Knowledge management in two universities before and during the COVID-19 effect in Peru

Knowledge management evaluation is an important and complex problem in Peruvian universities. An efficient policy in knowledge management is a critical success factor and will also advance teaching and research. Likewise, innovation is a key resource for the well-being of national economies and international competitive advantage, especially with COVID-19 9 restrictions. This research article has demonstrated that an assessment of knowledge management (KM) capability in two private universities could be used for an efficient policy, in order to obtain the best result in knowledge management maturity. Having discussed how to construct a model for KM, the final section of this paper addresses ways of the incorporation of this technology in the society. The case study has been developed in Lima, associated to two universities with 271 teachers and 5928 students; with this model could be used for KM assessment in all university in Peru. This study uses the “Intellectus Model, Mejia model organizational knowledge capability areas and Knowledge Management Capability Assessment (KMCA) model for the assessment, before and during COVID-19. Our findings have demonstrated the university performance and challenges associated to knowledge management during COVID-19, the new policy for virtual instructions and digital transformation of the educational process have improved the cost efficiencies. However, this system also has a number of serious drawbacks and challenges in innovation, researching, connections with industry and academy; this 2021 requires a new perspective in innovation with a knowledge management scheme in Peruvian universities.     

Effects of excessive use of mobile phone technology in India on human health during COVID-19 lockdown

ObjectiveThe global health crisis in the form of COVID-19 has forced people to shift their routine activities into a remote environment with the help of technology. The outbreak of the COVID-19 has caused several organizations to be shut down and forced them to initiate work from home employing technology. Now more than ever, it's important for people and institutions to understand the impact of excessive use of mobile phone technology and electronic gadgets on human health, cognition, and behavior. It is important to understand their perspective and how individuals are coping with this challenge in the wake of the COVID-19 pandemic. The investigation is an effort to answer the research question: whether dependency on technology during lockdown has more effects on human health in comparison to normal times.MethodsThe study included participants from India (n = 122). A questionnaire was framed and the mode of conducting the survey chosen was online to maintain social distancing during the time of the Pandemic. The gathered data was statistically analysed employing RStudio and multiple regression techniques.ResultsThe statistical analysis confirms that lockdown scenarios have led to an increase in the usage of mobile phone technology which has been confirmed by around 90% of participants. Moreover, 95% of the participants perceive an increased risk of developing certain health problems due to excessive usage of mobile phones and technology. It has been evaluated that participants under the age group 15–30 years are highly affected (45.9%) during lockdown due to excessive dependence on technology. And, amongst different professions, participants involved in online teaching-learning are the most affected (42.6%).ConclusionThe findings indicate that dependency on technology during lockdown has more health effects as compared to normal times. So, it is suggested that as more waves of pandemics are being predicted, strategies should be planned to decrease the psychological and physiological effects of the overuse of technology during lockdown due to pandemics. As the lockdown situation unfolds, people and organization functioning styles should be rolled back to the limited dependency on technology.     

A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare

COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in 2019. It affects the whole world through person-to-person communication. This virus spreads by the droplets of coughs and sneezing, which are quickly falling over the surface. Therefore, anyone can get easily affected by breathing in the vicinity of the COVID-19 patient. Currently, vaccine for the disease is under clinical investigation in different pharmaceutical companies. Until now, multiple medical companies have delivered health monitoring kits. However, a wireless body area network (WBAN) is a healthcare system that consists of nano sensors used to detect the real-time health condition of the patient. The proposed approach delineates is to fill a gap between recent technology trends and healthcare structure. If COVID-19 affected patient is monitored through WBAN sensors and network, a physician or a doctor can guide the patient at the right time with the correct possible decision. This scenario helps the community to maintain social distancing and avoids an unpleasant environment for hospitalized patients Herein, a Monte Carlo algorithm guided protocol is developed to probe a secured cipher output. Security cipher helps to avoid wireless network issues like packet loss, network attacks, network interference, and routing problems. Monte Carlo based covid-19 detection technique gives 90% better results in terms of time complexity, performance, and efficiency. Results indicate that Monte Carlo based covid-19 detection technique with edge computing idea is robust in terms of time complexity, performance, and efficiency and thus, is advocated as a significant application for lessening hospital expenses.     

