Demand Forecasting in SCM for Automobile Industry based on Time-series Modeling

In supply chain management (SCM), product classification and demand forecasting are crucial pillars to ensure companies to have production in the right category and quantity for long-term profitability. Due to COVID-19 from 2019, the automobile industry has been seriously negatively affected as the demand dropped dramatically. Therefore, it is necessary to make reasonable product classification and accurate demand forecasting to facilitate automobile companies in SCM to reduce unpopular product manufacture and unnecessary storage costs. In this paper, the Canada automobile market has been chosen with the period from 1946 to 2022. To classify a number of different types of motor vehicles into several categories with general characteristics, K-means Clustering method is applied. With the seasonal patterns and random generated features for auto sales, the time series models ARIMA and SARIMA are adopted for demand forecasting. According to the analysis, the automobiles fitting in the category with high demand and low price are valuable for further production. In addition, SARIMA Model is more accurate and fits better than ARIMA Model for both the training and test datasets for long-term prediction. The classification and forecasting results shed light on guiding manufacturers to adjust production schemes and ensuring auto dealers to predict more accurate sales in order to optimize the strategic planning. © 2023 SPIE.

Intelligent Methods and Models for Assessing Level of Student Adaptation to Online Learning

The problem of introducing online learning is becoming more and more popular in our society. Due to COVID-19 and the war in Ukraine, there is an urgent need for the transition of educational institutions to online learning, so this paper will help people not make mistakes in the process and afterward. The paper's primary purpose is to investigate the effectiveness of machine learning tools that can solve the problem of assessing student adaptation to online learning. These tools include intelligent methods and models, such as classification techniques and neural networks. This work uses data from an online survey of students at different levels: school, college, and university. The survey consists of questions such as gender, age, level of education, whether the student is in the city, class duration, quality of Internet connection, government/non-government educational institution, availability of virtual learning environment, whether the student is familiar with IT, financial conditions, type of Internet connection, a device used for studying, etc. To obtain the results on the effectiveness of online education were used the following machine learning algorithms and models: Random Forest (RF), Extra Trees (ET), Extreme, Light, and Simple Gradient Boosting (GB), Decision Trees (DT), K-neighbors (K-mean), Logistic Regression (LR), Support Vector Machine (SVM), Naїve Bayes (NB) classifier and others. An intelligent neural network model (NNM) was built to address the main issue. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

The Relationship Between COVID-19 Cases and ArcGIS Technology to Present in Indonesia

Giving the COVID-19 vaccine has many benefits, including increasing immunity from exposure to COVID-19 and preventing new mutations from COVID-19. In addition, the COVID-19 vaccine that has been injected into the community has gone through a series of strict tests, so that it is guaranteed to be safe, quality and efficacious. The research aims to cluster the spread of the corona virus in DKI Jakarta province which is displayed on a visual map using ArcGIS Technology. Based on the data on the spread of the corona virus which has been grouped using K-means clustering, it is hoped that it can help make the right decisions in vaccination and the priority of COVID-19 assistance that is determined and directed based on information cluster, so this research is expected to help the government in tackling the COVID-19 pandemic in Indonesia, especially DKI Jakarta. In addition, this research also aims to see the correlation between the COVID-19 vaccine and the number of positive cases of Covid-19. © 2022 IEEE.

Comparison of machine learning algorithms with regression analysis to predict the COVID-19 outbreak in Thailand

Coronavirus disease (COVID-19) is a public health problem in Thailand. Currently, there are more than 5 million infected people and the rate has been increasing at some point. It is therefore important to forecast the number of new cases over a short period of time to assist in strategic planning for the response to COVID-19. The purpose of this research paper was to compare the efficiency and prediction of the number of COVID-19 cases in Thailand using machine learning of 8 models using a regression analysis method. Using the 475-day dataset of COVID-19 cases in Thailand, the results showed that the predictive accuracy model (R2 score) from the testing dataset was the random forest (RF) model, which was 99.06%, followed by K-nearest neighbor (KNN), XGBoost. And the decision tree (DT) had the precision of 98.97, 98.67, and 98.64, respectively. And the results of the comparison of the number of infected people obtained from the prediction The models that predicted the number of real infections were the decision tree, random forest, and XGBoost, which were effective at predicting the number of infections correctly in the 2-4 day period. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

External and service factors influencing outcomes in a model of stratified mental health services for Young People

