bluuebunny/crossref_metadata_2025 huggingface dataset:
Coxsackievirus A6 U.K. Genetic and Clinical Epidemiology Pre- and Post-SARS-CoV-2 Emergence
Authors: Joyce Alice M. , Hill Jack D. , Tsoleridis Theocharis , Astbury Stuart , Berry Louise , Howson-Wells Hannah C. , Allen Nancy , Canning Ben , Jones Carl B. , Clark Gemma , Irving William L. , Tarr Alexander W. , McClure C. Patrick
Published: November 2024

Coxsackievirus A6 (CVA6) has become increasingly clinically relevant as a cause of Hand, Foot and Mouth Disease (HFMD) globally since 2008. However, most laboratories do not routinely determine the enteroviral type of positive samples. The non-pharmaceutical measures introduced to curb transmission during the COVID-19 pandemic may also have perturbed CVA6 epidemiology. We thus aimed to determine the prevalence, clinical presentation and genetic relationship of CVA6 across three complete epidemic seasons: one pre-SARS-CoV-2 emergence and two post-SARS-CoV-2 emergence in our regional healthcare setting. Surplus diagnostic nucleic acid from diagnosed enteroviral positives diagnosed between September and December of 2018 and between May 2021 and April of 2023 was subject to VP1 gene sequencing to determine the CVA6 cases and interrogate their phylogenetic relationship. The confirmed CVA6 cases were also retrospectively clinically audited. CVA6 infections were identified in 33 and 69 individuals pre- and post-pandemic, respectively, with cases peaking in November of 2018 and 2022, but in October of 2021. HFMD was the primary diagnosis in 85.5% of the post-pandemic cases, but only 69.7% of the pre-pandemic cases, where respiratory and neurological symptoms (45.5% and 12.1%, respectively) were significantly elevated. A complete VP1 sequence was retrieved for 94% of the CVA6 cases, revealing that studied infections were genetically diverse and suggestive of multiple local and international transmission chains. CVA6 presented a significant clinical burden in our regional U.K. hospital setting both pre- and post-pandemic and was subject to dynamic clinical and genetic epidemiology.
Analysis of the Correlation Between Toxoplasma gondii Seropositivity and Alzheimer’s Disease
Authors: Wang Jianjun , Lin Ping , Li Dan , Yang Biyu , Wang Jiaqi , Feng Meng , Cheng Xunjia
Published: November 2024

Alzheimer’s disease (AD) is a multifactorial brain disorder and infectious diseases are considered as one of the predisposing factors for AD. Toxoplasma gondii, an obligate intracellular parasitic protozoan, is suspected of being associated with AD. Serum samples were collected from 109 AD patients and 114 age-matched healthy controls. ELISA was performed using recombinant T. gondii cyst wall protein 1 (CST1) to detect T. gondii antibodies. A parallel experiment was performed with Toxoplasma gondii tachyzoites lysate protein. To analyze whether factors associated with the onset of AD included chronic T. gondii infection, a multivariate logistic regression model was applied, further validating the correlation between chronic T. gondii infection and AD. AD patients exhibited significantly higher levels of Toxoplasma-specific antibodies in their serum compared to the control group, with statistically significant differences (p < 0.05). Multivariate logistic regression analysis revealed that Toxoplasma infection is a risk factor for AD (p < 0.01), and the CST1 antigen can significantly improve the model’s performance in predicting the occurrence of AD. The results indicate that chronic infection with Toxoplasma gondii could be one of the risk factors for the development of AD, potentially predisposing individuals with underlying health conditions to the disease. This further validates the correlation between Toxoplasma gondii and AD.
Antimicrobial Peptides: The Game-Changer in the Epic Battle Against Multidrug-Resistant Bacteria
Authors: Hetta Helal F. , Sirag Nizar , Alsharif Shumukh M. , Alharbi Ahmad A. , Alkindy Tala T. , Alkhamali Alanoud , Albalawi Abdullah S. , Ramadan Yasmin N. , Rashed Zainab I. , Alanazi Fawaz E.
Published: November 2024

The rapid progress of antibiotic resistance among bacteria has prompted serious medical concerns regarding how to manage multidrug-resistant (MDR) bacterial infections. One emerging strategy to combat antibiotic resistance is the use of antimicrobial peptides (AMPs), which are amino acid chains that act as broad-spectrum antimicrobial molecules and are essential parts of the innate immune system in mammals, fungi, and plants. AMPs have unique antibacterial mechanisms that offer benefits over conventional antibiotics in combating drug-resistant bacterial infections. Currently, scientists have conducted multiple studies on AMPs for combating drug-resistant bacterial infections and found that AMPs are a promising alternative to conventional antibiotics. On the other hand, bacteria can develop several tactics to resist and bypass the effect of AMPs. Therefore, it is like a battle between the bacterial community and the AMPs, but who will win? This review provides thorough insights into the development of antibiotic resistance as well as detailed information about AMPs in terms of their history and classification. Furthermore, it addresses the unique antibacterial mechanisms of action of AMPs, how bacteria resist these mechanisms, and how to ensure AMPs win this battle. Finally, it provides updated information about FDA-approved AMPs and those that were still in clinical trials. This review provides vital information for researchers for the development and therapeutic application of novel AMPs for drug-resistant bacterial infections.
Analysis of Randomization Capacity in Quantum Noise Randomized Cipher System
Authors: Zhang Mingrui , Wei Shuang , Li Yuang , Li Yajie , Zhao Yongli , Zhang Jie
Published: November 2024

