Making vending machines smarter with the use of Machine Learning and Artificial Intelligence: Set-up and Architecture

Machine Learning and Robust Optimization techniques can significantly improve logistics operations and improve stock quantity and maintenance intervals. Machine Learning will be used to forecast item demands for each of the vending machines, taking into account past demands and calendar effects. By performing such predictions which are forwarded to a Robust Optimization model, and whose outputs will be the cash transport that each vending machine should require. These transports guarantee that demand is fulfilled up to the desired confidence level, preventing downtime of vending machines due to unplanned maintenance and out-of-stock situations, while also satisfying additional constraints arising in this particular domain. As a result of such operations, we expect productivity improvements of vending machines from 20-40%

КАЛИРАЊЕ НА ЈАБОЛКОТО ПРИ ЧУВАЊЕ ВО ОБИЧНИ ПЛОДОЧУВАЛИШТА ВО ЗАВИСНОСТ ОД СОРТАТА

Apple is the leading fruit species in the Republic of Macedonia. Apart assortments (more than 65% is idared), a significant problem due to inadequate storage conditions is the abatement. With proper storage, the losses are reduced, and the quality of the fruits is maintained. The survey was conducted in the period from 2010 to 2012. In the survey were included six varieties of apples (Golden Delicious, Red chif, Mutsu, Granny Smith, Chadel, Idared), set in adapted warehouse.The measurement of the fruits weight began on October 25 and lasted until April 25. The fruits were packed in two ways. First the fruits where directly set in wooden crates.Second the fruits were packed in zip bags and then placed in a crate. The measurement of the mass and determining losses were carried out every 15 days. Each variety has its own specificity in terms of sustainability and preservation of quality and quantity. The fruits of the variety Granny Smith in the two variants, have the longest storage time and fewest losses, while the biggest loss in the first variant is among the varieties Golden Delicious and Red chif or Mutsu in the second variant. The variety Idared has least abatement, or is best kept in adapted warehouse and that is one of the reasons why this variety is mostly represented in Prespa

Comparison of local image descriptors for plant identification from leaf images

In this paper we present several descriptors used for the task of Plant recognition based on the images of leaves. The set of descriptors include texture based descriptors, fractal descriptors as well as some of the state of the art descriptors for image retrieval and object recognition in images. The descriptors are generated from the leaf images taken from single leaves on homogenous background. The descriptors are then used for training classifiers from a dataset of leaf images. The comparison of the obtained results will be presented in this paper

BIOLOGICAL CONTROL OF VENTURIA INAEQUALIS – THE CAUSE OF APPLE SCAB IN APPLE

The main goal of the experiment was to study the possibility for biological control of apple scab by use of innovative biofungicide Vacciplant (a.m. Laminarin) and to compare the results of biological control with those from standard chemical control of this disease. Experiment was conducted during the 2016 in region of Prespa and region of Tetovo, on two apple varieties, idared and golden delicious. In untreated variant in region of Tetovo, was observed very high level of infection (77.21% on the leaves and 24.35% on the fruits), which demonstrated the destructive potential of this apple disease in our country. In region of Prespa, significantly lower level of infection was observed in untreated variant (30% on the leaves and 9.5% on the fruits). Regarding the efficacy of tested fungicides, in region of Tetovo, standard fungicide Merpan (a.m. captan) used in chemical variant provided considerably lower degree of efficacy on leaves and fruits (71.38% and 60.86% respectively), compared with biofungicide used in biological variant (95.13% and 94.78% respectively). In region of Prespa, the efficacy performance of standard fungicides on the leaves and fruits (98.33% and 100% respectively) was almost equal with the performance of biofungicide (99.16% and 100%)

Is the timed-up and go test feasible in mobile devices? A systematic review

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject's performance during the test execution.info:eu-repo/semantics/publishedVersio