Background: Stratified care aims at matching the intensity and setting of mental health interventions to the needs of help-seeking Young People. In Australia, a 5-tiered system of mental health services is in operation. To aid patient triage to the most appropriate tier, a Decision Support Tool (DST) has been developed and is being rolled out nationally Methods: We analysed outcome data pre-and post-enrolment of about 1500 Young People (aged 16-25) referred to a Youth Mental Health Service delivering medium- and high intensity psychological treatment programs (tiers 3 and 4). We compared outcomes in both tiers during three 12-month periods: (a) in the inaugural phase of tier 4, prior to service saturation and stringent triaging, and prior to the COVID-19 pandemic (2019);(b) during the COVID-19 pandemic when all services were delivered remotely over phone- and video facilities, and when DST triaging was introduced (2020);(c) following return of face-to-face consultations, in a situation of service saturation and stringent DST triaging (2021) Findings: About 22% of Young People in the tier 3 program experienced reliable improvement according to their Kessler-10 (K-10) scale ratings, regardless of changing circumstances. In contrast, 40% of people in the tier 4 program reliably improved during the inaugural phase When circumstances and service delivery changed (COVID-19 restrictions service saturation, DST triaging), the rate of reliable improvement halved to about 20% Conclusion(s): Access to higher intensity psychological programs improves treatment outcomes for help-seeking Young People. However high-intensity services are more sensitive to external and service factors than less intense treatment models.

COVID-19-Related Encephalitis in a Child with PIK3CD Defect

Introduction: COVID-19 related encephalitis has been reported in pediatric patients;however, there are no reports in patients with inborn errors of immunity (IEI). Activated PI3K Delta Syndrome (APDS) is a disease of immune dysregulation with immunodeficiency, autoimmunity, and abnormal lymphoproliferation resulting from autosomal dominant gain-offunction variants in PIK3CD or PIK3R1 genes. We investigate a family with APDS, one mother and three children, one of whom developed COVID-19 related encephalitis. Method(s): Patients were consented to an IRB-approved protocol at our institution. Medical records and detailed immunophenotyping were reviewed. Family members were sequenced for IEI with a targeted gene panel. Result(s): The index case is a 10-year-old female with a known pathogenic variant in PIK3CD (c.3061 G > A, p.Glu1021Lys), who contracted SARS-COV-2 despite one COVID-19 vaccination in the series. Her disease course included COVID-related encephalitis with cerebellitis and compression of the pons, resulting in lasting truncal ataxia and cerebellar mutism. At that time, the patient was not on immunoglobulin replacement therapy (IgRT), but was receiving Sirolimus. Besides the index case, 3 family members (2 brothers, 1 mother) also share the same PIK3CD variant with variable clinical and immunological phenotypes. All children exhibited high transitional B-cells, consistent with developmental block to follicular B cell stage. Increased non-class switched IgM+ memory B cells and skewing towards CD21lo B cell subset, which is considered autoreactive-like, was observed in all patients. Of note, the patient had low plasmablasts, but normal immunoglobulins. Of her family members, only one was receiving both sirolimus and IgRT. Conclusion(s): We describe a rare case of COVID-19-related encephalitis in a patient with inborn error of immunity while not on IgRT. This may indicate infection susceptibility because of a lack of sufficient immunity to SARS-CoV-2, unlike the rest of her family with the same PIK3CD variant.Copyright © 2023 Elsevier Inc.

Collective minds: Emotional support and counselling program using information and communications technologies

Aims: The counselling and support program of the Collective Minds Ecosystem [Mentes Colectivas] is a university-based program that aims to provide free and available counselling services in mental and sexual and reproductive health to people over 14 years in Colombia. Method(s): The program uses diverse information and communications technologies such as: traditional phone, SMS, and Internet mediated chat and video calls. Results and Conclusion(s): Since September 2020 to October 2022, 4873 users have been counselled, most of them are female (78.2%, n = 3809/4873), 46% of the users are between 18 to 29 years old. The program has served most of the Colombian territory, reaching 28/32 departments;as expected, the 4 remained are in the Amazon region, which is the area with the lowest internet connectivity. Most of the counselled (84%) had some type of psychological distress (measured with the Kessler-6 scale): 27% were classified as having mild psychological discomfort, 37% moderate and 20% severe. The most frequent topics in mental health include anxiety, depression, and relationship problems. In relation to sexual and reproductive health counselling, they were sexual education, anticonception, and pregnancy. By making use of diverse technologies, the Collective Minds program has managed to reach different parts of Colombia, providing free counselling and support to individuals in need. It has also assisted to mitigate the post-COVID-19 negative effects on mental and sexual and reproductive health by breaking down economic, geographic, and specialized human capital barriers.

