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17 pages, 1171 KiB  
Article
The Interplay of Values and Skill: How Do They Impact Graduates’ Employability?
by Alba Kruja, Belita Hysaj and Ahmet Oztas
Adm. Sci. 2024, 14(9), 201; https://doi.org/10.3390/admsci14090201 (registering DOI) - 2 Sep 2024
Abstract
The purpose of this research is to explore the development of values and skills throughout university education and their subsequent manifestation in the workplace, with a particular focus on the impact on graduates’ employability and the creation of value for society. The study’s [...] Read more.
The purpose of this research is to explore the development of values and skills throughout university education and their subsequent manifestation in the workplace, with a particular focus on the impact on graduates’ employability and the creation of value for society. The study’s research question probes the dynamics of values, skills, employability, and social value creation by analyzing and evaluating the main missions of universities, which essentially involve teaching, researching, and extracurricular activities. An exploratory factor analysis is used to extract the relevant factors of graduates’ performance. A progressive model is developed pointing out the interplay of values and skills that lead to professional performance. The research comes up with practical and theoretical implications. It seeks to provide helpful findings for higher education institutions, industry, and policymakers in promoting the enhancement of graduates’ values and skills, ensuring their successful transition into the job market and generating long-term societal benefits. Full article
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19 pages, 373 KiB  
Article
A New Integer Model for Selecting Students at Higher Education Institutions: Preparatory Classes of Engineers as Case Study
by Soufyane Majdoub, Chakir Loqman and Jaouad Boumhidi
Information 2024, 15(9), 529; https://doi.org/10.3390/info15090529 (registering DOI) - 2 Sep 2024
Abstract
This study addresses the challenge of selecting outstanding students at higher education institutions under multiple constraints. We propose a novel integer programming solution to manage this process, formulating it as a constrained assignment problem with a maximization objective function. This function prioritizes the [...] Read more.
This study addresses the challenge of selecting outstanding students at higher education institutions under multiple constraints. We propose a novel integer programming solution to manage this process, formulating it as a constrained assignment problem with a maximization objective function. This function prioritizes the fair selection of students while respecting criteria such as academic qualifications, required skills, and student preferences. The goal is to develop a decision support system that efficiently selects qualified students at higher education institutions within a reasonable time. The model was tested using real data from Moroccan preparatory classes, achieving important assignment rates across all student categories. Results demonstrate significance in execution time, fulfillment of student choices, and prioritization of outstanding students. This approach offers a flexible, efficient solution for managing academic merit-based selections, optimizing resource utilization, and enhancing fairness in the selection process. Full article
(This article belongs to the Special Issue Optimization Algorithms and Their Applications)
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13 pages, 7935 KiB  
Article
Future Parabolic Trough Collector Absorber Coating Development and Service Lifetime Estimation
by Ana Drinčić, Luka Noč, Franci Merzel and Ivan Jerman
Coatings 2024, 14(9), 1111; https://doi.org/10.3390/coatings14091111 (registering DOI) - 2 Sep 2024
Abstract
This work presents a study on the optical and mechanical degradation of parabolic trough collector absorber coatings produced through the spray coating application technique of in-house developed paint. The main aim of this investigation is to prepare, cure, load, and analyze the absorber [...] Read more.
This work presents a study on the optical and mechanical degradation of parabolic trough collector absorber coatings produced through the spray coating application technique of in-house developed paint. The main aim of this investigation is to prepare, cure, load, and analyze the absorber coating on the substrate under conditions that mimic the on-field thermal properties. This research incorporates predicted isothermal and cyclic loads for parabolic trough systems as stresses. Biweekly inspections of loaded, identical samples monitored the degradation process. We further used the cascade of data from optical, oxide-thickening, crack length, and pull-off force measurements in mathematical modelling to predict the service life of the parabolic trough collector. The results collected and used in modelling suggested that cyclic load in combination with iso-thermal load is responsible for coating fatigue, influencing the solar absorber optical values and resulting in lower energy transformation efficiency. Finally, easy-to-apply coatings made out of spinel-structured black pigment and durable binder could serve as a low-cost absorber coating replacement for a new generation of parabolic trough collectors, making it possible to harvest solar energy to provide medium-temperature heat to decarbonize future food, tobacco, and paint production industrial processes. Full article
(This article belongs to the Special Issue Coatings for Advanced Devices)
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27 pages, 2771 KiB  
Article
Contextual Intelligence: An AI Approach to Manufacturing Skills’ Forecasting
by Xolani Maphisa, Mpho Nkadimeng and Arnesh Telukdarie
Big Data Cogn. Comput. 2024, 8(9), 101; https://doi.org/10.3390/bdcc8090101 (registering DOI) - 2 Sep 2024
Abstract
The manufacturing industry is skill-intensive and plays a pivotal role in South Africa’s economy, reflecting the nation’s progress and development. The advent of technology has initiated a transformative era within the manufacturing sector. Workforce skills are at the heart of ensuring the sustained [...] Read more.
