Stages Of Supply Chain Research Articles

Niche-specific evolution and gene exchange of Salmonella in retail pork and chicken

Salmonella exhibits extensive genetic diversity, facilitated by horizontal gene transfer occurring within and between species, playing a pivotal role in this diversification. Nevertheless, most studies focus on clinical and farm animal isolates, and research on the pangenome dynamics of Salmonella isolates from retail stage of the animal food supply chain is limited. Here, we investigated the genomes of 950 Salmonella isolates recovered from retail chicken and pork meats in seven provinces and one municipality of China in 2018. We observed a strong correlation between Salmonella sublineage diversity and the accessory genome with meat type, revealing reduced diversity associated with increased resistance. Importantly, genes associated with antibiotic, biocide, and heavy metal resistance were unevenly distributed in Salmonella from retail chicken and pork. Pork Salmonella isolates showed a higher prevalence of copper and silver resistance genes, while chicken Salmonella isolates displayed a significant predominance of genetic determinants associated with cephalosporin and ciprofloxacin resistance. Moreover, co-occurrence patterns of resistance determinants and their interaction with mobile genetic elements also correlated with meat type. In summary, our findings shed light on how Salmonella achieves their ecological niche success driven by evolution and gene changes in the retail stage of the animal food supply chain.

Production losses in Russian agriculture: Impact on food security and environmental sustainability

This paper provides a quantitative assessment of the impact of losses on specific aspects of food security and environmental sustainability. The lack of full information on the benefits of food loss reduction destimulates developing appropriate effective measures at the public level and in the private sector. The study covers losses generated at the initial stages of the food supply chain, including agricultural production and primary processing. Methodological assessments are based on works of foreign authors, recommendations of the Food and Agriculture Organization of the United Nations (FAO), the Intergovernmental Panel on Climate Change (IPCC) and domestic methodological developments in this area. The results indicate that at the primary stages of the food supply chain, food losses generate a relatively small amount of greenhouse gas emissions, so their reduction in order to lower the carbon footprint is not as effective as at the stage of retail and household consumption. However, preventing losses in agriculture contributes to the creation of additional volumes of food, which can be directed to the consumption of vulnerable groups of population. At the same time, the consequences of loss reduction vary greatly depending on the type of product

Quantitative and qualitative assessment of food waste in rural markets in Morocco

Background: Food waste, a significant issue in sustainable food systems, occurs at various stages of the food supply chain, from production to consumption. However, there is limited data assessing the quantity and nutritional value of food waste at the retail level, especially in rural markets (Souks). Aims: This research aimed to characterize and quantify the nutritional value of food waste in rural retail markets of El Jadida and Sidi Bennour in Morocco. Material and Methods: A quantitative approach was employed, combining observational diagnosis of food waste types and a survey of sellers to collect information, through a questionnaire, on purchased and remaining quantities, as well as the nature and disposal of food waste. The nutritional value of wasted food was estimated using the Ciqual food composition table and the bilnut calculation program. Results: The estimated quantity of unsold food in the studied retail markets studied was 1135.36 tons/year, equivalent to 0.85 kg/inhabitant/year for the region’s population. Vegetables accounted for the largest proportion of unsold food (45.7%), followed by fruits (27.4%), and meat and offal (10.8%). The estimated quantity of food waste was 677.3 tons/year or 0.5 kg/inhabitant/year. The daily nutritional value of wasted food in the studied Souks was estimated to be 118.173 kcal, 7.616 g of carbohydrates, 6.026 g of proteins and 6.386 g of total fats, 1.579 g of dietary fiber, 334 mg of vitamin A, 1.6 mg of vitamin D, 945.9 mg of iron, 21.712 mg of calcium, 1.903 mg of potassium, and 4.56 mg of iodine. These nutrient contents could potentially contribute to the daily needs of a significant portion of the population, particularly for iron, as the estimated daily loss of 945.9 mg/day could meet the recommended daily intake of 59 adult women or 236 children under five years old. Conclusion: This study provides a novel comprehensive assessment of the nutritional value lost due to food waste in rural retail markets in El Jadida-Sidi Bennour. The findings reveal the need for improved supply chain management and demand forecasting to reduce food waste I these. Strengthening efforts to redistribute and reutilize wasted food can contribute to addressing nutritional deficiencies and food insecurity among vulnerable rural populations. Keywords: Food waste, retail trade, valorization, nutritional loss, rural markets.

