bluuebunny/crossref_metadata_2025 huggingface dataset:
Loss and Downtime Assessment of RC Dual Wall–Frame Office Buildings Toward Resilient Seismic Performance
Authors: Gallegos Marco F. , Araya-Letelier Gerardo , Lopez-Garcia Diego , Molina Hutt Carlos
Published: February 2025

This study quantitatively assesses the impact of seismic design strategies on the performance of reinforced concrete (RC) dual wall–frame office buildings by comparing direct and indirect economic losses and downtime in life-cycle terms. A high-rise archetype building located in Santiago, Chile, on stiff soil was evaluated as a benchmark case study. Three design strategies to potentially enhance the seismic performance of a building designed conventionally were explored: (i) incorporating fluid viscous dampers (FVDs) in the lateral load-resisting structure; (ii) replacing conventional non-structural components with enhanced ones (ENCs); and (iii) a combination of the previous two strategies. First, probabilistic structural responses were estimated through incremental dynamic analyses using three-dimensional nonlinear models of the archetypes subjected to a set of hazard-consistent Chilean ground motions. Second, FEMA P-58 time-based assessment was conducted to estimate expected annual losses (EALs) for economic loss estimation. Finally, for downtime assessment, a novel probabilistic framework, built on the FEMA P-58 methodology and the REDi guidelines, was employed to estimate the expected annual downtimes (EADs) to achieve specific target recovery states, such as reoccupancy (RO) and functional recovery (FR). Results revealed that seismically enhancing RC dual wall–frame buildings with FVDs significantly improves resilience by reducing loss and downtime. For example, the enhanced building with FVDs achieved an EAL of 0.093% and EAL of 8.6 days for FR, compared to the archetype base building without design improvements, which exhibited an EAL of 0.125% and an EAD of 9.5 days for FR. In contrast, the impact of ENCs alone was minor, compared to the effect of FVDs, with an EAL of 0.106% and an EAD of 9.1 days for FR. With this detailed recovery modeling, probabilistic methods, and a focus on intermediate recovery states, this framework represents a significant advancement in resilience-based seismic design and recovery planning.
Effects of Soil Modification Materials on the Quality of Sandy Soil in Mine Dumps
Authors: Shao Luying , Chen Shichao , Zhang Qing , Li Juan , Jia Zhi
Published: February 2025

Large-scale coal mine dumps are formed during the mining process of coal resources. These coal mine dumps comprise impoverished soil, posing significant challenges for vegetation restoration. To address this problem, soil microbial (EM) agents and fly ash have effectively improved soil quality. However, the effects of different application ratios on the quality of sandy soil in coal mine dumps are still unclear. This study aims to explore the applicable ratio for sandy soil in coal mine dumps. This study employed a field-based potted experiment design. A two-factor complete factorial experimental setup was utilized, with four levels of EM microbial agent to sandy soil weight ratio (0 g/kg, 0.1 g/kg, 0.2 g/kg, and 0.3 g/kg) and four levels of fly ash to sandy soil weight ratio (0 g/kg, 25 g/kg, 50 g/kg, and 75 g/kg), and the mixing of EM microbial agents and fly ash with the sandy soil was carried out at different ratios. Subsequently, the study examined the impacts of various dosages on the physicochemical properties of soil within the mine spoil heap, and a soil quality index was derived to quantify these effects. The application of EM microbial and fly ash resulted in significant improvements in the physicochemical properties of the soil compared to the control group. Notably, the combined application of EM microbial agent and fly ash exhibited superior effects on soil physicochemical properties compared to the individual applications of EM microbial agent or fly ash. Specifically, when the EM microbial agent concentration was 0.2 g/kg and the fly ash content was 75 g/kg, the enhancement in soil quality was most pronounced, with a soil quality index of 0.78. Mantel analysis revealed that the growth index and photosynthetic index of Corethrodendron fruticosum were primarily driven by soil total nitrogen and organic carbon. The research results can provide guidance and technical support for soil improvement in mining areas.
Expediting Recovery: Lessons and Challenges from the Great East Japan Earthquake to War-Torn Ukraine
Authors: Ishiwatari Mikio , Sakamoto Akiko , Nakayama Mikiyasu
Published: February 2025

The ongoing war in Ukraine poses an uncertain future, highlighting the need to prioritize reconstruction efforts even before the war ends. Japan’s recovery mechanisms have developed based on the experiences and lessons learned from natural disasters such as the Great Kanto Earthquake in 1923 and from war damage by World War II. This study aims to draw practical insights and lessons from the 2011 Great East Japan Earthquake for Ukraine’s postwar recovery and help achieve rapid and high-quality results. This study reviews the literature, policy documents, and reports on recovery from the disaster. It was found that streamlining environmental impact assessments, land use regulations, and land acquisition could accelerate recovery. Engaging the private sector promoted the reconstruction of critical infrastructure and housing projects. This Japanese case study highlights the importance of ensuring sustained commitment and avoiding setbacks in the recovery process by establishing legislative, planning, and institutional frameworks, as well as financial mechanisms. Protecting human health is paramount, even if more resources are needed to manage hazardous materials. Applying these valuable lessons to Ukraine requires careful consideration, including leveraging foreign investments to supplement limited domestic resources and establishing effective governance structures that promote transparency, accountability, and coordination.
Transport-Related Synthetic Time Series: Developing and Applying a Quality Assessment Framework
Authors: Gal-Tzur Ayelet
Published: February 2025

