Newey-West standard errors estimated the effect of the deployment process—encompassing driver adaptation and the dispatch reallocation enabled by the system’s enhanced handling capacity—while controlling for the pre-intervention trend. After stabilization, daily throughput was approximately 19.8 percent higher than the learning-phase baseline (approximately 14.8 percent relative to the model-implied counterfactual trend), a gain that was statistically significant, held for both drivers, and corresponded to an increase from two to three delivery rounds per shift. A transparent total cost of ownership model incorporating payload-loss, power, maintenance, and downtime yielded a base-case break-even point of 4.1 months, and a Monte Carlo simulation indicated a positive net benefit across all sampled parameter combinations under the assumed input distributions, with a median break-even point of 4.6 months. As an exploratory single-company field validation without an untreated control series, the study estimates the effect of the deployment as implemented in practice rather than the isolated effect of the hardware. The findings provide field-based evidence that in-vehicle handling automation can deliver measurable throughput and economic benefits in last-mile operations, pending confirmation in larger multi-company and multi-region deployments. Full article

Background/Objectives: High-risk human papillomavirus (HR-HPV) causes cervical cancer, but other sexually transmitted infections (STIs) act as possible cofactors. We herein evaluated the feasibility, logistics, and epidemiology of a community-based screening program in Romania using an innovative veil-based self-sampling device and a digital platform. Methods: Adult women self-collected genital secretions using the Vaginal Veil Collector V-Veil UP2™ device (V-Veil-Up Production SRL, Pitesti, Romania). A digital platform managed registration and results. Dry impregnated veils were transported at ambient temperature via standard courier to an accredited French laboratory for molecular testing using in parallel the Allplex™ HPV HR Detection assay (Seegene, Seoul, Republic of Korea), detecting 14 HR-HPVs, and the Allplex™ STI Essential Assay (Seegene), detecting 7 major pathogens causing STIs [Chlamydia trachomatis, Neisseria gonorrhoeae, Mycoplasma genitalium, Trichomonas vaginalis, Mycoplasma hominis, Ureaplasma urealyticum, Ureaplasma parvum]. Results: Among 960 included women (mean age 41.3 years), technical success was high: 97.7% of samples yielded valid results for HR-HPV and 97.4% for STIs. Sample stability at ambient temperature eliminated cold-chain requirements. Overall, 17.2% of women were positive for HR-HPV and 26.7% for an STI. The most frequent genotypes were HPV-68 (3.0%), HPV-16 (2.9%), and HPV-31 (2.3%); Ureaplasma parvum (24.0%) was the most prevalent bacterial pathogen. The 18–29 age group exhibited the highest risk for HR-HPV (22.2%) and STIs (36.8%). Multivariate analysis revealed strong biological associations: Chlamydia trachomatis was independently associated with overall HR-HPV (aOR: 4.52; 95% CI: 1.26–16.11) and multiple HR-HPV infections (aOR: 13.96; 95% CI: 3.70–52.64). Mycoplasma hominis and Ureaplasma species were also independently associated with HR-HPV (aOR: 1.83 and 1.67, respectively) or nonvaccine types (aOR: 2.75 and 5.03, respectively). Conclusions: Veil-based self-sampling combined with digital logistics is highly feasible, scalable, and overcomes geographical barriers. The marked association between Chlamydia trachomatis, Mycoplasma hominis, Ureaplasma species, and HR-HPV strongly advocates for integrated molecular co-testing models in public health screening programs. Full article

The global pasta industry is increasingly challenged to reconcile consumer demand for convenience with the need to reduce environmental impacts across the food supply chain. This study presents a cradle-to-grave Product Environmental Footprint (PEF) assessment integrated with Monte Carlo stochastic simulations to evaluate four durum wheat (Triticum durum) semolina pasta systems: traditional dry pasta, fresh pasta, instant pasta in a rigid cup, and an eco-designed instant pasta in a flexible pouch. Systems were evaluated using a primary functional unit of 1 kg of commercial product and normalized to an isocaloric serving to account for variations in moisture content and preparation. When evaluated across the full cradle-to-grave system boundary, traditional dry pasta (1.91 ± 0.08 kg CO_2e/kg) and flexible-pouch instant pasta (1.72 ± 0.08 kg CO_2e/kg) achieve comparable, lowest overall impacts, while the rigid cup format (3.85 ± 0.17 kg CO_2e/kg) is heavily penalized by packaging mass intensity and transport inefficiency. Industrial starch pre-gelatinization creates a porous structure enabling rapid passive rehydration (0.90 kWh/kg domestic energy), which fully offsets factory thermal inputs (0.326 kWh/kg) and dramatically outperforms traditional stovetop boiling (2.40 kWh/kg). Crucially, replacing rigid cups with flexible pouches reduces total packaging material mass per kg of net pasta product by 72.5% (279.8 g/kg vs. 1016.2 g/kg), avoiding severe volumetric logistics penalties. Conversely, fresh pasta incurs the highest Climate Change impact (4.14 ± 0.17 kg CO_2e/kg) due to continuous cold-chain distribution and storage requirements. Overall, this work demonstrates that shifting thermal energy processing from domestic preparation to factory pre-gelatinization—when combined with ambient shelf stability and lightweight flexible packaging—provides a promising eco-design strategy to decarbonize convenience foods, subject to commercial validation of packaging barrier performance and consumer acceptance. Full article

Fourteen novel antioxidant peptides were screened from the enzymatic hydrolysates of Asian swamp eel bone (ASEB) through an in silico analysis. Their ABTS and ORAC radical scavenging capacities were 1.70–4.29-fold and 2.79–5.90-fold higher than those of Trolox, respectively. Additionally, two novel peptide sequences (NVGW and WALN) were identified. Quantum chemical calculations combined with active–-site methylation experiments demonstrated that hydrogen atoms on tryptophan’s indole nitrogen, tyrosine’s phenolic hydroxyl, and arginine’s guanidinium group play crucial roles in enhancing ABTS and ORAC activities. Molecular docking further showed stable binding of ASEB peptides to myeloperoxidase (MPO) through hydrogen bonds and electrostatic interactions. Further studies indicated that ASEB alleviated oxidative stress in zebrafish by effectively reducing ROS accumulation, restoring redox homeostasis, and modulating the expression of Keap1–Nrf2 pathway-related antioxidant genes. These results enhance our understanding of the antioxidant properties of ASEB-derived peptides and support the high-value utilization of animal byproducts. Full article

This study presents an integrated approach for sustainable food supply chain design by evaluating how sourcing geography and logistics network structure influence Global Warming Potential (GWP) in a multi-echelon Mexican cold chain integrating Life Cycle Assessment (LCA) and Linear Programming (LP) network optimization. Three soy products are evaluated: edamame from China, tofu from the U.S., and textured vegetable protein (TVP) modeled as a soy-based alternative. Results are calculated using a cradle-to-retailer system boundary, normalized to 100 g of delivered protein. Four network configurations are evaluated, varying sourcing geography, port selection, and warehouse allocation. Distribution-stage emissions are minimized through LP optimization, while upstream emissions are incorporated as exogenous LCA parameters. Sourcing geography, distribution-network design, and protein density significantly affect GWP per functional unit, with domestic sourcing yielding the lowest impacts for all products and network configurations. Tofu under the baseline configuration exhibits the highest GWP (1.2236 kg CO₂e/100 g protein), whereas TVP with domestic sourcing exhibits the lowest (0.1146 kg CO₂e/100 g protein), representing a 90.64% difference. The integrated approach provides a decision-support framework for lower-emission sourcing and distribution in emerging-economy food supply chains. Full article