The manufacturing industry is skill-intensive and plays a pivotal role in South Africa’s economy, reflecting the nation’s progress and development. The advent of technology has initiated a transformative era within the manufacturing sector. Workforce skills are at the heart of ensuring the sustained growth of the industry. This study delves into the skill-related aspects of the occupational landscape of the South African manufacturing sector, with a particular focus on two important manufacturing sectors: the food and beverage manufacturing (FoodBev) sector and the chemical manufacturing (CHIETA) sector. Leveraging the forecasting prowess of Autoregressive Integrated Moving Average (ARIMA), this paper outlines a sectorial occupational forecasting modeling exercise to reveal which job roles are poised for expansion and which are expected to decline. The approach predicted future skills’ demand 80% accuracy for 473 out of 713 (66%) occupations for FoodBev and 474 out of 522 (91%) for CHIETA. These insights are invaluable for industry stakeholders and educational institutions, providing guidance to support the sector’s growth in an era marked by technological advancement. Full article
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19 pages, 576 KiB  
Article
The Impact of Environmental Protection Investment and Equity Balance Degree on Economic Performance and Eco-Autonomy: An Empirical Study of China’s A-Share Listed Companies
by Ying Sun, Kexin Zhang and Xuyang Li
Sustainability 2024, 16(17), 7581; https://doi.org/10.3390/su16177581 (registering DOI) - 2 Sep 2024
Abstract
Enterprises are not only the main source of energy consumption and pollution emissions but also a key force in environmental governance. There is no doubt that the positive impact of enterprise environmental protection investment (EPI) on other stakeholders, but the impact on its [...] Read more.
Enterprises are not only the main source of energy consumption and pollution emissions but also a key force in environmental governance. There is no doubt that the positive impact of enterprise environmental protection investment (EPI) on other stakeholders, but the impact on its own economic performance is the key to determining the scale of EPI and increasing the motivation for Eco-autonomy. This paper selects 691 companies listed on China’s A-share market from 2012 to 2022 as research samples, introducing the equity balance degree as the moderator variable, and empirically investigating the impact of the relationship by using a panel multivariate regression model. The results show that the relationship between EPI and its economic performance is a U-shaped curve, and it is related to Eco-autonomy. The equity balance degree can mitigate the negative influence of the relationship, but it is significantly different between state-owned enterprises and private enterprises. Accordingly, it prompts the following policy implementation: the Chinese government should develop differentiated environmental incentives and regulatory policies. It should focus on private enterprises with high-equity balance degrees and high pollution levels, and it should encourage state-owned enterprises to increase the scale of ex ante preventive investment through policy incentives. Full article
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20 pages, 3349 KiB  
Article
Carbon Emission Analysis of RC Core Wall-Steel Frame Structures
by Jiangjun Gao, Zhengliang Shen, Zerui Shao, Xinyu Pan, Deshuang Tang, Kun Zhao, Yao Chen and Hengzhu Lv
Appl. Sci. 2024, 14(17), 7727; https://doi.org/10.3390/app14177727 (registering DOI) - 2 Sep 2024
Abstract
The development of super high-rise building projects has become crucial for mitigating land shortages in rapidly growing urban areas. Super high-rise steel structures, particularly RC core wall-steel frame systems, have become the preferred choice due to their superior performance, high prefabrication level, and [...] Read more.