Advanced Supply Chain Management Using Adaptive Serial Cascaded Autoencoder with LSTM and Multi-Layered Perceptron Framework

Supply chain management is essential for businesses to handle uncertainties, maintain efficiency, and stay competitive. Financial risks can arise from various internal and external sources, impacting different supply chain stages. Companies that effectively manage these risks gain a deeper understanding of their procurement activities and implement strategies to mitigate financial threats. This paper explores financial risk assessment in supply chain management using advanced deep learning techniques on big data. The Adaptive Serial Cascaded Autoencoder (ASCA), combined with Long Short-Term Memory (LSTM) and Multi-Layered Perceptron (MLP), is used to evaluate financial risks. A data transformation process is used to clean and prepare financial data for analysis. Additionally, Sandpiper Galactic Swarm Optimization (SGSO) is employed to optimize the deep learning model’s performance. The SGSO-ASCALSMLP-based financial risk prediction model demonstrated superior accuracy compared to traditional methods. It outperformed GRU (gated recurrent unit)-ASCALSMLP by 3.03%, MLP-ASCALSMLP by 7.22%, AE-LSTM-ASCALSMLP by 10.7%, and AE-LSTM-MLP-ASCALSMLP by 10.9% based on F1-score performance. The SGSO-ASCALSMLP model is highly efficient in predicting financial risks, outperforming conventional prediction techniques and heuristic algorithms, making it a promising approach for enhancing financial risk management in supply chain networks.

Transmission centers of global embodied mercury emissions are moving from developed to developing regions

Mercury (Hg) can accumulate in the food chain, posing significant threats to human health and biodiversity. Existing studies have identified critical primary suppliers, producers, and final consumers driving Hg emissions in global supply chains. However, the hotspots in the intermediate transmission stages of global supply chains remain unknown. This study reveals critical transmission sectors and transactions of global embodied Hg emissions during 1995–2022. Results show that the critical transmission sectors and transactions of global embodied Hg emissions have been moving from developed (e.g., Japan and Russia) to developing (e.g., China and India) economies. Critical transmission sectors were primarily concentrated in the metal products, transportation, machinery and equipment manufacturing, and construction industries. Domestic transactions were the primary transmission pathways for embodied Hg emissions, with nearly half of these being intra-sectoral transactions. These findings can help policymakers develop transmission-bound Hg reduction policies and multi-level cooperation to support joint Hg emission reduction.

An integrated blockchain-enabled multi-channel vaccine supply chain network under hybrid uncertainties

The recent pandemic caused by COVID-19 is considered an unparalleled disaster in history. Developing a vaccine distribution network can provide valuable support to supply chain managers. Prioritizing the assigned available vaccines is crucial due to the limited supply at the final stage of the vaccine supply chain. In addition, parameter uncertainty is a common occurrence in a real supply chain, and it is essential to address this uncertainty in planning models. On the other hand, blockchain technology, being at the forefront of technological advancements, has the potential to enhance transparency within supply chains. Hence, in this study, we develop a new mathematical model for designing a COVID-19 vaccine supply chain network. In this regard, a multi-channel network model is designed to minimize total cost and maximize transparency with blockchain technology consideration. This addresses the uncertainty in supply, and a scenario-based multi-stage stochastic programming method is presented to handle the inherent uncertainty in multi-period planning horizons. In addition, fuzzy programming is used to face the uncertain price and quality of vaccines. Vaccine assignment is based on two main policies including age and population-based priority. The proposed model and method are validated and tested using a real-world case study of Iran. The optimum design of the COVID-19 vaccine supply chain is determined, and some comprehensive sensitivity analyses are conducted on the proposed model. Generally, results demonstrate that the multi-stage stochastic programming model meaningfully reduces the objective function value compared to the competitor model. Also, the results show that one of the efficient factors in increasing satisfied demand and decreasing shortage is the price of each type of vaccine and its agreement.