Data scarcity and privacy concerns in various fields, including transportation, have fueled a growing interest in synthetic data generation. Synthetic datasets offer a practical solution to address data limitations, such as the underrepresentation of minority classes, while maintaining privacy when needed. Notably, recent studies have highlighted the potential of combining real and synthetic data to enhance the accuracy of demand predictions for shared transport services, thereby improving service quality and advancing sustainable transportation. This study introduces a systematic methodology for evaluating the quality of synthetic transport-related time series datasets. The framework incorporates multiple performance indicators addressing six aspects of quality: fidelity, distribution matching, diversity, coverage, and novelty. By combining distributional measures like Hellinger distance with time-series-specific metrics such as dynamic time warping and cosine similarity, the methodology ensures a comprehensive assessment. A clustering-based evaluation is also included to analyze the representation of distinct sub-groups within the data. The methodology was applied to two datasets: passenger counts on an intercity bus route and vehicle speeds along an urban road. While the synthetic speed dataset adequately captured the diversity and patterns of the real data, the passenger count dataset failed to represent key cluster-specific variations. These findings demonstrate the proposed methodology’s ability to identify both satisfactory and unsatisfactory synthetic datasets. Moreover, its sequential design enables the detection of gaps in deeper layers of similarity, going beyond basic distributional alignment. This work underscores the value of tailored evaluation frameworks for synthetic time series, advancing their utility in transportation research and practice.
Time-Domain Aggregation of Interharmonics from Parallel Operation of Multiple Sustainable Sources and Electric Vehicles
Authors: Ravindran Vineetha , Letha Shimi Sudha , Rönnberg Sarah , Bollen Math H. J.
Published: February 2025

This paper examines the random nature of interharmonics generated by power converters connected to sustainable energy sources and loads, such as wind turbines, photovoltaic (PV) panels, and electric vehicles (EVs). Current research often overlooks the stochastic behavior of interharmonics and their impact on power system reliability and resilience, leading to gaps in effective modeling and mitigation strategies. Thus, this study examines a low-voltage installation with a PV panel, an EV and a microwave operating simultaneously, providing practical insights into real-world scenarios of interharmonic related disruptions and solutions for enhancing the reliability and resilience of sustainable energy grids. By leveraging real-time measurements of interharmonics, suitable probability distribution functions (PDFs) are initialized to develop a probabilistic model using Monte Carlo simulation. This enables the derivation of a time-domain aggregation model of interharmonics from multiple sources operating together at the point of common coupling (PCC). The findings reveal that the peak values of voltage or current fluctuations at the PCC are influenced by the randomness in the number of devices connected and the frequency components originating from different sources. Through multiple case studies, the dependency of these fluctuations on stochastic parameters is systematically established. Empirical relationships are formulated to predict aggregated interharmonic values under varying scenarios, enhancing the accuracy and applicability of the model. The results demonstrate that higher interharmonic frequencies and fewer randomly connected devices significantly increase the probability of elevated aggregated peak values. These insights can serve as benchmarks for grid operators and policymakers in mitigating interharmonic related issues in modern power systems.
Sharing and Co-Creating Value: Innovation in Platform-Based Agricultural Service Models Driven by Service Demand Collaboration—A Case Study of the JN Life
Authors: Sun Xin , Ma Yike
Published: February 2025

Platform-based agricultural service models have increasingly become a primary approach to agricultural socialized services in China. This study investigates a service-demand-driven platform-based agricultural service model through the case of JN Life initiated by nine ecological farms. Based on the theory of value co-creation, it traces the development of the JN platform ecosystem across three phases: emergence, formation, and expansion. The study examines how farm members and key stakeholders of the JN platform actively collaborate throughout these phases, transitioning from value propositions to institutional arrangements, resource integration, and achieving value co-creation. This process is referred to as an endogenous (vs. exogenous) platform-based agricultural service model. The findings provide valuable insights into advancing the sustainable development of ecological farms and agricultural socialized services, as well as enhancing collaborative innovation in the platform ecosystem.
Building Occupant Energy Labels (OEL): Capturing the Human Factors in Buildings for Energy Efficiency
Authors: Harputlugil Timuçin , de Wilde Pieter
Published: February 2025