A cross-country comparison of innovation efficiency

The purpose of this paper is to highlight the weakness of innovative activities and guide the improvement of innovation efficiency at country-level through carefully comparing innovation efficiency across countries. Following the conceptual framework which divides innovation processes into knowledge production process (KPP) and knowledge commercialization process (KCP) and applying dual network-DEA models, this paper tries to take economic benefit of R&D outputs into account. Moreover, we construct the production frontier of the innovation processes and two component processes under different assumptions (e.g., constant returns-to-scale, variable returns-to-scale and non-increasing returns-to-scale) for 35 countries over the period 2007–2011. Based on the production frontier, we do not only estimate technical efficiency and scale efficiency for each country but also investigate and verify whether returns-to-scale of each country are decreasing or increasing. Furthermore, we add together the radial movement and the slack movement to acquire input redundancy. We decompose the input redundancy into two parts: redundancy caused by technical inefficiency (R_TI) and redundancy caused by scale inefficiency (R_SI), and carry out a detail analysis of the input redundancy. We find specific circumstances of inefficiency and redundancy vary with the different countries’ characteristics and development stages. Moreover, innovation efficiency statistically mainly depends on the KCP efficiency. In particular, the study reveals that China suffers scale inefficiency is attributed to insufficient macro-level coordination, malfunctioning funding system, and flawed evaluations and incentives. Finally, public policy implications are proposed for the inefficient countries.     

Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning

This article aims to assess health habits, safety behaviors, and anxiety factors in the community during the novel coronavirus disease (COVID-19) pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 respondents. In other words, this paper aims to provide empirical insights into the correlation and the correspondence between socio-demographic factors (gender, nationality, age, citizenship factors, income, and education), and psycho-behavioral effects on individuals in response to the emergence of this new pandemic. To focus on the interaction between these variables and their effects, we suggest different methods of analysis, comprising regression trees and support vector machine regression (SVMR) algorithms. According to the regression tree results, the age variable plays a predominant role in health habits, safety behaviors, and anxiety. The health habit index, which focuses on the extent of behavioral change toward the commitment to use the health and protection methods, is highly affected by gender and age factors. The average monthly income is also a relevant factor but has contrasting effects during the COVID-19 pandemic period. The results of the SVMR model reveal a strong positive effect of income, with R² values of 99.59%, 99.93% and 99.88% corresponding to health habits, safety behaviors, and anxiety.     

The determinants of virtual reality adoption for marine conservation

Initiatives for marine conservation are funded by revenue from marine sites. However, the emergence of COVID-19 has restricted travelling, thus leading to decreased revenue and weakening conservation efforts. Virtual reality technology to support marine conservation efforts is explored as it allows users to experience marine sites, without having to be there physically. This study examines factors influencing users' desire to adopt virtual reality technology in marine ecotourism. With the integration of Stimulus-Organism-Response framework and three behavioural theories (i.e. health belief model, attitude, and eTrust), a hypothetical model was constructed to investigate consumers’ intention to adopt virtual reality technology in marine ecotourism amidst the pandemic. The data were analysed and interpreted using structural equation modelling after 451 survey responses were obtained via an online questionnaire. Outcome expectation, perceived susceptibility, perceived severity, self-efficacy, and cues to action exhibit significant positive effects on attitude towards virtual reality technology in marine ecotourism. Consequently, attitude has a direct and indirect influence on use intention via eTrust. Post hoc analysis revealed that self-efficacy has a direct impact on eTrust and use intention. Total effect analysis further concluded that attitude has the greatest influence on use intention of virtual reality technology in marine ecotourism. This research contributes to the existing literature by utilizing health belief factors to explain use intention of technology towards marine conservation efforts. Recommendations including policy formulation and marketing initiatives were generated for marine ecotourism operators and policymakers.     