We propose and verify a method for analyzing the randomization capacity in a 160 km quantum noise randomized cipher system with different data modulation formats. The randomization capacity is defined as the difference in mutual information between Alice and Bob while the randomization level is at 0 and at its maximum, under the condition of error-free transmission. Our experimental analysis examines the capacity of quantum noise randomized cipher systems under different optical signal-to-noise ratios for each modulation format. Additionally, we analyze the noise masking values while the randomization reaches its capacity. The experimental results indicate that the binary phase shift-keying-based quantum noise randomized cipher system achieves the highest randomization capacity and highest noise masking value.
Accelerating Charge: Add-Ons to Rest Mass and Field Energy
Authors: Tito Elizabeth P. , Pavlov Vadim I.
Published: November 2024

We present—in the framework of classical theory—a self-consistent derivation scheme which produces equations for the calculation of add-ons to the full field energy and to the effective mass of a charge moving with acceleration, which may be practically used for analyses in various scenarios. The charge is treated as a quasi-point-like charge; this helps to resolve the complications of the “infinite” electromagnetic energy, which are avoided by the procedure of slightly “spreading” the charge. As a result, the concept of the size of the particle takes a straightforward physical interpretation. Indeed, it is within the charge spread, at scales smaller than Compton’s length, where the quantum-field-mechanics approach to be applied. Beyond this region, no “infinite” tails of quantities accumulate. The seeming divergences of the integrals at the upper limits are not physical if one takes into account that the charge moves with acceleration only for a finite duration of time; every real physical process has its beginning and its end. The key focus of this paper is on the methodological aspects of the calculations.
Morpho-Phenological, Chemical, and Genetic Characterization of Italian Maize Landraces from the Lazio Region
Authors: Redaelli Rita , Bassolino Laura , Balconi Carlotta , Terracciano Irma , Torri Alessio , Nicoletti Federica , Benedetti Gianluca , Iacoponi Valentina , Rea Roberto , Taviani Paola
Published: November 2024

In the framework of a Collaboration Agreement between CREA and ARSIAL, a morpho-phenological, chemical, and genetic characterization of maize populations native to the Lazio region was carried out. During 2022 and 2023, a set of 50 accessions, belonging both to ARSIAL and CREA maize collections, were multiplied in Bergamo. Morpho-phenological descriptors were recorded in the field: plant height, ear height, and male and female flowering time. The grain chemical composition in terms of protein, lipid, starch, ash and fiber was evaluated by near-infrared spectroscopy (NIRS). A double-digest restriction-site-associated DNA sequencing (ddRADseq) strategy was used to genotype the landraces. The two collections were not significantly different in terms of grain chemical composition. On the other hand, the ARSIAL and CREA germplasm showed a different distribution in the three cluster-based population structure obtained by ddRADseq, which largely corresponded to the distribution map of their collection sites. The materials from the Lazio region maintained by ARSIAL and CREA were revealed to be different. The comparison between the two groups of landraces showed the importance of characterizing germplasm collections to promote the recovery and valorization of local biodiversity.
Contrasting Weather and Stocking Effects on Eucalyptus Initial Coppice Response in Brazil
Authors: Fernandes Pietro Gragnolati , Alvares Clayton Alcarde , Queiroz Túlio Barroso , Pimenta Pedro Vitor , Borges Jarbas Silva , Stahl James , Mendes Flávio Teixeira , Souza Amanda , Silva Gustavo Matheus , da Silva Gualter Guenther Costa , Milhomem Sara Bezerra Bandeira , Sousa Rosilvam Ramos de , Hakamada Rodrigo Eiji
Published: November 2024

In Eucalyptus plantations, coppice rotations often yield less than initial rotations. The TECHS project (Tolerance of Eucalyptus Clones to Hydric, Thermal and Biotic Stresses) studied short rotation coppicing across a 3000 km gradient. The main objective of this work was to compare the survival, sprouting, and initial growth of Eucalyptus clones managed and to examine factors that might influence the productivity of the coppice rotation: climate, genotypes, and stocking. Eight of the TECHS sites spread from latitudes 6° S to 30° S were included in the coppice study, with 17 genotypes at each site. The initial rotation had been planted at a 3 m × 3 m spacing and also in a spacing trial at densities from 500 to 3500 trees ha−1. Six months after harvesting the initial Eucalyptus rotation, average survival was 88%, with tropical clones showing over twice the sprouting biomass (6.7 vs. 2.9 Mg ha−1) and four times the woody biomass compared to subtropical clones (4.7 vs. 1.1 Mg ha−1). Greater initial water deficits had stronger sprouting and growth. Clones with higher belowground carbon allocation in the initial rotation performed better in coppicing, and precipitation became more influential after 12 months. Drought and spacing trials significantly affected growth.
Development of Analytical Model to Describe Reflectance Spectra in Leaves with Palisade and Spongy Mesophyll
Authors: Sukhova Ekaterina , Zolin Yuriy , Grebneva Kseniya , Berezina Ekaterina , Bondarev Oleg , Kior Anastasiia , Popova Alyona , Ratnitsyna Daria , Yudina Lyubov , Sukhov Vladimir
Published: November 2024

