bluuebunny/crossref_metadata_2025 huggingface dataset:
Monitoring Occupational Radiation Dose in Radiography Students: Implications for Safety and Training
Authors: Abuzaid Mohamed , Noorajan Zarmeena , Elshami Wiam , Ibham Manal
Published: April 2024

Background: This study aimed to investigate the occupational exposure of undergraduate radiography students to ionising radiation and evaluate the effectiveness of safety protocols and training in reducing radiation exposure. Methods: This study tracked undergraduate radiography students from the University of Sharjah, UAE, using thermoluminescent dosimeters (TLDs) from 2015 to 2023. TLD readings were conducted every 15 weeks during 384 h of clinical placement. This study encompassed various radiographic procedures, and the TLDs were used to measure shallow (HP (0.07)) and deep doses (HP (10)). Results: A data analysis from 599 dosimeters revealed an average of 74 students annually. The average effective doses for HP (10) and HP (0.07) were 0.227 mSv and 0.222 mSv, respectively. These doses were well-below the recommended annual limits. Conclusion: This study’s results indicated that radiography students’ occupational radiation exposure during clinical training was within the safe limits, demonstrating the effectiveness of training and safety protocols. A comparison with international data corroborated the low exposure levels. Clinical training is essential for radiography students, and this study highlights the success of safety protocols in minimising occupational radiation exposure. Continuous monitoring and education are crucial to sustaining these positive outcomes.
Effects of Anthropogenic Activities on Sardinella maderensis (Lowe, 1838) Fisheries in Coastal Communities of Ibeju-Lekki, Lagos, Nigeria
Authors: Adewale Temitope , Aheto Denis , Okyere Isaac , Soyinka Olufemi , Dekolo Samuel
Published: April 2024

Small-scale fisheries are significant sources of nutrition and livelihood globally. However, increasing anthropogenic activities in coastal areas of developing countries have threatened the sustainability of artisanal fisheries and species. Fisheries of Sardinella maderensis, towards the global stock of which Nigeria contributes 9% and which is a significant livelihood source in the coastal communities of Ibeju-Lekki, Lagos, faces sustainability threats. This research investigated the effects of anthropogenic activities on S. maderensis fisheries in the coastal areas of Ibeju-Lekki, Lagos, Nigeria. The study adopted a mixed-method approach involving qualitative and quantitative research methods. These included species identification, water quality analysis, land-use change analysis, field surveys, focus group discussions, and interviews. Genetic analysis of the fish samples from the study area revealed that the species had a mean of 98% similarity to S. maderensis. While major urban and industrial land use has increased by 175% in the last four decades, the catch per unit effort (CPUE) of S. maderensis declined monthly to 0.0072 kg/H between 2003 and 2019. Linear regression indicated that anthropogenic variables explained approximately 39.58% of the variation in the CPUE (p < 0.001, R2 = 0.40). Water samples showed that heavy metal levels were above international limits, with high total petroleum hydrocarbon (TPH) pollution in all stations (27.56 mg/L–3985.40 mg/L). Physiochemical analysis of water samples indicated TDS levels higher than the acceptable limits (mean = 24,971.1 mg/L) and inadequate chlorophyll-a levels (mean = 0.01 µg/L). Hence, urgent strategies are required to mitigate anthropogenic threats through inclusive coastal management policies supporting resilient artisanal fisheries.
Assessing the Feasibility and Socioecological Benefits of Climate-Smart Practices at the Watershed Scale
Authors: Lithgow Debora , Von Thaden Juan José , Revollo-Fernández Daniel A. , Salazar-Vargas María del Pilar , Rodríguez de los Santos Aram
Published: April 2024

Resource allocation in climate-smart productive practices depends on the explicit recognition and accountability of the expected costs and benefits in socioeconomic and ecological terms. This study assessed the private and social costs and benefits of 10 practices compatible with the transition to sustainable agricultural practices under an integrated landscape management (ILM) approach. First, the financial and economic viability of the alternatives was evaluated with a cost-benefit analysis. Then, the potential contribution of these practices in terms of carbon sequestration and landscape connectivity was determined in an ILM scenario where at least three practices (live fences, isolated trees in pastures, and riparian vegetation recovery) could be implemented and assessed at the watershed scale. These practices were evaluated in three Mexican pilot watersheds with contrasting biophysical and sociocultural contexts but with high importance in biodiversity conservation and cattle production. The results showed that most climate-smart practices are viable in the medium and long term from a private standpoint. However, more significant benefits are achievable over a shorter period when social co-benefits are included. The results could contribute to decision-making in terms of public policy, providing evidence of the financial and economic feasibility of the analyzed climate-smart practices that also have ecological benefits. In this sense, decision-makers who promote such practices have more arguments to seek funding for implementation.
Tradition and Innovation in the Italian Wine Industry: The Best Practices of Casa Paladin
Authors: Grechi Daniele , Pavione Enrica , Gazzola Patrizia , Cardini Francesca
Published: April 2024

