Automation of monitoring of drying parameters in hybrid solar-electric dryer for agricultural products

ABSTRACT Increasing the efficiency of solar dryers with ensuring that the system remains accessible to all users can be achieved with their automation through low-cost and easy-to-use technique sensors. The objective was to develop, implement and evaluate an automatic system for monitoring drying parameters in a hybrid solar-electric dryer (HSED). Initially, an automated data acquisition system for collecting the parameters of sample mass, air temperature, and relative air humidity was developed and installed. The automatic mass data acquisition system was calibrated in the hybrid solar-electric dryer. The automated system was validated by comparing it with conventional devices for measuring the parameters under study. The data obtained were subjected to analysis of variance, Tukey test and linear regression at p ≤ 0.05. The system to turn on/off the exhaust worked efficiently, helping to reduce the errors related to the mass measurement. The GERAR Mobile App showed easy to be used since it has intuitive icons and compatibility with the most used operating systems for mobile devices. The responses in communication via Bluetooth were fast. The use of Arduino, a low-cost microcontroller, to automate the monitoring activity allowed estimating the mass of the product and collecting the drying air temperature and relative air humidity data through the DHT22. This sensor showed a good correlation of mass and air temperature readings between the automatic and conventional system, but low correlation for relative air humidity. In general, the automatic data acquisition system monitored in real time the parameters for drying agricultural products in the HSED.

Automating Finite Element Methods for Geodynamics via Firedrake

Firedrake is an automated system for solving partial differential equations using the finite element method. By applying sophisticated performance optimisations through automatic code-generation techniques, it provides a means to create accurate, efficient, flexible, easily extensible, scalable, transparent and reproducible research software, that is ideally suited to simulating a wide-range of problems in geophysical fluid dynamics. Here, we demonstrate the applicability of Firedrake for geodynamical simulation, with a focus on mantle dynamics. The accuracy and efficiency of the approach is confirmed via comparisons against a suite of analytical and benchmark cases of systematically increasing complexity, whilst parallel scalability is demonstrated up to 12288 compute cores, where the problem size and the number of processing cores are simultaneously increased. In addition, Firedrake's flexibility is highlighted via straightforward application to different physical (e.g. complex nonlinear rheologies, compressibility) and geometrical (2-D and 3-D Cartesian and spherical domains) scenarios. Finally, a representative simulation of global mantle convection is examined, which incorporates 230 Myr of plate motion history as a kinematic surface boundary condition, confirming its suitability for addressing research problems at the frontiers of global mantle dynamics research.

Gender Identification and Classification of Drosophila melanogaster Flies Using Machine Learning Techniques

Drosophila melanogaster is an important genetic model organism used extensively in medical and biological studies. About 61% of known human genes have a recognizable match with the genetic code of Drosophila flies, and 50% of fly protein sequences have mammalian analogues. Recently, several investigations have been conducted in Drosophila to study the functions of specific genes exist in the central nervous system, heart, liver, and kidney. The outcomes of the research in Drosophila are also used as a unique tool to study human-related diseases. This article presents a novel automated system to classify the gender of Drosophila flies obtained through microscopic images (ventral view). The proposed system takes an image as input and converts it into grayscale illustration to extract the texture features from the image. Then, machine learning (ML) classifiers such as support vector machines (SVM), Naive Bayes (NB), and K -nearest neighbour (KNN) are used to classify the Drosophila as male or female. The proposed model is evaluated using the real microscopic image dataset, and the results show that the accuracy of the KNN is 90%, which is higher than the accuracy of the SVM classifier.

Development and experimental study of an automated laser-foil-printing additive manufacturing system

Purpose This paper aims to present the development and experimental study of a fully automated system using a novel laser additive manufacturing technology called laser foil printing (LFP), to fabricate metal parts layer by layer. The mechanical properties of parts fabricated with this novel system are compared with those of comparable methodologies to emphasize the suitability of this process. Design/methodology/approach Test specimens and parts with different geometries were fabricated from 304L stainless steel foil using an automated LFP system. The dimensions of the fabricated parts were measured, and the mechanical properties of the test specimens were characterized in terms of mechanical strength and elongation. Findings The properties of parts fabricated with the automated LFP system were compared with those of parts fabricated with the powder bed fusion additive manufacturing methods. The mechanical strength is higher than those of parts fabricated by the laser powder bed fusion and directed energy deposition technologies. Originality/value To the best knowledge of authors, this is the first time a fully automated LFP system has been developed and the properties of its fabricated parts were compared with other additive manufacturing methods for evaluation.