Online Threats vs. Mitigation Efforts: Keeping Children Safe in the Era of Online Learning

As the COVID-19 pandemic resulted in school closures since early 2020, children have spent more time online through virtual classrooms using educational technology (EdTech) and videoconferencing applications. This increased presence of children online exposes them to more risk of cyber threats. Here, we present a review of the current research and policies to protect children while online. We seek to answer four key questions: what are the online threats against children when learning online, what is known about children's cybersecurity awareness, what government policies and recommendations are implemented and proposed to protect children online, and what are the proposed and existing efforts to teach cybersecurity to childrenƒ Our study emphasizes the online risks to children and the importance of protective government policies and educational initiatives that give kids the knowledge and empowerment to protect themselves online. © 2023 IEEE.

Learning through radio and television during COVID-19: perspectives of K-12 stakeholders

The COVID-19 pandemic has disrupted traditional education, leading to the adoption of alternative methods, such as learning through radio and television for K-12 students. Television and radio became popularly adopted platforms to disseminate educational resources during the pandemic in developing countries, such as Nigeria. This study gathers the perspective of K-12 teachers and students during the crisis to find out the effectiveness of the utilized platforms, examine the challenges encountered, and suggest the way forward in case of future occurrence. The concerns-Based Adoption Model (CBAM) guided the study. A qualitative methodology of interpretivism was employed using 20 participants that comprise students and teachers across the five south-western states in Nigeria. Findings show that teachers adapted their lessons to be delivered through broadcasts, while school administrators have worked closely with broadcasters to develop and implement educational content. Students have had mixed experiences, with some finding radio and television engaging, while others face challenges with engagement and adaptability. In essence, the result shows that most of the respondents though acknowledged the effectiveness of the radio and television approach to learning but opined that the lessons are not detailed enough. Furthermore, educational television broadcast is preferable to radio lessons as the visual effect contributes significantly to learning. The study concludes that broadcasters have played a critical role in delivering educational content, partnering with schools, and developing programs that align with the curriculum during the pandemic. The study discussed its implication, followed by limitations, and gave direction for future studies.

Identifying Clinical Subtypes of Long COVID: An Unsupervised Machine Learning Approach

Objectives: The symptoms of patients with post-acute COVID-19 syndrome are heterogenous, impact multiple systems, and are often non-specific. To better understand the symptomatic profile of this population, this study used real-world data and unsupervised machine learning techniques to identify distinct groupings of long COVID patients. Method(s): Children/adolescents (age 0-17) and adults (age 18-64 and >=65) with >=2 primary diagnoses for U09.9 "Post COVID-19 condition" from 10/01/2021 (ICD-10 code introduction) until 03/31/2022 were selected from Optum's de-identified Clinformatics Data Mart Database, with the first diagnosis deemed index. Included patients had >=1 diagnosis for COVID-19 at least 4 weeks before index and continuous enrollment during the 12 months prior to index. Diagnoses recorded +/-2 weeks from index that were not present prior to the initial COVID-19 diagnosis were captured and used as patient features for k-means clustering. Final cluster assignments were selected based on silhouette coefficient and clinical relevancy of groupings. Result(s): 3,587 patients met eligibility criteria, yielding three clusters. Concurrent symptom domains surrounding index included breathing, fatigue, pain, cognitive, and cardiovascular diagnoses. The first cluster (N=2,578, 71.8%) was characterized by patients with only a single symptom domain (33% breathing, 33% cardiovascular, 20% fatigue, 11% cognitive). The second cluster (N=651, 18.1%) all presented with breathing symptoms accompanied by one additional domain (cardiovascular 40%, fatigue 28%, pain 18%). The final cluster (N=358, 9.9%) experienced breathing symptoms accompanied by two additional domains (fatigue and cardiovascular 34%, cardiovascular and cognitive 34%). Cluster 3 was slightly older than clusters 1 or 2 (mean age 66 vs. 58 years, respectively). Conclusion(s): Unsupervised machine learning identified distinct groups of long COVID patients, which may help inform multidisciplinary care needs. Our analysis suggests that many patients with long COVID may experience symptoms from only a single domain, and multi-system illness may generally include breathing complications accompanied by fatigue and/or cardiovascular complications.Copyright © 2023

Prediction of Corona Virus Disease Outcome and Future Forecast the Trend of COVID-19 in India: Observational Study