Remote sensing plays an important role in plant cultivation and ecological monitoring. This sensing is often based on measuring spectra of leaf reflectance, which are dependent on morphological, biochemical, and physiological characteristics of plants. However, interpretation of the reflectance spectra requires the development of new tools to analyze relations between plant characteristics and leaf reflectance. The current study was devoted to the development, parameterization, and verification of the analytical model to describe reflectance spectra of the dicot plant leaf with palisade and spongy mesophyll layers (on the example of pea leaves). Four variables (intensities of forward and backward collimated light and intensities of forward and backward scattered light) were considered. Light reflectance and transmittance on borders of lamina (Snell’s and Fresnel’s laws), light transmittance in the palisade mesophyll (Beer–Bouguer–Lambert law), and light transmittance and scattering in the spongy mesophyll (Kubelka–Munk theory) were described. The developed model was parameterized based on experimental results (reflectance spectra, contents of chlorophylls and carotenoid, and thicknesses of palisade and spongy mesophyll in pea leaves) and the literature data (final R2 was 0.989 for experimental and model-based reflectance spectra). Further model-based and experimental investigations showed that decreasing palisade and spongy mesophyll thicknesses in pea leaves (from 35.5 to 25.2 µm and from 58.6 to 47.8 µm, respectively) increased reflectance of green light and decreased reflectance of near-infrared light. Similarity between model-based and experimental results verified the developed model. Thus, the model can be used to analyze leaf reflectance spectra and, thereby, to increase efficiency of the plant remote and proximal sensing.
Chemical and Biological Properties of Different Romanian Populations of Hyssopus officinalis Correlated via Molecular Docking
Authors: Imbrea Ilinca Merima , Osiceanu Magdalena , Hulea Anca , Suleiman Mukhtar Adeiza , Popescu Iuliana , Floares (Oarga) Doris , Onisan Emilian , Neacșu Alina-Georgeta , Popescu Cosmin Alin , Hulea Calin , Pop Georgeta , Niță Simona , Imbrea Florin , Obistioiu Diana
Published: November 2024

This study compares three Romanian Hyssopus officinalis species—H. officinalis f. ruber (HOR), H. officinalis f. albus (HOA), and H. officinalis f. cyaneus (HOC)—evaluating their chemical composition and biological activities, specifically protein denaturation, haemolysis inhibition, and antibacterial effects. Chemical profiles were determined using Gas Chromatography–Mass Spectrometry (GC-MS). The species were cultivated at two distinct locations: the Didactic and Experimental Station DESUSVT and the Agricultural Research and Development Station Lovrin (ARDSL). This study investigates the correlation between chemical composition, biological activities, and local climate data at each site. The results show significant variations in chemical profiles, with species and cultivation location influencing the biological activities. H. officinalis f. albus (HOA) exhibited the strongest antimicrobial activity, particularly against Gram-positive bacteria. The molecular docking analysis highlighted key compounds, such as cyclohexene,4-isopropenyl-1-methoxymethoxymethyl and elemol, with binding solid affinities to microbial and inflammatory proteins. This study provides valuable insights into the chemical and biological properties of Hyssopus officinalis, emphasising its potential in combating microbial infections, protein denaturation, and haemolysis inhibition.
Mechanical and Tribological Performance of Epoxy Composites Reinforced with YSZ Waste Ceramics for Sustainable Green Engineering Applications
Authors: Alsaeed Talal , Alajmi Ayedh Eid , Alotaibi Jasem Ghanem , Ganthavee Voravich , Yousif Belal F.
Published: November 2024

The growing need for sustainable materials in engineering applications has led to increased interest in the use of waste-derived ceramics as reinforcing fillers in polymer composites. This study investigates the mechanical and tribological performance of epoxy composites reinforced with Yttria-Stabilized Zirconia (YSZ) waste ceramics, focusing on the effects of varying ceramic content (0–40 wt.%). The results demonstrate that while the tensile strength decreases with increasing ceramic content, the wear resistance and surface hardness improve, particularly at 20 wt.% YSZ. These findings are highly relevant for industries such as automotive, aerospace, and industrial manufacturing, where the demand for eco-friendly, high-performance materials is growing. This work aligns with the journal’s focus on sustainable engineering by offering new insights into the practical application of waste materials in high-performance composite systems.
Chemical Composition, Structural Properties, and Bioactivity of Carrageenan from Field-Cultivated Betaphycus gelatinus
Authors: Huynh Tran Van , Cao Hang Thi Thuy , Hieu Vo Mai Nhu , Tran Vy Ha Nguyen , Van Tran Thi Thanh , Nguyen Thuan Thi , Thuy Thanh Thi Thu , Trung Vo Thanh , Thinh Pham Duc , Trinh Phan Thi Hoai , Duc Tran Mai
Published: November 2024

