Overcoming Vaccine Development Challenges

According to recent market research, the vaccines market is expected to grow at a compound annual rate of 14.7% for the forecast period of 2020-2026 (1), the growth of which has been accelerated by the recent COVID-19 pandemic. Durability of glass vials at very low temperatures and permeability of plastic vials has complicated the packaging decisions as well." Since the beginning of the pandemic, the bio/pharma industry has been under pressure to produce stable formulations for effective vaccines in accelerated timescales, Blouet asserts. [...]the drive for a COVID-19 vaccine has occurred during a period of increased basic scientific understanding, such as in genomics and structural biology, supporting a new wave of vaccine development and production, she says. According to Phadnis, in addition to single-use technologies, automation for high throughput and robust analytical assays are necessary for rapid turnover during development and manufacturing of vaccines.

COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. Note to Practitioners—In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate. IEEE

IMPACT OF COVID-19 PANDEMIC ON THE EFFECTIVENESS OF AUTOMOTIVE PRODUCTION LINES-CASE STUDY

Since its discovery, the COVID-19 virus spread all over the world and caused millions of deaths, this paper focuses on studying the impact of the pandemic on the connected and non-connected automotive production lines. This study is developed on two production lines in an automotive manufacturing factory that assembles 700 cars per day and the study is elaborated following two main steps: firstly, studying the impact of the virus spreading on the OEE "Overall Equipment Effectiveness” of the production lines, which is a quantitative metric used for the evaluation of the line effectiveness based on availability, performance and quality, and secondly analyzing the relationship between these factors and the OEE using the Design of Experiments method © 2023, Suranaree Journal of Science and Technology.All Rights Reserved.

Modeling and Analyzing Online Food Delivery Services Using Design Thinking: An Optimization Approach

The COVID-19 pandemic has significantly strained online food delivery services (OFDS) globally. This has challenged OFDS businesses to redesign and deploy technologies to meet customer demand. The purpose of this article is to identify the optimal factors contributing to customer experience with OFDS services during a black swan event such as the COVID-19 pandemic. We followed a four-step research design to identify the optimal factors for OFDS. First, we identified the major episodes in the OFDS process. Second, these episodes were evaluated by customers using the sequential incidence technique. Third, we used an orthogonal design to analyze the episodes at different levels based on customer preferences. Finally, we used the Taguchi approach to calculate the signal-to-noise ratios and identify the optimal factors and their preferred levels. We classify the optimal factors into customer-oriented and service-provider-oriented propositions. The option to select the delivery person and delivery conditions was found to be the most optimal customer-oriented attribute. We discuss the theoretical and managerial implications of the study and suggest major avenues for digital transformations in OFDS for better customer experience. IEEE

Fish Cam: An Online Program to Deliver Behavioral Experiments to the Classroom

First pioneered in 2010, Fish Cam provides the opportunity for students to engage in novel behavioral research without the need for extensive materials or for leaving the classroom. Fish Cam utilizes a robust behavioral paradigm, shoaling behavior in fish, and enables students to collect information from simple, easy-to-understand observations, allowing for student-led experimental design, data collection, analysis, and discussions on the scientific process. In these ways Fish Cam removes the cost and time-intensive aspects of doing this sort of work in the classroom. Shoaling behavior, which is well represented in the scientific literature, refers to social aggregations of fish. Almost all species of fish form shoals, and this process is easy to study under laboratory conditions. An evolutionary adaptation, shoaling provides individuals better access to resources and decreases the risk of predation. In its initial launch, Fish Cam was highly successful as a learning tool but suffered from difficulties associated with delivering the information online in 2010. Now, with the rapid development of online communication tools associated with the COVID-19 pandemic, we have the second iteration of Fish Cam. The flexibility of new delivery platforms enables partner organizations to view experiments and adapt the experience to their specific educational goals. Here we present an overview of Fish Cam, including lesson plans, a description of shoaling behavior in fish, and the results of Fish Cam studies run in the fall of 2020.