This work is motivated by the disease caused by the novel corona virus Covid-19, rapid spread in India. An encyclopaedic search from India and worldwide social networking sites was performed between 1 March 2020 and 20 Jun 2020. Nowadays social network platform plays a vital role to track spreading behaviour of many diseases earlier then government agencies. Here we introduced the approach to predict and future forecast the disease outcome spread through corona virus in society to give earlier warning to save from life threats. We compiled daily data of Covid-19 incidence from all state regions in India. Five states (Maharashtra, Delhi, Gujarat, Rajasthan and Madhya-Pradesh) with higher incidence and other states considered for time series analysis to construct a predictive model based on daily incidence training data. In this study we have applied the predictive model building approaches like k-nearest neighbour technique, Random-Forest technique and stochastic gradient boosting technique in COVID-19 dataset and the simulated outcome compared with the observed outcome to validate model and measure the performance of model by accuracy (ACC) and Kappa measures. Further forecast the future trends in number of cases of corona virus deceased patients using the Holt Winters Method. Time series analysis is effective tool for predict the outcome of corona virus disease. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Thermal Shock Viral RNA Release in COVID-19 Testing

Introduction/Objective The public health emergency of the COVID-19 pandemic emphasized the crucial role of medical laboratory professionals and scientists in molecular diagnostics laboratories to ensure success in infection control strategies. The demand for laboratory testing using nucleic acid amplification tests to detect SARS-CoV-2 RNA imposed strains in laboratory supplies. Here, we explored an alternative cost-effective solution that will simplify the pre-PCR steps by using a simple heating method to release viral RNA. Methods/Case Report Samples tested using the reference automated extraction method were used:100 samples identified as positive for SARS-CoV-2 RNA and 500 samples tested negative for SARS-CoV-2 RNA were used for the study and sorted with equal distribution according to Ct values of (a) <20, (b) 20–30, and (c) >30.100 ul from swab preserved in Universal Transport Medium was treated with 30 μg of proteinase K, and another set was tested without proteinase K pre-treatment. All samples with or without proteinase K were diluted to minimize PCR inhibitors. The thermal shock protocol was set at (98°C, 5 minutes;4°C, 2 minutes) and screened for purity. Performance and method verification studies were performed. Internal extraction, positive template, and no template controls were markers used for testing quality. The experimental study was performed by qualified testing personnel and all under the same experimental conditions. Results (if a Case Study enter NA) The Ct values from the thermal shock RNA release were compared to the automated extraction method and statistically analyzed.The criteria for acceptability for validation of this new RNA extraction proceeding were set to 100% concordance compared to the commercial kit using an automated extraction. PCR efficiency was at 98% and a slope of -3.3. Within run precision of 2% and limits of detection from 200 to 20,000 copies/uL The method 100% (50/50) concordance on samples previously identified as negative by automated methods and identified 86% (86/100) with a mean difference of 3 Ct. Conclusion Our findings suggest that the thermal shock treatment of nasopharyngeal swabs in viral transport media can successfully extract viral nucleic acid for nucleic acid amplification and is a reasonable alternative for chemical extraction methods when molecular diagnostic laboratories persistently encounter supply chain issues.

"[To Live] Most Agreeably in a World Full of an Increasing Number of Disagreeable Surprises”: Revisiting How to Cook a Wolf by M. F. K. Fisher in 2020 During Quarantine

M. F. K. Fisher's food memoirs have been interpreted as works that possess literary sta-tus. Fisher's 1942 book How to Cook a Wolf, a collection of essays about food rationing during World War II, became popular again during the 2020 lockdown because it pro-vided a timely reflection on the domestic kitchen as a shelter in uncertain times. This article includes an overview of food writing as a genre and considers the relevance of Fisher's book through the concepts of healthism and hypervigilant subject, which are involved in contemporary discourses on eating and the human body. My analysis aims to demonstrate that How to Cook a Wolf invites its readers to reconcile with their appetite and their corporeality and to chase the new perils away with the delight and soothing ability to eat amid periods of crisis. © 2023, Universidad de los Andes, Colombia. All rights reserved.