Occupancy is one of the primary contributors to the energy performance gap, defined as the difference between actual and predicted energy usage, in buildings. This paper limits its scope to residential buildings, where occupant-centric consumption often goes unaccounted for in standard energy metrics. This paper starts from the hypothesis that a simple occupant energy efficiency label is needed to capture the essence of occupant behaviour. Such a label would help researchers and practitioners study a wide range of behavioural patterns and may better frame occupant interventions, potentially contributing more than expected to the field. Focusing on the residential sector, this research recognises that the complexity of occupant behaviour and its links to different scientific calculations requires that researchers deal with several intricate factors in their building performance assessments. Moreover, complexity arising from changing attitudes and behaviours—based on building typology, social environment, seasonal effects, and personal comfort levels—further complicates the challenge. Starting with these problems, this paper proposes a framework for an occupant energy labelling (OEL) model to overcome these issues. The contribution of the paper is twofold. Firstly, the literature is reviewed in depth to reveal current research related to occupant behaviour for labelling of humans based on their energy consumption. Secondly, a case study with energy simulations is implemented in the UK, using the CREST tool, to demonstrate the feasibility and potential of OEL. The results show that labelling occupants may help societies reduce building energy consumption by combining insights from energy statistics, surveys, and bills gathered with less effort, and can assist decision-makers in determining the best match between buildings and occupants. While the focus of this study is on residential buildings, future research is recommended to explore the applicability of OEL in office environments, where occupant behaviour and energy dynamics may differ significantly.
Challenging to Change? Examining the Link Between Public Participation and Greenwashing Based on Organizational Inertia
Authors: Liu Bei , Li Chengwu , Zhong Yin
Published: February 2025

This study investigates the impact of public participation on corporate greenwashing behavior among listed companies in China from 2011 to 2019, set against a backdrop of increasing global environmental regulations. Corporate greenwashing, characterized by misleading environmental claims, poses a significant barrier to sustainable development. Despite the recognition of public participation in social governance, organizational inertia often prevents companies from responding effectively. Our analysis reveals that public participation leads to stricter environmental regulations, thereby reducing greenwashing practices. This finding remains robust through various tests, including variable substitution and model adjustments. However, we also find that higher levels of organizational inertia weaken the positive influence of public participation on combating greenwashing. Thus, this study underscores the necessity of establishing mechanisms for public participation that can effectively shape corporate decision-making, offering crucial insights for enhancing corporate accountability and advancing sustainable practices.
Nanoscale Organic Contaminant Detection at the Surface Using Nonlinear Bond Model
Authors: Hardhienata Hendradi , Ahyad Muhammad , Nabilah Fasya , Alatas Husin , Handayasari Faridah , Kartono Agus , Sumaryada Tony , Birowosuto Muhammad D.
Published: February 2025

Environmental pollution from organic dyes such as malachite green and rhodamine B poses significant threats to ecosystems and human health due to their toxic properties. The rapid detection of these contaminants with high sensitivity and selectivity is crucial and can be effectively achieved using nonlinear optical methods. In this study, we combine the Simplified Bond Hyperpolarizability Model (SBHM) and molecular docking (MD) simulations to investigate the Second-Harmonic Generation (SHG) intensity of organic dyes on a silicon (Si(001)) substrate for nanoscale pollutant detection. Our simulations show good agreement with rotational anisotropy (RA) SHG intensity experimental data across all polarization angles, with a total error estimate of 3%. We find for the first time that the SBHM not only identifies the different organic pollutant dyes on the surface, as in conventional SHG detection, but can also determine their relative orientation and different concentrations on the surface. Meanwhile, MD simulations reveal that rhodamine B shows a strong adsorption affinity of −10.4kcal/mol to a single-layer graphene oxide (GO) substrate, primarily through π-π stacking interactions (36 instances) and by adopting a perpendicular molecular orientation. These characteristics significantly enhance SHG sensitivity. A nonlinear susceptibility analysis reveals good agreement between the SBHM and group theory. The susceptibility tensors confirm that the dominant contributions to the SHG signal arise from both the molecular structure and the surface interactions. This underscores the potential of GO-coated silicon substrates for detecting trace levels of organic pollutants with interaction distances ranging from 3.75Å to 5.81Å. This approach offers valuable applications in environmental monitoring, combining the sensitivity of SHG with the adsorption properties of GO for nanoscale detection.
Thioureas Derived from (S)-1-(2-pyridyl)ethylamine Enantiomer: Synthesis and Selected Applications as an Organocatalyst
Authors: Chrzanowski Jacek , Sancineto Luca , Deska Malgorzata , Rachwalski Michal , Drabowicz Jozef
Published: February 2025

In order to expand the group of chiral thiourea structures, several optically active thioureas derived from the (S)-1-(2-pyridyl)ethylamine enantiomer were prepared via its reaction with achiral or optically active isothiocyanates. To show their synthetic potential as chiral auxiliaries the isolated thioureas were tested as an optically active organocatalyst in the asymmetric version of the selected aldol condensation and addition of diethylzinc to benzaldehyde. The observation of asymmetric induction in these model reactions encourages further research on the use of this group of thioureas in asymmetric versions of multicomponent reactions and cycloadditions. The mechanistic aspects of the reactions under study are also briefly discussed.
Safety of Uncertain Session Types with Interval Probability
Authors: Aman Bogdan , Ciobanu Gabriel
Published: February 2025

In this article, we introduce and study a new kind of multiparty session type for a probabilistic process calculus that incorporates two forms of choice: probabilistic and nondeterministic. The main novelty of our approach is the use of interval probabilities in a type system in order to deal with uncertainty in a probabilistic process calculus. We present a decidable proof system that ensures deadlock freedom, type preservation and type safety, even when several types can be assigned to a process. The new typing system represents a conservative extension of the standard typing system based on multiparty session types (removing the probabilities from processes does not affect their well-typedness). We also define a probabilistic bisimulation between processes that are typed by using the same sorting and typing.
Estimation and Bayesian Prediction for the Generalized Exponential Distribution Under Type-II Censoring
Authors: Wang Wei , Gui Wenhao
Published: February 2025

