Vending Machines Research Articles

IJCM_95A: To Assess the knowledge and practice regarding reproductive tract infection among female of reproductive age group in Raipur city Chhattisgarh

Background: Reproductive tract infection (RTI) is a global health problem among women, especially in South East Asia Region (SEAR) countries. They may progress to serious complications and cause a high degree of morbidity during the sexually active period of life. More than a million women and infants die of the complications of RTI every year. RTI has become a silent epidemic that devastates women’s lives. Globally, three different types of RTI cause significant amounts of morbidity and mortality among women – sexually transmitted infections (STIs) (including gonorrhea, chlamydia, syphilis, trichomonas, and HIV infections); endogenous infections resulting from the overgrowth of organisms normally present in the reproductive tract (such as candidiasis and bacterial vaginosis); and iatrogenic infections related to medical procedures (such as menstrual regulation, abortion or IUD insertion). The presence of STI increases the risk of acquiring and transmitting HIV infection by three to five times. Objectives: 1- To Assess the knowledge of female what is reproductive tract infection how it occur and spread among residents of Raipur City, Chhattisgarh. 2- To Assess the Factors associated with Knowledge among residents of Raipur City, Chhattisgarh. Methodology: This is an observational cross-sectional study conducted in Raipur City of Chhattisgarh state during the month October 23-January 2024. A pre-designed, self-administered, semi-structured questionnaire was used for data collection. Systematic Random sampling technique was adopted to select the 350 Study participants. Results: The majority of subjects were between the age of 15-34 year. Most of the subject were of lower middle class socioeconomic status. Additionally, 37.8% of participants were graduates, 59.4% were married and 70% were unemployed. 30.3% subjects had not heard about Reproductive tract infection. 50.6% subjects don’t know about symptoms of RTI, almost half 49.4% subject don’t have knowledge about effect of RTI on health and only 84% female believed that good personal hygiene prevent RTI. 45.1% subjects did not wash their private part after urination, 10.5% female did not wash their used cloth daily after each use. 63.9% female used feminine hygiene products to maintain personal hygiene, 10% female did not change their cloth daily, 33.1% women did not change their pad/ cloth within every 4–6-hour interval during menstruation. Conclusion: Educate women through camps about genital hygiene practices to reduce the risk of RTI. For increased usage of Sanitary pads, more number of sanitary vending machine should be installed in schools, colleges, and health facilities. Implement awareness programs, promoting regular check- ups and safe sex practices to mitigate the risk of RTI.

Review on VEND IT!- A Smart Vending Machine

Abstract: Traditional vending machines, though reliable, have often lagged behind in terms of convenience, personalization, and sustainability. As society rely on digital connectivity and automation continues to grow, the need for a smarter, more responsive vending solution has become increasingly evident. In a world marked by constant technological innovation and evolving consumer preferences, the time has come to reimagine the traditional vending machine. "Vend It" is a visionary project that aims to reinvent the conventional vending machine, leveraging cutting-edge smart technology to create a seamless, efficient, and user-friendly vending experience. VEND IT! encompasses a comprehensive suite of innovations, ranging from intelligent inventory management to user-centric interface design, and remote monitoring and management capabilities.

QUALITY CHARACTERISTICS OF RAW MILK SAMPLES PURCHASED FROM AUTOMATIC MİLK VENDING MACHINES IN NİĞDE PROVINCE

In the regulation published by the Ministry of Agriculture and Forestry in Turkey, it is stated that only automatic milk machines can be used to put raw milk on sale. This study aims to examine some physical, chemical and microbiological properties of a total of 40 raw milk samples sold in automatic vending machines and unpackaged, by egulations in Niğde province, and to compare the milk samples according to the retailer from which they were purchased. The study also aimed to quantitatively determine carbonate and hydrogen peroxide residues in milk samples and the presence of beta-lactam and tetracycline group antibiotics with a commercial kit. At the end of the study, the average value of lactic acid (%) density, fat, protein, non-fat dry matter, lactose, freezing point and pH values of the samples were determined as 0.153% ± (0.022); 1.028± (0.03) g/ml; 3.525%± (0.656); 3.5%± (0.107); 9% 4± (0.277); 5.134± (0.152); -0.549°C ± (0.018) and 6.55 ± (0.102) respectively. As a result of microbiological analysis of milk, the average number of total aerobic mesophilic organisms, coliforms, fecal coliforms, yeast-molds and micrococci-staphylococci was determined as 5.38 ± (0.47); 3.73 ± (1.11); 2.76 ± (1.66); 2.33 ± (1.86) and 4.29± (1.20) log cfu/ml respectively. Carbonate, hydrogen peroxide, beta-lactam and tetracycline antibiotic residues could not be detected in the milk samples. The fact that some of the results obtained in the study were outside the limits specified in the codexes and different from the study average reveals that the milk sold in businesses selling street milk should be analyzed regularly.

