bluuebunny/crossref_metadata_2025 huggingface dataset:
Children’s Mental Health During the First Two Years of the COVID-19 Pandemic: Burden, Risk Factors and Posttraumatic Growth – A Mixed-Methods Parents’ Perspective
Authors: Wenter Anna , Schickl Maximilian , Sevecke Kathrin , Juen Barbara , Exenberger Silvia
Published: June 2022

The COVID-19 pandemic and the accompanying containment measures such as physical distancing and school closures led to major changes in children’s everyday lives. By means of a mixed-methods study, the “Tyrolean COVID-19 Children’s Study” investigated the effects of the pandemic and factors influencing mental health and health-related quality of life of North Tyrolean (Austria) and South Tyrolean (Italy) children aged 3–13 years. Parents filled out N = 2,691 online questionnaires (951 preschool children: 3–6 years; 1,740 schoolchildren: 7–13 years) at four measurement time points (March 2020, December 2020, June 2021, December 2021). For both age groups, children’s mental health outcomes (internalising problems, posttraumatic stress symptoms) were worse in December 2021 (t4) than children’s mental health outcomes in March 2020 (t1). With regard to aggressive behaviour, this difference was only found among schoolchildren. Thematic analysis of an open ended, written question revealed the following positive changes in children during the Corona crisis: (1) the importance of intra- and extra-familial relationships, (2) new competences and experiences, (3) values and virtues, (4) use of time, and (5) family strength. Using multilevel modelling, threat experience, economic disruption, and perceived posttraumatic growth were shown to be the strongest predictors of all outcomes. Additionally, male gender was shown to be a predictor of aggressive behaviour. In terms of age, schoolchildren showed more internalising problems, aggressive behaviour, and threat experience than preschool children. With regard to time, parents in December 2021 reported more threat experience in older children and less perceived posttraumatic growth in both older and younger children, than parents at the beginning of the pandemic. Targeted support for vulnerable children may prevent longer-term development of psychopathologies and contribute to society’s psychosocial resilience in the current COVID-19 pandemic. Moreover, sustainable promotion of children’s posttraumatic growth can also contribute to children’s mental health and could even offer a chance to turn the crisis into an opportunity.
Research on Construction of High-Quality Application-Oriented Talent Cultivation System for Internet of Things Engineering: Based on Educational Psychology
Authors: Nie Yalin , Wang Haijun , Liu Baoluo , Yang Shangsen
Published: June 2022

High-quality talent cultivating Internet of Things (IoT) Engineering is the basis for the rapid development of IoT technology. To train high-quality application-oriented IoT technical talents, guided by educational psychology, this article conducts in-depth research; analyzes the characteristics of IoT Engineering; makes professional talent cultivating programs and cyclical adjustment plans; builds a high-quality teaching system based on the professional knowledge system of the IoT; explores the “spiritual level” and “psychological level” characteristics of teachers and students in teaching; highly integrates “Industry-University-Research-Competition” from the perspective of students, teachers, and colleges; infiltrates positive psychological cues appropriately; formulates the construction method of the “student teaching assistant” auxiliary system to enhance the efforts to promote learning by learning; and finally innovates the talent cultivating system for the IoT Engineering. The implementation results show that the students trained by this system have a solid foundation of knowledge related to IoT Engineering and strong engineering practice application, adaptability, and innovation ability.
Impact of Perceived Skillset and Organizational Traits on Digital Wellbeing of Teachers: Mediating Role of Resilience
Authors: Yu Fang , Mirza Farhan , Chaudhary Naveed Iqbal , Arshad Rida , Wu Yingyu
Published: June 2022

Given the current unstable and unpredictable circumstances, especially due to the COVID-19 education system has evolved, requiring frequently distinct skills, and coping behavior. This study intended to empirically test the impact of perceived skillset and organizational traits on teachers' digital wellbeing with the mediating role of three levels of resilience. To serve the cause, non-probability convenience sampling was chosen, and data was gathered through an online survey from 336 on-duty teachers in the education sector of Pakistan. The results of the study have been drawn by using the PLS-SEM partial least squares structural equation modeling technique through the Smart-PLS software 3.0 version. The findings show that perceived skillset had a positive and significant impact on digital wellbeing and organizational traits had an insignificant effect on digital wellbeing. Moreover, results indicate that organizational resilience and employee resilience positively mediate the relationship between perceived skillset and organizational traits on digital wellbeing. Similarly, findings illustrate that team resilience positively mediates the relationship between perceived skillset and digital wellbeing. Furthermore, results show that team resilience negatively and insignificantly mediates the relationship between organizational traits and digital wellbeing. Lastly, discussion, theoretical and practical implications were also discussed in this research article.
The Influence of EFL Teachers’ Hope and Trust on Their Academic Grit: A Theoretical Review
Authors: Xu Yushu
Published: June 2022