Design and Evaluation of Remote Driving Architecture on 4G and 5G Mobile Networks

Despite the progress in the development of automated vehicles in the last decade, reaching the level of reliability required at large-scale deployment at an economical price and combined with safety requirements is still a long road ahead. In certain use cases, such as automated shuttles and taxis, where there is no longer even a steering wheel and pedals required, remote driving could be implemented to bridge this gap; a remote operator can take control of the vehicle in situations where it is too difficult for an automated system to determine the next actions. In logistics, it could even be implemented to solve already more pressing issues such as shortage of truck drivers, by providing more flexible working conditions and less standstill time of the truck. An important aspect of remote driving is the connection between the remote station and the vehicle. With the current roll-out of 5G mobile technology in many countries throughout the world, the implementation of remote driving comes closer to large-scale deployment. 5G could be a potential game-changer in the deployment of this technology. In this work, we examine the remote driving application and network-level performance of remote driving on a recently deployed sub-6-GHz commercial 5G stand-alone (SA) mobile network. It evaluates the influence of the 5G architecture, such as mobile edge computing (MEC) integration, local breakout, and latency on the application performance of remote driving. We describe the design, development (based on Hardware-in-the-Loop simulations), and performance evaluation of a remote driving solution, tested on both 5G and 4G mobile SA networks using two different vehicles and two different remote stations. Two test cases have been defined to evaluate the application and network performance and are evaluated based on position accuracy, relative reaction times, and distance perception. Results show the performance of the network to be sufficient for remote driving applications at relatively low speeds (<40 km/h). Network latencies compared with 4G have dropped to half. A strong correlation between latency and remote driving performance is not clearly seen and requires further evaluation taking into account the influence of the user interface.

An Artificial - Intelligent - Based System to Automate the Design of Complex Mechanical Products

Over the last decades, there has been growing pressure on industrial companies to offer to their costumers products with high quality, in the minimum deadlines and with reasonable prices. Since the design phase plays a key role to achieve these difficult goals, many traditional, DFX (Design For X) and integrated approaches have been proposed. However, many limits are still present. Thus, the main objectives of this work were first to identify these limits and then to overcome them by proposing and developing an automated framework for integrated product design. In this work, we automated the integrated DFMMA (Design For Materials, Manufacturing and Assembly) approach by developing an architecture composed of four levels, namely: the Common Information Modeling Level, the Selection Systems Level, the Inference and Computation Level and finally the Application Level. The proposed automated system is based on ontologies, on the CBR (Cases Based Reasoning) and the RBR (Rules Based Reasoning). The first main result obtained throughout the contributions consists on the integration of Manufacturing process selection, Assembly solution selection and materials selection in one integrated design approach. The second main result obtained consists on the exploitation of all the previous design studies developed by the design team and the ability to reuse the designers experience throughout the case based reasoning used in the proposed architecture. Another important result consists on the formalization and the automation of the execution of the design rules and the ability to infer new results and to check inconsistencies in the developed product using the data and information modeled in the ontological model and throughout the Cases Based Reasoning that we have incorporated in the developed approach. In this way, the redundancy in work and the difficulties faced in case of having a high number of design alternatives are avoided. Consequently, the product quality increases and wastes of time and money decrease. Finally, to validate the functioning and the efficacy of the proposed DFMMA system, an application on the design of a complex mechanical product is developed in the end of the work.