This research focuses on the prediction and estimation problems for the generalized exponential distribution under Type-II censoring. Firstly, maximum likelihood estimations for the parameters of the generalized exponential distribution are computed using the EM algorithm. Additionally, confidence intervals derived from the Fisher information matrix are developed and analyzed alongside two bootstrap confidence intervals for comparison. Compared to classical maximum likelihood estimation, Bayesian inference proves to be highly effective in handling censored data. This study explores Bayesian inference for estimating the unknown parameters, considering both symmetrical and asymmetrical loss functions. Utilizing Gibbs sampling to produce Markov Chain Monte Carlo samples, we employ an importance sampling approach to obtain Bayesian estimates and compute the corresponding highest posterior density (HPD) intervals. Furthermore, for one-sample prediction and, separately, for the two-sample case, we provide the corresponding posterior distributions, along with methods for computing point predictions and predictive intervals. Through Monte Carlo simulations, we evaluate the performance of Bayesian estimation in contrast to maximum likelihood estimation. Finally, we conduct an analysis of a real dataset derived from deep groove ball bearings, calculating Bayesian point predictions and predictive intervals for future samples.
Synthetic Biology-Based Approaches to Investigate Host–Pathogen Interactions
Authors: Banerjee Rajdeep
Published: February 2025

The increasing prevalence of multi-drug-resistant (MDR) bacterial pathogens presents a critical global health threat, highlighting the urgent need for innovative approaches to understanding bacterial pathogenesis and developing effective therapies. This review underscores the potential of synthetic biology in elucidating host–pathogen interactions and facilitating the creation of advanced diagnostic tools and targeted therapies to combat MDR infections. We first explore CRISPR-based strategies that modulate essential gene expression, providing insights into the molecular mechanisms underlying host–pathogen interactions. Next, we discuss engineered microbial synthetic circuits for rapid pathogen detection by identifying molecular signatures involved in interspecies communication and facilitating swift pathogen elimination. Additionally, we explore phage therapy (PT), which leverages bacteriophages to selectively target and eliminate specific bacterial pathogens, presenting a targeted and promising approach to combat MDR infections. Finally, we review the application of organ-on-a-chip (OOAC) technology, which overcomes the limitations of animal models in predicting human immune responses by using microfluidic devices that simulate organ-level physiology and pathophysiology, thereby enabling more accurate disease modeling, drug testing, and the development of personalized medicine. Collectively, these synthetic biology tools provide transformative insights into the molecular mechanisms of host–pathogen interactions, advancing the development of precise diagnostic and therapeutic strategies against MDR infections.
PictureGuard: Enhancing Software-Defined Networking–Internet of Things Security with Novel Image-Based Authentication and Artificial Intelligence-Powered Two-Stage Intrusion Detection
Authors: Hatamleh Hazem (Moh’d Said) , Alnaser As’ad Mahmoud As’ad , Saloum Said S. , Sharadqeh Ahmed , Alkasassbeh Jawdat S.
Published: February 2025

Software-defined networking (SDN) represents a transformative approach to network management, enabling the centralized and programmable control of network infrastructure. This paradigm facilitates enhanced scalability, flexibility, and security in managing complex systems. When integrated with the Internet of Things (IoT), SDN addresses critical challenges such as security and efficient network management, positioning the SDN-IoT paradigm as an emerging and impactful technology in modern networking. The rapid proliferation of IoT applications has led to a significant increase in security threats, posing challenges to the safe operation of IoT systems. Consequently, SDN-IoT-based applications and services have been widely adopted to address these issues and challenges. However, this platform faces critical limitations in ensuring scalability, optimizing energy consumption, and addressing persistent security vulnerabilities. To overcome these issues, we proposed a secure SDN-IoT environment for intrusion detection and prevention using virtual blockchain (V-Block). Initially, IoT users are registered and authenticated to the shadow blockchain nodes using a picture-based authentication mechanism. After that, authenticated user flows validation was provided by considering effective metrics utilizing the Trading-based Evolutionary Game Theory (TEGT) approach. Then, we performed a local risk assessment based on evaluated malicious flows severity and then the attack graph was constructed using an Isomorphism-based Graph Neural Network (IGNN) model. Further, multi-controllers were placed optimally using fox optimization algorithm. The generated global paths were securely stored in the virtual blockchain Finally, the two agents in the multi-controllers were responsible for validating and classifying the incoming suspicious flow packets into normal and malicious packets by considering the operative metrics using the Dueling Deep Q Network (DDQN) algorithm. The presented work was conducted by Network Simulator-3.26 and the different performance matrices were used to itemize the suggested V-Block model based on its malicious traffic, attack detection rate, link failure rate, anomaly detection rate, and scalability.
Analysis and Optimization of DC-DC Converters Through Sensitivity to Parametric Variations
Authors: Hinov Nikolay , Stanchev Plamen , Vacheva Gergana
Published: February 2025