The role of emotions in second/foreign language education has been exponentially highlighted in the literature. However, the interplay of English as a foreign language (EFL) teachers’ hope, trust, and grit has witness a scant attention among L2 researchers. Against this shortcoming, the present mini-review article made an effort to offer a theoretical analysis of the theoretical and empirical underpinnings of these three constructs. In so doing, it presented the definitions, conceptualizations, dimensions, theories, related studies, and the way these variables can influence one another. Drawing on scientific findings in the literature, this study proposed some implications for EFL teachers, teacher trainers, principals, and scholars to enhance their knowledge of psycho-emotional factors and how establishing an environment based on hope and trust can generate success in L2 education. Finally, some recommendations for future research are made to drive this line of research forward.
Psychological Distress and Access to Mental Health Services Among Undergraduate Students During the COVID-19 Lockdown in Uganda
Authors: Nantaayi Brandy , Ndawula Rodney Kato , Musoke Phillip , Ssewante Nelson , Nakyagaba Lourita , Wamala Joyce Nakiganda , Makai Emmanuel Arthur , Wannyana Babrah , Wamala Nicholas Kisaakye , Kanyike Andrew Marvin , Akech Gabriel Madut , Ojilong Daniel , Agira Drake , Nakimuli Ann Barbra , Asiimwe Asaph , Bongomin Felix
Published: June 2022

Background

Lockdown is an important public health approach aimed at curbing the raging effect of the coronavirus disease-2019 (COVID-19). This study aimed at determining the impact of prolonged lockdown on mental health and access to mental health services among undergraduate students in Uganda.

Methods

An online cross-sectional study was conducted anonymously among undergraduates across 10 universities in Uganda. The Distress Questionnaire-5 (DQ-5) and the Patient Health Questionnaire-2 (PHQ-2) were used. Logistic regression analysis was conducted to determine factors associated with psychological distress.

Results

We enrolled 366 participants with a mean age of 24.5 ± 4.6 years. The prevalence of psychological distress was 40.2% (n = 147) (cut off 14/25 based on DQ-5) while depression stood at 25.7% (n = 94; cut off 3/6 based on PHQ-2) with mean scores of 12.1 ± 4.6 and 1.7 ± 1.6 respectively. Female gender (aOR: 1.6, 95%CI: 1.0–2.6, p = 0.032), pursuing a non-medical program (aOR: 2.2, 95%CI: 1.3–3.7, p = 0.005) were factors associated with psychological distress while non-medical program (aOR: 2.2, 95%CI: 1.3–3.7, p = 0.005) was associated with increased depression. Access to mental health services was associated with both reduced distress (aOR: 0.5, 95%CI: 0.3–0.8, p = 0.005) and depression (aOR: 0.6, 95%CI: 0.3–0.9, p = 0.034). A majority (65.3%) of the participants reported knowing how to access mental health care and 188 (51.4%) reported having needed emotional support but, only 67 (18.3%) ever sought care from a mental health professional. Of those who had access, only 10 (7%), and 13 (9%) accessed a counselor or a mental health unit, respectively. The barriers to accessibility of mental health care included financial limitations (49.5%), lack of awareness (32.5%), lack of mental health professionals (28.4%), and stigma (13.9%).

Conclusion

Among university students in Uganda during the COVID- 19 lockdown, the burden of psychological distress and depression was substantial. However, access to mental health services was limited by several factors.
The Impact of the COVID-19 Pandemic on the Mental Health of Healthcare Workers in Italy: Analyzing the Role of Individual and Workplace-Level Factors in the Reopening Phase After Lockdown
Authors: Moro Maria Francesca , Calamandrei Gemma , Poli Ranieri , Di Mattei Valentina , Perra Alessandra , Kurotschka Peter Konstantin , Restrepo Alexandra , Romano Ferdinando , La Torre Giuseppe , Preti Emanuele , Mascayano Franco , Picardi Angelo , Chiarotti Flavia , Rapisarda Venerando , Urban Antonio , Alvarado Ruben , Susser Ezra , Carta Mauro Giovanni
Published: June 2022

Introduction

Italy is one of the high-income countries hit hardest by Covid-19. During the first months of the pandemic, Italian healthcare workers were praised by media and the public for their efforts to face the emergency, although with limited knowledge and resources. However, healthcare workers soon had to face new challenges at a time when the national health system was working hard to recover. This study focuses on this difficult period to assess the impact of the COVID-19 pandemic on the mental health of Italian healthcare workers.

Materials and Methods

Healthcare workers from all Italian regions [n = 5,502] completed an online questionnaire during the reopening phase after the first wave lockdown. We assessed a set of individual-level factors (e.g., stigma and violence against HCWs) and a set of workplace-level factors (e.g., trust in the workplace capacity to handle COVID-19) that were especially relevant in this context. The primary outcomes assessed were score ≥15 on the Patient Health Questionnaire-9 and score ≥4 on the General Health Questionnaire-12, indicators of clinically significant depressive symptoms and psychological distress, respectively. Logistic regression analyses were performed on depressive symptoms and psychological distress for each individual- and workplace-level factor adjusting for gender, age, and profession.

