Vaccination Identification System using Raspberry Pi

The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) causes Covid-19, an infectious illness. A methodology was created to track the vaccination history of people with the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that causes Covid-19, an infectious illness. The system operates on a Raspberry Pi processor that is designed to authenticate the vaccination records of individuals. The Vaccination Identification System consists of various components connected to the Raspberry Pi Zero 2W microprocessor, Pi camera, an LCD display, LED indicators, a buzzer, a DC servo motor, and a PCB converter. The proposed system grants access to vaccinated individuals and denies access to those who are not vaccinated.

Prediction and Analysis of Heart Diseases Using Heterogeneous Computing Platform

The work in this paper helps study cardiac rhythms and the electrical activity of the heart for two of the most critical cardiac arrhythmias. Various consumer devices exist, but implementation of an appropriate device at a certain position on the body at a certain pressure point containing an enormous number of blood vessels and developing filtering techniques for the most accurate signal extraction from the heart is a challenging task. In this paper, we provide evidence of prediction and analysis of Atrial Fibrillation (AF) and Ventricular Fibrillation (VF). Long-term monitoring of diseases such as AF and VF occurrences is very important, as these will lead to occurrence of ischemic stroke, cardiac arrest and complete heart failure. The AF and VF signal classification accuracy are much higher when processed on a Graphics Processor Unit (GPU) than Central Processing Unit (CPU) or traditional Holter machines. The classifier COMMA-Z filter is applied to the highly-sensitive industry certified Bio PPG sensor placed at the earlobe and computed on GPU.

Teaching interventions in occupational health and safety during COVID19 pandemic in a small exporter and processor company of hydrobiological products in Piura, Peru

IntroductionIn Peru there are many companies dedicated to fishing and exporting hydrobiological products that carry out their work informally. Most companies in this sector do not have occupational health and safety (OHS) systems. Accidents at work occur frequently but are not registered in the statistics of the Ministry of Labor. Workers also suffer from diseases such as musculoskeletal disorders, respiratory and skin infections, metabolic and cardiovascular diseases. Interventions of education and training workers and employers in OHS are becoming more important in small workplaces in developing countries as Peru, especially since the covid19 pandemic started. The purpose of the present study was to describe the implementation and its progressive improvement of teaching interventions during 3 years in a small exporter and processor company of hydrobiological products in Peru, including the covid19 pandemic, and to show its impact in the OHS system.Matherials & MethodsThe unit of this case report study was the indicators of teaching interventions as number of participants, professions, time working in OHS, education methods used and a knowledge assessment at the end of intervention. Besides, it was analyzed the impact of the intervention on the frequency of accidents and illnesses in workers, on absenteeism and the indicators of workers ‘health (such as frequency of diseases, workers under treatment, etc). The instrument used was Data collection sheet.ResultsDuring 3 years, the teaching intervention implemented included ‘In Person' and online sessions and tools. Some of the methods included Cases discussion, Role games, Performance-feedback, Video analysis and interactive games. The frequency of accidents was reduced in 20%. Absenteeism was reduced in 33%. Workers with diseases could follow medical exams and start their treatment.ConclusionTeaching interventions had goods results in OHS system reducing accidents and absenteeism at this small company and improving medical surveillance in workers.

A Novel Multi-Factor Three-Step Feature Selection and Deep Learning Framework for Regional GDP Prediction: Evidence from China

Gross domestic product (GDP) is an important index reflecting the economic development of a region. Accurate GDP prediction of developing regions can provide technical support for sustainable urban development and economic policy formulation. In this paper, a novel multi-factor three-step feature selection and deep learning framework are proposed for regional GDP prediction. The core modeling process is mainly composed of the following three steps: In Step I, the feature crossing algorithm is used to deeply excavate hidden feature information of original datasets and fully extract key information. In Step II, BorutaRF and Q-learning algorithms analyze the deep correlation between extracted features and targets from two different perspectives and determine the features with the highest quality. In Step III, selected features are used as the input of TCN (Temporal convolutional network) to build a GDP prediction model and obtain final prediction results. Based on the experimental analysis of three datasets, the following conclusions can be drawn: (1) The proposed three-stage feature selection method effectively improves the prediction accuracy of TCN by more than 10%. (2) The proposed GDP prediction framework proposed in the paper has achieved better forecasting performance than 14 benchmark models. In addition, the MAPE values of the models are lower than 5% in all cases.