This study aims to make a significant contribution to the development of a model for integrating research in the wine sector, innovative knowledge, and family businesses with the traditional mode of production in the context of the introduction of modern production technologies with a view to sustainability and the improvement of services in tourism. From a methodological point of view, the research is based on the case study and, in particular, on Casa Paladin, a family business in the Italian wine sector, which bases its strategy on innovation in production processes and customer relations to obtain high product quality with the aim of meeting consumer needs. The findings underscore the crucial role of family businesses in preserving cultural and traditional elements in the wine sector, with technology and innovation serving as vital drivers for their development. Casa Paladin’s commitment to innovation in production processes and products is evident, emphasizing sustainability as a core element that impacts customer relationships and product quality. Enotourism, including tastings, festivals, and fairs, emerges as a significant aspect contributing to the promotion of the company’s history, culture, and traditions. This study posits Casa Paladin as a notable example in the Italian wine industry, offering transferable insights for other businesses. Its successful integration of culture, innovation, and sustainability contributes to a broader understanding of the contemporary role of family businesses in the Italian wine sector.
The Influence of Digital Literacy on the Phenomenon of Deviation between Farmers’ E-Commerce Sales Willingness and Behavior: Evidence from Rural China
Authors: Zhang Yonghu , Zhang Yifeng
Published: April 2024

E-commerce, as a prominent feature of the digital economy era, is highly expected to promote economic growth and sustainable development. Based on the 2020 “China Rural Revitalization Survey” data, this study empirically analyzed the impact and mechanism of digital literacy on the discrepancy between the willingness and behavior of farmers in e-commerce sales. Eleven indicator items were selected, and factor analysis was used to obtain the comprehensive score of digital literacy. Firstly, benchmark regression was performed using the probit model, and then potential endogeneity issues were addressed using the IV probit method and endogenous conversion probit model. The following main conclusions were drawn: Digital literacy has a suppressive effect on the deviation between the willingness and behavior of farmers in e-commerce sales. Farmers with media operation literacy, information acquisition literacy, digital transaction literacy, e-commerce cognitive literacy, and digital social literacy have a 12.3%, 18.3%, 26.2%, 37.5%, and 21.9% reduction in the probability of deviation between the willingness and behavior of farmers in e-commerce sales compared to those without these literacy counterfactual assumptions. Digital literacy helps curb the deviation between farmers’ willingness and behavior in e-commerce sales by facilitating their access to financial resources and agricultural production services. This study demonstrates that enhancing the digital literacy of farmers will promote their willingness to convert e-commerce sales into behavior, help promote sustainable agricultural products more efficiently, and make important contributions to economic growth and sustainable development.
The Impact of Multi-Dimensional Urbanization on CO2 Emissions: Empirical Evidence from Jiangsu, China, at the County Level
Authors: Zhai Jun , Kong Fanbin
Published: April 2024

Understanding the underlying mechanism of how various dimensions of urbanization affect CO2 emissions could be helpful for achieving the goal of low-carbon cities in China. However, previous research has seldomly examined this relationship granularly in economically developed regions at the micro level, nor did they examine the mediating effects of economic development, industrial structure, and coal consumption. Using the panel dataset of 80 counties from 2002 to 2021 at the county level in Jiangsu, China, this study investigates the direct and indirect effects of population, economic, and land urbanizations on CO2 emissions in Jiangsu province and examines the regional heterogeneity. The findings indicate that population and economic urbanization have positive impacts on CO2 emissions, whereas land urbanization has insignificant effects. This finding is supported by various robustness tests. Population and economic urbanizations are found to have significantly positive impacts on CO2 emissions in the southern and northern Jiangsu regions, whereas none of the three dimensions are significant in the middle Jiangsu region. Economic urbanization contributes the most to CO2 emissions in southern Jiangsu. In addition, our results indicate that multi-dimensional urbanizations affect CO2 emissions through the mediating roles of economic development, industrial structure, and coal consumption. Our analysis shed some insights into the nuanced relationship between multi-dimensional urbanization and carbon emissions, which could contribute to sustainable urban transformation.
Impact of Environmental Regulation on Corporate Green Technological Innovation: The Moderating Role of Corporate Governance and Environmental Information Disclosure
Authors: Ying Ying , Jin Shanyue
Published: April 2024