This study investigates seasonal biomass variations in Betaphycus gelatinus, a red alga cultivated in the field in Ninh Thuan, Vietnam, along with the chemical composition, structural properties, and bioactivity of its carrageenan. Monthly measurements over a one-year period revealed peak growth (2.02% per day) and carrageenan yield (59.61%) in June, identifying it as the optimal harvest period. FTIR and NMR analyses of carrageenan extracted from field-cultivated B. gelatinus showed hybrid κ- and β-carrageenan forms and a unique pyruvylated β-carrageenan structure not previously reported for this species. Bioactivity assays indicated high antioxidant potential, with a total antioxidant capacity equivalent to 48.30 mg ascorbic acid/g carrageenan and an ABTS radical scavenging IC50 of 3.64 µg/mL. Additionally, antibacterial tests demonstrated strong inhibition of Listeria monocytogenes (12.00 mm inhibition zone). These findings suggest that field cultivation is a sustainable approach for carrageenan production, yielding bioactive compounds with promising applications in pharmaceuticals, cosmetics, and food preservation as a viable alternative to wild harvesting.
Industrial Internet of Things Enabled Kata Methodology of Assembly Line Productivity Improvement: Insights from a Case Study
Authors: Sundar Pratap Sriram , Chowdhury Chandan , Kamarthi Sagar
Published: November 2024

Lean manufacturing focuses on perfection, trying to eliminate all types of Muda (waste), Mura (inconsistency), Muri (overburden), defects, injuries, and accidents through a continuous improvement process. Assembly lines are the final stages of manufacturing before the product is delivered to customers. Kata methodology provides a practical approach to achieving perfection in assembly lines, but its effectiveness is often hindered by delays in data collection, analysis, and diagnostics. In this study, we address these challenges by leveraging industrial internet of things (IIoT) solutions in an industrial setting. The research question of this paper is as follows: “Why was the full potential of traditional Kata to achieve assembly line perfection not realized, and will IIoT-integrated Kata address the limitations of the traditional Kata?” We demonstrate the integration of IIoT and Kata methodology in a factory assembling automobile heating, ventilation, and air conditioning (HVAC) systems to enhance assembly line productivity. We observe that the integration of IIoT with Kata methodology not only addresses existing limitations but drives substantial gains in assembly line performance. We validate improvements in both productivity and efficiency through quantitative and qualitative outcomes. We underscore the pivotal role of real-time data for Kata’s effectiveness, discuss the process for digital transformation, and explain the need for data monetization. We recommend the development of an IIoT-savvy workforce, traceability of 4M (men, method, materials, and machine), and present the task scorecards and dashboards for real-time monitoring and decision-making. We highlight the positive impact of IIoT-enabled traceability on overall equipment effectiveness (OEE). The company reduced its workforce from 15 to 13 operators, increased OEE from 75% to 85%, and improved average throughput from 60 to 90 assemblies per hour. The time for traceability of 4M (men, machines, material, and method) was reduced from hours to minutes. The factory eliminated 350 paper documents to achieve a paperless shop floor. This real-world case study serves as a model for companies looking to transition from traditional continuous improvement processes to IIoT-supported lean manufacturing.
Influence of Simulated Skin Color on the Accuracy of Face Scans
Authors: Brintouch Ido , Ali Aisha , Romanos Georgios E. , Delgado-Ruiz Rafael A.
Published: November 2024

Aims: this study aims to investigate the impact of simulated skin color and the use of fiducial markers on the accuracy of 3D facial scans, comparing two types of structured light scanners under constant ambient lighting conditions. Materials and Methods: Three mannequins with different skin colors—black, white, and pink—were scanned using two light based hand-held scanners (infrared light and blue-light). Each mannequin was scanned with and without fiducial markers placed on defined anatomical landmarks. A total of one hundred thirty-two scans were performed and converted into standard tessellation language (STL) files. STL files from each scanner were compared to their respective control scans using point cloud comparison software. Accuracy was evaluated based on root mean square (RMS) values. Descriptive statistics summarized the data, and a t-test was performed to assess differences in RMS values between scans with and without fiducial markers for each scanner type. Results: The infrared light scanner showed the highest accuracy for the white mannequin, as evidenced by lower RMS values compared to the pink and black mannequins. Adding fiducial markers significantly enhanced scan accuracy for the pink and black mannequins. The blue-light scanner achieved accuracy for the white and pink mannequins comparable to that of the infrared scanner. However, it was unable to scan the black mannequin, even with the use of markers. Conclusions: Within the limitations of this study, simulated skin color significantly affects the accuracy of facial 3D scans. Scans of lighter (white) tones demonstrate higher accuracy compared to darker tones. Fiducial markers enhance the accuracy for an infrared scanner; however, a blue-light scanner is unable to capture dark simulated skin, even with the addition of fiducial markers.
Global Assessment of Mesoscale Eddies with TOEddies: Comparison Between Multiple Datasets and Colocation with In Situ Measurements
Authors: Ioannou Artemis , Guez Lionel , Laxenaire Rémi , Speich Sabrina
Published: November 2024