Results

Clinically significant depressive symptoms were observed in 7.5% and psychological distress in 37.9% of HCWs. 30.5% of healthcare workers reported having felt stigmatized or discriminated, while 5.7% reported having experienced violence. Feeling stigmatized or discriminated and experiencing violence due to being a healthcare worker were strongly associated with clinically significant depressive symptoms [OR 2.98, 95%CI 2.36–3.77 and OR 4.72 95%CI 3.41–6.54] and psychological distress [OR 2.30, 95%CI 2.01–2.64 and OR 2.85 95%CI 2.16–3.75]. Numerous workplace-level factors, e.g., trust in the workplace capacity to handle COVID-19 [OR 2.43, 95%CI 1.92–3.07] and close contact with a co-worker who died of COVID-19 [OR 2.05, 95%CI 1.56–2.70] were also associated with clinically significant depressive symptoms. Similar results were found for psychological distress.

Conclusions

Our study emphasizes the need to address discrimination and violence against healthcare professionals and improve healthcare work environments to strengthen the national health system's capacity to manage future emergencies.
Inter-Country COVID-19 Contagiousness Variation in Eight African Countries
Authors: Singini Geoffrey Chiyuzga , Manda Samuel O. M.
Published: June 2022

The estimates of contiguousness parameters of an epidemic have been used for health-related policy and control measures such as non-pharmaceutical control interventions (NPIs). The estimates have varied by demographics, epidemic phase, and geographical region. Our aim was to estimate four contagiousness parameters: basic reproduction number (R0), contact rate, removal rate, and infectious period of coronavirus disease 2019 (COVID-19) among eight African countries, namely Angola, Botswana, Egypt, Ethiopia, Malawi, Nigeria, South Africa, and Tunisia using Susceptible, Infectious, or Recovered (SIR) epidemic models for the period 1 January 2020 to 31 December 2021. For reference, we also estimated these parameters for three of COVID-19's most severely affected countries: Brazil, India, and the USA. The basic reproduction number, contact and remove rates, and infectious period ranged from 1.11 to 1.59, 0.53 to 1.0, 0.39 to 0.81; and 1.23 to 2.59 for the eight African countries. For the USA, Brazil, and India these were 1.94, 0.66, 0.34, and 2.94; 1.62, 0.62, 0.38, and 2.62, and 1.55, 0.61, 0.39, and 2.55, respectively. The average COVID-19 related case fatality rate for 8 African countries in this study was estimated to be 2.86%. Contact and removal rates among an affected African population were positively and significantly associated with COVID-19 related deaths (p-value < 0.003). The larger than one estimates of the basic reproductive number in the studies of African countries indicate that COVID-19 was still being transmitted exponentially by the 31 December 2021, though at different rates. The spread was even higher for the three countries with substantial COVID-19 outbreaks. The lower removal rates in the USA, Brazil, and India could be indicative of lower death rates (a proxy for good health systems). Our findings of variation in the estimate of COVID-19 contagiousness parameters imply that countries in the region may implement differential COVID-19 containment measures.
Research on Infant Health Diagnosis and Intelligence Development Based on Machine Learning and Health Information Statistics
Authors: Wang Siyu , Li Min , Ng Soo Boon
Published: June 2022

Intelligent health diagnosis for young children aims at maintaining and promoting the healthy development of young children, aiming to make young children have a healthy state and provide a better future for their physical and mental health development. The biological basis of intelligence is the structure and function of human brain and the key to improve the intelligence level of infants is to improve the quality of brain development, especially the early development of brain. Based on machine learning and health information statistics, this paper studies the development of infant health diagnosis and intelligence, physical and mental health. Pre-process the sample data, and use the filtering method based on machine learning and health information statistics for feature screening. Compared with traditional statistical methods, machine learning and health information statistical methods can better obtain the hidden information in the big data of children's physical and mental health development, and have better learning ability and generalization ability. The machine learning theory is used to analyze and mine the infant's health diagnosis and intelligence development, establish a health state model, and intuitively show people the health status of their infant's physical and mental health development by means of data. Moreover, the accumulation of these big data is very important in the field of medical and health research driven by big data.
Influencing Factors Related to Female Sports Participation Under the Implementation of Chinese Government Interventions: An Analysis Based on the China Family Panel Studies
Authors: Fang Ping , Sun Lei , Shi Shu Sheng , Ahmed Laar Rizwan , Lu Yan
Published: June 2022

Objectives

Regular sports participation is a gendered phenomenon in China. Women have reported much higher constraints than men on time, partner, psychology, knowledge, and interest. This study explores personal, family, lifestyle, and health factors associated with sports participation.

Study Design

This study is a cross-sectional study.

Methods

Data were collected from the national reprehensive China Family Panel Studies (CFPS) database (2018) to analyze personal information, family background, lifestyle, and health in relation to women's sports participation. Multiple classification logistic regression was used to quantify the association between independent variables and sports time.

Results

Women with high personal income and education, who were unmarried, in faster economic development areas have more awareness and more time for sports participation. Women who were overweight and self-rated as unattractive spent less time on sports participation. Women with a small family population and no children have more time for sports participation. Less time on the internet and moderate sleep contribute to active sports participation. Women with chronic diseases and high medical costs are less likely to participate in sports.