Development of a multivariate model with desirability-based optimization for simultaneous determination of five co-administrated drugs for the management of COVID-19 infection in their dosage forms via validated RP-HPLC method

More than 2.9 million people have died as a result of the global demographic impact of the coronavirus illness of 2019 (COVID-19). Numerous antiviral and anti-inflammatory medications have FDA approval to treat COVID-19 patients. For the simultaneous determination of COVID-19 utilized medications (Remdesivir, Moxifloxacin, Dexamethasone, Apixaban, and paracetamol) in their dosage forms, a sensitive technique has been developed and validated. The aforementioned medications were separated and quantified with the help of experimental design. The Box-Behnken design was used in the experiment to optimize the chromatographic method's analytical parameters. It employed RP-HPLC with a UV detector. An INERTSIL ODS-3 C18 column (5 µm, 250 × 4.6 mm) with mobile phase composed of acetonitrile: 30 mmoL potassium dihydrogen phosphate buffer (pH = 7.5) (50:50, v/v), at room temperature was employed to separate the aforementioned drugs. Paracetamol was linear over the concentration range (1–50 µg/mL), Moxifloxacin (5–70 µg/mL), Apixaban (5–70 µg/mL), Dexamethasone (1–100 µg/mL), and Remdesivir (5–100 µg/mL). According to ICH guidelines, the new approach underwent thorough validation. Between the proposed method's results and those from the reference or reported methods, there was no significant difference. The technique is simple to use in research of the cited medications in their dosage forms for quality control aspects.

Explainable Machine Learning for Drug Shortage Prediction in a Pandemic Setting

The COVID-19 pandemic poses new challenges on pharmaceutical supply chain including the delays and shortages of resources which lead to product backorders. Backorder is a common supply chain problem for pharmaceutical companies which affects inventory management and customer demand. A product is on backorder when the received quantity from the suppliers is less than the quantity ordered. Knowing when a product will be on backorder can help companies effectively plan their inventories, propose alternative solutions, schedule deliveries, and build trust with their customers. In this paper, we investigate two problems. One is how to use machine learning classifiers to predict product backorders for a pharmaceutical distributor. The second problem we focused on is what are the particular challenges and solutions for such task under a pandemic setting. This backorder dataset is different from existing benchmark backorder datasets with very limited features. We discuss our challenges for this task including empirical data pre-processing, feature engineering and understanding the special definitions of backorder under the pandemic situation. We discuss experimental design for predicting algorithm and comparison metrics, and demonstrate through experiments that decision tree algorithm outperforms other algorithms for our particular task. We show through explainable machine learning approaches how to interpret the prediction results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Informative initiatives as a useful tool to raise awareness of food waste. An application to higher education

PurposeBecause food waste is a serious problem today, society is currently aiming for more responsible consumption to minimize it, as defined in the 12th goal of the United Nations Sustainable Development Goals. This study aims to examine whether an informative initiative can help to raise university students' awareness of food waste consequences.Design/methodology/approachThe initiative consisted of explaining the problem of food waste to students of two marketing subject modules within economics and business administration degrees and asking them to participate in an activity in which they analyzed their own behavior. To assess its impact, two questionnaires about the students' food waste behaviors were administered, before and after the initiative, adopting an experimental design.FindingsThe results show that the information and awareness activities were successful, because, after the initiative, the students were more aware about the food waste problem and its consequences and were more critical of their behavior regarding the management of leftovers at home.Research limitations/implicationsDespite some circumstances under which the study was conducted (the COVID-19 pandemic and the lockdown), the practical and social implications are relevant.Practical implicationsThis study offers some interesting practical implications for educational institutions that want to inform and train students in more responsible consumption behavior. It shows that an initiative in which students are involved, like collecting data about food waste, in their homes with a diary, and informative sessions can be useful to increase students' awareness of food waste to behave in a more sustainable way.Social implicationsThese findings may be of interest to academics for designing initiatives that try to train and educate young people in making more responsible personal and professional decisions.Originality/valueThis study analyzes the impact of an awareness-raising initiative about food waste in higher education, which is a relatively neglected topic in the literature.