The development of super high-rise building projects has become crucial for mitigating land shortages in rapidly growing urban areas. Super high-rise steel structures, particularly RC core wall-steel frame systems, have become the preferred choice due to their superior performance, high prefabrication level, and construction efficiency. Despite their benefits, super high-rise buildings face challenges related to higher energy consumption and carbon emissions. Consequently, it is important to analyze the carbon emissions of these buildings throughout their lifecycle and propose low-carbon construction strategies. A carbon emission analysis focused on super high-rise buildings with RC core wall-steel frame structures is conducted in this study. A carbon emission analysis model is constructed based on BIM-enabled LCA through a real-world case study. The emission factor method is combined with the BIM model to calculate carbon emission. Furthermore, carbon emissions across various construction strategies are compared, with a particular focus on the manufacturing processes of the main materials. The results indicate that incorporating admixtures in concrete, along with adopting the electric arc furnace (EAF) method and utilizing recycled scrap steel in steel manufacturing, significantly reduces the carbon emissions of the buildings. Lastly, effective low-carbon approaches for these buildings are proposed. Full article
(This article belongs to the Section Civil Engineering)
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12 pages, 668 KiB  
Article
Predictions of the Effect of Non-Homogeneous Ocean Bubbles on Sound Propagation
by Yuezhu Cheng, Jie Shi, Yuan Cao and Haoyang Zhang
J. Mar. Sci. Eng. 2024, 12(9), 1510; https://doi.org/10.3390/jmse12091510 (registering DOI) - 2 Sep 2024
Abstract
In the ocean, bubbles rarely appear alone and are often not evenly distributed, which makes it complicated to predict the effect of ocean bubbles on sound propagation. To solve this problem, researchers have tried to use approximations such as equivalent and multiple scattering [...] Read more.
In the ocean, bubbles rarely appear alone and are often not evenly distributed, which makes it complicated to predict the effect of ocean bubbles on sound propagation. To solve this problem, researchers have tried to use approximations such as equivalent and multiple scattering models, but these approximations are accompanied by large errors. Therefore, we propose a semi-numerical and semi-analytical calculation method for underwater sound fields containing non-homogeneous bubbles in this paper. Based on the attenuation cross section and scattering cross section of a single bubble, the non-homogeneous medium is divided into multiple layers of uniform medium. Each layer of the bubble group is regarded as a whole, which can fully reflect the influence of bubble group vibration and scattering on sound wave propagation and is conducive to faster calculation of the sound field of non-homogeneous bubbly liquids. Compared with the classic coupling model, the calculation process of this method is simpler and faster, which solves the problem of fast calculation of sound fields in bubbly liquids and simulation of distributed bubble groups containing non-homogeneous distributed bubbles. Full article
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14 pages, 2781 KiB  
Article
Assessing the Fatigue Stress Behavior of Starch Biodegradable Films with Nanoclay Using Accelerated Survival Test Methods
by Theofilos Frangopoulos, Sophia Dimitriadou, Joanis Ozuni, Anna Marinopoulou, Athanasios Goulas, Dimitrios Petridis and Vassilis Karageorgiou
Appl. Sci. 2024, 14(17), 7728; https://doi.org/10.3390/app14177728 (registering DOI) - 2 Sep 2024
Abstract
A destructive degradation model was applied on films made from different concentrations of starch, glycerol and nanoclay using various elongation levels as a stress variable at different stress times and stretch cycles. The log tensile quotient (logarithm of the tensile strength to the [...] Read more.
A destructive degradation model was applied on films made from different concentrations of starch, glycerol and nanoclay using various elongation levels as a stress variable at different stress times and stretch cycles. The log tensile quotient (logarithm of the tensile strength to the corresponding break cycle) was recorded as the response variable. The log tensile quotient increased, and the log exact break time decreased, as the elongation level increased. The treatment containing the highest starch and nanoclay and lowest glycerol content proved to be the most resistant to stress conditions and the most versatile in relation to the varying log tensile quotients, while the treatments containing the lowest nanoclay and highest glycerol contents, regardless of the starch concentration, manifested the lowest log tensile quotient at higher levels of log exact break time. According to multiple regression findings, the break cycle governed mostly the stress conditions in the degradation model, followed by the sample ID and the log exact break time. The term log tensile quotient, attempted for the first time on data concerning biodegradable films enhanced with nanoclay, seems very promising for deeper research due to its ability to retrieve predictive information from survival equations and to discriminate the difference between film structures. Full article
(This article belongs to the Special Issue Synthesis and Application of Advanced Polymeric Materials)
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22 pages, 9693 KiB  
Article
A Trusted Supervision Paradigm for Autonomous Driving Based on Multimodal Data Authentication
by Tianyi Shi, Ruixiao Wu, Chuantian Zhou, Siyang Zheng, Zhu Meng, Zhe Cui, Jin Huang, Changrui Ren and Zhicheng Zhao
Big Data Cogn. Comput. 2024, 8(9), 100; https://doi.org/10.3390/bdcc8090100 (registering DOI) - 2 Sep 2024
Abstract
At the current stage of autonomous driving, monitoring the behavior of safety stewards (drivers) is crucial to establishing liability in the event of an accident. However, there is currently no method for the quantitative assessment of safety steward behavior that is trusted by [...] Read more.
