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1.
2022 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2022 ; 12330, 2022.
Article in English | Scopus | ID: covidwho-2029454

ABSTRACT

Due to the sudden outbreak of COVID-19, there is a high volatility in stock price of vaccine manufacturers in China (Between December 15, 2020 and December 13, 2021, average monthly volatility of these companies is 986). The aim of this paper is to compare the price prediction result of four algorithms: Multivariable Regression Model (MLR), Auto Regressive Integrated Moving Average Model (ARIMA), Back Propagation Neural Network Model (BP-NN), Random Forest Regression (RF), and decide which one has a better performance. Data from December 2020 to December 2021 is collected from Wind and the 8 stocks is selected in leading companies in vaccine industry. It turns out that BP-NN Model gives the best result in predicting stock price of vaccine manufacturers, measured using commonly used indicator, i.e., root-mean-square error (RMSE) and R Square (R2). So next time in the similar situation, BP-NN can be seen as a powerful tool to help us make decision. This paper would help investors build an optimal strategy in selecting stocks in terms of pharmaceutical industry. © 2022 SPIE.

2.
Industrial and Engineering Chemistry Research ; 2022.
Article in English | Scopus | ID: covidwho-2028629

ABSTRACT

Two years into the COVID-19 pandemic and more than one year after the approval of the first vaccine, bottlenecks in production and supply chain infrastructure continue to delay vaccination campaigns in the Global South. Mobile on Demand (MOD) vaccine manufacture may help quickly ramp up production capacity while bypassing infrastructure bottlenecks. Such decentralized small-scale factories can help tip the scales in the battle against COVID-19 and future pandemics. In this work, we designed two MOD vaccine manufacturing units based on a protein antigen expressed in yeast and in vitro transcription of mRNA. Each unit consists of three shipping containers and can produce on the order of 10,000 vaccine doses daily for competitive prices and in close proximity of their end users. Abandoning economies of scale may lead to a moderate increase in production costs that may be outweighed by reduced closed-vial dose wastage and an earlier protection of vulnerable populations. © 2022 American Chemical Society.

3.
Alanya Academic Review ; 6(2):2261-2274, 2022.
Article in Turkish | CAB Abstracts | ID: covidwho-2026413

ABSTRACT

The aim of this study is to investigate the effects of COVID-19 pandemic on the financial performance of companies operating in manufacturing subsectors and listed on the Stock Exchange Istanbul. In this direction, the financial efficiency and productivity change values of manufacturing companies before and during the Covid-19 pandemic were examined using the Malmquist Total Factor Productivity Index (MTFP) method. The findings obtained in the study indicate that the COVID-19 pandemic has negative effects on the financial performance of manufacturing companies. At the same time, the findings show that the COVID-19 pandemic has most affected the main metal industry, food, drink, and tobacco, metal and metals products and machinery industry subsectors.

4.
IISE Annual Conference and Expo 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2012915

ABSTRACT

The semiconductor industry has faced supply chain manufacturing shortages that ultimately led to a worldwide chip shortage during the COVID-19 pandemic. These chip manufacturers use sophisticated and advanced manufacturing machinery in their fabs to manufacture chips. As experienced during the pandemic, manufacturing unavailability is often due to the lack of critical manufacturing-related spare parts. This thesis evaluates the effectiveness of machine learning algorithms to identify significant factors contributing to manufacturing part outages (i.e., zero-bin) to keep manufacturing equipment running at total capacity within the organization. We propose clustering methods to segment the data and use logistic regression, logistic lasso regression, and kNN approaches to identify important factors for those parts that could go to zero-bin. Extant research applies classic inventory management strategies based on expenditure, criticality, or usage to manage their parts' inventory throughout the year. Instead, the proposed methods explore whether predefined, static inventory parameters can predict whether a spare part reaches zero bin. To demonstrate the viability of this approach, we present a case study using one year's worth of data from a leading chip manufacturing company. Based on the modeling approaches, a lasso-based logistic regression proved the best predictive model amongst the five clusters with lead-time, current quantity available, days on inventory (usage remained relevant), and the part's reorder point being the most significant parameters. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