Price transmission relationships in Taiwan seafood supply chain: Tilapia (Oreochromis niloticus) and milkfish (Chanos chanos)

Tilapia and milkfish are aquaculture species associated with the highest production volume in Taiwan. With both fish species as the research focus, this study explored their price transmission relationships along the seafood supply chain. Our study results revealed that tilapia and milkfish prices had a long-run equilibrium relationship at different stages of the seafood supply chain and presented inconsistent price transmissions in the short run. There are domestic and foreign market channels for tilapia, enabling producers and wholesalers to transmit prices to retailers. By contrast, milkfish are mostly supplied to domestic markets, there is not perfect price transmission in the milkfish supply chain besides between farmers and wholesalers. An accurate price forecasting model can effectively indicate future price volatility and predict seafood prices, especially following government policy changes or in the face of external impacts. Such model can also serve as a crucial reference for future research of tilapia and milkfish industry, producers, and aquaculture farmers. Finally, seafood prices are a key determinant of fisheries and supply chain sustainability. Therefore, the stability of seafood prices, fish market wholesale pricing, and retail pricing are issues meriting continuous attention for a sustainable seafood supply chain.

Enhancing International Trade Security: Real-Time Risk Assessment in Brazilian Customs with Blockchain Technology

This study introduces an innovative approach by integrating blockchain technology with risk analysis methods for international trade. The practical application and tangible improvements observed in a real case study highlight the originality and value of this approach, offering a robust framework for future research and practical implementation. The security of exports presents a challenge for customs authorities, requiring the analysis of vast volumes of data from diverse sources and stakeholders. Blockchain technology offers a viable solution by facilitating the sharing of reliable and secure data among participants in the international supply chain. This study proposes a distributed risk analysis method based on Blockchain to detect and prevent fraud and irregularities in Brazilian exports. The technique aims to enhance efficiency and protection in customs clearance, providing real-time risk analysis feedback at various supply chain stages. The method includes four stages: data collection, risk classification, validation, and feedback. It employs a Model Form for Insertion into the Blockchain, the DBSCAN algorithm to identify out-of-pattern transactions (outliers) at the manufacturing stage, and a Monte Carlo simulation to analyze the volume and specific products exported from Rio Grande do Sul. We applied this method in a case study of beef exports from Brazil. Using this method led to significant improvements in processing times, operational costs, transparency, and security in the supply chain. The blockchain-based approach identified fraudulent activities, such as value tampering and undeclared goods, enhancing compliance and trust among trade partners. Implementing this method can significantly reduce operational costs and processing times in international trade while increasing security and transparency. This approach benefits customs authorities and private operators, promoting a more reliable and efficient commercial environment. Adopting blockchain technology in international trade can reduce fraudulent and illicit practices, promoting greater fairness and equity in commercial transactions. Increased transparency strengthens trust among various actors in the supply chain, fostering a sense of social responsibility.

Exploring Community-Supported Agriculture through Maslow’s Hierarchy: A Systematic Review of Research Themes and Trends

Community-supported agriculture (CSA) has emerged as a pivotal model for sustainable and humanistic agricultural practices, emphasizing the symbiotic relationship between food production, consumption, and sustainable ecosystems. Despite the growing interest, a comprehensive analysis of research themes and trends within the CSA framework remains sparse. This paper undertakes a systematic review of CSA literature from 1999 to 2023, identifying evolving research hotspots, dominant themes, and prospective directions by keyword analysis to corroborate Maslow’s hierarchy of needs theory. The research analysis location is categorized into four temporal phases, revealing a geographical expansion from North America to encompass Asia, Africa, and other continents. This expansion corroborates Maslow’s theory, illustrating a global shift from fulfilling basic physiological needs towards recognizing sustainable practices, particularly in developing regions. The results of temporal trends (5 phases) and the hotspots of keyword analysis support each other by showing a societal shift from basic sustenance to a deeper understanding of nutrition and diet. Most of the recent research keywords are grouped into the “environment” and “health and education” categories, indicating an increasing emphasis on transforming the food system and nutrition education. This review suggests conducting an integrated analysis that links the various stages of the food supply chain with the criteria outlined in the Sustainable Agriculture Matrix (SAM). It highlights that the “environment” theme is a stage of building up esteem and self-realization that needs to be unfolded in the future, given that most research on community-supported agriculture (CSA) focuses on the “economy and society” aspect and consumption stage, which burnish self-morality in the theory of Maslow. Overall, this review proposes an analysis of the relevance among different subject categories and between food supply chain stages, which reveals that the trend of research under CSA development is accorded to the theory of Maslow’s hierarchy of needs and calls for a more holistic approach to agricultural research that considers ecological, health, and social imperatives.