Environmental degradation is an important issue facing the world today. Microcosmically, green technical innovation is needed to decrease environmental pollution. Therefore, exploring the relationship between the two is of great significance for promoting environmental protection and sustainable development. Thus, this research elucidates the interaction between green innovation (GI) and environmental regulations (ERs). This study utilizes the fixed effects model to examine how government environmental protection subsidies (EPSs) in market-incentive ER and environmental management system certification (EMSC) in voluntary participatory ER affect GI among listed companies in China. The sample observation period is from 2012 to 2021. Additionally, the impact of corporate governance (CGL) and environmental information disclosure (EID) on the relationship between ERs and GI within businesses is investigated. The empirical results show that both government environmental protection subsidies and environmental management system certification positively affect green innovation, and both corporate governance and environmental information disclosure positively moderate the impact of government environmental protection subsidies and environmental management system certification on green innovation. The above empirical results are still valid after a robustness test and can guide the formulation of government ERs, as well as corporate strategies for environmental management and GI.
Measurement Techniques, Calculation Methods, and Reduction Measures for Greenhouse Gas Emissions in Inland Navigation—A Preliminary Study
Authors: Hörandner Laura , Duldner-Borca Bianca , Beil Denise , Putz-Egger Lisa-Maria
Published: April 2024

Emissions originating from inland navigation should be reduced to achieve climate targets. This paper aims to identify (1) onboard GHG emission measurement systems, (2) calculation methods for GHG emissions of inland vessels and (3) reduction measures. A systematic literature review, examining 6 databases, yielded 105 initial outcomes, with 17 relevant references. The review reveals a scarcity of studies, with the majority concentrated in Europe and Asia, while North America, Africa, Australia, and South America remain largely unexplored. Four of the seventeen relevant studies focused on real-world GHG emissions measurement. Future research should explore more efficient and calibrated approaches for real-time CO2 insights in inland vessels. In the section on calculating GHG emissions, most papers attempt to adapt the EEDI or EEXI to inland navigation. Reduction measures for GHG emissions concentrate on alternative fuels, like LNG, methanol, hydrogen, or alternative power sources. As the research in this area is limited, prioritizing it in academic discourse is not only essential for advancing our understanding but also imperative for shaping a resilient and environmentally conscious future for inland navigation.
Study on Sustainable Application of Low-Carbon Supersulfated Cement with Alkanolamines
Authors: Zhou Runduo , Jin Bingxin , Wu Shuanglei , Fan Shujing , Hang Fafu , Chen Huxing
Published: April 2024

As an environmentally friendly cement material in green buildings, due to its low contribution to air pollution and its substantial use of solid waste, supersulfated cement (SSC) has been extensively studied. However, the low early strength of sustainably utilized SSC needs to be addressed. In order to use SSC to achieve great reductions in energy consumption during industrial production, the effects of triethanolamine (TEA), diethanolisopropanolamine (DEIPA) and triisopropanolamine (TIPA) (with dosages ranging from 0.02% to 0.08%) on the strength and hydration of SSC were studied, and the underlying mechanism was analyzed by TGA, XRD and SEM. The results show that TEA and DEIPA significantly improve the 3-day and 28-day strength of SSC. The former is better at low dosages, while the latter is more suitable for high dosages. TIPA also enhances the 3-day strength of SSC, but it is not as good as the other two alkanolamines. The chelation of alkanolamine with Al3+ ions plays an important role in the strength development of SSC, which accelerates the decomposition of slag and the formation of ettringite. In summary, adding alkanolamines to low-carbon cement systems with a high proportion of industrial by-products such as SSC is a potential and effective solution. In addition, alkanolamines can be used as a strength promoter for most low-carbon blends, which fully utilize solid waste.
How Environmental Regulations Affect Green Total Factor Productivity—Evidence from Chinese Cities
Authors: Ye Ziqi , Liu Yujia , Rong Ying
Published: April 2024

Environmental pollution and resource waste in Chinese cities have become important obstacles to sustainable economic development, and it is urgent to change the mode of economic development and improve the quality of economic development. In response to this challenge, this study proposes environmental regulation as a solution and empirically tests the impact of environmental regulation on green total factor productivity. The empirical results show that environmental regulation can significantly improve urban green total factor productivity, the public environmental concern and green finance strengthen the positive effect of environmental regulation on urban green total factor productivity. The mechanism test shows that environmental regulation can improve urban green total factor productivity through green technological innovation and industrial structure upgrading. Heterogeneity analysis reveals that, compared to the resource-based cities, the positive effect of environmental regulation on urban green total factor productivity is more significant in the non-resource-based cities with relatively developed traditional finance and high levels of industrial modernisation. Compared to the central and western as well as the northeast regions of China, the positive effect of environmental regulation on urban green total factor productivity is more significant in the eastern region due to capital accumulation and technological constraints. The results of the study remain reliable after a series of endogeneity and robustness tests. These studies provide an important research basis for providing more targeted environmental regulation programmes and better improving green total factor productivity.
Evaluation of the Quality of Higher Education Services by Revised IPA in the Perspective of Digitization
Authors: Ban Olimpia Iuliana , Hatos Roxana , Bugnar Nicoleta-Georgeta , Sasu Dinu , Popa Adela Laura , Fora Andreea-Florina
Published: April 2024

