Identifying the Influencing Factors of Depressive Symptoms among Nurses in China by Machine Learning: A Multicentre Cross-Sectional Study

Background. Nurses' high workload can result in depressive symptoms. However, the research has underexplored the internal and external variables, such as organisational support, career identity, and burnout, which may predict depressive symptoms among Chinese nurses via machine learning (ML). Aim. To predict nurses' depressive symptoms and identify the relevant factors by machine learning (ML) algorithms. Methods. A self-administered smartphone questionnaire was delivered to nurses to evaluate their depressive symptoms;1,431 questionnaires and 28 internal and external features were collected. In the training set, the use of maximum relevance minimum redundancy ranked the features' importance. Five ML algorithms were used to establish models to identify nurses' depressive symptoms using different feature subsets, and the area under the curve (AUC) determined the optimal feature subset. Demographic characteristics were added to the optimal feature subset to establish the combined models. Each model's performance was evaluated using the test set. Results. The prevalence rate of depressive symptoms among Chinese nurses was 31.86%. The optimal feature subset comprised of sleep disturbance, chronic fatigue, physical fatigue, exhaustion, and perceived organisation support. The five models based on the optimal feature subset had good prediction performance on the test set (AUC: 0.871–0.895 and accuracy: 0.798–0.815). After adding the significant demographic characteristics, the performance of the five combined models slightly improved;the AUC and accuracy increased to 0.904 and 0.826 on the test set, respectively. The logistic regression analysis results showed the best and most stable performance while the univariate analysis results showed that external and internal personal features (AUC: 0.739–0.841) were more effective than demographic characteristics (AUC: 0.572–0.588) for predicting nurses' depressive symptoms. Conclusions. ML could effectively predict nurses' depressive symptoms. Interventions to manage physical fatigue, sleep disorders, burnout, and organisational support may prevent depressive symptoms.

Joint Transmission Scheme and Coded Content Placement in Cluster-Centric UAV-Aided Cellular Networks

Recently, as a consequence of the COVID-19 pandemic, dependence on telecommunication for remote learning/working and telemedicine has significantly increased. In this context, preserving high Quality of Service (QoS) and maintaining low-latency communication are of paramount importance. In cellular networks, the incorporation of unmanned aerial vehicles (UAVs) can result in enhanced connectivity for outdoor users due to the high probability of establishing Line of Sight (LoS) links. The UAV's limited battery life and its signal attenuation in indoor areas, however, make it inefficient to manage users' requests in indoor environments. Referred to as the cluster-centric and coded UAV-aided femtocaching (CCUF) framework, the network's coverage in both indoor and outdoor environments increases by considering a two-phase clustering framework for Femto access points (FAPs)' formation and UAVs' deployment. Our first objective is to increase the content diversity. In this context, we propose a coded content placement in a cluster-centric cellular network, which is integrated with the coordinated multipoint (CoMP) approach to mitigate the intercell interference in edge areas. Then, we compute, experimentally, the number of coded contents to be stored in each caching node to increase the cache-hit-ratio, signal-to-interference-plus-noise ratio (SINR), and cache diversity and decrease the users' access delay and cache redundancy for different content popularity profiles. Capitalizing on clustering, our second objective is to assign the best caching node to indoor/outdoor users for managing their requests. In this regard, we define the movement speed of ground users as the decision metric of the transmission scheme for serving outdoor users' requests to avoid frequent handovers between FAPs and increase the battery life of UAVs. Simulation results illustrate that the proposed CCUF implementation increases the cache-hit-ratio, SINR, and cache diversity and decrease the users' access delay, cache redundancy, and UAVs' energy consumption.

The impact of governmental COVID‐19 measures on manufacturers' stock market valuations: The role of labor intensity and operational slack

This study investigates the impact of the Chinese government's Level I emergency response policy on manufacturers' stock market values. We empirically examine the roles of human resource dependence (labor intensity) and operational slack within the context of supply chain resilience. Through an event study of 1357 Chinese manufacturing companies, we find that the government's emergency response policy triggered statistically significant positive abnormal returns for manufacturers. However, we also find that there exists a negative impact on abnormal returns for manufacturers that are labor‐intensive, giving rise to arguments based in resource dependence theory. In addition, the results indicate the positive role played by operational slack (e.g., financial and inventory slack) in helping manufacturers maintain operations and business continuity, effectively mitigating risks and adding to the manufacturers' resilience. With these findings, we contribute to operations and supply chain management by calling attention to the importance of human resource redundancy while at the same time identifying financial slack and inventory as supply chain resilience strategies that were able to mitigate pandemic‐related risks.