Improved post‐harvest preservation effects of mushroom (Agaricus bisporus) using bacterial cellulose nanocrystals‐gelatin/cinnamon essential oil emulsion coatings

SummaryThis work investigated the effect of bacterial cellulose nanocrystals (BCNCs)‐gelatin (GelA)/cinnamon essential oil (CEO) emulsion coatings (BCNCs‐GelA/CEO) on the shelf‐life extension of button mushrooms (Agaricus bisporus). CEO loadings between 1200 and 2400 μL/L were used in the coatings applied on the mushrooms' surface. The overall quality of coated mushrooms was monitored over 15 days of cold storage (4 ± 0.5 °C). Regardless of the storage time, using the highest amount of CEO within the coating formulation minimised the impact on tested parameters (weight loss, firmness, percentage of opened caps, total soluble solids, content of ascorbic acid and soluble protein, PPO/POD enzymatic activity) as compared to control samples. Interestingly, a linear relationship (R2 = 0.96 on average) between CEO concentration and the respiration rate of coated mushrooms was disclosed. Overall, this study pinpointed the capability of BCNCs‐GelA/CEO coatings to delay the aging process of button mushrooms under cold storage. Our findings could be applied to address the issue of food losses, highlighting the positive role of coating technology in enhancing the efficiency of the early stages of the food supply chain, especially in the case of button mushrooms. However, an assessment of the impact of the concentration of other coating components (BCNC and fish gelatin) on the shelf‐life extension of button mushrooms, as well as an evaluation of the coating's effectiveness in prolonging the shelf‐life of other food items, particularly non‐respiring products, is necessary to widespread the applicability of the proposed technology.

Determination of Some Element’s Migrants in Aqueous Simulant from Plastic Food Contact Products by Inductively Coupled Plasma Mass Spectrometer

Various chemicals present at different stages in the food supply chain can lead to the leaching of heavy metals. These metals can accumulate in the human body through the consumption of contaminated food. Consequently, it is necessary to validate an analytical technique for the quantification chemical that could contaminate food. This study presents a rapid, straightforward, and efficient analytical method for the direct quantification of some potentially toxic elements in aqueous simulants from plastic food contact products using an inductively coupled mass spectrometer (ICP-MS). The method’s validation encompassed the study of the estimated detection limits, practical quantification limits, linearity, accuracy, and measurement uncertainty of aluminium (Al), antimony (Sb), arsenic (As), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), iron (Fe), lead (Pb), manganese (Mn), nickel (Ni), and zinc (Zn) under optimized ICP-MS conditions. The estimated detection limits ranged from 7.5 × 10−4 to 0.074 mg/kg, while practical quantification limits spanned from 0.02 to 0.8 mg/kg. The average recoveries ± standard deviations at different spiking levels were varied between 85.7 ± 1.51 and 115.6 ± 0.88% with coefficients of variation between 0.42 and 5.85%. The method trueness was verified by using references materials (test material in aqueous acetic acid) purchased from Food Chemistry Proficiency Testing and Analysis (FAPAS) yielding satisfactory results within acceptable recovery and Z-score values. The method precision, in terms of relative standard deviation (RSD), was being below 4.22%. The method uncertainty expressed as expanded uncertainty of all validated elements was found to be ≤ 21.9%. Validated method was employed to determine specific elements in aqueous simulants of thirty commercial plastic food packaging samples, representing three distinct types of plastic polymers. The results showed that the mean concentrations, in mg/kg, were as follows: 2.04 (Al), 0.02 (As), 0.02 (Cd), 0.02 (Co), 0.06 (Cr), 0.41 (Cu), 1.55 (Fe), 0.09 (Mn), 0.15 (Ni), 0.07 (Pb), 0.05 (Sb), and 0.81 (Zn). Furthermore, 30% of analyzed samples exceeding the maximum permissible limits of Al for plastic materials and articles intended to come into contact with food.