Higher education services are at a crossroads created by the disruptive impact of digitalization. The COVID-19 pandemic experience forced Romanian universities to use digital educational platforms and other online education tools. The questions put into discussion are: Where is Romanian university education headed, what attributes of service quality are relevant for students, how do they see the digitization experience, and what needs to be done to increase satisfaction? A survey was conducted based on an online questionnaire on a sample of 314 students from the University of Oradea that used, for the first time in empirical research, a scale to measure satisfaction with the quality of educational services to which a dimension of digital education is added. Revised importance–performance analysis (importance–performance analysis in nine quadrants with one parameter c-9Qc-IPA) was used for data processing. The results show a still-reserved attitude towards certain aspects of the digitization of services, with priority remaining on aspects related to the material base and the prospects of finding a suitable job after graduation.
Utilizing a Porcine Fat Grafting Model for Translational Research: Surgical Approach, Complications, and Expected Outcomes
Authors: Loder Shawn Jeffrey , Vagonis Alexandra M. , Shaaban Bahaa , Elmeanawy Amr , Bengur Fuat Baris , Villalvazo Yadira , Nerone Vincent W. , Surucu Yusuf , Humar Pooja , Arellano Jose Antonio , Malekzadeh Hamid , Gavrilescu Andreea , Ricketts Rachel E. , Rubin J. Peter , Kokai Lauren E.
Published: April 2024

Autologous fat grafting, or lipotransfer, is an important surgical approach to relocate adipose tissue within an individual to create volume. While used extensively in plastic and reconstructive surgery, significant drawbacks exist, including unpredictable volume retention. Thus, considerable research has been undertaken to identify surgical or therapeutic approaches that improve outcomes, primarily utilizing a xenograft immunocompromised mouse model. Large animal models are an important step in translating preclinical laboratory studies to the clinic, and previous studies utilizing pigs have been applied successfully for fat grafting research, but protocol variability exists across studies, and no previous publication has adequately described the impact of the swine breed on the experimental outcomes. In this report, we provide information on the critical attributes of the swine fat grafting model, including the following: (1) model selection; (2) donor site and surgical harvest approach; (3) tissue processing; (4) recipient site location and preparation; (5) post-operative care; and (6) longitudinal fat grafting assessments. Our experience comparing the use of Yorkshire and Yucatan breeds in our model showed that Yorkshire adipose tissue was fibrotic, extremely difficult to obtain through liposuction, and labor intensive to process into injectable formats. Alternatively, Yucatan adipose was more similar to human tissue, could be readily obtained through the surgical excision of inguinal fat pads, was amenable to mincing with surgical scissors, and yielded injectable tissue with a 95% efficiency. We determined that generation of a surgical pocket reduced the graft migration and spread, consequently facilitating the graft retrieval without significantly impacting retention. Using 5 cc grafts, the ultimate 3-month volume retention in 16 grafts was 19% ± 17% (or 1.14 cc ± 1.08 cc). While the use of ultrasound did not readily enable graft volume approximation during the study, it was a useful method to visualize the graft placement and ensure injection into the subcutaneous adipose layers.
Patients with High Pre-Operative Physical Activity Take Longer to Return to Baseline
Authors: Redfern Roberta E. , Crawford David A. , Lombardi Adolph V. , Tripuraneni Krishna R. , Van Andel David C. , Anderson Mike B. , Cholewa Jason M.
Published: April 2024

Patients with end-stage osteoarthritis are recommended to engage in physical activity (PA) to reduce pain and improve function but may avoid PA due to joint pain. Our goal was to investigate patient-reported outcomes and objective mobility metrics (step counts) in total hip arthroplasty (THA) patients as a function of pre-operative PA levels. In total, 1647 patients enrolled in a multicenter prospective cohort study investigating a smartphone-based care management platform for self-directed rehabilitation that underwent THA and were included in analysis. The entire cohort’s step count was divided into quartiles to categorize patients with low, moderate, and high baseline PA. Outcomes including pain, EQ-5D-5L, HOOS JR, and step counts were compared according to activity group by ANOVA. Pre-operative pain scores were lowest, with smallest improvements, in the high-baseline PA group. Low-PA patients demonstrated the greatest improvements in EQ-5D-5L, while changes in EQ-VAS, HOOS JR, and satisfaction were similar between groups. Low- and moderate-PA patients increased physical activity by six weeks, reaching 180% and 114% of pre-operative steps; high-PA patients did not return to full step counts until one-year post-operation. Patients who perform high levels of PA undergoing THA report lower levels of pain and higher function pre-operatively but may appreciate less improvement in PA up to one year post-operatively. These results may be helpful in appropriate counseling of patient expectations prior to surgery.
Velocity Estimations in Blood Microflows via Machine Learning Symmetries
Authors: Alfonso Perez Gerardo , Colchero Paetz Jaime Virgilio
Published: April 2024