The Influence of a Virtual Physics Experiment Learning Environment on Grade 9 Students' Motivation towards Physics Learning

Experiments are essential in physics to help students comprehend concepts. Although studies had been conducted to explore whether virtual physics experiments could affect students' learning motivation, they showed inconclusive results. Therefore, this research aimed to investigate the influence of a virtual physics experiment learning environment on the physics learning motivation of Grade 9 students, combining quantitative (quasi-experimental design) and qualitative (student interviews) research methods. Participants of this research were from two different classes, divided into an experimental group (n=37) and a control group (n=37). The intervention lasted for three weeks, with one 45-min physics experiment class per week. Learning motivation was measured by the Physics Learning Motivation Test, which included dimensions like interest-enjoyment, tension-pressure, perceived choice, perceived competence, and perceived value. Based on the data analysis, we found that the virtual experiment learning environment could significantly increase students' learning motivation, especially for the perceived value dimension. Moreover, students who perceived a higher level of competence in the virtual environment were more likely to appreciate its value and utilize virtual experiments again. We expect that the implications of this study and intervention design can be a reference for teachers in incorporating virtual experiments in future physics education and provide a possible solution for conducting physics lessons during the COVID-19 pandemic. More in-depth teacher interviews are recommended to investigate the issues from different pedagogical perspectives. © 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings.

Optimization of ultrasonication-assisted extraction conditions using RSM-I-Optimal experimental design to recover vitamin D2 and K1 from selected green leafy vegetable samples

This study employed the response surface methodology to optimize the extraction conditions for recovering vitamins D2 and K1 from green leafy vegetables using ultrasonication-assisted extraction. The vitamin content was determined using an Accucore C18 column and a UPLC-Q-ToF/MS method. An RSM-I-Optimal design was used for designing the experiment to find the best combination of solvent level (mL), sonication time (min), sonication frequency (kHz), and temperature (°C). The experimental data from a 25-sample set were fitted to a second-order polynomial equation using multiple regression analysis. The extraction models had R2 values of 0.895 and 0.896, respectively, and the probability values (p < 0.0001) indicated that the regression model was highly significant. The optimal extraction conditions were: solvent level of 65 mL, sonication time of 45 min, sonication frequency of 70 kHz, and temperature of 45 °C. Under these conditions, the predicted recovery (%) values for vitamins D2 and K1 were 90.7% and 90.4%, respectively. This study has the potential to use the reported extraction method for routine quantification of vitamins D2 and K1 in the laboratory using UPLC-Q-ToF/MS.

Public Stations towards Immigration, News and Social Media Exposure, and Political Stations from Across-Cultural Perspective

The data presented in this composition give the occasion to comparatively assay anti-immigrant and anti-refugee stations, news and social media consumption, and political stations(e.g., social dominance exposure, right- sect despotism) of the adult population in seven European countries( Austria, Belgium, Germany, Hungary, Italy, Spain, Sweden), the United States, and Colombia in 2021( N=,645). Through an online check, we collected quantitative data on stations towards emigrants, deportees, Muslims, Hispanics, news media consumption, trust in news media and societal institutions, frequency and valence of intergroup contact, realistic and emblematic intergroup trouble, right- sect despotism, social dominance exposure, political efficacy, personality characteristics, perceived COVID-trouble, and socio- demographic characteristics for the adult population aged 25 to 65 in seven European countries Austria, Belgium, Germany, Hungary, Italy, Spain, Sweden. Experimental Design, Accoutrements and styles Despite the growing attention to the part of news media as a contextual motorist in the station conformation to migration, numerous public and transnational data sources continue to parade a variety of failings (limited to a single country, lack of detail in media consumption dimension, vague station measures). 3 United Nations (1951) Convention and protocol relating to the status of refugees. 4 Diehl T, Huber B, Gil de Zuniga H, Liu JH (2019) Social media and beliefs about climate change: A cross-national analysis of news use, political ideology, and trust in science.

DC Motor System Identification Simulator App for Online Learning of Automatic Control System Subject

With Online Learning being essential during the COVID-19 pandemic, practical laboratories were forced to be replaced by virtual or remote alternatives. This paper proposes a Simulator App built on MATLAB environment that provides students of introductory Automatic Control System subject a simulated experimental environment to learn system and transfer function identification of DC Motor-Tachogenerator system. The features of the Simulator App are discussed, followed by design of experiments suitable to be carried out using the Simulator App. Experimental results and student feedback show that the Simulator App is a feasible virtual alternative to the physical laboratory experiments. © 2022 IEEE.

