In this work, a feasible and low-cost approach is proposed for level measurement in multiphase systems inside tanks used for petroleum-derived oil production. The developed level sensor system consisted of light-emitting diodes (LEDs), light-dependent resistor (LDR), and a low-cost microprocessor. Two different types of oil were tested: AW460 and AW68. Linear regression (LR) was applied for 11 scenarios and showed a direct correlation between the level of oil and the sensor’s output. The measurement with AW460 oil presented a perfect linear behavior, while for AW68, a higher standard deviation was obtained justifying the occurrence of the nonlinearity in several scenarios. In order to overcome the nonlinear effect, two machine learning (ML) techniques were tested: K-nearest neighbors regression (KNNR) and multilayer perceptron (MLP) neural network regression. The highest correlation coefficient ( R2 ) and the lowest root mean squared error (RMSE) were obtained for AW68 with MLP. Therefore, MLP was used for regression (level prediction for water, oil, and emulsion) as well as classification (identify the type of oil in the reservoir) simultaneously. The suggested network exhibited a high accuracy for oil identification (99.801%) and improved linear performance in regression ( R2 = 0.9989 and RMSE = 0.065).
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Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduc- tion of spectral bands. Therefore, we present a methodology for per-patch classification combined with hyper-spectral band selection. In controlled experiments performed on a set of individual leaves, we measure the performance of five classifiers and three dimensionality-reduction methods with three patch sizes. With the best-performing classifier an error rate of 1.5% is achieved for distinguishing two important potato-plant diseases.
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Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.
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The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.
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This paper describes prototypes for transition pathways towards inclusive, sustainable development for seven regions in five European Countries. The approach for developing transition pathways was based on three theoretical building blocks. First, the ABCD-Roadmap that outlines the various steps to be developed in the design process of the transition pathway, secondly, the Socio-Ecological-System framework was used to describe the current situation and analyze the interactions within the system and lastly, the X-curve model provided guidance in categorizing activities and policies that should be adapted, developed new or stopped. The international team showed how transition pathways for sustainable development can be developed in different contexts and scale levels, all over Europe. The resulting advice can be helpful to professionals active in regional development, on municipal, provincial, national, or European level.
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Accurate assessment of rolling resistance is important for wheelchair propulsion analyses. However, the commonly used drag and deceleration tests are reported to underestimate rolling resistance up to 6% due to the (neglected) influence of trunk motion. The first aim of this study was to investigate the accuracy of using trunk and wheelchair kinematics to predict the intra-cyclical load distribution, more particularly front wheel loading, during hand-rim wheelchair propulsion. Secondly, the study compared the accuracy of rolling resistance determined from the predicted load distribution with the accuracy of drag test-based rolling resistance. Twenty-five able-bodied participants performed hand-rim wheelchair propulsion on a large motor-driven treadmill. During the treadmill sessions, front wheel load was assessed with load pins to determine the load distribution between the front and rear wheels. Accordingly, a machine learning model was trained to predict front wheel load from kinematic data. Based on two inertial sensors (attached to the trunk and wheelchair) and the machine learning model, front wheel load was predicted with a mean absolute error (MAE) of 3.8% (or 1.8 kg). Rolling resistance determined from the predicted load distribution (MAE: 0.9%, mean error (ME): 0.1%) was more accurate than drag test-based rolling resistance (MAE: 2.5%, ME: −1.3%).
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Treatment guidelines difer signifcantly, not only between Europe and North America but also among European countries [1–4]. Reasons for these diferences include antimicrobial resistance patterns, accessibility to and reimbursement policies for medicines, and culturally and historically determined prescribing attitudes. The European Association of Clinical Pharmacology and Therapeutics’ Education Working Group has launched several initiatives to improve and harmonize European pharmacotherapy education, but international diferences have proven to be a major barrier to these eforts [5–7]. While we have taken steps to chart these diferences [6, 8], it will probably not be possible to fully resolve them. Rather than viewing these diferences as a barrier, we should perhaps see them as an opportunity for intercultural learning by providing students and teachers a valuable lesson in the context-dependent nature of prescribing medication and the diferent interpretations of evidence-based medicine. Here, we extend our experience with interprofessional student-run clinics [9, 10], to report on our first experiences with the “International and Interprofessional Student-run Clinic.” We organized three successful video meetings with medical and pharmacy students of the Amsterdam UMC, location VU University (the Netherlands), and the University of Bologna (Italy). During these meetings, one of the students presented a real-life case of a patient on polypharmacy. Then, in a 45-min session, the students split into smaller groups (break-out rooms) to review the patient’s medication, using the prescribing optimization method and STOPP/ START criteria [11, 12]. The teachers rotated between the diferent rooms and assisted the students when necessary. Teachers and students reconvened for 60 min for debriefng, with students presenting their fndings and suggestions to revise the medication list and teachers stimulating discussion and indicating how they would alter the medication list. Participation was voluntary, and the meetings were held in the evenings to accommodate students in clinical rotations. Third-to-fnal-year medical and pharmacy students participated in the three meetings (n=17, n=20, n=12, respectively). They reported learning a lot from each other, gaining an international and interprofessional perspective. Moreover, they learned to always consider the patient’s perspective, that evidence-based medicine is context-dependent, and that guidelines should be adapted to the patient’s situation.
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De transities naar een regionaal kenniscentrum (RKC) zijn te kenmerken als lokale chaotische processen, die moeilijk te plannen zijn. Hoe kun je hier als bestuur grip op krijgen? Hoe kun je iets structureren wat in essentie veelvormig is (de toekomst voor (v)mbo–studenten), en hoe daarmee om te gaan in de dagelijkse onderwijspraktijk? Dit onderzoek geeft daar handvatten voor.
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De transities naar een regionaal kenniscentrum (RKC) zijn te kenmerken als lokale chaotische processen, die moeilijk te plannen zijn. Hoe kun je hier als bestuur grip op krijgen? Hoe kun je iets structureren wat in essentie veelvormig is (de toekomst voor (v)mbo–studenten), en hoe daarmee om te gaan in de dagelijkse onderwijspraktijk? Dit onderzoek geeft daar handvatten voor.
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Dit handboek is het tweede resultaat van een Haagse samenwerking van onderwijsinstellingen, studenten, werkgevers, overheid en overige partners. Op initiatief van Hogeschool Inholland Den Haag met het lectoraat Diversiteitsvraagstukken, de Gemeente Den Haag en ontwerpcollectief idiotes in het jaar 2022. Het beoogde doel van dit ontwerpend onderzoek is het creëren van gelijke stagekansen voor alle Haagse studenten, versnelling en verbinding door een gezamenlijke aanpak en het verstevigen en verduurzamen van het netwerk rondom gelijke onderwijs- en arbeidsmarktkansen in Den Haag.
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