At the current stage of autonomous driving, monitoring the behavior of safety stewards (drivers) is crucial to establishing liability in the event of an accident. However, there is currently no method for the quantitative assessment of safety steward behavior that is trusted by multiple stakeholders. In recent years, deep-learning-based methods can automatically detect abnormal behaviors with surveillance video, and blockchain as a decentralized and tamper-resistant distributed ledger technology is very suitable as a tool for providing evidence when determining liability. In this paper, a trusted supervision paradigm for autonomous driving (TSPAD) based on multimodal data authentication is proposed. Specifically, this paradigm consists of a deep learning model for driving abnormal behavior detection based on key frames adaptive selection and a blockchain system for multimodal data on-chaining and certificate storage. First, the deep-learning-based detection model enables the quantification of abnormal driving behavior and the selection of key frames. Second, the key frame selection and image compression coding balance the trade-off between the amount of information and efficiency in multiparty data sharing. Third, the blockchain-based data encryption sharing strategy ensures supervision and mutual trust among the regulatory authority, the logistic platform, and the enterprise in the driving process. Full article
(This article belongs to the Special Issue Big Data Analytics and Edge Computing: Recent Trends and Future)
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18 pages, 4318 KiB  
Article
Intelligent Framework Design for Quality Control in Industry 4.0
by Yousaf Ali, Syed Waqar Shah, Arsalan Arif, Mehdi Tlija and Mudasir Raza Siddiqi
Appl. Sci. 2024, 14(17), 7726; https://doi.org/10.3390/app14177726 (registering DOI) - 2 Sep 2024
Abstract
This research aims to develop an intelligent framework for quality control and fault detection in pre-production and post-production systems in Industry 4.0. In the pre-production system, the health of the manufacturing machine is monitored. In this study, we examine the gear system of [...] Read more.
This research aims to develop an intelligent framework for quality control and fault detection in pre-production and post-production systems in Industry 4.0. In the pre-production system, the health of the manufacturing machine is monitored. In this study, we examine the gear system of induction motors used in industries. In post-production, the product is tested for quality using a machine vision system. Gears are fundamental components in countless mechanical systems, ranging from automotive transmissions to industrial machinery, where their reliable operation is vital for overall system efficiency. A faulty gear system in the induction motor directly affects the quality of the manufactured product. Vibration data, collected from the gear system of the induction motor using vibration sensors, are used to predict the motor’s health condition. The gear system is monitored for six different fault conditions. In the second part, the quality of the final product is inspected with the machine vision system. Faults on the surface of manufactured products are detected, and the product is classified as a good or bad product. The quality control system is developed with different deep learning models. Finally, the quality control framework is validated and tested with the evaluation metrics. Full article
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14 pages, 501 KiB  
Article
Microdosimetric Simulation of Gold-Nanoparticle-Enhanced Radiotherapy
by Maxim Azarkin, Martin Kirakosyan and Vladimir Ryabov
Int. J. Mol. Sci. 2024, 25(17), 9525; https://doi.org/10.3390/ijms25179525 (registering DOI) - 2 Sep 2024
Abstract
Conventional X-ray therapy (XRT) is commonly applied to suppress cancerous tumors; however, it often inflicts collateral damage to nearby healthy tissue. In order to provide a better conformity of the dose distribution in the irradiated tumor, proton therapy (PT) is increasingly being used [...] Read more.