A Flipped-Based WebQuest Model as an Alternative Proposal for Students of English as a Foreign Language in the COVID-19 Era

To find alternative strategies to create meaningful learning for their students during COVID-19, teachers resorted to using digital tools. This paper aims at investigating the impact of the Flipped-Based WebQuest (FWQ) model on the improvement of English L2 writing competence among sixty-nine secondary education English as a Foreign Language (EFL) students in Egypt. The study adopted a quasi-experimental design with quantitative tools. Two groups of students were selected: a control group studying in the traditional way (face-to-face) and an experimental group learning English through FWQ. A pretest was used to assess students' L2 writing competence in both groups. After the intervention, a posttest was conducted to explore students' L2 writing skills gains in both groups. Additionally, a pre/post-anxiety questionnaire was used to check the effect of FWQ on decreasing L2 writing anxiety. The findings revealed that the FWQ model is effective in teaching English L2 writing skills and decreasing students' English L2 anxiety.

Network Pharmacological Analysis on the Herbal Combinations for Mitigating Inflammation in Respiratory Tracts and Experimental Evaluation.

The regulation of inflammatory mediators, such as TNF-α, IL-6, IL-1ß, and leukotriene B4, could play a crucial role in suppressing inflammatory diseases such as COVID-19. In this study, we investigated the potential mechanisms of drug combinations comprising Ephedrae Herba, Schisandra Fructus, Platycodonis Radix, and Ginseng Radix; validated the anti-inflammatory effects of these drugs; and determined the optimal dose of the drug combinations. By constructing a herb-compound-target network, associations were identified between the herbs and tissues (such as bronchial epithelial cells and lung) and pathways (such as the TNF, NF-κB, and calcium signaling pathways). The drug combinations exerted anti-inflammatory effects in the RAW264.7 cell line treated with lipopolysaccharide by inhibiting the production of nitric oxide and inflammatory mediators, including TNF-α, IL-6, IL-1ß, and leukotriene B4. Notably, the drug combinations inhibited PMA-induced MUC5AC mRNA expression in NCI-H292 cells. A design space analysis was carried out to determine the optimal herbal medicine combinations using the design of experiments and synergy score calculation. Consequently, a combination study of the herbal preparations confirmed their mitigating effect on inflammation in COVID-19.

Measuring the Impact of Data Augmentation Techniques in Lung Radiograph Classification Using a Fractional Factorial Design: A Covid-19 Case Study

Convolutional neural networks (CNNs) have become dominant in various computer vision tasks, obtaining state-of-the-art results in medical image analysis. Nevertheless, CNNs require large datasets to achieve high performance, which might not always be available in medical settings. Hence, different data augmentation strategies have been proposed to synthetically increase the size and diversity of a dataset. According to the state of the art, the relationship between data augmentation operations and the classification accuracy of a neural network has not been fully explored. In this work, the effect that basic augmentation techniques have in the detection of COVID-19 on chest X-Ray images is analyzed using a 2(7-1) fractional factorial experimental design. The experimental results show that zoom in and height shift operations have a significant positive effect on the accuracy, while horizontal flip operation hinders the performance. Moreover, by applying a cube plot analysis, the data augmentation operations and values that maximize the accuracy of the CNN are found. A 97% accuracy, 93% precision, and 97.7% recall scores are attained on a publicly available COVID-19 dataset using these data augmentation operations. © 2022 IEEE.

Experimental Design of Self-Tracking Camera Gimbal Robot Based on OpenCV

The emergence of COVID-19 has reduced the opportunities for offline meetings, making people's work and study more transfer to the internet platform. However, the viewing angle and distance of the camera cannot be considered both. Therefore, machine vision is used to identify and track the presenter, and the camera pan-tilt control function of automatically tracking the presenter is realized. In many tests, the target tracking function works normally and works well. The experimental design involves relatively comprehensive disciplines, with good functional scalability and high practicability. It is an innovative experiment integrating robotics teaching, machine learning practice and embedded systems. © 2022 IEEE.