Role of the Copernicus Satellite Programme in Building the Resilience of European Supply Chains: Results of a Delphi Study

Abstract The paper aims to analyse the challenges European supply chains face in the context of satellite communications (specifically, in the Copernicus programme) amidst geopolitical and pandemic disruptions. It focuses on identifying factors and barriers and recommended solutions to enhance resilience in these supply chains. The study employs a comprehensive approach, incorporating Delphi surveys, a literature review, and the STEEPED analysis. Experts from the satellite communications field participated in the Delphi survey, and the study scrutinised the impact of Delphi theses on various stages of supply chains. STEEPED analysis was used to identify factors enhancing the resilience of European supply chains in satellite communications. An analysis of their validity and uncertainty was also carried out. The research highlights the factors influencing supply chain resilience within satellite communications, emphasising the importance of coping with uncertainty, shocks, and disruptions. The study presents valuable recommendations to strengthen supply chain resilience amid infrastructural and geopolitical challenges. This research enriches insights into building resilience strategies for European supply chains operating within intricate and uncertain environments. By analysing the role of the Copernicus programme and Earth observation data, the paper contributes to the theoretical framework, providing essential knowledge for managing supply chains in a dynamic and complex environment. The study’s recommendations offer practical guidance for EU institutions overseeing the Copernicus programme and its users. By effectively leveraging Copernicus and Earth observation data, stakeholders can enhance their analysis methods. These insights enable practical actions to be taken, ensuring the continuity and stability of European supply chains amidst challenging global scenarios.

Managing the microbiological safety of tilapia from farm to consumer.

Tilapia stands out as one of the most extensively farmed and consumed fish species globally, valued for its ease of preparation and relative affordability. Although tilapia is a valuable protein source, it can also function as a vector for foodborne pathogens. This literature review reveals that tilapia could carry a variety of contamination with various foodborne pathogens, including Plesiomonas shigelloides, diarrheagenic Escherichia coli, Vibrio parahaemolyticus, Salmonella Weltevreden, Salmonella enterica, Shigella, Staphylococcus aureus, Campylobacter jejuni, Clostridium botulinum, and Listeria monocytogenes. Although guidelines from entities, such as the Global Seafood Alliance, Aquaculture Stewardship Council, and International Organization for Standardization, have been established to ensure the microbiological safety of tilapia, the unique challenges posed by pathogens in tilapia farming call for a more nuanced and targeted approach. Recognizing that contaminants could emerge at various stages of the tilapia supply chain, there is a crucial need for enhanced detection and monitoring of pathogens associated with this fish and its culturing environment. Additionally, it is essential to acknowledge the potential impact of climate change on the safety of tilapia, which may elevate the prevalence and contamination levels of pathogens in this fish. Proactive measures are essential to understand and mitigate the effects of climate change on tilapia production, ensuring the sustainability and safety of this seafood product for both present and future generations.

The Impact of Technology on Food Waste: Smart Packaging

Food waste, which is constantly increasing and requires urgent intervention at all stages of the food supply chain, is a major problem. Failure to address this issue leads to larger problems such as biodiversity degradation, the climate crisis, and migration. Technological products are seen as a significant opportunity to prevent waste. Therefore, this study aims to explore the impact of technology on food waste, with a specific focus on smart packaging. By reviewing the literature on the subject, the study discusses and explains technological solutions to food waste (such as mobile phone applications, smart devices, waste recycling, and smart packaging), the relationship between packaging and food waste, and the importance of smart packaging in reducing food waste. In conclusion, it has been determined that technology is a crucial element in combating waste, new technologies should be supported, and the smart packaging system, in particular, plays a key role in preventing waste at the retail and consumption levels.