NEW OPPORTUNITIES FOR CHEMICAL CONTROL OF VENTURIA INAEQUALIS AND PODOSPHAERA LEUCOTRICHA IN APPLE ORCHARDS IN MACEDONIA

The possibility of simultaneous effective control of apple scab and apple powdery mildew, by using single fungicidal active substance, and to compare the obtained results with those from standard chemical control, were the main goal of these experiments. In this study two fungicides were included: Sercadis (a.m. fluxapyroxad) and Indar 5 EW (a.m. fenbuconazole). Experiment was conducted during the 2016 in region of Prespa and region of Tetovo, on two apple varieties, idared and golden delicious. In untreated variant in region of Tetovo, significantly high level of infection of apple scab (43%) and apple powdery mildew (18,2%) were recorded. In region of Prespa, the situation with the control variant was quite the opposite (15,2% apple scab and 36% apple powdery mildew infection). Regarding the efficacy of tested fungicides, in region of Tetovo, the standard fungicide Indar 5 EW (a.m. fenbuconazole) provided protection efficiency of 94,58% against apple scab and 98,18% against apple powdery mildew. In the same region, the efficacy in variants treated with fungicide Sercadis was quite similar (92,65% efficacy protect against apple scab and 98,68 against apple powdery mildew). In region of Prespa, the efficacy performance of standard fungicide against apple scab and powdery mildew (95,65% and 94,44% respectively) was almost equal with the performance of fungicide Sercadis (94,53% and 96,66%)

A Comparative Analysis of the Chronic Effects of Fine Particulate Matter

The American Cancer Society study (ACS) and the Harvard Six Cities study (SCS) are the two landmark cohort studies for estimating the chronic effects of fine particulate matter PM2.5 on mortality. To date, no comparative analysis of these studies has been carried out using a different study design, study period, data, and modeling approach. In this paper, we estimate the chronic effects of PM on mortality for the period 2000-2002 by using mortality data from Medicare and \PM levels from the National Air Pollution Monitoring Network for the same counties included in the SCS and the ACS. We use a log-linear regression model which controls for individual-level risk factors (age and gender) and area-level covariates (education, income level, poverty and employment). We found that a 10 units increase in the yearly average PM2.5 is associated with 10.9% (95% CI: 9.0, 12.8) and with 20.8% (95% CI: 12.3, 30.0) increase in all-cause mortality by using Medicare data for the ACS and SCS counties. The results are similar to those reported by the original SCS and ACS indicating that fine particulate matter is still significantly associated with mortality when more recent air pollution and mortality data are used. Our findings suggest that national government based data, like the Medicare, are useful for advancing our understanding of the chronic effects of ambient air pollution on health

Experimental study for determining the parameters required for detecting ECG and EEG related diseases during the timed-up and go test

The use of smartphones, coupled with different sensors, makes it an attractive solution for measuring different physical and physiological features, allowing for the monitoring of various parameters and even identifying some diseases. The BITalino device allows the use of different sensors, including Electroencephalography (EEG) and Electrocardiography (ECG) sensors, to study different health parameters. With these devices, the acquisition of signals is straightforward, and it is possible to connect them using a Bluetooth connection. With the acquired data, it is possible to measure parameters such as calculating the QRS complex and its variation with ECG data to control the individual’s heartbeat. Similarly, by using the EEG sensor, one could analyze the individual’s brain activity and frequency. The purpose of this paper is to present a method for recognition of the diseases related to ECG and EEG data, with sensors available in off-the-shelf mobile devices and sensors connected to a BITalino device. The data were collected during the elderly’s experiences, performing the Timed-Up and Go test, and the different diseases found in the sample in the study. The data were analyzed, and the following features were extracted from the ECG, including heart rate, linear heart rate variability, the average QRS interval, the average R-R interval, and the average R-S interval, and the EEG, including frequency and variability. Finally, the diseases are correlated with different parameters, proving that there are relations between the individuals and the different health conditions.info:eu-repo/semantics/publishedVersio

Proceedings of the “Think Tank Hackathon’’, Big Data Training School for Life Sciences Follow-up, Ljubljana 6th – 7th February 2018

On 6th and 7th February 2018, a Think Tank took place in Ljubljana, Slovenia. It was a follow-up of the “Big Data Training School for Life Sciences” held in Uppsala, Sweden, in September 2017. The focus was on identifying topics of interest and optimising the programme for a forthcoming “Advanced” Big Data Training School for Life Science, that we hope is again supported by the COST Action CHARME (Harmonising standardisation strategies to increase efficiency and competitiveness of European life-science research - CA15110). The Think Tank aimed to go into details of several topics that were - to a degree - covered by the former training school. Likewise, discussions embraced the recent experience of the attendees in light of the new knowledge obtained by the first edition of the training school and how it comes from the perspective of their current and upcoming work. The 2018 training school should strive for and further facilitate optimised applications of Big Data technologies in life sciences. The attendees of this hackathon entirely organised this workshop.Peer ReviewedPostprint (published version