Search Results (88)

The rapid growth of online shopping has increased demand for home deliveries, leading to sustainability issues and logistical challenges such as labour shortages and congestion. Autonomous delivery vehicles, including drones, street robots, autonomous vans, and mobile vending machines, are emerging as potential solutions. Understanding consumers’ perceptions of these technologies is critical for sustainable implementation. This exploratory study aimed to examine consumer reactions to emerging autonomous delivery services, providing insights into how consumers may respond to autonomous delivery systems encompassing multiple vehicle modes and the resulting policy implications. Eight online focus groups (n = 55) were conducted with a diverse range of participants to examine community attitudes to autonomous delivery services. Participants were shown videos depicting various autonomous delivery methods to foster informed responses. Thematic analysis of the transcripts identified recurring themes relating to participants’ preferences, concerns, and expectations. While participants had some concerns, they were largely receptive to using autonomous delivery services. Positive reactions centred around: (i) convenience, (ii) cost reductions, and (iii) novelty. Identified concerns included: (i) job losses, (ii) practical limitations of the delivery devices, (iii) degradation of urban environments, and (iv) facilitation of unhealthy lifestyles. Overall, the results suggest autonomous delivery systems have the potential to be popular, and proactive government policies are thus likely to be needed to ensure they are implemented in a manner that aligns with community expectations and minimises any negative sustainability outcomes. Full article

Purified water from vending machines offers consumers an alternative source of clean, safe water. However, data regarding its microbiological quality are limited, particularly concerning the prevalence of Pseudomonas aeruginosa harboring virulence traits. This study aimed to evaluate the microbiological quality of 125 purified water samples collected from vending machines across six cities of Michoacan, Mexico. Additionally, it sought to assess the occurrence of Pseudomonas aeruginosa and characterize its antimicrobial resistance profiles and biofilm-forming capacity. Aerobic mesophilic bacteria (AMB) were detected in all analyzed samples. A total of 71 (56.8%), 40 (32.0%), and 31 (24.8%) samples were positive for total coliforms (TC), fecal coliforms (FC), and Escherichia coli, respectively. Among the samples, 43 (34.4%) were positive for P. aeruginosa. There were significant correlations between the presence of P. aeruginosa and AMB (rho = 0.4445; p < 0.0001), TC (rho = 0.4094; p < 0.0001), FC (rho = 0.3389; p = 0.0001), and E. coli (rho = 0.3242; p = 0.0002). Moreover, the presence of TC in purified water samples increased the risk of P. aeruginosa nearly seven-fold (odds ratio = 6.91; p < 0.001). The resistance rate among P. aeruginosa strains to the most tested antibiotics ranged from 2.3 to 16.3%, and two (4.6%) of the isolates were multidrug-resistant. All P. aeruginosa strains were strong biofilm producers. Consequently, we recommend periodic maintenance of vending machines, the establishment of P. aeruginosa control protocols, and enhanced regulatory monitoring of the water vending industry. Full article

Highly pathogenic avian influenza virus (HPAIV) H5N1 has been detected in dairy cattle in the United States, with high viral loads observed in milk from infected animals. This raises public health concerns regarding potential transmission through exposure to raw milk. The sale of raw milk via vending machines represents a well-established distribution model in many European countries, including Switzerland. Although a notice must be posted on these milk vending machines stating that it is raw milk, together with appropriate processing instructions (heating to over 70 °C required, storage below 5 °C, consumption within 3 days), these notices are sometimes missing, and consumers often do not follow these guidelines. Over a four-month period, spanning from June 2025 to September 2025, 124 raw milk samples were collected from vending machines across Switzerland. Samples were screened for influenza A using reverse-transcription quantitative PCR (RT-qPCR). No samples tested positive for influenza A virus. The data from this study demonstrate the feasibility of implementing a sampling and detection system for HPAIV H5N1 in direct-to consumer raw milk samples and highlight the currently very low risk of HPAIV in raw milk samples sold via vending machines in Switzerland. Full article