Improving velocity forecasts of blood microflows could be useful in biomedical applications. We focus on estimating the velocity of the blood in capillaries. Modeling blood microflow in capillaries is a complex process. In this paper, we use artificial intelligence techniques for this modeling: more precisely, artificial neural networks (ANNs). The selected model is able to accurately forecast the velocity, with an R2 of 0.8992 comparing the forecast with the actual velocity. A key part of ANN model creation is selecting the appropriate parameters for the ANN, such as the number of neurons, the number of layers and the type of training algorithm used. A grid approach with 327,600 simulations was used. It is shown that there are substantial, statistically significant differences when different types of ANN structures are used. It is also shown that the proposed model is robust regarding the initial random initialization of weights in the ANN. Additionally, the sensitivity of the selected models to additional noise was also tested.
Mainline Railway Modeled with 2100 MHz 5G-R Channel Based on Measured Data of Test Line of Loop Railway
Authors: Liang Yiqun , Li Hui , Li Yi , Li Anning
Published: April 2024

Railway communication is undergoing a transitional period worldwide, and 5G for railway (5G-R), which has been adopted by the Railway International Union (UIC) in China and in many other countries, which is a common concern all over the world. The 2100 MHz frequency band, which ranks among the top three mainstream frequency bands for public 5G network deployment globally, has recently been issued by MIIT for a 5G-R trial in China, and the frequency is fairly close to 5G-R frequency band of 1900 MHz in Europe. Propagation study for railway communication, especially for mainline, which is the most common and widely used scenario, is of great importance, because it is closely related to passengers’ lives and properties. In this article, based on the 5G-R dedicated network testing environment constructed along the test line of loop railway of National Railway Track Test Center of China, the 2100 MHz 5G-R propagation measurement campaign is conducted, with an in-service SS-RSRP signal-based passive 2100 MHz large-scale channel testing system, as well as a SDR-based 2100 MHz 5G-R channel sounding system, based on which the large-scale as well as small-scale channel characteristics are derived and extensively analyzed. With data derived from the passive testing system, the classical FI, CI as well as TR 38.901 large-scale channel models are properly fitted and compared by calculating RMSE as well as MAE values. Moreover, based on the SDR-based channel sounding system, conclusions on the relationship between the parameters of multi-path component numbers, RMS time spread, received power and distance between the transmitter and the receiver are drawn. The 5G-R system studied in this letter is symmetric FDD communication system, apart from the 2 aspects of channel modeling characteristics, which are large-scale and small-scale complementing each other, the research methodology adopted of the letter also has two aspects, passive measurement, as well as active measurement, and both methods have their own advantages and different focuses, which can also complements each other. Relevant research results of this letter will be helpful for facilitating the R&D, deployment, as well as network optimization of a future mobile communication system under railway mainline scenario.
A Novel Method for Bearing Fault Diagnosis Based on a Parallel Deep Convolutional Neural Network
Authors: Lin Zhuonan , Wang Yongxing , Guo Yining , Tong Xiangrui , Wei Fanrong , Tong Ning
Published: April 2024

The symmetry of vibration signals collected from healthy machinery, which gradually degenerates with the development of faults, must be detected for timely diagnosis and prognosis. However, conventional methods may miss spatiotemporal relationships, struggle with varying sampling rates, and lack adaptability to changing loads and conditions, affecting diagnostic accuracy. A novel bearing fault diagnosis approach is proposed to address these issues, which integrates the Gramian angular field (GAF) transformation with a parallel deep convolutional neural network (DCNN). The crux of this method lies in the preprocessing of input signals, where sampling rate normalization is employed to minimize the effects of varying sampling rates on diagnostic outcomes. Subsequently, the processed signals undergo GAF transformation, converting them into an image format that effectively represents their spatiotemporal relationships in a two-dimensional space. These images serve as inputs to the parallel DCNN, facilitating feature extraction and fault classification through deep learning techniques and leading to improved generalization capabilities on test data. The proposed method achieves an overall accuracy of 96.96%, even in the absence of training data within the test set. Discussions are also conducted to quantify the effects of sampling rate normalization and model structures on diagnostic accuracy.
Recent Developments in Engineering Non-Paralytic Botulinum Molecules for Therapeutic Applications
Authors: Zhantleuova Aisha , Leese Charlotte , Andreou Anna P. , Karimova Altynay , Carpenter Guy , Davletov Bazbek
Published: April 2024

This review discusses the expanding application of botulinum neurotoxin in treating neurological conditions. The article specifically explores novel approaches to using non-paralytic botulinum molecules. These new molecules, such as BiTox or el-iBoNT, offer an alternative for patients who face limitations in using paralytic forms of botulinum neurotoxin due to concerns about muscle function loss. We highlight the research findings that confirm not only the effectiveness of these molecules but also their reduced paralytic effect. We also discuss a potential cause for the diminished paralytic action of these molecules, specifically changes in the spatial parameters of the new botulinum molecules. In summary, this article reviews the current research that enhances our understanding of the application of new botulinum neurotoxins in the context of common conditions and suggests new avenues for developing more efficient molecules.
In Ovo Models to Predict Virulence of Highly Pathogenic Avian Influenza H5-Viruses for Chickens and Ducks
Authors: Bordes Luca , Gonzales José L. , Vreman Sandra , Venema Sandra , Portier Nadia , Germeraad Evelien A. , van der Poel Wim H. M. , Beerens Nancy
Published: April 2024