Conventional X-ray therapy (XRT) is commonly applied to suppress cancerous tumors; however, it often inflicts collateral damage to nearby healthy tissue. In order to provide a better conformity of the dose distribution in the irradiated tumor, proton therapy (PT) is increasingly being used to treat solid tumors. Furthermore, radiosensitization with gold nanoparticles (GNPs) has been extensively studied to increase the therapeutic ratio. The mechanism of radiosensitization is assumed to be connected to an enhancement of the absorbed dose due to huge photoelectric cross-sections with gold. Nevertheless, numerous theoretical studies, mostly based on Monte Carlo (MC) simulations, did not provide a consistent and thorough picture of dose enhancement and, therefore, the radiosensitization effect. Radiosensitization by nanoparticles in PT is even less studied than in XRT. Therefore, we investigate the physics picture of GNP-enhanced RT using an MC simulation with Geant4 equipped with the most recent physics models, taking into account a wide range of physics processes relevant for realistic PT and XRT. Namely, we measured dose enhancement factors in the vicinity of GNP, with diameters ranging from 10 nm to 80 nm. The dose enhancement in the vicinity of GNP reaches high values for XRT, while it is very modest for PT. The macroscopic dose enhancement factors for realistic therapeutic GNP concentrations are rather low for all RT scenarios; therefore, other physico-chemical and biological mechanisms should be additionally invoked for an explanation of the radiosensitization effect observed in many experiments. Full article
(This article belongs to the Special Issue Nanoparticles in Nanobiotechnology and Nanomedicine: 2nd Edition)
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34 pages, 1692 KiB  
Review
Enduring Neurobiological Consequences of Early-Life Stress: Insights from Rodent Behavioral Paradigms
by Luisa Speranza, Kardelen Dalim Filiz, Pellegrino Lippiello, Maria Grazia Ferraro, Silvia Pascarella, Maria Concetta Miniaci and Floriana Volpicelli
Biomedicines 2024, 12(9), 1978; https://doi.org/10.3390/biomedicines12091978 (registering DOI) - 2 Sep 2024
Abstract
Stress profoundly affects physical and mental health, particularly when experienced early in life. Early-life stress (ELS) encompasses adverse childhood experiences such as abuse, neglect, violence, or chronic poverty. These stressors can induce long-lasting changes in brain structure and function, impacting areas involved in [...] Read more.
Stress profoundly affects physical and mental health, particularly when experienced early in life. Early-life stress (ELS) encompasses adverse childhood experiences such as abuse, neglect, violence, or chronic poverty. These stressors can induce long-lasting changes in brain structure and function, impacting areas involved in emotion regulation, cognition, and stress response. Consequently, individuals exposed to high levels of ELS are at an increased risk for mental health disorders like depression, anxiety, and post-traumatic stress disorders, as well as physical health issues, including metabolic disorders, cardiovascular disease, and cancer. This review explores the biological and psychological consequences of early-life adversity paradigms in rodents, such as maternal separation or deprivation and limited bedding or nesting. The study of these experimental models have revealed that the organism’s response to ELS is complex, involving genetic and epigenetic mechanisms, and is associated with the dysregulation of physiological systems like the nervous, neuroendocrine, and immune systems, in a sex-dependent fashion. Understanding the impact of ELS is crucial for developing effective interventions and preventive strategies in humans exposed to stressful or traumatic experiences in childhood. Full article
(This article belongs to the Special Issue Dopamine Signaling Pathway in Health and Disease—2nd Edition)
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17 pages, 2748 KiB  
Article
Method for Remaining Useful Life Prediction of Turbofan Engines Combining Adam Optimization-Based Self-Attention Mechanism with Temporal Convolutional Networks
by Hairui Wang, Dongjun Li, Ya Li, Guifu Zhu and Rongxiang Lin
Appl. Sci. 2024, 14(17), 7723; https://doi.org/10.3390/app14177723 (registering DOI) - 2 Sep 2024
Abstract
Conducting the remaining useful life (RUL) prediction for an aircraft engines is of significant importance in enhancing aircraft operation safety and formulating reasonable maintenance plans. Addressing the issue of low prediction model accuracy due to traditional neural networks’ inability to fully extract key [...] Read more.