The sale of raw milk via vending machines represents a well-established distribution model in many European countries, including Switzerland. As part of this study, data on the microbiological quality of raw milk sold via vending machines in Switzerland were collected. A total of 124 raw milk samples from 124 raw milk vending machines across Switzerland were analysed. In addition to standard hygiene parameters (TVC and E. coli), the scope of the investigation particularly included foodborne pathogens as well as methicillin-resistant Staphylococcus aureus (MRSA) and extended-spectrum β-lactamase (ESBL)-producing Enterobacterales. Isolates were further characterised by whole-genome sequencing. Shiga toxin-producing Escherichia coli (STEC) were detected in 3.2%, Staphylococcus aureus was detected in 12.1%, Listeria monocytogenes was detected in 2.4%, Campylobacter spp. were detected in 1.6%, Yersinia enterocolitica was detected in 29.8%, and Salmonella spp. were detected in 0% of the samples. MRSA and ESBL-producing Enterobacterales were each detected in 0.8% of samples. The results highlight the potential risk of foodborne infections associated with the consumption of untreated raw milk, as well as hygiene deficiencies linked to several raw milk vending machines. Based on the generated data, the importance of the requested heat treatment of raw milk in Switzerland is clearly underscored. Furthermore, more precise and binding guidelines for self-monitoring and the management of raw milk vending machines appear necessary. Full article

At present, the global logistics sector faces critical challenges, including rising energy costs and pressure to reduce CO₂ emissions. Traditional linear supply chains are becoming inefficient, necessitating a transition toward shared logistics based on the principles of the sharing economy. This paper presents a progressive three-layer architecture that transforms conventional reactive data collection into an autonomous, proactive management system for the distribution of consumable materials. While previous research established foundations in IoT connectivity for smart vending machines, this study advances the process by integrating an intelligent layer of artificial intelligence (AI) algorithms. The framework utilizes Long Short-Term Memory (LSTM) neural networks for demand forecasting, dynamic route optimization (VRP/ACO) for replenishment, and Isolation Forest/DBSCAN algorithms for real-time anomaly detection. To evaluate the framework, a numerical simulation was conducted using representative pilot scenarios. The results indicate that within the simulated environment, the system achieves over 95% accuracy in inventory depletion prediction (MAPE = 4.02%). In these analyzed instances, this leads to a 25–30% reduction in stock-out risks and a 25% reduction in replenishment distance. These findings demonstrate the significant potential for reducing operational costs and carbon footprints in green logistics. The study confirms that the synergy between IoT infrastructure and AI-driven analysis provides a robust foundation for transitioning from static methodologies to resilient, collaborative logistics ecosystems. Full article

Background/Objectives: Vending machines in hospitals offer convenient access to snacks and beverages for employees, visitors, and patients. However, their contents are typically energy-dense and nutritionally poor, which can potentially reinforce unhealthy eating habits. This study aimed to evaluate the impact of introducing healthier vending machine options on purchasing behaviour and consumer perceptions in a hospital setting. Methods: An interventional study was conducted at a university clinical centre in Slovenia. Sales data were collected from a standard vending machine and a pilot machine stocked with healthier products over two 14-day periods. Additionally, a consumer survey assessed factors influencing purchasing decisions and opinions on the healthier offerings. Results: The proportion of healthy items purchased increased from 22% to 39% in the pilot vending machine, indicating a positive shift toward healthier choices. However, total sales declined by 18.81%, suggesting consumer hesitation toward the new product mix. Survey results identified price, ingredients, and visual appeal as the primary factors influencing purchase decisions. Conclusions: The introduction of healthier vending machine options can promote better food choices in hospital environments, though challenges remain regarding consumer acceptance and sales performance. Expanding the variety of healthy items and adopting more competitive pricing strategies may enhance uptake. Further long-term research is needed to assess the sustainability of such interventions and their broader impact on hospital food environments. Full article