Highly pathogenic avian influenza (HPAI) H5-viruses are circulating in wild birds and are repeatedly introduced to poultry causing outbreaks in the Netherlands since 2014. The largest epizootic ever recorded in Europe was caused by HPAI H5N1 clade 2.3.4.4b viruses in the period 2021–2022. The recent H5-clade 2.3.4.4 viruses were found to differ in their virulence for chickens and ducks. Viruses causing only mild disease may remain undetected, increasing the risk of virus spread to other farms, wild birds and mammals. We developed in ovo models to determine the virulence of HPAI viruses for chickens and ducks, which are fast and have low costs. The virulence of five contemporary H5-viruses was compared studying replication rate, average time to death and virus spread in the embryo. Remarkable differences in virulence were observed between H5-viruses and between poultry species. The H5N1-2021 virus was found to have a fast replication rate in both the chicken and duck in ovo models, but a slower systemic virus dissemination compared to three other H5-clade 2.3.4.4b viruses. The results show the potential of in ovo models to quickly determine the virulence of novel HPAI viruses, and study potential virulence factors which can help to better guide the surveillance in poultry.
Deciphering the Role of Epstein–Barr Virus Latent Membrane Protein 1 in Immune Modulation: A Multifaced Signalling Perspective
Authors: Šimičić Petra , Batović Margarita , Stojanović Marković Anita , Židovec-Lepej Snjezana
Published: April 2024

The disruption of antiviral sensors and the evasion of immune defences by various tactics are hallmarks of EBV infection. One of the EBV latent gene products, LMP1, was shown to induce the activation of signalling pathways, such as NF-κB, MAPK (JNK, ERK1/2, p38), JAK/STAT and PI3K/Akt, via three subdomains of its C-terminal domain, regulating the expression of several cytokines responsible for modulation of the immune response and therefore promoting viral persistence. The aim of this review is to summarise the current knowledge on the EBV-mediated induction of immunomodulatory molecules by the activation of signal transduction pathways with a particular focus on LMP1-mediated mechanisms. A more detailed understanding of the cytokine biology molecular landscape in EBV infections could contribute to the more complete understanding of diseases associated with this virus.
Efficacy of Alum-Adjuvanted Peptide and Carbohydrate Conjugate Vaccine Candidates against Group A Streptococcus Pharyngeal Infection in a Non-Human Primate Model
Authors: Rivera-Hernandez Tania , Carnathan Diane G. , Richter Johanna , Marchant Patrick , Cork Amanda J. , Elangovan Gayathiri , Henningham Anna , Cole Jason N. , Choudhury Biswa , Moyle Peter M. , Toth Istvan , Batzloff Michael R. , Good Michael F. , Agarwal Paresh , Kapoor Neeraj , Nizet Victor , Silvestri Guido , Walker Mark J.
Published: April 2024

Vaccine development against group A Streptococcus (GAS) has gained traction in the last decade, fuelled by recognition of the significant worldwide burden of the disease. Several vaccine candidates are currently being evaluated in preclinical and early clinical studies. Here, we investigate two conjugate vaccine candidates that have shown promise in mouse models of infection. Two antigens, the J8 peptide from the conserved C-terminal end of the M protein, and the group A carbohydrate lacking N-acetylglucosamine side chain (ΔGAC) were each conjugated to arginine deiminase (ADI), an anchorless surface protein from GAS. Both conjugate vaccine candidates combined with alum adjuvant were tested in a non-human primate (NHP) model of pharyngeal infection. High antibody titres were detected against J8 and ADI antigens, while high background antibody titres in NHP sera hindered accurate quantification of ΔGAC-specific antibodies. The severity of pharyngitis and tonsillitis signs, as well as the level of GAS colonisation, showed no significant differences in NHPs immunised with either conjugate vaccine candidate compared to NHPs in the negative control group.
Effectiveness of Nirsevimab Immunoprophylaxis Administered at Birth to Prevent Infant Hospitalisation for Respiratory Syncytial Virus Infection: A Population-Based Cohort Study
Authors: Ezpeleta Guillermo , Navascués Ana , Viguria Natividad , Herranz-Aguirre Mercedes , Juan Belloc Sergio Enrique , Gimeno Ballester Juan , Muruzábal Juan Carlos , García-Cenoz Manuel , Trobajo-Sanmartín Camino , Echeverria Aitziber , Martínez-Baz Iván , Vera-Punzano Noelia , Casado Itziar , López-Mendoza Héctor , Ezpeleta Carmen , Castilla Jesús
Published: April 2024