Conclusions

Negative body aesthetic perception, the burden of family environment, modernization of lifestyle, and the normalization of sub-health are essential factors affecting women's sports participation. The government should understand the inner and outer barriers to women's participation in sports, develop policies and regulations to protect and support women's sports participation, and guide and monitor the effective implementation of women's sports activities.
Signal Perceptron: On the Identifiability of Boolean Function Spaces and Beyond
Authors: Mendez Lucero Miguel-Angel , Karampatsis Rafael-Michael , Bojorquez Gallardo Enrique , Belle Vaishak
Published: June 2022

In a seminal book, Minsky and Papert define the perceptron as a limited implementation of what they called “parallel machines.” They showed that some binary Boolean functions including XOR are not definable in a single layer perceptron due to its limited capacity to learn only linearly separable functions. In this work, we propose a new more powerful implementation of such parallel machines. This new mathematical tool is defined using analytic sinusoids—instead of linear combinations—to form an analytic signal representation of the function that we want to learn. We show that this re-formulated parallel mechanism can learn, with a single layer, any non-linear k-ary Boolean function. Finally, to provide an example of its practical applications, we show that it outperforms the single hidden layer multilayer perceptron in both Boolean function learning and image classification tasks, while also being faster and requiring fewer parameters.
Assessing Banks' Distress Using News and Regular Financial Data
Authors: Cerchiello Paola , Nicola Giancarlo , Rönnqvist Samuel , Sarlin Peter
Published: June 2022

In this paper, we focus our attention on leveraging the information contained in financial news to enhance the performance of a bank distress classifier. The news information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with the issues related to Natural Language interpretation and to the analysis of news media. Among the different models proposed for such purpose, we investigate a deep learning approach. The methodology is based on a distributed representation of textual data obtained from a model (Doc2Vec) that maps the documents and the words contained within a text onto a reduced latent semantic space. Afterwards, a second supervised feed forward fully connected neural network is trained combining news data distributed representations with standard financial figures in input. The goal of the model is to classify the corresponding banks in distressed or tranquil state. The final aim is to comprehend both the improvement of the predictive performance of the classifier and to assess the importance of news data in the classification process. This to understand if news data really bring useful information not contained in standard financial variables.
Exploring Metro and Non-metro Differences in Satisfaction With Services and Community Participation Among Low-Income Personal Assistance Service Users
Authors: Sage Rayna , Standley Krys , Mashinchi Genna M.
Published: June 2022

Personal assistance services (PAS) are supports provided by workers to assist disabled people with their activities of daily living. Access to in-home PAS allows people who need assistance with these activities to live in their own homes and communities, rather than moving to congregate living facilities. Because metro and non-metro areas differ in many ways, we explored the following research questions: (1) Are there differences between non-metro and metro PAS users?, (2) What factors are associated with satisfaction with services?, and (3) What factors are associated with satisfaction with community participation?. We randomly surveyed PAS consumers in five states about their experiences with PAS. To answer the first question, we compared metro or non-metro consumers using independent samplest-tests. We found few statistically significant differences between metro and non-metro respondents. To answer the second and third research questions, we conducted linear regressions predicting our dependent variables. In terms of satisfaction with services, our model explained very little of the variance, other than finding that being partnered or married was significantly, positively related to satisfaction with services. In predicting satisfaction with community participation, the model explained about a quarter of the variance, with having fewer disabilities and higher health status predicting more satisfaction. This research indicates that there are few differences between metro and non-metro low-income PAS consumers and that more research is needed to understand what factors are related to satisfaction with services and community participation in this population.
Cognition of Regional Economic Growth Based on China's Intellectual Class
Authors: Liu Ming-Yang
Published: June 2022

To understand the cognition of China's intellectual class on economic growth, this paper consulted the related documents and conducted questionnaires in China. A sample of 6,326 talented persons from 16 large and medium-sized cities was selected in a descriptive analysis study. The attitude of talents toward regional economic growth has been assessed by a conceptual pathway model of organizational environmental variables. The collected data is analyzed by the SPSS software package to establish a comprehensive framework model, which can quantitatively analyze the relationship between regional economic growth and its predictors. The relationship can be described as follows: YREG = 0.201XEI + 0.149XTEP + 0.217XPO + 0.148XTE + 0.480XNR + θ or YREG = 0.53XTRE + 0.480XNR + θ. The model suggested that regional economic growth was driven predominantly by talent rooting environment and natural resources. Compared with policy orientation, the talent exchanging platform was profound to pay more attention to the future development of talent. The model also demonstrated that increased education investment plays a pivotal role in China's intellectual class, which will directly or indirectly affect talent development, talent introduction, and the environment for talents to be rooted.
Artificial Intelligence (AI) for Sustainable Institutional Food Systems: Implementation of AI Tools for School Nutrition Program Management in the United States of America
Authors: Camaréna Stéphanie
Published: June 2022