Definition, harms, and prevention of redundant systematic reviews.

BACKGROUND: Along with other types of research, it has been stated that the extent of redundancy in systematic reviews has reached epidemic proportions. However, it was also emphasized that not all duplication is bad, that replication in research is essential, and that it can help discover unfortunate behaviors of scientists. Thus, the question is how to define a redundant systematic review, the harmful consequences of such reviews, and what we could do to prevent the unnecessary amount of this redundancy. MAIN BODY: There is no consensus definition of a redundant systematic review. Also, it needs to be defined what amount of overlap between systematic reviews is acceptable and not considered a redundancy. One needs to be aware that it is possible that the authors did not intend to create a redundant systematic review. A new review on an existing topic, which is not an update, is likely justified only when it can be shown that the previous review was inadequate, for example, due to suboptimal methodology. Redundant meta-analyses could have scientific, ethical, and economic questions for researchers and publishers, and thus, they should be avoided, if possible. Potential solutions for preventing redundant reviews include the following: (1) mandatory prospective registration of systematic reviews; (2) editors and peer reviewers rejecting duplicate/redundant and inadequate reviews; (3) modifying the reporting checklists for systematic reviews; (4) developing methods for evidence-based research (EBR) monitoring; (5) defining systematic reviews; (6) defining the conclusiveness of systematic reviews; (7) exploring interventions for the adoption of methodological advances; (8) killing off zombie reviews (i.e., abandoned registered reviews); (9) better prevention of duplicate reviews at the point of registration; (10) developing living systematic reviews; and (11) education of researchers. CONCLUSIONS: Disproportionate redundancy of the same or very similar systematic reviews can lead to scientific, ethical, economic, and societal harms. While it is not realistic to expect that the creation of redundant systematic reviews can be completely prevented, some preventive measures could be tested and implemented to try to reduce the problem. Further methodological research and development in this field will be welcome.

Transforming resilience in the context of a pandemic: results from a cross-industry case study exploring supply chain viability

Lately, there has been increased interest among researchers in studying the resilience of manufacturing supply chains. However, the Covid-19 pandemic has caused severe disruptions in global supply chains, which have led to calls for greater resilience in these supply chains. This study provides insights into the impact of the Covid-19 pandemic on supply chain resilience by conducting a multiple case study in three intertwined industries based on the dynamic capability view and the relational capability view as a theoretical underpinning. Data were collected during the pandemic in a two-stage interview process with 18 supply chain and production experts directly involved in crisis management. Internal and external documents supplemented the interviews. The results revealed seven higher-level capability groups for building resilience in intertwined supply chains during a pandemic outbreak: agility, collaboration, digital preparedness, flexible redundancy, human resource management, contingency planning, and transparency and visibility. Each capability group is supported by associated capabilities extracted from the data analysis. The findings obtained based on the results of the multiple case study are discussed, and implications for management and future research directions are presented.

Reconfigurable and In-sensor Computing Pb-free Perovskite Array towards Intelligent X-ray Imaging