5.
BioPharm International ; 35(5), 2022.
Article in English | Scopus | ID: covidwho-2012705
6.
BioPharm International ; 35(2):26-29, 2022.
Article in English | Scopus | ID: covidwho-2012042
7.
Pharmazeutische Zeitung ; 167(17):12, 2022.
Article in German | EMBASE | ID: covidwho-2006905
8.
Journal of Computational Design and Engineering ; 9(4):1369-1387, 2022.
Article in English | Web of Science | ID: covidwho-1985080

ABSTRACT

Modular building is an innovative and sustainable construction method and a notable architectural, engineering, and construction trend. Owing to the new constructability and sustainability, significant research efforts have been focused on the engineering aspects of modular building. Since the global outbreak of the COVID-19 pandemic, space utilization has changed radically, and a rethinking of modular building design has become essential. However, current literature lacks a comprehensive understanding of occupants' newly developed requirements and the relevant changes associated with engineering developments. Therefore, this study aims to review the current status of residential modular building design and construction, define its problems, and identify the key factors necessary for modular design and construction during the post-COVID-19 period from the perspective of architectural design. A total of 220 articles were selected from the Scopus database, out of which 94 papers were selected for a systematic review. The findings indicate that the present academic research primarily focuses on the technical aspects of optimizing architecture and on modularized plans to facilitate cost-effective factory production. Modular residential design has rarely considered people and cultural factors. Therefore, the authors derived 15 problems by adapting four architectural programming frameworks;function, form, economy, and time. The identified problems are mapped for future development with 11 problem-solving proposals. The proposed method contributes to developing new insights into COVID-19's impacts on dwelling environments and can help introduce modular residential design responses that enhance the quality of life by creating better residentials in the post-pandemic.

9.
Pharmaceutical Technology ; 46(4):36–37 and 53, 2022.
Article in English | EMBASE | ID: covidwho-1980483
10.
Pharmaceutical Technology ; 46(2):14-15, 2022.
Article in English | EMBASE | ID: covidwho-1976316
11.
Pharmaceutical Technology ; 2022:s18-s21, 2022.
Article in English | EMBASE | ID: covidwho-1976215
12.
JMIR Biomedical Engineering ; 7(2), 2022.
Article in English | ProQuest Central | ID: covidwho-1974476

ABSTRACT

Background: Precision public health (PPH) can maximize impact by targeting surveillance and interventions by temporal, spatial, and epidemiological characteristics. Although rapid diagnostic tests (RDTs) have enabled ubiquitous point-of-care testing in low-resource settings, their impact has been less than anticipated, owing in part to lack of features to streamline data capture and analysis. Objective: We aimed to transform the RDT into a tool for PPH by defining information and data axioms and an information utilization index (IUI);identifying design features to maximize the IUI;and producing open guidelines (OGs) for modular RDT features that enable links with digital health tools to create an RDT-OG system. Methods: We reviewed published papers and conducted a survey with experts or users of RDTs in the sectors of technology, manufacturing, and deployment to define features and axioms for information utilization. We developed an IUI, ranging from 0% to 100%, and calculated this index for 33 World Health Organization–prequalified RDTs. RDT-OG specifications were developed to maximize the IUI;the feasibility and specifications were assessed through developing malaria and COVID-19 RDTs based on OGs for use in Kenya and Indonesia. Results: The survey respondents (n=33) included 16 researchers, 7 technologists, 3 manufacturers, 2 doctors or nurses, and 5 other users. They were most concerned about the proper use of RDTs (30/33, 91%), their interpretation (28/33, 85%), and reliability (26/33, 79%), and were confident that smartphone-based RDT readers could address some reliability concerns (28/33, 85%), and that readers were more important for complex or multiplex RDTs (33/33, 100%). The IUI of prequalified RDTs ranged from 13% to 75% (median 33%). In contrast, the IUI for an RDT-OG prototype was 91%. The RDT open guideline system that was developed was shown to be feasible by (1) creating a reference RDT-OG prototype;(2) implementing its features and capabilities on a smartphone RDT reader, cloud information system, and Fast Healthcare Interoperability Resources;and (3) analyzing the potential public health impact of RDT-OG integration with laboratory, surveillance, and vital statistics systems. Conclusions: Policy makers and manufacturers can define, adopt, and synergize with RDT-OGs and digital health initiatives. The RDT-OG approach could enable real-time diagnostic and epidemiological monitoring with adaptive interventions to facilitate control or elimination of current and emerging diseases through PPH.