Bolsa Nasdaq: Creación de un portafolio óptimo de acciones de empresas tecnológicas

Palabras-clave: Nasdaq, telecomunicaciones, redes sociales : The objective of this research work was to build an optimal portfolio of shares of technology companies listed on the Nasdaq Stock Exchange during the period Dec 2019-Nov 2020, the methodology was framed in a quantitative approach, non-experimental design, descriptive level -correlational. The Markowitz model was used as a basis to determine the optimal investment portfolio, relying on the Excel Solver add-on. [...]it represents the best investment alternative in shares. E incluso, a mediano plazo, la posibilidad de una pronta aplicación de una vacuna contra el Covid-19;y las perspectivas de un mayor estímulo fiscal por parte del gobierno del recién electo Joe Biden, crean un escenario favorable para el rendimiento de los activos con riesgo.

Design for Six Sigma: Material Design for 3D Printing of the New Product

The starting point of the research was the demand of customers for 3D printing of face shield frames. In this context, but also due to the possibilities of relatively cheap 3D printing of various products, a large amount of waste has started to be generated, which needs to be disposed of. The goal of the research was to contribute to the development of new 3D printed products while balancing their level of quality, and cost and minimizing their environmental footprint. For this purpose, based on customer requirements, the research team printed samples of PLA (polylactic acid) material and recycled PLA using Fused Filament Fabrication (FFM) technology, thus significantly helping especially during the Covid-19 pandemic. The research used a design method known as Design for Six Sigma (DFSS) and its framework of successive steps of defining, measuring, analyzing, designing, and validating DMADV (Define, Measure, Analyze, Design, and Validate). The research concerns the development of Quality Function Deployment (QFD) functions that respect the requirement of a minimum environmental footprint of 3D printing. The result is an original QFD methodology, taking into account the choice of material in terms of minimizing the impact on the environment. © 2022 IEEE.

Improving the Separation of PS and ABS Plastics Using Modified Induced Air Flotation with A Mixing Device

A dramatic increase in plastic waste has resulted in a strong need to increase plastic recycling accordingly. A selective flotation has been highlighted due to its outstanding efficiency for the separation of mixed plastics with analogous physicochemical characteristics. In this study, the effects of design and operational factors on the bubble’s hydrodynamic and mixing parameters in induced air flotation (IAF) with a mixing device were investigated through a design of experiment method (DOE) analysis for improving the plastic separation efficiency (i.e., PS and ABS). As a result of DOE analysis, the increase in the induced air tube diameter together with the rotational speed could generate a smaller bubble size. This led to the enhancement of the ratio of interfacial area to velocity gradient (a/G), which was interestingly found to be a significant factor affecting plastic recovery apart from the chemical agents. It demonstrates that operating IAF with a mixing device at a greater a/G ratio improved the plastic separation performance. These findings suggest that operating an IAF process with a mixing device at suitable a/G conditions could be a promising technique for separating plastic wastes, which have similar physicochemical characteristics as PS and ABS.

Energy Consumption Prediction for Fused Deposition Modelling 3D Printing Using Machine Learning

Additive manufacturing (AM) technologies are growing more and more in the manufacturing industry;the increase in world energy consumption encourages the quantification and optimization of energy use in additive manufacturing processes. Orientation of the part to be printed is very important for reducing energy consumption. Our work focuses on defining the most appropriate direction for minimizing energy consumption. In this paper, twelve machine learning (ML) algorithms are applied to model energy consumption in the fused deposition modelling (FDM) process using a database of the FDM 3D printing of isovolumetric mechanical components. The adequate predicted model was selected using four performance criteria: mean absolute error (MAE), root mean squared error (RMSE), R-squared (R2), and explained variance score (EVS). It was clearly seen that the Gaussian process regressor (GPR) model estimates the energy consumption in FDM process with high accuracy: R2 > 99%, EVS > 99%, MAE < 3.89, and RMSE < 5.8.