A Study to Know the Impact of AI on Sustainability of Products in the Carbonated Beverages

Carbonated beverage businesses are using artificial intelligence (AI) technology to sustainability in a variety of ways to streamline their processes, boost output, and improve the customer experience. The businesses in the carbonated beverage sector use AI in the following ways, Coca-Cola is enhancing its supply chain administration by utilizing AI. The business has created a platform powered by AI that applies machine learning algorithms to improve its distribution and manufacturing procedures. The platform aids the business in improved demand forecasting, inventory level optimization, cost-effective transportation, voice activated vending machines. PepsiCo: To create novel product formulations, PepsiCo is utilizing AI. The business has developed an AI-powered platform that examines customer preferences and spots market trends. The business can develop new products thanks to this platform. Dr. Pepper Snapple Group: Dr. Pepper Snapple Group is enhancing its marketing initiatives with artificial intelligence. The business has created an AI-driven platform that analyses customer data and enables it to develop more specialized and individualized marketing campaigns. The platform enables the business to deliver more pertinent and efficient marketing messages by assisting in the better understanding of customer preferences and behaviour. Keurig Dr Pepper: Keurig Dr Pepper is using AI to enhance its customer service. The business has created an AI-powered chatbot that can instantly respond to customer inquiries and help. The chatbot uses machine learning algorithms and natural language processing to comprehend customer queries and deliver pertinent and useful responses. Overall, the carbonated beverage industry is using AI technology in a variety of methods. Keywords- carbonated beverage, artificial intelligence (AI), supply chain administration, demand forecasting, inventory optimization, transportation optimization, voice activated vending machines, product formulation, customer preferences, market trends, marketing campaigns, customer service, chatbot, machine learning algorithms, natural language processing.

Twenty-Four-Hour Urinary Sugars Biomarker in a Vending Machine Intake Paradigm in a Diverse Population.

Accurately measuring dietary sugars intake in large-scale epidemiological studies is necessary to understand dietary sugars' true impact on health. Researchers have developed a biomarker that can be used to assess total sugars intake. Our objective is to test this biomarker in diverse populations using an ad libitum intake protocol. Healthy adult participants (n = 63; 58% Indigenous Americans/Alaska Natives; 60% male; BMI (mean ± SD) = 30.6 ± 7.6 kg.m2) were admitted for a 10-day inpatient stay. On day 2, body composition was measured by DXA, and over the last 3 days, ad libitum dietary intake was measured using a validated vending machine paradigm. Over the same days, participants collected daily 24 h urine used to measure sucrose and fructose. The 24 h urinary sucrose and fructose biomarker (24hruSF) (mg/d) represents the sum of 24 h urinary sucrose and fructose excretion levels. The association between the 3-day mean total sugars intake and log 24uSF level was assessed using the Pearson correlation. A linear mixed model regressing log-biomarker on total sugars intake was used to investigate further the association between biomarker, diet, and other covariates. Mean (S.D.) total sugars intake for the group was 197.7 g/d (78.9). Log 24uSF biomarker was moderately correlated with total sugars intake (r = 0.33, p = 0.01). In stratified analyses, the correlation was strongest in females (r = 0.45, p = 0.028), the 18-30 age group (r = 0.44, p = 0.079), Indigenous Americans (r = 0.51, p = 0.0023), and the normal BMI category (r = 0.66, p = 0.027). The model adjusted for sex, age, body fat percent, and race/ethnicity demonstrated a statistically significant association between 24uSF and total sugars intake (β = 0.0027, p < 0.0001) and explained 31% of 24uSF variance (marginal R2 = 0.31). Our results demonstrated a significant relationship between total sugars intake and the 24uSF biomarker in this diverse population. However, the results were not as strong as those of controlled feeding studies that investigated this biomarker.