新冠肺炎疫情对影院空气质量控制提出的挑战和对策建议

在突发新冠肺炎疫情的新形势下,为进一步强化影院公共卫生安全,提升影院空气质量,筑牢观众健康底线,本文对国家及相关行业有关影院空气质量规范标准进行了较为全面的归纳和梳理,对电影业内外有关室内空气净化、灭菌杀毒等情况进行了调研,就疫情对影院空气质量控制提出的挑战,以及有关室内空气净化消毒的技术路径进行了比较深入的分析和探讨,并针对挑战从八个方面提出了对策建议,即增强影院公共卫生安全意识;处理好当前影院空气质量隐患问题;加快推进新标准落地;做好顶层设计和总体规划;完善电影行业空气质量科研、评价与认证体系;发挥政府和市场两方面作用;加强监管平台建设;明确责任与目标,加快落实。     

Index

Abstract

The central questions of this article are: How can the design of program management contribute to the success of complex software implementations? How do we deal with the complexity that large implementation projects encounter from changes over time and from strong integration needs? These questions become increasingly pertinent as software projects include the implementation of new internet-based IT architectures and business models. To answer these questions, we propose ways that have worked well in recent complex multi-project Enterprise Resource Planning (ERP) implementations (“programs”). We start by defining complexity in this context and making it measurable through three dimensions: variety, variability, and integration. We then investigate 15 cases and outline how our framework can be applied to deal with increasing ERP implementation complexity. One of our conclusions is that successful implementation programs leave more room to explore innovative technology but are more rigorous in rolling out proven technology. They differentiate their approaches according to the extent of the different complexities they encounter. In particular, they adjust the number of parallel rollout activities to the static complexity (variety) and organize the alignment of IT and business needs during the parallel activities according to the integration needs.     

基于废弃矿山改造的文化旅游产品设计

目的 针对废弃矿山的资源转化和文化旅游开发,结合设计创新及其产业化发展,研究文化旅游产品设计的原则、方法和模式。方法 通过设计理论基础研究、相关政策法规解读及国内外产品设计案例剖析,经过对比和归纳,总结出废弃矿山改造的文化旅游产品设计方式。结论 废弃矿山改造的文化旅游产品设计是以政策为引领、技术为支撑、创意为核心、资源为主体的创新过程。整个过程需明确创意方向、规范开发行为。通过技术应用和转化来实现矿山修复、土地改造、生态恢复。以创意产品主题化、文旅项目多样化、矿山IP品牌化来促进设计创意和创新。采用就地取材、跨界融合等方法,来提升资源转化率并增强产品体验感。通过政策落实、技术应用、设计创意、资源整合等协同创新方式,完成废弃矿山改造产品的创意设计和创新开发。     

Practice as policy in ICT for education: Catalysing communities of practice in education in South Africa

The e-Education policy was introduced into schools with the intention of “transforming learning and teaching” ([14], p. 1). The policy places an obligation on education to use educational technology to deliver on expectations of quality education for economic growth and social development. Utilising a case study approach and backward mapping principles to policy implementation, this study sets out to explore how teachers appropriate¹ information and communication technology (ICT) policy to influence teaching and learning in South African schools. Qualitative methods were employed to capture data through classroom observations, interviews and document analysis. Data was analysed using grounded theory methods. Findings that are unique to the South African context were fivefold. First, the national e-Education policy existed as an invisible policy within the school context. Second, there was a lack of policy support and district presence in schools. Third, districts and schools had conflicting ideas of establishing collaborative support. Fourth, teachers' beliefs, attitudes and agency promoted ICT practice as policy. And fifth, the absence of district support catalysed the emergence of communities of practice. This study asserts the notion that for policy to be implemented teachers should be instrumental partners in the formulation of policy. Teachers should be encouraged to form ICT communities of practice to support their teaching practice and foster policy implementation.     