The present study introduces a comprehensive, open-access atlas of mesoscale eddies in the global ocean, as identified and tracked by the TOEddies algorithm implemented on a global scale. Unlike existing atlases, TOEddies detects eddies directly from absolute dynamic topography (ADT) without spatial filtering, preserving the natural spatial variability and enabling precise, high-resolution tracking of eddy dynamics. This dataset provides daily information on eddy characteristics, such as size, intensity, and polarity, over a 30-year period (1993–2023), capturing complex eddy interactions, including splitting and merging events that often produce networks of interconnected eddies. This unique approach challenges the traditional single-trajectory perspective, offering a nuanced view of eddy life cycles as dynamically linked trajectories. In addition to traditional metrics, TOEddies identifies both the eddy core (characterized by maximum azimuthal velocity) and the outer boundary, offering a detailed representation of eddy structure and enabling precise comparisons with in situ data. To demonstrate its value, we present a statistical overview of eddy characteristics and spatial distributions, including generation, disappearance, and merging/splitting events, alongside a comparative analysis with existing global eddy datasets. Among the multi-year observations, TOEddies captures coherent, long-lived eddies with lifetimes exceeding 1.5 years, while highlighting significant differences in the dynamic properties and spatial patterns across datasets. Furthermore, this study integrates TOEddies with 23 years of colocalized Argo profile data (2000–2023), allowing for a novel examination of eddy-induced subsurface variability and the role of mesoscale eddies in the transport of global ocean heat and biogeochemical properties. This atlas aims to be a valuable resource for the oceanographic community, providing an open dataset that can support diverse applications in ocean dynamics, climate research, and marine resource management.
Privacy-Preserving Synthetic Data Generation Method for IoT-Sensor Network IDS Using CTGAN
Authors: Alabdulwahab Saleh , Kim Young-Tak , Son Yunsik
Published: November 2024

The increased usage of IoT networks brings about new privacy risks, especially when intrusion detection systems (IDSs) rely on large datasets for machine learning (ML) tasks and depend on third parties for storing and training the ML-based IDS. This study proposes a privacy-preserving synthetic data generation method using a conditional tabular generative adversarial network (CTGAN) aimed at maintaining the utility of IoT sensor network data for IDS while safeguarding privacy. We integrate differential privacy (DP) with CTGAN by employing controlled noise injection to mitigate privacy risks. The technique involves dynamic distribution adjustment and quantile matching to balance the utility–privacy tradeoff. The results indicate a significant improvement in data utility compared to the standard DP method, achieving a KS test score of 0.80 while minimizing privacy risks such as singling out, linkability, and inference attacks. This approach ensures that synthetic datasets can support intrusion detection without exposing sensitive information.
Enhancing Direction-of-Arrival Estimation with Multi-Task Learning
Authors: Bianco Simone , Celona Luigi , Crotti Paolo , Napoletano Paolo , Petraglia Giovanni , Vinetti Pietro
Published: November 2024

There are numerous methods in the literature for Direction-of-Arrival (DOA) estimation, including both classical and machine learning-based approaches that jointly estimate the Number of Sources (NOS) and DOA. However, most of these methods do not fully leverage the potential synergies between these two tasks, which could yield valuable shared information. To address this limitation, in this article, we present a multi-task Convolutional Neural Network (CNN) capable of simultaneously estimating both the NOS and the DOA of the signal. Through experiments on simulated data, we demonstrate that our proposed model surpasses the performance of state-of-the-art methods, especially in challenging environments characterized by high noise levels and dynamic conditions.
Energy Efficiency for 5G and Beyond 5G: Potential, Limitations, and Future Directions
Authors: Ichimescu Adrian , Popescu Nirvana , Popovici Eduard C. , Toma Antonela
Published: November 2024