Conventional X-ray imaging architectures feature data redundancy and hardware consumption due to the separated sensory terminal and computing units. In-sensor computing architectures is promising to overcome such drawbacks. However, its realization in X-ray range remains elusive. We propose ion distribution induced reconfigurable mechanism, and demonstrate the first X-ray band in-sensor computing array based on Pb-free perovskite. Redistribution of Br- ion in perovskite induces the switching of PN and NP modes under electrical pooling. X-ray detection sensitivity can be switched between two stable self-power sensing modes with 4373±298 and -7804±429 mu mathrm{CGy}-{ mathrm{a} mathrm{i} mathrm{r}}{}{-1} mathrm{cm}{-2} respectively, which are superior than that of commercial a-Se detectors (20 mu mathrm{C} mathrm{G} mathrm{y}-{ mathrm{a} mathrm{i} mathrm{r}}{}{-1} mathrm{c} mathrm{m}{-2}). Both modes exhibit low detection limit of 48.4 mathrm{n} mathrm{G} mathrm{y}-{ mathrm{a} mathrm{i} mathrm{r}} mathrm{s}{-1}, which is two orders lower than typical medical dose rate of 5.5 mu mathrm{G} mathrm{y}-{ mathrm{a} mathrm{i} mathrm{r}} mathrm{s}{-1}. The perovskite array sensors can integrate with thin film transistors (TFTs) with low-temperature (80oC) process with good uniformity. An in-sensor computing algorithm of attention mechanism is performed on array sensors for chest X-ray images COVID-19 recognition, which enables an accuracy improvement up to 98.2%. Our results can pave the way for future intelligent X-ray imaging. © 2022 IEEE.

Blurred lines: the timeline of supply chain resilience strategies in the grocery industry in the time of Covid-19

To anticipate, adapt and respond to, and recover from disruptions, firms need to enhance supply chain (SC) resilience. The spread of the COVID-19 pandemic in 2020 represented a unique opportunity to investigate it empirically. This study focuses on the exploration of the resilience strategies adopted to deepen their temporal characteristics and contribute to developing the current understanding of proactivity and reactivity, something that needs to be further investigated. Multiple-case study research was conducted considering 21 Italian companies in the grocery industry. Results show that with the outbreak of the pandemic, companies adopted a set of 21 strategies that spanned five resilience categories: redundancy, flexibility, agility, collaboration, and innovation. To explain the temporal characteristics of the identified resilience strategies we propose an original taxonomy that elaborates the previous theory by introducing two new dimensions related to the strategies' timing ("when?” and "how long?”). Each dimension can be complemented with other sub-dimensions that explain the design and activation of resilience strategies, and their utilisation and availability. The proposed taxonomy broadens the narrow view offered by existing research on the temporal dimension of resilience, as multiple layers are needed to disentangle the temporal characteristics of different strategies. It also provides an original viewpoint on interpreting the strategies' proactivity or reactivity as their boundary is increasingly blurred. Lastly, the study opens up to future investigations of the antecedents of the design and utilisation/activation of resilience strategies, as companies could rethink their managerial decisions based on the continuous evolution of their operating environment.

Building supply chain resilience through ambidexterity: an information processing perspective

This paper makes an initial attempt to develop a theory of supply chain resilience through ambidexterity in the context of the COVID-19 pandemic. We conducted a single-case analysis focusing on Zong-Teng Group, one of the biggest cross-border e-commerce enterprises in China, as our sample. Data were mainly collected from interviews with Zong-Teng managers and public online resources. Through case analysis, this paper identifies that a fit between the information processing requirements of a firm and its information processing capability leads to greater ambidexterity for exploitation and exploration, which in turn improves supply chain resilience (SCR) in the form of agility, redundancy and flexibility. In addition, ambidexterity in terms of morality improves SCR culture. This paper may be the first to adopt information processing theory to examine SCR and consider the role of ambidexterity, noting that crises such as COVID-19 impose an exponential increase in information processing requirements, to which many firms fail to respond effectively.

COVID-19 and the uncertain future of HRM: Furlough, job retention and reform

The article argues that job retention should be a central aim and practice of human resource management (HRM). Set against the global COVID-19 crisis, theoretical insights are drawn from strategic HRM planning and the economics of 'labour hoarding' to consider the potential benefits of workforce furloughing. Furlough has been supported in the UK by the Coronavirus Job Retention Scheme, which represents a novel, but temporary, state-led shift from the UK's market-orientated restructuring regime. We argue that the withdrawal of state-financed furlough may mean a quick return in UK firms to the management of redundancy. Yet, if the crisis is to generate any benefit it must create the conditions for a more collaborative HRM that delivers for workers as well as business, with job retention as a core priority. While change in this direction will mean confronting deep-rooted challenges-such as job security, good work and worker voice-such change remains vital in creating better and healthier workplaces. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Title: Post-pandemic planning: Do we have enough and efficient access to parks?