Beyond R0: heterogeneity in secondary infections and probabilistic epidemic forecasting

The basic reproductive number, R₀, is one of the most common and most commonly misapplied numbers in public health. Often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that different epidemics can exhibit, even when they have the same R₀. Here, we reformulate and extend a classic result from random network theory to forecast the size of an epidemic using estimates of the distribution of secondary infections, leveraging both its average R₀ and the underlying heterogeneity. Importantly, epidemics with lower R₀ can be larger if they spread more homogeneously (and are therefore more robust to stochastic fluctuations). We illustrate the potential of this approach using different real epidemics with known estimates for R₀, heterogeneity and epidemic size in the absence of significant intervention. Further, we discuss the different ways in which this framework can be implemented in the data-scarce reality of emerging pathogens. Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging infectious diseases like COVID-19 the uncertainty in outbreak size ranges dramatically. Taken together, our work highlights the critical need for contact tracing during emerging infectious disease outbreaks and the need to look beyond R₀.     

Analyzing COVID-19 Impact on the Researchers Productivity through Their Perceptions

Context: Since the end of 2019, the COVID-19 pandemic had a worst impact on world’s economy, healthcare, and education. There are several aspects where the impact of COVID-19 could be visualized. Among these, one aspect is the productivity of researcher, which plays a significant role in the success of an organization. Problem: There are several factors that could be aligned with the researcher’s productivity of each domain and whose analysis through researcher’s feedback could be beneficial for decision makers in terms of their decision making and implementation of mitigation plans for the success of an organization. Method: We perform an empirical study to investigate the substantial impact of COVID-19 on the productivity of researchers by analyzing the relevant factors through their perceptions. Our study aims to find out the impact of COVID-19 on the researcher’s productivity that are working in different fields. In this study, we conduct a questionnaire-based analysis, which included feedback of 152 researchers of certain domains. These researchers are currently involved in different research activities. Subsequently, we perform a statistical analysis to analyze the collected responses and report the findings. Findings: The results indicate the substantial impact of COVID-19 pandemics on the researcher’s productivity in terms of mental disturbance, lack of regular meetings, and field visits for the collection of primary data. Conclusion: Finally, it is concluded that researcher’s daily or weekly meetings with their supervisors and colleagues are necessary to keep them more productive in task completion. These findings would help the decision makers of an organization in the settlement of their plan for the success of an organization.     

试析战略创新的复杂性

指明了战略创新研究属于复杂性科学,依据系统学原理诠释创新概念;论述了战略创新的复杂性在 于它自身内含的对立统一;论证了国家战略创新是开放复杂巨系统,并指出驾驭这种复杂性需要建设创新型 国家。     

Effectiveness of non-pharmaceutical interventions to contain COVID-19: a case study of the 2020 spring pandemic wave in New York City

As COVID-19 continues to pose significant public health threats, quantifying the effectiveness of different public health interventions is crucial to inform intervention strategies. Using detailed epidemiological and mobility data available for New York City and comprehensive modelling accounting for under-detection, we reconstruct the COVID-19 transmission dynamics therein during the 2020 spring pandemic wave and estimate the effectiveness of two major non-pharmaceutical interventions—lockdown-like measures that reduce contact rates and universal masking. Lockdown-like measures were associated with greater than 50% transmission reduction for all age groups. Universal masking was associated with an approximately 7% transmission reduction overall and up to 20% reduction for 65+ year olds during the first month of implementation. This result suggests that face covering can substantially reduce transmission when lockdown-like measures are lifted but by itself may be insufficient to control SARS-CoV-2 transmission. Overall, findings support the need to implement multiple interventions simultaneously to effectively mitigate COVID-19 spread before the majority of population can be protected through mass-vaccination.     