Respiratory syncytial virus (RSV) infection is a frequent cause of hospitalisation in the first few months of life; however, this risk rapidly decreases with age. Nirsevimab immunoprophylaxis was approved in the European Union for the prevention of RSV-associated lower respiratory tract disease in infants during their first RSV season. We evaluated the effectiveness of nirsevimab in preventing hospitalisations for confirmed RSV infection and the impact of a strategy of immunisation at birth. A population-based cohort study was performed in Navarre, Spain, where nirsevimab was offered at birth to all children born from October to December 2023. Cox regression was used to estimate the hazard ratio of hospitalisation for PCR-confirmed RSV infection between infants who received and did not receive nirsevimab. Of 1177 infants studied, 1083 (92.0%) received nirsevimab. The risk of hospitalisation for RSV was 8.5% (8/94) among non-immunised infants versus 0.7% (8/1083) in those that were immunised. The estimated effectiveness of nirsevimab was 88.7% (95% confidence interval, 69.6–95.8). Immunisation at birth of infants born between October and December 2023 prevented one hospitalisation for every 15.3 immunised infants. Immunisation of children born from September to January might prevent 77.5% of preventable hospitalisations for RSV in infants born in 2023–2024. These results support the recommendation of nirsevimab immunisation at birth to children born during the RSV epidemic or in the months immediately before to prevent severe RSV infections and alleviate the overload of paediatric hospital resources.
An Estimation of the Energy Savings of a Mainline Diesel Locomotive Equipped with an Energy Storage Device
Authors: Riabov Ievgen , Goolak Sergey , Neduzha Larysa
Published: April 2024

The method of improving a two-section mainline diesel locomotive by using energy storage in the traction system is considered. A mathematical model was developed to study the movement of a diesel locomotive based on the recommendations and provisions of the theory of locomotive traction. For this purpose, the movement of a diesel locomotive as part of a train along a given section of a track was studied. It was determined that the use of an energy storage device on a diesel locomotive will allow up to 64% of the energy spent on train traction to accumulate. The use of energy storage in the accumulator during electrodynamic braking ensured a reduction in fuel consumption by about 50%, regardless of the options for equipping the traction system of the diesel locomotive with an energy accumulator. It is established that regardless of the options for equipping the traction system of the diesel locomotive with an energy storage device, the indicators characterizing the degree of use of the diesel engine do not change. These research results can be used in works devoted to the improvement of the control system of energy exchange between the accumulator and traction engines of diesel locomotives.
Enhancing Urban Intersection Efficiency: Utilizing Visible Light Communication and Learning-Driven Control for Improved Traffic Signal Performance
Authors: Vieira Manuela , Vieira Manuel Augusto , Galvão Gonçalo , Louro Paula , Véstias Mário , Vieira Pedro
Published: April 2024

This paper introduces an approach to enhance the efficiency of urban intersections by integrating Visible Light Communication (VLC) into a multi-intersection traffic control system. The main objectives include the reduction in waiting times for vehicles and pedestrians, the improvement of overall traffic safety, and the accommodation of diverse traffic movements during multiple signal phases. The proposed system utilizes VLC to facilitate communication among interconnected vehicles and infrastructure. This is achieved by utilizing streetlights, headlamps, and traffic signals for transmitting information. By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established. A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively. Agents placed at each intersection control traffic lights by incorporating information from VLC-ready cars, including their positions, destinations, and intended routes. The agents devise optimal strategies to improve traffic flow and engage in communication to optimize the collective traffic performance. An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times. The reinforcement learning approach effectively schedules traffic signals, and the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios. The discussion emphasizes the possibility of applying reinforcement learning in everyday traffic scenarios, showcasing the potential for the dynamic identification of control actions and improved traffic management.
Interval from Oestrus to Ovulation in Dairy Cows—A Key Factor for Insemination Time: A Review
Authors: De Rensis Fabio , Dall’Olio Eleonora , Gnemmi Giovanni Maria , Tummaruk Padet , Andrani Melania , Saleri Roberta
Published: April 2024

This review describes the oestrus-to-ovulation interval, the possibility of predicting the time of ovulation, and the optimum time for insemination relative to oestrus in dairy cows. The duration of oestrus in dairy cows is approximately 8–20 h, with differences possibly related to the methods of oestrus detection and the frequency of observations. Most cows ovulate approximately 24–33 h after the onset of oestrus and 15–22 h after the end of oestrus. The interval from the preovulatory luteinising hormone (LH) surge to ovulation is approximately 4–30 h. Ovulation occurs when follicle diameter averages 18–20 mm. When it is possible to correctly determine the beginning of oestrus, artificial insemination can be performed utilizing the “a.m.–p.m. rule”, and only one insemination may be applied. In cows with too long or too short oestrus-to-ovulation intervals, fertility can be compromised. One important factor that can alter the oestrus-to-ovulation interval is acute or chronic heat stress during the warm season. When there is a risk that insemination may occur too early or too late with respect to the time of ovulation, GnRH administration can be considered.
Ensemble Forecasts of Extreme Flood Events with Weather Forecasts, Land Surface Modeling and Deep Learning
Authors: Liu Yuxiu , Yuan Xing , Jiao Yang , Ji Peng , Li Chaoqun , An Xindai
Published: April 2024