In this study, 104 purified water samples were collected from vending machines in the three main cities of Michoacan, Mexico, to assess microbiological quality and the occurrence of diarrheagenic Escherichia coli pathotypes (DEP) and nontuberculous mycobacteria (NTM). Aerobic mesophilic bacteria were detected in all samples, with concentrations ranging from 0.95 to 3.71 log₁₀ CFU/mL. A total of 62, 34, and 25 samples tested positive for total coliforms, fecal coliforms, and E. coli, respectively. Sixty-two samples exceeded Mexico’s official guideline. None of the 58 E. coli strains isolated from the 25 E. coli-positive samples belonged to DEP. NTM species were recovered from 47 samples, including M. mucogenicum (n = 18), M. abscessus (n = 11), M. chelonae (n = 7), M. porcinum (n = 3), M. fortuitum (n = 2), M. septicum (n = 1), M. phocaicum (n = 1), and M. brisbanense (n = 1). Three additional isolates could not be identified. All NTM strains produced biofilm and exhibited sliding motility. These findings highlight significant microbiological risks associated with vending machine water and underscore the need for manufacturers to ensure regular maintenance to provide safe and reliable purified water to consumers. Full article

Healthy food service guidelines (HFSG) comprise food, nutrition, behavioral design, and other standards to guide the purchasing, preparation, and offering of foods and beverages in worksites and institutional food service. To date, there have been few attempts to synthesize evidence for HFSG effectiveness in non-K-12 or early childhood education sectors, particularly at worksites and institutional food services. We conducted a scoping review to achieve the following: (1) characterize the existing literature on the effectiveness of HFSG for improving the institution’s food environment, financial outcomes, and consumers’ diet quality and health, and (2) identify gaps in the literature. The initial search in PubMed and Web of Science retrieved 10,358 articles; after screening and snowball searching, 68 articles were included for analysis. Studies varied in terms of HFSG implementation settings, venues, and outcomes in both U.S. (n = 34) and non-U.S. (n = 34) contexts. The majority of HFSG interventions occurred in venues where food is sold (e.g., worksite cafeterias, vending machines). A diversity of HFSG terminology and measurement tools demonstrates the literature’s breadth. Literature gaps include quasi-experimental study designs, as well as interventions in settings that serve dependent populations (e.g., universities, elderly feeding programs, and prisons). Full article

The development of retail stores is determined by many factors, including the availability of retail space. The construction of a new building requires time, resources, and permits. This article aims to examine the possibilities of implementing small modular retail facilities built on the principles of vending machines, which do not require constant service and social space, by examining important groups of factors: architectural and structural, production, environmental, and costs. A vending machine in modular construction technology provides new opportunities for the development of a retail network in previously inaccessible places. The research presented in this article was conducted based on a literature review and interviews with experts, on the basis of which, using the network thinking methodology, critical factors were isolated and analysed in detail. The research results show the benefits of using modular technology, meeting the assumptions of the circular economy in terms of reducing the carbon footprint and improving the construction stage and investment costs, while taking into account the aesthetics of the surroundings. The results can contribute to the popularisation of the use of modular facilities, which can complement the development of downtown areas, making cities more sustainable. Full article