Detection and categorization of severe cardiac disorders based solely on pulse interval measurements

Cardiac disorders are common conditions associated with a high mortality rate. Due to their potential for causing serious symptoms, it is desirable to constantly monitor cardiac status using an accessible device such as a smartwatch. While electrocardiograms (ECGs) can make the detailed diagnosis of cardiac disorders, the examination is typically performed only once a year for each individual during health checkups, and it requires expert medical practitioners to make comprehensive judgments. Here we describe a newly developed automated system for alerting individuals about cardiac disorders solely based on pulse interval measurements. For this purpose, we examined two metrics of heart rate variability (HRV) and analyzed 1-day ECG recordings of more than 1,000 subjects in total. We found that a newly introduced metric of local variation was more efficient than conventional HRV metrics for detecting premature contraction, and furthermore, that a suitable combination of the old and new metrics resulted in much superior detectability particularly for atrial fibrillation, which requires more attention. Even with a 1-minute recording of pulse intervals, our new detection system had a diagnostic performance even better than that of the conventional analysis method applied to a 1-day recording.

Stream Stage Monitoring with Community Science-Contributed Stage Data

Volunteered Geographic Information, data contributed by community scientists, is an increasingly popular tool to collect scientific data, involve the community in scientific research, and provide information and education about a prominent issue. Johnson City, Tennnessee, USA has a long history of downtown flooding, and recent redevelopment of two land parcels has created new city parks that mitigate flooding through floodwater storage, additional channel capacity, and reduced impervious surfaces. At Founders Park, a project to collect stage data using text messages from community scientists has collected 1479 stage measurements from 597 participants from May 2017 through July 2021. Text messages were parsed to extract the stage and merged with local precipitation data to assess the stream’s response to precipitation. Of 1479 observations, 96.7% were correctly parsed. Only 3% of observations were false positives (parser extracted incorrect stage value) or false negatives (parser unable to extract correct value but usable data were reported). Less than 2% of observations were received between 11 p.m. and 7 a.m., creating an overnight data gap, and fewer than 7% of observations were made during or immediately following precipitation. Regression models for stage using antecedent precipitation explained 21.6% of the variability in stream stage. Increased participation and development of an automated system to record stage data at regular intervals will provide data to validate community observations and develop more robust rainfall–runoff models.

Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques

Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can deteriorate significantly with time. Hence, a rapid, reliable, and cost-effective computer-aided approach that can initially capture the hematoma features is highly relevant for real-time clinical diagnostics. In this study, the Gray Level Occurrence Matrix (GLCM), the Gray Level Run Length Matrix (GLRLM), and Hu moments are used to generate the texture features. The best set of discriminating features are obtained using various meta-heuristic algorithms, and these optimal features are subjected to different classifiers. The synthetic samples are generated using ADASYN to compensate for the data imbalance. The proposed CAD system attained 95.74% accuracy, 96.93% sensitivity, and 94.67% specificity using statistical and GLRLM features along with KNN classifier. Thus, the developed automated system can enhance the accuracy of hematoma detection, aid clinicians in the fast interpretation of CT images, and streamline triage workflow.

Optimization of RNA storage in a biobank, as well as methods for manual and automated isolation of RNA from whole blood and leukocyte fraction

Aim. To optimize the technique for the isolation and storage of ribonucleic acid (RNA) from whole blood and leukocyte fraction.Materials and methods. Comparison of isolation quality was carried out for RNA samples obtained from 228 leukocyte samples and 198 whole blood samples. Isolation was performed from fresh and frozen samples using ExtractRNA™ reagent and a MagNA Pure Compact automated system. Various methods of removing erythrocytes (centrifugation and treatment with hemolytic agents from two manufacturers) were tested, as well as freezing with and without preservatives for subsequent RNA isolation.Results. Twenty-one combinations of conditions were tested. The highest quality RNA was isolated by manual extraction using the ExtractRNA™ reagent from a fresh leukocyte fraction, purified by the Amplisens hemolytic agent (successful extraction — 94%, median RIN=8,4); frozen in IntactRNA™, purified by leukocyte fraction centrifugation (successful extraction — 100%, median RIN=8); frozen in ExtractRNA™, purified by leukocyte fraction centrifugation (successful extraction — 100%, median RIN=9,3).Conclusion. RNA can be isolated from frozen blood fractions, which is not inferior in quality to that isolated from fresh samples. Thus, it is not necessary to isolate RNA immediately after the receipt of biological material.