The optimization of DC-DC converters is crucial for enhancing their performance and efficiency in various applications. This study focuses on the sensitivity analysis of DC-DC converters to parametric variations, which plays a key role in designing robust and efficient systems. The methodology involves developing a simulation model that describes the behavior of converters under different conditions and analyzing the effects of parameter variations through simulation tools. Sensitivity analysis of DC-DC converters involves understanding the sources of harmonics, modeling the converter, analyzing the harmonic content, and implementing mitigation techniques. By combining theoretical analysis with practical design modifications, engineers can optimize DC-DC converters for improved performance, efficiency, and compliance with electromagnetic compatibility standards. Examples of harmonic analysis of the main types of DC-DC converters—Buck, Boost, and Buck-Boost—are discussed in the manuscript. Based on a study of the influence of harmonics in the operating modes, ratios have been derived to be applied during design. In this respect, the research presented is useful for designers and for use in power electronics education.
Enhancing Electricity Load Forecasting with Machine Learning and Deep Learning
Authors: Perçuku Arbër , Minkovska Daniela , Hinov Nikolay
Published: February 2025

The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and stability of the power grid. The accuracy of electricity load forecasting plays an important role in ensuring safe operation and improving the reliability of power systems and is a key component in the operational planning and efficient market. For many years, a conventional method has been used by using historical data as input parameters. With swift progress and improvement in technology, which shows more potential due to its accuracy, different methods can be applied depending on the identified model. To enhance the forecast of load, this paper introduces and proposes a framework developed on graph database technology to archive large amounts of data, which collects measured data from electrical substations in Pristina, Kosovo. The data includes electrical and weather parameters collected over a four-year timeframe. The proposed framework is designed to handle short-term load forecasting. Machine learning Linear Regression and deep learning Long Short-Term Memory algorithms are applied to multiple datasets and mean absolute error and root mean square error are calculated. The results show the promising performance and effectiveness of the proposed model, with high accuracy in load forecasting.
Per- and Poly-Fluoroalkyl Substances, and Organophosphate Flame Retardants in the Upper Yangtze River: Occurrence, Spatiotemporal Distribution, and Risk Assessment
Authors: Sun Wen , Fu Zhiyou , Liu Yueyue , Bai Yingchen , Zhao Yuyan , Wang Chen , Wu Fengchang
Published: February 2025

Contaminants of Emerging Concern (CECs), including per- and polyfluoroalkyl substances (PFASs) and organophosphate flame retardants (OPFRs), have raised global concerns due to their persistence, bioaccumulation potential, and toxicity. This study presents a comprehensive investigation of the occurrence, spatiotemporal distribution, potential sources, and the ecological and human health risks associated with 18 PFASs and 9 OPFRs in the surface waters of the upper Yangtze River, China. The water samples were collected from the main stream and five major tributaries (Min, Jinsha, Tuo, Jialing, and Wu Rivers) in 2022 and 2023. The total concentration of PFASs and OPFRs ranged from 16.07 to 927.19 ng/L, and 17.36 to 190.42 ng/L, respectively, with a consistently higher concentration observed in the main stream compared to the tributaries. Ultra-short-chain PFASs (e.g., TFMS) and halogenated OPFRs (e.g., TCPP) were the predominant compounds, likely originating from industrial discharges, wastewater effluents, and other anthropogenic sources. Ecological risk assessments indicated low-to-moderate risks at most sampling sites, with higher risks near wastewater discharge points. Human health risk evaluations suggested negligible non-carcinogenic risks but identified potential carcinogenic risks from OPFR exposure for adults at specific locations, particularly in Leshan city. This study highlights the importance of understanding the fate and impacts of PFASs and OPFRs in the upper Yangtze River, and provides valuable insights for developing targeted pollution control strategies and risk management measures.
Ozone Exposure and Gestational Diabetes in Twin Pregnancies: Exploring Critical Windows and Synergistic Risks
Authors: Zhao Anda , Xia Yuanqing , Lu Ruoyu , Kang Wenhui , Huang Lili , Hua Renyi , Lyu Shuping , Zhao Yan , Chen Jianyu , Wang Yanlin , Li Shenghui
Published: February 2025

The relationship between ozone (O3) exposure and gestational diabetes mellitus (GDM) in twin pregnancies remains unexplored. This study aimed to investigate the association between O3 exposure and GDM risk in twin pregnancies, and to explore the synergistic effects of O3 exposure with other maternal factors. A total of 428 pregnancies recruited from a prospective twin cohort were included. Cox proportional hazard models with distributed lag non-linear models (DLNMs) were applied to examine the associations between O3 exposure and the risk of GDM and to identify the critical windows. The multiplicative and additive interaction were further analyzed to test the synergistic effects. A 10 μg/m3 increase in average O3 exposure during the 12 weeks before pregnancy was associated with a 26% higher risk of GDM. The critical windows were identified in the period from the 3rd week before gestation to the 2nd gestational week as well as from the 17th to 19th gestational week. There were synergistic effects between high O3 exposure during preconception and advanced maternal age, and a history of preterm birth/abortion/stillbirth. Periconceptional O3 exposure could increase the risk of GDM in twin pregnancy women, and the synergism of O3 exposure with certain GDM risk factors was observed.
The Role of Epigenetic Mechanisms in the Development of PM2.5-Induced Cognitive Impairment
Authors: Jiang Lishan , Shao Mingxia , Song Chao , Zhou Li , Nie Wenke , Yu Hang , Wang Siqi , Liu Yongping , Yu Li
Published: February 2025

