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.     

Technological Innovation: Regaining the Competitive Edge

ABSTRACT

Global competition has become a business reality. To become competitive, we must improve the rate at which new technical concepts are incorporated into our products and processes. This article describes the sources and driving forces of innovation and the creative environment that the engineering manager must consider to increase the innovative productivity of the organization. Managers must also recognize the impact that the technical education system has on future innovative productivity and take the initiative to improve it. They must emphasize the importance of systems and process research and support such programs. In addition, idea generation must be considered just as important as analysis to the long-term success of the technological enterprise.     

A Decision Impact Model

ABSTRACT

Current and forecasted world economic trends call for an examination of traditional management organizational behavior. Changes in organizational behavior require a downward decision-making responsibility transfer. An examination of human motivational theories and experimental data provides insight into this needed decision transfer.

A decision impact model of current operational management trends is presented. The model shows the necessity for streamlining the traditional organization in an effort to accommodate rapidly changing technology and increase organizational competitiveness. An investigation of decision transfer is also presented.     

The state of social science research on COVID-19

Research on COVID-19 has proliferated rapidly since the outbreak of the pandemic at the end of 2019. Many articles have aimed to provide insight into this fast-growing theme. The social sciences have also put effort into research on problems related to COVID-19, with numerous documents having been published. Some studies have evaluated the growth of scientific literature on COVID-19 based on scientometric analysis, but most of these analyses focused on medical research while ignoring social science research on COVID-19. This is the first scientometric study of the performance of social science research on COVID-19. It provides insight into the landscape, the research fields, and international collaboration in this domain. Data obtained from SSCI on the Web of Science platform was analyzed using VOSviewer. The overall performance of the documents was described, and then keyword co-occurrence and co-authorship networks were visualized. The six main research fields with highly active topics were confirmed by analysis and visualization. Mental health and psychology were clearly shown to be the focus of most social science research related to COVID-19. The USA made the most contributions, with the most extensive collaborations globally, with Harvard University as the leading institution. Collaborations throughout the world were strongly related to geographical location. Considering the social impact of the COVID-19 pandemic, this scientometric study is significant for identifying the growth of literature in the social sciences and can help researchers within this field gain quantitative insights into the development of research on COVID-19. The results are useful for finding potential collaborators and for identifying the frontier and gaps in social science research on COVID-19 to shape future studies.

     

Future publication success in science is better predicted by traditional measures than by the <Emphasis Type="Italic">h</Emphasis> index

Although the use of bibliometric indicators for evaluations in science is becoming more and more ubiquitous, little is known about how future publication success can be predicted from past publication success. Here, we investigated how the post-2000 publication success of 85 researchers in oncology could be predicted from their previous publication record. Our main findings are: (i) Rates of past achievement were better predictors than measures of cumulative achievement. (ii) A combination of authors’ past productivity and the past citation rate of their average paper was most successful in predicting future publication success (R ² ≈ 0.60). (iii) This combination of traditional bibliographic indicators clearly outperformed predictions based on the rate of the h index (R ² between 0.37 and 0.52). We discuss implications of our findings for views on creativity and for science evaluation.     

New dogs,old tricks: making the case for the use of ABC effect evaluation in the ROI analysis of new technology and knowledge products: WR-AH

Abstract

The intended and unintended affective, behavioral, and cognitive (ABC) effects of a new product on end users often go unrecognized, yet these effects are critical components in its successful deployment, especially when the product claims to have powerful ABC effects on individuals who engage with it. Procurement groups need a method by which they can obtain evidence on the robustness of these ABC claims to inform their decision making. This will require the use of uniform principles of ABC evaluations to generate novel evidence that can support decision makers tasked with investing in technology and knowledge products. We propose developing five principles to support successful product evaluation: 1. Technology evaluators must identify the intended and unintended ABC consequences of the technology under investigation; 2. Technology evaluators must establish a rigorous design framework to evaluate the technology; 3. Conclusions of the investigation must inform a return-on-investment framework; 4. Turnaround time on rapid cycle evaluations of technology must show fidelity to their title (i.e. the evaluation needs to be complete is an acceptable timeframe to be used for decision making); 5. The information generated must be actionable for stakeholders charged with making a decision concerning which technologies to invest in.     