School food programs can improve the nutritional status at community level, and can be a powerful tool to facilitate a transition to sustainable food systems, particularly through their purchasing methods and meal/waste management. Artificial Intelligence (AI) can enable or inhibit transitions to sustainability, due to its capacity to facilitate transformational change and disruption. Thus, AI can have major impacts on achieving the United Nations' Sustainable Development Goals (SDGs), including those related to food systems. This paper focuses on critically exploring different aspects during the implementation of a software project in US schools that used AI techniques to enable agility and provide healthy food options for schoolchildren. Participant observation, semi-structured interviews, and document analysis were used to inform the case study and uncover the new processes developed using the technology. As a key barrier to the effective management of school food programs and nutrition is the administrative load associated with statutory compliance, this case study demonstrates the difference that AI-powered tools can make in alleviating the weight of administrative processes. An Information and Communication Technology (ICT)-enabled boundary spanning framework is used to extend the case study toward an analysis of the systems, boundaries, relationships and perspectives which starts mapping the areas where the private sector, public institutions and civil society can meet to fast-track sustainable transition activities, particularly in the context of food systems. This article presents some possible approaches to facilitating these activities, inviting policy makers to bridge the gap with businesses and use business agility to support common societal goals to achieve sustainable food systems.
Robust Behrens–Fisher Statistic Based on Trimmed Means and Its Usefulness in Analyzing High-Throughput Data
Authors: Kang Guolian , Mirzaei Sedigheh S. , Zhang Hui , Zhu Liang , Rai Shesh N. , Srivastava Deo Kumar
Published: June 2022

In the context of high-throughput data, the differences in continuous markers between two groups are usually assessed by ordering the p-values obtained from the two-sample pooled t-test or Wilcoxon–Mann–Whitney test and choosing a stringent cutoff such as 10–8 to control the family-wise error rate (FWER) or false discovery rate (FDR). All markers with p-values below the cutoff are declared to be significantly associated with the phenotype. This inherently assumes that the test procedure provides valid type I error estimates in extreme tails of the null distribution. The aforementioned tests assume homoscedasticity in the two groups, and the t-test further assumes underlying distributions to be normally distributed. Cao et al. (Biometrika, 2013, 100, 495–502) have shown that in the context of multiple hypotheses testing the approach based on FDR may not be valid under non-normality and/or heteroscedasticity. Therefore, having a test statistic that is robust to these violations is needed. In this study, we propose a robust analog of Behrens–Fisher statistic based on trimmed means, conduct an extensive simulation study to compare its performance with other competing approaches, and demonstrate its usefulness by applying it to DNA methylation data used by Teschendorff et al. (Genome Res., 2010, 20, 440–446). An R program to implement the proposed method is provided in the Supplementary Material.
Post-Transplantation Cytomegalovirus Infection Interplays With the Development of Anastomotic Biliary Strictures After Liver Transplantation
Authors: Georges Pauline , Clerc Clémentine , Turco Célia , Di Martino Vincent , Paquette Brice , Minello Anne , Calame Paul , Magnin Joséphine , Vuitton Lucine , Weil-Verhoeven Delphine , Lakkis Zaher , Vanlemmens Claire , Latournerie Marianne , Heyd Bruno , Doussot Alexandre
Published: June 2022

Background: Anastomotic biliary stricture (ABS) remains the most frequent complication after liver transplantation (LT). This study aimed to identify new anastomotic biliary stricture risk factors, with a specific focus on postoperative events. Additionally, ABS management and impact on patient and graft survival were assessed.

Methods: All consecutive patients who underwent LT with duct-to-duct anastomosis between 2010 and 2019 were included. All patients who died within 90 days after LT due to non-ABS-related causes were excluded.

Results: Among 240 patients, 65 (27.1%) developed ABS after a median time of 142 days (range, 13–1265). Median follow-up was 49 months (7–126). Upon multivariable analysis, donor BMI (OR=0.509, p = 0.037), post-LT CMV primoinfection (OR = 5.244, p < 0.001) or reactivation (OR = 2.421, p = 0.015) and the occurrence of post-LT anastomotic biliary fistula (OR = 2.691, p = 0.021) were associated with ABS. Anastomotic technical difficulty did not independently impact the risk of ABS (OR = 1.923, p = 0.051). First-line ABS treatment was systematically endoscopic (100%), and required a median of 2 (range, 1–11) procedures per patient. Repeat LT was not required in patients developing ABS. The occurrence of ABS was not associated with overall patient survival (p = 0.912) nor graft survival (p = 0.521).

Conclusion: The risk of developing ABS after LT seems driven by the occurrence of postoperative events such as CMV infection and anastomotic fistula. In this regard, the role of CMV prophylaxis warrants further investigations.
Energy Assessment for First and Second Season Conventional and Transgenic Corn
Authors: Silber Rodolfo Michelassi , Romanelli Thiago Libório
Published: June 2022

The exploitation of natural resources for agriculture is growing to fulfill the demand for food, which requires the rational use of inputs for sustainable production. Brazilian agricultural production stands out on the international scene. For instance, corn is one of the most exported products in Brazil, which is possible through the planting in the second crop season within a year, called the “off-season”. In addition to being a technique that allows soil conservation, it also reduces the use of inputs and soil tillage. The agricultural production systems require a large amount of energy throughout their processes, mainly through inputs and fuels. Energy flows allow for the identification of the efficiency of the production system and, consequently, its sustainability. Indicators regarding net energy gain per area (Energy balance) and energy profitability (Energy Return on Investment) were applied. The first-season system presented higher energy demand when compared to the second-season system, with a difference of 10.24 GJ ha−1 between the conventional ones and 10.47 GJ ha−1 between the transgenic ones. However, the indicators showed higher energy efficiency in the transgenic off-season corn production, in which the return on energy was 55% higher, and the energy incorporation was 35% lower when compared to conventional first-season corn.
A New Strategy to Improve Vineyard Resilience: Grapevine Morphological Adaptation to Short-Term Nitrogen Deficiency
Authors: Dinu Daniel Grigorie , Popescu Carmen Florentina , Sumedrea Dorin Ioan , Manolescu Andreea Elena , Pandelea Letitia Mariana , Rustioni Laura
Published: June 2022