K-12 school leaders: Social and emotional challenges during COVID-19

The purpose of this qualitative phenomenological study is to examine the lived experiences of K-12 school leaders who were presented with a variety of challenges during the COVID-19 pandemic. The first primary research question was: What are the lived experiences of K-12 school leaders as it pertains to the social, emotional, and mental health difficulties and challenges while leading during the COVID-19 pandemic (March 2020 to August 2021)? Saturation was reached in this study with 8 participants, who were K-12 school leaders during the COVID-19 pandemic (March 2020 to August 2021), due to no new categories or patterns being discovered (Creswell, 2007). The research methodology was phenomenological and used interviews and an online questionnaire. From the data gleaned from the lived experiences of K-12 school leaders, who participated in the study, experience, facing the challenges, overcoming stressors, putting mitigation and preventative strategies into place, and advocating for self-care and well-being became the main themes related to the research questions. The COVID-19 pandemic exposed compassion fatigue and the extreme need to promote self-care for those in the field of K-12 school leadership during the pandemic, and for immediate and consistent access to mental health, educational and fiscal resources. The pandemic has overwhelmingly necessitated and precipitated into the lived experiences of K-12 school leaders as they faced conflict, challenges, struggle, stressors, and fatigue in the areas of social, emotional health and well-being. The disruption school leaders faced during the COVID-19 crisis, has brought forth how necessary it is for the voices of school leaders, educators and needs of the students to be heard and acted on. Findings from the data from this study provide evidence for crisis measures to be put into place for K-12 school leaders as they respond to such as a pandemic, recover from crisis, and to strengthen their resilience, faith, and promotion of self-care and well-being for any future crises. The data also support an increase in research related to school leaders and having resiliency when bouncing back from crisis. The school leader's plan always needs prepared, in sight, and ready to implement, just in case. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Assessing the Reactionary Response of High School Engineering Teachers Offering a Novel Pre-College Engineering Curriculum: Lessons Learned from the COVID-19 Pandemic

The coronavirus (COVID-19) pandemic forced a rapid transition of K-16 education to remote and online learning in the final quarter of the 2019–2020 school year. The disruption was extreme for all teachers in K-12 but particularly for teachers involved in pilot programs, such as the NSF-funded Engineering for Us All (e4usa) project. This paper reports the key findings obtained through systematic data collection from a pilot cohort of high school teachers who adapted a brand-new engineering curriculum during the COVID-19 pandemic, students who experienced the adapted curriculum, and a new cohort of teachers who were tasked with teaching the updated curriculum. © 2023 by the authors.

A Novel CNN Framework for the Detection of COVID-19 Using Manta Ray Optimization and KNN Classifier in LUS Images

Reverse transcription polymerase chain reaction (RT-PCR) is the gold standard for the diagnosis of COVID-19. Studies have proven that non-invasive techniques based on medical imaging can be used as an alternative to RT-PCR. The use of medical imag-ing technologies along with RT-PCR could improve the diagnosis and management of the disease. Even though several methods exist for diagnosing COVID-19 from X-ray images and CT scans, ultrasound image has not been explored much to diagnose the disease. In this study, we built a deep learning model using ultrasound images for a fast and efficient disease diagnosis. Pre-trained Convolutional Neural Networks (CNN), trained on the ImageNet database has been utilized for feature extraction. The nature-inspired Manta Ray Foraging Optimization (MRFO) algorithm is applied for dimensionality reduction and K-Nearest-Neighbour (KNN) for classification. Model training has been performed using a publicly available POCUS dataset consisting of 2944 ultrasound images sampled from more than 200 Lung Ultrasound (LUS) videos. Experimentations conducted in this study prove the efficiency of the model in the diagnosis of COVID-19. The model achieved an accuracy of 99.4337% using MobilenetV2 as the pre-trained network. © 2023, Ismail Saritas. All rights reserved.

Liquid biopsy testing in metastatic or advanced breast cancer patients during the COVID-19 pandemic