“Optimizing the Interaction Between the Food and Drug Administration and the Industry” The Need for Cooperation for the “Public Good”

Abstract

It is a pleasure for me to be a speaker at this conference, to share the podium with a friend and former colleague, Merve Schumate, of the FDA and also to have the opportunity to appear for the first time with Dr. Parariello of Wyeth Laboratories. The title for this morning's session, “Optimizing the Interaction Between the Food and Drug Administration and the Industry”, is an interesting one. As I was preparing my remarks, I wondered if they would be different if I was giving this talk to an FDA seminar at the agency or at the Public Citizen Litigation Group, Nader's organization. If I was talking to Dr. Sidney Wolf of the Litigation Group, I m sure that his optimization plan between FDA and the Industry would be tied to the concept of having open files, everything in writing and memoranda for all meetings. I feel fairly certain that Dr. Wolf would wish to have the opportunity to have either himself or one of his colleagues attend any of the meetings that they thought worthy of their time and effort. Now if I was an FDA reviewer and was talking about optimizing the interaction, I would see Industry representatives submitting well researched NDA files and supplements. These Industry representives would quickly understand and appreciate the wisdom of my request for additional studies, more data for the NDA, and the use of certain terms and concepts in relationship to labeling and promotional activities. They would find my suggestion of a patient package insert to be extremely helpful. On the other hand, if I were to become one of Dr. Papariello's researchers, I'm sure that I would see optimization of the system occur when my explanation to the FDA reviewer was quickly understood, all my views on the relationship of the data to the study submitted were accepted without question, and the agency looked most favorably on everything that was submitted to support the application.     

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.     

Bayesian Rule Modeling for Interpretable Mortality Classification of COVID-19 Patients

Coronavirus disease 2019 (COVID-19) has been termed a “Pandemic Disease” that has infected many people and caused many deaths on a nearly unprecedented level. As more people are infected each day, it continues to pose a serious threat to humanity worldwide. As a result, healthcare systems around the world are facing a shortage of medical space such as wards and sickbeds. In most cases, healthy people experience tolerable symptoms if they are infected. However, in other cases, patients may suffer severe symptoms and require treatment in an intensive care unit. Thus, hospitals should select patients who have a high risk of death and treat them first. To solve this problem, a number of models have been developed for mortality prediction. However, they lack interpretability and generalization. To prepare a model that addresses these issues, we proposed a COVID-19 mortality prediction model that could provide new insights. We identified blood factors that could affect the prediction of COVID-19 mortality. In particular, we focused on dependency reduction using partial correlation and mutual information. Next, we used the Class-Attribute Interdependency Maximization (CAIM) algorithm to bin continuous values. Then, we used Jensen Shannon Divergence (JSD) and Bayesian posterior probability to create less redundant and more accurate rules. We provided a ruleset with its own posterior probability as a result. The extracted rules are in the form of “if antecedent then results, posterior probability()”. If the sample matches the extracted rules, then the result is positive. The average AUC Score was 96.77% for the validation dataset and the F1-score was 92.8% for the test data. Compared to the results of previous studies, it shows good performance in terms of classification performance, generalization, and interpretability.     

Disentangling the influence of cell phone usage in the dilemma zone: An econometric approach

This paper focuses on developing an analysis framework to study the impact of cell phone treatment (cell phone type and call status) on driver behavior in the presence of a dilemma zone. Specifically, we examine how the treatment influences the driver maneuver decision at the intersection (stop or cross) and the eventual success of the maneuver. For a stop maneuver, success is defined as stopping before the stop line. Similarly, for a cross maneuver, success is defined as clearing the intersection safely before the light turns red. The eventual success or failure of the driver's decision process is dependent on the factors that affected the maneuver decision. Hence it is important to recognize the interconnectedness of the stop or cross decision with its eventual success (or failure). Toward this end, we formulate and estimate a joint framework to analyze the stop/cross decision with its eventual success (or failure) simultaneously. The study is conducted based on driving simulator data provided online for the 2014 Transportation Research Board Data Contest at http://depts.washington.edu/hfsm/upload.php. The model is estimated to analyze drivers’ behavior at the onset of yellow by employing exogenous variables from three broad categories: driver characteristics, cell phone attributes and driving attributes. We also generate probability surfaces to identify dilemma zone distribution associated with different cell phone treatment types. The plots clearly illustrate the impact of various cellphone treatments on driver dilemma zone behavior.     