COVID-19 hospitalization rates rise exponentially with age,inversely proportional to thymic T-cell production

Here, we report that COVID-19 hospitalization rates follow an exponential relationship with age, doubling for every 16 years of age or equivalently increasing by 4.5% per year of life (R² = 0.98). This mirrors the well-studied exponential decline of both thymus volume and T-cell production, which halve every 16 years. COVID-19 can therefore be added to the list of other diseases with this property, including those caused by methicillin-resistant Staphylococcus aureus, MERS-CoV, West Nile virus, Streptococcus pneumoniae and certain cancers, such as chronic myeloid leukaemia and brain cancers. In addition, the incidence of severe disease and mortality due to COVID-19 are both higher in men, consistent with the degree to which thymic involution (and the decrease in T-cell production with age) is more severe in men compared to women. Since these properties are shared with some non-contagious diseases, we hypothesized that the age dependence does not come from social-mixing patterns, i.e. that the probability of hospitalization given infection rises exponentially, doubling every 16 years. A Bayesian analysis of daily hospitalizations, incorporating contact matrices, found that this relationship holds for every age group except for the under 20s. While older adults have fewer contacts than young adults, our analysis suggests that there is an approximate cancellation between the effects of fewer contacts for the elderly and higher infectiousness due to a higher probability of developing severe disease. Our model fitting suggests under 20s have 49–75% additional immune protection beyond that predicted by strong thymus function alone, consistent with increased juvenile cross-immunity from other viruses. We found no evidence for differences between age groups in susceptibility to infection or infectiousness to others (given disease state), i.e. the only important factor in the age dependence of hospitalization rates is the probability of hospitalization given infection. These findings suggest the existence of a T-cell exhaustion threshold, proportional to thymic output and that clonal expansion of peripheral T-cells does not affect disease risk. The strikingly simple inverse relationship between risk and thymic T-cell output adds to the evidence that thymic involution is an important factor in the decline of the immune system with age and may also be an important clue in understanding disease progression, not just for COVID-19 but other diseases as well.     

Clinical Characteristics and Outcomes of Type 2 Diabetes Patients Infected with COVID-19: A Retrospective Study

Diabetes and its related metabolic disorders have been reported as the leading comorbidities in patients with coronavirus disease 2019 (COVID-19). This clinical study aims to investigate the clinical features, radiographic and laboratory tests, complications, treatments, and clinical outcomes in COVID-19 patients with or without diabetes. This retrospective study included 208 hospitalized patients (≥ 45 years old) with laboratory-confirmed COVID-19 during the period between 12 January and 25 March 2020. Information from the medical record, including clinical features, radiographic and laboratory tests, complications, treatments, and clinical outcomes, were extracted for the analysis. 96 (46.2%) patients had comorbidity with type 2 diabetes. In COVID-19 patients with type 2 diabetes, the coexistence of hypertension (58.3% vs 31.2%), coronary heart disease (17.1% vs 8.0%), and chronic kidney diseases (6.2% vs 0%) was significantly higher than in COVID-19 patients without type 2 diabetes. The frequency and degree of abnormalities in computed tomography (CT) chest scans in COVID-19 patients with type 2 diabetes were markedly increased, including ground-glass opacity (85.6% vs 64.9%, P < 0.001) and bilateral patchy shadowing (76.7% vs 37.8%, P < 0.001). In addition, the levels of blood glucose (7.23 mmol·L⁻¹ (interquartile range (IQR): 5.80–9.29) vs 5.46 mmol·L⁻¹ (IQR: 5.00–6.46)), blood low-density lipoprotein cholesterol (LDL-C) (2.21 mmol·L⁻¹ (IQR: 1.67–2.76) vs 1.75 mmol·L⁻¹ (IQR: 1.27–2.01)), and systolic pressure (130 mmHg (IQR: 120–142) vs 122 mmHg (IQR: 110–137)) (1 mmHg = 133.3 Pa) in COVID-19 patients with diabetes were significantly higher than in patients without diabetes (P < 0.001). The coexistence of type 2 diabetes and other metabolic disorders is common in patients with COVID-19, which may potentiate the morbidity and aggravate COVID-19 progression. Optimal management of the metabolic hemostasis of glucose and lipids is the key to ensuring better clinical outcomes. Increased clinical vigilance is warranted for COVID-19 patients with diabetes and other metabolic diseases that are fundamental and chronic conditions.     