DESIGN OF AN AUTOMATED MONITORING AND CONTROL SYSTEM FOR THE SOFT DRINK CAPPING MACHINE AND PROCESS MIXER

Hierdie artikel is gebaseer op botteleringproses optimering deur deurlopende verbetering. n Gevallestudie is by maatskappy XYZ geloods. Die Ses Sigma Definieer, Meet, Ontleed, Verbeter, en Beheer metodologie het getoon dat die bottelering en doppie-opsit prosesse defekte by die drie sigma vlak lewer. Die 5 Hoekoms, Pareto-kaart, visgraatdiagram en Verskaffers, Insette, Prosesse, Uitsette, Kliente model het getoon dat los doppies (31.6%), lae opvulvlakke (29.2%) en leě bottels (28.9%) die vernaamste redes tot hoě kostes as gevolg van swak gehalte is. Die moniteringstelsel is ontwerp op die toegepaste wringkrag te meet, die doppie werkstuk status te monitor en die drankie temperatuur soos dit die hitteruiler verlaat te meet. Die verkoelingstelsel van die menger is ontwerp met n geslote lus beheerstelsel. As die drankie se temperatuur nie binne een of twee grade Celsius is nie, word dit gestuur vir sekondere verkoeling, anders beweeg dit aan. Die glikol inlaatklep word aangedryf sodat die vloei van die verkoelingsmiddel aangepas word om te verseker die primere verkoeling is doeltreffend. Die resultate toon dat dit moontlik is om die proses binne die ses sigma vlak te bedryf.Alternate :This paper is based on bottling process optimisation through continuous improvement. A case study was done at XYZ company. The Six Sigma Define, Measure, Analyze, Improve, and Control (DMAIC) methodology revealed that the bottling and capping processes were producing defects at 3 Sigma level. The 5 Whys, Pareto chart, fish bone diagram, and Suppliers, Inputs, Process, Outputs, Customers (SIPOC) model showed that loose-capped bottles (31.6%), under-fills (29.2%), and empty bottles (28.9%) caused the highest cost through poor quality. The monitoring system was designed to monitor the applied torque value, the capping head status, and the beverage temperature upon leaving the heat exchanger. The cooling system on the mix processor was designed using the closed loop control strategy. If the beverage temperature is not within 1 or 2 degrees Celsius, it is directed to secondary cooling;otherwise, it proceeds. The glycol inlet valve is actuated such that the flow of the coolant is adjusted to ensure that the primary cooling is efficient. The results show that it is possible to operate production within the Six Sigma level.

Transformation of the Business Process Manager Profession in Poland: The Impact of Digital Technologies

The increasing role of emerging technologies, such as big data, the Internet of Things, artificial intelligence (AI), cognitive technologies, cloud computing, and mobile technologies, is essential to the business process manager profession’s sustainable development. Nevertheless, these technologies could involve new challenges in labor markets. The era of intelligent business process management (BPM) has begun, but how does it look in real labor markets? This paper examines the hypothesis that the transformation of the business process manager profession has been caused by certain determinants that involve the need for an improvement in BPM skills. The main contribution is a model of the dimensions of the impact of digital technologies on business process management supplemented with skills that influence the business process manager profession. The paper fills the gap in research on perspectives of the impact of digital technologies on business process management, considering both a literature analysis and labor market research. The purpose of the literature review was to identify the core dimensions that drive the use of emerging technologies in business process management. The labor market study was conducted in order to analyze the current demand for core skills of business process managers in the Polish labor market with a particular emphasis on the intelligent BPM concept. Additionally, to study the determinants that slow down the iBPM concept’s development, the digital intensity level of the enterprises and public administration units in Poland was studied. Finally, a fuzzy cognitive map presenting the core determinants of the business process manager profession’s transformation is described.