Analysis of the Successful Implementation of Tazi's Important Innovations in Tackling Stunting in the Work Area of the Rusip Antara Public Health Center

The research objectives were to analyze the successful implementation of Tazi's Important Innovations in Strategy, Interventions, increasing the scope of interventions for the target of 1000 HPK households, improving nutrition intake and reducing infections and the impact of Tazi's important innovations in preventing stunting in the working area of ​​the Rusip Health Center. The type and design of the research is descriptive qualitative. Data sources consist of Primary Data and Secondary Data. Data collection techniques consist of interviews, observations and documentation, data analysis techniques in the form of data reduction, data presentation, and drawing conclusions. Testing the validity of the data using source triangulation and member check. Quality control process is carried out by applying PDCA. Research ethics such as Informed consent, Anominity and confidentiality. The Regent of Central Aceh has made Regulation Number 14 of 2019 concerning stunting handling in Central Aceh Regency and is very committed to the prevention and reduction of stunting by implementing a stunting reduction strategy through 5 pillars, specific nutrition interventions and sensitive nutrition carried out in an integrated and converged manner with a target of 1000 HPK . The impact of Tazi's Important innovation is able to reduce the prevalence of stunting in Tirmiara Village. Implementation of the Strategy for the Acceleration of Stunting Prevention is based on five main pillars. Interventions to accelerate stunting prevention consist of specific and sensitive interventions. increasing the scope of intervention in the target of 1000 HPK households

Detection of Contaminants in Hydrogen Fuel for Fuel Cell Electrical Vehicles with Sensors—Available Technology, Testing Protocols and Implementation Challenges

Europe’s low-carbon energy policy favors a greater use of fuel cells and technologies based on hydrogen used as a fuel. Hydrogen delivered at the hydrogen refueling station must be compliant with requirements stated in different standards. Currently, the quality control process is performed by offline analysis of the hydrogen fuel. It is, however, beneficial to continuously monitor at least some of the contaminants onsite using chemical sensors. For hydrogen quality control with regard to contaminants, high sensitivity, integration parameters, and low cost are the most important requirements. In this study, we have reviewed the existing sensor technologies to detect contaminants in hydrogen, then discussed the implementation of sensors at a hydrogen refueling stations, described the state-of-art in protocols to perform assessment of these sensor technologies, and, finally, identified the gaps and needs in these areas. It was clear that sensors are not yet commercially available for all gaseous contaminants mentioned in ISO14687:2019. The development of standardized testing protocols is required to go hand in hand with the development of chemical sensors for this application following a similar approach to the one undertaken for air sensors.

COMPREHENSIVE RADAR DATA FOR THE CONTIGUOUS UNITED STATES: MULTI-YEAR REANALYSIS OF REMOTELY SENSED STORMS

The Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) data set blends radar data from the WSR-88D network and Near-Storm Environmental (NSE) model analyses using the Multi-Radar Multi-Sensor (MRMS) framework. The MYRORSS data set uses the WSR-88D archive starting in 1998 through 2011, processing all valid single-radar volumes to produce a seamless three-dimensional reflectivity volume over the entire contiguous United States with an approximate 5-min update frequency. The three-dimensional grid has an approximate 1-km by 1-km horizontal dimension and is on a stretched vertical grid that extends to 20 km MSL with a maximal vertical spacing of 1 km. Several reflectivity-derived, severe storm related products are also produced, which leverage the ability to merge the MRMS and NSE data. Two Doppler velocity-derived azimuthal shear layer maximum products are produced at a higher horizontal resolution of approximately 0.5-km by 0.5-km. The initial period of record for the data set is 1998-2011. The data set underwent intensive manual quality control to ensure that all available and valid data were included while excluding highly problematic radar volumes that were a negligible percentage of the overall data set, but which caused large data errors in some cases. This data set has applications towards radar-based climatologies, post-event analysis, machine learning applications, model verification, and warning improvements. Details of the manual quality control process are included and examples of some of these applications are presented.

QUALITY CONTROL OF TROBOS LIVESTOCK MAGAZINE PRODUCTION AT PT AKSARA GRAFIKA PRATAMA

Quality control is an important factor for the industry because continuously control process in production will be able to detect abnormalities results quickly, so that it can be anticipated immediately. This study aims to determine how the quality control process has been implemented, what printing problems often occur, and what activities have been carried out to control the production quality of Trobos Live Stock magazine at PT Aksara Grafika Pratama. This research method is descriptive with direct observation of the production process at PT Aksara Grafika Pratama. Descriptive method has been used by direct observation of the production process at PT Aksara Grafika Pratama. Data analysis was carried out using the p-chart control chart analysis method of the production quantity and misprint of Trobos Live Stock Magazine in January - December 2019. The results showed that the total production of Trobos Live Stock Magazine by PT Aksara Grafika Pratama in 2019 was 225,521 sheets with 7,178 misprints, and the average proportion of damaged products was 0.030. The proportion of damaged production in April and May occupied the highest position, namely 0.045 and 0.049, exceeding the UCL (Upper Control Limit) control limit. However, in the production process the following month the damage to production is within control limits, which indicates that the production department has carried out the maximum evaluation and repair. Misprints that occur in the production process of Trobos Live Stock Magazine include Scumming, Ghosting, and no images or text appearing in certain parts. Quality control activities carried out by PT Aksara Grafika Pratama to reduce the level of damage to production include implementing a layered system such as directly monitoring the production process, always carrying out maintenance on machines and assigning special experts to repair machines, as well as placing experienced workers to minimize possible error rate during the production process.Keywords — Quality Control, Diagrams Control p-chart, Statistical Assistance Tools

Business Process Re-Engineering to Digitalise Quality Control Checks for Reducing Physical Waste and Resource Use in a Food Company