Quickly designing digital circuits that are both correct and efficient poses significant challenges. Electronics, especially those incorporating sequential logic circuits, are complex to design and test. While Electronic Design Automation (EDA) tools aid designers, they do not fully automate the creation of synthesisable circuits that can be directly translated into hardware. This paper introduces a system that employs Grammatical Evolution (GE) to automatically generate synthesisable Hardware Description Language (HDL) code for the Finite State Machine (FSM) of a Multi-Sequence Detector (MSD). This MSD differs significantly from prior work as it can detect multiple sequences in contrast to the single-sequence detectors discussed in existing literature. Sequence Detectors (SDs) are essential in circuits that detect sequences of specific events to produce timely alerts. The proposed MSD applies to a real-time vending machine scenario, enabling customer selections upon successful payment. However, this technique can evolve any MSD, such as a traffic light control system or a robot navigation system. We examine two parent selection techniques, Tournament Selection (TS) and Lexicase Selection (LS), demonstrating that LS performs better than TS, although both techniques successfully produce synthesisable hardware solutions. Both hand-crafted “Gold” and evolved circuits are synthesised using Generic Process Design Kit (GPDK) technologies at 45 nm, 90 nm, and 180 nm scales, demonstrating their efficacy. Full article

Background: The majority of Australian children exceed the World Health Organization’s recommended dietary intake of free sugar, particularly through the consumption of sugar-sweetened beverages. Front-of-pack nutrition labels increase perceived risk and deter the consumption of sugar-sweetened beverages. However, past studies of young children have focused almost exclusively on a parent’s choice of beverage for children. This study investigated the influence of four label designs (text-based warning, tooth decay pictorial, teaspoons of sugar, and Health Star Rating) on the beverage choices of N = 1229 Australian children (aged 4–11 years) and their parents. Methods: In an online vending machine scenario, parent–child dyads were separately asked to select which beverage they would choose for themselves before and after being randomised to one label condition. The beverages displayed included 100% fruit juice, soft drink, soft drink with a non-nutritive sweetener, flavoured milk, plain milk and bottled water. Beverage healthiness was determined by a 1–10 rating based on a review by a panel of experts (10 dietitians and nutritionists). Results: Mixed-model ANOVAs showed that for parents, each label design performed comparably; however, for children, small but significant differences were seen in the effectiveness of different label designs, with the teaspoons of sugar label, text-based warning, and tooth decay pictorial found to be more impactful in promoting healthier drink choices than the Health Star Rating. Conclusions: These findings can inform public health advocacy efforts to improve food labelling and could be incorporated into educational resources to help children understand the nutritional profiles of different sugary drinks. Full article

According to the Regulation on the Quality of Fresh Raw Milk, up to 100,000 microorganisms/mL are allowed in milk obtained by the hygienic milking of healthy cows, which represents the natural microbiota of milk and has no negative impact on the overall quality of milk. However, with unprofessional handling during and after milking, milk is easily contaminated and becomes a potential medium for the growth and reproduction of microorganisms, some of which can be harmful to human health. Since the number of aerobic mesophilic bacteria in milk is one of the indicators of the hygienic quality of milk, their number and identification are fundamental in the control of raw milk from milk vending machines. From five different milk vending machines, 35 samples were collected, from which the total number of aerobic mesophilic bacteria was determined using the flow cytometry method and the classic method of counting colonies on a nutrient medium. Randomly selected colonies based on morphological differences (n = 700) were identified by comparing MALDI-TOF mass spectra with reference spectra stored in the microorganism library and processing using the MALDI Biotyper computer program. Thirty-eight genera and eighty-one bacterial species and five genera and seven fungal species were successfully identified. The species that predominate are Lactococcus lactis, Hafnia alvei, Escherichia coli, Leuconostoc mesenteroides, and Kluyveromyces lactis. By integrating advanced methods like flow cytometry and MALDI-TOF MS for precise microbial identification, this study highlights the need for enhanced monitoring and adherence to hygienic standards in raw milk vending machines. This approach not only safeguards public health but also supports consumer confidence in milk quality from vending machines. Full article