13.
7th Brazilian Technology Symposium, BTSym 2021 ; 207 SIST:294-300, 2023.
Article in English | Scopus | ID: covidwho-1971368

ABSTRACT

Traditionally the furniture market in Brazil is owned by family businesses and of national origin, with few exceptions. Based on this premise and the high demand promoted, especially in the Covid-19 pandemic, the market has adapted to the growing demand for residential furniture. Therefore, a conceptual restructuring needs to be carried out due to the great variability of products in the same factory, driven by the target audience, which goes beyond the traditional concepts of factory standardization and enables a new way of thinking according to the demand for products offered by catalog and/or bespoke. In addition, as they are family businesses, there is an inherent risk of closing activities due to the lack of family motivation of the following generations linked to the lack of knowledge to update manufacturing processes. This article concludes that the variability of products offered in each factory is a high option. New concepts must be adopted from handcrafted to manual transition. And, with positional and functional factory configurations, ensuring high efficiency and quality concerning the degree of difficulty, associated with the characteristics of furniture dealerships. To get success, the companies must be directed towards the sustainable production chain to the companies involved. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

14.
BioPharm International ; 35(2):3, 2022.
Article in English | Scopus | ID: covidwho-1970509
15.
Pharmaceutical Technology ; 45(11):14-15, 2021.
Article in English | EMBASE | ID: covidwho-1955749
16.
2nd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2021 ; : 784-789, 2021.
Article in English | Scopus | ID: covidwho-1948776

ABSTRACT

To achieve high prediction accuracy of human body keeps an open issue for decades of years, especially when COVID comes and online retail becomes the major consumption channels. The body measurement is the key to solve cloth matching and recommendation in clothing e-commerce. This paper proposes a practical framework of image-based body measurement, by only taking the user's front and side photos. This framework does not require pure background or precise standing position, and supports manual modification of the measurement results. The framework takes people's height, weight and gender as params to initialize a common body size set, and corrects each part of the set by analyzing the body proportion via the front and side images. The prediction accuracy was tested with the 50 digital models and 10 real people. Results showed that the circumference sizes such as chest, waist, hips, have errors less then 5%, while the length sizes such as arm, leg approach to actual length on net body models. For real people, the errors depend on the wearing clothes. In addition to high accuracy, the method has a rapid process speed, reaching 19QPS on a NVIDIA RTX5000 GPU server. © 2021 IEEE.

20.
6th International Conference on Trends in Electronics and Informatics, ICOEI 2022 ; : 541-545, 2022.
Article in English | Scopus | ID: covidwho-1901462

ABSTRACT

The core idea available is IoT, which can be integrated into a traditional communication network to solve many problems. This paper deals with the development of a Social Distance Detector for safety purpose in this COVID-19 pandemic. To keep us safe from maintaining a social distance from people. A detector system is developed to make an alarm while the social distancing is not maintained properly. The detector system is developed by using Tinker cad IoT Simulation software. It is a Circuit design Arduino Simulator created by Soft logic to simulate and assemble. The sensors and tools make the circuit working by embedding Arduino-UNO-R3, ultrasonic sensor, breadboard and Piezo Buzzer. © 2022 IEEE.

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