PM2.5 is fine particulate matter with a diameter of less than 2.5 μm. Recent evidence has shown that exposure to PM2.5 markedly elevates the risk of neurodegenerative diseases, neurodevelopmental disorders, and cardiovascular diseases, which may culminate in cognitive impairment. Nevertheless, the precise mechanisms through which PM2.5 affects cognitive function are unclear. Recent studies have demonstrated that PM2.5-induced epigenetic alterations are associated with the development of cognitive impairment. Epigenetic alterations include modifications to DNA methylation, histone modifications, and non-coding RNAs. The underlying mechanisms of epigenetic alterations are related to inflammation, synaptic dysfunction, cardiovascular factors, and alterations in neuronal structure and function. This review reports the latest findings on the relationship between PM2.5-induced epigenetic alterations and the development of cognitive disorders, offering novel insights into the cognitive effects of air pollution.
Non-Keplerian Charged Accretion Disk Orbiting a Black Hole Pulsar
Authors: Trova Audrey , Hackmann Eva
Published: February 2025

Recent studies have focused on how spinning black holes (BHs) within a binary system containing a strongly magnetized neutron star, then immersed in external magnetic fields, can acquire charge through mechanisms like the Wald process and how this charge could power pulsar-like electromagnetic radiation. Those objects called “Black hole pulsar” mimic the behaviour of a traditional pulsar, and they can generate electromagnetic fields, such as magnetic dipoles. Charged particles within an accretion disk around the black hole would then be influenced not only by the gravitational forces but also by electromagnetic forces, leading to different geometries and dynamics. In this context, we focus here on the interplay of the magnetic dipole and the accretion disk. We construct the equilibrium structures of non-conducting charged perfect fluids orbiting Kerr black holes under the influence of a dipole magnetic field aligned with the rotation axis of the BH. The dynamics of the accretion disk in such a system are shaped by a complex interplay between the non-uniform, non-Keplerian angular momentum distribution, the black hole’s induced magnetic dipole, and the fluid’s charge. We show how these factors jointly influence key properties of the disk, such as its geometry, aspect ratio, size, and rest mass density.
Current Dengue Virus Vaccine Developments and Future Directions
Authors: Anumanthan Govindaraj , Sahay Bikash , Mergia Ayalew
Published: February 2025

Dengue fever (DF), a leading arboviral disease globally, is caused by the Dengue virus (DENV) and represents a significant public health concern, with an estimated 390 million cases reported annually. Due to the complexity of the various dengue variants and the severity of the disease, vaccination emerges as the essential strategy for combating this widespread infectious disease. The absence of specific antiviral medications underscores the critical need for developing a Dengue vaccine. This review aims to present the current status and future prospects of Dengue vaccine development. Further, this review elaborates on the various strategies employed in vaccine development, including attenuated, inactivated, subunit, and viral vector vaccines. Each approach is evaluated based on its immunogenicity, safety, and efficacy, drawing on data from preclinical and clinical studies to highlight the strengths and limitations of each candidate vaccine. The current study sheds light on future directions and research priorities in developing Dengue vaccines. In conclusion, the development of a Dengue vaccine holds significant potential for reducing the global burden of DF. However, challenges remain in terms of vaccine safety, efficacy, delivery, and availability. Overcoming these challenges, coupled with advancements in vaccine technology, could lead to better control and prevention of Dengue, thereby enhancing public health and quality of life.
Tobacco Mosaic Virus Movement: From Capsid Disassembly to Transport Through Plasmodesmata
Authors: Ibrahim Amr , Sasaki Nobumitsu , Schoelz James E. , Nelson Richard S.
Published: February 2025

Determining mechanisms to establish an initial infection and form intracellular complexes for accumulation and movement of RNA plant viruses are important areas of study in plant virology. The impact of these findings on the basic understanding of plant molecular virology and its application in agriculture is significant. Studies with tobacco mosaic virus (TMV) and related tobamoviruses often provide important foundational knowledge for studies involving other viruses. Topics discussed here include capsid disassembly, establishment of a virus replication complex (VRC), and transport of the VRCs or virus components within the cell to locations at the plasmodesmata for intercellular virus RNA (vRNA) movement. Seminal findings with TMV and related tobamoviruses include detecting co-translational disassembly of the vRNA from the virus rod, full sequencing of genomic vRNA and production of infectious transcript for genetic studies determining virus components necessary for intercellular movement, and biochemical and cell biological studies determining the host factors, protein and membrane, needed for replication and movement. This review highlights many of the studies through the years on TMV and selected tobamoviruses that have impacted not only our understanding of tobamovirus accumulation and movement but also that of other plant viruses.
Imaging Studies of the Stifle Joint in Puma concolor (Linnaeus, 1771)
Authors: Cardoso Eduardo Burgarelli Mayrink , Rahal Sheila Canevese , Silva Jeana Pereira da , Mamprim Maria Jaqueline , Filho Jair Theodoro , Frigerio Gabriela Montezel , Saldiva Paulo Hilário Nascimento , Ginja Mário Manuel Dinis , Werther Karin
Published: February 2025