Evaluating source credibility effects in health labelling using vending machines in a hospital setting.

Providing advice to consumers in the form of labelling may mitigate the increased availability and low cost of foods that contribute to the obesity problem. Our objective was to test whether making the source of the health advice on the label more credible makes labelling more effective. Vending machines in different locations were stocked with healthy and unhealthy products in a hospital. Healthy products were randomly assigned to one of three conditions (i) a control condition in which no labelling was present (ii) a low source credibility label, "Lighter choices", and (iii) a high source credibility label that included the UK National Health Service (NHS) logo and name, "NHS lighter choices". Unhealthy products received no labelling. The outcome measure was sales volume. There were no main effects of labelling. However, there were significant interactions between labelling, vending machine location and payment type. For one location and payment type, sales of products increased in the high credibility label condition compared to control, particularly for unhealthy products, contrary to expectations. Our findings suggest that high source credibility health labels (NHS endorsement) on food either have little effect, or worse, can "backfire" and lead to effects opposite to those intended. The primary limitations are the limited range of source credibility labels and the scale of the study.

A vending machine coffee in an emergency department waiting room.

A vending machine coffee in an emergency department waiting room.

Safety and quality assessment of hot-drinks vending machines in Southern Italy

Vending machines (VMs) are common and convenient sources of various food and beverage items. Due to limited available information, concerns have been raised about the hygiene and safety of products dispensed by VMs. This study aims to assess the microbiological contamination of VMs in the Campania region (Italy), by combining microbiological and molecular biology analyses. The results revealed notable microbial contamination in VMs. Total Viable Count exceeded acceptable limits in VMs components, particularly in the inner walls of beverage dispensing nozzles and in the hot-drink product. Coffee and ginseng powders exhibited comparatively lower Total Viable Count values, while chocolate displayed none. Regarding pathogens, Listeria monocytogenes and Salmonella spp. were not detected in any of the samples subjected to analysis. Bacillus cereus (B. cereus) and Staphylococcus aureus (S. aureus) were detected in various VMs components. B. cereus contamination was prevalent, exceeding 80% in some surface swabs (water intake pipe, mixer bowl, and dispensing nozzle), and coincided with a high occurrence in the hot-drink final product. S. aureus was frequently found in milk powder, dispensing nozzles, and cappuccino samples. This research offers valuable insights into VM hygiene and safety, emphasizing the importance of multifaceted approaches to address microbiological contamination and promote public health. Regulatory compliance with cleaning procedures and proactive measures, such as robust self-monitoring plans and adherence to Good Manufacturing Practices, are crucial in mitigating contamination hazards and safeguarding product quality.

“The machine doesn't judge”: Counternarratives on surveillance among people accessing a safer opioid supply via biometric machines

People who use illegal drugs experience routine surveillance, including in healthcare and harm reduction settings. The MySafe Project - a safer supply pilot project that dispenses prescription opioids via a biometric vending machine - exists in the Canadian province of British Columbia. The machine scans a participant's palmprint and has a built-in camera that records every machine interaction. The aim of this paper is to understand participants' experiences of surveillance, privacy, and personal security when accessing this novel program. An integrative case study and grounded theory methodology was employed. Qualitative one-to-one interviews were conducted with 46 MySafe participants across three different program sites in Vancouver. We used a team-based approach to code interview transcripts and utilized directed and conventional content analyses for deductive and inductive analyses. While participants described negative experiences of surveillance in other public and harm reduction settings, they did not have concerns regarding cameras, collection of personal information, tracking, nor staff issues associated with MySafe. Similarly, while some participants had privacy concerns in other settings, very few privacy and confidentiality concerns were expressed regarding accessing the machine in front of others. Lastly, while some participants reported being targeted by others when accessing the machines, most participants described how cameras, staff, and machine locations helped ensure a sense of safety. Despite negative experiences of surveillance and privacy issues elsewhere, participants largely lacked concern regarding the MySafe program and machines. The machine-human interaction was characterized as different than some human-human interactions as the machine is completing tasks in a manner that is acceptable and comfortable to participants, leading to a social preference toward the machines in comparison to other surveilled means of accessing medications. These findings provide an opportunity to rethink how we conceptualize surveillance, medication access, and harm reduction programs targeting people who use drugs.