Integrating Circular Economy Principles into Agri-Food Supply Chain Management: A Systematic Literature Review

This systematic literature review (SLR) examines the integration of circular economy (CE) principles into the agri-food supply chain over the past 20 years. The review aims to consolidate existing knowledge, identify research gaps, and provide actionable insights for future research. A comprehensive search across major databases yielded 1200 articles, which were screened, filtered, and assessed, resulting in a final selection of 100 high-quality studies. This review highlights the significant potential of the CE to enhance sustainability, reduce waste, and improve resource efficiency in the agri-food sector. These studies were categorized based on thematic issues, geographical regions, and supply chain stages. The findings highlight the predominant focus on waste management, resource efficiency, sustainable practices, and economic impacts, reflecting regional priorities and regulatory frameworks. The review also underscores the multiplier effect of CE implementation, which enhances sustainability and efficiency within the agri-food sector and generates significant economic and environmental benefits on a regional and global scale. Key challenges and areas for further research are identified, providing a comprehensive foundation for advancing CE practices in the agri-food supply chain.

Mismanagement and poor transparency in the European processed seafood supply revealed by DNA metabarcoding

In the global processed seafood industry, disparate actors play different roles along the supply chain, creating multiple opportunities for mistakes, malpractice, and fraud. As a consequence, consumers may be exposed to non-authentic products, which hinder informed purchasing decisions and broader efforts to improve trade transparency and sustainability. Here, we characterised the taxonomic composition of 62 processed seafood products in Italian, British and Albanian retailers, purposefully obtained from different supply routes, using multiple DNA metabarcoding markers. By combining molecular results with metadata reported on labels, we revealed patterns of mislabelling in 24 products (39%) across sampling regions, denoting lack of transparency of processed seafood products based on resources sourced from either Europe or globally. We show that the accuracy of label claims and the mis-represented and underestimated levels of traded biodiversity are largely determined by the management of raw material by global processors. Our study shows that DNA metabarcoding is a powerful and novel authentication tool that is mature for application at different stages of the seafood supply chain to protect consumers and improve the sustainable management of fish stocks.

S-Tune: SOT-MTJ manufacturing parameters tuning for securing the next generation of computing

Hardware-based acceleration approaches for Machine Learning (ML) workloads have been embracing the significant potential of post-CMOS switching devices to attain reduced footprint and/or energy-efficient execution relative to transistor-based GPU and/or TPU-based accelerator architectures. Meanwhile, the promulgation of fabless IC chip manufacturing paradigms has heightened the hardware security concerns inherent in such approaches. Namely, unauthorized access to various supply chain stages may expose significant vulnerabilities resulting in malfunctions including subtle adversarial outcomes via the malicious generation of differentially-corrupted outputs. Whereas the Spin-Orbit Torque Magnetic Tunnel Junction (SOT-MTJ) is a leading spintronic device for use in ML accelerators, as well as holding security tokens, their manufacturing-only security exposures are identified and evaluated herein. Results indicate a novel vulnerability profile whereby an adversary without access to the circuit netlist could differentially-influence the machine learning application’s behavior. Specifically, ML recognition outputs can be significantly swayed via a global modification of oxide thickness (Tox) resulting in bit-flips of the weights in the crossbar array, thus corrupting the recognition of selected digits in MNIST dataset differentially creating an opportunity for an adversary. With just 0.05% of bits in crossbar having a flipped resistance state, digits “4” and “5” show the highest overall error rates, and digit “9” exhibit the lowest impact, with recognition accuracy of digits “2,” “3,” and “8” unaffected by changing the oxide thickness of SOT-MTJs uniformly from 0.75 nm to 1.2 nm without modifying the netlist nor even having access to the circuit design itself. Exposures and mitigation approaches to such novel and potentially damaging manufacturing-side intrusions are identified, postulated, and quantitatively assessed.

Staphylococcus spp. in Salad Vegetables: Biodiversity, Antimicrobial Resistance, and First Identification of Methicillin-Resistant Strains in the United Arab Emirates Food Supply.