Introduction: The COVID-19 pandemic has placed neighborhood parks as a key asset in mitigating the negative implications of extended lockdowns, when parks turned into a sanctuary for residents. With increased scholarly work focusing on developing pots-pandemic neighborhoods, providing access to community parks via efficient routes, is central to such debate. Network connectivity provides a comprehensive assessment of the efficiency of network systems. Methods: A total of 16 samples, from the city of Abu-Dhabi, have been selected to study their network connectivity, with regard to accessing parks. Three distance-based connectivity measures are used: the pedestrian route directness (PRD), the count of redundant routes Redundancy Count (RC), and the route redundancy index (RI). The samples reflect different street's typologies and their urban form attributes are quantified. Results and Discussion: Connectivity analyses results are interrupted with regard to the quantified physical attributes. Findings indicate that gridded, and semi-gridded layouts provide more direct routes to parks, but less route's redundancy. Conversely, interlocked, and fragmented networks, when having sufficient intersection densities, have less direct routes but more redundancy. The inclusion of alleyways proved to alter typologies into gridded ones and improve both route directness and redundancy. The majority of the selected samples reported sufficient levels of route directness. The current design and planning guidelines, implemented by the Department of Transport and Municipalities are overly descriptive with regard to how neighborhood parks are accessed;therefore, the study's methodology provides a possible more evidence-based approach to policy development. Copyright © 2023 Alkhaja, Alawadi and Ibrahim.

E-learning course recommendation based on sentiment analysis using hybrid Elman similarity

Online learning is also referred to as E-learning which has gained huge attention and attracted most people during the COVID-19 lockdowns. Due to the excess of online information, users face severe challenges and difficulties realizing the best course that is being competitive in the global market. Therefore, it is necessary to develop an online recommendation system that supports the users in selecting the finest course with E-learning. Thus, the proposed work develops a robust RS model using different approaches. Initially, the pre-processing stage is performed to reduce the presented noise in the website data. Then, the feature extraction stage is done to extract the needed features using Improved TF-IDF, W2V (Word 2 Vector), and Hybrid N-gram. Finally, Elman Minimal Redundancy Maximum Relevance and Enhanced Aquila Optimization (EMRMR_EAO) model is proposed to provide Robust course recommendations. In this work, the ERNN method is used to classify the sentiments based on the similarity measure of the MRMR model. The top course recommendation is afforded depending on the similarity scores like Jaccard similarity, cosine similarity and euclidean similarity. Also, the loss function in the classifier is reduced by optimizing the weight parameters using the EAO approach. The performance analysis shows that the proposed recommendation model obtains improved results in terms of accuracy of 99.98%, recall of 99.81%, precision of 99.65%, and F-measure of 99.95%. The comparative analysis exhibit that the proposed EMRMR_EAO model attains better performance than the other existing works in the literature. © 2022 Elsevier B.V.

Null-values imputation using different modification random forest algorithm

Today, the world lives in the era of information and data. Therefore, it has become vital to collect and keep them in a database to perform a set of processes and obtain essential details. The null value problem will appear through these processes, which significantly influences the behaviour of processes such as analysis and prediction and gives inaccurate outcomes. In this concern, the authors decide to utilise the random forest technique by modifying it to calculate the null values from datasets got from the University of California Irvine (UCL) machine learning repository. The database of this scenario consists of connectionist bench, phishing websites, breast cancer, ionosphere, and COVID-19. The modified random forest algorithm is based on three matters and three number of null values. The samples chosen are founded on the proposed less redundancy bootstrap. Each tree has distinctive features depending on hybrid features selection. The final effect is considered based on ranked voting for classification. This scenario found that the modified random forest algorithm executed more suitable accuracy results than the traditional algorithm as it relied on four parameters and got sufficient accuracy in imputing the null value, which is grown by 9.5%, 6.5%, and 5.25% of one, two and three null values in the same row of datasets, respectively.