Acceptability of digital vending machines to access STI and HIV tests in two UK cities

ObjectivesPrompt HIV and STI diagnosis and treatment is a public health priority and relies on accessible testing. Technology-based approaches to distribute test kits have the potential to increase access to...

Product Recognition for Unmanned Vending Machines.

Recently, the emerging concept of "unmanned retail" has drawn more and more attention, and the unmanned retail based on the intelligent unmanned vending machines (UVMs) scene has great market demand. However, existing product recognition methods for intelligent UVMs cannot adapt to large-scale categories and have insufficient accuracy. In this article, we propose a method for large-scale categories product recognition based on intelligent UVMs. It can be divided into two parts: 1) first, we explore the similarities and differences between products through manifold learning, and then we build a hierarchical multigranularity label to constrain the learning of representation; and 2) second, we propose a hierarchical label object detection network, which mainly includes coarse-to-fine refine module (C2FRM) and multiple granularity hierarchical loss (MGHL), which are used to assist in capturing multigranularity features. The highlights of our method are mine potential similarity between large-scale category products and optimization through hierarchical multigranularity labels. Besides, we collected a large-scale product recognition dataset GOODS-85 based on the actual UVMs scenario. Experimental results and analysis demonstrate the effectiveness of the proposed product recognition methods.

UVM++: A Large-Scale Benchmark for Beverage Recognition in Intelligent Vending Machine

UVM++: A Large-Scale Benchmark for Beverage Recognition in Intelligent Vending Machine

Accuracy study of image classification for reverse vending machine waste segregation using convolutional neural network

This study aims to create a sorting system with high accuracy that can classify various beverage containers based on types and separate them accordingly. This reverse vending machine (RVM) provides an image classification method and allows for recycling three types of beverage containers: drink carton boxes, polyethylene terephthalate (PET) bottles, and aluminium cans. The image classification method used in this project is transfer learning with convolutional neural networks (CNN). AlexNet, GoogLeNet, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, XceptionNet, ShuffleNet, ResNet 18, ResNet 50, and ResNet 101 are the neural networks that used in this project. This project will compare the F1-score and computational time among the eleven networks. The F1-score and computational time of image classification differs for each neural network. In this project, the AlexNet network gave the best F1-score, 97.50% with the shortest computational time, 2229.235 s among the eleven neural networks.

Construction and optimization of vending machine decision support system based on improved C4.5 decision tree

The intensification of market competition makes refined operation management become the focus of attention of major manufacturers. As an important branch of artificial intelligence (AI), machine learning (ML) plays a key role in it, and has its application prospect in various systems. Based on this situation, this paper takes vending machines as the research object. On the one hand, the product classification model of vending machine is constructed based on decision tree algorithm. On the other hand, based on neural network (NN), the sales forecast model of vending machines is built. Finally, based on the above research, the theoretical framework of decision support system (DSS) for vending machines is constructed. The research shows that: (1) The accuracy of C4.5 algorithm can reach 87 % at the highest and 68 % at the lowest. The accuracy of the improved C4.5 algorithm can reach 87 % at the highest and 67 % at the lowest, with little difference between them. (2) The maximum running time of the improved C4.5 algorithm is about 5500 ms, and the minimum is close to 1 ms. In addition, the running time of all seven datasets is better than that of the unmodified algorithm. (3) When the back propagation neural network (BPNN) is used to forecast the sales of vending machines, the curve of the predicted data basically coincides with the curve of the actual data, which shows that its accuracy is high. This paper aims to build a convenient and secure DSS by taking vending machines as an example. In addition, this paper also uses reinforcement learning to optimize the research methods of this paper. It can further optimize the performance and efficiency of vending machines and provide better service experience for customers. Meanwhile, the use of reinforcement learning can make the whole system more intelligent and adaptive to better cope with the changing market environment.

Efficient Defect Detection of Rotating Goods under the Background of Intelligent Retail.