Contamination of leafy greens with Staphylococcus spp. can occur at various supply chain stages, from farm to table. This study comprehensively analyzes the species diversity, antimicrobial resistance, and virulence factors of Staphylococci in salad vegetables from markets in the United Arab Emirates (UAE). A total of 343 salad items were sampled from three major cities in the UAE from May 2022 to February 2023 and tested for the presence of Staphylococcus spp. using standard culture-based methods. Species-level identification was achieved using matrix-assisted laser desorption ionization-time of flight mass spectrometry. Antimicrobial susceptibility testing was conducted using the VITEK-2 system with AST-P592 cards. Additionally, whole genome sequencing (WGS) of ten selected isolates was performed to characterize antimicrobial resistance determinants and toxin-related virulence factors. Nine Staphylococcus species were identified in 37.6% (129/343) of the tested salad items, with coagulase-negative staphylococci (CoNS) dominating (87.6% [113/129]) and S. xylosus being the most prevalent (89.4% [101/113]). S. aureus was found in 4.6% (14/343) of the salad samples, averaging 1.7 log10 CFU/g. One isolate was confirmed as methicillin-resistant S. aureus, harboring the mecA gene. It belonged to multi-locus sequence type ST-672 and spa type t384 and was isolated from imported fresh dill. Among the characterized S. xylosus (n = 45), 13.3% tested positive in the cefoxitin screen test, and 6.6% were non-susceptible to oxacillin. WGS analysis revealed that the cytolysin gene (cylR2) was the only toxin-associated factor found in S. xylosus, while a methicillin-sensitive S. aureus isolate harbored the Panton-Valentine Leukocidin (LukSF/PVL) gene. This research is the first to document the presence of methicillin-resistant S. aureus in the UAE food chain. Furthermore, S. xylosus (a coagulase-negative staphylococcus not commonly screened in food) has demonstrated phenotypic resistance to clinically relevant antimicrobials. This underscores the need for vigilant monitoring of antimicrobial resistance in bacterial contaminants, whether pathogenic or commensal, at the human-food interface.

Multi-dimensional early warning of the entire supply chain of power materials based on RFID technology

Early warning system needs to process a large amount of real-time data, and carry out in-depth analysis and mining of these data to identify potential risks and hidden dangers. However, existing data processing and analysis capabilities may not be able to meet this demand. To this end, a multi-dimensional early warning of the entire supply chain of power materials based on RFID technology is designed. According to the real-time failure probability of power materials, the probability of early warning accidents is calculated and classified, and the risk is graded based on these probabilities. Through the methods of bonus points, deduction points and grade evaluation, the risk early warning indicators of different stages in the whole supply chain of power materials were quantified. The objective and rationality of risk assessment can be ensured by means of comprehensive weight. A multi-dimensional early warning system based on RFID technology is established, combining multiple linear regression model and particle swarm optimization algorithm to determine the time window of multi-dimensional early warning, and carry out dynamic monitoring and early warning of supply chain. The experimental results show that the early warning effect of the design method can reach 95% and the highest early warning effect can reach 98% at 10 seconds. The average warning error is only 2.91%, and the average warning time is only 1.34 seconds, which is more accurate in identifying the number of first-level risks, second-level risks and third-level risks.

Development of a Prototype Solution for Reducing Soup Waste in an Institutional Canteen

Food waste has gained increasing attention and debate, given its economic, environmental, social, and nutritional implications. One-third of food intended for human consumption is wasted. Although it is present at all stages of the food supply chain, it is in the final stages of consumption, such as households and food services, that the problem becomes most evident. This work builds on a previous study by the same authors, which identified computer vision as a suitable technology for identifying and quantifying food waste in institutional canteens. Based on this result, this paper describes the proposal and implementation process of a prototype demonstration. It is based on a Raspberry Pi 4 platform, a ResNet-50 model adapted with the Faster Region-Convolutional Neural Network (Faster R-CNN) model, and an algorithm for feature extracting. A specially built dataset was used to meet the challenge of detecting soup bowls and classifying waste in their consumption. A web application was developed to visualize the data collected, supporting decision making for more efficient food waste management. The prototype was subjected to validation and functional tests, and proved to be a viable, low-cost solution.