Conducting the remaining useful life (RUL) prediction for an aircraft engines is of significant importance in enhancing aircraft operation safety and formulating reasonable maintenance plans. Addressing the issue of low prediction model accuracy due to traditional neural networks’ inability to fully extract key features, this paper proposes an engine RUL prediction model based on the adaptive moment estimation (Adam) optimized self-attention mechanism–temporal convolutional network (SAM-TCN) neural network. Firstly, the raw data monitored by sensors are normalized, and RUL labels are set. A sliding window is utilized for overlapping sampling of the data, capturing more temporal features while eliminating data dimensionality. Secondly, the SAM-TCN neural network prediction model is constructed. The temporal convolutional network (TCN) neural network is used to capture the temporal dependency between data, solving the mapping relationship of engine degradation characteristics. A self-attention mechanism (SAM) is employed to adaptively assign different weight contributions to different input features. In the experiments, the root mean square error (RMSE) values on four datasets are 11.50, 16.45, 11.62, and 15.47 respectively. These values indicate further reduction in errors compared to methods reported in other literature. Finally, the SAM-TCN prediction model is optimized using the Adam optimizer to improve the training effectiveness and convergence speed of the model. Experimental results demonstrate that the proposed method can effectively learn feature data, with prediction accuracy superior to other models. Full article
(This article belongs to the Special Issue Deep Learning and Predictive Maintenance)
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17 pages, 302 KiB  
Article
Sex and Age Differences in the Effects of Food Frequency on Metabolic Parameters in Japanese Adults
by Katsumi Iizuka, Kotone Yanagi, Kanako Deguchi, Chihiro Ushiroda, Risako Yamamoto-Wada, Kazuko Kobae, Yoshiko Yamada and Hiroyuki Naruse
Nutrients 2024, 16(17), 2931; https://doi.org/10.3390/nu16172931 (registering DOI) - 2 Sep 2024
Abstract
Owing to differences in dietary preferences between men and women, the associations between dietary intake frequency and metabolic parameters may differ between the sexes. A retrospective observational study of the checkup findings of 3147 Japanese individuals (968 men, 2179 women) aged 20–59 years [...] Read more.
Owing to differences in dietary preferences between men and women, the associations between dietary intake frequency and metabolic parameters may differ between the sexes. A retrospective observational study of the checkup findings of 3147 Japanese individuals (968 men, 2179 women) aged 20–59 years was conducted to examine differences in dietary habits and associations between food frequency and blood parameters (eGFR, HbA1c, uric acid, and lipids) by sex and age. Males were more likely to consume meat, fish, soft drinks, and alcohol, whereas women were more likely to consume soybeans, dairy products, vegetables, fruits, and snacks. Multivariate linear regression models adjusted for age and BMI revealed that meat intake frequency was positively associated with HbA1c (β = 0.007, p = 0.03) and negatively associated with eGFR (β = −0.3, p = 0.01) only in males, whereas fish intake frequency was positively associated with eGFR (β = 0.4, p = 0.005) only in females. Egg and soy intake frequencies were positively and negatively associated with non-HDL-C (egg: β = 0.6, p = 0.02; soy: β = −0.3, p = 0.03) only in females. Alcohol consumption frequency was associated with uric acid (M: β = 0.06, p < 0.001; F: β = 0.06, p < 0.001) and HDL-C (M: β = 1.0, p < 0.001; F: β = 1.3, p < 0.001) in both sexes. Future research is needed to determine whether varying the emphasis of dietary guidance by sex and age group is effective, since the effects of dietary preferences on metabolic parameters vary by age and sex. Full article
(This article belongs to the Special Issue Dietary Habits and Metabolic Health)
23 pages, 1397 KiB  
Article
Analyzing Media Content in Turkiye and the UK during the COVID-19 Pandemic Considering the Dimensions of Quadruple Helix Collaboration Processes
by Lutz Peschke, Seyedehshahrzad Seyfafjehi, Irmak Dündar and Yasemin Gümüş Ağca
Soc. Sci. 2024, 13(9), 458; https://doi.org/10.3390/socsci13090458 (registering DOI) - 2 Sep 2024
Abstract
The outbreak of COVID-19 between 2020 and 2022 highlighted the significant role of news media as a tool of communication among different social actors. Due to the novelty of the virus, most citizens turned to official news outlets to obtain reliable information about [...] Read more.
The outbreak of COVID-19 between 2020 and 2022 highlighted the significant role of news media as a tool of communication among different social actors. Due to the novelty of the virus, most citizens turned to official news outlets to obtain reliable information about the disease and pandemic regulations. Therefore, a content analysis of news coverage in different countries provides insight into their Quadruple Helix dynamics, which reflects new patterns of knowledge democracy under consideration of the media-based public sphere. This article aims to trace the patterns of prevalent topics related to COVID-19 news in Turkish and British mainstream news agencies between September 2020 and March 2022. By deploying content analysis, this research endeavours to elicit public discourses created around the pandemic. These media agencies engaged in critical commentary on the pandemic situation and the policies enacted during this period, updating citizens with the latest information. However, the differences in the political and social structures of each country influence their Quadruple Helix knowledge exchange, which has a high impact on transformation processes. Full article
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