Energy efficiency constitutes a pivotal performance indicator for 5G New Radio (NR) networks and beyond, and achieving optimal efficiency necessitates the meticulous consideration of trade-offs against other performance parameters, including latency, throughput, connection densities, and reliability. Energy efficiency assumes it is of paramount importance for both User Equipment (UE) to achieve battery prologue and base stations to achieve savings in power and operation cost. This paper presents an exhaustive review of power-saving research conducted for 5G and beyond 5G networks in recent years, elucidating the advantages, disadvantages, and key characteristics of each technique. Reinforcement learning, heuristic algorithms, genetic algorithms, Markov Decision Processes, and the hybridization of various standard algorithms inherent to 5G and 5G NR represent a subset of the available solutions that shall undergo scrutiny. In the final chapters, this work identifies key limitations, namely, computational expense, deployment complexity, and scalability constraints, and proposes a future research direction by theoretically exploring online learning, the clustering of the network base station, and hard HO to lower the consumption of networks like 2G or 4G. In lowering carbon emissions and lowering OPEX, these three additional features could help mobile network operators achieve their targets.
Work-Related Quality of Life During the COVID-19 Pandemic: Gender Perspectives Among a Brazilian Sample
Authors: Silva Patricia , Alckmin-Carvalho Felipe , Oliveira António , Ledo Jóni , Silva Verônica , Pereira Henrique
Published: November 2024

Work-related quality of life (WRQoL) was affected by the COVID-19 pandemic, particularly for women. This study sought to evaluate the impacts of COVID-19 on Brazilians’ WRQoL from a gender-based perspective. A sample of 326 participants, 187 women and 139 men, completed an online survey containing the Fear of COVID-19 scale, the Negative Impacts of COVID-19 scale, and the Work-Related Quality of Life scale. t-tests compared the results between genders. Pearson correlation tested the association between the variables. Linear regressions assessed the predictive factors of WRQoL. Women reported significantly higher levels of COVID-19-related fears, and lower levels of all seven dimensions of WRQoL, with significant differences for overall WRQoL, well-being, career satisfaction, and control over work. A negative correlation was found among overall WRQoL, fear of COVID-19, and the negative impact of COVID-19. Gender, fear, and negative impacts of COVID-19 were significant predictors of general well-being (explaining 24.6% of variance); regarding the seven dimensions evaluated, gender explained two of them, fear of COVID-19 explained four, and the negative impact of COVID-19 explained six of them. These results contribute to the analysis of the COVID-19 pandemic’s effects on increasing gender inequality in a manner unfavorable to women in the Brazilian organizational context.
Education as a Determinant of Tolerance
Authors: Lee Sanghoon
Published: November 2024

This study empirically examines the relationship between education and tolerance for sexual minorities. The empirical results provide support for the positive effect of education on tolerance towards sexual minorities, with this effect being particularly pronounced in high-income countries. This study makes a contribution to the literature by employing robust methodologies. This empirical analysis utilizes country-level panel data and the concept of institutionalized tolerance, and employs various panel regression and machine learning techniques.
Comparing the Relative Efficacy of Generalized Estimating Equations, Latent Growth Curve Modeling, and Area Under the Curve with a Repeated Measures Discrete Ordinal Outcome Variable
Authors: Rodriguez Daniel , Verma Ryan , Upchurch Juliana
Published: November 2024

Researchers are often interested in how changes in one variable influence changes in a second variable, requiring the repeated measures of two variables. There are several multivariate statistical methods appropriate for this research design, including generalized estimating equations (GEE) and latent growth curve modeling (LGCM). Both methods allow for variables that are not continuous in measurement level and not normally distributed. More recently, researchers have begun to employ area under the curve (AUC) as a potential alternative when the nature of change is less important than the overall effect of time on repeated measures of a random variable. The research showed that AUC is an acceptable alternative to LGCM with repeated measures of a continuous and a zero-inflated Poisson random variable. However, less is known about its performance relative to GEE and LGCM when the repeated measures are ordinal random variables. Further, to our knowledge, no study has compared AUC to LGCM or GEE when there are two longitudinal processes. We thus compared AUC to LGCM and GEE, assessing the effects of repeated measures of psychological distress on repeated measures of smoking. Results suggest AUC performed equally well with both methods, although missing data management is an issue with both AUC and GEE.
Enablers and Barriers of Sustainability for Small Public Purchases
Authors: Schotanus Fredo , Gelderman Cees J. , Jupijn René
Published: November 2024

This study examines the sustainability of small public purchases, a largely overlooked area despite its substantial aggregate volume and legal flexibility compared to large purchases. We aim to understand if the sustainability potential in small public purchases is utilized and how it can be leveraged, providing insights into the dynamics of small purchases and offering practical guidance for policy and organizational change. Through case studies of five Dutch water authorities, incorporating interviews and document analyses, we examine internal and external factors that may inhibit or promote sustainability in small purchases. The findings indicate that sustainability currently plays a limited role in small purchases. Contrary to previous research, external factors do not drive sustainability in small purchases, as the focus remains on large purchases. All internal factors studied are perceived as barriers to achieving sustainable small purchases. We discuss that the external factor of “legal requirements” holds the most untapped potential to become a driver for sustainable small purchases. Additionally, we highlight “accountability” as an important internal factor, suggesting that improved accountability could lead to greater resources, higher risk tolerance, and increased organizational attention. Finally, we suggest that promoting sustainability in small purchases requires addressing underlying challenges, including its limited visibility.
Personal Data Management in Smart Product-Service Systems: Preliminary Design Strategies to Avoid User Manipulation in Democratic Processes
Authors: Canfield Petrecca Alessandra C. , Vezzoli Carlo , Ceschin Fabrizio
Published: November 2024