Integrating numerical weather forecasts that provide ensemble precipitation forecasts, land surface hydrological modeling that resolves surface and subsurface hydrological processes, and artificial intelligence techniques that correct the forecast bias, known as the “meteo-hydro-AI” approach, has emerged as a popular flood forecast method. However, its performance during extreme flood events across different interval basins has received less attention. Here, we evaluated the meteo-hydro-AI approach for forecasting extreme flood events from headwater to downstream sub-basins in the Luo River basin during 2010–2017, with forecast lead times up to 7 days. The proposed meteo-hydro approach based on ECMWF weather forecasts and the Conjunctive Surface-Subsurface Process version 2 land surface model with a spatial resolution of 1 km captured the flood hydrographs quite well. Compared with the ensemble streamflow prediction (ESP) approach based on initial conditions, the meteo-hydro approach increased the Nash-Sutcliffe efficiency of streamflow forecasts at the three outlet stations by 0.27–0.82, decreased the root-mean-squared-error by 22–49%, and performed better in reliability and discrimination. The meteo-hydro-AI approach showed marginal improvement, which suggested further evaluations with larger samples of extreme flood events should be carried out. This study demonstrated the potential of the integrated meteo-hydro-AI approach for ensemble forecasting of extreme flood events.
Multi-Objective Optimization Based on Simulation Integrated Pareto Analysis to Achieve Low-Carbon and Economical Operation of a Wastewater Treatment Plant
Authors: Liao Jianbo , Li Shuang , Liu Yihong , Mao Siyuan , Tian Tuo , Ma Xueyan , Li Bing , Qiu Yong
Published: April 2024

It is essential to reduce carbon emissions in wastewater treatment plants (WWTPs) to achieve carbon neutrality in society. However, current optimization of WWTPs prioritizes the operation cost index (OCI) and effluent quality index (EQI) over greenhouse gas (GHG) emissions. This study aims to conduct a multi-objective optimization of a WWTP, considering GHG emissions, EQI, and OCI. The anaerobic-anoxic-oxic integrated membrane bioreactor (AAO-MBR) process in an actual WWTP was selected as a typical case, tens of thousands of scenarios with combinations of six operational parameters (dissolved oxygen (DO), external carbon resource (ECR), poly aluminum chloride (PAC), internal reflux ratio (IRR), external reflux ratio (ERR), and sludge discharge (SD)) were simulated by GPS-X software (Hydromantics 8.0.1). It was shown that ECR has the greatest impact on optimization objectives. In the optimal scenario, the main parameters of ATDO, MTDO, IRR, and ERR were 0.1 mg/L, 4 mg/L, 50%, and 100%, respectively. The EQI, OCI, and GHG of the best scenario were 0.046 kg/m3, 0.27 ¥/m3, and 0.51 kgCO2/m3, which were 2.1%, 72.2%, and 34.6% better than the current situation of the case WWTP, respectively. This study provides an effective method for realizing low-carbon and economical operation of WWTPs.
Risk Assessment of Oil Spills along the Coastline of Jiaozhou Bay Using GIS Techniques and the MEDSLIK-II Model
Authors: Peng Jialong , Wang Shaoqiang , Mu Lin , Wang Si
Published: April 2024

With the increasing global reliance on maritime oil transportation, oil spills pose significant environmental hazards to coastal ecosystems. This study presents a comprehensive quantitative framework for assessing oil spill risks along the Jiaozhou Bay coastline in China. The research begins with an analysis of historical spill data to construct representative oil spill simulation scenarios. The advanced MEDSLIK-II oil spill prediction model is then employed to simulate oil spill trajectories under these scenarios, focusing on key parameters such as oil thickness and mass to evaluate the hazard levels associated with each scenario. Subsequently, the Environmental Sensitivity Index (ESI) is utilized to assess the vulnerability of coastal zones, while Geographic Information System (GIS) techniques are employed for a spatial analysis and visualization of the results. The case study, covering a 26.87 km stretch of the Jiaozhou Bay coastline, reveals 10 high-risk zones with a total length of 8561.2 m. These areas are predominantly characterized by saltwater marshes, brackish water marshes, and inundated low-lying areas, with ESI rankings of 9 and 10, accounting for 24% of the 339 analyzed segments. The modeling results indicate that in the simulated scenarios, oil spills originating from the Huangdao Oil Port and Qianwan Port pose the greatest risks, with potential impacts extending up to 12 km and 15 km along the coastline, respectively. The study highlights the importance of considering multiple factors, including oil spill trajectories, coastal geomorphology, and ecological sensitivity, in comprehensive risk assessments. The proposed framework demonstrates potential for adaptation and application to other coastal regions facing similar oil spill risks, contributing to the advancement of coastal management practices worldwide.