Dynamic visual vending machines are rapidly growing in popularity, offering convenience and speed to customers. However, there is a prevalent issue with consumers damaging goods and then returning them to the machine, severely affecting business interests. This paper addresses the issue from the standpoint of defect detection. Although existing industrial defect detection algorithms, such as PatchCore, perform well, they face challenges, including handling goods in various orientations, detection speeds that do not meet real-time monitoring requirements, and complex backgrounds that hinder detection accuracy. These challenges hinder their application in dynamic vending environments. It is crucial to note that efficient visual features play a vital role in memory banks, yet current memory repositories for industrial inspection algorithms do not adequately address the problem of location-specific feature redundancy. To tackle these issues, this paper introduces a novel defect detection algorithm for goods using adaptive subsampling and partitioned memory banks. Firstly, Grad-CAM is utilized to extract deep features, which, in combination with shallow features, mitigate the impact of complex backgrounds on detection accuracy. Next, graph convolutional networks extract rotationally invariant features. The adaptive subsampling partitioned memory bank is then employed to store features of non-defective goods, which reduces memory consumption and enhances training speed. Experimental results on the MVTec AD dataset demonstrate that the proposed algorithm achieves a marked improvement in detection speed while maintaining accuracy that is comparable to state-of-the-art models.

Estimation of informal park-and-ride

Curbside parking in a residential area may be induced by the presence of a public transport stop. Travelers may park their car near the stop and continue their trip to the city center by other means. This is called informal park-and-ride. The magnitude of the phenomenon is estimated by simulation. Parking demand is derived from the history of parking time (tickets) sold at vending machines. For each ticket, an activity location is determined by stochastic sampling from a buildings (facilities) database based on the position of the vending machine. The activity timing is derived from the parking duration specified by the ticket. Suitable parking spots for an activity are determined for the cases (i) drive-park-walk and (ii) drive-park-publicTransport-walk respectively. The generalized cost (based on money and travel duration) is determined for both options. The decision is sampled by means of a behavioural model. Several scenarios are considered and the results allow to evaluate the complaints issued by residents of a study area because microsimulation enables the generation of probability densities for parking occupation in such area in each scenario. This paper reports how the method is applied to a study area in Amsterdam.

The Day I Got My Period at the Museum

The author unexpectedly gets her period when she is visiting the Museum of the City of New York and uses the incident to explore how women’s desire to be free is thwarted when period products are not available in public restrooms. The author touches upon several aspects of menstrual equity—period stigma, outdated and empty dispensers, tampon tax, menstruating while homeless or incarcerated, and trans bodies that menstruate. Building on Judy Grahn’s idea that all culture is a result of women’s menstrual rites, the author points out the absurdity of not accommodating menstruating bodies in high-culture spaces like museums and asks for modern vending machines for period products in public restrooms.

Machine Learning-Based Predictive Inventory for a Vending Machine Warehouse

Machine Learning-Based Predictive Inventory for a Vending Machine Warehouse

Effect of formulations over a Periodic Capacitated Vehicle Routing Problem with multiple depots, heterogeneous fleet, and hard time-windows.

This article presents a novel contribution to the Periodic Vehicle Routing Problem (PVRP) by introducing two new problem formulations which differ in the usage of the crucial flow variable. The formulations are tailored to meet the specific demands of the vending machine industry in Medellín, Colombia, and require considering a PVRP with time windows, a heterogeneous fleet, and multiple depots. This scenario, tailored to address real-world complexity and computational challenges, brings to light an exponential surge in integer variables as customer numbers increase. The research presents an analysis of PVRPs that include the four mentioned attributes, compares their similarities, and delves into their nuances. From the analysis it is derived that the variant of the PVRP presented has not been considered previously, taking into account not only these attributes, but also the restrictions involved. Empirical experiments are conducted to examine the intricate interplay between the two proposed formulations, highlighting their impact on the performance of the GUROBI solver. The study provides valuable insights into problem-specific adaptations and algorithmic approaches, emphasizing the significance of the proposed formulations in addressing multifaceted PVRPs. In essence, this research positions the introduction of these two formulations as a pioneering step, offering a new paradigm for approaching the PVRP.

A qualitative exploration of user perceptions and expectations of raw milk vending machines

A qualitative exploration of user perceptions and expectations of raw milk vending machines