The use of digital technologies in Product Service Systems (PSSs) has increased in recent years. More and more smart devices are used in these models, collecting significant amounts of data to provide personalized and responsive products and services. However, data extraction has been causing disruptions in the social sphere, manipulating users, threatening democratic processes, and harming the social dimension of sustainability. To mitigate these problems associated with user data, some solutions on the market claim to take a more ethical approach. This article presents the preliminary results of a study aiming to understand what features in these solutions may favour the resilience of democratic processes and reduction in user manipulation due to personal data extraction and personalized activity. It also examines how designers can use them to develop smart PSSs that incorporate these elements and features in their process. Based on a literature review, three key elements relevant to personal data and democracy were assessed and applied to analyze 30 cases. The results provided a preliminary list of 46 features and 15 strategies for designers to embed these elements in the design of smart PSSs, as well as a conceptual framework. The study concludes with recommendations for future research.
Green Energy, Economic Growth, and Innovation for Sustainable Development in OECD Countries
Authors: Zhao Tianhao , Shah Syed Ahsan Ali
Published: November 2024

This study explores the interrelationship between green energy adoption, economic growth, and innovation in promoting sustainable development within OECD countries. Using a random forest regression model, the research analyzes secondary data from 2013 to 2022 to identify the most significant contributors to sustainable development. The random forest model was selected for its ability to handle non-linear relationships and feature importance ranking, providing a comprehensive understanding of the variables’ impacts. The analysis reveals that green energy adoption has the strongest influence on the human development index (HDI), with an importance score of 0.43, followed by gross domestic product (GDP) and the global innovation index (GII). These findings underscore the pivotal role of green energy adoption, amplified by economic growth and technological innovation, in advancing sustainable development. While the study focuses on OECD countries, the insights offer valuable implications for global sustainability initiatives. The evidence supports the argument that prioritizing green energy, supported by economic and innovative drivers, is crucial for achieving broader sustainable development goals. This research provides a methodological contribution by demonstrating the effectiveness of machine learning models in analyzing complex sustainability data and offers empirical evidence that informs policy and future research in a broader context.
Predictive Model of Pedestrian Crashes Using Markov Chains in the City of Badajoz
Authors: Moreno-Sanfélix Alejandro , Gragera-Peña F. Consuelo , Jaramillo-Morán Miguel A.
Published: November 2024

Driving a vehicle, whether motorized or not, is a risky activity that can lead to a traffic accident and directly or indirectly affect all road users. In particular, road crashes involving pedestrians have caused the highest number of deaths and serious injuries in recent years. In order to prevent and reduce the occurrence of these types of traffic accidents and to optimize the use of the available resources of the administrations in charge of road safety, an updatable predictive model using Markov chains is proposed in this work. Markov chains are used in fields as diverse as hospital management or electronic engineering, but their application in the field of road safety is considered innovative. They are prediction and decision techniques that allow the estimation of the state of a given system by simulating its stochastic risk level. To carry out this study, the available information on traffic accidents involving pedestrians in the database of the Local Police of Badajoz (a medium-sized city in the southwest of Spain) in the period 2016 to 2023 were analyzed. These data were used to train a predictive model that was subsequently used to estimate the probability of occurrence of a traffic crash involving pedestrians in different areas of this city, information that could be used by the authorities to focus their efforts in those areas with the highest probability of a road crash occurring. This model can improve the identification of high-risk locations, and urban planners can optimize decision making in designing appropriate preventive measures and increase efficiency to reduce pedestrian crashes.
Policy Recommendations to Enhance Small-to-Medium-Sized Enterprise Support for Achieving the UK’s Net Zero Targets
Authors: Khosravi Fatemeh , Jelliman Sam , Uchendu Chidinma , Haddad Hebba , Chandler Alan , Connop Stuart
Published: November 2024

In the UK, SMEs account for 99.9% of the business population, with from 43% to 53% of UK business emissions coming from SMEs. However, SMEs face specific barriers in relation to net zero targets, such as a lack of knowledge about strategies to achieve net zero targets. Despite the ongoing lack of a formal decarbonisation plan for SMEs, various types of projects have been designed to help SMEs to move towards the UK’s net zero target. The Eastern New Energy (ENE) project received funding from the European Regional Development Fund (ERDF) to support SMEs across significant economic areas of the UK, namely the East of England, to help address the barriers that prevent SMEs from decarbonising. The project provided a range of measures, including raising their level of knowledge and providing technical support to help with SME decarbonisation. Following the completion of our targeted support activities, semi-structured interviews (n = 21) were conducted with SME beneficiaries to evaluate the effectiveness of the support with the ambition of providing some policy recommendations based on understanding how national and strategic support can be effective for a diverse, often unconnected ecosystem of businesses. The findings from the interviews indicated that engagement in the ENE project had positive impacts on the SMEs’ understanding of net zero, or how to start their journey towards decarbonisation by identifying their source of emissions and learning how to calculate them. However, accounting for emissions from Scope 3 activities, transition costs and building restrictions were the main barriers that undermined the ENE recommendations. Our research concludes that in designing engagement projects and support, one needs to acknowledge such restrictions and barriers, which requires government interventions to cover all aspects of the SME decarbonisation journey, from nuanced and responsive policy making to developing bespoke knowledge transfer and supporting low-carbon material provision that can support a decarbonised marketplace.
Inclusive Maritime Spatial Planning: Stakes at the Regional Level
Authors: Papageorgiou Marilena , Pozoukidou Georgia , Istoriou Theodora , Kostopoulou Theoni
Published: November 2024