Milk has been consumed by humans for thousands of years for its nutritional properties. In recent years, raw milk demand has increased, valued for its authenticity and connection to local traditions. In Italy, the sale of raw milk is allowed exclusively through direct sale from the producing farm to the final consumer, either at the producing farm itself or through vending machines. However, the consumption of raw milk is not without risks. Among these, microbiological ones are relevant. These can lead to severe symptoms, particularly in vulnerable populations. For this reason, although consumers are advised to boil raw milk before consumption, producing farms in Italy are required to meet the microbiological criteria outlined in the Provision of 25 January 2007. In this retrospective study, the results of the analyses performed on 355 raw milk samples collected in Liguria between 2014 and 2023 for the detection of Campylobacter spp., Salmonella spp., Listeria monocytogenes, Staphylococcus aureus, and Escherichia coli O157 were analysed to better characterise the associated risk for consumers. The samples were collected during official controls by the local veterinary health services at vending machines of seven producing farms. Overall, six samples tested positive for C. jejuni, while only one sample tested positive for Salmonella enterica subsp. enterica, Serovar Veneziana. Listeria monocytogenes, S. aureus, and E. coli O157 were never responsible for non-compliances. Interestingly, three of the six samples positive for C. jejuni derived from the same producer. In farms where positive samples were detected, certain structural and/or operational non-compliances were identified. It can be concluded that, although the scenario in question does not present any cause for concern, it is nevertheless essential to implement a series of preventive measures in order to guarantee the safety of raw milk. These measures include the implementation of biosecurity practices, the maintenance of strict hygiene protocols during milking, and the adherence to the cold chain distribution protocol until the final stage of distribution. Full article

Vending machines have evolved into a critical element of the intelligent healthcare service system. To enhance the precision of pill box detection counting and cater to the lightweight requirements of its internal embedded controller for deep learning frameworks, an enhanced lightweight YOLOv8n model is introduced. A dataset comprising 4080 images is initially compiled for model training and assessment purposes. The refined YOLOv8n-ShuffleNetV2 model is crafted, featuring the integration of ShuffleNetv2 as the new backbone network, the incorporation of the VoVGSCSP module to bolster feature extraction capabilities, and the utilization of the Wise-IoU v3 loss function for bounding box regression enhancement. Moreover, a model pruning strategy based on structured pruning (SFP) and layer-wise adaptive magnitude pruning (LAMP) is implemented. Comparative experimental findings demonstrate that the enhanced and pruned model has elevated the mean Average Precision (mAP) rate from 94.5% to 95.1%. Furthermore, the model size has been reduced from 11.1 MB to 6.0 MB, and the inference time has been notably decreased from 1.97 s to 0.34 s. The model’s accuracy and efficacy are validated through experiments conducted on the Raspberry Pi 4B platform. The outcomes of the experiments underscore how the refined model significantly amplifies the deployment efficiency of the deep learning model on resource-limited devices, thus greatly supporting the advancement of intelligent medicine management and medical vending machine applications. Full article

Parcel vending machines (PVMs) are receiving more recognition as an environmentally friendly last-mile delivery service. However, their high popularity creates the risk of microbial contamination of touchscreens and keypads resulting in the spread of pathogens in humans. The study aimed at assessing the degree of bacterial contamination of PVMs and characterizing the microbial population using mass spectrometry-based identification. In total, 64 PVMs located in 16 districts of city Lublin (Poland) were studied for the total number of aerobic bacteria (TNAB) using contact plates, whereas bacterial identification was conducted using the MALDI-TOF MS. Study revealed that the average TNAB for the analyzed districts ranged from 1 ± 0.4 CFU/cm² to 8.54 ± 10.77 CFU/cm². Statistical analysis showed a positive correlation between the TNAB and the population density (p = 0.0193), emphasizing the influence of human microbiota on the level of bacterial contamination. Among 140 reliably identified bacterial species (96.3%), the most prevalent were Bacillus spp. (52.8%) and Staphylococcus spp. (10.7%). Multiple opportunistic pathogens were detected, including B. cereus and Escherichia coli. This study indicates the need to develop procedures for disinfection or to implement modified materials for the PVMs touchscreens to limit the adhesion of potentially pathogenic microorganisms. Full article