Nitrogen (N) deficiency causes a morphological disequilibrium between shoots and roots, favoring hypogeal growth. Our hypothesis is that short-term nitrogen deficiency could induce an increase in vine resilience, inducing root growth before the appearance of significant dysfunctions in the epigeal part of the plant. Then, nitrogen availability could be promptly restored through a well-thought-out fertilization plan. In this experiment, two table grape cultivars grafted onto three different rootstocks were studied in three hydroponic growing conditions characterized by different nitrogen availability. After 53 days of treatment, plant morphologies were described, and leaf photosynthetic pigments were quantified. The short-term nitrogen deficiency induced root growth, leading to significantly different proportions among the hypogeal and the epigeal parts, despite no significant differences were recorded concerning shoot growth. Some small differences in the leaf pigmentations indicated an incipient symptom of nitrogen deficiency, and significant differences in the phytomer morphology indicated the possibility of developing low-cost indices for prompt identification of incipient symptoms. This will allow the exploitation of the advantages of short-term nitrogen deficiency while avoiding negative effects on shoot growth by promptly restoring the nitrogen availability.
Mortality in Thai Nursing Homes Based on Antimicrobial-Resistant Enterobacterales Carriage and COVID-19 Lockdown Timing: A Prospective Cohort Study
Authors: Ngamprasertchai Thundon , Vanaporn Muthita , Muangnoicharoen Sant , Pan-ngum Wirichada , Ruenroengbun Narisa , Piroonamornpun Pittaya , Ponam Thitiya , Duangdee Chatnapa , Chankete Phanita , Jitmuang Anupop , Thamlikitkul Visanu
Published: June 2022

Antimicrobial-resistant Enterobacterales carriage and the coronavirus disease 2019 (COVID-19) lockdown measures may impact the incidence all-cause mortality rate among nursing home residents. To determine the all-cause mortality rate in the presence/absence of antimicrobial-resistant Enterobacterales carriage and the incidence all-cause mortality rate before and during COVID-19 pandemic lockdown, this prospective closed-cohort study was conducted at various types of nursing homes in Bangkok, Thailand, from June 2020 to December 2021. The elderly residents included 142 participants (aged ≥60 years) living in nursing homes ≥3 months, who did not have terminal illnesses. Time-to-event analyses with Cox proportional hazards models and stratified log-rank tests were used. The all-cause mortality rate was 18%, and the incidence all-cause mortality rate was 0.59/1000 person-days in residents who had antimicrobial-resistant Enterobacterales carriage at baseline. Meanwhile, the incidence all-cause mortality rate among noncarriage was 0.17/1000 person-days. The mortality incidence rate of carriage was three times higher than residents who were noncarriage without statistical significance (HR 3.2; 95% CI 0.74, 13.83). Residents in nonprofit nursing homes had a higher mortality rate than those in for-profit nursing homes (OR 9.24; 95% CI 2.14, 39.86). The incidence mortality rate during and before lockdown were 0.62 and 0.30, respectively. Effective infection-control policies akin to hospital-based systems should be endorsed in all types of nursing homes. To limit the interruption of long-term chronic care, COVID-19 prevention should be individualized to nursing homes.
Persistent Coagulase-Negative Staphylococcal Bacteremia in Neonates: Clinical, Microbiological Characteristics and Changes within a Decade
Authors: Bellou Venetia , Gkentzi Despoina , Giormezis Nikolaos , Vervenioti Aggeliki , Spiliopoulou Iris , Dimitriou Gabriel
Published: June 2022

Atypical outbreaks of persistent coagulase-negative staphylococci (CoNS) bacteremias, defined as three or more consecutive positive blood cultures with the same CoNS species, at least 48 h apart, have been reported in neonatal intensive-care units (NICUs). Our aim was to describe the profile of these cases in our NICU over a two-year period with the objective of assessing possible changes within a decade. Demographics, clinical and microbiological data were recorded for all CoNS bacteremias in our tertiary NICU during 2016–2017 and compared with the results of the same study in 2006–2007. Fifty-six cases of CoNS sepsis were recorded. Fourteen (25%) of them were persistent. There were no significant differences in demographic and clinical characteristics between cases with persistent vs. non-persistent bacteremia. Staphylococcus epidermidis was the most common species. In logistic regression analysis, biofilm production (β = 2.464, p = 0.04) was the most significant determinant for the development of persistent CoNS bacteremia. Our isolates were less likely to produce biofilm and carry ica operon as compared to those of 2006–2007. The cases of persistent CoNS sepsis have decreased within a decade, which could be attributed to the implementation of intensive infection control practices. Biofilm production remains the most important risk factor.
Antioxidants in Arrhythmia Treatment—Still a Controversy? A Review of Selected Clinical and Laboratory Research
Authors: Szyller Jakub , Jagielski Dariusz , Bil-Lula Iwona
Published: June 2022