The paper focuses on Maritime Spatial Planning (MSP) and the importance of incorporating participatory democracy and inclusivity, from a region-wise perspective. Traditionally MSP is a top-down (central government) process. Most responsibilities, competencies and jurisdictions lie at the national level, with usually limited input from local or regional stakeholders. However, the growing complexity of marine activities and the need for sustainable management of the marine environment require more inclusive and collaborative approaches. In other words, it calls for a more bottom-up approach, where local and regional stakeholders are involved in a meaningful way. Drawing on the REGINA-MSP project, the study presented in this paper identifies categories of regional and local stakeholders relevant to MSP. The paper identifies seven categories of MSP stakeholders deriving from the local communities of coastal regions. Following an in depth stakeholder analysis and mapping, fishers were identified as the ones having the lowest level of engagement and degree of representativeness in the MSP process, despite being among the most traditional marine users. The general public is also considered a “weak” MSP stakeholder. Regarding regional authorities/governments (NUTS 2 level according to the EU classification), their role and voice may also need further strengthening in the—nationally driven—MSP process. Communities of Practice is a tool that may encourage and advance participation and inclusivity in MSP, especially as regards local stakeholders of coastal regions.
Understanding Turnover Intentions: The Interplay of Organizational Politics, Employee Resilience, and Person-Job Fit in Ghana’s Healthcare Sector
Authors: Obeng Hayford Asare , Atan Tarik
Published: November 2024

This study explores the dynamics of organizational politics, employee resilience, person-job fit, and turnover intentions among health workers in Ghanaian public and private hospitals, all under the framework of the person-environment fit theory. The convenience sampling technique was used to collect data from 371 participants across six distinct regions of Ghana. The collected data were analyzed using partial least squares structural equation modeling. This study indicates that organizational politics have a significant positive impact on turnover intentions and employee resilience, and higher resilience is linked to a lower inclination to leave. This study found that employee resilience partially mediates the relationship between organizational politics and turnover intentions. Furthermore, it was discovered that person-job fit moderates the relationship between organizational politics and both inclination to leave and resilience. These findings highlight the importance for health service administrators to foster staff resilience and person-job fit while addressing organizational politics through open communication and equitable decision-making.
Increase in Households Triggered by Accommodation Closure Due to the COVID-19 Pandemic in the Historical Center of Kyoto City
Authors: Kamino Shunpei , Kato Haruka
Published: November 2024

The COVID-19 pandemic has forced many accommodations to close. However, the pandemic might play an important role in providing an opportunity to achieve sustainable tourism with a good balance between housing for residents and accommodation for tourists. As the theoretical framework, this study aims to investigate the change in households triggered by accommodation closure due to the COVID-19 pandemic in Kyoto City’s historical center. Furthermore, the causes of these changes were examined by analyzing the real estate properties traded on the market. For the analysis, this study considered the COVID-19 pandemic as a natural experiment to investigate the causal relationship between the number of households, closed accommodations, and real estate properties. As a result, it was found that households increased by approximately 1.34 in neighborhood associations with closed simple accommodations. Regarding the causes of the increase, closed simple accommodation properties tend to change to short-term rentals. This study also highlighted that closed simple accommodations have significantly smaller room sizes than other property types, with only slightly higher prices. As a theoretical contribution, our findings suggest that the pandemic might have suppressed tourism gentrification, but increased the number of households.
A New Method of Teaching Quality Evaluation Based on the α-Pessimistic Shapley Value
Authors: Gao Yin
Published: November 2024

Coalitional game with uncertain payoffs driven by uncertain variables stands for uncertain coalitional game. A α-pessimistic Shapley value as the solution of uncertain coalitional game is proposed in this paper, which can be played an important role in solving the problem of profit allocation. Moreover, the α-pessimistic Shapley value is proved to be the only value satisfying symmetry, dummy and additivity. Based on the α-pessimistic Shapley value, a new method of teaching quality evaluation in the institutions of higher learning is presented. In order to obtain the index of teaching quality evaluation in uncertain environments, an α-pessimistic multilinear extension is defined. Meanwhile, some properties of α-pessimistic multilinear extension are investigated successfully. Moreover, the problem of teaching quality evaluation are considered in this paper.