Crisis-induced financial anxiety, social support, socio-psychological wellbeing, and commitment to work in the tourism sector

Understanding the causal influence of financial anxiety on future work commitment with social support and socio-psychological wellbeing as mediators amongst crisis-induced redundant tourism employees remains limited. Using data collected from 547 COVID-19-induced redundant tourism employees, this paper examines the influence of financial anxiety on future work commitment with social support and socio-psychological wellbeing as mediators. The findings reveal that financial anxiety has a negative influence on social support and social and psychological wellbeing. Social support has a negative influence on social wellbeing, while social support has a positive influence on future work commitment. Both social and psychological wellbeing has a negative influence on future work commitment. Meanwhile, the influence of financial anxiety on future work is fully mediated by social support and socio-psychological wellbeing. Insurance uptake and establishment of welfare funds amongst tourism employees can be used to buffer the effects of financial anxiety on future work commitment. © 2023 Government Technical Advisory Centre (GTAC).

Is the Covid-19 Pandemic Rewriting the Rules of Corporate Governance in Nigeria?

The corona virus (covid-19) pandemic which started as a public health emergency swiftly evolved into a global financial and economic crisis of epic proportions. Thus, it had a far-reaching effect on Nigerian corporations. The advent of covid-19 seems to have changed the guiding principles of corporate governance from the agency theory to stakeholder theory due to the heightened expectations for societal engagement from corporations in Nigeria. For instance, the management of Access Bank which donated 1 Billion and other materials like ambulances to the Federal Government of Nigeria, decided to downsize staff by 75 percent in the same week. The outcry by the public on the challenges caused by the aforesaid huge donation vis-à-vis the redundancy policy led Access Bank 's management to reverse its earlier decision and for the regulatory body for the banking industry, the Central Bank of Nigeria, to prohibit all banks in Nigeria from retrenching their employees during the pendency of the pandemic. The article examines the impact of covid-19 on corporate governance in Nigeria through the prism of stakeholder theories of corporate governance. The authors submit that covid-19 has a profound effect on corporate governance in Nigeria and it seems to be inducing a review and amendment of certain provisions of Companies and Allied Matters Act, 2020 to promote good corporate governance in Nigeria.

Null-values imputation using different modification random forest algorithm

Today, the world lives in the era of information and data. Therefore, it has become vital to collect and keep them in a database to perform a set of processes and obtain essential details. The null value problem will appear through these processes, which significantly influences the behaviour of processes such as analysis and prediction and gives inaccurate outcomes. In this concern, the authors decide to utilise the random forest technique by modifying it to calculate the null values from datasets got from the University of California Irvine (UCL) machine learning repository. The database of this scenario consists of connectionist bench, phishing websites, breast cancer, ionosphere, and COVID-19. The modified random forest algorithm is based on three matters and three number of null values. The samples chosen are founded on the proposed less redundancy bootstrap. Each tree has distinctive features depending on hybrid features selection. The final effect is considered based on ranked voting for classification. This scenario found that the modified random forest algorithm executed more suitable accuracy results than the traditional algorithm as it relied on four parameters and got sufficient accuracy in imputing the null value, which is grown by 9.5%, 6.5%, and 5.25% of one, two and three null values in the same row of datasets, respectively.

E-learning course recommendation based on sentiment analysis using hybrid Elman similarity

Online learning is also referred to as E-learning which has gained huge attention and attracted most people during the COVID-19 lockdowns. Due to the excess of online information, users face severe challenges and difficulties realizing the best course that is being competitive in the global market. Therefore, it is necessary to develop an online recommendation system that supports the users in selecting the finest course with E-learning. Thus, the proposed work develops a robust RS model using different approaches. Initially, the pre-processing stage is performed to reduce the presented noise in the website data. Then, the feature extraction stage is done to extract the needed features using Improved TF-IDF, W2V (Word 2 Vector), and Hybrid N-gram. Finally, Elman Minimal Redundancy Maximum Relevance and Enhanced Aquila Optimization (EMRMR_EAO) model is proposed to provide Robust course recommendations. In this work, the ERNN method is used to classify the sentiments based on the similarity measure of the MRMR model. The top course recommendation is afforded depending on the similarity scores like Jaccard similarity, cosine similarity and euclidean similarity. Also, the loss function in the classifier is reduced by optimizing the weight parameters using the EAO approach. The performance analysis shows that the proposed recommendation model obtains improved results in terms of accuracy of 99.98%, recall of 99.81%, precision of 99.65%, and F-measure of 99.95%. The comparative analysis exhibit that the proposed EMRMR_EAO model attains better performance than the other existing works in the literature.