Antioxidants are substances that can prevent damage to cells caused by free radicals. Production of reactive oxygen species and the presence of oxidative stress play an important role in cardiac arrhythmias. Currently used antiarrhythmic drugs have many side effects. The research on animals and humans using antioxidants (such as vitamins C and E, resveratrol and synthetic substances) yields many interesting but inconclusive results. Natural antioxidants, such as vitamins C and E, can reduce the recurrence of atrial fibrillation (AF) after successful electrical cardioversion and protect against AF after cardiac surgery, but do not affect the incidence of atrial arrhythmias in critically ill patients with trauma. Vitamins C and E may also effectively treat ventricular tachycardia, ventricular fibrillation and long QT-related arrhythmias. Another natural antioxidant—resveratrol—may effectively treat AF and ventricular arrhythmias caused by ischaemia–reperfusion injury. It reduces the mortality associated with life-threatening ventricular arrhythmias and can be used to prevent myocardial remodelling. Statins also show antioxidant activity. Their action is related to the reduction of oxidative stress and anti-inflammatory effect. Therefore, statins can reduce the post-operative risk of AF and may be useful in lowering its recurrence rate after successful cardioversion. Promising results also apply to polyphenols, nitric oxide synthase inhibitors and MitoTEMPO. Although few clinical trials have been conducted, the use of antioxidants in treating arrhythmias is an interesting prospect.
An Effective Approach to Detect and Identify Brain Tumors Using Transfer Learning
Authors: Ullah Naeem , Khan Javed Ali , Khan Mohammad Sohail , Khan Wahab , Hassan Izaz , Obayya Marwa , Negm Noha , Salama Ahmed S.
Published: June 2022

Brain tumors are considered one of the most serious, prominent and life-threatening diseases globally. Brain tumors cause thousands of deaths every year around the globe because of the rapid growth of tumor cells. Therefore, timely analysis and automatic detection of brain tumors are required to save the lives of thousands of people around the globe. Recently, deep transfer learning (TL) approaches are most widely used to detect and classify the three most prominent types of brain tumors, i.e., glioma, meningioma and pituitary. For this purpose, we employ state-of-the-art pre-trained TL techniques to identify and detect glioma, meningioma and pituitary brain tumors. The aim is to identify the performance of nine pre-trained TL classifiers, i.e., Inceptionresnetv2, Inceptionv3, Xception, Resnet18, Resnet50, Resnet101, Shufflenet, Densenet201 and Mobilenetv2, by automatically identifying and detecting brain tumors using a fine-grained classification approach. For this, the TL algorithms are evaluated on a baseline brain tumor classification (MRI) dataset, which is freely available on Kaggle. Additionally, all deep learning (DL) models are fine-tuned with their default values. The fine-grained classification experiment demonstrates that the inceptionresnetv2 TL algorithm performs better and achieves the highest accuracy in detecting and classifying glioma, meningioma and pituitary brain tumors, and hence it can be classified as the best classification algorithm. We achieve 98.91% accuracy, 98.28% precision, 99.75% recall and 99% F-measure values with the inceptionresnetv2 TL algorithm, which out-performs the other DL algorithms. Additionally, to ensure and validate the performance of TL classifiers, we compare the efficacy of the inceptionresnetv2 TL algorithm with hybrid approaches, in which we use convolutional neural networks (CNN) for deep feature extraction and a Support Vector Machine (SVM) for classification. Similarly, the experiment’s results show that TL algorithms, and inceptionresnetv2 in particular, out-perform the state-of-the-art DL algorithms in classifying brain MRI images into glioma, meningioma, and pituitary. The hybrid DL approaches used in the experiments are Mobilnetv2, Densenet201, Squeeznet, Alexnet, Googlenet, Inceptionv3, Resnet50, Resnet18, Resnet101, Xception, Inceptionresnetv3, VGG19 and Shufflenet.
Research Status of and Trends in 3D Geological Property Modeling Methods: A Review
Authors: Liu Yuyang , Zhang Xiaowei , Guo Wei , Kang Lixia , Gao Jinliang , Yu Rongze , Sun Yuping , Pan Mao
Published: June 2022

Three-dimensional (3D) geological property modeling is used to quantitatively characterize various geological attributes in 3D space based on geostatistics with the help of computer visualization technology, and the results are often stored in grid data. The 3D geological property modeling includes two main components, grid model generation and property interpolation. In this review article, the existing grid generation methods are systematically investigated, and both traditional and multiple-point geostatistical algorithms involved in interpolation methods are comprehensively analyzed. It is shown that considering the numerical simulation of oil reservoirs, the orthogonal hexahedral grid remains the most suitable grid model for simulations in petroleum exploration and development. For the interpolation methods aspect, most geological phenomena are nonstationary, to simulate various types of reservoirs; the main development trends are increasing geological constraints and reducing the limitation of stationarity. Both methods have certain constraints, and the multiscale problem of multiple-point geostatistics poses a main challenge to the field. In addition, the deep-learning based method is a new trend in geological property modeling.
Wireless Body Area Routing Protocols Impact Analysis on Entity Mobility Models with Static Sink Node
Authors: Singh Sunny , Prasad Devendra , Rani Shalli , Singh Aman , Alharithi Fahd S. , Almotiri Jasem
Published: June 2022