Using Issue/Impact/Probability (I2P) Analysis to Facilitate Large and Diverse Group Meetings

ABSTRACT

Engineering managers are increasingly required to lead effective and efficient problem-solving meetings. This article presents a five-step process that engineering managers can use to facilitate large and diverse group meetings where the purpose is to identify and define complex and contentious problems. We describe the conceptual foundation of impact/probability analysis and illustrate its application to a 1-day conference that was held to address a solid-waste crisis faced by one small state in the Northeast. A model is proposed to explain why this process worked well in several field and laboratory applications.     

Strategic Thrust of Manufacturing Automation Decisions: A Conceptual Framework

American manufacturing firms increasingly consider automation as the means to increase productivity and improve their competitiveness. However, decisions to automate do not always produce the expected results and lead to further frustration. The so called “islands of automation” are often blamed for such failures and other technological alternatives such as computer integrated manufacturing (CIM) are adopted on a piece-meal fashion. This paper examines the adoption of new technology in general, and die decision to automate in particular, from a strategic perspective. In order to deal with the underlying complexities of automation, a conceptual framework is developed that can guide decision makers through a step-by-step process. Within an experimental analysis context, a decision support system is built that uses the “Expert Choice” cell to test the effectiveness of the introduced framework. The results indicate that the study's method can greatly enhance the decision making capabilities of firms contemplating automation     

How to evaluate individual researchers working in the natural and life sciences meaningfully? A proposal of methods based on percentiles of citations

Although bibliometrics has been a separate research field for many years, there is still no uniformity in the way bibliometric analyses are applied to individual researchers. Therefore, this study aims to set up proposals how to evaluate individual researchers working in the natural and life sciences. 2005 saw the introduction of the h index, which gives information about a researcher’s productivity and the impact of his or her publications in a single number (h is the number of publications with at least h citations); however, it is not possible to cover the multidimensional complexity of research performance and to undertake inter-personal comparisons with this number. This study therefore includes recommendations for a set of indicators to be used for evaluating researchers. Our proposals relate to the selection of data on which an evaluation is based, the analysis of the data and the presentation of the results.     

Impact Assessment of COVID-19 Pandemic Through Machine Learning Models

Ever since its outbreak in the Wuhan city of China, COVID-19 pandemic has engulfed more than 211 countries in the world, leaving a trail of unprecedented fatalities. Even more debilitating than the infection itself, were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus. Such enforced alienation affected both the mental and social condition of people significantly. Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement. However, COVID-19 brought all such communication to a grinding halt. Digital interactions have failed to enthuse the fervor that one enjoys in face-to-face meets. The pandemic has shoved the entire planet into an unstable state. The main focus and aim of the proposed study is to assess the impact of the pandemic on different aspects of the society in Saudi Arabia. To achieve this objective, the study analyzes two perspectives: the early approach, and the late approach of COVID-19 and the consequent effects on different aspects of the society. We used a Machine Learning based framework for the prediction of the impact of COVID-19 on the key aspects of society. Findings of this research study indicate that financial resources were the worst affected. Several countries are facing economic upheavals due to the pandemic and COVID-19 has had a considerable impact on the lives as well as the livelihoods of people. Yet the damage is not irretrievable and the world’s societies can emerge out of this setback through concerted efforts in all facets of life.     