Unemployment Analysis during and after COVID-19

In this paper, we focus the unemployability in India and various parts of the country with their earning resources. The main of the paper is to review and analysis the situation at the time of covid-19 and after pandemic specially the stream down impact, inequality and unemployment, joblessness among the young generation, wages and earnings of young generation workers and labor force participation rate (%) by age and location. Then, the comparison of some states with India overall unemployability with special reference Madhya Pradesh is proposed and states the challenges of unemployment situation in India. © 2022 University of Stockholm. All rights reserved.

COVID-19 Biomarkers Recognition & Classification Using Intelligent Systems

Background: SARS-CoV-2 has paralyzed mankind due to its high transmissibility and its associated mortality, causing millions of infections and deaths worldwide. The search for gene expression biomarkers from the host transcriptional response to infection may help understand the underlying mechanisms by which the virus causes COVID-19. This research proposes a smart methodology integrating different RNA-Seq datasets from SARS-CoV-2, other respiratory diseases, and healthy patients. Methods: The proposed pipeline exploits the functionality of the ‘KnowSeq’ R/Bioc package, integrating different data sources and attaining a significantly larger gene expression dataset, thus endowing the results with higher statistical significance and robustness in comparison with previous studies in the literature. A detailed preprocessing step was carried out to homogenize the samples and build a clinical decision system for SARS-CoV-2. It uses machine learning techniques such as feature selection algorithm and supervised classification system. This clinical decision system uses the most differentially expressed genes among different diseases (including SARS-Cov-2) to develop a four-class classifier. Results: The multiclass classifier designed can discern SARS-CoV-2 samples, reaching an accuracy equal to 91.5%, a mean F1-Score equal to 88.5%, and a SARS-CoV-2 AUC equal to 94% by using only 15 genes as predictors. A biological interpretation of the gene signature extracted reveals relations with processes involved in viral responses. Conclusion: This work proposes a COVID-19 gene signature composed of 15 genes, selected after applying the feature selection ‘minimum Redundancy Maximum Relevance’ algorithm. The integration among several RNA-Seq datasets was a success, allowing for a considerable large number of samples and therefore providing greater statistical significance to the results than in previous studies. Biological interpretation of the selected genes was also provided.

Using Curriculum Mapping as a Tool to Match Student Learning Outcomes and Social Studies Curricula.

The interest in program- and colleges of education- level evaluation and alignment of student learning outcomes to course content has been increasing over the past several decades. Curriculum mapping establishes the links between content and expected student learning outcomes. Curriculum map is an overview of what is taking place in the classroom; and it includes evaluation tools and activities. Social Studies Department, Federal Capital Territory (FCT) College of Education Zuba, Abuja, recently completed an accreditation exercise by National Commission for Colleges of Education Abuja, Nigeria. The audit reported that there was no match between the student learning outcomes and Social Studies curricula. The purpose of this paper was to align the Nigeria Certificate in Education (NCE) (Social Studies) minimum standards with student learning outcomes to determine gaps and redundancies. The paper also looked at how virtual education enhances curriculum mapping during COVID-19 pandemic. Minimum standards learning outcomes were modified from existing learning outcomes to better align with college learning outcomes and the Social Studies Core and Elective Competencies. All NCE Social Studies courses were mapped to the Social Studies Core and Elective Competencies and assessed to determine the gaps and redundancies. The study used the documentary research method. The purposeful sampling strategy was used to select the research site. Potential gaps were defined as coverage for each competency in about ≤20% of the courses and potential redundancies was considered as coverage of ≥80% of the courses. The mapping exercise revealed gaps; and no redundancies in course content. The findings of the mapping exercises should be used to improve the content provided to NCE Social Studies students at FCT College of Education Zuba, with the overall objective of enhancing the quality of the education provided to those students and helping them to be better students that are prepared for a successful career in Social Studies.