The most important and emerging characteristic of Wireless Body Area Networks (WBANs), which differentiates them from other wired and wireless area networks, is mobility. Therefore, the routing protocols for WBAN are designed in such a way that they can deal with dynamic changes in topology and provide maximum throughput, packet delivery ratio, average end-to-end delay, and minimum energy consumption. Thus, achieving optimal values for every performance parameter becomes a big challenge. This work investigates the performance of three separate path discovery protocols, such as Destination-Sequenced Distance-Vector Routing (DSDV), Ad Hoc On-demand Distance Vector (AODV), and Ad Hoc On-demand Multipath Distance Vector Routing protocol (AOMDV), for two different mobility models with a fixed-positioned sink. During experimentation, the AOMDV routing protocol achieves a high packet delivery ratio (PDR), average end-to-end delay, and throughput as compared to other routing protocols.
Theory of Electrical Breakdown in a Nanocomposite Capacitor
Authors: Bordo Vladimir , Ebel Thomas
Published: June 2022

The electrostatic field in a nanocomposite represented by spherical nanoparticles (NPs) embedded into a dielectric between two parallel metallic electrodes is derived from first principles. The NPs are modeled by point dipoles which possess the polarizability of a sphere, and their image potential in the electrodes is found using a dyadic Green’s function. The derived field is used to obtain the parameters which characterize the electrical breakdown in a nanocomposite capacitor. It is found, in particular, that for relatively low volume fractions of NPs, the breakdown voltage linearly decreases with the volume fraction, and the slope of this dependence is explicitly found in terms of the dielectric permittivities of the NPs and the dielectric host. The corresponding decrease in the maximum energy density accumulated in the capacitor is also determined. A comparison with the experimental data on the breakdown strength in polymer films doped with BaTiO3 NPs available in the literature reveals a dominant role of the interface polarization at the NP-polymer interface and an existence of a nonferroelectric surface layer in NPs. This research provides a rigorous approach to the electrical breakdown phenomenon and can be used for a proper design of nanocomposite capacitors.
Comparison of Unbound Granular Materials’ Resilient Moduli Determined by Cyclic Triaxial Test and Innovative FWD Device
Authors: Stehlik Dusan , Hyzl Petr , Dasek Ondrej , Malis Ludek , Kaderka Robert , Komenda Radek , Sachr Jiri , Vesely Petr , Spies Karel , Varaus Michal
Published: June 2022

In addition to the conventional laboratory tests of input materials and construction mixtures, the technically correct design of pavement structure involves more frequent functional tests of the input materials and mixtures. Our study summarizes findings from a long-term monitoring of pavement on selected road sections with lower traffic intensity. The research involved the comparison of resilient moduli as a design parameter of unbound pavement base layers’ unbound granular materials (UGMs), determined in a laboratory by a test device for a cyclical triaxial test and in situ by impact load test using a falling weight deflectometer (FWD). In particular, the resilient moduli determined in the laboratory by a cyclic triaxial test (Mr,CTT) were compared with the real values of resilient moduli measured by FWD (Mr,FWD). A new, unique, and innovative FWD device was used to determine the Mr,FWD of the UGMs. The innovation lay in the dual-axis deflection measurement—measurement performed not just in the axis parallel to the road, but also in the axis in the transversal direction. It was found that, especially on roads with lower traffic load, such dual-axis (spatial) determination of Mr,FWD moduli was of great importance because it often allowed the identification of local pavement sections with low bearing capacities, especially at the edges, which have a significantly lower value of Mr,FWD.
Infrared Bird Target Detection Based on Temporal Variation Filtering and a Gaussian Heat-Map Perception Network
Authors: Zhao Fan , Wei Renjie , Chao Yu , Shao Sidi , Jing Cuining
Published: June 2022

Flying bird detection has recently attracted increasing attention in computer vision. However, compared to conventional object detection tasks, it is much more challenging to trap flying birds in infrared videos due to small target size, complex backgrounds, and dim shapes. In order to solve the problem of poor detection performance caused by insufficient feature information of small and dim birds, this paper suggests a method of detecting birds in outdoor environments using image pre-processing and deep learning, called temporal Variation filtering (TVF) and Gaussian heatmap perception network (GHPNet), respectively. TVF separates the dynamic background from moving creatures. Using bird appearance features that are brightest at the center and gradually darker outwards, the size-adaptive Gaussian kernel is used to generate the ground truth of the region of interest (ROI). In order to fuse the features from different scales and to highlight the saliency of the target, the GHPNet network integrates VGG-16 and maximum-no-pooling filterer into a U-Net network. The comparative experiments demonstrate that the proposed method outperforms those that are state-of-the-art in detecting bird targets in real-world infrared images.