Quality control is an essential element of manufacturing operations that reduces product defects and provides excellent products of the right specifications to the end consumer. Industry 4.0 solutions, such as digitalisation, along with lean manufacturing tools, may support quality control operations. This paper presents a case study of a food company wherein quality control checks were optimised using business process re-engineering to reduce physical waste and resource usage. Following close analysis of the company’s pack-house operations, it was proposed to adopt elements of Industry 4.0 by digitalising the quality control process. Implementing such a solution led to a reduction in the time needed to complete recorded checks, an increase in the time the pack-house quality control team spends with packers on the production lines, and the facilitation of defects identification. It also ensured that the product met the customers’ specifications and reduced the likelihood of rejection at the customers’ depot. The new system also enabled monitoring of each line in real-time and gathering of additional information faster and more accurately. This article proves how employing lean principles in combination with Industry 4.0 technologies can lead to savings in resources and a reduction in waste, which leads to improvements in operational efficiency.

Deep learning for the quality control of thermoforming food packages

Quality control is a key process designed to ensure that only products satisfying the defined quality requirements reach the end consumer or the next step in a production line. In the food industry, in the packaging step, there are many products that are still evaluated by human operators. To automate the process and improve efficiency and effectiveness, computer vision and artificial intelligence techniques can be applied. This automation is challenging since specific strategies designed according to the application scenario are required. Focusing on the quality control of the sealing and closure of matrix-shaped thermoforming food packages, the aim of the article is to propose a deep-learning-based solution designed to automatically perform the quality control while satisfying production cadence and ensuring 100% inline inspection of the products. Particularly, the designed computer vision system and the image-based criteria defined to determine when a product has to be accepted or rejected are presented. In addition, the vision control software is described with special emphasis on the different convolutional neural network (CNN) architectures that have been considered (ResNet18, ResNet50, Vgg19 and DenseNet161, non-pre-trained and pre-trained on ImageNet) and on the specifically designed dataset. To test the solution, different experiments are carried out in the laboratory and also in a real scenario, concluding that the proposed CNN-based approach improves the efficiency and security of the quality control process. Optimal results are obtained with the pre-trained DenseNet161, achieving false positive rates that range from 0.03 to 0.30% and false negative rates that range from 0 to 0.07%, with a rejection rate between 0.64 and 5.09% of production, and being able to detect at least 99.93% of the sealing defects that occur in any production. The modular design of our solution as well as the provided description allow it to adapt to similar scenarios and to new deep-learning models to prevent the arrival of faulty products to end consumers by removing them from the automated production line.

P.070 Introducing Stroke Endovascular Thrombectomy Into A Smaller Canadian Site, Is It Safe?

Background: Success of Endovascular Thrombectomy (EVT) requires ultra-fast access to specialized neuro imaging, neurological assessment and an angio suite with interventional radiologists. Prior access was via transport to Vancouver and outcomes were poor, with a high rate of disability or death. This appeared primarily due to long delays. Methods: Quality control process, in parallel to the introduction of a new intervention, EVT, to Vancouver Island, to determine if this intervention could be delivered with reasonable safety and good outcomes. Patients receiving EVT from May, 2016 until Sep, 2019 are included, with 90-day outcomes. Data was collected by stroke nurses. Results: The proportion of patients having a good outcome was comparable to that of the major clinical trial involving Canadian academic centres. The proportion sustaining a poor outcome was comparable to the control group in that trial population (who still received tPA treatment where possible). This was despite a median age 4.5 years greater than in that trial. Conclusions: EVT required coordination of multiple services. Victoria General Hospital performance in terms of speed to treatment was slower than in the published trials. This is a factor in determining outcome and is therefore an important quality improvement target moving forward.

THE APPLICATION OF COMPUTER VISION FOR THE ESTIMATION OF SHRINKAGE AND DEFORMATION OF THIN MANGO SLICES DURING AIR DRYING PROCESS

Computer vision has been currently a new trend in developing new tools for automatic real-time quality control process in food drying. During drying process, the size and shape of mango slides are critically changed. These changes usually determine the sensorial value of products, as well as which drying conditions would be needed to obtain the highest quality of dried products. In this study, we report on the development of a computer vision tool, requiring a normal digital camera installed, to evaluate the changes in size and shape of mango slides during the drying process. The technique is expected to replace the observation with human eyes to evaluate the changes of food products during the drying process, which might not be able to provide reliable and consistent judgements all the time. Image of drying mango slide is taken by a digital camera, then the feature extraction is implemented. The area of mango slice is determined by the area ratio via the pixel number counting and the comparison to an original sample with predefined reference size. The shrinkage deformation is evaluated by elliptical fitting to develop the automated utility. The utility is built on MATLAB platform. The variations in size and shape of the mango slices during a convective drying process with different processing conditions are examined and acquired by the built software which achieves real-time performance on the personal laptop.

Integrated Quality Control Process for Hydrological Database: A Case Study of Daecheong Dam Basin in South Korea

In our intelligent society, water resources are being managed using vast amounts of hydrological data collected through telemetric devices. Recently, advanced data quality control technologies for data refinement based on hydrological observation history, such as big data and artificial intelligence, have been studied. However, these are impractical due to insufficient verification and implementation periods. In this study, a process to accurately identify missing and false-reading data was developed to efficiently validate hydrological data by combining various conventional validation methods. Here, false-reading data were reclassified into suspected and confirmed groups by combining the results of individual validation methods. Furthermore, an integrated quality control process that links data validation and reconstruction was developed. In particular, an iterative quality control feedback process was proposed to achieve highly reliable data quality, which was applied to precipitation and water level stations in the Daecheong Dam Basin, South Korea. The case study revealed that the proposed approach can improve the quality control procedure of hydrological database and possibly be implemented in practice.