Although the stifle joint of wild felines shares several characteristics observed in domestic cats, other characteristics are specific to each species. This study aimed to evaluate the stifle joints of eight Puma concolor, including two young and six adults, through different imaging examinations. All stifles were assessed using radiographs and computed tomography (CT). Magnetic resonance imaging (MRI) was performed on the stifles of one animal using 7 Tesla equipment. In all imaging modalities, the four sesamoid bones were detected. Meniscal mineralization was identified in the stifles of three adult animals and one young animal. The cruciate ligaments and menisci were identified on CT, with MRI providing better visualization. The mean values of CT measurements (cm2) in the sagittal section included patella (2.475), medial fabella (0.481), lateral fabella (0.772), popliteal sesamoid (0.222), and medial meniscus (0.051). No differences were found in HU values between the central trabecular bone of the patella and popliteal sesamoid, the cortical bone of the patella and lateral and medial fabellas, or the cortical bone of the patella and popliteal sesamoid. In conclusion, the descriptions of the stifle of Puma concolor from the different imaging methods contribute to understanding the species and can serve as a basis for identifying alterations.
Comparative Transcriptomics Analysis of Foot-and-Mouth Disease Virus-Infected Cell Model Systems
Authors: Ma Haibin , Zheng Zhenzhen , Liu Min , Zhang Mengsi , Qu Xiaoyun , Ren Jingqiang , Liao Ming
Published: February 2025

The highly contagious FMDV is the agent responsible for foot-and-mouth disease, significantly impacting animals with cloven hooves and incurring substantial economic losses globally. The FMDV genome, composed of single-stranded RNA, consists of approximately 8500 nucleotides and harbors a single open reading frame (ORF) encoding both structural and non-structural proteins vital for the virus’s pathogenicity and replication. BHK-21 (baby hamster kidney) cells are the optimal cell line for FMDV culture due to their robust viral replication ability and high infection susceptibility. The insufficient elucidation of the host response to FMDV hampers progress towards the establishment of precise therapeutic interventions. To fill this void in understanding, samples from FMDV-challenged and control BHK-21 cells were systematically procured, with comprehensive transcriptome sequencing subsequently undertaken to delineate the gene expression landscapes of each group. A total of 4018 differentially expressed genes were identified, of which 2044 were downregulated and 1974 were upregulated. The data indicate that FMDV infection significantly enhances transcription initiation in BHK-21. According to GO and KEGG enrichment analysis, FMDV affects a number of immune-related processes as well as the movement of chemicals within cells. In the analysis of the protein–protein interaction network, Fos, Flt3lg, Rpl22l1, Ifi35, Ep300, and Rps16 emerged as pivotal hub proteins, underscoring their significant roles within the cellular interactome. The RT-qPCR experiment of Lgfb5, Ler2, Vgll3, and Ahr verified that the DEGs’ expression profiles matched the results of the RNA-seq investigation. The study’s findings have enhanced our understanding of the molecular pathways underlying FMDV pathogenesis and host interactions. Furthermore, the identification of key genes could serve as potential targets for therapeutic strategies and diagnostic tools, thereby enhancing control measures for livestock foot-and-mouth disease and mitigating its economic impact.
Genome-Wide Association Integrating a Transcriptomic Meta-Analysis Suggests That Genes Related to Fat Deposition and Muscle Development Are Closely Associated with Growth in Huaxi Cattle
Authors: Liu Cheng-Li , Ren Tao , Ruan Peng-Cheng , Huang Yong-Fu , Ceccobelli Simone , Huang De-Jun , Zhang Lu-Pei , E Guang-Xin
Published: February 2025

Growth traits are among the most important economic phenotypes targeted in the genetic improvement of beef cattle. To understand the genetic basis of growth traits in Huaxi cattle, we performed a genome-wide association study (GWAS) on body weight, eye muscle area, and back fat thickness across five developmental stages in a population of 202 Huaxi cattle. Additionally, publicly available RNA-seq data from the longissimus dorsi muscle of both young and adult cattle were analyzed to identify key genes and genetic markers associated with growth in Huaxi cattle. In total, 7.19 million high-quality variant loci (SNPs and INDELs) were identified across all samples. In the GWAS, the three multilocus models (FarmCPU, MLMM, and BLINK) outperformed the conventional single-locus models (CMLM, GLM, and MLM). Consequently, GWAS analysis was conducted using multilocus models, which identified 99 variant loci significantly associated with growth traits and annotated a total of 83 candidate genes (CDGs). Additionally, 23 of the 83 CDGs overlapped with significantly differentially expressed genes identified from public RNA-seq datasets of longissimus dorsi muscle between young and adult cattle. Furthermore, gene functional enrichment (KEGG and GO) analyses revealed that over 30% of the pathways and GO terms were associated with muscle development and fat deposition, crucial factors for beef production. Specifically, key genes identified included MGLL, SGMS1, SNX29 and AKAP6, which are implicated in lipid metabolism, adipogenesis, and muscle growth. In summary, this study provides new insights into the genetic mechanisms underlying growth traits in Huaxi cattle and presents promising markers for future breeding improvements.
Administration of a Recombinant Fusion Protein of IFN-γ and CD154 Inhibited the Infection of Chicks with Salmonella enterica
Authors: Zhang Jingya , Ren Guofan , Li Wei , Xie Honglin , Yang Zengqi , Wang Juan , Zhou Yefei , Wang Xinglong
Published: February 2025