Objective: During the COVID-19 pandemic, cancer patients had restricted access to standard of care tissue biopsy. Liquid biopsy assays using next generation sequencing technology provides a less invasive method for determining circulating tumour mutations (ctDNA) associated with targeted treatments or prognosis. As part of deploying technology to help cancer patients obtain molecular testing, a clinical program was initiated to offer liquid biopsy testing for Canadian patients with advanced or metastatic breast cancer. Method(s): Blood was drawn in two 10 mL StreckTM DNA BCTs and sent to the CAP/CLIA/DAP accredited Imagia Canexia Health laboratory for testing using the clinically validated Follow ItTM liquid biopsy assay. Plasma was isolated using a double spin protocol and plasma cell-free DNA (cfDNA) extracted using an optimized Promega Maxwell RSC method. Extracted cfDNA was amplified using the multiplex amplicon-based hotspot 30 or 38 gene panel and sequenced. An inhouse developed bioinformatics pipeline and reporting platform were used to identify pathogenic single nucleotide variants (SNVs), indels (insertions and deletions), and gene amplification. Included in the panel are genes associated with metastatic breast cancer: AKT1, BRAF, ERBB2, ESR1, KRAS, PIK3CA, TP53. Result(s): To identify biomarkers, 1214 metastatic or advanced breast cancer patient cfDNA samples were tested. There were 15 cases sent for repeat testing. We reported 48% of samples harboring pathogenic ctDNA mutations in TP53 (22%), PIK3CA (19%), ESR1 (18%), AKT1 (2%), ERBB2 (1.5%). Co-occurring variants were identified in samples with ESR1/PIK3CA as well as TP53/PIK3CA (both p-values <0.001). Interestingly, 29% of samples with mutated ESR1 harbored >= 2 ESR1 ctDNA mutations. In 56% of cases, previous molecular testing indicated the cancer subtype as hormone receptor (ER, PR) positive with/without HER2 negative status. In this specific subgroup, 49% harbored ctDNA mutations with 63% of those being PIK3CA and/or ESR1 mutations. Conclusion(s): A population of Canadian women with metastatic breast cancer were tested using a liquid biopsy gene panel during the COVID-19 pandemic for identification of biomarkers for targeted therapeutic options. Over 50% of the samples were identified as hormone positive, with greater than 60% harboring PIK3CA and ESR1 ctDNA mutations. Studies have shown that metastatic PIK3CA mutated ER-positive/HER2-negative tumors are predictive to respond to alpelisib therapy and have FDA and Health Canada approval. Additionally, ESR1 mutations are associated with acquired resistance to antiestrogen therapies, and interestingly we identified 29% of ESR1 mutated samples with multiple mutations possibly indicating resistance subclones. In future studies, longitudinal monitoring for presence of multiple targetable and resistance mutations could be utilized to predict or improve clinical management.

VUCA bibliometric and content analysis

VUCA is an acronym for volatility, uncertainty, complexity, and ambiguity, used to describe an environment that defies confident predictions. An example of this environment is the Covid-19 pandemic, which has created uncer-tainty worldwide because it is an unknown and highly contagious disease that neither society nor institutions were pre-pared to face. This article aims to describe the scientific production of VUCA to understand its main research focus. This research analyzes 105 documents from the Web of Science (WoS) database using Bibliometrics and Content Analysis. The bibliometric analysis reported several production indexes: annual, personal, national, institutional, and journal productiv-ity. The content analysis analyzed 95 article s in nineteen clusters selected by comparing two clustering methods, Latent Dirichlet Allocation and K-Means, using the coherence and silhouette indices, respectively. VUCA is an emerging topic with increased scientific production in the last four years. However, there are no major producers to date. The most frequent topics are management, leadership, and change, where several works emphasize the role of the leader in deal-ing with change. The literature has focused on understanding the skills needed to cope with a VUCA environment and how to teach them. In addition, the use of two methods based on machine learning techniques to estimate the number of clusters of scientific papers is highlighted as an alternative to splitting articles into topics when the dataset is small. © 2023 CSIC. Este es un artículo de acceso abierto distribuido bajo los términos de la licencia de uso y distribución Creative Commons Reconocimiento 4.0 Internacional (CC BY 4.0).

Analisis bibliometrico y de contenido sobre VUCA

analysis : VUCA is an acronym for volatility, uncertainty, complexity, and ambiguity, used to describe an environment that defies confident predictions. An example of this environment is the Covid-19 pandemic, which has created uncer-tainty worldwide because it is an unknown and highly contagious disease that neither society nor institutions were pre-pared to face. This article aims to describe the scientific production of VUCA to understand its main research focus. This research analyzes 105 documents from the Web of Science (WoS) database using Bibliometrics and Content Analysis. The bibliometric analysis reported several production indexes: annual, personal, national, institutional, and journal productiv-ity. The content analysis analyzed 95 article s in nineteen clusters selected by comparing two clustering methods, Latent Dirichlet Allocation and K-Means, using the coherence and silhouette indices, respectively. VUCA is an emerging topic with increased scientific production in the last four years. However, there are no major producers to date. The most frequent topics are management, leadership, and change, where several works emphasize the role of the leader in deal-ing with change. The literature has focused on understanding the skills needed to cope with a VUCA environment and how to teach them. In addition, the use of two methods based on machine learning techniques to estimate the number of clusters of scientific papers is highlighted as an alternative to splitting articles into topics when the dataset is small.