MXene-Based Aptameric Fluorosensor for Sensitive and Rapid Detection of COVID-19 (Small 23/2023)

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-caused COVID-19 pandemic has rapidly escalated into the largest global health emergency, which pushes to develop detection kits for the detection of COVID-19 with high sensitivity, specificity, and fast analysis. Here, aptamer-functionalized MXene nanosheet is demonstrated as a novel bionanosensor that detects COVID-19. Upon binding to the spike receptor binding domain of SARS-CoV-2, the aptamer probe is released from MXene surface restoring the quenched fluorescence. The performances of the fluorosensor are evaluated using antigen protein, cultured virus, and swab specimens from COVID-19 patients. It is evidenced that this sensor can detect SARS-CoV-2 spike protein at final concentration of 38.9 fg mL⁻¹ and SARS-CoV-2 pseudovirus (limit of detection: 7.2 copies) within 30 min. Its application for clinical samples analysis is also demonstrated successfully. This work offers an effective sensing platform for sensitive and rapid detection of COVID-19 with high specificity.     

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the Lahore General Hospital, Lahore, Pakistan.     

Analysis of European competitiveness based on its innovative capacity and digitalization level

The competitiveness of countries can be measured through their innovative capacity and the level of digitalization they have achieved. Technological advances have proven to be an engine of economic growth, promoting employment and sustainable development. In Europe, policies are being focused on investments to make this the “Digital Decade”. Against this backdrop, the aim of the study is to analyse the competitiveness of the 27 countries of the European Union by producing a synthetic indicator that includes factors relating to innovation (measured by the Global Innovation Index) and to digitalization (based on the Digital Economy and Society Index), which in turn yields an annual ranking of the analysed economies between 2017 and 2021. Furthermore, in a second stage of the analysis, three panel data models are estimated to determine how factors relating to economic, social and environmental development foster advances in technology and innovation. The multicriteria decision-making method Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is applied to establish a ranking of EU members according to their level of digitalization and innovation, while the Prais-Winsten regression with Panel Corrected Standard Errors is used to obtain robust estimators. The results of the ranking indicate that the countries hold similar positions in the five years analysed, with Sweden leading the way throughout, which reflects its stable development in terms of digitalization and innovation. A digital and innovation gap can be seen between the top and bottom positions of the ranking, that is, between north-central and south-eastern Europe. Finally, the estimated models suggest that governments should promote wealth, employment, research and infrastructure investments in order to improve innovative and technological development in their countries.     

Monitoring the social gradient: Inequalities in use of blood pressure monitors in the HUNT study

AimTo investigate the cross-sectional and longitudinal social gradient in use of blood pressure monitors, an innovative health technology.BackgroundThis is one of the first studies of social inequalities in the utilization of an end-user health technology in a universal health care context. The diffusion of innovation (DoI) and fundamental cause (FCT) theories predicts a widening of inequalities with the introduction of a new technology.Data and methodsTwo waves (N > 18,000) of the Nord-Trøndelag Health Study (HUNT), conducted in 1997 and 2008. Dependent variables were three self-reported indicators of blood pressure monitor use. Independent variables were educational attainment and income quartiles. Control variables were gender, age, and blood pressure.ResultsFor the blood pressure monitor variable from 1997, there was evidence of an educational gradient. No social inequalities were found for the 2008 monitor variable. When interacting socio-economic status with a survey wave dummy, results showed a social gradient from 1997 becoming smaller or non-significant in 2008. These results are supportive of the DoI and FCT, suggesting that the use of technology may initially generate health inequalities, which decrease as the technology is diffused across all social strata.