The cytokines IFN-γ and CD154 have been well established, and they play pivotal roles in immune protection against Salmonella in mice, but their effects and specific mechanisms in Salmonella-infected chickens are less understood. In this study, we conducted animal experiments to screen the highly immunoprotective chIFN-γ-chCD154 fusion protein compared with single protein chIFN-γ or chCD154 in white Leghorn chickens. The results showed that compared with separate pretreatments with chIFN-γ and chCD154, the fusion protein, chIFN-γ-chCD154, synergistically increased survival of infected chickens, reduced bacterial load in feces and organs, and attenuated pathological damage to the liver and cecum. Pretreatment with chIFN-γ-chCD154 also increased humoral immune responses, expression of the tight junction proteins zo-1, occludin, and claudin-1, and the relative abundance of Enterococcus_cecorum, Lactobacillus_helveticus, and Lactobacillus_agilis, which protect against intestinal inflammation. Compared with single protein pretreatment, chIFN-γ-chCD154 significantly upregulated STAT1, IRF1, and GBP1 in infected chickens while decreasing mRNA expression of TLR4, MyD88, NF-κB, TNF-α, IL-6, and IL-1β. In summary, damage to the cecal epithelial barrier and the inflammation induced by S. typhimurium infection was alleviated by chIFN-γ-chCD154 pretreatment through a mechanism involving the TLR4/MyD88/NF-κB and IFN-γ/STAT/IRF1/GBP1 pathways.
Agreement and Diagnostic Accuracy of New Linear Deflection Oscillometry and Doppler Devices for Hypotension Detection Compared to Invasive Blood Pressure in Anesthetized Dogs
Authors: Mantovani Matheus M. , Costa Any C. A. , de Lima Mayara T. , Santos Luis F. N. dos , Silva Kimberly F. , Martins Alessandro R. de C. , Navarro Adan W. M. , Akabane Renata S. , Fantoni Denise T.
Published: February 2025

Blood pressure (BP) monitoring is essential during anesthesia to maintain cardiovascular stability and detect hypotension. This study evaluated the agreement and diagnostic accuracy of linear deflection oscillometry (LDO) and Doppler compared to invasive blood pressure (IBP) in anesthetized dogs. Eleven healthy dogs were anesthetized, and BP measurements were taken using LDO, Doppler, and IBP methods under normotensive and hypotensive conditions. The LDO device demonstrated superior agreement, assessed using Bland–Altman analysis, with IBP, especially in hypotensive conditions, compared to the Doppler method. LDO showed bias and standard deviation in the hypotensive state, with a mean and systolic arterial pressure (MAP and SAP) of −5.1 ± 7.9 and −5.6 ± 12.5 mmHg, respectively. Conversely, Doppler measurements tended to overestimate SAP during hypotension, presenting a bias of −13 ± 15.45 mmHg. The LDO achieved an area under the curve (AUC) of 0.809 for hypotension detection, with an MAP cutoff of ≤72 mmHg (sensitivity: 90%, specificity: 63%). Meanwhile, the best threshold for Doppler measurements was an AUC of 0.798, SAP ≤ 100 mmHg (sensitivity: 77.8%, specificity: 81.8%). These results indicate that LDO is a reliable method for hypotension detection in anesthetized dogs, with potential applications for real-time monitoring. In contrast, the Doppler method may help confirm hypotension diagnoses.
Soil–Structure Interaction and Damping by the Soil—Effects of Foundation Groups, Foundation Flexibility, Soil Stiffness and Layers
Authors: Auersch Lutz
Published: February 2025

In many tasks of railway vibration, the structure, that is, the track, a bridge, and a nearby building and its floors, is coupled to the soil, and the soil–structure interaction and the damping by the soil should be included in the analysis to obtain realistic resonance frequencies and amplitudes. The stiffness and damping of a variety of foundations is calculated by an indirect boundary element method which uses fundamental solutions, is meshless, uses collocation points on the boundary, and solves the singularity by an appropriate averaging over a part of the surface. The boundary element method is coupled with the finite element method in the case of flexible foundations such as beams, plates, piles, and railway tracks. The results, the frequency-dependent stiffness and damping of single and groups of rigid foundations on homogeneous and layered soil and the amplitude and phase of the dynamic compliance of flexible foundations, show that the simple constant stiffness and damping values of a rigid footing on homogeneous soil are often misleading and do not represent well the reality. The damping may be higher in some special cases, but, in most cases, the damping is lower than expected from the simple theory. Some applications and measurements demonstrate the importance of the correct damping by the soil.