Efficient Deep CNN Model for COVID-19 Classification

Coronavirus (COVID-19) infection was initially acknowledged as a global pandemic in Wuhan in China. World Health Organization (WHO) stated that the COVID-19 is an epidemic that causes a 3.4% death rate. Chest X-Ray (CXR) and Computerized Tomography (CT) screening of infected persons are essential in diagnosis applications. There are numerous ways to identify positive COVID-19 cases. One of the fundamental ways is radiology imaging through CXR, or CT images. The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality. Hence, automated classification techniques are required to facilitate the diagnosis process. Deep Learning (DL) is an effective tool that can be utilized for detection and classification this type of medical images. The deep Convolutional Neural Networks (CNNs) can learn and extract essential features from different medical image datasets. In this paper, a CNN architecture for automated COVID-19 detection from CXR and CT images is offered. Three activation functions as well as three optimizers are tested and compared for this task. The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it. The performance is tested and investigated on the CT and CXR datasets. Three activation functions: Tanh, Sigmoid, and ReLU are compared using a constant learning rate and different batch sizes. Different optimizers are studied with different batch sizes and a constant learning rate. Finally, a comparison between different combinations of activation functions and optimizers is presented, and the optimal configuration is determined. Hence, the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage. The proposed model achieves a classification accuracy of 91.67% on CXR image dataset, and a classification accuracy of 100% on CT dataset with training times of 58 min and 46 min on CXR and CT datasets, respectively. The best results are obtained using the ReLU activation function combined with the SGDM optimizer at a learning rate of 10⁻⁵ and a minibatch size of 16.     

Evaluating the COVID-19 impacts on the construction and demolition waste management and resource recovery industry: experience from the Australian built environment sector

The COVID-19 crisis has brought unprecedented challenges to many sectors, including the built environment. The construction and demolition (C&D) waste management and recovery industry is an essential service provider to this sector. Like other industries, this industry has been affected by the pandemic in many ways. However, in Australia, this impact has not been thoroughly investigated. This study, therefore, explores COVID-19 impacts on the Australian C&D waste recovery and construction industry as the major waste consumer and generator. To achieve this aim, a literature review and a series of semi-structured interviews were conducted with 27 participants representing five stakeholder groups (government, construction, waste recovery, material supplying and consultancy) across five Australian states. The research findings established that there is a critical need for leveraging digital technologies, developing business contingency plans, creating coalitions between government and industry, and diversifying supply chains to reduce supply chain risks. This study also uncovered a range of targeted responses and recommendations to manage pandemic-induced disruptions and improve the circular economy in the industry. Our findings can immediately assist industrial practitioners and government decision-makers in managing the impacts of COVID-19 on the waste recovery activities in C&D waste and other waste streams.

Graphical abstract

     

Rapid System Development (RSD) Methodologies: Proposing a Selection Framework

Abstract:

The current global customer trend requires companies across domains to reduce their product development lifecycle. As a result the exploration of methodologies that will support rapid system development has been gaining importance. The primary focus of this article is to provide a framework for comparative analysis of rapid system development methodologies. The purpose of this framework is to help the project managers and systems engineers choose and tailor an appropriate rapid development methodology to suit their development context and environment. Toward this, the framework identifies and defines a set of critical rapid development attributes. The article redefines rapid system development as adopting methodologies, tools, and techniques that can introduce rapidity into the system development processes while optimizing the success factors of development. The success factors are specific to the system under development and they depend on the system, product line, organization, and customers. Some of the common success factors are return-on-investment (ROI), cost of ownership, other performance factors, and customer satisfaction. The article provides a fundamental discussion on the current rapid system development methodologies, metrics, tools, and techniques.     

Career Planning and Progression for Engineering Management Graduates: An Exploratory Study

Abstract:

When faced with the decision of selecting an advanced degree, many engineers opt for management related studies rather than further specialization in a technical field. This article attempts to highlight the reasons behind such choices, and explores the role that a Master's degree in Engineering Management (MEM) plays in career planning and progression. A survey of 58 MEM graduates, who completed their studies at a prominent university in Lebanon between 1992 and 2009, reveals that the majority of the respondents follow a linear career path, rapidly paving the way towards managerial positions. For most of the respondents, earning a graduate degree in engineering management played a primary role in, or at least contributed to, making this shift. The article concludes with a diagram sketching the possible career paths for MEM graduates. By showing the number of years spent at different career stages, the diagram serves as a career-planning tool for MEM graduates, engineers, managers, and researchers.